Last updated on 2025-06-06 01:51:28 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.0 | 32.50 | 127.66 | 160.16 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.0 | 22.37 | 89.81 | 112.18 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.0 | 263.39 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.0 | 255.48 | OK | |||
| r-devel-windows-x86_64 | 0.1.0 | 40.00 | 145.00 | 185.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1.0 | 34.27 | 120.12 | 154.39 | OK | |
| r-release-linux-x86_64 | 0.1.0 | 32.08 | 121.88 | 153.96 | OK | |
| r-release-macos-arm64 | 0.1.0 | 70.00 | OK | |||
| r-release-macos-x86_64 | 0.1.0 | 136.00 | OK | |||
| r-release-windows-x86_64 | 0.1.0 | 38.00 | 147.00 | 185.00 | OK | |
| r-oldrel-macos-arm64 | 0.1.0 | 76.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.1.0 | 132.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.1.0 | 50.00 | 185.00 | 235.00 | ERROR |
clang-ASAN gcc-ASAN M1mac valgrind
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'GAPR-Ex.R' failed
The error most likely occurred in:
> ### Name: computeProximity
> ### Title: Compute Proximity Matrix
> ### Aliases: computeProximity
>
> ### ** Examples
>
> # =======================
> # Example 1: Crabs dataset with distance method (Euclidean distance)
> # =======================
> # Step 1: Compute proximity matrix
> if (requireNamespace("MASS", quietly = TRUE)) {
+ df_crabs <- as.matrix(MASS::crabs[, -c(1:3)]) # Use continuous variables only
+ row_prox_crabs <- computeProximity(
+ data = df_crabs,
+ proxType = 0, # 0 = Euclidean distance
+ side = 0, # 0 = row-wise proximity
+ isContainMissingValue = 0
+ )
+
+ # Step 2: Obtain R2E ordering
+ r2e_order_crabs <- ellipse_sort(row_prox_crabs) # R2E ordering
+
+ # Step 3: Apply AVG-R2E ordering
+ hctree_result_crabs <- hctree_sort(
+ row_prox_crabs, # use distance matrix directly
+ externalOrder = r2e_order_crabs, # apply r2e order
+ orderType = 2, # 2 = Average-linkage
+ flipType = 1 # 1 = Flip based on externalOrder
+ )
+
+ avg_r2e_order_crabs <- hctree_result_crabs$order + 1
+
+ # Inspect results
+ avg_r2e_order_crabs
+ }
[1] 150 149 50 200 148 145 144 147 146 143 142 199 198 197 195 45 46 49
[19] 48 47 44 100 196 193 192 188 194 189 190 191 187 186 141 140 139 138
[37] 134 135 133 42 43 41 40 37 35 99 98 180 179 185 184 176 183 97
[55] 136 137 132 131 127 130 36 38 39 34 32 31 33 28 177 178 182 181
[73] 172 171 175 174 95 92 96 94 29 30 128 129 126 88 91 83 89 93
[91] 90 87 173 170 169 26 25 24 27 23 22 21 84 86 85 82 168 167
[109] 166 163 164 165 162 125 123 124 161 121 122 120 81 80 79 160 159 78
[127] 118 20 158 157 113 115 112 114 119 117 111 77 76 73 19 75 18 17
[145] 14 13 16 71 74 12 15 11 72 70 116 110 108 156 109 155 107 65
[163] 69 67 68 64 66 10 63 154 9 8 106 153 105 7 62 60 6 51
[181] 101 1 151 152 104 61 59 58 57 56 103 102 5 4 55 54 3 52
[199] 53 2
>
> # =======================
> # Example 2: Crabs dataset with distance method (Pearson correlation)
> # =======================
> if (requireNamespace("MASS", quietly = TRUE)) {
+ df_crabs <- as.matrix(MASS::crabs[, -c(1:3)]) # Use continuous variables only
+ row_prox_pearson <- computeProximity(
+ data = df_crabs,
+ proxType = 1, # 1 = Pearson correlation (internally 1 - cor)
+ side = 0, # 0 = row-wise proximity
+ isContainMissingValue = 0
+ )
+
+ # Step 2: Obtain R2E ordering
+ r2e_order_pearson <- ellipse_sort(row_prox_pearson) # R2E ordering
+
+ # Step 3: Inspect results
+ dist_pearson <- as.dist(1 - row_prox_pearson) # convert correlation matrix to distance matrix
+ dist_pearson_MT <- as.matrix(dist_pearson)
+
+ hctree_result_pearson <- hctree_sort(
+ dist_pearson_MT, # use distance matrix directly
+ externalOrder = r2e_order_pearson, # apply r2e order
+ orderType = 2, # 2 = Average-linkage
+ flipType = 1 # 1 = Flip based on externalOrder
+ )
+
+ avg_r2e_order_pearson <- hctree_result_pearson$order + 1
+
+ # Inspect results
+ avg_r2e_order_pearson
+ }
[1] 145 144 142 146 150 137 130 143 149 131 139 129 135 148 138 112 114 126
[19] 140 120 121 115 113 127 136 124 133 117 147 134 132 45 125 128 141 122
[37] 106 119 116 109 105 123 103 110 102 104 107 42 111 161 195 108 28 193
[55] 190 186 31 40 34 50 49 48 39 22 46 199 23 30 38 197 198 118
[73] 191 153 182 174 43 27 24 180 37 41 33 29 26 172 25 13 36 47
[91] 8 181 101 194 152 44 32 192 6 100 96 176 90 14 1 156 162 185
[109] 35 21 200 155 187 189 184 171 175 20 173 9 196 170 5 178 18 158
[127] 17 166 4 165 160 167 19 169 177 15 80 179 16 89 159 163 164 151
[145] 154 157 7 66 11 84 93 168 2 55 188 10 74 91 77 76 86 54
[163] 58 3 78 75 68 62 51 53 60 88 56 85 71 69 12 79 82 97
[181] 73 83 67 92 64 87 94 99 98 70 183 81 95 72 52 63 57 65
[199] 59 61
>
>
>
> ### * <FOOTER>
> ###
> cleanEx()
Flavor: r-devel-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'GAPR-Ex.R' failed
The error most likely occurred in:
> ### Name: computeProximity
> ### Title: Compute Proximity Matrix
> ### Aliases: computeProximity
>
> ### ** Examples
>
> # =======================
> # Example 1: Crabs dataset with distance method (Euclidean distance)
> # =======================
> # Step 1: Compute proximity matrix
> if (requireNamespace("MASS", quietly = TRUE)) {
+ df_crabs <- as.matrix(MASS::crabs[, -c(1:3)]) # Use continuous variables only
+ row_prox_crabs <- computeProximity(
+ data = df_crabs,
+ proxType = 0, # 0 = Euclidean distance
+ side = 0, # 0 = row-wise proximity
+ isContainMissingValue = 0
+ )
+
+ # Step 2: Obtain R2E ordering
+ r2e_order_crabs <- ellipse_sort(row_prox_crabs) # R2E ordering
+
+ # Step 3: Apply AVG-R2E ordering
+ hctree_result_crabs <- hctree_sort(
+ row_prox_crabs, # use distance matrix directly
+ externalOrder = r2e_order_crabs, # apply r2e order
+ orderType = 2, # 2 = Average-linkage
+ flipType = 1 # 1 = Flip based on externalOrder
+ )
+
+ avg_r2e_order_crabs <- hctree_result_crabs$order + 1
+
+ # Inspect results
+ avg_r2e_order_crabs
+ }
[1] 150 149 50 200 148 145 144 147 146 143 142 199 198 197 195 45 46 49
[19] 48 47 44 100 196 193 192 188 194 189 190 191 187 186 141 140 139 138
[37] 134 135 133 42 43 41 40 37 35 99 98 180 179 185 184 176 183 97
[55] 136 137 132 131 127 130 36 38 39 34 32 31 33 28 177 178 182 181
[73] 172 171 175 174 95 92 96 94 29 30 128 129 126 88 91 83 89 93
[91] 90 87 173 170 169 26 25 24 27 23 22 21 84 86 85 82 168 167
[109] 166 163 164 165 162 125 123 124 161 121 122 120 81 80 79 160 159 78
[127] 118 20 158 157 113 115 112 114 119 117 111 77 76 73 19 75 18 17
[145] 14 13 16 71 74 12 15 11 72 70 116 110 108 156 109 155 107 65
[163] 69 67 68 64 66 10 63 154 9 8 106 153 105 7 62 60 6 51
[181] 1 101 151 152 104 61 59 58 57 56 103 102 5 4 55 54 3 52
[199] 53 2
>
> # =======================
> # Example 2: Crabs dataset with distance method (Pearson correlation)
> # =======================
> if (requireNamespace("MASS", quietly = TRUE)) {
+ df_crabs <- as.matrix(MASS::crabs[, -c(1:3)]) # Use continuous variables only
+ row_prox_pearson <- computeProximity(
+ data = df_crabs,
+ proxType = 1, # 1 = Pearson correlation (internally 1 - cor)
+ side = 0, # 0 = row-wise proximity
+ isContainMissingValue = 0
+ )
+
+ # Step 2: Obtain R2E ordering
+ r2e_order_pearson <- ellipse_sort(row_prox_pearson) # R2E ordering
+
+ # Step 3: Inspect results
+ dist_pearson <- as.dist(1 - row_prox_pearson) # convert correlation matrix to distance matrix
+ dist_pearson_MT <- as.matrix(dist_pearson)
+
+ hctree_result_pearson <- hctree_sort(
+ dist_pearson_MT, # use distance matrix directly
+ externalOrder = r2e_order_pearson, # apply r2e order
+ orderType = 2, # 2 = Average-linkage
+ flipType = 1 # 1 = Flip based on externalOrder
+ )
+
+ avg_r2e_order_pearson <- hctree_result_pearson$order + 1
+
+ # Inspect results
+ avg_r2e_order_pearson
+ }
[1] 145 144 142 146 150 137 130 143 149 131 139 129 135 148 138 112 114 126
[19] 140 120 121 115 113 127 136 124 133 117 147 134 132 45 125 128 141 122
[37] 106 119 116 109 105 123 103 110 102 104 107 42 111 161 195 108 28 193
[55] 190 186 31 40 34 50 49 48 39 22 46 199 23 30 38 197 198 118
[73] 191 153 182 174 43 27 24 180 37 41 33 29 26 172 25 13 36 47
[91] 8 181 101 194 152 44 32 192 6 100 96 176 90 14 156 1 162 185
[109] 35 21 200 155 187 189 184 171 175 20 173 9 196 170 5 178 18 158
[127] 17 166 4 165 160 167 19 169 177 15 80 179 16 89 159 163 164 151
[145] 154 157 7 66 11 84 93 168 2 55 188 10 74 91 77 76 86 54
[163] 58 3 78 75 68 62 51 53 60 88 56 85 71 69 12 79 82 97
[181] 73 83 67 92 64 87 94 99 98 70 183 81 95 72 52 63 57 65
[199] 59 61
>
>
>
> ### * <FOOTER>
> ###
> cleanEx()
Error in gc() :
GC encountered a node (00000179F35F92A0) with an unknown SEXP type: 27 at memory.c:1840
Calls: cleanEx -> showConnections -> gc
Execution halted
Flavor: r-oldrel-windows-x86_64