[R] for help about R--probit

[Prof Brian Ripley]

And did you actually look at the fitted values?  I got 22 ones.  For a
substantial part of your x1-x2 space there are no failures.  The warning 
is telling you that the fitted probabilities are so close to one as to be 
unreliable.  The largest is 1-exp-20!

On Thu, 6 Nov 2003, L Z wrote:

> Not real data. It was gererated randomly. The original codes are the following:
>  
> par(mfrow=c(2,1))
> n <- 500
> 
> #########################
> #DATA GENERATING PROCESS#
> #########################
> x1  <- rnorm(n,0,1)
> x2  <- rchisq(n,df=3,ncp=0)-3
> sigma <- 1
> u1   <- rnorm(n,0,sigma)
> ylatent1 <-x1+x2+u1
> y1   <- (ylatent1 >=0)  # create the binary indicator
> #######################
> #THE Probit Estimation#
> #######################
> probit<-glm(y1~x1+x2-1, family=binomial(link=probit))
> bp<-probit$coef[2]/probit$coef[1]
> bp;
> I also tried family=quasibinomial. There seems no error message. But the result is different from what I got from Gauss. For u1 belongs to another distribution (not normal), the difference is even larger. I used the same data for the comparison.
>  
> Thanks a lot!
> 
> Steve Sullivan <ssullivan at qedgroupllc.com> wrote:
> Is this simulated or actual data?
> 
> STS
> 
> Steven Sullivan, Ph.D.
> Senior Associate
> The QED Group, LLC
> 1250 Eye St. NW, Suite 802
> Washington, DC 20005
> ssullivan at qedgroupllc.com
> 202.898.1910.x15 (v)
> 202.898.0887 (f)
> 202.421.8161 (m)
> 
> 
> -----Original Message-----
> From: L Z [mailto:cougar3721 at yahoo.com] 
> Sent: Wednesday, November 05, 2003 12:10 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] for help about R 
> 
> just want to ask the following
> > > question:
> > > > probit<-glm(y1~x1+x2-1,
> > > family=binomial(link=probit))
> > > Warning message:
> > > fitted probabilities numerically 0 or 1 occurred
> in:
> > > glm.fit(x = X, y = Y,
> > > weights = weights, start = start, etastart =
> > > etastart,
> > > why does that happen?
> 
> 
> 
> 
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-- 
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