library(lavaan)
library(semPlot)
library(semTools)
#FFM + HH OVERALL (Common Source & Non-Common Source) ####
correl = lav_matrix_lower2full(
c(1,
0.211715797123219,	1,
0.420527937576375,	0.27,	1,
-0.148416187270875,	0.25,	0.2,	1,
0.150791906364563,	0.14,	0.16,	0.09,	1,
-0.00973603121181242,	0.19,	0.21,	0.21,	0.27,	1,
0.12,	0.1,	0.22,	0.13,	0.12,	0.23,	1,
0.273,	0.14,	0.14,	0.11,	0.16,	0.12,	0.627,	1))
rownames(correl) = colnames(correl) = c(
"HH",
"Conscientiousness",
"Agreeableness",
"EmotionalStability",
"Openness",
"Extraversion",
"TFL_C",
"LdEf")
library(lavaan)
library(semPlot)
library(semTools)
#FFM + HH OVERALL (Common Source & Non-Common Source) ####
correl = lav_matrix_lower2full(
c(1,
0.211715797123219,	1,
0.420527937576375,	0.27,	1,
-0.148416187270875,	0.25,	0.2,	1,
0.150791906364563,	0.14,	0.16,	0.09,	1,
-0.00973603121181242,	0.19,	0.21,	0.21,	0.27,	1,
0.12,	0.1,	0.22,	0.13,	0.12,	0.23,	1,
0.273,	0.14,	0.14,	0.11,	0.16,	0.12,	0.627,	1))
rownames(correl) = colnames(correl) = c(
"HH",
"Conscientiousness",
"Agreeableness",
"EmotionalStability",
"Openness",
"Extraversion",
"TFL_C",
"LdEf")
harmonic = 6558.112414
Model1a = '
LdEf ~ a * TFL_C
TFL_C ~ b1 * Extraversion
TFL_C ~ b2 * Agreeableness
'
results1a = sem(Model1a, sample.cov = correl, sample.nobs = harmonic)
standardizedsolution(results1a)
summary(results1a)
parameterEstimates(results1a)
standardizedsolution(results1a)
monteCarloMed("a * b1", object = standardizedSolution(results1a))
summary(results1a)
summary(results1a, standardized = TRUE)
inspect(results1a)
?inspect
lanInspect(result1b)
lavInspect(result1b)
lavInspect(results1a)
lavInspect(results1a, "std.all")
lavInspect(results1a, "std.all")$beta
monteCarloMed("a * b1", object = lavInspect(results1a, "std.all")$beta)
monteCarloMed("a * b1", object = lavInspect(results1a, "std.all")$beta[1])
?monteCarloMed
print(monteCarloMed("scale(a) * scale(b1)", object = results1a))
print(monteCarloMed("std(a) * std(b1)", object = results1a))
print(monteCarloMed("std.all(a) * std.all(b1)", object = results1a))
summary(result1a)
summary(results1a)
parameterEstimates(results1a)
harmonic = 6558.112414
Model1a = '
LdEf ~ a * TFL_C
TFL_C ~ b1 * Extraversion
TFL_C ~ b2 * Agreeableness
'
results1a = sem(Model1a, sample.cov = correl, sample.nobs = harmonic, standardized = TRUE)
?`semPlot-package`
standardizedSolution(results1a)
standardizedSolution(results1a)[1,]
standardizedSolution(results1a)[2,]
harmonic = 6558.112414
Model1a = '
LdEf ~ a * TFL_C
TFL_C ~ b1 * Extraversion
TFL_C ~ b2 * Agreeableness
'
results1a = sem(Model1a, sample.cov = correl, sample.nobs = harmonic, std = TRUE)
library(lavaan)
?sem
summary(results1a, fit.measures = TRUE, standardized=TRUE, rsquare=TRUE)
semPaths(
results1a,
what = "path",
whatLabels = "stand",
layout = "tree3",
residuals = FALSE,
exoCov = FALSE
)
library(lavaan)
library(semPlot)
#FFM + HH OVERALL (Common Source & Non-Common Source) ####
correl2 = lav_matrix_lower2full(
c(1,
-0.01,	1,
0.42,	0.166721482213838,	1,
0.57,	-0.09,	0.34,	1,
-0.08,	0.77,	0.05,	0.01,	1,
0.22,	0,	0.71,	0.46,	0.12,	1,
-0.16,	0.18,	0.092,	0.26,	0.457,	0.579,	1,
0.188,	0.11,	0.125,	0.343,	0.277,	0.405,	0.627,	1))
rownames(correl2) = colnames(correl2) = c(
"HHSelf",
"ExSelf",
"AgSelf",
"HHOther",
"ExOther",
"AgOther",
"TFL_C",
"LdEf")
harmonic = 1470.280392
Model3a = '
LdEf ~ a * TFL_C
#LdEf ~ HHSelf #added later
TFL_C ~ b1 * HHOther
TFL_C ~ b2 * ExOther
TFL_C ~ b3 * AgOther
#TFL_C ~ HHSelf #added later
HHOther ~~ ExOther
HHOther ~~ AgOther
ExOther ~~ AgOther
HHOther ~ c1 * HHSelf
ExOther ~ c2 * ExSelf
AgOther ~ c3 * AgSelf
'
results3a = sem(Model3a, sample.cov = correl2, sample.nobs = harmonic)
summary(results3a, fit.measures = TRUE, standardized=TRUE, rsquare=TRUE)
semPaths(
results3a,
what = "path",
whatLabels = "stand",
layout = "tree3",
residuals = FALSE,
exoCov = FALSE
)
standardizedSolution(results3a)
library(tm)
library(SentimentAnalysis)
library(tm)
library(psych)
?trimfill
library(metafor)
?trimfill
Sys.setenv(LANGUAGE = "en")
Sys.setenv(LANGUAGE = "en")
sqrt(0.0039)
sqrt(.2265)
sqrt(.0317)
sqrt(.0521)
sqrt(.0112)
sqrt(.0146)
sqrt(.0054)
sqrt(.0001)
sqrt(.0356)
sqrt(.0681)
sqrt(.0021)
sqrt(.2633)
sqrt(.0517)
sqrt(.0035)
sqrt(.0979)
sqrt(.0408)
sqrt(.0086)
sqrt(.0030)
sqrt(.0095)
sqrt(.0551)
sqrt(.0405)
sqrt(.3351)
sqrt(.1560)
sqrt(.0047)
sqrt(.1215)
sqrt(.0038)
sqrt(.0506)
sqrt(.0199)
sqrt(.1396\)
sqrt(.1396)
sqrt(.0329)
sqrt(.2782)
sqrt(.1142)
sqrt(.0019)
sqrt(.0064)
sqrt(.0878)
sqrt(.0778)
sqrt(.0292)
sqrt(.141)
sqrt(.089)
x <- 11
y <- 8
x * y
?rnorm()
dnorm(100)
rnorm(100)
plot(1:100, rnorm(100))
plot(1:100, runif(100))
plot(1:100, sin(1:100))
sin(-100*pi:100*pi)
sin((-100*pi):(100*pi))
?seq()
plot(-10:10,(-10:10)^2)
x <- -10:10
plot(x, x^3 - 3*x^2 + 4x - 14)
plot(x, (x^3 - 3*x^2 + 4x - 14))
plot(x, (x^3 - 3*x^2 + 4*x - 14))
plot(x, (-x^3 - 3*x^2 + 4*x - 14))
plot(x, (-1*x^3 - 3*x^2 + 4*x - 14))
plot(x, (x^3 - 3*x^2 + 4*x - 14))
mean(.056, .073, .107, -.018, .101, -.174, -.256, -.194, -.135, -.147)
sd(.056, .073, .107, -.018, .101, -.174, -.256, -.194, -.135, -.147)
mean(c(.056, .073, .107, -.018, .101, -.174, -.256, -.194, -.135, -.147))
sd(c(.056, .073, .107, -.018, .101, -.174, -.256, -.194, -.135, -.147))
mean(c(.038, .076, .006, .018, -.002, .105, .196, .206, .089, -.075))
sd(c(.038, .076, .006, .018, -.002, .105, .196, .206, .089, -.075))
mean(c(-.051, .112, .135, .179, .093, -.101, -.070, .200, -.044, .121))
sd(c(-.051, .112, .135, .179, .093, -.101, -.070, .200, -.044, .121))
pbinom(0.5, 17, 0.5)
?pbinom
1/(2^17)
pbinom(0.35, 17, 0.5)
pbinom(1, 17, 0.5)
pbinom(0.05, 17, 0.5)
pbinom(0.1, 17, 0.5)
pbinom(0.9, 17, 0.5)
pbinom(c(0.9, 0.1), 17, 0.5)
pbinom(c(0.9, 0.999999), 17, 0.5)
pbinom(0.9, 1, 0.5)
pbinom(0.9, 2, 0.5)
pbinom(0.9, 3, 0.5)
pbinom(0.9, 4, 0.5)
pbinom(0.001, 4, 0.5)
qbinom(0.01, 4, 23)
qbinom(0.01, 4, 0.5)
qbinom(0.1, 4, 0.5)
qbinom(0.0001, 4, 0.5)
qbinom(0.0001, 16, 0.5)
qbinom(0.0001, 16, 0.23)
qbinom(0.0001, 16, 0.25)
0.9……130
0.9^130
0.9^130
0.95^130
0.50^130
0.90^130
129*0.9^129 * 0.1
0.5^139
0.9^139
pbinom(q = 0.5, size = 139, prob = 0.9)
pbinom(q = 0.5, size = 139, prob = 0.1)
0.9^139
library(metafor)
?rma
library(metafor)
?rma
library(thurstonianIRT)
?fit_TIRT_stan
?thurstonianIRT
library(mirt)
?mirt
citation(semTools)
citation("semTools")
?iccde
library(iccde)
?icc.de
load("D:/Full Research Image 0815.RData")
library(iccde)
round(cor(VD_FULLGRM), 2
round(cor(VD_FULLGRM), 2
)
round(cor(VD_FULLGRM), 2)
round(cor(VD_FULLGRM), 2)[1:6,8:19]
VD1 <- round(cor(VD_matrix_FC1)[1:12,14:25], 2)
VD2 <- round(cor(VD_matrix_FC2)[1:12,14:25], 2)
VD3 <- round(cor(VD_matrix_FC3)[1:12,14:25], 2)
VD4 <- round(cor(VD_matrix_FC4)[1:12,14:25], 2)
VD_GRM <- round(cor(VD_FULLGRM), 2)[1:6,8:19]
VD1
VD2
VD_GRM
VD_GRM[1,]
icc.de(VD_GRM[1,], VD1[1,])
icc.de(VD_GRM[1,], VD1[1,])
icc.de(VD_GRM[1,], VD2[1,])
icc.de(VD_GRM[1,], VD3[1,])
icc.de(VD_GRM[1,], VD4[1,])
icc.de(VD_GRM[1,], VD3[1,])
icc.de(VD_GRM[1,], VD4[1,])
icc.de(VD_GRM[1,], VD1[1,])
icc.de(VD_GRM[1,], VD2[1,])
icc.de(VD_GRM[1,], VD3[1,])
icc.de(VD_GRM[1,], VD4[1,])
icc.de(VD_GRM[2,], VD1[2,])
icc.de(VD_GRM[2,], VD2[2,])
icc.de(VD_GRM[2,], VD3[2,])
icc.de(VD_GRM[2,], VD4[2,])
icc.de(VD_GRM[3,], VD1[3,])
icc.de(VD_GRM[3,], VD2[3,])
icc.de(VD_GRM[3,], VD3[3,])
icc.de(VD_GRM[3,], VD4[3,])
VD_GRM
VD1
icc.de(VD_GRM[4,], VD1[4,])
icc.de(VD_GRM[4,], VD2[4,])
icc.de(VD_GRM[4,], VD3[4,])
icc.de(VD_GRM[4,], VD4[4,])
icc.de(VD_GRM[5,], VD1[5,])
icc.de(VD_GRM[5,], VD2[5,])
icc.de(VD_GRM[5,], VD3[5,])
icc.de(VD_GRM[5,], VD4[5,])
VD_FULLGRM <- GRM_ALL %>% select(PID: O) %>% left_join(
full_dataset %>% select(DTDD,DTDD_Mach, DTDD_Psych, DTDD_Narci, CWB, JS, Burnout, FinancialSecurity, OCB, SWB, TI, JP, PHQ, PROLIFIC_PID),
by = c("PID" = "PROLIFIC_PID")
) %>% select(-PID) %>% drop_na()
VD_FULLGRM <- VD_FULLGRM %>% mutate(H = -1*H, C = -1*C, O = -1*O)
VD_GRM <- round(cor(VD_FULLGRM), 2)[1:6,8:19]
library(dplyr)
VD_FULLGRM <- GRM_ALL %>% select(PID: O) %>% left_join(
full_dataset %>% select(DTDD,DTDD_Mach, DTDD_Psych, DTDD_Narci, CWB, JS, Burnout, FinancialSecurity, OCB, SWB, TI, JP, PHQ, PROLIFIC_PID),
by = c("PID" = "PROLIFIC_PID")
) %>% select(-PID) %>% drop_na()
VD_FULLGRM <- VD_FULLGRM %>% mutate(H = -1*H, C = -1*C, O = -1*O)
VD_GRM <- round(cor(VD_FULLGRM), 2)[1:6,8:19]
library(tidyr)
VD_FULLGRM <- GRM_ALL %>% select(PID: O) %>% left_join(
full_dataset %>% select(DTDD,DTDD_Mach, DTDD_Psych, DTDD_Narci, CWB, JS, Burnout, FinancialSecurity, OCB, SWB, TI, JP, PHQ, PROLIFIC_PID),
by = c("PID" = "PROLIFIC_PID")
) %>% select(-PID) %>% drop_na()
VD_FULLGRM <- VD_FULLGRM %>% mutate(H = -1*H, C = -1*C, O = -1*O)
VD_GRM <- round(cor(VD_FULLGRM), 2)[1:6,8:19]
icc.de(VD_GRM[1,], VD1[1,])
icc.de(VD_GRM[1,], VD2[1,])
icc.de(VD_GRM[1,], VD3[1,])
icc.de(VD_GRM[1,], VD4[1,])
icc.de(VD_GRM[5,], VD1[5,])
icc.de(VD_GRM[5,], VD2[5,])
icc.de(VD_GRM[5,], VD3[5,])
icc.de(VD_GRM[5,], VD4[5,])
icc.de(VD_GRM[6,], VD1[6,])
icc.de(VD_GRM[6,], VD2[6,])
icc.de(VD_GRM[6,], VD3[6,])
icc.de(VD_GRM[6,], VD4[6,])
mean(RR_dataset_T2_FC1$RecomInt)
mean(RR_dataset_T2_FC1$RecomInt)/2
sd(RR_dataset_T2_FC1$RecomInt)
sd(RR_dataset_T2_FC1$RecomInt)/4
?icc.de
library(metafor)
?rma
remove.packages("Rcpp")
library(autoFC)
library(dplyr)
library(thurstonianIRT)
updateR()
installr::updateR()
installr::updateR()
installr::updateR()
installr::updateR()
install.packages("lavaan")
install.packages("tidymodels")
library(tidymodels)
install.packages("rlang")
install.packages("rlang")
install.packages("rlang")
update.packages(oldPkgs = "rlang")
library(tidymodels)
install.packages("dials")
library(tidymodels)
install.packages("tidymodels")
install.packages("tidymodels")
update.packages(oldPkgs = "vctrs")
setwd("D:/Anoop Meta-Analysis/Supplementary Materials - 032023/Analysis/2. HEXACO Trait-Leadership Relationships")
source("../Utilities/Utilities-Main.R")
data_HEXACO <- readxl::read_excel("../../DataSet/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
# Leadership Emergence - Main Effect --------------------------------------
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
HEXACO_emg_results <- HEX_analysis(data_HEXACO_emg, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
HEXACO_eff_J_results <- HEX_analysis(data_HEXACO_eff_J, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
# Leadership Effectiveness (Narrow) - Main Effect --------------------------------------
data_HEXACO_eff_J <- data_HEXACO %>% filter(DV_JUDGE_EFF == 1)
HEXACO_eff_J_results <- HEX_analysis(data_HEXACO_eff_J, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
format_file(HEXACO_eff_J_results$overall)
HEXACO_eff_results <- HEX_analysis(data_HEXACO_eff, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
data_HEXACO
data_HEXACO$INCLUDE_TYPE
data_HEXACO$OVERALL_INCLUSION
# Leadership Emergence - Main Effect --------------------------------------
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
HEXACO_emg_results <- HEX_analysis(data_HEXACO_emg, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
data_HEXACO_emg
data_HEXACO_emg$IV
data_HEXACO_emg$N
data_HEXACO <- readxl::read_excel("../../DataSet/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
data_HEXACO
data_HEXACO$DV
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1)
HEXACO_emg_results <- HEX_analysis(data_HEXACO_emg, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
var_rhp
var_rho
### If rxx/ryy is missing, we use mean imputation of the input data frame.
### If you provide a data frame where no study has rxx or ryy - impute 1 as conservative means.
calculation <- function(input, valueX, valueY, method = "HS",
var_names = list(rxx = "rxx",
ryy = "ryy",
rxy = "rxy",
N = "N")) {
digits <- 2
if (is.null(input)) return(NULL)
if (nrow(input) < 1) return(NULL)
ri <- as.numeric(unlist(input[var_names$rxy]))
ni <- as.numeric(unlist(input[var_names$N]))
rxx <- as.numeric(unlist(input[var_names$rxx]))
ryy <- as.numeric(unlist(input[var_names$ryy]))
## Mean Imputation
rxx = rxx %>% tidyr::replace_na(mean(na.omit(rxx)))
ryy = ryy %>% tidyr::replace_na(mean(na.omit(ryy)))
## If mean(na.omit()) fails, result will become all NaN. In this case we replace NaN with 1
if (is.na(rxx[1])) {
# print("Artificial Alpha X was imputed")
if (missing(valueX)) valueX <- 1
# print(valueX)
rxx <- valueX
}
if (is.na(ryy[1])) {
# print("Artificial Alpha Y was imputed")
if (missing(valueY)) valueY <- 1
# print(valueY)
ryy <- valueY
}
data <- cbind(ri, ni, rxx, ryy)
k = length(ri)
N = sum(ni)
ri <- ifelse(ri==0,0.0000001, ri)
M1 <- sum(ni*ri)/sum(ni)  # weighted mean, uncorrected (bare bones)
# print(sprintf("Bare bones correlation:%.3f", M1))
var_M1 <- sum(ni*(ri-M1)^2)/sum(ni)
var.i <-((1-M1^2)^2)/(ni-1) # individual study sampling variance (bare bones)
ai <- sqrt(rxx*ryy) # individual study attenuation for unreliability
var.i2 <- var.i/ai^2 # corrected individual study sampling variance for unreliability
ri.m <- ri/ai # corrected individual study ES for unreliability
ri.m_weighted <- sum(ri.m*ni)/sum(ni)
corrx <- rma(yi = ri.m,vi = var.i2,method = method, weights = 1/(var.i2))
# corrx <- rma(yi = ri.m,vi = var.i2, method = "HS", weights = ni - 3)
rho <- corrx$b
sd_r <- sqrt(var_M1)
se_rho <- corrx$se
sd_rho <- sqrt(corrx$tau2)
avg_var.i2 <- sum(ni*var.i2)/sum(ni)
var_corrected_r <- sum(ni * (ri.m-ri.m_weighted)^2)/sum(ni)
var_rho <- var_corrected_r - avg_var.i2
sd_rho2 <- ifelse(var_rho < 0, 0, sqrt(var_rho))
var_rho1 <- sum(ni*(ri.m - c(rho))^2)/sum(ni)
CI_u <- rho + 1.96 * se_rho
CI_l <- rho - 1.96 * se_rho
CI_95 <- paste("[",roundif(CI_l, digits),",",
roundif(CI_u, digits),"]",sep = "", collapse = "")
CV_u <- rho + 1.28 * sd_rho
CV_l <- rho - 1.28 * sd_rho
CV_80 <- paste("[", roundif(CV_l, digits),",",
roundif(CV_u, digits),"]",sep = "", collapse = "")
CV_u2 <- rho + 1.28 * sd_rho2
CV_l2 <- rho - 1.28 * sd_rho2
CV_80_2 <- paste("[", roundif(CV_l2, digits),",",
roundif(CV_u2, digits),"]",sep = "", collapse = "")
### Results for fixed effects
corrx_fe <- rma(yi = ri.m,vi = var.i2,method = "FE", weights = 1/(var.i2))
rho_fe <- corrx_fe$b
sd_r_fe <- sqrt(var_M1)
se_rho_fe <- corrx_fe$se
sd_rho_fe = sqrt(corrx_fe$tau2)
var_rho1_fe <- sum(ni*(ri.m - c(rho_fe))^2)/sum(ni)
CI_u_fe <- rho_fe + 1.96 * se_rho_fe
CI_l_fe <- rho_fe - 1.96 * se_rho_fe
CI_95_fe <- paste("[",roundif(CI_l_fe, digits),",",
roundif(CI_u_fe, digits),"]",sep = "", collapse = "")
CV_u_fe <- rho_fe + 1.28 * sd_rho_fe
CV_l_fe <- rho_fe - 1.28 * sd_rho_fe
CV_80_fe <- paste("[", roundif(CV_l_fe, digits),",",
roundif(CV_u_fe, digits),"]",sep = "", collapse = "")
return(list(result = corrx,
result_fe = corrx_fe,
details = data.frame(k = k, N = N, r = round(M1, 3),
sd_r = roundif(sqrt(var_M1), digits),
rho = round(rho, 3), rho2 = ri.m_weighted,
sd_rho = roundif(sqrt(corrx$tau2), digits), CI_95 = CI_95, CV_80 = CV_80, CV_80_2 = CV_80_2,
se_rho = roundif(corrx$se, digits), rxx = mean(rxx), ryy = mean(ryy), var_rho = var_rho1)))
}
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
HEXACO_emg_results <- HEX_analysis(data_HEXACO_emg, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
format_file(HEXACO_emg_results$overall)
# Leadership Effectiveness (Narrow) - Main Effect --------------------------------------
data_HEXACO_eff_J <- data_HEXACO %>% filter(DV_JUDGE_EFF == 1)
HEXACO_eff_J_results <- HEX_analysis(data_HEXACO_eff_J, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
format_file(HEXACO_eff_J_results$overall)
