[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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