Naive error data for high dimensional simulations

Author: perej1

functions.R

@@ -307,7 +307,7 @@ compute_error_elliptical <- function(sigma_hat, r_hat, alpha, p) {
 #'
 #' @return Double, naive error.
 compute_error_axes <- function(sigma_hat, r_hat, alpha, p) {
-  d <- ncol(sigma)
+  d <- ncol(sigma_hat)
 
   # (1-p)-quantile of the generating variate
   r <- sqrt(d * stats::qf(1 - p, d, alpha))

high-dim-data/data/n_1000_k_large_p_high.csv

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high-dim-data/data/n_1000_k_large_p_low.csv

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high-dim-data/data/n_1000_k_large_p_medium.csv

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high-dim-data/data/n_1000_k_medium_p_high.csv

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high-dim-data/data/n_1000_k_medium_p_low.csv

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-100,23,1000,low,medium,278,0.6766122512508519,42
-100,24,1000,low,medium,278,0.748843772243728,41
-100,25,1000,low,medium,278,0.6794951719479586,50
-100,26,1000,low,medium,278,0.7607851346832329,44
-100,27,1000,low,medium,278,0.6917710256772065,46
-100,28,1000,low,medium,278,0.6681280891568755,54
-100,29,1000,low,medium,278,0.829052018810905,56
-100,30,1000,low,medium,278,0.6451471993378071,64
+s,d,n,p,k,seed,error_naive_med,error_elliptical_med,num_na
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+100,3,1000,low,medium,278,158.64577734603995,0.3406653476252219,8
+100,4,1000,low,medium,278,204.88823498241914,0.35634627630337135,17
+100,5,1000,low,medium,278,247.05284221185502,0.39953713821291903,21
+100,6,1000,low,medium,278,215.47965064826033,0.38833244354162866,21
+100,7,1000,low,medium,278,249.54351631353842,0.4429786319460729,25
+100,8,1000,low,medium,278,324.0573490755553,0.4707309715734418,25
+100,9,1000,low,medium,278,313.21809767101047,0.48000783504695854,25
+100,10,1000,low,medium,278,352.48724306433513,0.5193570587486256,23
+100,11,1000,low,medium,278,324.6111017812194,0.505552646903312,31
+100,12,1000,low,medium,278,363.23369836716506,0.5104044620569217,32
+100,13,1000,low,medium,278,383.30293619913823,0.5320537781716937,32
+100,14,1000,low,medium,278,338.31350244958264,0.5655766641167003,43
+100,15,1000,low,medium,278,469.6568430137236,0.6062440232605543,32
+100,16,1000,low,medium,278,492.4115237109583,0.7465242059831236,33
+100,17,1000,low,medium,278,442.0960653615559,0.6037875736489462,41
+100,18,1000,low,medium,278,465.1180110069041,0.6773068961255841,39
+100,19,1000,low,medium,278,382.09338272403147,0.5872791603622141,49
+100,20,1000,low,medium,278,525.6728654142103,0.6350246515701787,46
+100,21,1000,low,medium,278,440.0772456037722,0.7280247373246784,45
+100,22,1000,low,medium,278,484.62471543250643,0.6925763863686251,48
+100,23,1000,low,medium,278,530.0511314383514,0.6766122512508519,42
+100,24,1000,low,medium,278,589.9840408274774,0.748843772243728,41
+100,25,1000,low,medium,278,471.61015823867956,0.6794951719479586,50
+100,26,1000,low,medium,278,590.73627444078,0.7607851346832329,44
+100,27,1000,low,medium,278,603.3876868625334,0.6917710256772065,46
+100,28,1000,low,medium,278,515.5707978191056,0.6681280891568755,54
+100,29,1000,low,medium,278,507.1586185114015,0.829052018810905,56
+100,30,1000,low,medium,278,470.2024462185029,0.6451471993378071,64