influence.mlm {mvinfluence} | 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.
In addition, the functions provide diagnostics for deletion of
subsets of observations of size m>1
.
## 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, ...)
model |
An |
do.coef |
logical. Should the coefficients be returned in the |
m |
Size of the subsets for deletion diagnostics |
x |
An |
FUN |
For |
funnames |
logical. Should the |
... |
Other arguments passed to methods |
digits |
Number of digits for the print method |
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 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) # 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 Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake) influence(Sake.mod) influencePlot(Sake.mod, id.n=3, type="cookd")