I made a logistic regression model using glm in R. I have two independent variables. How can I plot the decision boundary of my model in the scatter plot of the two variables. For example, how can I plot a figure like here.

  • 3
    $\begingroup$ The link to the figure is dead. $\endgroup$ Oct 19, 2015 at 22:48

2 Answers 2


x1 <- rnorm(20, 1, 2)
x2 <- rnorm(20)

y <- sign(-1 - 2 * x1 + 4 * x2 )

y[ y == -1] <- 0

df <- cbind.data.frame( y, x1, x2)

mdl <- glm( y ~ . , data = df , family=binomial)

slope <- coef(mdl)[2]/(-coef(mdl)[3])
intercept <- coef(mdl)[1]/(-coef(mdl)[3]) 

xyplot( x2 ~ x1 , data = df, groups = y,
       panel.abline(intercept , slope)

alt text

I must remark that perfect separation occurs here, therefore the glm function gives you a warning. But that is not important here as the purpose is to illustrate how to draw the linear boundary and the observations colored according to their covariates.

  • $\begingroup$ I hope I am not old fashioned if I use lattice :-) $\endgroup$
    – suncoolsu
    Jan 13, 2011 at 2:47
  • 2
    $\begingroup$ I also hope that if this is a HW problem, you will not simply copy paste. $\endgroup$
    – suncoolsu
    Jan 13, 2011 at 2:54
  • $\begingroup$ Thanks. This is not a HW question and the answer is helpful for me to understand my model. $\endgroup$
    – user2755
    Jan 13, 2011 at 4:25
  • $\begingroup$ oh yes you are :) $\endgroup$
    – mpiktas
    Jan 13, 2011 at 8:09
  • 1
    $\begingroup$ Can someone explain me the logic behind the slope and intercept? (regarding the logistic model) $\endgroup$
    – Fernando
    Jan 9, 2013 at 12:29

Wanted to address the question in comment to the accepted answer above from Fernando: Can someone explain the logic behind the slope and intercept?

The hypothesis for logistics regression takes the form of:

$$h_{\theta} = g(z)$$

where, $g(z)$ is the sigmoid function and where $z$ is of the form:

$$z = \theta_{0} + \theta_{1}x_{1} + \theta_{2}x_{2}$$

Given we are classifying between 0 and 1, $y = 1$ when $h_{\theta} \geq 0.5$ which given the sigmoid function is true when:

$$\theta_{0} + \theta_{1}x_{1} + \theta_{2}x_{2} \geq 0$$

the above is the decision boundary and can be rearranged as:

$$x_{2} \geq \frac{-\theta_{0}}{\theta_{2}} + \frac{-\theta_{1}}{\theta_{2}}x_{1}$$

This is an equation in the form of $y = mx + b$ and you can see then why $m$ and $b$ are calculated the way they are in the accepted answer

  • 2
    $\begingroup$ Good explanation accompanying the answer above! $\endgroup$
    – Augustin
    Dec 29, 2015 at 11:04

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.