gcv.lambda: Calculation of Generalized Cross-validation
In SeoncheolPark/SphereWaveu:

Description Usage Arguments Value See Also Examples

This function calculates generalized cross-validation for ridge regression.

1	gcv.lambda(obs, latlon, netlab, eta, approx=FALSE, lambda)

`obs`	observations
`latlon`	grid points of observation sites in degree
`netlab`	vector of labels representing sub-networks
`eta`	bandwidth parameters for Poisson kernel
`approx`	if TRUE, approximation is used.
`lambda`	smoothing parameter for penalized least squares method

gcv

generalized cross-validation for ridge regression.

ridge.diacomp, ridge.comp.

### Observations of year 1967
data(temperature)
names(temperature)

# Temperatures on 939 weather stations of year 1967    
temp67 <- temperature$obs[temperature$year == 1967] 
# Locations of 939 weather stations    
latlon <- temperature$latlon[temperature$year == 1967, ]

### Network design by BUD
data(netlab)

### Bandwidth for Poisson kernel
eta <- c(0.961, 0.923, 0.852, 0.723, 0.506)

### Select smoothing parameter lambda by generalized cross-validation
lam <- seq(0.1, 0.9, ,9)
gcv <- NULL
for(i in 1:length(lam))
    gcv <- c(gcv, gcv.lambda(obs=temp67, latlon=latlon, 
        netlab=netlab, eta=eta, lambda=lam[i])$gcv)
lam[gcv == min(gcv)]