Modelacion muestreo de plagas, fenologia y enfermedades
Luis Castillo
2023-03-27
# To clean environment
rm(list = ls(all.names = TRUE))
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 518249 27.7 1158588 61.9 644245 34.5
## Vcells 923541 7.1 8388608 64.0 1634757 12.5Lote
xy<-expand.grid(x=seq(0,19.75,0.25),
y=seq(0,29.25,0.75))
dim(xy)## [1] 3200 2cat("Hileras:",length(seq(0,29.25,0.75)),"\n")## Hileras: 40cat("Planta:",length(seq(0,19.75,0.25)))## Planta: 80plot(xy,cex=0.5,xlab="Planta",ylab="Hilera",axes=F)
xy2=expand.grid(x=seq(0,19.75,1),
y=seq(0,29.25,1.5))plot(xy,cex=0.5,xlab="Planta",ylab="Hilera",axes=F)
points(xy2,col="red",cex=1,pch=15)
Lote 2
set.seed(2023)
enfermedad=round(runif(400,0,0.6),0)
colores=ifelse(enfermedad=="1","red","green")
plot(xy2,xlab="Planta",ylab="Hilera",axes=F,pch=15,col=colores)
Tamaño de muestra para estimar una prevalencia
\[ n=\frac{Np(1-p)}{(N-1)(\frac{e}{Z})^2+p(1-p)} \]
\[ N:\text{400 cuadros}\\ \text{Estudio piloto: 10 cuadros (4/10) = 0.4} \\ \text{p: 0.4} \\ \text{e=5%} \\ \text{Z=1.96} \]
muestran1<-function(N,p,e,z=1.96){
n=ceiling((N*p*(1-p))/((N-1)*(e/z)^2+p*(1-p)))
return(n)
}
muestran1(N = 400,p = 0.4,e = 0.08)## [1] 107cuadros=sample(400,107,replace = F)plot(xy2,xlab="Planta",ylab="Hilera",axes=F,pch=15,col=colores)
points(xy2[cuadros,],pch=0,cex=2)
table(colores[cuadros])##
## green red
## 89 1814/107*100## [1] 13.08411table(colores)## colores
## green red
## 329 7155/400*100## [1] 13.75Dependencia espacial
enfermedad2=sort.int(round(runif(400,0,0.6),0),partial = 200)
colores2=ifelse(enfermedad2=="1","red","green")
plot(xy2,xlab="Planta",ylab="Hilera",axes=F,pch=15,col=colores2)
points(xy2[cuadros,],pch=0,cex=2)
cuadros=sample(400,107,replace = F)
plot(xy2,xlab="Planta",ylab="Hilera",axes=F,pch=15,col=colores2)
titulo=table(colores2[cuadros])[2]/107
titulo=round(titulo,3)
points(xy2[cuadros,],pch=0,cex=2)
text(5,1,titulo,cex=3)
Muestreo espacial
set.seed(2023)
enfermedad2=(runif(400,0,0.6))
enfermedad2_round=sort.int(round(enfermedad2,0),partial = 200)
NDVI=1-enfermedad2
df=data.frame(xy2,enfermedad=enfermedad2_round)
df$NDVI=NDVIlibrary(ggplot2)
df |>
ggplot(aes(x,y,fill=NDVI))+
geom_tile()+
scale_fill_gradientn(colors = hcl.colors(20, "RdYlGn"))
df## x y enfermedad NDVI
## 1 0 0.0 0 0.7200316
## 2 1 0.0 0 0.7988854
## 3 2 0.0 0 0.9023095
## 4 3 0.0 0 0.7623280
## 5 4 0.0 0 0.9817650
## 6 5 0.0 0 0.9274691
## 7 6 0.0 0 0.7443006
## 8 7 0.0 0 0.6292853
## 9 8 0.0 0 0.8420750
## 10 9 0.0 0 0.7142057
## 11 10 0.0 0 0.4826086
## 12 11 0.0 0 0.9106728
## 13 12 0.0 0 0.8917420
## 14 13 0.0 0 0.4004360
## 15 14 0.0 0 0.4949553
## 16 15 0.0 0 0.9144106
## 17 16 0.0 0 0.7930910
## 18 17 0.0 0 0.4662247
## 19 18 0.0 0 0.8080910
## 20 19 0.0 0 0.5745905
## 21 0 1.5 0 0.6231090
## 22 1 1.5 0 0.7370965
## 23 2 1.5 0 0.7960624
## 24 3 1.5 0 0.4401444
## 25 4 1.5 0 0.5722274
## 26 5 1.5 0 0.6214889
## 27 6 1.5 0 0.5521303
## 28 7 1.5 0 0.9331298
## 29 8 1.5 0 0.8360269
## 30 9 1.5 0 0.9408891
## 31 10 1.5 0 0.5454823
## 32 11 1.5 0 0.6018447
## 33 12 1.5 0 0.5653833
## 34 13 1.5 0 0.7246321
## 35 14 1.5 0 0.5953609
## 36 15 1.5 0 0.5796918
## 37 16 1.5 0 0.5109567
## 38 17 1.5 0 0.5852911
## 39 18 1.5 0 0.5701526
## 40 19 1.5 0 0.4656521
## 41 0 3.0 0 0.7958219
## 42 1 3.0 0 0.5988623
## 43 2 3.0 0 0.7694476
## 44 3 3.0 0 0.8589320
## 45 4 3.0 0 0.4669018
## 46 5 3.0 0 0.5191290
## 47 6 3.0 0 0.6421434
## 48 7 3.0 0 0.4137894
## 49 8 3.0 0 0.8031370
## 50 9 3.0 0 0.5168920
## 51 10 3.0 0 0.9806108
## 52 11 3.0 0 0.7310261
## 53 12 3.0 0 0.8411610
## 54 13 3.0 0 0.7544395
## 55 14 3.0 0 0.8832419
## 56 15 3.0 0 0.6739075
## 57 16 3.0 0 0.4389378
## 58 17 3.0 0 0.5355774
## 59 18 3.0 0 0.4018700
## 60 19 3.0 0 0.5064245
## 61 0 4.5 0 0.7649768
## 62 1 4.5 0 0.7688503
## 63 2 4.5 0 0.4605173
## 64 3 4.5 0 0.8086882
## 65 4 4.5 0 0.8747869
## 66 5 4.5 0 0.6614937
## 67 6 4.5 0 0.7107493
## 68 7 4.5 0 0.9609312
## 69 8 4.5 0 0.8432336
## 70 9 4.5 0 0.8017986
## 71 10 4.5 0 0.8016615
## 72 11 4.5 0 0.9445880
## 73 12 4.5 0 0.5836039
## 74 13 4.5 0 0.9301062
## 75 14 4.5 0 0.4776416
## 76 15 4.5 0 0.9227976
## 77 16 4.5 0 0.4987732
## 78 17 4.5 0 0.8573849
## 79 18 4.5 0 0.7297488
## 80 19 4.5 0 0.4994208
## 81 0 6.0 0 0.6005218
## 82 1 6.0 0 0.6735285
## 83 2 6.0 0 0.6026248
## 84 3 6.0 0 0.4758926
## 85 4 6.0 0 0.5989672
## 86 5 6.0 0 0.8718728
## 87 6 6.0 0 0.7485554
## 88 7 6.0 0 0.7223344
## 89 8 6.0 0 0.6287148
## 90 9 6.0 0 0.7097643
## 91 10 6.0 0 0.5462374
## 92 11 6.0 0 0.8670329
## 93 12 6.0 0 0.4052923
## 94 13 6.0 0 0.4121623
## 95 14 6.0 0 0.4845822
## 96 15 6.0 0 0.8550915
## 97 16 6.0 0 0.6212005
## 98 17 6.0 0 0.5535726
## 99 18 6.0 0 0.5353578
## 100 19 6.0 0 0.5950981
## 101 0 7.5 0 0.5519329
## 102 1 7.5 0 0.4155447
## 103 2 7.5 0 0.9174026
## 104 3 7.5 0 0.5527489
## 105 4 7.5 0 0.7982613
## 106 5 7.5 0 0.6113750
## 107 6 7.5 0 0.4709950
## 108 7 7.5 0 0.7429064
## 109 8 7.5 0 0.4339017
## 110 9 7.5 0 0.8483439
## 111 10 7.5 0 0.4067671
## 112 11 7.5 0 0.4563267
## 113 12 7.5 0 0.9883361
## 114 13 7.5 0 0.6679686
## 115 14 7.5 0 0.7950426
## 116 15 7.5 0 0.6215738
## 117 16 7.5 0 0.6290210
## 118 17 7.5 0 0.4462353
## 119 18 7.5 0 0.5458135
## 120 19 7.5 0 0.8485441
## 121 0 9.0 0 0.4969188
## 122 1 9.0 0 0.7367644
## 123 2 9.0 0 0.7670914
## 124 3 9.0 0 0.6076849
## 125 4 9.0 0 0.9836284
## 126 5 9.0 0 0.7682341
## 127 6 9.0 0 0.9278478
## 128 7 9.0 0 0.9104233
## 129 8 9.0 0 0.4889064
## 130 9 9.0 0 0.5073020
## 131 10 9.0 0 0.7317249
## 132 11 9.0 0 0.4710453
## 133 12 9.0 0 0.5194538
## 134 13 9.0 0 0.7159342
## 135 14 9.0 0 0.7869411
## 136 15 9.0 0 0.5468291
## 137 16 9.0 0 0.4884491
## 138 17 9.0 0 0.9406117
## 139 18 9.0 0 0.5064249
## 140 19 9.0 0 0.5107286
## 141 0 10.5 0 0.9582263
## 142 1 10.5 0 0.8529337
## 143 2 10.5 0 0.8931408
## 144 3 10.5 0 0.8222180
## 145 4 10.5 0 0.7202983
## 146 5 10.5 0 0.9511637
## 147 6 10.5 0 0.8841368
## 148 7 10.5 0 0.8319196
## 149 8 10.5 0 0.5498947
## 150 9 10.5 0 0.8631014
## 151 10 10.5 0 0.7192970
## 152 11 10.5 0 0.9605912
## 153 12 10.5 0 0.7299837
## 154 13 10.5 0 0.8979451
## 155 14 10.5 0 0.7906687
## 156 15 10.5 0 0.6685974
## 157 16 10.5 0 0.6959723
## 158 17 10.5 0 0.7250607
## 159 18 10.5 0 0.9219893
## 160 19 10.5 0 0.9418314
## 161 0 12.0 0 0.6481465
## 162 1 12.0 0 0.9789451
## 163 2 12.0 0 0.4243982
## 164 3 12.0 0 0.6187641
## 165 4 12.0 0 0.7285549
## 166 5 12.0 0 0.6285423
## 167 6 12.0 0 0.5880519
## 168 7 12.0 0 0.5601844
## 169 8 12.0 0 0.6662736
## 170 9 12.0 0 0.7181087
## 171 10 12.0 0 0.6274321
## 172 11 12.0 0 0.5486793
## 173 12 12.0 0 0.4960550
## 174 13 12.0 0 0.7186421
## 175 14 12.0 0 0.8186231
## 176 15 12.0 0 0.7625492
## 177 16 12.0 0 0.8741236
## 178 17 12.0 0 0.4465987
## 179 18 12.0 0 0.9836627
## 180 19 12.0 0 0.8721779
## 181 0 13.5 0 0.9505628
## 182 1 13.5 0 0.7884249
## 183 2 13.5 0 0.6004490
## 184 3 13.5 0 0.6827622
## 185 4 13.5 0 0.7693225
## 186 5 13.5 0 0.4879164
## 187 6 13.5 0 0.4067823
## 188 7 13.5 0 0.4720570
## 189 8 13.5 0 0.9267349
## 190 9 13.5 0 0.9205644
## 191 10 13.5 0 0.4643964
## 192 11 13.5 0 0.4465277
## 193 12 13.5 0 0.9595322
## 194 13 13.5 0 0.7876535
## 195 14 13.5 0 0.7549539
## 196 15 13.5 0 0.9328916
## 197 16 13.5 0 0.8771658
## 198 17 13.5 0 0.9725726
## 199 18 13.5 0 0.9443663
## 200 19 13.5 0 0.8802405
## 201 0 15.0 0 0.6811065
## 202 1 15.0 0 0.4575390
## 203 2 15.0 0 0.7260967
## 204 3 15.0 0 0.6441469
## 205 4 15.0 0 0.5095562
## 206 5 15.0 0 0.5312106
## 207 6 15.0 0 0.4988123
## 208 7 15.0 0 0.8791675
## 209 8 15.0 0 0.5036915
## 210 9 15.0 0 0.7019407
## 211 10 15.0 0 0.7040625
## 212 11 15.0 0 0.8005281
## 213 12 15.0 0 0.8588374
## 214 13 15.0 0 0.5334699
## 215 14 15.0 0 0.6291293
## 216 15 15.0 0 0.6696113
## 217 16 15.0 0 0.8900609
## 218 17 15.0 0 0.9940549
## 219 18 15.0 0 0.4495690
## 220 19 15.0 0 0.9027125
## 221 0 16.5 0 0.5508791
## 222 1 16.5 0 0.5914100
## 223 2 16.5 0 0.6985297
## 224 3 16.5 0 0.4078756
## 225 4 16.5 0 0.6372644
## 226 5 16.5 0 0.7268559
## 227 6 16.5 0 0.9882118
## 228 7 16.5 0 0.7429919
## 229 8 16.5 0 0.5354391
## 230 9 16.5 0 0.7143692
## 231 10 16.5 0 0.6977445
## 232 11 16.5 0 0.8070706
## 233 12 16.5 0 0.9055081
## 234 13 16.5 0 0.4899944
## 235 14 16.5 0 0.4029705
## 236 15 16.5 0 0.6198562
## 237 16 16.5 0 0.5449326
## 238 17 16.5 0 0.6082246
## 239 18 16.5 0 0.4180636
## 240 19 16.5 0 0.9708749
## 241 0 18.0 0 0.8095771
## 242 1 18.0 0 0.4443345
## 243 2 18.0 0 0.6138441
## 244 3 18.0 0 0.9078180
## 245 4 18.0 0 0.6752602
## 246 5 18.0 0 0.9352995
## 247 6 18.0 0 0.6187258
## 248 7 18.0 0 0.5050656
## 249 8 18.0 0 0.8673189
## 250 9 18.0 0 0.9791040
## 251 10 18.0 0 0.4815122
## 252 11 18.0 0 0.8719643
## 253 12 18.0 0 0.8537876
## 254 13 18.0 0 0.5494490
## 255 14 18.0 0 0.7484807
## 256 15 18.0 0 0.5051774
## 257 16 18.0 0 0.7775282
## 258 17 18.0 0 0.6304703
## 259 18 18.0 0 0.4633113
## 260 19 18.0 0 0.8153953
## 261 0 19.5 0 0.7288209
## 262 1 19.5 0 0.9797029
## 263 2 19.5 0 0.4049439
## 264 3 19.5 0 0.8140072
## 265 4 19.5 0 0.6172148
## 266 5 19.5 0 0.5052566
## 267 6 19.5 0 0.5023892
## 268 7 19.5 0 0.4430282
## 269 8 19.5 1 0.7743460
## 270 9 19.5 1 0.5522531
## 271 10 19.5 1 0.4564995
## 272 11 19.5 1 0.6989168
## 273 12 19.5 0 0.9802165
## 274 13 19.5 1 0.9960366
## 275 14 19.5 1 0.5576457
## 276 15 19.5 0 0.8420013
## 277 16 19.5 1 0.7429689
## 278 17 19.5 1 0.4731265
## 279 18 19.5 1 0.6110653
## 280 19 19.5 0 0.5050032
## 281 0 21.0 0 0.7546056
## 282 1 21.0 0 0.8936010
## 283 2 21.0 1 0.7363628
## 284 3 21.0 0 0.5557145
## 285 4 21.0 1 0.5479127
## 286 5 21.0 1 0.9550122
## 287 6 21.0 1 0.9753562
## 288 7 21.0 1 0.8914682
## 289 8 21.0 0 0.6999893
## 290 9 21.0 0 0.5035729
## 291 10 21.0 1 0.5726642
## 292 11 21.0 1 0.4690844
## 293 12 21.0 0 0.6142686
## 294 13 21.0 1 0.4247378
## 295 14 21.0 0 0.6638275
## 296 15 21.0 1 0.4433054
## 297 16 21.0 1 0.7270671
## 298 17 21.0 0 0.9387862
## 299 18 21.0 1 0.8177948
## 300 19 21.0 0 0.7378482
## 301 0 22.5 0 0.5940933
## 302 1 22.5 0 0.9036134
## 303 2 22.5 1 0.4020511
## 304 3 22.5 1 0.8731396
## 305 4 22.5 0 0.9274621
## 306 5 22.5 1 0.5121712
## 307 6 22.5 1 0.5065578
## 308 7 22.5 1 0.4800592
## 309 8 22.5 0 0.8396761
## 310 9 22.5 1 0.7045629
## 311 10 22.5 0 0.5061806
## 312 11 22.5 1 0.6410605
## 313 12 22.5 1 0.6105176
## 314 13 22.5 1 0.4080372
## 315 14 22.5 0 0.6996030
## 316 15 22.5 1 0.7558936
## 317 16 22.5 0 0.5347690
## 318 17 22.5 1 0.4722249
## 319 18 22.5 1 0.4333411
## 320 19 22.5 0 0.6125484
## 321 0 24.0 1 0.4254527
## 322 1 24.0 0 0.9907744
## 323 2 24.0 1 0.6232289
## 324 3 24.0 1 0.9328105
## 325 4 24.0 1 0.5819798
## 326 5 24.0 1 0.9791117
## 327 6 24.0 0 0.5271119
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## 330 9 24.0 1 0.7410447
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## 332 11 24.0 1 0.9391587
## 333 12 24.0 0 0.7278865
## 334 13 24.0 0 0.5968180
## 335 14 24.0 0 0.7191339
## 336 15 24.0 0 0.7802721
## 337 16 24.0 1 0.6340180
## 338 17 24.0 1 0.4129652
## 339 18 24.0 0 0.7017871
## 340 19 24.0 0 0.7273099
## 341 0 25.5 0 0.5905491
## 342 1 25.5 1 0.4753150
## 343 2 25.5 1 0.6306410
## 344 3 25.5 0 0.6269294
## 345 4 25.5 1 0.8818662
## 346 5 25.5 1 0.8643861
## 347 6 25.5 0 0.9801322
## 348 7 25.5 0 0.8072534
## 349 8 25.5 0 0.9194519
## 350 9 25.5 0 0.6410174
## 351 10 25.5 1 0.8127032
## 352 11 25.5 0 0.9343114
## 353 12 25.5 1 0.4140649
## 354 13 25.5 0 0.6903738
## 355 14 25.5 0 0.9431428
## 356 15 25.5 0 0.5312064
## 357 16 25.5 0 0.8219999
## 358 17 25.5 1 0.4811872
## 359 18 25.5 1 0.4723134
## 360 19 25.5 1 0.4514776
## 361 0 27.0 0 0.6877531
## 362 1 27.0 1 0.4939148
## 363 2 27.0 0 0.6986776
## 364 3 27.0 1 0.7585079
## 365 4 27.0 0 0.5017487
## 366 5 27.0 0 0.5500370
## 367 6 27.0 1 0.6425234
## 368 7 27.0 0 0.9536470
## 369 8 27.0 1 0.9076285
## 370 9 27.0 0 0.5449850
## 371 10 27.0 0 0.5031301
## 372 11 27.0 1 0.9955066
## 373 12 27.0 0 0.7838334
## 374 13 27.0 0 0.9964805
## 375 14 27.0 0 0.5351420
## 376 15 27.0 0 0.7348855
## 377 16 27.0 0 0.6690337
## 378 17 27.0 1 0.6586328
## 379 18 27.0 0 0.8642931
## 380 19 27.0 1 0.4065435
## 381 0 28.5 1 0.6367477
## 382 1 28.5 1 0.5316251
## 383 2 28.5 1 0.7756326
## 384 3 28.5 1 0.9151380
## 385 4 28.5 1 0.8971336
## 386 5 28.5 1 0.5556333
## 387 6 28.5 0 0.8584752
## 388 7 28.5 0 0.8680335
## 389 8 28.5 1 0.8335562
## 390 9 28.5 1 0.8280266
## 391 10 28.5 1 0.4458492
## 392 11 28.5 1 0.4650102
## 393 12 28.5 1 0.4224210
## 394 13 28.5 1 0.5331979
## 395 14 28.5 0 0.5858987
## 396 15 28.5 0 0.9318912
## 397 16 28.5 1 0.4359596
## 398 17 28.5 1 0.8884903
## 399 18 28.5 0 0.6081885
## 400 19 28.5 0 0.8519536Muestreo espacial
5*400/100## [1] 20library(clhs)
res <- clhs(df[,c(1,2)], size = 12, progress = FALSE, simple = TRUE)plot(df$x,df$y)
points(df$x[res],df$y[res],pch=16)
res2 <- clhs(df, size = 12, progress = FALSE, simple = TRUE)plot(df$x,df$y)
points(df$x[res2],df$y[res2],pch=16)
res3 <- clhs(xy, size = 20, progress = FALSE, simple = TRUE)plot(xy,cex=0.6)
points(xy$x[res3],xy$y[res3],pch=16,cex=1)