drop1.merMod {lme4} | 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 |

`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 |

`k` |
the penalty constant in AIC |

`trace` |
print tracing information? |

`sumFun` |
a summary |

`...` |
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))
```

*lme4*version 1.1-35.3 Index]