[R] errors with lme4
gunter.berton at gene.com
Thu Nov 24 15:27:04 CET 2011
Ben et. al:
Shouldn't this thread be taken to R-sig-mixed-models ?
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),
>> > 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.
> 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
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Genentech Nonclinical Biostatistics
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