R help - Jun 2014 - Marginal Effects for Hurdle (Two-part) models in R

I am very new to R. I am using R package "pscl" for hurdle regression
(Binary with "cloglog" link and Poisson with "log" link). My
problem is to model road crash frequencies at given locations as a function of
road geometry and surface condition. All of my dependent variable are
covariates. I want to test their marginal effects on the response. However, I am
hard-pressed to find a way to do that. I just wish to ask if there is a way at
all to accomplish this in R? Or is there any other better way to quantify the
impact of individual variables on the response for hurdle regression? Thanks in
advance.

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On Thu, 26 Jun 2014, Ashutosh Arun wrote:
> I am very new to R. I am using R package "pscl" for hurdle
regression
> (Binary with "cloglog" link and Poisson with "log"
link). My problem is
> to model road crash frequencies at given locations as a function of road 
> geometry and surface condition. All of my dependent variable are 
> covariates. I want to test their marginal effects on the response.
I'm not sure what exactly you want to test here. The hurdle model has the 
advantage that its two parts can be estimated (and hence also tested) 
separately. The summary() reports the usual marginal Wald tests.

If you want to test whether a certain variable has a significant effect on 
either one of the two parts, then you can fit the model with and without 
the variable and use waldtest() or lrtest() to assess the difference.

If you just want to assess changes in terms of probabilities or means, 
then you can set up auxiliary data sets that vary one variable but keep 
the others fixed and predict() the quantity you are interested in.

hth,
Z
> However, I am hard-pressed to find a way to do that. I just wish to ask 
> if there is a way at all to accomplish this in R? Or is there any other 
> better way to quantify the impact of individual variables on the 
> response for hurdle regression? Thanks in advance.