Latin hypercube sampling matlab
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package lhs (Carnell, 2009), and the function lhsdesign in the Matlab.
#Latin hypercube sampling matlab code#
The cLHS code was run in Matlabâ„¢ (Mathworks, 2008) and statistical analysis was performed using the R statistical language (R Development Core Team, 2009). (1979) suggested a sampling approach based on a Latin hypercube design with n runs. This paper briefly reviews cLHS and investigates different sample sizes for representing five environmental covariates in a 30,000-ha complex landscape in the Great Basin of southwestern Utah. Draws a Latin Hypercube Sample from a set of uniform distributions for use in creating a Latin Hypercube Design. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping? An optimal sample size accurately represents the variability in the environmental covariates and provides enough samples for predictive models. Through this analysis, the uncertainty of the parameters and therefore the variability of the model output in response to this uncertainty can be.
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Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. Latin hypercube sampling and Partial Rank Correlation Coe cient procedure (LHS/ PRCC) can be used in combination to perform a sensitivity analysis that assesses a model over a global parameter space.