geosptdb (version 1.0-1)

rbfST.cv1: RMSPE value result of leave-one-out cross validation for rbfST

Description

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

Usage

rbfST.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 (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.

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

See Also

rbfST, rbfST.cv, graph.rbfST

Examples

# NOT RUN {
require(minqa)
data(croatiadb)
coordinates(croatiadb) <- ~x+y

# }
# NOT RUN {
rbf.im <- bobyqa(c(0.5, 0.5), rbfST.cv1, lower=c(1e-05,0), upper=c(2,2), 
              formula=MTEMP~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10, data=croatiadb, n.neigh=25, 
              func="IM", control=list(maxfun=50))         
# }
# NOT RUN {
# obtained with the optimal values previously estimated
rbfST.cv1(c(0.847050095690357,0.104157855356128), MTEMP~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10, 
           croatiadb, n.neigh=25, func="IM")
# }