This function creates a “bubble” plot of functions, R = log(Studentized residuals^2) by L = log(H/p*(1-H)) of the hat values, with the areas of the circles representing the observations proportional to Cook's distances.
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.
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.
mlm.influence is the main computational function in this package. It is usually not called directly, but rather via its alias, influence.mlm, the S3 method for a mlm object.
This collection of functions is designed to compute regression deletion diagnostics for multivariate linear models following Barrett & Ling (1992) that are close analogs of methods for univariate and generalized linear models handled by the influence.measures in the stats package.
Provides index plots of some diagnostic measures for a multivariate linear model: Cook's distance, a generalized (squared) studentized residual, hat-values (leverages), and Mahalanobis squared distances of the residuals.
This function creates various types of “bubble” plots of influence measures with the areas of the circles representing the observations proportional to Cook's distances.
These functions implement the general classes of influence measures for multivariate regression models defined in Barrett and Ling (1992), Eqn 2.3, 2.4, as shown in their Table 1.
providing 
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