Displaying 2 results from an estimated 2 matches for "countmean".
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countmax
2012 Aug 02
0
predictions from hurdle model
...9;*' 0.05 '.' 0.1 ' ' 1
Theta: count = 0.1562
Number of iterations in BFGS optimization: 20
Log-likelihood: -931.7 on 5 Df
> y.prev<-seq(0,100,by=1)
> newdata<-data.frame(y.prev)
> nonzero<-1-predict(nbhurdle.prev,newdata=newdata,type="prob")
> countmean<-predict(nbhurdle.prev,newdata=newdata,type="count")
> allmean<-predict(nbhurdle.prev,newdata=newdata,type="response")
> plot(y.prev,nonzero[,1],type="b")
> plot(y.prev,countmean,type="b")
> plot(y.prev, allmean, type = "b")
-...
2012 Jan 17
2
pscl package and hurdle model marginal effects
This request is related to the following post from last year:
https://stat.ethz.ch/pipermail/r-help/2011-June/279752.html
After reading the thread, the idea is still not clear. I have fitted a model using HURDLE from the PSCL package. I am trying to get marginal effects / slopes by multiplying the coefficients by the mean of the marginal effects (I think this is right). To my understanding, this