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# last modified 2022-01-10 by J. Fox
# the following function to be imported from admisc and then deleted here:
# `tryCatchWEM` <- function(expr, capture = FALSE) {
# toreturn <- list()
#
# output <- withVisible(withCallingHandlers(
# tryCatch(expr, error = function(e) {
# toreturn$error <<- e$message
# NULL
# }),
# warning = function(w) {
# toreturn$warning <<- c(toreturn$warning, w$message)
# invokeRestart("muffleWarning")
# },
# message = function(m) {
# toreturn$message <<- paste(toreturn$message, m$message, sep = "")
# invokeRestart("muffleMessage")
# }
# ))
#
# if (capture && output$visible && !is.null(output$value)) {
# toreturn$output <- capture.output(output$value)
# toreturn$value <- output$value
# }
#
# if (length(toreturn) > 0) {
# return(toreturn)
# }
# }
hetcor.data.frame <- function(data, ML=FALSE, std.err=TRUE, use=c("complete.obs", "pairwise.complete.obs"),
bins=4, pd=TRUE, parallel=FALSE, ncores=detectCores(logical=FALSE),
thresholds=FALSE, ...){
se.r <- function(r, n){
rho <- r*(1 + (1 - r^2)/(2*(n - 3))) # approx. unbiased estimator
v <- (((1 - rho^2)^2)/(n + 6))*(1 + (14 + 11*rho^2)/(2*(n + 6)))
sqrt(v)
}
computeCor <- function(pair){
type <- ""
se <- NA
test <- NA
i <- rows[pair]
j <- cols[pair]
x <- data[, i]
y <- data[, j]
n <- sum(complete.cases(x, y))
if (n == 0) {
test <- r <- se <- NA
warning("no cases for pair ", j, ", ", i)
}
if (inherits(x, c("numeric", "integer")) && inherits(y, c("numeric", "integer"))) {
r <- cor(x, y, use="complete.obs")
type <- "Pearson"
if (std.err) {
se <- se.r(r, n)
test <- pchisq(chisq(x, y, r, bins=bins), bins^2 - 2, lower.tail=FALSE)
}
Thresholds <- list(NULL)
}
else if (inherits(x, c("factor", "logical", "character")) &&
inherits(y, c("factor", "logical", "character"))) {
type <- "Polychoric"
result <- tryCatchWEM(polychor(x, y, ML=ML, std.err=std.err, thresholds=thresholds),
capture=TRUE)
error <- !is.null(result$error)
if (!is.null(result$warning)){
warning("polychoric correlation between variables ", vnames[j], " and ", vnames[i],
if (length(result$warning) == 1) " produced a warning:\n" else " produced warnings:\n",
paste(paste(" ", result$warning), collapse="\n"))
}
if (error){
msg <- result$error
warning("could not compute polychoric correlation between variables ", vnames[j], " and ", vnames[i], "\n",
" Error message: ", msg)
result <- NA
}
if (std.err && !error){
result <- result$value
if (!(length(result) == 1 && is.na(result))){
r <- result$rho
se <- sqrt(result$var[1,1])
test <- if (result$df > 0)
pchisq(result$chisq, result$df, lower.tail=FALSE)
else NA
}
else {
r <- if (is.list(result)) result$value else result
test <- se <- NA
}
}
else {
r <- if (is.list(result)) result$value else result
test <- se <- NA
}
Thresholds <- if (thresholds) {
list(row.cuts=as.vector(result$row.cuts),
col.cuts=as.vector(result$col.cuts))
} else {
NULL
}
}
else {
if (inherits(x, c("factor", "logical", "character")) &&
inherits(y, c("numeric", "integer")))
result <- tryCatchWEM(polyserial(y, x, ML=ML, std.err=std.err, bins=bins,
thresholds=thresholds),
capture=TRUE)
else if (inherits(x, c("numeric", "integer")) &&
inherits(y, c("factor", "logical", "character")))
result <- tryCatchWEM(polyserial(x, y, ML=ML, std.err=std.err, bins=bins),
capture=TRUE)
else {
stop("columns must be numeric, factors, logical, or character.")
}
type <- "Polyserial"
error <- !(is.null(result$error))
if (!is.null(result$warning)){
warning("polyserial correlation between variables ", vnames[j], " and ", vnames[i],
if (length(result$warning) == 1) " produced a warning:\n" else " produced warnings:\n",
paste(paste( " ", result$warning), collapse="\n"))
}
if (error){
msg <- result$error
warning("could not compute polyserial correlation between variables ", vnames[j], " and ", vnames[i], "\n",
" Error message: ", msg)
result <- NA
}
if (std.err && !error){
result <- result$value
if (!(length(result) == 1 && is.na(result))){
r <- result$rho
se <- sqrt(result$var[1,1])
test <- pchisq(result$chisq, result$df, lower.tail=FALSE)
}
else {
r <- if (is.list(result)) result$value else result
test <- se <- NA
}
}
else {
r <- if (is.list(result)) result$value else result
se <- test <- NA
}
Thresholds <- if (thresholds) {
list(cuts=as.vector(result$cuts))
} else {
NULL
}
}
list(n=n, r=r, Type=type, SE=se, Test=test, Thresholds=Thresholds)
}
vnames <- names(data)
if (any(sapply(data, function(x) inherits(x, "character")))){
message("data contain one or more character variables",
"\nthe values of which are ordered alphabetically")
}
use <- match.arg(use)
if (use == "complete.obs") {
data <- na.omit(data)
n <- nrow(data)
}
p <- length(data)
if (p < 2) stop("fewer than 2 variables.")
R <- matrix(1, p, p)
Type <- matrix("", p, p)
SE <- matrix(0, p, p)
N <- matrix(0, p, p)
Test <- matrix(0, p, p)
if (thresholds){
Thresholds <- vector(p^2, mode="list")
Thresholds <- matrix(Thresholds, p, p)
}
diag(N) <- if (use == "complete.obs") nrow(data)
else sapply(data, function(x) sum(!is.na(x)))
if (all(diag(N) == 0)) stop("no non-missing cases")
npairs <- p*(p -1)/2
rows <- matrix(1:p, p, p)
cols <- t(rows)
rows <- rows[lower.tri(rows)]
cols <- cols[lower.tri(cols)]
result <- if (parallel && ncores > 1){
message("Note: using a cluster of ", ncores, " cores")
cl <- parallel::makeCluster(ncores)
on.exit(parallel::stopCluster(cl))
parallel::clusterApply(cl, 1:npairs, computeCor)
} else {
lapply(1:npairs, computeCor)
}
for (pair in 1:npairs){
i <- rows[pair]
j <- cols[pair]
res <- result[[pair]]
N[i, j] <- N[j, i] <- res$n
R[i, j] <- R[j, i] <- res$r
Type[i, j] <- Type[j, i] <- res$Type
SE[i, j] <- SE[j, i] <- res$SE
Test[i, j] <- Test[j, i] <- res$Test
if (thresholds) {
Thresholds[[i, j]] <- res$Thresholds
Thresholds[[j, i]] <- res$Type
}
}
if (pd && !any(is.na(R)) && min(eigen(R, only.values=TRUE)$values) < 0){
cor <- Matrix::nearPD(R, corr=TRUE)
if (!cor$converged) warning("attempt to make correlation matrix positive-definite failed")
else warning("the correlation matrix has been adjusted to make it positive-definite")
R <- as.matrix(cor$mat)
}
rownames(R) <- colnames(R) <- names(data)
result <- list(correlations=R, type=Type, NA.method=use, ML=ML)
if (thresholds) result$thresholds <- Thresholds
if (std.err) {
rownames(SE) <- colnames(SE) <- names(data)
rownames(N) <- colnames(N) <- names(N)
rownames(Test) <- colnames(Test) <- names(data)
result$std.errors <- SE
result$n <- if (use == "complete.obs") n else N
result$tests <- Test
}
if (0 < (nNA <- sum(is.na(R[lower.tri(R)])))){
warning(nNA, if (nNA == 1) " correlation" else " correlations",
" couldn't be computed and", if (nNA == 1) " is" else " are", " NA")
}
class(result) <- "hetcor"
result
}
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