# drop1.merMod

0th

Percentile

##### Drop all possible single fixed-effect terms from a mixed effect model

Drop allowable single terms from the model: see drop1 for details of how the appropriate scope for dropping terms is determined.

Keywords
misc
##### Usage
# S3 method for 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 $\chi^2$ 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.

• drop1.merMod
##### Examples
# NOT RUN {
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))
# }

Documentation reproduced from package lme4, version 1.1-21, License: GPL (>= 2)

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