R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("BHH2-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('BHH2')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "anovaPlot"
> 
> ### * anovaPlot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anovaPlot
> ### Title: Graphical Anova
> ### Aliases: anovaPlot
> ### Keywords: design hplot regression
> 
> ### ** Examples
> 
> library(BHH2)
> data(heads.data)
> heads.data$periods <- factor(heads.data$periods)
> heads.data$heads <- factor(heads.data$heads)
> 
> heads.aov <- aov(resp~periods+heads,data=heads.data)
> anovaPlot(heads.aov)
> 
> anovaPlot(heads.aov,labels=TRUE,faclab=TRUE)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "corrosion.data"
> 
> ### * corrosion.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corrosion.data
> ### Title: Corrosion data
> ### Aliases: corrosion.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(corrosion.data)
> str(corrosion.data)
`data.frame':	24 obs. of  4 variables:
 $ run       : Factor w/ 6 levels "r1","r2","r3",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ heats     : Factor w/ 3 levels "T360","T370",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ coating   : Factor w/ 4 levels "C1","C2","C3",..: 2 3 1 4 1 3 4 2 3 1 ...
 $ resistance: int  73 83 67 89 65 87 86 91 147 155 ...
> plot(corrosion.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "dotPlot"
> 
> ### * dotPlot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dotPlot
> ### Title: Dot plot: scatter plot with stacked dots similar to the
> ###   stem-and-leaf plot
> ### Aliases: dotPlot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> library(BHH2)
> data(tab03B1)
> attach(tab03B1)
> stem(yield) #stem-leaf plot

  The decimal point is at the |

  76 | 5
  77 | 59
  78 | 14
  79 | 0355566778
  80 | 0012233455667799
  81 | 00144566678888999
  82 | 00012222333344566778899
  83 | 000000111233333455555556667789
  84 | 00112223344445566777778888899
  85 | 0000011222344445666778899
  86 | 01222234555666667777788
  87 | 12222233478
  88 | 002246899
  89 | 034779
  90 | 04558
  91 | 7

> plt <- dotPlot(yield) # equivalent dotPlot
> 
> # same dot plot with max and min observations labelled
> plt <- dotPlot(yield,xlim=c(75,95),xlab="yield",pch.size="x",hcex=1)
> text(c(min(yield),max(yield),80),rep(0.05,3),c("min","max",80))
> segments(80,min(plt$y),80,max(plt$y),lty=2)
> detach()
> 
> 
> 
> cleanEx(); ..nameEx <- "dots"
> 
> ### * dots
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dots
> ### Title: Dots display
> ### Aliases: dots
> ### Keywords: aplot hplot
> 
> ### ** Examples
> 
> library(BHH2)
> set.seed(4)
> # Defines the height of the plot area between c(0,1)
> dotPlot(rnorm(100),xlab="x")
> 
> x <- rnorm(100)
> 
> # plots (possibly) overlapping points at y=0.3
> dots(x,y=0.3)
> # plots (possibly) overlapping points at y=0.4
> dots(x,y=0.4,stacked=TRUE,base=FALSE)
> # plots (hopefully) stacked points at y=0.5 allowing the dots to as high as 0.9
> dots(x,y=0.5,stacked=TRUE,base=FALSE,hmax=.9)
> 
> 
> 
> cleanEx(); ..nameEx <- "ffDesMatrix"
> 
> ### * ffDesMatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ffDesMatrix
> ### Title: Full or fractional factorial design matrix generation
> ### Aliases: ffDesMatrix
> ### Keywords: design
> 
> ### ** Examples
> 
> ffDesMatrix(5) # Full 2^5 factorial design
      [,1] [,2] [,3] [,4] [,5]
 [1,]   -1   -1   -1   -1   -1
 [2,]    1   -1   -1   -1   -1
 [3,]   -1    1   -1   -1   -1
 [4,]    1    1   -1   -1   -1
 [5,]   -1   -1    1   -1   -1
 [6,]    1   -1    1   -1   -1
 [7,]   -1    1    1   -1   -1
 [8,]    1    1    1   -1   -1
 [9,]   -1   -1   -1    1   -1
[10,]    1   -1   -1    1   -1
[11,]   -1    1   -1    1   -1
[12,]    1    1   -1    1   -1
[13,]   -1   -1    1    1   -1
[14,]    1   -1    1    1   -1
[15,]   -1    1    1    1   -1
[16,]    1    1    1    1   -1
[17,]   -1   -1   -1   -1    1
[18,]    1   -1   -1   -1    1
[19,]   -1    1   -1   -1    1
[20,]    1    1   -1   -1    1
[21,]   -1   -1    1   -1    1
[22,]    1   -1    1   -1    1
[23,]   -1    1    1   -1    1
[24,]    1    1    1   -1    1
[25,]   -1   -1   -1    1    1
[26,]    1   -1   -1    1    1
[27,]   -1    1   -1    1    1
[28,]    1    1   -1    1    1
[29,]   -1   -1    1    1    1
[30,]    1   -1    1    1    1
[31,]   -1    1    1    1    1
[32,]    1    1    1    1    1
> ffDesMatrix(5,gen=list(c(5,1,2,3,4))) # 2^(5-1) factorial design
      [,1] [,2] [,3] [,4] [,5]
 [1,]   -1   -1   -1   -1    1
 [2,]    1   -1   -1   -1   -1
 [3,]   -1    1   -1   -1   -1
 [4,]    1    1   -1   -1    1
 [5,]   -1   -1    1   -1   -1
 [6,]    1   -1    1   -1    1
 [7,]   -1    1    1   -1    1
 [8,]    1    1    1   -1   -1
 [9,]   -1   -1   -1    1   -1
[10,]    1   -1   -1    1    1
[11,]   -1    1   -1    1    1
[12,]    1    1   -1    1   -1
[13,]   -1   -1    1    1    1
[14,]    1   -1    1    1   -1
[15,]   -1    1    1    1   -1
[16,]    1    1    1    1    1
> ffDesMatrix(5,gen=list(c(4,1,2),c(-5,1,3))) # 2^(5-2) factorial design
     [,1] [,2] [,3] [,4] [,5]
[1,]   -1   -1   -1    1   -1
[2,]    1   -1   -1   -1    1
[3,]   -1    1   -1   -1   -1
[4,]    1    1   -1    1    1
[5,]   -1   -1    1    1    1
[6,]    1   -1    1   -1   -1
[7,]   -1    1    1   -1    1
[8,]    1    1    1    1   -1
> 
> 
> 
> cleanEx(); ..nameEx <- "ffFullMatrix"
> 
> ### * ffFullMatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ffFullMatrix
> ### Title: Full model matrix from a design matrix
> ### Aliases: ffFullMatrix
> ### Keywords: design
> 
> ### ** Examples
> 
> print(X <- ffDesMatrix(5,gen=list(c(5,1,2,3,4))))
      [,1] [,2] [,3] [,4] [,5]
 [1,]   -1   -1   -1   -1    1
 [2,]    1   -1   -1   -1   -1
 [3,]   -1    1   -1   -1   -1
 [4,]    1    1   -1   -1    1
 [5,]   -1   -1    1   -1   -1
 [6,]    1   -1    1   -1    1
 [7,]   -1    1    1   -1    1
 [8,]    1    1    1   -1   -1
 [9,]   -1   -1   -1    1   -1
[10,]    1   -1   -1    1    1
[11,]   -1    1   -1    1    1
[12,]    1    1   -1    1   -1
[13,]   -1   -1    1    1    1
[14,]    1   -1    1    1   -1
[15,]   -1    1    1    1   -1
[16,]    1    1    1    1    1
> ffFullMatrix(X[,1:4],x=c(1,2,3,4),maxInt=2,blk=X[,5])
$Xa
   one bk1 x1 x2 x3 x4 x1*x2 x1*x3 x1*x4 x2*x3 x2*x4 x3*x4
1    1   1 -1 -1 -1 -1     1     1     1     1     1     1
2    1  -1  1 -1 -1 -1    -1    -1    -1     1     1     1
3    1  -1 -1  1 -1 -1    -1     1     1    -1    -1     1
4    1   1  1  1 -1 -1     1    -1    -1    -1    -1     1
5    1  -1 -1 -1  1 -1     1    -1     1    -1     1    -1
6    1   1  1 -1  1 -1    -1     1    -1    -1     1    -1
7    1   1 -1  1  1 -1    -1    -1     1     1    -1    -1
8    1  -1  1  1  1 -1     1     1    -1     1    -1    -1
9    1  -1 -1 -1 -1  1     1     1    -1     1    -1    -1
10   1   1  1 -1 -1  1    -1    -1     1     1    -1    -1
11   1   1 -1  1 -1  1    -1     1    -1    -1     1    -1
12   1  -1  1  1 -1  1     1    -1     1    -1     1    -1
13   1   1 -1 -1  1  1     1    -1    -1    -1    -1     1
14   1  -1  1 -1  1  1    -1     1     1    -1    -1     1
15   1  -1 -1  1  1  1    -1    -1    -1     1     1     1
16   1   1  1  1  1  1     1     1     1     1     1     1

$x
[1] 1 2 3 4

$maxInt
[1] 2

$nTerms
  blk  main int.2 
    1     4     6 

> ffFullMatrix(X[,1:5],x=c(1,3,5),maxInt=3)
$Xa
   one x1 x3 x5 x1*x3 x1*x5 x3*x5 x1*x3*x5
1    1 -1 -1  1     1    -1    -1        1
2    1  1 -1 -1    -1    -1     1        1
3    1 -1 -1 -1     1     1     1       -1
4    1  1 -1  1    -1     1    -1       -1
5    1 -1  1 -1    -1     1    -1        1
6    1  1  1  1     1     1     1        1
7    1 -1  1  1    -1    -1     1       -1
8    1  1  1 -1     1    -1    -1       -1
9    1 -1 -1 -1     1     1     1       -1
10   1  1 -1  1    -1     1    -1       -1
11   1 -1 -1  1     1    -1    -1        1
12   1  1 -1 -1    -1    -1     1        1
13   1 -1  1  1    -1    -1     1       -1
14   1  1  1 -1     1    -1    -1       -1
15   1 -1  1 -1    -1     1    -1        1
16   1  1  1  1     1     1     1        1

$x
[1] 1 3 5

$maxInt
[1] 3

$nTerms
  blk  main int.2 int.3 
    0     3     3     1 

> 
> 
> 
> cleanEx(); ..nameEx <- "heads.data"
> 
> ### * heads.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: heads.data
> ### Title: Machine heads data
> ### Aliases: heads.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(heads.data)
> str(heads.data)
`data.frame':	30 obs. of  6 variables:
 $ obs    : int  1 2 3 4 5 6 7 8 9 10 ...
 $ periods: Factor w/ 6 levels "P1","P2","P3",..: 1 1 1 1 1 2 2 2 2 2 ...
 $ heads  : Factor w/ 5 levels "H1","H2","H3",..: 1 2 3 4 5 1 2 3 4 5 ...
 $ days   : Factor w/ 2 levels "D1","D2": 1 1 1 1 1 1 1 1 1 1 ...
 $ shifts : Factor w/ 3 levels "S1","S2","S3": 1 1 1 1 1 2 2 2 2 2 ...
 $ resp   : int  20 14 17 12 22 16 19 16 17 21 ...
> plot(heads.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "lambdaPlot"
> 
> ### * lambdaPlot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lambdaPlot
> ### Title: Lambda plot: traces the t and F statistics in Box-Cox
> ###   transformation of the response
> ### Aliases: lambdaPlot
> ### Keywords: design hplot
> 
> ### ** Examples
> 
> library(BHH2)
> # Lambda Plot tracing t values.
> data(woolen.data)
> woolen.lm <- lm(y~x1+x2+x3+I(x1^2)+I(x2^2)+I(x3^2)+
+                     I(x1*x2)+I(x1*x3)+I(x2*x3)+I(x1*x2*x3),data=woolen.data)
> lambdaPlot(woolen.lm,cex=.8,stat="t")
                term label
1        (Intercept)      
2                 x1     A
3                 x2     B
4                 x3     C
5          `I(x1^2)`     D
6          `I(x2^2)`     E
7          `I(x3^2)`     F
8       `I(x1 * x2)`     G
9       `I(x1 * x3)`     H
10      `I(x2 * x3)`     I
11 `I(x1 * x2 * x3)`     J
> 
> # Lambda Plot tracing F values.
> woolen2.lm <- lm(y~x1+x2+x3,data=woolen.data)
> lambdaPlot(woolen2.lm,lambda=seq(-1,1,length=41),stat="F",global=TRUE)
[1] "y"
   term label
1 Model     A
> 
> # Lambda Plot tracing F values.
> data(poison.data)
> poison.lm <- lm(y~treat*poison,data=poison.data)
> lambdaPlot(poison.lm,lambda=seq(-3,1,by=.1),stat="F",global=FALSE)
[1] "y"
          term label
1        treat     A
2       poison     B
3 treat:poison     C
> 
> 
> 
> cleanEx(); ..nameEx <- "penicillin.data"
> 
> ### * penicillin.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: penicillin.data
> ### Title: Penicillin data
> ### Aliases: penicillin.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(penicillin.data)
> str(penicillin.data)
`data.frame':	20 obs. of  4 variables:
 $ blend: Factor w/ 5 levels "B1","B2","B3",..: 1 2 3 4 5 1 2 3 4 5 ...
 $ run  : int  1 4 2 1 3 3 2 1 3 4 ...
 $ treat: Factor w/ 4 levels "A","B","C","D": 1 1 1 1 1 2 2 2 2 2 ...
 $ yield: int  89 84 81 87 79 88 77 87 92 81 ...
> plot(penicillin.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "permtest"
> 
> ### * permtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: permtest
> ### Title: Permutation test: randomization test for small size samples
> ### Aliases: permtest
> ### Keywords: design distribution
> 
> ### ** Examples
> 
> library(BHH2)
> 
> # Permutation test for Tomato Data
> data(tomato.data)
> cat("Tomato Data (not paired):\n")
Tomato Data (not paired):
> attach(tomato.data)
> a <- pounds[fertilizer=="A"]
> b <- pounds[fertilizer=="B"]
> print(round(test <- permtest(b,a),3))
             N          t.obs   t-Dist:P(>t) PermDist:P(>t)          F.obs 
       462.000          0.444          0.334          0.327          0.562 
  F-Dist:P(>F) PermDist:P(>F) 
         0.730          0.784 
> detach()
> 
> # Permutation test for Boy's Shoes Example
> data(shoes.data)
> cat("Shoes Data (paired):\n")
Shoes Data (paired):
> attach(shoes.data)
> x <- matB-matA
> print(round(test <- permtest(x),3))
             N          t.obs   t-Dist-P(>t) PermDist-P(>t) 
      1024.000          3.349          0.004          0.005 
> detach()
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "poison.data"
> 
> ### * poison.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: poison.data
> ### Title: Poison example data set
> ### Aliases: poison.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(poison.data)
> str(poison.data)
`data.frame':	48 obs. of  3 variables:
 $ poison: Factor w/ 3 levels "I","II","III": 1 1 1 1 2 2 2 2 3 3 ...
 $ treat : Factor w/ 4 levels "A","B","C","D": 1 1 1 1 1 1 1 1 1 1 ...
 $ y     : num  0.31 0.45 0.46 0.43 0.36 0.29 0.4 0.23 0.22 0.21 ...
> plot(poison.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "shoes.data"
> 
> ### * shoes.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: shoes.data
> ### Title: Boys' shoes data set
> ### Aliases: shoes.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(shoes.data)
> str(shoes.data)
`data.frame':	10 obs. of  5 variables:
 $ boy  : int  1 2 3 4 5 6 7 8 9 10
 $ matA : num  13.2 8.2 10.9 14.3 10.7 6.6 9.5 10.8 8.8 13.3
 $ sideA: Factor w/ 2 levels "L","R": 1 1 2 1 2 1 1 1 2 1
 $ matB : num  14 8.8 11.2 14.2 11.8 6.4 9.8 11.3 9.3 13.6
 $ sideB: Factor w/ 2 levels "L","R": 2 2 1 2 1 2 2 2 1 2
> plot(shoes.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "subsets"
> 
> ### * subsets
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: subsets
> ### Title: Generation of all the combinations of k elements from n possible
> ### Aliases: subsets
> ### Keywords: manip misc
> 
> ### ** Examples
> 
> library(BHH2)
> subsets(5,3)
      [,1] [,2] [,3]
 [1,]    1    2    3
 [2,]    1    2    4
 [3,]    1    2    5
 [4,]    1    3    4
 [5,]    1    3    5
 [6,]    1    4    5
 [7,]    2    3    4
 [8,]    2    3    5
 [9,]    2    4    5
[10,]    3    4    5
> subsets(5,3,letters)
      [,1] [,2] [,3]
 [1,] "a"  "b"  "c" 
 [2,] "a"  "b"  "d" 
 [3,] "a"  "b"  "e" 
 [4,] "a"  "c"  "d" 
 [5,] "a"  "c"  "e" 
 [6,] "a"  "d"  "e" 
 [7,] "b"  "c"  "d" 
 [8,] "b"  "c"  "e" 
 [9,] "b"  "d"  "e" 
[10,] "c"  "d"  "e" 
> subsets(5,3,c(10,20,30,50,80))
      [,1] [,2] [,3]
 [1,]   10   20   30
 [2,]   10   20   50
 [3,]   10   20   80
 [4,]   10   30   50
 [5,]   10   30   80
 [6,]   10   50   80
 [7,]   20   30   50
 [8,]   20   30   80
 [9,]   20   50   80
[10,]   30   50   80
> 
> 
> 
> cleanEx(); ..nameEx <- "tab03B1"
> 
> ### * tab03B1
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tab03B1
> ### Title: Table 3.2
> ### Aliases: tab03B1
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(BHH2)
> data(tab03B1)
> attach(tab03B1)
> stem(yield)

  The decimal point is at the |

  76 | 5
  77 | 59
  78 | 14
  79 | 0355566778
  80 | 0012233455667799
  81 | 00144566678888999
  82 | 00012222333344566778899
  83 | 000000111233333455555556667789
  84 | 00112223344445566777778888899
  85 | 0000011222344445666778899
  86 | 01222234555666667777788
  87 | 12222233478
  88 | 002246899
  89 | 034779
  90 | 04558
  91 | 7

> stem(ave10)

  The decimal point is 1 digit(s) to the left of the |

  828 | 2
  830 | 298
  832 | 12384
  834 | 33466790113455788889
  836 | 1122588889900122344577
  838 | 2234455788899990111122233444668999
  840 | 01244456667788889991122455566678899
  842 | 01133667882333356788
  844 | 0001122224770111347889
  846 | 678002467788899
  848 | 122556700024555
  850 | 138
  852 | 0147
  854 | 75

> plot(yield,xlab="time order",ylab="yield")
> detach()
> 
> 
> 
> cleanEx(); ..nameEx <- "tab03B2"
> 
> ### * tab03B2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tab03B2
> ### Title: Table 3.3
> ### Aliases: tab03B2
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(BHH2)
> data(tab03B2)
> attach(tab03B2)
> # displays the differences as dot plot (similar to histograms)
> plt <- dotPlot(diff10,xlim=2.55*c(-1,+1),xlab="differences")
> segments(1.3,0,1.3,max(plt$y))  #vertical line at x=1.3
> detach()
> 
> 
> 
> cleanEx(); ..nameEx <- "tomato.data"
> 
> ### * tomato.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tomato.data
> ### Title: Tomato plants data set
> ### Aliases: tomato.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tomato.data)
> str(tomato.data)
`data.frame':	11 obs. of  3 variables:
 $ pos       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ pounds    : num  29.9 11.4 26.6 23.7 25.3 28.5 14.2 17.9 16.5 21.1 ...
 $ fertilizer: Factor w/ 2 levels "A","B": 1 1 2 2 1 2 2 2 1 1 ...
> plot(tomato.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "woolen.data"
> 
> ### * woolen.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: woolen.data
> ### Title: Textile experiment data set
> ### Aliases: woolen.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(woolen.data)
> str(woolen.data)
`data.frame':	27 obs. of  4 variables:
 $ x1: int  -1 -1 -1 -1 -1 -1 -1 -1 -1 0 ...
 $ x2: int  -1 -1 -1 0 0 0 1 1 1 -1 ...
 $ x3: int  -1 0 1 -1 0 1 -1 0 1 -1 ...
 $ y : int  674 370 292 338 266 210 170 118 90 1414 ...
> plot(woolen.data)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  3.47 0.11 3.6 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')