### Step 1 library("metaSEM") ### Step 2 data3 <- read.csv("/Users/srikanthparameswaran/Desktop/TSSEM 3/M2/M2 Data - Before Preprocessing.csv") nvar <- 5 varnames <- c("A","B","C","D","E") labels <- list(varnames,varnames) cordat <- list() for (i in 1:nrow(data3)){ cordat[[i]] <- vec2symMat(as.matrix(data3[i,2:11]),diag = FALSE) dimnames(cordat[[i]]) <- labels} data3$data<-cordat pattern.na(data3$data, show.na = FALSE) pattern.n(data3$data, data3$Corr_Sample) is.pd(data3$data) ### Step 3 for (i in 1:length(data3$data)){ for (j in 1:nrow(data3$data[[i]])){ if (sum(is.na(data3$data[[i]][j,]))==nvar-1) {data3$data[[i]][j,j] <- NA} }} pattern.na(data3$data, show.na = FALSE) pattern.n(data3$data, data3$Corr_Sample) is.pd(data3$data) ### Step 4 for (i in 1:length(data3$data)){ for (j in 1:nrow(data3$data[[i]])){ for (k in 1:nvar){ if (is.na(data3$data[[i]][j,k])==TRUE &is.na(data3$data[[i]][j,j])!=TRUE &is.na(data3$data[[i]][k,k])!=TRUE){ if(sum(is.na(data3$data[[i]])[j,])>sum(is.na(data3$data[[i]])[k,])) {data3$data[[i]][k,k] = NA} if(sum(is.na(data3$data[[i]])[j,])<=sum(is.na(data3$data[[i]])[k,])) {data3$data[[i]][j,j] = NA} }}}} pattern.na(data3$data, show.na = FALSE) pattern.n(data3$data, data3$Corr_Sample) is.pd(data3$data)