| transmatdual {adlift} | R Documentation |
transmatdual
Description
Works out the transform matrix for a particular prediction scheme and neighbourhood structure.
Usage
transmatdual(x, f, Pred = AdaptNeigh, neigh = 1, int = TRUE, clo = TRUE,
keep = 2,varonly=FALSE)
Arguments
x |
A vector of grid values. Can be of any length, not necessarily equally spaced. |
f |
A vector of function values corresponding to x. Must be of the same length as x. |
Pred |
The type of regression to be performed. Possible options are |
neigh |
The number of neighbours over which the regression is performed at each step. If clo is false, then this in fact denotes the number of neighbours on each side of the removed point. |
int |
Indicates whether or not the regression curve includes an intercept. |
clo |
Refers to the configuration of the chosen neighbours. If clo is false, the neighbours will be chosen symmetrically around the removed point. Otherwise, the closest neighbours will be chosen. |
keep |
The number of scaling coefficients to be kept in the final representation of the initial signal. This must be at least two. |
varonly |
A boolean variable indicating whether only the
coefficient variances should be returned, i.e. just the diagonal of
the transform matrix |
Details
The function uses Amatdual to form the refinement matrices A_j, from which the augmented matrices T_j are constructed. This process is iterated, to find the transform matrix (the top level augmented matrix). The rows and columns of this matrix are then reordered to be in the order of out$coeff, i.e. so that the columns correspond to f_1 \dots f_n.
Value
out |
the output from the forward transform. |
Wnew |
the matrix associated to the wavelet transform. |
x |
the original gridpoint vector. |
Note
This function has been left in the package for completeness. However, the transform matrix is (optionally) computed within the forward lifting
transform function fwtnp.
Author(s)
Matt Nunes (nunesrpackages@gmail.com), Marina Knight
See Also
Examples
x1<-runif(10)
y1<-make.signal2("doppler",x=x1)
#
a<-transmatdual(x1,y1,AdaptNeigh,2,TRUE,TRUE,2)
#
a$Wnew
#
#the transform matrix for this adaptive lifting scheme