similar to: fixed effect significance with lmer() vs. t-test

I'm running lmer repeatedly on artificial data with two fixed factors (called 'gender' and 'stress') and one random factor ('speaker'). Gender is a between-speaker variable, stress is a within-speaker variable, if that matters. Each dataset has 100 rows from each of 20 speakers, 2000 rows in all. About 5% of the time I get a strange result, where the lmer() model with

I looked for examples of count data that might interest the students and found this project about dropout rates in Los Angeles High Schools. It is discussed in the UCLA stats help pages for the Stata users: http://www.ats.ucla.edu/stat/stata/library/count.htm and See: http://www.ats.ucla.edu/stat/stata/library/longutil.htm To replicate those results, I used R's excellent foreign package to

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

I am encountering an error I do not know how to debug. The error arises when I try to add an interaction term involving two continuous variables (defined using rcs functions) to an existing (and working) model. The new model reads: model5 <- lrm( B_fainting ~ gender+ rcs(exactage, 7) + rcs(DW_nadler_bv, 7) + rcs(drawtimefrom8am, 7)+ DW_firsttime+ DW_race_eth +

Dear R people, I am having a problem with modeling the following SAS code in R: Class ID Gr Hemi Region Gender Model Y = Gr Region Hemi Gender Gr*Hemi Gr*Region Hemi*Region Gender*Region Gender*Hemi Gr*Hemi*Region Gender*Hemi*Region Gr*Gender*Hemi*Region Random Intercept Region Hemi /Subject = ID (Gr Gender) I.e., ID is a random effect nested in Gr and Gender, leading to ID-specific

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

Dear R users My data set is e > names(e) [1] "yearctry" "discent" "age" "gender" "gemeduc" "gemhhinc" "ref_group" "fearfail_ref" "knowent_ref" "nbgoodc_ref" [11] "nbstatus_ref" "estbbuso_ref" "lngdp" "lngdpsq"

Hi, I am having problems trying to assess the significance of random terms in a generalized linear mixed model using lme4 package. The model describes bird species richness R along roads (offset by log length of road log_length) as a function of fixed effects Shrub (%shrub cover) and Width (width of road), and random effect Site (nested within Site Cluster). >From reading answers to previous

Greetings, I am trying to run a logistic regression model for binary data with a random intercept and slope in R 2.6.1. When I use the code: lmer1<-lmer(infect ~ time+gender + (1+time|id), family=binomial, data=ichs, method="Laplace") Then from: summary(lmer1) I get the message: Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing

Does anyone know if the lmer function of lme4 works fine for unbalanced designs? I have the examination results of 1000 pupils on three subjects, one score every term. So, I have three scores for English (one for every term), three scores for maths etc. However, not everybody was examined in maths, not everybody was examined in English etc, but everybody was in effect examined on four subjects. I

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

Hi, I receive the following warning message when I run a poisson GLM in R: "glm.fit: fitted rates numerically 0 occurred" The model summary is shown below. The variable 'Species' consists of counts of different species ranging from 0 to 4. I suspect this may have something to do with the warning message but I'm not sure. Can anybody help? Thank you! Anna Call:

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

hello, all. while i have a mcmc running, i am looking at the frequestist method of my model. i have never done HLM so i am looking for ways to plot them that might yeild something useful like dr. fox's effects plot package. this is my model, where dem is democracy ranked continuous 1:10, trsut is a 3 level categorical variable, cpi is 1:10, etc... > hier.jags2.mod <- lmer(dem ~

Hello all, I'm attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the

Dear Adrian and Ian (and r-helpers), I encountered a curious result in developing an example using the bicreg function in the BMA package: I noticed that pairs of models with equal R^2 and equal numbers of predictors had nevertheless different BIC values. Looking at the bicreg function, the definition of BIC appears to be the usual one, or close to it [bic <- n * log(1 - r2/100) + (size - 1) *

hi, at first; thanks for the help on getting confidence intervals in R. now I have a pure statistical question. I hope you don't mind if I ask ... I have an expectation of how large my beta-weight in a regression should be - so I have an "ideal" or expected regression line. Now the real beta-weight is less then the expected and when I draw the confidence interval lines

Hi everyone, I'm trying to determine the significance of a trendline. From my internet search months ago, I came across the following post. I modified tim and dat for simiplicity. tim <- 1:10 dat <- c(0.17, 1.09 ,0.11, 0.82, 0.23, 0.38 ,2.47 ,0.41 ,0.75, 1.44) fstat <- summary(lm(dat~tim))$fstatistic p.val <-

Hello All, I'm looking for an R package that will run the "Brien's Test" (see Brien et al., 1984, Biometrika) on a correlation matrix. Or if anyone can help with the equations used to calculated the chi-squared statistic for the grand mean, main effects, interactions, and equal correlation that would also be great. Thanks in advance, B Wiltse -- View this message in

Hi there, I tried this code from homepage: http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html <http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html> corstarsl <- function(x){ require(Hmisc) x <- as.matrix(x) R <- rcorr(x)$r p <- rcorr(x)$P ## define notions for significance levels; spacing is important. mystars <- ifelse(p <