# drop1.merMod

##### 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.

##### 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)*