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