clhs-package: Conditioned Latin Hypercube Sampling

Description Details Author(s) References See Also Examples

Description

This package implements the conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) and the DLHS variant method (Minasny and McBratney, 2010).. This method proposes to stratify sampling in presence of ancillary data.

Details

An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012).

Author(s)

Pierre Roudier

References

* For the initial cLHS method:

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.

*For the DLHS variant method:

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.

* For the cost-constrained implementation:

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

* For the similarity buffer prediction:

Brungard, C. and Johanson, J. 2015. The gate's locked! I can't get to the exact sampling spot... can I sample nearby? Pedometron, 37:8–10.

See Also

sample

Examples

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df <- data.frame(
  a = runif(1000), 
  b = rnorm(1000), 
  c = sample(LETTERS[1:5], size = 1000, replace = TRUE)
)
res <- clhs(df, size = 50, iter = 2000, progress = FALSE)
str(res)

clhs documentation built on Oct. 11, 2018, 1:04 a.m.