Displaying 20 results from an estimated 8000 matches similar to: "lme4_0.6-1 uploaded"
2004 Apr 28
0
Release candidate 1 of lme4_0.6-1
Deepayan Sarkar and I have a source package of release candidate 1 of
the 0.6 series of the lme4 package available at
http://www.stat.wisc.edu/~bates/lme4_0.6-0-1.tar.gz
This package requires Matrix_0.8-6 which has been uploaded to CRAN and
should be available in a few days. A copy of the source package is
available as
http://www.stat.wisc.edu/~bates/Matrix_0.8-6.tar.gz
2004 Apr 28
0
Release candidate 1 of lme4_0.6-1
Deepayan Sarkar and I have a source package of release candidate 1 of
the 0.6 series of the lme4 package available at
http://www.stat.wisc.edu/~bates/lme4_0.6-0-1.tar.gz
This package requires Matrix_0.8-6 which has been uploaded to CRAN and
should be available in a few days. A copy of the source package is
available as
http://www.stat.wisc.edu/~bates/Matrix_0.8-6.tar.gz
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
2007 Sep 25
0
R lmer with problem of 'sd slot has negative entries'
Dear R Users,
I want to fit GLMM with lmer with binomial data and a one-way random
effects model with an overall mean and random effects. From R help,
Laplace is slower than PQL, but more accurate. When I fit my model with
Laplace method with
control = list (usePQL = FALSE)),
for most data sets it works well, but for some I get an error message
(Error in if (any(sd < 0))
2007 Sep 28
0
lmer giving negative, or no, estimated standard errors
R Users,
Emine Bayman sent this out earlier and we do not think it went through.
Appologies if it did.
We want to fit GLMM with lmer with binomial data and a one-way random
effects model (overall mean is a fixed effect and there are random
effects for each binomial).
We are using the Laplace method. We are simulating multiple data sets
and use the
Laplace method with control = list
2005 Jun 16
1
identical results with PQL and Laplace options in lmer function (package lme4)
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
2007 Jan 26
0
R crash with modified lmer code
Hi all,
I've now got a problem with some modified lmer code (function lmer1
pasted at end) - I've made only three changes to the lmer code (marked),
and I'm not really looking for comments on this function, but would like
to know why execution of the following commands that use it almost
invariably (but not quite predictably) leads to the R session
terminating.
Here's the command
2009 Aug 28
0
Help with glmer {lme4} function: how to return F or t statistics instead of z statistics?
Hi,
I'm new to R and GLMMs, and I've been unable to find the answers to my
questions by trawling through the R help archives. I'm hoping someone
here can help me.
I'm running an analysis on Seedling survival (count data=Poisson
distribution) on restoration sites, and my main interest is in
determining whether the Nutrients (N) and water absorbing polymer Gel
(G) additions to the
2009 Aug 28
1
Help with glmer {lme4) function: how to return F or t statistics instead of z statistics.
Hi,
I'm new to R and GLMMs, and I've been unable to find the answers to my
questions by trawling through the R help archives. I'm hoping someone
here can help me.
I'm running an analysis on Seedling survival (count data=Poisson
distribution) on restoration sites, and my main interest is in
determining whether the Nutrients (N) and water absorbing polymer Gel
(G) additions to the
2008 Jul 06
2
Error: cannot use PQL when using lmer
> 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
2004 Jan 30
0
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 revision, and to G?ran Brostr?m who run a
simulation.
In response to my first posting, Prof.
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
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
2005 Oct 13
3
Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works?
Do Users of Nonlinear Mixed Effects Models Know
Whether Their Software Really Works?
Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335)
analyzed
some simple clinical trials data using a logistic random effects
model. Several packages and methods MIXOR, SAS NLMIXED were employed.
They reported obtaining very different parameter estimates and
P
2006 Dec 31
0
(no subject)
> > If one compares the random effect estimates, in fact, one sees that
> > they are in the correct proportion, with the expected signs. They are
> > just approximately eight orders of magnitude too small. Is this a bug?
>
> BLUPs are essentially shrinkage estimates, where shrinkage is
> determined with magnitude of variance. Lower variance more
> shrinkage towards
2005 Sep 04
1
Question regarding lmer with binary response
Dear all, dear Prof. Bates,
my dependent variable (school absenteeism, truancy[1]) is a binary
response for which I am trying to compute an unconditional mixed
effects model. I've got observations (monday, wednesday and friday)
nested in individuals (ID2), which were nested in classes (KID2) and
schools (SID), i.e. a 4-level mixed effects model.
In short, I was trying without success. I
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
2004 Nov 23
2
Convergence problem in GLMM
Dear list members,
In re-running with GLMM() from the lme4 package a generalized-linear mixed
model that I had previously fit with glmmPQL() from MASS, I'm getting a
warning of a convergence failure, even when I set the method argument of
GLMM() to "PQL":
> bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban,
+ random=~as.factor(children) + cage +
2007 Oct 12
0
change of variance components depending on scaling of fixed effects
Dear all,
I am trying to understand the output from a binomial lmer object and why
the scaling of a fixed effect changes the variance components.
In the model p2rec is cbind(number recruits2,number recruits 1), Pop is
populations (five level factor) and ja is year (covariate running from
1955-2004). I.e. biologically I am interested to see how the proportion
of recruits from the second
2005 Apr 18
1
lmer question
Hi --
I'm using lmer for binomial data. I am trying to
replicate estimates provided by Agresti (2002,
Categorical data analysis, Wiley) using abortion data
in table 10.13 (estimates provided in table 12.3 p.
505).
I fit the same model using these three commands:
a1 <- lmer(resp ~ sex + option1 + option2 + (1|id),
data=abort,family=binomial, method = c("AGQ"))
a2 <-