These functions calculate measures of the change in the fixed effects
estimates based on the deletion of an observation, or group of
observations, for a hierarchical linear model fit using lmer.
# S3 method for default mdffits(object, ...) # S3 method for mer cooks.distance(model, level = 1, delete = NULL, ...) # S3 method for lmerMod cooks.distance(model, level = 1, delete = NULL, include.attr = FALSE, ...) # S3 method for lme cooks.distance(model, level = 1, delete = NULL, include.attr = FALSE, ...) # S3 method for mer mdffits(object, level = 1, delete = NULL, ...) # S3 method for lmerMod mdffits(object, level = 1, delete = NULL, include.attr = FALSE, ...) # S3 method for lme mdffits(object, level = 1, delete = NULL, include.attr = FALSE, ...)
| object | fitted object of class |
|---|---|
| ... | do not use |
| model | fitted model of class |
| level | variable used to define the group for which cases will be
deleted. If |
| delete | 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 |
| include.attr | logical value determining whether the difference between
the full and deleted parameter estimates should be included. If |
Both functions return a numeric vector (or single value if
delete has been specified) as the default. If include.attr = TRUE,
then a tibble is returned. The first column consists of the Cook's distance or
MDFFITS values, and the later columns capture the difference between the full
and deleted parameter estimates.
Both Cook's distance and MDFFITS measure the change in the fixed effects estimates based on the deletion of a subset of observations. The key difference between the two diagnostics is that Cook's distance uses the covariance matrix for the fixed effects from the original model while MDFFITS uses the covariance matrix from the deleted model.
Because MDFFITS requires the calculation of the covariance matrix for the fixed effects for every model, it will be slower.
Christensen, R., Pearson, L., & Johnson, W. (1992) Case-deletion diagnostics for mixed models. Technometrics, 34, 38--45.
Schabenberger, O. (2004) Mixed Model Influence Diagnostics, in Proceedings of the Twenty-Ninth SAS Users Group International Conference, SAS Users Group International.
Adam Loy loyad01@gmail.com
data(sleepstudy, package = 'lme4') ss <- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy) # Cook's distance for individual observations ss.cd.lev1 <- cooks.distance(ss) # Cook's distance for each Subject ss.cd.subject <- cooks.distance(ss, level = "Subject") if (FALSE) { data(Exam, package = 'mlmRev') fm <- lme4::lmer(normexam ~ standLRT * schavg + (standLRT | school), Exam) # Cook's distance for individual observations cd.lev1 <- cooks.distance(fm) # Cook's distance for each school cd.school <- cooks.distance(fm, level = "school") # Cook's distance when school 1 is deleted cd.school1 <- cooks.distance(fm, level = "school", delete = 1) } # MDFFITS for individual observations ss.m1 <- mdffits(ss) # MDFFITS for each Subject ss.m.subject <- mdffits(ss, level = "Subject") if (FALSE) { # MDFFITS for individual observations m1 <- mdffits(fm) # MDFFITS for each school m.school <- mdffits(fm, level = "school") }