gcv.lambda {SpherWave} | R Documentation |
Calculation of Generalized Cross-validation
Description
This function calculates generalized cross-validation for ridge regression.
Usage
gcv.lambda(obs, latlon, netlab, eta, approx=FALSE, lambda)
Arguments
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 |
Value
gcv |
generalized cross-validation for ridge regression. |
See Also
ridge.diacomp
, ridge.comp
.
Examples
### 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)]
[Package
SpherWave version 1.1.0
Index]