Quantile regression is now supported in the Design package and bootcov
has been updated accordingly. To get the new code run
require(Design)
source('http://biostat.mc.vanderbilt.edu/cgi-bin/viewvc.cgi/*checkout*/Design/trunk/R/bootcov.s')
source('http://biostat.mc.vanderbilt.edu/cgi-bin/viewvc.cgi/*checkout*/Design/trunk/R/Rq.s')
f <- Rq(y ~ rcs(age,4)*sex, x=TRUE, y=TRUE, tau=.25)
b <- bootcov(f, B=1000, pr=TRUE)
nomogram(f)
anova(b)
summary(b, age=c(21,65), sex='male')
This allows one to do quantile regression (but only one tau at a time)
and get effect plots, nomograms, anova, general contrasts, etc.
Frank
John Gardner wrote:
I have a quick question, and I apologize in advance if, in asking, I
expose my woeful ignorance of R and its packages. I am trying to use
the bootcov function to estimate the standard errors for some
regression quantiles using a cluster bootstrap. However, it seems that
bootcov passes arguments that rq.fit doesn't like, preventing the
command from executing. Here is an example:
e<-bootcov(rq(y~x),clust,B=10,fitter=rq.fit)
(where clust is my clustering variable) results in
Error in rq.fit.br(x, y, tau = tau, ...) :
unused argument(s) (maxit = 15, penalty.matrix = NULL)
In contrast, the lm.fit function seems to just ignore these arguments,
resulting in the following warning:
10: In fitter(X[obs, , drop = FALSE], Y[obs, , drop = FALSE], maxit = maxit, :
extra arguments maxitpenalty.matrix are just disregarded.
Is there a way that I can either (a) modify bootcov so that it doesn't
pass these arguments or (b) modify rq so that it ignores them?
Thanks in advance,
John Gardner
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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