Displaying 3 results from an estimated 3 matches for "allmean".
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allman
2012 Aug 02
0
predictions from hurdle model
...rations 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")
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2013 Mar 27
1
Creating mean C columns out of As and Bs
I am working on a gene expression microarray dataset, each sample has been taking from animal A and animal B on the same type of microarray.
I would like to take the mean of these columns and create a dataset with columns with data which are means of the corresponding rows of A and B
For example:
TissueExpressionA <- HTissue[1:22284, c(29, 39, 57, 59, 63, 69, 71, 73, 77, 79, 83, 89, 99, 117,
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