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. 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? Thanks for your help! Kirsten -- View this message in context: http://r.789695.n4.nabble.com/Conditional-model-in-R-tp4651188.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
