logistic regression's criteria of prediction probability in r I performed classification analysis as using the glm(). The dependent variable has a value of 0 and 1, and the probability is measured using the predict function as follows.
glm.prob<-predict(A, newdata=test, type="response")
glm.prob

 4         11         15         17         22         23         30         31         35         36         42         50 
0.83793310 0.51753857 0.54858443 0.76921368 0.82107932 0.07838337 0.83934274 0.84484728 0.61028261 0.74274305 0.84628820 0.88751409 

If it exceeds 0.5, it is 1, and if it is less than 0.5, it is considered as 0. But I have a question. Why is the standard 0.5? 0.7 or 0.3 etc .. Can not other values?
 A: The cutoff 0.5 is not a standard, and if it is communicated as such, you should have some suspicion about any other information you recieve from the same source.
It is the job of the regression only to estimate the predicted conditional probabilities
$$ P(y = 1 \mid X) $$
Assigning hard class assignments is another layer of decision making above and beyond estimating the probabilities.  It should not be done unless there is a pressing need, and if there is a need, it should be done in accordance of that need.  One way to do this is to threshold the predicted probabilities, but the threshold chosen should be in service of some objective.  
There should not be any need for a standard threshold, or a rule of thumb.  If you find yourself in need of one, it's better to think more carefully about whether you really need hard classification, or about what objective you are attempting to accomplish with the hard classification.
A: Typically, unless you make changes, it will create the cutoff at whatever the average response rate of the training data is. If your training data has 13.5% y=1, then it will classify anything where predicted probability >0.135 as a 1. 
