Thank you very much for your comments!
however, i still do not get it right. robcov() accepts fit objects like lrm or ols objects as arguments, but obviously not the glmD objects (or at least not as simple as that). below some code to demonstrate. what am i still doing wrong?
thx for your efforts- lutz
id<-1:500 outcome<-sample(c(0,1), 500, replace=T, prob=c(.6, .4)) exposed<-sample(c(0,1), 500, replace=T, prob=c(.5, .5)) my.data<-data.frame(id=id, ou=outcome, ex=exposed)
model1<-glmD(ou~ex, my.data, family=poisson(link="log")) robcov(model1)
Error in match.arg(type) : ARG should be one of deviance, pearson, working, response, partial
Sorry I didn't think of that sooner. robcov needs the residuals method for the fitter to allow a type="score" or type="hscore" (for Efron's method) argument. Until someone adds score residuals to residuals.glm robcov will not work for you. residuals.lrm and residuals.coxph are examples where score residuals are computed. You can get robust variance-covariance estimates with the bootstrap using bootcov for glmD fits. Oddly in your example I am finding that the bootstrap variances are lower than the information-matrix-based ones.
Frank Harrell
At 17:25 02.06.2004, Frank E Harrell Jr wrote:
Thomas Lumley wrote:
On Wed, 2 Jun 2004, Lutz Ph. Breitling wrote:
Dear all,
i am trying to redo the 'eyestudy' analysis presented on the site http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm with R (1.9.0), with special interest in the section on "relative risk estimation by poisson regression with robust error variance".
so i guess rlm is the function to use. but what is its equivalent to the
glm's argument "family" to indicate 'poisson'? or am i somehow totally
wrong and this is not applicable here?
No, no. You want glm() and then a function to compute the robust
covariance matrix (there's robcov() in the Hmisc package), or use gee()
from the "gee" package or geese() from "geepack" with independence working
correlation.
Slight correction: robcov in the Design package, can easily be used with Design's glmD function. -Frank
These are not outlier-resistant estimates of the regression coefficients,
they are model-agnostic estimates of the standard errors.
Stata is unusual in providing these covariance matrix estimates for just
about every regression estimator. I think R should consider doing
something similar, but haven't got around to it.
-thomas
thx a lot- lutz
============================= Lutz Ph. Breitling, CMd
Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle
============================= Lutz Ph. Breitling Unit� des Recherches M�dicale H�pital Albert Schweitzer B.P. 118 Lambar�n� (GABON)
-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University
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