influence.mlm {mvinfluence}

Regression Deletion Diagnostics for Multivariate Linear Models

Description

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.

Usage

## S3 method for class 'mlm'
influence(model, do.coef = TRUE, m = 1, ...)

Arguments

model

An mlm object, as returned by lm

do.coef

logical. Should the coefficients be returned in the inflmlm object?

m

Size of the subsets for deletion diagnostics

...

Other arguments passed to methods

Details

In addition, the functions provide diagnostics for deletion of subsets of observations of size m>1.

influence.mlm is a simple wrapper for the computational function, mlm.influence designed to provide an S3 method for class "mlm" objects.

There are still infelicities in the methods for the m>1 case in the current implementation. In particular, for m>1, you must call influence.mlm directly, rather than using the S3 generic influence().

Value

influence.mlm returns an S3 object of class inflmlm, a list with the following components

m

Deletion subset size

H

Hat values, H_I. If m=1, a vector of diagonal entries of the ‘hat’ matrix. Otherwise, a list of m \times m matrices corresponding to the subsets.

Q

Residuals, Q_I.

CookD

Cook's distance values

L

Leverage components

R

Residual components

subsets

Indices of the observations in the subsets of size m

labels

Observation labels

call

Model call for the mlm object

Beta

Deletion regression coefficients– included ifdo.coef=TRUE

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.

See Also

influencePlot.mlm, mlm.influence

Examples

# Rohwer data
data(Rohwer, package="heplots")
Rohwer2 <- subset(Rohwer, subset=group==2)
rownames(Rohwer2)<- 1:nrow(Rohwer2)
Rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)

# m=1 diagnostics
influence(Rohwer.mod) |> head()

# try an m=2 case
## res2 <- influence.mlm(Rohwer.mod, m=2, do.coef=FALSE)
## res2.df <- as.data.frame(res2)
## head(res2.df)
## scatterplotMatrix(log(res2.df))


influencePlot(Rohwer.mod, id.n=4, type="cookd")


# Sake data
data(Sake, package="heplots")
Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
influence(Sake.mod)
influencePlot(Sake.mod, id.n=3, type="cookd")