Add code examples for CV functions

Author: nanxstats

R/cv_lightgbm.R

@@ -17,7 +17,33 @@
 #' @export
 #'
 #' @examplesIf is_installed_lightgbm()
-#' # Check the vignette for code examples
+#' sim_data <- msaenet::msaenet.sim.binomial(
+#'   n = 100,
+#'   p = 10,
+#'   rho = 0.6,
+#'   coef = rnorm(5, mean = 0, sd = 10),
+#'   snr = 1,
+#'   p.train = 0.8,
+#'   seed = 42
+#' )
+#'
+#' params <- suppressWarnings(
+#'   cv_lightgbm(
+#'     sim_data$x.tr,
+#'     sim_data$y.tr,
+#'     params = cv_param_grid(
+#'       n_iterations = c(100, 200),
+#'       max_depth = c(3, 5),
+#'       learning_rate = c(0.1, 0.5)
+#'     ),
+#'     n_folds = 5,
+#'     n_threads = 1,
+#'     seed = 42,
+#'     verbose = FALSE
+#'   )
+#' )
+#'
+#' params$df
 cv_lightgbm <- function(
     x, y,
     params = cv_param_grid(),

R/cv_xgboost.R

@@ -19,7 +19,31 @@
 #' @export
 #'
 #' @examplesIf is_installed_xgboost()
-#' # Check the vignette for code examples
+#' sim_data <- msaenet::msaenet.sim.binomial(
+#'   n = 100,
+#'   p = 10,
+#'   rho = 0.6,
+#'   coef = rnorm(5, mean = 0, sd = 10),
+#'   snr = 1,
+#'   p.train = 0.8,
+#'   seed = 42
+#' )
+#'
+#' params <- cv_xgboost(
+#'   sim_data$x.tr,
+#'   sim_data$y.tr,
+#'   params = cv_param_grid(
+#'     n_iterations = c(100, 200),
+#'     max_depth = c(3, 5),
+#'     learning_rate = c(0.1, 0.5)
+#'   ),
+#'   n_folds = 5,
+#'   n_threads = 1,
+#'   seed = 42,
+#'   verbose = FALSE
+#' )
+#'
+#' params$df
 cv_xgboost <- function(
     x, y,
     params = cv_param_grid(),

man/cv_lightgbm.Rd

@@ -49,16 +49,20 @@ training set and the rest 200 will be in the (independent) test set.
 25 out of the 50 features will be informative and follows $N(0, 10)$.
 
 ```{r}
-dat <- msaenet.sim.binomial(
-  n = 1000, p = 50, rho = 0.6,
-  coef = rnorm(25, 0, 10), snr = 1, p.train = 0.8,
+sim_data <- msaenet.sim.binomial(
+  n = 1000,
+  p = 50,
+  rho = 0.6,
+  coef = rnorm(25, mean = 0, sd = 10),
+  snr = 1,
+  p.train = 0.8,
   seed = 42
 )
 
-x_train <- dat$x.tr
-x_test <- dat$x.te
-y_train <- as.vector(dat$y.tr)
-y_test <- as.vector(dat$y.te)
+x_train <- sim_data$x.tr
+x_test <- sim_data$x.te
+y_train <- as.vector(sim_data$y.tr)
+y_test <- as.vector(sim_data$y.te)
 ```
 
 ## Parameter tuning
@@ -95,7 +99,7 @@ params_cat <- readRDS("params_cat.rds")
 
 ```{r}
 model_stack <- stackgbm(
-  dat$x.tr, dat$y.tr,
+  sim_data$x.tr, sim_data$y.tr,
   params = list(
     xgb.nrounds = params_xgb$nrounds,
     xgb.eta = params_xgb$eta,