lmsglm | R Documentation |
This function computes the Least Median Square robust fit for generalized linear models using deviance residuals.
lmsglm(x, y, family, weights, offset, n.samples = 100, max.samples = 200, epsilon = 1e-04, maxit = 50, trace = FALSE)
x |
a matrix or data frame containing the explanatory variables. |
y |
the response: a vector of length the number of rows of |
family |
a description of the error distribution and link function to be used in the model. See |
weights |
an optional vector of weights to be used in the fitting process. |
offset |
optional, a priori known component to be included in the linear predictor during fitting. |
n.samples |
number of good subsets to fit. It can be a numeric value or |
max.samples |
maximal number of subsets to fit. By default is set to twice |
epsilon |
positive convergence tolerance epsilon. See |
maxit |
integer giving the maximal number of IWLS iterations. See |
trace |
logical, if |
This function is used by fwdglm
to select the
starting subset for the forward search. For this reason, users do not
generally need to use it.
The function returns a list with the following components:
bsb |
a vector giving the best subset found |
dev.res |
a vector giving the deviance residuals for all the observations |
message |
a short message about the status of the algorithm |
model |
the model provided by |
Originally written for S-Plus by:
Kjell Konis kkonis@insightful.com and Marco Riani mriani@unipr.it
Ported to R by Luca Scrucca luca@stat.unipg.it
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapter 6.
fwdglm
, fwdlm
, lmsreg
, fwdsco
.
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