[R] errors with lme4

[Bert Gunter]

Ben et. al:

Shouldn't this thread be taken to R-sig-mixed-models ?

Cheers,
Bert

On Thu, Nov 24, 2011 at 6:14 AM, Ben Bolker <bbolker at gmail.com> wrote:
> Alessio Unisi <franceschi6 <at> unisi.it> writes:
>
>>
>> Dear R-users,
>> i need help for this topic!
>>
>> I'm trying to determine if the reproductive success
>> (0=fail, 1=success) of a species of bird
>> is related to a list of covariates.
>>
>> These are the covariates:
>> §    elev: elevation of nest (meters)
>> §    seadist: distance from the sea (meters)
>> §    meanterranova: records of temperature
>> §    minpengS1: records of temperature
>> §    wchillpengS1: records of temperature
>> §    minpengS2: records of temperature
>> §    wchillpengS2: records of temperature
>> §    nnd: nearest neighbour distance
>> §    npd: nearest penguin distance
>> §    eggs: numbers of eggs
>> §    lay: laying date (julian calendar)
>> §    hatch: hatching date (julian calendar)
>> I have some NAs in the data.
>>
>> I want to test the model with all the variable then i want to remove
>> some, but the ideal model:
>> GLM.1 <-lmer(fledgesucc ~ +lay +hatch +elev +seadist +nnd +npd
>> +meanterranova +minpengS1 +minpengS2 +wchillpengS1 +wchillpengS2
>> +(1|territory), family=binomial(logit), data=fledge)
>>
>> doesn't work because of these errors:
>> 'Warning message: In mer_finalize(ans) : gr cannot be computed at
>> initial par (65)'.
>> "matrix is not symmetric [1,2]"
>>
>> If i delete one or more of the T records (i.e. minpengS2 +wchillpengS2)
>> the model works...below and example:
>>
>>  GLM.16 <-lmer(fledgesucc ~ lay +hatch +elev +seadist +nnd +npd
>> +meanterranova +minpengS1 +(1|territory), family=binomial(logit),
>> data=fledge)
>>
>>  > summary(GLM.16)
>> Generalized linear mixed model fit by the Laplace approximation
>> Formula: fledgesucc ~ lay + hatch + elev + seadist + nnd + npd +
>> meanterranova +      minpengS1 + (1 | territory)
>>    Data: fledge
>>  AIC   BIC logLik deviance
>>  174 204.2    -77      154
>> Random effects:
>>  Groups    Name        Variance Std.Dev.
>>  territory (Intercept) 0.54308  0.73694
>> Number of obs: 152, groups: territory, 96
>>
>
>  I can't prove it, but I strongly suspect that some of your
> coefficients are perfectly multicollinear.  Try running your
> model as a regular GLM:
>
> g1 <- glm(fledgesucc ~ +lay +hatch +elev +seadist +nnd +npd
>  +meanterranova +minpengS1 +minpengS2 +wchillpengS1 +wchillpengS2
>
> and see if some of the coefficients are NA.
>
> coef(g1)
>
> lm() and glm() can handle this sort of "rank-deficient" or
> multicollinear input, (g)lmer can't, as of now.
>
> I suspect that you may be overfitting your model anyway:
> you should aim for not more than 10 observations per parameter
> (in your case, since all your predictors appear to be continuous,
> How many observations are left after na.omit(fledge)?
>
>  What is the difference between your 'S1' and 'S2' temperature
> records?
>
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>



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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