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, ...)
# Default S3 method
hlm_augment(object, ...)
# S3 method for class 'lmerMod'
hlm_augment(object, level = 1, include.ls = TRUE, data = NULL, ...)
# S3 method for class '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.
hlm_augment does not allow for the deletion of specific cases, the specification of other
types of leverage, or the use of full refits of the model instead of one step approximations for influence
diagnostics. If this additional functionality is desired, hlm_influence should be used instead. The additional
parameter standardize is available in hlm_resid; if this are desired, hlm_resid
should be used instead.