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Autocorrelated conditioned Latin hypercube method

by Van Huong Le1, Rodrigo Vargas1

1Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA

Corresponding author affiliation and e-mail:

Rodrigo Vargas

Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA

[email protected]

Description

This repository contains the source code for different sampling methods: Fixed sampling, conditioned Latin Hypercube Sampling (cLHS), and autocorrelated conditioned Latin Hypercube Sampling (acLHS). Predictions are based using a Bernstein copula-based stochastic cosimulation (BCSCS) method. We provide two case studeies: first using a time series and the second for spatial applications.

Content

RProject_acLHS_1D: The case study represents data from a time series
  • Data: this folder contains the data
  • Functions: this folder contains the useful functions
  • Scripts: this folder contains the scripts
  • Results: this folder contains the results
  • RProject_acLHS_1D.Rproj: This file is the R Project
RProject_acLHS_2D: The case study represents data from spatial data
  • Data: this folder contains the data
  • Functions: this folder contains the useful functions
  • Scripts: this folder contains the scripts
  • Results: this folder contains the results
  • RProject_acLHS_2D.Rproj: This file is the R Project

install

The code has been tested using packages of:

  • R version 4.2.1

  • RStudio 2022.07.1

How to run the code?

RProject_acLHS_1D

Opening the project RProject_acLHS_1D.Rproj with Rstudio. Then open all the scripts in the "scripts" folder. The scripts are run in the following order: 0_Getting_Started.R, 1_Exploratory_data_analysis.R, 2_Variogram_analysis.R, 3_Sampling_Design.R, 4_Simulations.R.

  • 0_Getting_Started.R: this script is for installing and loading R packages, and also loading functions from the functions folder.
  • 1_Exploratory_data_analysis.R: this script is for exploring and calculating univariate statistical properties and dependency relationships between variables.
  • 2_Variogram_analysis.R: this script is to explore the temporal or spatial distribution of the variable of interest and calculate its autocorrelation function.
  • 3_Sampling_Design.R: this script is for applying sampling methods based on the data.
  • 4_Simulations.R: this script is to model the characteristic functions of the variables and perform the simulation.

RProject_acLHS_2D

Opening the project RProject_acLHS_2D.Rproj with Rstudio. Then open all the scripts in the "scripts" folder. The scripts are run in the following order: 0_Getting_Started.R, 1_Exploratory_data_analysis.R, 2_Variogram_analysis.R, 3_Sampling_Design.R, 4_Simulations.R.

  • 0_Getting_Started.R: this script is for installing and loading R packages, and also loading functions from the functions folder.
  • 1_Exploratory_data_analysis.R: this script is for exploring and calculating univariate statistical properties and dependency relationships between variables.
  • 2_Variogram_analysis.R: this script is to explore the temporal or spatial distribution of the variable of interest and calculate its autocorrelation function.
  • 3_Sampling_Design.R: this script is for applying sampling methods based on the data.
  • 4_Simulations.R: this script is to model the characteristic functions of the variables and perform the simulation.

License

MIT License

Copyright (c) 2022 Van Huong Le, Rodrigo Vargas