Conditional model in R
On Nov 28, 2012, at 11:32 AM, Kirsten Martin wrote:
Hello all, I have a data set where the response variable is the percent cover of a specific plant (represented in cover classes 0,1,2,3,4,5, or 6). This data set has a lot of zeros (plots where the plant was not present). I am trying to model cover class of the plant as a function of both total nitrogen and shrub cover. After quite a bit of research I have come across a conditional approach to modeling data with a lot of zeros (Fletcher et al. 2005, Welsh et al. 1996). In this approach you model the presence/absence data using a logistic regression and then model the presence only data using ordinary (least squares) regression.
Just because you have zeroes does not mean a Poisson model for instance might no be a good fit. You are dealing with count data and you really ought to at least attempt to model it using an appropriate distribution. Achim Zeileis has written avery nice tutorial on using R for count data. A google-search with his name and 'count data' will likely get it as the first hit.
I have successfully come up with both a logistic model and an ols model with good fits. I am running into trouble combining the two (as outlined in the third step of the Fletcher et al. 2005 paper). Does anyone have any experience or any advice on doing this? How does one come up with an overall model for the data using this approach?
You might search on "hurdle models".
require(sos)
findFn("hurdle model")
found 43 matches; retrieving 3 pages 2 3 Downloaded 23 links in 8 packages.
David Winsemius, MD Alameda, CA, USA