#' Obtain correlated factors reliability estimates of two-dimensional data
#'
#' Obtain correlated factors reliability estimates of two-dimensional data. It is a kind
#' of multidimensional CFA reliability, and it is the reliability derived from
#' the correlated factors model. Items should be grouped by each sub-dimension. For
#' example, the first dimension consists of the first item through the until item.
#'
#' @param x observed item scores or their covariances
#' @param nobs number of observations (i.e., sample size)
#' @param until The number of items up to the first sub-construct
#' @param print If TRUE, the result is printed to the screen.
#' @return a correlated factors reliability estimate
#' @examples correlated_factors(Osburn_moderate, 4)
#' @examples correlated_factors(Sijtsma2a, c(2, 4))
#' @references Cho, E. (2016). Making reliability reliable: A systematic
#' approach to reliability coefficients. Organizational Research Methods, 19(4),
#' 651-682. https://doi.org/10.1177/1094428116656239
#' @author Eunseong Cho, \email{bene@kw.ac.kr}
#'
correlated_factors <- function(x, nobs = NULL, until, print = TRUE) {
stopifnot(requireNamespace("lavaan"))
if(nrow(x) == ncol(x)) {
m <- x
nobs <- ifelse(is.null(nobs), 200, nobs)
} else {
m <- cov(x)
nobs <- nrow(x)
}
colnames(m) <- rownames(m) <- character(length = nrow(m))
grp_start <- c(1, until + 1)
grp_end <- c(until, nrow(m))
model_str <- vector("character")
for (i in 1:nrow(m)) {
rownames(m)[i] <- paste0("V", i)
}
for (i in 1:nrow(m)) {
if (any(i == grp_start)) {
which <- which(i == grp_start)
for (j in i:grp_end[which]) {
if (j == i) {
model_str <- paste0(model_str, "\n G", which, "=~ NA*V", j)
} else {
model_str <- paste0(model_str, " + b", j, "*V", j)
}
} # end of for (j in i:grp_end[which])
} # end of if(any(i == grp_start))
} # end of for (i in 1:nrow(m))
for (i in seq_along(grp_start)) {
model_str <- paste0(model_str, "\n G", i, " ~~ 1 * G", i)
if (i > 1) { # to prevent correlation less than -1 or greater than 1
for (j in 1:(i - 1)) {
model_str <- paste0(model_str, "\n G", i, " ~~ a", i, j, " * G", j)
model_str <- paste0(model_str, "\n a", i, j, " < 1")
model_str <- paste0(model_str, "\n a", i, j, " > -1")
}
}
}
for (i in 1:nrow(m)) { # to prevent negative errors
model_str <- paste0(model_str, "\n V", i, " ~~ e", i, "*V", i, "\n e", i, "> 0")
}
lav_out <- lavaan::cfa(model_str, sample.cov = m, sample.nobs = nobs)
if (lavaan::inspect(lav_out, what = "converged")) {
fit <- lavaan::inspect(lav_out, what = "fit")
est <- lavaan::inspect(lav_out, what = "est")
implied <- lavaan::inspect(lav_out, what = "implied")[[1]]
rel <- 1 - sum(est$theta)/sum(implied)
# to obtain subdimensional reliability
sub_rel <- vector("double", length = length(grp_start))
for (i in seq_along(grp_start)) { # to conveniently compute the average correlation using na.rm
sub_uniq <- sum(est$theta[grp_start[i]:grp_end[i], grp_start[i]:grp_end[i]])
sub_implied <- sum(implied[grp_start[i]:grp_end[i], grp_start[i]:grp_end[i]])
sub_rel[i] <- 1 - sub_uniq / sub_implied
}
# output
out <- list(rel = rel,
sub_rel = sub_rel,
fit = fit,
est = est)
} else { # NA for non-convergent solutions
out <- NA
}
if (print) {
cat("correlated factors reliability (multidimensional CFA) ", rel, "\n")
cat("Sub-dimensional reliability (congeneric reliability) ", sub_rel, "\n")
}
invisible(out)
}
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