I agree with Ted: "in model-fitting terms, it is a
resounding success!" With any data set having at least one point with a
binomial yield of 0 or 100%, you can get this phenomenon by adding
series of random numbers sequentially to a model. Eventually, you will
add enough variables t
On 02-Jul-05 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 =
>
On 2 Jul 2005, at 06:01, Spencer Graves wrote:
> 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 <-
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, eac
