mlm.influence  R Documentation 
mlm.influence
is the main computational function in this package. It
is usually not called directly, but rather via its alias,
influence.mlm
, the S3 method for a mlm
object.
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 
... 
Further arguments passed to other methods 
The computations and methods for the m=1
case are straightforward,
as are the computations for the m>1
case. Associated methods for
m>1
are still under development.
mlm.influence
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 subsets 
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), 184191.
Barrett, B. E. (2003). Understanding Influence in Multivariate Regression. Communications in Statistics – Theory and Methods, 32, 3, 667680.
influencePlot.mlm
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) Rohwer.mod influence(Rohwer.mod) # extract the most influential cases influence(Rohwer.mod) > as.data.frame() > dplyr::arrange(dplyr::desc(CookD)) > head() # Sake data Sake.mod < lm(cbind(taste,smell) ~ ., data=Sake) influence(Sake.mod) > as.data.frame() > dplyr::arrange(dplyr::desc(CookD)) > head()
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