How to capture the most important variables in Bootstrapped models in R?

Question

I have several models that I would like to compare their choices of important predictors over the same data set, Lasso being one of them. The data set I am using consists of census data with around a thousand variables that have been renamed to "x1", "x2" and so on for convenience sake (The original names are extremely long). I would like to report the top features then rename these variables with a shorter more concise name.

My attempt to solve this is by extracting the top variables in each iterated model, put it into a list, then finding the mean of the top variables in X amount of loops. However, my issue is I still find variability with the top 10 most used predictors and so I cannot manually alter the variable names as each run on the code chunk yields different results. I suspect this is because I have so many variables in my analysis and due to CV causing the creation of new models every bootstrap.

For the sake of a simple example I used mtcars and will look for the top 3 most common predictors due to only having 10 variables in this data set.

library(glmnet)

data("mtcars") # Base R Dataset
df <- mtcars


topvar <- list()

for (i in 1:100) {
  
  # CV and Splitting
  
  ind <- sample(nrow(df), nrow(df), replace = TRUE)
  ind <- unique(ind)
  
  train <- df[ind, ]
  xtrain <- model.matrix(mpg~., train)[,-1]
  ytrain <- df[ind, 1]
  
  test <- df[-ind, ]
  xtest <- model.matrix(mpg~., test)[,-1]
  ytest <- df[-ind, 1]
  
  # Create Model per Loop
 
  model <- glmnet(xtrain, ytrain, alpha = 1, lambda = 0.2) 
                     
  # Store Coeffecients per loop
  
  coef_las <- coef(model, s = 0.2)[-1, ] # Remove intercept
  
  # Store all nonzero Coefficients
  
  topvar[[i]] <- coef_las[which(coef_las != 0)]
  
}

# Unlist 

varimp <- unlist(topvar)

# Count all predictors

novar <- table(names(varimp))

# Find the mean of all variables

meanvar <- tapply(varimp, names(varimp), mean)

# Return top 3 repeated Coefs

repvar <- novar[order(novar, decreasing = TRUE)][1:3]

# Return mean of repeated Coefs

repvar.mean <- meanvar[names(repvar)]

repvar

Now if you were to rerun the code chunk above you would notice that the top 3 variables change and so if I had to rename these variables it would be difficult to do if they are not constant and changing every run. Any suggestions on how I could approach this?

Answer 1

You can use function set.seed() to ensure your sample will return the same sample each time. For example

set.seed(123)

When I add this to above code and then run twice, the following is returned both times:

  wt carb   hp 
  98   89   86