[R] probit analysis

Prof Brian Ripley ripley at stats.ox.ac.uk
Sat May 6 15:42:54 CEST 2006

On Sat, 6 May 2006, White, Charles E WRAIR-Wash DC wrote:

> The model becomes nonlinear when you add the natural response rate. In
> R, that means that you switch from using the glm function to using the
> nls function.

Not in the usual sense of `linear': it is still just as linear in the 
explanatory variables as a glm is.

>  As long as you're willing to use logistic regression
> instead of Probit analysis, nls has a 'self starting' option (SSLogis)
> for a three parameter logistic model. The third parameter will be your
> natural response rate. Unless you are looking at the tails of the
> distribution, the Probit and logistic models will agree closely. If you
> are highly motivated to use Probit analysis, you can use SSLogis to
> figure out how to do that.

Quick comment: logistic regression via nls is by least-squares, not the 
meaning of the term for glm(family=binomial(logit)).  If you want the 
latter, it is easy to adapt the code on MASS4 p.445 to a probit+const link 
function (and even to estimate the constant).


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)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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