############################ # Author: Joshua M. Tebbs # # Date: 20 Dec 2009 # # Update: 25 Jul 2011 # # Purpose: STAT 520 R code # # CHAPTER 2 # ############################ # Example 2.1. # Page 30 white.noise = rnorm(150,0,1) plot(white.noise,ylab="Simulated white noise process",xlab="Time",type="o") # Example 2.2. # Uses white noise process from Example 2.1. # Page 33 random.walk = white.noise*0 for(i in 1:length(white.noise)){ random.walk[i]=sum(white.noise[1:i]) } plot(random.walk,ylab="Simulated random walk process",xlab="Time",type="o") # Example 2.3. # Uses white noise process from Example 2.1. # Page 36 moving.average = filter(x = white.noise, filter = rep(x = 1/3, times = 3), method = "convolution", sides = 1) plot(moving.average,ylab="Simulated moving average process",xlab="Time",type="o") # Example 2.4. # Autoregressive model simulation # Page 37 autoregressive = arima.sim(model = list(ar = c(0.75)), n = 150, rand.gen = rnorm, sd = 1) plot(autoregressive,ylab="Simulated autoregressive process",xlab="Time",type="o") # Example 2.5. # Sinusoidal process # Page 38 mean.function = 2*sin(2*pi*1:156/52+0.6*pi) w = rnorm(156,0,1) par(mfrow=c(2,2)) plot(mean.function,ylab="",xlab="Time",type="l") plot(mean.function+w,ylab="",xlab="Time",type="o") plot(mean.function+2*w,ylab="",xlab="Time",type="o") plot(mean.function+4*w,ylab="",xlab="Time",type="o")