swthresh: Thresholding of Spherical Wavelet Decomposition ('swd')...
In SpherWave: Spherical Wavelets and SW-based Spatially Adaptive Methods

Description Usage Arguments Details Value References See Also Examples

This function performs various ways to threshold a ‘swd’ class object.

1 2	swthresh(swd, policy, by.level, type, nthresh, value = 0.1, Q = 0.05)

`swd`	an object of class ‘swd’
`policy`	threshold technique. At present the possible policies are ‘"universal"’, ‘"probability"’, ‘"fdr"’, ‘"Lorentz"’ and ‘"sure"’.
`by.level`	If FALSE, then perform a global threshold. If TRUE, a thresholding value is computed and applied separately to each resolution level.
`type`	the type of thresholding. This can be ‘"hard"’, ‘"soft"’ or ‘"Lorentz"’.
`nthresh`	the number of resolution levels to be thresholded in the decomposition
`value`	the user supplied threshold represented by quantile level for ‘"probability"’ policy
`Q`	parameter for the false discovery rate of ‘"fdr"’ policy

This function thresholds or shrinks details stored in a ‘swd’ object and returns the thresholded details in a modified ‘swd’ object. For level-dependent thresholding, ‘"universal"’, ‘"Lorentz"’ and ‘"fdr"’ are provided. Only hard type thresholding is proper for ‘"probability"’ thresholding. Also note that only soft type thresholding is proper for ‘"sure"’ thresholding.

An object of class ‘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, …, 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`	spherical wavelet coefficients
`thresh.info`	thresholding information and ranges of local components before thresholding

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.

sbf, swd, swr.

### 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)

### Thresholding
#out.univ <- swthresh(out.dpls, policy="universal", by.level=TRUE, 
#    type="hard", nthresh=4)