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...e (or variance, b_j) parameter and 2 position parameters (a_cj)? Doesn't it? Do I need at least 4 diagnostic tests for such a model? What happens if I specify options blocksize and byclass? How many diagnostic tests (or rater) I need? Extract from see "randomLCA examples", by Ken Beath. > dentistry.lca2random <- randomLCA(dentistry[, + 1:5], freq = dentistry$freq, initmodel = dentistry.lca2, + nclass = 2, random = TRUE, quadpoints = 31, + probit = TRUE) Thank you.

It seems like the Bayesian folks like to hide. Hey, I just want to get the job done whether using frequency or Bayesian stats. Can anybody suggest a good introduction to Bayesian Analysis? ----- Forwarded message from Ken Beath <kjbeath at kagi.com> ----- To: Joshua N Pritikin <jpritikin at pobox.com> From: Ken Beath <kjbeath at kagi.com> Subject: Remedial stats Hi, Gelman's book "Bayesian Data Analysis' while excellent tends to move through the introductory material at a rapid pace. Y...

I am trying to plot multiple densityplots on each panel, and using panel.groups to do some additional plotting (not included in the example) as in this example. library(lattice) thedata <- data.frame(x=rnorm(1200),class=rep(1:3,each=200,times=2), group=rep(1:2,each=100,times=6)) densityplot(~x | class,groups=group, xlab='x', panel.groups =

I'm maximising a reasonably complex function using nlme (version 3.1-65, have also tried 3.1-66) and am having trouble with fixed parameter estimates slightly away from the maximum of the log likelihood. I have profiled the log likelihood and it is a parabola but with sum dips. Interestingly changing the parameterisation moves the dips around slightly. Unfortunately the PNLS step is

I'm not sure if this is the right target to which to direct this post, but I couldn't think of anything more appropriate. I just downloaded the Hmisc package to the Imac that I use. When I attempted to load it I got an error to the effect that it could not load the library: /Library/Frameworks/R.framework/Versions/2.5/Resources/lib/ libgfortran.2.dylib Note the ``2.5'' in

A new package 'randomLCA' is available on CRAN. Its main purpose is to fit latent class models with random effects, such as those used in diagnostic testing. This methodology can also be applied in other areas. It also fits standard latent class and will plot. Ken _______________________________________________ R-packages mailing list R-packages at r-project.org