Create a loop for model selection in R - r

Question

I am trying to test a whole bunch of different models easily and compare AIC / R-sq values to select the right one. I am having some trouble saving things how I want to between lists and data frames.
data frame I am going to model:
set.seed(1)
df <- data.frame(response=runif(50,min=50,max=100),
var1 = sample(1:20,50,replace=T),
var2 = sample(40:60,50,replace = T))
list of formulas to test:
formulas <- list( response ~ NULL,
response ~ var1,
response ~ var2,
response ~ var1 + var2,
response ~ var1 * var2)
So, what I want to do is create a loop that will model all of these formulas, extract Formula, AIC, and R-sq values into a table, and let me sort it to find the best one. The problem I'm having is I can't extract the formula name as "Response ~ var1", instead, it keeps coming out as "Response" "~" "var1" if I try to extract as a character object. Or, if I extract as a list (like below), then it comes out like this:
[[1]]
response ~ NULL
[[2]]
[1] 415.89
[[3]]
[1] 0
And I can't easily plug those list elements into a data frame. Here is what I tried:
selection <- matrix(ncol=3)
colnames(selection) <- c("formula","AIC","R2") # create a df to store results in
for ( i in 1:length(formulas)){
mod <- lm( formula = formulas[[i]], data= df)
mod_vals <- c(extract(formulas[[i]]),
round(AIC(mod),2),
round(summary(mod)$adj.r.squared,2)
)
selection[i,] <- mod_vals[]
}
Any ideas? I don't have to keep it as a for loop either, I just want a way to test a long list of models together.
Thanks.

Answer 1

You could use lapply to loop over each formula and extract relevant statistic from the model and bind the datasets together.
do.call(rbind, lapply(formulas, function(x) {
mod <- lm(x, data= df)
data.frame(formula = format(x),
AIC = round(AIC(mod),2),
r_square = round(summary(mod)$adj.r.squared,2))
}))
# formula AIC r_square
#1 response ~ NULL 405.98 0.00
#2 response ~ var1 407.54 -0.01
#3 response ~ var2 407.90 -0.02
#4 response ~ var1 + var2 409.50 -0.03
#5 response ~ var1 * var2 410.36 -0.03
Or with purrr
purrr::map_df(formulas, ~{
mod <- lm(.x, data= df)
data.frame(formula = format(.x),
AIC = round(AIC(mod),2),
r_square = round(summary(mod)$adj.r.squared,2))
})