Nutter, Benjamin wrote:
I hope you'll forgive me for resurrecting this thread. My question
refers to John Fox's comments in the discussion of lsmeans from
https://stat.ethz.ch/pipermail/r-help/2008-June/164106.html
John you said, "It wouldn't be hard, however, to do the computations
yourself, using the coefficient vector for the fixed effects and a
suitably constructed model-matrix to compute the effects; you could also
get standard errors by using the covariance matrix for the fixed
effects."
I've been able to make use of all of that except for the 'suitably
constructed model-matrix' part. I've looked through some other threads
on this topic, but am still a little in the dark as to what I'd need to
do to construct a suitable matrix.
I would like to use the least squares means to develop parameter
estimates for a parametric ROC analysis, as described by Mithat Gonen's
book (Analyzing Receiver Operating Characteristic Curves with SAS,
2007).
Any suggestions on references that would explain how to go about
constructing the suitable model matrix?
Many Thanks
Benjamin
As an aside, what advantages does modeling an ROC curve have over doing
direct covariate modeling of the response variable as usual?
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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