# @rdname prediction
# @export
prediction.bigglm <-
function(model,
data = find_data(model, parent.frame()),
at = NULL,
type = "response",
calculate_se = TRUE,
...) {
type <- match.arg(type)
# extract predicted values
data <- data
if (missing(data) || is.null(data)) {
stop("prediction() for objects of class 'bigglm' only work when 'data' is specified")
} else {
# reduce memory profile
model[["model"]] <- NULL
# setup data
data <- build_datalist(data, at = at, as.data.frame = TRUE)
at_specification <- attr(data, "at_specification")
# calculate predictions
if (isTRUE(calculate_se)) {
tmp <- predict(model, newdata = data, type = type, se.fit = TRUE, ...)
# cbind back together
pred <- make_data_frame(data, fitted = tmp[["fit"]], se.fitted = tmp[["se.fit"]])
} else {
tmp <- predict(model, newdata = data, type = type, se.fit = FALSE, ...)
# cbind back together
pred <- make_data_frame(data, fitted = tmp, se.fitted = rep(NA_real_, nrow(data)))
}
}
# 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 = type,
call = if ("call" %in% names(model)) model[["call"]] else NULL,
model_class = class(model),
row.names = seq_len(nrow(pred)),
vcov = vc,
jaccobian = NULL,
weighted = FALSE)
}
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