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

[Christopher David Desjardins]

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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