Minor docstring fix: training -> prediction (#1420)

Author: erikcs

r-package/grf/R/causal_forest.R

@@ -312,7 +312,7 @@ causal_forest <- function(X, Y, W,
 #' @param ll.lambda Ridge penalty for local linear predictions. Defaults to NULL and will be cross-validated.
 #' @param ll.weight.penalty Option to standardize ridge penalty by covariance (TRUE),
 #'                  or penalize all covariates equally (FALSE). Penalizes equally by default.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals).

r-package/grf/R/causal_survival_forest.R

@@ -405,7 +405,7 @@ causal_survival_forest <- function(X, Y, W, D,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals).

r-package/grf/R/instrumental_forest.R

@@ -273,7 +273,7 @@ instrumental_forest <- function(X, Y, W, Z,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals).

r-package/grf/R/ll_regression_forest.R

@@ -231,7 +231,7 @@ ll_regression_forest <- function(X, Y,
 #' @param ll.lambda Ridge penalty for local linear predictions. Defaults to NULL and will be cross-validated.
 #' @param ll.weight.penalty Option to standardize ridge penalty by covariance (TRUE),
 #'                            or penalize all covariates equally (FALSE). Defaults to FALSE.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals).

r-package/grf/R/lm_forest.R

@@ -260,7 +260,7 @@ lm_forest <- function(X, Y, W,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat h_k(x)} are desired
 #'                          (for confidence intervals). This option is currently

r-package/grf/R/multi_arm_causal_forest.R

@@ -314,7 +314,7 @@ multi_arm_causal_forest <- function(X, Y, W,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals). This option is currently

r-package/grf/R/multi_regression_forest.R

@@ -130,7 +130,7 @@ multi_regression_forest <- function(X, Y,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param drop If TRUE, coerce the prediction result to the lowest possible dimension. Default is FALSE.
 #' @param ... Additional arguments (currently ignored).

r-package/grf/R/probability_forest.R

@@ -160,7 +160,7 @@ probability_forest <- function(X, Y,
 #'                Xi using only trees that did not use the i-th training example). Note
 #'                that this matrix should have the number of columns as the training
 #'                matrix, and that the columns must appear in the same order.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for P[Y = k | X] are desired (for confidence intervals).
 #' @param ... Additional arguments (currently ignored).

r-package/grf/R/quantile_forest.R

@@ -153,7 +153,7 @@ quantile_forest <- function(X, Y,
 #'                matrix, and that the columns must appear in the same order.
 #' @param quantiles Vector of quantiles at which estimates are required. If NULL, the quantiles
 #'  used to train the forest is used. Default is NULL.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param ... Additional arguments (currently ignored).
 #'

r-package/grf/R/regression_forest.R

@@ -199,7 +199,7 @@ regression_forest <- function(X, Y,
 #' @param ll.lambda Ridge penalty for local linear predictions. Defaults to NULL and will be cross-validated.
 #' @param ll.weight.penalty Option to standardize ridge penalty by covariance (TRUE),
 #'                            or penalize all covariates equally (FALSE). Defaults to FALSE.
-#' @param num.threads Number of threads used in training. If set to NULL, the software
+#' @param num.threads Number of threads used in prediction. If set to NULL, the software
 #'                    automatically selects an appropriate amount.
 #' @param estimate.variance Whether variance estimates for \eqn{\hat\tau(x)} are desired
 #'                          (for confidence intervals).