Kirsten,

The overall model is the combination of both models.  If you call the 
parameter estimates from the logistic regression betas and the parameter 
estimates from the linear regression alpha, you could write the predictive 
equation something like this (ignoring error terms):
        cover = (alpha0 + alpha1*nitr + alpha2*shrub) / {1 + exp[-(beta0 + 
beta1*nitr + beta2*shrub)]}
That's not really an R question, though, so perhaps what you really want 
to know is how to calculate predicted values?  If so, you could do 
something like this.  I am assuming that your data is in a data frame 
called "df", with variables "cover", "nitr", and "shrub".

# fit a logistic regression to the presence absence data
present <- cover>0
fitL <- glm(present ~ nitr + shrub, family="binomial", data=df)

# fit a regression to the abundance data, when present
fitD <- lm(log(cover) ~ nitr + shrub, data=df[present , ])

# calculate predicted values from the "combined" model
pcomb <- fitL$fitted * exp(predict(fitD, newdata=df))

Jean



Kirsten Martin <kmmartin@knights.ucf.edu> wrote on 11/28/2012 01:32:43 PM:
> 
> 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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