R: Drop all possible single fixed-effect terms from a mixed...
drop1.merMod
R Documentation
Drop all possible single fixed-effect terms from a mixed effect model
Description
Drop allowable single terms from the model: see drop1
for details of how the appropriate scope for dropping terms
is determined.
Usage
## S3 method for class 'merMod'
drop1(object, scope, scale = 0,
test = c("none", "Chisq", "user"),
k = 2, trace = FALSE, sumFun, ...)
Arguments
object
a fitted merMod object.
scope
a formula giving the terms to be considered for adding or
dropping.
scale
Currently ignored (included for S3 method compatibility)
test
should the results include a test statistic relative to the
original model?
The Chisq test is a likelihood-ratio test,
which is approximate due to finite-size effects.
k
the penalty constant in AIC
trace
print tracing information?
sumFun
a summary function to be used when
test=="user". It must allow arguments scale and
k, but these may be ignored (e.g. specified in dots).
The first two arguments must be object, the full model fit,
and objectDrop, a reduced model. If objectDrop is missing,
sumFun should return a vector of with the appropriate
length and names (the actual contents are ignored).
...
other arguments (ignored)
Details
drop1 relies on being able to find the appropriate information
within the environment of the formula of the original model. If the
formula is created in an environment that does not contain the data,
or other variables passed to the original model (for example, if
a separate function is called to define the formula), then
drop1 will fail. A workaround (see example below) is to
manually specify an appropriate environment for the formula.
Value
An object of class anova summarizing the differences in fit
between the models.
Examples
fm1 <- lmer(Reaction~Days+(Days|Subject),sleepstudy)
## likelihood ratio tests
drop1(fm1,test="Chisq")
## use Kenward-Roger corrected F test, or parametric bootstrap,
## to test the significance of each dropped predictor
if (require(pbkrtest) && packageVersion("pbkrtest")>="0.3.8") {
KRSumFun <- function(object, objectDrop, ...) {
krnames <- c("ndf","ddf","Fstat","p.value","F.scaling")
r <- if (missing(objectDrop)) {
setNames(rep(NA,length(krnames)),krnames)
} else {
krtest <- KRmodcomp(object,objectDrop)
unlist(krtest$stats[krnames])
}
attr(r,"method") <- c("Kenward-Roger via pbkrtest package")
r
}
drop1(fm1,test="user",sumFun=KRSumFun)
if(lme4:::testLevel() >= 3) { ## takes about 16 sec
nsim <- 100
PBSumFun <- function(object, objectDrop, ...) {
pbnames <- c("stat","p.value")
r <- if (missing(objectDrop)) {
setNames(rep(NA,length(pbnames)),pbnames)
} else {
pbtest <- PBmodcomp(object,objectDrop,nsim=nsim)
unlist(pbtest$test[2,pbnames])
}
attr(r,"method") <- c("Parametric bootstrap via pbkrtest package")
r
}
system.time(drop1(fm1,test="user",sumFun=PBSumFun))
}
}
## workaround for creating a formula in a separate environment
createFormula <- function(resp, fixed, rand) {
f <- reformulate(c(fixed,rand),response=resp)
## use the parent (createModel) environment, not the
## environment of this function (which does not contain 'data')
environment(f) <- parent.frame()
f
}
createModel <- function(data) {
mf.final <- createFormula("Reaction", "Days", "(Days|Subject)")
lmer(mf.final, data=data)
}
drop1(createModel(data=sleepstudy))