[R] Is it possible to use glm() with 30 observations?

[Spencer Graves]

	  The issue is not 30 observations but whether it is possible to 
perfectly separate the two possible outcomes.  Consider the following:

tst.glm <- data.frame(x=1:3, y=c(0, 1, 0))
glm(y~x, family=binomial, data=tst.glm)

tst2.glm <- data.frame(x=1:1000,
                      y=rep(0:1, each=500))
glm(y~x, family=binomial, data=tst2.glm)

	  The algorithm fits y~x to tst.glm without complaining for tst.glm, 
but issues warnings for tst2.glm.  This is called the Hauck-Donner 
effect, and RSiteSearch("Hauck-Donner") just now produced 8 hits.  For 
more information, look for "Hauck-Donnner" in the index of Venables, W. 
N. and Ripley, B. D. (2002) _Modern Applied Statistics with S._ New 
York: Springer.  (If you don't already have this book, I recommend you 
give serious consideration to purchasing a copy.  It is excellent on 
many issues relating to statistical analysis and R.

	  Spencer Graves

Kerry Bush wrote:

> I have a very simple problem. When using glm to fit
> binary logistic regression model, sometimes I receive
> the following warning:
> 
> Warning messages:
> 1: fitted probabilities numerically 0 or 1 occurred
> in: glm.fit(x = X, y = Y, weights = weights, start =
> start, etastart = etastart,  
> 2: fitted probabilities numerically 0 or 1 occurred
> in: glm.fit(x = X, y = Y, weights = weights, start =
> start, etastart = etastart,  
> 
> What does this output tell me? Since I only have 30
> observations, i assume this is a small sample problem.
> Is it possible to fit this model in R with only 30
> observations? Could any expert provide suggestions to
> avoid the warning?
> 
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