Removed plot commands from R examples

Author: andrewherren

R/bart.R

@@ -99,10 +99,6 @@
 #' y_train <- y[train_inds]
 #' bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test, 
 #'                    num_gfr = 10, num_burnin = 0, num_mcmc = 10)
-#' \dontrun{
-#' plot(rowMeans(bart_model$y_hat_test), y_test, xlab = "predicted", ylab = "actual")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' }
 bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train = NULL, 
                  rfx_basis_train = NULL, X_test = NULL, leaf_basis_test = NULL, 
                  rfx_group_ids_test = NULL, rfx_basis_test = NULL, 
@@ -996,10 +992,6 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
 #' bart_model <- bart(X_train = X_train, y_train = y_train, 
 #'                    num_gfr = 10, num_burnin = 0, num_mcmc = 10)
 #' y_hat_test <- predict(bart_model, X_test)$y_hat
-#' \dontrun{
-#' plot(rowMeans(y_hat_test), y_test, xlab = "predicted", ylab = "actual")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' }
 predict.bartmodel <- function(object, X, leaf_basis = NULL, rfx_group_ids = NULL, rfx_basis = NULL, ...){
     # Preprocess covariates
     if ((!is.data.frame(X)) && (!is.matrix(X))) {
@@ -1561,10 +1553,6 @@ createBARTModelFromJsonFile <- function(json_filename){
 #' bart_json <- saveBARTModelToJsonString(bart_model)
 #' bart_model_roundtrip <- createBARTModelFromJsonString(bart_json)
 #' y_hat_mean_roundtrip <- rowMeans(predict(bart_model_roundtrip, X_train)$y_hat)
-#' \dontrun{
-#' plot(rowMeans(bart_model$y_hat_train), y_hat_mean_roundtrip, 
-#'      xlab = "original", ylab = "roundtrip")
-#' }
 createBARTModelFromJsonString <- function(json_string){
     # Load a `CppJson` object from string
     bart_json <- createCppJsonString(json_string)

R/bcf.R

@@ -140,14 +140,6 @@
 #'                  propensity_train = pi_train, X_test = X_test, Z_test = Z_test, 
 #'                  propensity_test = pi_test, num_gfr = 10, 
 #'                  num_burnin = 0, num_mcmc = 10)
-#' \dontrun{
-#' plot(rowMeans(bcf_model$mu_hat_test), mu_test, xlab = "predicted", 
-#'      ylab = "actual", main = "Prognostic function")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' plot(rowMeans(bcf_model$tau_hat_test), tau_test, xlab = "predicted", 
-#'      ylab = "actual", main = "Treatment effect")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' }
 bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_ids_train = NULL, 
                 rfx_basis_train = NULL, X_test = NULL, Z_test = NULL, propensity_test = NULL, 
                 rfx_group_ids_test = NULL, rfx_basis_test = NULL, 
@@ -1446,14 +1438,6 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id
 #'                  propensity_train = pi_train, num_gfr = 10, 
 #'                  num_burnin = 0, num_mcmc = 10)
 #' preds <- predict(bcf_model, X_test, Z_test, pi_test)
-#' \dontrun{
-#' plot(rowMeans(preds$mu_hat), mu_test, xlab = "predicted",
-#'      ylab = "actual", main = "Prognostic function")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' plot(rowMeans(preds$tau_hat), tau_test, xlab = "predicted", 
-#'      ylab = "actual", main = "Treatment effect")
-#' abline(0,1,col="red",lty=3,lwd=3)
-#' }
 predict.bcfmodel <- function(object, X, Z, propensity = NULL, rfx_group_ids = NULL, rfx_basis = NULL, ...){
     # Preprocess covariates
     if ((!is.data.frame(X)) && (!is.matrix(X))) {