This function is used to compute residuals, fitted values, and influence diagnostics for a hierarchical linear model. The residuals and fitted values are computed using Least Squares(LS) and Empirical Bayes (EB) methods. The influence diagnostics are computed through one step approximations.
hlm_augment(object, ...)# S3 method for default
hlm_augment(object, ...)
# S3 method for lmerMod
hlm_augment(object, level = 1, include.ls = TRUE, data = NULL, ...)
# S3 method for lme
hlm_augment(object, level = 1, include.ls = TRUE, ...)
an object of class lmerMod or lme.
currently not used
which residuals should be extracted and what cases should be deleted for influence diagnostics.
If level = 1 (default), then within-group (case-level) residuals are returned and influence diagnostics
are calculated for individual observations. Otherwise, level should be the name of a grouping
factor as defined in flist for a lmerMod object or as in groups for a lme object.
This will return between-group residuals and influence diagnostics calculated for each group.
a logical indicating if LS residuals should be included in the
return tibble. include.ls = FALSE decreases runtime substantially.
the original data frame passed to `lmer`. This is only necessary for `lmerMod` models where `na.action = "na.exclude"`
The hlm_augment function combines functionality from hlm_resid
and hlm_influence for a simpler way of obtaining residuals and influence
diagnostics. Please see ?hlm_resid and ?hlm_influence for additional information
about the returned values.