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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.frame(x = xseq), se = se,
level = level, interval = if(se) "confidence" else "none")
if (se) {
fit <- as.data.frame(pred$fit)
names(fit) <- c("y", "ymin", "ymax")
data.frame(x = xseq, fit, se = pred$se)
} else {
data.frame(x = xseq, y = as.vector(pred))
}
}
#' @export
predictdf.glm <- function(model, xseq, se, level) {
pred <- stats::predict(model, newdata = data.frame(x = xseq), se = se,
type = "link")
if (se) {
std <- qnorm(level / 2 + 0.5)
data.frame(
x = xseq,
y = model$family$linkinv(as.vector(pred$fit)),
ymin = model$family$linkinv(as.vector(pred$fit - std * pred$se)),
ymax = model$family$linkinv(as.vector(pred$fit + std * pred$se)),
se = as.vector(pred$se)
)
} else {
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.frame(x = xseq), se = se)
if (se) {
y = pred$fit
ci <- pred$se.fit * qt(level / 2 + .5, pred$df)
ymin = y - ci
ymax = y + ci
data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
} else {
data.frame(x = xseq, y = as.vector(pred))
}
}
#' @export
predictdf.locfit <- function(model, xseq, se, level) {
pred <- predict(model, newdata = data.frame(x = xseq), se.fit = se)
if (se) {
y = pred$fit
ymin = y - pred$se.fit
ymax = y + pred$se.fit
data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
} else {
data.frame(x = xseq, y = as.vector(pred))
}
}
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