Plot the results of a multivariate logistic regression model in R

Question

I would like to plot the results of a multivariate logistic regression analysis (GLM) for a specific independent variables adjusted (i.e. independent of the confounders included in the model) relationship with the outcome (binary).

I have seen posts that recommend the following method using the predict command followed by curve, here's an example;

x     <- data.frame(binary.outcome, cont.exposure)
model <- glm(binary.outcome ~ cont.exposure, family=binomial, data=x)
plot(cont.exposure, binary.outcome, xlab="Temperature",ylab="Probability of Response") 
curve(predict(model, data.frame(cont.exposure=x), type="resp"), add=TRUE, col="red")

However this does not seem to work for multivariate regression models. I get the following error when I add 'age' (arbitrary - could be any variable of same length) as a confounding variable;

> x     <- data.frame(binary.outcome, cont.exposure, age)
> model <- glm(binary.outcome ~ cont.exposure + age, family=binomial, data=x)
> plot(cont.exposure, binary.outcome, xlab="Temperature",ylab="Probability of Response") 
> curve(predict(model, data.frame(cont.exposure=x), type="resp"), add=TRUE, col="red")
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : 
  variable lengths differ (found for 'age')
In addition: Warning message:
  'newdata' had 101 rows but variable(s) found have 698 rows

The above model is a simplified version of the models I'd like to run, but the principle is the same; I would like to plot the relationship between a binary outcome variable and a continuous exposure, independent of confounding factors..

It would be great to get either a workaround for the above, or an alternative way to view the relationship I am interested in. Many thanks.

You could have a look at the crPlots function in the car package. — BenBarnes
@BenBarnes thanks for that. I've had a look and a quick play with the data, and the function doesn't recognize that I am doing logistic regressions. However, the if I use a linear regression (ie. .my exposure is now my outcome, my binary variable an independent variable) then I do get exactly what I want. Will you post this as an answer for me to accept, or shall I? — Luke
@Luke did you ever find a good solution to this question? The accepted answer sort of missed the intent of the question, as you point out in your comment. — colin
@colin sadly no, and I have not seen anything in the last few years to do what I'm after really. Do you have any suggestions? — Luke

Thierry Thierry · Accepted Answer · 2012-07-02T11:47:16

set.seed(12345)
dataset <- expand.grid(Temp = rnorm(30), Age = runif(10))
dataset$Truth <- with(dataset, plogis(2 * Temp - 3 * Age))
dataset$Sample <- rbinom(nrow(dataset), size = 1, prob = dataset$Truth)
model <- glm(Sample ~ Temp + Age, data = dataset, family = binomial)
newdata <- expand.grid(
  Temp = pretty(dataset$Temp, 20), 
  Age = pretty(dataset$Age, 5))
newdata$Sample <- predict(model, newdata = newdata, type = "response")
library(ggplot2)
ggplot(newdata, aes(x = Temp, y = Sample)) + geom_line() + facet_wrap(~Age)

enter image description here

ggplot(newdata, aes(x = Temp, y = Sample, colour = Age, group = Age)) + 
  geom_line()

enter image description here

Plot the results of a multivariate logistic regression model in R

1 Answers