library(readxl)
install.packages("metaSEM")
library("metaSEM")
install.packages("OpenMx")
library("OpenMx")
#-------------------------------------------------------------------------------------------------------------------
#get sample size  
practisesamplesize <- read_excel("C:/Users/JingJing/Desktop/samplesize for practise2.xlsx")

samplesize=practisesamplesize$`sample size`
samplesize


#input correlation metrics
#if cannot load the data, try with read table function to see the problem, usually the data format thing
my.vec1=read.table(file="C:/Users/JingJing/Desktop/practise2.csv",sep=",",header=T,fill=TRUE)

nvar=6
varname=c("par","indiriver","commdriver","instrumentdriver","indiout","hostout")
labels=list(varname,varname)

my.vec <- list() 
for (i in 1:nrow(my.vec1)){
  my.vec[[i]] <- vec2symMat(as.matrix(my.vec1[i,1:15]), 
  diag = FALSE)
  dimnames(my.vec[[i]]) <- labels
  }
head(my.vec)

# put NA on diagonal if variable is missing 

for (i in 1:length(my.vec)){ 
  for (j in 1:nrow(my.vec[[i]])){ 
    if (sum(is.na(my.vec[[i]][j,]))==nvar-1) 
      {my.vec[[i]][j,j] <- NA} 
  }}
my.vec[1]
#take care of missing correlation
# put NA on diag for var with least present correlations 

for (i in 1:length(my.vec)){ 
  for (j in 1:nrow(my.vec[[i]])){ 
    for (k in 1:nvar){ 
      if (is.na(my.vec[[i]][j,k])==TRUE 
          &is.na(my.vec[[i]][j,j])!=TRUE 
          &is.na(my.vec[[i]][k,k])!=TRUE){ 
        
if(sum(is.na(my.vec[[i]])[j,])>sum(is.na(my.vec[[i]])[k,])) 
  {my.vec[[i]][k,k] <- NA} 
if(sum(is.na(my.vec[[i]])[j,])<=sum(is.na(my.vec[[i]])[k,])) 
  {my.vec[[i]][j,j] <- NA} 
        }}}}

# try fix efect stage 1-assume homogeneity across study
stage1fixed <- tssem1(Cov=my.vec, n=samplesize, method="FEM") 
#got returned errors that the matrix are not positively definite
install.packages("Matrix")
library("Matrix")
my.vec[c(133,135,136,137,139)]

nearPD(my.vec[133])