View source: R/influence.mlm.R
influence.mlm | R Documentation |
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.
## S3 method for class 'mlm' influence(model, do.coef = TRUE, m = 1, ...)
model |
An |
do.coef |
logical. Should the coefficients be returned in the
|
m |
Size of the subsets for deletion diagnostics |
... |
Other arguments passed to methods |
In addition, the functions provide diagnostics for deletion of subsets of
observations of size m>1
.
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()
.
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 |
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 |
labels |
Observation labels |
call |
Model call for the |
Beta |
Deletion regression coefficients– included if |
Michael Friendly
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.
influencePlot.mlm
, mlm.influence
# Rohwer data 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) # m=1 diagnostics influence(Rohwer.mod) |> head() # try an m=2 case ## res2 <- influence.mlm(Rohwer.mod, m=2, do.coef=FALSE) ## res2.df <- as.data.frame(res2) ## head(res2.df) ## scatterplotMatrix(log(res2.df)) influencePlot(Rohwer.mod, id.n=4, type="cookd") # Sake data data(Sake, package="heplots") Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake) influence(Sake.mod) influencePlot(Sake.mod, id.n=3, type="cookd")
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