similar to: AIC in lmer when using PQL

I am analysing some binary data with a mixed effects model using glmmPQL. I am aware that I cannot use the AIC values to help me find the minimum adequate model so how do I perform likelihood ratio tests? I need to fix on the minimum adequate model but I'm not sure of the proper way to do this. Thank you very much, Elizabeth Boakes Elizabeth Boakes PhD Student Institute of Zoology

Hello all, Is AIC calculated incorrectly in lmer? It appears as though it uses AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is output from one of many models I have tried: Generalized linear mixed model fit using PQL Formula: cswa ~ pcov.ess1k + (1 | year) Data: ptct50.5 Family: poisson(log link) AIC BIC logLik deviance 224.8466 219.19 -114.4233 228.8466

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

> 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

Can you please tell me the minimum system spec for R on a windows machine? Thanks _______________________________________ Matthew Longley IT Administrator Zoological Society of London Regent's Park London NW1 4RY Charity No: 208728 Tel: 020 74496412 Fax: 020 74496294 email: Matthew.Longley at zsl.org WWW: www.zsl.org _______________________________________

Dear R community, I have two questions regarding fitting GLMM using maximum likelihood method. The first one arises from trying repeat an analysis in the Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the

Hello, I was wondering if anyone knew of an R function/R code to plot bivariate (3 dimensional) partial dependence plots in random forests (randomForest package). It is apparently possible using the rgl package (http://esapubs.org/archive/ecol/E088/173/appendix-C.htm) or there may be a more direct function such as the pairplot() in MART (multiple additive regression trees)? Many

Hi, I have been trying to plot density plots using the example on: http://addictedtor.free.fr/graphiques/graphcode.php?graph=139 I used to use this function, but I cannot get any old code or even the example to work. library("geneplotter") require("RColorBrewer") x1 <- matrix(rnorm(1e4), ncol=2) x2 <- matrix(rnorm(1e4, mean=3, sd=1.5), ncol=2) x <-

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

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

It appears that the degrees of freedom reported by logLik changed between R 1.4.1 and R 1.5.1. Is this true? Detail: > I have been using the lm and logLik functions in R to develop code using > version 1.4.1. When I run it on version 1.5.1, I'm getting different > degrees of freedom with the logLik function. Version 1.5.1 seems to give > one extra degree of freedom than

Douglas Bates wrote: >From 0.62 onward you should not have to create a symbolic link in >/usr/local/bin. It should be that you can run > cd $RSOURCE > ./configure --prefix=/usr/local > make install >and you will end up with the R script installed in /usr/local/bin and >all the files needed to run R in /usr/local/lib/R. > >Can you tell what the prefix is set to after

Hi- I am trying to fit a log function to my data, with the ultimate goal of finding the second derivative of the function. However, I am stalled on the first step of fitting a curve. When I use the following code: FG2.model<-(nls((CO2~log(a*Time)+b), start=setNames(coef(lm(CO2 ~ log(Time), data=FG2)), c("a", "b")),data=FG2)) I get the following error: Error in

Hi there I'm trying to construct a model of mortality risk in 2D space that requires numerical integration of a hazard function, for which I'm using the integrate function. I'm occasionally encountering parameter combinations that cause integrate to terminate with error "Error in integrate... the integral is probably divergent", which I'm not sure how to interpret. The

Hey, Can anyone answer this question. I am working with really large datasets and most of the programs I have been running take quite some time. I heard that R may be faster in Unix. I sthis true and if so can anyone reccomend which system and requirements may allow things to go faster for? Thanks!! Elizabeth Lawson --------------------------------- [[alternative

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

Hey, I am estiamting a partially linear model y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets. Has anyone heard of methods to test if the betas are significant or to address model fit? Thanks for any thoughts or comments. Elizabeth Lawson __________________________________________________ [[alternative HTML version deleted]]

Hello all, I would like to perform SVM-RFE (Guyon et al. 2002) in R and have only found one implementation of this algorithm. The function belongs to the MCRestimate package but when I try to use it I encounter a problem - the function appears to be missing a required package or other function that I simply cannot find available anywhere. Here is my session info followed by a simple example

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between