These functions calculate measures of the change in the covariance
matrices for the fixed effects based on the deletion of an
observation, or group of observations, for a hierarchical
linear model fit using lmer.
# Default S3 method
covratio(model, ...)
# Default S3 method
covtrace(model, ...)
# S3 method for class 'mer'
covratio(model, level = 1, delete = NULL, ...)
# S3 method for class 'lmerMod'
covratio(model, level = 1, delete = NULL, ...)
# S3 method for class 'lme'
covratio(model, level = 1, delete = NULL, ...)
# S3 method for class 'mer'
covtrace(model, level = 1, delete = NULL, ...)
# S3 method for class 'lmerMod'
covtrace(model, level = 1, delete = NULL, ...)
# S3 method for class 'lme'
covtrace(model, level = 1, delete = NULL, ...)fitted model object of class mer or lmerMod
do not use
variable used to define the group for which cases will be
deleted. If level = 1 (default), then individual cases will be deleted.
index of individual cases to be deleted. To delete specific
observations the row number must be specified. To delete higher level
units the group ID and group parameter must be specified.
If delete = NULL then all cases are iteratively deleted.
If delete = NULL then a vector corresponding to each deleted
observation/group is returned.
If delete is specified then a single value is returned corresponding
to the deleted subset specified.
Both the covariance ratio (covratio) and the covariance trace
(covtrace) measure the change in the covariance matrix
of the fixed effects based on the deletion of a subset of observations.
The key difference is how the variance covariance matrices are compared:
covratio compares the ratio of the determinants while covtrace
compares the trace of the ratio.
Christensen, R., Pearson, L., & Johnson, W. (1992) Case-deletion diagnostics for mixed models. Technometrics, 34(1), 38–45.
Schabenberger, O. (2004) Mixed Model Influence Diagnostics, in Proceedings of the Twenty-Ninth SAS Users Group International Conference, SAS Users Group International.
data(sleepstudy, package = 'lme4')
ss <- lme4::lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
# covratio for individual observations
ss.cr1 <- covratio(ss)
# covratio for subject-level deletion
ss.cr2 <- covratio(ss, level = "Subject")
if (FALSE) { # \dontrun{
## A larger example
data(Exam, package = 'mlmRev')
fm <- lme4::lmer(normexam ~ standLRT * schavg + (standLRT | school), data = Exam)
# covratio for individual observations
cr1 <- covratio(fm)
# covratio for school-level deletion
cr2 <- covratio(fm, level = "school")
} # }
# covtrace for individual observations
ss.ct1 <- covtrace(ss)
# covtrace for subject-level deletion
ss.ct2 <- covtrace(ss, level = "Subject")
if (FALSE) { # \dontrun{
## Returning to the larger example
# covtrace for individual observations
ct1 <- covtrace(fm)
# covtrace for school-level deletion
ct2 <- covtrace(fm, level = "school")
} # }