Dear R crew:
I think I am in the right mailing list. I have a very simple dataset consisting
of two variables: cestode intensity and chick size (defined as CAPI). Intensity
is clearly overdispersed, with way too many zeroes. I'm interested in looking
at the association between these two variables, i.e. how well does chick size
predict tape intensity?
I fit a zero inflated negat. binomial model using the "pscl" package.
I built my model as follows and got the output below.
> model <- zeroinfl(Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE)
> model
Call:
zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE)
Count model coefficients (negbin with log link):
(Intercept) CAPI
-2.99182 0.06817
Theta = 0.4528
Zero-inflation model coefficients (binomial with logit link):
(Intercept) CAPI
12.1364 -0.1572
> summary(model)
Call:
zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE)
Pearson residuals:
Min 1Q Median 3Q Max
-0.62751 -0.38842 -0.21303 -0.06899 7.29566
Count model coefficients (negbin with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.99182 3.39555 -0.881 0.3783
CAPI 0.06817 0.04098 1.664 0.0962 .
Log(theta) -0.79222 0.45031 -1.759 0.0785 .
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.13636 3.71918 3.263 0.00110 **
CAPI -0.15720 0.04989 -3.151 0.00163 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Theta = 0.4528
Number of iterations in BFGS optimization: 1
Log-likelihood: -140.2 on 5 Df
QUESTIONS
1. Is my model adequately specified?
2. CAPI is included in block 1 of output containing negative binomial
regression coefficients the variable, and in block 2 corresponding to the
inflation model. Does this make sense? If so...
3. How should one interprete these results?
Thanks in advance!
LFLS
Yahoo! Cocina
Encontra las mejores recetas con Yahoo! Cocina.
http://ar.mujer.yahoo.com/cocina/
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