Add documentation

DESCRIPTION

@@ -13,7 +13,8 @@ Authors@R:
              role = "aut",
              email = "wouter.saelens@gmail.com",
              comment = c(ORCID = "0000-0002-7114-6248", github = "zouter")))
-Description: Calculating feature importance scores from trajectories using the random forests algorithm and more.
+Description: Calculating feature importance scores from trajectories using the random forests algorithm and more. Saelens and Cannoodt et
+    al. (2019) <doi:10.1038/s41587-019-0071-9>.
 License: GPL-3
 Encoding: UTF-8
 Imports:

NEWS.md

@@ -6,7 +6,7 @@ Initial release of dynfeature on CRAN.
 
 * MINOR CHANGE: Support sparse matrices.
 
-* DOCUMENTATION: Add examples.
+* DOCUMENTATION: Add examples and returns.
 
 # dynfeature 0.2.0 (25-10-2018)
 

R/calculate_feature_importances.R

@@ -4,6 +4,15 @@
 #' @param Y A data frame of predictor variables, with `nrow(Y) == nrow(X)`.
 #' @param fi_method A feature importance method. Default: `fi_ranger_rf_lite()`. Check `?fi_methods` for a full list of available feature importance methods.
 #' @param verbose Whether to print out extra information.
+#'
+#' @returns A data frame with three columns, `predictor_id`, `feature_id`, and `importance`. `predictor_id` is a column in `Y`, while `feature_id` is a column in `X`.
+#'
+#' @examples
+#' X <- data.frame(matrix(runif(25*10), ncol = 10))
+#' Y <- data.frame(matrix(runif(25*2), ncol = 2))
+#'
+#' # don't run since this function is not exported
+#' # calculate_feature_importances(X, Y)
 calculate_feature_importances <- function(
   X,
   Y,

R/calculate_overall_feature_importance.R

@@ -12,6 +12,8 @@
 #' @param milestones_oi The milestone(s) for which to calculate feature importance
 #' @param waypoints The waypoints, optional
 #'
+#' @returns A data frame with two or more columns, `feature_id`, and `importance`. `feature_id` is a column in the trajectory expression matrix. Additional columns may be available depending on the function called.
+#'
 #' @inheritParams calculate_feature_importances
 #'
 #' @importFrom reshape2 acast

R/fi_methods.R

@@ -14,7 +14,9 @@ apply_function_params <- function(params, nrow, ncol) {
 #' @param num_variables_per_split (fi_ranger_rf_lite) The number of variables to sample per split
 #' @param num_samples_per_tree (fi_ranger_rf_lite) The number of samples to bootstrap per split
 #' @param min_node_size (fi_ranger_rf_lite) The minimum node size, no split will be made if the node size is less than this value.
-#' @param ... Extra parameters to pass onto the underlying feature importnce function.
+#' @param ... Extra parameters to pass onto the underlying feature importance function.
+#'
+#' @returns A list containing a helper function for calling a feature importance function.
 #'
 #' @rdname fi_methods
 #'

cran-comments.md

@@ -6,7 +6,7 @@ Initial release of dynfeature on CRAN.
 
 * MINOR CHANGE: Support sparse matrices.
 
-* DOCUMENTATION: Add examples.
+* DOCUMENTATION: Add examples and returns.
 
 ## Test environments
 * local Fedora install, R 4.0
@@ -17,6 +17,10 @@ Initial release of dynfeature on CRAN.
 
 ```
 ── R CMD check results ─────────────────────────────────── dynfeature 1.0.0 ────
-...
+Duration: 3m 0.1s
+
+0 errors ✓ | 0 warnings ✓ | 0 notes ✓
+
+R CMD check succeeded
 ```