|
Essential Wavelets for Statistical Applications and Data Analysis R.T. Ogden, University of South Carolina 0-8176-3864-4 * 1996 * $40.00 * Hardcover * 285 pages * 40 Illustrations |
Fig5.16s <- function()
{
# postscript(file = "Fig5.16.ps", height = 3.9, width = 7.37, horiz = F)
par(mfrow = c(1, 2), mar = c(1.5, 2.5, 1.5, 1.5), mgp = c(1.5, 0.4, 0))
rs <- c(57, 14, 55, 51, 30, 0, 53, 44, 34, 53, 49, 2)
.Random.seed <- rs
blocky <- make.signal("blocks", n = 1024)
sigma <- sqrt(var(blocky))/3
bnoise <- blocky + rnorm(1024, sd = sigma)
bnoisewt <- dwt(bnoise, wavelet = "d10")
boxplot(bnoisewt$d5, bnoisewt$d4, bnoisewt$d3, bnoisewt$d2, bnoise$d1,
names = c("j=5", "j=6", "j=7", "j=8", "j=9"))
mtext("Noisy blocky function", side = 3, line = 0.1)
.Random.seed <- rs
wn <- rnorm(1024)
wnwt <- dwt(wn, wavelet = "d10")
boxplot(wnwt$d5, wnwt$d4, wnwt$d3, wnwt$d2, wnwt$d1,
names = c("j=5", "j=6", "j=7", "j=8", "j=9"))
mtext("White noise", side = 3, line = 0.1)
# graphics.off()
NULL
}