Re: [R] GLMM: MEEM error due to dichotomous variables

At 14:31 07/08/2007, Elva Robinson wrote: I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family=binomial) I get the error: iteration 1 Error in

Re: [R] GLMM: MEEM error due to dichotomous variables

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family= binomial) I get the error: iteration 1 Error in MEEM(object, conLin,

[R] GLMM: MEEM error due to dichotomous variables

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family=binomial) I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) :

[R] GLMM for unbalanced data

Hi friends, I need some help regarding generalized linear mixed model of unbalanced data. 1. Is their any package for applying Monte-Carlo Newton-Raphson (MCNR) or Monte-Carlo EM (MCEM) to estimate fixed and random effects? 2. My data is unbalanced (groups having unequal number of

[R] GLMM plots

Hi R-users, I would like to plot the effects of one of the predictor variables on the response variable in the GLMM I ran with the lme4 package. Usually when doing a multivariate analysis I would obtain the residuals of the model without the predictor variable of interest (x1) and then plot

[R] GLMM in lme4 and Tweedie dist.

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to

Re: [R] GLMM: measure for significance of random variable?

1. To evalute the significance of the random variable (a random effect?) using 'lmer', have you considered fitting models with and without that effect, as in the example with 'example(lmer)'? 2. Regarding 'predict.lmer', I tried the following: predict(fm1) Error in

[R] GLMM: measure for significance of random variable?

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable.

Re: [R] Glmm for multiple outcomes

Does the following help: n.subjects - 3 J - 4 K - 5 n.ijk - rep(2, each=n.subjects*J*K) x - rep(1:K, n.subjects, each=J) subj - factor(rep(1:n.subjects, each=K*J)) sa.subject - 1 sb.subject - 1 set.seed(2) a.subj - rep(sa.subject*rnorm(n.subjects), each=K*J) b.subj -

[R] Glmm for multiple outcomes

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a

[R] GLMM - Am I trying the impossible?

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

Re: [R] GLMM - Am I trying the impossible?

It is not supported to call anova() on a glmmPQL fit. For the glmmPQL fit you show, you have very large parameter estimates for a logistic and have partial separation (as you comment on for the control group): in that case PQL is not a reasonable method. Try fit - glm(dead ~ Parasite *

[R] glmm with negative binomial

Hello, At present, can generalized linear mixed models with negative binomial distribution and estimating the shape parameter be fit using R? I am aware of glm.nb but am wondering about incorporation of mixed effects. Thanks in advance, Brian Aukema

Re: [R] glmm with negative binomial

On Wed, 16 Feb 2005, Brian Aukema wrote: At present, can generalized linear mixed models with negative binomial distribution and estimating the shape parameter be fit using R? I am aware of glm.nb but am wondering about incorporation of mixed effects. I am not aware of anyone who knows how to

Re: [R] glmm multinomial?

On Fri, 14 Jan 2005, ecatchpole wrote: I'm looking for something like Brian Ripley's glmmPQL that will handle multinomial data. Does anyone know of anything? It's a lot more complicated conceptually. A multinomial model has K-1 linear predictors which should probably have a correlated joint

[R] glmm multinomial?

I'm looking for something like Brian Ripley's glmmPQL that will handle multinomial data. Does anyone know of anything? Thanks, Ted. -- Dr E.A. Catchpole Visiting Fellow Univ of New South Wales at ADFA, Canberra, Australia and University of Kent, Canterbury, England - [EMAIL PROTECTED] -

RE: [R] GLMM and crossed effects

I am analysing data with a dependent variable of insect counts, a fixed effect of site and two random effects, day, which is the same set of 10 days for each site, and then transect, which is nested within site (5 each). And what exactly are you interested in? Just the differences between

[R] GLMM and crossed effects

Hi again. Perhaps a simple question this time I am analysing data with a dependent variable of insect counts, a fixed effect of site and two random effects, day, which is the same set of 10 days for each site, and then transect, which is nested within site (5 each). I am trying to fit

[R] GLMM

Hi all, Could someone please tell me if we have to group data in the units with a command such factor() or groupedData() before using the functions glmmPQL or GLMM. I didn't do that and at first my results seem OK, but I'd like to solve this doubt. Thanks in advance, Alex

Re: [R] GLMM

On Wednesday 08 December 2004 07:35, Alex wrote: Hi all, Could someone please tell me if we have to group data in the units with a command such factor() or groupedData() before using the functions glmmPQL or GLMM. I didn't do that and at first my results seem OK, but I'd like to solve this

[R] GLMM

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

Re: [R] GLMM

On Mon, 1 Nov 2004 [EMAIL PROTECTED] wrote: I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). You haven't really attributed the functions you use to particular packages. If this is glmm() from Jim Lindsey's packages then it was our

[R] ?glmm with correlation structure?

Hi folks, I am looking for the package that will allow me to do a generalized (poisson) linear mixed model with spatial correlation structure. If gls() in nlme does this, I don't understand how to implement different families. If glmmPQL() in MASS does this, I don't understand what correlation

[R] glmm

I am trying to use R. My question is if R can calculate a random effect probit model {e.g. glmm} but including sampling weights. I am desperately looking for a random effect model but wanted to use it on survey data. Thanks for an answer: Niko Speybroeck.

Re: [R] glmm

/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm - Original Message - From: Niko Speybroeck [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Thursday, September 02, 2004 10:42 AM Subject: [R] glmm I am trying to use R. My

RE: [R] glmm

or glmmPQL, in which you use sampling (probability) weights? Thanks in advance. Niko Van: Dimitris Rizopoulos [mailto:[EMAIL PROTECTED] Verzonden: do 2/09/2004 10:51 Aan: Niko Speybroeck CC: [EMAIL PROTECTED] Onderwerp: Re: [R] glmm Hi Niko, look at functions

Re: [R] glmm

PROTECTED] To: [EMAIL PROTECTED] Sent: Thursday, September 02, 2004 10:42 AM Subject: [R] glmm I am trying to use R. My question is if R can calculate a random effect probit model {e.g. glmm} but including sampling weights. I am desperately looking for a random effect model but wanted

RE: [R] glmm

Aan: Dimitris Rizopoulos CC: Niko Speybroeck; [EMAIL PROTECTED] Onderwerp: Re: [R] glmm On Thu, 2 Sep 2004, Dimitris Rizopoulos wrote: Hi Niko, look at functions `GLMM' (package: lme4) and `glmmPQL' (package: MASS). Yes, but they don't take sampling weights. We had this discussion a while

RE: [R] glmm

[mailto:[EMAIL PROTECTED] Sent: Thursday, September 02, 2004 10:28 AM To: Thomas Lumley; Dimitris Rizopoulos Cc: [EMAIL PROTECTED] Subject: RE: [R] glmm Thanks a lot for you answer Thomas. Do you have a reference which supports this solution? Can you give an example of a weight that depends

RE: [R] glmm

On Thu, 2 Sep 2004, Niko Speybroeck wrote: Thanks a lot for you answer Thomas. Do you have a reference which supports this solution? Can you give an example of a weight that depends on variables that shouldn't be in the model? Robert Baskin has answered some of this. Additional points 1)

Re: [R] GLMM

Bossarte, Robert wrote: I am trying to use the LME package to run a multilevel logistic model using the following code: --- Model1 = GLMM(WEAP ~ TSRAT2 , random = ~1 | GROUP ,

[R] glmm in R

Dear all, I'm new to R and to the list, and I have a problem which I'm unable to solve. Consider the following simple simulated data frame:

[R] GLMM

I am trying to use the LME package to run a multilevel logistic model using the following code: --- Model1 = GLMM(WEAP ~ TSRAT2 , random = ~1 | GROUP , family = binomial,

Re: [R] GLMM(..., family=binomial(link=cloglog))?

On Tue, Jun 08, 2004 at 08:32:24AM -0700, Spencer Graves wrote: Hi, Doug: Thanks. I'll try the things you suggests. The observed proportions ranged from roughly 0.2 to 0.8 in 100 binomial random samples where sigma is at most 0.05. Jim Lindsey's glmm does Gauss-Hermite

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Hi, Go"ran: (B (BThanks for the analysis. Unfortunately, it still leaves me with 2 (Bproblems. First, I'm dealing with extremely small defect rates involving (Bthousands and millions of Bernoulli trials, so creating bigDF would (Brequire computers with much more memory and processing speed

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Spencer Graves [EMAIL PROTECTED] writes: Data: DF log-likelihood: -55.8861 Random effects: Groups NameVariance Std.Dev. smpl (Intercept) 1.7500e-12 1.3229e-06 Estimated scale (compare to 1) 3.280753 Fixed effects: Estimate Std. Error z value Pr(|z|)

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Spencer Graves [EMAIL PROTECTED] writes: Another GLMM/glmm problem: I simulate rbinom(N, 100, pz), where logit(pz) = rnorm(N). I'd like to estimate the mean and standard deviation of logit(pz). I've tried GLMM{lme4}, glmmPQL{MASS}, and glmm{Jim Lindsey's

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Hi, Peter: Thanks. The help page on GLMM in lme4 0.6-1 2004/05/31 mentions GLMM(formula, family, data, random, ...) with additional arguments subset, method, na.action, control, and model, x logicals. I may try reading the source code. On the other hand, my need is sufficiently

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Hi, Doug: Thanks. I'll try the things you suggests. The observed proportions ranged from roughly 0.2 to 0.8 in 100 binomial random samples where sigma is at most 0.05. Jim Lindsey's glmm does Gauss-Hermite quadrature, but I don't know if it bothers with the adaptive step. With it,

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Thanks, Andy, Doug, Deepayan. I now have lme4 0.6-1 2004/05/31 installed for R 1.9.1 alpha under Windows 2000. When I tried the example below, GLMM ran, but the print method reported an error: Generalized Linear Mixed Model Fixed: immun ~ 1 Data: guImmun log-likelihood: -1440.052

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Spencer Graves [EMAIL PROTECTED] writes: Thanks, Andy, Doug, Deepayan. I now have lme4 0.6-1 2004/05/31 installed for R 1.9.1 alpha under Windows 2000. When I tried the example below, GLMM ran, but the print method reported an error: Generalized Linear Mixed Model As I write this I

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Hi, Doug: Thanks. I ran 'tst - getMethod(show, summary.ssclme)', then edited tst as you indicated and ran 'setMethod(show, summary.ssclme, tst)', and it fixed the problem. Best Wishes, spencer graves Douglas Bates wrote: Spencer Graves [EMAIL PROTECTED] writes:

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Another GLMM/glmm problem: I simulate rbinom(N, 100, pz), where logit(pz) = rnorm(N). I'd like to estimate the mean and standard deviation of logit(pz). I've tried GLMM{lme4}, glmmPQL{MASS}, and glmm{Jim Lindsey's repeated}. In several replicates of this for N = 10, 100, 500, etc., my

[R] GLMM(..., family=binomial(link=cloglog))?

I'm having trouble using binomial(link=cloglog) with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 - GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need

Re: [R] GLMM(..., family=binomial(link=cloglog))?

On Tuesday 01 June 2004 17:25, Spencer Graves wrote: I'm having trouble using binomial(link=cloglog) with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 - GLMM(immun~1, data=guImmun, family=binomial,

Re: [R] GLMM(..., family=binomial(link=cloglog))?

Hi, Deepayan: Thanks for your reply. How can I get the new release in a Windows 2000 format, downloaded and properly installed? I tried update.packages, but the new version has not yet migrated within reach of the default update.packages function call. I tried downloading lme4

RE: [R] GLMM(..., family=binomial(link=cloglog))?

As Doug said in his announcement, version 0.6-1 of lme4 (which is pure R code) depends on the Matrix package, version 0.8-7. AFAICT the Windows binary on CRAN for Matrix is version 0.8-6. Not sure if that will work with the current lme4... It's probably best to wait for the right versions of

Re: [R] glmm?

Spencer Graves [EMAIL PROTECTED] writes: Is there an easy way to get confidence intervals from glmm in Jim Lindsey's library(repeated)? Consider the following slight modification of an example from the help page: df - data.frame(r=rbinom(10,10,0.5), n=rep(10,10),

[R] glmm?

Is there an easy way to get confidence intervals from glmm in Jim Lindsey's library(repeated)? Consider the following slight modification of an example from the help page: df - data.frame(r=rbinom(10,10,0.5), n=rep(10,10), x=c(rep(0,5), + rep(1,5)), nest=1:10) fit -

[R] GLMM error in ..1?

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n - 10 N - 1000 DF - data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit - GLMM(yield~1, random=~1|nest, family=binomial, data=DF,

Re: [R] GLMM error in ..1?

Is this the new experimental lme4 (version 0.6-x) ? If so, this is due to an error in our use of method dispatch. It has been fixed in the development version, and there should be a new release in a few days. On Friday 28 May 2004 19:32, Spencer Graves wrote: I'm trying to use GLMM in

[R] GLMM error message

Hi, I wrote a few days ago about an error message I'm getting when I use GLMM from lme4 to do random effects modelling. When I add random effects, I get the following error message: Error in EMsteps-(`*tmp*`, value = control) : invalid source matrix. (I wanted to note that I've only just

Re: [R] GLMM error message

On Sunday 16 May 2004 16:03, Matt Loveland wrote: Hi, I wrote a few days ago about an error message I'm getting when I use GLMM from lme4 to do random effects modelling. When I add random effects, I get the following error message: Error in EMsteps-(`*tmp*`, value = control) : invalid

Re: [R] GLMM error message

Matt Loveland [EMAIL PROTECTED] writes: Hi, I wrote a few days ago about an error message I'm getting when I use GLMM from lme4 to do random effects modelling. When I add random effects, I get the following error message: Error in EMsteps-(`*tmp*`, value = control) : invalid source

[R] GLMM question

Hi I'm using lme4 to do random effects modelling. I keep getting the following error message: Error in EMsteps-(*tmp*', value = control) : invalid source matrix I get the error when I include more than one random effect in the model, sometime I'm able to get two. I've looked into

Re: [R] GLMM question

On Wednesday 12 May 2004 16:48, Matt Loveland wrote: Hi I'm using lme4 to do random effects modelling. I keep getting the following error message: Error in EMsteps-(*tmp*', value = control) : invalid source matrix I get the error when I include more than one random effect in

Re: [R] GLMM with random slope

Liliana, At 23:20 2004-05-05, Liliana Forzani wrote: I was using GLMM to fit a model (binomial) with random slope. When I put random~1|ID I got the results (random intercept) I assume that you used random = ~ 1 | ID. ^^^ when I put random~time|ID I

[R] GLMM with random slope

I was using GLMM to fit a model (binomial) with random slope. When I put random~1|ID I got the results (random intercept) when I put random~time|ID I got an error Thanks. Liliana __ [EMAIL PROTECTED] mailing list

[R] GLMM

Dear all, I'm working with count data following over-dispersed poisson distribution and have to work with mixed-models on them (like proc GENMOD on SAS sys.). I'm still not to sure about what function to use. It seems to me that a glmmPQL will do the job I want, but I'll be glad if people who

Re: [R] GLMM

At 11:17 2004-03-24, you wrote: I'm working with count data following over-dispersed poisson distribution and have to work with mixed-models on them (like proc GENMOD on SAS sys.). I'm still not to sure about what function to use. This is confusing: Proc GENMOD fits generalized linear models

[R] GLMM (lme4) vs. glmmPQL output (summary with lme4 revised)

This is a summary and extension of the thread GLMM (lme4) vs. glmmPQL output http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the

Re: [R] GLMM (lme4) vs. glmmPQL output

Goran, from my reply to a message from Douglas Bates; is quoted from a mail by DG. I believe the distinction is explained in the lme4 documentation but, in any case, the standard errors and the approximate log-likelihood for glmmPQL are from the lme model that is the last step in the

Re: [R] GLMM (lme4) vs. glmmPQL output

Dieter Menne [EMAIL PROTECTED] writes: I have compared glmmPQL, glmmML, geese and GLMM, results and code see below. I am aware that glmmPQL uses another method to handle the problem, and geese (geepack) has considerable different assumptions, but the results are very similar. On the other

Re: [R] GLMM (lme4) vs. glmmPQL output

Although it has not been stated nor credited, this is very close to an example in MASS4 (there seems a difference in coding). Both the dataset and much of the alternative analyses are from the work of my student James McBroom (and other students have contributed). MASS4 does contain

Re: [R] GLMM (lme4) vs. glmmPQL output

On Fri, Jan 09, 2004 at 12:26:21PM -0600, Douglas Bates wrote: I believe the distinction is explained in the lme4 documentation but, in any case, the standard errors and the approximate log-likelihood for glmmPQL are from the lme model that is the last step in the optimization. The

Re: [R] GLMM (lme4) vs. glmmPQL output

I believe the distinction is explained in the lme4 documentation but, in any case, the standard errors and the approximate log-likelihood for glmmPQL are from the lme model that is the last step in the optimization. The corresponding quantities from GLMM are from another approximation that should

[R] GLMM (lme4) vs. glmmPQL output

Dear List, As I understand, GLMM (in experimental lme4) and glmmPQL (MASS) do similar things using somewhat different methods. Trying both, I get the same coefficients, but markedly different std. errors and p-values. Any help in understanding the models tested by both procedures? Dieter Menne

Re: [R] glmm and overall goodness of fit

Hi, exist in R any glmm function that have any tools for test for overall goodness of fit? True measures of overall goodness of fit may be difficult to formulate for such mixed models. Relative goodness of fit (as compared to glm) is available through the AIC produced by my glmm

[R] glmm and overall goodness of fit

Hi, exist in R any glmm function that have any tools for test for overall goodness of fit? Thanks Ronaldo -- O papel da impressora é sempre mais forte na parte picotada. -- | // | \\ [***] | ( õ õ ) [Ronaldo Reis Júnior] | V