Displaying 20 results from an estimated 20000 matches similar to: "generalized linear mixed models - how to compare?"
2002 Apr 12
1
summary: Generalized linear mixed model software
Thanks to those who responded to my inquiry about generalized linear
mixed models on R and S-plus. Before I summarize the software, I note
that there are several ways of doing statistical inference for
generalized linear mixed models:
(1)Standard maximum likelihood estimation, computationally intensive
due to intractable likelihood function
(2) Penalized quasi likelihood or similar
2005 Dec 15
1
generalized linear mixed model by ML
Dear All,
I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The
2013 Feb 22
1
How to do generalized linear mixed effects models
I want to analyze binary, multinomial, and count outcomes (as well as
the occasional continuous one) for clustered data.
The more I search the less I know, and so I'm hoping the list can
provide me some guidance about which of the many alternatives to choose.
The nlme package seemed the obvious place to start. However, it seems
to be using specifications from nls, which does non-linear
2008 Aug 19
1
R vs Stata on generalized linear mixed models: glmer and xtmelogit
Hello,
I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway).
Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not
2005 Dec 14
3
glmmADMB: Generalized Linear Mixed Models using AD Model Builder
Dear R-users,
Half a year ago we put out the R package "glmmADMB" for fitting
overdispersed count data.
http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html
Several people who used this package have requested
additional features. We now have a new version ready.
The major new feature is that glmmADMB allows Bernoulli responses
with logistic and probit links. In addition there
2004 Nov 09
1
Some questions to GLMM
Hello all R-user
I am relative new to the R-environment and also to GLMM, so please don't be
irritated if some questions don't make sense.
I am using R 2.0.0 on Windows 2000.
I investigated the occurrence of insects (count) in different parts of
different plants (plantid) and recorded as well some characteristics of the
plant parts (e.g. thickness). It is an unbalanced design with 21
2006 Apr 23
1
Comparing GLMMs and GLMs with quasi-binomial errors?
Dear All,
I am analysing a dataset on levels of herbivory in seedlings in an
experimental setup in a rainforest.
I have seven classes/categories of seedling damage/herbivory that I want to
analyse, modelling each separately.
There are twenty maternal trees, with eight groups of seedlings around each.
Each tree has a TreeID, which I use as the random effect (blocking factor).
There are two
2002 Apr 01
2
writing a package for generalized linear mixed modesl
Happy new month, everyone!
I am planning to write a NIH grant proposal to study ways to speed
Monte Carlo based maximum likelihood algorithm for hierarchical models
with a focus on generalized linear mixed models (GLM with random
effects). I thought it would be nice and also increase the chance of
funding if I could produce an R package in the process. I understand
that Prof. Pinheiro ang Bates
2007 Feb 10
2
error using user-defined link function with mixed models (LMER)
Greetings, everyone. I've been trying to analyze bird nest survival
data using generalized linear mixed models (because we documented
several consecutive nesting attempts by the same individuals; i.e.
repeated measures data) and have been unable to persuade the various
GLMM models to work with my user-defined link function. Actually,
glmmPQL seems to work, but as I want to evaluate a suite of
2011 Mar 04
1
AIC on GLMM pscl package
Hello,
I'm using GLMM on the pscl package and i'm not getting the AIC on the
summary.
The code i'm using is (example) :
mmall3 <-glmmPQL(allclues ~ cycloc + male, data=dados, family=poisson,
random=~1|animal/idfid)
and the results:
Linear mixed-effects model fit by maximum likelihood
Data: dados
AIC BIC logLik
NA NA NA
Random effects:
Formula: ~1 | animal
2006 Sep 04
1
Problem with Variance Components (and general glmm confusion)
Dear list,
I am having some problems with extracting Variance Components from a random-effects model:
I am running a simple random-effects model using lme:
model<-lme(y~1,random=~1|groupA/groupB)
which returns the output for the StdDev of the Random effects, and model AIC etc as expected.
Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the
2011 Mar 17
1
generalized mixed linear models, glmmPQL and GLMER give very different results that both do not fit the data well...
Hi,
I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER).
I wanted to fit the following model:
2004 Mar 24
2
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 worked on
this type of data can share what they learned. Thanks for your time.
simon
2006 Jan 10
1
extracting coefficients from lmer
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)$tTable
2005 Nov 30
1
likelihood ratio tests using glmmPQL
I am analysing some binary data with a mixed effects model using
glmmPQL.
I am aware that I cannot use the AIC values to help me find the minimum
adequate model so how do I perform likelihood ratio tests? I need to
fix on the minimum adequate model but I'm not sure of the proper way to
do this.
Thank you very much,
Elizabeth Boakes
Elizabeth Boakes
PhD Student
Institute of Zoology
2005 Aug 18
1
GLMM - Am I trying the impossible?
Dear all,
I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL
(MASS), I also used glm for comparison.
I am getting very different results from different functions, and I
suspect that the problem is with our dataset rather than the functions,
but I would appreciate help in deciding whether my suspicions are right.
If indeed we are attempting the wrong type of analysis, some
2004 May 13
3
GLMMs & LMEs: dispersion parameters, fixed variances, design matrices
Three related questions on LMEs and GLMMs in R:
(1) Is there a way to fix the dispersion parameter (at 1) in either glmmPQL (MASS) or GLMM (lme4)?
Note: lme does not let you fix any variances in advance (presumably because it wants to "profile out" an overall sigma^2 parameter) and glmmPQL repeatedly calls lme, so I couldn't see how glmmPQL would be able to fix the dispersion
2008 Nov 19
2
GAMM and anove.lme question
Greetings all
The help file for GAMM in mgcv indicates that the log likelihood for a
GAMM reported using
summary(my.gamm$lme) (as an example) is not correct.
However, in a past R-help post (included below), there is some indication
that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is
valid.
How can I tell if anova.lme results are meaningful (are AIC, BIC, and
logLik
2003 Jan 14
1
glmmPQL and anova
Dear R-users,
I have conducted an experiment with a 2*2*2 factorial within-subjects design. All factors are binary and the dependent measure is a frequency of successes between 0 and 4. Treating this as a normally distributed variable, I would perform a repeated-measures ANOVA as follows:
> aov(y ~ A*B*C + Error(subj/(A+B+C)))
but since the distribution of the dependent measure is clearly
2006 Mar 31
1
loglikelihood and lmer
Dear R users,
I am estimating Poisson mixed models using glmmPQL
(MASS) and lmer (lme4). We know that glmmPQL do not
provide the correct loglikelihood for such models (it
gives the loglike of a 'pseudo' or working linear
mixed model). I would like to know how the loglike is
calculated by lmer.
A minor question is: why do glmmPQL and lmer give
different degrees-of-freedom for the same