Calculating residuals and influence diagnostics for HLMs
hlm_augment 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
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an object of class
currently not used
which residuals should be extracted and what cases should be deleted for influence diagnostics.
a logical indicating if LS residuals should be included in the
the original data frame passed to 'lmer'. This is only necessary for 'lmerMod' models where 'na.action = "na.exclude"'
hlm_augment function combines functionality from
hlm_influence for a simpler way of obtaining residuals and influence
diagnostics. Please see
?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
standardize is available in
hlm_resid; if this are desired,
should be used instead.
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