optimLanduse (version 1.2.1)

initScenario: Initialize the robust optimization

Description

The function initializes an optimLanduse S3 object on the basis of a coefficients table. Please note that the coefficients table must follow the expected optimLanduse format. The expected format is explained in the example on the GitHub project page and in the publication in Methods in Ecology and Evolution (Husmann et al. ,2022)

Usage

initScenario(
  coefTable,
  uValue = 1,
  optimisticRule = "expectation",
  fixDistance = 3
)

Value

An initialized optimLanduse S3 object ready for optimization.

Arguments

coefTable

Coefficient table in the expected optimLanduse format.

uValue

u Value. The uncertainty value delivered in the coefTable is multiplied with this u value. The value, therefore, enables scenario analyses with differing uncertainties in relation to indicator values. Higher u values can be interpreted as a higher risk aversion of the decision maker.

optimisticRule

Either expectation or uncertaintyAdjustedExpectation. The rule indicates whether the optimistic outcomes of an indicator are directly reflected by their expectations or if the indicator is calculated as expectation + uncertainty when "more is better" or expectation - uncertainty respectively when "less is better". An optimization based on expectation considers only downside risks.

fixDistance

This optional numeric value allows to define distinct uncertainty levels for the calculation of the uncertainty space and the averaged distances of a certain land-cover composition (see Equation 9 in Husmann et al. (2020)). Passing NA disables fixDistance. In this case, the uncertainty space is defined by uValue.

Details

Separating the initialization from the optimization is to save computation time in batch analysis. The separated function calls allow the user to perform multiple optimizations from one initialized object. This could save time in the scenario or sensitivity analysis.

A detailed description of the input parameters can be found in Husmann et al. (2022).

References

Husmann, K., von Groß, V., Bödeker, K., Fuchs, J. M., Paul, C., & Knoke, T. (2022). optimLanduse: A package for multiobjective land-cover composition optimization under uncertainty. Methods in Ecology and Evolution, 00, 1– 10. https://doi.org/10.1111/2041-210X.14000

Examples

require(readxl)
dat <- read_xlsx(exampleData("exampleGosling.xlsx"))

init <- initScenario(dat,
                     uValue = 2,
                     optimisticRule = "expectation",
                     fixDistance = 3)