# Hurdle models and word count

Question: why negative binominal part of hurdle model does not provide coefficients for intercept and word count?

I have counted positive emotional words (Y) in some conversations that have a different length. Positive emotion variable has a Poission distribution. A Poission regression controlling for the total amount of words in the conversation do not satisfy a dispersion test. (Test suggest that the data overdispersed). I choose to run a hurdle model instead. I am trying to fit the data in a hurdle model figuring out whether the presence and amount of positive words (Y) depend on the advice given in the conversation (X). I also control for the total word count in the conversations (WC) since conversations have a different length. Here is what I have:

Y <- cbind(PosEmotCount)
X <- cbind(AdviceAS, WC)
hnegbin <- hurdle(Y ~ X, link = "logit", dist = "negbin")

Pearson residuals:
Min      1Q  Median      3Q     Max
-1.7208 -0.7297 -0.2498  0.4924  3.5845
Count model coefficients (truncated negbin with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept)  0.7551942         NA      NA       NA
XAdviceAS   -0.0541452  0.1096667  -0.494    0.621
XWC          0.0003255         NA      NA       NA
Log(theta)   1.3652328         NA      NA       NA
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3011979  0.3781910   0.796    0.426
XAdviceAS   0.7367846  0.6598110   1.117    0.264
XWC         0.0009060  0.0001132   8.002 1.22e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


QUESTION: why negative binominal part of the hurdle model does not provide coefficients for intercept and word count?

• The hurdle model allows for clumping at zero but that is not necessary if you are just worried about over-dispersion. You might want to state which package hurdle() comes from. – mdewey Jan 16 at 16:53
• thank you so, much for your response. I am using R, pscl library for the analysis – Ilona Jan 16 at 17:36