#Latin hypercube sampling r manual#
#Latin hypercube sampling r code#

Where else can I sample when a cLHS location cannot be visited because of difficult terrain, locked gate, safety reasons etc.? However, our own experience, and from personal communication with other researchers and field technicians, a common set of methodological questions arise when using cLHS. Presuming that soil variation is a function of the chosen environmental variables, it is reasoned that models fitted using data collected via cLHS, capture all the soil spatial variability and will be applicable across the whole spatial extent to be mapped. For example, in optimal soil spectral model calibration ( Ramirez-Lopez et al., 2014 KopaДЌkovГЎ et al., 2017), understanding the conditions which determine Phytophthora distribution in rainforests ( Scarlett et al., 2015), and assessing the uncertainty of digital elevation models derived from light detection and ranging technology ( Chu et al., 2014).ДЇor DSM, the algorithm exploits collections of environmental variables pertaining to soil forming factors and proxies thereof ( McBratney, MendonГ§a Santos & Minasny, 2003 e.g., digital elevation model derivatives, remote sensing imagery of vegetation type and distribution, climatic data, and geological maps) to derive a sample configuration (of specified size), such that the empirical distribution function of each environmental variable is replicated ( Clifford et al., 2014). cLHS has also been used for other purposes and contexts too.

cLHS has been used extensively in DSM projects throughout the world with recent examples in the last 5 years including Sun et al. cLHS is a random stratified procedure that choses sampling locations based on prior information pertaining to a suite of environmental variables in a given area. The conditioned Latin hypercube sampling (cLHS) algorithm ( Minasny & McBratney, 2006) was designed with digital soil mapping (DSM) in mind. Some methods to improve the utility of conditioned Latin hypercube sampling. Cite this article Malone BP, Minansy B, Brungard C.

For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. Finally, you can change the names of things like the output file (from 1.out to myfile.out or myfile.dat or anything you want) and also the distribution names.Д®xample input file unif.3 Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA DOI 10.7717/peerj.6451 Published Accepted Received Academic Editor Paolo Giordani Subject Areas Agricultural Science, Soil Science, Statistics, Data Science, Spatial and Geographic Information Science Keywords Soil sampling, Conditioned Latin Hypercube, Digital soil mapping, Optimization, Sampling, Sample optimization, Legacy soil data, Pedometrics, Soil survey, Fieldwork Copyright В© 2019 Malone et al. In this case, we are specifying everything (the data, the correlations, and histograms of the samples) so all of that is printed in fort.4. The file 1.OUT has the samples, but the file fort.4 has everything you specify in the line вЂњlhsrptsвЂќ (reports). Note also that there are several fortran files generated, and the one that is most useful is fort.4. The output will go to the file 1.out, any messages will go to 2.out. For example, the unif.dat input will generate 60 observations (samples) from 4 uniform distributions between -1 and 1, with a seed 1342. Then open a terminal or command prompt, change directory to the input files and run lhsdrv, e.g.:

#Latin hypercube sampling r manual#

The LHS manual referenced above explains the format of the input file in more detail. Input file, e.g., unif.dat (example below) which is a simple example which generates 60 samples from four uniform random variables on the interval -1 to 1, or KEYWORD.DAT (which is included in the LHS source package), which is a more comprehensive example of possible random variable types and syntax.Initialization file SIPRA.INI (example included in LHS source package).Place the following files in the directory from which you run lhsdrv.exe and Wyss, G.D., 'A User's Guide to Sandia's Latin Hypercube Sampling Software: LHS UNIX Library Standalone Version, Sandia Technical Report SAND2004-2439, July 2004.

#Latin hypercube sampling r code#

The LHS Fortran source code is included in packages/external/LHS. Once the package is unpacked, the lhsdrv or lhsdrv.exe binary is included in dakota/bin/. Shortencarier)Дownload: See Dakota Downloads for binary or source packages. LHS is distributed with Dakota and can be used as a distinct standalone capability.ДЄuthors: G.

Sandia's Latin Hypercube Sampling package predates Dakota, though is no longer distributed as a standalone package.