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
set.seed(1)
## 10-fold cross validation
nfolds = 10
## Sample fold-assignment index
fold_index = sample(nfolds, n, replace=TRUE)
## Print first few fold-assignments
head(fold_index)
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)
return(test_error)
}
```

```
lasso_fit = glmnet(
X1,
y,
family = "binomial",
alpha = 1,
standardize = FALSE,
lambda = grid
)
```