Tord:   The logistic zero-inflation portion of the zeroinfl() 
implementation of ZIP or ZINB predict the probability of 0 rather than the 
probability of 1 (>0 counts) so the signs of the coefficients are often 
reversed from how you would expect them to be if you had just performed a 
logistic regression.  I'm guessing that the hurdle model as a two-stage 
model is using a logistic regression predicting the probability of 1, 
hence the reversed signs of the estimates in the logistic regression 
portion of the model.

Brian


Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  brian_cade@usgs.gov
tel:  970 226-9326



From:
Tord Snäll <tord.snall@ekol.slu.se>
To:
r-help@r-project.org
Date:
10/23/2009 07:40 AM
Subject:
[R] opposite estimates from zeroinfl() and hurdle()
Sent by:
r-help-bounces@r-project.org



Dear all,
A question related to the following has been asked on R-help before, but 
I could not find any answer to it. Input will be much appreciated.

I got an unexpected sign of the "slope" parameter associated with a 
covariate (diam) using zeroinfl(). It led me to compare the estimates 
given by zeroinfl() and hurdle():

The (significant) negative estimate here is surprising, given the 
biology of the species:

 > summary(zeroinfl(bnl ~ 1| diam, dist = "poisson", data = valdaekar, 
EM = TRUE))
Count model coefficients (poisson with log link):
           Estimate Std. Error z value Pr(>|z|)   (Intercept) 
3.74604    0.02635   142.2   <2e-16 ***

Zero-inflation model coefficients (binomial with logit link):
           Estimate Std. Error z value Pr(>|z|)  (Intercept) 
21.7510     7.6525   2.842  0.00448 **
diam         -1.1437     0.3941  -2.902  0.00371 **

Number of iterations in BFGS optimization: 1
Log-likelihood: -582.8 on 3 Df


The hurdle model gives the same estimates, but with opposite (and 
expected) signs of the parameters:

summary(hurdle(bnl ~ 1| diam, dist = "poisson", data = valdaekar))
Count model coefficients (truncated poisson with log link):
           Estimate Std. Error z value Pr(>|z|)   (Intercept) 
3.74604    0.02635   142.2   <2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
           Estimate Std. Error z value Pr(>|z|)  (Intercept) 
-21.7510     7.6525  -2.842  0.00448 **
diam          1.1437     0.3941   2.902  0.00371 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Number of iterations in BFGS optimization: 8
Log-likelihood: -582.8 on 3 Df

Why is this so?

thanks,
Tord
Windows NT, R 2.8.1, pcsl 1.03

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