On Sun, 4 Mar 2012, j.straka wrote:
Hello,
I?m using zero-inflated Poisson regression via the zeroinfl() function to
analyze data on seed-set of plants, but for some reason, I don?t seem to be
getting the output for all three levels of my two categorical predictors.
More about my data and model:
My response variable is the number of viable seeds (AVInt), and my two
categorical predictors are elevation (Elev) and Treatment (Treatment). Elev
has three levels: 01-Low, 02-Mid, and 03-High; Treatment also has three
possibilities: B, F, or O.
Because the response variable (AVInt) is zero-inflated and
Poisson-distributed, I?m using zeroinfl() under the pcsl library as an
alternative to factorial ANOVA (I?ve also tried the zero-inflated negative
binomial). This is early in my data-analysis, but I will likely incorporate
additional categorical and continuous predictors at a later time.
This gives me the following model:
zipclay=zeroinfl(AVInt ~ Elev + Treatment)
So running the model, I have:
zipclay=zeroinfl(AVInt ~ Elev + Treatment)
summary(zipclay)
Call:
zeroinfl(formula = AVInt ~ Elev + Treatment)
Pearson residuals:
Min 1Q Median 3Q Max
-1.1958 -0.5612 -0.3764 0.2704 5.5130
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2035 0.3435 -0.592 0.55368
Elev02-Mid 0.3937 0.1806 2.180 0.02923 *
Elev03-High 0.1635 0.1792 0.912 0.36159
TreatmentF 1.0026 0.3305 3.033 0.00242 **
TreatmentO 0.5915 0.3293 1.796 0.07244 .
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.6086 0.5080 3.167 0.00154 **
Elev02-Mid -0.3813 0.4345 -0.878 0.38020
Elev03-High -0.9512 0.4532 -2.099 0.03584 *
TreatmentF -0.9774 0.4690 -2.084 0.03718 *
TreatmentO -3.0242 0.6561 -4.609 4.05e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 16
Log-likelihood: -363.2 on 10 Df
So my question is, where did my "Elev01-Low" and "TreatmentB" go?? Why
aren't they appearing in the output table?
Both factors are coded with treatment contrasts and hence the main effect
of the first category is constrained to zero to make the model
identifiable. But this is the same as in a 2-way ANOVA. Compare with:
summary(lm(AVInt ~ Elev + Treatment))
where the intercept corresponds to the mean for Elev01-Low/TreatmentB.
The regressors in the zero-inflated models are set up in exactly the same
way as in such a 2-way ANOVA.
hth,
Z
Any insight would be greatly appreciated!
- Jason
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