similar to: interacting factors in lmer [was: error using user-defined link function]

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 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

Dear R users, I'm struggling with applying a user-defined link function in lmer. For analyzing data of a 2AFC psychophysical experiment, I would like to model my binary data with a logistic function with a lower limit at 0.5 instead of 0. In a previous question this has been described as a halflogit function. To do so I wrote my own link function and would like to submit it to lmer, however

Hello, I am a relative novice at both using regression analysis and at using R in general (and at object oriented programing). A colleague convinced me that binary logistic regression is the most appropriate analysis for the data that I have though, so I've been trying to muddle through. I'm currently stumped on how to interpret/compute odds ratios for two and three way interactions

Hi, I was hoping to get some advice regarding the testing of interactions, when one factor is modelled as a random effect... I have a model with binomial error structure where the response variable is the proportion of time spent at the main sett (animals were tracked for 28 consecutive days in each season, and were recorded either at the main sett or an outlier sett, so the response variable is

I'm trying to specify a model using lmer with a binary response and interaction term, but I get an error I can't parse (see below). Here is some sample data: Subject Concord Age Disc SVC999MX148SU-F yes u int TOU999JU030S1 yes u int TOU999JU030S1 yes u int TOU999JU030S1 yes u int TUT578MX037S2 yes g int COL140MX114S2 yes yf

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Greetings, everyone. I've been trying to analyze bird nest survival data using generalized linear mixed models (because we documented several consecutive nesting attempts by the same individuals; i.e. repeated measures data) and have been unable to persuade the various GLMM models to work with my user-defined link function. Actually, glmmPQL seems to work, but as I want to evaluate a suite of

Hi, Bert, Thank you?very much for the comments and suggestions! Ace On Wednesday, November 15, 2017 10:44 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See ?library. As for the appropriateness of your modeling, you should do what David already suggested and post to the r-sig-mixed-models list instead. -- Bert Bert Gunter

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

Hi, I am an R beginner and having problems structuring my REML models. I have a model with y=weight x1=time x2=timesquared id=individual identity I need to structure the model such that in the random effects there is a constant intercept for all individuals but a separate individual slope for both x1 and x2 (a coefficient score for every individual).

Hi dears while modeling an interaction random effect in lmer i receive the instantaneous error message > ldlM4<-lmer(ldl~rt*cd4+age+rf+pharmac+factor(hcv)+ + hivdur+(rt:cd4|id),na.action=na.omit,REML=F) *Warning message: In mer_finalize(ans) : false convergence (8) * I think the matter lies in syntax, 'cause i sistematically receive the same message even when changing response... PS:

Hi, I have coding question on mixed model in R. I am using R2.11.0 in windows. I have an experiment with 2 fixed effect factors - A and B. The levels of B are within the levels of A factor. The model is very similar to a split plot design except the nesting relationship between the 2 fixed effect factors. For example: there are 2 levels for A - GM and ZM. There are 7 levels of B in total

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels

Dear Colleagues, I fit this model: mod1 <- lmer(x~category*comp+(1|id),data=impchiefsrm) where category has 4 levels and comp has 8 levels. These work: glht(mod1, linfct=mcp(category="Tukey") glht(mod1, linfct=mcp(comp="Tukey") What I'd like is (conceptually): glht(mod1, linfct=mcp(category:comp="Tukey") but it gives a syntax error. Any help is

I have data from a design in which items are completely nested within subjects. Subject is the only second-level factor, but I have multiple first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can