Updated variance and preprocessing docs

Author: andrewherren

R/utils.R

@@ -429,6 +429,12 @@ savePreprocessorToJsonString <- function(object){
 #'
 #' @returns Preprocessor object that can be used with the `preprocessPredictionData` function
 #' @export
+#'
+#' @examples
+#' cov_mat <- matrix(1:12, ncol = 3)
+#' preprocess_list <- preprocessTrainData(cov_mat)
+#' preprocessor_json <- convertPreprocessorToJson(preprocess_list$metadata)
+#' preprocessor_roundtrip <- createPreprocessorFromJson(preprocessor_json)
 createPreprocessorFromJson <- function(json_object){
     # Initialize the metadata list
     metadata <- list()
@@ -472,6 +478,12 @@ createPreprocessorFromJson <- function(json_object){
 #'
 #' @return Preprocessor object that can be used with the `preprocessPredictionData` function
 #' @export
+#'
+#' @examples
+#' cov_mat <- matrix(1:12, ncol = 3)
+#' preprocess_list <- preprocessTrainData(cov_mat)
+#' preprocessor_json_string <- savePreprocessorToJsonString(preprocess_list$metadata)
+#' preprocessor_roundtrip <- createPreprocessorFromJsonString(preprocessor_json_string)
 createPreprocessorFromJsonString <- function(json_string){
     # Load a `CppJson` object from string
     preprocessor_json <- createCppJsonString(json_string)

R/variance.R

@@ -5,8 +5,18 @@
 #' @param rng C++ random number generator
 #' @param a Global variance shape parameter
 #' @param b Global variance scale parameter
-#'
 #' @export
+#' 
+#' @examples
+#' X <- matrix(runif(10*100), ncol = 10)
+#' y <- -5 + 10*(X[,1] > 0.5) + rnorm(100)
+#' y_std <- (y-mean(y))/sd(y)
+#' forest_dataset <- createForestDataset(X)
+#' outcome <- createOutcome(y_std)
+#' rng <- createCppRNG(1234)
+#' a <- 1.0
+#' b <- 1.0
+#' sigma2 <- sampleGlobalErrorVarianceOneIteration(outcome, forest_dataset, rng, a, b)
 sampleGlobalErrorVarianceOneIteration <- function(residual, dataset, rng, a, b) {
     return(sample_sigma2_one_iteration_cpp(residual$data_ptr, dataset$data_ptr, rng$rng_ptr, a, b))
 }
@@ -19,6 +29,17 @@ sampleGlobalErrorVarianceOneIteration <- function(residual, dataset, rng, a, b)
 #' @param b Leaf variance scale parameter
 #'
 #' @export
+#' 
+#' @examples
+#' num_trees <- 100
+#' leaf_dimension <- 1
+#' is_leaf_constant <- TRUE
+#' is_exponentiated <- FALSE
+#' active_forest <- createForest(num_trees, leaf_dimension, is_leaf_constant, is_exponentiated)
+#' rng <- createCppRNG(1234)
+#' a <- 1.0
+#' b <- 1.0
+#' tau <- sampleLeafVarianceOneIteration(active_forest, rng, a, b)
 sampleLeafVarianceOneIteration <- function(forest, rng, a, b) {
     return(sample_tau_one_iteration_cpp(forest$forest_ptr, rng$rng_ptr, a, b))
 }