S-PLUS : Copyright (c) 1988, 2007 Insightful Corp.
S : Copyright Insightful Corp.
Enterprise Developer Version 8.0.4  for Microsoft Windows : 200
7 
Working data will be in C:\DDRIVE~1\MYDOCU~1\S-PLUS~1\Project1 

> #                    Exploring the function acf()
> 
> # The function acf(y) will compute the autocorrelation function of the object y, which can
> # either be a time series or a vector that is treated like a time series. We'll observe later
> # that it can deal with multiple time series, but we will stick with the univaritate case for the moment.
> 
> #To have someting to work with, let's just create a random sequence.
> 
> y<-rnorm(200)
> 
> #Now look at its ACF
> 
> yacf<-acf(y)
> 
> # Now yacf is the output of the acf function. Let's check its "class"
> 
> class(yacf)
[1] "acf"
[1] "acf"
> 
> 
> #This is an object of type "list".
> # How do we ge the names of the items in the list?
> 
> names(yacf)
[1] "acf"    "lag"    "n.used" "type"   "series" "units"  "call"  
> 
> # We see that the names are   "acf"    "lag"    "n.used" "type"   "series" "units"  "call" 
> 
> # We can explore these in turn...
> # The "bottom line" looks like it might be acf. Let's use the list selector $ to look at it.
> 
> yacf$acf

, , 1
              [,1] 
 [1,]  1.000000000
 [2,] -0.025375930
 [3,]  0.072179473
 [4,] -0.062246031
 [5,] -0.010783330
 [6,] -0.005818639
 [7,]  0.052141828
 [8,] -0.133777373
 [9,]  0.016325404
[10,] -0.027241653
[11,] -0.026463356
[12,] -0.014267245
[13,] -0.098341668
[14,] -0.060309540
[15,] -0.069225995
[16,] -0.046347466
[17,]  0.105606908
[18,] -0.002364707
[19,] -0.047133909
[20,] -0.013808956
[21,] -0.044105855
[22,]  0.071698490
[23,] -0.056716158
[24,] -0.012477802
> 
> # This looks like an array... lets check
> 
> temp=yacf$acf
> 
> class(temp)
[1] "array"
> 
> # Yup, it is an array. We can now pull out the pieces that we want.
> # Say we want the first 10 nontrivial correlations in a vector.
> 
> v=temp[2:11]
> 
> # If you look closely (sometimes a good idea, sometimes not) you will see that temp is # actually a three
> # dimensional array. 
> # Why? This is because acf is set up to handle multiple series.
> # 
> # Fortunately we get easy access to the autocorrelations without specifying the "other # dimensions"
> # but we could have used the more explicit extraction
> 
> v=temp[2:11,1,1]
> v
 [1] -0.025375930  0.072179473 -0.062246031 -0.010783330 -0.005818639  0.052141828 -0.133777373  0.016325404 -0.027241653 -0.026463356
>