Dear All,
I would like to know if I can use the Wald test or the Score test for testing
a set of parameter in a glmm model estimated by the glmmPQL method. I am
asking this question because I don't know whether the glmmPQL's estimates are
maximum likelihood estimates or not, and also
http://www.uib.no/people/cgo022/
Re: [R] glmmPQL in 2.3.1
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[ [R] glmmPQL in 2.3.1 ]
From: Prof Brian Ripley ripley_at_stats.ox.ac.uk
Date: Tue 26 Sep 2006 - 09:58:19 GMT
On Mon
Christian Gold c.gold at magnet.at writes:
I have come across the previous communication on this list in September
(copied below) because I had received the same error message.
I understand from Brian Ripley's reply that anova should not be used
with glmmPQL because it is not an
On Mon, 25 Sep 2006, Justin Rhodes wrote:
Dear R-help,
I recently tried implementing glmmPQL in 2.3.1,
I thought *I* had implemented it: are you talking about my function in
package MASS or your own implementation?
and I discovered a few differences as compared to 2.2.1.
You appear to be
Dear R-help,
I recently tried implementing glmmPQL in 2.3.1, and I discovered a
few differences as compared to 2.2.1. I am fitting a regression with
fixed and random effects with Gamma error structure. First, 2.3.1
gives different estimates than 2.2.1, and 2.3.1, takes more
iterations to
Will this do? best, Simon
## simulate some data...
set.seed(1)
joint - c(rep(1,20),rep(2,20),rep(3,20))
time - runif(60)+1
subject - factor(rep(1:12,rep(5,12)))
mu - time*joint
joint - factor(joint)
y - rgamma(mu,mu)
## fit model
b -
I don't think there is an easy way to do that. If it were my problem,
I think I'd start by trying to the model using 'lmer' associated with
the 'lme4' package. I would then try to pass the fit to 'mcmcsamp' to
get a random sample of parameter estimates following the posterior
distribution.
Hello Folks-
Is there a way to create confidence bands with 'glmmPQL' ???
I am performing a stroke study for Northwestern University in Chicago,
Illinois. I am trying to
decide a way to best plot the model which we created with the glmmPQL function
in R. I would like
to plot my actual
I need to use the glmmPQL function for an assignment, but when I call for the
summary of the function, it gives the AIC a value of NA. How do I get R to
give me the AIC value?
--
View this message in context:
http://www.nabble.com/glmmPQL-help-tf1955675.html#a5363876
Sent from the R help forum
On Mon, 17 Jul 2006, Zython wrote:
I need to use the glmmPQL function for an assignment, but when I call for the
summary of the function, it gives the AIC a value of NA. How do I get R to
give me the AIC value?
You did!
Hint: have you read the reference of which this is part of the support
With repeated measures, you will want to use the correlation
argument in glmmPQL. For help with how to do that, you need to know
that glmmPQL calls lme repeatedly. Therefore, if you have any
trouble using it, I suggest you consult Pinheiro and Bates (2000)
Mixed-Effects Models in S
Hello R users,
I am trying to run a model with a binary response variable (nesting
success: 0 failure, 1 success) and 8 fixed terms. Nesting success was
examined in 72 cases in 34 territories (TER) during a 6 study years.
Territories are nested within 14 patches (PATCH). I want to run a model
I have not seen a reply to this post, and unfortunately, I'm not
smart enough to help you. If you would still like help from this
listserve, please provide a terse, reproducible example that someone can
in seconds copy from your email into R and presumably get the same error
message
Did you try traceback()? What do you get?
I've had good luck with problems like this in listing the function
then using debug to review while I walk throught the code line by line.
This may not be the issue here, but with family=binomial, if the
model being fit
Thanks for providing a partially reproducible example. I believe the
error message you cite came from lme. I say this, because I modified
your call to glmmPQL2 to call lme and got the following:
library(nlme)
fit.lme - lme(y ~ trt + I(week 2), random = ~ 1 | ID,
+ data
I just identified an error in my recent post on this subject: There
is a very good reason that Venables Ripley's glmmPQL did NOT include
an argument like the weights.lme in the glmmPQL. included in my
recent post: Their function calls glm first and then provides weights
computed
I'm using glmmPQL, and I still have a few problems with it.
In addition to the issue reported earlier, I'm getting the following
error and I was wondering if there's something I can do about it.
Error in logLik.reStruct(object, conLin) : Na/NaN/Inf in foreign
function call (arg 3)
...
1. The function glmmPQL is in the MASS package, as can be seen by
looking at the top line in the help file for glmmPQL. To find the
maintainer, type 'help(package=MASS)'. The results say, Maintainer:
Brian Ripley [EMAIL PROTECTED].
2. It is generally NOT appropriate to
Hi,
I'm having trouble with glmmPQL from the MASS package.
I'm trying to fit a model with a binary response variable, two fixed
and two random variables (nested), with a sample of about 200,000
data points.
Unfortunately, I'm getting an error message that is difficult to
understand without
Dear listers,
On the line of a last (unanswered) question about glmmPQL() of the
library MASS, I am still wondering if it is possible to pass a variance
structure object to the call to lme() within the functions (e.g.
weights=varPower(1), etc...). The current weights argument of glmmPQL is
Have you received a reply to this post? I haven't seen one. I don't
have an answer for you, but if you'd still like help from this list, I
suggest you prepare the simplest possible toy example that you can
conceive and send it to this list, restating your question in terms of
that
Dear listers,
glmmPQL (package MASS) is given to work by repeated call to lme. In the
classical outputs glmmPQL the Variance Structure is given as fixed
weights, Formula: ~invwt. The script shows that the function
varFixed() is used, though the place where 'invwt' is defined remains
1. Did you try anova(f1)? The documentation for glmmPQL says it
produces an object of class lme, and anova.glm might not work properly
for it. Also, did you try summary(f1), as suggested in the example in
?glmmPQL?
2. Have you considered using lmer in the lme4 package?
Hi,
My name is José María Gómez, and I am pretty new in R. Thus, I apologize
deeply if my questions are extremmely naïve.I have checked several
available books and URL's, without finding any answer.
I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide
the structure of my
Hello,
I'm running a simulation study of a multilevel model with binary
response using the binomial probit link. It is a random intercept and
random slope model. GLMMPQL and lmer fail to converge on a
*significant* portion of the *generated* datasets, while MlWin gives
reasonable estimates
Formulated more directly, are there plans for the implementation of the
crude but more robust Marginal Quasi Likelihood estimation in for
example LME?
Regards,
Roel de Jong
Roel de Jong wrote:
Hello,
I'm running a simulation study of a multilevel model with binary
response using
Message-
From: [EMAIL PROTECTED] on behalf of Roel de Jong
Sent: Tue 11/1/2005 6:05 PM
To: r-help
Cc:
Subject:Re: [R] glmmpql and lmer keep failing
Formulated more directly, are there plans for the implementation of the
crude but more robust Marginal Quasi Likelihood
Hi,
I believe we could extend our upcoming release of our
freely available AD Model Builder negative binomial mixed model
http://otter-rsch.com/admbre/admbre.html
packaqge for R to include your model. Writing the model is
simple, it is the interface with R that is a bit more
PROTECTED] on behalf of Roel de Jong
Sent: Tue 11/1/2005 6:05 PM
To: r-help
Cc:
Subject:Re: [R] glmmpql and lmer keep failing
Formulated more directly, are there plans for the implementation of the
crude but more robust Marginal Quasi Likelihood estimation in for
example LME
Have you received a reply to this question? If you still want an
answer, please submit a much simpler, self-contained example that
someone can copy from your email into R, examine possibly with other
tools and offer comments in a minute or two. Please include the output,
labeling
Hi,
I'm running a GLMM on binomial choice data. The outputs I receive are
sensible except for the degrees of freedom, which come out much larger than
expected. Can anyone advise please?
Exptl design:
Response = Choice
Fixed Factors = Position, Treatment and Sex
Random Factor = ID nested
I fit the following model using glmmPQL from MASS:
fit.glmmPQL -
glmmPQL(ifelse(class==Disease,1,0)~age+x1+x2,random=~1|subject,family=binomial)
summary(fit.glmmPQL)
The response is paired (pairing denoted by subject), although some
subjects only have one response. Also, there is a perfect
Dear R users,
I'm attempting to fit a GLM with random effects using the tweedie family
for the error structure. I'm getting the error:
iteration 1
Error in logLik.reStruct(object, conLin) :
NA/NaN/Inf in foreign function call (arg 3)
I'm running V2.1.0
I notice from searching the
Dear all,
As an update to my previous post, for anyone who is interested: Someone
has kindly told me that this error does not occur in the 2.2 pre-release
version. I've run my particular model with my data and this problem is
solved in the 2.2 pre-release.
Cheers,
Kechi Nzerem
-Original
I am looking a risk factors for disease in cattle and am interested in modelling
farm and sampling cluster as random effects (My outcome is positive or negative
at the level of the farm). I am using R version 2.0.1 on a Mac and have
identified glmmPQL as hopefully the correct function to use. I
Does someone know what is the integration method of the random effect in
glmmPQL function??
Please advise. Tnks, Mariana.
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
Dear listmembers,
I've adjusted a mixed model with glmmPQL:
nulo-glmmPQL(POS~1,random=~1|GRUPO, family=binomial, data=new)
and I've reched the following results:
Linear mixed-effects model fit by maximum likelihood
Data: new
AIC BIClogLik
53238.5 53260.74 -26616.25
Random
Hi,
I am trying to use glmmPQL package for Generalized linear mixed models.
This package works by repeated calls to lme. lme uses by default REML
method for estimation. Then, does glmmmPQL use REML too? In contrast,
how can I change it?
I have tried it, writing : method=REML, but the
On Fri, 29 Oct 2004 [EMAIL PROTECTED] wrote:
I am trying to use glmmPQL package for Generalized linear mixed models.
This package works by repeated calls to lme. lme uses by default REML
method for estimation. Then, does glmmmPQL use REML too? In contrast,
how can I change it?
I have
Running Mac OS 10.3.5 and R 2.0
Does glmmPQL use the same spatial dependence models as gls in nmle? It
does not seem to - I get the following, for example:
m2 - glmmPQL(S.Early ~ fertilized*watered, data=geodat,
family=poisson, random=~1|col)
iteration 1
iteration 2
plot(Variogram(m2,
Have you tried GLMM in lme4? Doug Bates is the primary architect
of both nlme and lme4. Therefore, I would think that a spatial
dependence model that works in nlme might also work in GLMM.
hope this helps. spencer graves
Martin Henry H. Stevens wrote:
Running Mac OS 10.3.5 and R
I have just realised that I sent this to Per only. For those interested on
the list:
-Original Message-
From: Gygax Lorenz FAT
Sent: Tuesday, September 14, 2004 4:35 PM
To: 'Per Toräng'
Subject: RE: [R] glmmPQL and random factors
Hi Per,
glmmPQL(Fruit.set~Treat1*Treat2+offset(log10
glmmPQL calls lme, and it is that which differs between S-PLUS and R.
These are optimization problems with multiple local maxima, and like any
complex statistical fitting problem you should not expect all programs to
give the same answer. The short answer is to believe all of them, and to
Professor Ripley,
These are optimization problems with multiple local maxima, and
like any complex statistical fitting problem you should not expect all
programs to give the same answer.
Examining the log-likelihoods from the anova statements, while fit1 and m1 are
similar, the maximum
Greetings R-ers,
A colleague and I have been exploring the behaviour of glmmPQL in R
and S-PLUS 6 and we appear to get different results using the same
code and the same data set, which worries us. I have checked the
behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the
results
Dear friends,
I am apologise to ask again about PQL, because I did
not receive any answer from the first question.
Is it possible to fit a crossed random effects model
using glmmPQL and, because I tyred to use this codes
Dear all,
I have two questions concerning model simplification in GlmmPQL, for for random
and fixed effects:
1. Fixed effects: I don't know if I can simply specify anova(model) and trust
the table that comes up with the p value for each variable in the fixed
effects formula. I have read that
I think you need to read the references given in ?glmmPQL and its
reference to understand what PQL actually does. If you don't know the
theory behind a statistical method, you should try to understand it before
trying to use it.
The same applies to AIC. Even if AIC were computable, do you know
I'm getting a strange error from glmmPQL. Consider the following
sample code:
set.seed(8)
N. - 1000
z - rnorm(N.)
pr.good - exp(-1e-4*exp(2+2*z))
quantile(pr.good)
DF. - data.frame(yield=rbinom(N., N., pr.good)/N.,
Offset=rep(-10, N.), nest=1:N.)
fit -
Dear R users,
Is there something like predict (..., type= 'response') for glmmPQL objects
or how would I get fitted values on the scale of the response variable for
the binomial and the poisson family?
Any pointers are appreciated.
Thanks, Lorenz
-
Lorenz Gygax, Dr. sc. nat.
Tel: +41 (0)52
when I tried the example in glmmPQL I got an error
library(nlme)
summary(glmmPQL(y ~ trt + I(week 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
Error: No slot of name reStruct for this
I thinks I understand the porblem, you can not use glmmPQL if you have
open lme4
On Wed, 28 Apr 2004, Liliana Forzani wrote:
when I tried the example in glmmPQL I got an error
library(nlme)
summary(glmmPQL(y ~ trt + I(week 2), random = ~ 1 | ID,
family =
Hi all!
I hope somebody can help me solve some doubts which must be very basic,
but I haven't been able to solve by myself.
The first one, is how to assess for overdispersion in GlmmPQL when fitting
binomial or poisson errors. The second one is whether GlmmPQL can compare
models with
glmmPQL is part of MASS, and that is support software for a book. Do look
in the book and its references
On Sat, 6 Mar 2004, O Tosas Auguet wrote:
I hope somebody can help me solve some doubts which must be very basic,
but I haven't been able to solve by myself.
The first one, is
At 13:52 2004-03-06, you wrote:
The reason for this question, is that I am trying to fit a variance
components analysis with a single random effect and no fixed effects.The
only way I know to test for the significance of the single level of random
effects is by comparing the model with a glm
Dear R users
I am using GLMMpql to analyse some nested negative binomial response data.
How do I summarise the significance of my main effects? For example, in a standard
linear mixed model (lme), I would use anova.lme to obtain an F statistic and P value
for each of my main effects: how do
Greetings-
a reviewer for a paper of mine noted an anomaly in some models I ran using
glmmPQL (from the MASS package). Specifically, the models are three-level
hierarchical probit models estimated using PQL under R. The anomaly is
that the log-likelihoods decrease (or, alternatively -2logLik
glmmPQL does not fit by maximum likelihood, and what is being quoted is
not a likelihood for the original problem.
On Fri, 21 Nov 2003, Andrew Perrin wrote:
Greetings-
a reviewer for a paper of mine noted an anomaly in some models I ran using
glmmPQL (from the MASS package). Specifically,
Sorry for my ignorance, but could you explain a little further? I'm
guessing from your response that this makes the log-likelihood that is
quoted by glmmPQL a poor measure of model fit. Are there are statistics
that would be better for reporting model fit?
thanks.
Andrew Perrin [EMAIL PROTECTED] writes:
Sorry for my ignorance, but could you explain a little further? I'm
guessing from your response that this makes the log-likelihood that is
quoted by glmmPQL a poor measure of model fit. Are there are statistics
that would be better for reporting model
On Fri, 21 Nov 2003, Douglas Bates wrote:
Andrew Perrin [EMAIL PROTECTED] writes:
Sorry for my ignorance, but could you explain a little further? I'm
guessing from your response that this makes the log-likelihood that is
quoted by glmmPQL a poor measure of model fit. Are there are
Dear listers,
First let me appologize if the same mail arrives multiple times. Recently I
had some probelms sending my e-mails to the list.
I encountered a problem when running glmmPQL procuedure doing multilevel
modeling with a dichotomous outcome.
Those are the two error messages I usually
Nothing to do with glmmPQL (as traceback() would have shown you).
I think you are looking for ?try.
On Wed, 3 Sep 2003, Andrej Kveder wrote:
Dear listers,
First let me appologize if the same mail arrives multiple times. Recently I
had some probelms sending my e-mails to the list.
I
Hi, all,
When running glmmPQL(), I keep getting errors like
Error: cannot allocate vector of size 61965 Kb
Execution halted
This is R-1.7.1. The data set consists of about 11,000 binary responses
from 16 subjects. The model is
fixed =
SonResp ~ (ordered
Elliott -
I don't know if you've had any other responses off-list yet; none
have shown up on the r-help mailing list during the day today.
I'm really NOT the most expert person to answer this, but I'll give
it a try.
Your option (1) seems entirely possible to me.
Let me do some thinking out
Hi,
In glmmPQL in the MASS library, the function uses
repeated calls to the function lme(), using ML. Does
anyone know how you can change this to REML? I know
that in lme(), the default is actually set to REML and
you can also specify this as 'method=REML' or
'method'ML' but this isn't
On 25 Jul 2003 at 16:24, Emma Tan wrote:
Hi,
In glmmPQL in the MASS library, the function uses
repeated calls to the function lme(), using ML. Does
anyone know how you can change this to REML? I know
that in lme(), the default is actually set to REML and
you can also specify this as
Is it possible to fit a crossed random effects model
using glmmPQL and, e.g.
random=pdBlocked(list(pdIdent(~a-1),pdIdent(~b-1))) as
the random part of the model? I seem to be getting
errors on R using glmmPQL in this way, although if I
try something very similar using 'lme' instead of
'glmmPQL',
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