My apologies for posting yet another message.
I have tried several options to change the initial values of the model
parameters for my GLMM poisson since it does seem to get stuck at just two
evaluations (see below). Following your suggestion, I discarded the value
of the deviance (the first one), and wrote start=rep(1,14) or
start=c(1,1,1,1,1,etc).  Following the example in the lmer help page, I
have also tried giving parameter name= parameter value but this option is
invalid. The only example in the lmer help page seems to apply only to a
different case:

## nonlinear mixed models
(nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~
Asym|Tree,Orange, start = c(Asym = 200, xmid = 725, scal = 350)))

However, I do not think that I manage to ever change the initial parameter
values, and would need some extra help on how to do it (if this is what I
should actually do). All my attempts have always yielded  the same two
lines of the verbose output that are posted below. I should add that there
is no problem in fitting the same model with just one of the random effects
(either one), which suggests to me that the problem may lie in their being
two uncorrelated random effects (but this is only my crude guess). I could
of course provide my data if needed.
Thanks in advance,
Cheers
Pablo

On 31 October 2012 15:56, Douglas Bates <bates@stat.wisc.edu> wrote:

> On Wed, Oct 31, 2012 at 12:40 PM, Pablo Inchausti
> <pablo.inchausti.f@gmail.com> wrote:
> > Douglas,
> > Many thanks for the speedy response.
> > When I tried:
> >
> m5=with(DF3,lmer(n.spp~log(hs.arrastre+1)+zona+prof+SSTA+zona*log(hs.arrastre+1)+zona*SSTA+zona*prof+
> > (1|año)+(1|grado),weight=n.lances,family="poisson", verbose=T))
> >   0:     1150.8846: 0.396545 0.388196  2.44097 0.0156075 -0.221623
> > -0.0492026 -0.000271677 0.0909335 -0.0106744 -0.00526810 -0.161031
> > -0.0626493 -0.000706752 -0.000404302
> >   1:     1150.8846: 0.396545 0.388196  2.44097 0.0156075 -0.221623
> > -0.0492026 -0.000271677 0.0909335 -0.0106744 -0.00526810 -0.161031
> > -0.0626493 -0.000706752 -0.000404302
> > Error in mer_finalize(ans) : Downdated X'X is not positive definite, 1.
> > In addition: Warning messages:
> > 1: In mer_finalize(ans) :
> >   Cholmod warning 'not positive definite' at
> > file:../Cholesky/t_cholmod_rowfac.c, line 432
> > 2: In mer_finalize(ans) :
> >   Cholmod warning 'not positive definite' at
> > file:../Cholesky/t_cholmod_rowfac.c, line 432
>
> The value before the ':' is the objective function, in this case the
> deviance.  The parameters are listed after the deviance.  If I recall
> correctly they are in the order of the standard deviations of the
> random effects (both around 0.39) and then the fixed-effects
> parameters.
>
> > I cannot tell to the correspondence of the parameter values in the first
> two
> > iteractions with the parameters in the model. Guessing that the
> 1150.8846 (a
> > huge value?) corresponds to the intercept, I suppose that I could provide
> > other initial values? I added: start=rep(0.5,15) since there are 15
> columns
> > in the failed output above, but I could not change the initial values.
> > Any suggestions?
> >
> > Thanks in advance
> > Cheers
> > Pablo
> >
> >
> > On 31 October 2012 15:29, Douglas Bates <bates@stat.wisc.edu> wrote:
> >>
> >> On Wed, Oct 31, 2012 at 12:00 PM, Pablo Inchausti
> >> <pablo.inchausti.f@gmail.com> wrote:
> >> > Dear all,
> >> > I am experiencing problems when trying to fit the following GLMM with
> >> > library lme4:
> >> >
> >> >
> m5=with(DF3,lmer(n.spp~log(hs.arrastre+1)+zona+prof+SSTA+zona*log(hs.arrastre+1)+zona*SSTA+zona*prof+
> >> > (1|año) +(1|grado),weight=n.lances,family="poisson"))
> >> >
> >> > The response variable are counts (number of species), and there are
> four
> >> > explanatory variables with fixed effects: three continuous variables
> >> > (SSTA,
> >> > arrastre, prof) and one is a three-level factor (zona). There are two
> >> > crossed (uncorrelated random effects): (1|año) with año having 24
> >> > levels;
> >> > the other random effect (1|grado) has by coincidence also 24 levels.
> The
> >> > DF
> >> > has 419 rows and no missing values.
> >> > The numbers of cases for each of the random effects are unbalanced but
> >> > not
> >> > zero:
> >> >> with(DF2, table(año))
> >> > 1984 1985 1987 1988 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999
> >> > 2000
> >> > 2001 2002 2003 2005 2006 2007 2008 2009 2010
> >> >    3       4       3       29    12     23      8        8      40
> 31
> >> >   9      12   12         6     14   32       7      8       28      31
> >> > 35     26    26     12
> >> >> with(DF2, table(grado))
> >> > 333 342 343 344 345 352 353 354 355 356 363 364 365 366 374 375 376
> 385
> >> > 386
> >> > 387 395 396 397 398
> >> >   7    35    22  18    9    22   31    25  18  17    28   32  10     6
> >> > 22  26   11   13   18  13      9     9   12   6
> >> >> with(DF2, table(grado, año)) is very sparse, which is why I am trying
> >> >> to fit uncorrelated random effects.
> >> >
> >> > I have used the very same dataset, with the same model structure) for
> >> > other analyses with response variables having Gaussian distribution
> and have
> >> > had no problems at all.
> >> >
> >> > I invariably obtain the error message:
> >> > Error in mer_finalize(ans) : Downdated X'X is not positive definite,
> 1.
> >> > In addition: Warning messages:
> >> > 1: In mer_finalize(ans) :
> >> >   Cholmod warning 'not positive definite' at
> >> > file:../Cholesky/t_cholmod_rowfac.c, line 432
> >> > 2: In mer_finalize(ans) :
> >> >   Cholmod warning 'not positive definite' at
> >> > file:../Cholesky/t_cholmod_rowfac.c, line 432
> >>
> >> > I have absolutely no idea how to interpret this message or resolve the
> >> > error.
> >>
> >> The error messages are coming from the sparse matrix library CHOLMOD.
> >> One of the disadvantages of adopting such code in the Matrix package
> >> for R is that it wants to provide its own error messages and without
> >> considerable effort at rewriting it, which would need to be repeated
> >> every time a new version is released, these messages show up as R
> >> error messages.
> >>
> >> The best thing to do is to trace the progress of the iterations in the
> >> optimization of the parameter estimates.  You will likely find that
> >> the fixed-effects parameters or the random effects are heading to
> >> extreme values.
> >>
> >> > After trying to fit the same model with library glmmAMDB, I obtain
> >> > another
> >> > (equally cryptical) error message:
> >> > Error in II[, ii] = II[, ii] + REmat$codes[[i]] :
> >> >   number of items to replace is not a multiple of replacement length
> >> > In addition: Warning messages:
> >> > 1: In glmmadmb(n.spp ~ log(hs.arrastre + 1) + zona + prof + SSTA +  :
> >> >   NAs removed in constructing fixed-effect model frame: you should
> >> > probably
> >> > remove them manually, e.g. with na.omit()
> >> > 2: In II[, ii] + REmat$codes[[i]] :
> >> >   longer object length is not a multiple of shorter object length:
> >> >
> >> > I have tried many of the control options of either library (i.e.
> >> > changing
> >> > the # of iterations, of evaluations, etc) but the error messages
> remain
> >> > unchanged.
> >> >
> >> > I would really appreciate if any of you could possible spare some
> >> > minutes
> >> > and suggest some alternative.
> >> > Many thanks in advance,
> >> > Cheers
> >> >
> >> >         [[alternative HTML version deleted]]
> >> >
> >> >
> >> > _______________________________________________
> >> > R-sig-mixed-models@r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >> >
> >
> >
>

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