I'm not an expert on robust modeling. However, as far as I know,
most robust regression procedures are based on heuristics, justified by
claims that "it seems to work" rather than reference to assumptions
about a probability model that makes the procedures "optimal". There
may be exceptions for procedures that assume a linear model plus noise
that follows a student's t distribution or a contaminated normal. Thus,
if you can't get traditional R-squares from a standard robust regression
function, it may be because the people who wrote the function thought
that R-squared (as, "percent of variance explained") did not make sense
in that context. This is particularly true for robust general linear
models.
Fortunately, the prospects are not as grim as this explanation
might seem: The summary method for an "lmrob" object (from the
robustbase package) returned for me the standard table with estimated,
standard errors, t values, and p values for the regression
coefficients. The robustbase package also includes an anova method for
two nested lmrob models. This returns pseudoDF (a replacement for the
degrees of freedom), Test.Stat (analogous to 2*log(likelihood ratio)),
Df, and Pr(>chisq). In addition to the 5 References in the lmrob help
page, help(pac=robustbase) says, it is ' "Essential" Robust Statistics.
The goal is to provide tools allowing to analyze data with robust
methods. This includes regression methodology including model
selections and multivariate statistics where we strive to cover the book
"Robust Statistics, Theory and Methods" by Maronna, Martin and Yohai;
Wiley 2006.'
I chose to use lmrob, because it seemed the obvious choice from a
search I did of Jonathan Baron's database of contributed R packages:
library(sos)
rls <- findFn('robust fit') # 477 matches; retrieved 400
rls.m <- findFn('robust model')# 2404 matches; retrieved 400
rls. <- rls|rls.m # union of the two searchs
installPackages(rls.)
# install missing packages with many matches
# so we can get more information about those packages
writeFindFn2xls(rls.)
# Produce an Excel file with a package summary
# as well a table of the individual matches
Hope this helps.
Spencer Graves
p.s. The functions in MASS are very good. I did not use rlm in this
case primarily because MASS was package number 27 in the package summary
in the Excel file produced by the above script. Beyond that,
methods(class='rlm') identified predict, print, se.contrast, summary and
vcov methods for rlm objects, and showMethods(class='rlm') returned
nothing. Conclusion: If there is an anova method for rlm objects, I
couldn't find it.
On 3/14/2011 7:00 AM, agent dunham wrote:
I also have the same problem, can anybody help?
and I would also like to see the p-values associated with the t-value of the
coefficients.
At present I type summary (mod1.rlm) and neither of these things appear.
Thanks, u...@host.com
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