SpherWave (version 1.2.2)

swd: Decomposition

Description

This function performs decomposition with multi-sale SBF's.

Usage

swd(sbf)

Arguments

sbf

an object of class `sbf'

Value

An object of class spherical wavelet decomposition(`swd'). This object is a list with the following components.

obs

observations

latlon

grid points of observation sites in degree

netlab

vector of labels representing sub-networks

eta

bandwidth parameters for Poisson kernel

method

extrapolation methods, `"ls"' or `"pls"'

approx

if TRUE, approximation is used.

grid.size

grid size (latitude, longitude) of extrapolation site

lambda

smoothing parameter for penalized least squares method

p0

starting level for extrapolation. Resolution levels $p0+1, \ldots, L$ is used for extrapolation.

gridlon

longitudes of extrapolation sites in degree

gridlat

latitudes of extrapolation sites in degree

nlevels

the number of multi-resolution levels

coeff

interpolation coefficients

field

extrapolation on grid.size

density1

density of SBF

latlim

range of latitudes in degree

lonlim

range of longitudes in degree

global

List of successively smoothed data

density

density of SW coefficients

detail

List of details at different resolution levels

swcoeff

SW coefficients

thresh.info

`"None"'

Details

This function performs decomposition with multi-sale SBF's.

References

Oh, H-S. and Li, T-H. (2004) Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method. Journal of the Royal Statistical Society Ser. B, 66, 221--238.

See Also

sbf, swthresh, swr.

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)

### SBF representation of the observations by pls
#out.pls <- sbf(obs=temp67, latlon=latlon, netlab=netlab, eta=eta, 
#    method="pls", grid.size=c(50, 100), lambda=0.89)

### Decomposition
#out.dpls <- swd(out.pls)