[R-sig-eco] Error in solve.default(as.matrix(fit$hessian))

[Alvinda Nisma Yusniar]

Thanks for the tip!

Pada tanggal Sel, 10 Des 2019 pukul 23.23 Christopher David Desjardins <
cddesjardins using gmail.com> menulis:

> Alvinda,
> Do you have a predictor that has only a 0 or > 0 for the Y and is a
> factor? That error message is telling you have perfect discrimination. You
> will need to drop that predictor or use a different method.
> Chris
>
> On Mon, Dec 9, 2019 at 8:16 PM Alvinda Nisma Yusniar <
> alvindanismay using gmail.com> wrote:
>
>> *Dear list,*
>>
>>
>>
>> *I'm trying to construct a zero-inflated poisson model but I get  greeted
>> by an error. I haven't had the chance to try my dataset on different OSs
>> or
>> different R version, but I did mange to try that for the "cod parasite"
>> data from Zuur et al book (Mixed effect models...) and I get a similar
>> error (models with different formulas may or may not go through, depending
>> on R  version and the system). This is the error I get for the cod data.*
>>
>>
>>
>> M3 <- zeroinfl(Y ~ X1+X2+X3+X4+X5+X6+X7 | ## Predictor for the Poisson
>> process
>>
>> +                  X1+X2+X3+X4+X5+X6+X7, ## Predictor for the Bernoulli
>> process;
>>
>> +                dist = 'poisson',
>>
>> +                data = DB)
>>
>> Error in solve.default(as.matrix(fit$hessian)) :
>>
>>   system is computationally singular: reciprocal condition number =
>> 1.12074e-52
>>
>> In addition: Warning message:
>>
>> glm.fit: fitted probabilities numerically 0 or 1 occurred
>>
>>
>>
>> *I get the same error on my data:*
>>
>>
>>
>> frm <- formula(Y ~ X1+X2+X3+X4+X5+X6+X7| X1+X2+X3+X4+X5+X6+X7)
>>
>> nb <- zeroinfl(frm, dist="negbin", link="logit", data=DB)
>>
>> Error in solve.default(as.matrix(fit$hessian)) :
>>
>>   system is computationally singular: reciprocal condition number =
>> 2.80889e-26
>>
>> In addition: Warning message:
>>
>> glm.fit: fitted probabilities numerically 0 or 1 occurred
>>
>>
>>
>>
>>
>> I would suggest to simplify your model (dropping covariates). I guess
>>
>> the code has difficulties estimating standard errors, or it may be in a
>>
>> local optimum. Or contact the owner of the package.
>>
>>
>>
>> If some of your covariates are factors with many levels, then this may
>>
>> also cause numerical instabilities. Perhaps you can simplify the binary
>>
>> part of the model?
>>
>>
>>
>>
>>
>>
>>
>> * Has anyone any idea how to solve this? It has been suggested that it's
>> something in my data, but I don't know what to think if the cod parasite
>> data shows different success/failures on different versions for the same
>> model.*
>>
>>
>>
>>
>>
>> *Cheers,*
>>
>> Alvinda
>>
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>>
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>

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