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# function for extracting MSE and CVs from saeTrafo object
mse_saeTrafo <- function(object, indicator = "all", CV = FALSE) {
if (is.null(object$MSE) && CV == TRUE) {
stop(paste("No MSE estimates in saeTrafo object: arguments MSE and CV have",
"to be FALSE or a new saeTrafo object with variance/MSE needs",
"to be generated."))
}
if (inherits(object, "fh")) {
object$MSE <- object$MSE[, c("Domain", "Direct", "FH")]
object$ind <- object$ind[, c("Domain", "Direct", "FH")]
}
all_cv <- sqrt(object$MSE[, -1]) / object$ind[, -1]
if (any(indicator == "fh") || any(indicator == "FH")) {
ind <- object$MSE[, c("Domain", "FH")]
ind_cv <- cbind(Domain = object$MSE[, 1], all_cv)
ind_name <- "Fay-Herriot estimates"
} else if (any(class(object) == "NER")) {
ind <- object$MSE[, c("Domain", "Mean")]
ind_cv <- data.frame(Domain = object$MSE[, 1], Mean = c(all_cv))
ind_name <- "Nested error regression model estimates"
} else if (any(indicator == "Direct") || any(indicator == "direct")) {
ind <- object$MSE[, c("Domain", "Direct")]
ind_cv <- cbind(Domain = object$MSE[, 1], all_cv)
ind_name <- "Direct estimates used in Fay-Herriot approach"
} else {
selection <- colnames(object$MSE[-1]) %in% indicator
ind <- object$MSE[, c(TRUE, selection)]
ind_cv <- data.frame(Domain = object$MSE[, 1], all_cv[, selection])
colnames(ind_cv) <- colnames(ind)
ind_name <- paste(unique(indicator), collapse = ", ")
}
if (CV == FALSE) {
mse_saeTrafo <- list(ind = ind, ind_name = ind_name)
} else {
mse_saeTrafo <- list(ind = ind, ind_cv = ind_cv, ind_name = ind_name)
}
class(mse_saeTrafo) <- "mse.saeTrafo"
return(mse_saeTrafo)
}
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