CHOLMOD error in lmer-specifying nested random effects

Hi Emmanuel,

? ?Ok, point very well taken, all my values are integers, but you are
correct that they were discretized by the measurement process.

? ?In that case, is there any input on what the current state of
negative binomial distributions is? ?My understanding is that one can
use lmer as long as you specify a theta value, or use glmmADMB. ?Are
these approaches still valid? ?The conversations I found on the boards
were a couple years old.

Do you think I would I be better off using
one of the transformations you suggest?

Thanks!
Eli




--
Eli Swanson
Department of Zoology
Ecology, Evolutionary Biology, and Behavior Program
Michigan State University


On Mon, Aug 10, 2009 at 3:51 AM, Emmanuel
Charpentier<charpent at bacbuc.dyndns.org> wrote:

Dear Luca, dear list,

Sorry for buttin' in after the fact, but I wonder why you want to use a
(quasi-)Poisson model ? Unless I misunderstand your problem, your
dependent variable is, in essence, continuous, even if discretized by
the measurement process : when you record, for example, 5 minutes, that
means that the time (in mins) is some *real* value t in [4.5 5.5). Using
a model based on a discrete distribution does not make sense to me ;
ditto for "overdispersion".

Of course, your data may have a non-normal residuals' distribution
and/or heteroscedasticity. That means that you may have to use a
variable transformation such as log, or something more sophisticated
such as Box & Cox or logtrans transformation : see V&R4, for example.
MASS has a couple of functions for determination of the "optimal" Box &
Cox or logtrans parameter of the dependent variable transformation in
fixed effect models ; ISTR that the car package has something more
sophisticate, proposing transformation of the dependent and independent
variables ; ISTR also that there exist a couple of packages on CRAN
dedicated to this class of problems.

However, as far as I know, all these functions are written for
fixed-effects models. You may have to adapt them to random effects
models and/or find the optimal transformations on analogous fixed
effects models. You may also have to settle for a "reasonable"
transformation on general principles and check a posteriori that your
residuals are not (wildly)non-normal or heteroscedastic (ditto for the
random effects) ... and remember that the linear model is somewhat
robust to (small) deviations from these preconditions.

Last resort : bootstrap and permutation tests, but this is
non-trivial... Maybe a look at the "coin" package may be useful.

Hope this helps (even a bit late),

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Emmanuel Charpentier

Le vendredi 07 ao?t 2009 ? 15:44 -0400, Eli Swanson a ?crit :

Hi,
Thanks Luca, number 1 certainly makes sense to me- i only had sex
nested within cub like that as a random effect due to something i'd
read in a previous post.

Your answer number 2 was really helpful, I did not know that. ?I do in
fact already have a unique ID for both each individual mom and each
individual cub. ?Recoding this did not instantly solve my problem, but
mean centering and standardizing instantly fixed it. ?My new model:
ger<-lmer(Nurse.min~Mom.mins+Mom.rank+Sex+(1|Cub)+(1|Mom),data=data,family=quasipoisson)

Is mean centering and standardizing totally valid in this case? ?I
dont know why it wouldnt be, it just seems, i dont know, a very simple
fix for an ongoing problem ive been having.

I have tried to read all the old posts about overdispersion, and in
fact have calculated other values that have been suggested, as Doug
said that sigma probably wasn't valid, ie.
ger at deviance["pwrss"]/ger at dims["n"]
? ?pwrss
6.933783

But, I can't find anywhere what the meaning is of deviance/n . ?Is it
an estimate of the scale parameter? ?In that case my data is clearly
overdispersed, but I don't have a meaningful scale on which to think
about this that I know is accurate. ?I may have missed some of the
info however, so I'll go back and read through the old posts again.

Thanks again!
Eli

On Fri, Aug 7, 2009 at 2:31 PM, Luca Borger<lborger at uoguelph.ca> wrote:

Hello,

a quick reply to get this started (you may get more expert feedback by other members of the list).

1. I'd expect sex to have an effect on nursing time (longer time for males?). Sex is definitively a fixed effect (see also the mailing list archives).

2. Specify a unique ID code for each cub and for each mother, then lmer will work out the nesting without the need to specify it explicitly (see posts by Doug Bates on this issue).


Thus I'd try something like:


ger<-lmer(Nurse.min~Mom.min+Mom.rank+Sex+(1|Cub)+(1|Mom),data=data,family=poisson,control=list(msVerbose=T))


If you don't have multiple cubs for each mother it might be that a model with only cubID might be more appropriate (but am guessing here). If the warning persists try also centrering/standardizing Mom.min.

Re the overdispersion issue have a look at the mailing list archive (e.g. postings by Ben Bolker).


HTH



Cheers,


Luca


----- Messaggio originale -----
Da: "Eli Swanson" <eliswanson at gmail.com>
A: r-sig-mixed-models at r-project.org
Inviato: Venerd?, 7 agosto 2009 18:17:06 GMT +01:00 Amsterdam/Berlino/Berna/Roma/Stoccolma/Vienna
Oggetto: [R-sig-ME] CHOLMOD error in lmer-specifying nested random effects

Hi all,



I'm working on an analyzing some data right now that's causing me some
difficulties. ?I have 2 questions, and would really appreciate any
advice that people can offer. ?I don't post data because there are
over 2000 cases. ?sessionInfo() is posted at the end of my post.



The data was collected during focal animal surveys (FAS). ?The
response variable is the number of minutes a cub was observed nursing
(Nurse.min). ?The fixed predictors are 1) the number of minutes the
mother was present during the FAS (Mom.min), 2) the mother's social
rank (Mom.rank), and 3)sex of cub(Sex). ?The random effects are 1)
Identity of mom, and 2) identity of cub, as there are a large number
of observations for every mother, and most of the cubs. ?Cub is nested
within mom, and when I include sex in the model, my understanding is
that I have to nest it within cub.

Question the first:
I try to create the following model, leaving out sex for the moment,
because my understanding is that with only two cases, sex complicates
matters:

ger<-lmer(Nurse.min~Mom.min+Mom.rank+(1|Mom:Cub)+(1|Mom),data=data,family=poisson,control=list(msVerbose=T))

Is this model correctly specified? ?I receive a CHOLMOD warning, as follows:

? 11918.870: 0.289063 0.172748 -0.0419576 0.0575048 0.00979307
CHOLMOD warning: 7u?e_
Error in mer_finalize(ans) :
?Cholmod error `not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432

When I try to include Sex as a fixed effect, I'm even less sure I'm
specifying the model correctly but my model looks like this:

ger<-lmer(Nurse.min~Mom.min+Mom.rank+Sex+(1|Mom:Cub:Sex)+(1|Mom),data=data,family=poisson,control=list(msVerbose=T))

And my output:
?0: ? ? 11926.160: 0.289063 0.172748 0.172336 0.0572882 0.00311534
-0.244655 -0.683584
CHOLMOD warning: 7u?e_
Error in mer_finalize(ans) :
?Cholmod error `not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432

The model works if I specify it as follows, but I can't get it to work
with sex, and I think I'm specifying the random effects incorrectly
here:

ger<-lmer(Nurse.min~Mom.min+Mom.rank+(1|Mom:Cub),data=data,family=poisson)

summary(ger)
Generalized linear mixed model fit by the Laplace approximation
Formula: Nurse.min ~ Mom.min + Mom.rank + (1 | Mom:Cub)
? Data: data
? AIC ? BIC logLik deviance
?11381 11404 ?-5687 ? ?11373
Random effects:
?Groups ?Name ? ? ? ?Variance Std.Dev.
?Mom:Cub (Intercept) 2.1313 ? 1.4599
Number of obs: 2234, groups: Mom:Cub, 70

Fixed effects:
? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.4669098 ?0.1962970 ? -7.47 7.84e-14 ***
Mom.min ? ? ?0.0688668 ?0.0007541 ? 91.32 ?< 2e-16 ***
Mom.rank ? ? 0.0676121 ?0.0069510 ? ?9.73 ?< 2e-16 ***
---
Signif. codes: ?0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Correlation of Fixed Effects:
? ? ? ? (Intr) Mom.mn
Mom.min ?-0.134
Mom.rank -0.332 ?0.115



So, am I specifying these models correctly? ?What could be the
problem? ?I only have 1 observation for some of the cubs, and I
thought this might be the problem, but when I remove these cubs, the
error remains.







?I'm not sure if it makes a difference, but I do have slightly
overdispersed data (its 0-inflated i think, but not hugely so), hence
my second question:

When I use the quasipoisson family to estimate the model like so
(specifying the model the only way i could get it to work, even though
this may be incorrect)
:
?ger<-lmer(Nurse.min~Mom.min+Mom.rank+(1|Mom:Cub),data=data,family=quasipoisson,control=list(msVerbose=T))

Then, by my understanding, estimate the degree of overdispersion:

lme4:::sigma(ger)
?sigmaML
1.233926

How badly overdispersed is this? ?I tried using MCMCglmm with a
"zipoisson", but its giving me strange results (in fact, estimates for
the fixed effects that appear to have an opposite sign of those I find
with lmer). ?As I'm not an expert in Bayesian methods, I assume I'm
specifying something wrong there and would prefer to stick with lmer
for now if possible. ?Can I just continue to use quasipoisson? ?I know
that the standard errors are inflated, but if im not worried about
that does this mean that parameters are correctly estimated?





sessionInfo()
R version 2.9.0 (2009-04-17)
i386-pc-mingw32

locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

attached base packages:
[1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base

other attached packages:
[1] lme4_0.999375-28 ? Matrix_0.999375-24 lattice_0.17-22

loaded via a namespace (and not attached):
[1] grid_2.9.0





i would appreciate any help that people can offer,
Thank you very much,
Eli



--
Eli Swanson
Department of Zoology
Ecology, Evolutionary Biology, and Behavior Program
Michigan State University

--
Eli Swanson
Department of Zoology
Ecology, Evolutionary Biology, and Behavior Program
Michigan State University