optimization_function: Optimization equation to select spatial predictors

In BlasBenito/spatialRF: Easy Spatial Modeling with Random Forest

Optimization equation to select spatial predictors

Description

Optimizes the selection of spatial predictors using two different methods: "moran.i", and "p.value".

Usage

optimization_function(
  x = NULL,
  weight.r.squared = NULL,
  weight.penalization.n.predictors = NULL,
  optimization.method = "moran.i"
)

Arguments

x	Optimization data frame generated internally by select_spatial_predictors_sequential() or select_spatial_predictors_recursive(). Default: NULL
weight.r.squared	Numeric between 0 and 1, weight of R-squared in the optimization process. Default: NULL
weight.penalization.n.predictors	Numeric between 0 and 1, weight of the penalization on the number of added spatial predictors. Default: NULL
optimization.method	Character, one of "moran.i", and "p.value". Default: "moran.i"

Details

The method "moran.i" tries to maximize 1 - Moran's I while taking into account the R-squared of the model and a penalization on the number of introduced spatial predictors through the expression

(1 - Moran's I) + w1 * r.squared - w2 * penalization

The method "p.value" uses a binary version of the p-values of Moran's I (1 if >= 0.05, 0 otherwise), and uses the expression

max(1 - Moran's I, binary p-value) + w1 * r.squared - w2 * penalization

The "moran.i" method generally selects more spatial predictors than the "p.value" method.

Value

A numeric vector with the optimization criteria.

See Also

select_spatial_predictors_recursive(), select_spatial_predictors_sequential()

BlasBenito/spatialRF documentation built on Sept. 1, 2022, 1:42 p.m.