Problem with computing gr and false convergence
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On 06/15/2011 01:14 PM, Iker Vaquero Alba wrote:
Thank you very much once again. The code for the graphs is really useful. However, I have to be a bit annoying again. When I have this model:
> summary(s.g1.15)
Generalized linear mixed model fit by the Laplace approximation Formula: nhatch ~ mod + briventral + brithr + year + (1 | site/pair)
[snip]
and I try to simplify, again, the variable "briventral", I get the same error message as usual:
> s.g1.21<-update(s.g1.15,~.-briventral)
Mensajes de aviso perdidos 1: In mer_finalize(ans) : Cholmod warning 'not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 2: In mer_finalize(ans) : false convergence (8) I cannot understand why, I mean, if the models with more parameters in it worked, why this one, which is much less suspicious of being overfitted, doesn't? Obviously it looks like there's a big problem with the variable itself. Any ideas about what could it be, or posible solutions? Sorry again. Thank you very much
I agree that this is a big pain.
I was able to get it to work by dropping the 'site' random effect
(which has zero variance anyway, so doesn't affect the results much).
I completely agree that from a distance none of this stuff seems to
have much logic to it. I believe (but this belief is admittedly
faith-based, and not evidence-based in this case) that *if* one were to
spend a lot of time digging in the gory details of the shapes of the
objective functions and the precise workings of the optimization
algorithm that one could understand why this particular problem is being
problematic in this particular way. It will be a *little* bit easier
if/when lme4a has the capability to do likelihood profiles on all
parameters of GLMMs (not yet). In the meantime, it's back to trial and
error based on reasonable ways of simplifying the problem.
I have a bigger question about your modeling strategy, though, which
is: why are you trying to remove this parameter? In order to test its
significance against the full model? In general, if you're going to
follow the approach of estimating significance of individual
predictors/parameters by LRT, you should *not* reduce the model from the
fullest version that works first (i.e., don't throw out non-significant
predictors before testing for significance among the remaining
predictors). Something like drop1(g1,test="Chisq") or
drop1(g2,test="Chisq") [see below for def. of g2] or drop1(g2) [which
gives AIC rather than Chisq/LRT significance values] is the right way to
proceed if you want to test hypotheses about the predictors ...
g2 <-
glmer(nhatch~sex+mod+briventral+brithr+tlength+cond+year+(1|site:pair),
data=X,family=poisson)
s.g2.15<-update(g2,~.-sex-tlength-cond)
update(s.g2.15,~.-briventral,verbose=TRUE)
good luck
Ben Bolker
Iker ------------------------------------------------------------------------ *De:* Ben Bolker <bbolker at gmail.com> *Para:* Iker Vaquero Alba <karraspito at yahoo.es> *CC:* r-sig-mixed-models at r-project.org *Enviado:* mi?,15 junio, 2011 16:14 *Asunto:* Re: [R-sig-ME] Problem with computing gr and false convergence On 06/14/2011 06:45 PM, Iker Vaquero Alba wrote:
Thank you so much for that, Ben.
I?m trying to do a split plot simplification with that model, but I still get this message with briventral:
s.g1.5<-update(g1,~.-briventral) Mensajes de aviso perdidos 1: In mer_finalize(ans) : Cholmod warning 'not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 2: In mer_finalize(ans) : false convergence (8)
Do you think I should try to centre and scale that variable as well?
Thank you.
Iker.
Oddly enough I am able to fit the model better when the continuous
variables are *not* centered and scaled. I can't explain this
particularly well, but weird things sometimes happen when you have a
model that is on the edge of being overfitted (which this one still is
-- 12 parameters plus two nested random effects for 94 data points in a
fairly unbalanced design). Some code is also included below to take a
quick look at the distribution of the data. There are some patterns,
but the continuous variables don't seem to be doing too much ...
X <- read.table("nfledge-nhatch insects 2009-2010.txt",header=TRUE)
X <- transform(X,site=factor(site),pair=factor(pair),year=factor(year))
X <- transform(X,ctlength=scale(tlength),
cbriventral=scale(briventral))
f1 <- nhatch~(sex+mod+briventral+brithr+tlength+cond)^2-sex:mod
ncol(model.matrix(f1,data=X)) ## 41 parameters!
f2 <- nhatch~sex+mod+briventral+brithr+tlength+cond+year
ncol(model.matrix(f2,data=X)) ## 12 parameters (still dicey)
with(X,table(site,pair,year))
library(lme4)
X <-
na.omit(subset(X,select=c(site,pair,year,sex,mod,nhatch,briventral,brithr,tlength,cond)))
g1 <-
glmer(nhatch~sex+mod+briventral+brithr+tlength+cond+year+(1|site/pair),
data=X,family=poisson)
s.g1.5<-update(g1,~.-briventral)
library(ggplot2)
X2 <- melt(X,id.var=1:6)
ggplot(X2,
aes(x=value,y=nhatch,colour=mod,shape=sex))+
geom_point()+facet_grid(year~variable,scale="free_x")
ggplot(X,
aes(x=mod,y=nhatch,colour=mod))+geom_boxplot()+facet_grid(year~sex)
dd <- drop1(g1)
------------------------------------------------------------------------ *De:* Ben Bolker <bbolker at gmail.com <mailto:bbolker at gmail.com>> *Para:* Iker Vaquero Alba <karraspito at yahoo.es
<mailto:karraspito at yahoo.es>>
*CC:* r-sig-mixed-models at r-project.org
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*Enviado:* mar,14 junio, 2011 23:02 *Asunto:* Re: [R-sig-ME] Problem with computing gr and false convergence
On 06/14/2011 04:12 PM, Iker Vaquero Alba wrote:
Thank you very much. Data is attached.
Unfortunately, looking at your data makes it very clear that you will have a lot of trouble fitting this model: maybe this isn't the complete data set ... ?
* there are only two years, which makes it nearly impossible to handle year as a random effect
* you have a total of 94 observations in the data set, and your model involves 41 (!!) fixed effect parameter. There is just no way you can fit this many parameters (even neglecting the random effects).
I had some success with this model by dropping the interactions and fitting only the main effects. The site-level variance is estimated as zero, but there is some pair*site variance (and a significant difference between years, at least as inferred from a Wald Z test)
X <- read.table("nfledge-nhatch insects 2009-2010.txt",header=TRUE)
X <- transform(X,site=factor(site),pair=factor(pair),year=factor(year))
X <- transform(X,ctlength=scale(tlength,center=TRUE))
f1 <- nhatch~(sex+mod+briventral+brithr+tlength+cond)^2-sex:mod
ncol(model.matrix(f1,data=X)) ## 41 parameters!
f2 <- nhatch~sex+mod+briventral+brithr+tlength+cond+year
ncol(model.matrix(f2,data=X)) ## 12 parameters
with(X,table(site,pair,year))
library(lme4)
g1 <-
glmer(nhatch~sex+mod+briventral+brithr+ctlength+cond+year+(1|site/pair),
data=X,family=poisson,na.action=na.omit)
To center and scale continuous variables, I've tried standardizing them, as someone suggested in some post, and I have succesfully done before. But when fitting the model with standardized variables, I get: "Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels" Any ideas? Thank you very much! ------------------------------------------------------------------------ *De:* Ben Bolker <bbolker at gmail.com <mailto:bbolker at gmail.com>
<mailto:bbolker at gmail.com <mailto:bbolker at gmail.com>>>
*Para:* r-sig-mixed-models at r-project.org
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*Enviado:* mar,14 junio, 2011 21:09 *Asunto:* Re: [R-sig-ME] Problem with computing gr and false convergence On 06/14/2011 02:57 PM, Iker Vaquero Alba wrote:
Dear R-users: I am fitting a model with quite many terms, and I'm having a lot of problems. Either I get: "In mer_finalize(ans) : gr cannot be computed at initial par (65)" or false convergence problems, when the model is a little bit simpler. I attach the most complex one with "verbose=TRUE" to see if you can help me detectt where the problem is:
hatchcoltailmodel1<-lmer(nhatch~sex+mod+briventral+brithr+tlength+cond+briventral:brithr+briventral:tlength+briventral:cond+briventral:sex+briventral:mod+
brithr:tlength+brithr:cond+brithr:sex+brithr:mod+tlength:cond+tlength:sex+tlength:mod+
cond:sex+cond:mod+(1|site/pair)+(1|year),family=poisson,na.action=na.omit,verbose=TRUE)
0: nan: 1.06217 0.584705 0.261488 -15.3440 -0.757161 16.9953 3.85760 1.53215 1.30924 5.14768 -0.0259057 0.0675839 0.172804 9.56398 0.000488889 -0.000119170 0.0156963 0.00445958 -0.109915 -0.0119783 -0.0113610 -0.00132653 0.00594852 -2.29204e-05 -0.0678690 0.00288760 0.321050 -0.0107392 0.0195566 -0.00242138 -0.0538382 -0.0866086 -0.00632440 -0.168793 -0.0123981 0.00379237 -0.00313671 -0.0228579 0.504940 nan -0.722186 -1.31712 -0.618912 -1.25492 Mensajes de aviso perdidos In mer_finalize(ans) : gr cannot be computed at initial par (65)
Very hard to say without a reproducible example. Can you post the data somewhere? How big is your data set? If any of your variables are continuous, consider centering and scaling them (e.g. using scale()) Other than that, I only have a couple of coding style suggestions. 1. I *think* but am not sure that your very long model above is equivalent to (briventral+brithr+tlength+cond+sex+mod)^2-mod:sex (all main effects + all pairwise interactions except mod:sex) but you should of course check that (and the order might not be the same as the model you have above). 2. It is best to use the 'data' argument to specify a data frame (rather than attach()ing or having the variables floating around in the workspace)
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