Trabajo PPP
“Daniel Felipe Malagon Vega - 1193096924”
set.seed(1193096924)
sec_lat <- seq(from=-73.30, to=-73.25, by=0.001)
sec_long <- seq(from=5.54, to=5.58, by=0.001)
Latitud <- sample(sec_lat, size = 100, replace= TRUE)
longitud <- sample(sec_long, size = 100, replace= TRUE)
posicion <- data.frame(x= longitud, y= Latitud)
plot(posicion$x,posicion$y)
Ind_humedad_suelo = sort.int(runif(100, 0.7, 0.95), partial = 10)
Ind_vegetacion_diferencia_normalizado = sort.int(rnorm(100, 0.45, 0.06), partial = 10)
Temp_superfi_suelo = sort.int(26*rbeta(100, shape1 = 0.87,shape2 = 0.91), partial = 10)
presencia_ausencia_cultivos = factor(ifelse(rgamma(n=100, rate = 0.8, shape = 0.5)<0.5,0,1))
tabla_1 =data.frame(posicion,Ind_humedad_suelo,Ind_vegetacion_diferencia_normalizado,Temp_superfi_suelo,presencia_ausencia_cultivos)
tabla_1## x y Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado
## 1 5.575 -73.268 0.7026732 0.2991360
## 2 5.543 -73.286 0.7087010 0.3340606
## 3 5.576 -73.298 0.7017520 0.3393141
## 4 5.543 -73.251 0.7059049 0.3171926
## 5 5.554 -73.251 0.7054854 0.3358501
## 6 5.543 -73.297 0.7110817 0.3477945
## 7 5.550 -73.254 0.7012688 0.3411699
## 8 5.553 -73.273 0.7137616 0.3492701
## 9 5.573 -73.275 0.7105483 0.3567919
## 10 5.570 -73.273 0.7202223 0.3568015
## 11 5.573 -73.250 0.7362153 0.3597202
## 12 5.574 -73.296 0.7339790 0.3628262
## 13 5.566 -73.291 0.7514377 0.3682714
## 14 5.572 -73.272 0.7421811 0.3708850
## 15 5.555 -73.281 0.7331554 0.3798633
## 16 5.574 -73.253 0.7533281 0.3912434
## 17 5.567 -73.286 0.7247215 0.3827131
## 18 5.559 -73.261 0.7534498 0.3803317
## 19 5.572 -73.256 0.7391675 0.3810648
## 20 5.558 -73.277 0.7543980 0.3843086
## 21 5.547 -73.279 0.7384275 0.3969765
## 22 5.564 -73.283 0.7441869 0.3798142
## 23 5.550 -73.258 0.7450494 0.3984910
## 24 5.541 -73.299 0.7289576 0.3815276
## 25 5.561 -73.272 0.8490427 0.3947378
## 26 5.559 -73.278 0.7920595 0.3892577
## 27 5.547 -73.261 0.8112685 0.4383685
## 28 5.578 -73.265 0.9007474 0.4340920
## 29 5.556 -73.264 0.8018667 0.4123739
## 30 5.556 -73.273 0.7688961 0.4330543
## 31 5.546 -73.254 0.8283577 0.4101861
## 32 5.580 -73.283 0.8841883 0.4334208
## 33 5.547 -73.263 0.7878809 0.4000654
## 34 5.540 -73.270 0.8434585 0.4359619
## 35 5.577 -73.275 0.8067768 0.4461145
## 36 5.554 -73.269 0.8149295 0.4334073
## 37 5.563 -73.266 0.8509928 0.4098335
## 38 5.541 -73.270 0.8659456 0.4243419
## 39 5.564 -73.273 0.7641143 0.4043342
## 40 5.565 -73.261 0.8645853 0.4491995
## 41 5.540 -73.263 0.7952560 0.3998971
## 42 5.560 -73.253 0.7632455 0.4302931
## 43 5.552 -73.300 0.8488116 0.4472068
## 44 5.553 -73.254 0.7643642 0.4267996
## 45 5.552 -73.272 0.7853386 0.4306879
## 46 5.544 -73.253 0.8893194 0.4438585
## 47 5.574 -73.297 0.7879383 0.4515931
## 48 5.541 -73.288 0.7777631 0.4295091
## 49 5.572 -73.262 0.7788205 0.4156109
## 50 5.568 -73.271 0.7664077 0.4343663
## 51 5.540 -73.299 0.7724759 0.4395660
## 52 5.562 -73.300 0.8091705 0.4244798
## 53 5.547 -73.284 0.9099795 0.4376342
## 54 5.544 -73.273 0.7694987 0.3986369
## 55 5.576 -73.266 0.8670346 0.4312013
## 56 5.576 -73.254 0.7985044 0.4420126
## 57 5.564 -73.300 0.9086912 0.4478757
## 58 5.559 -73.264 0.7963039 0.4312391
## 59 5.566 -73.297 0.7898150 0.4336521
## 60 5.574 -73.281 0.8469558 0.4648882
## 61 5.543 -73.255 0.8500429 0.4539320
## 62 5.540 -73.299 0.7604993 0.4701093
## 63 5.544 -73.251 0.8631985 0.4692793
## 64 5.579 -73.298 0.8494692 0.4604512
## 65 5.542 -73.287 0.7554524 0.4617572
## 66 5.578 -73.275 0.7878699 0.4549603
## 67 5.547 -73.255 0.7833852 0.4646405
## 68 5.553 -73.264 0.8258192 0.4623018
## 69 5.546 -73.285 0.8345510 0.4681131
## 70 5.578 -73.271 0.7957878 0.4703274
## 71 5.579 -73.252 0.7781054 0.4518307
## 72 5.540 -73.250 0.7719382 0.4602901
## 73 5.544 -73.268 0.8377899 0.4532498
## 74 5.557 -73.297 0.7873545 0.4911202
## 75 5.553 -73.297 0.8652038 0.4718040
## 76 5.564 -73.264 0.7767990 0.4799779
## 77 5.562 -73.263 0.8730995 0.4866341
## 78 5.576 -73.290 0.8187260 0.4876494
## 79 5.563 -73.290 0.7910377 0.4780392
## 80 5.576 -73.283 0.7900116 0.4958211
## 81 5.556 -73.298 0.8965371 0.4933919
## 82 5.547 -73.272 0.8196465 0.4948647
## 83 5.571 -73.276 0.7653513 0.4766855
## 84 5.573 -73.268 0.8840478 0.4799968
## 85 5.572 -73.260 0.8573880 0.4712669
## 86 5.546 -73.280 0.8380801 0.4814654
## 87 5.569 -73.272 0.8778693 0.4719824
## 88 5.559 -73.257 0.8995971 0.4720583
## 89 5.556 -73.283 0.8584176 0.5345085
## 90 5.548 -73.286 0.8435062 0.5050466
## 91 5.557 -73.270 0.7752904 0.5432064
## 92 5.578 -73.265 0.9260580 0.5398717
## 93 5.580 -73.278 0.9246158 0.5247375
## 94 5.560 -73.263 0.9193302 0.5331479
## 95 5.558 -73.254 0.9314929 0.5802553
## 96 5.561 -73.273 0.9362472 0.5228870
## 97 5.553 -73.253 0.9364005 0.5173503
## 98 5.555 -73.266 0.9243760 0.5377988
## 99 5.576 -73.263 0.9188795 0.5019796
## 100 5.549 -73.264 0.9406476 0.5304693
## Temp_superfi_suelo presencia_ausencia_cultivos
## 1 0.826539827 1
## 2 0.017394917 1
## 3 0.351011699 1
## 4 0.336168445 0
## 5 0.193621764 0
## 6 0.215849049 0
## 7 0.584549947 0
## 8 1.483054908 1
## 9 0.006872773 0
## 10 1.753836488 0
## 11 2.843300949 1
## 12 2.582178248 1
## 13 2.852630476 1
## 14 3.500861115 0
## 15 2.814266943 0
## 16 3.686865468 0
## 17 3.618736131 0
## 18 6.239539182 0
## 19 25.520887833 0
## 20 12.817117273 1
## 21 18.230129061 0
## 22 14.646657127 1
## 23 11.681789665 1
## 24 16.442318327 0
## 25 4.757313609 1
## 26 24.769329095 1
## 27 11.302171311 1
## 28 23.196069515 0
## 29 5.470260922 1
## 30 15.210452393 1
## 31 9.281275270 0
## 32 25.000765169 0
## 33 16.993937823 0
## 34 5.103845254 0
## 35 15.090956978 1
## 36 14.640395270 0
## 37 10.127788157 0
## 38 8.422640488 0
## 39 11.886389973 0
## 40 25.816393158 1
## 41 8.710845223 0
## 42 19.093821499 0
## 43 20.871494635 1
## 44 7.044146645 0
## 45 15.033394136 0
## 46 11.508236091 1
## 47 22.598347709 0
## 48 7.023766152 0
## 49 7.915431207 1
## 50 24.771317373 0
## 51 15.766005895 0
## 52 3.756143586 0
## 53 4.889933105 1
## 54 16.918064667 0
## 55 8.333239533 0
## 56 21.396693401 0
## 57 10.205550911 1
## 58 20.389451853 1
## 59 18.368361255 0
## 60 17.663204696 1
## 61 9.416196640 1
## 62 13.560064282 1
## 63 21.746179395 0
## 64 8.552850345 0
## 65 3.849805406 0
## 66 13.635003214 0
## 67 8.335741235 1
## 68 7.195944292 0
## 69 10.624148205 1
## 70 24.112370528 0
## 71 25.053117412 0
## 72 13.816253932 1
## 73 12.877793376 1
## 74 24.589545862 1
## 75 22.807899692 0
## 76 24.889900947 1
## 77 22.425090742 0
## 78 25.807794689 0
## 79 15.640323852 0
## 80 5.974928398 0
## 81 19.246478485 1
## 82 10.038566094 1
## 83 20.299743510 0
## 84 6.598101036 0
## 85 13.864498408 1
## 86 17.126823814 0
## 87 24.069060162 0
## 88 19.653361010 0
## 89 8.625337943 1
## 90 12.271206713 0
## 91 13.201976116 0
## 92 11.330843968 0
## 93 11.824962656 0
## 94 10.541765847 0
## 95 8.252502616 0
## 96 12.119605010 0
## 97 4.921196095 0
## 98 17.817380758 0
## 99 24.119785104 1
## 100 12.039486496 1library(spatstat)## Warning: package 'spatstat' was built under R version 4.1.2## Loading required package: spatstat.data## Warning: package 'spatstat.data' was built under R version 4.1.2## Loading required package: spatstat.geom## Warning: package 'spatstat.geom' was built under R version 4.1.2## spatstat.geom 2.3-0## Loading required package: spatstat.core## Warning: package 'spatstat.core' was built under R version 4.1.2## Loading required package: nlme## Loading required package: rpart## spatstat.core 2.3-1## Loading required package: spatstat.linnet## Warning: package 'spatstat.linnet' was built under R version 4.1.2## spatstat.linnet 2.3-0##
## spatstat 2.2-0 (nickname: 'That's not important right now')
## For an introduction to spatstat, type 'beginner'datos_posicion = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)))## Warning: data contain duplicated pointsany(duplicated(datos_posicion))## [1] TRUEy <- unique(datos_posicion)
plot(y)
datos_posicion_2 = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)), marks = tabla_1[,6])## Warning: data contain duplicated pointsany(duplicated(datos_posicion_2))## [1] TRUEY_1 <- unique(datos_posicion_2)
plot(Y_1)
datos_posicion_3 = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)), marks = tabla_1[,4])
any(duplicated(datos_posicion_3))## [1] FALSEY_2 <- unique(datos_posicion_3)
plot(Y_2, size =0.002)
datos_posicion_4 = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)), marks = tabla_1[,c(3:6)] )
y_3 <- unique(datos_posicion_4)
marks(y_3)## Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado Temp_superfi_suelo
## 1 0.7026732 0.2991360 0.826539827
## 2 0.7087010 0.3340606 0.017394917
## 3 0.7017520 0.3393141 0.351011699
## 4 0.7059049 0.3171926 0.336168445
## 5 0.7054854 0.3358501 0.193621764
## 6 0.7110817 0.3477945 0.215849049
## 7 0.7012688 0.3411699 0.584549947
## 8 0.7137616 0.3492701 1.483054908
## 9 0.7105483 0.3567919 0.006872773
## 10 0.7202223 0.3568015 1.753836488
## 11 0.7362153 0.3597202 2.843300949
## 12 0.7339790 0.3628262 2.582178248
## 13 0.7514377 0.3682714 2.852630476
## 14 0.7421811 0.3708850 3.500861115
## 15 0.7331554 0.3798633 2.814266943
## 16 0.7533281 0.3912434 3.686865468
## 17 0.7247215 0.3827131 3.618736131
## 18 0.7534498 0.3803317 6.239539182
## 19 0.7391675 0.3810648 25.520887833
## 20 0.7543980 0.3843086 12.817117273
## 21 0.7384275 0.3969765 18.230129061
## 22 0.7441869 0.3798142 14.646657127
## 23 0.7450494 0.3984910 11.681789665
## 24 0.7289576 0.3815276 16.442318327
## 25 0.8490427 0.3947378 4.757313609
## 26 0.7920595 0.3892577 24.769329095
## 27 0.8112685 0.4383685 11.302171311
## 28 0.9007474 0.4340920 23.196069515
## 29 0.8018667 0.4123739 5.470260922
## 30 0.7688961 0.4330543 15.210452393
## 31 0.8283577 0.4101861 9.281275270
## 32 0.8841883 0.4334208 25.000765169
## 33 0.7878809 0.4000654 16.993937823
## 34 0.8434585 0.4359619 5.103845254
## 35 0.8067768 0.4461145 15.090956978
## 36 0.8149295 0.4334073 14.640395270
## 37 0.8509928 0.4098335 10.127788157
## 38 0.8659456 0.4243419 8.422640488
## 39 0.7641143 0.4043342 11.886389973
## 40 0.8645853 0.4491995 25.816393158
## 41 0.7952560 0.3998971 8.710845223
## 42 0.7632455 0.4302931 19.093821499
## 43 0.8488116 0.4472068 20.871494635
## 44 0.7643642 0.4267996 7.044146645
## 45 0.7853386 0.4306879 15.033394136
## 46 0.8893194 0.4438585 11.508236091
## 47 0.7879383 0.4515931 22.598347709
## 48 0.7777631 0.4295091 7.023766152
## 49 0.7788205 0.4156109 7.915431207
## 50 0.7664077 0.4343663 24.771317373
## 51 0.7724759 0.4395660 15.766005895
## 52 0.8091705 0.4244798 3.756143586
## 53 0.9099795 0.4376342 4.889933105
## 54 0.7694987 0.3986369 16.918064667
## 55 0.8670346 0.4312013 8.333239533
## 56 0.7985044 0.4420126 21.396693401
## 57 0.9086912 0.4478757 10.205550911
## 58 0.7963039 0.4312391 20.389451853
## 59 0.7898150 0.4336521 18.368361255
## 60 0.8469558 0.4648882 17.663204696
## 61 0.8500429 0.4539320 9.416196640
## 62 0.7604993 0.4701093 13.560064282
## 63 0.8631985 0.4692793 21.746179395
## 64 0.8494692 0.4604512 8.552850345
## 65 0.7554524 0.4617572 3.849805406
## 66 0.7878699 0.4549603 13.635003214
## 67 0.7833852 0.4646405 8.335741235
## 68 0.8258192 0.4623018 7.195944292
## 69 0.8345510 0.4681131 10.624148205
## 70 0.7957878 0.4703274 24.112370528
## 71 0.7781054 0.4518307 25.053117412
## 72 0.7719382 0.4602901 13.816253932
## 73 0.8377899 0.4532498 12.877793376
## 74 0.7873545 0.4911202 24.589545862
## 75 0.8652038 0.4718040 22.807899692
## 76 0.7767990 0.4799779 24.889900947
## 77 0.8730995 0.4866341 22.425090742
## 78 0.8187260 0.4876494 25.807794689
## 79 0.7910377 0.4780392 15.640323852
## 80 0.7900116 0.4958211 5.974928398
## 81 0.8965371 0.4933919 19.246478485
## 82 0.8196465 0.4948647 10.038566094
## 83 0.7653513 0.4766855 20.299743510
## 84 0.8840478 0.4799968 6.598101036
## 85 0.8573880 0.4712669 13.864498408
## 86 0.8380801 0.4814654 17.126823814
## 87 0.8778693 0.4719824 24.069060162
## 88 0.8995971 0.4720583 19.653361010
## 89 0.8584176 0.5345085 8.625337943
## 90 0.8435062 0.5050466 12.271206713
## 91 0.7752904 0.5432064 13.201976116
## 92 0.9260580 0.5398717 11.330843968
## 93 0.9246158 0.5247375 11.824962656
## 94 0.9193302 0.5331479 10.541765847
## 95 0.9314929 0.5802553 8.252502616
## 96 0.9362472 0.5228870 12.119605010
## 97 0.9364005 0.5173503 4.921196095
## 98 0.9243760 0.5377988 17.817380758
## 99 0.9188795 0.5019796 24.119785104
## 100 0.9406476 0.5304693 12.039486496
## presencia_ausencia_cultivos
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 1
## 9 0
## 10 0
## 11 1
## 12 1
## 13 1
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 1
## 21 0
## 22 1
## 23 1
## 24 0
## 25 1
## 26 1
## 27 1
## 28 0
## 29 1
## 30 1
## 31 0
## 32 0
## 33 0
## 34 0
## 35 1
## 36 0
## 37 0
## 38 0
## 39 0
## 40 1
## 41 0
## 42 0
## 43 1
## 44 0
## 45 0
## 46 1
## 47 0
## 48 0
## 49 1
## 50 0
## 51 0
## 52 0
## 53 1
## 54 0
## 55 0
## 56 0
## 57 1
## 58 1
## 59 0
## 60 1
## 61 1
## 62 1
## 63 0
## 64 0
## 65 0
## 66 0
## 67 1
## 68 0
## 69 1
## 70 0
## 71 0
## 72 1
## 73 1
## 74 1
## 75 0
## 76 1
## 77 0
## 78 0
## 79 0
## 80 0
## 81 1
## 82 1
## 83 0
## 84 0
## 85 1
## 86 0
## 87 0
## 88 0
## 89 1
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 1
## 100 1plot(y_3, which.marks ="presencia_ausencia_cultivos", size= 0.5)
plot(y_3, which.marks = "Ind_vegetacion_diferencia_normalizado", size= 0.0009)
marks(y_3)## Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado Temp_superfi_suelo
## 1 0.7026732 0.2991360 0.826539827
## 2 0.7087010 0.3340606 0.017394917
## 3 0.7017520 0.3393141 0.351011699
## 4 0.7059049 0.3171926 0.336168445
## 5 0.7054854 0.3358501 0.193621764
## 6 0.7110817 0.3477945 0.215849049
## 7 0.7012688 0.3411699 0.584549947
## 8 0.7137616 0.3492701 1.483054908
## 9 0.7105483 0.3567919 0.006872773
## 10 0.7202223 0.3568015 1.753836488
## 11 0.7362153 0.3597202 2.843300949
## 12 0.7339790 0.3628262 2.582178248
## 13 0.7514377 0.3682714 2.852630476
## 14 0.7421811 0.3708850 3.500861115
## 15 0.7331554 0.3798633 2.814266943
## 16 0.7533281 0.3912434 3.686865468
## 17 0.7247215 0.3827131 3.618736131
## 18 0.7534498 0.3803317 6.239539182
## 19 0.7391675 0.3810648 25.520887833
## 20 0.7543980 0.3843086 12.817117273
## 21 0.7384275 0.3969765 18.230129061
## 22 0.7441869 0.3798142 14.646657127
## 23 0.7450494 0.3984910 11.681789665
## 24 0.7289576 0.3815276 16.442318327
## 25 0.8490427 0.3947378 4.757313609
## 26 0.7920595 0.3892577 24.769329095
## 27 0.8112685 0.4383685 11.302171311
## 28 0.9007474 0.4340920 23.196069515
## 29 0.8018667 0.4123739 5.470260922
## 30 0.7688961 0.4330543 15.210452393
## 31 0.8283577 0.4101861 9.281275270
## 32 0.8841883 0.4334208 25.000765169
## 33 0.7878809 0.4000654 16.993937823
## 34 0.8434585 0.4359619 5.103845254
## 35 0.8067768 0.4461145 15.090956978
## 36 0.8149295 0.4334073 14.640395270
## 37 0.8509928 0.4098335 10.127788157
## 38 0.8659456 0.4243419 8.422640488
## 39 0.7641143 0.4043342 11.886389973
## 40 0.8645853 0.4491995 25.816393158
## 41 0.7952560 0.3998971 8.710845223
## 42 0.7632455 0.4302931 19.093821499
## 43 0.8488116 0.4472068 20.871494635
## 44 0.7643642 0.4267996 7.044146645
## 45 0.7853386 0.4306879 15.033394136
## 46 0.8893194 0.4438585 11.508236091
## 47 0.7879383 0.4515931 22.598347709
## 48 0.7777631 0.4295091 7.023766152
## 49 0.7788205 0.4156109 7.915431207
## 50 0.7664077 0.4343663 24.771317373
## 51 0.7724759 0.4395660 15.766005895
## 52 0.8091705 0.4244798 3.756143586
## 53 0.9099795 0.4376342 4.889933105
## 54 0.7694987 0.3986369 16.918064667
## 55 0.8670346 0.4312013 8.333239533
## 56 0.7985044 0.4420126 21.396693401
## 57 0.9086912 0.4478757 10.205550911
## 58 0.7963039 0.4312391 20.389451853
## 59 0.7898150 0.4336521 18.368361255
## 60 0.8469558 0.4648882 17.663204696
## 61 0.8500429 0.4539320 9.416196640
## 62 0.7604993 0.4701093 13.560064282
## 63 0.8631985 0.4692793 21.746179395
## 64 0.8494692 0.4604512 8.552850345
## 65 0.7554524 0.4617572 3.849805406
## 66 0.7878699 0.4549603 13.635003214
## 67 0.7833852 0.4646405 8.335741235
## 68 0.8258192 0.4623018 7.195944292
## 69 0.8345510 0.4681131 10.624148205
## 70 0.7957878 0.4703274 24.112370528
## 71 0.7781054 0.4518307 25.053117412
## 72 0.7719382 0.4602901 13.816253932
## 73 0.8377899 0.4532498 12.877793376
## 74 0.7873545 0.4911202 24.589545862
## 75 0.8652038 0.4718040 22.807899692
## 76 0.7767990 0.4799779 24.889900947
## 77 0.8730995 0.4866341 22.425090742
## 78 0.8187260 0.4876494 25.807794689
## 79 0.7910377 0.4780392 15.640323852
## 80 0.7900116 0.4958211 5.974928398
## 81 0.8965371 0.4933919 19.246478485
## 82 0.8196465 0.4948647 10.038566094
## 83 0.7653513 0.4766855 20.299743510
## 84 0.8840478 0.4799968 6.598101036
## 85 0.8573880 0.4712669 13.864498408
## 86 0.8380801 0.4814654 17.126823814
## 87 0.8778693 0.4719824 24.069060162
## 88 0.8995971 0.4720583 19.653361010
## 89 0.8584176 0.5345085 8.625337943
## 90 0.8435062 0.5050466 12.271206713
## 91 0.7752904 0.5432064 13.201976116
## 92 0.9260580 0.5398717 11.330843968
## 93 0.9246158 0.5247375 11.824962656
## 94 0.9193302 0.5331479 10.541765847
## 95 0.9314929 0.5802553 8.252502616
## 96 0.9362472 0.5228870 12.119605010
## 97 0.9364005 0.5173503 4.921196095
## 98 0.9243760 0.5377988 17.817380758
## 99 0.9188795 0.5019796 24.119785104
## 100 0.9406476 0.5304693 12.039486496
## presencia_ausencia_cultivos
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 1
## 9 0
## 10 0
## 11 1
## 12 1
## 13 1
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 1
## 21 0
## 22 1
## 23 1
## 24 0
## 25 1
## 26 1
## 27 1
## 28 0
## 29 1
## 30 1
## 31 0
## 32 0
## 33 0
## 34 0
## 35 1
## 36 0
## 37 0
## 38 0
## 39 0
## 40 1
## 41 0
## 42 0
## 43 1
## 44 0
## 45 0
## 46 1
## 47 0
## 48 0
## 49 1
## 50 0
## 51 0
## 52 0
## 53 1
## 54 0
## 55 0
## 56 0
## 57 1
## 58 1
## 59 0
## 60 1
## 61 1
## 62 1
## 63 0
## 64 0
## 65 0
## 66 0
## 67 1
## 68 0
## 69 1
## 70 0
## 71 0
## 72 1
## 73 1
## 74 1
## 75 0
## 76 1
## 77 0
## 78 0
## 79 0
## 80 0
## 81 1
## 82 1
## 83 0
## 84 0
## 85 1
## 86 0
## 87 0
## 88 0
## 89 1
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 1
## 100 1datos_posicion_5 = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)), marks= tabla_1[, c(3:6)])
Y_5 <- unique (datos_posicion_5)
marks(Y_5)## Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado Temp_superfi_suelo
## 1 0.7026732 0.2991360 0.826539827
## 2 0.7087010 0.3340606 0.017394917
## 3 0.7017520 0.3393141 0.351011699
## 4 0.7059049 0.3171926 0.336168445
## 5 0.7054854 0.3358501 0.193621764
## 6 0.7110817 0.3477945 0.215849049
## 7 0.7012688 0.3411699 0.584549947
## 8 0.7137616 0.3492701 1.483054908
## 9 0.7105483 0.3567919 0.006872773
## 10 0.7202223 0.3568015 1.753836488
## 11 0.7362153 0.3597202 2.843300949
## 12 0.7339790 0.3628262 2.582178248
## 13 0.7514377 0.3682714 2.852630476
## 14 0.7421811 0.3708850 3.500861115
## 15 0.7331554 0.3798633 2.814266943
## 16 0.7533281 0.3912434 3.686865468
## 17 0.7247215 0.3827131 3.618736131
## 18 0.7534498 0.3803317 6.239539182
## 19 0.7391675 0.3810648 25.520887833
## 20 0.7543980 0.3843086 12.817117273
## 21 0.7384275 0.3969765 18.230129061
## 22 0.7441869 0.3798142 14.646657127
## 23 0.7450494 0.3984910 11.681789665
## 24 0.7289576 0.3815276 16.442318327
## 25 0.8490427 0.3947378 4.757313609
## 26 0.7920595 0.3892577 24.769329095
## 27 0.8112685 0.4383685 11.302171311
## 28 0.9007474 0.4340920 23.196069515
## 29 0.8018667 0.4123739 5.470260922
## 30 0.7688961 0.4330543 15.210452393
## 31 0.8283577 0.4101861 9.281275270
## 32 0.8841883 0.4334208 25.000765169
## 33 0.7878809 0.4000654 16.993937823
## 34 0.8434585 0.4359619 5.103845254
## 35 0.8067768 0.4461145 15.090956978
## 36 0.8149295 0.4334073 14.640395270
## 37 0.8509928 0.4098335 10.127788157
## 38 0.8659456 0.4243419 8.422640488
## 39 0.7641143 0.4043342 11.886389973
## 40 0.8645853 0.4491995 25.816393158
## 41 0.7952560 0.3998971 8.710845223
## 42 0.7632455 0.4302931 19.093821499
## 43 0.8488116 0.4472068 20.871494635
## 44 0.7643642 0.4267996 7.044146645
## 45 0.7853386 0.4306879 15.033394136
## 46 0.8893194 0.4438585 11.508236091
## 47 0.7879383 0.4515931 22.598347709
## 48 0.7777631 0.4295091 7.023766152
## 49 0.7788205 0.4156109 7.915431207
## 50 0.7664077 0.4343663 24.771317373
## 51 0.7724759 0.4395660 15.766005895
## 52 0.8091705 0.4244798 3.756143586
## 53 0.9099795 0.4376342 4.889933105
## 54 0.7694987 0.3986369 16.918064667
## 55 0.8670346 0.4312013 8.333239533
## 56 0.7985044 0.4420126 21.396693401
## 57 0.9086912 0.4478757 10.205550911
## 58 0.7963039 0.4312391 20.389451853
## 59 0.7898150 0.4336521 18.368361255
## 60 0.8469558 0.4648882 17.663204696
## 61 0.8500429 0.4539320 9.416196640
## 62 0.7604993 0.4701093 13.560064282
## 63 0.8631985 0.4692793 21.746179395
## 64 0.8494692 0.4604512 8.552850345
## 65 0.7554524 0.4617572 3.849805406
## 66 0.7878699 0.4549603 13.635003214
## 67 0.7833852 0.4646405 8.335741235
## 68 0.8258192 0.4623018 7.195944292
## 69 0.8345510 0.4681131 10.624148205
## 70 0.7957878 0.4703274 24.112370528
## 71 0.7781054 0.4518307 25.053117412
## 72 0.7719382 0.4602901 13.816253932
## 73 0.8377899 0.4532498 12.877793376
## 74 0.7873545 0.4911202 24.589545862
## 75 0.8652038 0.4718040 22.807899692
## 76 0.7767990 0.4799779 24.889900947
## 77 0.8730995 0.4866341 22.425090742
## 78 0.8187260 0.4876494 25.807794689
## 79 0.7910377 0.4780392 15.640323852
## 80 0.7900116 0.4958211 5.974928398
## 81 0.8965371 0.4933919 19.246478485
## 82 0.8196465 0.4948647 10.038566094
## 83 0.7653513 0.4766855 20.299743510
## 84 0.8840478 0.4799968 6.598101036
## 85 0.8573880 0.4712669 13.864498408
## 86 0.8380801 0.4814654 17.126823814
## 87 0.8778693 0.4719824 24.069060162
## 88 0.8995971 0.4720583 19.653361010
## 89 0.8584176 0.5345085 8.625337943
## 90 0.8435062 0.5050466 12.271206713
## 91 0.7752904 0.5432064 13.201976116
## 92 0.9260580 0.5398717 11.330843968
## 93 0.9246158 0.5247375 11.824962656
## 94 0.9193302 0.5331479 10.541765847
## 95 0.9314929 0.5802553 8.252502616
## 96 0.9362472 0.5228870 12.119605010
## 97 0.9364005 0.5173503 4.921196095
## 98 0.9243760 0.5377988 17.817380758
## 99 0.9188795 0.5019796 24.119785104
## 100 0.9406476 0.5304693 12.039486496
## presencia_ausencia_cultivos
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 1
## 9 0
## 10 0
## 11 1
## 12 1
## 13 1
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 1
## 21 0
## 22 1
## 23 1
## 24 0
## 25 1
## 26 1
## 27 1
## 28 0
## 29 1
## 30 1
## 31 0
## 32 0
## 33 0
## 34 0
## 35 1
## 36 0
## 37 0
## 38 0
## 39 0
## 40 1
## 41 0
## 42 0
## 43 1
## 44 0
## 45 0
## 46 1
## 47 0
## 48 0
## 49 1
## 50 0
## 51 0
## 52 0
## 53 1
## 54 0
## 55 0
## 56 0
## 57 1
## 58 1
## 59 0
## 60 1
## 61 1
## 62 1
## 63 0
## 64 0
## 65 0
## 66 0
## 67 1
## 68 0
## 69 1
## 70 0
## 71 0
## 72 1
## 73 1
## 74 1
## 75 0
## 76 1
## 77 0
## 78 0
## 79 0
## 80 0
## 81 1
## 82 1
## 83 0
## 84 0
## 85 1
## 86 0
## 87 0
## 88 0
## 89 1
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 1
## 100 1plot(Y_5)
npoints(Y_5)## [1] 100marks(Y_5)## Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado Temp_superfi_suelo
## 1 0.7026732 0.2991360 0.826539827
## 2 0.7087010 0.3340606 0.017394917
## 3 0.7017520 0.3393141 0.351011699
## 4 0.7059049 0.3171926 0.336168445
## 5 0.7054854 0.3358501 0.193621764
## 6 0.7110817 0.3477945 0.215849049
## 7 0.7012688 0.3411699 0.584549947
## 8 0.7137616 0.3492701 1.483054908
## 9 0.7105483 0.3567919 0.006872773
## 10 0.7202223 0.3568015 1.753836488
## 11 0.7362153 0.3597202 2.843300949
## 12 0.7339790 0.3628262 2.582178248
## 13 0.7514377 0.3682714 2.852630476
## 14 0.7421811 0.3708850 3.500861115
## 15 0.7331554 0.3798633 2.814266943
## 16 0.7533281 0.3912434 3.686865468
## 17 0.7247215 0.3827131 3.618736131
## 18 0.7534498 0.3803317 6.239539182
## 19 0.7391675 0.3810648 25.520887833
## 20 0.7543980 0.3843086 12.817117273
## 21 0.7384275 0.3969765 18.230129061
## 22 0.7441869 0.3798142 14.646657127
## 23 0.7450494 0.3984910 11.681789665
## 24 0.7289576 0.3815276 16.442318327
## 25 0.8490427 0.3947378 4.757313609
## 26 0.7920595 0.3892577 24.769329095
## 27 0.8112685 0.4383685 11.302171311
## 28 0.9007474 0.4340920 23.196069515
## 29 0.8018667 0.4123739 5.470260922
## 30 0.7688961 0.4330543 15.210452393
## 31 0.8283577 0.4101861 9.281275270
## 32 0.8841883 0.4334208 25.000765169
## 33 0.7878809 0.4000654 16.993937823
## 34 0.8434585 0.4359619 5.103845254
## 35 0.8067768 0.4461145 15.090956978
## 36 0.8149295 0.4334073 14.640395270
## 37 0.8509928 0.4098335 10.127788157
## 38 0.8659456 0.4243419 8.422640488
## 39 0.7641143 0.4043342 11.886389973
## 40 0.8645853 0.4491995 25.816393158
## 41 0.7952560 0.3998971 8.710845223
## 42 0.7632455 0.4302931 19.093821499
## 43 0.8488116 0.4472068 20.871494635
## 44 0.7643642 0.4267996 7.044146645
## 45 0.7853386 0.4306879 15.033394136
## 46 0.8893194 0.4438585 11.508236091
## 47 0.7879383 0.4515931 22.598347709
## 48 0.7777631 0.4295091 7.023766152
## 49 0.7788205 0.4156109 7.915431207
## 50 0.7664077 0.4343663 24.771317373
## 51 0.7724759 0.4395660 15.766005895
## 52 0.8091705 0.4244798 3.756143586
## 53 0.9099795 0.4376342 4.889933105
## 54 0.7694987 0.3986369 16.918064667
## 55 0.8670346 0.4312013 8.333239533
## 56 0.7985044 0.4420126 21.396693401
## 57 0.9086912 0.4478757 10.205550911
## 58 0.7963039 0.4312391 20.389451853
## 59 0.7898150 0.4336521 18.368361255
## 60 0.8469558 0.4648882 17.663204696
## 61 0.8500429 0.4539320 9.416196640
## 62 0.7604993 0.4701093 13.560064282
## 63 0.8631985 0.4692793 21.746179395
## 64 0.8494692 0.4604512 8.552850345
## 65 0.7554524 0.4617572 3.849805406
## 66 0.7878699 0.4549603 13.635003214
## 67 0.7833852 0.4646405 8.335741235
## 68 0.8258192 0.4623018 7.195944292
## 69 0.8345510 0.4681131 10.624148205
## 70 0.7957878 0.4703274 24.112370528
## 71 0.7781054 0.4518307 25.053117412
## 72 0.7719382 0.4602901 13.816253932
## 73 0.8377899 0.4532498 12.877793376
## 74 0.7873545 0.4911202 24.589545862
## 75 0.8652038 0.4718040 22.807899692
## 76 0.7767990 0.4799779 24.889900947
## 77 0.8730995 0.4866341 22.425090742
## 78 0.8187260 0.4876494 25.807794689
## 79 0.7910377 0.4780392 15.640323852
## 80 0.7900116 0.4958211 5.974928398
## 81 0.8965371 0.4933919 19.246478485
## 82 0.8196465 0.4948647 10.038566094
## 83 0.7653513 0.4766855 20.299743510
## 84 0.8840478 0.4799968 6.598101036
## 85 0.8573880 0.4712669 13.864498408
## 86 0.8380801 0.4814654 17.126823814
## 87 0.8778693 0.4719824 24.069060162
## 88 0.8995971 0.4720583 19.653361010
## 89 0.8584176 0.5345085 8.625337943
## 90 0.8435062 0.5050466 12.271206713
## 91 0.7752904 0.5432064 13.201976116
## 92 0.9260580 0.5398717 11.330843968
## 93 0.9246158 0.5247375 11.824962656
## 94 0.9193302 0.5331479 10.541765847
## 95 0.9314929 0.5802553 8.252502616
## 96 0.9362472 0.5228870 12.119605010
## 97 0.9364005 0.5173503 4.921196095
## 98 0.9243760 0.5377988 17.817380758
## 99 0.9188795 0.5019796 24.119785104
## 100 0.9406476 0.5304693 12.039486496
## presencia_ausencia_cultivos
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 1
## 9 0
## 10 0
## 11 1
## 12 1
## 13 1
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 1
## 21 0
## 22 1
## 23 1
## 24 0
## 25 1
## 26 1
## 27 1
## 28 0
## 29 1
## 30 1
## 31 0
## 32 0
## 33 0
## 34 0
## 35 1
## 36 0
## 37 0
## 38 0
## 39 0
## 40 1
## 41 0
## 42 0
## 43 1
## 44 0
## 45 0
## 46 1
## 47 0
## 48 0
## 49 1
## 50 0
## 51 0
## 52 0
## 53 1
## 54 0
## 55 0
## 56 0
## 57 1
## 58 1
## 59 0
## 60 1
## 61 1
## 62 1
## 63 0
## 64 0
## 65 0
## 66 0
## 67 1
## 68 0
## 69 1
## 70 0
## 71 0
## 72 1
## 73 1
## 74 1
## 75 0
## 76 1
## 77 0
## 78 0
## 79 0
## 80 0
## 81 1
## 82 1
## 83 0
## 84 0
## 85 1
## 86 0
## 87 0
## 88 0
## 89 1
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 1
## 100 1coords(Y_5)## x y
## 1 5.575 -73.268
## 2 5.543 -73.286
## 3 5.576 -73.298
## 4 5.543 -73.251
## 5 5.554 -73.251
## 6 5.543 -73.297
## 7 5.550 -73.254
## 8 5.553 -73.273
## 9 5.573 -73.275
## 10 5.570 -73.273
## 11 5.573 -73.250
## 12 5.574 -73.296
## 13 5.566 -73.291
## 14 5.572 -73.272
## 15 5.555 -73.281
## 16 5.574 -73.253
## 17 5.567 -73.286
## 18 5.559 -73.261
## 19 5.572 -73.256
## 20 5.558 -73.277
## 21 5.547 -73.279
## 22 5.564 -73.283
## 23 5.550 -73.258
## 24 5.541 -73.299
## 25 5.561 -73.272
## 26 5.559 -73.278
## 27 5.547 -73.261
## 28 5.578 -73.265
## 29 5.556 -73.264
## 30 5.556 -73.273
## 31 5.546 -73.254
## 32 5.580 -73.283
## 33 5.547 -73.263
## 34 5.540 -73.270
## 35 5.577 -73.275
## 36 5.554 -73.269
## 37 5.563 -73.266
## 38 5.541 -73.270
## 39 5.564 -73.273
## 40 5.565 -73.261
## 41 5.540 -73.263
## 42 5.560 -73.253
## 43 5.552 -73.300
## 44 5.553 -73.254
## 45 5.552 -73.272
## 46 5.544 -73.253
## 47 5.574 -73.297
## 48 5.541 -73.288
## 49 5.572 -73.262
## 50 5.568 -73.271
## 51 5.540 -73.299
## 52 5.562 -73.300
## 53 5.547 -73.284
## 54 5.544 -73.273
## 55 5.576 -73.266
## 56 5.576 -73.254
## 57 5.564 -73.300
## 58 5.559 -73.264
## 59 5.566 -73.297
## 60 5.574 -73.281
## 61 5.543 -73.255
## 62 5.540 -73.299
## 63 5.544 -73.251
## 64 5.579 -73.298
## 65 5.542 -73.287
## 66 5.578 -73.275
## 67 5.547 -73.255
## 68 5.553 -73.264
## 69 5.546 -73.285
## 70 5.578 -73.271
## 71 5.579 -73.252
## 72 5.540 -73.250
## 73 5.544 -73.268
## 74 5.557 -73.297
## 75 5.553 -73.297
## 76 5.564 -73.264
## 77 5.562 -73.263
## 78 5.576 -73.290
## 79 5.563 -73.290
## 80 5.576 -73.283
## 81 5.556 -73.298
## 82 5.547 -73.272
## 83 5.571 -73.276
## 84 5.573 -73.268
## 85 5.572 -73.260
## 86 5.546 -73.280
## 87 5.569 -73.272
## 88 5.559 -73.257
## 89 5.556 -73.283
## 90 5.548 -73.286
## 91 5.557 -73.270
## 92 5.578 -73.265
## 93 5.580 -73.278
## 94 5.560 -73.263
## 95 5.558 -73.254
## 96 5.561 -73.273
## 97 5.553 -73.253
## 98 5.555 -73.266
## 99 5.576 -73.263
## 100 5.549 -73.264as.owin(Y_5)## window: rectangle = [5.54, 5.58] x [-73.3, -73.25] unitsas.data.frame(Y_5)## x y Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado
## 1 5.575 -73.268 0.7026732 0.2991360
## 2 5.543 -73.286 0.7087010 0.3340606
## 3 5.576 -73.298 0.7017520 0.3393141
## 4 5.543 -73.251 0.7059049 0.3171926
## 5 5.554 -73.251 0.7054854 0.3358501
## 6 5.543 -73.297 0.7110817 0.3477945
## 7 5.550 -73.254 0.7012688 0.3411699
## 8 5.553 -73.273 0.7137616 0.3492701
## 9 5.573 -73.275 0.7105483 0.3567919
## 10 5.570 -73.273 0.7202223 0.3568015
## 11 5.573 -73.250 0.7362153 0.3597202
## 12 5.574 -73.296 0.7339790 0.3628262
## 13 5.566 -73.291 0.7514377 0.3682714
## 14 5.572 -73.272 0.7421811 0.3708850
## 15 5.555 -73.281 0.7331554 0.3798633
## 16 5.574 -73.253 0.7533281 0.3912434
## 17 5.567 -73.286 0.7247215 0.3827131
## 18 5.559 -73.261 0.7534498 0.3803317
## 19 5.572 -73.256 0.7391675 0.3810648
## 20 5.558 -73.277 0.7543980 0.3843086
## 21 5.547 -73.279 0.7384275 0.3969765
## 22 5.564 -73.283 0.7441869 0.3798142
## 23 5.550 -73.258 0.7450494 0.3984910
## 24 5.541 -73.299 0.7289576 0.3815276
## 25 5.561 -73.272 0.8490427 0.3947378
## 26 5.559 -73.278 0.7920595 0.3892577
## 27 5.547 -73.261 0.8112685 0.4383685
## 28 5.578 -73.265 0.9007474 0.4340920
## 29 5.556 -73.264 0.8018667 0.4123739
## 30 5.556 -73.273 0.7688961 0.4330543
## 31 5.546 -73.254 0.8283577 0.4101861
## 32 5.580 -73.283 0.8841883 0.4334208
## 33 5.547 -73.263 0.7878809 0.4000654
## 34 5.540 -73.270 0.8434585 0.4359619
## 35 5.577 -73.275 0.8067768 0.4461145
## 36 5.554 -73.269 0.8149295 0.4334073
## 37 5.563 -73.266 0.8509928 0.4098335
## 38 5.541 -73.270 0.8659456 0.4243419
## 39 5.564 -73.273 0.7641143 0.4043342
## 40 5.565 -73.261 0.8645853 0.4491995
## 41 5.540 -73.263 0.7952560 0.3998971
## 42 5.560 -73.253 0.7632455 0.4302931
## 43 5.552 -73.300 0.8488116 0.4472068
## 44 5.553 -73.254 0.7643642 0.4267996
## 45 5.552 -73.272 0.7853386 0.4306879
## 46 5.544 -73.253 0.8893194 0.4438585
## 47 5.574 -73.297 0.7879383 0.4515931
## 48 5.541 -73.288 0.7777631 0.4295091
## 49 5.572 -73.262 0.7788205 0.4156109
## 50 5.568 -73.271 0.7664077 0.4343663
## 51 5.540 -73.299 0.7724759 0.4395660
## 52 5.562 -73.300 0.8091705 0.4244798
## 53 5.547 -73.284 0.9099795 0.4376342
## 54 5.544 -73.273 0.7694987 0.3986369
## 55 5.576 -73.266 0.8670346 0.4312013
## 56 5.576 -73.254 0.7985044 0.4420126
## 57 5.564 -73.300 0.9086912 0.4478757
## 58 5.559 -73.264 0.7963039 0.4312391
## 59 5.566 -73.297 0.7898150 0.4336521
## 60 5.574 -73.281 0.8469558 0.4648882
## 61 5.543 -73.255 0.8500429 0.4539320
## 62 5.540 -73.299 0.7604993 0.4701093
## 63 5.544 -73.251 0.8631985 0.4692793
## 64 5.579 -73.298 0.8494692 0.4604512
## 65 5.542 -73.287 0.7554524 0.4617572
## 66 5.578 -73.275 0.7878699 0.4549603
## 67 5.547 -73.255 0.7833852 0.4646405
## 68 5.553 -73.264 0.8258192 0.4623018
## 69 5.546 -73.285 0.8345510 0.4681131
## 70 5.578 -73.271 0.7957878 0.4703274
## 71 5.579 -73.252 0.7781054 0.4518307
## 72 5.540 -73.250 0.7719382 0.4602901
## 73 5.544 -73.268 0.8377899 0.4532498
## 74 5.557 -73.297 0.7873545 0.4911202
## 75 5.553 -73.297 0.8652038 0.4718040
## 76 5.564 -73.264 0.7767990 0.4799779
## 77 5.562 -73.263 0.8730995 0.4866341
## 78 5.576 -73.290 0.8187260 0.4876494
## 79 5.563 -73.290 0.7910377 0.4780392
## 80 5.576 -73.283 0.7900116 0.4958211
## 81 5.556 -73.298 0.8965371 0.4933919
## 82 5.547 -73.272 0.8196465 0.4948647
## 83 5.571 -73.276 0.7653513 0.4766855
## 84 5.573 -73.268 0.8840478 0.4799968
## 85 5.572 -73.260 0.8573880 0.4712669
## 86 5.546 -73.280 0.8380801 0.4814654
## 87 5.569 -73.272 0.8778693 0.4719824
## 88 5.559 -73.257 0.8995971 0.4720583
## 89 5.556 -73.283 0.8584176 0.5345085
## 90 5.548 -73.286 0.8435062 0.5050466
## 91 5.557 -73.270 0.7752904 0.5432064
## 92 5.578 -73.265 0.9260580 0.5398717
## 93 5.580 -73.278 0.9246158 0.5247375
## 94 5.560 -73.263 0.9193302 0.5331479
## 95 5.558 -73.254 0.9314929 0.5802553
## 96 5.561 -73.273 0.9362472 0.5228870
## 97 5.553 -73.253 0.9364005 0.5173503
## 98 5.555 -73.266 0.9243760 0.5377988
## 99 5.576 -73.263 0.9188795 0.5019796
## 100 5.549 -73.264 0.9406476 0.5304693
## Temp_superfi_suelo presencia_ausencia_cultivos
## 1 0.826539827 1
## 2 0.017394917 1
## 3 0.351011699 1
## 4 0.336168445 0
## 5 0.193621764 0
## 6 0.215849049 0
## 7 0.584549947 0
## 8 1.483054908 1
## 9 0.006872773 0
## 10 1.753836488 0
## 11 2.843300949 1
## 12 2.582178248 1
## 13 2.852630476 1
## 14 3.500861115 0
## 15 2.814266943 0
## 16 3.686865468 0
## 17 3.618736131 0
## 18 6.239539182 0
## 19 25.520887833 0
## 20 12.817117273 1
## 21 18.230129061 0
## 22 14.646657127 1
## 23 11.681789665 1
## 24 16.442318327 0
## 25 4.757313609 1
## 26 24.769329095 1
## 27 11.302171311 1
## 28 23.196069515 0
## 29 5.470260922 1
## 30 15.210452393 1
## 31 9.281275270 0
## 32 25.000765169 0
## 33 16.993937823 0
## 34 5.103845254 0
## 35 15.090956978 1
## 36 14.640395270 0
## 37 10.127788157 0
## 38 8.422640488 0
## 39 11.886389973 0
## 40 25.816393158 1
## 41 8.710845223 0
## 42 19.093821499 0
## 43 20.871494635 1
## 44 7.044146645 0
## 45 15.033394136 0
## 46 11.508236091 1
## 47 22.598347709 0
## 48 7.023766152 0
## 49 7.915431207 1
## 50 24.771317373 0
## 51 15.766005895 0
## 52 3.756143586 0
## 53 4.889933105 1
## 54 16.918064667 0
## 55 8.333239533 0
## 56 21.396693401 0
## 57 10.205550911 1
## 58 20.389451853 1
## 59 18.368361255 0
## 60 17.663204696 1
## 61 9.416196640 1
## 62 13.560064282 1
## 63 21.746179395 0
## 64 8.552850345 0
## 65 3.849805406 0
## 66 13.635003214 0
## 67 8.335741235 1
## 68 7.195944292 0
## 69 10.624148205 1
## 70 24.112370528 0
## 71 25.053117412 0
## 72 13.816253932 1
## 73 12.877793376 1
## 74 24.589545862 1
## 75 22.807899692 0
## 76 24.889900947 1
## 77 22.425090742 0
## 78 25.807794689 0
## 79 15.640323852 0
## 80 5.974928398 0
## 81 19.246478485 1
## 82 10.038566094 1
## 83 20.299743510 0
## 84 6.598101036 0
## 85 13.864498408 1
## 86 17.126823814 0
## 87 24.069060162 0
## 88 19.653361010 0
## 89 8.625337943 1
## 90 12.271206713 0
## 91 13.201976116 0
## 92 11.330843968 0
## 93 11.824962656 0
## 94 10.541765847 0
## 95 8.252502616 0
## 96 12.119605010 0
## 97 4.921196095 0
## 98 17.817380758 0
## 99 24.119785104 1
## 100 12.039486496 1marks(Y_5) <- 200Grafico_densidad <- contour(density(y, 0.005), axes = FALSE)
Grafico_densidad## window: rectangle = [5.54, 5.58] x [-73.3, -73.25] unitshist(y$x, nclass = 20)
hist(y$y, nclass = 20)
densidad <- owin(c(min(datos_posicion$x), max(datos_posicion$x)), c(min(datos_posicion$y), max(datos_posicion$y)))
cultivos <- y[densidad]
plot(density(cultivos))
contour(density(Y_2, 0.005),axes = FALSE)
z <-as.im(y_3)
plot(z)
z_1 <-as.im(y_3)
plot(z_1)
plot(y_3, add = TRUE, size =0.4, pch =3)## Plotting the first column of marks
summary(y_3)## Marked planar point pattern: 100 points
## Average intensity 50000 points per square unit
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Mark variables: Ind_humedad_suelo, Ind_vegetacion_diferencia_normalizado,
## Temp_superfi_suelo, presencia_ausencia_cultivos
## Summary:
## Ind_humedad_suelo Ind_vegetacion_diferencia_normalizado Temp_superfi_suelo
## Min. :0.7013 Min. :0.2991 Min. : 0.006873
## 1st Qu.:0.7592 1st Qu.:0.3981 1st Qu.: 6.173386
## Median :0.7937 Median :0.4380 Median :11.855676
## Mean :0.8079 Mean :0.4371 Mean :12.354472
## 3rd Qu.:0.8576 3rd Qu.:0.4718 3rd Qu.:18.264687
## Max. :0.9406 Max. :0.5803 Max. :25.816393
## presencia_ausencia_cultivos
## 0:62
## 1:38
##
##
##
##
##
## Window: rectangle = [5.54, 5.58] x [-73.3, -73.25] units
## (0.04 x 0.05 units)
## Window area = 0.002 square unitsset.seed(1193096924)
sec_lat <- seq(from=0, to=2000, by=2)
sec_long <- seq(from=0, to=500, by=2)
Latitud <- sample(sec_lat, size = 150, replace= TRUE)
longitud <- sample(sec_long, size = 150, replace= TRUE)
posicion_n <- data.frame(x= longitud, y= Latitud)
Ind_humedad_suelo = sort.int(runif(150, 0.7, 0.95), partial = 10)
Ind_vegetacion_diferencia_normalizado = sort.int(rnorm(150, 0.45, 0.06), partial = 10)
Temp_superfi_suelo = sort.int(26*rbeta(150, shape1 = 0.87,shape2 = 0.91), partial = 10)
presencia_ausencia_cultivos = factor(ifelse(rgamma(n=150, rate = 0.8, shape = 0.5)<0.5,0,1))
tabla_2 = data.frame(posicion_n,Ind_humedad_suelo,Ind_vegetacion_diferencia_normalizado,Temp_superfi_suelo,presencia_ausencia_cultivos)
library(spatstat)
datos_posicion_n = ppp(posicion$x, posicion$y, xrange=c(min(posicion$x), max(posicion$x)), yrange=c(min(posicion$y), max(posicion$y)))## Warning: data contain duplicated pointsany(duplicated(datos_posicion_n))## [1] TRUEy_n <- unique(datos_posicion_n)
plot(y_n)
summary(y_n)## Planar point pattern: 98 points
## Average intensity 49000 points per square unit
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: rectangle = [5.54, 5.58] x [-73.3, -73.25] units
## (0.04 x 0.05 units)
## Window area = 0.002 square unitspuntos_cm2 <- summary(y_n)$intensity
puntos_m2 <- puntos_cm2/0.0001
puntos_m2## [1] 4.9e+08cuadricula <- quadratcount(y_n, nx=4, ny=4)
plot(cuadricula)
grafico_3d <- density(y_n, sigma = 60)
plot(grafico_3d)
plot(y_n, cex=0.5)
contorno <- contour(density(y_n, 0.005) , axes = TRUE)
CSR_hipotesis <- quadrat.test(y_n, nx=4)
plot(y_n)
plot(CSR_hipotesis, add=TRUE, cex=1)
CSR_hipotesis$p.value## [1] 0.3284858modelo_poisson <- ppm(y_n,~1)
modelo_poisson## Stationary Poisson process
## Intensity: 49000
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## log(lambda) 10.79958 0.1010153 10.60159 10.99756 *** 106.9103dep_latitud <- ppm(y_n, ~y)
dep_latitud## Nonstationary Poisson process
##
## Log intensity: ~y
##
## Fitted trend coefficients:
## (Intercept) y
## 815.69266 10.98472
##
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## (Intercept) 815.69266 516.908505 -197.429395 1828.81471 1.578021
## y 10.98472 7.054583 -2.842005 24.81145 1.557105dep_longitud <- ppm(y_n, ~x)
dep_longitud## Nonstationary Poisson process
##
## Log intensity: ~x
##
## Fitted trend coefficients:
## (Intercept) x
## 36.371085 -4.599446
##
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## (Intercept) 36.371085 48.690533 -59.06061 131.80278 0.7469847
## x -4.599446 8.758236 -21.76527 12.56638 -0.5251566funcion_x_mas_y <- ppm(y_n, ~x+y)
funcion_x_mas_y## Nonstationary Poisson process
##
## Log intensity: ~x + y
##
## Fitted trend coefficients:
## (Intercept) x y
## 841.149622 -4.594583 10.983529
##
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## (Intercept) 841.149622 519.183770 -176.431869 1858.73111 1.6201385
## x -4.594583 8.757940 -21.759830 12.57066 -0.5246191
## y 10.983529 7.054632 -2.843295 24.81035 1.5569246fun_poli_cuadarico <- ppm(y_n, ~polynom(x,y,2))
fun_poli_cuadarico## Error in solve.default(M) :
## sistema es computacionalmente singular: número de condición recíproco = 2.06402e-22## Warning: Cannot compute variance: Fisher information matrix is singular## Error in solve.default(M) :
## sistema es computacionalmente singular: número de condición recíproco = 2.06402e-22## Warning: Cannot compute variance: Fisher information matrix is singular## Nonstationary Poisson process
##
## Log intensity: ~x + y + I(x^2) + I(x * y) + I(y^2)
##
## Fitted trend coefficients:
## (Intercept) x y I(x^2) I(x * y) I(y^2)
## 2100320.8352 -39950.1048 54285.0339 1213.9183 -360.9431 356.6567
##
## Standard errors unavailable; variance-covariance matrix is singularcomp_modelo <- anova(dep_latitud, funcion_x_mas_y)
comp_modelo## Analysis of Deviance Table
##
## Model 1: ~y Poisson
## Model 2: ~x + y Poisson
## Npar Df Deviance
## 1 2
## 2 3 1 0.27546otra_comparacion <- anova(funcion_x_mas_y,fun_poli_cuadarico)
otra_comparacion## Analysis of Deviance Table
##
## Model 1: ~x + y Poisson
## Model 2: ~x + y + I(x^2) + I(x * y) + I(y^2) Poisson
## Npar Df Deviance
## 1 3
## 2 6 3 3.1068