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

In friendly/mvinfluence: Influence Measures and Diagnostic Plots for Multivariate Linear Models

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, ...)

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

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.

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

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.

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()

friendly/mvinfluence documentation built on Sept. 28, 2022, 10:05 a.m.