
Influence Measures and Diagnostic Plots for Multivariate Linear Models
mvinfluence-package.Rd
This collection of functions is designed to compute regression deletion
diagnostics for multivariate linear models following Barrett & Ling (1992).
These are close analogs of standard
methods for univariate and generalized linear models handled by the
influence.measures
in the stats
package.
These functions also extend plots of influence diagnostic measures such as those
provided by influencePlot
in the stats
package.
In addition, the functions provide diagnostics for deletion of
subsets of observations of size m>1
. This case is theoretically interesting
because sometimes pairs (m=2
) of influential observations can mask each other,
sometimes they can have joint influence far exceeding their individual effects,
as well as other interesting phenomena described by Lawrence (1995).
Associated methods for the case
m>1
are still under development in this package.
Details
Package: | mvinfluence |
Type: | Package |
Version: | 0.7 |
Date: | 2013–9-06 |
License: | GPL-2 |
The design goal for this package is that, as an extension of standard methods for univariate linear models, you should be able to fit a linear model with a multivariate response,
and then get useful diagnostics and plots with
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.
Barrett, B. E. (2003). Understanding Influence in Multivariate Regression. Communications in Statistics – Theory and Methods, 32, 3, 667-680.
A. J. Lawrence (1995). Deletion Influence and Masking in Regression Journal of the Royal Statistical Society. Series B (Methodological) , Vol. 57, No. 1, pp. 181-189.
See also
influence.measures
, influence.mlm
, influencePlot.mlm
, ...
Jdet
, Jtr
provide some theoretical description and definitions of influence measures in the
Barrett & Ling framework.