Regression (logistic) in R: Finding x value (predictor) for a particular y value (outcome) -

i've fitted logistic regression model predicts binary outcome vs mpg (mtcars dataset). plot shown below. how can determine mpg value particular vs value? example, i'm interested in finding out mpg value when probability of vs 0.50. appreciate can provide!

model <- glm(vs ~ mpg, data = mtcars, family = binomial)  ggplot(mtcars, aes(mpg, vs)) +      geom_point() +      stat_smooth(method = "glm", method.args = list(family = "binomial"), se = false) 

the easiest way calculate predicted values model predict() function. can use numerical solver find particular intercepts. example

findint <- function(model, value) {     function(x) {         predict(model, data.frame(mpg=x), type="response") - value      } }  uniroot(findint(model, .5), range(mtcars$mpg))$root # [1] 20.52229 

here findint takes model , particular target value , returns function uniroot can solve 0 find solution.