#' Predict from CFA model
#'
#' This function predicts based on the estimation of the CFA
#' model \code{train_CFA}.
#'
#' @export
#' @param tr_fa An object resulting from \code{train_CFA}.
#' @param gp_test vector. Groups for each observation in the test set.
#' @param test data frame. Test data set for evaluation.
#' @param logl logical. Return log-likelihood? Defaults to TRUE.
#' @return A list.
#' \item{predictions}{A numeric vector of predictions for all observations
#' in the test set.}
#' \item{likelihoods}{A numeric vector of (log-)likelihoods for all
#' observations in the test set.}
#' @seealso \code{\link{train_CFA}}
pred_CFA <- function (tr_fa, gp_test, test, logl = TRUE) {
if (is.na(tr_fa$Sigma)) {
predictions <- NA
log_lik <- NA
} else {
predictions <- matrix(data = NA, nrow=length(gp_test), ncol=ncol(tr_fa$train))
loadings <- matrix(tr_fa$loadings, ncol = ncol(tr_fa$loadings))
scores <- t(as.matrix(tr_fa$scores))
gp_score <- data.frame(group = tr_fa$gp_train, scores = scores)
gp_score <- aggregate(gp_score[ ,-1], by = list(gp_score$group), FUN = mean)
gp_uniq <- unique(gp_score)
colnames(gp_uniq)[1] <- "group"
pr_factors <- join(data.frame(group = gp_test), gp_uniq, by = "group")
if (anyNA(pr_factors)) {
ind <- apply(is.na(pr_factors), 1, sum)
ind[ind != 0] <- 1
ind <- as.logical(ind)
pr_factors[ind, -1] <- matrix(rep(apply(as.matrix(gp_score[ ,-1]), 2, mean),
times = sum(ind)),
nrow = sum(ind), byrow = T)
}
predictions <- as.matrix(pr_factors[ ,-1]) %*% t(loadings)
means <- apply(tr_fa$train, MARGIN = 2, FUN = mean)
sds <- apply(tr_fa$train, MARGIN = 2, FUN = sd)
predictions <- sweep(predictions, 2, sds, '*')
predictions <- sweep(predictions, 2, means, '+')
predictions <- round(predictions)
mu_mat <- as.matrix(pr_factors[ ,-1]) %*% t(loadings)
sig_mat <- tr_fa$noise_org
cop_cov <- tr_fa$Sigma
test_st <- scale(test, center = means, scale = sds)
log_lik <- vector(mode = "numeric", length = nrow(test))
for (i in 1L:length(gp_test)) {
mus <- mu_mat[i, ]
sigs <- sqrt(diag(sig_mat))
log_lik[i] <- L_CFA_approx(test_st[i,],
mus,
sigs,
cop_cov,
logl = TRUE,
sd_train = sds)
}
}
return (list(predictions = predictions,
likelihoods = log_lik))
}
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