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#' @rdname prediction
#' @export
prediction.mnp <-
function(model,
data = find_data(model, parent.frame()),
at = NULL,
type = NULL,
calculate_se = FALSE,
category,
...) {
if (!is.null(type)) {
warning(sprintf("'type' is ignored for models of class '%s'", class(model)))
}
# extract predicted values
data <- data
if (missing(data) || is.null(data)) {
probs <- make_data_frame(predict(model, type = "prob", ...)[["p"]])
names(probs) <- paste0("Pr(", names(probs), ")")
tmp <- predict(model, type = "choice", ...)[["y"]]
d <- dim(tmp)
if (length(d) == 3) {
stop("'prediction.mnp' only works when 'n.draws = 1'")
}
probs[["fitted.class"]] <- lapply(seq_len(d[1L]), function(i) tmp[i,])
pred <- probs
rm(probs, tmp)
} else {
# setup data
if (is.null(at)) {
out <- data
} else {
out <- build_datalist(data, at = at, as.data.frame = TRUE)
at_specification <- attr(out, "at_specification")
}
# calculate predictions
tmp_probs <- make_data_frame(predict(model, newdata = out, type = "prob", ...)[["p"]])
names(tmp_probs) <- paste0("Pr(", names(tmp_probs), ")")
tmp <- predict(model, newdata = out, type = "choice", ...)[["y"]]
d <- dim(tmp)
if (length(d) == 3) {
stop("'prediction.mnp' only works when 'n.draws = 1'")
}
tmp_probs[["fitted.class"]] <- lapply(seq_len(d[1L]), function(i) tmp[i,])
# cbind back together
pred <- make_data_frame(out, tmp_probs)
rm(tmp, tmp_probs)
}
# handle category argument
if (missing(category)) {
w <- grep("^Pr\\(", names(pred))[1L]
category <- names(pred)[w]
pred[["fitted"]] <- pred[[w]]
} else {
w <- which(names(pred) == paste0("Pr(", category, ")"))
if (!length(w)) {
stop(sprintf("category %s not found", category))
}
pred[["fitted"]] <- pred[[ w[1L] ]]
}
pred[["se.fitted"]] <- NA_real_
# variance(s) of average predictions
vc <- NA_real_
# output
structure(pred,
class = c("prediction", "data.frame"),
at = if (is.null(at)) at else at_specification,
type = NA_character_,
call = if ("call" %in% names(model)) model[["call"]] else NULL,
model_class = class(model),
row.names = seq_len(nrow(pred)),
vcov = vc,
jacobian = NULL,
category = category,
weighted = FALSE)
}
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