Error en mer_finalize(ans) : Downdated X'X is not positive definite, 1. What is wrong with my model?

Just looking quickly, is seems strange that for the last two species
(Vu and Vv), _all_ coefficients are to NA? If you try without these
two species, does it work better?

After that, I'm not specialist enough to traceback why for these two
species there are only NAs --- may be only one observation only for
each of them? or associated to another fBud_type not used in the
analysis for some reason? --- if unused levels have been removed...

Hope this helps,

Best regards,

On Tue, Mar 05, 2013 at 08:29:45AM +0100, PALACIO BLASCO, SARA wrote:
? Dear Ben
?
? This is what the summary(M_bud_type0) says. As expected, there are
? plenty of NAs in the interactions between (uncrossed) levels of the
? interaction between the nested factors (fBud_type:Species):
?
?
? Call:
? glm(formula = Dead ~ Treatment * fBud_type + fBud_type:Species,
?     family = binomial, data = species)
?
? Deviance Residuals:
?     Min       1Q   Median       3Q      Max
? -5.8281  -0.2220   0.0703   0.3323   2.3882
?
? Coefficients: (18 not defined because of singularities)
?                       Estimate Std. Error z value Pr(>|z|)
? (Intercept)           -7.77657    0.85126  -9.135  < 2e-16 ***
? Treatment             -0.31190    0.03200  -9.747  < 2e-16 ***
? fBud_typena            0.19449    1.38718   0.140  0.88850
? fBud_typesc            5.36751    0.91869   5.843 5.14e-09 ***
? Treatment:fBud_typena -0.05374    0.05892  -0.912  0.36172
? Treatment:fBud_typesc  0.06949    0.03837   1.811  0.07012 .
? fBud_typehy:SpeciesEc  3.96261    0.52793   7.506 6.10e-14 ***
? fBud_typena:SpeciesEc       NA         NA      NA       NA
? fBud_typesc:SpeciesEc       NA         NA      NA       NA
? fBud_typehy:SpeciesEn  3.01308    0.48926   6.158 7.35e-10 ***
? fBud_typena:SpeciesEn       NA         NA      NA       NA
? fBud_typesc:SpeciesEn       NA         NA      NA       NA
? fBud_typehy:SpeciesLp       NA         NA      NA       NA
? fBud_typena:SpeciesLp  1.21835    0.49753   2.449  0.01433 *
? fBud_typesc:SpeciesLp       NA         NA      NA       NA
? fBud_typehy:SpeciesRf       NA         NA      NA       NA
? fBud_typena:SpeciesRf       NA         NA      NA       NA
? fBud_typesc:SpeciesRf  0.14214    0.39921   0.356  0.72180
? fBud_typehy:SpeciesRh       NA         NA      NA       NA
? fBud_typena:SpeciesRh       NA         NA      NA       NA
? fBud_typesc:SpeciesRh -1.18370    0.37535  -3.154  0.00161 **
? fBud_typehy:SpeciesVm       NA         NA      NA       NA
? fBud_typena:SpeciesVm       NA         NA      NA       NA
? fBud_typesc:SpeciesVm -1.09756    0.37513  -2.926  0.00344 **
? fBud_typehy:SpeciesVu       NA         NA      NA       NA
? fBud_typena:SpeciesVu       NA         NA      NA       NA
? fBud_typesc:SpeciesVu       NA         NA      NA       NA
? fBud_typehy:SpeciesVv       NA         NA      NA       NA
? fBud_typena:SpeciesVv       NA         NA      NA       NA
? fBud_typesc:SpeciesVv       NA         NA      NA       NA
? ---
? Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
?
? (Dispersion parameter for binomial family taken to be 1)
?
?     Null deviance: 1797.06  on 1385  degrees of freedom
? Residual deviance:  736.43  on 1374  degrees of freedom
? AIC: 760.43
?
? Number of Fisher Scoring iterations: 7
?
?
? ### If I try your second suggestion and run the model in glm, the
? number of NAs goes down, but there are still a few:
?
? Call:
? glm(formula = Dead ~ Treatment * fBud_type + budspecies, family = binomial,
?     data = species)
?
? Deviance Residuals:
?     Min       1Q   Median       3Q      Max
? -5.8281  -0.2220   0.0703   0.3323   2.3882
?
? Coefficients: (2 not defined because of singularities)
?                       Estimate Std. Error z value Pr(>|z|)
? (Intercept)           -7.77657    0.85126  -9.135  < 2e-16 ***
? Treatment             -0.31190    0.03200  -9.747  < 2e-16 ***
? fBud_typena            0.19449    1.38718   0.140  0.88850
? fBud_typesc            5.36751    0.91869   5.843 5.14e-09 ***
? budspecieshy.Ec        3.96261    0.52793   7.506 6.10e-14 ***
? budspecieshy.En        3.01308    0.48926   6.158 7.35e-10 ***
? budspeciesna.Lp        1.21835    0.49753   2.449  0.01433 *
? budspeciessc.Rf        0.14214    0.39921   0.356  0.72180
? budspeciessc.Rh       -1.18370    0.37535  -3.154  0.00161 **
? budspeciessc.Vm       -1.09756    0.37513  -2.926  0.00344 **
? budspeciessc.Vu             NA         NA      NA       NA
? budspecieshy.Vv             NA         NA      NA       NA
? Treatment:fBud_typena -0.05374    0.05892  -0.912  0.36172
? Treatment:fBud_typesc  0.06949    0.03837   1.811  0.07012 .
? ---
? Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
?
? (Dispersion parameter for binomial family taken to be 1)
?
?     Null deviance: 1797.06  on 1385  degrees of freedom
? Residual deviance:  736.43  on 1374  degrees of freedom
? AIC: 760.43
?
? Number of Fisher Scoring iterations: 7
?
? I also don't know how to include the new factor with droplevels in
? the glmer model... should this new factor replace the nested one?
?
? Cheers,
?
? Sara
?
?
?
?
? Quoting Ben Bolker <bbolker at gmail.com>:
?
?
? >
? >  Did you try to fit
? >
? >M_bud_type0 = glm(Dead~Treatment* fBud_type +
? >   fBud_type:Species, family=binomial, data=species)
? >
? >as suggested in the FAQ to see where the rank-deficiencies are
? >(i.e. are there NA-valued coefficients?)
? >
? >  It's not immediately obvious to me that the fBud_type:Species
? >interaction should be causing trouble, because lme4 internally
? >drops unused levels of factors. You could *try*
? >
? >species$budspecies <- with(species,
? >   droplevels(interaction(fBud_type,Species)))
? >
? >just to check that, but I don't think it will help.
? >
? >  Using Species as a random effect does *not* mean you "will not be able
? >to know its effect" -- you just won't be able to test hypotheses about
? >differences between particular species/combinations of species.
? >You can still use ranef() to get a value (technically not an "estimate")
? >for the conditional mode of each species.
? >
? >
? >>
? >>Quoting Ben Bolker <bbolker at gmail.com>:
? >>
? >>>PALACIO BLASCO, SARA <s.palacio at ...> writes:
? >>>
? >>>[snip]
? >>>
? >>>>I am trying to run the following model in glmer:
? >>>>
? >>>>> M_bud_type1=glmer(Dead~Treatment* fBud_type + fBud_type:Species +
? >>>>> (1|fRep), family=binomial, data=species)
? >>>>
? >>>>where:
? >>>>- Dead is a binomial response variable
? >>>>- fBud_type is a fixed factor with 3 levels
? >>>>- Species is a fixed factor with 9 levels nested within fBud_type and
? >>>>- fRep is a random factor with 27 levels nested within Species
? >>>>
? >>>>I have 1386 observations.
? >>>>The error message I receive reads:
? >>>>
? >>>>Error en mer_finalize(ans) : Downdated X'X is not positive definite, 1.
? >>>>
? >>>
? >>>  Did you already read the http://glmm.wikidot.com/faq#errors section?
? >>>
? >>>  It sounds like all your predictors are categorical (although we don't
? >>>know about Treatment), so centering isn't really as important/as
? >>>practical
? >>>an option (you can use sum-to-zero contrasts, but it probably won't
? >>>make a big difference).
? >>>
? >>>  Ben Bolker
? >>>
? >>>_______________________________________________
? >>>R-sig-mixed-models at r-project.org mailing list
? >>>https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
? >>
? >>
? >>
?
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? https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

--
                                Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

Page WWW: http://emmanuel.curis.online.fr/index.html