rbf.cv: rbf cross validation leave-one-out

In amsantac/geospt: Geostatistical Analysis and Design of Optimal Spatial Sampling Networks

rbf cross validation leave-one-out

Description

Generate the RMSPE value, which is given by the radial basis function with smoothing parameter eta and robustness parameter rho.

Usage

rbf.cv(formula, data, eta, rho, n.neigh, func)

Arguments

formula	formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name z, for a rbf detrended use z~1, for a rbf with trend, suppose z is linearly dependent on x and y, use the formula z~x+y (linear trend).
data	SpatialPointsDataFrame: should contain the dependent variable, independent variables, and coordinates.
eta	the optimal smoothing parameter; we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation
rho	value of optimal robustness parameter; we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation. eta and rho parameters can be optimized simultaneously, through the bobyqa function from nloptr or minqa packages
n.neigh	number of nearest observations that should be used for a rbf prediction, where nearest is defined in terms of the spatial locations
func	radial basis function model type, e.g. "GAU", "EXPON", "TRI", "TPS", "CRS", "ST", "IM" and "M", are currently available

Value

returns the RMSPE value

See Also

rbf

Examples

data(preci)
coordinates(preci)<-~x+y
rbf.cv(prec~1, preci, eta=0.2589, rho=0, n.neigh=9, func="M") 

amsantac/geospt documentation built on Feb. 21, 2024, 12:23 p.m.