Amatdual: Amatdual

Description

Combines filter matrices to produce a refinement matrix A for a wavelet transform.

Usage

Amatdual(steps, pointsin, removelist, nbrs, weights, alpha)

Arguments

steps

a value indicating which refinement matrix to construct. It refers to the number of points already removed during the transform.

pointsin

The indices of gridpoints still to be removed.

removelist

a vector of indices into env{X} of the lifted coefficients during the transform (in the order of removal).

nbrs

indices of the neighbours used in the last step of the decomposition.

weights

the prediction weights obtained from the regression in the prediction step of the transform.

alpha

the update weights used to update lengths and coeff.

Value

Adualthe refinement matrix for the particular step of the transform.
Hdualthe high-pass filter matrix for the current step of the transform.
Gdualthe low-pass filter matrix for the current step of the transform.
othe indices of nbrs into the vector of pointsin and the steps removed points of the transform.
alphathe update weights used to update lengths and coeff.
weightsthe prediction weights obtained from the regression in the prediction step of the transform.

Details

The function uses the prediction and update weights to construct the filter matrices Hdual and Gdual. Combining these two matrices results in the refinement matrix Adual.

Examples

Run this code

#
x<-runif(256)
y<-make.signal2("doppler",x=x)
a<-fwtnp(x,y,LocalPred=AdaptNeigh,neighbours=2)
#
A<-Amatdual(90,a$pointsin,a$removelist,a$neighbrs[[90]],a$gamlist[[90]],a$alphalist[[90]])
#
A$Adual
#
#the 90th refinement matrix for the transform above.
#