Logistic regression: how to try every combination of predictors in R?

Question

This is a duplicate of https://stats.stackexchange.com/questions/293988/logistic-regression-how-to-try-every-combination-of-predictors.

I want to perform a logistic

Answer 1

I am not sure about the value of this "educational exercise", but for the sake of programming, here would be my approach:

First, let's create some example predictor names. I use 5 predictors as in your example, but for 10, you would obviously need to replace 5 with 10.

X = paste0("x",1:5)
X
[1] "x1" "x2" "x3" "x4" "x5"    

Now, we can get the combinations with combn.

For instance, for one variable at a time:

 t(combn(X,1))
     [,1]
[1,] "x1"
[2,] "x2"
[3,] "x3"
[4,] "x4"
[5,] "x5"

Two variables at a time:

> t(combn(X,2))
      [,1] [,2]
 [1,] "x1" "x2"
 [2,] "x1" "x3"
 [3,] "x1" "x4"
 [4,] "x1" "x5"
 [5,] "x2" "x3"
 [6,] "x2" "x4"
 [7,] "x2" "x5"
 [8,] "x3" "x4"
 [9,] "x3" "x5"
[10,] "x4" "x5"

etc.

We can use lapply to call these functions successively with an increasing number of variables to consider, and to catch the results in a list. For instance, have a look at the output of lapply(1:5, function(n) t(combn(X,n))). To turn these combinations into formulas, we can use the following:

out <- unlist(lapply(1:5, function(n) {
  # get combinations
  combinations <- t(combn(X,n))
  # collapse them into usable formulas:
  formulas <- apply(combinations, 1, 
                    function(row) paste0("y ~ ", paste0(row, collapse = "+")))}))

Or equivalently using the FUN argument of combn (as pointed out by user20650):

out <- unlist(lapply(1:5, function(n) combn(X, n, FUN=function(row) paste0("y ~ ", paste0(row, collapse = "+")))))

This gives:

out
 [1] "y ~ x1"             "y ~ x2"             "y ~ x3"             "y ~ x4"             "y ~ x5"            
 [6] "y ~ x1+x2"          "y ~ x1+x3"          "y ~ x1+x4"          "y ~ x1+x5"          "y ~ x2+x3"         
[11] "y ~ x2+x4"          "y ~ x2+x5"          "y ~ x3+x4"          "y ~ x3+x5"          "y ~ x4+x5"         
[16] "y ~ x1+x2+x3"       "y ~ x1+x2+x4"       "y ~ x1+x2+x5"       "y ~ x1+x3+x4"       "y ~ x1+x3+x5"      
[21] "y ~ x1+x4+x5"       "y ~ x2+x3+x4"       "y ~ x2+x3+x5"       "y ~ x2+x4+x5"       "y ~ x3+x4+x5"      
[26] "y ~ x1+x2+x3+x4"    "y ~ x1+x2+x3+x5"    "y ~ x1+x2+x4+x5"    "y ~ x1+x3+x4+x5"    "y ~ x2+x3+x4+x5"   
[31] "y ~ x1+x2+x3+x4+x5"

This can now be passed to your logistic regression function.

---

Example:

Let's use the mtcars dataset, with mpg as dependent variable.

X = names(mtcars[,-1])
X
 [1] "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"

Now, let's use the aforementioned function:

out <- unlist(lapply(1:length(X), function(n) combn(X, n, FUN=function(row) paste0("mpg ~ ", paste0(row, collapse = "+")))))

which gives us a vector of all combinations as formulas.

To run the corresponding models, we can do for instance

mods = lapply(out, function(frml) lm(frml, data=mtcars))

Since you want to capture specific statistics and order the models accordingly, I would use broom::glance. broom::tidy turns lm output into a dataframe (useful if you want to compare coefficients etc) and broom::glance turns e.g. r-squared, sigma, the F-statistic, the logLikelihood, AIC, BIC etc into a dataframe. For instance:

library(broom)
library(dplyr)
tmp = bind_rows(lapply(out, function(frml) {
  a = glance(lm(frml, data=mtcars))
  a$frml = frml
  return(a)
}))

head(tmp)
  r.squared adj.r.squared    sigma statistic      p.value df    logLik      AIC      BIC deviance df.residual       frml
1 0.7261800     0.7170527 3.205902 79.561028 6.112687e-10  2 -81.65321 169.3064 173.7036 308.3342          30  mpg ~ cyl
2 0.7183433     0.7089548 3.251454 76.512660 9.380327e-10  2 -82.10469 170.2094 174.6066 317.1587          30 mpg ~ disp
3 0.6024373     0.5891853 3.862962 45.459803 1.787835e-07  2 -87.61931 181.2386 185.6358 447.6743          30   mpg ~ hp
4 0.4639952     0.4461283 4.485409 25.969645 1.776240e-05  2 -92.39996 190.7999 195.1971 603.5667          30 mpg ~ drat
5 0.7528328     0.7445939 3.045882 91.375325 1.293959e-10  2 -80.01471 166.0294 170.4266 278.3219          30   mpg ~ wt
6 0.1752963     0.1478062 5.563738  6.376702 1.708199e-02  2 -99.29406 204.5881 208.9853 928.6553          30 mpg ~ qsec

which you can sort as you wish.

Answer 2

There's a package that does this, MuMIn (multimodel inference), as part of a more principled multi-model approach (i.e. it doesn't just pick the best model(s) and ignore the fact that selection has been done):

Set up data and full model:

set.seed(101)
d <- data.frame(replicate(5,rnorm(100)))
d$y <- rbinom(100,size=1,prob=0.5)
full <- glm(y~.,data=d,na.action=na.fail)

"dredge" the result:

library(MuMIn)
allfits <- dredge(full)

results (also contains all fitted parameters):

head(allfits[,7:11])
##    df    logLik     AICc    delta     weight
## 3   3 -69.66403 145.5781 0.000000 0.15916685
## 11  4 -69.22909 146.8792 1.301191 0.08304293
## 19  4 -69.30856 147.0382 1.460123 0.07669921
## 7   4 -69.31233 147.0457 1.467655 0.07641093
## 4   4 -69.40589 147.2328 1.654775 0.06958615
## 1   2 -72.07662 148.2769 2.698896 0.04128523