similar to: Bradley Terry model and glmmPQL

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

I have the following code: ----------------------------------------------------------------------------------------------------- result <- matrix(NA, nrow=1, ncol=5) for(i in 1:(nsnp-1)) { for(j in (i+1):nsnp){ tempsnp1 <- data.lme[,i] tempsnp2 <- data.lme[,j] fm1 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2+tempsnp1*tempsnp2, random=~1|famid, na.action=na.omit) fm2 <-

Hi all, I'm having some difficulty with lme. I am currently trying to run the following simple model anova(lme(x ~ f1 + f2 + f1:f2, data=m, random=~1|r1)) Which is currently producing the error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 x is a numeric vector containing 194 observations. f1 is a factor vector containing two levels, and

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

Hello everyone: It's the first time I write to this mailing list. Sorry in advance if my doubt has already been posted before, but I have been checking the archives and I haven't been able to find anything satisfactory. I am running a mixed effects model with nested effects (site and pair, referred to barn swallow nests located in different places in different farms). My dependent

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(base) sens<-(VP+adj)/(VP+FN+2*adj)

Dear R users, I have a data structure as follows: id two res1 res2 c1 c2 inter 1 -0.786093166 1 0 1 2 6 3 -0.308495749 1 0 0 1 2 5 -0.738033048 1 0 0 0 1 7 -0.52176252 1 0

Hi, Sorry to bother those who are not interested in the question. I am using R to run a multiphase mixed-effects model. I simulated a data set by using the following model. And now I want to use R to estimate the parameters and compare the results with the true values. The equations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))+e[ij] b0[i]=b00+e[i0]

Hi everyone, I have posted a similar question to this list, but I don't get a reply. I really want to solve this problem, so I post it again... I am trying to use R to fit some mixed-effects models for a nested data. The data is a simulated data with 111 subjects. Each subject has 6 waves' data. Below are the first two subjects' data : > simu1[1:12,] Grouped Data: gf ~ age |

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

Hello, I am trying to design new variance structures by using fixed effects variables in combination with the VarPower function. That is, I would like to create and evaluate my own variance function in the data frame and then incorporate it into the model using varPower, with value=.5. As a start, I am trying to recreate the function of VarConstPower by introducing two new variables in the

Hi, I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no statistics wizard, so part of my problem my be a more fundamental lack of knowledge in the field. I have a dataset that looks something like this: Week Subj Group Readout 0 1 A 352.2 1 1 A

Dear lme and lmer -ers, I have some problems using "home-made" contrast matrix in lme and lmer. I did an experiment to investigate the relationship between the response of an animal and some factors, namely the light wavelength (WA), the light intensity to which this animal was exposed and the sex of the animal tested. - The response can be a variable LA (normal distribution) or

Hi, I posted the message below last week, but no answers, so I'm giving it another attempt in case somebody who would be able to help might have missed it and it has now dropped off the end of the list of mails. I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

I'm using glmmPQL, and I still have a few problems with it. In addition to the issue reported earlier, I'm getting the following error and I was wondering if there's something I can do about it. Error in logLik.reStruct(object, conLin) : Na/NaN/Inf in foreign function call (arg 3) ... Warnings: 1: Singular precistion matrix in level -1, block 4 (...) 4: "" The