|
Essential Wavelets for Statistical Applications and Data Analysis R.T. Ogden, University of South Carolina 0-8176-3864-4 * 1996 * $40.00 * Hardcover * 285 pages * 40 Illustrations |
Fig7.1n <- function()
{
# postscript(file = "Fig7.1.ps", height = 6.5, width = 6.5, horiz = F)
par(mfrow = c(2, 2), mar = c(1.5, 1.5, 1.5, 0.5), mgp = c(5, 0.4, 0))
x <- seq(-3.3, 3.3, length = 512)
y <- sapply(x, fff)
ywd <- wd(y, filter.number = 10)
ywd1 <- ywd
ywd2 <- ywd
ywd3 <- ywd
ywd4 <- ywd
coefvals <- rev(sort(abs(ywd$D)))
ywd1$D <- ywd$D * (abs(ywd$D) >= coefvals[3])
ywd2$D <- ywd$D * (abs(ywd$D) >= coefvals[7])
ywd3$D <- ywd$D * (abs(ywd$D) >= coefvals[11])
ywd4$D <- ywd$D * (abs(ywd$D) >= coefvals[15])
plot(x, wr(ywd1), type = "l", xlab = "", ylab = "")
mtext("4 coefficients", side = 3, line = 0.1)
plot(x, wr(ywd2), type = "l", xlab = "", ylab = "")
mtext("8 coefficients", side = 3, line = 0.1)
plot(x, wr(ywd3), type = "l", xlab = "", ylab = "")
mtext("12 coefficients", side = 3, line = 0.1)
plot(x, wr(ywd4), type = "l", xlab = "", ylab = "")
mtext("16 coefficients", side = 3, line = 0.1)
# graphics.off()
NULL
}