putC.mwd | R Documentation |
The smoothed and original data from a multiple wavelet decomposition structure, mwd.object
, (e.g. returned from mwd
) are packed into a single matrix in that structure. This function copies the mwd.object
, replaces some smoothed data in the copy, and then returns the copy.
## S3 method for class 'mwd'
putC(mwd, level, M, boundary = FALSE, index = FALSE, ...)
mwd |
Multiple wavelet decomposition structure whose coefficients you wish to replace. |
level |
The level that you wish to replace. |
M |
Matrix of replacement coefficients. |
boundary |
If |
index |
If index is |
... |
any other arguments |
The mwd
function produces a wavelet decomposition structure.
The need for this function is a consequence of the pyramidal structure of Mallat's algorithm and the memory efficiency gain achieved by storing the pyramid as a linear matrix of coefficients. PutC obtains information about where the smoothed data appears from the fl.dbase component of mwd, in particular the array fl.dbase$first.last.c
which gives a complete specification of index numbers and offsets for mwd$C
.
Note also that this function only puts information into mwd
class objects. To extract coefficients from mwd
structures you have to use the accessC.mwd
function.
See Downie and Silverman, 1998.
An object of class mwd.object
if index
is FALSE
, otherwise the index numbers indicating where the M
matrix would have been inserted into the mwd$C
object are returned.
Version 3.9.6 (Although Copyright Tim Downie 1995-6).
G P Nason
accessC.mwd
, accessD.mwd
, draw.mwd
, mfirst.last
, mfilter.select
, mwd
, mwd.object
, mwr
, plot.mwd
, print.mwd
, putD.mwd
, summary.mwd
, threshold.mwd
, wd
, wr.mwd
.
#
# Generate an mwd object
#
tmp <- mwd(rnorm(32))
#
# Now let's examine the finest resolution smooth...
#
accessC(tmp, level=3)
# [,1] [,2] [,3] [,4] [,5] [,6]
#[1,] -0.4669103 -1.3150580 -0.7094966 -0.1979214 0.32079986 0.5052254
#[2,] -0.7645379 -0.8680941 0.1004062 0.6633268 -0.05860848 0.5757286
# [,7] [,8]
#[1,] 0.5187380 0.6533843
#[2,] 0.2864293 -0.4433788
#
# A matrix. There are two rows one for each father wavelet in this
# two-ple multiple wavelet transform and at level 3 there are 2^3 columns.
#
# Let's set the coefficients of the first father wavelet all equal to zero
# for this examples
#
newcmat <- accessC(tmp, level=3)
newcmat[1,] <- 0
#
# Ok, let's insert it back at level 3
#
tmp2 <- putC(tmp, level=3, M=newcmat)
#
# And check it
#
accessC(tmp2, level=3)
# [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#[1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000 0.0000000
#[2,] -0.7645379 -0.8680941 0.1004062 0.6633268 -0.05860848 0.5757286 0.2864293
# [,8]
#[1,] 0.0000000
#[2,] -0.4433788
#
# Yep, all the first father wavelet coefficients at level 3 are now zero.
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