### This file contains some examples from Ch. 1 R

library(ts)

# to read jj.dat from a file.

#x <- scan("E:\\Stat207\\Class\\Examples\\Ch1\\jj.dat")

# read data from the class website.

x <- scan("http://www.sci.csueastbay.edu/~esuess/Statistics_6871/Handouts/Examples/Ch1/jj.dat")

# you can make x a regular time series object

x <- ts(x)

# plot the data

win.graph()

ts.plot(x,gpars=list(xlab="Quarter", ylab="J&J Earning Per Share"))

# log and plot

win.graph()

x.log <- log(x)
ts.plot(x.log,gpars=list(xlab="Quarter", ylab="Log of J&J Earning Per Share"))

# difference the log data and plot

win.graph()

dx <- diff(x.log)
ts.plot(dx, gpars=list(xlab="Quarter", ylab="J&J Earning Per Share Growth Rate"))

# all 3 plots - one on top of the other

win.graph()

par(mfrow=c(3,1)) 
ts.plot(x,gpars=list(xlab="Quarter", ylab="J&J Earning Per Share"))
ts.plot(x.log,gpars=list(xlab="Quarter", ylab="Log of J&J Earning Per Share"))
ts.plot(dx, gpars=list(xlab="Quarter", ylab="J&J Earning Per Share Growth Rate"))

# Detrend & Regression        
z <- 1:length(x.log)
fit <- lm(x.log~z)

win.graph()

par(mfrow=c(1,1))
min(x.log)
max(x.log)
ts.plot(x.log, gpars=list(xlab="Quarter", ylab="Log of J&J Earning Per Share",ylim=c(-1,3)) )
par(new=T)
x.hat <- fit$fitted
ts.plot(x.hat, gpars=list(xlab="", ylab="", ylim=c(-1,3)))  # both lines on plot

#detrended series

win.graph()

dt <- x.log-x.hat     
ts.plot(dt)

# compare difference with detrended data

win.graph()

par(mfrow=c(2,1))
acf(dx)           # remember dx <- diff(x.log)
acf(dt)

# matrix of plots of dx(t) vs dx(t-h) for h=1,2,...,9

win.graph()

lag.plot(dx, lags=9, layout=c(3,3)) 

# dummy variable regression

u <- matrix(contr.sum(4, contrasts=F),4,84)  # matrix of dummy variables
u <- t(u)               # transpose
fit <- lm(x.log~z + u[,1:3])     # fit on 3 cols of dummy
summary(fit)   # get details of the regression
noise <- fit$residuals  # get residuals  

win.graph()

acf(noise)

win.graph()

ts.plot(noise)