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# Prediction data frame
# Get predictions with standard errors into data frame
#
# @keyword internal
# @alias predictdf.default
# @alias predictdf.glm
# @alias predictdf.loess
# @alias predictdf.locfit
predictdf <- function(model, xseq, se, level) UseMethod("predictdf")
#' @export
predictdf.default <- function(model, xseq, se, level) {
pred <- stats::predict(
model,
newdata = data_frame0(x = xseq),
se.fit = se,
level = level,
interval = if (se) "confidence" else "none"
)
if (se) {
fit <- as.data.frame(pred$fit)
names(fit) <- c("y", "ymin", "ymax")
base::data.frame(x = xseq, fit, se = pred$se.fit)
} else {
base::data.frame(x = xseq, y = as.vector(pred))
}
}
#' @export
predictdf.glm <- function(model, xseq, se, level) {
pred <- stats::predict(
model,
newdata = data_frame0(x = xseq),
se.fit = se,
type = "link"
)
if (se) {
std <- stats::qnorm(level / 2 + 0.5)
base::data.frame(
x = xseq,
y = model$family$linkinv(as.vector(pred$fit)),
ymin = model$family$linkinv(as.vector(pred$fit - std * pred$se.fit)),
ymax = model$family$linkinv(as.vector(pred$fit + std * pred$se.fit)),
se = as.vector(pred$se.fit)
)
} else {
base::data.frame(x = xseq, y = model$family$linkinv(as.vector(pred)))
}
}
#' @export
predictdf.loess <- function(model, xseq, se, level) {
pred <- stats::predict(
model,
newdata = data_frame0(x = xseq),
se = se
)
if (se) {
y <- pred$fit
ci <- pred$se.fit * stats::qt(level / 2 + .5, pred$df)
ymin <- y - ci
ymax <- y + ci
base::data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
} else {
base::data.frame(x = xseq, y = as.vector(pred))
}
}
#' @export
predictdf.locfit <- function(model, xseq, se, level) {
pred <- stats::predict(
model,
newdata = data_frame0(x = xseq),
se.fit = se
)
if (se) {
y <- pred$fit
ci <- pred$se.fit * stats::qt(level / 2 + .5, model$dp["df2"])
ymin <- y - ci
ymax <- y + ci
base::data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
} else {
base::data.frame(x = xseq, y = as.vector(pred))
}
}
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