fit <- glm(am ~ mpg, family=binomial(), data=mtcars) # Logistic regression
p <- c(.05,.10,.20,.50,.80,.90,.95)                  # Specify probabilities
x <- (log(p/(1-p)) - coef(fit)[1])/coef(fit)[2]      # Compute corresponding x's
i <- ordered(1:length(p))                            # Identify p's and x's for plotting
#
# Compute the fitted curve.
#
Z <- data.frame(mpg=with(mtcars, seq(min(mpg), max(mpg), length.out=101)))
Z$am <- predict(fit, Z, type="response")
#
# Plot everything.
#
library (ggplot2)
ggplot(mtcars, aes(mpg, am)) +
  geom_hline(aes(yintercept=p, color=i), data.frame(p, i), alpha=0.8, show.legend=FALSE)+
  geom_vline(aes(xintercept=x, color=i), data.frame(x, i), alpha=0.8, show.legend=FALSE)+
  geom_point(size=2, alpha=0.25, fill="Black", shape=21) +
  geom_line(aes(mpg, am), Z, size=1.25, color="#303030") +
  ylab("Probability(am=1)") +
  ggtitle("Probability grid with associated regressor values") +
  theme(panel.grid=element_blank())