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#' Confidence Intervals
#'
#' @author Ivan Jacob Agaloos Pesigan
#'
#' @param object object of class `semmcci`.
#' @param alpha Numeric vector.
#' Significance level.
#' If `alpha = NULL`,
#' extract `alpha` from `semmcci`.
#' @return Returns a matrix with the following columns:
#' \describe{
#' \item{`est`}{Parameter estimates.}
#' \item{`se`}{Standard errors or the square root
#' of the diagonals of the Monte Carlo sampling distribution
#' of parameter estimates.}
#' \item{`R`}{Number of valid Monte Carlo replications.}
#' \item{...}{Percentiles that correspond to the confidence intervals
#' defined by `alpha`.}
#' }
#' Note that the rows in `ci` correspond to the model parameters.
#'
#' @family Monte Carlo in Structural Equation Modeling Functions
#' @keywords semmcci mc internal
#' @noRd
.MCCI <- function(object,
alpha = NULL) {
stopifnot(
inherits(
object,
"semmcci"
)
)
thetahatstar <- object$thetahatstar
thetahat <- object$thetahat
if (is.null(alpha)) {
alpha <- object$args$alpha
}
stopifnot(
all(alpha > 0 & alpha < 1)
)
probs <- .PCProbs(alpha = alpha)
ci <- vector(
mode = "list",
length = dim(thetahatstar)[2]
)
for (i in seq_len(dim(thetahatstar)[2])) {
ci[[i]] <- .PCCI(
thetahatstar = thetahatstar[, i],
thetahat = thetahat$est[[i]],
probs = probs
)
}
ci <- do.call(
what = "rbind",
args = ci
)
varnames <- colnames(thetahatstar)
if (!is.null(varnames)) {
rownames(ci) <- varnames
}
return(ci)
}
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