search for: lrts

...version 1.5 might include estimation of the residual variance into the model parameters. Does anyone know if a) this is the only change or if there is somewhere I can read about how the degrees of freedom are calculated? b) the degrees of freedom in 1.5.1 are correct for using in differences for LRTs? > Thanks very much for you consideration, > Deanne Wright > Deanne.Wright at pioneer.com > > > This communication is for use by the intended recipient and contains information that may be privileged, confidential or copyrighted under applicable law. If you are not the int...

...dosetime /rl ties=efron; dosetime=time*dose; run; Without the interaction terms I get the same results for the two models. By including the interaction terms I do not. The model in R gives a negative coefficient for the interaction term which is expected to be positive (and is so in SAS). The LRTs are also completely different. TWO QUESTIONS: 1) Is it reasonable to bring in an interaction term when dose is only measured once? 2) If yes, can anyone give a hint on explaining the difference between the models in R and SAS? Thanx in advance, marianne ______________________________________...

...od ratio > statistic is being used (the documentation does not say, but it appears > that it is probably REML-based) if it is not a legitimate test, then why > is it included in the package? I *have* looked at the documentation. It does not give a reference for the validity of REML-based LRTs, so can you please supply one? There is a warning note: Likelihood comparisons are not meaningful for objects fit using restricted maximum likelihood and with different fixed effects. which does not say that the converse *is* meaningful. nlme2 even gives the comparisons in the excluded...

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

The concrete problem is that I am refereeing a paper where a confidence interval is presented for the risk ratio and I do not find it credible. I show below my attempts to do this in R. The example is slightly changed from the authors'. I can obtain a confidence interval for the odds ratio from fisher.test of course === fisher.test example === > outcome <- matrix(c(500, 0, 500, 8),

...tic regression with mutiple predictors? family=binomial("logit"))               drop1(Confidence.glm, test="Chisq") The summary z-table suggests a direction of the effect, and notably the large LRT statistics are the significant ones. I am used to thinking of extremely small LRTs as significant (negative natural logarithms of LRTs). I must assume that the LRT in *R* is alternative hypothesis over null hypothesis, rather than the convention I learned of null/alternative, where a small number (negative logLR) represents strong evidence and zero/zed evidence is represented...