[R] Conditional model in R

[Jagat.K.Sheth at wellsfargo.com]

Sounds like a finite mixture model. I haven't read your references but an overall model for such an approach could be 

f(Y=0; pi, kappa) = 1- pi + pi*f(Y=0|Z=1; kappa)

where pi=Pr(Z=1) is the probability of an event, z, and y is the value observed when the event occurs and f is the probability density of Y with parameters kappa. 

You could try 'fmr' in Jim Lindsey's gnlm package (available at http://www.commanster.eu/rcode.html ) which fits generalized nonlinear regression models with two or three point mixtures using maximum likelihood.



> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Kirsten Martin
> Sent: Wednesday, November 28, 2012 1:33 PM
> To: r-help at r-project.org
> Subject: [R] Conditional model in R
> 
> 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
> 
> 
> 
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