Hi,
I am using a zero inflated Poisson to model a dependent variable. How do I find out whether the zero-inflated Poisson is a good approximation of the variable?

So far I have:

> m1 <- zeroinfl(Join_Count_6m  ~ 1, data = Megdata)

> summary(m1)

Call:
zeroinfl(formula = Join_Count_6m ~ 1, data = Megdata)

Pearson residuals:
    Min      1Q  Median      3Q     Max
-1.1517 -1.1517 -1.1297 -0.1326 42.5377

Count model coefficients (poisson with log link):
            Estimate Std. Error z value Pr(>|z|)
(Intercept) 7.002181   0.002889    2424   <2e-16 ***

Zero-inflation model coefficients (binomial with logit link):
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.2846     0.1462  -1.947   0.0515 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Number of iterations in BFGS optimization: 6
Log-likelihood: -1.264e+05 on 2 Df


However plot () and qqplot () both give error messages:

> plot(m1)
Error in xy.coords(x, y, xlabel, ylabel, log) :
  'x' is a list, but does not have components 'x' and 'y'

> qqplot(m1)
Error in rank(x, ties.method = "min", na.last = "keep") :
  unimplemented type 'list' in 'greater'

Many thanks,
Meg

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