geosptdb (version 1.0-1)

rbfST.cv: Leave-one-out cross validation for spatio-temporal radial basis function

Description

It generates the RMSPE value, which is given by the radial basis function with smoothing eta and robustness rho parameters.

Usage

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

Arguments

formula

formula that defines the dependent variable as a linear model of independent variables (covariates or the principal coordinates); suppose the dependent variable has name \(z_{st}\), for a rbfST detrended use \(z_{st}\)~1, for a rbfST with trend, suppose \(z_{st}\) is linearly dependent on x and y, use the formula \(z_{st}\)~x+y (linear trend).

data

SpatialPointsDataFrame: should contain the spatio-temporal dependent variable, independent variables (statics and/or dynamics), spatial coordinates and the time as an integer or numerical variable.

eta

the optimal smoothing parameter, we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation.

rho

optimal robustness parameter, we recommend using the value obtained by minimizing the root-mean-square prediction errors with 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 rbfST prediction, where nearest is defined in terms of the spatio-temporal locations.

func

spatio-temporal radial basis function; model type: "GAU", "EXPON", "TRI", "TPS", "CRS", "ST", "IM" and "M", are currently available

Value

returns the RMSPE value

References

Melo, C. E. (2012). Analisis geoestadistico espacio tiempo basado en distancias y splines con aplicaciones. PhD. Thesis. Universitat de Barcelona. 276 p. [link]

See Also

rbfST, graph.rbfST

Examples

# NOT RUN {
data(croatiadb)
coordinates(croatiadb) <- ~x+y
rbfST.cv(MTEMP~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10, croatiadb, eta=0.0108, rho=0.00004, 
          n.neigh=25, func="TPS")
# }