[R] Non-negativity constraints for logistic regression

[Prof Brian Ripley]

On 21/12/2011 18:26, tw at netstorm.be wrote:
> Dear R users,
>
> I am currently attempting to fit logistic regression models in R, where
> the slopes should be restricted to positive values. Although I am aware

I guess non-negative, as in the subject line, so there actually is a 
solution.

> of the package nnls (which does the trick for linear regression models),
> I did not find any solution for logistic regression. If there is any
> package available for this purpose, I would be interested to know them.
>
> Alternatively, I realize it is possible to optimize a specialized
> likelihood function that does the trick. Although I know how to optimize
> the log-likelihood of logistic regression models, I am not sure how to
> implement non-negativity constraints for slope parameters without
> messing up the Newton optimization. Therefore, I am also interested in
> solutions for this problem.

There is an example of this in the 'Optimization' chapter of MASS (the 
book, page 445 to be precise).  You simply use an optimizer with box 
constraints: see ?optim and ?nlminb, for example.

> Best regards,
>
> Thomas Debray


-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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