similar to: help with lme

> 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

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 -- 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 <-

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 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),

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 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

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]]

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

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

I'm getting the following error in a script: "Error: could not find function "lmer."??? I'm wondering of my lme4 package is installed incorrectly.? Can someone tell me the installation procedure?? I looked at the support docs but couldn't translate that into anything that would work.

Hi everyone, I've been having difficulty writing wrapper functions for some functions where those same functions include other functions with eval() calls where the environment is specified. A very simple example using function lmer from lme4: lmerWrapper <- function(formula, data, family = gaussian, method = c("REML", "ML", "PQL", "Laplace",

Hi, We released individual birds into a room with 2 trees. We counted the number of visits to each of the 2 tree. One of the trees is always a control tree and the other tree is either treatment 1, treatment 2 or treatment3 or treatment 4. Ind Treat ContrTree ExpTree Total visits 1 1 11 16 27 1 2 6 9 15 1 3 5 13 18 1 4 11 25 36 2 1 2 3 5 4 1 6 7 13 4 3 4 4 8 4 4 2 5 7 6 1 1 1 2 6 4 5 16 21

Hi! I get the following message trying to install the matrix pakcage, can anyone help me please? trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' Error in download.file(url, destfile, method, mode = "wb") : cannot open URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' In addition: Warning message: cannot open: HTTP

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Hello all, I'm attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same

I use lmer to fit a mixed effect model.It give some warnings.what does this warnings mean? and what should I do? > (fm2.mlm <- lmer(qd ~ edu + jiankang + peixun +hunyin + cadcj + age + age2 + sex + dangyuan + Comp.1 + Comp.2+trust.cz1 +(trust.cz1|commid), data = individual,na.action = "na.exclude",family="quasibinomial")) Generalized linear mixed model fit using PQL