# Confused with the reference level in logistic regression in R

I am confused with the answer from https://stackoverflow.com/questions/23282048/logistic-regression-defining-reference-level-in-r

It said if you want to predict the probability of "Yes", you set as relevel(auth$class, ref = "YES"). However, in my experiment, if we have a binary response variable with "0" and "1". We only get the estimation for probability of "1" when we set relevel(factor(y),ref="0"). n <- 200 x <- rnorm(n) sumx <- 5 + 3*x exp1 <- exp(sumx)/(1+exp(sumx)) y <- rbinom(n,1,exp1) #probability here is for 1 model1 <- glm(y~x,family = "binomial") summary(model1)$coefficients
Estimate Std. Error  z value     Pr(>|z|)
(Intercept) 5.324099  1.0610921 5.017565 5.233039e-07
x           2.767035  0.7206103 3.839849 1.231100e-04
model2 <- glm(relevel(factor(y),ref="0")~x,family = "binomial")

summary(model2)$coefficients Estimate Std. Error z value Pr(>|z|) (Intercept) 5.324099 1.0610921 5.017565 5.233039e-07 x 2.767035 0.7206103 3.839849 1.231100e-04  I think if we want to get probability of "Yes", we should set relevel(auth$class, ref = "No"), am I correct? And what is reference level here means? Actually, what is glm() to predict in default if we use response other than "0" and "1"?

    y = factor(y, levels=c(0,1), labels=c("No","Yes")

Sorry, but I am unsure about what you're asking about for default behavior for the glm function when you've specified family="binomial" -- the function will expect a two-level factor as the response variable.