similar to: extracting p-values from lmer()

Dear R users, I've been struggling some days with the following problem: I'm interesting in producing the following plot x <- seq(0.01, 10, length = 20) plot(c(0, 10), c(-20, 20), type = "n", xlab = "x", ylab = expression(2 * alpha * log(x))) pch. <- rep(NA, length(x)) for(i in 1:4){ pch.[7] <- as.character(i) lines(x, 2 * i * log(x), type

Dear R-users, I was wondering for the following: Let 'x' be a matrix and 'ind' and indicator matrix, i.e., x <- array(1:20, dim = c(4, 5)) ind <- array(c(1:3, 3:1), dim = c(3, 2)) I'd like to get (as a vector) the elements of 'x' which are not indexed by 'ind'. Since negative indices are not allowed in index matrices I thought of using something like:

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

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

> 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

Dear R users, I've been trying to build R from sources (in Windows) using Dr. Goto's BLAS, unsuccessfully! I've followed the instructions in Section 3.1.2-3.1.3 of "R Installation and Administration" manual (but maybe I did something wrong), but I keep receiving the following error: -- initially I get -- make: ./Rpwd.exe: Command not found make[1]: ./Rpwd.exe: Command not

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

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 -- 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)

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

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

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

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

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

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]]

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",