conbar which is the main
function in WaveThresh to do filter convolution with data.
conbar is used to carry out the data reconstruction in WaveThresh.
(Although use wr to actually do the inverse of
a wavelet transform: wr uses conbar
repeatedly once for each level to reconstruct each level).
conbar(c.in, d.in, filter)
Internally, the wr function uses the
C conbar function. Other functions also make use of
conbar and some R functions also would benefit from using the
fast C code of the conbar reconstruction hence this
WaveThresh function. Some of the other functions that use conbar
are listed in the SEE ALSO section.
# # Let's generate some test data, just some 32 normal variates. # v <- rnorm(32) # # Now take the wavelet transform with default filter arguments (which # are filter.number=10, family="DaubLeAsymm") # vwd <- wd(v) # # Now, let's take an arbitrary level, say 2, and reconstruct level 3 # scaling function coefficients # c.in <- accessC(vwd, lev=2) d.in <- accessD(vwd, lev=2) # conbar(c.in, d.in, filter.select(filter.number=10, family="DaubLeAsymm")) #[1] -0.50368115 0.04738620 -0.90331807 1.08497622 0.90490528 0.06252717 #[7] 2.55894899 -1.26067508 # # Ok, this was the pure reconstruction from using only level 2 information. # # Let's check this against the "original" level 3 coefficients (which get # stored on the decomposition step in wd) # accessC(vwd, lev=3) #[1] -0.50368115 0.04738620 -0.90331807 1.08497622 0.90490528 0.06252717 #[7] 2.55894899 -1.26067508 # # Yep, the same numbers! #