Generate a matrix showing the amount of each feature in each planning unit (also known as an rij matrix).
Usage
rij_matrix(x, y, ...)
# S4 method for SpatRaster,SpatRaster
rij_matrix(x, y, memory, ...)
# S4 method for sf,SpatRaster
rij_matrix(x, y, fun, memory, ...)
# S4 method for Raster,Raster
rij_matrix(x, y, memory, ...)
# S4 method for sf,Raster
rij_matrix(x, y, fun, memory, ...)
# S4 method for Spatial,Raster
rij_matrix(x, y, fun, memory, ...)Arguments
- x
terra::rast()orsf::sf()object representing planning units.- y
terra::rast()object.- ...
not used.
- memory
logicalshould calculations be performed using a method that prioritizes reduced memory consumption over speed? This is useful when processing particularly large raster datasets. IfTRUE, then calculations are performed by processing each raster layer inyin a sequential manner. IfFALSE, then calculations are performed by processing all raster layers inytogether. Defaults toNA, such that calculations are automatically performed using the best method given data requirements and the amount of available memory.- fun
characterfor summarizing values inside each planning unit. This parameter is only used when the argument toxis asf::sf()object. Defaults to"sum".
Value
A Matrix::dgCMatrix sparse matrix object.
The sparse matrix represents the spatial intersection between the
planning units and the features. Rows correspond to features,
and columns correspond to planning units. Values correspond to the amount
(or presence/absence) of the feature in the planning unit. For example,
the amount of the third species in the second planning unit would be
stored in the third column and second row.
Details
Generally, processing sf::st_sf() data takes much longer to process than
terra::rast() data.
As such, it is recommended to use terra::rast() data for planning units
where possible.
The performance of this function for large terra::rast() datasets
can be improved by increasing the GDAL cache size.
The default cache size is 25 MB.
For example, the following code can be used to set the cache size to 4 GB.
Examples
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_pu_polygons <- get_sim_pu_polygons()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_features <- get_sim_features()
# create rij matrix using raster layer planning units
rij_raster <- rij_matrix(sim_pu_raster, sim_features)
print(rij_raster)
#> 5 x 90 sparse Matrix of class "dgCMatrix"
#>
#> feature_1 0.7150548 0.6990429 0.6859317 0.6783193 0.6782107 0.6863253 0.7017231
#> feature_2 0.2900901 0.3052216 0.3266036 0.3510977 0.3750470 0.3950113 0.4084749
#> feature_3 0.8178213 0.8064534 0.7852441 0.7541320 0.7131559 0.6628435 0.6046203
#> feature_4 0.2199663 0.2713800 0.3296247 0.3900085 0.4468281 0.4946333 0.5292249
#> feature_5 0.4533809 0.4413639 0.4343547 0.4355628 0.4474871 0.4714105 0.5068508
#>
#> feature_1 0.7219284 0.7435505 0.7631509 0.7505900 0.7357790 0.7233151 0.7156627
#> feature_2 0.4142271 0.4123446 0.4038949 0.2800841 0.2967547 0.3181723 0.3409791
#> feature_3 0.5411391 0.4763017 0.4147364 0.8275983 0.8194545 0.8013062 0.7730504
#> feature_4 0.5479991 0.5498153 0.5348758 0.2208512 0.2738525 0.3350935 0.3998241
#> feature_5 0.5510725 0.5991256 0.6448475 0.4741697 0.4569167 0.4453572 0.4428185
#>
#> feature_1 0.7147517 0.7212831 0.7343615 0.7516415 0.7699543 0.7897989 0.7772875
#> feature_2 0.3615030 0.3765606 0.3841341 0.3836641 0.3759393 0.2716298 0.2901825
#> feature_3 0.7345400 0.6860553 0.6288231 0.5654560 0.5000330 0.8346555 0.8298830
#> feature_4 0.4618047 0.5148343 0.5541170 0.5767631 0.5815260 0.2249796 0.2776009
#> feature_5 0.4517499 0.4732935 0.5068285 0.5495481 0.5965013 0.5022737 0.4802429
#>
#> feature_1 0.7661407 0.7585160 0.7561970 0.7599327 0.7690603 0.7816119 0.7948577
#> feature_2 0.3119889 0.3334326 0.3508138 0.3612252 0.3631965 0.3568911 0.3438731
#> feature_3 0.8151305 0.7903745 0.7553823 0.7102172 0.6558247 0.5945687 0.5304188
#> feature_4 0.3390011 0.4047008 0.4684589 0.5238040 0.5655910 0.5906737 0.5976492
#> feature_5 0.4646212 0.4587578 0.4649526 0.4840959 0.5153215 0.5556964 0.6003282
#>
#> feature_1 0.8059966 0.8286461 0.8189873 0.8095434 0.8020377 0.7980313 0.7983423
#> feature_2 0.3265679 0.2650109 0.2854743 0.3077757 0.3280176 0.3424767 0.3485511
#> feature_3 0.4684656 0.8381119 0.8367193 0.8254115 0.8043530 0.7733527 0.7323690
#> feature_4 0.5863706 0.2330116 0.2831343 0.3416658 0.4047077 0.4665769 0.5210694
#> feature_5 0.6432595 0.5353515 0.5095593 0.4907793 0.4822721 0.4860960 0.5028092
#>
#> feature_1 0.8026563 0.8095316 0.8167838 0.8220091 0.8633036 0.8564520 0.8487799
#> feature_2 0.3453627 0.3338433 0.3163452 0.2959188 0.2603208 0.2824472 0.3051395
#> feature_3 0.6821137 0.6246373 0.5636095 0.5039577 0.8370439 0.8390267 0.8310691
#> feature_4 0.5629582 0.5888172 0.5969403 0.5869119 0.2456164 0.2910969 0.3438600
#> feature_5 0.5312405 0.5683321 0.6093528 0.6487381 0.5700379 0.5418938 0.5211593
#>
#> feature_1 0.8415392 0.8360026 0.8325552 0.8337363 0.8339533 0.8912849 0.8867860
#> feature_2 0.3242198 0.3359588 0.3303278 0.3143945 0.2708325 0.2575975 0.2809245
#> feature_3 0.8136049 0.7866027 0.7045768 0.6519656 0.5398929 0.8305082 0.8359634
#> feature_4 0.4008068 0.4572360 0.5471036 0.5718452 0.5700791 0.2632827 0.3020677
#> feature_5 0.5108758 0.5127704 0.5521633 0.5850800 0.6559584 0.6024207 0.5734186
#>
#> feature_1 0.8807106 0.8738915 0.8672825 0.8616340 0.8571536 0.8533099 0.8488533
#> feature_2 0.3037493 0.3216412 0.3308881 0.3295497 0.3179542 0.2985197 0.2749763
#> feature_3 0.8312740 0.8171942 0.7939453 0.7616869 0.7210739 0.6737961 0.6228802
#> feature_4 0.3465595 0.3945501 0.4425012 0.4859430 0.5203217 0.5418532 0.5479865
#> feature_5 0.5520171 0.5408934 0.5413573 0.5531961 0.5746993 0.6028225 0.6335756
#>
#> feature_1 0.8419809 0.9117805 0.9090526 0.9042812 0.8979800 0.8833313 0.8758136
#> feature_2 0.2512416 0.2570026 0.2809260 0.3035274 0.3201754 0.3229680 0.3083414
#> feature_3 0.5724977 0.8175488 0.8267525 0.8253873 0.8145533 0.7665224 0.7305762
#> feature_4 0.5375278 0.2860727 0.3163391 0.3506471 0.3876576 0.4590604 0.4860971
#> feature_5 0.6626552 0.6287730 0.6001593 0.5792111 0.5681102 0.5773113 0.5951498
#>
#> feature_1 0.8679237 0.8586633 0.8463772 0.9251764 0.9199524 0.9141710 0.9067838
#> feature_2 0.2863446 0.2611997 0.2370352 0.2589605 0.3047683 0.3200938 0.3251705
#> feature_3 0.6886563 0.6435894 0.5991429 0.7972733 0.8129275 0.8053969 0.7890073
#> feature_4 0.5023710 0.5050812 0.4927354 0.3133764 0.3566663 0.3816158 0.4070643
#> feature_5 0.6183284 0.6433893 0.6667633 0.6461805 0.5990580 0.5886663 0.5878728
#>
#> feature_1 0.8981802 0.8643680 0.8473654 0.9322748 0.9317639 0.9287528 0.9159813
#> feature_2 0.3186414 0.2521341 0.2280966 0.2641903 0.2872474 0.3081174 0.3253427
#> feature_3 0.7645150 0.6570162 0.6186335 0.7690766 0.7873422 0.7936492 0.7768533
#> feature_4 0.4304725 0.4559778 0.4408970 0.3437657 0.3535038 0.3646749 0.3908502
#> feature_5 0.5957192 0.6486412 0.6667338 0.6528234 0.6269820 0.6089827 0.5991743
#>
#> feature_1 0.9066665 0.8822224 0.8658993 0.8449602 0.9336696 0.9339561 0.9315392
#> feature_2 0.3170430 0.2740393 0.2478478 0.2243090 0.2736019 0.2951441 0.3143908
#> feature_3 0.7561399 0.6972504 0.6635963 0.6310227 0.7330271 0.7567351 0.7677462
#> feature_4 0.4031866 0.4135759 0.4061247 0.3879679 0.3750617 0.3743479 0.3741982
#> feature_5 0.6058227 0.6325923 0.6479832 0.6617020 0.6480033 0.6238689 0.6078230
#>
#> feature_1 0.9265291 0.9190510 0.8968893 0.8817701 0.8629507 0.8388748
#> feature_2 0.3265712 0.3282949 0.2992975 0.2742827 0.2483226 0.2255383
#> feature_3 0.7679649 0.7589920 0.7195554 0.6927857 0.6644490 0.6371886
#> feature_4 0.3753241 0.3774461 0.3785580 0.3729115 0.3601426 0.3391285
#> feature_5 0.6002170 0.6002851 0.6166496 0.6288149 0.6410043 0.6515710
# create rij matrix using polygon planning units
rij_polygons <- rij_matrix(sim_pu_polygons, sim_features)
print(rij_polygons)
#> 5 x 90 sparse Matrix of class "dgCMatrix"
#>
#> feature_1 0.7150548 0.6990429 0.6859317 0.6783193 0.6782107 0.6863253 0.7017231
#> feature_2 0.2900901 0.3052216 0.3266036 0.3510977 0.3750470 0.3950113 0.4084749
#> feature_3 0.8178213 0.8064534 0.7852441 0.7541320 0.7131559 0.6628435 0.6046203
#> feature_4 0.2199663 0.2713800 0.3296247 0.3900085 0.4468281 0.4946333 0.5292249
#> feature_5 0.4533809 0.4413639 0.4343547 0.4355628 0.4474871 0.4714105 0.5068508
#>
#> feature_1 0.7219284 0.7435505 0.7631509 0.7505900 0.7357790 0.7233151 0.7156627
#> feature_2 0.4142271 0.4123446 0.4038949 0.2800841 0.2967547 0.3181723 0.3409791
#> feature_3 0.5411391 0.4763017 0.4147364 0.8275983 0.8194545 0.8013062 0.7730504
#> feature_4 0.5479991 0.5498153 0.5348758 0.2208512 0.2738525 0.3350935 0.3998241
#> feature_5 0.5510725 0.5991256 0.6448475 0.4741697 0.4569167 0.4453572 0.4428185
#>
#> feature_1 0.7147517 0.7212831 0.7343615 0.7516415 0.7699543 0.7897989 0.7772875
#> feature_2 0.3615030 0.3765606 0.3841341 0.3836641 0.3759393 0.2716298 0.2901825
#> feature_3 0.7345400 0.6860553 0.6288231 0.5654560 0.5000330 0.8346555 0.8298830
#> feature_4 0.4618047 0.5148343 0.5541170 0.5767631 0.5815260 0.2249796 0.2776009
#> feature_5 0.4517499 0.4732935 0.5068285 0.5495481 0.5965013 0.5022737 0.4802429
#>
#> feature_1 0.7661407 0.7585160 0.7561970 0.7599327 0.7690603 0.7816119 0.7948577
#> feature_2 0.3119889 0.3334326 0.3508138 0.3612252 0.3631965 0.3568911 0.3438731
#> feature_3 0.8151305 0.7903745 0.7553823 0.7102172 0.6558247 0.5945687 0.5304188
#> feature_4 0.3390011 0.4047008 0.4684589 0.5238040 0.5655910 0.5906737 0.5976492
#> feature_5 0.4646212 0.4587578 0.4649526 0.4840959 0.5153215 0.5556964 0.6003282
#>
#> feature_1 0.8059966 0.8286461 0.8189873 0.8095434 0.8020377 0.7980313 0.7983423
#> feature_2 0.3265679 0.2650109 0.2854743 0.3077757 0.3280176 0.3424767 0.3485511
#> feature_3 0.4684656 0.8381119 0.8367193 0.8254115 0.8043530 0.7733527 0.7323690
#> feature_4 0.5863706 0.2330116 0.2831343 0.3416658 0.4047077 0.4665769 0.5210694
#> feature_5 0.6432595 0.5353515 0.5095593 0.4907793 0.4822721 0.4860960 0.5028092
#>
#> feature_1 0.8026563 0.8095316 0.8167838 0.8220091 0.8633036 0.8564520 0.8487799
#> feature_2 0.3453627 0.3338433 0.3163452 0.2959188 0.2603208 0.2824472 0.3051395
#> feature_3 0.6821137 0.6246373 0.5636095 0.5039577 0.8370439 0.8390267 0.8310691
#> feature_4 0.5629582 0.5888172 0.5969403 0.5869119 0.2456164 0.2910969 0.3438600
#> feature_5 0.5312405 0.5683321 0.6093528 0.6487381 0.5700379 0.5418938 0.5211593
#>
#> feature_1 0.8415392 0.8360026 0.8325552 0.8337363 0.8339533 0.8912849 0.8867860
#> feature_2 0.3242198 0.3359588 0.3303278 0.3143945 0.2708325 0.2575975 0.2809245
#> feature_3 0.8136049 0.7866027 0.7045768 0.6519656 0.5398929 0.8305082 0.8359634
#> feature_4 0.4008068 0.4572360 0.5471036 0.5718452 0.5700791 0.2632827 0.3020677
#> feature_5 0.5108758 0.5127704 0.5521633 0.5850800 0.6559584 0.6024207 0.5734186
#>
#> feature_1 0.8807106 0.8738915 0.8672825 0.8616340 0.8571536 0.8533099 0.8488533
#> feature_2 0.3037493 0.3216412 0.3308881 0.3295497 0.3179542 0.2985197 0.2749763
#> feature_3 0.8312740 0.8171942 0.7939453 0.7616869 0.7210739 0.6737961 0.6228802
#> feature_4 0.3465595 0.3945501 0.4425012 0.4859430 0.5203217 0.5418532 0.5479865
#> feature_5 0.5520171 0.5408934 0.5413573 0.5531961 0.5746993 0.6028225 0.6335756
#>
#> feature_1 0.8419809 0.9117805 0.9090526 0.9042812 0.8979800 0.8833313 0.8758136
#> feature_2 0.2512416 0.2570026 0.2809260 0.3035274 0.3201754 0.3229680 0.3083414
#> feature_3 0.5724977 0.8175488 0.8267525 0.8253873 0.8145533 0.7665224 0.7305762
#> feature_4 0.5375278 0.2860727 0.3163391 0.3506471 0.3876576 0.4590604 0.4860971
#> feature_5 0.6626552 0.6287730 0.6001593 0.5792111 0.5681102 0.5773113 0.5951498
#>
#> feature_1 0.8679237 0.8586633 0.8463772 0.9251764 0.9199524 0.9141710 0.9067838
#> feature_2 0.2863446 0.2611997 0.2370352 0.2589605 0.3047683 0.3200938 0.3251705
#> feature_3 0.6886563 0.6435894 0.5991429 0.7972733 0.8129275 0.8053969 0.7890073
#> feature_4 0.5023710 0.5050812 0.4927354 0.3133764 0.3566663 0.3816158 0.4070643
#> feature_5 0.6183284 0.6433893 0.6667633 0.6461805 0.5990580 0.5886663 0.5878728
#>
#> feature_1 0.8981802 0.8643680 0.8473654 0.9322748 0.9317639 0.9287528 0.9159813
#> feature_2 0.3186414 0.2521341 0.2280966 0.2641903 0.2872474 0.3081174 0.3253427
#> feature_3 0.7645150 0.6570162 0.6186335 0.7690766 0.7873422 0.7936492 0.7768533
#> feature_4 0.4304725 0.4559778 0.4408970 0.3437657 0.3535038 0.3646749 0.3908502
#> feature_5 0.5957192 0.6486412 0.6667338 0.6528234 0.6269820 0.6089827 0.5991743
#>
#> feature_1 0.9066665 0.8822224 0.8658993 0.8449602 0.9336696 0.9339561 0.9315392
#> feature_2 0.3170430 0.2740393 0.2478478 0.2243090 0.2736019 0.2951441 0.3143908
#> feature_3 0.7561399 0.6972504 0.6635963 0.6310227 0.7330271 0.7567351 0.7677462
#> feature_4 0.4031866 0.4135759 0.4061247 0.3879679 0.3750617 0.3743479 0.3741982
#> feature_5 0.6058227 0.6325923 0.6479832 0.6617020 0.6480033 0.6238689 0.6078230
#>
#> feature_1 0.9265291 0.9190510 0.8968893 0.8817701 0.8629507 0.8388748
#> feature_2 0.3265712 0.3282949 0.2992975 0.2742827 0.2483226 0.2255383
#> feature_3 0.7679649 0.7589920 0.7195554 0.6927857 0.6644490 0.6371886
#> feature_4 0.3753241 0.3774461 0.3785580 0.3729115 0.3601426 0.3391285
#> feature_5 0.6002170 0.6002851 0.6166496 0.6288149 0.6410043 0.6515710
# create rij matrix using raster planning units with multiple zones
rij_zones_raster <- rij_matrix(sim_zones_pu_raster, sim_features)
print(rij_zones_raster)
#> 5 x 90 sparse Matrix of class "dgCMatrix"
#>
#> feature_1 0.7150548 0.6990429 0.6859317 0.6783193 0.6782107 0.6863253 0.7219284
#> feature_2 0.2900901 0.3052216 0.3266036 0.3510977 0.3750470 0.3950113 0.4142271
#> feature_3 0.8178213 0.8064534 0.7852441 0.7541320 0.7131559 0.6628435 0.5411391
#> feature_4 0.2199663 0.2713800 0.3296247 0.3900085 0.4468281 0.4946333 0.5479991
#> feature_5 0.4533809 0.4413639 0.4343547 0.4355628 0.4474871 0.4714105 0.5510725
#>
#> feature_1 0.7435505 0.7631509 0.7505900 0.7357790 0.7233151 0.7156627 0.7147517
#> feature_2 0.4123446 0.4038949 0.2800841 0.2967547 0.3181723 0.3409791 0.3615030
#> feature_3 0.4763017 0.4147364 0.8275983 0.8194545 0.8013062 0.7730504 0.7345400
#> feature_4 0.5498153 0.5348758 0.2208512 0.2738525 0.3350935 0.3998241 0.4618047
#> feature_5 0.5991256 0.6448475 0.4741697 0.4569167 0.4453572 0.4428185 0.4517499
#>
#> feature_1 0.7212831 0.7343615 0.7699543 0.7861227 0.7897989 0.7772875 0.7561970
#> feature_2 0.3765606 0.3841341 0.3759393 0.3627013 0.2716298 0.2901825 0.3508138
#> feature_3 0.6860553 0.6288231 0.5000330 0.4375452 0.8346555 0.8298830 0.7553823
#> feature_4 0.5148343 0.5541170 0.5815260 0.5684388 0.2249796 0.2776009 0.4684589
#> feature_5 0.4732935 0.5068285 0.5965013 0.6415349 0.5022737 0.4802429 0.4649526
#>
#> feature_1 0.7599327 0.7690603 0.7816119 0.7948577 0.8059966 0.8286461 0.8189873
#> feature_2 0.3612252 0.3631965 0.3568911 0.3438731 0.3265679 0.2650109 0.2854743
#> feature_3 0.7102172 0.6558247 0.5945687 0.5304188 0.4684656 0.8381119 0.8367193
#> feature_4 0.5238040 0.5655910 0.5906737 0.5976492 0.5863706 0.2330116 0.2831343
#> feature_5 0.4840959 0.5153215 0.5556964 0.6003282 0.6432595 0.5353515 0.5095593
#>
#> feature_1 0.8095434 0.8020377 0.7980313 0.7983423 0.8026563 0.8095316 0.8167838
#> feature_2 0.3077757 0.3280176 0.3424767 0.3485511 0.3453627 0.3338433 0.3163452
#> feature_3 0.8254115 0.8043530 0.7733527 0.7323690 0.6821137 0.6246373 0.5636095
#> feature_4 0.3416658 0.4047077 0.4665769 0.5210694 0.5629582 0.5888172 0.5969403
#> feature_5 0.4907793 0.4822721 0.4860960 0.5028092 0.5312405 0.5683321 0.6093528
#>
#> feature_1 0.8220091 0.8633036 0.8564520 0.8487799 0.8360026 0.8330038 0.8337363
#> feature_2 0.2959188 0.2603208 0.2824472 0.3051395 0.3359588 0.3380832 0.3143945
#> feature_3 0.5039577 0.8370439 0.8390267 0.8310691 0.7866027 0.7500573 0.6519656
#> feature_4 0.5869119 0.2456164 0.2910969 0.3438600 0.4572360 0.5076754 0.5718452
#> feature_5 0.6487381 0.5700379 0.5418938 0.5211593 0.5127704 0.5270232 0.5850800
#>
#> feature_1 0.8348882 0.8339533 0.8912849 0.8867860 0.8807106 0.8738915 0.8672825
#> feature_2 0.2933767 0.2708325 0.2575975 0.2809245 0.3037493 0.3216412 0.3308881
#> feature_3 0.5955431 0.5398929 0.8305082 0.8359634 0.8312740 0.8171942 0.7939453
#> feature_4 0.5797670 0.5700791 0.2632827 0.3020677 0.3465595 0.3945501 0.4425012
#> feature_5 0.6213430 0.6559584 0.6024207 0.5734186 0.5520171 0.5408934 0.5413573
#>
#> feature_1 0.8616340 0.8571536 0.8488533 0.9117805 0.9090526 0.9042812 0.8979800
#> feature_2 0.3295497 0.3179542 0.2749763 0.2570026 0.2809260 0.3035274 0.3201754
#> feature_3 0.7616869 0.7210739 0.6228802 0.8175488 0.8267525 0.8253873 0.8145533
#> feature_4 0.4859430 0.5203217 0.5479865 0.2860727 0.3163391 0.3506471 0.3876576
#> feature_5 0.5531961 0.5746993 0.6335756 0.6287730 0.6001593 0.5792111 0.5681102
#>
#> feature_1 0.8908018 0.8833313 0.8758136 0.8679237 0.8586633 0.8463772 0.9251764
#> feature_2 0.3271982 0.3229680 0.3083414 0.2863446 0.2611997 0.2370352 0.2589605
#> feature_3 0.7947903 0.7665224 0.7305762 0.6886563 0.6435894 0.5991429 0.7972733
#> feature_4 0.4249398 0.4590604 0.4860971 0.5023710 0.5050812 0.4927354 0.3133764
#> feature_5 0.5676758 0.5773113 0.5951498 0.6183284 0.6433893 0.6667633 0.6461805
#>
#> feature_1 0.9237208 0.9199524 0.9141710 0.9067838 0.8981802 0.8885294 0.8775561
#> feature_2 0.2828033 0.3047683 0.3200938 0.3251705 0.3186414 0.3017897 0.2781030
#> feature_3 0.8107003 0.8129275 0.8053969 0.7890073 0.7645150 0.7330004 0.6962687
#> feature_4 0.3337335 0.3566663 0.3816158 0.4070643 0.4304725 0.4485815 0.4580145
#> feature_5 0.6187615 0.5990580 0.5886663 0.5878728 0.5957192 0.6102524 0.6288607
#>
#> feature_1 0.8643680 0.8473654 0.9322748 0.9317639 0.9287528 0.9234146 0.9159813
#> feature_2 0.2521341 0.2280966 0.2641903 0.2872474 0.3081174 0.3219892 0.3253427
#> feature_3 0.6570162 0.6186335 0.7690766 0.7873422 0.7936492 0.7897301 0.7768533
#> feature_4 0.4559778 0.4408970 0.3437657 0.3535038 0.3646749 0.3773969 0.3908502
#> feature_5 0.6486412 0.6667338 0.6528234 0.6269820 0.6089827 0.5997976 0.5991743
#>
#> feature_1 0.9066665 0.8955188 0.8822224 0.8658993 0.8449602 0.9336696 0.9339561
#> feature_2 0.3170430 0.2986799 0.2740393 0.2478478 0.2243090 0.2736019 0.2951441
#> feature_3 0.7561399 0.7289606 0.6972504 0.6635963 0.6310227 0.7330271 0.7567351
#> feature_4 0.4031866 0.4117466 0.4135759 0.4061247 0.3879679 0.3750617 0.3743479
#> feature_5 0.6058227 0.6177441 0.6325923 0.6479832 0.6617020 0.6480033 0.6238689
#>
#> feature_1 0.9315392 0.9190510 0.9091912 0.8968893 0.8629507 0.8388748
#> feature_2 0.3143908 0.3282949 0.3186099 0.2992975 0.2483226 0.2255383
#> feature_3 0.7677462 0.7589920 0.7423053 0.7195554 0.6644490 0.6371886
#> feature_4 0.3741982 0.3774461 0.3792402 0.3785580 0.3601426 0.3391285
#> feature_5 0.6078230 0.6002851 0.6064383 0.6166496 0.6410043 0.6515710
# }