rbf.cv1: Generates a RMSPE value, result of cross validation...

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

Description

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

Usage

rbf.cv1(param, formula, data, n.neigh, func)

Arguments

param	vector starting points (eta and rho respectively) for searching the RMSPE optimum.
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.
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)
require(nloptr)
coordinates(preci) <- ~x+y
bobyqa(c(0.5, 0.5), rbf.cv1, lower=c(1e-05,0), upper=c(2,2), formula=prec~x+y, data=preci,
    n.neigh=9, func="TRI")
# obtained with the optimal values previously estimated
rbf.cv1(c(0.2126191,0.1454171), prec~x+y, preci, n.neigh=9, func="TRI")  

geospt documentation built on May 2, 2019, 4:51 p.m.