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 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.


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,

  mymlm <- lm( cbind(y1, y2, y3) ~ x1 + x2 +x3, data=mydata)

and then get useful diagnostics and plots with

  influencePlot(mymlm, ...)  


Michael Friendly

Maintainer: Michael Friendly <>


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.


# none here
comments powered by Disqus