On Thu, 4 Feb 2010, Luciano La Sala wrote:
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?
Hard to say from only the output. But given that you only have these two
variables it seems like a natural model. You could also compare it with
the corresponding hurdle() model.
In both models, you can look at observed and expected frequencies for the
counts 0, 1, 2, etc.
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...
As I said above: It seems natural to me but I don't have any background
knowledge in this application.
3. How should one interprete these results?
count_CAPI: The mean "Int_Cesto" seems to increase slightly with CAPI but
not very much.
count_theta: There is clear overdispersion (compared to Poisson).
zero_CAPI: The probability for an inflated zero "Int_Cesto" decreases
clearly with CAPI.
See
vignette("countreg", package = "pscl")
for a hands-on introduction to Poisson and negative binomial models with
and without excess zeros.
hth,
Z
Thanks in advance!
LFLS
Yahoo! Cocina
Encontra las mejores recetas con Yahoo! Cocina.
http://ar.mujer.yahoo.com/cocina/
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and provide commented, minimal, self-contained, reproducible code.
