Compute Moran's Eigenvector Maps across multiple distance thresholds
Source:R/mem_multithreshold.R
mem_multithreshold.RdComputes Moran's Eigenvector Maps (MEMs) using mem() at multiple distance thresholds and combines them into a single data frame. This creates spatial predictors capturing patterns at different spatial scales.
Usage
mem_multithreshold(
distance.matrix = NULL,
distance.thresholds = NULL,
max.spatial.predictors = NULL
)Arguments
- distance.matrix
Numeric distance matrix between spatial locations.
- distance.thresholds
Numeric vector of distance thresholds. Each threshold defines the maximum distance for spatial neighbors at that scale. Default:
NULL(automatically computed withdefault_distance_thresholds()).- max.spatial.predictors
Integer specifying the maximum number of spatial predictors to return. If the total number of MEMs exceeds this value, only the first
max.spatial.predictorscolumns are returned. Default:NULL(no limit).
Value
Data frame with one row per observation (matching distance.matrix dimensions) and columns representing MEMs at different distance thresholds. Column names follow the pattern spatial_predictor_<threshold>_<number> (e.g., "spatial_predictor_0_1", "spatial_predictor_1000_2").
Details
This function generates spatial predictors at multiple spatial scales by computing MEMs at different distance thresholds. Different thresholds capture spatial patterns at different scales:
Smaller thresholds (e.g., 0) capture fine-scale spatial patterns
Larger thresholds capture broad-scale spatial patterns
Algorithm:
For each distance threshold, calls
mem()to compute MEMsEach
mem()call applies the threshold, double-centers the matrix, and extracts positive eigenvectorsCombines all MEMs into a single data frame
Optionally limits the total number of predictors with
max.spatial.predictors
The resulting MEMs are used as spatial predictors in rf_spatial() to model spatial autocorrelation at multiple scales simultaneously.
See also
mem(), rf_spatial(), default_distance_thresholds(), double_center_distance_matrix()
Other spatial_analysis:
filter_spatial_predictors(),
mem(),
moran(),
moran_multithreshold(),
pca(),
pca_multithreshold(),
rank_spatial_predictors(),
residuals_diagnostics(),
residuals_test(),
select_spatial_predictors_recursive(),
select_spatial_predictors_sequential()
Examples
data(plants_distance)
# Compute MEMs for multiple distance thresholds
mems <- mem_multithreshold(
distance.matrix = plants_distance,
distance.thresholds = c(0, 1000, 5000)
)
# View structure
head(mems)
#> spatial_predictor_0_1 spatial_predictor_0_2 spatial_predictor_0_3
#> 1 -0.025921732 0.005220262 -0.04169686
#> 2 -0.099667898 0.053971309 -0.13244795
#> 3 -0.001047654 -0.014304635 0.04436019
#> 4 -0.016569475 0.004799116 -0.03074566
#> 5 -0.022576055 0.001959546 -0.02303677
#> 6 -0.015525218 0.002374152 -0.01979535
#> spatial_predictor_0_4 spatial_predictor_0_5 spatial_predictor_0_6
#> 1 -0.0363323858 -0.029427120 -0.004345679
#> 2 0.3826927819 0.205013057 0.006481398
#> 3 -0.0031386301 -0.026240412 0.036601404
#> 4 0.0005170325 -0.014444892 -0.003356330
#> 5 -0.0524239401 0.006729439 -0.006797669
#> 6 -0.0338955963 -0.007884866 -0.002744701
#> spatial_predictor_0_7 spatial_predictor_0_8 spatial_predictor_0_9
#> 1 0.0059417525 -0.003509625 -0.006296265
#> 2 -0.0338063656 0.174542054 0.024450222
#> 3 -0.0616077280 0.019577234 -0.001448598
#> 4 0.0009804302 0.010526820 -0.002889298
#> 5 0.0025571100 -0.011762268 0.004846323
#> 6 0.0029345255 -0.007054664 -0.002369704
#> spatial_predictor_0_10 spatial_predictor_0_11 spatial_predictor_0_12
#> 1 0.002487306 0.005395791 -0.011379866
#> 2 0.005829746 -0.054961827 0.153839059
#> 3 -0.018299683 0.039246420 -0.008007024
#> 4 0.008730811 0.002146499 0.007681169
#> 5 -0.045296964 -0.039538738 -0.011040322
#> 6 -0.009715209 -0.004288805 -0.009536937
#> spatial_predictor_0_13 spatial_predictor_0_14 spatial_predictor_0_15
#> 1 0.191604368 -0.0072850442 -0.026412798
#> 2 0.019835793 -0.0057251178 -0.062892949
#> 3 -0.014692044 -0.0201202412 0.008020865
#> 4 -0.008201179 0.0001369046 0.006005988
#> 5 0.164381811 -0.0088932051 -0.021214181
#> 6 0.178202420 -0.0060291344 -0.016907370
#> spatial_predictor_0_16 spatial_predictor_0_17 spatial_predictor_0_18
#> 1 0.109791768 0.12559194 0.107268296
#> 2 -0.003832431 0.03668636 0.014722504
#> 3 0.048006557 -0.17744495 0.150866603
#> 4 -0.024858297 -0.01844209 -0.009470203
#> 5 0.108145269 0.02998075 -0.013711486
#> 6 0.083504053 0.06773982 0.047243449
#> spatial_predictor_0_19 spatial_predictor_0_20 spatial_predictor_0_21
#> 1 -0.012273527 0.046229850 -0.04237342
#> 2 0.202464657 0.082229660 0.15771710
#> 3 -0.003222866 0.030550230 0.08951193
#> 4 -0.011830035 0.040433340 -0.02567659
#> 5 0.034505110 -0.178892320 0.04343963
#> 6 -0.009624029 0.008015184 -0.03834844
#> spatial_predictor_0_22 spatial_predictor_0_23 spatial_predictor_0_24
#> 1 0.03596611 0.01118606 0.10224462
#> 2 0.04038242 -0.11437678 -0.01012395
#> 3 -0.04504866 0.13461198 -0.02047940
#> 4 -0.06842079 -0.01692236 0.10377418
#> 5 -0.13917976 -0.04071620 0.05601777
#> 6 -0.05776603 -0.02652804 0.09311909
#> spatial_predictor_0_25 spatial_predictor_0_26 spatial_predictor_0_27
#> 1 -0.0011848037 -0.02412275 -0.0007539716
#> 2 -0.0013068838 -0.07838816 0.0235252584
#> 3 -0.0387798586 -0.07535325 0.1064394216
#> 4 0.0049076695 0.03958842 0.0382303597
#> 5 -0.0005085273 -0.04327854 0.0018750185
#> 6 -0.0021059632 -0.04019586 0.0116133257
#> spatial_predictor_0_28 spatial_predictor_0_29 spatial_predictor_0_30
#> 1 -0.01644154 -0.052111626 0.065321865
#> 2 -0.03939033 0.003796367 -0.003727503
#> 3 0.04395511 -0.014889858 -0.004292680
#> 4 -0.01956660 0.097801181 -0.133857876
#> 5 -0.02127332 -0.066337960 0.089123320
#> 6 -0.04411492 -0.104538187 0.131117350
#> spatial_predictor_0_31 spatial_predictor_0_32 spatial_predictor_0_33
#> 1 -0.009110300 -0.005527607 0.0024141508
#> 2 0.006294934 -0.009297452 -0.0011867862
#> 3 -0.145501124 0.048790879 0.1471245843
#> 4 0.009342614 -0.004236143 0.0002171203
#> 5 -0.008281460 0.005782912 -0.0083726169
#> 6 -0.014943405 -0.004594169 -0.0026991507
#> spatial_predictor_0_34 spatial_predictor_0_35 spatial_predictor_0_36
#> 1 0.0995535458 -0.02277398 0.032366522
#> 2 -0.0019609284 -0.02249876 0.003705815
#> 3 0.0009205415 -0.02456633 -0.047683754
#> 4 -0.0217659867 -0.01207111 -0.002365715
#> 5 -0.0976154193 0.02262826 -0.068423826
#> 6 0.0986535382 -0.02665171 0.001086085
#> spatial_predictor_0_37 spatial_predictor_0_38 spatial_predictor_0_39
#> 1 -0.095207273 0.001470171 0.0220490866
#> 2 -0.009986122 0.001834603 0.0072022025
#> 3 -0.030017972 0.194260820 0.0002023167
#> 4 0.003665707 -0.002490411 0.0516209027
#> 5 0.207826521 0.003515905 0.0673115372
#> 6 -0.021607620 0.005222174 0.0807527077
#> spatial_predictor_0_40 spatial_predictor_0_41 spatial_predictor_0_42
#> 1 -0.020744827 0.063465665 -0.001583198
#> 2 0.012513154 0.006773117 0.005153518
#> 3 0.117669489 0.028793256 0.002015785
#> 4 -0.004805407 -0.052984447 -0.011659562
#> 5 0.034927798 -0.171082675 0.006518095
#> 6 -0.013496439 -0.019748363 0.002035536
#> spatial_predictor_0_43 spatial_predictor_0_44 spatial_predictor_0_45
#> 1 0.0008763528 -0.004924952 0.004441966
#> 2 -0.0061488312 0.005445949 0.004192209
#> 3 0.0145429165 0.054685966 -0.031594959
#> 4 0.0007896651 0.012327561 -0.125926338
#> 5 0.0029789094 0.004475698 -0.086190522
#> 6 0.0001205283 -0.003080538 -0.102438242
#> spatial_predictor_0_46 spatial_predictor_0_47 spatial_predictor_0_48
#> 1 -0.010614314 0.109509260 -0.0291230983
#> 2 -0.008707069 0.004100545 -0.0083583731
#> 3 0.154873292 0.012040765 -0.0001406001
#> 4 -0.008602096 -0.251552263 0.1535405874
#> 5 -0.026927424 0.054298969 0.0705021721
#> 6 -0.027814286 0.034531636 -0.0040461329
#> spatial_predictor_0_49 spatial_predictor_0_50 spatial_predictor_0_51
#> 1 -0.014141207 0.065556939 0.008376850
#> 2 0.004919289 0.002300612 -0.051908297
#> 3 -0.020129602 -0.006858614 0.007107829
#> 4 0.034783817 -0.001360919 -0.012019909
#> 5 0.001021822 -0.206500175 -0.033419293
#> 6 -0.008599773 -0.018817582 0.010105730
#> spatial_predictor_0_52 spatial_predictor_0_53 spatial_predictor_0_54
#> 1 -0.270094108 0.016737186 -0.0008228533
#> 2 -0.017095345 -0.030601816 -0.0067216761
#> 3 -0.004057733 -0.033720564 0.0245429177
#> 4 -0.203816105 0.006645515 0.0057952190
#> 5 -0.008175125 -0.005998713 0.0025015180
#> 6 -0.113070059 0.004311746 0.0054377734
#> spatial_predictor_0_55 spatial_predictor_0_56 spatial_predictor_0_57
#> 1 0.015664149 -0.029544538 -0.08246695
#> 2 -0.031129552 0.054424622 0.02365053
#> 3 -0.211044319 -0.115905532 0.01459723
#> 4 0.016373143 -0.030249584 0.03505309
#> 5 -0.003434644 -0.014103637 -0.01536707
#> 6 0.010990643 -0.004441768 0.11325329
#> spatial_predictor_0_58 spatial_predictor_0_59 spatial_predictor_0_60
#> 1 0.037100611 0.096731288 0.005897200
#> 2 0.044103974 -0.007519343 0.030608519
#> 3 -0.016563181 0.010981383 0.050403472
#> 4 0.002291122 0.068845992 -0.004341585
#> 5 -0.035271720 -0.122375412 0.002701816
#> 6 -0.043342941 -0.007111342 -0.005799743
#> spatial_predictor_0_61 spatial_predictor_0_62 spatial_predictor_0_63
#> 1 0.008479632 -0.09335058 -0.077496495
#> 2 -0.098699533 -0.07575240 0.105276944
#> 3 -0.007539959 -0.01065693 0.008150333
#> 4 0.024917090 0.03804528 0.019850232
#> 5 -0.010265142 0.07225392 0.040042615
#> 6 0.029787167 0.02760241 -0.138702962
#> spatial_predictor_0_64 spatial_predictor_0_65 spatial_predictor_0_66
#> 1 -0.011023456 0.013749019 -0.00670314
#> 2 0.004368849 -0.026456375 -0.06720143
#> 3 -0.042359390 0.036145409 -0.02388848
#> 4 0.009653611 0.003252935 0.03549700
#> 5 -0.020165538 -0.041935828 -0.01486902
#> 6 -0.057357348 -0.169761130 -0.23725453
#> spatial_predictor_0_67 spatial_predictor_0_68 spatial_predictor_0_69
#> 1 -0.092711212 0.04605361 0.14007779
#> 2 0.026432216 -0.04457336 0.05193081
#> 3 -0.002307292 0.05047973 -0.02176933
#> 4 0.047805935 0.01809031 0.01189454
#> 5 0.086121023 -0.07767593 -0.12162162
#> 6 0.012066368 -0.08022491 -0.20666531
#> spatial_predictor_0_70 spatial_predictor_0_71 spatial_predictor_0_72
#> 1 -0.03865851 0.01149307 0.06794522
#> 2 0.03527919 0.04203337 0.01282620
#> 3 0.05057488 -0.02103948 -0.01111083
#> 4 -0.09374814 -0.19466068 0.02195679
#> 5 0.06707658 0.05352161 -0.03062204
#> 6 0.08652559 0.01833518 0.15627827
#> spatial_predictor_0_73 spatial_predictor_0_74 spatial_predictor_0_75
#> 1 -0.035343901 -0.017915068 0.015542313
#> 2 -0.009111780 0.005375863 0.017540288
#> 3 -0.009049244 0.036453627 0.233927322
#> 4 0.063050823 0.063122301 0.030319256
#> 5 -0.047062419 -0.031912283 0.023691678
#> 6 0.016779720 0.013204202 0.005625772
#> spatial_predictor_0_76 spatial_predictor_0_77 spatial_predictor_0_78
#> 1 0.03292822 0.069727084 -0.017757708
#> 2 -0.03399917 -0.005818549 -0.009577624
#> 3 0.04447370 -0.032276461 -0.003526079
#> 4 -0.09559430 0.063758722 0.064366459
#> 5 0.08129817 0.182586145 0.077074235
#> 6 -0.08945159 -0.169983941 0.035904400
#> spatial_predictor_0_79 spatial_predictor_0_80 spatial_predictor_0_81
#> 1 0.013920614 -0.00821663 -0.015901522
#> 2 0.034844339 0.01212693 -0.003600061
#> 3 0.062467634 0.03735965 -0.087326353
#> 4 0.087933528 -0.04193539 -0.041426923
#> 5 0.049456016 0.18633301 0.023410926
#> 6 0.005426446 0.05857672 -0.020355821
#> spatial_predictor_0_82 spatial_predictor_0_83 spatial_predictor_0_84
#> 1 0.02664505 0.051483998 -0.059455466
#> 2 0.02960288 0.018758921 0.013363577
#> 3 -0.03510723 -0.009796530 -0.061048344
#> 4 0.08514307 0.054298933 0.005360485
#> 5 0.04332644 -0.044720417 -0.006508432
#> 6 0.03165506 -0.006830768 -0.034532156
#> spatial_predictor_0_85 spatial_predictor_0_86 spatial_predictor_0_87
#> 1 0.09722934 -0.097284331 0.086610223
#> 2 0.01470533 0.003332324 -0.020797134
#> 3 0.04897573 0.034998512 -0.040119569
#> 4 -0.01867608 -0.022669681 0.007774045
#> 5 -0.01168749 -0.030586171 -0.119438460
#> 6 -0.01424540 -0.044059874 -0.104983179
#> spatial_predictor_1000_1 spatial_predictor_1000_2 spatial_predictor_1000_3
#> 1 0.08658532 -0.03231611 0.068231938
#> 2 0.02042442 -0.15314615 0.013698668
#> 3 -0.08057675 0.07390865 0.071567363
#> 4 0.06696771 -0.06176788 0.075926333
#> 5 0.10024445 0.04832441 -0.011952038
#> 6 0.08412779 0.02766366 0.005880981
#> spatial_predictor_1000_4 spatial_predictor_1000_5 spatial_predictor_1000_6
#> 1 3.217489e-03 -0.07018759 -0.026959299
#> 2 -2.473432e-02 0.11286917 -0.003267409
#> 3 -5.019297e-02 -0.02021901 -0.015663412
#> 4 -3.161629e-05 -0.03105710 -0.063646355
#> 5 -3.334773e-03 -0.04076160 -0.003326743
#> 6 -1.666075e-03 -0.04259223 -0.035163054
#> spatial_predictor_1000_7 spatial_predictor_1000_8 spatial_predictor_1000_9
#> 1 0.0215975143 0.0790860856 0.005040687
#> 2 -0.0026196641 -0.0536572424 -0.005489766
#> 3 0.0162036065 -0.0605032737 -0.103856648
#> 4 -0.0009505308 -0.0007683611 0.020450989
#> 5 0.0328064995 0.1410146912 -0.101773566
#> 6 0.0340545764 0.1560500779 -0.106090804
#> spatial_predictor_1000_10 spatial_predictor_1000_11 spatial_predictor_1000_12
#> 1 0.003350271 0.07608810 -0.03150507
#> 2 -0.046485866 -0.01337414 0.04083626
#> 3 -0.075473384 0.05253653 -0.04096583
#> 4 -0.035159892 -0.08299955 -0.11650787
#> 5 0.026591665 0.09650615 0.11436048
#> 6 0.016861260 0.03868709 0.06749009
#> spatial_predictor_1000_13 spatial_predictor_1000_14 spatial_predictor_1000_15
#> 1 -0.07222673 0.04009140 -0.054078382
#> 2 -0.03145447 -0.08060326 0.025209320
#> 3 -0.04615498 -0.09056808 0.004550364
#> 4 0.06517494 0.01875903 -0.099293633
#> 5 -0.05184261 0.06171003 0.052480136
#> 6 -0.01316298 0.01867532 -0.005327623
#> spatial_predictor_1000_16 spatial_predictor_1000_17 spatial_predictor_1000_18
#> 1 0.017810969 0.0113269073 -0.010257510
#> 2 0.028927460 0.0477794844 0.114391523
#> 3 -0.003050005 -0.0498092053 0.080532853
#> 4 -0.027069687 -0.0398669021 0.074806234
#> 5 -0.038380623 0.0002771587 0.032738537
#> 6 0.071443001 0.0495003413 -0.009328155
#> spatial_predictor_1000_19 spatial_predictor_1000_20 spatial_predictor_1000_21
#> 1 0.04813674 -0.0437974692 0.01102296
#> 2 -0.03764689 -0.0183330366 -0.03000959
#> 3 0.07959245 -0.0712543891 -0.03850909
#> 4 -0.02019075 0.0099556770 -0.04529636
#> 5 0.03940559 0.0004961722 -0.02981489
#> 6 0.03468993 -0.0032133431 0.03370076
#> spatial_predictor_1000_22 spatial_predictor_1000_23 spatial_predictor_1000_24
#> 1 -0.027440375 -0.034200698 -0.13298626
#> 2 0.218439020 -0.187647453 -0.20219303
#> 3 -0.090324377 0.073754536 -0.04432696
#> 4 0.022049436 0.003090971 -0.03809253
#> 5 0.006696832 0.023545706 -0.03388560
#> 6 -0.040124541 -0.008380628 -0.01783676
#> spatial_predictor_1000_25 spatial_predictor_1000_26 spatial_predictor_1000_27
#> 1 0.11636893 0.132667519 0.151602883
#> 2 -0.02570768 -0.084066858 -0.040487923
#> 3 -0.10873386 0.003123965 -0.032845800
#> 4 0.02217474 0.061350239 0.002773503
#> 5 0.04045397 0.055444249 -0.067371147
#> 6 0.01030714 0.007858013 0.103901541
#> spatial_predictor_1000_28 spatial_predictor_1000_29 spatial_predictor_1000_30
#> 1 0.12106686 0.0317377071 -0.1594341401
#> 2 0.03077180 0.0822242624 -0.0252894818
#> 3 0.01742517 0.0005693548 0.0002860974
#> 4 0.04540237 0.0294925171 -0.0148152924
#> 5 -0.05796736 -0.0203579338 0.1131264022
#> 6 0.05061757 0.0118455318 -0.1125735350
#> spatial_predictor_1000_31 spatial_predictor_1000_32 spatial_predictor_1000_33
#> 1 -0.04135196 -0.04034484 0.03137691
#> 2 0.07494952 0.03754340 -0.05337929
#> 3 0.02038282 -0.01248751 -0.11382103
#> 4 0.13600669 0.02155827 0.06537506
#> 5 0.04101160 0.09961856 -0.02509947
#> 6 -0.13869427 -0.11630926 0.02115162
#> spatial_predictor_1000_34 spatial_predictor_1000_35 spatial_predictor_1000_36
#> 1 -0.01452556 0.13409851 -0.07015630
#> 2 -0.08013209 0.06133915 -0.04826374
#> 3 -0.02198527 -0.03069170 0.03953152
#> 4 -0.05692688 0.11252861 0.09945197
#> 5 0.02887879 0.17843541 0.03495999
#> 6 0.05676607 0.03817728 0.03808089
#> spatial_predictor_1000_37 spatial_predictor_1000_38 spatial_predictor_1000_39
#> 1 -0.16441816 -0.15123087 -0.01569234
#> 2 0.04761633 0.06699646 0.04794570
#> 3 0.02162981 -0.02815115 0.01841747
#> 4 0.01412370 0.06762155 0.15921531
#> 5 -0.02014062 0.20978338 -0.17502231
#> 6 -0.03451317 -0.06725425 -0.25219520
#> spatial_predictor_1000_40 spatial_predictor_1000_41 spatial_predictor_1000_42
#> 1 -0.052881697 0.041360648 -0.03712966
#> 2 -0.045060093 0.080340444 -0.06707884
#> 3 -0.036774983 0.063167729 -0.08381353
#> 4 0.012532971 0.002011998 0.06939317
#> 5 0.001508912 -0.045524768 0.02468731
#> 6 -0.032933814 0.014634112 -0.09950179
#> spatial_predictor_1000_43 spatial_predictor_1000_44 spatial_predictor_1000_45
#> 1 0.0009924187 0.03406906 -0.063176151
#> 2 0.0475707151 -0.06485353 -0.029631986
#> 3 -0.0795885376 -0.09240317 -0.109308147
#> 4 -0.0707769709 0.03382653 0.012411380
#> 5 0.0608986522 -0.08283213 -0.008718039
#> 6 -0.0270807705 -0.06530144 0.117763752
#> spatial_predictor_1000_46 spatial_predictor_1000_47 spatial_predictor_1000_48
#> 1 -0.002894634 0.04297005 -0.050901484
#> 2 -0.016717014 0.02501987 0.032720687
#> 3 -0.132058215 -0.07947253 0.009571893
#> 4 0.065341767 -0.01786437 0.038196552
#> 5 -0.073750376 -0.03367780 -0.144606160
#> 6 -0.038034542 0.03967850 0.138418127
#> spatial_predictor_1000_49 spatial_predictor_1000_50 spatial_predictor_1000_51
#> 1 0.14359592 0.01846099 0.10635259
#> 2 -0.03772670 -0.02690010 -0.05430504
#> 3 0.02445001 -0.13600703 0.02110699
#> 4 -0.03080022 0.06074740 0.14563081
#> 5 0.05615738 0.01735644 -0.02263605
#> 6 -0.16474718 -0.07841596 -0.06888592
#> spatial_predictor_1000_52 spatial_predictor_1000_53 spatial_predictor_1000_54
#> 1 -0.000241508 -0.10875651 0.01367290
#> 2 0.015850104 -0.08289016 -0.03913177
#> 3 -0.093901157 0.05106307 -0.01105852
#> 4 -0.076717763 -0.15232997 -0.01537210
#> 5 -0.015168571 -0.01763253 -0.01283656
#> 6 -0.011287484 0.06269636 0.01206870
#> spatial_predictor_1000_55 spatial_predictor_1000_56 spatial_predictor_1000_57
#> 1 -0.08309078 -0.032753222 0.03403235
#> 2 0.02253975 0.001842601 -0.02742228
#> 3 0.24374902 0.033593021 0.31853032
#> 4 -0.05601574 -0.008248447 0.04937029
#> 5 -0.05061777 -0.035996369 0.06950188
#> 6 0.06594581 0.078808216 -0.09638643
#> spatial_predictor_1000_58 spatial_predictor_1000_59 spatial_predictor_1000_60
#> 1 -0.06191693 0.04216419 -0.0231419456
#> 2 0.02030021 -0.08578727 0.0000734037
#> 3 -0.03935156 0.22351219 -0.0436377191
#> 4 -0.02356013 0.03949120 -0.0371362255
#> 5 0.14295371 -0.01720984 -0.0278205957
#> 6 -0.04028605 -0.02803037 -0.0027459329
#> spatial_predictor_1000_61 spatial_predictor_1000_62 spatial_predictor_1000_63
#> 1 0.098280214 0.079847623 -0.07078457
#> 2 0.022023086 -0.073366242 -0.07414266
#> 3 0.035103433 -0.041340942 -0.10674560
#> 4 0.012275864 0.046810287 0.10887253
#> 5 0.004667341 -0.017673312 0.07930793
#> 6 0.002605628 -0.003060575 -0.11106312
#> spatial_predictor_1000_64 spatial_predictor_1000_65 spatial_predictor_1000_66
#> 1 -0.009341237 0.09502692 0.026757354
#> 2 -0.032701838 0.08127296 -0.031106869
#> 3 -0.047211284 0.02020700 -0.082326152
#> 4 -0.032450026 -0.04638701 -0.083501222
#> 5 -0.017794081 -0.06405042 0.003938521
#> 6 -0.032329644 0.12832392 -0.038232356
#> spatial_predictor_1000_67 spatial_predictor_1000_68 spatial_predictor_1000_69
#> 1 0.017717805 -0.056097200 0.02021046
#> 2 -0.036041310 0.053447156 0.03501488
#> 3 -0.024005564 -0.035042736 -0.12319399
#> 4 0.057734523 0.090578300 0.00989136
#> 5 0.001937527 -0.002230929 -0.07911011
#> 6 0.094831138 0.108278776 0.14358105
#> spatial_predictor_1000_70 spatial_predictor_1000_71 spatial_predictor_1000_72
#> 1 -0.037729770 0.05672643 -0.14152073
#> 2 0.044920980 -0.09919566 -0.10969028
#> 3 -0.069083674 0.02324144 0.01200051
#> 4 -0.069607759 0.02891570 -0.08000965
#> 5 -0.002556965 -0.07422993 -0.07798042
#> 6 -0.030013400 -0.08394401 -0.03210298
#> spatial_predictor_1000_73 spatial_predictor_5000_1 spatial_predictor_5000_2
#> 1 -0.019293352 0.07199820 0.0239353948
#> 2 -0.039012292 0.02746351 -0.0172216615
#> 3 -0.099882513 -0.07597655 -0.0485147776
#> 4 0.084892409 0.05829797 0.0422465233
#> 5 -0.057933974 0.08477245 -0.0139102904
#> 6 -0.002781528 0.08231523 -0.0002748449
#> spatial_predictor_5000_3 spatial_predictor_5000_4 spatial_predictor_5000_5
#> 1 -0.05486655 -0.107006150 -0.01648821
#> 2 0.07561315 0.004184952 -0.10274275
#> 3 0.05281807 -0.039695517 0.01183390
#> 4 -0.07324577 0.089557939 -0.06019356
#> 5 0.06659431 -0.077785063 -0.02715719
#> 6 0.04308426 -0.105113236 -0.02640728
#> spatial_predictor_5000_6 spatial_predictor_5000_7 spatial_predictor_5000_8
#> 1 0.06380175 0.0596363719 0.04055366
#> 2 -0.22859211 -0.0555953998 0.11515747
#> 3 -0.01442345 -0.0679633788 -0.02186725
#> 4 -0.01573504 0.0005949253 0.01316262
#> 5 -0.03831650 -0.0073180397 -0.05883968
#> 6 -0.04949227 0.0051429213 -0.03793394
#> spatial_predictor_5000_9 spatial_predictor_5000_10 spatial_predictor_5000_11
#> 1 0.07460767 -0.073165410 0.003796257
#> 2 0.03351724 -0.082353191 0.162996915
#> 3 0.01942256 0.007168803 0.052610228
#> 4 0.06946470 0.005520727 0.031301475
#> 5 0.05320099 0.057930020 -0.106560925
#> 6 -0.01678641 0.065520476 -0.142799548
#> spatial_predictor_5000_12 spatial_predictor_5000_13 spatial_predictor_5000_14
#> 1 0.019068820 -0.047265993 -0.027035214
#> 2 -0.004935663 0.053820272 -0.087927830
#> 3 -0.017920179 0.053665862 -0.008093601
#> 4 0.098079018 0.053854091 0.038132732
#> 5 -0.056446799 -0.003229838 0.098252346
#> 6 -0.028161194 0.059303749 0.068278870
#> spatial_predictor_5000_15 spatial_predictor_5000_16 spatial_predictor_5000_17
#> 1 0.09498974 -0.042605193 -0.018035611
#> 2 0.03089895 0.041740618 -0.007017234
#> 3 0.06327196 0.028093803 0.044044863
#> 4 -0.01158486 0.009666273 0.018656379
#> 5 -0.05696720 0.038722124 -0.022586472
#> 6 -0.10562548 0.067736839 0.015524225
#> spatial_predictor_5000_18 spatial_predictor_5000_19 spatial_predictor_5000_20
#> 1 0.08895054 0.03057443 -0.036816173
#> 2 0.02175828 0.03546826 -0.020434265
#> 3 0.02615244 -0.03165395 0.043372885
#> 4 -0.04650178 0.04379053 -0.057824899
#> 5 0.03234194 0.12648227 0.003336959
#> 6 -0.00410192 0.06011295 0.024366217
#> spatial_predictor_5000_21 spatial_predictor_5000_22 spatial_predictor_5000_23
#> 1 -0.01734864 0.01266707 -0.03726276
#> 2 0.04108243 0.02825731 -0.10133496
#> 3 -0.01109768 0.06256047 0.11106185
#> 4 -0.07377196 -0.14922578 0.05013686
#> 5 0.12247721 -0.09068544 0.07892492
#> 6 -0.07167867 -0.04517031 0.03675831
#> spatial_predictor_5000_24 spatial_predictor_5000_25 spatial_predictor_5000_26
#> 1 -0.085181897 0.05823798 -0.04566431
#> 2 -0.080434704 -0.06620975 0.05519300
#> 3 -0.061308086 0.06006801 0.04667698
#> 4 -0.063911399 -0.02140509 -0.03184073
#> 5 -0.048421387 -0.01488175 -0.02739511
#> 6 -0.005783037 0.02433378 0.02815328
#> spatial_predictor_5000_27 spatial_predictor_5000_28 spatial_predictor_5000_29
#> 1 0.028342715 0.016901231 -0.03203252
#> 2 -0.129198741 0.025422107 0.05315748
#> 3 -0.006492682 -0.011466010 0.03215453
#> 4 -0.096787057 0.037257125 0.12248712
#> 5 -0.119201398 -0.120391473 -0.07296944
#> 6 -0.096943873 -0.001978548 -0.02276658
#> spatial_predictor_5000_30 spatial_predictor_5000_31 spatial_predictor_5000_32
#> 1 -0.001273783 0.08780673 -0.080724095
#> 2 0.102338090 -0.06626997 -0.001334075
#> 3 -0.042822088 -0.01387536 -0.003407348
#> 4 -0.198818368 -0.13416812 -0.024851630
#> 5 0.010207573 0.07288329 0.092361713
#> 6 -0.033499684 0.11612793 -0.162980755
#> spatial_predictor_5000_33 spatial_predictor_5000_34 spatial_predictor_5000_35
#> 1 0.01781027 0.002248750 0.01671515
#> 2 -0.02990437 -0.032641670 0.08678797
#> 3 0.06393024 0.010420819 -0.02761100
#> 4 0.03092086 -0.137247555 -0.12131250
#> 5 -0.01943636 -0.054533286 -0.04251760
#> 6 -0.08695361 -0.005047018 -0.11509346
#> spatial_predictor_5000_36 spatial_predictor_5000_37 spatial_predictor_5000_38
#> 1 -0.006172086 0.11198682 -0.059864246
#> 2 -0.004448553 -0.12383714 0.079488834
#> 3 0.018851232 -0.02941015 0.042666612
#> 4 0.178075022 0.08977122 -0.007076506
#> 5 0.066770627 0.03876279 -0.051671055
#> 6 0.093604019 -0.08014813 0.069799965
#> spatial_predictor_5000_39 spatial_predictor_5000_40 spatial_predictor_5000_41
#> 1 -0.06315411 -0.024782056 0.0127075254
#> 2 -0.06293867 -0.007467738 0.0181999516
#> 3 -0.01662694 0.018912417 0.0007662147
#> 4 0.07881042 -0.047469580 -0.1646598813
#> 5 0.04899792 -0.134123616 0.0773160118
#> 6 -0.05497356 0.031775739 0.0949174035
#> spatial_predictor_5000_42 spatial_predictor_5000_43 spatial_predictor_5000_44
#> 1 -0.01778167 -0.08506967 0.004379197
#> 2 0.05644518 -0.04580026 0.124306897
#> 3 -0.01423978 -0.06279209 0.063065312
#> 4 -0.08040396 0.08214645 0.105889599
#> 5 0.01939318 0.09937598 0.031432852
#> 6 0.11631277 0.14451829 0.041000682
#> spatial_predictor_5000_45 spatial_predictor_5000_46 spatial_predictor_5000_47
#> 1 -0.014888884 0.03924662 0.14769008
#> 2 0.064188224 0.06410487 0.08532677
#> 3 -0.036593562 -0.02231719 -0.11293032
#> 4 0.006766716 0.14574672 -0.17149452
#> 5 0.089360133 0.03172565 0.02744822
#> 6 0.041933253 -0.03777955 0.02752247
#> spatial_predictor_5000_48 spatial_predictor_5000_49 spatial_predictor_5000_50
#> 1 -0.048251334 0.04714192 0.14637112
#> 2 -0.012187926 -0.14150864 -0.01302295
#> 3 0.046154718 0.05587225 0.01455556
#> 4 0.098008877 -0.07854284 0.15023238
#> 5 -0.009439831 -0.02011616 0.06439308
#> 6 -0.121534134 -0.07436057 0.09902297
#> spatial_predictor_5000_51 spatial_predictor_5000_52 spatial_predictor_5000_53
#> 1 -0.068668099 0.05772877 0.05739608
#> 2 -0.022575358 -0.07449298 -0.02429778
#> 3 0.005354177 -0.11190406 0.03200119
#> 4 0.115493802 0.06475079 -0.07822792
#> 5 -0.005855112 -0.11116350 0.06640452
#> 6 -0.176250052 0.01193667 -0.05406238
#> spatial_predictor_5000_54 spatial_predictor_5000_55 spatial_predictor_5000_56
#> 1 -0.11781180 0.0004691497 -0.049783698
#> 2 -0.01868333 0.0457440347 -0.049031389
#> 3 0.05618605 0.0336912718 -0.033694761
#> 4 -0.10116513 -0.0548091289 -0.035641947
#> 5 0.05319686 -0.1104736116 -0.006035261
#> 6 -0.15229959 0.0141379097 -0.056821992
dim(mems)
#> [1] 227 216
# Check column names showing threshold and predictor number
colnames(mems)[1:6]
#> [1] "spatial_predictor_0_1" "spatial_predictor_0_2" "spatial_predictor_0_3"
#> [4] "spatial_predictor_0_4" "spatial_predictor_0_5" "spatial_predictor_0_6"
# Limit number of spatial predictors
mems_limited <- mem_multithreshold(
distance.matrix = plants_distance,
distance.thresholds = c(0, 1000, 5000),
max.spatial.predictors = 20
)
dim(mems_limited)
#> [1] 227 20