I know I'm missing something in my understanding of logistic regression, and would really appreciate any help.
As far as I understand it, the logistic regression assumes that the probability of a '1' outcome given the inputs, is a linear combination of the inputs, passed through an inverse-logistic function. This is exemplified in the following R code:
#create data: x1 = rnorm(1000) # some continuous variables x2 = rnorm(1000) z = 1 + 2*x1 + 3*x2 # linear combination with a bias pr = 1/(1+exp(-z)) # pass through an inv-logit function y = pr > 0.5 # take as '1' if probability > 0.5 #now feed it to glm: df = data.frame(y=y,x1=x1,x2=x2) glm =glm( y~x1+x2,data=df,family="binomial")
and I get the following error message:
Warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred
I've worked with R for some time now; enough to know that probably I'm the one to blame.. what is happening here?