Spatially correlated data are simulated assuming a multivariate normal
random error vector. For simplicity, only "Exponential" and "Spherical" simulation options are given here.
geostatSim(
loc.data,
xcol = "x",
ycol = "y",
parsil = 1,
range = 1,
nugget = 0,
minorp = 1,
rotate = 90,
extrap = NULL,
CorModel = "Exponential"
)data.frame with x- and y-coordinates of locations for simulated data
name of the column in loc.data with x-coordinates, default is "x"
name of the column loc.data with y-coordinates, default is "y"
partial sill of autocorrelation model, default = 1
range of autocorrelation model, default = 1
range of autocorrelation model, default = 0
proportion of range in x direction to that of y direction for unrotated anisotropic model, default = 1
rotation of anisotropic axes, default = 90
extra covariance parameter
autocorrelation model, default = "Exponential". Other possibilities are "Spherical".
data.frame of three columns, the original location data appended with a 3rd column of simulated geostatistical data
locations <- expand.grid(1:10, 1:10)
geostatSim(locations, xcol = "Var1", ycol = "Var2",
parsil = 4, range = 20, nugget = 1, CorModel = "Exponential")
#> Var1 Var2 z
#> 1 1 1 1.038416490
#> 2 2 1 -0.471126320
#> 3 3 1 -1.667411598
#> 4 4 1 1.298771460
#> 5 5 1 -0.487296926
#> 6 6 1 -1.151277657
#> 7 7 1 -2.298625236
#> 8 8 1 -0.810536240
#> 9 9 1 -2.122589828
#> 10 10 1 -0.935802561
#> 11 1 2 -0.264263295
#> 12 2 2 -0.727034027
#> 13 3 2 1.464676279
#> 14 4 2 -2.403357123
#> 15 5 2 -0.949552051
#> 16 6 2 -0.640875398
#> 17 7 2 -0.006393008
#> 18 8 2 -1.388363210
#> 19 9 2 0.083576070
#> 20 10 2 0.438680733
#> 21 1 3 -0.263591707
#> 22 2 3 0.362093921
#> 23 3 3 0.106734212
#> 24 4 3 0.223098464
#> 25 5 3 1.348833327
#> 26 6 3 2.587807223
#> 27 7 3 0.932680310
#> 28 8 3 1.746438270
#> 29 9 3 2.428211593
#> 30 10 3 0.211567759
#> 31 1 4 -0.285708037
#> 32 2 4 1.185017778
#> 33 3 4 -1.556338221
#> 34 4 4 -0.158619836
#> 35 5 4 -0.399068580
#> 36 6 4 1.128818106
#> 37 7 4 0.826208595
#> 38 8 4 1.294247463
#> 39 9 4 1.069944320
#> 40 10 4 0.445630755
#> 41 1 5 -0.552311699
#> 42 2 5 -0.289806817
#> 43 3 5 0.665833261
#> 44 4 5 0.350314836
#> 45 5 5 -0.147680919
#> 46 6 5 0.800487080
#> 47 7 5 1.532777339
#> 48 8 5 0.944422484
#> 49 9 5 1.959658537
#> 50 10 5 2.434469912
#> 51 1 6 -0.292542365
#> 52 2 6 1.407796019
#> 53 3 6 -0.001165708
#> 54 4 6 1.932478372
#> 55 5 6 3.169824968
#> 56 6 6 0.973311642
#> 57 7 6 2.216533990
#> 58 8 6 1.903271868
#> 59 9 6 1.102973662
#> 60 10 6 1.385180645
#> 61 1 7 -1.135730413
#> 62 2 7 1.445645282
#> 63 3 7 0.599536253
#> 64 4 7 0.501302606
#> 65 5 7 2.712900258
#> 66 6 7 0.335394279
#> 67 7 7 -0.054698583
#> 68 8 7 -1.351833329
#> 69 9 7 0.033421920
#> 70 10 7 0.145623867
#> 71 1 8 1.679741552
#> 72 2 8 -0.800263860
#> 73 3 8 0.508065190
#> 74 4 8 -2.386498097
#> 75 5 8 0.569045884
#> 76 6 8 0.618277663
#> 77 7 8 -1.119653993
#> 78 8 8 2.073465170
#> 79 9 8 1.464899489
#> 80 10 8 -2.471278706
#> 81 1 9 1.865021104
#> 82 2 9 1.417836170
#> 83 3 9 1.373895180
#> 84 4 9 0.132062261
#> 85 5 9 -1.043122340
#> 86 6 9 -0.838027982
#> 87 7 9 -0.530508251
#> 88 8 9 1.424692371
#> 89 9 9 0.161558613
#> 90 10 9 -1.593798260
#> 91 1 10 1.286820581
#> 92 2 10 0.400373393
#> 93 3 10 1.794387625
#> 94 4 10 0.655421619
#> 95 5 10 0.059149967
#> 96 6 10 0.618199524
#> 97 7 10 -0.653622011
#> 98 8 10 1.251387082
#> 99 9 10 0.308299802
#> 100 10 10 0.051150670