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.
## S3 method for class 'merMod' drop1(object, scope, scale = 0, test = c("none", "Chisq", "user"), k = 2, trace = FALSE, sumFun, ...)
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 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 |
... |
other arguments (ignored) |
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.
An object of class anova
summarizing the differences in fit
between the models.
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))
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.