rbf.cv {geospt}

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")