#' @export
get_predictions.rqs <- function(model,
data_grid = NULL,
terms = NULL,
ci_level = 0.95,
type = NULL,
typical = NULL,
vcov = NULL,
vcov_args = NULL,
condition = NULL,
interval = "confidence",
bias_correction = FALSE,
link_inverse = insight::link_inverse(model),
model_info = NULL,
verbose = TRUE,
...) {
# predictions, no SE
prdat <- stats::predict(model, newdata = data_grid, ...)
# numeric matrix to data frame
prdat <- as.data.frame(prdat)
# predictions in long format
prediction_data <- .gather(prdat, names_to = "tau", values_to = "predicted")
prediction_data <- cbind(data_grid, prediction_data)
# name cleanup
prediction_data$tau <- gsub("tau= ", "", prediction_data$tau, fixed = TRUE)
if (!is.na(ci_level) && !is.null(ci_level)) {
# standard errors
model_data <- insight::get_data(model, verbose = FALSE)
data_grid[setdiff(colnames(model_data), colnames(data_grid))] <- 0
vcm <- suppressWarnings(insight::get_varcov(model))
mm <- insight::get_modelmatrix(model, data = data_grid)
standard_errors <- unlist(lapply(vcm, function(vmatrix) {
colSums(t(mm %*% vmatrix) * t(mm))
}))
# ci-value
ci <- (1 + ci_level) / 2
# degrees of freedom
dof <- .get_df(model)
tcrit <- stats::qt(ci, df = dof)
prediction_data$conf.low <- prediction_data$predicted - tcrit * standard_errors
prediction_data$conf.high <- prediction_data$predicted + tcrit * standard_errors
# copy standard errors
attr(prediction_data, "std.error") <- standard_errors
}
prediction_data
}
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