R: Regression Deletion Diagnostics for Multivariate Linear...
influence.mlm
R Documentation
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.
In addition, the functions provide diagnostics for deletion of
subsets of observations of size m>1.
Usage
## S3 method for class 'mlm'
influence(model, do.coef = TRUE, m = 1, ...)
## S3 method for class 'inflmlm'
as.data.frame(x, ..., FUN = det, funnames = TRUE)
## S3 method for class 'inflmlm'
print(x, digits = max(3, getOption("digits") - 4), FUN = det, ...)
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
x
An inflmlm object, as returned by mlm.influence
FUN
For m>1, the function to be applied to the H and Q
matrices returning a scalar value. FUN=det and FUN=tr
are possible choices, returning the |H| and tr(H)
respectively.
funnames
logical. Should the FUN name be prepended to the statistics when
creating a data frame?
...
Other arguments passed to methods
digits
Number of digits for the print method
Details
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 if do.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.