Displaying 20 results from an estimated 8000 matches similar to: "Representation of lmer objects will change soon"
2007 Apr 03
1
lmer, CHOLMOD warning: matrix not positive definite
Hi,
I am getting a warning message when I am fitting a generalized linear
mixed model (m1.2 below).
CHOLMOD warning: matrix not positive definite
Error in objective(.par, ...) : Cholmod error `matrix not positive
definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614
Any idea?
Thanks for your help,
Reza
> sessionInfo()
R version 2.4.1 (2006-12-18)
i386-pc-mingw32
2007 Jan 23
0
New lmer: How to recode random effect ?
Dear all,
I ran the following model without any problem previously to the update of
lme4:
fm2<-lmer(data=NGud,family=poisson,
seed~hab*seedtray
+(1|site)+(1|site:hab))
I have 25 sites, 2 habitats ("hab") per site, 8 seedtrays per habitat (4 as
control, and 4 as treatment), and I'm interested in comparing the number of
seed in seedtrays as a function of the treatment and its
2008 Feb 14
1
Cholmod error `matrix not positive definite'
Dear R-users,
I'm new to R, so my apologies if this question doesn't make sense.
I've tried the following model in lmer, and it works perfectly:
model<-lmer(aphids~densroot+zone+(1|zone/site), family=quasipoisson)
But if I try the exact same model with a different variable, totmas, the model looks as follows:
model<-lmer(aphids~totmas+zone+(1|zone/site), family=quasipoisson)
2009 Dec 24
1
Multiple CHOLMOD errors when attempting poisson glmm
Hello,
I have been attempting to run a poisson glmm using lme4 for some time now
and have had a lot of trouble. I would say 9 times out of 10 I receive the
following warning:
CHOLMOD warning: %h
Error in mer_finalize(ans) :
Cholmod error `not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432
My data are counts of microbe colony forming units (CFUs) collected from
2012 Feb 06
1
lmer with spatial and temporal random factors, not nested
Hi, I am new to this list.
I have a question regarding including both spatial and temporal random
factors in lmer. These two are not nested, and an example of model I
try to fit is
model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year),
family=poisson, REML=FALSE),
where
richness = integer
Y & Treatment = factor
Canopy & Veg_cm = numerical, continous
2007 Mar 09
0
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
other people advising them to use the Tweedie distribution. I'm still
not sure if this
2005 Dec 26
4
lme X lmer results
Hi,
this is not a new doubt, but is a doubt that I cant find a good response.
Look this output:
> m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3)
> anova(m.lme)
numDF denDF F-value p-value
(Intercept) 1 860 210.2457 <.0001
Xvar 1 2 1.2352 0.3821
> summary(m.lme)
Linear mixed-effects model fit by REML
Data: NULL
AIC BIC
2006 Jun 09
1
binomial lmer and fixed effects
Hi Folks,
I think I have searched exhaustively, including, of course R-help (D.
Bates, S. Graves, and others) and but I remain uncertain about
testing fixed effects with lmer(..., family=binomial).
I gather that mcmcsamp does not work with Do we rely exclusively on z
values of model parameters, or could we use anova() with likelihood
ratios, AIC and BIC, with (or without)
2010 Jul 01
0
Cholmod warning when fitting a poisson GLMM
Hi,
I am getting a warning message when I am fitting a generalized mixed model (mod_2) and I don't understand why because when I add just an interaction factor the model works perfectly (mod_1).
Does anyone know what it happpens ?
Thanks,
Aïda
> mod_1<-lmer(sur15~soeviv15_4plus+frviv15_4plus+frat_15death+dad_class_new+soeviv15_4plus:dad_class_new +frviv15_4plus:dad_class_new+
2006 Apr 20
1
lmer{lme4}, poisson family and residuals
Hello,
I’m trying to fit the following model:
Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during
a given dive)
Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird),
STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the
interaction SUCCESSMN*STUDYDAY
Random effect: BIRD (the bird id, as each bird is performing several dives)
2007 Apr 24
1
Matrix: how to re-use the symbolic Cholesky factorization?
I have been playing around with sparse matrices in the Matrix
package, in particularly with the Cholesky factorization of matrices
of class dsCMatrix. And BTW, what a fantastic package.
My problem is that I have to carry out repeated Cholesky
factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n,
where the Q's have the same non-zero pattern. I know in this case one
does
2011 Dec 29
1
Cholesky update/downdate
Dear R-devel members,
I am looking for a fast Cholesky update/downdate. The matrix A being
symmetric positive definite (n, n) and factorized as
A = L %*% t(L), the goal is to factor the new matrix A +- C %*% t(C)
where C is (n, r). For instance, C is 1-column when adding/removing an
observation in a linear regression. Of special interest is the case
where A is sparse.
Looking at the
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
2005 Dec 09
1
lmer for 3-way random anova
I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4.
I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge.
Here is my lme code:
dat <-
2006 Oct 03
1
New versions of Matrix and lme4 packages for R-2.4.0
Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on
CRAN for use with R version 2.4.0 or later.
Purpose of the packages:
The Matrix package provides S4 classes and methods for sparse
and dense matrices. The lme4 package provides functions for fitting
and assessing linear or generalized linear mixed effects models (also
called multilevel models). Like the Matrix package,
2006 Oct 03
1
New versions of Matrix and lme4 packages for R-2.4.0
Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on
CRAN for use with R version 2.4.0 or later.
Purpose of the packages:
The Matrix package provides S4 classes and methods for sparse
and dense matrices. The lme4 package provides functions for fitting
and assessing linear or generalized linear mixed effects models (also
called multilevel models). Like the Matrix package,
2006 Jan 10
2
lmer(): nested and non-nested factors in logistic regression
Thanks to some help by Doug Bates (and the updated version of the Matrix
package), I've refined my question about fitting nested and non-nested
factors in lmer(). I can get it to work in linear regression but it
crashes in logistic regression. Here's my example:
# set up the predictors
n.age <- 4
n.edu <- 4
n.rep <- 100
n.state <- 50
n <- n.age*n.edu*n.rep
age.id
2011 Jun 01
1
different results from lme() and lmer()
Hello R-help,
I'm studying an example in the R book.?
The data file is available from the link below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt
Could you explain Why the results from lme() and lmer() are different in the following case? In other examples, I can get the same results using the two functions, but not here...?
Thank you.Miya
library(lme4)library(nlme)#
2015 Mar 19
0
RFC: Matrix package: Matrix products (%*%, crossprod, tcrossprod) involving "nsparseMatrix" aka sparse pattern matrices
Hi Martin
I got stung by this last week.
glmnet produces a coefficient matrix of class ?dgCMatrix?
If a predictor matrix was created using sparseMatrix as follows,
one gets unexpected results, as this simple example shows.
My fix was easy (I always convert the predictor matrix to class ?dgCMatrix? now)
Trevor
> y=Matrix(diag(4))
> y
4 x 4 diagonal matrix of class "ddiMatrix"
2015 Mar 20
0
RFC: Matrix package: Matrix products (%*%, crossprod, tcrossprod) involving "nsparseMatrix" aka sparse pattern matrices
Hi Martin,
package arules heavily relies on ngCMatrix and uses multiplication and
addition for logical operations. I think it makes sense that in a mixed
operation with one dgCMatrix and one ngCMatrix the ngCMatrix should be
"promoted" to a dgCMatrix.
The current behavior of %*% and friends is in deed confusing:
> m <- matrix(sample(c(0,1), 5*5, replace=TRUE), nrow=5)
>