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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