[R-sig-ME] Error en mer_finalize(ans) : Downdated X'X is not positive definite, 1. What is wrong with my model?
PALACIO BLASCO, SARA
s.palacio at ipe.csic.es
Tue Mar 5 08:29:45 CET 2013
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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