search for: downdating

Dear R-devel members, I am looking for a fast Cholesky update/downdate. The matrix A being symmetric positive definite (n, n) and factorized as A = L %*% t(L), the goal is to factor the new matrix A +- C %*% t(C) where C is (n, r). For instance, C is 1-column when adding/removing an observation in a linear regression. Of special interest is the case where A is sparse. Looking at the

Dear All, I have conducted an experiment in order to examine predation pressure in the surroundings of potential wildlife road-crossing structures. I have documented predation occurrence (binary?) in these structures and calculated several possible explanatory variables describing the spatial heterogeneity in several scales. At the landscape scale I have calculated the percentage of different

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

Dear list, I am new to this list and I am new to the world of R. Additionally I am not very firm in statistics either but have to deal. So here is my problem: I have a dataset (which I attach at the end of the post) with a binomial response variable (alive or not) and three fixed factors (trapping,treat,sex). I do have repeated measures and would like to include one (enclosure) random factor. I

Dear List: I was able to run the following in nlme successfully, but the same model and code (same dataset) failed to run in lme4 and gave me the error message below. Any thoughts? lme(math~year, data=egsingle, random=~year|schoolid/childid) Error in lme(formula = math ~ year, data = egsingle, random = structure(list( : Unable to invert singular factor of downdated X'X

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

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

Hello, I'm trying to run an lmer model with family poisson but receive the following warning message: model<-lmer(count~tem+dat+alt+year+tem:dat+tem:alt+tem:year+dat:alt+dat:year+alt:year+(1|id),family=poisson) Error in objective(.par, ...) : Leading minor of order 2 in downdated X'X is not positive definite I'm using the newest version of R and recently installed the

Hi everybody, I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of

Dear R users, I am building a hierarchical model on a large data set. It can take quite some time to finish one fit, I was just wondering whether it is possible to store the fit object (the result) to a file for later (offline) analysis. thanks Julia -- View this message in context: http://www.nabble.com/how-to-store-lme-lmer-fit-result-tp19910951p19910951.html Sent from the R help mailing

Using lmer() on my data results in an error. The problem, I think, is my model specification. However, lm() works ok. I recreated this error with a more simple dataset. (See code below.) # word and letter recognition data # two within factors: # word length: 4, 5, 6 letters # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in 6-letter words) # one dependent variable: #

Hello, I hope that you can bare with me. I am new to models, but I think I have a pretty godd understanding of how to run them now, including how to use AICc and Anova. The issue is that I have many factors that I wish to compare so doing each one at a time would take forever. I came across the MuMIn package and I was so excited, however I am getting an error message and i don't know why.

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

Hi, I would appreciate help with the following model <<1>>= gunload <- read.table(hh('datasets/gunload.dat'), header = T) gunload$method <- factor(gunload$method, labels = c('new', 'old')) gunload$physique <- factor(gunload$group, labels = c('slight', 'average', 'heavy')) gunload$team9 <- factor(rep(1:9, each = 2)) @ This

...d algorithms discovered over the years by a number of researchers (e.g. faster or more accurate eigenvalue and SVD algorithms, extra precise iterative refinement, recursive blocking for some linear solvers, etc.). We also know of a variety of other possible functions we could add (e.g. updating and downdating factorizations), but are uncertain of their impact. Please see http://icl.cs.utk.edu/lapack-survey.html for the survey. We would like to have your input by June 16th, 2004. Regards, Jack Dongarra, Jim Demmel, and Sven Hammarling

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

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Dear R-users, I have a frustrating problem that I am hoping has a simple fix. I am running a series of lmer models from the lme4 package of the general form: model<-lmer(y~x1 + x2 ..... + xn + (1|site),data=dataframe,family=poisson,method="Laplace",control=list(usePQL=FALSE,msVerbose=TRUE)) where the same model is executed multiple times on a bootstrapped dataframe. For each

I've been using lme4 to fit Poisson GLMMs with crossed random effects. The data are counts(y) sampled at 55 sites over 4 (n=12) or 5 (n=43) years. Most models use three fixed effects: x1 is a two level factor; x2 and x3 are continuous. We are including random intercepts for YEAR and SITE. On subject-matter considerations, we are also including a random coefficient for x3 within YEAR.