I have some questions regarding Zero Inflation Poisson models.
I am using count data to analyze abundance trends of salamanders. However,
I have surveys which differ in the amount of effort (i.e. the number of
people searching and amount of time - I am using a museum database so not
all surveys were conducted by me). Therefore I need to account for the
effort. If change the count (response variable) then it will have decimals
and not be usable in this model. So I decided to put this term into the
independent variable. I am analyzing Historic vs. Current surveys.
Here is an example of my code:
require(pscl)
model <- zeroinfl(Sallys~Survey:Person.Hours, dist="poisson", EM=TRUE)
summary(model)
I have received some very significant results on most of them and on some
that I thought wouldn't be significant turned out to be. So I am concerned
with the model being appropriate. I created a simulated database and ran a
simple glm to see if y/b ~ x is the same as y~x:b and it is not (not
surprisingly). Does anyone have suggestions for how to adjust my model to
allow for these comparisons? I cannot use a glm with Poisson error because
of overdispersion and a lot of zeroes. I thought about either rounding up
my ratios or multiplying everything by 100 to eliminate the decimals but to
keep the variation (I am not pleased with either of those options)
On another note, I am having a little trouble interpreting the results (I
think). Which this may not matter if I cannot use the ZIP model. Is the
Count model coefficients (poisson with log link) the measure of if the sites
differ and if so what do the estimates for both surveys indicate? Is that
the mean for both surveys and it is testing them against zero? If so I want
to test them against each other and I don't know exactly how to do that.
Here is the output:
Estimate Std. Error z value
Pr(>|z|)
(Intercept) 1.97418 0.06570 30.048 <2e-16
***
SurveyCurrent:Person.Hours 0.04192 0.07597 0.552 0.581
SurveyHistoric:Person.Hours 0.40221 0.01540 26.110 <2e-16 ***
As for the "Zero-inflation model coefficients( binomial with logit link). I
read that this is a measure of 1) suitability or 2) if the predictor of
excess zeros was significant. Which one of these (or is it something else)
is correct and how do I interpret this?
Here is a sample of a read out:
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value
Pr(>|z|)
(Intercept) -1.1625 0.9833 -1.182
0.237
SurveyCurrent:Person.Hours -1.1787 1.1304 -1.043 0.297
SurveyHistoric:Person.Hours -0.5050 0.3440 -1.468 0.142
<http://search.twitter.com/search?q=%0D%0A><http://www.google.com/search?q=%0D%0A><http://smarterfox.com/wikisearch/search?q=%0D%0A&locale=en-US><http://www.oneriot.com/search?p=smarterfox&ssrc=smarterfox_popup_bubble&spid=8493c8f1-0b5b-4116-99fd-f0bcb0a3b602&q=%0D%0A>
Thanks for any suggestions/help!!
--
Nicholas M Caruso
Graduate Student
CLFS-Biology
4219 Biology-Psychology Building
University of Maryland, College Park, MD 20742-5815
phone: 301-405-6884
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