Hi all,
I have a group-year panel data as attahced. Apologies the data is very low quality.
There are 3 groups, each has 20 observations. Outcome y is a dummy variable for success. The first column in x is a continuous variable "effort". The second column is a dummy indicates group A. The third column is a dummy for group B. There is no dummy for group C to avoid collinearity.
I want to predict the probability of success using the probit model. The code I try is:
b = glmfit(x,y,'binomial','Link','probit');
b =
0.1857 (constant)
-1.8149 (effort)
-16.1148 (group A)
-16.2994 (group B)
As you can see in the data, all outcomes for group A are failures. So the second column in x predicts y == 0 perfectly. Matlab also raises a warning:
Warning: The estimated coefficients perfectly separate failures from successes. This means the theoretical best estimates are not finite.
For the fitted linear combination XB of the predictors, the sample proportions P of Y=N in the data satisfy:
XB<-0.834093: P=0
XB=-0.834093: P=1
XB>-0.834093: P=0
However, it still returns an estimated coefficient for group A dummy, which is b(3) = -16.1148.
Question:
Since x(:,2) perfectly predict failures, b(3) should be 0. Is there an option in glmfit to remove observations for group A within glmfit function, then return the coefficient as 0 for this column? So I can get something like:
b =
0.1857 (constant)
-1.8149 (effort)
0 (group A)
xxx (group B)
Stata does this automatically using the command:
It turns out the estiamtes for the constant and effort are the same. So the perfect failure issue only affects the group dummies coefficients...
Thank you!!!
