Amatdual: Amatdual
In nunesmatt/adlift: An Adaptive Lifting Scheme Algorithm

Description Usage Arguments Details Value Note Author(s) See Also Examples

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

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

`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 envX 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.

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.

`Adual`	the refinement matrix for the particular step of the transform.
`Hdual`	the high-pass filter matrix for the current step of the transform.
`Gdual`	the low-pass filter matrix for the current step of the transform.
`o`	the indices of nbrs into the vector of pointsin and the steps removed points of the transform.
`alpha`	the update weights used to update lengths and coeff.
`weights`	the prediction weights obtained from the regression in the prediction step of the transform.

This function has been left in the package for completeness. However, the transform matrix is (optionally) computed within the forward lifting transform function fwtnp.

Matt Nunes (nunesrpackages@gmail.com), Marina Knight

transmatdual, fwtnp

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