tools:::Rd_package_description("LSDsensitivity")
tools:::Rd_package_author("LSDsensitivity")
Maintainer: tools:::Rd_package_maintainer("LSDsensitivity")
The LSDsensitivity R package provides tools to analyze simulated experiments from LSD. LSD offers native tools to sample the configuration (factor) space of a simulation model using different design of experiments (DoE). The produced experimental design data can be transparently imported to R by using the function read.doe.lsd().
The package offers two sensitivity analysis (SA) methods (elementary.effects.lsd() and sobol.decomposition.lsd()) pre-configured for application on LSD simulations: Morris Elementary Effects (EE) and Sobol Variance Decomposition (SVD).
EE (elementary.effects.lsd()) employs a simple one-factor-at-a-time (OAT) SA and is usually applied as an initial screening method while selecting relevant factors to a SVD global SA. EE requires an appropriate set of sample points (the DoE) which can be generated in LSD when "EE Sampling" is selected in the "Data" menu. Please make sure to take note of the DoE parameters used for the sampling, as they will be required for the configuration of the R analysis script.
Due to its high computational cost, sobol.decomposition.lsd() (SVD) is performed over a meta-model fitted from the experimental data produced by the LSD original model. The meta-model can be fitted using different sampling strategies offered by LSD, being "NOLH Sampling" (Near Orthogonal Latin Hypercube) usually the most efficient. Additionally to the set of samples used to fit the meta-model, it is recommended to also generate another set for the (external) validation of the meta-model ("MC Range Sampling" is the recommended option).
The package offers two meta-modeling (MM) methods for using with SVD: Kriging and polynomial. Kriging (kriging.model.lsd()) is offered under five different variance kernels (Matern 5/2, Matern3/2, Gaussian, exponential and power exponential) and two trend models (constant or first order polynomial) to choose, including auto-selection to the best fitting alternative. Polynomial meta-models of first or second order, with or without interactions, and auto-selection are also offered (polynomial.model.lsd()). Kriging is the recommended option in most cases.
Additionally, the package offers tools for the graphical representation of the meta-models response surfaces (2D and 3D) (response.surface.lsd()), to predict meta-model response in specific points in the factor space (model.pred.lsd()), to identify maximum and minimum responses from a set of factors (model.limits.lsd()), and to find optimal parameter settings using the meta-model (model.optim.lsd()).
For a complete list of exported functions, use library(help = "LSDsensitivity").
LSD 7.0+ default installation provides example scripts for the usage of the LSDsensitivity package. LSD can be downloaded at https://github.com/marcov64/Lsd/. They can also be retrieved from the package itself using the commands:
EE example: file.show(system.file("examples", "elementary-effects-SA.R", package = "LSDsensitivity"))
Kriging SVD example: file.show(system.file("examples", "kriging-sobol-SA.R", package = "LSDsensitivity"))
Polynomial SVD example: file.show(system.file("examples", "poly-sobol-SA.R", package = "LSDsensitivity"))
Optimize MM example: file.show(system.file("examples", "optimize-MM.R", package = "LSDsensitivity"))
LSD documentation is available at https://www.labsimdev.org/
The latest LSD binaries and source code can be downloaded at https://github.com/marcov64/Lsd/.
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LSDinterface-package