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...problem with lmer under FreeBSD, but not under Windows. Anyone knows why? Thanks. > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in UseMethod("as.logical") : no applicable method for "as.logical" > traceback() 9: as.logical(EMverbose) 8: as.logical(EMverbose) 7: lmerControl() 6: do.call("lmerControl", control) 5: lmer(Reaction ~ Days + (Days | Subject), sleepstudy) 4: eval.with.vis(expr, envir, enclos) 3: eval.with.vis(ei, envir) 2: source(zfile, local, echo = echo, prompt.echo = paste(prompt.prefix, getOption(...

...c only tolerance = sqrt(.Machine$double.eps),# ditto msMaxIter = 200, ## msTol = sqrt(.Machine$double.eps), ## FIXME: should be able to pass tolerances to nlminb() msVerbose = getOption("verbose"), niterEM = 15, EMverbose = getOption("verbose"), PQLmaxIt = 30,# FIXME: unused; PQL currently uses 'maxIter' instead gradient = TRUE, Hessian = FALSE # unused _FIXME_ ) { list(maxIter = as.integer(maxIter), tolerance = as.double(tolerance),...

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

...The only object in my .RData is last.warning, thus: > last.warning $"optim or nlminb returned message false convergence (8)" "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, tolerance = 1.49011611938477e-08, msMaxIter = 200, msVerbose = 0, niterEM = 15, EMverbose = FALSE, PQLmaxIt = 30, analyticGradient = TRUE, analyticHessian = FALSE)) This was generated by a call, in a previous session,to lmer() There may be a problem with the package Matrix, but it seems a bit extreme that this prevents restoration of the session. John Maindonald john.maindonald...

Dear All, Can anyone give me some hints about how to set starting values for a lmer model? For complicated models like this, good starting values can help the numerical computation and make the model converge faster. Thanks! Shige [[alternative HTML version deleted]]

...t;rbind", Ztl) fl <- FL$fl if (fltype < 0) { mer <- .Call(mer_create, fl, Zt, X, as.double(Y), method == "REML", nc, cnames) if (!is.null(start)) mer <- setOmega(mer, start) .Call(mer_ECMEsteps, mer, cv$niterEM, cv$EMverbose) LMEoptimize(mer) <- cv return(new("lmer", mer, frame = if (model) fr$mf else data.frame(), terms = mt, call = mc)) } if (method %in% c("ML", "REML")) method <- "Laplace" if (method == "AGQ")...