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- #LATIN HYPERCUBE SAMPLING EQUIPROBABLE INSTALL#
- #LATIN HYPERCUBE SAMPLING EQUIPROBABLE FULL#
- #LATIN HYPERCUBE SAMPLING EQUIPROBABLE CODE#
Establishing a prediction model from secondary or ancillary variables (Hengl et al., 2003 Lesch et al., 1995).īrus and de Gruijter (1997) discussed the two fundamental approaches to soil sampling: namely design-based, which follows the classical survey, and model-based, following geostatistical analysis. Providing an optimal spatial coverage (Royle and Nychka, 1998). This includes methods that minimise the kriging variance (McBratney et al., 1981 van Groenigen et al., 1999).ĪRTICLE IN PRESS B.
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Estimating variables within an area using spatial interpolation. Minasny).Ġ098-3004/$ - see front matter r 2006 Elsevier Ltd.
#LATIN HYPERCUBE SAMPLING EQUIPROBABLE CODE#
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The method is illustrated with a simple 3-D example and an application in digital soil mapping of part of the Hunter Valley of New South Wales, Australia. This paper presents the cLHS method with a search algorithm based on heuristic rules combined with an annealing schedule. For conditioned Latin hypercube sampling (cLHS) the problem is: given N sites with ancillary variables (X), select x a sub-sample of size n ðn5NÞ in order that x forms a Latin hypercube, or the multivariate distribution of X is maximally stratified.
#LATIN HYPERCUBE SAMPLING EQUIPROBABLE FULL#
It provides a full coverage of the range of each variable by maximally stratifying the marginal distribution. Latin hypercube sampling (LHS) is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. McBratney Australian Centre for Precision Agriculture, Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Australia Received 9 September 2005 received in revised form 3 December 2005 accepted 16 December 2005Ībstract This paper presents the conditioned Latin hypercube as a sampling strategy of an area with prior information represented as exhaustive ancillary data. Now that we have the samples, the next step is to find the sensors that areĬlosest to the sample points in some manner.Computers & Geosciences 32 (2006) 1378–1388 A conditioned Latin hypercube method for sampling in the presence of ancillary information$ Budiman Minasny, Alex B. The sample coordinates generated in the unit disk can then be transformed so that Polar plots of the mapped LHS points show how the two maps operate. \(\theta = \tan^\right) \,\)Ī slightly better map is the Shirley-Chew idea that attempts to reduce distortion during mapping. To get a set of sample points that samples the sensor positions equally well, we have toĪ simple map that takes lines in the \(\) square to ellipses in the unit circle is Blue circles indicate the distance of the point TheĪdjacent figure shows some of the points. The sampling algorithm produces a set of points whose coordinates are in \(\).
#LATIN HYPERCUBE SAMPLING EQUIPROBABLE INSTALL#
# Install and load the lhs library install.packages ( "lhs" ) library ( "lhs" ) # Create a Latin Hypercube sample of coordinates between 0 and 1 # 100 samples, 3 dimensions sampleCoords <- randomLHS ( 100, 3 )