Plot logistic regression curve in R

0 votes

I want to plot a logistic regression curve of my data, but whenever I try to my plot produces multiple curves. Here's a picture of my last attempt:

last attempt

Here's the relevant code I am using:

fit = glm(output ~ maxhr, data=heart, family=binomial)
predicted = predict(fit, newdata=heart, type="response")

 plot(output~maxhr, data=heart, col="red4")
 lines(heart$maxhr, predicted, col="green4", lwd=2)

My professor uses the following code, but when I try to run it I get an error on the last line saying that the x and y lengths do not match:

# fit logistic regression model
fit = glm(output ~ maxhr, data=heart, family=binomial)
# plot the result
hr = data.frame(maxhr=seq(80,200,10))
probs = predict(fit, newdata=dat, type="response")
plot(output ~ maxhr, data=heart, col="red4", xlab ="max HR", ylab="P(heart disease)")
lines(hr$maxhr, probs, col="green4", lwd=2)

Any help would be appreciated.

Edit:

As requested, reproduceable code using the mtcars dataset:

fit = glm(vs ~ hp, data=mtcars, family=binomial)
predicted= predict(fit, newdata=mtcars, type="response")
plot(vs~hp, data=mtcars, col="red4")
lines(mtcars$hp, predicted, col="green4", lwd=2)
Mar 26, 2022 in Machine Learning by Dev
• 6,000 points
1,993 views

1 answer to this question.

0 votes

The Code looks something like this:

fit = glm(vs ~ hp, data=mtcars, family=binomial)
newdat <- data.frame(hp=seq(min(mtcars$hp), max(mtcars$hp),len=100))
newdat$vs = predict(fit, newdata=newdat, type="response")
plot(vs~hp, data=mtcars, col="red4")
lines(vs ~ hp, newdat, col="green4", lwd=2)

​The Graph looks like this:

enter image description here
The predictions are made with the newdatobject. This dataset has a considerably better resolution of possible hpvalues than the original dataset, and it is organised to make charting easier. When you constructed hr, you were on your way to implementing it right, but you didn't use it in the prediction stage, instead using newdata=dat.

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answered Apr 4, 2022 by Nandini
• 5,480 points

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