CWavDE {wavethresh} | R Documentation |
This function implements the density estimator with hard thresholding described by Hall, P. and Patil, P. (1995) Formulae for mean integrated squared error of nonlinear wavelet-based density estimators, Ann. Statist., 23, 905-928.
CWavDE(x, Jmax, threshold=0, nout=100, primary.resolution=1, filter.number=10, family="DaubLeAsymm", verbose=0, SF=NULL, WV=NULL)
x |
Vector of real numbers. This is the data for which you want a density estimate for |
Jmax |
The maximum resolution of wavelets |
threshold |
The hard threshold value for the wavelet coefficients |
nout |
The number of ordinates in the density estimate |
primary.resolution |
The usual wavelet density estimator primary resolution |
filter.number |
The wavelet filter number, see filter.select |
family |
The wavelet family, see filter.select |
verbose |
The level of reporting performed by the function, legit values are 0, 1 or 2, with 2 being more reports |
SF |
Scaling function values in format as returned by
draw.default |
WV |
Wavelet function values in format as returned by
draw.default |
As the description.
A list containing the following components:
x |
A vector of length nout that covers the range of the
input data x , plus some more depending on the
support of the wavelet and the primary resolution. |
y |
A vector of length nout that contains the output
wavelet density estimate |
sfix |
The integer values of the translates of the scaling functions used in the estimate |
wvixmin |
As for sfix, but a vector of length Jmax which
contains the minimum integer wavelet translates |
wvixmax |
As for wvixmin, but with the maxima |
G P Nason
# # Let's generate a bi-modal artificial set of data. # x <- c( rnorm(100), rnorm(100, 10)) # # Now perform simple wavelet density estimate # wde <- CWavDE(x, Jmax=10, threshold=1) # # Plot results # ## Not run: plot(wde$x, wde$y, type="l")