library(metaSEM)
# Input correlation matrices
dt <- readFullMat("cordata1.txt")

mtnam1 <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12")


dt <- lapply(dt, function(x) {
  dimnames(x) <- list(mtnam1, mtnam1)
  x})

# Input sample sizes 
sn <- as.numeric(read.table("samplesize.txt"))

# Combine correlation matrices and sample sizes to create a list of semdata
semdata <- list(data=dt, n=sn )

## Run random-effect model to compute the correlations and population variability τ2 for MASEM 
stage1random <- tssem1(semdata$data, n=semdata$n, method="REM")

# Retrieve the results of random-effect model 
summary(stage1random)
#Error in 1:(rsum * csum) : NA/NaN argument
#In addition: Warning message:
#In rsum * csum : NAs produced by integer overflow