buildcustom {buildmer} R Documentation

## Use buildmer to perform stepwise elimination using a custom fitting function

### Description

Use buildmer to perform stepwise elimination using a custom fitting function

### Usage

buildcustom(
formula,
data = NULL,
fit = function(p, formula) stop("'fit' not specified"),
crit = function(p, ref, alt) stop("'crit' not specified"),
elim = function(x) stop("'elim' not specified"),
REML = FALSE,
buildmerControl = buildmerControl()
)


### Arguments

 formula See the general documentation under buildmer-package data See the general documentation under buildmer-package fit A function taking two arguments, of which the first is the buildmer parameter list p and the second one is a formula. The function must return a single object, which is treated as a model object fitted via the provided formula. The function must return an error ('stop()') if the model does not converge crit A function taking one argument and returning a single value. The argument is the return value of the function passed in fit, and the returned value must be a numeric indicating the goodness of fit, where smaller is better (like AIC or BIC). elim A function taking one argument and returning a single value. The argument is the return value of the function passed in crit, and the returned value must be a logical indicating if the small model must be selected (return TRUE) or the large model (return FALSE) REML A logical indicating if the fitting function wishes to distinguish between fits differing in fixed effects (for which p$reml will be set to FALSE) and fits differing only in the random part (for which p$reml will be TRUE). Note that this ignores the usual semantics of buildmer's optional REML argument, because they are redundant: if you wish to force REML on or off, simply code it so in your custom fitting function. buildmerControl Control arguments for buildmer — see the general documentation under buildmerControl

buildmer-package

### Examples

## Use \code{buildmer} to do stepwise linear discriminant analysis
library(buildmer)
migrant[,-1] <- scale(migrant[,-1])
flipfit <- function (p,formula) {
# The predictors must be entered as dependent variables in a MANOVA
# (i.e. the predictors must be flipped with the dependent variable)
Y <- model.matrix(formula,migrant)
m <- lm(Y ~ 0+migrant$changed) # the model may error out when asking for the MANOVA test <- try(anova(m)) if (inherits(test,'try-error')) test else m } crit.F <- function (p,a,b) { # use whole-model F pvals <- anova(b)$'Pr(>F)' # not valid for backward!
pvals[length(pvals)-1]
}
crit.Wilks <- function (p,a,b) {
if (is.null(a)) return(crit.F(p,a,b)) #not completely correct, but close as F approximates X2
Lambda <- anova(b,test='Wilks')\$Wilks[1]
p <- length(coef(b))
n <- 1
m <- nrow(migrant)
Bartlett <- ((p-n+1)/2-m)*log(Lambda)
pchisq(Bartlett,n*p,lower.tail=FALSE)
}

# First, order the terms based on Wilks' Lambda