## File: predict.gl1ce.Rd

package info (click to toggle)
r-cran-lasso2 1.2-20-1
 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859 \name{predict.gl1ce} \alias{predict.gl1ce} \title{Prediction Method for a gl1ce' Object} \description{ This is a method for the generic function \code{\link{predict}} for class \code{"gl1ce"}, typically produced from \code{\link{gl1ce}()}. When \code{newdata} is missing, the fitted values are extracted, otherwise returns \emph{new} predictions. } \usage{ \method{predict}{gl1ce}(object, newdata, type=c("link", "response"), se.fit = FALSE, \dots) } \arguments{ \item{object}{a fitted \code{\link{gl1ce}} object.} \item{newdata}{ a data frame containing the values at which predictions are required. This argument can be missing, in which case predictions are made at the same values used to compute the object. Only those predictors referred to in the right side of the formula in object need be present by name in \code{newdata}.} \item{type}{type of predictions, with choices "link" (the default), or "response". The default produces predictions on the scale of the additive predictors, and with \code{newdata} missing, \code{predict()} is simply an extractor function for this component of a \code{gl1ce} object. If "response" is selected, the predictions are on the scale of the response, and are monotone transformations of the additive predictors, using the inverse link function.} \item{se.fit}{logical indicating if standard errors should be returned as well. \bold{Not yet available}.} \item{\dots}{further potential arguments passed to methods.} } \value{ a vector of predictions. } \section{Warning}{ \code{predict} can produce incorrect predictions when the \code{newdata} argument is used if the formula in \code{object} involves data-dependent transformations, such as \code{poly(Age, 3)} %% MM: this was for S-plus -- which has such problems with glm() -- maybe not R or \code{sqrt(Age - min(Age))}. } \examples{ ## start with example(gl1ce) predict(gl1c.E, new = esoph[1:7,])# type 'link' predict(gl1c.E, new = esoph[1:7,], type = "response") ## identities / consistency checks : stopifnot(predict(gl1c.E, type = "response") == fitted(gl1c.E), all.equal(predict(gl1c.E)[1:7], as.vector(predict(gl1c.E, new = esoph[1:7,]))), all.equal(fitted(gl1c.E)[1:7], as.vector(predict(gl1c.E, new = esoph[1:7,], type = "response"))) ) } \keyword{models} `