R/md.vcov.R

In metavcov: Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

md.vcov <- function(r, nt, nc, n_rt = NA, n_rc = NA, sdt, sdc)
{ K <- length(r)
  if (length(as.vector(nt)) == length(as.matrix(nt)[, 1])) {
    colum.number <- 1} else { colum.number <- ncol(nt)}
  col.vac.number <- (colum.number + 1)*colum.number/2

  if (is.na(n_rt)&(length(n_rt) == 1)){
    n_rt <- rep(list(matrix(NA, colum.number, colum.number)), K) }

  for (k in 1:K) {
    for (i in 1:colum.number){
      for (j in 1:colum.number){
        if (is.na(n_rt[[k]][i, j]))
          n_rt[[k]][i, j] <- min(nt[k, i], nt[k, j])
      }
    }
  }

  if (is.na(n_rc)&(length(n_rc) == 1)){
    n_rc <- rep(list(matrix(NA, colum.number, colum.number)), K) }

  for (k in 1:K) {
    for (i in 1:colum.number){
      for (j in 1:colum.number){
        if (is.na(n_rc[[k]][i, j]))
          n_rc[[k]][i, j] <- min(nc[k, i], nc[k, j])
      }
    }
  }

  list.corr.st.varcovar <- list()
  for (k in 1:K){
    list.corr.st.varcovar[[k]] <- matrix(NA, colum.number, colum.number)
    for (i in 1:colum.number){
      for (j in 1:colum.number)
      {list.corr.st.varcovar[[k]][i, j] <- unlist(r[[k]][i, j]*n_rc[[k]][i, j]*sdc[k, i]*sdc[k, j]/(nc[k, i]*nc[k, j]) +
                                                    r[[k]][i, j]*n_rt[[k]][i, j]*sdt[k, i]*sdt[k, j]/(nt[k, i]*nt[k, j]))
      }
    }
  }
  corr.st.varcovar <- matrix(unlist(lapply(1:K, function(k){
    smTovec(list.corr.st.varcovar[[k]])})), K, col.vac.number, byrow = TRUE)
  list(list.vcov = list.corr.st.varcovar,
       matrix.vcov = corr.st.varcovar)
}