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Conditioned Latin Hypercube Sampling

Source: R/sample_clhs.R
sample_clhs.Rd

Conditioned Latin Hypercube Sampling using clhs functionality.

Usage

sample_clhs(
  mraster,
  nSamp,
  iter = 10000,
  cost = NULL,
  existing = NULL,
  access = NULL,
  buff_inner = NULL,
  buff_outer = NULL,
  plot = FALSE,
  details = FALSE,
  filename = NULL,
  overwrite = FALSE,
  ...
)

Arguments

mraster

spatRaster. ALS metrics raster.

nSamp

Numeric. Number of desired samples.

iter

Numeric. Value giving the number of iterations within the Metropolis-Hastings process.

cost

Numeric/Character. Index or name of covariate within mraster to be used to constrain cLHS sampling. If default (NULL), a cost constraint is not used.

existing

sf 'POINT'. Existing plot network.

access

sf 'LINESTRING' or 'MULTILINESTRING'. Access network.

buff_inner

Numeric. Inner buffer boundary specifying distance from access where plots cannot be sampled.

buff_outer

Numeric. Outer buffer boundary specifying distance from access where plots can be sampled.

plot

Logical. Plots output strata raster with samples.

details

Logical. If FALSE (default) output is only stratification raster. If TRUE return a list where $details is additional stratification information and $raster is the output stratification spatRaster.

filename

Character. Path to write output samples.

overwrite

Logical. Choice to overwrite existing filename if it exists.

...

Additional arguments for clhs sampling. See clhs.

Value

An sf object with nSamp stratified samples.

References

Minasny, B. and McBratney, A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32:1378-1388.

Minasny, B. and A. B. McBratney, A.B.. 2010. Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties. In: Proximal Soil Sensing, Progress in Soil Science, pages 111-119.

Roudier, P., Beaudette, D.E. and Hewitt, A.E. 2012. A conditioned Latin hypercube sampling algorithm incorporating operational constraints. In: Digital Soil Assessments and Beyond. Proceedings of the 5th Global Workshop on Digital Soil Mapping, Sydney, Australia.

See also

Other sample functions: sample_ahels(), sample_balanced(), sample_existing(), sample_nc(), sample_srs(), sample_strat(), sample_sys_strat(), sample_systematic()

Author

Tristan R.H. Goodbody

Examples

#--- Load raster and existing plots---#
r <- system.file("extdata", "mraster.tif", package = "sgsR")
mr <- terra::rast(r)

e <- system.file("extdata", "existing.shp", package = "sgsR")
e <- sf::st_read(e)
#> Reading layer `existing' from data source 
#>   `/home/runner/work/_temp/Library/sgsR/extdata/existing.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 200 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337710 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM_Zone_17_Northern_Hemisphere

a <- system.file("extdata", "access.shp", package = "sgsR")
ac <- sf::st_read(a)
#> Reading layer `access' from data source 
#>   `/home/runner/work/_temp/Library/sgsR/extdata/access.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 167 features and 2 fields
#> Geometry type: MULTILINESTRING
#> Dimension:     XY
#> Bounding box:  xmin: 431100 ymin: 5337700 xmax: 438560 ymax: 5343240
#> Projected CRS: UTM_Zone_17_Northern_Hemisphere

sample_clhs(
  mraster = mr,
  nSamp = 200,
  plot = TRUE,
  iter = 100
)

#> Simple feature collection with 200 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337730 xmax: 438530 ymax: 5343190
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>        zq90 pzabove2  zsd type               geometry
#> 53648 15.40     85.7 4.60  new POINT (432070 5340090)
#> 56987 18.50     82.0 5.81  new POINT (438390 5339910)
#> 53479 20.20     71.5 6.19  new POINT (435850 5340110)
#> 41784 12.20     75.3 3.40  new POINT (432170 5340790)
#> 13849  4.47     72.0 0.86  new POINT (435570 5342410)
#> 3681  16.70     91.5 4.42  new POINT (438510 5343010)
#> 37582  3.95     21.5 0.82  new POINT (433990 5341050)
#> 84618  8.24     91.4 1.72  new POINT (437150 5338070)
#> 4820   5.42     66.0 1.18  new POINT (433490 5342930)
#> 72403 13.70     47.2 3.16  new POINT (435690 5338870)

sample_clhs(
  mraster = mr,
  nSamp = 400,
  existing = e,
  iter = 250,
  details = TRUE
)
#> $clhs
#>   [1] 15151 61657 31518  8482 37636 19381 42107 67289  2699 73969 36068
#>  [12] 22164  5320 25791 54985 10158 74454 68856 79500 16164 58659 67935
#>  [23] 20433 85555 71022 85135 64642 17635 15695 49701 13617 13217 12633
#>  [34]  1932 46873  4937 70153 61088 50135 90059 16867 37095 28415 24619
#>  [45] 56906 89113 51948 55320 63927 43535 52235 16019 71862  4127 29442
#>  [56] 54578 42606 14103 85699 36255   103 77556 15049 49254 90452 67494
#>  [67] 44835 53441 88496  7655 22810  8150 59769 51115 50766  2292 61435
#>  [78] 61542 28576 27520 40423 46933 63448 35881 36326  6629 48012 24958
#>  [89] 18913 52851 65170 80606 30660 27639 86414 73377 14574 27183 30682
#> [100] 35999 70333 56212 75135 25278 71529 18522 38225 89496 16612 75078
#> [111] 68372 36770 86263 44002 82377 67729 44367 41772 21499 23778 81923
#> [122] 14752 46310 31766 32595 70036 87618  6136 57171 69757 25207 69386
#> [133] 21258 58088 29699 35160  1125 37888 45294 75471 70281 16890 49426
#> [144] 87890 20449 44738 63123 65519 25100 33156 47778 64436 79120 73492
#> [155] 34131 83680 11178 52059 87270 68834 59822 80783 56255 20903 62779
#> [166] 52240 89713 65661 49777 27826 60008 63865 15259 51553 88750 54527
#> [177] 34377 54953 60728 53203 26862 35191 15643 80905 37937 26021 10431
#> [188]  2519 33327 75891 23860 38347   992 60891 62583 66462 57831 23620
#> [199] 63894 72526     1     2     3     4     5     6     7     8     9
#> [210]    10    11    12    13    14    15    16    17    18    19    20
#> [221]    21    22    23    24    25    26    27    28    29    30    31
#> [232]    32    33    34    35    36    37    38    39    40    41    42
#> [243]    43    44    45    46    47    48    49    50    51    52    53
#> [254]    54    55    56    57    58    59    60    61    62    63    64
#> [265]    65    66    67    68    69    70    71    72    73    74    75
#> [276]    76    77    78    79    80    81    82    83    84    85    86
#> [287]    87    88    89    90    91    92    93    94    95    96    97
#> [298]    98    99   100   101   102   103   104   105   106   107   108
#> [309]   109   110   111   112   113   114   115   116   117   118   119
#> [320]   120   121   122   123   124   125   126   127   128   129   130
#> [331]   131   132   133   134   135   136   137   138   139   140   141
#> [342]   142   143   144   145   146   147   148   149   150   151   152
#> [353]   153   154   155   156   157   158   159   160   161   162   163
#> [364]   164   165   166   167   168   169   170   171   172   173   174
#> [375]   175   176   177   178   179   180   181   182   183   184   185
#> [386]   186   187   188   189   190   191   192   193   194   195   196
#> [397]   197   198   199   200
#> 
#> $samples
#> Simple feature collection with 400 features and 5 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337710 xmax: 438550 ymax: 5343230
#> Projected CRS: UTM_Zone_17_Northern_Hemisphere
#> First 10 features:
#>    type  zq90 pzabove2  zsd FID               geometry
#> 1   new 17.80     88.3 4.95  NA POINT (436790 5342350)
#> 2   new  6.37     70.8 1.44  NA POINT (437910 5339630)
#> 3   new 16.70     94.8 3.79  NA POINT (432350 5341410)
#> 4   new 17.80     90.8 5.51  NA POINT (437310 5342750)
#> 5   new 15.70     83.3 4.52  NA POINT (438330 5341070)
#> 6   new 14.20     88.5 3.13  NA POINT (434050 5342110)
#> 7   new 18.40     81.7 4.69  NA POINT (435430 5340790)
#> 8   new 10.80     52.5 2.80  NA POINT (435830 5339270)
#> 9   new 15.40     59.9 4.12  NA POINT (432130 5343070)
#> 10  new 16.70     93.5 4.77  NA POINT (433410 5338750)
#>