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
}