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biasCorrectionGaussian <-
function(m, sigma.penalty, analytic) {
# A function that calls the bias correction functions.
#
# Args:
# mer = Object of class lmerMod or lme
# sigma.penalty = Number of estimated variance components in the residual error covariance
# analytic = FALSE if the numeric hessian of the (restricted) marginal log-
# likelihood from the lmer optimization procedure should be used.
# Otherwise (default) TRUE, i.e. use a analytical version that
# has to be computed.
#
# Returns:
# bc = Bias correction for a mixed model.
#
zeroLessModel <- deleteZeroComponents(m)
if (inherits(zeroLessModel, "lm")) {
return(zeroLessModel$rank)
}
model <- getModelComponents(zeroLessModel, analytic)
if (identical(m, zeroLessModel)) {
bc <- calculateGaussianBc(model, sigma.penalty, analytic)
newModel <- NULL
new <- FALSE
} else {
bc <- calculateGaussianBc(model, sigma.penalty, analytic)
newModel <- zeroLessModel
new <- TRUE
}
return(list(bc = bc, newModel = newModel, new = new))
}
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