View source: R/cross_validate.R
cross_validate | R Documentation |
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 |
A |
.combine |
A |
.combine_control |
A |
.old_results |
A |
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) # function to calculate cross-validated 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$SE) # cross-validated estimate folds <- make_folds(mtcars) cv_results <- cross_validate( cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ ." ) mean(cv_results$SE) ############################################################################### # 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 cross-validated 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 ~ ." ) }) plan(multicore) time_mc <- system.time({ results_mc <- cross_validate( cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ ." ) }) if (availableCores() > 1) { time_mc["elapsed"] < 1.2 * time_seq["elapsed"] }
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