Indice de Moran

library(ape)
set.seed(12345)
MO=rnorm(n=150, mean= 3, sd=0.5)
xy= expand.grid(x=seq(1,10), y =seq(1,15))
df=data.frame(MO, xy)
plot(xy, col=MO, pch=18)

as.data.frame(MO)

mo.dis <- as.matrix(dist(cbind(xy$x, xy$y)))
mo.dis.inv <- 1/mo.dis ##(Inf) Inverso de la Matriz 
### Matriz de peso, el inverso de las distancias = mas lejos va a tener menos relevancia y un menor valor
diag(mo.dis.inv) <- 0
dim(mo.dis)

[1] 150 150

mo.dis.inv[1:10, 1:10]

           1         2         3         4         5         6
1  0.0000000 1.0000000 0.5000000 0.3333333 0.2500000 0.2000000
2  1.0000000 0.0000000 1.0000000 0.5000000 0.3333333 0.2500000
3  0.5000000 1.0000000 0.0000000 1.0000000 0.5000000 0.3333333
4  0.3333333 0.5000000 1.0000000 0.0000000 1.0000000 0.5000000
5  0.2500000 0.3333333 0.5000000 1.0000000 0.0000000 1.0000000
6  0.2000000 0.2500000 0.3333333 0.5000000 1.0000000 0.0000000
7  0.1666667 0.2000000 0.2500000 0.3333333 0.5000000 1.0000000
8  0.1428571 0.1666667 0.2000000 0.2500000 0.3333333 0.5000000
9  0.1250000 0.1428571 0.1666667 0.2000000 0.2500000 0.3333333
10 0.1111111 0.1250000 0.1428571 0.1666667 0.2000000 0.2500000
           7         8         9        10
1  0.1666667 0.1428571 0.1250000 0.1111111
2  0.2000000 0.1666667 0.1428571 0.1250000
3  0.2500000 0.2000000 0.1666667 0.1428571
4  0.3333333 0.2500000 0.2000000 0.1666667
5  0.5000000 0.3333333 0.2500000 0.2000000
6  1.0000000 0.5000000 0.3333333 0.2500000
7  0.0000000 1.0000000 0.5000000 0.3333333
8  1.0000000 0.0000000 1.0000000 0.5000000
9  0.5000000 1.0000000 0.0000000 1.0000000
10 0.3333333 0.5000000 1.0000000 0.0000000

Moran.I(df$MO, mo.dis.inv)

$observed
[1] -0.009650003

$expected
[1] -0.006711409

$sd
[1] 0.007694112

$p.value
[1] 0.7025151

No hay dependencia espacial según el p.value al calcular el Indice de Moran.

Ejercicio Conductividad Electrica

library(readxl)
BD_MORAN <- read_excel("./BD_MORAN.xlsx", 
    sheet = "Hoja1")
View(BD_MORAN)
plot(BD_MORAN$X_WGS84,BD_MORAN$Y_WGS84,col=0.5*BD_MORAN$z)

plot(BD_MORAN$X_WGS84,BD_MORAN$Y_WGS84,col=0.5*BD_MORAN$CEa_075)

plot(BD_MORAN$X_WGS84,BD_MORAN$Y_WGS84,col=0.5*BD_MORAN$CEa_150)

plot(BD_MORAN$Y_WGS84,BD_MORAN$X_WGS84,col=0.5*BD_MORAN$Y_MCE)

Índice de Moran - CE a 75cm (primeras 4000 muestras)

library(ape)
Longitud <- BD_MORAN$X_WGS84[1:4000]
Latitud <- BD_MORAN$Y_WGS84[1:4000]
Ce_dist <- as.matrix(dist(cbind(Longitud, Latitud)))
dim(Ce_dist)

[1] 4000 4000

Ce_dist_inv <- 1/Ce_dist
Ce_dist_inv[is.infinite(Ce_dist_inv)] <- 0
diag(Ce_dist_inv) <- 0
CEa_075_1 <- BD_MORAN$CEa_075[1:4000]
Ce_dist_inv[1:10,1:10]

           1         2         3         4         5         6
1       0.00 406473.84 183582.68 136552.66  99512.00  80767.51
2  406473.84      0.00 334028.09 205286.88 131603.44 100690.59
3  183582.68 334028.09      0.00 532631.20 217163.07 144140.71
4  136552.66 205286.88 532631.20      0.00 366652.29 197620.35
5   99512.00 131603.44 217163.07 366652.29      0.00 428663.73
6   80767.51 100690.59 144140.71 197620.35 428663.73      0.00
7   66276.45  79119.77 103677.22 128735.53 198393.56 369323.16
8   57404.68  66798.00  83495.12  99016.99 135648.88 198443.56
9   49363.96  56156.93  67505.83  77303.00  97952.59 126960.00
10  44785.68  50307.49  59227.30  66636.81  81434.82 100529.27
           7         8         9        10
1   66276.45  57404.68  49363.96  44785.68
2   79119.77  66798.00  56156.93  50307.49
3  103677.22  83495.12  67505.83  59227.30
4  128735.53  99016.99  77303.00  66636.81
5  198393.56 135648.88  97952.59  81434.82
6  369323.16 198443.56 126960.00 100529.27
7       0.00 428837.13 193437.71 138109.95
8  428837.13      0.00 352381.47 203715.00
9  193437.71 352381.47      0.00 482860.83
10 138109.95 203715.00 482860.83      0.00

Moran.I(CEa_075_1,Ce_dist_inv)

$observed
[1] 0.5692932

$expected
[1] -0.0002500625

$sd
[1] 0.002184709

$p.value
[1] 0

Índice de Moran - CE a 75cm (de la muestra 4001 a la 8000)

library(ape)
Longitud <- BD_MORAN$X_WGS84[4001:8000]
Latitud <- BD_MORAN$Y_WGS84[4001:8000]
Ce_dist <- as.matrix(dist(cbind(Longitud, Latitud)))
dim(Ce_dist)

[1] 4000 4000

Ce_dist_inv <- 1/Ce_dist
Ce_dist_inv[is.infinite(Ce_dist_inv)] <- 0
diag(Ce_dist_inv) <- 0
CEa_075_1 <- BD_MORAN$CEa_075[4001:8000]
Ce_dist_inv[1:10,1:10]

           1         2         3         4         5         6
1       0.00 146925.43  84295.79  53911.96  41984.94  33629.84
2  146925.43      0.00 197748.59  85158.95  58781.71  43611.86
3   84295.79 197748.59      0.00 149570.27  83645.82  55951.51
4   53911.96  85158.95 149570.27      0.00 189776.55  89391.07
5   41984.94  58781.71  83645.82 189776.55      0.00 168991.75
6   33629.84  43611.86  55951.51  89391.07 168991.75      0.00
7   27690.34  34120.66  41235.70  56931.34  81329.50 156783.54
8   23967.31  28638.85  33488.86  43150.19  55848.72  83416.21
9   21335.13  24959.33  28564.69  35307.69  43378.13  58357.84
10  18987.88  21805.81  24508.35  29311.23  34665.34  43611.31
           7         8         9        10
1   27690.34  23967.31  21335.13  18987.88
2   34120.66  28638.85  24959.33  21805.81
3   41235.70  33488.86  28564.69  24508.35
4   56931.34  43150.19  35307.69  29311.23
5   81329.50  55848.72  43378.13  34665.34
6  156783.54  83416.21  58357.84  43611.31
7       0.00 178257.65  92958.93  60417.06
8  178257.65      0.00 194265.97  91392.98
9   92958.93 194265.97      0.00 172587.03
10  60417.06  91392.98 172587.03      0.00

Moran.I(CEa_075_1,Ce_dist_inv)

$observed
[1] 0.5215655

$expected
[1] -0.0002500625

$sd
[1] 0.002154601

$p.value
[1] 0

Índice de Moran - CE a 75cm (de la muestra 8001 a la 1200)

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio

library(ape)
Longitud <- BD_MORAN$X_WGS84[8001:1200]

Error: object 'BD_MORAN' not found