Cross validation using K folds

I am trying use the function below to carry cross validation on some of the regression model using K folds instead of set validation. I am struggling to work out how to use the code to test the model using the LASSO regression. I would really appreciate any help with this please many thanks.

## Set the seed to make the analysis reproducible

## 10-fold cross validation
nfolds = 10
## Sample fold-assignment index
fold_index = sample(nfolds, n, replace=TRUE)
## Print first few fold-assignments

reg_cv = function(X1, y, fold_ind) {
  Xy = data.frame(X1, y=y)
  nfolds = max(fold_ind)
  if(!all.equal(sort(unique(fold_ind)), 1:nfolds)) stop("Invalid fold partition.")
  cv_errors = numeric(nfolds)
  for(fold in 1:nfolds) {
    tmp_fit = lm(y ~ ., data=Xy[fold_ind!=fold,])
    yhat = predict(tmp_fit, Xy[fold_ind==fold,])
    yobs = y[fold_ind==fold]
    cv_errors[fold] = mean((yobs - yhat)^2)
  fold_sizes = numeric(nfolds)
  for(fold in 1:nfolds) fold_sizes[fold] = length(which(fold_ind==fold))
  test_error = weighted.mean(cv_errors, w=fold_sizes)
lasso_fit = glmnet(
  family = "binomial",
  alpha = 1,
  standardize = FALSE,
  lambda = grid

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Thanks for your help!

fold ind,contains a consecutive set of integers, starting at 1. On line 4, that create a vector to hold the average MSE computed over each fold. Then loop over the folds.

I am required to do the cross validation in a fair comparison way. This way ensure the same folds is used by each model.

Thanks for your help