|
Essential Wavelets for Statistical Applications and Data Analysis R.T. Ogden, University of South Carolina 0-8176-3864-4 * 1996 * $40.00 * Hardcover * 285 pages * 40 Illustrations |
Fig7.8s <- function(n = 128)
{
# postscript(file = "Fig7.8.ps", height = 6.5, width = 6.5, horiz = F)
par(mfrow = c(2, 2), mar = c(1.5, 1.5, 1.5, 0.5), mgp = c(5, 0.4, 0))
rs <- c(57, 14, 55, 51, 30, 0, 53, 44, 34, 53, 49, 2)
.Random.seed <- rs
y1 <- rnorm(n)
y2 <- arima.sim(n, model = list(ar = 0.5))
y3 <- arima.sim(n, model = list(ar = -0.5))
y4 <- arima.sim(n, model = list(ar = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5)))
a <- spec.wave(y1, wavelet = "d10", plot = F)
omega <- 2 * pi * a$freq
plot(omega, 2 * 10^(a$spec/10), type = "l")
mtext("White noise", side = 3, line = 0.1)
a <- spec.wave(y2, wavelet = "d10", plot = F)
plot(omega, 2 * 10^(a$spec/10), type = "l")
mtext("AR(1), r = 0.5", side = 3, line = 0.1)
a <- spec.wave(y3, wavelet = "d10", plot = F)
plot(omega, 2 * 10^(a$spec/10), type = "l")
mtext("AR(1), r = -0.5", side = 3, line = 0.1)
a <- spec.wave(y4, wavelet = "d10", plot = F)
plot(omega, 2 * 10^(a$spec/10), type = "l")
mtext("Seasonal time series", side = 3, line = 0.1)
# graphics.off()
NULL
}