similar to: GLMM (lme4) vs. glmmPQL output (summary with lme4 revised)

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

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

I apologise for writing again about the problem with using stepAIC + multinom, but I think the reason why I had it in the first place is perhaps there may be a bug in either stepAIC or multinom. Just to repeat the problem, I have 126 variables and 99 cases. I don't know if the large number of variables could be the problem. Of couse the reason for doing a stepwise method is to reduce this

Hello I try to find probability values of some predictor combinations using logistic reg. in response level. Firstly I found coefficients by glm function. Then I followed two ways to get probability values: 1- probility <- exp(X0+bX1+cX2+...)/((1+exp(X0+bX1+cX2+...)) 2- probility <- predict(glm.obj,type="resp") Should have these two given same result ? if so, I did not have. Why

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

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

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

Hi, i have many variables for in example 4weeks and want to do aggregations, like mean standard , deviation etc.. With mean it works but how i can calculate the standard deviation for the 4weeks and for every ID. many thanks & regards, christian week1 <- grep("(_PRO_001)",names(dmx3),perl=T) week1table <- subset(dmx3,select=c(ID,week1)) week2 <-

Odette Gaston <odette.gaston <at> gmail.com> writes: > > Dear all, > > I have a problem with accessing class attributes. > I was unable to solve this > yet, but someone may know how to solve it. My best guess at your immediate problem (doing things by hand) is that you're not using the whole vector. From your example: Delta <- c(m1 = 0, m2 = 1.8, m3 =

Hello, I am a new user of R, and I am not able to read/scan external files. I am working in a Linux environment. I have read through the R FAQ and documents and have not been successful using the recommendations. Below are several scripts I've used and the error messages. . I've cc'd Brandon Whitcher because it was recommended in another FAQ. I read that this is a bug that

I have a data frame that looks like this: > gctablechromonly[1:5,] refseq geometry gccontent X60_origin X60_terminus length kingdom 1 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 2 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 3 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 4 NC_009484 cir 0.6799

I'm trying to use the glmmML package on a Windows machine. When I try to install the package, I get the message: > {pkg <- select.list(sort(.packages(all.available = TRUE))) + if(nchar(pkg)) library(pkg, character.only=TRUE)} Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

I have the following data frame: > algaedata = year DIV cellsperml 2001 BAC 72.808 2001 CHL 3.273 2002 BAC 14.002 2002 CYA 220.896 2002 UNI 464.699 2003 BAP 0 2003 BAC 1.782 2004 CYA 315.799 2005 UNI 222.532 2005 BAP 0.2 2005 CYA 163.627 2005 BAC 324.949 2006 CHL 1.636 2006 BAC 199.145 2007 CHL 19.635 2007 CYA 134.174 2007 BAC 485.405 2007 CHL 11.454 2007 CYA 104.721 ...which makes a fine

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

Hello, I am closely following the RTAQ documentation in order to load my dataset into R, however I get this warning when running the convert function in the following way: convert(from="2010-11-01", to="2010-11-01",datasource=datasource, datadestination=datadestination,trades=T,quotes=T,ticker="BAC",dir=T, extention="csv", header=T,

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family =

Hello! Just would like to make sure I am not doing something wrong. I am running an OLS regression. I have several subgroups in the data set (locations) - and in each location I have weekly data for 2 years - on my DV and on all predictors. Looks like this: location week DV Predictor1 Predictor 2 location1 week1 xxx xxxxxxx xxxxxxxxx location1 week2 xxx xxxxxxx xxxxxxxxx . .

Hi, I was recently misfortunate enough to have to use regular expressions to sort out some data in R. I'm working on a data file which contains taxonomical data of bacteria in hierarchical order. A sample of this file can be generated using: tax.data <- read.table(header=F, con <- textConnection(' G9SS7BA01D15EC Bacteria(100) Cyanobacteria(84) unclassified G9SS7BA01C9UIR