R help - Apr 2011 - Regression model with proportional dependent variable

Hello, dear experts. I don't have much experience in building
regression models, so sorry if this is too simple and not very
interesting question.
Currently I'm working on the model that have to predict proportion of
the debt returned by the debtor in some period of time. So the
dependent variable can be any number between 0 and 1 with very high
probability of 0 (if there are no payment) and if there are some
payments it can very likely be 1 (all debt paid) although can be any
number from 0 to 1.
Not having much knowledge in this area I can't think about any
appropriate model and wasn't able to find much on the Internet. Can
anyone give me some ideas about possible models, any information
on-line and some R functions and packages that can implement it.
Thank you in advance for any help.

Ihor.

On Mon, 11 Apr 2011, ty ty wrote:
> Hello, dear experts. I don't have much experience in building
> regression models, so sorry if this is too simple and not very
> interesting question.
> Currently I'm working on the model that have to predict proportion of
> the debt returned by the debtor in some period of time. So the
> dependent variable can be any number between 0 and 1 with very high
> probability of 0 (if there are no payment) and if there are some
> payments it can very likely be 1 (all debt paid) although can be any
> number from 0 to 1.
> Not having much knowledge in this area I can't think about any
> appropriate model and wasn't able to find much on the Internet. Can
> anyone give me some ideas about possible models, any information
> on-line and some R functions and packages that can implement it.
> Thank you in advance for any help.
Beta regression is one possibility to model proportions in the open unit 
interval (0, 1). It is available in R in the package "betareg":

   http://CRAN.R-project.org/package=betareg
   http://www.jstatsoft.org/v34/i02/

If 0 and 1 can occur, some authors have suggested to scale the response so 
that 0 and 1 are avoided. See the paper linked above for an example. If, 
however, there are many 0s and/or 1s, one might want to take a hurdle or 
inflation type approach. One such approach is implemented in the
"gamlss"
package:

   http://CRAN.R-project.org/package=gamlss
   http://www.jstatsoft.org/v23/i07/
   http://www.gamlss.org/

The hurdle approach can be implemented using separate building blocks.
First a binary regression model that captures whether the dependent 
variable is greater than 0 (i.e., crosses the hurdle): glm(I(y > 0) ~ ...,
family = binomial). Second a beta regression for only the observations in 
(0, 1) that crossed the hurdle: betareg(y ~ ..., subset = y > 0). A recent 
technical report introduces such a family of models along with many 
further techniques (specialized residuals and regression diagnostics) that 
are not yet available in R:

   http://arxiv.org/abs/1103.2372

Best,
Z

> Ihor.
>
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