Nothing
bcMer <-
function(object, method = NULL, B = NULL, sigma.penalty = 1, analytic = TRUE) {
# A function that calls the bias correction functions.
#
# Args:
# object = Object of class lmerMod or glmerMod. Obtained by lmer() or glmer()
# or of class lme
# method = How the bias correction should be evaluated. If NULL than method
# is chosen by family, i.e. analytical if family is Poisson or
# Gaussian and with parametric bootstrap for other. Method may also
# be specified before, either "steinian" or "conditionalBootstrap".
# "steinian" only available for Gaussian, Poisson and Bernoulli.
# B = Number of Bootstrap replications. Default is NULL then it is
# chosen as maximum of the number of observations and 100.
#
# Returns:
# bc = Bias correction for a mixed model.
#
if (is.null(method) | class(object) == "lme") {
switch(family(object)$family,
binomial = {
bc <- biasCorrectionBernoulli(object)
},
poisson = {
bc <- biasCorrectionPoisson(object)
},
gaussian = {
bc <- biasCorrectionGaussian(object, sigma.penalty, analytic)
},
{
cat("For this family no bias correction is currently available \n")
bc <- NA
}
)
} else {
if(method == "steinian") {
switch(family(object)$family,
binomial = {
bc <- biasCorrectionBernoulli(object)
},
poisson = {
bc <- biasCorrectionPoisson(object)
},
gaussian = {
bc <- biasCorrectionGaussian(object, sigma.penalty, analytic)
},
{
cat("For this family no bias correction is currently available \n")
bc <- NA
}
)
}
if(method == "conditionalBootstrap") {
if (is.null(B)) {
B <- max(length(getME(object, "y")), 100)
}
bc <- conditionalBootstrap(object, B)
}
}
return(bc)
}
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