mvinfluence (version 0.9.0)

mlm.influence: Calculate Regression Deletion Diagnostics for Multivariate Linear Models

Description

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.

Usage

mlm.influence(model, do.coef = TRUE, m = 1, ...)

Value

mlm.influence 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 subsets

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

Arguments

model

An mlm object, as returned by lm with a multivariate response.

do.coef

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

m

Size of the subsets for deletion diagnostics

...

Further arguments passed to other methods

Author

Michael Friendly

Details

The computations and methods for the m=1 case are straight-forward, as are the computations for the m>1 case. Associated methods for m>1 are still under development.

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.

See Also

influencePlot.mlm

Examples

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)
Rohwer.mod
influence(Rohwer.mod)

# extract the most influential cases
influence(Rohwer.mod) |> 
    as.data.frame() |> 
    dplyr::arrange(dplyr::desc(CookD)) |> 
    head()

# Sake data
Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
influence(Sake.mod) |>
    as.data.frame() |> 
    dplyr::arrange(dplyr::desc(CookD)) |> head()