View source: R/hatvalues.mlm.R
hatvalues.mlm | R Documentation |
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.
## S3 method for class 'mlm' hatvalues(model, m = 1, infl, ...)
model |
An object of class |
m |
The size of subsets to be considered |
infl |
An |
... |
Other arguments, for compatibility with the generic; ignored. |
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
.
A vector of hatvalues
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.
cooks.distance.mlm
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)
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