Nothing
library(buildmer)
library(testthat)
test_that('buildcustom',{
skip_on_cran()
# Use \code{buildmer} to do stepwise linear discriminant analysis
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')$Wilks[1]
p <- length(coef(b))
n <- 1
m <- nrow(migrant)
Bartlett <- ((p-n+1)/2-m)*log(Lambda)
pchisq(Bartlett,n*p,lower.tail=FALSE)
}
# First, order the terms based on Wilks' Lambda
model <- buildcustom(changed ~ friends.nl+friends.be+multilingual+standard+hearing+reading+
attention+sleep+gender+handedness+diglossic+age+years,buildmerControl=list(
direction='order',fit=flipfit,crit=crit.Wilks))
buildmer:::testthat.compare.df(model@p$tab,'buildcustom')
})
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