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#####################################################################################
## Author: Daniel Sabanés Bové [daniel *.* sabanesbove *a*t* ifspm *.* uzh *.* ch]
## Project: hypergsplines
##
## Time-stamp: <[getFitSamples.R] by DSB Die 04/10/2011 15:50 (CEST)>
##
## Description:
## Extract fit samples on linear predictor scale
## from posterior coefficients samples.
##
## History:
## 30/09/2010 file creation
## 11/01/2011 adapt to change of samples$linearCoefs
## 04/04/2011 - rename to "getFitSamples" because prediction samples would
## comprise the additional observation error.
## - Note that we can also use this function for GLMs!
#####################################################################################
##' @include modelData.R
##' @include getFunctionSamples.R
{}
##' Extract fit samples on linear predictor scale
##' from posterior coefficients samples
##'
##' @param X the numeric matrix with new covariate values on the
##' \emph{original} scale, with the same column layout as originally
##' provided to \code{\link{modelData}} or \code{\link{glmModelData}}.
##' @param samples the samples object (either from \code{\link{getSamples}} or
##' the \code{samples} element from \code{\link{glmGetSamples}})
##' @param modelData the corresponding model data object
##' @return the fit samples as a matrix.
##'
##' @example examples/getFitSamples.R
##'
##' @export
##' @keywords regression
##' @author Daniel Sabanes Bove \email{daniel.sabanesbove@@ifspm.uzh.ch}
getFitSamples <- function(X,
samples,
modelData)
{
## checks and extracts:
stopifnot(is.matrix(X),
is.numeric(X),
all(! is.na(X)),
identical(ncol(X), modelData$nCovs))
nNew <- nrow(X)
covNames <- colnames(modelData$X)
## start with the intercept samples
fitSamples <- tcrossprod(rep(1L, nNew),
samples$intercept)
## so the layout will be nNew x nSamples
## then sequentially add the function samples, from all covariates
## included in the model which has been sampled:
for(thisCov in names(samples$linearCoefs))
{
## note that we do only rely on the fact that X has the
## same column order as modelData$X, but not the same
## column names possibly.
fitSamples <- fitSamples +
getFunctionSamples(x=X[, match(thisCov, covNames)],
covName=thisCov,
samples=samples,
modelData=modelData)
}
## return the fit samples
return(fitSamples)
}
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