lsmeans
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