R: Regression Deletion Diagnostics for Multivariate Linear...
influence.measures
R Documentation
Regression Deletion Diagnostics for Multivariate Linear Models
Description
The functions cooks.distance.mlm and hatvalues.mlm
are designed as extractor functions for regression deletion
diagnostics for multivariate linear models following Barrett & Ling (1992).
These are close analogs of
methods for univariate and generalized linear models handled by the
influence.measures in the stats package.
In addition, the functions provide diagnostics for deletion of
subsets of observations of size m>1.
Usage
## S3 method for class 'mlm'
cooks.distance(model, infl = mlm.influence(model, do.coef = FALSE), ...)
## S3 method for class 'mlm'
hatvalues(model, m = 1, infl, ...)
Arguments
model
A mlm object, as returned by lm with a multivariate response
do.coef
logical. Should the coefficients be returned in the inflmlm object?
m
Size of the subsets for deletion diagnostics
infl
An influence structure, of class inflmlm as returned by mlm.influence
...
Other arguments, passed on
Value
When m=1, these functions return a vector, corresponding to the observations
in the data set.
When m>1, they return a list of m \times m matrices,
corresponding to deletion of subsets of size m.
Author(s)
Michael Friendly
References
Barrett, B. E. and Ling, R. F. (1992).
General Classes of Influence Measures for Multivariate Regression.
Journal of the American Statistical Association, 87(417), 184-191.