similar to: Mixed effects model with binomial errors - problem

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Dear Madam or Sir I am writing you hoping, that you can help me with a problem concerning the output of regressions done with the function lme in R. I would need the standard deviations for intercepts and predictors, but in the output I can only find those for the intercepts. Could it be, that this is my fault? (I am just a beginner with R and multilevel modeling). I am sorry to annoy you with

Hello I have a problem when fitting a mixed generalised linear model with the lmer-function in the Matrix package, version 0.98-7. I have a respons variable (sfox) that is 1 or 0, whether a roe deer fawn is killed or not by red fox. This is expected to be related to e.g. the density of red fox (roefoxratio) or other variables. In addition, we account for family effects by adding the mother

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

Hi Andrew, I seem to be making good progress on the P5600 scheduler using the new machine model but I've got a few questions about it. How would you represent an instruction that splits into two micro-ops and is dispatched to two different reservation stations? For example, I have two reservation stations (AGQ and FPQ). An FPU load instruction is split into a load micro-op which is

Hi there, I am using SAS Proc NLMIXED to maximize a likelihood with multivariate normal random effects. An example is the two part random effects model for repeated measures semi-continous data with a cluster at 0. I use the "model y ~ general(loglike)" statement in Proc NLMIXED, so I can specify a general log likelihood function constructed by SAS programming statements. Then the

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

Hi -- I'm using lmer for binomial data. I am trying to replicate estimates provided by Agresti (2002, Categorical data analysis, Wiley) using abortion data in table 10.13 (estimates provided in table 12.3 p. 505). I fit the same model using these three commands: a1 <- lmer(resp ~ sex + option1 + option2 + (1|id), data=abort,family=binomial, method = c("AGQ")) a2 <-

From: Andrew Trick [mailto:atrick at apple.com] Sent: 24 January 2014 21:52 To: Daniel Sanders Cc: LLVM Developers Mailing List (llvmdev at cs.uiuc.edu) Subject: Re: New machine model questions On Jan 24, 2014, at 2:21 AM, Daniel Sanders <Daniel.Sanders at imgtec.com<mailto:Daniel.Sanders at imgtec.com>> wrote: Hi Andrew, I seem to be making good progress on the P5600 scheduler

Hi All, I have a dataset, longitudinal in nature, each row is a 'visit' to a clinic, which has numerous data fields and a count variable for the number of 'events' that occurred since the previous visit. ~50k rows, ~2k unique subjects so ~25 rows/visits per subject, some have 50 some have 3 or 4. In STATA there is an adjustment for the fact that you have multiple rows per

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Dear R users, I've been struggling with the following problem: I want to extract the Wald p-value from an lmer() fit, i.e., consider library(lme4) n <- 120 x1 <- runif(n, -4, 4) x2 <- sample(0:1, n, TRUE) z <- rnorm(n) id <- 1:n N <- sample(20:200, n, TRUE) y <- rbinom(n, N, plogis(0.1 + 0.2 * x1 - 0.5 * x2 + 1.5 * z)) m1 <- lmer(cbind(y, N - y) ~ x1 + x2 + (1 | id),

Hi all, I have a very simple problem, but i cant find a solution probably because i search for the wrong keywords. I have coordinates x and y. They look like this: > str(data) 'data.frame': 13979 obs. of 2 variables: $ x : Factor w/ 3815 levels "","186011.4971280",..: 1528 1524 1524 1524 1524 ... $ y : Factor w/ 3792 levels

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

Is H323 built into the current CVS? If so, could someone give me an idea of a simple config? thanks, darran -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20030429/fffec1d1/attachment.htm

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

Hi! I have a table with visits with a visited_at: attribute which is a datetime field. They seem to be stored as UTC. Now I want to count all visits each day and return something like: { 2013-01-01: 8, 2013-01-02: 4, 2013-01-07: 9, ... } So, I did it like this which kind of works...: def self.total_grouped_by_day(start_date, end_date) visits = where(visited_at:

Version 0.98-1 of the lme4 package and of the Matrix package are now on CRAN. This version provides the adaptive Gauss-Hermite quadrature (AGQ) method for fitting generalized linear mixed models. A new generic function mcmcsamp has been added with a method for objects in the "lmer" (linear mixed model fit or generalized linear mixed-effects model fit) class. This function provides a

Version 0.98-1 of the lme4 package and of the Matrix package are now on CRAN. This version provides the adaptive Gauss-Hermite quadrature (AGQ) method for fitting generalized linear mixed models. A new generic function mcmcsamp has been added with a method for objects in the "lmer" (linear mixed model fit or generalized linear mixed-effects model fit) class. This function provides a