Influence Measures and Diagnostic Plots for Multivariate Linear Models
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
These functions also extend plots of influence diagnostic measures such as those
influencePlot in the
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.
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
1 2 3
Maintainer: Michael Friendly <firstname.lastname@example.org>
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.
provide some theoretical description and definitions of influence measures in the
Barrett & Ling framework.
# none here
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.