Latin hypercube sampling vs. importance sampling

LATIN HYPERCUBE SAMPLING VS. IMPORTANCE SAMPLING PLUS

*For the cost-constrained implementation: Soil Sensing, Progress in Soil Science, pages 111-119. Sampling for Calibrating Soil Sensor Data to Soil Properties. A conditioned Latin hypercube methodДЇor sampling in the presence of ancillary information. The Metropolis-Hastings iterations (if available). Objective function throughout the Metropolis-Hastings iterations.ДЄ vector giving the evolution of the cost function throughout Vector giving the indices of the chosen samples. * If the simple option is set to FALSE: An object of classД¬LHS_result, with the following elements: index_samples Numeric vector containing the indices of the selected samples is returned * If the simple option is set to TRUE (default behaviour): A The b parameter, users can defined their own eta matrix so that theyД¬an give more complex probability design of sampling each strata of theДistribution instead of just be able to give more importance to both edges of Probability b times higher to be sampled. , 1, b) in order to give the edge of the distribution a The final sample and K the number of continuous variables) is defined, toД¬ompute the objective function of the algorithm, where each column equal the A matrix eta (size N x K, where N is the size of Defaults to FALSE.ДЇor the DLHS method, the original paper defines parameter b as the importance Logical, if TRUE the spatial coordinates of supported spatial objects (either a 'SpatialPointsDataFrame' object if using 'sp', or a 'sf' object if using 'sf') are included in the Latin hypercube calculations. Process will not be constrained by this attribute. However, this method will only track the cost - the sampling Of the attribute in x that gives a cost associated with each If set to FALSE, aД¬LHS_result object is returned (takes more memory but allows to make use ofД¬LHS_results methods such as plot.cLHS_result).ДЄ character giving the name or an integer giving the index Selected samples are returned, as a numeric vector. The duration (number of iterations) of the The minimal value at which the optimisation is stopped. Defaults to 0.95.ДЄ list a length 3, giving the relative weights forД¬ontinuous data, categorical data, and correlation between variables.Дefaults to list(numeric = 1, factor = 1, correlation = 1).Д®ither a number equal 1 to perform a classic cLHS or a constrainedД¬LHS or a matrix to perform a cLHS that samples more on the edge of the Temperature decreases in the simulated annealing process.

The initial temperature at which the simulated annealingДЄ number between 0 and 1, giving the rate at which This is ~ 150 times faster than the R version, but is less stable and currentlyДoesn't accept track or obj.limit parameters. If set to TRUE, annealing process uses C++ code. If NULL (default), the cost-constrained implementation is notДЄ positive number, giving the number of iterations for the The attribute in x that gives a cost that can be use to constrain theД¬LHS sampling. The option is only available in theД¬++ version if use.cpp = FALSE, this parameter will be ignored.ДЄ character giving the name or an integer giving the index of The algorithm will use all of x as the referenceДistribution, but will only select samples from possible.sample.

LATIN HYPERCUBE SAMPLING VS. IMPORTANCE SAMPLING PLUS

Size of mandatory samples given by must.include plus the size of the randomlyДЄ numeric vector giving indices of the rows from x If must.include is not NULL,ДЄrgument size must include the total size of the final sample i.e. If NULL (default), all data are randomlyД¬hosen according to the classic cLHS method. For the cost-constrained cLHS method, cost of Optimisation process, is also proposed (Roudier et al., 2012).ДЄ ame, SpatialPointsDataFrame, sf, or RasterДЄ non-negative integer giving the total number of items to selectДЄ numeric vector giving the indices of the rows from x that must be An extension of this method, which propose toДЄssociate a cost to each individual and take it into account during the These methods propose to stratify sampling in Minasny and McBratney (2006) and the DLHS variant method (Minasny and Implementation of the conditioned Latin hypercube sampling, as published by Conditioned Latin Hypercube Sampling Description

Latin hypercube sampling vs. importance sampling