install.packages("metaSEM")
require("metaSEM")
library(readxl)
dataset <- read_excel("c:/Users/user/Documents/R/paper6_3/paper6_3 data/masem_data_3_SMPL.xlsx")
head(dataset)

## make list of cormatrices (cordat), NA on diagonal
nvar <- 6
varnames <- c("RND","CEO","ABS","COM","REL","PER")
labels <- list(varnames, varnames)
cordat <- list()
for (i in 1:nrow(dataset)){	
 cordat[[i]] <- vec2symMat(as.matrix(dataset[i,3:17]),diag = FALSE)
 dimnames(cordat[[i]]) <- labels
 }
# put NA on diagonal if variable is missing
for (i in 1:length(cordat)){
 for (j in 1:nrow(cordat[[i]])){	
     if (sum(is.na(cordat[[i]][j,]))==nvar-1) 
     {cordat[[i]][j,j] <- NA}
   }}
# put NA on diagonal for variable with least present correlations
for (i in 1:length(cordat)){
 for (j in 1:nrow(cordat[[i]])){
     for (k in 1:nvar){				
     if (is.na(cordat[[i]][j,k])==TRUE
         &is.na(cordat[[i]][j,j])!=TRUE
         &is.na(cordat[[i]][k,k])!=TRUE){
     
         if(sum(is.na(cordat[[i]])[j,])>sum(is.na(cordat[[i]])[k,]))
         {cordat[[i]][k,k] = NA}
         if(sum(is.na(cordat[[i]])[j,])<=sum(is.na(cordat[[i]])[k,]))
         {cordat[[i]][j,j] = NA}	
       }}}}
head(cordat)

dataset$N

#### stage 1 
stage1random <- tssem1(Cov=cordat, n=dataset$N, method="REM", RE.type="Diag")
summary(stage1random)

# returns models via maTryHard()
random1_rerun <- rerun(stage1random)