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#' Model Predictions for a Linear Model
#'
#' Uses the main output and some error messages from R function 'predict' but
#' gives you more output. (Error messages are not reliable when used in Splus.)
#'
#' Note: The data frame, newdata, must have the same column order and data
#' types (e.g. numeric or factor) as those used in fitting the model.
#'
#'
#' @param object an \code{lm} object, i.e. the output from \code{lm}.
#' @param newdata prediction data frame.
#' @param cilevel confidence level of the interval.
#' @param digit decimal numbers after the point.
#' @param print.out if \code{TRUE}, print out the prediction matrix.
#' @param \dots optional arguments that are passed to the generic 'predict'
#' @return \item{frame}{vector or matrix including predicted values, confidence
#' intervals and predicted intervals.} \item{fit}{prediction values.}
#' \item{se.fit}{standard error of predictions.} \item{residual.scale}{residual
#' standard deviations.} \item{df}{degrees of freedom for residual.}
#' \item{cilevel}{confidence level of the interval.}
#' @seealso \code{\link{predict}}, \code{\link{predict.lm}}, \code{\link{as.data.frame}}.
#' @note This function is deprecated. It will be removed in future versions of the package.
#' @keywords htest
#' @examples
#'
#' # Zoo data
#' data(zoo.df)
#' zoo.df = within(zoo.df, {day.type = factor(day.type)})
#' zoo.fit = lm(log(attendance) ~ time + sun.yesterday + nice.day + day.type + tv.ads,
#' data = zoo.df)
#' pred.zoo = data.frame(time = 8, sun.yesterday = 10.8, nice.day = 0,
#' day.type = factor(3), tv.ads = 1.181)
#' predict20x(zoo.fit, pred.zoo)
#'
#' # Peruvian Indians data
#' data(peru.df)
#' peru.fit = lm(BP ~ age + years + I(years^2) + weight + height, data = peru.df)
#' pred.peru = data.frame(age = 21, years = 2, `I(years^2)` = 2, weight = 71, height = 1629)
#' predict20x(peru.fit, pred.peru)
#'
#' @export predict20x
#' @note this function is deprecated as it is never used in class any more. We prefer the standard \code{\link{predict}} method.
predict20x = function(object, newdata, cilevel = 0.95, digit = 3, print.out = TRUE, ...) {
## prediction which allows for factors and a data frame with data entered in the same order as the data frame that was used in fitting the above model (note: the variable names do not need to be specified)
if (!inherits(object, "lm"))
stop("First input is not an \"lm\" object")
if (!is.data.frame(newdata))
stop("Argument \"newdata\" is not a data frame!")
name.row = paste("pred", 1:nrow(newdata), sep = ".")
name.row = 1:nrow(newdata)
# name.col = attr(object$terms,'term.labels')
x = attr(object$terms, "term.labels")
y = unlist(strsplit(x, "factor\\("))
z = unlist(strsplit(y, "\\)"))
name.col = z
# print(name.col)
if (ncol(newdata) != length(name.col))
stop("Incorrectly input the new data!")
dimnames(newdata) = list(name.row, name.col)
pred = predict.lm(object, newdata, se.fit = TRUE, ...)
Predicted = pred$fit
percent = 1 - (1 - cilevel)/2
Conf.lower = pred$fit - qt(percent, pred$df) * pred$se.fit
Conf.upper = pred$fit + qt(percent, pred$df) * pred$se.fit
pred.se = sqrt(pred$residual.scale^2 + pred$se.fit^2)
Pred.lower = pred$fit - qt(percent, pred$df) * pred.se
Pred.upper = pred$fit + qt(percent, pred$df) * pred.se
mat = cbind(Predicted, Conf.lower, Conf.upper, Pred.lower, Pred.upper)
mat = round(mat, digit)
mat.df = as.data.frame(mat)
dimnames(mat.df)[[1]] = dimnames(newdata)[[1]]
dimnames(mat.df)[[2]] = c("Predicted", " Conf.lower", "Conf.upper", " Pred.lower", " Pred.upper")
if (print.out)
print(mat.df)
invisible(list(frame = mat.df, fit = pred$fit, se.fit = pred$se.fit, residual.scale = pred$residual.scale, df = pred$df, cilevel = cilevel))
}
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