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Why am I getting “algorithm did not converge” and “fitted prob numerically 0 or 1” warnings with glm?
I am trying to to fit glm using the following data with response variable y1 as categorical. 
The code is giving me the following Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
Sometimes, it does give me error
glm.fit: algorithm did not converge
From the data it is evident that there is a clear relation between predictor and response variable.
- Is the 'did not converge' error because of less number of data points? 
- glmis converting the response variable into factor as shown below. Is this normal?
- Having an - x1and- x2value, how can I know the response?- x1 = c(runif(10,50,100) , runif(10,101,150) ) x2 = c(runif(10,1,50) , runif(10,51,100) ) y1 = c(rep('n',10), rep('y',10)) tmpData = data.frame(x1,x2,y1) tmpData str(tmpData) model <- glm(formula = 'y1~x1+x2', family=binomial(), na.action=na.omit, data=tmpData) summary(model) >str(tmpData) 'data.frame': 20 obs. of 3 variables: $ x1: num 97.9 90.3 62.1 76 63.5 ... $ x2: num 18.6 49.4 21.2 47.7 24.8 ... $ y1: Factor w/ 2 levels "n","y": 1 1 1 1 1 1 1 1 1 1 ...
 
    