Updated from ifelse to if else to support NULL return

NorskRegnesentral · Dec 8, 2023 · b9728d7 · b9728d7
1 parent 6a391b2
commit b9728d7
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Showing 2 changed files with 13 additions and 12 deletions.
diff --git a/R/RcppExports.R b/R/RcppExports.R
@@ -14,7 +14,7 @@
 #' @return Matrix of dimension \code{ncol(X)*ncol(X)}
 #' @author Martin Jullum
 hat_matrix_cpp <- function(X, mcov, S_scale_dist, h) {
-  .Call(`_shapr_hat_matrix_cpp`, X, mcov, S_scale_dist, h)
+    .Call(`_shapr_hat_matrix_cpp`, X, mcov, S_scale_dist, h)
 }
 
 #' sigma_hat_sq-function
@@ -29,7 +29,7 @@ hat_matrix_cpp <- function(X, mcov, S_scale_dist, h) {
 #'
 #' @author Martin Jullum
 rss_cpp <- function(H, y) {
-  .Call(`_shapr_rss_cpp`, H, y)
+    .Call(`_shapr_rss_cpp`, H, y)
 }
 
 #' correction term with trace_input in AICc formula
@@ -42,7 +42,7 @@ rss_cpp <- function(H, y) {
 #' @return Scalar
 #' @author Martin Jullum
 correction_matrix_cpp <- function(tr_H, n) {
-  .Call(`_shapr_correction_matrix_cpp`, tr_H, n)
+    .Call(`_shapr_correction_matrix_cpp`, tr_H, n)
 }
 
 #'  Temp-function for computing the full AICc with several X's etc
@@ -59,7 +59,7 @@ correction_matrix_cpp <- function(tr_H, n) {
 #' @return Scalar with the numeric value of the AICc formula
 #' @author Martin Jullum
 aicc_full_single_cpp <- function(X, mcov, S_scale_dist, h, y) {
-  .Call(`_shapr_aicc_full_single_cpp`, X, mcov, S_scale_dist, h, y)
+    .Call(`_shapr_aicc_full_single_cpp`, X, mcov, S_scale_dist, h, y)
 }
 
 #'  AICc formula for several sets, alternative definition
@@ -77,7 +77,7 @@ aicc_full_single_cpp <- function(X, mcov, S_scale_dist, h, y) {
 #'
 #' @author Martin Jullum
 aicc_full_cpp <- function(h, X_list, mcov_list, S_scale_dist, y_list, negative) {
-  .Call(`_shapr_aicc_full_cpp`, h, X_list, mcov_list, S_scale_dist, y_list, negative)
+    .Call(`_shapr_aicc_full_cpp`, h, X_list, mcov_list, S_scale_dist, y_list, negative)
 }
 
 #' (Generalized) Mahalanobis distance
@@ -97,12 +97,12 @@ aicc_full_cpp <- function(h, X_list, mcov_list, S_scale_dist, y_list, negative)
 #' @return Array of three dimensions. Contains the squared distance for between all training and test observations for all feature combinations passed to the function.
 #' @author Martin Jullum
 mahalanobis_distance_cpp <- function(featureList, Xtrain_mat, Xtest_mat, mcov, S_scale_dist) {
-  .Call(`_shapr_mahalanobis_distance_cpp`, featureList, Xtrain_mat, Xtest_mat, mcov, S_scale_dist)
+    .Call(`_shapr_mahalanobis_distance_cpp`, featureList, Xtrain_mat, Xtest_mat, mcov, S_scale_dist)
 }
 
 #' @keywords internal
 sample_features_cpp <- function(m, n_features) {
-  .Call(`_shapr_sample_features_cpp`, m, n_features)
+    .Call(`_shapr_sample_features_cpp`, m, n_features)
 }
 
 #' Get imputed data
@@ -141,7 +141,7 @@ sample_features_cpp <- function(m, n_features) {
 #'
 #' @author Nikolai Sellereite
 observation_impute_cpp <- function(index_xtrain, index_s, xtrain, xtest, S) {
-  .Call(`_shapr_observation_impute_cpp`, index_xtrain, index_s, xtrain, xtest, S)
+    .Call(`_shapr_observation_impute_cpp`, index_xtrain, index_s, xtrain, xtest, S)
 }
 
 #' Calculate weight matrix
@@ -160,7 +160,7 @@ observation_impute_cpp <- function(index_xtrain, index_s, xtrain, xtest, S) {
 #' @return Matrix of dimension n x m + 1
 #' @author Nikolai Sellereite
 weight_matrix_cpp <- function(subsets, m, n, w) {
-  .Call(`_shapr_weight_matrix_cpp`, subsets, m, n, w)
+    .Call(`_shapr_weight_matrix_cpp`, subsets, m, n, w)
 }
 
 #' Get feature matrix
@@ -174,5 +174,6 @@ weight_matrix_cpp <- function(subsets, m, n, w) {
 #' @return Matrix
 #' @author Nikolai Sellereite
 feature_matrix_cpp <- function(features, m) {
-  .Call(`_shapr_feature_matrix_cpp`, features, m)
+    .Call(`_shapr_feature_matrix_cpp`, features, m)
 }
+
diff --git a/R/plot.R b/R/plot.R
@@ -974,7 +974,7 @@ make_waterfall_plot <- function(dt_plot,
 plot_MSEv_eval_crit <- function(explanation_list,
                                 index_x_explain = NULL,
                                 id_combination = NULL,
-                                CI_level = ifelse(length(explanation_list[[1]]$pred_explain) < 20, NULL, 0.95),
+                                CI_level = if (length(explanation_list[[1]]$pred_explain) < 20) NULL else 0.95,
                                 geom_col_width = 0.9,
                                 plot_type = "overall") {
   # Setup and checks ----------------------------------------------------------------------------
@@ -1008,7 +1008,7 @@ plot_MSEv_eval_crit <- function(explanation_list,
   # Get the number of observations and combinations and the quantile of the T distribution
   n_explain <- explanation_list[[1]]$internal$parameters$n_explain
   n_combinations <- explanation_list[[1]]$internal$parameters$n_combinations
-  tfrac <- ifelse(is.null(CI_level), NULL, qt((1 + CI_level) / 2, n_explain - 1))
+  tfrac <- if (is.null(CI_level)) NULL else qt((1 + CI_level) / 2, n_explain - 1)
 
   # Create data.tables of the MSEv values
   MSEv_dt_list <- MSEv_extract_MSEv_values(