Modelacion
Laura Valbuena
26/10/2020
MODELADOTaxonomia de Elhorts->Modelos regresión espacial MODELOS DE REGRESION ESPACIAL falta:187
modelado <- read_excel("BD_MODELADO.xlsx")
data = data.frame(modelado)
data## Avg_X_MCB Avg_Y_MCE Avg_CEa_07 Avg_CEa_15 NDVI DEM SLOPE
## 1 843449.6 955962.0 7.237480 18.02656 0.863030 199.0000 6.385167
## 2 843454.8 955962.4 6.787250 18.02737 0.866502 197.1667 1.981082
## 3 843493.6 955951.3 6.848250 18.70444 0.874883 197.0000 0.577682
## 4 843439.6 955977.6 7.135162 18.34237 0.845838 197.0000 1.175075
## 5 843468.7 955972.8 6.826763 17.92409 0.797179 197.0000 0.210996
## 6 843500.6 955975.3 6.699966 18.39441 0.758272 197.6667 4.357386
## 7 843525.7 955982.6 6.180742 17.84332 0.763436 199.7500 6.628445
## 8 843413.4 956014.0 8.539024 18.75812 0.823320 197.1667 1.462050
## 9 843433.6 956013.4 8.869958 18.85396 0.759923 197.3333 1.663344
## 10 843468.8 956010.1 7.231308 18.34269 0.757382 197.6667 3.541936
## 11 843502.3 956003.0 7.372200 18.35662 0.775947 199.6667 5.092919
## 12 843527.4 956009.6 7.556792 18.40508 0.757534 201.5000 2.800611
## 13 843558.6 956009.9 6.613547 18.00057 0.786412 201.4444 2.361177
## 14 843409.2 956037.7 8.707629 18.60609 0.822730 198.5000 4.355658
## 15 843444.0 956031.2 8.619512 18.65902 0.751389 198.5000 2.763125
## 16 843462.4 956045.2 9.443404 18.87923 0.782599 199.0000 2.106899
## 17 843498.4 956034.7 7.948763 18.66895 0.837023 200.6667 3.431262
## 18 843530.9 956034.9 7.617205 18.72236 0.827783 202.0000 1.192970
## 19 843554.9 956043.5 6.952229 18.63938 0.815532 201.6667 1.590627
## 20 843389.9 956077.3 8.900977 19.16011 0.849303 198.4444 5.012258
## 21 843405.1 956074.3 8.362279 18.82934 0.784440 200.5000 2.915162
## 22 843439.7 956065.7 9.246182 19.41561 0.792788 199.7778 2.759113
## 23 843470.2 956066.1 9.565551 19.12467 0.830265 199.3333 2.352002
## 24 843496.6 956073.4 9.514172 19.31950 0.836988 200.4444 3.071998
## 25 843532.6 956063.6 7.765429 18.73330 0.856579 202.0000 2.045438
## 26 843557.1 956069.6 7.740431 19.30920 0.848950 201.2222 3.057894
## 27 843587.7 956070.4 8.005415 18.90811 0.846561 200.1667 3.000087
## 28 843621.8 956062.2 6.561038 18.64561 0.851043 200.7778 3.986067
## 29 843639.8 956077.0 6.283077 18.36692 0.854749 201.8333 2.074468
## 30 843387.3 956093.4 8.319138 18.83954 0.808107 199.0000 4.708445
## 31 843405.5 956102.7 9.039500 19.16427 0.704767 201.0000 1.474276
## 32 843439.1 956100.5 8.967420 18.79867 0.829590 200.8333 1.545263
## 33 843473.6 956093.5 10.180382 19.61115 0.796595 200.7500 3.036000
## 34 843495.0 956102.8 10.306887 20.20234 0.800730 201.3333 3.673525
## 35 843523.1 956095.7 10.387930 19.50311 0.822610 202.7500 3.780945
## 36 843564.3 956090.1 8.079340 19.52706 0.812524 201.6667 5.572433
## 37 843584.4 956103.3 7.416591 18.46507 0.851740 198.7500 6.139762
## 38 843619.4 956096.3 7.794147 18.55415 0.861789 199.0000 6.402925
## 39 843651.4 956093.7 6.358915 18.25549 0.855028 200.5000 5.598307
## 40 843673.6 956105.1 7.251424 19.10358 0.868394 199.0000 6.668860
## 41 843211.2 956141.8 9.239875 20.23771 0.863231 198.4444 3.801271
## 42 843222.1 956140.1 8.808246 19.87461 0.842880 199.0000 3.790430
## 43 843263.1 956132.1 9.690171 19.67751 0.821701 198.3333 4.396922
## 44 843288.7 956126.8 10.155757 19.46209 0.798429 196.8333 3.904072
## 45 843385.9 956129.3 8.804591 19.08687 0.737705 199.3333 2.923768
## 46 843409.0 956124.0 8.434166 19.03361 0.750449 200.5000 2.458677
## 47 843434.3 956134.7 9.156519 18.77033 0.835305 201.3333 1.687156
## 48 843469.4 956125.4 9.274048 18.43818 0.752140 202.3333 3.230637
## 49 843501.5 956124.2 10.207658 19.63138 0.715250 203.5556 4.476171
## 50 843523.1 956134.7 10.902909 19.60230 0.739356 205.0000 4.121137
## 51 843597.5 956117.9 7.761483 18.37293 0.856687 200.3333 10.745049
## 52 843615.1 956132.8 8.006260 18.36260 0.852410 197.4444 5.309658
## 53 843651.3 956123.5 7.533355 18.36884 0.849109 197.5000 3.681294
## 54 843681.2 956124.6 6.599943 18.65739 0.855608 196.8889 3.572474
## 55 843705.6 956131.9 6.165389 18.22589 0.837410 196.1667 1.894812
## 56 843203.3 956159.1 9.801222 20.57460 0.813314 201.0000 6.860100
## 57 843232.4 956154.0 9.883363 19.67821 0.785680 201.2500 5.779408
## 58 843258.6 956159.4 9.356057 19.23891 0.828626 200.5000 5.703887
## 59 843288.7 956160.8 9.417184 18.72799 0.843965 199.2500 6.287442
## 60 843324.3 956151.0 8.755571 18.37157 0.847600 199.0000 5.979193
## 61 843343.0 956166.2 8.698579 18.29050 0.837913 200.0000 5.017095
## 62 843382.9 956157.4 8.703409 18.35272 0.758228 200.5000 4.258302
## 63 843414.1 956155.5 8.273370 18.69217 0.782020 201.5000 5.531510
## 64 843436.7 956160.5 8.349463 18.67237 0.843852 202.0000 1.791052
## 65 843467.1 956161.7 9.406651 19.00990 0.803169 201.5000 3.412350
## 66 843502.3 956152.3 9.437600 18.38075 0.805965 203.3333 6.380927
## 67 843529.8 956157.1 10.106193 18.76165 0.786369 205.0000 5.288365
## 68 843555.8 956162.5 10.622833 19.52911 0.815856 206.5000 5.275273
## 69 843600.5 956158.8 9.710588 19.22582 0.862998 205.2500 11.876602
## 70 843625.8 956148.9 8.792682 18.39832 0.848414 200.6667 12.718485
## 71 843647.5 956160.4 8.157281 18.32854 0.847895 198.0000 7.269520
## 72 843684.3 956150.0 7.586421 18.00271 0.865199 197.1667 5.617575
## 73 843709.2 956158.8 7.571196 17.90945 0.870017 196.7500 3.788360
## 74 843738.7 956159.4 6.827806 17.68600 0.871002 197.1667 3.364663
## 75 843231.2 956183.7 10.413632 19.74619 0.758252 204.5000 5.642858
## 76 843257.7 956190.6 9.166925 19.62213 0.867244 203.7778 6.482583
## 77 843288.4 956192.2 8.504000 18.89116 0.867638 202.3333 6.083620
## 78 843319.3 956186.0 8.788548 18.37173 0.870942 201.1111 2.459150
## 79 843350.9 956186.5 8.662250 18.26270 0.872671 201.0000 0.455108
## 80 843376.0 956193.7 8.719192 18.85450 0.868578 202.1111 4.101172
## 81 843412.2 956186.1 8.341883 18.48699 0.848237 203.6667 2.456326
## 82 843443.6 956182.1 8.293013 18.90836 0.851545 202.3333 4.217897
## 83 843464.3 956188.5 8.894091 18.87077 0.819059 200.8333 2.518604
## 84 843499.2 956188.4 9.640773 18.75718 0.804251 200.2222 4.984981
## 85 843533.1 956182.7 9.492250 18.26178 0.815396 201.8333 8.670177
## 86 843558.1 956189.6 9.782962 18.64194 0.823529 206.0000 7.582604
## 87 843589.4 956190.0 11.163060 20.20002 0.841616 207.3333 4.667620
## 88 843650.6 956193.7 9.308194 19.18990 0.849396 203.0000 8.109317
## 89 843679.3 956187.1 8.156393 18.35493 0.866166 202.5556 11.049718
## 90 843714.6 956180.8 8.287346 18.28569 0.864788 201.1667 11.253322
## 91 843738.6 956190.7 8.951000 18.28127 0.855187 199.1111 7.486137
## 92 843769.3 956184.9 7.039985 17.21669 0.856369 200.3333 6.899040
## 93 843264.4 956212.3 9.423294 19.52137 0.824327 207.0000 5.684828
## 94 843284.4 956224.4 9.017756 19.17876 0.862673 205.2500 7.447565
## 95 843319.5 956217.8 8.648365 19.24670 0.861911 203.0000 7.774040
## 96 843350.3 956215.5 8.578609 18.44053 0.870474 201.5000 4.397030
## 97 843377.7 956220.8 8.499200 18.28480 0.877252 202.8333 4.143000
## 98 843408.6 956220.7 8.404081 18.78468 0.860681 204.2500 1.997852
## 99 843439.4 956216.5 8.742085 19.17412 0.845406 204.0000 3.836572
## 100 843465.0 956224.4 9.369309 19.24459 0.841513 202.0000 6.512908
## 101 843499.4 956220.3 9.560190 18.78436 0.825845 199.6667 4.350432
## 102 843531.1 956214.7 9.754492 18.74436 0.808722 200.0000 4.851805
## 103 843558.5 956218.2 9.550490 18.27571 0.817211 203.1667 7.158753
## 104 843586.2 956222.9 9.488833 18.19102 0.844389 205.2500 3.089440
## 105 843619.5 956214.7 11.076870 20.15741 0.848573 204.8333 1.423512
## 106 843650.0 956216.6 9.998806 19.54010 0.848647 205.0000 1.982079
## 107 843679.0 956221.1 9.759255 19.09805 0.856381 206.6667 3.747822
## 108 843710.6 956213.4 8.134407 18.10189 0.849230 205.7500 7.037877
## 109 843734.2 956222.4 8.283045 18.08600 0.843651 202.1667 5.566600
## 110 843769.5 956215.0 9.061986 18.65290 0.843616 201.0000 1.501851
## 111 843798.8 956215.9 8.171761 18.13158 0.849739 202.3333 5.030968
## 112 843819.9 956226.9 7.553833 17.89883 0.860130 203.0000 4.671025
## 113 843296.8 956240.2 9.105000 19.12731 0.843663 207.8333 3.271004
## 114 843314.4 956254.9 9.540674 19.70359 0.853745 206.4444 6.858296
## 115 843351.4 956243.9 8.603241 19.29691 0.856553 205.0000 7.369337
## 116 843380.5 956246.8 8.789031 18.58095 0.866439 204.1111 4.006299
## 117 843405.1 956254.6 8.815902 18.78757 0.840555 204.8333 1.462050
## 118 843439.5 956246.4 8.671873 18.86411 0.841259 204.8889 1.218358
## 119 843472.5 956241.3 9.639222 18.64261 0.861820 204.0000 3.836572
## 120 843496.1 956253.1 9.724586 18.89210 0.832131 201.6667 5.265142
## 121 843530.0 956245.7 9.861596 18.64730 0.827554 201.5000 5.385043
## 122 843560.6 956244.9 10.127983 18.61331 0.824167 204.1111 7.128587
## 123 843587.3 956250.3 10.388884 18.67009 0.827009 206.0000 5.358540
## 124 843619.0 956250.1 9.952259 18.44155 0.836960 204.7778 3.626926
## 125 843652.5 956242.1 11.144444 19.95739 0.847317 203.6667 2.962247
## 126 843673.6 956253.9 10.916097 19.64090 0.865341 204.7778 5.460382
## 127 843710.1 956245.8 9.710557 18.55275 0.857880 205.5000 4.828302
## 128 843742.9 956241.8 8.085140 17.91605 0.843587 202.3333 5.443827
## 129 843802.9 956240.6 9.437786 18.36079 0.870549 201.5556 1.737311
## 130 843829.3 956246.8 8.461278 18.23818 0.862629 202.0000 0.397830
## 131 843853.4 956254.3 7.954150 18.13873 0.836774 200.8889 4.574136
## 132 843329.5 956267.5 9.158409 19.58104 0.831675 209.3333 3.588530
## 133 843347.9 956282.4 9.320712 19.21278 0.837706 208.0000 6.125635
## 134 843385.0 956271.8 8.914465 19.23244 0.856900 206.1667 5.162935
## 135 843410.4 956277.5 8.762164 18.87300 0.865854 205.2500 2.218411
## 136 843437.1 956282.5 9.031770 18.57587 0.872514 205.3333 2.080359
## 137 843472.2 956274.0 8.958058 18.34819 0.872568 204.5000 3.006227
## 138 843499.8 956277.9 9.390675 18.47270 0.852908 202.3333 4.674987
## 139 843528.6 956280.6 9.799225 18.57563 0.833239 201.5000 4.727215
## 140 843562.4 956273.0 10.491500 19.03598 0.841123 206.3333 9.757872
## 141 843590.3 956276.3 10.666597 19.02223 0.838892 209.2500 5.480415
## 142 843616.1 956282.5 10.220761 18.92078 0.854460 206.3333 8.254676
## 143 843649.9 956276.3 9.576524 18.23724 0.866290 203.7500 3.371925
## 144 843682.4 956273.3 11.325523 19.28098 0.858144 203.3333 2.225776
## 145 843703.8 956284.5 11.492385 19.30587 0.863999 203.7500 2.808880
## 146 843776.6 956268.4 7.968871 17.46971 0.862478 201.5000 2.990262
## 147 843795.3 956283.6 8.121050 17.72365 0.873844 201.8333 2.530224
## 148 843836.8 956268.1 9.461541 18.51354 0.836021 202.5000 2.276920
## 149 843859.7 956277.0 9.119667 18.28281 0.812793 201.8333 2.716172
## 150 843887.1 956281.6 9.490019 18.76485 0.827297 200.5000 4.612540
## 151 843363.1 956294.8 9.033125 19.33087 0.820116 210.3333 4.002342
## 152 843379.8 956308.8 9.038783 19.51658 0.836631 209.8889 7.841606
## 153 843414.6 956302.6 8.930578 19.08338 0.863915 207.8333 8.257585
## 154 843438.3 956310.7 8.875571 18.92654 0.873334 206.4444 3.857106
## 155 843470.0 956309.9 9.428071 18.44366 0.875065 205.1667 3.487768
## 156 843503.9 956302.0 8.788283 18.25748 0.866960 203.2222 6.914147
## 157 843524.9 956315.1 9.177440 18.19536 0.834331 201.1667 4.310694
## 158 843559.5 956306.3 9.794014 18.57058 0.839068 205.7778 11.296110
## 159 843591.6 956304.3 10.494813 18.72233 0.856831 210.0000 1.747159
## 160 843620.1 956307.7 10.637610 18.78246 0.857267 208.5556 6.130330
## 161 843647.4 956312.0 10.651236 18.81255 0.857484 205.5000 5.711950
## 162 843682.1 956303.3 9.873600 17.76493 0.850752 205.0000 4.719129
## 163 843709.8 956307.1 11.176595 18.51562 0.857664 204.8333 5.374358
## 164 843736.9 956311.7 11.420059 19.12765 0.855681 203.3333 5.159998
## 165 843780.4 956302.2 10.373150 18.40430 0.863387 203.8333 5.796872
## 166 843798.9 956308.6 9.611918 18.19295 0.874638 204.1111 4.943022
## 167 843828.9 956310.9 8.906259 17.73712 0.848241 204.0000 3.505737
## 168 843870.1 956295.3 10.253087 18.28652 0.840399 203.3333 3.730053
## 169 843894.3 956311.4 10.481411 18.95797 0.841652 201.5000 4.479237
## 170 843915.9 956304.0 13.058916 20.93098 0.795073 199.7778 3.182941
## 171 843411.1 956334.0 9.350082 19.53766 0.810811 211.7500 7.183032
## 172 843441.5 956336.9 9.242393 19.02414 0.864632 208.3333 7.453835
## 173 843465.3 956340.8 9.099338 18.98034 0.874916 206.7500 3.146847
## 174 843499.6 956334.8 9.063097 18.44244 0.854908 205.8333 6.620357
## 175 843531.5 956333.6 9.204898 18.10246 0.844776 204.2500 7.818020
## 176 843554.4 956344.6 8.911786 17.86002 0.850159 206.1667 7.750718
## 177 843591.9 956333.8 10.290754 18.50285 0.856557 209.2500 4.140995
## 178 843620.4 956336.2 10.683234 18.46745 0.834661 209.5000 2.622039
## 179 843649.3 956339.1 11.102933 18.37955 0.836137 208.2500 6.048235
## 180 843679.8 956338.9 11.555725 18.25584 0.847999 208.1667 6.774420
## 181 843713.9 956331.7 10.262660 17.39117 0.846368 208.0000 6.397650
## 182 843737.7 956340.0 10.833848 17.85763 0.844550 205.6667 5.088587
## 183 843770.0 956339.0 11.726238 18.83256 0.862181 206.0000 3.834015
## 184 843809.1 956325.9 10.057179 18.69707 0.865831 206.5000 3.339427
## 185 843829.7 956339.0 10.225946 18.36255 0.851753 205.5000 3.257628
## 186 843858.2 956334.4 9.190459 17.95162 0.853442 203.6667 3.698943
## 187 843890.9 956335.2 10.466905 17.97419 0.857861 202.7500 3.159929
## 188 843919.8 956335.3 11.088537 18.78384 0.845244 201.3333 4.151367
## 189 843949.8 956334.1 10.537560 20.02344 0.774291 200.2500 4.271058
## 190 843445.3 956360.3 9.588048 18.82817 0.804610 209.8889 4.661827
## 191 843469.1 956369.9 8.890474 18.78765 0.855494 209.3333 5.267915
## 192 843496.6 956367.1 9.178667 18.73316 0.849169 209.2222 6.872354
## 193 843533.1 956363.1 8.939582 17.58596 0.848935 208.6667 7.365235
## 194 843560.2 956367.5 9.121469 17.82763 0.853871 209.0000 5.953982
## 195 843587.3 956372.2 9.161358 17.63924 0.850449 210.5000 5.253798
## 196 843624.1 956361.2 9.624125 18.32854 0.832890 211.0000 3.526908
## 197 843648.8 956368.9 11.124750 17.95658 0.823946 211.3333 5.185570
## 198 843679.1 956370.3 11.890033 17.76955 0.840283 211.3333 4.988912
## 199 843710.6 956363.8 11.216469 17.67884 0.848761 210.5000 5.430198
## 200 843741.0 956365.4 11.074617 17.49098 0.853740 208.0000 5.881513
## 201 843767.0 956373.3 11.575519 17.08335 0.870374 207.1667 3.109163
## 202 843800.2 956364.2 11.625279 18.87552 0.859950 207.2222 1.517558
## 203 843823.1 956374.8 12.533951 18.86659 0.831997 206.0000 5.347407
## 204 843858.2 956365.6 12.664912 18.38086 0.825287 203.4444 3.258100
## 205 843890.5 956363.6 10.729181 17.21194 0.829966 203.0000 0.281328
## 206 843910.8 956376.4 9.710667 17.63315 0.831045 203.0000 2.432128
## 207 843952.6 956361.3 13.227629 21.51339 0.804012 202.5000 5.575297
## 208 843980.6 956364.4 9.126116 19.53128 0.773180 199.6667 7.781784
## 209 844005.8 956371.5 6.894246 18.29086 0.794779 197.5000 5.738217
## 210 843479.5 956387.9 8.503577 18.33692 0.804623 210.7500 4.400325
## 211 843496.9 956402.6 8.451278 18.34056 0.851234 212.3333 4.360802
## 212 843530.2 956395.2 8.930413 18.50075 0.850611 211.5000 4.597890
## 213 843561.9 956394.9 9.979037 18.06068 0.836540 211.1667 1.883871
## 214 843588.3 956400.2 10.039082 17.67702 0.831368 211.2500 1.786949
## 215 843620.1 956398.7 10.834800 17.87506 0.840000 211.8333 1.462050
## 216 843655.4 956390.7 11.127763 18.10650 0.838095 212.2500 1.786949
## 217 843676.3 956402.6 11.791851 18.21894 0.841190 212.3333 1.680566
## 218 843710.3 956396.0 12.863088 17.80500 0.865160 211.5000 2.549555
## 219 843741.2 956394.4 12.366322 17.62178 0.849108 210.0000 3.990678
## 220 843769.6 956398.6 12.096839 17.61648 0.841961 208.5000 3.257628
## 221 843794.5 956396.0 11.738320 16.88126 0.848782 207.5000 3.292012
## 222 843832.5 956390.4 12.740534 18.68069 0.828928 205.0000 6.269660
## 223 843852.1 956406.1 13.601094 18.82575 0.811157 203.1667 2.430204
## 224 843892.2 956392.2 11.457347 18.02683 0.820088 203.5000 3.299475
## 225 843920.0 956395.9 11.166191 18.37085 0.830938 204.5000 3.254442
## 226 843944.7 956403.6 11.297233 18.90360 0.804511 204.0000 2.554135
## 227 843986.5 956388.3 11.039111 21.20233 0.817266 202.1667 4.492692
## 228 844015.0 956397.3 7.963308 19.38825 0.789837 200.5000 4.355658
## 229 843512.2 956415.5 9.048643 18.20879 0.812592 212.3333 3.567334
## 230 843529.4 956430.5 8.881460 17.97930 0.837166 212.5000 2.958468
## 231 843563.1 956422.5 10.835075 18.14640 0.830340 210.6667 3.911258
## 232 843590.2 956427.5 10.506455 17.76495 0.815107 210.1667 2.740343
## 233 843616.8 956432.7 9.840000 16.97415 0.837437 210.8889 2.434887
## 234 843651.0 956423.7 10.916683 17.68562 0.839855 211.8333 1.462050
## 235 843680.7 956426.6 11.010927 17.45429 0.843084 211.6667 1.574458
## 236 843708.0 956431.1 13.424340 18.47732 0.849661 211.1667 1.191299
## 237 843742.6 956422.9 12.675188 17.95838 0.829123 210.3333 1.963581
## 238 843774.0 956423.0 12.447763 17.93237 0.814726 209.3333 2.510633
## 239 843802.0 956424.4 12.444867 17.68103 0.859755 208.1111 3.357586
## 240 843830.4 956428.2 11.632409 17.55151 0.860111 205.6667 5.573225
## 241 843865.0 956419.9 12.971378 18.85073 0.836932 205.0000 3.883573
## 242 843886.0 956433.0 13.262604 19.25704 0.833459 205.3333 3.425505
## 243 843925.6 956418.2 10.691591 18.11443 0.862390 205.3333 2.397526
## 244 843950.3 956426.9 10.110577 18.56411 0.835272 203.5000 3.529828
## 245 843979.7 956430.9 10.214864 18.50148 0.820129 202.3333 1.757121
## 246 844017.8 956412.4 9.330150 20.68170 0.813438 201.8333 1.321386
## 247 843544.5 956443.5 8.808750 17.69575 0.801299 211.0000 3.843770
## 248 843560.6 956457.1 9.284254 17.66615 0.814951 209.6667 2.339072
## 249 843592.3 956454.0 9.965915 17.60387 0.810631 210.0000 3.372290
## 250 843619.9 956459.8 10.322586 17.42128 0.830254 210.8333 1.331963
## 251 843650.0 956460.1 9.753217 16.80468 0.829867 211.5000 2.331430
## 252 843684.4 956450.6 10.107022 17.01939 0.830576 211.3333 1.753202
## 253 843707.9 956460.8 12.064159 17.70423 0.829442 210.7500 1.982079
## 254 843741.6 956459.3 13.318076 18.44122 0.807566 210.6667 1.994592
## 255 843772.7 956449.8 11.840000 18.03844 0.830631 210.5000 3.652555
## 256 843801.1 956456.3 11.691589 17.92762 0.869058 208.8333 4.000510
## 257 843828.8 956460.5 12.471000 18.34732 0.876872 207.2500 4.344182
## 258 843861.3 956452.7 11.860000 17.76506 0.856497 206.5000 2.025263
## 259 843890.0 956457.0 11.974023 18.34123 0.855178 206.7500 2.143036
## 260 843919.6 956459.4 12.494547 19.32316 0.865053 205.6667 3.054682
## 261 843960.3 956445.0 9.845591 18.51041 0.856615 203.5000 3.507347
## 262 843978.2 956454.1 10.792130 18.86935 0.833807 202.3333 1.699703
## 263 843592.1 956482.6 9.899631 17.45368 0.799654 210.5000 3.195748
## 264 843620.6 956487.7 10.453354 17.65788 0.841748 211.7778 3.074642
## 265 843648.4 956490.9 10.972456 18.56572 0.840825 212.6667 2.340314
## 266 843680.8 956485.4 9.714850 17.08600 0.842335 211.8889 3.682146
## 267 843712.1 956484.6 11.064222 17.15713 0.842948 210.0000 1.380508
## 268 843736.3 956492.1 12.026567 17.66037 0.823280 210.2222 1.494623
## 269 843770.9 956483.4 12.136029 17.96648 0.835574 211.0000 1.180966
## 270 843801.3 956485.2 12.298185 18.43244 0.855167 210.3333 3.630946
## 271 843828.9 956490.4 12.122456 18.10990 0.856197 208.0000 4.508540
## 272 843860.3 956485.2 12.715985 18.00555 0.846907 207.0000 1.229972
## 273 843891.9 956483.2 11.433000 17.89280 0.848410 207.5000 3.308189
## 274 843916.0 956492.3 11.097524 18.34174 0.861704 207.0000 5.720357
## 275 843951.1 956484.3 12.078147 19.67345 0.866817 204.5000 5.589362
## 276 843626.5 956509.8 9.787512 17.72488 0.818078 212.6667 2.291358
## 277 843680.6 956518.9 9.977851 17.93497 0.844989 212.0000 3.971073
## 278 843707.0 956512.5 10.401154 17.04784 0.848807 210.0000 1.192970
## 279 843739.0 956519.2 10.185048 17.19309 0.838659 210.0000 0.281328
## 280 843770.5 956519.4 11.695016 17.65305 0.831923 210.5000 2.276920
## 281 843804.1 956510.8 12.247611 18.35720 0.843948 210.3333 1.731291
## 282 843829.2 956518.5 12.025920 18.36328 0.839912 209.5000 3.270202
## 283 843860.5 956518.7 11.256382 18.32035 0.829359 208.3333 4.489935
## 284 843892.1 956512.4 11.515895 18.41900 0.832789 209.0000 4.915900
## 285 843920.2 956516.8 11.611375 18.73189 0.852465 209.3333 4.350432
## 286 843660.3 956536.8 9.577556 17.74167 0.804340 213.1667 1.191299
## 287 843674.5 956548.0 9.894512 17.67345 0.831047 212.1111 3.048190
## 288 843710.2 956542.8 10.346464 17.80915 0.838290 211.3333 3.132002
## 289 843741.2 956545.8 10.386561 17.27867 0.828603 210.7778 3.312014
## 290 843766.1 956552.8 9.972537 16.80343 0.833425 211.3333 2.262116
## 291 843801.1 956543.4 11.363155 17.82667 0.843895 210.7778 2.962719
## 292 843831.9 956544.6 11.511292 18.15106 0.836312 209.8333 2.286170
## 293 843857.5 956552.3 11.321532 18.56879 0.808821 209.0000 2.715691
## 294 843892.2 956543.8 11.403831 18.60643 0.804109 208.6667 4.780660
## 295 843919.6 956540.9 12.237587 19.39965 0.828916 209.8889 2.595346
## 296 843694.2 956563.6 10.013111 17.44778 0.802328 212.8333 2.800913
## 297 843710.2 956576.4 9.645260 17.53826 0.826260 212.5000 3.006227
## 298 843743.4 956572.6 10.961389 18.04187 0.835826 212.5000 2.802492
## 299 843770.5 956577.7 9.826764 17.54689 0.843641 212.5000 2.385940
## 300 843800.3 956580.0 9.692833 17.14611 0.840611 212.6667 3.459422
## 301 843835.4 956570.0 11.477929 18.16683 0.824012 210.0000 7.497325
## 302 843859.4 956579.0 11.563340 18.74846 0.814689 207.1667 6.410306
## 303 843886.9 956575.1 10.787655 18.61794 0.815784 205.5000 6.839028
## 304 843743.6 956602.9 10.040680 17.56948 0.819922 213.1111 1.389929
## 305 843771.6 956605.5 11.507673 17.99358 0.839661 212.6667 1.531505
## 306 843798.2 956611.2 9.891250 18.13896 0.826877 213.3333 2.869667
## 307 843830.7 956603.6 9.972969 17.30525 0.825225 211.8333 8.230437
## 308 843862.9 956604.0 10.590700 18.89458 0.834841 207.0000 9.589556
## 309 843776.7 956629.2 9.565263 18.37484 0.800520 211.7500 3.309488
## 310 843799.6 956639.4 9.002500 18.15950 0.820314 212.1667 4.362813
## 311 843832.1 956638.0 9.762534 18.99889 0.837665 212.7500 4.403135
## 312 843857.0 956627.6 9.225618 18.51444 0.796948 209.6667 8.798860
## 313 843809.0 956654.7 9.394625 18.19100 0.777866 212.0000 2.791830
## Avg_z
## 1 193.0512
## 2 193.2986
## 3 193.5659
## 4 194.4116
## 5 193.9931
## 6 195.3814
## 7 196.6780
## 8 194.9936
## 9 196.1356
## 10 197.8522
## 11 196.9330
## 12 198.0175
## 13 197.7762
## 14 195.8610
## 15 196.5075
## 16 197.4861
## 17 199.9242
## 18 199.1996
## 19 199.2844
## 20 197.4021
## 21 197.2999
## 22 197.7400
## 23 198.8052
## 24 199.7561
## 25 200.2470
## 26 200.0841
## 27 200.0516
## 28 198.6690
## 29 198.8767
## 30 198.0322
## 31 198.9863
## 32 199.0529
## 33 199.4091
## 34 200.2416
## 35 200.4325
## 36 200.7606
## 37 201.2179
## 38 201.1689
## 39 198.6975
## 40 197.1639
## 41 195.1000
## 42 195.2742
## 43 195.7090
## 44 196.6163
## 45 196.6843
## 46 197.5293
## 47 199.8418
## 48 200.1707
## 49 200.8024
## 50 201.0412
## 51 201.2643
## 52 201.3608
## 53 200.7411
## 54 198.9216
## 55 198.8203
## 56 195.6087
## 57 196.0870
## 58 196.1448
## 59 196.4108
## 60 197.4217
## 61 197.9413
## 62 198.2749
## 63 198.1177
## 64 198.8206
## 65 200.6089
## 66 201.0759
## 67 201.1798
## 68 200.8173
## 69 203.1204
## 70 201.7512
## 71 201.4652
## 72 201.3465
## 73 201.2001
## 74 200.1021
## 75 198.3504
## 76 197.8452
## 77 197.3573
## 78 197.0722
## 79 198.1134
## 80 198.8306
## 81 199.8018
## 82 199.2333
## 83 200.0127
## 84 201.2964
## 85 202.1158
## 86 202.1501
## 87 201.8758
## 88 204.5230
## 89 201.9955
## 90 201.7849
## 91 201.6944
## 92 199.8109
## 93 198.8574
## 94 198.8165
## 95 198.1910
## 96 198.3609
## 97 199.0324
## 98 199.9175
## 99 201.0765
## 100 202.0215
## 101 201.3854
## 102 202.4915
## 103 203.2807
## 104 203.4541
## 105 203.0027
## 106 204.2055
## 107 204.8999
## 108 201.7845
## 109 201.8522
## 110 201.4426
## 111 199.7676
## 112 199.0601
## 113 198.3139
## 114 197.9799
## 115 199.1988
## 116 199.6428
## 117 200.2269
## 118 201.0782
## 119 202.3012
## 120 202.6912
## 121 202.6254
## 122 203.7258
## 123 204.1337
## 124 204.7050
## 125 203.4641
## 126 203.7717
## 127 204.8091
## 128 201.9826
## 129 200.9807
## 130 199.6393
## 131 198.6265
## 132 198.2778
## 133 199.2195
## 134 200.2588
## 135 200.8218
## 136 201.5015
## 137 202.4989
## 138 203.3595
## 139 204.4179
## 140 203.6451
## 141 204.4681
## 142 205.4655
## 143 206.1004
## 144 204.2725
## 145 204.0167
## 146 201.6800
## 147 201.5062
## 148 200.5268
## 149 199.7099
## 150 198.5984
## 151 199.7960
## 152 200.2779
## 153 201.3893
## 154 202.0203
## 155 202.6132
## 156 203.7657
## 157 204.5432
## 158 205.7407
## 159 204.5445
## 160 204.9341
## 161 205.8528
## 162 206.3428
## 163 205.1603
## 164 204.1542
## 165 202.9000
## 166 203.4336
## 167 201.4334
## 168 200.1442
## 169 199.8272
## 170 198.7313
## 171 201.5489
## 172 202.5080
## 173 203.1635
## 174 203.5593
## 175 204.7211
## 176 205.2250
## 177 206.9010
## 178 205.6902
## 179 204.9596
## 180 206.3974
## 181 206.1927
## 182 205.5291
## 183 204.3245
## 184 202.7165
## 185 202.3656
## 186 201.2474
## 187 200.5020
## 188 199.8491
## 189 198.5972
## 190 202.7712
## 191 203.2853
## 192 203.6961
## 193 204.2412
## 194 205.0853
## 195 205.9503
## 196 207.7375
## 197 206.7987
## 198 204.8491
## 199 206.4087
## 200 206.0711
## 201 205.9899
## 202 204.7037
## 203 204.3314
## 204 202.1066
## 205 201.1195
## 206 200.8034
## 207 199.7839
## 208 198.6119
## 209 197.4881
## 210 203.6242
## 211 204.1438
## 212 204.2841
## 213 204.7361
## 214 205.4088
## 215 206.3681
## 216 207.6834
## 217 207.2423
## 218 205.3834
## 219 205.9602
## 220 205.7391
## 221 205.9970
## 222 204.5509
## 223 204.8136
## 224 201.8857
## 225 201.3896
## 226 200.8387
## 227 199.5112
## 228 198.6991
## 229 204.5661
## 230 204.0985
## 231 204.9926
## 232 205.3672
## 233 205.4851
## 234 206.6006
## 235 206.9225
## 236 206.9044
## 237 205.8225
## 238 205.4299
## 239 207.9852
## 240 211.9958
## 241 205.4668
## 242 204.7264
## 243 202.1130
## 244 201.4283
## 245 200.8799
## 246 199.5737
## 247 204.1000
## 248 204.1985
## 249 205.5335
## 250 205.8982
## 251 205.7017
## 252 206.8975
## 253 206.8196
## 254 206.3962
## 255 205.9474
## 256 205.4009
## 257 204.7141
## 258 212.3547
## 259 208.9608
## 260 204.7026
## 261 201.5060
## 262 201.5401
## 263 205.3600
## 264 205.9236
## 265 206.6825
## 266 205.9591
## 267 206.3634
## 268 206.4892
## 269 206.3979
## 270 205.9306
## 271 205.4732
## 272 204.6796
## 273 210.9083
## 274 212.1226
## 275 205.4466
## 276 205.9218
## 277 207.1207
## 278 206.7147
## 279 206.9503
## 280 206.2647
## 281 206.4828
## 282 206.1123
## 283 205.3742
## 284 204.6418
## 285 208.2343
## 286 206.9770
## 287 207.8832
## 288 207.5258
## 289 207.4212
## 290 207.1158
## 291 206.1141
## 292 206.2850
## 293 205.8256
## 294 205.4207
## 295 204.7865
## 296 208.1229
## 297 208.1408
## 298 207.5593
## 299 206.4362
## 300 205.2322
## 301 206.2511
## 302 206.1078
## 303 205.4652
## 304 207.7260
## 305 207.4729
## 306 207.5030
## 307 205.3955
## 308 206.0401
## 309 207.5483
## 310 207.6438
## 311 207.4406
## 312 205.7614
## 313 207.6968plot(data$Avg_X_MCB,data$Avg_Y_MCE,col="brown",pch=16,main="PUNTOS DE MUESTREO ESPACIAL")
###VARIABLE RESPUESTA: CONDUCTIVIDAD ELECTRICA APARENTE 07 Parte exploratoria
#Variables explicativas
X=as.matrix(data.frame(data$Avg_CEa_15,data$NDVI,data$DEM,data$SLOPE,data$Avg_z))
X## data.Avg_CEa_15 data.NDVI data.DEM data.SLOPE data.Avg_z
## [1,] 18.02656 0.863030 199.0000 6.385167 193.0512
## [2,] 18.02737 0.866502 197.1667 1.981082 193.2986
## [3,] 18.70444 0.874883 197.0000 0.577682 193.5659
## [4,] 18.34237 0.845838 197.0000 1.175075 194.4116
## [5,] 17.92409 0.797179 197.0000 0.210996 193.9931
## [6,] 18.39441 0.758272 197.6667 4.357386 195.3814
## [7,] 17.84332 0.763436 199.7500 6.628445 196.6780
## [8,] 18.75812 0.823320 197.1667 1.462050 194.9936
## [9,] 18.85396 0.759923 197.3333 1.663344 196.1356
## [10,] 18.34269 0.757382 197.6667 3.541936 197.8522
## [11,] 18.35662 0.775947 199.6667 5.092919 196.9330
## [12,] 18.40508 0.757534 201.5000 2.800611 198.0175
## [13,] 18.00057 0.786412 201.4444 2.361177 197.7762
## [14,] 18.60609 0.822730 198.5000 4.355658 195.8610
## [15,] 18.65902 0.751389 198.5000 2.763125 196.5075
## [16,] 18.87923 0.782599 199.0000 2.106899 197.4861
## [17,] 18.66895 0.837023 200.6667 3.431262 199.9242
## [18,] 18.72236 0.827783 202.0000 1.192970 199.1996
## [19,] 18.63938 0.815532 201.6667 1.590627 199.2844
## [20,] 19.16011 0.849303 198.4444 5.012258 197.4021
## [21,] 18.82934 0.784440 200.5000 2.915162 197.2999
## [22,] 19.41561 0.792788 199.7778 2.759113 197.7400
## [23,] 19.12467 0.830265 199.3333 2.352002 198.8052
## [24,] 19.31950 0.836988 200.4444 3.071998 199.7561
## [25,] 18.73330 0.856579 202.0000 2.045438 200.2470
## [26,] 19.30920 0.848950 201.2222 3.057894 200.0841
## [27,] 18.90811 0.846561 200.1667 3.000087 200.0516
## [28,] 18.64561 0.851043 200.7778 3.986067 198.6690
## [29,] 18.36692 0.854749 201.8333 2.074468 198.8767
## [30,] 18.83954 0.808107 199.0000 4.708445 198.0322
## [31,] 19.16427 0.704767 201.0000 1.474276 198.9863
## [32,] 18.79867 0.829590 200.8333 1.545263 199.0529
## [33,] 19.61115 0.796595 200.7500 3.036000 199.4091
## [34,] 20.20234 0.800730 201.3333 3.673525 200.2416
## [35,] 19.50311 0.822610 202.7500 3.780945 200.4325
## [36,] 19.52706 0.812524 201.6667 5.572433 200.7606
## [37,] 18.46507 0.851740 198.7500 6.139762 201.2179
## [38,] 18.55415 0.861789 199.0000 6.402925 201.1689
## [39,] 18.25549 0.855028 200.5000 5.598307 198.6975
## [40,] 19.10358 0.868394 199.0000 6.668860 197.1639
## [41,] 20.23771 0.863231 198.4444 3.801271 195.1000
## [42,] 19.87461 0.842880 199.0000 3.790430 195.2742
## [43,] 19.67751 0.821701 198.3333 4.396922 195.7090
## [44,] 19.46209 0.798429 196.8333 3.904072 196.6163
## [45,] 19.08687 0.737705 199.3333 2.923768 196.6843
## [46,] 19.03361 0.750449 200.5000 2.458677 197.5293
## [47,] 18.77033 0.835305 201.3333 1.687156 199.8418
## [48,] 18.43818 0.752140 202.3333 3.230637 200.1707
## [49,] 19.63138 0.715250 203.5556 4.476171 200.8024
## [50,] 19.60230 0.739356 205.0000 4.121137 201.0412
## [51,] 18.37293 0.856687 200.3333 10.745049 201.2643
## [52,] 18.36260 0.852410 197.4444 5.309658 201.3608
## [53,] 18.36884 0.849109 197.5000 3.681294 200.7411
## [54,] 18.65739 0.855608 196.8889 3.572474 198.9216
## [55,] 18.22589 0.837410 196.1667 1.894812 198.8203
## [56,] 20.57460 0.813314 201.0000 6.860100 195.6087
## [57,] 19.67821 0.785680 201.2500 5.779408 196.0870
## [58,] 19.23891 0.828626 200.5000 5.703887 196.1448
## [59,] 18.72799 0.843965 199.2500 6.287442 196.4108
## [60,] 18.37157 0.847600 199.0000 5.979193 197.4217
## [61,] 18.29050 0.837913 200.0000 5.017095 197.9413
## [62,] 18.35272 0.758228 200.5000 4.258302 198.2749
## [63,] 18.69217 0.782020 201.5000 5.531510 198.1177
## [64,] 18.67237 0.843852 202.0000 1.791052 198.8206
## [65,] 19.00990 0.803169 201.5000 3.412350 200.6089
## [66,] 18.38075 0.805965 203.3333 6.380927 201.0759
## [67,] 18.76165 0.786369 205.0000 5.288365 201.1798
## [68,] 19.52911 0.815856 206.5000 5.275273 200.8173
## [69,] 19.22582 0.862998 205.2500 11.876602 203.1204
## [70,] 18.39832 0.848414 200.6667 12.718485 201.7512
## [71,] 18.32854 0.847895 198.0000 7.269520 201.4652
## [72,] 18.00271 0.865199 197.1667 5.617575 201.3465
## [73,] 17.90945 0.870017 196.7500 3.788360 201.2001
## [74,] 17.68600 0.871002 197.1667 3.364663 200.1021
## [75,] 19.74619 0.758252 204.5000 5.642858 198.3504
## [76,] 19.62213 0.867244 203.7778 6.482583 197.8452
## [77,] 18.89116 0.867638 202.3333 6.083620 197.3573
## [78,] 18.37173 0.870942 201.1111 2.459150 197.0722
## [79,] 18.26270 0.872671 201.0000 0.455108 198.1134
## [80,] 18.85450 0.868578 202.1111 4.101172 198.8306
## [81,] 18.48699 0.848237 203.6667 2.456326 199.8018
## [82,] 18.90836 0.851545 202.3333 4.217897 199.2333
## [83,] 18.87077 0.819059 200.8333 2.518604 200.0127
## [84,] 18.75718 0.804251 200.2222 4.984981 201.2964
## [85,] 18.26178 0.815396 201.8333 8.670177 202.1158
## [86,] 18.64194 0.823529 206.0000 7.582604 202.1501
## [87,] 20.20002 0.841616 207.3333 4.667620 201.8758
## [88,] 19.18990 0.849396 203.0000 8.109317 204.5230
## [89,] 18.35493 0.866166 202.5556 11.049718 201.9955
## [90,] 18.28569 0.864788 201.1667 11.253322 201.7849
## [91,] 18.28127 0.855187 199.1111 7.486137 201.6944
## [92,] 17.21669 0.856369 200.3333 6.899040 199.8109
## [93,] 19.52137 0.824327 207.0000 5.684828 198.8574
## [94,] 19.17876 0.862673 205.2500 7.447565 198.8165
## [95,] 19.24670 0.861911 203.0000 7.774040 198.1910
## [96,] 18.44053 0.870474 201.5000 4.397030 198.3609
## [97,] 18.28480 0.877252 202.8333 4.143000 199.0324
## [98,] 18.78468 0.860681 204.2500 1.997852 199.9175
## [99,] 19.17412 0.845406 204.0000 3.836572 201.0765
## [100,] 19.24459 0.841513 202.0000 6.512908 202.0215
## [101,] 18.78436 0.825845 199.6667 4.350432 201.3854
## [102,] 18.74436 0.808722 200.0000 4.851805 202.4915
## [103,] 18.27571 0.817211 203.1667 7.158753 203.2807
## [104,] 18.19102 0.844389 205.2500 3.089440 203.4541
## [105,] 20.15741 0.848573 204.8333 1.423512 203.0027
## [106,] 19.54010 0.848647 205.0000 1.982079 204.2055
## [107,] 19.09805 0.856381 206.6667 3.747822 204.8999
## [108,] 18.10189 0.849230 205.7500 7.037877 201.7845
## [109,] 18.08600 0.843651 202.1667 5.566600 201.8522
## [110,] 18.65290 0.843616 201.0000 1.501851 201.4426
## [111,] 18.13158 0.849739 202.3333 5.030968 199.7676
## [112,] 17.89883 0.860130 203.0000 4.671025 199.0601
## [113,] 19.12731 0.843663 207.8333 3.271004 198.3139
## [114,] 19.70359 0.853745 206.4444 6.858296 197.9799
## [115,] 19.29691 0.856553 205.0000 7.369337 199.1988
## [116,] 18.58095 0.866439 204.1111 4.006299 199.6428
## [117,] 18.78757 0.840555 204.8333 1.462050 200.2269
## [118,] 18.86411 0.841259 204.8889 1.218358 201.0782
## [119,] 18.64261 0.861820 204.0000 3.836572 202.3012
## [120,] 18.89210 0.832131 201.6667 5.265142 202.6912
## [121,] 18.64730 0.827554 201.5000 5.385043 202.6254
## [122,] 18.61331 0.824167 204.1111 7.128587 203.7258
## [123,] 18.67009 0.827009 206.0000 5.358540 204.1337
## [124,] 18.44155 0.836960 204.7778 3.626926 204.7050
## [125,] 19.95739 0.847317 203.6667 2.962247 203.4641
## [126,] 19.64090 0.865341 204.7778 5.460382 203.7717
## [127,] 18.55275 0.857880 205.5000 4.828302 204.8091
## [128,] 17.91605 0.843587 202.3333 5.443827 201.9826
## [129,] 18.36079 0.870549 201.5556 1.737311 200.9807
## [130,] 18.23818 0.862629 202.0000 0.397830 199.6393
## [131,] 18.13873 0.836774 200.8889 4.574136 198.6265
## [132,] 19.58104 0.831675 209.3333 3.588530 198.2778
## [133,] 19.21278 0.837706 208.0000 6.125635 199.2195
## [134,] 19.23244 0.856900 206.1667 5.162935 200.2588
## [135,] 18.87300 0.865854 205.2500 2.218411 200.8218
## [136,] 18.57587 0.872514 205.3333 2.080359 201.5015
## [137,] 18.34819 0.872568 204.5000 3.006227 202.4989
## [138,] 18.47270 0.852908 202.3333 4.674987 203.3595
## [139,] 18.57563 0.833239 201.5000 4.727215 204.4179
## [140,] 19.03598 0.841123 206.3333 9.757872 203.6451
## [141,] 19.02223 0.838892 209.2500 5.480415 204.4681
## [142,] 18.92078 0.854460 206.3333 8.254676 205.4655
## [143,] 18.23724 0.866290 203.7500 3.371925 206.1004
## [144,] 19.28098 0.858144 203.3333 2.225776 204.2725
## [145,] 19.30587 0.863999 203.7500 2.808880 204.0167
## [146,] 17.46971 0.862478 201.5000 2.990262 201.6800
## [147,] 17.72365 0.873844 201.8333 2.530224 201.5062
## [148,] 18.51354 0.836021 202.5000 2.276920 200.5268
## [149,] 18.28281 0.812793 201.8333 2.716172 199.7099
## [150,] 18.76485 0.827297 200.5000 4.612540 198.5984
## [151,] 19.33087 0.820116 210.3333 4.002342 199.7960
## [152,] 19.51658 0.836631 209.8889 7.841606 200.2779
## [153,] 19.08338 0.863915 207.8333 8.257585 201.3893
## [154,] 18.92654 0.873334 206.4444 3.857106 202.0203
## [155,] 18.44366 0.875065 205.1667 3.487768 202.6132
## [156,] 18.25748 0.866960 203.2222 6.914147 203.7657
## [157,] 18.19536 0.834331 201.1667 4.310694 204.5432
## [158,] 18.57058 0.839068 205.7778 11.296110 205.7407
## [159,] 18.72233 0.856831 210.0000 1.747159 204.5445
## [160,] 18.78246 0.857267 208.5556 6.130330 204.9341
## [161,] 18.81255 0.857484 205.5000 5.711950 205.8528
## [162,] 17.76493 0.850752 205.0000 4.719129 206.3428
## [163,] 18.51562 0.857664 204.8333 5.374358 205.1603
## [164,] 19.12765 0.855681 203.3333 5.159998 204.1542
## [165,] 18.40430 0.863387 203.8333 5.796872 202.9000
## [166,] 18.19295 0.874638 204.1111 4.943022 203.4336
## [167,] 17.73712 0.848241 204.0000 3.505737 201.4334
## [168,] 18.28652 0.840399 203.3333 3.730053 200.1442
## [169,] 18.95797 0.841652 201.5000 4.479237 199.8272
## [170,] 20.93098 0.795073 199.7778 3.182941 198.7313
## [171,] 19.53766 0.810811 211.7500 7.183032 201.5489
## [172,] 19.02414 0.864632 208.3333 7.453835 202.5080
## [173,] 18.98034 0.874916 206.7500 3.146847 203.1635
## [174,] 18.44244 0.854908 205.8333 6.620357 203.5593
## [175,] 18.10246 0.844776 204.2500 7.818020 204.7211
## [176,] 17.86002 0.850159 206.1667 7.750718 205.2250
## [177,] 18.50285 0.856557 209.2500 4.140995 206.9010
## [178,] 18.46745 0.834661 209.5000 2.622039 205.6902
## [179,] 18.37955 0.836137 208.2500 6.048235 204.9596
## [180,] 18.25584 0.847999 208.1667 6.774420 206.3974
## [181,] 17.39117 0.846368 208.0000 6.397650 206.1927
## [182,] 17.85763 0.844550 205.6667 5.088587 205.5291
## [183,] 18.83256 0.862181 206.0000 3.834015 204.3245
## [184,] 18.69707 0.865831 206.5000 3.339427 202.7165
## [185,] 18.36255 0.851753 205.5000 3.257628 202.3656
## [186,] 17.95162 0.853442 203.6667 3.698943 201.2474
## [187,] 17.97419 0.857861 202.7500 3.159929 200.5020
## [188,] 18.78384 0.845244 201.3333 4.151367 199.8491
## [189,] 20.02344 0.774291 200.2500 4.271058 198.5972
## [190,] 18.82817 0.804610 209.8889 4.661827 202.7712
## [191,] 18.78765 0.855494 209.3333 5.267915 203.2853
## [192,] 18.73316 0.849169 209.2222 6.872354 203.6961
## [193,] 17.58596 0.848935 208.6667 7.365235 204.2412
## [194,] 17.82763 0.853871 209.0000 5.953982 205.0853
## [195,] 17.63924 0.850449 210.5000 5.253798 205.9503
## [196,] 18.32854 0.832890 211.0000 3.526908 207.7375
## [197,] 17.95658 0.823946 211.3333 5.185570 206.7987
## [198,] 17.76955 0.840283 211.3333 4.988912 204.8491
## [199,] 17.67884 0.848761 210.5000 5.430198 206.4087
## [200,] 17.49098 0.853740 208.0000 5.881513 206.0711
## [201,] 17.08335 0.870374 207.1667 3.109163 205.9899
## [202,] 18.87552 0.859950 207.2222 1.517558 204.7037
## [203,] 18.86659 0.831997 206.0000 5.347407 204.3314
## [204,] 18.38086 0.825287 203.4444 3.258100 202.1066
## [205,] 17.21194 0.829966 203.0000 0.281328 201.1195
## [206,] 17.63315 0.831045 203.0000 2.432128 200.8034
## [207,] 21.51339 0.804012 202.5000 5.575297 199.7839
## [208,] 19.53128 0.773180 199.6667 7.781784 198.6119
## [209,] 18.29086 0.794779 197.5000 5.738217 197.4881
## [210,] 18.33692 0.804623 210.7500 4.400325 203.6242
## [211,] 18.34056 0.851234 212.3333 4.360802 204.1438
## [212,] 18.50075 0.850611 211.5000 4.597890 204.2841
## [213,] 18.06068 0.836540 211.1667 1.883871 204.7361
## [214,] 17.67702 0.831368 211.2500 1.786949 205.4088
## [215,] 17.87506 0.840000 211.8333 1.462050 206.3681
## [216,] 18.10650 0.838095 212.2500 1.786949 207.6834
## [217,] 18.21894 0.841190 212.3333 1.680566 207.2423
## [218,] 17.80500 0.865160 211.5000 2.549555 205.3834
## [219,] 17.62178 0.849108 210.0000 3.990678 205.9602
## [220,] 17.61648 0.841961 208.5000 3.257628 205.7391
## [221,] 16.88126 0.848782 207.5000 3.292012 205.9970
## [222,] 18.68069 0.828928 205.0000 6.269660 204.5509
## [223,] 18.82575 0.811157 203.1667 2.430204 204.8136
## [224,] 18.02683 0.820088 203.5000 3.299475 201.8857
## [225,] 18.37085 0.830938 204.5000 3.254442 201.3896
## [226,] 18.90360 0.804511 204.0000 2.554135 200.8387
## [227,] 21.20233 0.817266 202.1667 4.492692 199.5112
## [228,] 19.38825 0.789837 200.5000 4.355658 198.6991
## [229,] 18.20879 0.812592 212.3333 3.567334 204.5661
## [230,] 17.97930 0.837166 212.5000 2.958468 204.0985
## [231,] 18.14640 0.830340 210.6667 3.911258 204.9926
## [232,] 17.76495 0.815107 210.1667 2.740343 205.3672
## [233,] 16.97415 0.837437 210.8889 2.434887 205.4851
## [234,] 17.68562 0.839855 211.8333 1.462050 206.6006
## [235,] 17.45429 0.843084 211.6667 1.574458 206.9225
## [236,] 18.47732 0.849661 211.1667 1.191299 206.9044
## [237,] 17.95838 0.829123 210.3333 1.963581 205.8225
## [238,] 17.93237 0.814726 209.3333 2.510633 205.4299
## [239,] 17.68103 0.859755 208.1111 3.357586 207.9852
## [240,] 17.55151 0.860111 205.6667 5.573225 211.9958
## [241,] 18.85073 0.836932 205.0000 3.883573 205.4668
## [242,] 19.25704 0.833459 205.3333 3.425505 204.7264
## [243,] 18.11443 0.862390 205.3333 2.397526 202.1130
## [244,] 18.56411 0.835272 203.5000 3.529828 201.4283
## [245,] 18.50148 0.820129 202.3333 1.757121 200.8799
## [246,] 20.68170 0.813438 201.8333 1.321386 199.5737
## [247,] 17.69575 0.801299 211.0000 3.843770 204.1000
## [248,] 17.66615 0.814951 209.6667 2.339072 204.1985
## [249,] 17.60387 0.810631 210.0000 3.372290 205.5335
## [250,] 17.42128 0.830254 210.8333 1.331963 205.8982
## [251,] 16.80468 0.829867 211.5000 2.331430 205.7017
## [252,] 17.01939 0.830576 211.3333 1.753202 206.8975
## [253,] 17.70423 0.829442 210.7500 1.982079 206.8196
## [254,] 18.44122 0.807566 210.6667 1.994592 206.3962
## [255,] 18.03844 0.830631 210.5000 3.652555 205.9474
## [256,] 17.92762 0.869058 208.8333 4.000510 205.4009
## [257,] 18.34732 0.876872 207.2500 4.344182 204.7141
## [258,] 17.76506 0.856497 206.5000 2.025263 212.3547
## [259,] 18.34123 0.855178 206.7500 2.143036 208.9608
## [260,] 19.32316 0.865053 205.6667 3.054682 204.7026
## [261,] 18.51041 0.856615 203.5000 3.507347 201.5060
## [262,] 18.86935 0.833807 202.3333 1.699703 201.5401
## [263,] 17.45368 0.799654 210.5000 3.195748 205.3600
## [264,] 17.65788 0.841748 211.7778 3.074642 205.9236
## [265,] 18.56572 0.840825 212.6667 2.340314 206.6825
## [266,] 17.08600 0.842335 211.8889 3.682146 205.9591
## [267,] 17.15713 0.842948 210.0000 1.380508 206.3634
## [268,] 17.66037 0.823280 210.2222 1.494623 206.4892
## [269,] 17.96648 0.835574 211.0000 1.180966 206.3979
## [270,] 18.43244 0.855167 210.3333 3.630946 205.9306
## [271,] 18.10990 0.856197 208.0000 4.508540 205.4732
## [272,] 18.00555 0.846907 207.0000 1.229972 204.6796
## [273,] 17.89280 0.848410 207.5000 3.308189 210.9083
## [274,] 18.34174 0.861704 207.0000 5.720357 212.1226
## [275,] 19.67345 0.866817 204.5000 5.589362 205.4466
## [276,] 17.72488 0.818078 212.6667 2.291358 205.9218
## [277,] 17.93497 0.844989 212.0000 3.971073 207.1207
## [278,] 17.04784 0.848807 210.0000 1.192970 206.7147
## [279,] 17.19309 0.838659 210.0000 0.281328 206.9503
## [280,] 17.65305 0.831923 210.5000 2.276920 206.2647
## [281,] 18.35720 0.843948 210.3333 1.731291 206.4828
## [282,] 18.36328 0.839912 209.5000 3.270202 206.1123
## [283,] 18.32035 0.829359 208.3333 4.489935 205.3742
## [284,] 18.41900 0.832789 209.0000 4.915900 204.6418
## [285,] 18.73189 0.852465 209.3333 4.350432 208.2343
## [286,] 17.74167 0.804340 213.1667 1.191299 206.9770
## [287,] 17.67345 0.831047 212.1111 3.048190 207.8832
## [288,] 17.80915 0.838290 211.3333 3.132002 207.5258
## [289,] 17.27867 0.828603 210.7778 3.312014 207.4212
## [290,] 16.80343 0.833425 211.3333 2.262116 207.1158
## [291,] 17.82667 0.843895 210.7778 2.962719 206.1141
## [292,] 18.15106 0.836312 209.8333 2.286170 206.2850
## [293,] 18.56879 0.808821 209.0000 2.715691 205.8256
## [294,] 18.60643 0.804109 208.6667 4.780660 205.4207
## [295,] 19.39965 0.828916 209.8889 2.595346 204.7865
## [296,] 17.44778 0.802328 212.8333 2.800913 208.1229
## [297,] 17.53826 0.826260 212.5000 3.006227 208.1408
## [298,] 18.04187 0.835826 212.5000 2.802492 207.5593
## [299,] 17.54689 0.843641 212.5000 2.385940 206.4362
## [300,] 17.14611 0.840611 212.6667 3.459422 205.2322
## [301,] 18.16683 0.824012 210.0000 7.497325 206.2511
## [302,] 18.74846 0.814689 207.1667 6.410306 206.1078
## [303,] 18.61794 0.815784 205.5000 6.839028 205.4652
## [304,] 17.56948 0.819922 213.1111 1.389929 207.7260
## [305,] 17.99358 0.839661 212.6667 1.531505 207.4729
## [306,] 18.13896 0.826877 213.3333 2.869667 207.5030
## [307,] 17.30525 0.825225 211.8333 8.230437 205.3955
## [308,] 18.89458 0.834841 207.0000 9.589556 206.0401
## [309,] 18.37484 0.800520 211.7500 3.309488 207.5483
## [310,] 18.15950 0.820314 212.1667 4.362813 207.6438
## [311,] 18.99889 0.837665 212.7500 4.403135 207.4406
## [312,] 18.51444 0.796948 209.6667 8.798860 205.7614
## [313,] 18.19100 0.777866 212.0000 2.791830 207.6968#variable respuesta
CEa07 = data[,3]
options(digits = 12)
#Estandarizar matriz de pesos (hacer que la suma de los pesos valga 1)
distancias=as.matrix(data[,1:2])
matrizpesos= as.matrix(dist(distancias,diag = T, upper = T))
matpesinv <-as.matrix(1/matrizpesos)
diag(matpesinv) <- 0
W = as.matrix(matpesinv)
SUMA=apply(W,1,sum)
We=W/SUMA #estandarizacion
apply(We,1,sum) #verificar estandarizacion## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 301 302 303 304 305 306 307 308 309 310 311 312 313
## 1 1 1 1 1 1 1 1 1 1 1 1 1#Indice de moran
im=list()
for(j in 3:8){
im[j]=(Moran.I(data[,j], We))$p.value
}
im #parece que las variables tienen dependencia espacial## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## [1] 0
##
## [[4]]
## [1] 0
##
## [[5]]
## [1] 0
##
## [[6]]
## [1] 0
##
## [[7]]
## [1] 0
##
## [[8]]
## [1] 0#Matrices correlacion "redundancia entre variables"
mcp = rcorr(as.matrix(data[,3:8]),type="pearson")
mcs = rcorr(as.matrix(data[,3:8]),type="spearman")
mcorp = mcp$r
mcorp## Avg_CEa_07 Avg_CEa_15 NDVI DEM
## Avg_CEa_07 1.0000000000000 0.0108976446727 0.0355258288637 0.532294803241
## Avg_CEa_15 0.0108976446727 1.0000000000000 -0.1763621587086 -0.384197582508
## NDVI 0.0355258288637 -0.1763621587086 1.0000000000000 0.101588171642
## DEM 0.5322948032409 -0.3841975825076 0.1015881716416 1.000000000000
## SLOPE -0.1364233442100 0.1871994566118 0.0961053729259 -0.119589789382
## Avg_z 0.6218586550181 -0.4643154559311 0.2135202781596 0.790106047430
## SLOPE Avg_z
## Avg_CEa_07 -0.1364233442100 0.6218586550181
## Avg_CEa_15 0.1871994566118 -0.4643154559311
## NDVI 0.0961053729259 0.2135202781596
## DEM -0.1195897893818 0.7901060474300
## SLOPE 1.0000000000000 -0.0462311077091
## Avg_z -0.0462311077091 1.0000000000000mcors = mcs$r
mcors## Avg_CEa_07 Avg_CEa_15 NDVI
## Avg_CEa_07 1.0000000000000 -0.0701079155272 -0.08775845956612
## Avg_CEa_15 -0.0701079155272 1.0000000000000 -0.09073419813942
## NDVI -0.0877584595661 -0.0907341981394 1.00000000000000
## DEM 0.5546506518114 -0.3909867462043 -0.00100131186398
## SLOPE -0.1373202076802 0.2188553773020 0.10790987884449
## Avg_z 0.6497519696071 -0.5066230141524 0.08779798476284
## DEM SLOPE Avg_z
## Avg_CEa_07 0.55465065181140 -0.137320207680 0.6497519696071
## Avg_CEa_15 -0.39098674620425 0.218855377302 -0.5066230141524
## NDVI -0.00100131186398 0.107909878844 0.0877979847628
## DEM 1.00000000000000 -0.135329719189 0.8161090885564
## SLOPE -0.13532971918932 1.000000000000 -0.1057240527070
## Avg_z 0.81610908855642 -0.105724052707 1.0000000000000library(corrplot)## Warning: package 'corrplot' was built under R version 4.0.3## corrplot 0.84 loadedpar(mfrow=c(1,2))
corrplot(mcorp,order="hclust",tl.col="black",main="pearson")
corrplot(mcors,order="hclust",tl.col="black",main="spearman")
#Las mayores relaciones de dan entre DEM y Avg_z, pero tambien se ve una relacion considerable de Avg_z con CEa_07. Negativamente, CEa_15 se relaciona conAvg_z y en menor medida con DEM. "DEM y Avg_z son ambas medidas de alturas"
library(psych) #Normalidad##
## Attaching package: 'psych'## The following object is masked from 'package:asbio':
##
## skew## The following object is masked from 'package:Hmisc':
##
## describe## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaoptions(digits = 5)
describe(data[,3:8])## vars n mean sd median trimmed mad min max range skew
## Avg_CEa_07 1 313 9.77 1.53 9.65 9.76 1.40 6.17 13.60 7.44 0.10
## Avg_CEa_15 2 313 18.50 0.74 18.44 18.47 0.64 16.80 21.51 4.71 0.57
## NDVI 3 313 0.83 0.03 0.84 0.84 0.02 0.70 0.88 0.17 -1.39
## DEM 4 313 205.11 4.55 204.83 205.13 5.68 196.17 213.33 17.17 0.06
## SLOPE 5 313 4.13 2.16 3.78 3.94 2.10 0.21 12.72 12.51 0.95
## Avg_z 6 313 202.47 3.66 202.49 202.57 4.34 193.05 212.35 19.30 -0.14
## kurtosis se
## Avg_CEa_07 -0.28 0.09
## Avg_CEa_15 1.22 0.04
## NDVI 2.48 0.00
## DEM -1.13 0.26
## SLOPE 1.25 0.12
## Avg_z -0.54 0.21boxplot(data[,3], main ="Boxplot Conductividad Electrica 07")
hist(data[,3],main = "histograma Conductividad Electrica 07", xlab="CE_07")
cvm.test(data[,3])##
## Cramer-von Mises normality test
##
## data: data[, 3]
## W = 0.131, p-value = 0.043shapiro=list()
for(j in 3:8){
shapiro[j]=(sf.test(data[,j]))$p.value
}
shapiro #No todas las variables son normales, revisar simetria## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## [1] 0.20125
##
## [[4]]
## [1] 0.00014139
##
## [[5]]
## [1] 5.1932e-12
##
## [[6]]
## [1] 1.6549e-06
##
## [[7]]
## [1] 4.5506e-08
##
## [[8]]
## [1] 0.00057707##MODELOS MODELO NO ESPACIAL (clásico)
modc = lm(data$Avg_CEa_07~data$Avg_CEa_15+data$NDVI+data$DEM+data$SLOPE+data$Avg_z)
summary(modc) #CEa15,SLOPE,Avg_z relacionados a CE07##
## Call:
## lm(formula = data$Avg_CEa_07 ~ data$Avg_CEa_15 + data$NDVI +
## data$DEM + data$SLOPE + data$Avg_z)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3453 -0.6871 -0.0586 0.5934 3.0400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -72.7183 4.9502 -14.69 < 2e-16 ***
## data$Avg_CEa_15 0.8582 0.0931 9.22 < 2e-16 ***
## data$NDVI -2.3132 2.1204 -1.09 0.28
## data$DEM 0.0281 0.0216 1.30 0.19
## data$SLOPE -0.1164 0.0284 -4.10 5.2e-05 ***
## data$Avg_z 0.3124 0.0282 11.08 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.05 on 307 degrees of freedom
## Multiple R-squared: 0.536, Adjusted R-squared: 0.529
## F-statistic: 71 on 5 and 307 DF, p-value: <2e-16resmodc=modc$residuals
shapiro.test(resmodc) #no normalidad##
## Shapiro-Wilk normality test
##
## data: resmodc
## W = 0.99, p-value = 0.024cvm.test(resmodc) #normales al 4% ##
## Cramer-von Mises normality test
##
## data: resmodc
## W = 0.132, p-value = 0.041imrc=Moran.I(resmodc,We)
imrc #Como hay dependencia espacial en residuales este modelo NO SIRVE## $observed
## [1] 0.16159
##
## $expected
## [1] -0.0032051
##
## $sd
## [1] 0.0046648
##
## $p.value
## [1] 0library(normtest)
skewness.norm.test(resmodc) #simetria para normalidad 5%##
## Skewness test for normality
##
## data: resmodc
## T = 0.273, p-value = 0.042estimadoCE7=modc$fitted.values
plot(data$Avg_CEa_07,estimadoCE7) #se ve cierta relacion muy marcada pero de igualmente no se ajusta perfectamente
MODELO AUTOREGRESIVO PURO
#contorno convexo
cc = chull(distancias)
cc = c(cc,cc[1])
plot(distancias,main="CONTORNO CONVEXO",col="orange",pch=16)
lines(distancias[cc,], type='l',col="purple")
distan = as.matrix(dist(distancias))
min(distan[distan!=0])## [1] 5.1759max(distan)## [1] 853.01dim(distan)## [1] 313 313contnb = dnearneigh(coordinates(distancias),0,max(distan),longlat = F)
contnb## Neighbour list object:
## Number of regions: 313
## Number of nonzero links: 97656
## Percentage nonzero weights: 99.681
## Average number of links: 312dlist <- nbdists(contnb,distancias)
dlist <- lapply(dlist, function(x) 1/x)
wve=nb2listw(contnb,glist=dlist,style="W") #estandarizado;acotar landa para poder comparar e interpretar el valor
map= spautolm(CEa07~1,data=data,listw=wve)
summary(map) #landa significativo; autodependencia##
## Call: spautolm(formula = CEa07 ~ 1, data = data, listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.258254 -0.650679 -0.071829 0.824652 3.063002
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.6941 5.5177 1.032 0.3021
##
## Lambda: 0.98811 LR test value: 162.5 p-value: < 2.22e-16
## Numerical Hessian standard error of lambda: 0.011866
##
## Log likelihood: -494.82
## ML residual variance (sigma squared): 1.347, (sigma: 1.1606)
## Number of observations: 313
## Number of parameters estimated: 3
## AIC: 995.65#menor AIC, mejor modelo
#sacar modelo
CE07e = map$fit$fitted.values
CE07e## 1 2 3 4 5 6 7 8 9 10
## 8.6308 8.6776 8.9153 8.9056 8.8236 8.8560 8.9267 9.0756 9.0054 8.9769
## 11 12 13 14 15 16 17 18 19 20
## 8.8846 8.8911 9.0060 9.1184 9.0633 9.0784 9.0171 8.9640 9.0108 9.1408
## 21 22 23 24 25 26 27 28 29 30
## 9.1678 9.1535 9.1619 9.1763 9.0825 9.0567 9.0478 9.0672 9.0447 9.1880
## 31 32 33 34 35 36 37 38 39 40
## 9.1637 9.2192 9.2347 9.2775 9.2092 9.1145 9.1417 9.0571 9.0632 9.0851
## 41 42 43 44 45 46 47 48 49 50
## 9.3782 9.4087 9.3506 9.3033 9.1994 9.2065 9.2210 9.2981 9.3359 9.3437
## 51 52 53 54 55 56 57 58 59 60
## 9.1671 9.2176 9.1321 9.1396 9.2313 9.3898 9.3622 9.3639 9.3071 9.2695
## 61 62 63 64 65 66 67 68 69 70
## 9.2370 9.2178 9.2240 9.2513 9.3058 9.3910 9.4173 9.4125 9.3594 9.2655
## 71 72 73 74 75 76 77 78 79 80
## 9.3239 9.2164 9.2656 9.3774 9.3644 9.3646 9.3213 9.2619 9.2327 9.2311
## 81 82 83 84 85 86 87 88 89 90
## 9.2489 9.2862 9.3333 9.4118 9.4733 9.5075 9.4876 9.4922 9.4381 9.3610
## 91 92 93 94 95 96 97 98 99 100
## 9.4106 9.5162 9.3367 9.3264 9.2866 9.2545 9.2576 9.2849 9.3203 9.3817
## 101 102 103 104 105 106 107 108 109 110
## 9.4590 9.5114 9.5719 9.6277 9.5854 9.6143 9.6095 9.5499 9.5690 9.5446
## 111 112 113 114 115 116 117 118 119 120
## 9.6038 9.6738 9.3280 9.3182 9.2953 9.2850 9.3124 9.3656 9.4151 9.4837
## 121 122 123 124 125 126 127 128 129 130
## 9.5574 9.6224 9.6810 9.7327 9.7106 9.7839 9.7116 9.6820 9.6372 9.7157
## 131 132 133 134 135 136 137 138 139 140
## 9.8259 9.3429 9.3451 9.3261 9.3542 9.3945 9.4620 9.5138 9.5859 9.6685
## 141 142 143 144 145 146 147 148 149 150
## 9.7419 9.8130 9.8626 9.8637 9.9185 9.8194 9.8963 9.7944 9.9096 10.0180
## 151 152 153 154 155 156 157 158 159 160
## 9.3705 9.3963 9.3978 9.4401 9.4755 9.5495 9.5940 9.6980 9.7920 9.8731
## 161 162 163 164 165 166 167 168 169 170
## 9.9467 10.0081 10.0306 10.0494 9.9619 10.0184 10.0643 10.0020 10.1681 10.0806
## 171 172 173 174 175 176 177 178 179 180
## 9.4504 9.4740 9.5055 9.5540 9.6177 9.7048 9.8234 9.9248 10.0229 10.0915
## 181 182 183 184 185 186 187 188 189 190
## 10.1567 10.2052 10.1909 10.1217 10.2187 10.2377 10.2423 10.2441 10.2290 9.4994
## 191 192 193 194 195 196 197 198 199 200
## 9.5343 9.5627 9.6535 9.7414 9.8607 10.0037 10.0919 10.1894 10.2583 10.3270
## 201 202 203 204 205 206 207 208 209 210
## 10.3895 10.3665 10.4452 10.3516 10.3532 10.4099 10.1819 10.1901 10.1525 9.5697
## 211 212 213 214 215 216 217 218 219 220
## 9.6149 9.6843 9.7804 9.8942 10.0048 10.1651 10.2510 10.3339 10.4222 10.4946
## 221 222 223 224 225 226 227 228 229 230
## 10.5156 10.5303 10.5659 10.4488 10.3972 10.3374 10.0945 10.1034 9.6453 9.7259
## 231 232 233 234 235 236 237 238 239 240
## 9.8020 9.9227 10.0388 10.1558 10.3017 10.3613 10.4819 10.5475 10.5711 10.6251
## 241 242 243 244 245 246 247 248 249 250
## 10.6139 10.5648 10.4490 10.3797 10.2696 10.0658 9.7964 9.8558 9.9555 10.0398
## 251 252 253 254 255 256 257 258 259 260
## 10.1606 10.3221 10.3718 10.4630 10.5717 10.6140 10.6161 10.6422 10.6120 10.4919
## 261 262 263 264 265 266 267 268 269 270
## 10.3797 10.2952 9.9715 10.0433 10.1069 10.2565 10.3711 10.4114 10.5254 10.5896
## 271 272 273 274 275 276 277 278 279 280
## 10.6187 10.5942 10.6018 10.5574 10.4023 10.0749 10.1916 10.2864 10.3859 10.4369
## 281 282 283 284 285 286 287 288 289 290
## 10.5489 10.5654 10.5766 10.5609 10.5157 10.1271 10.1347 10.2299 10.3079 10.3587
## 291 292 293 294 295 296 297 298 299 300
## 10.4448 10.5030 10.4969 10.5031 10.4499 10.1627 10.1992 10.2353 10.3047 10.3435
## 301 302 303 304 305 306 307 308 309 310
## 10.4126 10.3965 10.4263 10.2179 10.1799 10.2282 10.2952 10.3118 10.1904 10.1663
## 311 312 313
## 10.1790 10.2662 10.1322plot(CEa07,CE07e,ylab="CEa estimada",xlab="CEa observada")
cor(CEa07,CE07e) #Se observa cierto tipo de correlación## [1] 0.79772resCE7=map$fit$residuals
moran.mc(map$fit$residuals,wve,nsim = 2000) #residuales u por dependencia espacial##
## Monte-Carlo simulation of Moran I
##
## data: map$fit$residuals
## weights: wve
## number of simulations + 1: 2001
##
## statistic = 0.167, observed rank = 2001, p-value = 5e-04
## alternative hypothesis: greater#$$Y=\lambda W Y +u$$
plot(CEa07,CE07e,ylab="estimado modelo autoregresivo puro",xlab="valores reales")
plot(distancias[,1],distancias[,2],cex=abs(resCE7)*0.8,pch=20)
points(distancias[,1],distancias[,2],col=floor(abs(CE07e))+2,pch=1,cex=0.5)
library(plotly)## Warning: package 'plotly' was built under R version 4.0.3##
## Attaching package: 'plotly'## The following object is masked from 'package:Hmisc':
##
## subplot## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot_ly(data.frame(data[,1:2]),
x = data$Avg_X_MCB,
y = data$Avg_Y_MCE,
size = CE07e)## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: `line.width` does not currently support multiple values.plot_ly(data.frame(data[,1:2]),
x = data$Avg_X_MCB,
y = data$Avg_Y_MCE,
size = CEa07)## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: `line.width` does not currently support multiple values.plot_ly(data.frame(data[,1:2]),
x = data$Avg_X_MCB,
y = data$Avg_Y_MCE,
size = map$fit$residuals)## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: `line.width` does not currently support multiple values.hist(map$fit$residuals,main="histograma residuales")
modap<-lm(CEa07~1,data=data)
modap##
## Call:
## lm(formula = CEa07 ~ 1, data = data)
##
## Coefficients:
## (Intercept)
## 9.77#Normalidad de residuales
shapiro.test(modap$residuals) #residuales normales al 9%##
## Shapiro-Wilk normality test
##
## data: modap$residuals
## W = 0.992, p-value = 0.098cvm.test(modap$residuals) #residuales normales al 4%##
## Cramer-von Mises normality test
##
## data: modap$residuals
## W = 0.131, p-value = 0.043ad.test(modap$residuals) #residuales normales al 6%##
## Anderson-Darling normality test
##
## data: modap$residuals
## A = 0.702, p-value = 0.066lillie.test(modap$residuals) #residuales normales al 8%##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: modap$residuals
## D = 0.0473, p-value = 0.088pearson.test(modap$residuals) #residuales normales##
## Pearson chi-square normality test
##
## data: modap$residuals
## P = 19.2, p-value = 0.32Moran.I(modap$residuals,We) #resiuales con dependencia espacial## $observed
## [1] 0.26875
##
## $expected
## [1] -0.0032051
##
## $sd
## [1] 0.0046659
##
## $p.value
## [1] 0#Simetria
skewness.norm.test(modap$residuals)##
## Skewness test for normality
##
## data: modap$residuals
## T = 0.101, p-value = 0.46map_sarar=sacsarlm(CEa07~1,data=data,listw=wve)## Warning in sacsarlm(CEa07 ~ 1, data = data, listw = wve): inversion of asymptotic covariance matrix failed for tol.solve = 2.22044604925031e-16
## número de condición recíproco = 5.77785e-20 - using numerical Hessian.summary(map_sarar) #Modelo muy malo, según AIC; pero mejor que el anterior##
## Call:sacsarlm(formula = CEa07 ~ 1, data = data, listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.405867 -0.620449 -0.028054 0.640915 2.891325
##
## Type: sac
## Coefficients: (numerical Hessian approximate standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7267 3.2577 -0.53 0.5961
##
## Rho: 0.97863
## Approximate (numerical Hessian) standard error: 0.021305
## z-value: 45.935, p-value: < 2.22e-16
## Lambda: 0.97863
## Approximate (numerical Hessian) standard error: 0.021299
## z-value: 45.946, p-value: < 2.22e-16
##
## LR test value: 254.56, p-value: < 2.22e-16
##
## Log likelihood: -448.79 for sac model
## ML residual variance (sigma squared): 0.98538, (sigma: 0.99267)
## Number of observations: 313
## Number of parameters estimated: 4
## AIC: 905.59, (AIC for lm: 1156.2)cee7mm2=map_sarar$fitted.values
resce72=map_sarar$residuals
moran.mc(resce72,wve,nsim=2000) ##
## Monte-Carlo simulation of Moran I
##
## data: resce72
## weights: wve
## number of simulations + 1: 2001
##
## statistic = 0.105, observed rank = 2001, p-value = 5e-04
## alternative hypothesis: greatershapiro.test(resce72) #CUMPLE NORMALIDAD##
## Shapiro-Wilk normality test
##
## data: resce72
## W = 0.994, p-value = 0.27#Rho es significativo por lo que también hay autocorrelación de los residuales, residuales con dependencia espacial
plot(CEa07,cee7mm2,ylab="estimado modelo sarar autoregresivo puro",xlab="valores reales")
INTERPOLACIÓN MODELO AUTOREGRESIVO PURO
library(akima)## Warning: package 'akima' was built under R version 4.0.3plot(data$Avg_X_MCB,data$Avg_Y_MCE, main="CONTORNO CONVEXO", col="orange",pch=16)
points(843750,956280,col="red",pch=15,cex=1)
interpola=interp(x=distancias[,1],y=distancias[,2],z=CE07e,nx=500,ny=500,linear=F)
image(interpola)
contour(interpola,add=T)
points(843750,956280,col="blue",pch=10,cex=1) #valores estimados
interpola1=interp(x=distancias[,1],y=distancias[,2],z=data$Avg_CEa_07,nx=500,ny=500,linear=F)
image(interpola1)
contour(interpola1,add=T)
points(843750,956280,col="blue",pch=10,cex=1) #valores reales
distancias1=rbind(distancias,c(843750,956280)) #posición 314
matrizpesos1= as.matrix(dist(distancias1,diag = T, upper = T))
matpesinv1 <-as.matrix(1/matrizpesos1)
diag(matpesinv1) <- 0
W1 = as.matrix(matpesinv1)
SUMA1=apply(W1,1,sum)
we1=W1/SUMA1
i=diag(1,314,314)
CEest=5.6941*(i-(0.98811*we1))^-1
CEest[314,314] #Se acerca un poco a los valores reales, más no a los estimados## [1] 5.6941Como el modelo es muy malo los valores estimados son muy altos con respecto a los datos reales
MODELOS QUE INVOLUCRAN VARIABLES EXPLICATIVAS
#MODELO ESPACIAL DEL ERROR
mser1=errorsarlm(formula = CEa07~NDVI+Avg_CEa_15+DEM+SLOPE+Avg_z,data = data,listw = wve)
summary(mser1)##
## Call:errorsarlm(formula = CEa07 ~ NDVI + Avg_CEa_15 + DEM + SLOPE +
## Avg_z, data = data, listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.019160 -0.540466 -0.045367 0.513314 2.592838
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -64.737579 5.752902 -11.2530 < 2.2e-16
## NDVI -2.395368 1.907913 -1.2555 0.209301
## Avg_CEa_15 0.859898 0.083054 10.3535 < 2.2e-16
## DEM 0.036792 0.020974 1.7542 0.079402
## SLOPE -0.073067 0.024760 -2.9510 0.003168
## Avg_z 0.257034 0.028465 9.0299 < 2.2e-16
##
## Lambda: 0.9825, LR test value: 99.359, p-value: < 2.22e-16
## Asymptotic standard error: 0.012342
## z-value: 79.604, p-value: < 2.22e-16
## Wald statistic: 6336.8, p-value: < 2.22e-16
##
## Log likelihood: -406.1 for error model
## ML residual variance (sigma squared): 0.76603, (sigma: 0.87523)
## Number of observations: 313
## Number of parameters estimated: 8
## AIC: 828.2, (AIC for lm: 925.56)mser2=errorsarlm(formula = CEa07~Avg_CEa_15+DEM+SLOPE+Avg_z,data = data,listw = wve)
summary(mser2)##
## Call:errorsarlm(formula = CEa07 ~ Avg_CEa_15 + DEM + SLOPE + Avg_z,
## data = data, listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.068942 -0.573110 -0.041672 0.535538 2.620533
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -66.334323 5.621358 -11.8004 < 2.2e-16
## Avg_CEa_15 0.871288 0.082765 10.5273 < 2.2e-16
## DEM 0.039380 0.020925 1.8819 0.059845
## SLOPE -0.074849 0.024782 -3.0203 0.002525
## Avg_z 0.251732 0.028220 8.9203 < 2.2e-16
##
## Lambda: 0.98246, LR test value: 98.998, p-value: < 2.22e-16
## Asymptotic standard error: 0.012369
## z-value: 79.427, p-value: < 2.22e-16
## Wald statistic: 6308.6, p-value: < 2.22e-16
##
## Log likelihood: -406.89 for error model
## ML residual variance (sigma squared): 0.76989, (sigma: 0.87744)
## Number of observations: 313
## Number of parameters estimated: 7
## AIC: 827.77, (AIC for lm: 924.77)mser3=errorsarlm(formula = CEa07~Avg_CEa_15+SLOPE+Avg_z,data = data,listw = wve) #ya se seleccionaron las mejores variables significativas para la variable respuesta
summary(mser3) #mejor modelo por el AIC pero este sigue siendo muy alto##
## Call:errorsarlm(formula = CEa07 ~ Avg_CEa_15 + SLOPE + Avg_z, data = data,
## listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.150527 -0.558459 -0.045187 0.540349 2.578564
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -65.325177 5.620712 -11.622 < 2.2e-16
## Avg_CEa_15 0.874324 0.083217 10.507 < 2.2e-16
## SLOPE -0.079881 0.024777 -3.224 0.001264
## Avg_z 0.286926 0.021256 13.498 < 2.2e-16
##
## Lambda: 0.98237, LR test value: 97.514, p-value: < 2.22e-16
## Asymptotic standard error: 0.012433
## z-value: 79.011, p-value: < 2.22e-16
## Wald statistic: 6242.7, p-value: < 2.22e-16
##
## Log likelihood: -408.65 for error model
## ML residual variance (sigma squared): 0.77863, (sigma: 0.8824)
## Number of observations: 313
## Number of parameters estimated: 6
## AIC: 829.3, (AIC for lm: 924.81)resCE7m3=mser3$residuals
shapiro.test(resCE7m3)#Normalidad##
## Shapiro-Wilk normality test
##
## data: resCE7m3
## W = 0.993, p-value = 0.19moran.mc(resCE7m3,wve,nsim=2000)#dependencia espacial##
## Monte-Carlo simulation of Moran I
##
## data: resCE7m3
## weights: wve
## number of simulations + 1: 2001
##
## statistic = 0.128, observed rank = 2001, p-value = 5e-04
## alternative hypothesis: greaterplot(CEa07,mser3$fitted.values,ylab="estimado modelo espacial del error",xlab="valores reales")
mser3.1=sacsarlm(formula = CEa07~Avg_CEa_15+SLOPE+Avg_z,data = data,listw = wve)
summary(mser3.1)#rho significativo, y tiene el mejor AIC de todos los modelos ##
## Call:sacsarlm(formula = CEa07 ~ Avg_CEa_15 + SLOPE + Avg_z, data = data,
## listw = wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11409261 -0.48405766 -0.00093026 0.51350443 2.20507300
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -59.437373 14.726433 -4.0361 5.435e-05
## Avg_CEa_15 0.857117 0.073143 11.7183 < 2.2e-16
## SLOPE -0.065991 0.021538 -3.0639 0.002185
## Avg_z 0.213388 0.028874 7.3904 1.463e-13
##
## Rho: 0.97498
## Asymptotic standard error: 0.37591
## z-value: 2.5937, p-value: 0.0094956
## Lambda: 0.97212
## Asymptotic standard error: 0.41932
## z-value: 2.3183, p-value: 0.020433
##
## LR test value: 179.22, p-value: < 2.22e-16
##
## Log likelihood: -367.79 for sac model
## ML residual variance (sigma squared): 0.58887, (sigma: 0.76738)
## Number of observations: 313
## Number of parameters estimated: 7
## AIC: 749.59, (AIC for lm: 924.81)shapiro.test(mser3.1$residuals)#Normalidad##
## Shapiro-Wilk normality test
##
## data: mser3.1$residuals
## W = 0.995, p-value = 0.5moran.mc(mser3.1$residuals,wve,nsim=2000)#dependencia espacial##
## Monte-Carlo simulation of Moran I
##
## data: mser3.1$residuals
## weights: wve
## number of simulations + 1: 2001
##
## statistic = 0.0916, observed rank = 2001, p-value = 5e-04
## alternative hypothesis: greaterplot(CEa07,mser3$fitted.values,ylab="estimado modelo espacial del error sarar",xlab="valores reales") #mejor ajuste
INTERPOLACIÓN MODELO ESPACIAL DEL ERROR SARAR
interpola2=interp(x=distancias[,1],y=distancias[,2],z=mser3$fitted.values,nx=500,ny=500,linear=F)
image(interpola2)
contour(interpola2,add=T)
points(843750,956280,col="blue",pch=10,cex=1) #valores estimados
#los estimados se ajustan mucho más a los valores realesARTICULO
La conductividad electrica muestra la salinidad en un suelo o en el agua debido a que cuantifica la cantidad de sales que estan presentes en el medio; según el protocolo para la identificación y evaluación de la degradación de suelos por salinización desarrollado por el IDEAM, CAR y U.D.C.A. (http://www.andi.com.co/Uploads/11.%20Protocolo_Salinizacion.pdf) la salinización en el suelo se presenta, generalmente, en zonas de poca pendiente o terrenos concavos; esto se debe a que en estos lugares todas las sales que son “lavadas” de los suelos se precipitan y acumulan en estos lugares aumentando la conductividad electrica; por el contrario, en zonas con mucha pendiente se favorece el lavado de las sales por el gradiente de altura y por ende esta propiedad disminuye. Teniendo en cuenta esto, también es común que estas sales se desplacen entre horizontes de horizontes superiores a otros subsuperficiales. Adicional a esto, Coitiño-Lopez, J et al en 2015 tambien determinaron que en donde la pendiente era menor y la altura mayor era donde se registraban los mayores valors de CEa (http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S2301-15482015000100012); esto puede explicarse debido a que según agrosal (http://agrosal.ivia.es/evaluar.html) e infoagro (https://www.infoagro.com/documentos/la_conductividad_electrica_al_servicio_agricultura_y_cespedes_deportivos.asp) la conductividad electrica de un suelo depende fuertemente con la temperatura, si la tempertura aumenta la CEa tambien; teniendo en cuenta que la temperatura depende a su vez de la altura del terreno ya que a mayor altura hay menor presión atmosferica y por ende menos temperatura (http://meteo.navarra.es/definiciones/elementosFactores.cfm#:~:text=Es%20la%20distancia%20vertical%20de,al%20perder%20presi%C3%B3n%20pierde%20temperatura.) por ende, se puede decir que la altura y la CEa tienen una relación indirecta pero significativa.