Influence Diagnostics for Mixed-Effects Models
These functions compute deletion influence diagnostics for linear mixed-effects models fit by lmer in the lme4 package and
lme in the nlme package and for generalized linear mixed-effects models fit by glmer in the
lme4 package. The main functions are methods for the influence generic function. Other functions are provided for
computing dfbeta, dfbetas, cooks.distance, and influence on variance-covariance components based
on the objects computed by influence.merMod and influence.lme.
## S3 method for class 'merMod'
influence(model, groups, data, maxfun=1000, ncores=1, ...)
## S3 method for class 'lme'
influence(model, groups, data, ncores=1, ...)
## S3 method for class 'influence.merMod'
cooks.distance(model, ...)
## S3 method for class 'influence.lme'
cooks.distance(model, ...)
## S3 method for class 'influence.merMod'
dfbeta(model, which = c("fixed", "var.cov"), ...)
## S3 method for class 'influence.lme'
dfbeta(model, which = c("fixed", "var.cov"), ...)
## S3 method for class 'influence.merMod'
dfbetas(model, ...)
## S3 method for class 'influence.lme'
dfbetas(model, ...)model |
in the case of |
groups |
a character vector containing the name of a grouping factor or names of grouping factors; if more than one name is supplied, then groups are defined by all combinations of levels of the grouping factors that appear in the data. If omitted, then each individual row of the data matrix is treated as a "group" to be deleted in turn. |
data |
an optional data frame with the data to which |
maxfun |
The maximum number of function evaluations (for |
ncores |
number of cores for parallel computation of diagnostics; if |
which |
if |
... |
ignored. |
influence.merMod and influence.lme start with the estimated variance-covariance components from model and then refit
the model omitting each group in turn, not necessarily iterating to completion. For example, maxfun=20 takes up to 20 function evaluations
step away from the ML or REML solution for the full data, which usually provides decent approximations to the fully iterated estimates.
The other functions are methods for the dfbeta, dfbetas, and cooks.distance generics, to be applied to the
"influence.merMod" or "influence.lme" object produced by the influence function; the dfbeta methods can also return
influence on the variance-covariance components.
influence.merMod and influence.lme return objects of class "influence.merMod" and "influence.lme" respectively,
each of which contains the following elements:
"fixed.effects"the estimated fixed effects for the model.
"fixed.effects[-groups]"a matrix with columns corresponding to the fixed-effects coefficients and rows corresponding to groups, giving the estimated fixed effects with each group deleted in turn; groups is formed from the name(s) of the grouping factor(s).
"var.cov.comps"the estimated variance-covariance parameters for the model.
"var.cov.comps[-groups]"a matrix with the estimated covariance parameters (in columns) with each group deleted in turn.
"vcov"The estimated covariance matrix of the fixed-effects coefficients.
"vcov[-groups]"a list each of whose elements is the estimated covariance matrix of the fixed-effects coefficients with one group deleted.
"groups"a character vector giving the names of the grouping factors.
"deleted"the possibly composite grouping factor, each of whose elements is deleted in turn.
"converged"for influence.merMod, a logical vector indicating whether the computation converged for each group.
"function.evals"for influence.merMod, a vector of the number of function evaluations performed for each group.
For plotting "influence.merMod" and "influence.lme" objects, see infIndexPlot.
J. Fox jfox@mcmaster.ca
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
lmer, glmer, lme, infIndexPlot.
if (require("lme4")){
print(fm1 <- lmer(Reaction ~ Days + (Days | Subject),
sleepstudy)) # from ?lmer
infIndexPlot(influence(fm1, "Subject"))
}
if (require("lme4")){
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
infIndexPlot(influence(gm1, "herd", maxfun=100))
gm1.11 <- update(gm1, subset = herd != 11) # check deleting herd 11
compareCoefs(gm1, gm1.11)
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