在 R 中的逐步回归中限制预测变量的数量

Question

I would like to be able to do a forward stepwise linear regression, but constrain the number of predictor variables to a maximum (in my specific case, three). Here is some sample data.

set.seed(123)
myDep <- runif(100)

pred1 <- myDep + runif(100)
pred2 <- myDep + rnorm(100)
pred3 <- myDep + runif(100) + rnorm(100)
pred4 <- myDep + runif(100) + runif(100)
pred5 <- runif(100)

myDF <- data.frame(myDep, pred1, pred2, pred3, pred4, pred5)

If I were to simply run a linear regression using the following code below, I would get all five predictor variables, obviously.

myModel <- lm(myDep ~ ., data = myDF)

What I would like to do it use step() or other R command to run a forward-direction stepwise that picks only three predictor variables and then stops.

For what it is worth, I tried this:

step(lm(myDep ~ ., data = myDF), steps = 3, direction = "forward")

and the results were the following -- but not what I want because it uses all five predictor variables.

Start:  AIC=-378.09
myDep ~ pred1 + pred2 + pred3 + pred4 + pred5

Call:
lm(formula = myDep ~ pred1 + pred2 + pred3 + pred4 + pred5, data = myDF)

Coefficients:
(Intercept)        pred1        pred2        pred3        pred4        pred5  
   -0.16617      0.30043      0.07983      0.03670      0.17869      0.01606 

I'm sure there's a way to do this, but I cannot seem to figure out the proper formatting. Thanks in advance.

Answer 1

You could use the regsubsets package in R, where you can limit the variables and choose your method ("forward").

https://www.rdocumentation.org/packages/leaps/versions/2.1-1/topics/regsubsets

library(regsubsets)

b <- regsubsets(myDep ~ ., data=myDF, nbest=1, nvmax=[enter your max # of predictors])
summary(b)