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...g_s' and 's_s'), and also compared to the glm() model with both fixed factors and no random factor ('gs'). This doesn't make much sense to me. I've placed a dataset on the Web that exhibits this behavior, as follows: dat <- read.csv("http://www.ling.upenn.edu/~johnson4/strange.csv") gs <- glm(outcome~gender+stress,binomial,dat) g_s <- lmer(outcome~gender+(1|speaker),dat,binomial) s_s <- lmer(outcome~stress+(1|speaker),dat,binomial) gs_s <- lmer(outcome~gender+stress+(1|speaker),dat,binomial) logLik(gs) # -1344 (df=3) logLik(g_s)...

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

I am running lrm() with a single factor. I then run anova() on the fitted model to obtain a p-value associated with having that factor in the model. I am noticing that the "Model L.R." in the lrm results is almost the same as the "Chi-Square" in the anova results, but not quite; the latter value is always slightly smaller. anova() calculates the p-value based on

For what it's worth, I've been getting the exact same error message as in the previous thread (see http://tolstoy.newcastle.edu.au/R/e2/ help/07/02/10269.html etc.): Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing value where TRUE/FALSE needed as well as NaN's all through the model, by using lmer on a formula that includes a

I have data consisting of binary responses from a large number of subjects on seven similar items. I have been using lmer with (crossed) random effects for subject and item. These effects are almost always (in the case of subject, always) significant additions to the model, testing this with anova. Including them also increases the Somers' Dxy value substantially. Even without those

I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties

> > If one compares the random effect estimates, in fact, one sees that > > they are in the correct proportion, with the expected signs. They are > > just approximately eight orders of magnitude too small. Is this a bug? > > BLUPs are essentially shrinkage estimates, where shrinkage is > determined with magnitude of variance. Lower variance more > shrinkage towards