calculate_lhsOpt: Analyze optimal Latin hypercube sample number

In tgoodbody/sgsR: Structurally Guided Sampling

Analyze optimal Latin hypercube sample number

Description

Population level analysis of metric raster data to determine optimal Latin Hypercube sample size

Usage

calculate_lhsOpt(
  mats,
  PCA = TRUE,
  quant = TRUE,
  KLdiv = TRUE,
  minSamp = 10,
  maxSamp = 100,
  step = 10,
  rep = 10,
  iter = 10000
)

Arguments

mats	List. Output from calculate_pop function.
PCA	Logical. Calculates principal component loadings of the population for PCA similarity factor testing. default = FALSE.
quant	Logical. Perform quantile comparison testing.
KLdiv	Logical. Perform Kullback–Leibler divergence testing.
minSamp	Numeric. Minimum sample size to test. default = 10.
maxSamp	Numeric. Maximum sample size to test. default = 100.
step	Numeric. Sample step size for each iteration. default = 10.
rep	Numeric. Internal repetitions for each sample size. default = 10.
iter	Positive Numeric. The number of iterations for the Metropolis-Hastings annealing process. Defaults to 10000. Internal to clhs.

Value

data.frame with summary statistics.

Note

Special thanks to Dr. Brendan Malone for the original implementation of this algorithm.

Author(s)

Tristan R.H. Goodbody

References

Malone BP, Minasny B, Brungard C. 2019. Some methods to improve the utility of conditioned Latin hypercube sampling. PeerJ 7:e6451 DOI 10.7717/peerj.6451

Examples

## Not run: 
#--- Load raster and access files ---#
r <- system.file("extdata", "mraster.tif", package = "sgsR")
mr <- terra::rast(r)

#--- calculate lhsPop details ---#
mats <- calculate_pop(mraster = mr)

calculate_lhsOpt(mats = mats)

calculate_lhsOpt(
  mats = mats,
  PCA = FALSE,
  iter = 200
)

## End(Not run)

tgoodbody/sgsR documentation built on March 7, 2024, 2:20 a.m.