hatvalues.mlm {mvinfluence} | R Documentation |
Hatvalues for a MLM
Description
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.
Usage
## S3 method for class 'mlm'
hatvalues(model, m = 1, infl, ...)
Arguments
model |
An object of class |
m |
The size of subsets to be considered |
infl |
An |
... |
Other arguments, for compatibility with the generic; ignored. |
Details
Hat values are a component of influence diagnostics, measuring the leverage or outlyingness of observations in the space of the predictor variables.
The usual
case considers observations one at a time (m=1
), where the hatvalue is
proportional to the squared Mahalanobis distance, D^2
of each observation
from the centroid of all observations. This function extends that definition
to calculate a comparable quantity for subsets of size m>1
.
Value
A vector of hatvalues
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
Examples
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)
options(digits=3)
hatvalues(Rohwer.mod)
cooks.distance(Rohwer.mod)