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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
# FUNCTION: DESCRIPTION REGRESSION METHODS:
# predict.fREG Predicts values from a fitted regression model
################################################################################
setMethod(f = "predict", signature(object = "fREG"), definition =
function(object, newdata, se.fit = FALSE, type = "response", ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Predict method for Regression Modelling, an object of class "fREG"
# FUNCTION:
# Fit:
fit <- object@fit
# Data as data.frame:
if (missing(newdata)) newdata <- object@data$data
# Predict:
if (object@method == "nnet" & type == "response") type = "raw"
ans <- .predict(object = fit, newdata = newdata, se.fit = se.fit,
type = type, ...)
# Make the output from 'predict' unique:
if (se.fit) {
if (!is.list(ans)) {
if (is.matrix(ans)) ans = as.vector(ans)
names(ans) = rownames(newdata)
ans = list(fit = ans, se.fit = NA*ans)
} else {
ans = ans[1:2]
}
} else {
if (is.matrix(ans)) ans = as.vector(ans)
names(ans) = rownames(newdata)
}
# Return Value:
ans
})
# ------------------------------------------------------------------------------
# Note, in the following "object" concerns to the slot @fit:
.predict.lm <- function(...) stats::predict.lm(...)
# <- function (object, newdata, se.fit = FALSE, scale = NULL, df = Inf,
# interval = c("none", "confidence", "prediction"), level = 0.95,
# type = c("response", "terms"), terms = NULL, na.action = na.pass,
# pred.var = res.var/weights, weights = 1, ...)
.predict.rlm <- function(...) stats::predict.lm(...)
#
.predict.glm <- function(...) stats::predict.glm(...)
# <- function (object, newdata = NULL, type = c("link", "response",
# "terms"), se.fit = FALSE, dispersion = NULL, terms = NULL,
# na.action = na.pass, ...)
.predict.gam <- function(...) mgcv::predict.gam(...)
# <- function (object, newdata, type = "link", se.fit = FALSE, terms = NULL,
# block.size = 1000, newdata.guaranteed = FALSE, na.action = na.pass,
# ...)
.predict.ppr <- function(object, ...) { stats::predict(object, ...) }
# <- function(object, newdata, ...)
##.predict.nnet <- function(object, ...) { nnet::predict(object, ...) }
# <- function(object, newdata, type=c("raw","class"), ...)
##.predict.polspline <- function(object, ...) { polspline::predict(object, ...) }
# ---- can be found in polymars.R
# <- function(object, newdata, se.fit = FALSE, type = "response", ...)
################################################################################
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