Applies cv_fun
to the folds using future_lapply
and combines
the results across folds using combine_results
.
cross_validate(cv_fun, folds, ..., use_future = TRUE, .combine = TRUE, .combine_control = list(), .old_results = NULL)
cv_fun  a function that takes a 'fold' as it's first argument and
returns a list of results from that fold. NOTE: the use of an argument named
'X' is specifically disallowed in any input function for compliance with the
functions 

folds  a list of folds to loop over generated using

...  other arguments passed to 
use_future 

.combine  (logical)  should 
.combine_control  (list)  arguments to 
.old_results  (list)  the returned result from a previous call to This function. Will be combined with the current results. This is useful for adding additional CV folds to a results object. 
A list of results, combined across folds.
############################################################################### # This example explains how to use the cross_validate function naively. ############################################################################### data(mtcars) # resubstitution MSE r < lm(mpg ~ ., data = mtcars) mean(resid(r)^2)#> [1] 4.609201# function to calculate crossvalidated squared error cv_lm < function(fold, data, reg_form) { # get name and index of outcome variable from regression formula out_var < as.character(unlist(stringr::str_split(reg_form, " "))[1]) out_var_ind < as.numeric(which(colnames(data) == out_var)) # split up data into training and validation sets train_data < training(data) valid_data < validation(data) # fit linear model on training set and predict on validation set mod < lm(as.formula(reg_form), data = train_data) preds < predict(mod, newdata = valid_data) # capture results to be returned as output out < list(coef = data.frame(t(coef(mod))), SE = ((preds  valid_data[, out_var_ind])^2)) return(out) } # replicate the resubstitution estimate resub < make_folds(mtcars, fold_fun = folds_resubstitution)[[1]] resub_results < cv_lm(fold = resub, data = mtcars, reg_form = "mpg ~ .") mean(resub_results$SE)#> [1] 4.609201# crossvalidated estimate folds < make_folds(mtcars) cv_results < cross_validate(cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ .") mean(cv_results$SE)#> [1] 14.59195############################################################################### # This example explains how to use the cross_validate function with # parallelization using the framework of the future package. ############################################################################### suppressMessages(library(data.table)) library(future)#> #>#>#> #>data(mtcars) set.seed(1) # make a lot of folds folds < make_folds(mtcars, fold_fun = folds_bootstrap, V = 1000) # function to calculate crossvalidated squared error for linear regression cv_lm < function(fold, data, reg_form) { # get name and index of outcome variable from regression formula out_var < as.character(unlist(str_split(reg_form, " "))[1]) out_var_ind < as.numeric(which(colnames(data) == out_var)) # split up data into training and validation sets train_data < training(data) valid_data < validation(data) # fit linear model on training set and predict on validation set mod < lm(as.formula(reg_form), data = train_data) preds < predict(mod, newdata = valid_data) # capture results to be returned as output out < list(coef = data.frame(t(coef(mod))), SE = ((preds  valid_data[, out_var_ind])^2)) return(out) } plan(sequential) time_seq < system.time({ results_seq < cross_validate(cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ .") })#> Warning: All iterations resulted in errorsplan(multicore) time_mc < system.time({ results_mc < cross_validate(cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ .") })#> Warning: All iterations resulted in errors#> elapsed #> TRUE