#install packages
install.packages(c("psych","car","flextable","officer","data.table","dplyr","numform","lavaan","multilevel","MplusAutomation"))

#load packages
library(psych)
library(car)
library(flextable)
library(officer)
library(data.table)
library(dplyr)
library(numform)
library(lavaan)
library(multilevel)
library(MplusAutomation)


#save directory to the folder to which files created in R should be saved (e.g., "C:/NarcSub/")
files_wd <- "C:/NarcSub/"


########################################
#S1: Additional Results of Study 1 
########################################


#read data
dat1 <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/427/3/NarcSub_Study1_Data.csv", header = T, sep = ","))


#apply data-exclusion criteria
dat1 <- subset(dat1, subset = participated_seriously != 2 & not_use_data != 1)


#recode variables
dat1$im1r <- car::recode(dat1$im1, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat1$im3r <- car::recode(dat1$im3, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat1$im4r <- car::recode(dat1$im4, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat1$im5r <- car::recode(dat1$im5, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat1$description_aut_r <- car::recode(dat1$description_aut, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq2r <- car::recode(dat1$lbdq2, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq4r <- car::recode(dat1$lbdq4, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq6r <- car::recode(dat1$lbdq6, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq8r <- car::recode(dat1$lbdq8, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq10r <- car::recode(dat1$lbdq10, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq12r <- car::recode(dat1$lbdq12, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq14r <- car::recode(dat1$lbdq14, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq16r <- car::recode(dat1$lbdq16, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq18r <- car::recode(dat1$lbdq18, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat1$lbdq20r <- car::recode(dat1$lbdq20, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")


#compute variables
dat1$narq_adm <- rowMeans( dat1[c("narq1", "narq2", "narq3", "narq5", "narq7", "narq8", "narq15", "narq16", "narq18")], na.rm=TRUE)
dat1$Znarq_adm <- scale(dat1$narq_adm)
dat1$narq_riv <- rowMeans( dat1[c("narq4", "narq6", "narq9", "narq10", "narq11", "narq12", "narq13", "narq14", "narq17")], na.rm=TRUE)
dat1$Znarq_riv <- scale(dat1$narq_riv)
dat1$IM <- rowMeans( dat1[c("im1r","im2","im3r","im4r","im5r","im6","im7","im8")], na.rm=TRUE)
dat1$ZIM <- scale(dat1$IM)
dat1$descriptions <- rowMeans(dat1[c("description_dem","description_aut_r")], na.rm=TRUE)
dat1$Zdescriptions <- scale(dat1$descriptions)
dat1$lbdq <- rowMeans(dat1[c("lbdq1","lbdq3","lbdq5","lbdq7","lbdq9","lbdq11","lbdq13","lbdq15","lbdq17","lbdq19", 
                             "lbdq2r","lbdq4r","lbdq6r","lbdq8r","lbdq10r","lbdq12r","lbdq14r","lbdq16r","lbdq18r","lbdq20r")], na.rm=TRUE)
dat1$Zlbdq <- scale(dat1$lbdq)
dat1$pref_index <- rowMeans(dat1[c("Zdescriptions","Zlbdq")], na.rm=TRUE)
dat1$Zpref_index <- scale(dat1$pref_index)
dat1$Zbipolar <- scale(dat1$bipolar_measure)
dat1$Zorder <- scale(dat1$description_order)
dat1$ZADMxOrder <- scale(dat1$Znarq_adm*dat1$Zorder)
dat1$ZRIVxOrder <- scale(dat1$Znarq_riv*dat1$Zorder)
dat1$narq <- rowMeans(dat1[c(2:19)], na.rm=TRUE)
dat1$Znarq <- scale(dat1$narq)
dat1$ZNARQxOrder <- scale(dat1$Znarq*dat1$Zorder)
dat1$npi <- rowMeans(dat1[c(20:59)], na.rm=TRUE)
dat1$Znpi <- scale(dat1$npi)
dat1$ZNPIxOrder <- scale(dat1$Znpi*dat1$Zorder)
dat1$npi_authority <- rowMeans( dat1[c("npi1", "npi5", "npi10", "npi11", "npi12", "npi27", "npi32", "npi33", "npi34", "npi36", "npi40")], na.rm=TRUE)
dat1$znpi_authority <- scale(dat1$npi_authority)
dat1$npi_exhibitionism <- rowMeans( dat1[c("npi4", "npi7", "npi15", "npi19", "npi20", "npi26", "npi28", "npi29", "npi30", "npi38")], na.rm=TRUE)
dat1$znpi_exhibitionism <- scale(dat1$npi_exhibitionism)
dat1$npi_entitlement <- rowMeans(dat1[c("npi13", "npi14", "npi24", "npi25")], na.rm=TRUE)
dat1$znpi_entitlement <- scale(dat1$npi_entitlement)


#Separate Analyses for Leadership Measures

##Table S1.1: Descriptive Statistics and Zero-Order Correlations for the Leadership Measures in Study 1
dat_table_S1.1 <- data.frame(matrix(nrow=6, ncol=3))
dat_table_S1.1[,1] <- c("ADM","RIV","IM","Leadership Descriptions","Leadership Behaviors","Bipolar Leadership Measure")
dat_table_S1.1[,2] <- c(mean(dat1$narq_adm, na.rm=T),mean(dat1$narq_riv, na.rm=T),mean(dat1$IM, na.rm=T),mean(dat1$descriptions, na.rm=T),mean(dat1$lbdq, na.rm=T),mean(dat1$bipolar_measure, na.rm=T))
dat_table_S1.1[,3] <- c(sd(dat1$narq_adm, na.rm=T),sd(dat1$narq_riv, na.rm=T),sd(dat1$IM, na.rm=T),sd(dat1$descriptions, na.rm=T),sd(dat1$lbdq, na.rm=T),sd(dat1$bipolar_measure, na.rm=T))
dat1_1 <- subset(dat1, select = c(narq_adm,narq_riv,IM,descriptions,lbdq,bipolar_measure))
cor_dat1_1 <- corr.test(dat1_1)
dat_cor <- as.data.frame(matrix(unlist(cor_dat1_1), nrow=365, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_r <- as.data.frame(matrix(dat_cor[1:36,], nrow=6))
dat_cor_p <- as.data.frame(matrix(dat_cor[74:109,], nrow=6))

dat_table_S1.1[,2:3] <- sapply(dat_table_S1.1[,2:3], as.numeric)
format_msd <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S1.1[,2:3] <- dat_table_S1.1[,2:3] %>% mutate_if(is.numeric, format_msd)

dat_cor_r[,1:6] <- sapply(dat_cor_r[,1:6], as.numeric)
format_r <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_cor_r[,1:6] <- dat_cor_r[,1:6] %>% mutate_if(is.numeric, format_r)

dat_cor_p[,1:6] <- sapply(dat_cor_p[,1:6], as.numeric)
format_p <- function(x){
  ifelse(abs(x) < 0.05 & abs(x) >= 0.01,  "*", ifelse(abs(x) < 0.01 & abs(x) >= 0.001,  "**", ifelse(abs(x) < 0.001,  "***", "")))
}
dat_cor_p[,1:6] <- dat_cor_p[,1:6] %>% mutate_if(is.numeric, format_p)

dat_table_S1.1$r1 <- paste0(dat_cor_r[,1],dat_cor_p[,1])
dat_table_S1.1$r2 <- paste0(dat_cor_r[,2],dat_cor_p[,2])
dat_table_S1.1$r3 <- paste0(dat_cor_r[,3],dat_cor_p[,3])
dat_table_S1.1$r4 <- paste0(dat_cor_r[,4],dat_cor_p[,4])
dat_table_S1.1$r5 <- paste0(dat_cor_r[,5],dat_cor_p[,5])

dat_table_S1.1[1,4:8] <- NA
dat_table_S1.1[2,5:8] <- NA
dat_table_S1.1[3,6:8] <- NA
dat_table_S1.1[4,7:8] <- NA
dat_table_S1.1[5,8] <- NA

dat_table_S1.1$blank1 <- NA
dat_table_S1.1 <- dat_table_S1.1[,c(1:3,9,4:8)]

col_keys <- c("X1","X2","X3","blank1","r1","r2","r3","r4","r5")
head1 <- c("Variable","M","SD","","1","2","3","4","5")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S1.1)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:9, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:9, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("X1"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("X2","X3"), part="header")
tbl <- width(tbl, j =~ X1, width=2.3)
tbl <- width(tbl, j =~ X2 + X3, width=.55)
tbl <- width(tbl, j =~ blank1, width=.1)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.1.docx")

rm(cor_dat1_1, dat_cor, dat_cor_p, dat_cor_r, dat_table_S1.1, dat1_1, doc, head, tbl, format_msd, format_p, format_r)


#Note of Table S1.1
dat.descriptions <- subset(dat1, select = c(description_dem,description_aut_r))
dat.lbdq <- subset(dat1, select = c(lbdq1,lbdq2r,lbdq3,lbdq4r,lbdq5,lbdq6r,lbdq7,lbdq8r,lbdq9,lbdq10r,lbdq11,lbdq12r,lbdq13, 
                                    lbdq14r,lbdq15,lbdq16r,lbdq17,lbdq18r,lbdq19,lbdq20r))
round(psych::alpha(dat.descriptions, na.rm=T)$total[1], 2)
round(psych::alpha(dat.lbdq, na.rm=T)$total[1], 2) #for three items, the item-total correlation was close to zero or negative; we kept those items anyway because (1) we wanted to maintain consistency across studies (there was no reason to exclude those items in the other studies) and (2) excluding those items did not alter the results.
rm(dat.descriptions, dat.lbdq)


##Table S1.2: Unique Relations of Narcissistic Admiration and Rivalry to the Leadership Measures in Study 1

##regression models
model1a <- lm(Zdescriptions ~ Znarq_adm + Znarq_riv, data=dat1)
model1b <- lm(Zlbdq ~ Znarq_adm + Znarq_riv, data=dat1)
model1c <- lm(Zbipolar ~ Znarq_adm + Znarq_riv, data=dat1)
dat1_1 <- rbind(data.frame(summary(model1a)$coef,confint(model1a)),data.frame(summary(model1b)$coef,confint(model1b)),data.frame(summary(model1c)$coef,confint(model1c)))

model2a <- lm(Zdescriptions ~ Znarq_adm + Znarq_riv + ZIM, data=dat1)
model2b <- lm(Zlbdq ~ Znarq_adm + Znarq_riv + ZIM, data=dat1)
model2c <- lm(Zbipolar ~ Znarq_adm + Znarq_riv + ZIM, data=dat1)
dat1_2 <- rbind(data.frame(summary(model2a)$coef,confint(model2a)),data.frame(summary(model2b)$coef,confint(model2b)),data.frame(summary(model2c)$coef,confint(model2c)))

##create table
dat1_1 <- dat1_1[!(row.names(dat1_1) %in% c("(Intercept)","(Intercept)1","(Intercept)2")), ]
dat1_2 <- dat1_2[!(row.names(dat1_2) %in% c("(Intercept)","(Intercept)1","(Intercept)2")), ]
dat1_1[nrow(dat1_1)+3,] <- NA
dat1_1$Predictor <- NA
dat1_1 <- dat1_1[c(1:2,7,3:4,8,5:6,9),c(7,1,5:6,3:4)]
dat1_1$Predictor <- c("ADM","RIV","IM","ADM","RIV","IM","ADM","RIV","IM")
dat1_2$blank <- NA
dat_table_S1.2 <- cbind(dat1_1,dat1_2[,c(7,1,5:6,3:4)])

colnames(dat_table_S1.2) <- c("Predictor","beta1","ci_lower1","ci_upper1","t1","p1","blank","beta2","ci_lower2","ci_upper2","t2","p2")

dat_table_S1.2[,2:12] <- sapply(dat_table_S1.2[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S1.2[,c(2:4,8:10)] <- dat_table_S1.2[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S1.2[,c(5,11)] <- dat_table_S1.2[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S1.2[,c(6,12)] <- dat_table_S1.2[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S1.2$ci1 <- paste0("[", dat_table_S1.2$ci_lower1, ", ", dat_table_S1.2$ci_upper1, "]")
dat_table_S1.2$ci2 <- paste0("[", dat_table_S1.2$ci_lower2, ", ", dat_table_S1.2$ci_upper2, "]")

dat_table_S1.2 <- dat_table_S1.2[,c("Predictor","beta1","ci1","t1","p1","blank","beta2","ci2","t2","p2")]
dat_table_S1.2[c(3,6,9),c(3:4)] <- NA

dat_table_S1.2[10,2] <- "Leadership Descriptions"
dat_table_S1.2[11,2] <- "Leadership Behaviors"
dat_table_S1.2[12,2] <- "Bipolar Leadership Measure"

dat_table_S1.2 <- dat_table_S1.2[c(10,1:3,11,4:6,12,7:9),]

col_keys <- c("Predictor","beta1","ci1","t1","p1","blank","beta2","ci2","t2","p2")
head1 <- c("","Model 1","Model 1","Model 1","Model 1","","Model 2","Model 2","Model 2","Model 2")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S1.2)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=5, j=2:10, part="body")
tbl <- merge_at(tbl, i=9, j=2:10, part="body")
tbl <- hline(tbl, i=c(4,8), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t1","p1","t2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=.9)
tbl <- width(tbl, j =~ beta1 + beta2, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t1 + t2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl <- colformat_lgl(tbl, j=2:6, na_str="")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.2.docx")

rm(model1a,model1b,model1c,dat1_1,model2a,model2b,model2c,dat1_2,dat_table_S1.2,head,tbl, doc, format_beta, format_p, format_t)


#Presentation Order of Leadership Descriptions

##Table S1.3: Unique Relations of Narcissistic Admiration and Rivalry to the Leadership Measures Moderated by Presentation Order of Leadership Descriptions in Study 1

###regression models
model1 <- lm(Zpref_index ~ Znarq_adm + Znarq_riv + Zorder + ZADMxOrder + ZRIVxOrder, dat=dat1)
model2 <- lm(Zdescriptions ~ Znarq_adm + Znarq_riv + Zorder + ZADMxOrder + ZRIVxOrder, dat=dat1)
model3 <- lm(Zlbdq ~ Znarq_adm + Znarq_riv + Zorder + ZADMxOrder + ZRIVxOrder, dat=dat1)
model4 <- lm(Zbipolar ~ Znarq_adm + Znarq_riv + Zorder + ZADMxOrder + ZRIVxOrder, dat=dat1)
dat_table_S1.3 <- rbind(data.frame(summary(model1)$coef,confint(model1)),data.frame(summary(model2)$coef,confint(model2)),data.frame(summary(model3)$coef,confint(model3)),data.frame(summary(model4)$coef,confint(model4)))

###create table
dat_table_S1.3 <- dat_table_S1.3[!(row.names(dat_table_S1.3) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat_table_S1.3$Predictor <- c("ADM","RIV","Order","ADM x Order","RIV x Order","ADM","RIV","Order","ADM x Order","RIV x Order","ADM","RIV","Order","ADM x Order","RIV x Order","ADM","RIV","Order","ADM x Order","RIV x Order")
dat_table_S1.3 <- dat_table_S1.3[,c(7,1,5:6,3:4)]

colnames(dat_table_S1.3) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S1.3[,2:6] <- sapply(dat_table_S1.3[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S1.3[,c(2:4)] <- dat_table_S1.3[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S1.3[,5] <- sapply(dat_table_S1.3[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S1.3[,6] <- sapply(dat_table_S1.3[,6], format_p)

dat_table_S1.3$ci <- paste0("[", dat_table_S1.3$ci_lower, ", ", dat_table_S1.3$ci_upper, "]")

dat_table_S1.3 <- dat_table_S1.3[,c("Predictor","beta","ci","t","p")]

dat_table_S1.3[21,2] <- "Index of Leadership Preferences"
dat_table_S1.3[22,2] <- "Leadership Descriptions"
dat_table_S1.3[23,2] <- "Leadership Behaviors"
dat_table_S1.3[24,2] <- "Bipolar Leadership Measure"

dat_table_S1.3 <- dat_table_S1.3[c(21,1:5,22,6:10,23,11:15,24,16:20),]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S1.3)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(6,12,18), j=1:5, border=fp_border(width=1.2), part="body")
tbl <- merge_at(tbl, i=1, j=2:5, part="body")
tbl <- merge_at(tbl, i=7, j=2:5, part="body")
tbl <- merge_at(tbl, i=13, j=2:5, part="body")
tbl <- merge_at(tbl, i=19, j=2:5, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.2)
tbl <- width(tbl, j =~ beta, width=.45)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t + p, width=.6)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.3.docx")

rm(model1,model2,model3,model4,dat_table_S1.3,head,tbl, doc, format_beta, format_p, format_t)


#NARQ Total Score

##Internal Consistency and Descriptive Statistics of the NARQ Total Score
dat.narq <- subset(dat1, select = c(2:19))
round(psych::alpha(dat.narq, na.rm=T)$total[1], digits = 2)
round(mean(dat1$narq),2)
round(sd(dat1$narq),2)
rm(dat.narq)

##Table S1.4: Relations of the NARQ Total Score to the Leadership Measures in Study 1

###regression models
model1a <- lm(Zpref_index ~ Znarq, data=dat1)
model1b <- lm(Zdescriptions ~ Znarq, data=dat1)
model1c <- lm(Zlbdq ~ Znarq, data=dat1)
model1d <- lm(Zbipolar ~ Znarq, data=dat1)
dat1_1 <- rbind(data.frame(summary(model1a)$coef,confint(model1a)),data.frame(summary(model1b)$coef,confint(model1b)),data.frame(summary(model1c)$coef,confint(model1c)),data.frame(summary(model1d)$coef,confint(model1d)))

model2a <- lm(Zpref_index ~ Znarq + ZIM, data=dat1)
model2b <- lm(Zdescriptions ~ Znarq + ZIM, data=dat1)
model2c <- lm(Zlbdq ~ Znarq + ZIM, data=dat1)
model2d <- lm(Zbipolar ~ Znarq + ZIM, data=dat1)
dat1_2 <- rbind(data.frame(summary(model2a)$coef,confint(model2a)),data.frame(summary(model2b)$coef,confint(model2b)),data.frame(summary(model2c)$coef,confint(model2c)),data.frame(summary(model2d)$coef,confint(model2d)))

model3a <- lm(Zpref_index ~ Znarq + Zorder + ZNARQxOrder, data=dat1)
model3b <- lm(Zdescriptions ~ Znarq + Zorder + ZNARQxOrder, data=dat1)
model3c <- lm(Zlbdq ~ Znarq + Zorder + ZNARQxOrder, data=dat1)
model3d <- lm(Zbipolar ~ Znarq + Zorder + ZNARQxOrder, data=dat1)
dat1_3 <- rbind(data.frame(summary(model3a)$coef,confint(model3a)),data.frame(summary(model3b)$coef,confint(model3b)),data.frame(summary(model3c)$coef,confint(model3c)),data.frame(summary(model3d)$coef,confint(model3d)))

###create table
dat1_1 <- dat1_1[!(row.names(dat1_1) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_2 <- dat1_2[!(row.names(dat1_2) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_3 <- dat1_3[!(row.names(dat1_3) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_1[nrow(dat1_1)+12,] <- NA
dat1_1$Predictor <- NA
dat1_1 <- dat1_1[c(1,5:7,2,8:10,3,11:13,4,14:16),c(7,1,5:6,3:4)]
dat1_1$Predictor <- c("NARQ","IM","Order","NARQ x Order","NARQ","IM","Order","NARQ x Order","NARQ","IM","Order","NARQ x Order","NARQ","IM","Order","NARQ x Order")
dat1_2[nrow(dat1_1)+8,] <- NA
dat1_2$blank1 <- NA
dat1_3[nrow(dat1_1)+4,] <- NA
dat1_3$blank2 <- NA
dat_table_S1.4 <- cbind(dat1_1,dat1_2[c(1:2,9:10,3:4,11:12,5:6,13:14,7:8,15:16),c(7,1,5:6,3:4)],dat1_3[c(1,13,2:4,14,5:7,15,8:10,16,11:12),c(7,1,5:6,3:4)])

colnames(dat_table_S1.4) <- c("Predictor","beta1","ci_lower1","ci_upper1","t1","p1","blank1","beta2","ci_lower2","ci_upper2","t2","p2","blank2","beta3","ci_lower3","ci_upper3","t3","p3")

dat_table_S1.4[,2:18] <- sapply(dat_table_S1.4[,2:18], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S1.4[,c(2:4,8:10,14:16)] <- dat_table_S1.4[,c(2:4,8:10,14:16)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S1.4[,c(5,11,17)] <- dat_table_S1.4[,c(5,11,17)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S1.4[,c(6,12,18)] <- dat_table_S1.4[,c(6,12,18)] %>% mutate_if(is.numeric, format_p)

dat_table_S1.4$ci1 <- paste0("[", dat_table_S1.4$ci_lower1, ", ", dat_table_S1.4$ci_upper1, "]")
dat_table_S1.4$ci2 <- paste0("[", dat_table_S1.4$ci_lower2, ", ", dat_table_S1.4$ci_upper2, "]")
dat_table_S1.4$ci3 <- paste0("[", dat_table_S1.4$ci_lower3, ", ", dat_table_S1.4$ci_upper3, "]")

dat_table_S1.4 <- dat_table_S1.4[,c("Predictor","beta1","ci1","t1","p1","blank1","beta2","ci2","t2","p2","blank2","beta3","ci3","t3","p3")]
dat_table_S1.4[c(2:4,6:8,10:12,14:16),c(3:4)] <- NA
dat_table_S1.4[c(3:4,7:8,11:12,15:16),c(8:9)] <- NA
dat_table_S1.4[c(2,6,10,14),c(13:14)] <- NA

dat_table_S1.4[17,2] <- "Index of Leadership Preferences"
dat_table_S1.4[18,2] <- "Leadership Descriptions"
dat_table_S1.4[19,2] <- "Leadership Behaviors"
dat_table_S1.4[20,2] <- "Bipolar Leadership Measure"

dat_table_S1.4 <- dat_table_S1.4[c(17,1:4,18,5:8,19,9:12,20,13:16),]

col_keys <- c("Predictor","beta1","ci1","t1","p1","blank1","beta2","ci2","t2","p2","blank2","beta3","ci3","t3","p3")
head1 <- c("","Model 1","Model 1","Model 1","Model 1","","Model 2","Model 2","Model 2","Model 2","","Model 3","Model 3","Model 3","Model 3")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","t","p","","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S1.4)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:15, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=12:15, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10,12:15), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:15, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(5,10,15), j=1:15, border=fp_border(width=1.2), part="body")
tbl <- merge_at(tbl, i=1, j=2:15, part="body")
tbl <- merge_at(tbl, i=6, j=2:15, part="body")
tbl <- merge_at(tbl, i=11, j=2:15, part="body")
tbl <- merge_at(tbl, i=16, j=2:15, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t1","p1","t2","p2","t3","p3"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.3)
tbl <- width(tbl, j =~ beta1 + beta2, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2 + ci3, width=1)
tbl <- width(tbl, j =~ t1 + t2 + t3, width=.6)
tbl <- width(tbl, j =~ p1 + p2 + p3, width=.6)
tbl <- width(tbl, j =~ blank1 + blank2, width=.3)
tbl <- colformat_lgl(tbl, j=2:15, na_str="")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.4.docx")

rm(model1a,model1b,model1c,model1d,dat1_1,model2a,model2b,model2c,model2d,dat1_2,model3a,model3b,model3c,model3d,dat1_3,dat_table_S1.4,head,tbl, doc, format_beta, format_p, format_t)


#NPI

##Internal Consistency and Descriptive Statistics of NPI
dat.npi <- subset(dat1, select = c(20:59))
round(psych::alpha(dat.npi, na.rm=T)$total[1], digits = 2)
round(mean(dat1$npi),2)
round(sd(dat1$npi),2)
rm(dat.npi)

##Table S1.5: Relations of NPI-Narcissism to the Leadership Measures in Study 1

###regression models
model1a <- lm(Zpref_index ~ Znpi, data=dat1)
model1b <- lm(Zdescriptions ~ Znpi, data=dat1)
model1c <- lm(Zlbdq ~ Znpi, data=dat1)
model1d <- lm(Zbipolar ~ Znpi, data=dat1)
dat1_1 <- rbind(data.frame(summary(model1a)$coef,confint(model1a)),data.frame(summary(model1b)$coef,confint(model1b)),data.frame(summary(model1c)$coef,confint(model1c)),data.frame(summary(model1d)$coef,confint(model1d)))

model2a <- lm(Zpref_index ~ Znpi + ZIM, data=dat1)
model2b <- lm(Zdescriptions ~ Znpi + ZIM, data=dat1)
model2c <- lm(Zlbdq ~ Znpi + ZIM, data=dat1)
model2d <- lm(Zbipolar ~ Znpi + ZIM, data=dat1)
dat1_2 <- rbind(data.frame(summary(model2a)$coef,confint(model2a)),data.frame(summary(model2b)$coef,confint(model2b)),data.frame(summary(model2c)$coef,confint(model2c)),data.frame(summary(model2d)$coef,confint(model2d)))

model3a <- lm(Zpref_index ~ Znpi + Zorder + ZNPIxOrder, data=dat1)
model3b <- lm(Zdescriptions ~ Znpi + Zorder + ZNPIxOrder, data=dat1)
model3c <- lm(Zlbdq ~ Znpi + Zorder + ZNPIxOrder, data=dat1)
model3d <- lm(Zbipolar ~ Znpi + Zorder + ZNPIxOrder, data=dat1)
dat1_3 <- rbind(data.frame(summary(model3a)$coef,confint(model3a)),data.frame(summary(model3b)$coef,confint(model3b)),data.frame(summary(model3c)$coef,confint(model3c)),data.frame(summary(model3d)$coef,confint(model3d)))

###create table
dat1_1 <- dat1_1[!(row.names(dat1_1) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_2 <- dat1_2[!(row.names(dat1_2) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_3 <- dat1_3[!(row.names(dat1_3) %in% c("(Intercept)","(Intercept)1","(Intercept)2","(Intercept)3")), ]
dat1_1[nrow(dat1_1)+12,] <- NA
dat1_1$Predictor <- NA
dat1_1 <- dat1_1[c(1,5:7,2,8:10,3,11:13,4,14:16),c(7,1,5:6,3:4)]
dat1_1$Predictor <- c("NPI","IM","Order","NPI x Order","NPI","IM","Order","NPI x Order","NPI","IM","Order","NPI x Order","NPI","IM","Order","NPI x Order")
dat1_2[nrow(dat1_1)+8,] <- NA
dat1_2$blank1 <- NA
dat1_3[nrow(dat1_1)+4,] <- NA
dat1_3$blank2 <- NA
dat_table_S1.5 <- cbind(dat1_1,dat1_2[c(1:2,9:10,3:4,11:12,5:6,13:14,7:8,15:16),c(7,1,5:6,3:4)],dat1_3[c(1,13,2:4,14,5:7,15,8:10,16,11:12),c(7,1,5:6,3:4)])

colnames(dat_table_S1.5) <- c("Predictor","beta1","ci_lower1","ci_upper1","t1","p1","blank1","beta2","ci_lower2","ci_upper2","t2","p2","blank2","beta3","ci_lower3","ci_upper3","t3","p3")

dat_table_S1.5[,2:18] <- sapply(dat_table_S1.5[,2:18], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S1.5[,c(2:4,8:10,14:16)] <- dat_table_S1.5[,c(2:4,8:10,14:16)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S1.5[,c(5,11,17)] <- dat_table_S1.5[,c(5,11,17)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S1.5[,c(6,12,18)] <- dat_table_S1.5[,c(6,12,18)] %>% mutate_if(is.numeric, format_p)

dat_table_S1.5$ci1 <- paste0("[", dat_table_S1.5$ci_lower1, ", ", dat_table_S1.5$ci_upper1, "]")
dat_table_S1.5$ci2 <- paste0("[", dat_table_S1.5$ci_lower2, ", ", dat_table_S1.5$ci_upper2, "]")
dat_table_S1.5$ci3 <- paste0("[", dat_table_S1.5$ci_lower3, ", ", dat_table_S1.5$ci_upper3, "]")

dat_table_S1.5 <- dat_table_S1.5[,c("Predictor","beta1","ci1","t1","p1","blank1","beta2","ci2","t2","p2","blank2","beta3","ci3","t3","p3")]
dat_table_S1.5[c(2:4,6:8,10:12,14:16),c(3:4)] <- NA
dat_table_S1.5[c(3:4,7:8,11:12,15:16),c(8:9)] <- NA
dat_table_S1.5[c(2,6,10,14),c(13:14)] <- NA

dat_table_S1.5[17,2] <- "Index of Leadership Preferences"
dat_table_S1.5[18,2] <- "Leadership Descriptions"
dat_table_S1.5[19,2] <- "Leadership Behaviors"
dat_table_S1.5[20,2] <- "Bipolar Leadership Measure"

dat_table_S1.5 <- dat_table_S1.5[c(17,1:4,18,5:8,19,9:12,20,13:16),]

col_keys <- c("Predictor","beta1","ci1","t1","p1","blank1","beta2","ci2","t2","p2","blank2","beta3","ci3","t3","p3")
head1 <- c("","Model 1","Model 1","Model 1","Model 1","","Model 2","Model 2","Model 2","Model 2","","Model 3","Model 3","Model 3","Model 3")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","t","p","","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S1.5)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:15, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=12:15, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10,12:15), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:15, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(5,10,15), j=1:15, border=fp_border(width=1.2), part="body")
tbl <- merge_at(tbl, i=1, j=2:15, part="body")
tbl <- merge_at(tbl, i=6, j=2:15, part="body")
tbl <- merge_at(tbl, i=11, j=2:15, part="body")
tbl <- merge_at(tbl, i=16, j=2:15, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t1","p1","t2","p2","t3","p3"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.2)
tbl <- width(tbl, j =~ beta1 + beta2, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2 + ci3, width=.95)
tbl <- width(tbl, j =~ t1 + t2 + t3, width=.55)
tbl <- width(tbl, j =~ p1 + p2 + p3, width=.6)
tbl <- width(tbl, j =~ blank1 + blank2, width=.3)
tbl <- colformat_lgl(tbl, j=2:15, na_str="")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.5.docx")

rm(model1a,model1b,model1c,model1d,dat1_1,model2a,model2b,model2c,model2d,dat1_2,model3a,model3b,model3c,model3d,dat1_3,dat_table_S1.5,head,tbl, doc, format_beta, format_p, format_t)

##Supplementary Footnote S1: Regression Models With NPI Facets
model1a_facets <- lm(Zdescriptions ~ znpi_authority + znpi_exhibitionism + znpi_entitlement, data=dat1)
model1b_facets <- lm(Zlbdq ~ znpi_authority + znpi_exhibitionism + znpi_entitlement, data=dat1)
model1c_facets <- lm(Zpref_index ~ znpi_authority + znpi_exhibitionism + znpi_entitlement, data=dat1)
model1d_facets <- lm(Zbipolar ~ znpi_authority + znpi_exhibitionism + znpi_entitlement, data=dat1)
dat1_facets <- rbind(data.frame(summary(model1a_facets)$coef,confint(model1a_facets)),data.frame(summary(model1b_facets)$coef,confint(model1b_facets)),data.frame(summary(model1c_facets)$coef,confint(model1c_facets)),data.frame(summary(model1d_facets)$coef,confint(model1d_facets)))

rm(model1a_facets,model1b_facets,model1c_facets,model1d_facets,dat1_facets)


rm(dat1)


########################################
#S2: Additional Results of Studies 2a-2b
########################################


#read data
dat2a <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/427/5/NarcSub_Study2a_Data.csv", header = T, sep = ","))
dat2b <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/427/7/NarcSub_Study2b_Data.csv", header = T, sep = ","))


#apply data-exclusion criteria for Study 2a
dat2a <- subset(dat2a, subset = exclude == 0)


#recode variables
##Study 2a
dat2a$description_aut_supervisor_r <- car::recode(dat2a$description_aut_supervisor, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat2a$lbdq2r_supervisor <- car::recode(dat2a$lbdq2_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq4r_supervisor <- car::recode(dat2a$lbdq4_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq6r_supervisor <- car::recode(dat2a$lbdq6_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq8r_supervisor <- car::recode(dat2a$lbdq8_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq10r_supervisor <- car::recode(dat2a$lbdq10_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq12r_supervisor <- car::recode(dat2a$lbdq12_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq14r_supervisor <- car::recode(dat2a$lbdq14_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq16r_supervisor <- car::recode(dat2a$lbdq16_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq18r_supervisor <- car::recode(dat2a$lbdq18_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq20r_supervisor <- car::recode(dat2a$lbdq20_supervisor, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$description_aut_self_r <- car::recode(dat2a$description_aut_self, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat2a$lbdq2r_self <- car::recode(dat2a$lbdq2_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq4r_self <- car::recode(dat2a$lbdq4_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq6r_self <- car::recode(dat2a$lbdq6_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq8r_self <- car::recode(dat2a$lbdq8_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq10r_self <- car::recode(dat2a$lbdq10_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq12r_self <- car::recode(dat2a$lbdq12_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq14r_self <- car::recode(dat2a$lbdq14_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq16r_self <- car::recode(dat2a$lbdq16_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq18r_self <- car::recode(dat2a$lbdq18_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$lbdq20r_self <- car::recode(dat2a$lbdq20_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat2a$bfi1r <- 6-dat2a$bfi1
dat2a$bfi4r <- 6-dat2a$bfi4
dat2a$bfi7r <- 6-dat2a$bfi7
dat2a$bfi8r <- 6-dat2a$bfi8
dat2a$bfi10r <- 6-dat2a$bfi10
dat2a$se3r <- 5-dat2a$se3
dat2a$se5r <- 5-dat2a$se5
dat2a$se8r <- 5-dat2a$se8
dat2a$se9r <- 5-dat2a$se9
dat2a$se10r <- 5-dat2a$se10
##Study 2b
dat2b$description_aut_supervisor_r <- car::recode(dat2b$description_aut_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq2r_supervisor <- car::recode(dat2b$lbdq2_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq4r_supervisor <- car::recode(dat2b$lbdq4_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq6r_supervisor <- car::recode(dat2b$lbdq6_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq8r_supervisor <- car::recode(dat2b$lbdq8_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq10r_supervisor <- car::recode(dat2b$lbdq10_supervisor, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$description_aut_self_r <- car::recode(dat2b$description_aut_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq2r_self <- car::recode(dat2b$lbdq2_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq4r_self <- car::recode(dat2b$lbdq4_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq6r_self <- car::recode(dat2b$lbdq6_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq8r_self <- car::recode(dat2b$lbdq8_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$lbdq10r_self <- car::recode(dat2b$lbdq10_self, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1")
dat2b$bfi1r <- 8-dat2b$bfi1
dat2b$bfi3r <- 8-dat2b$bfi3
dat2b$bfi7r <- 8-dat2b$bfi7
dat2b$bfi8r <- 8-dat2b$bfi8
dat2b$bfi10r <- 8-dat2b$bfi10
dat2b$bfi14r <- 8-dat2b$bfi14


#compute variables 
##Study 2a
###predictors
dat2a$narq_adm <- rowMeans(dat2a[c("narq1", "narq2", "narq3", "narq5", "narq7", "narq8", "narq15", "narq16", "narq18")], na.rm=TRUE)
dat2a$Znarq_adm <- as.numeric(scale(dat2a$narq_adm))
dat2a$narq_riv <- rowMeans(dat2a[c("narq4", "narq6", "narq9", "narq10", "narq11", "narq12", "narq13", "narq14", "narq17")], na.rm=TRUE)
dat2a$Znarq_riv <- as.numeric(scale(dat2a$narq_riv))
dat2a$narq <- rowMeans(dat2a[c(70:87)], na.rm=TRUE)
dat2a$Znarq <- as.numeric(scale(dat2a$narq))
dat2a$npi <- rowMeans(dat2a[c(30:69)], na.rm=TRUE)
dat2a$Znpi <- as.numeric(scale(dat2a$npi))
dat2a$npi_authority <- rowMeans( dat2a[c("npi1", "npi5", "npi10", "npi11", "npi12", "npi27", "npi32", "npi33", "npi34", "npi36", "npi40")], na.rm=TRUE)
dat2a$Znpi_authority <- as.numeric(scale(dat2a$npi_authority))
dat2a$npi_exhibitionism <- rowMeans( dat2a[c("npi4", "npi7", "npi15", "npi19", "npi20", "npi26", "npi28", "npi29", "npi30", "npi38")], na.rm=TRUE)
dat2a$Znpi_exhibitionism <- as.numeric(scale(dat2a$npi_exhibitionism))
dat2a$npi_entitlement <- rowMeans(dat2a[c("npi13", "npi14", "npi24", "npi25")], na.rm=TRUE)
dat2a$Znpi_entitlement <- as.numeric(scale(dat2a$npi_entitlement))
###moderators
dat2a$descriptions_supervisor <- rowMeans(dat2a[c("description_dem_supervisor","description_aut_supervisor_r")], na.rm = TRUE) 
dat2a$Zdescriptions_supervisor <- as.numeric(scale(dat2a$descriptions_supervisor))
dat2a$lbdq_supervisor <- rowMeans(dat2a[c("lbdq1_supervisor","lbdq3_supervisor","lbdq5_supervisor","lbdq7_supervisor","lbdq9_supervisor","lbdq11_supervisor","lbdq13_supervisor", 
                                          "lbdq15_supervisor","lbdq17_supervisor","lbdq19_supervisor","lbdq2r_supervisor","lbdq4r_supervisor","lbdq6r_supervisor","lbdq8r_supervisor",
                                          "lbdq10r_supervisor","lbdq12r_supervisor","lbdq14r_supervisor","lbdq16r_supervisor","lbdq18r_supervisor","lbdq20r_supervisor")], na.rm = TRUE)
dat2a$Zlbdq_supervisor <- as.numeric(scale(dat2a$lbdq_supervisor))
dat2a$style_index_supervisor <- rowMeans(dat2a[c("Zdescriptions_supervisor","Zlbdq_supervisor")], na.rm = TRUE)
dat2a$Zstyle_index_supervisor <- as.numeric(scale(dat2a$style_index_supervisor))
dat2a$Zorder <- as.numeric(scale(dat2a$completion_order))
###mediators
dat2a$descriptions_self <- rowMeans(dat2a[c("description_dem_self","description_aut_self_r")], na.rm=TRUE)
dat2a$Zdescriptions_self <- as.numeric(scale(dat2a$descriptions_self))
dat2a$lbdq_self <- rowMeans(dat2a[c("lbdq1_self","lbdq3_self","lbdq5_self","lbdq7_self","lbdq9_self","lbdq11_self","lbdq13_self", 
                                    "lbdq15_self","lbdq17_self","lbdq19_self","lbdq2r_self","lbdq4r_self","lbdq6r_self","lbdq8r_self",
                                    "lbdq10r_self","lbdq12r_self","lbdq14r_self","lbdq16r_self","lbdq18r_self","lbdq20r_self")], na.rm=TRUE)
dat2a$Zlbdq_self <- as.numeric(scale(dat2a$lbdq_self))
dat2a$style_index_self <- rowMeans(dat2a[c("Zdescriptions_self","Zlbdq_self")], na.rm = TRUE)
dat2a$Zstyle_index_self <- as.numeric(scale(dat2a$style_index_self))
###interactions
dat2a$ZADMxSLS <- as.numeric(scale(dat2a$Znarq_adm*dat2a$Zstyle_index_supervisor))
dat2a$ZRIVxSLS <- as.numeric(scale(dat2a$Znarq_riv*dat2a$Zstyle_index_supervisor))
dat2a$ZNARQxSLS <- as.numeric(scale(dat2a$Znarq*dat2a$Zstyle_index_supervisor))
dat2a$ZNPIxSLS <- as.numeric(scale(dat2a$Znpi*dat2a$Zstyle_index_supervisor))
dat2a$ZSHLSxSLS <- as.numeric(scale(dat2a$Zstyle_index_self*dat2a$Zstyle_index_supervisor))
dat2a$ZADMxSLS_descriptions <- as.numeric(scale(dat2a$Znarq_adm*dat2a$Zdescriptions_supervisor))
dat2a$ZRIVxSLS_descriptions <- as.numeric(scale(dat2a$Znarq_riv*dat2a$Zdescriptions_supervisor))
dat2a$ZNARQxSLS_descriptions <- as.numeric(scale(dat2a$Znarq*dat2a$Zdescriptions_supervisor))
dat2a$ZNPIxSLS_descriptions <- as.numeric(scale(dat2a$Znpi*dat2a$Zdescriptions_supervisor))
dat2a$ZSHLSxSLS_descriptions <- as.numeric(scale(dat2a$Zdescriptions_self*dat2a$Zdescriptions_supervisor))
dat2a$ZADMxSLS_lbdq <- as.numeric(scale(dat2a$Znarq_adm*dat2a$Zlbdq_supervisor))
dat2a$ZRIVxSLS_lbdq <- as.numeric(scale(dat2a$Znarq_riv*dat2a$Zlbdq_supervisor))
dat2a$ZNARQxSLS_lbdq <- as.numeric(scale(dat2a$Znarq*dat2a$Zlbdq_supervisor))
dat2a$ZNPIxSLS_lbdq <- as.numeric(scale(dat2a$Znpi*dat2a$Zlbdq_supervisor))
dat2a$ZSHLSxSLS_lbdq <- as.numeric(scale(dat2a$Zlbdq_self*dat2a$Zlbdq_supervisor))
dat2a$ZADMxORD <- as.numeric(scale(dat2a$Znarq_adm*dat2a$Zorder))
dat2a$ZRIVxORD <- as.numeric(scale(dat2a$Znarq_riv*dat2a$Zorder))
dat2a$ZNARQxORD <- as.numeric(scale(dat2a$Znarq*dat2a$Zorder))
dat2a$ZNPIxORD <- as.numeric(scale(dat2a$Znpi*dat2a$Zorder))
dat2a$ZSLSxORD <- as.numeric(scale(dat2a$Zstyle_index_supervisor*dat2a$Zorder))
dat2a$ZADMxSLSxORD <- as.numeric(scale(dat2a$Znarq_adm*dat2a$Zstyle_index_supervisor*dat2a$Zorder))
dat2a$ZRIVxSLSxORD <- as.numeric(scale(dat2a$Znarq_riv*dat2a$Zstyle_index_supervisor*dat2a$Zorder))
dat2a$ZNARQxSLSxORD <- as.numeric(scale(dat2a$Znarq*dat2a$Zstyle_index_supervisor*dat2a$Zorder))
dat2a$ZNPIxSLSxORD <- as.numeric(scale(dat2a$Znpi*dat2a$Zstyle_index_supervisor*dat2a$Zorder))
dat2a$ZNPI_AUTxSLS <- as.numeric(scale(dat2a$Znpi_authority*dat2a$Zstyle_index_supervisor))
dat2a$ZNPI_AUTxSLS_descriptions <- as.numeric(scale(dat2a$Znpi_authority*dat2a$Zdescriptions_supervisor))
dat2a$ZNPI_AUTxSLS_lbdq <- as.numeric(scale(dat2a$Znpi_authority*dat2a$Zlbdq_supervisor))
dat2a$ZNPI_EXHxSLS <- as.numeric(scale(dat2a$Znpi_exhibitionism*dat2a$Zstyle_index_supervisor))
dat2a$ZNPI_EXHxSLS_descriptions <- as.numeric(scale(dat2a$Znpi_exhibitionism*dat2a$Zdescriptions_supervisor))
dat2a$ZNPI_EXHxSLS_lbdq <- as.numeric(scale(dat2a$Znpi_exhibitionism*dat2a$Zlbdq_supervisor))
dat2a$ZNPI_ENTxSLS <- as.numeric(scale(dat2a$Znpi_entitlement*dat2a$Zstyle_index_supervisor))
dat2a$ZNPI_ENTxSLS_descriptions <- as.numeric(scale(dat2a$Znpi_entitlement*dat2a$Zdescriptions_supervisor))
dat2a$ZNPI_ENTxSLS_lbdq <- as.numeric(scale(dat2a$Znpi_entitlement*dat2a$Zlbdq_supervisor))
###dependent variable
dat2a$zvalence <- as.numeric(scale(rowMeans(dat2a[c("valence1","valence2","valence3")], na.rm=TRUE)))
dat2a$zsuccess <- as.numeric(scale(rowMeans(dat2a[c("success1","success2","success3")], na.rm=TRUE)))
dat2a$boss_desirability3r <- 8 - dat2a$boss_desirability3
dat2a$zboss_desirability <- as.numeric(scale(rowMeans(dat2a[c("boss_desirability1","boss_desirability2","boss_desirability3r")], na.rm=TRUE)))
dat2a$zliking <- as.numeric(scale(rowMeans(dat2a[c("liking1","liking2","liking3")], na.rm=TRUE)))
dat2a$zreputation <- as.numeric(scale(rowMeans(dat2a[c("reputation1","reputation2")], na.rm=TRUE)))
dat2a$zcompetence <- as.numeric(scale(rowMeans(dat2a[c("competence1","competence2","competence3")], na.rm=TRUE)))
dat2a$zwarmth <- as.numeric(scale(rowMeans(dat2a[c("warmth1","warmth2","warmth3")], na.rm=TRUE)))
dat2a$zworking_conditions <- as.numeric(scale(rowMeans(dat2a[c("working_conditions1","working_conditions2","working_conditions3")], na.rm=TRUE)))
dat2a$attraction <- rowMeans(dat2a[c("zvalence","zsuccess","zboss_desirability","zliking","zreputation","zcompetence","zwarmth","zworking_conditions")], na.rm=TRUE)
dat2a$Zattraction <- as.numeric(scale(dat2a$attraction))
###covariates and their interactions
dat2a$agr <- rowMeans(dat2a[c("bfi2","bfi7r")], na.rm=TRUE)
dat2a$Zagr <- as.numeric(scale(dat2a$agr))
dat2a$cns <- rowMeans(dat2a[c("bfi3","bfi8r")], na.rm=TRUE)
dat2a$Zcns <- as.numeric(scale(dat2a$cns))
dat2a$ext <- rowMeans(dat2a[c("bfi1r","bfi6")], na.rm=TRUE)
dat2a$Zext <- as.numeric(scale(dat2a$ext))
dat2a$neu <- rowMeans(dat2a[c("bfi4r","bfi9")], na.rm=TRUE)
dat2a$Zneu <- as.numeric(scale(dat2a$neu))
dat2a$opn <- rowMeans(dat2a[c("bfi5","bfi10r")], na.rm=TRUE)
dat2a$Zopn <- as.numeric(scale(dat2a$opn))
dat2a$selfesteem <- rowMeans(dat2a[c("se1","se2","se3r","se4","se5r","se6","se7","se8r","se9r","se10r")], na.rm=TRUE)
dat2a$Zse <- as.numeric(scale(dat2a$selfesteem))
dat2a$ZAGRxSLS <- as.numeric(scale(dat2a$Zagr*dat2a$Zstyle_index_supervisor))
dat2a$ZCNSxSLS <- as.numeric(scale(dat2a$Zcns*dat2a$Zstyle_index_supervisor))
dat2a$ZEXTxSLS <- as.numeric(scale(dat2a$Zext*dat2a$Zstyle_index_supervisor))
dat2a$ZNEUxSLS <- as.numeric(scale(dat2a$Zneu*dat2a$Zstyle_index_supervisor))
dat2a$ZOPNxSLS <- as.numeric(scale(dat2a$Zopn*dat2a$Zstyle_index_supervisor))
dat2a$ZSExSLS <- as.numeric(scale(dat2a$Zse*dat2a$Zstyle_index_supervisor))
##Study 2b
###predictors
dat2b$narq_adm <- rowMeans(dat2b[c("narq1", "narq2", "narq3", "narq5", "narq7", "narq8", "narq15", "narq16", "narq18")], na.rm=TRUE)
dat2b$Znarq_adm <- scale(dat2b$narq_adm)
dat2b$narq_riv <- rowMeans(dat2b[c("narq4", "narq6", "narq9", "narq10", "narq11", "narq12", "narq13", "narq14", "narq17")], na.rm=TRUE)
dat2b$Znarq_riv <- scale(dat2b$narq_riv)
dat2b$narq <- rowMeans(dat2b[c(41:58)], na.rm=TRUE)
dat2b$Znarq <- scale(dat2b$narq)
dat2b$npi <- rowMeans(dat2b[c(28:40)], na.rm=TRUE)
dat2b$Znpi <- scale(dat2b$npi)
dat2b$npi_authority <- rowMeans( dat2b[c("npi2", "npi10", "npi12","npi13")], na.rm=TRUE)
dat2b$Znpi_authority <- as.numeric(scale(dat2b$npi_authority))
dat2b$npi_exhibitionism <- rowMeans( dat2b[c("npi1", "npi5", "npi6", "npi7", "npi11")], na.rm=TRUE)
dat2b$Znpi_exhibitionism <- as.numeric(scale(dat2b$npi_exhibitionism))
dat2b$npi_entitlement <- rowMeans(dat2b[c("npi3", "npi4", "npi8", "npi9")], na.rm=TRUE)
dat2b$Znpi_entitlement <- as.numeric(scale(dat2b$npi_entitlement))
###moderators
dat2b$descriptions_supervisor <- rowMeans(dat2b[c("description_dem_supervisor","description_aut_supervisor_r")], na.rm = TRUE)
dat2b$lbdq_supervisor <- rowMeans(dat2b[c("lbdq1_supervisor","lbdq3_supervisor","lbdq5_supervisor","lbdq7_supervisor","lbdq9_supervisor",
                                          "lbdq2r_supervisor","lbdq4r_supervisor","lbdq6r_supervisor","lbdq8r_supervisor","lbdq10r_supervisor")], na.rm = TRUE)
dat2b_team <- aggregate(cbind(descriptions_supervisor,lbdq_supervisor,bipolar_measure_supervisor) ~ team, dat2b, mean)
colnames(dat2b_team) <- c("team","descriptions_supervisor_team","lbdq_supervisor_team","bipolar_measure_supervisor_team")
dat2b_team$Zdescriptions_supervisor_team <- scale(dat2b_team$descriptions_supervisor_team)
dat2b_team$Zlbdq_supervisor_team <- scale(dat2b_team$lbdq_supervisor_team)
dat2b_team$Zbipolar_measure_supervisor_team <- scale(dat2b_team$bipolar_measure_supervisor_team)
dat2b_team$style_index_supervisor_team <- rowMeans(dat2b_team[c("Zdescriptions_supervisor_team","Zlbdq_supervisor_team")], na.rm=TRUE)
dat2b_team$Zstyle_index_supervisor_team <- scale(dat2b_team$style_index_supervisor_team)
dat2b <- merge(dat2b,dat2b_team, by = "team")
rm(dat2b_team)
###mediators
dat2b$descriptions_self <- rowMeans(dat2b[c("description_dem_self","description_aut_self_r")], na.rm=TRUE) 
dat2b$Zdescriptions_self <- scale(dat2b$descriptions_self)
dat2b$lbdq_self <- rowMeans(dat2b[c("lbdq1_self","lbdq3_self","lbdq5_self","lbdq7_self","lbdq9_self","lbdq2r_self",
                                    "lbdq4r_self","lbdq6r_self","lbdq8r_self","lbdq10r_self")], na.rm=TRUE) 
dat2b$Zlbdq_self <- scale(dat2b$lbdq_self)
dat2b$Zbipolar_measure_self <- scale(dat2b$bipolar_measure_self)
dat2b$style_index_self <- rowMeans(dat2b[c("Zdescriptions_self","Zlbdq_self")], na.rm = TRUE)
dat2b$Zstyle_index_self <- scale(dat2b$style_index_self)
###dependent variable
dat2b$zvalence <- scale(rowMeans(dat2b[c("valence1","valence2")], na.rm=TRUE))
dat2b$zsuccess <- scale(rowMeans(dat2b[c("success1","success2")], na.rm=TRUE))
dat2b$boss_desirability2r <- 8 - dat2b$boss_desirability2
dat2b$zboss_desirability <- scale(rowMeans(dat2b[c("boss_desirability1","boss_desirability2r")], na.rm=TRUE))
dat2b$zliking <- scale(rowMeans(dat2b[c("liking1","liking2")], na.rm=TRUE))
dat2b$zcompetence <- scale(rowMeans(dat2b[c("competence1","competence2")], na.rm=TRUE))
dat2b$zwarmth <- scale(rowMeans(dat2b[c("warmth1","warmth2")], na.rm=TRUE))
dat2b$attraction <- rowMeans(dat2b[c("zvalence","zsuccess","zboss_desirability","zliking","zcompetence","zwarmth")], na.rm=TRUE)
dat2b$Zattraction <- scale(dat2b$attraction)
###covariates
dat2b$agr <- rowMeans(dat2b[c("bfi2","bfi7r","bfi12")], na.rm=TRUE)
dat2b$Zagr <- scale(dat2b$agr)
dat2b$cns <- rowMeans(dat2b[c("bfi3r","bfi8r","bfi13")], na.rm=TRUE)
dat2b$Zcns <- scale(dat2b$cns)
dat2b$ext <- rowMeans(dat2b[c("bfi1r","bfi6","bfi11")], na.rm=TRUE)
dat2b$Zext <- scale(dat2b$ext)
dat2b$neu <- rowMeans(dat2b[c("bfi4","bfi9","bfi14r")], na.rm=TRUE)
dat2b$Zneu <- scale(dat2b$neu)
dat2b$opn <- rowMeans(dat2b[c("bfi5","bfi10r","bfi15")], na.rm=TRUE)
dat2b$Zopn <- scale(dat2b$opn)
dat2b$Zse <- scale(dat2b$selfesteem)


#Mplus analyses for Study 2b

##################################################################################################################################

##### ACCESS TO MPLUS #####

#select variables for Mplus analyses
dat2b_mplus <- subset(dat2b, select = c(id,team,Znarq_adm,Znarq_riv,Znarq,Znpi,Zagr,Zcns,Zext,Zneu,Zopn,Zse,
                                        Zstyle_index_supervisor_team,Zdescriptions_supervisor_team,Zlbdq_supervisor_team,Zbipolar_measure_supervisor_team,
                                        Zstyle_index_self,Zdescriptions_self,Zlbdq_self,Zbipolar_measure_self,Zattraction,
                                        Znpi_authority,Znpi_exhibitionism,Znpi_entitlement))

#save dataset for Mplus analyses to folder "C:/NarcSub/" (or change path of dataset in FILE command of Mplus script)
setwd("C:/NarcSub/")
fwrite(dat2b_mplus, file = "NarcSub_Mplus-Data_Supplement.csv", col.names = FALSE)
rm(dat2b_mplus)

#download the folder "NarcSub_Mplus-Scripts" via https://madata.bib.uni-mannheim.de/427/15/NarcSub_Mplus-Scripts.zip and set working directory to that folder (e.g., "C:/NarcSub/NarcSub_Mplus-Scripts/")
mplus_wd <- "C:/NarcSub/NarcSub_Mplus-Scripts/"

#run Mplus analyses
setwd(mplus_wd)
runModels("Supplement/")


##### NO ACCESS TO MPLUS #####

#download the folder "NarcSub_Mplus-Outputs" via https://madata.bib.uni-mannheim.de/427/16/NarcSub_Mplus-Outputs.zip and set working directory to that folder (e.g., "C:/NarcSub/NarcSub_Mplus-Outputs/")
mplus_wd <- "C:/NarcSub/NarcSub_Mplus-Outputs/"

##################################################################################################################################


#Separate Analyses for Narcissism Dimensions

##Table S2.1: Effects of Narcissistic Admiration (Model 1) and Rivalry (Model 2) on Attraction to Supervisor Moderated by Supervisor's Leadership Style in Studies 2a-2b

###regression models for Study 2a
moderation_model2a_adm <- lm(Zattraction ~ Znarq_adm + Zstyle_index_supervisor + ZADMxSLS, data=dat2a)
dat2a_mod_adm <- data.frame(summary(moderation_model2a_adm)$coef,confint(moderation_model2a_adm))
moderation_model2a_riv <- lm(Zattraction ~ Znarq_riv + Zstyle_index_supervisor + ZRIVxSLS, data=dat2a)
dat2a_mod_riv <- data.frame(summary(moderation_model2a_riv)$coef,confint(moderation_model2a_riv))
dat2a_mod <- rbind(dat2a_mod_adm,dat2a_mod_riv)

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output_adm <- readModels("Supplement/adm_index_moderation.out", what = "parameters")
output_est_ci_adm <- sapply(output_adm, "[", "ci.unstandardized")
est_ci_adm <- sapply(output_est_ci_adm, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p_adm <- sapply(output_adm, "[", "unstandardized")
t_p_adm <- sapply(output_t_p_adm, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci_adm <- data.frame(matrix(unlist(est_ci_adm), nrow=8, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p_adm <- data.frame(matrix(unlist(t_p_adm), nrow=8, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_mod_adm <- cbind(dat2b_est_ci_adm,dat2b_t_p_adm[,c(3:4)])
dat2b_mod_adm <- dat2b_mod_adm[c(5,1,4,2),]

output_riv <- readModels("Supplement/riv_index_moderation.out", what = "parameters")
output_est_ci_riv <- sapply(output_riv, "[", "ci.unstandardized")
est_ci_riv <- sapply(output_est_ci_riv, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p_riv <- sapply(output_riv, "[", "unstandardized")
t_p_riv <- sapply(output_t_p_riv, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci_riv <- data.frame(matrix(unlist(est_ci_riv), nrow=9, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p_riv <- data.frame(matrix(unlist(t_p_riv), nrow=9, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_mod_riv <- cbind(dat2b_est_ci_riv,dat2b_t_p_riv[,c(3:4)])
dat2b_mod_riv_simpleslopes <- dat2b_mod_riv[c(8:9),]
dat2b_mod_riv <- dat2b_mod_riv[c(4,5,3,2),]
dat2b_mod <- rbind(dat2b_mod_adm,dat2b_mod_riv)

###create table
dat2a_mod$Predictor <- c("   (Intercept)","   ADM","   SLS","   ADM x SLS","   (Intercept)","   RIV","   SLS","   RIV x SLS")
dat2a_mod$blank <- NA
dat_table_S2.1 <- cbind(dat2a_mod[,c(7,1,5:6,3:4,8)],dat2b_mod[,c(3:7)])

colnames(dat_table_S2.1) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.1[,2:12] <- sapply(dat_table_S2.1[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.1[,c(2:4,8:10)] <- dat_table_S2.1[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.1[,c(5,11)] <- dat_table_S2.1[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.1[,c(6,12)] <- dat_table_S2.1[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.1$ci1 <- paste0("[", dat_table_S2.1$ci_lower1, ", ", dat_table_S2.1$ci_upper1, "]")
dat_table_S2.1$ci2 <- paste0("[", dat_table_S2.1$ci_lower2, ", ", dat_table_S2.1$ci_upper2, "]")

dat_table_S2.1 <- dat_table_S2.1[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.1[c(1,5),c(2:5)] <- NA
dat_table_S2.1[9,1] <- "Model 1"
dat_table_S2.1[10,1] <- "Model 2"
dat_table_S2.1 <- dat_table_S2.1[c(9,1:4,10,5:8),]

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.1)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=5, j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.1.docx")

###simple slopes of the interaction between rivalry and supervisor's leadership style in Study 2b
dat2b_mod_riv_simpleslopes[,3:7] <- sapply(dat2b_mod_riv_simpleslopes[,3:7], as.numeric)
paste0("relation between rivalry and attraction to democratic supervisors: zPE = ", round(dat2b_mod_riv_simpleslopes[2,3], digits = 2), ", 95% CI [", round(dat2b_mod_riv_simpleslopes[2,4], digits = 2),   
       ", ", round(dat2b_mod_riv_simpleslopes[2,5], digits = 3), "], z = ", round(dat2b_mod_riv_simpleslopes[2,6], digits = 2), ", p = ",round(dat2b_mod_riv_simpleslopes[2,7], digits = 3))
paste0("relation between rivalry and attraction to autocratic supervisors: zPE = ", round(dat2b_mod_riv_simpleslopes[1,3], digits = 2), ", 95% CI [", round(dat2b_mod_riv_simpleslopes[1,4], digits = 2),   
       ", ", round(dat2b_mod_riv_simpleslopes[1,5], digits = 2), "], z = ",round(dat2b_mod_riv_simpleslopes[1,6], digits = 2), ", p = ", round(dat2b_mod_riv_simpleslopes[1,7], digits = 3))

rm(dat_table_S2.1, dat2a_mod, dat2a_mod_adm, dat2a_mod_riv, dat2b_est_ci_adm, dat2b_est_ci_riv, 
   dat2b_mod, dat2b_mod_adm, dat2b_mod_riv, dat2b_t_p_adm, dat2b_t_p_riv, doc, est_ci_adm, est_ci_riv, 
   head, moderation_model2a_adm, moderation_model2a_riv, output_adm, output_est_ci_adm, output_est_ci_riv, 
   output_riv, output_t_p_adm, output_t_p_riv, t_p_adm, t_p_riv, tbl, format_beta, format_p, format_t, 
   dat2b_mod_riv_simpleslopes)


##Tables S2.2 and S2.3: Moderated Mediation Model With Narcissistic Admiration (Model 1) and Rivalry (Model 2) in Studies 2a-2b

###moderated mediation models for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a_adm <- '
Zattraction ~ c*Znarq_adm + Zstyle_index_supervisor + b*Zstyle_index_self + h*ZADMxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq_adm
## index of moderated mediaion for ADM
index_adm := a * g
## conditional indirect effects of ADM
indirect_aut := a*(b-2.404079*g)
indirect_dem := a*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h'
results_adm <- sem(mediation_model2a_adm, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_adm <- data.frame(parameterEstimates(results_adm, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_riv <- '
Zattraction ~ c*Znarq_riv + Zstyle_index_supervisor + b*Zstyle_index_self + h*ZRIVxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq_riv
## index of moderated mediaion for RIV
index_riv := a * g
## conditional indirect effects of RIV
indirect_aut := a*(b-2.404079*g)
indirect_dem := a*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h'
results_riv <- sem(mediation_model2a_riv, data = dat2a, meanstructure = TRUE,
                   se = "bootstrap", bootstrap = 10000)
dat2a_modmed_riv <- data.frame(parameterEstimates(results_riv, ci = TRUE, level = 0.95,
                                                  boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat2a_modmed_index_simpleslopes_adm <- dat2a_modmed_adm[c(25:29),]
dat2a_modmed_adm <- dat2a_modmed_adm[c(6,19,1,3,2,4:5),]
dat2a_modmed_index_simpleslopes_riv <- dat2a_modmed_riv[c(25:29),]
dat2a_modmed_riv <- dat2a_modmed_riv[c(6,19,1,3,2,4:5),]
dat2a_modmed <- rbind(dat2a_modmed_adm,dat2a_modmed_riv)
dat2a_modmed_index_simpleslopes <- rbind(dat2a_modmed_index_simpleslopes_adm,dat2a_modmed_index_simpleslopes_riv)

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output_adm <- readModels("Supplement/adm_index_mediation.out", what = "parameters")
output_est_ci_adm <- sapply(output_adm, "[", "ci.unstandardized")
est_ci_adm <- sapply(output_est_ci_adm, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p_adm <- sapply(output_adm, "[", "unstandardized")
t_p_adm <- sapply(output_t_p_adm, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci_adm <- data.frame(matrix(unlist(est_ci_adm), nrow=18, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p_adm <- data.frame(matrix(unlist(t_p_adm), nrow=18, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_modmed_adm <- cbind(dat2b_est_ci_adm,dat2b_t_p_adm[,c(3:4)])
dat2b_modmed_index_simpleslopes_adm <- dat2b_modmed_adm[c(14:18),]
dat2b_modmed_adm <- dat2b_modmed_adm[c(1,8:10,7,6,5),]

output_riv <- readModels("Supplement/riv_index_mediation.out", what = "parameters")
output_est_ci_riv <- sapply(output_riv, "[", "ci.unstandardized")
est_ci_riv <- sapply(output_est_ci_riv, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p_riv <- sapply(output_riv, "[", "unstandardized")
t_p_riv <- sapply(output_t_p_riv, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci_riv <- data.frame(matrix(unlist(est_ci_riv), nrow=18, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p_riv <- data.frame(matrix(unlist(t_p_riv), nrow=18, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_modmed_riv <- cbind(dat2b_est_ci_riv,dat2b_t_p_riv[,c(3:4)])
dat2b_modmed_index_simpleslopes_riv <- dat2b_modmed_riv[c(14:18),]
dat2b_modmed_riv <- dat2b_modmed_riv[c(1,8:10,7,6,5),]
dat2b_modmed <- rbind(dat2b_modmed_adm,dat2b_modmed_riv)
dat2b_modmed_index_simpleslopes <- rbind(dat2b_modmed_index_simpleslopes_adm,dat2b_modmed_index_simpleslopes_riv)

###create Table S.2.2: Results
dat2a_modmed$Predictor <- c("   ADM","   (Intercept)","   ADM","   SHLS","   SLS","   ADM x SLS","   SHLS x SLS","   RIV","   (Intercept)","   RIV","   SHLS","   SLS","   RIV x SLS","   SHLS x SLS")
dat2a_modmed$blank <- NA
dat_table_S2.2 <- cbind(dat2a_modmed[,c(11,5,9:10,7:8,12)],dat2b_modmed[,c(3:7)])

colnames(dat_table_S2.2) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.2[,2:12] <- sapply(dat_table_S2.2[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.2[,c(2:4,8:10)] <- dat_table_S2.2[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.2[,c(5,11)] <- dat_table_S2.2[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.2[,c(6,12)] <- dat_table_S2.2[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.2$ci1 <- paste0("[", dat_table_S2.2$ci_lower1, ", ", dat_table_S2.2$ci_upper1, "]")
dat_table_S2.2$ci2 <- paste0("[", dat_table_S2.2$ci_lower2, ", ", dat_table_S2.2$ci_upper2, "]")

dat_table_S2.2 <- dat_table_S2.2[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.2[c(2,9),c(2:5)] <- NA
dat_table_S2.2[15,1] <- "Model 1"
dat_table_S2.2[16,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S2.2[17,2] <- "Attraction to Supervisor"
dat_table_S2.2[18,1] <- "Model 2"
dat_table_S2.2[19,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S2.2[20,2] <- "Attraction to Supervisor"
dat_table_S2.2 <- dat_table_S2.2[c(15:16,1,17,2:7,18:19,8,20,9:14),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.2)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=2, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=12, j=2:10, part="body")
tbl <- merge_at(tbl, i=14, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=10, j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.2.docx")

###create Table S2.3: Index of Moderated Mediation, Conditional Indirect Effects, and Conditional Direct Effects
dat2a_modmed_index_simpleslopes <- dat2a_modmed_index_simpleslopes[c(1,3,2,5,4,6,8,7,10,9),]
dat2b_modmed_index_simpleslopes <- dat2b_modmed_index_simpleslopes[c(1,3,2,5,4,6,8,7,10,9),]
dat2a_modmed_index_simpleslopes$Effect <- c("   ADM","   Democratic","   Autocratic","   Democratic","   Autocratic","   RIV","   Democratic","   Autocratic","   Democratic","   Autocratic")
dat2a_modmed_index_simpleslopes$blank <- NA
dat_table_S2.3 <- cbind(dat2a_modmed_index_simpleslopes[,c(11,5,9:10,7:8,12)],dat2b_modmed_index_simpleslopes[,c(3:7)])

colnames(dat_table_S2.3) <- c("Effect","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.3[,2:12] <- sapply(dat_table_S2.3[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) <= 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.3[,c(2:4,8:10)] <- dat_table_S2.3[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.3[,c(5,11)] <- dat_table_S2.3[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.3[,c(6,12)] <- dat_table_S2.3[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.3$ci1 <- paste0("[", dat_table_S2.3$ci_lower1, ", ", dat_table_S2.3$ci_upper1, "]")
dat_table_S2.3$ci2 <- paste0("[", dat_table_S2.3$ci_lower2, ", ", dat_table_S2.3$ci_upper2, "]")

dat_table_S2.3 <- dat_table_S2.3[,c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.3[11,1] <- "Index of Moderated Mediation for ADM in Model 1"
dat_table_S2.3[12,1] <- "Conditional Indirect Effects of ADM on Attraction to Supervisor in Model 1"
dat_table_S2.3[13,1] <- "Conditional Direct Effects of ADM on Attraction to Supervisor in Model 1"
dat_table_S2.3[14,1] <- "Index of Moderated Mediation for RIV in Model 2"
dat_table_S2.3[15,1] <- "Conditional Indirect Effects of RIV on Attraction to Supervisor in Model 2"
dat_table_S2.3[16,1] <- "Conditional Direct Effects of RIV on Attraction to Supervisor in Model 2"

dat_table_S2.3 <- dat_table_S2.3[c(11,1,12,2:3,13,4:5,14,6,15,7:8,16,9:10),]

col_keys <- c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Effect","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.3)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=1:10, part="body")
tbl <- merge_at(tbl, i=3, j=1:10, part="body")
tbl <- merge_at(tbl, i=6, j=1:10, part="body")
tbl <- merge_at(tbl, i=9, j=1:10, part="body")
tbl <- merge_at(tbl, i=11, j=1:10, part="body")
tbl <- merge_at(tbl, i=14, j=1:10, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Effect"), align="left", part="body")
tbl <- align(tbl, i = c(1,3,6,9,11,14), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Effect, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.55)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.3.docx")


rm(mediation_model2a_adm,results_adm,mediation_model2a_riv,results_riv,dat2a_modmed_adm,dat2a_modmed_riv,dat2a_modmed,dat2a_modmed_index_simpleslopes_adm,dat2a_modmed_index_simpleslopes_riv,dat2a_modmed_index_simpleslopes,
   output_adm,output_est_ci_adm,est_ci_adm,dat2b_est_ci_adm,output_t_p_adm,t_p_adm,dat2b_t_p_adm,dat2b_modmed_index_simpleslopes_adm,dat2b_modmed_adm,
   output_riv,output_est_ci_riv,est_ci_riv,dat2b_est_ci_riv,output_t_p_riv,t_p_riv,dat2b_t_p_riv,dat2b_modmed_index_simpleslopes_riv,dat2b_modmed_riv,
   dat2b_modmed,dat2b_modmed_index_simpleslopes,format_beta,format_p,format_t,head,tbl,doc,dat_table_S2.2,dat_table_S2.3)


#Separate Analyses for Leadership Measures

##Table S2.4: Descriptive Statistics and Zero-Order Correlations of Each Leadership Measure in Studies 2a-2b
dat_table_S2.4 <- data.frame(matrix(nrow=9, ncol=5))
dat_table_S2.4[,1] <- c("ADM","RIV","SHLS-Descriptions","SHLS-Behaviors","SHLS-Bipolar","SLS-Descriptions","SLS-Behaviors","SLS-Bipolar","Attraction")
dat_table_S2.4[,2] <- c(mean(dat2a$narq_adm, na.rm=T),mean(dat2a$narq_riv, na.rm=T),mean(dat2a$descriptions_self, na.rm=T),mean(dat2a$lbdq_self, na.rm=T),NA,mean(dat2a$descriptions_supervisor, na.rm=T),mean(dat2a$lbdq_supervisor, na.rm=T),NA,mean(dat2a$attraction, na.rm=T))
dat_table_S2.4[,3] <- c(sd(dat2a$narq_adm, na.rm=T),sd(dat2a$narq_riv, na.rm=T),sd(dat2a$descriptions_self, na.rm=T),sd(dat2a$lbdq_self, na.rm=T),NA,sd(dat2a$descriptions_supervisor, na.rm=T),sd(dat2a$lbdq_supervisor, na.rm=T),NA,sd(dat2a$attraction, na.rm=T))
dat_table_S2.4[,4] <- c(mean(dat2b$narq_adm, na.rm=T),mean(dat2b$narq_riv, na.rm=T),mean(dat2b$descriptions_self, na.rm=T),mean(dat2b$lbdq_self, na.rm=T),mean(dat2b$bipolar_measure_self, na.rm=T),mean(dat2b$descriptions_supervisor_team, na.rm=T),mean(dat2b$lbdq_supervisor_team, na.rm=T),mean(dat2b$bipolar_measure_supervisor_team, na.rm=T),mean(dat2b$attraction, na.rm=T))
dat_table_S2.4[,5] <- c(sd(dat2b$narq_adm, na.rm=T),sd(dat2b$narq_riv, na.rm=T),sd(dat2b$descriptions_self, na.rm=T),sd(dat2b$lbdq_self, na.rm=T),sd(dat2b$bipolar_measure_self, na.rm=T),sd(dat2b$descriptions_supervisor_team, na.rm=T),sd(dat2b$lbdq_supervisor_team, na.rm=T),sd(dat2b$bipolar_measure_supervisor_team, na.rm=T),sd(dat2b$attraction, na.rm=T))
dat2a_1 <- subset(dat2a, select = c(narq_adm,narq_riv,descriptions_self,lbdq_self,descriptions_supervisor,lbdq_supervisor,attraction))
cor_dat2a_1 <- corr.test(dat2a_1)
dat_cor_2a <- as.data.frame(matrix(unlist(cor_dat2a_1), nrow=502, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2a_r <- as.data.frame(matrix(dat_cor_2a[1:49,], nrow=7))
dat_cor_2a_p <- as.data.frame(matrix(dat_cor_2a[100:148,], nrow=7))
dat2b_1 <- subset(dat2b, select = c(narq_adm,narq_riv,descriptions_self,lbdq_self,bipolar_measure_self,descriptions_supervisor_team,lbdq_supervisor_team,bipolar_measure_supervisor_team,attraction))
cor_dat2b_1 <- corr.test(dat2b_1)
dat_cor_2b <- as.data.frame(matrix(unlist(cor_dat2b_1), nrow=842, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2b_r <- as.data.frame(matrix(dat_cor_2b[1:81,], nrow=9))
dat_cor_2b_p <- as.data.frame(matrix(dat_cor_2b[164:244,], nrow=9))
dat_cor_2a_r[c(8:9),] <- NA 
dat_cor_r <- cbind(dat_cor_2a_r[c(1:4,8,5:6,9,7),],dat_cor_2b_r)
dat_cor_2a_p[c(8:9),] <- NA 
dat_cor_p <- cbind(dat_cor_2a_p[c(1:4,8,5:6,9,7),],dat_cor_2b_p)

dat_table_S2.4[,2:5] <- sapply(dat_table_S2.4[,2:5], as.numeric)
format_msd <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.4[,2:5] <- dat_table_S2.4[,2:5] %>% mutate_if(is.numeric, format_msd)
dat_table_S2.4[9,4] <- "0.00"

dat_cor_r[,1:16] <- sapply(dat_cor_r[,1:16], as.numeric)
format_r <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_cor_r[,1:16] <- dat_cor_r[,1:16] %>% mutate_if(is.numeric, format_r)

dat_cor_p[,1:16] <- sapply(dat_cor_p[,1:16], as.numeric)
format_p <- function(x){
  ifelse(abs(x) < 0.05 & abs(x) >= 0.01,  "*", ifelse(abs(x) < 0.01 & abs(x) >= 0.001,  "**", ifelse(abs(x) < 0.001,  "***", "")))
}
dat_cor_p[,1:16] <- dat_cor_p[,1:16] %>% mutate_if(is.numeric, format_p)

dat_table_S2.4$r1 <- paste0(dat_cor_r[,1],dat_cor_p[,1])
dat_table_S2.4$r2 <- paste0(dat_cor_r[,2],dat_cor_p[,2])
dat_table_S2.4$r3 <- paste0(dat_cor_r[,3],dat_cor_p[,3])
dat_table_S2.4$r4 <- paste0(dat_cor_r[,4],dat_cor_p[,4])
dat_table_S2.4$r5 <- NA
dat_table_S2.4$r6 <- paste0(dat_cor_r[,5],dat_cor_p[,6])
dat_table_S2.4$r7 <- paste0(dat_cor_r[,6],dat_cor_p[,7])
dat_table_S2.4$r8 <- NA
dat_table_S2.4$r9 <- paste0(dat_cor_r[,7],dat_cor_p[,7])
dat_table_S2.4[1,6] <- NA
dat_table_S2.4[1,7] <- paste0(dat_cor_r[2,8],dat_cor_p[2,8])
dat_table_S2.4[1,8] <- paste0(dat_cor_r[3,8],dat_cor_p[3,8])
dat_table_S2.4[1,9] <- paste0(dat_cor_r[4,8],dat_cor_p[4,8])
dat_table_S2.4[1,10] <- paste0(dat_cor_r[5,8],dat_cor_p[5,8])
dat_table_S2.4[1,11] <- paste0(dat_cor_r[6,8],dat_cor_p[6,8])
dat_table_S2.4[1,12] <- paste0(dat_cor_r[7,8],dat_cor_p[7,8])
dat_table_S2.4[1,13] <- paste0(dat_cor_r[8,8],dat_cor_p[8,8])
dat_table_S2.4[1,14] <- paste0(dat_cor_r[9,8],dat_cor_p[9,8])
dat_table_S2.4[2,7] <- NA
dat_table_S2.4[2,8] <- paste0(dat_cor_r[3,9],dat_cor_p[3,9])
dat_table_S2.4[2,9] <- paste0(dat_cor_r[4,9],dat_cor_p[4,9])
dat_table_S2.4[2,10] <- paste0(dat_cor_r[5,9],dat_cor_p[5,9])
dat_table_S2.4[2,11] <- paste0(dat_cor_r[6,9],dat_cor_p[6,9])
dat_table_S2.4[2,12] <- paste0(dat_cor_r[7,9],dat_cor_p[7,9])
dat_table_S2.4[2,13] <- paste0(dat_cor_r[8,9],dat_cor_p[8,9])
dat_table_S2.4[2,14] <- paste0(dat_cor_r[9,9],dat_cor_p[9,9])
dat_table_S2.4[3,8] <- NA
dat_table_S2.4[3,9] <- paste0(dat_cor_r[4,10],dat_cor_p[4,10])
dat_table_S2.4[3,10] <- paste0(dat_cor_r[5,10],dat_cor_p[5,10])
dat_table_S2.4[3,11] <- paste0(dat_cor_r[6,10],dat_cor_p[6,10])
dat_table_S2.4[3,12] <- paste0(dat_cor_r[7,10],dat_cor_p[7,10])
dat_table_S2.4[3,13] <- paste0(dat_cor_r[8,10],dat_cor_p[8,10])
dat_table_S2.4[3,14] <- paste0(dat_cor_r[9,10],dat_cor_p[9,10])
dat_table_S2.4[4,9] <- NA
dat_table_S2.4[4,10] <- paste0(dat_cor_r[5,11],dat_cor_p[5,11])
dat_table_S2.4[4,11] <- paste0(dat_cor_r[6,11],dat_cor_p[6,11])
dat_table_S2.4[4,12] <- paste0(dat_cor_r[7,11],dat_cor_p[7,11])
dat_table_S2.4[4,13] <- paste0(dat_cor_r[8,11],dat_cor_p[8,11])
dat_table_S2.4[4,14] <- paste0(dat_cor_r[9,11],dat_cor_p[9,11])
dat_table_S2.4[5,10] <- NA
dat_table_S2.4[5,11] <- paste0(dat_cor_r[6,12],dat_cor_p[6,12])
dat_table_S2.4[5,12] <- paste0(dat_cor_r[7,12],dat_cor_p[7,12])
dat_table_S2.4[5,13] <- paste0(dat_cor_r[8,12],dat_cor_p[8,12])
dat_table_S2.4[5,14] <- paste0(dat_cor_r[9,12],dat_cor_p[9,12])
dat_table_S2.4[6,11] <- NA
dat_table_S2.4[6,12] <- paste0(dat_cor_r[7,13],dat_cor_p[7,13])
dat_table_S2.4[6,13] <- paste0(dat_cor_r[8,13],dat_cor_p[8,13])
dat_table_S2.4[6,14] <- paste0(dat_cor_r[9,13],dat_cor_p[9,13])
dat_table_S2.4[7,12] <- NA
dat_table_S2.4[7,13] <- paste0(dat_cor_r[8,14],dat_cor_p[8,14])
dat_table_S2.4[7,14] <- paste0(dat_cor_r[9,14],dat_cor_p[9,14])
dat_table_S2.4[8,13] <- NA
dat_table_S2.4[8,14] <- paste0(dat_cor_r[9,15],dat_cor_p[9,15])
dat_table_S2.4[9,14] <- NA
dat_table_S2.4[c(5,8),c(2:3,6:9)] <- NA
dat_table_S2.4[8,c(11:12)] <- NA

dat_table_S2.4$blank1 <- NA
dat_table_S2.4$blank2 <- NA
dat_table_S2.4 <- dat_table_S2.4[,c(1:3,15,4:5,16,6:14)]

col_keys <- c("X1","X2","X3","blank1","X4","X5","blank2","r1","r2","r3","r4","r5","r6","r7","r8","r9")
head1 <- c("","Study 2a","Study 2a","","Study 2b","Study 2b","","","","","","","","","","")
head2 <- c("Variable","M","SD","","M","SD","","1","2","3","4","5","6","7","8","9")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.4)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:16, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:16, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("X1"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("X2","X3","X4","X5"), part="header")
tbl <- width(tbl, j =~ X1, width=1)
tbl <- width(tbl, j =~ X2 + X3 + X4 + X5, width=.55)
tbl <- width(tbl, j =~ blank1 + blank2, width=.1)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.4.docx")

##Note of Table S2.4
###Study 2a
dat2a.descriptions_self <- subset(dat2a, select = c(description_dem_self,description_aut_self_r))
dat2a.lbdq_self <- subset(dat2a, select = c(lbdq1_self,lbdq2r_self,lbdq3_self,lbdq4r_self,lbdq5_self,
                                            lbdq6r_self,lbdq7_self,lbdq8r_self,lbdq9_self,lbdq10r_self,
                                            lbdq11_self,lbdq12r_self,lbdq13_self,lbdq14r_self,lbdq15_self,
                                            lbdq16r_self,lbdq17_self,lbdq18r_self,lbdq19_self,lbdq20r_self))
dat2a.descriptions_supervisor <- subset(dat2a, select = c(description_dem_supervisor,description_aut_supervisor_r))
dat2a.lbdq_supervisor <- subset(dat2a, select = c(lbdq1_supervisor,lbdq2r_supervisor,lbdq3_supervisor,lbdq4r_supervisor,lbdq5_supervisor,
                                                  lbdq6r_supervisor,lbdq7_supervisor,lbdq8r_supervisor,lbdq9_supervisor,lbdq10r_supervisor,
                                                  lbdq11_supervisor,lbdq12r_supervisor,lbdq13_supervisor,lbdq14r_supervisor,lbdq15_supervisor,
                                                  lbdq16r_supervisor,lbdq17_supervisor,lbdq18r_supervisor,lbdq19_supervisor,lbdq20r_supervisor))
round(psych::alpha(dat2a.descriptions_self, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.lbdq_self, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.descriptions_supervisor, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.lbdq_supervisor, na.rm=T)$total[1], 2)
###Study 2b
dat2b.descriptions_self <- subset(dat2b, select = c(description_dem_self,description_aut_self_r))
dat2b.lbdq_self <- subset(dat2b, select = c(lbdq1_self,lbdq2r_self,lbdq3_self,lbdq4r_self,lbdq5_self,
                                            lbdq6r_self,lbdq7_self,lbdq8r_self,lbdq9_self,lbdq10r_self)) 
round(psych::alpha(dat2b.descriptions_self, na.rm=T)$total[1], 2)
round(psych::alpha(dat2b.lbdq_self, na.rm=T)$total[1], 2)
round(ICC2(aov(bipolar_measure_supervisor ~ as.factor(team),data=dat2b)),2)

rm(dat2a_1,cor_dat2a_1,dat_cor_2a,dat_cor_2a_p,dat_cor_2a_r,dat2b_1,cor_dat2b_1,dat_cor_2b,dat_cor_2b_p,dat_cor_2b_r,dat_cor_p,dat_cor_r,
   dat_table_S2.4,format_msd,format_p,format_r,head,tbl,doc,dat2a.descriptions_self,dat2a.lbdq_self,dat2a.descriptions_supervisor,dat2a.lbdq_supervisor,dat2b.descriptions_self,dat2b.lbdq_self)


##Tables S2.5 and S2.6: Unique Effects of Narcissistic Admiration and Rivalry on Attraction to Supervisor Moderated by Supervisor's Leadership Style Separately for Each Leadership Measure in Studies 2a-2b

###regression models for Study 2a
moderation_model2a_descriptions <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zdescriptions_supervisor + ZADMxSLS_descriptions + ZRIVxSLS_descriptions, data=dat2a)
moderation_model2a_lbdq <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zlbdq_supervisor + ZADMxSLS_lbdq + ZRIVxSLS_lbdq, data=dat2a)
dat2a_mod <- rbind(data.frame(summary(moderation_model2a_descriptions)$coef,confint(moderation_model2a_descriptions)),data.frame(summary(moderation_model2a_lbdq)$coef,confint(moderation_model2a_lbdq)))

###simple slope analysis for Study 2a
dat2a$Zdescriptions_supervisor_dem <- dat2a$Zdescriptions_supervisor - abs(max(dat2a$Zdescriptions_supervisor))
dat2a$Zdescriptions_supervisor_aut <- dat2a$Zdescriptions_supervisor + abs(min(dat2a$Zdescriptions_supervisor))
dat2a$Zlbdq_supervisor_dem <- dat2a$Zlbdq_supervisor - abs(max(dat2a$Zlbdq_supervisor))
dat2a$Zlbdq_supervisor_aut <- dat2a$Zlbdq_supervisor + abs(min(dat2a$Zlbdq_supervisor))
dat2a$ZADMxSLS_descriptions_dem <- scale(dat2a$Znarq_adm*dat2a$Zdescriptions_supervisor_dem)
dat2a$ZRIVxSLS_descriptions_dem <- scale(dat2a$Znarq_riv*dat2a$Zdescriptions_supervisor_dem)
dat2a$ZADMxSLS_descriptions_aut <- scale(dat2a$Znarq_adm*dat2a$Zdescriptions_supervisor_aut)
dat2a$ZRIVxSLS_descriptions_aut <- scale(dat2a$Znarq_riv*dat2a$Zdescriptions_supervisor_aut)
dat2a$ZADMxSLS_lbdq_dem <- scale(dat2a$Znarq_adm*dat2a$Zlbdq_supervisor_dem)
dat2a$ZRIVxSLS_lbdq_dem <- scale(dat2a$Znarq_riv*dat2a$Zlbdq_supervisor_dem)
dat2a$ZADMxSLS_lbdq_aut <- scale(dat2a$Znarq_adm*dat2a$Zlbdq_supervisor_aut)
dat2a$ZRIVxSLS_lbdq_aut <- scale(dat2a$Znarq_riv*dat2a$Zlbdq_supervisor_aut)

model_dem_descriptions <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zdescriptions_supervisor_dem + ZADMxSLS_descriptions_dem + ZRIVxSLS_descriptions_dem, data=dat2a)
model_aut_descriptions <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zdescriptions_supervisor_aut + ZADMxSLS_descriptions_aut + ZRIVxSLS_descriptions_aut, data=dat2a)
dat2a_mod_dem_descriptions <- data.frame(summary(model_dem_descriptions)$coef,confint(model_dem_descriptions))
dat2a_mod_aut_descriptions <- data.frame(summary(model_aut_descriptions)$coef,confint(model_aut_descriptions))
model_dem_lbdq <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zlbdq_supervisor_dem + ZADMxSLS_lbdq_dem + ZRIVxSLS_lbdq_dem, data=dat2a)
model_aut_lbdq <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zlbdq_supervisor_aut + ZADMxSLS_lbdq_aut + ZRIVxSLS_lbdq_aut, data=dat2a)
dat2a_mod_dem_lbdq <- data.frame(summary(model_dem_lbdq)$coef,confint(model_dem_lbdq))
dat2a_mod_aut_lbdq <- data.frame(summary(model_aut_lbdq)$coef,confint(model_aut_lbdq))
dat2a_mod_simpleslopes <- rbind(dat2a_mod_dem_descriptions[3,c(1,5:6,3:4)],dat2a_mod_aut_descriptions[3,c(1,5:6,3:4)],dat2a_mod_dem_lbdq[3,c(1,5:6,3:4)],dat2a_mod_aut_lbdq[3,c(1,5:6,3:4)])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/narq_descriptions_moderation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=11, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=11, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/narq_lbdq_moderation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=11, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=11, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/narq_bipolar_moderation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=12, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=12, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_mod_simpleslopes <- rbind(dat2b_mod1[c(10:11),],dat2b_mod2[c(10:11),],dat2b_mod3[c(11:12),])
dat2b_mod <- rbind(dat2b_mod1[c(6,1,7,5,2,4),],dat2b_mod2[c(6,1,7,5,2,4),],dat2b_mod3[c(5,7,6,4,2,3),])

###create Table S2.5: Results
dat2a_mod[nrow(dat2a_mod)+6,] <- NA
dat2a_mod$Predictor <- c("(Intercept)","ADM","RIV","SLS","ADM x SLS","RIV x SLS","(Intercept)","ADM","RIV","SLS","ADM x SLS","RIV x SLS","(Intercept)","ADM","RIV","SLS","ADM x SLS","RIV x SLS")
dat2a_mod$blank <- NA
dat_table_S2.5 <- cbind(dat2a_mod[,c(7,1,5:6,3:4,8)],dat2b_mod[,c(3:7)])

colnames(dat_table_S2.5) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.5[,2:12] <- sapply(dat_table_S2.5[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.5[,c(2:4,8:10)] <- dat_table_S2.5[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.5[,c(5,11)] <- dat_table_S2.5[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.5[,c(6,12)] <- dat_table_S2.5[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.5$ci1 <- paste0("[", dat_table_S2.5$ci_lower1, ", ", dat_table_S2.5$ci_upper1, "]")
dat_table_S2.5$ci2 <- paste0("[", dat_table_S2.5$ci_lower2, ", ", dat_table_S2.5$ci_upper2, "]")

dat_table_S2.5 <- dat_table_S2.5[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.5[c(1,7,13:18),c(2:5)] <- NA

dat_table_S2.5[19,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.5[20,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.5[21,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.5 <- dat_table_S2.5[c(19,1:6,20,7:12,21,13:18),]

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.5)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=8, j=2:10, part="body")
tbl <- merge_at(tbl, i=15, j=2:10, part="body")
tbl <- hline(tbl, i=c(7,14), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.5.docx")

###create Table S2.6: Conditional Total Effects of Narcissistic Rivalry on Attraction to Supervisor for Democratic and Autocratic Supervisors Separately for Each Leadership Measure
dat2a_mod_simpleslopes[nrow(dat2a_mod_simpleslopes)+2,] <- NA
dat2a_mod_simpleslopes$SLS <- c("Democratic","Autocratic","Democratic","Autocratic","Democratic","Autocratic")
dat2a_mod_simpleslopes$blank <- NA
dat_table_S2.6 <- cbind(dat2a_mod_simpleslopes[,c(6,1:5,7)],dat2b_mod_simpleslopes[c(2,1,4,3,6,5),c(3:7)])

colnames(dat_table_S2.6) <- c("SLS","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.6[,2:12] <- sapply(dat_table_S2.6[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.6[,c(2:4,8:10)] <- dat_table_S2.6[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.6[,c(5,11)] <- dat_table_S2.6[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.6[,c(6,12)] <- dat_table_S2.6[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.6$ci1 <- paste0("[", dat_table_S2.6$ci_lower1, ", ", dat_table_S2.6$ci_upper1, "]")
dat_table_S2.6$ci2 <- paste0("[", dat_table_S2.6$ci_lower2, ", ", dat_table_S2.6$ci_upper2, "]")

dat_table_S2.6 <- dat_table_S2.6[,c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.6[c(5:6),c(2:5)] <- NA

dat_table_S2.6[7,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.6[8,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.6[9,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.6 <- dat_table_S2.6[c(7,1:2,8,3:4,9,5:6),]

col_keys <- c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("SLS","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.6)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=7, j=2:10, part="body")
tbl <- hline(tbl, i=c(3,6), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("SLS"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ SLS, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.6.docx")

##Note of Tables S2.6, S2.8, S2.14, and S2.16
###Study 2a
max(dat2a$descriptions_supervisor) #leadership descriptions value of the most democratic supervisors: 7
max(dat2a$lbdq_supervisor) #leadership behaviors value of the most democratic supervisors: 4.75
min(dat2a$descriptions_supervisor) #leadership descriptions value of the most autocratic supervisors: 1
min(dat2a$lbdq_supervisor) #leadership behaviors value of the most autocratic supervisors: 1.2
boxplot(dat2a$Zdescriptions_supervisor, ylab = "Zdescriptions_supervisor") #no leadership descriptions values are outliers
boxplot(dat2a$Zlbdq_supervisor, ylab = "Zlbdq_supervisor") #no leadership behaviors values are outliers
###Study 2b
max(dat2b$descriptions_supervisor_team) #leadership descriptions value of the most democratic supervisors: 5.83
max(dat2b$lbdq_supervisor_team) #leadership behaviors value of the most democratic supervisors: 5.5
max(dat2b$bipolar_measure_supervisor_team) #the most democratic supervisors' value on the bipolar leadership measure: 10.75
min(dat2b$descriptions_supervisor_team) #leadership descriptions value of the most autocratic supervisors: 1.5
min(dat2b$lbdq_supervisor_team) #leadership behaviors value of the most autocratic supervisors: 1.63
min(dat2b$bipolar_measure_supervisor_team) #the most autocratic supervisors' value on the bipolar leadership measure: 2
boxplot(dat2b$Zdescriptions_supervisor_team, ylab = "Zdescriptions_supervisor_team") #no leadership descriptions values are outliers
boxplot(dat2b$Zlbdq_supervisor_team, ylab = "Zlbdq_supervisor_team") #no leadership behaviors values are extreme outliers
boxplot(dat2b$Zbipolar_measure_supervisor_team, ylab = "Zbipolar_measure_supervisor_team") #no values on the bipolar leadership measure are outliers

rm(moderation_model2a_descriptions,moderation_model2a_lbdq,dat2a_mod,model_dem_descriptions,model_aut_descriptions,dat2a_mod_dem_descriptions,dat2a_mod_aut_descriptions,
   model_dem_lbdq,model_aut_lbdq,dat2a_mod_dem_lbdq,dat2a_mod_aut_lbdq,dat2a_mod_simpleslopes,output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,
   output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,
   dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod,dat2b_mod_simpleslopes,dat_table_S2.5,dat_table_S2.6,format_beta,format_p,format_t,head,tbl,doc)


##Tables S2.7 and S2.8: Moderated Mediation Model With Narcissistic Admiration and Rivalry Separately for Each Leadership Measure in Studies 2a-2b

###standardized values of leadership measures for conditional effects 
####Study 2a
min(dat2a$Zdescriptions_supervisor) #standardized leadership descriptions value of the most autocratic supervisors: -1.85317
max(dat2a$Zdescriptions_supervisor) #standardized leadership descriptions value of the most democratic supervisors: 1.435109
min(dat2a$Zlbdq_supervisor) #standardized leadership behaviors value of the most autocratic supervisors: -2.721009
max(dat2a$Zlbdq_supervisor) #standardized leadership behaviors value of the most democratic supervisors: 2.040864
####Study 2b
min(dat2b$Zdescriptions_supervisor_team) #standardized leadership descriptions value of the most autocratic supervisors: -2.253091
max(dat2b$Zdescriptions_supervisor_team) #standardized leadership descriptions value of the most democratic supervisors: 1.387379
min(dat2b$Zlbdq_supervisor_team) #standardized leadership behaviors value of the most autocratic supervisors: -2.959622
max(dat2b$Zlbdq_supervisor_team) #standardized leadership behaviors value of the most democratic supervisors: 1.697091
min(dat2b$Zbipolar_measure_supervisor_team) #the most autocratic supervisors' standardized value on the bipolar leadership measure: -2.102371
max(dat2b$Zbipolar_measure_supervisor_team) #the most autocratic supervisors' standardized value on the bipolar leadership measure: 1.341297

###moderated mediation models for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a_descriptions <- '
Zattraction ~ Znarq_adm + c*Znarq_riv + Zdescriptions_supervisor + b*Zdescriptions_self + ZADMxSLS_descriptions + h*ZRIVxSLS_descriptions + g*ZSHLSxSLS_descriptions
Zdescriptions_self ~ a1*Znarq_adm + a2*Znarq_riv
## index of moderated mediaion
index_adm := a1 * g
index_riv := a2 * g
## conditional effects of RIV
indirect_aut := a2*(b-1.85317*g)
indirect_dem := a2*(b+1.435109*g)
direct_aut := c-1.85317*h
direct_dem := c+1.435109*h
## conditional effects of SHLS
SHLS_aut := b-1.85317*g
SHLS_dem := b+1.435109*g'
results_descriptions <- sem(mediation_model2a_descriptions, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_descriptions <- data.frame(parameterEstimates(results_descriptions, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_lbdq <- '
Zattraction ~ Znarq_adm + c*Znarq_riv + Zlbdq_supervisor + b*Zlbdq_self + ZADMxSLS_lbdq + h*ZRIVxSLS_lbdq + g*ZSHLSxSLS_lbdq
Zlbdq_self ~ a1*Znarq_adm + a2*Znarq_riv
## index of moderated mediaion
index_adm := a1 * g
index_riv := a2 * g
## conditional effects of RIV
indirect_aut := a2*(b-2.721009*g)
indirect_dem := a2*(b+2.040864*g)
direct_aut := c-2.721009*h
direct_dem := c+2.040864*h
## conditional effects of SHLS
SHLS_aut := b-2.721009*g
SHLS_dem := b+2.040864*g'
results_lbdq <- sem(mediation_model2a_lbdq, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_lbdq <- data.frame(parameterEstimates(results_lbdq, ci = TRUE, level = 0.95,
                                                    boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat2a_modmed_index_simpleslopes <- rbind(dat2a_modmed_descriptions[c(41:48),],dat2a_modmed_lbdq[c(41:48),])
dat2a_modmed <- rbind(dat2a_modmed_descriptions[c(8:9,33,1:2,4,3,5:7),],dat2a_modmed_lbdq[c(8:9,33,1:2,4,3,5:7),])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/narq_descriptions_mediation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=24, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=24, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/narq_lbdq_mediation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=24, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=24, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/narq_bipolar_mediation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=25, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=25, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_modmed_simpleslopes <- rbind(dat2b_mod1[c(17:24),],dat2b_mod2[c(17:24),],dat2b_mod3[c(18:25),])
dat2b_modmed <- rbind(dat2b_mod1[c(4,3,11,1,12,13,10,2,9,8),],dat2b_mod2[c(4,3,11,1,12,13,10,2,9,8),],dat2b_mod3[c(2,1,10,12,11,13,9,6,8,7),])

###create Table S2.7: Results
dat2a_modmed[nrow(dat2a_modmed)+10,] <- NA
dat2a_modmed$Predictor <- c("ADM","RIV","(Intercept)","ADM","RIV","SHLS","SLS","ADM x SLS","RIV x SLS","SHLS x SLS","ADM","RIV","(Intercept)","ADM","RIV","SHLS","SLS","ADM x SLS","RIV x SLS","SHLS x SLS","ADM","RIV","(Intercept)","ADM","RIV","SHLS","SLS","ADM x SLS","RIV x SLS","SHLS x SLS")
dat2a_modmed$blank <- NA
dat_table_S2.7 <- cbind(dat2a_modmed[,c(11,5,9:10,7:8,12)],dat2b_modmed[,c(3:7)])

colnames(dat_table_S2.7) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.7[,2:12] <- sapply(dat_table_S2.7[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.7[,c(2:4,8:10)] <- dat_table_S2.7[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.7[,c(5,11)] <- dat_table_S2.7[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.7[,c(6,12)] <- dat_table_S2.7[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.7$ci1 <- paste0("[", dat_table_S2.7$ci_lower1, ", ", dat_table_S2.7$ci_upper1, "]")
dat_table_S2.7$ci2 <- paste0("[", dat_table_S2.7$ci_lower2, ", ", dat_table_S2.7$ci_upper2, "]")

dat_table_S2.7 <- dat_table_S2.7[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.7[c(3,13,21:30),c(2:5)] <- NA
dat_table_S2.7[31,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.7[32,2] <- "Outcome: SHLS"
dat_table_S2.7[33,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.7[34,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.7[35,2] <- "Outcome: SHLS"
dat_table_S2.7[36,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.7[37,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.7[38,2] <- "Outcome: SHLS"
dat_table_S2.7[39,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.7 <- dat_table_S2.7[c(31:32,1:2,33,3:10,34:35,11:12,36,13:20,37:38,21:22,39,23:30),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.7)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=2:10, part="body")
tbl <- merge_at(tbl, i=5, j=2:10, part="body")
tbl <- merge_at(tbl, i=14, j=2:10, part="body")
tbl <- merge_at(tbl, i=15, j=2:10, part="body")
tbl <- merge_at(tbl, i=18, j=2:10, part="body")
tbl <- merge_at(tbl, i=27, j=2:10, part="body")
tbl <- merge_at(tbl, i=28, j=2:10, part="body")
tbl <- merge_at(tbl, i=31, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(13,26), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.7.docx")

###create Table S2.8: Index of Moderated Mediation, Conditional Indirect Effects, and Conditional Direct Effects
dat2a_modmed_index_simpleslopes[nrow(dat2a_modmed_index_simpleslopes)+8,] <- NA
dat2a_modmed_index_simpleslopes <- dat2a_modmed_index_simpleslopes[c(8,7,1:2,4,3,6,5,16,15,9:10,12,11,14,13,17:24),]
dat2a_modmed_index_simpleslopes$Effect <- c("   Democratic","   Autocratic","   ADM","   RIV","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   ADM","   RIV","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   ADM","   RIV","   Democratic","   Autocratic","   Democratic","   Autocratic")
dat2a_modmed_index_simpleslopes$blank <- NA
dat_table_S2.8 <- cbind(dat2a_modmed_index_simpleslopes[,c(11,5,9:10,7:8,12)],dat2b_modmed_simpleslopes[c(8,7,2,1,4,3,6,5,16,15,10,9,12,11,14,13,24,23,18,17,20,19,22,21),c(3:7)])

colnames(dat_table_S2.8) <- c("Effect","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.8[,2:12] <- sapply(dat_table_S2.8[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.8[,c(2:4,8:10)] <- dat_table_S2.8[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.8[,c(5,11)] <- dat_table_S2.8[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.8[,c(6,12)] <- dat_table_S2.8[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.8$ci1 <- paste0("[", dat_table_S2.8$ci_lower1, ", ", dat_table_S2.8$ci_upper1, "]")
dat_table_S2.8$ci2 <- paste0("[", dat_table_S2.8$ci_lower2, ", ", dat_table_S2.8$ci_upper2, "]")

dat_table_S2.8 <- dat_table_S2.8[,c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.8[c(17:24),c(2:5)] <- NA

dat_table_S2.8[25,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.8[26,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.8[27,1] <- "Index of Moderated Mediation"
dat_table_S2.8[28,1] <- "Conditional Indirect Effects of RIV on Attraction to Supervisor"
dat_table_S2.8[29,1] <- "Conditional Direct Effects of RIV on Attraction to Supervisor"
dat_table_S2.8[30,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.8[31,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.8[32,1] <- "Index of Moderated Mediation"
dat_table_S2.8[33,1] <- "Conditional Indirect Effects of RIV on Attraction to Supervisor"
dat_table_S2.8[34,1] <- "Conditional Direct Effects of RIV on Attraction to Supervisor"
dat_table_S2.8[35,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.8[36,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.8[37,1] <- "Index of Moderated Mediation"
dat_table_S2.8[38,1] <- "Conditional Indirect Effects of RIV on Attraction to Supervisor"
dat_table_S2.8[39,1] <- "Conditional Direct Effects of RIV on Attraction to Supervisor"

dat_table_S2.8 <- dat_table_S2.8[c(25:26,1:2,27,3:4,28,5:6,29,7:8,30:31,9:10,32,11:12,33,13:14,34,15:16,35:36,17:18,37,19:20,38,21:22,39,23:24),]

col_keys <- c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Effect","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.8)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=14, j=2:10, part="body")
tbl <- merge_at(tbl, i=27, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=1:10, part="body")
tbl <- merge_at(tbl, i=5, j=1:10, part="body")
tbl <- merge_at(tbl, i=8, j=1:10, part="body")
tbl <- merge_at(tbl, i=11, j=1:10, part="body")
tbl <- merge_at(tbl, i=15, j=1:10, part="body")
tbl <- merge_at(tbl, i=18, j=1:10, part="body")
tbl <- merge_at(tbl, i=21, j=1:10, part="body")
tbl <- merge_at(tbl, i=24, j=1:10, part="body")
tbl <- merge_at(tbl, i=28, j=1:10, part="body")
tbl <- merge_at(tbl, i=31, j=1:10, part="body")
tbl <- merge_at(tbl, i=34, j=1:10, part="body")
tbl <- merge_at(tbl, i=37, j=1:10, part="body")
tbl <- hline(tbl, i=c(13,26), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Effect"), align="left", part="body")
tbl <- align(tbl, i = c(2,5,8,11,15,18,21,24,28,31,34,37), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Effect, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.55)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.8.docx")

rm(mediation_model2a_descriptions,results_descriptions,dat2a_modmed_descriptions,mediation_model2a_lbdq,results_lbdq,dat2a_modmed_lbdq,dat2a_modmed,dat2a_modmed_index_simpleslopes,
   output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,
   output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_modmed,dat2b_modmed_simpleslopes,
   dat_table_S2.7,dat_table_S2.8,format_beta,format_p,format_t,head,tbl,doc)


#Completion Order in Study 2a

##Table S2.9: Unique Effects of Narcissistic Admiration and Rivalry on Attraction to Supervisor Moderated by Supervisor's Leadership Style and 
##            Completion Order of Supervisor's Leadership Style and Subordinate's Hypothetical Leadership Style in Study 2a

###regression model
model_order <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zstyle_index_supervisor + Zorder + ZADMxSLS + ZRIVxSLS + ZADMxORD + ZRIVxORD + ZSLSxORD + ZADMxSLSxORD + ZRIVxSLSxORD, data=dat2a)
dat_table_S2.9 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S2.9 <- dat_table_S2.9[!(row.names(dat_table_S2.9) %in% "(Intercept)"), ]
dat_table_S2.9$Predictor <- c("ADM","RIV","SLS","Order","ADM x SLS","RIV x SLS","ADM x Order","RIV x Order","SLS x Order","ADM x SLS x Order","RIV x SLS x Order")
dat_table_S2.9 <- dat_table_S2.9[,c(7,1,5:6,3:4)]

colnames(dat_table_S2.9) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S2.9[,2:6] <- sapply(dat_table_S2.9[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.9[,c(2:4)] <- dat_table_S2.9[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.9[,5] <- sapply(dat_table_S2.9[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.9[,6] <- sapply(dat_table_S2.9[,6], format_p)

dat_table_S2.9$ci <- paste0("[", dat_table_S2.9$ci_lower, ", ", dat_table_S2.9$ci_upper, "]")

dat_table_S2.9 <- dat_table_S2.9[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S2.9)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.6)
tbl <- width(tbl, j =~ beta, width=.5)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t + p, width=.6)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.9.docx")

##Footnote S2: simple slopes of three-way interaction between admiration, supervisor's leadership Style, and completion order  
dat2a$Zorder_SLS_first <- dat2a$Zorder - abs(max(dat2a$Zorder))
dat2a$Zorder_SHLS_first <- dat2a$Zorder + abs(min(dat2a$Zorder))
dat2a$Zsupervisor_dem <- dat2a$Zstyle_index_supervisor - abs(max(dat2a$Zstyle_index_supervisor))
dat2a$Zsupervisor_aut <- dat2a$Zstyle_index_supervisor + abs(min(dat2a$Zstyle_index_supervisor))
dat2a$ZADMxSLS_dem <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_dem)
dat2a$ZRIVxSLS_dem <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_dem)
dat2a$ZADMxSLS_aut <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_aut)
dat2a$ZRIVxSLS_aut <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_aut)
dat2a$ZADMxORD_SLS_first <- scale(dat2a$Znarq_adm*dat2a$Zorder_SLS_first)
dat2a$ZRIVxORD_SLS_first <- scale(dat2a$Znarq_riv*dat2a$Zorder_SLS_first)
dat2a$ZSLS_demxORD_SLS_first <- scale(dat2a$Zsupervisor_dem*dat2a$Zorder_SLS_first)
dat2a$ZSLS_autxORD_SLS_first <- scale(dat2a$Zsupervisor_aut*dat2a$Zorder_SLS_first)
dat2a$ZADMxORD_SHLS_first <- scale(dat2a$Znarq_adm*dat2a$Zorder_SHLS_first)
dat2a$ZRIVxORD_SHLS_first <- scale(dat2a$Znarq_riv*dat2a$Zorder_SHLS_first)
dat2a$ZSLS_demxORD_SHLS_first <- scale(dat2a$Zsupervisor_dem*dat2a$Zorder_SHLS_first)
dat2a$ZSLS_autxORD_SHLS_first <- scale(dat2a$Zsupervisor_aut*dat2a$Zorder_SHLS_first)
dat2a$ZADMxSLS_demxORD_SLS_first <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_dem*dat2a$Zorder_SLS_first)
dat2a$ZRIVxSLS_demxORD_SLS_first <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_dem*dat2a$Zorder_SLS_first)
dat2a$ZADMxSLS_autxORD_SLS_first <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_aut*dat2a$Zorder_SLS_first)
dat2a$ZRIVxSLS_autxORD_SLS_first <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_aut*dat2a$Zorder_SLS_first)
dat2a$ZADMxSLS_demxORD_SHLS_first <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_dem*dat2a$Zorder_SHLS_first)
dat2a$ZRIVxSLS_demxORD_SHLS_first <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_dem*dat2a$Zorder_SHLS_first)
dat2a$ZADMxSLS_autxORD_SHLS_first <- scale(dat2a$Znarq_adm*dat2a$Zsupervisor_aut*dat2a$Zorder_SHLS_first)
dat2a$ZRIVxSLS_autxORD_SHLS_first <- scale(dat2a$Znarq_riv*dat2a$Zsupervisor_aut*dat2a$Zorder_SHLS_first)

model_order_SLS_first_supervisor_dem <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zsupervisor_dem + Zorder + ZADMxSLS_dem + ZRIVxSLS_dem +   
                                             ZADMxORD_SLS_first + ZRIVxORD_SLS_first + ZSLS_demxORD_SLS_first +   
                                             ZADMxSLS_demxORD_SLS_first + ZRIVxSLS_demxORD_SLS_first, data=dat2a)
model_order_SLS_first_supervisor_aut <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zsupervisor_aut + Zorder + ZADMxSLS_aut + ZRIVxSLS_aut +   
                                             ZADMxORD_SLS_first + ZRIVxORD_SLS_first + ZSLS_autxORD_SLS_first +   
                                             ZADMxSLS_autxORD_SLS_first + ZRIVxSLS_autxORD_SLS_first, data=dat2a)
model_order_SHLS_first_supervisor_dem <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zsupervisor_dem + Zorder + ZADMxSLS_dem + ZRIVxSLS_dem +   
                                              ZADMxORD_SHLS_first + ZRIVxORD_SHLS_first + ZSLS_demxORD_SHLS_first +   
                                              ZADMxSLS_demxORD_SHLS_first + ZRIVxSLS_demxORD_SHLS_first, data=dat2a)
model_order_SHLS_first_supervisor_aut <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zsupervisor_aut + Zorder + ZADMxSLS_aut + ZRIVxSLS_aut +   
                                              ZADMxORD_SHLS_first + ZRIVxORD_SHLS_first + ZSLS_autxORD_SHLS_first +   
                                              ZADMxSLS_autxORD_SHLS_first + ZRIVxSLS_autxORD_SHLS_first, data=dat2a)
dat2a_mod_order_SLS_first_supervisor_dem <- data.frame(summary(model_order_SLS_first_supervisor_dem)$coef,confint(model_order_SLS_first_supervisor_dem))
dat2a_mod_order_SLS_first_supervisor_aut <- data.frame(summary(model_order_SLS_first_supervisor_aut)$coef,confint(model_order_SLS_first_supervisor_aut))
dat2a_mod_order_SHLS_first_supervisor_dem <- data.frame(summary(model_order_SHLS_first_supervisor_dem)$coef,confint(model_order_SHLS_first_supervisor_dem))
dat2a_mod_order_SHLS_first_supervisor_aut <- data.frame(summary(model_order_SHLS_first_supervisor_aut)$coef,confint(model_order_SHLS_first_supervisor_aut))

paste0("relation between admiration and attraction to democratic supervisors when supervisor's leadership style was completed first: beta = ", 
       round(dat2a_mod_order_SLS_first_supervisor_dem[2,1], digits= 2), ", 95% CI [", round(dat2a_mod_order_SLS_first_supervisor_dem[2,5], digits = 2), ", ",   
       round(dat2a_mod_order_SLS_first_supervisor_dem[2,6], digits = 2), "], t(144) = ", round(dat2a_mod_order_SLS_first_supervisor_dem[2,3], digits = 2), ", p = ",
       round(dat2a_mod_order_SLS_first_supervisor_dem[2,4], digits = 3))
paste0("relation between admiration and attraction to autocratic supervisors when supervisor's leadership style was completed first: beta = ", 
       round(dat2a_mod_order_SLS_first_supervisor_aut[2,1], digits= 2), ", 95% CI [", round(dat2a_mod_order_SLS_first_supervisor_aut[2,5], digits = 2), ", ",   
       round(dat2a_mod_order_SLS_first_supervisor_aut[2,6], digits = 2), "], t(144) = ", round(dat2a_mod_order_SLS_first_supervisor_aut[2,3], digits = 2), ", p = ",
       round(dat2a_mod_order_SLS_first_supervisor_aut[2,4], digits = 3))
paste0("relation between admiration and attraction to democratic supervisors when subordinate's hypothetical leadership style was completed first: beta = ", 
       round(dat2a_mod_order_SHLS_first_supervisor_dem[2,1], digits = 2), ", 95% CI [", round(dat2a_mod_order_SHLS_first_supervisor_dem[2,5], digits = 2), ", ", 
       round(dat2a_mod_order_SHLS_first_supervisor_dem[2,6], digits = 2), "], t(144) = ", round(dat2a_mod_order_SHLS_first_supervisor_dem[2,3], digits = 2), ", p = ", 
       round(dat2a_mod_order_SHLS_first_supervisor_dem[2,4], digits = 3))
paste0("relation between admiration and attraction to autocratic supervisors when subordinate's hypothetical leadership style was completed first: beta = ", 
       round(dat2a_mod_order_SHLS_first_supervisor_aut[2,1], digits = 2), ", 95% CI [", round(dat2a_mod_order_SHLS_first_supervisor_aut[2,5], digits = 2), ", ", 
       round(dat2a_mod_order_SHLS_first_supervisor_aut[2,6], digits = 2), "], t(144) = ", round(dat2a_mod_order_SHLS_first_supervisor_aut[2,3], digits = 2), ", p = ", 
       round(dat2a_mod_order_SHLS_first_supervisor_aut[2,4], digits = 3))

rm(model_order,dat_table_S2.9,format_beta,format_p,format_t,head,tbl,doc,model_order_SLS_first_supervisor_dem,model_order_SLS_first_supervisor_aut,model_order_SHLS_first_supervisor_dem,model_order_SHLS_first_supervisor_aut,
   dat2a_mod_order_SLS_first_supervisor_dem,dat2a_mod_order_SLS_first_supervisor_aut,dat2a_mod_order_SHLS_first_supervisor_dem,dat2a_mod_order_SHLS_first_supervisor_aut)


#The Big Five and Self-Esteem as Covariates

##Internal Consistencies of Covariates
###Study 2a
dat2a.agr <- subset(dat2a, select = c(bfi2, bfi7r))
dat2a.cns <- subset(dat2a, select = c(bfi3, bfi8r))
dat2a.ext <- subset(dat2a, select = c(bfi1r, bfi6))
dat2a.neu <- subset(dat2a, select = c(bfi4r, bfi9))
dat2a.opn <- subset(dat2a, select = c(bfi5, bfi10r))
dat2a.selfesteem <- subset(dat2a, select = c(se1, se2, se3r,se4,se5r,se6,se7,se8r,se9r,se10r))
round(psych::alpha(dat2a.agr, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.cns, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.ext, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.neu, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.opn, na.rm=T)$total[1], 2)
round(psych::alpha(dat2a.selfesteem, na.rm=T)$total[1], 2)
###Study 2b
dat2b.agr <- subset(dat2b, select = c(bfi2, bfi7r, bfi12))
dat2b.cns <- subset(dat2b, select = c(bfi3r, bfi8r, bfi13))
dat2b.ext <- subset(dat2b, select = c(bfi1r, bfi6, bfi11))
dat2b.neu <- subset(dat2b, select = c(bfi4, bfi9, bfi14r))
dat2b.opn <- subset(dat2b, select = c(bfi5, bfi10r, bfi15))
round(psych::alpha(dat2b.agr, na.rm=T)$total[1], 2)
round(psych::alpha(dat2b.cns, na.rm=T)$total[1], 2)
round(psych::alpha(dat2b.ext, na.rm=T)$total[1], 2)
round(psych::alpha(dat2b.neu, na.rm=T)$total[1], 2)
round(psych::alpha(dat2b.opn, na.rm=T)$total[1], 2)
rm(dat2a.agr,dat2a.cns,dat2a.ext,dat2a.neu,dat2a.opn,dat2a.selfesteem,dat2b.agr, dat2b.cns,dat2b.ext,dat2b.neu,dat2b.opn)

##Table S2.10: Descriptive Statistics and Zero-Order Correlations of the Big Five and Self-Esteem in Studies 2a-2b
dat_table_S2.10 <- data.frame(matrix(nrow=11, ncol=5))
dat_table_S2.10[,1] <- c("ADM","RIV","AGR","CNS","EXT","NEU","OPN","SE","SHLS","SLS","Attraction")
dat_table_S2.10[,2] <- c(mean(dat2a$narq_adm, na.rm=T),mean(dat2a$narq_riv, na.rm=T),mean(dat2a$agr, na.rm=T),mean(dat2a$cns, na.rm=T),mean(dat2a$ext, na.rm=T),mean(dat2a$neu, na.rm=T),mean(dat2a$opn, na.rm=T),mean(dat2a$selfesteem, na.rm=T),mean(dat2a$style_index_self, na.rm=T),mean(dat2a$style_index_supervisor, na.rm=T),mean(dat2a$attraction, na.rm=T))
dat_table_S2.10[,3] <- c(sd(dat2a$narq_adm, na.rm=T),sd(dat2a$narq_riv, na.rm=T),sd(dat2a$agr, na.rm=T),sd(dat2a$cns, na.rm=T),sd(dat2a$ext, na.rm=T),sd(dat2a$neu, na.rm=T),sd(dat2a$opn, na.rm=T),sd(dat2a$selfesteem, na.rm=T),sd(dat2a$style_index_self, na.rm=T),sd(dat2a$style_index_supervisor, na.rm=T),sd(dat2a$attraction, na.rm=T))
dat_table_S2.10[,4] <- c(mean(dat2b$narq_adm, na.rm=T),mean(dat2b$narq_riv, na.rm=T),mean(dat2b$agr, na.rm=T),mean(dat2b$cns, na.rm=T),mean(dat2b$ext, na.rm=T),mean(dat2b$neu, na.rm=T),mean(dat2b$opn, na.rm=T),mean(dat2b$selfesteem, na.rm=T),mean(dat2b$style_index_self, na.rm=T),mean(dat2b$style_index_supervisor_team, na.rm=T),mean(dat2b$attraction, na.rm=T))
dat_table_S2.10[,5] <- c(sd(dat2b$narq_adm, na.rm=T),sd(dat2b$narq_riv, na.rm=T),sd(dat2b$agr, na.rm=T),sd(dat2b$cns, na.rm=T),sd(dat2b$ext, na.rm=T),sd(dat2b$neu, na.rm=T),sd(dat2b$opn, na.rm=T),sd(dat2b$selfesteem, na.rm=T),sd(dat2b$style_index_self, na.rm=T),sd(dat2b$style_index_supervisor_team, na.rm=T),sd(dat2b$attraction, na.rm=T))
dat2a_1 <- subset(dat2a, select = c(narq_adm,narq_riv,agr,cns,ext,neu,opn,selfesteem,style_index_self,style_index_supervisor,attraction))
cor_dat2a_1 <- corr.test(dat2a_1)
dat_cor_2a <- as.data.frame(matrix(unlist(cor_dat2a_1), nrow=1270, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2a_r <- as.data.frame(matrix(dat_cor_2a[1:121,], nrow=11))
dat_cor_2a_p <- as.data.frame(matrix(dat_cor_2a[244:364,], nrow=11))
dat2b_1 <- subset(dat2b, select = c(narq_adm,narq_riv,agr,cns,ext,neu,opn,selfesteem,style_index_self,style_index_supervisor_team,attraction))
cor_dat2b_1 <- corr.test(dat2b_1)
dat_cor_2b <- as.data.frame(matrix(unlist(cor_dat2b_1), nrow=1270, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2b_r <- as.data.frame(matrix(dat_cor_2b[1:121,], nrow=11))
dat_cor_2b_p <- as.data.frame(matrix(dat_cor_2b[244:364,], nrow=11))

dat_cor_r <- cbind(dat_cor_2a_r,dat_cor_2b_r)
dat_cor_p <- cbind(dat_cor_2a_p,dat_cor_2b_p)

dat_table_S2.10[,2:5] <- sapply(dat_table_S2.10[,2:5], as.numeric)
format_msd <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.10[,2:5] <- dat_table_S2.10[,2:5] %>% mutate_if(is.numeric, format_msd)
dat_table_S2.10[9,2] <- "0.00"
dat_table_S2.10[11,4] <- "0.00"

dat_cor_r[,1:22] <- sapply(dat_cor_r[,1:22], as.numeric)
format_r <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_cor_r[,1:22] <- dat_cor_r[,1:22] %>% mutate_if(is.numeric, format_r)

dat_cor_p[,1:22] <- sapply(dat_cor_p[,1:22], as.numeric)
format_p <- function(x){
  ifelse(abs(x) < 0.05 & abs(x) >= 0.01,  "*", ifelse(abs(x) < 0.01 & abs(x) >= 0.001,  "**", ifelse(abs(x) < 0.001,  "***", "")))
}
dat_cor_p[,1:22] <- dat_cor_p[,1:22] %>% mutate_if(is.numeric, format_p)

dat_table_S2.10$r1 <- paste0(dat_cor_r[,1],dat_cor_p[,1])
dat_table_S2.10$r2 <- paste0(dat_cor_r[,2],dat_cor_p[,2])
dat_table_S2.10$r3 <- paste0(dat_cor_r[,3],dat_cor_p[,3])
dat_table_S2.10$r4 <- paste0(dat_cor_r[,4],dat_cor_p[,4])
dat_table_S2.10$r5 <- paste0(dat_cor_r[,5],dat_cor_p[,5])
dat_table_S2.10$r6 <- paste0(dat_cor_r[,6],dat_cor_p[,6])
dat_table_S2.10$r7 <- paste0(dat_cor_r[,7],dat_cor_p[,7])
dat_table_S2.10$r8 <- paste0(dat_cor_r[,8],dat_cor_p[,8])
dat_table_S2.10$r9 <- paste0(dat_cor_r[,9],dat_cor_p[,9])
dat_table_S2.10$r10 <- paste0(dat_cor_r[,10],dat_cor_p[,10])
dat_table_S2.10$r11 <- paste0(dat_cor_r[,11],dat_cor_p[,11])
dat_table_S2.10[1,6] <- NA
dat_table_S2.10[1,7] <- paste0(dat_cor_r[2,12],dat_cor_p[2,12])
dat_table_S2.10[1,8] <- paste0(dat_cor_r[3,12],dat_cor_p[3,12])
dat_table_S2.10[1,9] <- paste0(dat_cor_r[4,12],dat_cor_p[4,12])
dat_table_S2.10[1,10] <- paste0(dat_cor_r[5,12],dat_cor_p[5,12])
dat_table_S2.10[1,11] <- paste0(dat_cor_r[6,12],dat_cor_p[6,12])
dat_table_S2.10[1,12] <- paste0(dat_cor_r[7,12],dat_cor_p[7,12])
dat_table_S2.10[1,13] <- paste0(dat_cor_r[8,12],dat_cor_p[8,12])
dat_table_S2.10[1,14] <- paste0(dat_cor_r[9,12],dat_cor_p[9,12])
dat_table_S2.10[1,15] <- paste0(dat_cor_r[10,12],dat_cor_p[10,12])
dat_table_S2.10[1,16] <- paste0(dat_cor_r[11,12],dat_cor_p[11,12])
dat_table_S2.10[2,7] <- NA
dat_table_S2.10[2,8] <- paste0(dat_cor_r[3,13],dat_cor_p[3,13])
dat_table_S2.10[2,9] <- paste0(dat_cor_r[4,13],dat_cor_p[4,13])
dat_table_S2.10[2,10] <- paste0(dat_cor_r[5,13],dat_cor_p[5,13])
dat_table_S2.10[2,11] <- paste0(dat_cor_r[6,13],dat_cor_p[6,13])
dat_table_S2.10[2,12] <- paste0(dat_cor_r[7,13],dat_cor_p[7,13])
dat_table_S2.10[2,13] <- paste0(dat_cor_r[8,13],dat_cor_p[8,13])
dat_table_S2.10[2,14] <- paste0(dat_cor_r[9,13],dat_cor_p[9,13])
dat_table_S2.10[2,15] <- paste0(dat_cor_r[10,13],dat_cor_p[10,13])
dat_table_S2.10[2,16] <- paste0(dat_cor_r[11,13],dat_cor_p[11,13])
dat_table_S2.10[3,8] <- NA
dat_table_S2.10[3,9] <- paste0(dat_cor_r[4,14],dat_cor_p[4,14])
dat_table_S2.10[3,10] <- paste0(dat_cor_r[5,14],dat_cor_p[5,14])
dat_table_S2.10[3,11] <- paste0(dat_cor_r[6,14],dat_cor_p[6,14])
dat_table_S2.10[3,12] <- paste0(dat_cor_r[7,14],dat_cor_p[7,14])
dat_table_S2.10[3,13] <- paste0(dat_cor_r[8,14],dat_cor_p[8,14])
dat_table_S2.10[3,14] <- paste0(dat_cor_r[9,14],dat_cor_p[9,14])
dat_table_S2.10[3,15] <- paste0(dat_cor_r[10,14],dat_cor_p[10,14])
dat_table_S2.10[3,16] <- paste0(dat_cor_r[11,14],dat_cor_p[11,14])
dat_table_S2.10[4,9] <- NA
dat_table_S2.10[4,10] <- paste0(dat_cor_r[5,15],dat_cor_p[5,15])
dat_table_S2.10[4,11] <- paste0(dat_cor_r[6,15],dat_cor_p[6,15])
dat_table_S2.10[4,12] <- paste0(dat_cor_r[7,15],dat_cor_p[7,15])
dat_table_S2.10[4,13] <- paste0(dat_cor_r[8,15],dat_cor_p[8,15])
dat_table_S2.10[4,14] <- paste0(dat_cor_r[9,15],dat_cor_p[9,15])
dat_table_S2.10[4,15] <- paste0(dat_cor_r[10,15],dat_cor_p[10,15])
dat_table_S2.10[4,16] <- paste0(dat_cor_r[11,15],dat_cor_p[11,15])
dat_table_S2.10[5,10] <- NA
dat_table_S2.10[5,11] <- paste0(dat_cor_r[6,16],dat_cor_p[6,16])
dat_table_S2.10[5,12] <- paste0(dat_cor_r[7,16],dat_cor_p[7,16])
dat_table_S2.10[5,13] <- paste0(dat_cor_r[8,16],dat_cor_p[8,16])
dat_table_S2.10[5,14] <- paste0(dat_cor_r[9,16],dat_cor_p[9,16])
dat_table_S2.10[5,15] <- paste0(dat_cor_r[10,16],dat_cor_p[10,16])
dat_table_S2.10[5,16] <- paste0(dat_cor_r[11,16],dat_cor_p[11,16])
dat_table_S2.10[6,11] <- NA
dat_table_S2.10[6,12] <- paste0(dat_cor_r[7,17],dat_cor_p[7,17])
dat_table_S2.10[6,13] <- paste0(dat_cor_r[8,17],dat_cor_p[8,17])
dat_table_S2.10[6,14] <- paste0(dat_cor_r[9,17],dat_cor_p[9,17])
dat_table_S2.10[6,15] <- paste0(dat_cor_r[10,17],dat_cor_p[10,17])
dat_table_S2.10[6,16] <- paste0(dat_cor_r[11,17],dat_cor_p[11,17])
dat_table_S2.10[7,12] <- NA
dat_table_S2.10[7,13] <- paste0(dat_cor_r[8,18],dat_cor_p[8,18])
dat_table_S2.10[7,14] <- paste0(dat_cor_r[9,18],dat_cor_p[9,18])
dat_table_S2.10[7,15] <- paste0(dat_cor_r[10,18],dat_cor_p[10,18])
dat_table_S2.10[7,16] <- paste0(dat_cor_r[11,18],dat_cor_p[11,18])
dat_table_S2.10[8,13] <- NA
dat_table_S2.10[8,14] <- paste0(dat_cor_r[9,19],dat_cor_p[9,19])
dat_table_S2.10[8,15] <- paste0(dat_cor_r[10,19],dat_cor_p[10,19])
dat_table_S2.10[8,16] <- paste0(dat_cor_r[11,19],dat_cor_p[11,19])
dat_table_S2.10[9,14] <- NA
dat_table_S2.10[9,15] <- paste0(dat_cor_r[10,20],dat_cor_p[10,20])
dat_table_S2.10[9,16] <- paste0(dat_cor_r[11,20],dat_cor_p[11,20])
dat_table_S2.10[10,15] <- NA
dat_table_S2.10[10,16] <- paste0(dat_cor_r[11,21],dat_cor_p[11,21])
dat_table_S2.10[11,16] <- NA

dat_table_S2.10$blank1 <- NA
dat_table_S2.10$blank2 <- NA
dat_table_S2.10 <- dat_table_S2.10[,c(1:3,17,4:5,18,6:16)]
dat_table_S2.10[6,10] <- ".00"

col_keys <- c("X1","X2","X3","blank1","X4","X5","blank2","r1","r2","r3","r4","r5","r6","r7","r8","r9","r10","r11")
head1 <- c("","Study 2a","Study 2a","","Study 2b","Study 2b","","","","","","","","","","","","")
head2 <- c("Variable","M","SD","","M","SD","","1","2","3","4","5","6","7","8","9","10","11")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.10)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:18, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:18, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("X1"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("X2","X3","X4","X5"), part="header")
tbl <- width(tbl, j =~ X1, width=.9)
tbl <- width(tbl, j =~ X2 + X3 + X4 + X5, width=.55)
tbl <- width(tbl, j =~ blank1 + blank2, width=.05)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.10.docx")

rm(dat2a_1,cor_dat2a_1,dat_cor_2a,dat_cor_2a_p,dat_cor_2a_r,dat2b_1,cor_dat2b_1,dat_cor_2b,dat_cor_2b_p,dat_cor_2b_r,dat_cor_p,dat_cor_r,
   dat_table_S2.10,format_msd,format_p,format_r,head,tbl,doc)


##Table S2.11: Unique Effects of Narcissistic Admiration and Rivalry on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###regression model for Study 2a
moderation_model2a_cov <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor 
                         + ZADMxSLS + ZRIVxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS, data=dat2a)
dat2a_mod_cov <- data.frame(summary(moderation_model2a_cov)$coef,confint(moderation_model2a_cov))

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/narq_bfi_se_index_moderation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=30, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=30, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_mod_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_mod_cov <- dat2b_mod_cov[c(11,13,12,14:19,10,2,9,3:8),]

###create table
dat2a_mod_cov$Predictor <- c("(Intercept)","ADM","RIV","AGR","CNS","EXT","NEU","OPN","SE","SLS",
                             "ADM x SLS","RIV x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS")
dat2a_mod_cov$blank <- NA
dat_table_S2.11 <- cbind(dat2a_mod_cov[,c(7,1,5:6,3:4,8)],dat2b_mod_cov[,c(3:7)])

colnames(dat_table_S2.11) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.11[,2:12] <- sapply(dat_table_S2.11[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.11[,c(2:4,8:10)] <- dat_table_S2.11[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.11[,c(5,11)] <- dat_table_S2.11[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.11[,c(6,12)] <- dat_table_S2.11[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.11$ci1 <- paste0("[", dat_table_S2.11$ci_lower1, ", ", dat_table_S2.11$ci_upper1, "]")
dat_table_S2.11$ci2 <- paste0("[", dat_table_S2.11$ci_lower2, ", ", dat_table_S2.11$ci_upper2, "]")

dat_table_S2.11 <- dat_table_S2.11[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.11[1,c(2:5)] <- NA

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.11)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.11.docx")

rm(moderation_model2a_cov,dat2a_mod_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_mod_cov,dat_table_S2.11,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.12: Results of the Moderated Mediation Model With Narcissistic Admiration and Rivalry Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###moderated mediation model for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a_cov <- '
Zattraction ~ Znarq_adm + c*Znarq_riv + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor + b*Zstyle_index_self 
            + ZADMxSLS + h*ZRIVxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a1*Znarq_adm + a2*Znarq_riv + a3*Zagr + a4*Zcns + a5*Zext + a6*Zneu + a7*Zopn + a8*Zse
##index of moderated mediation
index_adm := a1 * g
index_riv := a2 * g
index_agr := a3 * g
index_cns := a4 * g
index_ext := a5 * g
index_neu := a6 * g
index_opn := a7 * g
index_se := a8 * g
##conditional effects of RIV
indirect_aut := a2*(b-2.404079*g)
indirect_dem := a2*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h
## conditional effects of SHLS
SHLS_aut := b-2.404079*g
SHLS_dem := b+1.826888*g'
results_cov <- sem(mediation_model2a_cov, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_cov <- data.frame(parameterEstimates(results_cov, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method
dat2a_modmed_cov <- dat2a_modmed_cov[c(20:27,201,1:19),]

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/narq_bfi_se_index_mediation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=47, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=47, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_modmed_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_modmed_cov <- dat2b_modmed_cov[c(2,1,3:8,22,24,23,26:31,25,21,12,20,13:19),]

###create table
dat2a_modmed_cov$Predictor <- c("ADM","RIV","AGR","CNS","EXT","NEU","OPN","SE",
                                "(Intercept)","ADM","RIV","AGR","CNS","EXT","NEU","OPN","SE","SHLS","SLS",
                                "ADM x SLS","RIV x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS","SHLS x SLS")
dat2a_modmed_cov$blank <- NA
dat_table_S2.12 <- cbind(dat2a_modmed_cov[,c(11,5,9:10,7:8,12)],dat2b_modmed_cov[,c(3:7)])

colnames(dat_table_S2.12) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.12[,2:12] <- sapply(dat_table_S2.12[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.12[,c(2:4,8:10)] <- dat_table_S2.12[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)
dat_table_S2.12[26,2] <- ".00"

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.12[,c(5,11)] <- dat_table_S2.12[,c(5,11)] %>% mutate_if(is.numeric, format_t)
dat_table_S2.12[26,5] <- "0.00"

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.12[,c(6,12)] <- dat_table_S2.12[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.12$ci1 <- paste0("[", dat_table_S2.12$ci_lower1, ", ", dat_table_S2.12$ci_upper1, "]")
dat_table_S2.12$ci2 <- paste0("[", dat_table_S2.12$ci_lower2, ", ", dat_table_S2.12$ci_upper2, "]")

dat_table_S2.12 <- dat_table_S2.12[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.12[9,c(2:5)] <- NA
dat_table_S2.12[29,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S2.12[30,2] <- "Attraction to Supervisor"
dat_table_S2.12 <- dat_table_S2.12[c(29,1:8,30,9:28),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.12)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=10, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.12.docx")

rm(mediation_model2a_cov,results_cov,dat2a_modmed_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_modmed_cov,dat_table_S2.12,format_beta,format_t,format_p,head,tbl,doc)


#NARQ Total Score

##Internal Consistency and Descriptive Statistics of the NARQ Total Score
###Study 2a
dat2a.narq <- subset(dat2a, select = c(70:87))
round(psych::alpha(dat2a.narq, na.rm=T)$total[1], digits = 2)
round(mean(dat2a$narq),2)
round(sd(dat2a$narq),2)
rm(dat2a.narq)
###Study 2b
dat2b.narq <- subset(dat2b, select = c(41:58))
round(psych::alpha(dat2b.narq, na.rm=T)$total[1], digits = 2)
round(mean(dat2b$narq),2)
round(sd(dat2b$narq),2)
rm(dat2b.narq)

##Tables S2.13 and S2.14: Effect of the NARQ Total Score on Attraction to Supervisor Moderated by Supervisor's Leadership Style Separately for Each Leadership Measure in Studies 2a-2b

###regression models for Study 2a
moderation_model2a <- lm(Zattraction ~ Znarq + Zstyle_index_supervisor + ZNARQxSLS, data=dat2a)
moderation_model2a_descriptions <- lm(Zattraction ~ Znarq + Zdescriptions_supervisor + ZNARQxSLS_descriptions, data=dat2a)
moderation_model2a_lbdq <- lm(Zattraction ~ Znarq + Zlbdq_supervisor + ZNARQxSLS_lbdq, data=dat2a)
dat2a_mod <- rbind(data.frame(summary(moderation_model2a)$coef,confint(moderation_model2a)),data.frame(summary(moderation_model2a_descriptions)$coef,confint(moderation_model2a_descriptions)),data.frame(summary(moderation_model2a_lbdq)$coef,confint(moderation_model2a_lbdq)))

###simple slope analysis for Study 2a
dat2a$Zstyle_index_supervisor_dem <- dat2a$Zstyle_index_supervisor - abs(max(dat2a$Zstyle_index_supervisor))
dat2a$Zstyle_index_supervisor_aut <- dat2a$Zstyle_index_supervisor + abs(min(dat2a$Zstyle_index_supervisor))
dat2a$Zdescriptions_supervisor_dem <- dat2a$Zdescriptions_supervisor - abs(max(dat2a$Zdescriptions_supervisor))
dat2a$Zdescriptions_supervisor_aut <- dat2a$Zdescriptions_supervisor + abs(min(dat2a$Zdescriptions_supervisor))
dat2a$Zlbdq_supervisor_dem <- dat2a$Zlbdq_supervisor - abs(max(dat2a$Zlbdq_supervisor))
dat2a$Zlbdq_supervisor_aut <- dat2a$Zlbdq_supervisor + abs(min(dat2a$Zlbdq_supervisor))
dat2a$ZNARQxSLS_index_dem <- scale(dat2a$Znarq*dat2a$Zstyle_index_supervisor_dem)
dat2a$ZNARQxSLS_index_aut <- scale(dat2a$Znarq*dat2a$Zstyle_index_supervisor_aut)
dat2a$ZNARQxSLS_descriptions_dem <- scale(dat2a$Znarq*dat2a$Zdescriptions_supervisor_dem)
dat2a$ZNARQxSLS_descriptions_aut <- scale(dat2a$Znarq*dat2a$Zdescriptions_supervisor_aut)
dat2a$ZNARQxSLS_lbdq_dem <- scale(dat2a$Znarq*dat2a$Zlbdq_supervisor_dem)
dat2a$ZNARQxSLS_lbdq_aut <- scale(dat2a$Znarq*dat2a$Zlbdq_supervisor_aut)

model_dem <- lm(Zattraction ~ Znarq + Zstyle_index_supervisor_dem + ZNARQxSLS_index_dem, data=dat2a)
model_aut <- lm(Zattraction ~ Znarq + Zstyle_index_supervisor_aut + ZNARQxSLS_index_aut, data=dat2a)
dat2a_mod_dem <- data.frame(summary(model_dem)$coef,confint(model_dem))
dat2a_mod_aut <- data.frame(summary(model_aut)$coef,confint(model_aut))
model_dem_descriptions <- lm(Zattraction ~ Znarq + Zdescriptions_supervisor_dem + ZNARQxSLS_descriptions_dem, data=dat2a)
model_aut_descriptions <- lm(Zattraction ~ Znarq + Zdescriptions_supervisor_aut + ZNARQxSLS_descriptions_aut, data=dat2a)
dat2a_mod_dem_descriptions <- data.frame(summary(model_dem_descriptions)$coef,confint(model_dem_descriptions))
dat2a_mod_aut_descriptions <- data.frame(summary(model_aut_descriptions)$coef,confint(model_aut_descriptions))
model_dem_lbdq <- lm(Zattraction ~ Znarq + Zlbdq_supervisor_dem + ZNARQxSLS_lbdq_dem, data=dat2a)
model_aut_lbdq <- lm(Zattraction ~ Znarq + Zlbdq_supervisor_aut + ZNARQxSLS_lbdq_aut, data=dat2a)
dat2a_mod_dem_lbdq <- data.frame(summary(model_dem_lbdq)$coef,confint(model_dem_lbdq))
dat2a_mod_aut_lbdq <- data.frame(summary(model_aut_lbdq)$coef,confint(model_aut_lbdq))
dat2a_mod_simpleslopes <- rbind(dat2a_mod_dem[2,c(1,5:6,3:4)],dat2a_mod_aut[2,c(1,5:6,3:4)],dat2a_mod_dem_descriptions[2,c(1,5:6,3:4)],dat2a_mod_aut_descriptions[2,c(1,5:6,3:4)],dat2a_mod_dem_lbdq[2,c(1,5:6,3:4)],dat2a_mod_aut_lbdq[2,c(1,5:6,3:4)])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/narq_total_index_moderation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/narq_total_descriptions_moderation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/narq_total_lbdq_moderation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4 <- readModels("Supplement/narq_total_bipolar_moderation.out", what = "parameters")
output4_est_ci <- sapply(output4, "[", "ci.unstandardized")
est_ci4 <- sapply(output4_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4_t_p <- sapply(output4, "[", "unstandardized")
t_p4 <- sapply(output4_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4 <- data.frame(matrix(unlist(est_ci4), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4 <- data.frame(matrix(unlist(t_p4), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_mod4 <- cbind(dat2b_est_ci4,dat2b_t_p4[,c(3:4)])
dat2b_mod_simpleslopes <- rbind(dat2b_mod1[c(9:10),],dat2b_mod2[c(9:10),],dat2b_mod3[c(9:10),],dat2b_mod4[c(9:10),])
dat2b_mod <- rbind(dat2b_mod1[c(5,6,3,2),],dat2b_mod2[c(5,6,3,2),],dat2b_mod3[c(5,6,3,2),],dat2b_mod4[c(5,6,3,2),])

###create Table S2.13: Results
dat2a_mod[nrow(dat2a_mod)+4,] <- NA
dat2a_mod$Predictor <- c("(Intercept)","NARQ","SLS","NARQ x SLS","(Intercept)","NARQ","SLS","NARQ x SLS","(Intercept)","NARQ","SLS","NARQ x SLS","(Intercept)","NARQ","SLS","NARQ x SLS")
dat2a_mod$blank <- NA
dat_table_S2.13 <- cbind(dat2a_mod[,c(7,1,5:6,3:4,8)],dat2b_mod[,c(3:7)])

colnames(dat_table_S2.13) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.13[,2:12] <- sapply(dat_table_S2.13[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.13[,c(2:4,8:10)] <- dat_table_S2.13[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.13[,c(5,11)] <- dat_table_S2.13[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.13[,c(6,12)] <- dat_table_S2.13[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.13$ci1 <- paste0("[", dat_table_S2.13$ci_lower1, ", ", dat_table_S2.13$ci_upper1, "]")
dat_table_S2.13$ci2 <- paste0("[", dat_table_S2.13$ci_lower2, ", ", dat_table_S2.13$ci_upper2, "]")

dat_table_S2.13 <- dat_table_S2.13[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.13[c(1,5,9,13:16),c(2:5)] <- NA

dat_table_S2.13[17,2] <- "SLS Based on Index of Leadership Style"
dat_table_S2.13[18,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.13[19,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.13[20,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.13 <- dat_table_S2.13[c(17,1:4,18,5:8,19,9:12,20,13:16),]

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.13)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=6, j=2:10, part="body")
tbl <- merge_at(tbl, i=11, j=2:10, part="body")
tbl <- merge_at(tbl, i=16, j=2:10, part="body")
tbl <- hline(tbl, i=c(5,10,15), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.13.docx")

###create Table S2.14: Conditional Total Effects of the NARQ Total Score on Attraction to Supervisor for Democratic and Autocratic Supervisors Separately for Each Leadership Measure
dat2a_mod_simpleslopes[nrow(dat2a_mod_simpleslopes)+2,] <- NA
dat2a_mod_simpleslopes$SLS <- c("Democratic","Autocratic","Democratic","Autocratic","Democratic","Autocratic","Democratic","Autocratic")
dat2a_mod_simpleslopes$blank <- NA
dat_table_S2.14 <- cbind(dat2a_mod_simpleslopes[,c(6,1:5,7)],dat2b_mod_simpleslopes[c(2,1,4,3,6,5,8,7),c(3:7)])

colnames(dat_table_S2.14) <- c("SLS","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.14[,2:12] <- sapply(dat_table_S2.14[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.14[,c(2:4,8:10)] <- dat_table_S2.14[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.14[,c(5,11)] <- dat_table_S2.14[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.14[,c(6,12)] <- dat_table_S2.14[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.14$ci1 <- paste0("[", dat_table_S2.14$ci_lower1, ", ", dat_table_S2.14$ci_upper1, "]")
dat_table_S2.14$ci2 <- paste0("[", dat_table_S2.14$ci_lower2, ", ", dat_table_S2.14$ci_upper2, "]")

dat_table_S2.14 <- dat_table_S2.14[,c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.14[c(7:8),c(2:5)] <- NA

dat_table_S2.14[9,2] <- "SLS Based on Index of Leadership Style"
dat_table_S2.14[10,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.14[11,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.14[12,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.14 <- dat_table_S2.14[c(9,1:2,10,3:4,11,5:6,12,7:8),]

col_keys <- c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("SLS","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.14)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=7, j=2:10, part="body")
tbl <- merge_at(tbl, i=10, j=2:10, part="body")
tbl <- hline(tbl, i=c(3,6,9), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("SLS"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ SLS, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=1)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.14.docx")

rm(moderation_model2a,moderation_model2a_descriptions,moderation_model2a_lbdq,dat2a_mod,model_dem,model_aut,dat2a_mod_dem,dat2a_mod_aut,model_dem_descriptions,model_aut_descriptions,dat2a_mod_dem_descriptions,dat2a_mod_aut_descriptions,
   model_dem_lbdq,model_aut_lbdq,dat2a_mod_dem_lbdq,dat2a_mod_aut_lbdq,dat2a_mod_simpleslopes,output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,
   output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,output4,output4_est_ci,est_ci4,output4_t_p,t_p4,dat2b_est_ci4,dat2b_t_p4,dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod4,dat2b_mod,dat2b_mod_simpleslopes,
   dat_table_S2.13,dat_table_S2.14,format_beta,format_p,format_t,head,tbl,doc)


##Tables S2.15 and S2.16: Moderated Mediation Model With NARQ Total Score Separately for Each Leadership Measure in Studies 2a-2b

###moderated mediation models for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a <- '
Zattraction ~ c*Znarq + Zstyle_index_supervisor + b*Zstyle_index_self + h*ZNARQxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq
## index of moderated mediaion
index_narq := a * g
## conditional effects of narq
indirect_aut := a*(b-2.404079*g)
indirect_dem := a*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h
## conditional effects of SHLS
SHLS_aut := b-2.404079*g
SHLS_dem := b+1.826888*g'
results1 <- sem(mediation_model2a, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed <- data.frame(parameterEstimates(results1, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_descriptions <- '
Zattraction ~ c*Znarq + Zdescriptions_supervisor + b*Zdescriptions_self + h*ZNARQxSLS_descriptions + g*ZSHLSxSLS_descriptions
Zdescriptions_self ~ a*Znarq
## index of moderated mediaion
index_narq := a * g
## conditional effects of narq
indirect_aut := a*(b-1.85317*g)
indirect_dem := a*(b+1.435109*g)
direct_aut := c-1.85317*h
direct_dem := c+1.435109*h
## conditional effects of SHLS
SHLS_aut := b-1.85317*g
SHLS_dem := b+1.435109*g'
results2 <- sem(mediation_model2a_descriptions, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed_descriptions <- data.frame(parameterEstimates(results2, ci = TRUE, level = 0.95,
                                                           boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_lbdq <- '
Zattraction ~ c*Znarq + Zlbdq_supervisor + b*Zlbdq_self + h*ZNARQxSLS_lbdq + g*ZSHLSxSLS_lbdq
Zlbdq_self ~ a*Znarq
## index of moderated mediaion
index_narq := a * g
## conditional effects of narq
indirect_aut := a*(b-2.721009*g)
indirect_dem := a*(b+2.040864*g)
direct_aut := c-2.721009*h
direct_dem := c+2.040864*h
## conditional effects of SHLS
SHLS_aut := b-2.721009*g
SHLS_dem := b+2.040864*g'
results3 <- sem(mediation_model2a_lbdq, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed_lbdq <- data.frame(parameterEstimates(results3, ci = TRUE, level = 0.95,
                                                   boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat2a_modmed_index_simpleslopes <- rbind(dat2a_modmed[c(25:31),],dat2a_modmed_descriptions[c(25:31),],dat2a_modmed_lbdq[c(25:31),])
dat2a_modmed <- rbind(dat2a_modmed[c(6,19,1,3,2,4:5),],dat2a_modmed_descriptions[c(6,19,1,3,2,4:5),],dat2a_modmed_lbdq[c(6,19,1,3,2,4:5),])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/narq_total_index_mediation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/narq_total_descriptions_mediation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/narq_total_lbdq_mediation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4 <- readModels("Supplement/narq_total_bipolar_mediation.out", what = "parameters")
output4_est_ci <- sapply(output4, "[", "ci.unstandardized")
est_ci4 <- sapply(output4_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4_t_p <- sapply(output4, "[", "unstandardized")
t_p4 <- sapply(output4_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4 <- data.frame(matrix(unlist(est_ci4), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4 <- data.frame(matrix(unlist(t_p4), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_mod4 <- cbind(dat2b_est_ci4,dat2b_t_p4[,c(3:4)])
dat2b_modmed_simpleslopes <- rbind(dat2b_mod1[c(15:21),],dat2b_mod2[c(15:21),],dat2b_mod3[c(15:21),],dat2b_mod4[c(15:21),])
dat2b_modmed <- rbind(dat2b_mod1[c(1,9:11,7,6,5),],dat2b_mod2[c(1,9:11,7,6,5),],dat2b_mod3[c(1,9:11,7,6,5),],dat2b_mod4[c(1,9:11,7,6,5),])

###create Table S2.15: Results
dat2a_modmed[nrow(dat2a_modmed)+7,] <- NA
dat2a_modmed$Predictor <- c("NARQ","(Intercept)","NARQ","SHLS","SLS","NARQ x SLS","SHLS x SLS","NARQ","(Intercept)","NARQ","SHLS","SLS","NARQ x SLS","SHLS x SLS","NARQ","(Intercept)","NARQ","SHLS","SLS","NARQ x SLS","SHLS x SLS","NARQ","(Intercept)","NARQ","SHLS","SLS","NARQ x SLS","SHLS x SLS")
dat2a_modmed$blank <- NA
dat_table_S2.15 <- cbind(dat2a_modmed[,c(11,5,9:10,7:8,12)],dat2b_modmed[,c(3:7)])

colnames(dat_table_S2.15) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.15[,2:12] <- sapply(dat_table_S2.15[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.15[,c(2:4,8:10)] <- dat_table_S2.15[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.15[,c(5,11)] <- dat_table_S2.15[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.15[,c(6,12)] <- dat_table_S2.15[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.15$ci1 <- paste0("[", dat_table_S2.15$ci_lower1, ", ", dat_table_S2.15$ci_upper1, "]")
dat_table_S2.15$ci2 <- paste0("[", dat_table_S2.15$ci_lower2, ", ", dat_table_S2.15$ci_upper2, "]")

dat_table_S2.15 <- dat_table_S2.15[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.15[c(2,9,16,22:28),c(2:5)] <- NA
dat_table_S2.15[29,2] <- "SLS and SHLS Based on Index of Leadership Style"
dat_table_S2.15[30,2] <- "Outcome: SHLS"
dat_table_S2.15[31,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.15[32,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.15[33,2] <- "Outcome: SHLS"
dat_table_S2.15[34,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.15[35,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.15[36,2] <- "Outcome: SHLS"
dat_table_S2.15[37,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.15[38,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.15[39,2] <- "Outcome: SHLS"
dat_table_S2.15[40,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.15 <- dat_table_S2.15[c(29:30,1,31,2:7,32:33,8,34,9:14,35:36,15,37,16:21,38:39,22,40,23:28),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.15)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=11, j=2:10, part="body")
tbl <- merge_at(tbl, i=12, j=2:10, part="body")
tbl <- merge_at(tbl, i=14, j=2:10, part="body")
tbl <- merge_at(tbl, i=21, j=2:10, part="body")
tbl <- merge_at(tbl, i=22, j=2:10, part="body")
tbl <- merge_at(tbl, i=24, j=2:10, part="body")
tbl <- merge_at(tbl, i=31, j=2:10, part="body")
tbl <- merge_at(tbl, i=32, j=2:10, part="body")
tbl <- merge_at(tbl, i=34, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(10,20,30), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.15.docx")

###create Table S2.16: Index of Moderated Mediation, Conditional Indirect Effects, and Conditional Direct Effects 
dat2a_modmed_index_simpleslopes[nrow(dat2a_modmed_index_simpleslopes)+7,] <- NA
dat2a_modmed_index_simpleslopes <- dat2a_modmed_index_simpleslopes[c(7,6,1,3,2,5,4,14,13,8,10,9,12,11,21,20,15,17,16,19,18,28,27,22,24,23,26,25),]
dat2a_modmed_index_simpleslopes$Effect <- c("   Democratic","   Autocratic","   NARQ","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NARQ","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NARQ","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NARQ","   Democratic","   Autocratic","   Democratic","   Autocratic")
dat2a_modmed_index_simpleslopes$blank <- NA
dat_table_S2.16 <- cbind(dat2a_modmed_index_simpleslopes[,c(11,5,9:10,7:8,12)],dat2b_modmed_simpleslopes[c(7,6,1,3,2,5,4,14,13,8,10,9,12,11,21,20,15,17,16,19,18,28,27,22,24,23,26,25),c(3:7)])

colnames(dat_table_S2.16) <- c("Effect","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.16[,2:12] <- sapply(dat_table_S2.16[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) <= 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.16[,c(2:4,8:10)] <- dat_table_S2.16[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.16[,c(5,11)] <- dat_table_S2.16[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.16[,c(6,12)] <- dat_table_S2.16[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.16$ci1 <- paste0("[", dat_table_S2.16$ci_lower1, ", ", dat_table_S2.16$ci_upper1, "]")
dat_table_S2.16$ci2 <- paste0("[", dat_table_S2.16$ci_lower2, ", ", dat_table_S2.16$ci_upper2, "]")

dat_table_S2.16 <- dat_table_S2.16[,c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.16[c(22:28),c(2:5)] <- NA

dat_table_S2.16[29,2] <- "SLS and SHLS Based on Index of Leadership Style"
dat_table_S2.16[30,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.16[31,1] <- "Index of Moderated Mediation"
dat_table_S2.16[32,1] <- "Conditional Indirect Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[33,1] <- "Conditional Direct Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[34,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.16[35,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.16[36,1] <- "Index of Moderated Mediation"
dat_table_S2.16[37,1] <- "Conditional Indirect Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[38,1] <- "Conditional Direct Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[39,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.16[40,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.16[41,1] <- "Index of Moderated Mediation"
dat_table_S2.16[42,1] <- "Conditional Indirect Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[43,1] <- "Conditional Direct Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[44,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.16[45,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.16[46,1] <- "Index of Moderated Mediation"
dat_table_S2.16[47,1] <- "Conditional Indirect Effects of NARQ on Attraction to Supervisor"
dat_table_S2.16[48,1] <- "Conditional Direct Effects of NARQ on Attraction to Supervisor"

dat_table_S2.16 <- dat_table_S2.16[c(29:30,1:2,31,3,32,4:5,33,6:7,34:35,8:9,36,10,37,11:12,38,13:14,39:40,15:16,41,17,42,18:19,43,20:21,44:45,22:23,46,24,47,25:26,48,27:28),]

col_keys <- c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Effect","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.16)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=13, j=2:10, part="body")
tbl <- merge_at(tbl, i=25, j=2:10, part="body")
tbl <- merge_at(tbl, i=37, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=1:10, part="body")
tbl <- merge_at(tbl, i=5, j=1:10, part="body")
tbl <- merge_at(tbl, i=7, j=1:10, part="body")
tbl <- merge_at(tbl, i=10, j=1:10, part="body")
tbl <- merge_at(tbl, i=14, j=1:10, part="body")
tbl <- merge_at(tbl, i=17, j=1:10, part="body")
tbl <- merge_at(tbl, i=19, j=1:10, part="body")
tbl <- merge_at(tbl, i=22, j=1:10, part="body")
tbl <- merge_at(tbl, i=26, j=1:10, part="body")
tbl <- merge_at(tbl, i=29, j=1:10, part="body")
tbl <- merge_at(tbl, i=31, j=1:10, part="body")
tbl <- merge_at(tbl, i=34, j=1:10, part="body")
tbl <- merge_at(tbl, i=38, j=1:10, part="body")
tbl <- merge_at(tbl, i=41, j=1:10, part="body")
tbl <- merge_at(tbl, i=43, j=1:10, part="body")
tbl <- merge_at(tbl, i=46, j=1:10, part="body")
tbl <- hline(tbl, i=c(12,24,36), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Effect"), align="left", part="body")
tbl <- align(tbl, i = c(2,5,7,10,14,17,19,22,26,29,31,34,38,41,43,46), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Effect, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=1)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.16.docx")

rm(mediation_model2a,results1,mediation_model2a_descriptions,results2,dat2a_modmed_descriptions,mediation_model2a_lbdq,results3,dat2a_modmed_lbdq,dat2a_modmed,dat2a_modmed_index_simpleslopes,
   output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,
   output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,output4,output4_est_ci,est_ci4,output4_t_p,t_p4,dat2b_est_ci4,dat2b_t_p4,
   dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod4,dat2b_modmed,dat2b_modmed_simpleslopes,
   dat_table_S2.15,dat_table_S2.16,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.17: Effect of the NARQ Total Score on Attraction to Supervisor Moderated by Supervisor's Leadership Style and 
##             Completion Order of Supervisor's Leadership Style and Subordinate's Hypothetical Leadership Style in Study 2a

###regression model
model_order <- lm(Zattraction ~ Znarq + Zstyle_index_supervisor + Zorder + ZNARQxSLS + ZNARQxORD + ZSLSxORD + ZNARQxSLSxORD, data=dat2a)
dat_table_S2.17 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S2.17 <- dat_table_S2.17[!(row.names(dat_table_S2.17) %in% "(Intercept)"), ]
dat_table_S2.17$Predictor <- c("NARQ","SLS","Order","NARQ x SLS","NARQ x Order","SLS x Order","NARQ x SLS x Order")
dat_table_S2.17 <- dat_table_S2.17[,c(7,1,5:6,3:4)]

colnames(dat_table_S2.17) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S2.17[,2:6] <- sapply(dat_table_S2.17[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.17[,c(2:4)] <- dat_table_S2.17[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.17[,5] <- sapply(dat_table_S2.17[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.17[,6] <- sapply(dat_table_S2.17[,6], format_p)

dat_table_S2.17$ci <- paste0("[", dat_table_S2.17$ci_lower, ", ", dat_table_S2.17$ci_upper, "]")

dat_table_S2.17 <- dat_table_S2.17[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S2.17)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.7)
tbl <- width(tbl, j =~ beta, width=.45)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t, width=.55)
tbl <- width(tbl, j =~ p, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.17.docx")

rm(model_order,dat_table_S2.17,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.18: Zero-Order Correlations of the Big Five and Self-Esteem with the NARQ Total Score in Studies 2a-2b
dat2a_1 <- subset(dat2a, select = c(narq,agr,cns,ext,neu,opn,selfesteem))
cor_dat2a_1 <- corr.test(dat2a_1)
dat_cor_2a <- as.data.frame(matrix(unlist(cor_dat2a_1), nrow=502, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2a_r <- as.data.frame(matrix(dat_cor_2a[2:7,], nrow=6))
dat_cor_2a_p <- as.data.frame(matrix(dat_cor_2a[101:106,], nrow=6))
dat2b_1 <- subset(dat2b, select = c(narq,agr,cns,ext,neu,opn,selfesteem))
cor_dat2b_1 <- corr.test(dat2b_1)
dat_cor_2b <- as.data.frame(matrix(unlist(cor_dat2b_1), nrow=502, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2b_r <- as.data.frame(matrix(dat_cor_2b[2:7,], nrow=6))
dat_cor_2b_p <- as.data.frame(matrix(dat_cor_2b[101:106,], nrow=6))
dat_cor_r <- cbind(dat_cor_2a_r,dat_cor_2b_r)
dat_cor_p <- cbind(dat_cor_2a_p,dat_cor_2b_p)

dat_cor_r[,1:2] <- sapply(dat_cor_r[,1:2], as.numeric)
format_r <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_cor_r[,1:2] <- dat_cor_r[,1:2] %>% mutate_if(is.numeric, format_r)

dat_cor_p[,1:2] <- sapply(dat_cor_p[,1:2], as.numeric)
format_p <- function(x){
  ifelse(abs(x) < 0.05 & abs(x) >= 0.01,  "*", ifelse(abs(x) < 0.01 & abs(x) >= 0.001,  "**", ifelse(abs(x) < 0.001,  "***", "")))
}
dat_cor_p[,1:2] <- dat_cor_p[,1:2] %>% mutate_if(is.numeric, format_p)

dat_table_S2.18 <- data.frame(matrix(nrow=6, ncol=3))

dat_table_S2.18[,1] <- c("Agreeableness","Conscientiousness","Extraversion","Neuroticism","Openness","Self-Esteem")
dat_table_S2.18[,2] <- paste0(dat_cor_r[,1],dat_cor_p[,1])
dat_table_S2.18[,3] <- paste0(dat_cor_r[,2],dat_cor_p[,2])

col_keys <- c("X1","X2","X3")
head1 <- c("Variable","Study 2a","Study 2b")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S2.18)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:3, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:3, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j =~ X1, align="left", part="body")
tbl <- width(tbl, j =~ X1, width=1.4)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.18.docx")

rm(dat2a_1,cor_dat2a_1,dat_cor_2a,dat_cor_2a_p,dat_cor_2a_r,dat2b_1,cor_dat2b_1,dat_cor_2b,dat_cor_2b_p,dat_cor_2b_r,dat_cor_p,dat_cor_r,
   dat_table_S2.18,format_p,format_r,head,tbl,doc)


##Table S2.19: Effect of the NARQ Total Score on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###regression model for Study 2a
moderation_model2a_cov <- lm(Zattraction ~ Znarq + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor 
                             + ZNARQxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS, data=dat2a)
dat2a_mod_cov <- data.frame(summary(moderation_model2a_cov)$coef,confint(moderation_model2a_cov))

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/narq_total_bfi_se_index_moderation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=28, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=28, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_mod_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_mod_cov <- dat2b_mod_cov[c(11:18,9,8,2:7),]

###create table
dat2a_mod_cov$Predictor <- c("(Intercept)","NARQ","AGR","CNS","EXT","NEU","OPN","SE","SLS",
                             "NARQ x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS")
dat2a_mod_cov$blank <- NA
dat_table_S2.19 <- cbind(dat2a_mod_cov[,c(7,1,5:6,3:4,8)],dat2b_mod_cov[,c(3:7)])

colnames(dat_table_S2.19) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.19[,2:12] <- sapply(dat_table_S2.19[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.19[,c(2:4,8:10)] <- dat_table_S2.19[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.19[,c(5,11)] <- dat_table_S2.19[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.19[,c(6,12)] <- dat_table_S2.19[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.19$ci1 <- paste0("[", dat_table_S2.19$ci_lower1, ", ", dat_table_S2.19$ci_upper1, "]")
dat_table_S2.19$ci2 <- paste0("[", dat_table_S2.19$ci_lower2, ", ", dat_table_S2.19$ci_upper2, "]")

dat_table_S2.19 <- dat_table_S2.19[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.19[1,c(2:5)] <- NA

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.19)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=1)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.19.docx")

rm(moderation_model2a_cov,dat2a_mod_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_mod_cov,dat_table_S2.19,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.20: Results of the Moderated Mediation Model With the NARQ Total Score Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###moderated mediation model for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a_cov <- '
Zattraction ~ c*Znarq + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor + b*Zstyle_index_self 
            + h*ZNARQxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a1*Znarq + a2*Zagr + a3*Zcns + a4*Zext + a5*Zneu + a6*Zopn + a7*Zse
## index of moderated mediaion
index_narq := a1 * g
index_agr := a2 * g
index_cns := a3 * g
index_ext := a4 * g
index_neu := a5 * g
index_opn := a6 * g
index_se := a7 * g
## conditional effects of narq
indirect_aut := a1*(b-2.404079*g)
indirect_dem := a1*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h
## conditional effects of SHLS
SHLS_aut := b-2.404079*g
SHLS_dem := b+1.826888*g'
results_cov <- sem(mediation_model2a_cov, data = dat2a, meanstructure = TRUE,
                   se = "bootstrap", bootstrap = 10000)
dat2a_modmed_cov <- data.frame(parameterEstimates(results_cov, ci = TRUE, level = 0.95,
                                                  boot.ci.type = "bca.simple")) #bias-corrected percentile method
dat2a_modmed_cov <- dat2a_modmed_cov[c(18:24,163,1:7,9,8,10:17),]

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/narq_total_bfi_se_index_mediation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=43, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=43, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_modmed_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_modmed_cov <- dat2b_modmed_cov[c(1:7,21:22,24:29,23,19,18,11:17),]

###create table
dat2a_modmed_cov$Predictor <- c("NARQ","AGR","CNS","EXT","NEU","OPN","SE",
                                "(Intercept)","NARQ","AGR","CNS","EXT","NEU","OPN","SE","SHLS","SLS",
                                "NARQ x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS","SHLS x SLS")
dat2a_modmed_cov$blank <- NA
dat_table_S2.20 <- cbind(dat2a_modmed_cov[,c(11,5,9:10,7:8,12)],dat2b_modmed_cov[,c(3:7)])

colnames(dat_table_S2.20) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.20[,2:12] <- sapply(dat_table_S2.20[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.20[,c(2:4,8:10)] <- dat_table_S2.20[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)
dat_table_S2.20[4,8] <- ".00"

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.20[,c(5,11)] <- dat_table_S2.20[,c(5,11)] %>% mutate_if(is.numeric, format_t)
dat_table_S2.20[4,11] <- "0.00"

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.20[,c(6,12)] <- dat_table_S2.20[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.20$ci1 <- paste0("[", dat_table_S2.20$ci_lower1, ", ", dat_table_S2.20$ci_upper1, "]")
dat_table_S2.20$ci2 <- paste0("[", dat_table_S2.20$ci_lower2, ", ", dat_table_S2.20$ci_upper2, "]")

dat_table_S2.20 <- dat_table_S2.20[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.20[8,c(2:5)] <- NA
dat_table_S2.20[26,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S2.20[27,2] <- "Attraction to Supervisor"
dat_table_S2.20 <- dat_table_S2.20[c(26,1:7,27,8:25),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.20)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=9, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.20.docx")

rm(mediation_model2a_cov,results_cov,dat2a_modmed_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_modmed_cov,dat_table_S2.20,format_beta,format_t,format_p,head,tbl,doc)


#NPI-Narcissism

##Internal Consistency and Descriptive Statistics of NPI-Narcissism
###Study 2a
dat2a.npi <- subset(dat2a, select = c(30:69))
round(psych::alpha(dat2a.npi, na.rm=T)$total[1], digits = 2)
round(mean(dat2a$npi),2)
round(sd(dat2a$npi),2)
rm(dat2a.npi)
###Study 2b
dat2b.npi <- subset(dat2b, select = c(28:40))
round(psych::alpha(dat2b.npi, na.rm=T)$total[1], digits = 2) #for one item, the item-total correlation was slightly negative; we kept that item anyway because excluding that item did not alter the results.
round(mean(dat2b$npi),2)
round(sd(dat2b$npi),2)
rm(dat2b.npi)

##Tables S2.21 and S2.22: Effect of NPI-Narcissism on Attraction to Supervisor Moderated by Supervisor's Leadership Style Separately for Each Leadership Measure in Studies 2a-2b

###regression models for Study 2a
moderation_model2a <- lm(Zattraction ~ Znpi + Zstyle_index_supervisor + ZNPIxSLS, data=dat2a)
moderation_model2a_descriptions <- lm(Zattraction ~ Znpi + Zdescriptions_supervisor + ZNPIxSLS_descriptions, data=dat2a)
moderation_model2a_lbdq <- lm(Zattraction ~ Znpi + Zlbdq_supervisor + ZNPIxSLS_lbdq, data=dat2a)
dat2a_mod <- rbind(data.frame(summary(moderation_model2a)$coef,confint(moderation_model2a)),data.frame(summary(moderation_model2a_descriptions)$coef,confint(moderation_model2a_descriptions)),data.frame(summary(moderation_model2a_lbdq)$coef,confint(moderation_model2a_lbdq)))

###simple slope analysis for Study 2a
dat2a$Zstyle_index_supervisor_dem <- dat2a$Zstyle_index_supervisor - abs(max(dat2a$Zstyle_index_supervisor))
dat2a$Zstyle_index_supervisor_aut <- dat2a$Zstyle_index_supervisor + abs(min(dat2a$Zstyle_index_supervisor))
dat2a$Zdescriptions_supervisor_dem <- dat2a$Zdescriptions_supervisor - abs(max(dat2a$Zdescriptions_supervisor))
dat2a$Zdescriptions_supervisor_aut <- dat2a$Zdescriptions_supervisor + abs(min(dat2a$Zdescriptions_supervisor))
dat2a$Zlbdq_supervisor_dem <- dat2a$Zlbdq_supervisor - abs(max(dat2a$Zlbdq_supervisor))
dat2a$Zlbdq_supervisor_aut <- dat2a$Zlbdq_supervisor + abs(min(dat2a$Zlbdq_supervisor))
dat2a$ZNPIxSLS_index_dem <- scale(dat2a$Znpi*dat2a$Zstyle_index_supervisor_dem)
dat2a$ZNPIxSLS_index_aut <- scale(dat2a$Znpi*dat2a$Zstyle_index_supervisor_aut)
dat2a$ZNPIxSLS_descriptions_dem <- scale(dat2a$Znpi*dat2a$Zdescriptions_supervisor_dem)
dat2a$ZNPIxSLS_descriptions_aut <- scale(dat2a$Znpi*dat2a$Zdescriptions_supervisor_aut)
dat2a$ZNPIxSLS_lbdq_dem <- scale(dat2a$Znpi*dat2a$Zlbdq_supervisor_dem)
dat2a$ZNPIxSLS_lbdq_aut <- scale(dat2a$Znpi*dat2a$Zlbdq_supervisor_aut)

model_dem <- lm(Zattraction ~ Znpi + Zstyle_index_supervisor_dem + ZNPIxSLS_index_dem, data=dat2a)
model_aut <- lm(Zattraction ~ Znpi + Zstyle_index_supervisor_aut + ZNPIxSLS_index_aut, data=dat2a)
dat2a_mod_dem <- data.frame(summary(model_dem)$coef,confint(model_dem))
dat2a_mod_aut <- data.frame(summary(model_aut)$coef,confint(model_aut))
model_dem_descriptions <- lm(Zattraction ~ Znpi + Zdescriptions_supervisor_dem + ZNPIxSLS_descriptions_dem, data=dat2a)
model_aut_descriptions <- lm(Zattraction ~ Znpi + Zdescriptions_supervisor_aut + ZNPIxSLS_descriptions_aut, data=dat2a)
dat2a_mod_dem_descriptions <- data.frame(summary(model_dem_descriptions)$coef,confint(model_dem_descriptions))
dat2a_mod_aut_descriptions <- data.frame(summary(model_aut_descriptions)$coef,confint(model_aut_descriptions))
model_dem_lbdq <- lm(Zattraction ~ Znpi + Zlbdq_supervisor_dem + ZNPIxSLS_lbdq_dem, data=dat2a)
model_aut_lbdq <- lm(Zattraction ~ Znpi + Zlbdq_supervisor_aut + ZNPIxSLS_lbdq_aut, data=dat2a)
dat2a_mod_dem_lbdq <- data.frame(summary(model_dem_lbdq)$coef,confint(model_dem_lbdq))
dat2a_mod_aut_lbdq <- data.frame(summary(model_aut_lbdq)$coef,confint(model_aut_lbdq))
dat2a_mod_simpleslopes <- rbind(dat2a_mod_dem[2,c(1,5:6,3:4)],dat2a_mod_aut[2,c(1,5:6,3:4)],dat2a_mod_dem_descriptions[2,c(1,5:6,3:4)],dat2a_mod_aut_descriptions[2,c(1,5:6,3:4)],dat2a_mod_dem_lbdq[2,c(1,5:6,3:4)],dat2a_mod_aut_lbdq[2,c(1,5:6,3:4)])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/npi_index_moderation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/npi_descriptions_moderation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/npi_lbdq_moderation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4 <- readModels("Supplement/npi_bipolar_moderation.out", what = "parameters")
output4_est_ci <- sapply(output4, "[", "ci.unstandardized")
est_ci4 <- sapply(output4_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4_t_p <- sapply(output4, "[", "unstandardized")
t_p4 <- sapply(output4_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4 <- data.frame(matrix(unlist(est_ci4), nrow=10, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4 <- data.frame(matrix(unlist(t_p4), nrow=10, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_mod4 <- cbind(dat2b_est_ci4,dat2b_t_p4[,c(3:4)])
dat2b_mod_simpleslopes <- rbind(dat2b_mod1[c(9:10),],dat2b_mod2[c(9:10),],dat2b_mod3[c(9:10),],dat2b_mod4[c(9:10),])
dat2b_mod <- rbind(dat2b_mod1[c(5,6,3,2),],dat2b_mod2[c(5,6,3,2),],dat2b_mod3[c(5,6,3,2),],dat2b_mod4[c(5,6,3,2),])

###create Table S2.21: Results
dat2a_mod[nrow(dat2a_mod)+4,] <- NA
dat2a_mod$Predictor <- c("(Intercept)","NPI","SLS","NPI x SLS","(Intercept)","NPI","SLS","NPI x SLS","(Intercept)","NPI","SLS","NPI x SLS","(Intercept)","NPI","SLS","NPI x SLS")
dat2a_mod$blank <- NA
dat_table_S2.21 <- cbind(dat2a_mod[,c(7,1,5:6,3:4,8)],dat2b_mod[,c(3:7)])

colnames(dat_table_S2.21) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.21[,2:12] <- sapply(dat_table_S2.21[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.21[,c(2:4,8:10)] <- dat_table_S2.21[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.21[,c(5,11)] <- dat_table_S2.21[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.21[,c(6,12)] <- dat_table_S2.21[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.21$ci1 <- paste0("[", dat_table_S2.21$ci_lower1, ", ", dat_table_S2.21$ci_upper1, "]")
dat_table_S2.21$ci2 <- paste0("[", dat_table_S2.21$ci_lower2, ", ", dat_table_S2.21$ci_upper2, "]")

dat_table_S2.21 <- dat_table_S2.21[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.21[c(1,5,9,13:16),c(2:5)] <- NA

dat_table_S2.21[17,2] <- "SLS Based on Index of Leadership Style"
dat_table_S2.21[18,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.21[19,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.21[20,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.21 <- dat_table_S2.21[c(17,1:4,18,5:8,19,9:12,20,13:16),]

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.21)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=6, j=2:10, part="body")
tbl <- merge_at(tbl, i=11, j=2:10, part="body")
tbl <- merge_at(tbl, i=16, j=2:10, part="body")
tbl <- hline(tbl, i=c(5,10,15), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.21.docx")

###create Table S2.22: Conditional Total Effects of NPI-Narcissism on Attraction to Supervisor for Democratic and Autocratic Supervisors Separately for Each Leadership Measure
dat2a_mod_simpleslopes[nrow(dat2a_mod_simpleslopes)+2,] <- NA
dat2a_mod_simpleslopes$SLS <- c("Democratic","Autocratic","Democratic","Autocratic","Democratic","Autocratic","Democratic","Autocratic")
dat2a_mod_simpleslopes$blank <- NA
dat_table_S2.22 <- cbind(dat2a_mod_simpleslopes[,c(6,1:5,7)],dat2b_mod_simpleslopes[c(2,1,4,3,6,5,8,7),c(3:7)])

colnames(dat_table_S2.22) <- c("SLS","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.22[,2:12] <- sapply(dat_table_S2.22[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.22[,c(2:4,8:10)] <- dat_table_S2.22[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.22[,c(5,11)] <- dat_table_S2.22[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.22[,c(6,12)] <- dat_table_S2.22[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.22$ci1 <- paste0("[", dat_table_S2.22$ci_lower1, ", ", dat_table_S2.22$ci_upper1, "]")
dat_table_S2.22$ci2 <- paste0("[", dat_table_S2.22$ci_lower2, ", ", dat_table_S2.22$ci_upper2, "]")

dat_table_S2.22 <- dat_table_S2.22[,c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.22[c(7:8),c(2:5)] <- NA

dat_table_S2.22[9,2] <- "SLS Based on Index of Leadership Style"
dat_table_S2.22[10,2] <- "SLS Based on Leadership Descriptions"
dat_table_S2.22[11,2] <- "SLS Based on Leadership Behaviors"
dat_table_S2.22[12,2] <- "SLS Based on Bipolar Leadership Measure"

dat_table_S2.22 <- dat_table_S2.22[c(9,1:2,10,3:4,11,5:6,12,7:8),]

col_keys <- c("SLS","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("SLS","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.22)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=7, j=2:10, part="body")
tbl <- merge_at(tbl, i=10, j=2:10, part="body")
tbl <- hline(tbl, i=c(3,6,9), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("SLS"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ SLS, width=1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.22.docx")

rm(moderation_model2a,moderation_model2a_descriptions,moderation_model2a_lbdq,dat2a_mod,model_dem,model_aut,dat2a_mod_dem,dat2a_mod_aut,model_dem_descriptions,model_aut_descriptions,dat2a_mod_dem_descriptions,dat2a_mod_aut_descriptions,
   model_dem_lbdq,model_aut_lbdq,dat2a_mod_dem_lbdq,dat2a_mod_aut_lbdq,dat2a_mod_simpleslopes,output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,
   output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,output4,output4_est_ci,est_ci4,output4_t_p,t_p4,dat2b_est_ci4,dat2b_t_p4,dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod4,dat2b_mod,dat2b_mod_simpleslopes,
   dat_table_S2.21,dat_table_S2.22,format_beta,format_p,format_t,head,tbl,doc)


##Tables S2.23 and S2.24: Moderated Mediation Model With NPI-Narcissism Separately for Each Leadership Measure in Studies 2a-2b

###moderated mediation models for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a <- '
Zattraction ~ c*Znpi + Zstyle_index_supervisor + b*Zstyle_index_self + h*ZNPIxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znpi
## index of moderated mediaion
index_npi := a * g
## conditional effects of NPI
indirect_aut := a*(b-2.404079*g)
indirect_dem := a*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h
## conditional effects of SHLS
SHLS_aut := b-2.404079*g
SHLS_dem := b+1.826888*g'
results1 <- sem(mediation_model2a, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed <- data.frame(parameterEstimates(results1, ci = TRUE, level = 0.95,
                                                    boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_descriptions <- '
Zattraction ~ c*Znpi + Zdescriptions_supervisor + b*Zdescriptions_self + h*ZNPIxSLS_descriptions + g*ZSHLSxSLS_descriptions
Zdescriptions_self ~ a*Znpi
## index of moderated mediaion
index_npi := a * g
## conditional effects of NPI
indirect_aut := a*(b-1.85317*g)
indirect_dem := a*(b+1.435109*g)
direct_aut := c-1.85317*h
direct_dem := c+1.435109*h
## conditional effects of SHLS
SHLS_aut := b-1.85317*g
SHLS_dem := b+1.435109*g'
results2 <- sem(mediation_model2a_descriptions, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_descriptions <- data.frame(parameterEstimates(results2, ci = TRUE, level = 0.95,
                                                    boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_lbdq <- '
Zattraction ~ c*Znpi + Zlbdq_supervisor + b*Zlbdq_self + h*ZNPIxSLS_lbdq + g*ZSHLSxSLS_lbdq
Zlbdq_self ~ a*Znpi
## index of moderated mediaion
index_npi := a * g
## conditional effects of NPI
indirect_aut := a*(b-2.721009*g)
indirect_dem := a*(b+2.040864*g)
direct_aut := c-2.721009*h
direct_dem := c+2.040864*h
## conditional effects of SHLS
SHLS_aut := b-2.721009*g
SHLS_dem := b+2.040864*g'
results3 <- sem(mediation_model2a_lbdq, data = dat2a, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat2a_modmed_lbdq <- data.frame(parameterEstimates(results3, ci = TRUE, level = 0.95,
                                                   boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat2a_modmed_index_simpleslopes <- rbind(dat2a_modmed[c(25:31),],dat2a_modmed_descriptions[c(25:31),],dat2a_modmed_lbdq[c(25:31),])
dat2a_modmed <- rbind(dat2a_modmed[c(6,19,1,3,2,4:5),],dat2a_modmed_descriptions[c(6,19,1,3,2,4:5),],dat2a_modmed_lbdq[c(6,19,1,3,2,4:5),])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1 <- readModels("Supplement/npi_index_mediation.out", what = "parameters")
output1_est_ci <- sapply(output1, "[", "ci.unstandardized")
est_ci1 <- sapply(output1_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1_t_p <- sapply(output1, "[", "unstandardized")
t_p1 <- sapply(output1_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1 <- data.frame(matrix(unlist(est_ci1), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1 <- data.frame(matrix(unlist(t_p1), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2 <- readModels("Supplement/npi_descriptions_mediation.out", what = "parameters")
output2_est_ci <- sapply(output2, "[", "ci.unstandardized")
est_ci2 <- sapply(output2_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2_t_p <- sapply(output2, "[", "unstandardized")
t_p2 <- sapply(output2_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2 <- data.frame(matrix(unlist(est_ci2), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2 <- data.frame(matrix(unlist(t_p2), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3 <- readModels("Supplement/npi_lbdq_mediation.out", what = "parameters")
output3_est_ci <- sapply(output3, "[", "ci.unstandardized")
est_ci3 <- sapply(output3_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3_t_p <- sapply(output3, "[", "unstandardized")
t_p3 <- sapply(output3_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3 <- data.frame(matrix(unlist(est_ci3), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3 <- data.frame(matrix(unlist(t_p3), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4 <- readModels("Supplement/npi_bipolar_mediation.out", what = "parameters")
output4_est_ci <- sapply(output4, "[", "ci.unstandardized")
est_ci4 <- sapply(output4_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4_t_p <- sapply(output4, "[", "unstandardized")
t_p4 <- sapply(output4_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4 <- data.frame(matrix(unlist(est_ci4), nrow=21, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4 <- data.frame(matrix(unlist(t_p4), nrow=21, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1,dat2b_t_p1[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2,dat2b_t_p2[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3,dat2b_t_p3[,c(3:4)])
dat2b_mod4 <- cbind(dat2b_est_ci4,dat2b_t_p4[,c(3:4)])
dat2b_modmed_simpleslopes <- rbind(dat2b_mod1[c(15:21),],dat2b_mod2[c(15:21),],dat2b_mod3[c(15:21),],dat2b_mod4[c(15:21),])
dat2b_modmed <- rbind(dat2b_mod1[c(1,9:11,7,6,5),],dat2b_mod2[c(1,9:11,7,6,5),],dat2b_mod3[c(1,9:11,7,6,5),],dat2b_mod4[c(1,9:11,7,6,5),])

###create Table S2.23: Results
dat2a_modmed[nrow(dat2a_modmed)+7,] <- NA
dat2a_modmed$Predictor <- c("NPI","(Intercept)","NPI","SHLS","SLS","NPI x SLS","SHLS x SLS","NPI","(Intercept)","NPI","SHLS","SLS","NPI x SLS","SHLS x SLS","NPI","(Intercept)","NPI","SHLS","SLS","NPI x SLS","SHLS x SLS","NPI","(Intercept)","NPI","SHLS","SLS","NPI x SLS","SHLS x SLS")
dat2a_modmed$blank <- NA
dat_table_S2.23 <- cbind(dat2a_modmed[,c(11,5,9:10,7:8,12)],dat2b_modmed[,c(3:7)])

colnames(dat_table_S2.23) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.23[,2:12] <- sapply(dat_table_S2.23[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.23[,c(2:4,8:10)] <- dat_table_S2.23[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.23[,c(5,11)] <- dat_table_S2.23[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.23[,c(6,12)] <- dat_table_S2.23[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.23$ci1 <- paste0("[", dat_table_S2.23$ci_lower1, ", ", dat_table_S2.23$ci_upper1, "]")
dat_table_S2.23$ci2 <- paste0("[", dat_table_S2.23$ci_lower2, ", ", dat_table_S2.23$ci_upper2, "]")

dat_table_S2.23 <- dat_table_S2.23[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.23[c(2,9,16,22:28),c(2:5)] <- NA
dat_table_S2.23[29,2] <- "SLS and SHLS Based on Index of Leadership Style"
dat_table_S2.23[30,2] <- "Outcome: SHLS"
dat_table_S2.23[31,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.23[32,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.23[33,2] <- "Outcome: SHLS"
dat_table_S2.23[34,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.23[35,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.23[36,2] <- "Outcome: SHLS"
dat_table_S2.23[37,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.23[38,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.23[39,2] <- "Outcome: SHLS"
dat_table_S2.23[40,2] <- "Outcome: Attraction to Supervisor"
dat_table_S2.23 <- dat_table_S2.23[c(29:30,1,31,2:7,32:33,8,34,9:14,35:36,15,37,16:21,38:39,22,40,23:28),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.23)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=11, j=2:10, part="body")
tbl <- merge_at(tbl, i=12, j=2:10, part="body")
tbl <- merge_at(tbl, i=14, j=2:10, part="body")
tbl <- merge_at(tbl, i=21, j=2:10, part="body")
tbl <- merge_at(tbl, i=22, j=2:10, part="body")
tbl <- merge_at(tbl, i=24, j=2:10, part="body")
tbl <- merge_at(tbl, i=31, j=2:10, part="body")
tbl <- merge_at(tbl, i=32, j=2:10, part="body")
tbl <- merge_at(tbl, i=34, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=c(10,20,30), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.23.docx")

###create Table S2.24: Index of Moderated Mediation, Conditional Indirect Effects, and Conditional Direct Effects 
dat2a_modmed_index_simpleslopes[nrow(dat2a_modmed_index_simpleslopes)+7,] <- NA
dat2a_modmed_index_simpleslopes <- dat2a_modmed_index_simpleslopes[c(7,6,1,3,2,5,4,14,13,8,10,9,12,11,21,20,15,17,16,19,18,28,27,22,24,23,26,25),]
dat2a_modmed_index_simpleslopes$Effect <- c("   Democratic","   Autocratic","   NPI","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NPI","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NPI","   Democratic","   Autocratic","   Democratic","   Autocratic","   Democratic","   Autocratic","   NPI","   Democratic","   Autocratic","   Democratic","   Autocratic")
dat2a_modmed_index_simpleslopes$blank <- NA
dat_table_S2.24 <- cbind(dat2a_modmed_index_simpleslopes[,c(11,5,9:10,7:8,12)],dat2b_modmed_simpleslopes[c(7,6,1,3,2,5,4,14,13,8,10,9,12,11,21,20,15,17,16,19,18,28,27,22,24,23,26,25),c(3:7)])

colnames(dat_table_S2.24) <- c("Effect","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.24[,2:12] <- sapply(dat_table_S2.24[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.24[,c(2:4,8:10)] <- dat_table_S2.24[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.24[,c(5,11)] <- dat_table_S2.24[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.24[,c(6,12)] <- dat_table_S2.24[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.24$ci1 <- paste0("[", dat_table_S2.24$ci_lower1, ", ", dat_table_S2.24$ci_upper1, "]")
dat_table_S2.24$ci2 <- paste0("[", dat_table_S2.24$ci_lower2, ", ", dat_table_S2.24$ci_upper2, "]")

dat_table_S2.24 <- dat_table_S2.24[,c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.24[c(22:28),c(2:5)] <- NA

dat_table_S2.24[29,2] <- "SLS and SHLS Based on Index of Leadership Style"
dat_table_S2.24[30,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.24[31,1] <- "Index of Moderated Mediation"
dat_table_S2.24[32,1] <- "Conditional Indirect Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[33,1] <- "Conditional Direct Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[34,2] <- "SLS and SHLS Based on Leadership Descriptions"
dat_table_S2.24[35,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.24[36,1] <- "Index of Moderated Mediation"
dat_table_S2.24[37,1] <- "Conditional Indirect Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[38,1] <- "Conditional Direct Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[39,2] <- "SLS and SHLS Based on Leadership Behaviors"
dat_table_S2.24[40,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.24[41,1] <- "Index of Moderated Mediation"
dat_table_S2.24[42,1] <- "Conditional Indirect Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[43,1] <- "Conditional Direct Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[44,2] <- "SLS and SHLS Based on Bipolar Leadership Measure"
dat_table_S2.24[45,1] <- "Conditional Direct Effects of SHLS on Attraction to Supervisor"
dat_table_S2.24[46,1] <- "Index of Moderated Mediation"
dat_table_S2.24[47,1] <- "Conditional Indirect Effects of NPI on Attraction to Supervisor"
dat_table_S2.24[48,1] <- "Conditional Direct Effects of NPI on Attraction to Supervisor"

dat_table_S2.24 <- dat_table_S2.24[c(29:30,1:2,31,3,32,4:5,33,6:7,34:35,8:9,36,10,37,11:12,38,13:14,39:40,15:16,41,17,42,18:19,43,20:21,44:45,22:23,46,24,47,25:26,48,27:28),]

col_keys <- c("Effect","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Effect","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.24)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=13, j=2:10, part="body")
tbl <- merge_at(tbl, i=25, j=2:10, part="body")
tbl <- merge_at(tbl, i=37, j=2:10, part="body")
tbl <- merge_at(tbl, i=2, j=1:10, part="body")
tbl <- merge_at(tbl, i=5, j=1:10, part="body")
tbl <- merge_at(tbl, i=7, j=1:10, part="body")
tbl <- merge_at(tbl, i=10, j=1:10, part="body")
tbl <- merge_at(tbl, i=14, j=1:10, part="body")
tbl <- merge_at(tbl, i=17, j=1:10, part="body")
tbl <- merge_at(tbl, i=19, j=1:10, part="body")
tbl <- merge_at(tbl, i=22, j=1:10, part="body")
tbl <- merge_at(tbl, i=26, j=1:10, part="body")
tbl <- merge_at(tbl, i=29, j=1:10, part="body")
tbl <- merge_at(tbl, i=31, j=1:10, part="body")
tbl <- merge_at(tbl, i=34, j=1:10, part="body")
tbl <- merge_at(tbl, i=38, j=1:10, part="body")
tbl <- merge_at(tbl, i=41, j=1:10, part="body")
tbl <- merge_at(tbl, i=43, j=1:10, part="body")
tbl <- merge_at(tbl, i=46, j=1:10, part="body")
tbl <- hline(tbl, i=c(12,24,36), j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Effect"), align="left", part="body")
tbl <- align(tbl, i = c(2,5,7,10,14,17,19,22,26,29,31,34,38,41,43,46), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Effect, width=1.2)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.24.docx")

rm(mediation_model2a,results1,mediation_model2a_descriptions,results2,dat2a_modmed_descriptions,mediation_model2a_lbdq,results3,dat2a_modmed_lbdq,dat2a_modmed,dat2a_modmed_index_simpleslopes,
   output1,output1_est_ci,est_ci1,output1_t_p,t_p1,dat2b_est_ci1,dat2b_t_p1,output2,output2_est_ci,est_ci2,output2_t_p,t_p2,dat2b_est_ci2,dat2b_t_p2,
   output3,output3_est_ci,est_ci3,output3_t_p,t_p3,dat2b_est_ci3,dat2b_t_p3,output4,output4_est_ci,est_ci4,output4_t_p,t_p4,dat2b_est_ci4,dat2b_t_p4,
   dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod4,dat2b_modmed,dat2b_modmed_simpleslopes,
   dat_table_S2.23,dat_table_S2.24,format_beta,format_p,format_t,head,tbl,doc)


##Supplementary Footnote S6: NPI Facets

###regression models for Study 2a
moderation_model2a <- lm(Zattraction ~ Znpi_authority + Znpi_exhibitionism + Znpi_entitlement + Zstyle_index_supervisor + ZNPI_AUTxSLS + ZNPI_EXHxSLS + ZNPI_ENTxSLS, data=dat2a)
moderation_model2a_descriptions <- lm(Zattraction ~ Znpi_authority + Znpi_exhibitionism + Znpi_entitlement + Zdescriptions_supervisor + ZNPI_AUTxSLS_descriptions + ZNPI_EXHxSLS_descriptions + ZNPI_ENTxSLS_descriptions, data=dat2a)
moderation_model2a_lbdq <- lm(Zattraction ~ Znpi_authority + Znpi_exhibitionism + Znpi_entitlement + Zlbdq_supervisor + ZNPI_AUTxSLS_lbdq + ZNPI_EXHxSLS_lbdq + ZNPI_ENTxSLS_lbdq, data=dat2a)
dat2a_mod_facets <- rbind(data.frame(summary(moderation_model2a)$coef,confint(moderation_model2a)),data.frame(summary(moderation_model2a_descriptions)$coef,confint(moderation_model2a_descriptions)),data.frame(summary(moderation_model2a_lbdq)$coef,confint(moderation_model2a_lbdq)))

###moderated mediation models for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a <- '
Zattraction ~ c1*Znpi_authority + c2*Znpi_exhibitionism + c3*Znpi_entitlement + Zstyle_index_supervisor + b*Zstyle_index_self + h1*ZNPI_AUTxSLS + h2*ZNPI_EXHxSLS + h3*ZNPI_ENTxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a1*Znpi_authority + a2*Znpi_exhibitionism + a3*Znpi_entitlement
## index of moderated mediaion
index_npi_authority := a1 * g
index_npi_exhibitionism := a2 * g
index_npi_entitlement := a3 * g
## indirect effects of NPI facets
indirect_aut1 := a1*(b-2.404079*g)
indirect_dem1 := a1*(b+1.826888*g)
indirect_aut2 := a2*(b-2.404079*g)
indirect_dem2 := a2*(b+1.826888*g)
indirect_aut3 := a3*(b-2.404079*g)
indirect_dem3 := a3*(b+1.826888*g)'
results1 <- sem(mediation_model2a, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed <- data.frame(parameterEstimates(results1, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_descriptions <- '
Zattraction ~ c1*Znpi_authority + c2*Znpi_exhibitionism + c3*Znpi_entitlement + Zdescriptions_supervisor + b*Zdescriptions_self + h1*ZNPI_AUTxSLS_descriptions + h2*ZNPI_EXHxSLS_descriptions + h3*ZNPI_ENTxSLS_descriptions + g*ZSHLSxSLS_descriptions
Zdescriptions_self ~ a1*Znpi_authority + a2*Znpi_exhibitionism + a3*Znpi_entitlement
## index of moderated mediaion
index_npi_authority := a1 * g
index_npi_exhibitionism := a2 * g
index_npi_entitlement := a3 * g
## indirect effects of NPI facets
indirect_aut1 := a1*(b-1.85317*g)
indirect_dem1 := a1*(b+1.435109*g)
indirect_aut2 := a2*(b-1.85317*g)
indirect_dem2 := a2*(b+1.435109*g)
indirect_aut3 := a3*(b-1.85317*g)
indirect_dem3 := a3*(b+1.435109*g)'
results2 <- sem(mediation_model2a_descriptions, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed_descriptions <- data.frame(parameterEstimates(results2, ci = TRUE, level = 0.95,
                                              boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model2a_lbdq <- '
Zattraction ~ c1*Znpi_authority + c2*Znpi_exhibitionism + c3*Znpi_entitlement + Zlbdq_supervisor + b*Zlbdq_self + h1*ZNPI_AUTxSLS_lbdq + h2*ZNPI_EXHxSLS_lbdq + h3*ZNPI_ENTxSLS_lbdq + g*ZSHLSxSLS_lbdq
Zlbdq_self ~ a1*Znpi_authority + a2*Znpi_exhibitionism + a3*Znpi_entitlement
## index of moderated mediaion
index_npi_authority := a1 * g
index_npi_exhibitionism := a2 * g
index_npi_entitlement := a3 * g
## indirect effects of NPI facets
indirect_aut1 := a1*(b-2.721009*g)
indirect_dem1 := a1*(b+2.040864*g)
indirect_aut2 := a2*(b-2.721009*g)
indirect_dem2 := a2*(b+2.040864*g)
indirect_aut3 := a3*(b-2.721009*g)
indirect_dem3 := a3*(b+2.040864*g)'
results3 <- sem(mediation_model2a_lbdq, data = dat2a, meanstructure = TRUE,
                se = "bootstrap", bootstrap = 10000)
dat2a_modmed_lbdq <- data.frame(parameterEstimates(results3, ci = TRUE, level = 0.95,
                                                   boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat2a_modmed_index_simpleslopes_facets <- rbind(dat2a_modmed[c(61:69),],dat2a_modmed_descriptions[c(61:69),],dat2a_modmed_lbdq[c(61:69),])
dat2a_modmed_facets <- rbind(dat2a_modmed[c(10:12,1:3,5,4,6:9),],dat2a_modmed_descriptions[c(10:12,1:3,5,4,6:9),],dat2a_modmed_lbdq[c(10:12,1:3,5,4,6:9),])

###extract coefficients from Mplus-Outputs for Study 2b
setwd(mplus_wd)

output1a <- readModels("Supplement/npi_facets_index_moderation.out", what = "parameters")
output1a_est_ci <- sapply(output1a, "[", "ci.unstandardized")
est_ci1a <- sapply(output1a_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1a_t_p <- sapply(output1a, "[", "unstandardized")
t_p1a <- sapply(output1a_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1a <- data.frame(matrix(unlist(est_ci1a), nrow=13, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1a <- data.frame(matrix(unlist(t_p1a), nrow=13, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2a <- readModels("Supplement/npi_facets_descriptions_moderation.out", what = "parameters")
output2a_est_ci <- sapply(output2a, "[", "ci.unstandardized")
est_ci2a <- sapply(output2a_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2a_t_p <- sapply(output2a, "[", "unstandardized")
t_p2a <- sapply(output2a_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2a <- data.frame(matrix(unlist(est_ci2a), nrow=13, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2a <- data.frame(matrix(unlist(t_p2a), nrow=13, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3a <- readModels("Supplement/npi_facets_lbdq_moderation.out", what = "parameters")
output3a_est_ci <- sapply(output3a, "[", "ci.unstandardized")
est_ci3a <- sapply(output3a_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3a_t_p <- sapply(output3a, "[", "unstandardized")
t_p3a <- sapply(output3a_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3a <- data.frame(matrix(unlist(est_ci3a), nrow=13, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3a <- data.frame(matrix(unlist(t_p3a), nrow=13, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4a <- readModels("Supplement/npi_facets_bipolar_moderation.out", what = "parameters")
output4a_est_ci <- sapply(output4a, "[", "ci.unstandardized")
est_ci4a <- sapply(output4a_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4a_t_p <- sapply(output4a, "[", "unstandardized")
t_p4a <- sapply(output4a_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4a <- data.frame(matrix(unlist(est_ci4a), nrow=13, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4a <- data.frame(matrix(unlist(t_p4a), nrow=13, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_mod1 <- cbind(dat2b_est_ci1a,dat2b_t_p1a[,c(3:4)])
dat2b_mod2 <- cbind(dat2b_est_ci2a,dat2b_t_p2a[,c(3:4)])
dat2b_mod3 <- cbind(dat2b_est_ci3a,dat2b_t_p3a[,c(3:4)])
dat2b_mod4 <- cbind(dat2b_est_ci4a,dat2b_t_p4a[,c(3:4)])
dat2b_mod_facets <- rbind(dat2b_mod1[c(6:9,5,2:4),],dat2b_mod2[c(6:9,5,2:4),],dat2b_mod3[c(6:9,5,2:4),],dat2b_mod4[c(6:9,5,2:4),])
colnames(dat2b_mod_facets) <- c("Outcome","Predictor","zPE","ci_lower","ci_upper","t","p")

output1b <- readModels("Supplement/npi_facets_index_mediation.out", what = "parameters")
output1b_est_ci <- sapply(output1b, "[", "ci.unstandardized")
est_ci1b <- sapply(output1b_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output1b_t_p <- sapply(output1b, "[", "unstandardized")
t_p1b <- sapply(output1b_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci1b <- data.frame(matrix(unlist(est_ci1b), nrow=32, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p1b <- data.frame(matrix(unlist(t_p1b), nrow=32, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output2b <- readModels("Supplement/npi_facets_descriptions_mediation.out", what = "parameters")
output2b_est_ci <- sapply(output2b, "[", "ci.unstandardized")
est_ci2b <- sapply(output2b_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output2b_t_p <- sapply(output2b, "[", "unstandardized")
t_p2b <- sapply(output2b_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci2b <- data.frame(matrix(unlist(est_ci2b), nrow=32, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p2b <- data.frame(matrix(unlist(t_p2b), nrow=32, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output3b <- readModels("Supplement/npi_facets_lbdq_mediation.out", what = "parameters")
output3b_est_ci <- sapply(output3b, "[", "ci.unstandardized")
est_ci3b <- sapply(output3b_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output3b_t_p <- sapply(output3b, "[", "unstandardized")
t_p3b <- sapply(output3b_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci3b <- data.frame(matrix(unlist(est_ci3b), nrow=32, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p3b <- data.frame(matrix(unlist(t_p3b), nrow=32, ncol = 4, byrow=F), stringsAsFactors=FALSE)

output4b <- readModels("Supplement/npi_facets_bipolar_mediation.out", what = "parameters")
output4b_est_ci <- sapply(output4b, "[", "ci.unstandardized")
est_ci4b <- sapply(output4b_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output4b_t_p <- sapply(output4b, "[", "unstandardized")
t_p4b <- sapply(output4b_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci4b <- data.frame(matrix(unlist(est_ci4b), nrow=32, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p4b <- data.frame(matrix(unlist(t_p4b), nrow=32, ncol = 4, byrow=F), stringsAsFactors=FALSE)

dat2b_modmed1 <- cbind(dat2b_est_ci1b,dat2b_t_p1b[,c(3:4)])
dat2b_modmed2 <- cbind(dat2b_est_ci2b,dat2b_t_p2b[,c(3:4)])
dat2b_modmed3 <- cbind(dat2b_est_ci3b,dat2b_t_p3b[,c(3:4)])
dat2b_modmed4 <- cbind(dat2b_est_ci4b,dat2b_t_p4b[,c(3:4)])
dat2b_modmed_facets <- rbind(dat2b_modmed1[c(1:3,12:16,11,8:10,7),],dat2b_modmed2[c(1:3,12:16,11,8:10,7),],dat2b_modmed3[c(1:3,12:16,11,8:10,7),],dat2b_modmed4[c(1:3,12:16,11,8:10,7),])
colnames(dat2b_modmed_facets) <- c("Outcome","Predictor","zPE","ci_lower","ci_upper","t","p")
dat2b_modmed_simpleslopes_facets <- rbind(dat2b_modmed1[c(22:32),c(2:7)],dat2b_modmed2[c(22:32),c(2:7)],dat2b_modmed3[c(22:32),c(2:7)],dat2b_modmed4[c(22:32),c(2:7)])
colnames(dat2b_modmed_simpleslopes_facets) <- c("Effect","zPE","ci_lower","ci_upper","t","p") #IND = index of moderated mediation, IND_AUT = indirect effect on attraction to autocratic supervisors, IND_DEM = indirect effect on attraction to democratic supervisors, 1 = npi_authority, 2 = npi_exhibitionism, 3 = npi_entitlement, SHLS_AUT = direct effect of subordinate's hypothetical leadership style on attraction to autocratic supervisors, SHLS_DEM = direct effect of subordinate's hypothetical leadership style on attraction to democratic supervisors.

rm(moderation_model2a,moderation_model2a_descriptions,moderation_model2a_lbdq,dat2a_mod_facets,
   mediation_model2a,results1,dat2a_modmed,mediation_model2a_descriptions,results2,dat2a_modmed_descriptions,mediation_model2a_lbdq,results3,dat2a_modmed_lbdq,dat2a_modmed_facets,dat2a_modmed_index_simpleslopes_facets,
   output1a,output1a_est_ci,est_ci1a,output1a_t_p,t_p1a,dat2b_est_ci1a,dat2b_t_p1a,output2a,output2a_est_ci,est_ci2a,output2a_t_p,t_p2a,dat2b_est_ci2a,dat2b_t_p2a,
   output3a,output3a_est_ci,est_ci3a,output3a_t_p,t_p3a,dat2b_est_ci3a,dat2b_t_p3a,output4a,output4a_est_ci,est_ci4a,output4a_t_p,t_p4a,dat2b_est_ci4a,dat2b_t_p4a,
   dat2b_mod1,dat2b_mod2,dat2b_mod3,dat2b_mod4,dat2b_mod_facets,
   output1b,output1b_est_ci,est_ci1b,output1b_t_p,t_p1b,dat2b_est_ci1b,dat2b_t_p1b,output2b,output2b_est_ci,est_ci2b,output2b_t_p,t_p2b,dat2b_est_ci2b,dat2b_t_p2b,
   output3b,output3b_est_ci,est_ci3b,output3b_t_p,t_p3b,dat2b_est_ci3b,dat2b_t_p3b,output4b,output4b_est_ci,est_ci4b,output4b_t_p,t_p4b,dat2b_est_ci4b,dat2b_t_p4b,
   dat2b_modmed1,dat2b_modmed2,dat2b_modmed3,dat2b_modmed4,dat2b_modmed_facets,dat2b_modmed_simpleslopes_facets)


##Table S2.25: Effect of NPI-Narcissism on Attraction to Supervisor Moderated by Supervisor's Leadership Style and 
##             Completion Order of Supervisor's Leadership Style and Subordinate's Hypothetical Leadership Style in Study 2a

###regression model
model_order <- lm(Zattraction ~ Znpi + Zstyle_index_supervisor + Zorder + ZNPIxSLS + ZNPIxORD + ZSLSxORD + ZNPIxSLSxORD, data=dat2a)
dat_table_S2.25 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S2.25 <- dat_table_S2.25[!(row.names(dat_table_S2.25) %in% "(Intercept)"), ]
dat_table_S2.25$Predictor <- c("NPI","SLS","Order","NPI x SLS","NPI x Order","SLS x Order","NPI x SLS x Order")
dat_table_S2.25 <- dat_table_S2.25[,c(7,1,5:6,3:4)]

colnames(dat_table_S2.25) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S2.25[,2:6] <- sapply(dat_table_S2.25[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.25[,c(2:4)] <- dat_table_S2.25[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.25[,5] <- sapply(dat_table_S2.25[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.25[,6] <- sapply(dat_table_S2.25[,6], format_p)

dat_table_S2.25$ci <- paste0("[", dat_table_S2.25$ci_lower, ", ", dat_table_S2.25$ci_upper, "]")

dat_table_S2.25 <- dat_table_S2.25[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S2.25)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.5)
tbl <- width(tbl, j =~ beta, width=.45)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t, width=.55)
tbl <- width(tbl, j =~ p, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.25.docx")

rm(model_order,dat_table_S2.25,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.26: Zero-Order Correlations of the Big Five and Self-Esteem with NPI-Narcissism in Studies 2a-2b
dat2a_1 <- subset(dat2a, select = c(npi,agr,cns,ext,neu,opn,selfesteem))
cor_dat2a_1 <- corr.test(dat2a_1)
dat_cor_2a <- as.data.frame(matrix(unlist(cor_dat2a_1), nrow=502, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2a_r <- as.data.frame(matrix(dat_cor_2a[2:7,], nrow=6))
dat_cor_2a_p <- as.data.frame(matrix(dat_cor_2a[101:106,], nrow=6))
dat2b_1 <- subset(dat2b, select = c(npi,agr,cns,ext,neu,opn,selfesteem))
cor_dat2b_1 <- corr.test(dat2b_1)
dat_cor_2b <- as.data.frame(matrix(unlist(cor_dat2b_1), nrow=502, ncol = 1, byrow=F), stringsAsFactors=FALSE)
dat_cor_2b_r <- as.data.frame(matrix(dat_cor_2b[2:7,], nrow=6))
dat_cor_2b_p <- as.data.frame(matrix(dat_cor_2b[101:106,], nrow=6))
dat_cor_r <- cbind(dat_cor_2a_r,dat_cor_2b_r)
dat_cor_p <- cbind(dat_cor_2a_p,dat_cor_2b_p)

dat_cor_r[,1:2] <- sapply(dat_cor_r[,1:2], as.numeric)
format_r <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_cor_r[,1:2] <- dat_cor_r[,1:2] %>% mutate_if(is.numeric, format_r)

dat_cor_p[,1:2] <- sapply(dat_cor_p[,1:2], as.numeric)
format_p <- function(x){
  ifelse(abs(x) < 0.05 & abs(x) >= 0.01,  "*", ifelse(abs(x) < 0.01 & abs(x) >= 0.001,  "**", ifelse(abs(x) < 0.001,  "***", "")))
}
dat_cor_p[,1:2] <- dat_cor_p[,1:2] %>% mutate_if(is.numeric, format_p)

dat_table_S2.26 <- data.frame(matrix(nrow=6, ncol=3))

dat_table_S2.26[,1] <- c("Agreeableness","Conscientiousness","Extraversion","Neuroticism","Openness","Self-Esteem")
dat_table_S2.26[,2] <- paste0(dat_cor_r[,1],dat_cor_p[,1])
dat_table_S2.26[,3] <- paste0(dat_cor_r[,2],dat_cor_p[,2])

col_keys <- c("X1","X2","X3")
head1 <- c("Variable","Study 2a","Study 2b")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S2.26)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:3, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:3, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j =~ X1, align="left", part="body")
tbl <- width(tbl, j =~ X1, width=1.4)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.26.docx")

rm(dat2a_1,cor_dat2a_1,dat_cor_2a,dat_cor_2a_p,dat_cor_2a_r,dat2b_1,cor_dat2b_1,dat_cor_2b,dat_cor_2b_p,dat_cor_2b_r,dat_cor_p,dat_cor_r,
   dat_table_S2.26,format_p,format_r,head,tbl,doc)


##Table S2.27: Effect of NPI-Narcissism on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###regression model for Study 2a
moderation_model2a_cov <- lm(Zattraction ~ Znpi + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor 
                             + ZNPIxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS, data=dat2a)
dat2a_mod_cov <- data.frame(summary(moderation_model2a_cov)$coef,confint(moderation_model2a_cov))

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/npi_bfi_se_index_moderation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=28, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=28, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_mod_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_mod_cov <- dat2b_mod_cov[c(11:18,9,8,2:7),]

###create table
dat2a_mod_cov$Predictor <- c("(Intercept)","NPI","AGR","CNS","EXT","NEU","OPN","SE","SLS",
                             "NPI x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS")
dat2a_mod_cov$blank <- NA
dat_table_S2.27 <- cbind(dat2a_mod_cov[,c(7,1,5:6,3:4,8)],dat2b_mod_cov[,c(3:7)])

colnames(dat_table_S2.27) <- c("Predictor","beta","ci_lower1","ci_upper1","t","p1","blank","zPE","ci_lower2","ci_upper2","z","p2")

dat_table_S2.27[,2:12] <- sapply(dat_table_S2.27[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.27[,c(2:4,8:10)] <- dat_table_S2.27[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.27[,c(5,11)] <- dat_table_S2.27[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.27[,c(6,12)] <- dat_table_S2.27[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.27$ci1 <- paste0("[", dat_table_S2.27$ci_lower1, ", ", dat_table_S2.27$ci_upper1, "]")
dat_table_S2.27$ci2 <- paste0("[", dat_table_S2.27$ci_lower2, ", ", dat_table_S2.27$ci_upper2, "]")

dat_table_S2.27 <- dat_table_S2.27[,c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")]

dat_table_S2.27[1,c(2:5)] <- NA

col_keys <- c("Predictor","beta","ci1","t","p1","blank","zPE","ci2","z","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.27)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","zPE","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.27.docx")

rm(moderation_model2a_cov,dat2a_mod_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_mod_cov,dat_table_S2.27,format_beta,format_p,format_t,head,tbl,doc)


##Table S2.28: Results of the Moderated Mediation Model With NPI-Narcissism Controlled for the Big Five and Self-Esteem in Studies 2a-2b

###moderated mediation model for Study 2a
set.seed(123) #setting the seed for reproducibility
mediation_model2a_cov <- '
Zattraction ~ c*Znpi + Zagr + Zcns + Zext + Zneu + Zopn + Zse + Zstyle_index_supervisor + b*Zstyle_index_self 
            + h*ZNPIxSLS + ZAGRxSLS + ZCNSxSLS + ZEXTxSLS + ZNEUxSLS + ZOPNxSLS + ZSExSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a1*Znpi + a2*Zagr + a3*Zcns + a4*Zext + a5*Zneu + a6*Zopn + a7*Zse
## index of moderated mediaion
index_npi := a1 * g
index_agr := a2 * g
index_cns := a3 * g
index_ext := a4 * g
index_neu := a5 * g
index_opn := a6 * g
index_se := a7 * g
## conditional effects of NPI
indirect_aut := a1*(b-2.404079*g)
indirect_dem := a1*(b+1.826888*g)
direct_aut := c-2.404079*h
direct_dem := c+1.826888*h
## conditional effects of SHLS
SHLS_aut := b-2.404079*g
SHLS_dem := b+1.826888*g'
results_cov <- sem(mediation_model2a_cov, data = dat2a, meanstructure = TRUE,
                   se = "bootstrap", bootstrap = 10000)
dat2a_modmed_cov <- data.frame(parameterEstimates(results_cov, ci = TRUE, level = 0.95,
                                                  boot.ci.type = "bca.simple")) #bias-corrected percentile method
dat2a_modmed_cov <- dat2a_modmed_cov[c(18:24,163,1:7,9,8,10:17),]

###extract coefficients from Mplus-Output for Study 2b
setwd(mplus_wd)

output <- readModels("Supplement/npi_bfi_se_index_mediation.out", what = "parameters")
output_est_ci <- sapply(output, "[", "ci.unstandardized")
est_ci <- sapply(output_est_ci, "[", c("paramHeader","param","est","low2.5","up2.5"))
output_t_p <- sapply(output, "[", "unstandardized")
t_p <- sapply(output_t_p, "[", c("paramHeader","param","est_se","pval"))
dat2b_est_ci <- data.frame(matrix(unlist(est_ci), nrow=43, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat2b_t_p <- data.frame(matrix(unlist(t_p), nrow=43, ncol = 4, byrow=F), stringsAsFactors=FALSE)
dat2b_modmed_cov <- cbind(dat2b_est_ci,dat2b_t_p[,c(3:4)])
dat2b_modmed_cov <- dat2b_modmed_cov[c(1:7,21:22,24:29,23,19,18,11:17),]

###create table
dat2a_modmed_cov$Predictor <- c("NPI","AGR","CNS","EXT","NEU","OPN","SE",
                                "(Intercept)","NPI","AGR","CNS","EXT","NEU","OPN","SE","SHLS","SLS",
                                "NPI x SLS","AGR x SLS","CNS x SLS","EXT x SLS","NEU x SLS","OPN x SLS","SE x SLS","SHLS x SLS")
dat2a_modmed_cov$blank <- NA
dat_table_S2.28 <- cbind(dat2a_modmed_cov[,c(11,5,9:10,7:8,12)],dat2b_modmed_cov[,c(3:7)])

colnames(dat_table_S2.28) <- c("Predictor","beta","ci_lower1","ci_upper1","z1","p1","blank","zPE","ci_lower2","ci_upper2","z2","p2")

dat_table_S2.28[,2:12] <- sapply(dat_table_S2.28[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S2.28[,c(2:4,8:10)] <- dat_table_S2.28[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)
dat_table_S2.28[4,8] <- ".00"

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S2.28[,c(5,11)] <- dat_table_S2.28[,c(5,11)] %>% mutate_if(is.numeric, format_t)
dat_table_S2.28[4,11] <- "0.00"

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S2.28[,c(6,12)] <- dat_table_S2.28[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S2.28$ci1 <- paste0("[", dat_table_S2.28$ci_lower1, ", ", dat_table_S2.28$ci_upper1, "]")
dat_table_S2.28$ci2 <- paste0("[", dat_table_S2.28$ci_lower2, ", ", dat_table_S2.28$ci_upper2, "]")

dat_table_S2.28 <- dat_table_S2.28[,c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")]

dat_table_S2.28[8,c(2:5)] <- NA
dat_table_S2.28[26,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S2.28[27,2] <- "Attraction to Supervisor"
dat_table_S2.28 <- dat_table_S2.28[c(26,1:7,27,8:25),]

col_keys <- c("Predictor","beta","ci1","z1","p1","blank","zPE","ci2","z2","p2")
head1 <- c("","Study 2a","Study 2a","Study 2a","Study 2a","","Study 2b","Study 2b","Study 2b","Study 2b")
head2 <- c("Predictor","\u03B2","95% CI","z","p","","zPE","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S2.28)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=9, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("z1","p1","zPE","z2","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta + zPE, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ z1 + z2 + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.28.docx")

rm(mediation_model2a_cov,results_cov,dat2a_modmed_cov,output,output_est_ci,est_ci,output_t_p,t_p,dat2b_est_ci,dat2b_t_p,dat2b_modmed_cov,dat_table_S2.28,format_beta,format_t,format_p,head,tbl,doc)


rm(dat2a,dat2b,mplus_wd)


########################################
#S3: Additional Results of Study 3
########################################


#read data
dat3 <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/427/9/NarcSub_Study3_Data.csv", header = T, sep = ","))


#recode variables
dat3$description_aut_self_r <- car::recode(dat3$description_aut_self, "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1")
dat3$lbdq2r_self <- car::recode(dat3$lbdq2_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq4r_self <- car::recode(dat3$lbdq4_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq6r_self <- car::recode(dat3$lbdq6_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq8r_self <- car::recode(dat3$lbdq8_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq10r_self <- car::recode(dat3$lbdq10_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq12r_self <- car::recode(dat3$lbdq12_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq14r_self <- car::recode(dat3$lbdq14_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq16r_self <- car::recode(dat3$lbdq16_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq18r_self <- car::recode(dat3$lbdq18_self, "1=5; 2=4; 3=3; 4=2; 5=1")
dat3$lbdq20r_self <- car::recode(dat3$lbdq20_self, "1=5; 2=4; 3=3; 4=2; 5=1")


#compute variables 
##predictors
dat3$narq_adm <- rowMeans(dat3[c("narq1", "narq2", "narq3", "narq5", "narq7", "narq8", "narq15", "narq16", "narq18")], na.rm=TRUE)
dat3$Znarq_adm <- scale(dat3$narq_adm)
dat3$narq_riv <- rowMeans(dat3[c("narq4", "narq6", "narq9", "narq10", "narq11", "narq12", "narq13", "narq14", "narq17")], na.rm=TRUE)
dat3$Znarq_riv <- scale(dat3$narq_riv)
dat3$narq <- rowMeans(dat3[c(42:59)], na.rm=TRUE)
dat3$Znarq <- scale(dat3$narq)
dat3$npi <- rowMeans(dat3[c(2:41)], na.rm=TRUE)
dat3$Znpi <- scale(dat3$npi)
dat3$npi_authority <- rowMeans( dat3[c("npi1", "npi5", "npi10", "npi11", "npi12", "npi27", "npi32", "npi33", "npi34", "npi36", "npi40")], na.rm=TRUE)
dat3$Znpi_authority <- as.numeric(scale(dat3$npi_authority))
dat3$npi_exhibitionism <- rowMeans( dat3[c("npi4", "npi7", "npi15", "npi19", "npi20", "npi26", "npi28", "npi29", "npi30", "npi38")], na.rm=TRUE)
dat3$Znpi_exhibitionism <- as.numeric(scale(dat3$npi_exhibitionism))
dat3$npi_entitlement <- rowMeans(dat3[c("npi13", "npi14", "npi24", "npi25")], na.rm=TRUE)
dat3$Znpi_entitlement <- as.numeric(scale(dat3$npi_entitlement))
##moderators
dat3$Zstyle_supervisor <- scale(dat3$style_supervisor)
dat3$Zorder <- scale(dat3$presentation_order)
##mediator
dat3$descriptions_self <- rowMeans(dat3[c("description_dem_self","description_aut_self_r")], na.rm=TRUE)
dat3$Zdescriptions_self <- scale(dat3$descriptions_self)
dat3$lbdq_self <- rowMeans(dat3[c("lbdq1_self","lbdq3_self","lbdq5_self","lbdq7_self","lbdq9_self","lbdq11_self","lbdq13_self", 
                                  "lbdq15_self","lbdq17_self","lbdq19_self","lbdq2r_self","lbdq4r_self","lbdq6r_self","lbdq8r_self",
                                  "lbdq10r_self","lbdq12r_self","lbdq14r_self","lbdq16r_self","lbdq18r_self","lbdq20r_self")], na.rm=TRUE)
dat3$Zlbdq_self <- scale(dat3$lbdq_self)
dat3$style_index_self <- rowMeans(dat3[c("Zdescriptions_self","Zlbdq_self")], na.rm = TRUE)
dat3$Zstyle_index_self <- scale(dat3$style_index_self)
##interactions
dat3$ZADMxSLS <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor)
dat3$ZRIVxSLS <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor)
dat3$ZNARQxSLS <- scale(dat3$Znarq*dat3$Zstyle_supervisor)
dat3$ZNPIxSLS <- scale(dat3$Znpi*dat3$Zstyle_supervisor)
dat3$ZSHLSxSLS <- scale(dat3$Zstyle_index_self*dat3$Zstyle_supervisor)
dat3$ZADMxORD <- scale(dat3$Znarq_adm*dat3$Zorder)
dat3$ZRIVxORD <- scale(dat3$Znarq_riv*dat3$Zorder)
dat3$ZNARQxORD <- scale(dat3$Znarq*dat3$Zorder)
dat3$ZNPIxORD <- scale(dat3$Znpi*dat3$Zorder)
dat3$ZSLSxORD <- scale(dat3$Zstyle_supervisor*dat3$Zorder)
dat3$ZADMxSLSxORD <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor*dat3$Zorder)
dat3$ZRIVxSLSxORD <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor*dat3$Zorder)
dat3$ZNARQxSLSxORD <- scale(dat3$Znarq*dat3$Zstyle_supervisor*dat3$Zorder)
dat3$ZNPIxSLSxORD <- scale(dat3$Znpi*dat3$Zstyle_supervisor*dat3$Zorder)
dat3$ZNPI_AUTxSLS <- as.numeric(scale(dat3$Znpi_authority*dat3$Zstyle_supervisor))
dat3$ZNPI_EXHxSLS <- as.numeric(scale(dat3$Znpi_exhibitionism*dat3$Zstyle_supervisor))
dat3$ZNPI_ENTxSLS <- as.numeric(scale(dat3$Znpi_entitlement*dat3$Zstyle_supervisor))
##dependent variables
dat3$Zperceived_style_supervisor <- scale(dat3$perceived_style_supervisor)
dat3$zvalence <- scale(rowMeans(dat3[c("valence1","valence2","valence3")], na.rm=TRUE))
dat3$zsuccess <- scale(rowMeans(dat3[c("success1","success2","success3")], na.rm=TRUE))
dat3$boss_desirability3r <- 8 - dat3$boss_desirability3
dat3$zboss_desirability <- scale(rowMeans(dat3[c("boss_desirability1","boss_desirability2","boss_desirability3r")], na.rm=TRUE))
dat3$zliking <- scale(rowMeans(dat3[c("liking1","liking2","liking3")], na.rm=TRUE))
dat3$zcompetence <- scale(rowMeans(dat3[c("competence1","competence2","competence3")], na.rm=TRUE))
dat3$zwarmth <- scale(rowMeans(dat3[c("warmth1","warmth2","warmth3")], na.rm=TRUE))
dat3$zworking_conditions <- scale(rowMeans(dat3[c("working_conditions1","working_conditions2","working_conditions3")], na.rm=TRUE))
dat3$attraction <- rowMeans(dat3[c("zvalence","zsuccess","zboss_desirability","zliking","zcompetence","zwarmth","zworking_conditions")], na.rm=TRUE)
dat3$Zattraction <- scale(dat3$attraction)


#Separate Analyses for Narcissism Dimensions

##Tables S3.1 and S3.2: Effects of Narcissistic Admiration (Model 1) and Rivalry (Model 2) on Attraction to Supervisor 
##                      Moderated by Supervisor's Leadership Style and Mediated by Subordinate's Hypothetical Leadership Style in Study 3

###moderation models
moderation_model_adm <- lm(Zattraction ~ Znarq_adm + Zstyle_supervisor + ZADMxSLS, data=dat3)
moderation_model_riv <- lm(Zattraction ~ Znarq_riv + Zstyle_supervisor + ZRIVxSLS, data=dat3)
dat3_mod <- rbind((data.frame(summary(moderation_model_adm)$coef,confint(moderation_model_adm))),(data.frame(summary(moderation_model_riv)$coef,confint(moderation_model_riv))))

###simple slopes
dat3$Zstyle_supervisor_dem <- dat3$Zstyle_supervisor - abs(max(dat3$Zstyle_supervisor))
dat3$Zstyle_supervisor_aut <- dat3$Zstyle_supervisor + abs(min(dat3$Zstyle_supervisor))
dat3$ZADMxSLS_dem <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor_dem)
dat3$ZRIVxSLS_dem <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor_dem)
dat3$ZADMxSLS_aut <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor_aut)
dat3$ZRIVxSLS_aut <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor_aut)

model_dem_adm <- lm(Zattraction ~ Znarq_adm + Zstyle_supervisor_dem + ZADMxSLS_dem, data=dat3)
model_aut_adm <- lm(Zattraction ~ Znarq_adm + Zstyle_supervisor_aut + ZADMxSLS_aut, data=dat3)
model_dem_riv <- lm(Zattraction ~ Znarq_riv + Zstyle_supervisor_dem + ZRIVxSLS_dem, data=dat3)
model_aut_riv <- lm(Zattraction ~ Znarq_riv + Zstyle_supervisor_aut + ZRIVxSLS_aut, data=dat3)
dat3_mod_dem <- rbind((data.frame(summary(model_dem_adm)$coef,confint(model_dem_adm))),(data.frame(summary(model_dem_riv)$coef,confint(model_dem_riv))))
dat3_mod_aut <- rbind((data.frame(summary(model_aut_adm)$coef,confint(model_aut_adm))),(data.frame(summary(model_aut_riv)$coef,confint(model_aut_riv))))

paste0("relation between admiration and atraction to democratic supervisor: beta = ", round(dat3_mod_dem[2,1], digits = 2), ", 95% CI [", round(dat3_mod_dem[2,5], digits = 2), ", ", round(dat3_mod_dem[2,6], digits = 2), "], t(597) = ", round(dat3_mod_dem[2,3], digits = 2), ", p = ", round(dat3_mod_dem[2,4], digits = 3))
paste0("relation between admiration and atraction to autocratic supervisor: beta = ", round(dat3_mod_aut[2,1], digits = 2), ", 95% CI [", round(dat3_mod_aut[2,5], digits = 2), ", ", round(dat3_mod_aut[2,6], digits = 2), "], t(597) = ", round(dat3_mod_aut[2,3], digits = 2), ", p = ", round(dat3_mod_aut[2,4], digits = 3))

###moderated mediation models
set.seed(123) #setting the seed for reproducibility
mediation_model_adm <- '
Zattraction ~ c*Znarq_adm + Zstyle_supervisor + b*Zstyle_index_self + h*ZADMxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq_adm
## index of moderated mediaion
index_adm := a * g
## conditional effects of ADM
indirect_aut := a*(b-0.9941926*g)
indirect_dem := a*(b+1.004168*g)
direct_aut := c-0.9941926*h
direct_dem := c+1.004168*h
## conditional effects of SHLS
SHLS_aut := b-0.9941926*g
SHLS_dem := b+1.004168*g'
results_adm <- sem(mediation_model_adm, data = dat3, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat3_modmed_adm <- data.frame(parameterEstimates(results_adm, ci = TRUE, level = 0.95,
                                             boot.ci.type = "bca.simple")) #bias-corrected percentile method

set.seed(123) #setting the seed for reproducibility
mediation_model_riv <- '
Zattraction ~ c*Znarq_riv + Zstyle_supervisor + b*Zstyle_index_self + h*ZRIVxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq_riv
## index of moderated mediaion
index_riv := a * g
## conditional effects of RIV
indirect_aut := a*(b-0.9941926*g)
indirect_dem := a*(b+1.004168*g)
direct_aut := c-0.9941926*h
direct_dem := c+1.004168*h
## conditional effects of SHLS
SHLS_aut := b-0.9941926*g
SHLS_dem := b+1.004168*g'
results_riv <- sem(mediation_model_riv, data = dat3, meanstructure = TRUE,
                   se = "bootstrap", bootstrap = 10000)
dat3_modmed_riv <- data.frame(parameterEstimates(results_riv, ci = TRUE, level = 0.95,
                                                 boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat3_modmed_index_simpleslopes <- rbind(dat3_modmed_adm[c(25:29),],dat3_modmed_riv[c(25:29),])
dat3_modmed <- rbind(dat3_modmed_adm[c(6,1,3,2,4:5),],dat3_modmed_riv[c(6,1,3,2,4:5),])

###create Table S3.1: Results
dat3_mod <- dat3_mod[-c(1,5), ]
dat3_mod[7:12,] <- NA
dat3_mod <- dat3_mod[c(7,1,8,2:3,9:10,4,11,5:6,12),]
dat3_mod$Predictor <- c("ADM","ADM","SHLS","SLS","ADM x SLS","SHLS x SLS","RIV","RIV","SHLS","SLS","RIV x SLS","SHLS x SLS")
dat3_mod$blank <- NA
dat_table_S3.1 <- cbind(dat3_mod[,c(7,1,5:6,3:4,8)],dat3_modmed[,c(5,9:10,7:8)])

colnames(dat_table_S3.1) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1","blank","beta2","ci_lower2","ci_upper2","z","p2")

dat_table_S3.1[,2:12] <- sapply(dat_table_S3.1[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.1[,c(2:4,8:10)] <- dat_table_S3.1[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.1[,c(5,11)] <- dat_table_S3.1[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.1[,c(6,12)] <- dat_table_S3.1[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S3.1$ci1 <- paste0("[", dat_table_S3.1$ci_lower1, ", ", dat_table_S3.1$ci_upper1, "]")
dat_table_S3.1$ci2 <- paste0("[", dat_table_S3.1$ci_lower2, ", ", dat_table_S3.1$ci_upper2, "]")

dat_table_S3.1 <- dat_table_S3.1[,c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")]

dat_table_S3.1[c(1,3,6,7,9,12),c(2:5)] <- NA
dat_table_S3.1[13,1] <- "Model 1"
dat_table_S3.1[14,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S3.1[15,2] <- "Attraction to Supervisor"
dat_table_S3.1[16,1] <- "Model 2"
dat_table_S3.1[17,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S3.1[18,2] <- "Attraction to Supervisor"
dat_table_S3.1 <- dat_table_S3.1[c(13:14,1,15,2:6,16:17,7,18,8:12),]

col_keys <- c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")
head1 <- c("","Moderation model","Moderation model","Moderation model","Moderation model","","Moderated mediation model","Moderated mediation model","Moderated mediation model","Moderated mediation model")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S3.1)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=2, j=2:10, part="body")
tbl <- merge_at(tbl, i=4, j=2:10, part="body")
tbl <- merge_at(tbl, i=11, j=2:10, part="body")
tbl <- merge_at(tbl, i=13, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=9, j=1:10, border=fp_border(width=1.2), part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta1 + beta2, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z + p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.1.docx")

###create Table S3.2: Index of Moderated Mediation, Conditional Indirect Effects, and Conditional Direct Effects
dat3_modmed_index_simpleslopes <- dat3_modmed_index_simpleslopes[c(1,3,2,5,4,6,8,7,10,9),]
dat3_modmed_index_simpleslopes$Effect <- c("   ADM","   Democratic","   Autocratic","   Democratic","   Autocratic","   RIV","   Democratic","   Autocratic","   Democratic","   Autocratic")
dat_table_S3.2 <- dat3_modmed_index_simpleslopes[,c(11,5,9:10,7:8)]

colnames(dat_table_S3.2) <- c("Effect","beta1","ci_lower1","ci_upper1","t","p1")

dat_table_S3.2[,2:6] <- sapply(dat_table_S3.2[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.2[,c(2:4)] <- dat_table_S3.2[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.2[,5] <- sapply(dat_table_S3.2[,5],format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.2[,6] <- sapply(dat_table_S3.2[,6],format_p)


dat_table_S3.2$ci1 <- paste0("[", dat_table_S3.2$ci_lower1, ", ", dat_table_S3.2$ci_upper1, "]")

dat_table_S3.2 <- dat_table_S3.2[,c("Effect","beta1","ci1","t","p1")]

dat_table_S3.2[11,1] <- "Index of Moderated Mediation for ADM in Model 1"
dat_table_S3.2[12,1] <- "Conditional Indirect Effects of ADM on Attraction to Supervisor in Model 1"
dat_table_S3.2[13,1] <- "Conditional Direct Effects of ADM on Attraction to Supervisor in Model 1"
dat_table_S3.2[14,1] <- "Index of Moderated Mediation for RIV in Model 2"
dat_table_S3.2[15,1] <- "Conditional Indirect Effects of RIV on Attraction to Supervisor in Model 2"
dat_table_S3.2[16,1] <- "Conditional Direct Effects of RIV on Attraction to Supervisor in Model 2"

dat_table_S3.2 <- dat_table_S3.2[c(11,1,12,2:3,13,4:5,14,6,15,7:8,16,9:10),]

col_keys <- c("Effect","beta1","ci1","t","p1")
head1 <- c("Effect","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.2)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=1:5, part="body")
tbl <- merge_at(tbl, i=3, j=1:5, part="body")
tbl <- merge_at(tbl, i=6, j=1:5, part="body")
tbl <- merge_at(tbl, i=9, j=1:5, part="body")
tbl <- merge_at(tbl, i=11, j=1:5, part="body")
tbl <- merge_at(tbl, i=14, j=1:5, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Effect"), align="left", part="body")
tbl <- align(tbl, i = c(1,3,6,9,11,14), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p1"), part="header")
tbl <- width(tbl, j =~ Effect, width=2.7)
tbl <- width(tbl, j =~ beta1, width=.5)
tbl <- width(tbl, j =~ ci1, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p1, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.2.docx")

rm(moderation_model_adm,moderation_model_riv,dat3_mod,model_dem_adm,model_aut_adm,model_dem_riv,model_aut_riv,dat3_mod_dem,dat3_mod_aut,
   mediation_model_adm,results_adm,dat3_modmed_adm,mediation_model_riv,results_riv,dat3_modmed_riv,dat3_modmed,dat3_modmed_index_simpleslopes,
   dat_table_S3.1,dat_table_S3.2,format_beta,format_t,format_p,head,tbl,doc)


#Perceived Leadership Style of the Fictitious Supervisor

##Table S3.3: Unique Effects of Narcissistic Admiration and Rivalry on Perceived Leadership Style of the Fictitious Supervisor Moderated by Supervisor's Leadership Style in Study 3

###regression model
moderation_model_perceived_style <- lm(Zperceived_style_supervisor ~ Znarq_adm + Znarq_riv + Zstyle_supervisor + ZADMxSLS + ZRIVxSLS, data=dat3)
dat3_perceived_style <- data.frame(summary(moderation_model_perceived_style)$coef,confint(moderation_model_perceived_style))

###create table
dat3_perceived_style <- dat3_perceived_style[-1, ]
dat3_perceived_style$Predictor <- c("ADM","RIV","SLS","ADM x SLS","RIV x SLS")
dat_table_S3.3 <- dat3_perceived_style[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.3) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1")

dat_table_S3.3[,2:6] <- sapply(dat_table_S3.3[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.3[,c(2:4)] <- dat_table_S3.3[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.3[,5] <- sapply(dat_table_S3.3[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.3[,6] <- sapply(dat_table_S3.3[,6], format_p)

dat_table_S3.3$ci1 <- paste0("[", dat_table_S3.3$ci_lower1, ", ", dat_table_S3.3$ci_upper1, "]")
dat_table_S3.3$ci2 <- paste0("[", dat_table_S3.3$ci_lower2, ", ", dat_table_S3.3$ci_upper2, "]")

dat_table_S3.3 <- dat_table_S3.3[,c("Predictor","beta1","ci1","t","p1")]

col_keys <- c("Predictor","beta1","ci1","t","p1")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.3)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p1"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta1, width=.5)
tbl <- width(tbl, j =~ ci1, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p1, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.3.docx")

###simple slopes of the interaction between admiration and supervisor's leadership style 
dat3$Znarq_adm_high <- dat3$Znarq_adm - 1
dat3$Znarq_adm_low <- dat3$Znarq_adm + 1
dat3$ZADM_highxSLS <- scale(dat3$Znarq_adm_high*dat3$Zstyle_supervisor)
dat3$ZADM_lowxSLS <- scale(dat3$Znarq_adm_low*dat3$Zstyle_supervisor)

model_adm_high <- lm(Zperceived_style_supervisor ~ Znarq_adm_high + Znarq_riv + Zstyle_supervisor + ZADM_highxSLS + ZRIVxSLS, data=dat3)
model_adm_low <- lm(Zperceived_style_supervisor ~ Znarq_adm_low + Znarq_riv + Zstyle_supervisor + ZADM_lowxSLS + ZRIVxSLS, data=dat3)
dat3_adm_high <- data.frame(summary(model_adm_high)$coef,confint(model_adm_high))
dat3_adm_low <- data.frame(summary(model_adm_low)$coef,confint(model_adm_low))

paste0("difference between the two leader profiles for subordinates higher in narcissistic admiration: beta = ", round(dat3_adm_high[4,1], digits = 2), ", 95% CI [", round(dat3_adm_high[4,5], digits = 2), ", ", round(dat3_adm_high[4,6], digits = 2), "], t(595) = ", round(dat3_adm_high[4,3], digits = 2), ", p = ", round(dat3_adm_high[4,4], digits = 3))
paste0("difference between the two leader profiles for subordinates lower in narcissistic admiration: beta = ", round(dat3_adm_low[4,1], digits = 2), ", 95% CI [", round(dat3_adm_low[4,5], digits = 2), ", ", round(dat3_adm_low[4,6], digits = 2), "], t(595) = ", round(dat3_adm_low[4,3], digits = 2), ", p = ", round(dat3_adm_low[4,4], digits = 3))

rm(moderation_model_perceived_style,dat3_perceived_style,dat_table_S3.3,format_beta,format_t,format_p,head,tbl,doc,model_adm_high,model_adm_low,dat3_adm_high,dat3_adm_low)


#Presentation Order of Democratic and Autocratic Leadership Items

##Table S3.4: Unique Effects of Narcissistic Admiration and Rivalry on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Presentation Order of Democratic and Autocratic Leadership Items in Study 3

###regression model
model_order <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zstyle_supervisor + Zorder + ZADMxSLS + ZRIVxSLS + ZADMxORD + ZRIVxORD + ZSLSxORD + ZADMxSLSxORD + ZRIVxSLSxORD, data=dat3)
dat_table_S3.4 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S3.4 <- dat_table_S3.4[!(row.names(dat_table_S3.4) %in% "(Intercept)"), ]
dat_table_S3.4$Predictor <- c("ADM","RIV","SLS","Order","ADM x SLS","RIV x SLS","ADM x Order","RIV x Order","SLS x Order","ADM x SLS x Order","RIV x SLS x Order")
dat_table_S3.4 <- dat_table_S3.4[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.4) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S3.4[,2:6] <- sapply(dat_table_S3.4[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.4[,c(2:4)] <- dat_table_S3.4[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.4[,5] <- sapply(dat_table_S3.4[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.4[,6] <- sapply(dat_table_S3.4[,6], format_p)

dat_table_S3.4$ci <- paste0("[", dat_table_S3.4$ci_lower, ", ", dat_table_S3.4$ci_upper, "]")

dat_table_S3.4 <- dat_table_S3.4[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.4)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.6)
tbl <- width(tbl, j =~ beta, width=.5)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.4.docx")

###simple slopes of the interaction between supervisor's leadership style and presentation order
dat3$Zorder_aut_dem <- dat3$Zorder - 0.5
dat3$Zorder_dem_aut <- dat3$Zorder + 0.5
dat3$ZADMxORD_aut_dem <- scale(dat3$Znarq_adm*dat3$Zorder_aut_dem)
dat3$ZRIVxORD_aut_dem <- scale(dat3$Znarq_riv*dat3$Zorder_aut_dem)
dat3$ZSLSxORD_aut_dem <- scale(dat3$Zstyle_supervisor*dat3$Zorder_aut_dem)
dat3$ZADMxSLSxORD_aut_dem <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor*dat3$Zorder_aut_dem)
dat3$ZRIVxSLSxORD_aut_dem <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor*dat3$Zorder_aut_dem)
dat3$ZADMxORD_dem_aut <- scale(dat3$Znarq_adm*dat3$Zorder_dem_aut)
dat3$ZRIVxORD_dem_aut <- scale(dat3$Znarq_riv*dat3$Zorder_dem_aut)
dat3$ZSLSxORD_dem_aut <- scale(dat3$Zstyle_supervisor*dat3$Zorder_dem_aut)
dat3$ZADMxSLSxORD_dem_aut <- scale(dat3$Znarq_adm*dat3$Zstyle_supervisor*dat3$Zorder_dem_aut)
dat3$ZRIVxSLSxORD_dem_aut <- scale(dat3$Znarq_riv*dat3$Zstyle_supervisor*dat3$Zorder_dem_aut)

model_order_aut_dem <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zstyle_supervisor + Zorder_aut_dem + ZADMxSLS + ZRIVxSLS + ZADMxORD_aut_dem + ZRIVxORD_aut_dem + ZSLSxORD_aut_dem + ZADMxSLSxORD_aut_dem + ZRIVxSLSxORD_aut_dem, data=dat3)
model_order_dem_aut <- lm(Zattraction ~ Znarq_adm + Znarq_riv + Zstyle_supervisor + Zorder_dem_aut + ZADMxSLS + ZRIVxSLS + ZADMxORD_dem_aut + ZRIVxORD_dem_aut + ZSLSxORD_dem_aut + ZADMxSLSxORD_dem_aut + ZRIVxSLSxORD_dem_aut, data=dat3)
dat3_order_aut_dem <- data.frame(summary(model_order_aut_dem)$coef,confint(model_order_aut_dem))
dat3_order_dem_aut <- data.frame(summary(model_order_dem_aut)$coef,confint(model_order_dem_aut))

paste0("difference between the two leader profiles when democratic leadership items were presented first: beta = ", round(dat3_order_dem_aut[4,1], digits = 2), ", 95% CI [", round(dat3_order_dem_aut[4,5], digits = 2), ", ", round(dat3_order_dem_aut[4,6], digits = 2), "], t(589) = ", round(dat3_order_dem_aut[4,3], digits = 2), ", p = ", round(dat3_order_dem_aut[4,4], digits = 3))
paste0("difference between the two leader profiles when autocratic leadership items were presented first: beta = ", round(dat3_order_aut_dem[4,1], digits = 2), ", 95% CI [", round(dat3_order_aut_dem[4,5], digits = 2), ", ", round(dat3_order_aut_dem[4,6], digits = 2), "], t(589) = ", round(dat3_order_aut_dem[4,3], digits = 2), ", p = ", round(dat3_order_aut_dem[4,4], digits = 3))

rm(model_order,dat_table_S3.4,format_beta,format_t,format_p,head,tbl,doc,model_order_aut_dem,model_order_dem_aut,dat3_order_aut_dem,dat3_order_dem_aut)


#NARQ Total Score

##Internal Consistency and Descriptive Statistics of the NARQ Total Score
dat.narq <- subset(dat3, select = c(42:59))
round(psych::alpha(dat.narq, na.rm=T)$total[1], digits = 2)
round(mean(dat3$narq),2)
round(sd(dat3$narq),2)
rm(dat.narq)

##Table S3.5: Effect of the NARQ Total Score on Perceived Leadership Style of the Fictitious Supervisor Moderated by Supervisor's Leadership Style in Study 3

###regression model
moderation_model_perceived_style <- lm(Zperceived_style_supervisor ~ Znarq + Zstyle_supervisor + ZNARQxSLS, data=dat3)
dat3_perceived_style <- data.frame(summary(moderation_model_perceived_style)$coef,confint(moderation_model_perceived_style))

###create table
dat3_perceived_style <- dat3_perceived_style[-1, ]
dat3_perceived_style$Predictor <- c("NARQ","SLS","NARQ x SLS")
dat_table_S3.5 <- dat3_perceived_style[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.5) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1")

dat_table_S3.5[,2:6] <- sapply(dat_table_S3.5[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.5[,c(2:4)] <- dat_table_S3.5[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.5[,5] <- sapply(dat_table_S3.5[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.5[,6] <- sapply(dat_table_S3.5[,6], format_p)

dat_table_S3.5$ci1 <- paste0("[", dat_table_S3.5$ci_lower1, ", ", dat_table_S3.5$ci_upper1, "]")

dat_table_S3.5 <- dat_table_S3.5[,c("Predictor","beta1","ci1","t","p1")]

col_keys <- c("Predictor","beta1","ci1","t","p1")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.5)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p1"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta1, width=.5)
tbl <- width(tbl, j =~ ci1, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p1, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.5.docx")

###simple slopes of the interaction between the NARQ total score and supervisor's leadership style
dat3$Znarq_high <- dat3$Znarq - 1
dat3$Znarq_low <- dat3$Znarq + 1
dat3$Znarq_highxSLS <- scale(dat3$Znarq_high*dat3$Zstyle_supervisor)
dat3$Znarq_lowxSLS <- scale(dat3$Znarq_low*dat3$Zstyle_supervisor)

model_narq_high <- lm(Zperceived_style_supervisor ~ Znarq_high + Zstyle_supervisor + Znarq_highxSLS, data=dat3)
model_narq_low <- lm(Zperceived_style_supervisor ~ Znarq_low + Zstyle_supervisor + Znarq_lowxSLS, data=dat3)
dat3_narq_high <- data.frame(summary(model_narq_high)$coef,confint(model_narq_high))
dat3_narq_low <- data.frame(summary(model_narq_low)$coef,confint(model_narq_low))

paste0("difference between the two leader profiles for more narcissistic subordinates: beta = ", round(dat3_narq_high[3,1], digits = 2), ", 95% CI [", round(dat3_narq_high[3,5], digits = 2), ", ", round(dat3_narq_high[3,6], digits = 2), "], t(597) = ", round(dat3_narq_high[3,3], digits = 2), ", p = ", round(dat3_narq_high[3,4], digits = 3))
paste0("difference between the two leader profiles for less narcissistic subordinates: beta = ", round(dat3_narq_low[3,1], digits = 2), ", 95% CI [", round(dat3_narq_low[3,5], digits = 2), ", ", round(dat3_narq_low[3,6], digits = 2), "], t(597) = ", round(dat3_narq_low[3,3], digits = 2), ", p = ", round(dat3_narq_low[3,4], digits = 3))

rm(moderation_model_perceived_style,dat3_perceived_style,dat_table_S3.5,format_beta,format_t,format_p,head,tbl,doc,model_narq_high,model_narq_low,dat3_narq_high,dat3_narq_low)


##Table S3.6: Effect of the NARQ Total Score on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Mediated by Subordinate's Hypothetical Leadership Style in Study 3

###moderation model
moderation_model_narq <- lm(Zattraction ~ Znarq + Zstyle_supervisor + ZNARQxSLS, data=dat3)
dat3_mod_narq <- data.frame(summary(moderation_model_narq)$coef,confint(moderation_model_narq))

###simple slopes
dat3$Zstyle_supervisor_dem <- dat3$Zstyle_supervisor - abs(max(dat3$Zstyle_supervisor))
dat3$Zstyle_supervisor_aut <- dat3$Zstyle_supervisor + abs(min(dat3$Zstyle_supervisor))
dat3$ZNARQxSLS_dem <- scale(dat3$Znarq*dat3$Zstyle_supervisor_dem)
dat3$ZNARQxSLS_aut <- scale(dat3$Znarq*dat3$Zstyle_supervisor_aut)

model_dem <- lm(Zattraction ~ Znarq + Zstyle_supervisor_dem + ZNARQxSLS_dem, data=dat3)
model_aut <- lm(Zattraction ~ Znarq + Zstyle_supervisor_aut + ZNARQxSLS_aut, data=dat3)
dat3_mod_dem <- data.frame(summary(model_dem)$coef,confint(model_dem))
dat3_mod_aut <- data.frame(summary(model_aut)$coef,confint(model_aut))

paste0("relation between the NARQ total score and atraction to the democratic supervisor: beta = ", round(dat3_mod_dem[2,1], digits = 2), ", 95% CI [", round(dat3_mod_dem[2,5], digits = 2), ", ", round(dat3_mod_dem[2,6], digits = 2), "], t(597) = ", round(dat3_mod_dem[2,3], digits = 2), ", p = ", round(dat3_mod_dem[2,4], digits = 3))
paste0("relation between the NARQ total score and atraction to the autocratic supervisor: beta = ", round(dat3_mod_aut[2,1], digits = 2), ", 95% CI [", round(dat3_mod_aut[2,5], digits = 2), ", ", round(dat3_mod_aut[2,6], digits = 2), "], t(597) = ", round(dat3_mod_aut[2,3], digits = 2), ", p = ", round(dat3_mod_aut[2,4], digits = 3))

###moderated mediation model
set.seed(123) #setting the seed for reproducibility
mediation_model_narq <- '
Zattraction ~ c*Znarq + Zstyle_supervisor + b*Zstyle_index_self + h*ZNARQxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znarq
## index of moderated mediaion
index_narq := a * g
## conditional effects of narq
indirect_aut := a*(b-0.9941926*g)
indirect_dem := a*(b+1.004168*g)
direct_aut := c-0.9941926*h
direct_dem := c+1.004168*h
## conditional effects of SHLS
SHLS_aut := b-0.9941926*g
SHLS_dem := b+1.004168*g'
results <- sem(mediation_model_narq, data = dat3, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat3_modmed_narq <- data.frame(parameterEstimates(results, ci = TRUE, level = 0.95,
                                                  boot.ci.type = "bca.simple")) #bias-corrected percentile method
dat3_modmed_index_simpleslopes <- dat3_modmed_narq[c(25:31),]
dat3_modmed_narq <- dat3_modmed_narq[c(6,1,3,2,4:5),]

###create table
dat3_mod_narq[c(1,5:6),] <- NA
dat3_mod_narq <- dat3_mod_narq[c(1:2,5,3:4,6),]
dat3_mod_narq$Predictor <- c("NARQ","NARQ","SHLS","SLS","NARQ x SLS","SHLS x SLS")
dat3_mod_narq$blank <- NA
dat_table_S3.6 <- cbind(dat3_mod_narq[,c(7,1,5:6,3:4,8)],dat3_modmed_narq[,c(5,9:10,7:8)])

colnames(dat_table_S3.6) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1","blank","beta2","ci_lower2","ci_upper2","z","p2")

dat_table_S3.6[,2:12] <- sapply(dat_table_S3.6[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.6[,c(2:4,8:10)] <- dat_table_S3.6[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.6[,c(5,11)] <- dat_table_S3.6[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.6[,c(6,12)] <- dat_table_S3.6[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S3.6$ci1 <- paste0("[", dat_table_S3.6$ci_lower1, ", ", dat_table_S3.6$ci_upper1, "]")
dat_table_S3.6$ci2 <- paste0("[", dat_table_S3.6$ci_lower2, ", ", dat_table_S3.6$ci_upper2, "]")

dat_table_S3.6 <- dat_table_S3.6[,c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")]

dat_table_S3.6[c(1,3,6),c(2:5)] <- NA
dat_table_S3.6[7,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S3.6[8,2] <- "Attraction to Supervisor"
dat_table_S3.6 <- dat_table_S3.6[c(7,1,8,2:6),]

col_keys <- c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")
head1 <- c("","Moderation model","Moderation model","Moderation model","Moderation model","","Moderated mediation model","Moderated mediation model","Moderated mediation model","Moderated mediation model")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S3.6)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=3, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta1 + beta2, width=.45)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z, width=.55)
tbl <- width(tbl, j =~ p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.6.docx")

###simple slopes of the interaction between subordinate's hypothetical leadership style and supervisor's leadership style 
paste0("relation between SHLS and atraction to democratic supervisor: beta = ", round(dat3_modmed_index_simpleslopes[7,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[7,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[7,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[7,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[7,8], digits = 3))
paste0("relation between SHLS and atraction to autocratic supervisor: beta = ", round(dat3_modmed_index_simpleslopes[6,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[6,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[6,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[6,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[6,8], digits = 3))

###indirect effects of the NARQ total score
paste0("Study 3: index of moderated mediation for the NARQ total score: estimate = ", round(dat3_modmed_index_simpleslopes[1,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[1,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[1,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[1,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[1,8], digits = 3))
paste0("Study 3: indirect effect of the NARQ total score for the democratic supervisor: estimate = ", round(dat3_modmed_index_simpleslopes[3,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[3,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[3,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[3,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[3,8], digits = 3))
paste0("Study 3: indirect effect of the NARQ total score for the autocratic supervisor: estimate = ", round(dat3_modmed_index_simpleslopes[2,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[2,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[2,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[2,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[2,8], digits = 3))

rm(moderation_model_narq,dat3_mod_narq,model_dem,model_aut,dat3_mod_dem,dat3_mod_aut,mediation_model_narq,results,dat3_modmed_narq,dat3_modmed_index_simpleslopes,dat_table_S3.6,format_beta,format_t,format_p,head,tbl,doc)


##Table S3.7: Effect of the NARQ Total Score on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Presentation Order of Democratic and Autocratic Leadership Items in Study 3

###regression model
model_order <- lm(Zattraction ~ Znarq + Zstyle_supervisor + Zorder + ZNARQxSLS + ZNARQxORD + ZSLSxORD + ZNARQxSLSxORD, data=dat3)
dat_table_S3.7 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S3.7 <- dat_table_S3.7[!(row.names(dat_table_S3.7) %in% "(Intercept)"), ]
dat_table_S3.7$Predictor <- c("NARQ","SLS","Order","NARQ x SLS","NARQ x Order","SLS x Order","NARQ x SLS x Order")
dat_table_S3.7 <- dat_table_S3.7[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.7) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S3.7[,2:6] <- sapply(dat_table_S3.7[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.7[,c(2:4)] <- dat_table_S3.7[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.7[,5] <- sapply(dat_table_S3.7[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.7[,6] <- sapply(dat_table_S3.7[,6], format_p)

dat_table_S3.7$ci <- paste0("[", dat_table_S3.7$ci_lower, ", ", dat_table_S3.7$ci_upper, "]")

dat_table_S3.7 <- dat_table_S3.7[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.7)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.7)
tbl <- width(tbl, j =~ beta, width=.45)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t, width=.55)
tbl <- width(tbl, j =~ p, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.7.docx")

rm(model_order,dat_table_S3.7,format_beta,format_t,format_p,head,tbl,doc)


#NPI-Narcissism

##Internal Consistency and Descriptive Statistics of NPI-Narcissism
dat.npi <- subset(dat3, select = c(2:41))
round(psych::alpha(dat.npi, na.rm=T)$total[1], digits = 2)
round(mean(dat3$npi),2)
round(sd(dat3$npi),2)
rm(dat.npi)

##Table S3.8: Effect of NPI-Narcissism on Perceived Leadership Style of the Fictitious Supervisor Moderated by Supervisor's Leadership Style in Study 3

###regression model
moderation_model_perceived_style <- lm(Zperceived_style_supervisor ~ Znpi + Zstyle_supervisor + ZNPIxSLS, data=dat3)
dat3_perceived_style <- data.frame(summary(moderation_model_perceived_style)$coef,confint(moderation_model_perceived_style))

###create table
dat3_perceived_style <- dat3_perceived_style[-1, ]
dat3_perceived_style$Predictor <- c("NPI","SLS","NPI x SLS")
dat_table_S3.8 <- dat3_perceived_style[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.8) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1")

dat_table_S3.8[,2:6] <- sapply(dat_table_S3.8[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.8[,c(2:4)] <- dat_table_S3.8[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.8[,5] <- sapply(dat_table_S3.8[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.8[,6] <- sapply(dat_table_S3.8[,6], format_p)

dat_table_S3.8$ci1 <- paste0("[", dat_table_S3.8$ci_lower1, ", ", dat_table_S3.8$ci_upper1, "]")

dat_table_S3.8 <- dat_table_S3.8[,c("Predictor","beta1","ci1","t","p1")]

col_keys <- c("Predictor","beta1","ci1","t","p1")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.8)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p1"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1)
tbl <- width(tbl, j =~ beta1, width=.5)
tbl <- width(tbl, j =~ ci1, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p1, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.8.docx")

###simple slopes of the interaction between NPI-narcissism and supervisor's leadership style
dat3$Znpi_high <- dat3$Znpi - 1
dat3$Znpi_low <- dat3$Znpi + 1
dat3$ZNPI_highxSLS <- scale(dat3$Znpi_high*dat3$Zstyle_supervisor)
dat3$ZNPI_lowxSLS <- scale(dat3$Znpi_low*dat3$Zstyle_supervisor)

model_npi_high <- lm(Zperceived_style_supervisor ~ Znpi_high + Zstyle_supervisor + ZNPI_highxSLS, data=dat3)
model_npi_low <- lm(Zperceived_style_supervisor ~ Znpi_low + Zstyle_supervisor + ZNPI_lowxSLS, data=dat3)
dat3_npi_high <- data.frame(summary(model_npi_high)$coef,confint(model_npi_high))
dat3_npi_low <- data.frame(summary(model_npi_low)$coef,confint(model_npi_low))

paste0("difference between the two leader profiles for subordinates higher in NPI-narcissism: beta = ", round(dat3_npi_high[3,1], digits = 2), ", 95% CI [", round(dat3_npi_high[3,5], digits = 2), ", ", round(dat3_npi_high[3,6], digits = 2), "], t(597) = ", round(dat3_npi_high[3,3], digits = 2), ", p = ", round(dat3_npi_high[3,4], digits = 3))
paste0("difference between the two leader profiles for subordinates lower in NPI-narcissism: beta = ", round(dat3_npi_low[3,1], digits = 2), ", 95% CI [", round(dat3_npi_low[3,5], digits = 2), ", ", round(dat3_npi_low[3,6], digits = 2), "], t(597) = ", round(dat3_npi_low[3,3], digits = 2), ", p = ", round(dat3_npi_low[3,4], digits = 3))

rm(moderation_model_perceived_style,dat3_perceived_style,dat_table_S3.8,format_beta,format_t,format_p,head,tbl,doc,model_npi_high,model_npi_low,dat3_npi_high,dat3_npi_low)


##Table S3.9: Effect of NPI-Narcissism on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Mediated by Subordinate's Hypothetical Leadership Style in Study 3

###moderation model
moderation_model_npi <- lm(Zattraction ~ Znpi + Zstyle_supervisor + ZNPIxSLS, data=dat3)
dat3_mod_npi <- data.frame(summary(moderation_model_npi)$coef,confint(moderation_model_npi))

###simple slopes
dat3$Zstyle_supervisor_dem <- dat3$Zstyle_supervisor - abs(max(dat3$Zstyle_supervisor))
dat3$Zstyle_supervisor_aut <- dat3$Zstyle_supervisor + abs(min(dat3$Zstyle_supervisor))
dat3$ZNPIxSLS_dem <- scale(dat3$Znpi*dat3$Zstyle_supervisor_dem)
dat3$ZNPIxSLS_aut <- scale(dat3$Znpi*dat3$Zstyle_supervisor_aut)

model_dem <- lm(Zattraction ~ Znpi + Zstyle_supervisor_dem + ZNPIxSLS_dem, data=dat3)
model_aut <- lm(Zattraction ~ Znpi + Zstyle_supervisor_aut + ZNPIxSLS_aut, data=dat3)
dat3_mod_dem <- data.frame(summary(model_dem)$coef,confint(model_dem))
dat3_mod_aut <- data.frame(summary(model_aut)$coef,confint(model_aut))

paste0("relation between NPI-narcissism and atraction to democratic supervisor: beta = ", round(dat3_mod_dem[2,1], digits = 2), ", 95% CI [", round(dat3_mod_dem[2,5], digits = 2), ", ", round(dat3_mod_dem[2,6], digits = 2), "], t(597) = ", round(dat3_mod_dem[2,3], digits = 2), ", p = ", round(dat3_mod_dem[2,4], digits = 3))
paste0("relation between NPI-narcissism and atraction to autocratic supervisor: beta = ", round(dat3_mod_aut[2,1], digits = 2), ", 95% CI [", round(dat3_mod_aut[2,5], digits = 2), ", ", round(dat3_mod_aut[2,6], digits = 2), "], t(597) = ", round(dat3_mod_aut[2,3], digits = 2), ", p = ", round(dat3_mod_aut[2,4], digits = 3))

###moderated mediation model
set.seed(123) #setting the seed for reproducibility
mediation_model_npi <- '
Zattraction ~ c*Znpi + Zstyle_supervisor + b*Zstyle_index_self + h*ZNPIxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a*Znpi
## index of moderated mediaion
index_npi := a * g
## conditional effects of NPI
indirect_aut := a*(b-0.9941926*g)
indirect_dem := a*(b+1.004168*g)
direct_aut := c-0.9941926*h
direct_dem := c+1.004168*h
## conditional effects of SHLS
SHLS_aut := b-0.9941926*g
SHLS_dem := b+1.004168*g'
results <- sem(mediation_model_npi, data = dat3, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat3_modmed_npi <- data.frame(parameterEstimates(results, ci = TRUE, level = 0.95,
                                             boot.ci.type = "bca.simple")) #bias-corrected percentile method
dat3_modmed_index_simpleslopes <- dat3_modmed_npi[c(25:31),]
dat3_modmed_npi <- dat3_modmed_npi[c(6,1,3,2,4:5),]

###create table
dat3_mod_npi[c(1,5:6),] <- NA
dat3_mod_npi <- dat3_mod_npi[c(1:2,5,3:4,6),]
dat3_mod_npi$Predictor <- c("NPI","NPI","SHLS","SLS","NPI x SLS","SHLS x SLS")
dat3_mod_npi$blank <- NA
dat_table_S3.9 <- cbind(dat3_mod_npi[,c(7,1,5:6,3:4,8)],dat3_modmed_npi[,c(5,9:10,7:8)])

colnames(dat_table_S3.9) <- c("Predictor","beta1","ci_lower1","ci_upper1","t","p1","blank","beta2","ci_lower2","ci_upper2","z","p2")

dat_table_S3.9[,2:12] <- sapply(dat_table_S3.9[,2:12], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.9[,c(2:4,8:10)] <- dat_table_S3.9[,c(2:4,8:10)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.9[,c(5,11)] <- dat_table_S3.9[,c(5,11)] %>% mutate_if(is.numeric, format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.9[,c(6,12)] <- dat_table_S3.9[,c(6,12)] %>% mutate_if(is.numeric, format_p)

dat_table_S3.9$ci1 <- paste0("[", dat_table_S3.9$ci_lower1, ", ", dat_table_S3.9$ci_upper1, "]")
dat_table_S3.9$ci2 <- paste0("[", dat_table_S3.9$ci_lower2, ", ", dat_table_S3.9$ci_upper2, "]")

dat_table_S3.9 <- dat_table_S3.9[,c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")]

dat_table_S3.9[c(1,3,6),c(2:5)] <- NA
dat_table_S3.9[7,2] <- "Subordinate's Hypothetical Leadership Style"
dat_table_S3.9[8,2] <- "Attraction to Supervisor"
dat_table_S3.9 <- dat_table_S3.9[c(7,1,8,2:6),]

col_keys <- c("Predictor","beta1","ci1","t","p1","blank","beta2","ci2","z","p2")
head1 <- c("","Moderation model","Moderation model","Moderation model","Moderation model","","Moderated mediation model","Moderated mediation model","Moderated mediation model","Moderated mediation model")
head2 <- c("Predictor","\u03B2","95% CI","t","p","","\u03B2","95% CI","z","p")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_S3.9)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=1, j=2:10, part="body")
tbl <- merge_at(tbl, i=3, j=2:10, part="body")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=2, j=c("t","p1","z","p2"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.1)
tbl <- width(tbl, j =~ beta1 + beta2, width=.5)
tbl <- width(tbl, j =~ ci1 + ci2, width=.95)
tbl <- width(tbl, j =~ t + z, width=.6)
tbl <- width(tbl, j =~ p1 + p2, width=.6)
tbl <- width(tbl, j =~ blank, width=.3)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.9.docx")

###simple slopes of the interaction between subordinate's hypothetical leadership style and supervisor's leadership style 
paste0("relation between SHLS and atraction to democratic supervisor: beta = ", round(dat3_modmed_index_simpleslopes[7,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[7,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[7,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[7,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[7,8], digits = 3))
paste0("relation between SHLS and atraction to autocratic supervisor: beta = ", round(dat3_modmed_index_simpleslopes[6,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[6,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[6,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[6,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[6,8], digits = 3))

###indirect effects of NPI-Narcissism
paste0("Study 3: index of moderated mediation for NPI-narcissism: estimate = ", round(dat3_modmed_index_simpleslopes[1,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[1,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[1,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[1,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[1,8], digits = 3))
paste0("Study 3: indirect effect of NPI-narcissism for the democratic supervisor: estimate = ", round(dat3_modmed_index_simpleslopes[3,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[3,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[3,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[3,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[3,8], digits = 3))
paste0("Study 3: indirect effect of NPI-narcissism for the autocratic supervisor: estimate = ", round(dat3_modmed_index_simpleslopes[2,5], digits = 2), ", 95% CI [", round(dat3_modmed_index_simpleslopes[2,9], digits = 2), ", ", round(dat3_modmed_index_simpleslopes[2,10], digits = 2), "], z = ", round(dat3_modmed_index_simpleslopes[2,7], digits = 2), ", p = ", round(dat3_modmed_index_simpleslopes[2,8], digits = 3))

rm(moderation_model_npi,dat3_mod_npi,model_dem,model_aut,dat3_mod_dem,dat3_mod_aut,mediation_model_npi,results,dat3_modmed_npi,dat3_modmed_index_simpleslopes,dat_table_S3.9,format_beta,format_t,format_p,head,tbl,doc)


##Supplementary Footnote S7: NPI Facets

###moderation model
moderation_model_npi_facets <- lm(Zattraction ~ Znpi_authority + Znpi_exhibitionism + Znpi_entitlement + Zstyle_supervisor + ZNPI_AUTxSLS + ZNPI_EXHxSLS + ZNPI_ENTxSLS, data=dat3)
dat3_mod_npi_facets <- data.frame(summary(moderation_model_npi_facets)$coef,confint(moderation_model_npi_facets))

###moderated mediation model
set.seed(123) #setting the seed for reproducibility
mediation_model_npi_facets <- '
Zattraction ~ c1*Znpi_authority + c2*Znpi_exhibitionism + c3*Znpi_entitlement + Zstyle_supervisor + b*Zstyle_index_self + h1*ZNPI_AUTxSLS + h2*ZNPI_EXHxSLS + h3*ZNPI_ENTxSLS + g*ZSHLSxSLS
Zstyle_index_self ~ a1*Znpi_authority + a2*Znpi_exhibitionism + a3*Znpi_entitlement
## index of moderated mediaion
index_npi_authority := a1 * g
index_npi_exhibitionism := a2 * g
index_npi_entitlement := a3 * g
## conditional effects of NPI facets
indirect_aut1 := a1*(b-0.9941926*g)
indirect_dem1 := a1*(b+1.004168*g)
indirect_aut2 := a2*(b-0.9941926*g)
indirect_dem2 := a2*(b+1.004168*g)
indirect_aut3 := a3*(b-0.9941926*g)
indirect_dem3 := a3*(b+1.004168*g)
## conditional effects of SHLS
SHLS_aut := b-0.9941926*g
SHLS_dem := b+1.004168*g'
results <- sem(mediation_model_npi_facets, data = dat3, meanstructure = TRUE,
               se = "bootstrap", bootstrap = 10000)
dat3_modmed_npi_facets <- data.frame(parameterEstimates(results, ci = TRUE, level = 0.95,
                                                 boot.ci.type = "bca.simple")) #bias-corrected percentile method

dat3_modmed_facets <- dat3_modmed_npi_facets[c(10:12,1:3,5,4,6:9),]
dat3_modmed_index_simpleslopes_facets <- dat3_modmed_npi_facets[c(61:71),]

rm(moderation_model_npi_facets,dat3_mod_npi_facets,mediation_model_npi_facets,results,dat3_modmed_npi_facets,dat3_modmed_index_simpleslopes_facets,dat3_modmed_facets)


##Table S3.10: Effect of NPI-Narcissism on Attraction to Supervisor Moderated by Supervisor's Leadership Style and Presentation Order of Democratic and Autocratic Leadership Items in Study 3

###regression model
model_order <- lm(Zattraction ~ Znpi + Zstyle_supervisor + Zorder + ZNPIxSLS + ZNPIxORD + ZSLSxORD + ZNPIxSLSxORD, data=dat3)
dat_table_S3.10 <- data.frame(summary(model_order)$coef,confint(model_order))

###create table
dat_table_S3.10 <- dat_table_S3.10[!(row.names(dat_table_S3.10) %in% "(Intercept)"), ]
dat_table_S3.10$Predictor <- c("NPI","SLS","Order","NPI x SLS","NPI x Order","SLS x Order","NPI x SLS x Order")
dat_table_S3.10 <- dat_table_S3.10[,c(7,1,5:6,3:4)]

colnames(dat_table_S3.10) <- c("Predictor","beta","ci_lower","ci_upper","t","p")

dat_table_S3.10[,2:6] <- sapply(dat_table_S3.10[,2:6], as.numeric)

format_beta <- function(x){
  ifelse(abs(x) < 0.005,  f_num(unlist(x), digits=3), f_num(unlist(x), digits=2))
}
dat_table_S3.10[,c(2:4)] <- dat_table_S3.10[,c(2:4)] %>% mutate_if(is.numeric, format_beta)

format_t <- function(x){
  formatC(x, format = "f", digits = 2)
}
dat_table_S3.10[,5] <- sapply(dat_table_S3.10[,5], format_t)

format_p <- function(x){
  ifelse(abs(x) < 0.001,  "< .001", f_num(unlist(x), digits=3))
}
dat_table_S3.10[,6] <- sapply(dat_table_S3.10[,6], format_p)

dat_table_S3.10$ci <- paste0("[", dat_table_S3.10$ci_lower, ", ", dat_table_S3.10$ci_upper, "]")

dat_table_S3.10 <- dat_table_S3.10[,c("Predictor","beta","ci","t","p")]

col_keys <- c("Predictor","beta","ci","t","p")
head1 <- c("Predictor","\u03B2","95% CI","t","p")
head <- data.frame(col_keys,head1, stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_S3.10)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:5, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:5, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- italic(tbl, i=1, j=c("t","p"), part="header")
tbl <- width(tbl, j =~ Predictor, width=1.5)
tbl <- width(tbl, j =~ beta, width=.5)
tbl <- width(tbl, j =~ ci, width=.95)
tbl <- width(tbl, j =~ t, width=.6)
tbl <- width(tbl, j =~ p, width=.6)
tbl <- height_all(tbl, height=.2, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.10.docx")

rm(model_order,dat_table_S3.10,format_beta,format_t,format_p,head,tbl,doc)


#clear environment
rm(list = ls())