similar to: degrees of freedom does not appear in the summary lmer :(

Hey, I am having trouble with lmer. I am looking at the presence/absence of water shrews against habitat and other factors e.g so I used this: m1<-lmer(Presencebsence~Habitatype*Width+(1|Sitename))summary(m1) But i keep getting this error up Error in mer_finalize(ans) : Downdated X'X is not positive definite, 16.> summary(m1)Error in asMethod(object) : matrix is not symmetric [1,2] What

Dear All, I used lmer for data with non-normally distributed error and both fixed and random effects. I tried to calculate a "Type III" sums of squares result, by I conducting likelihood ratio tests of the full model against a model reduced by one variable at a time (for each variable separately). These tests gave appropriate degrees of freedom for each of the two fixed effects, but

Hi, I am trying to fit a model with lmer in R and proc glimmix in SAS. I have simplified my code but I am surprised to see I get different results from the two softwares. My R code is : lmer(y~age_cat + (1|cat),data=fic,family=binomial(link = "logit"), NaGQ=1) My SAS code is : ods output Glimmix.Glimmix.ParameterEstimates=t_estimates; proc glimmix data=tab_psi method=laplace;

Hello all, I'm trying to do a nested linear model with a dataset that incorporates an observation for each of several classes within each of several plots. I have 219 plots, and 17 classes within each plot. data.frame has columns "plot","class","age","dep.var" With lm(dep.var~class*age), The summary(lm) function returns t-test and F-test values

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr

Hello, I have a little problem about degree of freedom in R. if you can help me, I will be happy. I used nlme?function to analyze my data and run the linear mixed effects model in R. I did the linear mixed effect analysis in SAS?and SPSS as well. However, R gave?the different degrees of freedom than SAS?and SPSS did. Can you help me to learn what the reason is to obtain different degrees of

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Hello I'm working with mixed effects models using lmer() and have some problems to get all variance components of the model's random effects. I can get the variance of the random effect out of the summary and use it for further calculations, but not the variance component of the residual term. Could somebody help me with that problem? Thanks a lot! Below an example. Aline ## EXAMPLE

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

I wonder whether any of you know of an efficient way to calculate the approximate degrees of freedom of a gamm() fit. Calculating the smoother/projection matrix S: y -> \hat y and then its trace by sum(eigen(S))$values is what I've been doing so far- but I was hoping there might be a more efficient way than doing the spectral decomposition of an NxN-matrix. The degrees of freedom

Dear all, I want to do a F test, which involves calculation of the degrees of freedom for the residuals. Now say, I have a nlme object "mod.nlme". I have two questions 1.How do I extract the degrees of freedom? 2.How is this degrees of freedom calculated in an nlme model? Thanks. Jun Shen Some sample code and data =================================================================

I'm running an ANOVA on some data for respiration in a forest. I am having a problem with my degrees of freedom. For one of my variables I get one fewer degrees of freedom than I should. I have 12 plots and I therefore expected 11 degrees of freedom, but instead I got 10. Any ideas? I have some code and output below: > class(Combined.Plot) [1] "character" >

Dear all, I wish to thank Christoph Buser and John Wilkinson for their input, and especially John his examples and for for pointing me to the thread 'Doubt about nested aov output' where the rat-example was hiding...:

Hello, I was re-generating the moduli for SSH, and during that process I noticed that, when running the following command: ssh-keygen -M screen -O prime-tests=600 -O generator=3 -f moduli-2048-01.candidates moduli-2048-01c It does not produce any errors, only the following: ebug2: ???1467763: (4) Sophie-Germain debug2: ???1467763: generator 0 != 3 debug2: ???1467764: (4) Sophie-Germain

Dear all, I have an R script that uses Rcpp, and I have been trying to parallelize it using mclapply (I tried with the multicore and the parallel library) Sometimes (not always, interestingly), the CPU use for each core drops, usually so that the total over all cores reaches 100%, i.e., as fast as if using just one single core fully. I tried my code directly from within emacs, and also using a

Dear Colleagues, I have x, y data (pollen and seed dispersal from oaks !) that I would like to fit with a function which look like this: p(a,b,x,y)=b/(2*pi*a?gamma(2/b))*exp(-(square_root(x?+y?)/a)power(b)) I am looking for a and b values that fit my data at best. Can someone give me hints to perform such an analysis with R ? Thanks a lot Sophie Sophie Gerber

Hi, I need to do several matrix multiplications with the corresponding matrices forming two 3-dimentional arrays. To illustrate my problem, let's say I have the following 3-dimensional arrays: array1 <- array(1:30,dim=c(3,2,5)) array2 <- array(1:20,dim=c(2,2,5)) I know that I can get what I want with the following computation : result <- array(dim=c(dim(array1)[1],

Hi, I?ve been trying to increase the number of open files for the dovecot user on Debian 9 and have so far, failed! I?ve tried this: # cat /etc/security/limits.d/limits_dovecot.conf dovecot soft nofile 2048 dovecot hard nofile 8192 # cat /etc/systemd/system/dovecot.service.d/service.conf LimitNOFILE=8192 But to no avail: # prlimit -p 27208|grep -i