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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