R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12.3
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+ ,46
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+ ,48
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+ ,219.25
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+ ,57
+ ,182.25
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+ ,29.1
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+ ,58
+ ,175.50
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+ ,161.75
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+ ,60
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+ ,32.6
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+ ,27.3
+ ,63
+ ,219.15
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+ ,29.6
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+ ,12.4
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+ ,94.5
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+ ,18.3
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+ ,189.75
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+ ,30.5
+ ,19.1
+ ,17.0
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+ ,87.6
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+ ,16.5
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+ ,114.3
+ ,61.3
+ ,42.1
+ ,23.4
+ ,34.9
+ ,30.1
+ ,19.4
+ ,30.4
+ ,66
+ ,234.25
+ ,72.00
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+ ,119.7
+ ,109.0
+ ,109.1
+ ,63.7
+ ,42.4
+ ,24.6
+ ,35.6
+ ,30.7
+ ,19.5
+ ,32.6
+ ,67
+ ,227.75
+ ,72.75
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+ ,29.8
+ ,19.5
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+ ,67
+ ,199.50
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+ ,101.6
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+ ,38.8
+ ,24.1
+ ,32.1
+ ,29.3
+ ,18.5
+ ,15.2
+ ,68
+ ,155.50
+ ,69.25
+ ,36.3
+ ,97.4
+ ,84.3
+ ,94.4
+ ,54.3
+ ,37.5
+ ,22.6
+ ,29.2
+ ,27.3
+ ,18.5
+ ,30.2
+ ,69
+ ,215.50
+ ,70.50
+ ,40.8
+ ,113.7
+ ,107.6
+ ,110.0
+ ,63.3
+ ,44.0
+ ,22.6
+ ,37.5
+ ,32.6
+ ,18.8
+ ,11.0
+ ,70
+ ,134.25
+ ,67.00
+ ,34.9
+ ,89.2
+ ,83.6
+ ,88.8
+ ,49.6
+ ,34.8
+ ,21.5
+ ,25.6
+ ,25.7
+ ,18.5
+ ,33.6
+ ,72
+ ,201.00
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+ ,108.5
+ ,105.0
+ ,104.5
+ ,59.6
+ ,40.8
+ ,23.2
+ ,35.2
+ ,28.6
+ ,20.1
+ ,29.3
+ ,72
+ ,186.75
+ ,66.00
+ ,38.9
+ ,111.1
+ ,111.5
+ ,101.7
+ ,60.3
+ ,37.3
+ ,21.5
+ ,31.3
+ ,27.2
+ ,18.0
+ ,26.0
+ ,72
+ ,190.75
+ ,70.50
+ ,38.9
+ ,108.3
+ ,101.3
+ ,97.8
+ ,56.0
+ ,41.6
+ ,22.7
+ ,30.5
+ ,29.4
+ ,19.8
+ ,31.9
+ ,74
+ ,207.50
+ ,70.00
+ ,40.8
+ ,112.4
+ ,108.5
+ ,107.1
+ ,59.3
+ ,42.2
+ ,24.6
+ ,33.7
+ ,30.0
+ ,20.9)
+ ,dim=c(14
+ ,252)
+ ,dimnames=list(c('Fat'
+ ,'Age'
+ ,'Weight'
+ ,'Height'
+ ,'Neck'
+ ,'Chest'
+ ,'Abdomen'
+ ,'Hip'
+ ,'Thigh'
+ ,'Knee'
+ ,'Ankle'
+ ,'Biceps'
+ ,'Forearm'
+ ,'Wrist')
+ ,1:252))
> y <- array(NA,dim=c(14,252),dimnames=list(c('Fat','Age','Weight','Height','Neck','Chest','Abdomen','Hip','Thigh','Knee','Ankle','Biceps','Forearm','Wrist'),1:252))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Fat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle Biceps
1 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 32.0
2 6.1 22 173.25 72.25 38.5 93.6 83.0 98.7 58.7 37.3 23.4 30.5
3 25.3 22 154.00 66.25 34.0 95.8 87.9 99.2 59.6 38.9 24.0 28.8
4 10.4 26 184.75 72.25 37.4 101.8 86.4 101.2 60.1 37.3 22.8 32.4
5 28.7 24 184.25 71.25 34.4 97.3 100.0 101.9 63.2 42.2 24.0 32.2
6 20.9 24 210.25 74.75 39.0 104.5 94.4 107.8 66.0 42.0 25.6 35.7
7 19.2 26 181.00 69.75 36.4 105.1 90.7 100.3 58.4 38.3 22.9 31.9
8 12.4 25 176.00 72.50 37.8 99.6 88.5 97.1 60.0 39.4 23.2 30.5
9 4.1 25 191.00 74.00 38.1 100.9 82.5 99.9 62.9 38.3 23.8 35.9
10 11.7 23 198.25 73.50 42.1 99.6 88.6 104.1 63.1 41.7 25.0 35.6
11 7.1 26 186.25 74.50 38.5 101.5 83.6 98.2 59.7 39.7 25.2 32.8
12 7.8 27 216.00 76.00 39.4 103.6 90.9 107.7 66.2 39.2 25.9 37.2
13 20.8 32 180.50 69.50 38.4 102.0 91.6 103.9 63.4 38.3 21.5 32.5
14 21.2 30 205.25 71.25 39.4 104.1 101.8 108.6 66.0 41.5 23.7 36.9
15 22.1 35 187.75 69.50 40.5 101.3 96.4 100.1 69.0 39.0 23.1 36.1
16 20.9 35 162.75 66.00 36.4 99.1 92.8 99.2 63.1 38.7 21.7 31.1
17 29.0 34 195.75 71.00 38.9 101.9 96.4 105.2 64.8 40.8 23.1 36.2
18 22.9 32 209.25 71.00 42.1 107.6 97.5 107.0 66.9 40.0 24.4 38.2
19 16.0 28 183.75 67.75 38.0 106.8 89.6 102.4 64.2 38.7 22.9 37.2
20 16.5 33 211.75 73.50 40.0 106.2 100.5 109.0 65.8 40.6 24.0 37.1
21 19.1 28 179.00 68.00 39.1 103.3 95.9 104.9 63.5 38.0 22.1 32.5
22 15.2 28 200.50 69.75 41.3 111.4 98.8 104.8 63.4 40.6 24.6 33.0
23 15.6 31 140.25 68.25 33.9 86.0 76.4 94.6 57.4 35.3 22.2 27.9
24 17.7 32 148.75 70.00 35.5 86.7 80.0 93.4 54.9 36.2 22.1 29.8
25 14.0 28 151.25 67.75 34.5 90.2 76.3 95.8 58.4 35.5 22.9 31.1
26 3.7 27 159.25 71.50 35.7 89.6 79.7 96.5 55.0 36.7 22.5 29.9
27 7.9 34 131.50 67.50 36.2 88.6 74.6 85.3 51.7 34.7 21.4 28.7
28 22.9 31 148.00 67.50 38.8 97.4 88.7 94.7 57.5 36.0 21.0 29.2
29 3.7 27 133.25 64.75 36.4 93.5 73.9 88.5 50.1 34.5 21.3 30.5
30 8.8 29 160.75 69.00 36.7 97.4 83.5 98.7 58.9 35.3 22.6 30.1
31 11.9 32 182.00 73.75 38.7 100.5 88.7 99.8 57.5 38.7 33.9 32.5
32 5.7 29 160.25 71.25 37.3 93.5 84.5 100.6 58.5 38.8 21.5 30.1
33 11.8 27 168.00 71.25 38.1 93.0 79.1 94.5 57.3 36.2 24.5 29.0
34 21.3 41 218.50 71.00 39.8 111.7 100.5 108.3 67.1 44.2 25.2 37.5
35 32.3 41 247.25 73.50 42.1 117.0 115.6 116.1 71.2 43.3 26.3 37.3
36 40.1 49 191.75 65.00 38.4 118.5 113.1 113.8 61.9 38.3 21.9 32.0
37 24.2 40 202.25 70.00 38.5 106.5 100.9 106.2 63.5 39.9 22.6 35.1
38 28.4 50 196.75 68.25 42.1 105.6 98.8 104.8 66.0 41.5 24.7 33.2
39 35.2 46 363.15 72.25 51.2 136.2 148.1 147.7 87.3 49.1 29.6 45.0
40 32.6 50 203.00 67.00 40.2 114.8 108.1 102.5 61.3 41.1 24.7 34.1
41 34.5 45 262.75 68.75 43.2 128.3 126.2 125.6 72.5 39.6 26.6 36.4
42 32.9 44 205.00 29.50 36.6 106.0 104.3 115.5 70.6 42.5 23.7 33.6
43 31.6 48 217.00 70.00 37.3 113.3 111.2 114.1 67.7 40.9 25.0 36.7
44 32.0 41 212.00 71.50 41.5 106.6 104.3 106.0 65.0 40.2 23.0 35.8
45 7.7 39 125.25 68.00 31.5 85.1 76.0 88.2 50.0 34.7 21.0 26.1
46 13.9 43 164.25 73.25 35.7 96.6 81.5 97.2 58.4 38.2 23.4 29.7
47 10.8 40 133.50 67.50 33.6 88.2 73.7 88.5 53.3 34.5 22.5 27.9
48 5.6 39 148.50 71.25 34.6 89.8 79.5 92.7 52.7 37.5 21.9 28.8
49 13.6 45 135.75 68.50 32.8 92.3 83.4 90.4 52.0 35.8 20.6 28.8
50 4.0 47 127.50 66.75 34.0 83.4 70.4 87.2 50.6 34.4 21.9 26.8
51 10.2 47 158.25 72.25 34.9 90.2 86.7 98.3 52.6 37.2 22.4 26.0
52 6.6 40 139.25 69.00 34.3 89.2 77.9 91.0 51.4 34.9 21.0 26.7
53 8.0 51 137.25 67.75 36.5 89.7 82.0 89.1 49.3 33.7 21.4 29.6
54 6.3 49 152.75 73.50 35.1 93.3 79.6 91.6 52.6 37.6 22.6 38.5
55 3.9 42 136.25 67.50 37.8 87.6 77.6 88.6 51.9 34.9 22.5 27.7
56 22.6 54 198.00 72.00 39.9 107.6 100.0 99.6 57.2 38.0 22.0 35.9
57 20.4 58 181.50 68.00 39.1 100.0 99.8 102.5 62.1 39.6 22.5 33.1
58 28.0 62 201.25 69.50 40.5 111.5 104.2 105.8 61.8 39.8 22.7 37.7
59 31.5 54 202.50 70.75 40.5 115.4 105.3 97.0 59.1 38.0 22.5 31.6
60 24.6 61 179.75 65.75 38.4 104.8 98.3 99.6 60.6 37.7 22.9 34.5
61 26.1 62 216.00 73.25 41.4 112.3 104.8 103.1 61.6 40.9 23.1 36.2
62 29.8 56 178.75 68.50 35.6 102.9 94.7 100.8 60.9 38.0 22.1 32.5
63 30.7 54 193.25 70.25 38.0 107.6 102.4 99.4 61.0 39.4 23.6 32.7
64 25.8 61 178.00 67.00 37.4 105.3 99.7 99.7 60.8 40.1 22.7 33.6
65 32.3 57 205.50 70.00 40.1 105.3 105.5 108.3 65.0 41.2 24.7 35.3
66 30.0 55 183.50 67.50 40.9 103.0 100.3 104.2 64.8 40.2 22.7 34.8
67 21.5 54 151.50 70.75 35.6 90.0 83.9 93.9 55.0 36.1 21.7 29.6
68 13.8 55 154.75 71.50 36.9 95.4 86.6 91.8 54.3 35.4 21.5 32.8
69 6.3 54 155.25 69.25 37.5 89.3 78.4 96.1 56.0 37.4 22.4 32.6
70 12.9 55 156.75 71.50 36.3 94.4 84.6 94.3 51.2 37.4 21.6 27.3
71 24.3 62 167.50 71.50 35.5 97.6 91.5 98.5 56.6 38.6 22.4 31.5
72 8.8 55 146.75 68.75 38.7 88.5 82.8 95.5 58.9 37.6 21.6 30.3
73 8.5 56 160.75 73.75 36.4 93.6 82.9 96.3 52.9 37.5 23.1 29.7
74 13.5 55 125.00 64.00 33.2 87.7 76.0 88.6 50.9 35.4 19.1 29.3
75 11.8 61 143.00 65.75 36.5 93.4 83.3 93.0 55.5 35.2 20.9 29.4
76 18.5 61 148.25 67.50 36.0 91.6 81.8 94.8 54.5 37.0 21.4 29.3
77 8.8 57 162.50 69.50 38.7 91.6 78.8 94.3 56.7 39.7 24.2 30.2
78 22.2 69 177.75 68.50 38.7 102.0 95.0 98.3 55.0 38.3 21.8 30.8
79 21.5 81 161.25 70.25 37.8 96.4 95.4 99.3 53.5 37.5 21.5 31.4
80 18.8 66 171.25 69.25 37.4 102.7 98.6 100.2 56.5 39.3 22.7 30.3
81 31.4 67 163.75 67.75 38.4 97.7 95.8 97.1 54.8 38.2 23.7 29.4
82 26.8 64 150.25 67.25 38.1 97.1 89.0 96.9 54.8 38.0 22.0 29.9
83 18.4 64 190.25 72.75 39.3 103.1 97.8 99.6 58.9 39.0 23.0 34.3
84 27.0 70 170.75 70.00 38.7 101.8 94.9 95.0 56.0 36.5 24.1 31.2
85 27.0 72 168.00 69.25 38.5 101.4 99.8 96.2 56.3 36.6 22.0 29.7
86 26.6 67 167.00 67.50 36.5 98.9 89.7 96.2 54.7 37.8 33.7 32.4
87 14.9 72 157.75 67.25 37.7 97.5 88.1 96.9 57.2 37.7 21.8 32.6
88 23.1 64 160.00 65.75 36.5 104.3 90.9 93.8 57.8 39.5 23.3 29.2
89 8.3 46 176.75 72.50 38.0 97.3 86.0 99.3 61.0 38.4 23.8 30.2
90 14.1 48 176.00 73.00 36.7 96.7 86.5 98.3 60.4 39.9 24.4 28.8
91 20.5 46 177.00 70.00 37.2 99.7 95.6 102.2 58.3 38.2 22.5 29.1
92 18.2 44 179.75 69.50 39.2 101.9 93.2 100.6 58.9 39.7 23.1 31.4
93 8.5 47 165.25 70.50 37.5 97.2 83.1 95.4 56.9 38.3 22.1 30.1
94 24.9 46 192.50 71.75 38.0 106.6 97.5 100.6 58.9 40.5 24.5 33.3
95 9.0 47 184.25 74.50 37.3 99.6 88.8 101.4 57.4 39.6 24.6 30.3
96 17.4 53 224.50 77.75 41.1 113.2 99.2 107.5 61.7 42.3 23.2 32.9
97 9.6 38 188.75 73.25 37.5 99.1 91.6 102.4 60.6 39.4 22.9 31.6
98 11.3 50 162.50 66.50 38.7 99.4 86.7 96.2 62.1 39.3 23.3 30.6
99 17.8 46 156.50 68.25 35.9 95.1 88.2 92.8 54.7 37.3 21.9 31.6
100 22.2 47 197.00 72.00 40.0 107.5 94.0 103.7 62.7 39.0 22.3 35.3
101 21.2 49 198.50 73.50 40.1 106.5 95.0 101.7 59.0 39.4 22.3 32.2
102 20.4 48 173.75 72.00 37.0 99.1 92.0 98.3 59.3 38.4 22.4 27.9
103 20.1 41 172.75 71.25 36.3 96.7 89.2 98.3 60.0 38.4 23.2 31.0
104 22.3 49 196.75 73.75 40.7 103.5 95.5 101.6 59.1 39.8 25.4 31.0
105 25.4 43 177.00 69.25 39.6 104.0 98.6 99.5 59.5 36.1 22.0 30.1
106 18.0 43 165.50 68.50 31.1 93.1 87.3 96.6 54.7 39.0 24.8 31.0
107 19.3 43 200.25 73.50 38.6 105.2 102.8 103.6 61.2 39.3 23.5 30.5
108 18.3 52 203.25 74.25 42.0 110.0 101.6 100.7 55.8 38.7 23.4 35.1
109 17.3 43 194.00 75.50 38.5 110.1 88.7 102.1 57.5 40.0 24.8 35.1
110 21.4 40 168.50 69.25 34.2 97.8 92.3 100.6 57.5 36.8 22.8 32.1
111 19.7 43 170.75 68.50 37.2 96.3 90.6 99.3 61.9 38.0 22.3 33.3
112 28.0 43 183.25 70.00 37.1 108.0 105.0 103.0 63.7 40.0 23.6 33.5
113 22.1 47 178.25 70.00 40.2 99.7 95.0 98.6 62.3 38.1 23.9 35.3
114 21.3 42 163.00 70.25 35.3 93.5 89.6 99.8 61.5 37.8 21.9 30.7
115 26.7 48 175.25 71.75 38.0 100.7 92.4 97.5 59.3 38.1 21.8 31.8
116 16.7 40 158.00 69.25 36.3 97.0 86.6 92.6 55.9 36.3 22.1 29.8
117 20.1 48 177.25 72.75 36.8 96.0 90.0 99.7 58.8 38.4 22.8 29.9
118 13.9 51 179.00 72.00 41.0 99.2 90.0 96.4 56.8 38.8 23.3 33.4
119 25.8 40 191.00 74.00 38.3 95.4 92.4 104.3 64.6 41.1 24.8 33.6
120 18.1 44 187.50 72.25 38.0 101.8 87.5 101.0 58.5 39.2 24.5 32.1
121 27.9 52 206.50 74.50 40.8 104.3 99.2 104.1 58.5 39.3 24.6 33.9
122 25.3 44 185.25 71.50 39.5 99.2 98.1 101.4 57.1 40.5 23.2 33.0
123 14.7 40 160.25 68.75 36.9 99.3 83.3 97.5 60.5 38.7 22.6 34.4
124 16.0 47 151.50 66.75 36.9 94.0 86.1 95.2 58.1 36.5 22.1 30.6
125 13.8 50 161.00 66.50 37.7 98.9 84.1 94.0 58.5 36.6 23.5 34.4
126 17.5 46 167.00 67.00 36.6 101.0 89.9 100.0 60.7 36.0 21.9 35.6
127 27.2 42 177.50 68.75 38.9 98.7 92.1 98.5 60.7 36.8 22.2 33.8
128 17.4 43 152.25 67.75 37.5 95.9 78.0 93.2 53.5 35.8 20.8 33.9
129 20.8 40 192.25 73.25 39.8 103.9 93.5 99.5 61.7 39.0 21.8 33.3
130 14.9 42 165.25 69.75 38.3 96.2 87.0 97.8 57.4 36.9 22.2 31.6
131 18.1 49 171.75 71.50 35.5 97.8 90.1 95.8 57.0 38.7 23.2 27.5
132 22.7 40 171.25 70.50 36.3 94.6 90.3 99.1 60.3 38.5 23.0 31.2
133 23.6 47 197.00 73.25 37.8 103.6 99.8 103.2 61.2 38.1 22.6 33.5
134 26.1 50 157.00 66.75 37.8 100.4 89.4 92.3 56.1 35.6 20.5 33.6
135 24.4 41 168.25 69.50 36.5 98.4 87.2 98.4 56.0 36.9 23.0 34.0
136 27.1 44 186.00 69.75 37.8 104.6 101.1 102.1 58.9 37.9 22.7 30.9
137 21.8 39 166.75 70.75 37.0 92.9 86.1 95.6 58.8 36.1 22.4 32.7
138 29.4 43 187.75 74.00 37.7 97.8 98.6 100.6 63.6 39.2 23.8 34.3
139 22.4 40 168.25 71.25 34.3 98.3 88.5 98.3 58.1 38.4 22.5 31.7
140 20.4 49 212.75 75.00 40.8 104.7 106.6 107.7 66.5 42.5 24.5 35.5
141 24.9 40 176.75 71.00 37.4 98.6 93.1 101.6 59.1 39.6 21.6 30.8
142 18.3 40 173.25 69.50 36.5 99.5 93.0 99.3 60.4 38.2 22.0 32.0
143 23.3 52 167.00 67.75 37.5 102.7 91.0 98.9 57.1 36.7 22.3 31.6
144 9.4 23 159.75 72.25 35.5 92.1 77.1 93.9 56.1 36.1 22.7 30.5
145 10.3 23 188.15 77.50 38.0 96.6 85.3 102.5 59.1 37.6 23.2 31.8
146 14.2 24 156.00 70.75 35.7 92.7 81.9 95.3 56.4 36.5 22.0 33.5
147 19.2 24 208.50 72.75 39.2 102.0 99.1 110.1 71.2 43.5 25.2 36.1
148 29.6 25 206.50 69.75 40.9 110.9 100.5 106.2 68.4 40.8 24.6 33.3
149 5.3 25 143.75 72.50 35.2 92.3 76.5 92.1 51.9 35.7 22.0 25.8
150 25.2 26 223.00 70.25 40.6 114.1 106.8 113.9 67.6 42.7 24.7 36.0
151 9.4 26 152.25 69.00 35.4 92.9 77.6 93.5 56.9 35.9 20.4 31.6
152 19.6 26 241.75 74.50 41.8 108.3 102.9 114.4 72.9 43.5 25.1 38.5
153 10.1 27 146.00 72.25 34.1 88.5 72.8 91.1 53.6 36.8 23.8 27.8
154 16.5 27 156.75 67.25 37.9 94.0 88.2 95.2 56.8 37.4 22.8 30.6
155 21.0 27 200.25 73.50 38.2 101.1 100.1 105.0 62.1 40.0 24.9 33.7
156 17.3 28 171.50 75.25 35.6 92.1 83.5 98.3 57.3 37.8 21.7 32.2
157 31.2 28 205.75 69.00 38.5 105.6 105.0 106.4 68.6 40.0 25.2 35.2
158 10.0 28 182.50 72.25 37.0 98.5 90.8 102.5 60.8 38.5 25.0 31.6
159 12.5 30 136.50 68.75 35.9 88.7 76.6 89.8 50.1 34.8 21.8 27.0
160 22.5 31 177.25 71.50 36.2 101.1 92.4 99.3 59.4 39.0 24.6 30.1
161 9.4 31 151.25 72.25 35.0 94.0 81.2 91.5 52.5 36.6 21.0 27.0
162 14.6 33 196.00 73.00 38.5 103.8 95.6 105.1 61.4 40.6 25.0 31.3
163 13.0 33 184.25 68.75 40.7 98.9 92.1 103.5 64.0 37.3 23.5 33.5
164 15.1 34 140.00 70.50 36.0 89.2 83.4 89.6 52.4 35.6 20.4 28.3
165 27.3 34 218.75 72.00 39.5 111.4 106.0 108.8 63.8 42.0 23.4 34.0
166 19.2 35 217.00 73.75 40.5 107.5 95.1 104.5 64.8 41.3 25.6 36.4
167 21.8 35 166.25 68.00 38.5 99.1 90.4 95.6 55.5 34.2 21.9 30.2
168 20.3 35 224.75 72.25 43.9 108.2 100.4 106.8 63.3 41.7 24.6 37.2
169 34.3 35 228.25 69.50 40.4 114.9 115.9 111.9 74.4 40.6 24.0 36.1
170 16.5 35 172.75 69.50 37.6 99.1 90.8 98.1 60.1 39.1 23.4 32.5
171 3.0 35 152.25 67.75 37.0 92.2 81.9 92.8 54.7 36.2 22.1 30.4
172 0.7 35 125.75 65.50 34.0 90.8 75.0 89.2 50.0 34.8 22.0 24.8
173 20.5 35 177.25 71.00 38.4 100.5 90.3 98.7 57.8 37.3 22.4 31.0
174 16.9 36 176.25 71.50 38.7 98.2 90.3 99.9 59.2 37.7 21.5 32.4
175 25.3 36 226.75 71.75 41.5 115.3 108.8 114.4 69.2 42.4 24.0 35.4
176 9.9 37 145.25 69.25 36.0 96.8 79.4 89.2 50.3 34.8 22.2 31.0
177 13.1 37 151.00 67.00 35.3 92.6 83.2 96.4 60.0 38.1 22.0 31.5
178 29.9 37 241.25 71.50 42.1 119.2 110.3 113.9 69.8 42.6 24.8 34.4
179 22.5 38 187.25 69.25 38.0 102.7 92.7 101.9 64.7 39.5 24.7 34.8
180 16.9 39 234.75 74.50 42.8 109.5 104.5 109.9 69.5 43.1 25.8 39.1
181 26.6 39 219.25 74.25 40.0 108.5 104.6 109.8 68.1 42.8 24.1 35.6
182 0.0 40 118.50 68.00 33.8 79.3 69.4 85.0 47.2 33.5 20.2 27.7
183 11.5 40 145.75 67.25 35.5 95.5 83.6 91.6 54.1 36.2 21.8 31.4
184 12.1 40 159.25 69.75 35.3 92.3 86.8 96.1 58.0 39.4 22.7 30.0
185 17.5 40 170.50 74.25 37.7 98.9 90.4 95.5 55.4 38.9 22.4 30.5
186 8.6 40 167.50 71.50 39.4 89.5 83.7 98.1 57.3 39.7 22.6 32.9
187 23.6 41 232.75 74.25 41.9 117.5 109.3 108.8 67.7 41.3 24.7 37.2
188 20.4 41 210.50 72.00 38.5 107.4 98.9 104.1 63.5 39.8 23.5 36.4
189 20.5 41 202.25 72.50 40.8 109.2 98.0 101.8 62.8 41.3 24.8 36.6
190 24.4 41 185.00 68.25 38.0 103.4 101.2 103.1 61.5 40.4 22.9 33.4
191 11.4 41 153.00 69.25 36.4 91.4 80.6 92.3 54.3 36.3 21.8 29.6
192 38.1 42 244.25 76.00 41.8 115.2 113.7 112.4 68.5 45.0 25.5 37.1
193 15.9 42 193.50 70.50 40.7 104.9 94.1 102.7 60.6 38.6 24.7 34.0
194 24.7 42 224.75 74.75 38.5 106.7 105.7 111.8 65.3 43.3 26.0 33.7
195 22.8 42 162.75 72.75 35.4 92.2 85.6 96.5 60.2 38.9 22.4 31.7
196 25.5 42 180.00 68.25 38.5 101.6 96.6 100.6 61.1 38.4 24.1 32.9
197 22.0 42 156.25 69.00 35.5 97.8 86.0 96.2 57.7 38.6 24.0 31.2
198 17.7 42 168.00 71.50 36.5 92.0 89.7 101.0 62.3 38.0 22.3 30.8
199 6.6 42 167.25 72.75 37.6 94.0 78.0 99.0 57.5 40.0 22.5 30.6
200 23.6 43 170.75 67.50 37.4 103.7 89.7 94.2 58.5 39.0 24.1 33.8
201 12.2 43 178.25 70.25 37.8 102.7 89.2 99.2 60.2 39.2 23.8 31.7
202 22.1 43 150.00 69.25 35.2 91.1 85.7 96.9 55.5 35.7 22.0 29.4
203 28.7 43 200.50 71.50 37.9 107.2 103.1 105.5 68.8 38.3 23.7 32.1
204 6.0 44 184.00 74.00 37.9 100.8 89.1 102.6 60.6 39.0 24.0 32.9
205 34.8 44 223.00 69.75 40.9 121.6 113.9 107.1 63.5 40.3 21.8 34.8
206 16.6 44 208.75 73.00 41.9 105.6 96.3 102.0 63.3 39.8 24.1 37.3
207 32.9 44 166.00 65.50 39.1 100.6 93.9 100.1 58.9 37.6 21.4 33.1
208 32.8 47 195.00 72.50 40.2 102.7 101.3 101.7 60.7 39.4 23.3 36.7
209 9.6 47 160.50 70.25 36.0 99.8 83.9 91.8 53.0 36.2 22.5 31.4
210 10.8 47 159.75 70.75 34.5 92.9 84.4 94.0 56.0 38.2 22.6 29.0
211 7.1 49 140.50 68.00 35.8 91.2 79.4 89.0 51.1 35.0 21.7 30.9
212 27.2 49 216.25 74.50 40.2 115.6 104.0 109.0 63.7 40.3 23.2 36.8
213 19.5 49 168.25 71.75 38.3 98.3 89.7 99.1 56.3 38.8 23.0 29.5
214 18.7 50 194.75 70.75 39.0 103.7 97.6 104.2 60.0 40.9 25.5 32.7
215 19.5 50 172.75 73.00 37.4 98.7 87.6 96.1 57.1 38.1 21.8 28.6
216 47.5 51 219.00 64.00 41.2 119.8 122.1 112.8 62.5 36.9 23.6 34.7
217 13.6 51 149.25 69.75 34.8 92.8 81.1 96.3 53.8 36.5 21.5 31.3
218 7.5 51 154.50 70.00 36.9 93.3 81.5 94.4 54.7 39.0 22.6 27.5
219 24.5 52 199.25 71.75 39.4 106.8 100.0 105.0 63.9 39.2 22.9 35.7
220 15.0 53 154.50 69.25 37.6 93.9 88.7 94.5 53.7 36.2 22.0 28.5
221 12.4 54 153.25 70.50 38.5 99.0 91.8 96.2 57.7 38.1 23.9 31.4
222 26.0 54 230.00 72.25 42.5 119.9 110.4 105.5 64.2 42.7 27.0 38.4
223 11.5 54 161.75 67.50 37.4 94.2 87.6 95.6 59.7 40.2 23.4 27.9
224 5.2 55 142.25 67.25 35.2 92.7 82.8 91.9 54.4 35.2 22.5 29.4
225 10.9 55 179.75 68.75 41.1 106.9 95.3 98.2 57.4 37.1 21.8 34.1
226 12.5 55 126.50 66.75 33.4 88.8 78.2 87.5 50.8 33.0 19.7 25.3
227 14.8 55 169.50 68.25 37.2 101.7 91.1 97.1 56.6 38.5 22.6 33.4
228 25.2 55 198.50 74.25 38.3 105.3 96.7 106.6 64.0 42.6 23.4 33.2
229 14.9 56 174.50 69.50 38.1 104.0 89.4 98.4 58.4 37.4 22.5 34.6
230 17.0 56 167.75 68.50 37.4 98.6 93.0 97.0 55.4 38.8 23.2 32.4
231 10.6 57 147.75 65.75 35.2 99.6 86.4 90.1 53.0 35.0 21.3 31.7
232 16.1 57 182.25 71.75 39.4 103.4 96.7 100.7 59.3 38.6 22.8 31.8
233 15.4 58 175.50 71.50 38.0 100.2 88.1 97.8 57.1 38.9 23.6 30.9
234 26.7 58 161.75 67.25 35.1 94.9 94.9 100.2 56.8 35.9 21.0 27.8
235 25.8 60 157.75 67.50 40.4 97.2 93.3 94.0 54.3 35.7 21.0 31.3
236 18.6 62 168.75 67.50 38.3 104.7 95.6 93.7 54.4 37.1 22.7 30.3
237 24.8 62 191.50 72.25 40.6 104.0 98.2 101.1 59.3 40.3 23.0 32.6
238 27.3 63 219.15 69.50 40.2 117.6 113.8 111.8 63.4 41.1 22.3 35.1
239 12.4 64 155.25 69.50 37.9 95.8 82.8 94.5 61.2 39.1 22.3 29.8
240 29.9 65 189.75 65.75 40.8 106.4 100.5 100.5 59.2 38.1 24.0 35.9
241 17.0 65 127.50 65.75 34.7 93.0 79.7 87.6 50.7 33.4 20.1 28.5
242 35.0 65 224.50 68.25 38.8 119.6 118.0 114.3 61.3 42.1 23.4 34.9
243 30.4 66 234.25 72.00 41.4 119.7 109.0 109.1 63.7 42.4 24.6 35.6
244 32.6 67 227.75 72.75 41.3 115.8 113.4 109.8 65.6 46.0 25.4 35.3
245 29.0 67 199.50 68.50 40.7 118.3 106.1 101.6 58.2 38.8 24.1 32.1
246 15.2 68 155.50 69.25 36.3 97.4 84.3 94.4 54.3 37.5 22.6 29.2
247 30.2 69 215.50 70.50 40.8 113.7 107.6 110.0 63.3 44.0 22.6 37.5
248 11.0 70 134.25 67.00 34.9 89.2 83.6 88.8 49.6 34.8 21.5 25.6
249 33.6 72 201.00 69.75 40.9 108.5 105.0 104.5 59.6 40.8 23.2 35.2
250 29.3 72 186.75 66.00 38.9 111.1 111.5 101.7 60.3 37.3 21.5 31.3
251 26.0 72 190.75 70.50 38.9 108.3 101.3 97.8 56.0 41.6 22.7 30.5
252 31.9 74 207.50 70.00 40.8 112.4 108.5 107.1 59.3 42.2 24.6 33.7
Forearm Wrist
1 27.4 17.1
2 28.9 18.2
3 25.2 16.6
4 29.4 18.2
5 27.7 17.7
6 30.6 18.8
7 27.8 17.7
8 29.0 18.8
9 31.1 18.2
10 30.0 19.2
11 29.4 18.5
12 30.2 19.0
13 28.6 17.7
14 31.6 18.8
15 30.5 18.2
16 26.4 16.9
17 30.8 17.3
18 31.6 19.3
19 30.5 18.5
20 30.1 18.2
21 30.3 18.4
22 32.8 19.9
23 25.9 16.7
24 26.7 17.1
25 28.0 17.6
26 28.2 17.7
27 27.0 16.5
28 26.6 17.0
29 27.9 17.2
30 26.7 17.6
31 27.7 18.4
32 26.4 17.9
33 30.0 18.8
34 31.5 18.7
35 31.7 19.7
36 29.8 17.0
37 30.6 19.0
38 30.5 19.4
39 29.0 21.4
40 31.0 18.3
41 32.7 21.4
42 28.7 17.4
43 29.8 18.4
44 31.5 18.8
45 23.1 16.1
46 27.4 18.3
47 26.2 17.3
48 26.8 17.9
49 25.5 16.3
50 25.8 16.8
51 25.8 17.3
52 26.1 17.2
53 26.0 16.9
54 27.4 18.5
55 27.5 18.5
56 30.2 18.9
57 28.3 18.5
58 30.9 19.2
59 28.8 18.2
60 29.6 18.5
61 31.8 20.2
62 29.8 18.3
63 29.9 19.1
64 29.0 18.8
65 31.1 18.4
66 30.1 18.7
67 27.4 17.4
68 27.4 18.7
69 28.1 18.1
70 27.1 17.3
71 27.3 18.6
72 27.3 18.3
73 27.3 18.2
74 25.7 16.9
75 27.0 16.8
76 27.0 18.3
77 29.2 18.1
78 25.7 18.8
79 26.8 18.3
80 28.7 19.0
81 27.2 19.0
82 25.2 17.7
83 29.6 19.0
84 27.3 19.2
85 26.3 18.0
86 27.7 18.2
87 28.0 18.8
88 28.4 18.1
89 29.3 18.8
90 29.6 18.7
91 27.7 17.7
92 28.4 18.8
93 28.2 18.4
94 29.6 19.1
95 27.9 17.8
96 30.8 20.4
97 30.1 18.5
98 27.8 18.2
99 27.5 18.2
100 30.9 18.3
101 31.0 18.6
102 26.2 17.0
103 29.2 18.4
104 30.3 19.7
105 27.2 17.7
106 29.4 18.8
107 28.5 18.1
108 29.6 19.1
109 30.7 19.2
110 26.0 17.3
111 28.2 18.1
112 27.8 17.4
113 31.1 19.8
114 27.6 17.4
115 27.3 17.5
116 26.3 17.3
117 28.0 18.1
118 29.8 19.5
119 29.5 18.5
120 28.6 18.0
121 31.2 19.5
122 29.6 18.4
123 28.0 17.6
124 27.5 17.6
125 29.2 18.0
126 30.2 17.6
127 30.3 17.2
128 28.2 17.4
129 29.6 18.1
130 27.8 17.7
131 26.5 17.6
132 28.4 17.1
133 28.6 17.9
134 29.3 17.3
135 29.8 18.1
136 28.8 17.6
137 28.3 17.1
138 28.4 17.7
139 27.4 17.6
140 29.8 18.7
141 27.9 16.6
142 28.5 17.8
143 27.5 17.9
144 27.2 18.2
145 29.7 18.3
146 28.3 17.3
147 30.3 18.7
148 29.7 18.4
149 25.2 16.9
150 30.4 18.4
151 29.0 17.8
152 33.8 19.6
153 26.3 17.4
154 28.3 17.9
155 29.2 19.4
156 27.7 17.7
157 30.7 19.1
158 28.0 18.6
159 34.9 16.9
160 28.2 18.2
161 26.3 16.5
162 29.2 19.1
163 30.6 19.7
164 26.2 16.5
165 31.2 18.5
166 33.7 19.4
167 28.7 17.7
168 33.1 19.8
169 31.8 18.8
170 29.8 17.4
171 27.4 17.7
172 25.9 16.9
173 28.7 17.7
174 28.4 17.8
175 21.0 20.1
176 26.9 16.9
177 26.6 16.7
178 29.5 18.4
179 30.3 18.1
180 32.5 19.9
181 29.0 19.0
182 24.6 16.5
183 28.3 17.2
184 26.4 17.4
185 28.9 17.7
186 29.3 18.2
187 31.8 20.0
188 30.4 19.1
189 32.4 18.8
190 29.2 18.5
191 27.3 17.9
192 31.2 19.9
193 30.1 18.7
194 29.9 18.5
195 27.1 17.1
196 29.8 18.8
197 27.3 17.4
198 27.8 16.9
199 30.0 18.5
200 28.8 18.8
201 28.4 18.6
202 26.6 17.4
203 28.9 18.7
204 29.2 18.4
205 30.7 17.4
206 23.1 19.4
207 29.5 17.3
208 31.6 18.4
209 27.5 17.7
210 26.2 17.6
211 28.8 17.4
212 31.0 18.9
213 27.9 18.6
214 30.0 19.0
215 26.7 18.0
216 29.1 18.4
217 26.3 17.8
218 25.9 18.6
219 30.4 19.2
220 25.7 17.1
221 29.9 18.9
222 32.0 19.6
223 27.0 17.8
224 26.8 17.0
225 31.1 19.2
226 22.0 15.8
227 29.3 18.8
228 30.0 18.4
229 30.1 18.8
230 29.7 19.0
231 27.3 16.9
232 29.1 19.0
233 29.6 18.0
234 26.1 17.6
235 28.7 18.3
236 26.3 18.3
237 28.5 19.0
238 29.6 18.5
239 28.9 18.3
240 30.5 19.1
241 24.8 16.5
242 30.1 19.4
243 30.7 19.5
244 29.8 19.5
245 29.3 18.5
246 27.3 18.5
247 32.6 18.8
248 25.7 18.5
249 28.6 20.1
250 27.2 18.0
251 29.4 19.8
252 30.0 20.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Age Weight Height Neck Chest
-18.18849 0.06208 -0.08844 -0.06959 -0.47060 -0.02386
Abdomen Hip Thigh Knee Ankle Biceps
0.95477 -0.20754 0.23610 0.01528 0.17400 0.18160
Forearm Wrist
0.45202 -1.62064
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.1687 -2.8639 -0.1014 3.2085 10.0068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -18.18849 17.34857 -1.048 0.29551
Age 0.06208 0.03235 1.919 0.05618 .
Weight -0.08844 0.05353 -1.652 0.09978 .
Height -0.06959 0.09601 -0.725 0.46925
Neck -0.47060 0.23247 -2.024 0.04405 *
Chest -0.02386 0.09915 -0.241 0.81000
Abdomen 0.95477 0.08645 11.044 < 2e-16 ***
Hip -0.20754 0.14591 -1.422 0.15622
Thigh 0.23610 0.14436 1.636 0.10326
Knee 0.01528 0.24198 0.063 0.94970
Ankle 0.17400 0.22147 0.786 0.43285
Biceps 0.18160 0.17113 1.061 0.28966
Forearm 0.45202 0.19913 2.270 0.02410 *
Wrist -1.62064 0.53495 -3.030 0.00272 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.305 on 238 degrees of freedom
Multiple R-squared: 0.749, Adjusted R-squared: 0.7353
F-statistic: 54.65 on 13 and 238 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.08756083 0.17512165 0.9124392
[2,] 0.28210694 0.56421387 0.7178931
[3,] 0.22358148 0.44716297 0.7764185
[4,] 0.25422015 0.50844030 0.7457799
[5,] 0.19043848 0.38087697 0.8095615
[6,] 0.53266256 0.93467488 0.4673374
[7,] 0.56250711 0.87498577 0.4374929
[8,] 0.56289252 0.87421497 0.4371075
[9,] 0.47842865 0.95685730 0.5215713
[10,] 0.59125048 0.81749905 0.4087495
[11,] 0.51703178 0.96593643 0.4829682
[12,] 0.61337276 0.77325448 0.3866272
[13,] 0.56576854 0.86846292 0.4342315
[14,] 0.54844891 0.90310219 0.4515511
[15,] 0.49365603 0.98731206 0.5063440
[16,] 0.58227158 0.83545683 0.4177284
[17,] 0.56759574 0.86480852 0.4324043
[18,] 0.56909279 0.86181443 0.4309072
[19,] 0.50508025 0.98983950 0.4949197
[20,] 0.46496065 0.92992130 0.5350394
[21,] 0.41716257 0.83432514 0.5828374
[22,] 0.38495108 0.76990215 0.6150489
[23,] 0.51219788 0.97560424 0.4878021
[24,] 0.45250061 0.90500121 0.5474994
[25,] 0.40524913 0.81049826 0.5947509
[26,] 0.43815689 0.87631379 0.5618431
[27,] 0.40850110 0.81700220 0.5914989
[28,] 0.45659178 0.91318356 0.5434082
[29,] 0.44662512 0.89325024 0.5533749
[30,] 0.40965445 0.81930890 0.5903455
[31,] 0.36565140 0.73130280 0.6343486
[32,] 0.35813374 0.71626748 0.6418663
[33,] 0.34708068 0.69416136 0.6529193
[34,] 0.32729842 0.65459683 0.6727016
[35,] 0.34843722 0.69687443 0.6515628
[36,] 0.31364352 0.62728704 0.6863565
[37,] 0.29580835 0.59161671 0.7041916
[38,] 0.30190072 0.60380144 0.6980993
[39,] 0.28356178 0.56712356 0.7164382
[40,] 0.28511542 0.57023084 0.7148846
[41,] 0.30474275 0.60948551 0.6952572
[42,] 0.28302689 0.56605379 0.7169731
[43,] 0.27008461 0.54016922 0.7299154
[44,] 0.23577726 0.47155452 0.7642227
[45,] 0.21209491 0.42418983 0.7879051
[46,] 0.23017557 0.46035115 0.7698244
[47,] 0.21015786 0.42031572 0.7898421
[48,] 0.18444344 0.36888688 0.8155566
[49,] 0.15706336 0.31412672 0.8429366
[50,] 0.14397291 0.28794581 0.8560271
[51,] 0.16256912 0.32513823 0.8374309
[52,] 0.13808775 0.27617550 0.8619123
[53,] 0.12171231 0.24342461 0.8782877
[54,] 0.10162754 0.20325507 0.8983725
[55,] 0.11428192 0.22856383 0.8857181
[56,] 0.12447620 0.24895240 0.8755238
[57,] 0.10835181 0.21670362 0.8916482
[58,] 0.09610977 0.19221954 0.9038902
[59,] 0.12386381 0.24772762 0.8761362
[60,] 0.15722639 0.31445277 0.8427736
[61,] 0.13707723 0.27415445 0.8629228
[62,] 0.14215557 0.28431114 0.8578444
[63,] 0.12216637 0.24433273 0.8778336
[64,] 0.15535462 0.31070924 0.8446454
[65,] 0.27957005 0.55914009 0.7204300
[66,] 0.40607214 0.81214428 0.5939279
[67,] 0.41094992 0.82189984 0.5890501
[68,] 0.42568329 0.85136657 0.5743167
[69,] 0.39141170 0.78282340 0.6085883
[70,] 0.39628133 0.79256266 0.6037187
[71,] 0.38626806 0.77253612 0.6137319
[72,] 0.38472857 0.76945714 0.6152714
[73,] 0.40936334 0.81872668 0.5906367
[74,] 0.38052150 0.76104299 0.6194785
[75,] 0.34594884 0.69189768 0.6540512
[76,] 0.31090072 0.62180144 0.6890993
[77,] 0.28886349 0.57772699 0.7111365
[78,] 0.27841616 0.55683233 0.7215838
[79,] 0.29935127 0.59870255 0.7006487
[80,] 0.27635079 0.55270157 0.7236492
[81,] 0.33766602 0.67533205 0.6623340
[82,] 0.37627424 0.75254848 0.6237258
[83,] 0.33982499 0.67964999 0.6601750
[84,] 0.32861239 0.65722478 0.6713876
[85,] 0.31093501 0.62187002 0.6890650
[86,] 0.27796183 0.55592367 0.7220382
[87,] 0.25876347 0.51752694 0.7412365
[88,] 0.27306125 0.54612250 0.7269387
[89,] 0.24568205 0.49136410 0.7543180
[90,] 0.21693428 0.43386856 0.7830657
[91,] 0.25771746 0.51543492 0.7422825
[92,] 0.25317441 0.50634882 0.7468256
[93,] 0.26407974 0.52815947 0.7359203
[94,] 0.23669460 0.47338921 0.7633054
[95,] 0.20862756 0.41725512 0.7913724
[96,] 0.21164419 0.42328838 0.7883558
[97,] 0.18840309 0.37680617 0.8115969
[98,] 0.16658242 0.33316483 0.8334176
[99,] 0.20512411 0.41024822 0.7948759
[100,] 0.17951285 0.35902571 0.8204871
[101,] 0.16895795 0.33791591 0.8310420
[102,] 0.14628001 0.29256002 0.8537200
[103,] 0.20099742 0.40199484 0.7990026
[104,] 0.20571558 0.41143116 0.7942844
[105,] 0.26726999 0.53453997 0.7327300
[106,] 0.24959717 0.49919433 0.7504028
[107,] 0.22145842 0.44291684 0.7785416
[108,] 0.19662668 0.39325337 0.8033733
[109,] 0.17540387 0.35080774 0.8245961
[110,] 0.16813672 0.33627345 0.8318633
[111,] 0.18361957 0.36723914 0.8163804
[112,] 0.26020858 0.52041717 0.7397914
[113,] 0.23576732 0.47153463 0.7642327
[114,] 0.20765120 0.41530241 0.7923488
[115,] 0.18289744 0.36579488 0.8171026
[116,] 0.16723014 0.33446028 0.8327699
[117,] 0.14759962 0.29519923 0.8524004
[118,] 0.15446719 0.30893438 0.8455328
[119,] 0.22064286 0.44128572 0.7793571
[120,] 0.19359746 0.38719492 0.8064025
[121,] 0.20651933 0.41303867 0.7934807
[122,] 0.20223827 0.40447654 0.7977617
[123,] 0.20915476 0.41830953 0.7908452
[124,] 0.29128922 0.58257844 0.7087108
[125,] 0.27687917 0.55375833 0.7231208
[126,] 0.25953938 0.51907877 0.7404606
[127,] 0.26605059 0.53210119 0.7339494
[128,] 0.26760501 0.53521001 0.7323950
[129,] 0.23855493 0.47710987 0.7614451
[130,] 0.21534840 0.43069681 0.7846516
[131,] 0.20139782 0.40279565 0.7986022
[132,] 0.25045583 0.50091166 0.7495442
[133,] 0.22159086 0.44318172 0.7784091
[134,] 0.19839095 0.39678189 0.8016091
[135,] 0.17485351 0.34970701 0.8251465
[136,] 0.16195363 0.32390725 0.8380464
[137,] 0.20664504 0.41329009 0.7933550
[138,] 0.18044087 0.36088175 0.8195591
[139,] 0.15885384 0.31770768 0.8411462
[140,] 0.18114347 0.36228694 0.8188565
[141,] 0.17097816 0.34195632 0.8290218
[142,] 0.18021164 0.36042327 0.8197884
[143,] 0.17791245 0.35582490 0.8220876
[144,] 0.18671744 0.37343488 0.8132826
[145,] 0.16742875 0.33485750 0.8325712
[146,] 0.15283166 0.30566333 0.8471683
[147,] 0.13825699 0.27651399 0.8617430
[148,] 0.11786045 0.23572090 0.8821395
[149,] 0.09938415 0.19876831 0.9006158
[150,] 0.08835373 0.17670745 0.9116463
[151,] 0.09156255 0.18312511 0.9084374
[152,] 0.07697411 0.15394823 0.9230259
[153,] 0.06932292 0.13864584 0.9306771
[154,] 0.06315990 0.12631981 0.9368401
[155,] 0.09945224 0.19890449 0.9005478
[156,] 0.12191625 0.24383249 0.8780838
[157,] 0.11831842 0.23663684 0.8816816
[158,] 0.09938411 0.19876823 0.9006159
[159,] 0.10443613 0.20887227 0.8955639
[160,] 0.09016277 0.18032554 0.9098372
[161,] 0.08107420 0.16214840 0.9189258
[162,] 0.06709199 0.13418399 0.9329080
[163,] 0.06159375 0.12318750 0.9384062
[164,] 0.08598534 0.17197068 0.9140147
[165,] 0.07215475 0.14430950 0.9278453
[166,] 0.07003964 0.14007928 0.9299604
[167,] 0.06544951 0.13089902 0.9345505
[168,] 0.07056120 0.14112240 0.9294388
[169,] 0.05745098 0.11490195 0.9425490
[170,] 0.06369311 0.12738623 0.9363069
[171,] 0.06106150 0.12212300 0.9389385
[172,] 0.05265150 0.10530299 0.9473485
[173,] 0.04306049 0.08612097 0.9569395
[174,] 0.04402745 0.08805491 0.9559725
[175,] 0.03464796 0.06929593 0.9653520
[176,] 0.04115012 0.08230024 0.9588499
[177,] 0.03401975 0.06803949 0.9659803
[178,] 0.02938198 0.05876396 0.9706180
[179,] 0.03795305 0.07590610 0.9620470
[180,] 0.03134892 0.06269784 0.9686511
[181,] 0.05276163 0.10552325 0.9472384
[182,] 0.04412229 0.08824459 0.9558777
[183,] 0.03489294 0.06978587 0.9651071
[184,] 0.09360168 0.18720336 0.9063983
[185,] 0.07833736 0.15667473 0.9216626
[186,] 0.10065021 0.20130042 0.8993498
[187,] 0.11311260 0.22622519 0.8868874
[188,] 0.19809660 0.39619321 0.8019034
[189,] 0.16559585 0.33119170 0.8344042
[190,] 0.19108657 0.38217314 0.8089134
[191,] 0.50868765 0.98262469 0.4913123
[192,] 0.61218296 0.77563407 0.3878170
[193,] 0.56777008 0.86445984 0.4322299
[194,] 0.51764256 0.96471488 0.4823574
[195,] 0.47643158 0.95286316 0.5235684
[196,] 0.42817995 0.85635990 0.5718201
[197,] 0.42835075 0.85670149 0.5716493
[198,] 0.38586481 0.77172963 0.6141352
[199,] 0.44803691 0.89607383 0.5519631
[200,] 0.70557839 0.58884323 0.2944216
[201,] 0.65363198 0.69273605 0.3463680
[202,] 0.59183847 0.81632307 0.4081615
[203,] 0.56159921 0.87680158 0.4384008
[204,] 0.53100073 0.93799854 0.4689993
[205,] 0.49296330 0.98592660 0.5070367
[206,] 0.43771278 0.87542556 0.5622872
[207,] 0.42111156 0.84222312 0.5788884
[208,] 0.61830460 0.76339080 0.3816954
[209,] 0.71231784 0.57536432 0.2876822
[210,] 0.64080634 0.71838732 0.3591937
[211,] 0.56161916 0.87676169 0.4383808
[212,] 0.70795001 0.58409998 0.2920500
[213,] 0.66442447 0.67115106 0.3355755
[214,] 0.56870193 0.86259614 0.4312981
[215,] 0.47605697 0.95211393 0.5239430
[216,] 0.36721609 0.73443219 0.6327839
[217,] 0.27093761 0.54187522 0.7290624
[218,] 0.63173581 0.73652837 0.3682642
[219,] 0.60302885 0.79394229 0.3969711
> postscript(file="/var/wessaorg/rcomp/tmp/1mxpn1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29to31353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3r7rr1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bs1s1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51qjx1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 252
Frequency = 1
1 2 3 4 5 6
-3.85278087 -2.74370386 6.74323194 -1.52145524 1.40892172 3.95966210
7 8 9 10 11 12
2.46115045 -1.51591458 -5.57746578 1.51642818 -1.91723978 -5.06741067
13 14 15 16 17 18
2.97464464 -3.85091201 -1.98392619 -2.07452297 5.46168396 3.45178803
19 20 21 22 23 24
-0.74128899 -6.67087455 -2.00905260 -4.68770078 6.04857998 6.57898264
25 26 27 28 29 30
5.86727055 -4.80909674 -1.20335612 4.94504408 -2.66291763 -2.82651365
31 32 33 34 35 36
-2.70301698 -5.33116321 5.66487710 -2.51851712 0.02974837 2.52041509
37 38 39 40 41 42
0.08004236 6.49658883 -8.83488922 0.07467651 -2.17594557 0.36516227
43 44 45 46 47 48
-2.63488024 5.62472921 -3.20411583 3.94345389 3.02869809 -3.94087988
49 50 51 52 53 54
-4.20872402 -1.52042426 -4.53767109 -2.89173918 -6.22321615 -4.49423589
55 56 57 58 59 60
-3.77501483 -0.95682775 -5.45712319 0.31967653 2.86646987 -1.66758680
61 62 63 64 65 66
0.74163862 6.28140133 3.21806956 -1.43822057 2.20623005 3.78563402
67 68 69 70 71 72
6.06824492 -1.89080508 -2.42373308 -0.94226659 4.77880706 -3.99740207
73 74 75 76 77 78
-2.55938876 1.89475945 -4.99563587 6.38867553 -0.16221332 2.83098085
79 80 81 82 83 84
-1.68099936 -6.01058474 9.22654663 8.88470318 -4.56691829 5.70358573
85 86 87 88 89 90
-0.17629968 5.69086006 -2.66853862 1.27555018 -4.37681867 -0.07262775
91 92 93 94 95 96
-1.20482625 0.56047139 -2.54098411 3.20537655 -5.76198195 1.11853064
97 98 99 100 101 102
-7.22865948 -4.78864894 -0.03201518 3.06764261 2.70529180 0.48184404
103 104 105 106 107 108
2.80781511 5.12847319 1.51676978 0.20089741 -6.55550552 -3.94869164
109 110 111 112 113 114
4.84132018 0.35867218 0.46251366 -4.44792138 1.23522466 1.43454189
115 116 117 118 119 120
6.57177812 0.27032672 3.22990397 -0.48701604 7.62411008 4.63985912
121 122 123 124 125 126
7.54000755 2.76459355 0.61212986 -1.10932506 -1.66865976 -3.46492832
127 128 129 130 131 132
5.71852045 8.40431499 2.14455897 -0.09578241 -0.22495989 2.53161092
133 134 135 136 137 138
-1.18122590 5.03423515 8.35292724 0.26717143 5.15361246 3.35056925
139 140 141 142 143 144
4.49761508 -8.36935132 3.70989345 -2.98748325 4.40308875 4.01202028
145 146 147 148 149 150
1.00649828 1.67421640 -3.29006824 6.62875623 -0.77669239 -1.48530885
151 152 153 154 155 156
0.90061109 -2.25679297 6.01604670 -0.10709487 -0.53737885 6.21895547
157 158 159 160 161 162
2.61108726 -5.58282920 0.75238084 3.63444546 -2.41356126 -3.10933225
163 164 165 166 167 168
-2.47870477 -0.20295292 0.28494023 1.26647465 4.05540390 1.27754676
169 170 171 172 173 174
-3.16475546 -3.67495163 -8.66149228 -7.51931356 3.83959023 0.32257550
175 176 177 178 179 180
3.93806198 -1.25804279 -2.85467583 1.88092469 1.82498386 -6.69334583
181 182 183 184 185 186
1.55062518 -4.48358712 -4.78576956 -4.79868221 -0.42626135 -2.57263289
187 188 189 190 191 192
-3.88821800 -1.55668052 -2.15467481 -2.12497867 1.03246127 7.87549228
193 194 195 196 197 198
-2.04125942 -1.94764349 5.99799262 2.75815486 4.90169496 -2.13416993
199 200 201 202 203 204
0.81044609 5.61117211 -3.36883109 6.08990344 1.24076212 -9.08840687
205 206 207 208 209 210
-0.93658262 1.54090918 10.00677252 6.00368701 -4.20414525 -3.82188965
211 212 213 214 215 216
-5.71466023 1.90189046 4.10242879 -2.73419337 5.25784578 6.36391329
217 218 219 220 221 222
2.09645922 -1.57253141 0.70212350 -2.89762943 -8.45703269 -4.62496378
223 224 225 226 227 228
-5.81422316 -11.16865524 -9.40671198 -0.41753962 -4.30494468 3.42863303
229 230 231 232 233 234
-2.33677970 -3.61982793 -9.42021978 -5.25026020 -1.06033120 3.30807495
235 236 237 238 239 240
4.65436673 -3.83875295 3.28730568 -7.17810546 -1.88213706 3.96680349
241 242 243 244 245 246
2.01668075 -1.76036244 2.42426118 -0.33786486 -0.41701823 1.26423963
247 248 249 250 251 252
-0.54713168 -3.72502837 7.74658114 -7.61926679 1.41883129 4.50054460
> postscript(file="/var/wessaorg/rcomp/tmp/631tx1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 252
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.85278087 NA
1 -2.74370386 -3.85278087
2 6.74323194 -2.74370386
3 -1.52145524 6.74323194
4 1.40892172 -1.52145524
5 3.95966210 1.40892172
6 2.46115045 3.95966210
7 -1.51591458 2.46115045
8 -5.57746578 -1.51591458
9 1.51642818 -5.57746578
10 -1.91723978 1.51642818
11 -5.06741067 -1.91723978
12 2.97464464 -5.06741067
13 -3.85091201 2.97464464
14 -1.98392619 -3.85091201
15 -2.07452297 -1.98392619
16 5.46168396 -2.07452297
17 3.45178803 5.46168396
18 -0.74128899 3.45178803
19 -6.67087455 -0.74128899
20 -2.00905260 -6.67087455
21 -4.68770078 -2.00905260
22 6.04857998 -4.68770078
23 6.57898264 6.04857998
24 5.86727055 6.57898264
25 -4.80909674 5.86727055
26 -1.20335612 -4.80909674
27 4.94504408 -1.20335612
28 -2.66291763 4.94504408
29 -2.82651365 -2.66291763
30 -2.70301698 -2.82651365
31 -5.33116321 -2.70301698
32 5.66487710 -5.33116321
33 -2.51851712 5.66487710
34 0.02974837 -2.51851712
35 2.52041509 0.02974837
36 0.08004236 2.52041509
37 6.49658883 0.08004236
38 -8.83488922 6.49658883
39 0.07467651 -8.83488922
40 -2.17594557 0.07467651
41 0.36516227 -2.17594557
42 -2.63488024 0.36516227
43 5.62472921 -2.63488024
44 -3.20411583 5.62472921
45 3.94345389 -3.20411583
46 3.02869809 3.94345389
47 -3.94087988 3.02869809
48 -4.20872402 -3.94087988
49 -1.52042426 -4.20872402
50 -4.53767109 -1.52042426
51 -2.89173918 -4.53767109
52 -6.22321615 -2.89173918
53 -4.49423589 -6.22321615
54 -3.77501483 -4.49423589
55 -0.95682775 -3.77501483
56 -5.45712319 -0.95682775
57 0.31967653 -5.45712319
58 2.86646987 0.31967653
59 -1.66758680 2.86646987
60 0.74163862 -1.66758680
61 6.28140133 0.74163862
62 3.21806956 6.28140133
63 -1.43822057 3.21806956
64 2.20623005 -1.43822057
65 3.78563402 2.20623005
66 6.06824492 3.78563402
67 -1.89080508 6.06824492
68 -2.42373308 -1.89080508
69 -0.94226659 -2.42373308
70 4.77880706 -0.94226659
71 -3.99740207 4.77880706
72 -2.55938876 -3.99740207
73 1.89475945 -2.55938876
74 -4.99563587 1.89475945
75 6.38867553 -4.99563587
76 -0.16221332 6.38867553
77 2.83098085 -0.16221332
78 -1.68099936 2.83098085
79 -6.01058474 -1.68099936
80 9.22654663 -6.01058474
81 8.88470318 9.22654663
82 -4.56691829 8.88470318
83 5.70358573 -4.56691829
84 -0.17629968 5.70358573
85 5.69086006 -0.17629968
86 -2.66853862 5.69086006
87 1.27555018 -2.66853862
88 -4.37681867 1.27555018
89 -0.07262775 -4.37681867
90 -1.20482625 -0.07262775
91 0.56047139 -1.20482625
92 -2.54098411 0.56047139
93 3.20537655 -2.54098411
94 -5.76198195 3.20537655
95 1.11853064 -5.76198195
96 -7.22865948 1.11853064
97 -4.78864894 -7.22865948
98 -0.03201518 -4.78864894
99 3.06764261 -0.03201518
100 2.70529180 3.06764261
101 0.48184404 2.70529180
102 2.80781511 0.48184404
103 5.12847319 2.80781511
104 1.51676978 5.12847319
105 0.20089741 1.51676978
106 -6.55550552 0.20089741
107 -3.94869164 -6.55550552
108 4.84132018 -3.94869164
109 0.35867218 4.84132018
110 0.46251366 0.35867218
111 -4.44792138 0.46251366
112 1.23522466 -4.44792138
113 1.43454189 1.23522466
114 6.57177812 1.43454189
115 0.27032672 6.57177812
116 3.22990397 0.27032672
117 -0.48701604 3.22990397
118 7.62411008 -0.48701604
119 4.63985912 7.62411008
120 7.54000755 4.63985912
121 2.76459355 7.54000755
122 0.61212986 2.76459355
123 -1.10932506 0.61212986
124 -1.66865976 -1.10932506
125 -3.46492832 -1.66865976
126 5.71852045 -3.46492832
127 8.40431499 5.71852045
128 2.14455897 8.40431499
129 -0.09578241 2.14455897
130 -0.22495989 -0.09578241
131 2.53161092 -0.22495989
132 -1.18122590 2.53161092
133 5.03423515 -1.18122590
134 8.35292724 5.03423515
135 0.26717143 8.35292724
136 5.15361246 0.26717143
137 3.35056925 5.15361246
138 4.49761508 3.35056925
139 -8.36935132 4.49761508
140 3.70989345 -8.36935132
141 -2.98748325 3.70989345
142 4.40308875 -2.98748325
143 4.01202028 4.40308875
144 1.00649828 4.01202028
145 1.67421640 1.00649828
146 -3.29006824 1.67421640
147 6.62875623 -3.29006824
148 -0.77669239 6.62875623
149 -1.48530885 -0.77669239
150 0.90061109 -1.48530885
151 -2.25679297 0.90061109
152 6.01604670 -2.25679297
153 -0.10709487 6.01604670
154 -0.53737885 -0.10709487
155 6.21895547 -0.53737885
156 2.61108726 6.21895547
157 -5.58282920 2.61108726
158 0.75238084 -5.58282920
159 3.63444546 0.75238084
160 -2.41356126 3.63444546
161 -3.10933225 -2.41356126
162 -2.47870477 -3.10933225
163 -0.20295292 -2.47870477
164 0.28494023 -0.20295292
165 1.26647465 0.28494023
166 4.05540390 1.26647465
167 1.27754676 4.05540390
168 -3.16475546 1.27754676
169 -3.67495163 -3.16475546
170 -8.66149228 -3.67495163
171 -7.51931356 -8.66149228
172 3.83959023 -7.51931356
173 0.32257550 3.83959023
174 3.93806198 0.32257550
175 -1.25804279 3.93806198
176 -2.85467583 -1.25804279
177 1.88092469 -2.85467583
178 1.82498386 1.88092469
179 -6.69334583 1.82498386
180 1.55062518 -6.69334583
181 -4.48358712 1.55062518
182 -4.78576956 -4.48358712
183 -4.79868221 -4.78576956
184 -0.42626135 -4.79868221
185 -2.57263289 -0.42626135
186 -3.88821800 -2.57263289
187 -1.55668052 -3.88821800
188 -2.15467481 -1.55668052
189 -2.12497867 -2.15467481
190 1.03246127 -2.12497867
191 7.87549228 1.03246127
192 -2.04125942 7.87549228
193 -1.94764349 -2.04125942
194 5.99799262 -1.94764349
195 2.75815486 5.99799262
196 4.90169496 2.75815486
197 -2.13416993 4.90169496
198 0.81044609 -2.13416993
199 5.61117211 0.81044609
200 -3.36883109 5.61117211
201 6.08990344 -3.36883109
202 1.24076212 6.08990344
203 -9.08840687 1.24076212
204 -0.93658262 -9.08840687
205 1.54090918 -0.93658262
206 10.00677252 1.54090918
207 6.00368701 10.00677252
208 -4.20414525 6.00368701
209 -3.82188965 -4.20414525
210 -5.71466023 -3.82188965
211 1.90189046 -5.71466023
212 4.10242879 1.90189046
213 -2.73419337 4.10242879
214 5.25784578 -2.73419337
215 6.36391329 5.25784578
216 2.09645922 6.36391329
217 -1.57253141 2.09645922
218 0.70212350 -1.57253141
219 -2.89762943 0.70212350
220 -8.45703269 -2.89762943
221 -4.62496378 -8.45703269
222 -5.81422316 -4.62496378
223 -11.16865524 -5.81422316
224 -9.40671198 -11.16865524
225 -0.41753962 -9.40671198
226 -4.30494468 -0.41753962
227 3.42863303 -4.30494468
228 -2.33677970 3.42863303
229 -3.61982793 -2.33677970
230 -9.42021978 -3.61982793
231 -5.25026020 -9.42021978
232 -1.06033120 -5.25026020
233 3.30807495 -1.06033120
234 4.65436673 3.30807495
235 -3.83875295 4.65436673
236 3.28730568 -3.83875295
237 -7.17810546 3.28730568
238 -1.88213706 -7.17810546
239 3.96680349 -1.88213706
240 2.01668075 3.96680349
241 -1.76036244 2.01668075
242 2.42426118 -1.76036244
243 -0.33786486 2.42426118
244 -0.41701823 -0.33786486
245 1.26423963 -0.41701823
246 -0.54713168 1.26423963
247 -3.72502837 -0.54713168
248 7.74658114 -3.72502837
249 -7.61926679 7.74658114
250 1.41883129 -7.61926679
251 4.50054460 1.41883129
252 NA 4.50054460
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.74370386 -3.85278087
[2,] 6.74323194 -2.74370386
[3,] -1.52145524 6.74323194
[4,] 1.40892172 -1.52145524
[5,] 3.95966210 1.40892172
[6,] 2.46115045 3.95966210
[7,] -1.51591458 2.46115045
[8,] -5.57746578 -1.51591458
[9,] 1.51642818 -5.57746578
[10,] -1.91723978 1.51642818
[11,] -5.06741067 -1.91723978
[12,] 2.97464464 -5.06741067
[13,] -3.85091201 2.97464464
[14,] -1.98392619 -3.85091201
[15,] -2.07452297 -1.98392619
[16,] 5.46168396 -2.07452297
[17,] 3.45178803 5.46168396
[18,] -0.74128899 3.45178803
[19,] -6.67087455 -0.74128899
[20,] -2.00905260 -6.67087455
[21,] -4.68770078 -2.00905260
[22,] 6.04857998 -4.68770078
[23,] 6.57898264 6.04857998
[24,] 5.86727055 6.57898264
[25,] -4.80909674 5.86727055
[26,] -1.20335612 -4.80909674
[27,] 4.94504408 -1.20335612
[28,] -2.66291763 4.94504408
[29,] -2.82651365 -2.66291763
[30,] -2.70301698 -2.82651365
[31,] -5.33116321 -2.70301698
[32,] 5.66487710 -5.33116321
[33,] -2.51851712 5.66487710
[34,] 0.02974837 -2.51851712
[35,] 2.52041509 0.02974837
[36,] 0.08004236 2.52041509
[37,] 6.49658883 0.08004236
[38,] -8.83488922 6.49658883
[39,] 0.07467651 -8.83488922
[40,] -2.17594557 0.07467651
[41,] 0.36516227 -2.17594557
[42,] -2.63488024 0.36516227
[43,] 5.62472921 -2.63488024
[44,] -3.20411583 5.62472921
[45,] 3.94345389 -3.20411583
[46,] 3.02869809 3.94345389
[47,] -3.94087988 3.02869809
[48,] -4.20872402 -3.94087988
[49,] -1.52042426 -4.20872402
[50,] -4.53767109 -1.52042426
[51,] -2.89173918 -4.53767109
[52,] -6.22321615 -2.89173918
[53,] -4.49423589 -6.22321615
[54,] -3.77501483 -4.49423589
[55,] -0.95682775 -3.77501483
[56,] -5.45712319 -0.95682775
[57,] 0.31967653 -5.45712319
[58,] 2.86646987 0.31967653
[59,] -1.66758680 2.86646987
[60,] 0.74163862 -1.66758680
[61,] 6.28140133 0.74163862
[62,] 3.21806956 6.28140133
[63,] -1.43822057 3.21806956
[64,] 2.20623005 -1.43822057
[65,] 3.78563402 2.20623005
[66,] 6.06824492 3.78563402
[67,] -1.89080508 6.06824492
[68,] -2.42373308 -1.89080508
[69,] -0.94226659 -2.42373308
[70,] 4.77880706 -0.94226659
[71,] -3.99740207 4.77880706
[72,] -2.55938876 -3.99740207
[73,] 1.89475945 -2.55938876
[74,] -4.99563587 1.89475945
[75,] 6.38867553 -4.99563587
[76,] -0.16221332 6.38867553
[77,] 2.83098085 -0.16221332
[78,] -1.68099936 2.83098085
[79,] -6.01058474 -1.68099936
[80,] 9.22654663 -6.01058474
[81,] 8.88470318 9.22654663
[82,] -4.56691829 8.88470318
[83,] 5.70358573 -4.56691829
[84,] -0.17629968 5.70358573
[85,] 5.69086006 -0.17629968
[86,] -2.66853862 5.69086006
[87,] 1.27555018 -2.66853862
[88,] -4.37681867 1.27555018
[89,] -0.07262775 -4.37681867
[90,] -1.20482625 -0.07262775
[91,] 0.56047139 -1.20482625
[92,] -2.54098411 0.56047139
[93,] 3.20537655 -2.54098411
[94,] -5.76198195 3.20537655
[95,] 1.11853064 -5.76198195
[96,] -7.22865948 1.11853064
[97,] -4.78864894 -7.22865948
[98,] -0.03201518 -4.78864894
[99,] 3.06764261 -0.03201518
[100,] 2.70529180 3.06764261
[101,] 0.48184404 2.70529180
[102,] 2.80781511 0.48184404
[103,] 5.12847319 2.80781511
[104,] 1.51676978 5.12847319
[105,] 0.20089741 1.51676978
[106,] -6.55550552 0.20089741
[107,] -3.94869164 -6.55550552
[108,] 4.84132018 -3.94869164
[109,] 0.35867218 4.84132018
[110,] 0.46251366 0.35867218
[111,] -4.44792138 0.46251366
[112,] 1.23522466 -4.44792138
[113,] 1.43454189 1.23522466
[114,] 6.57177812 1.43454189
[115,] 0.27032672 6.57177812
[116,] 3.22990397 0.27032672
[117,] -0.48701604 3.22990397
[118,] 7.62411008 -0.48701604
[119,] 4.63985912 7.62411008
[120,] 7.54000755 4.63985912
[121,] 2.76459355 7.54000755
[122,] 0.61212986 2.76459355
[123,] -1.10932506 0.61212986
[124,] -1.66865976 -1.10932506
[125,] -3.46492832 -1.66865976
[126,] 5.71852045 -3.46492832
[127,] 8.40431499 5.71852045
[128,] 2.14455897 8.40431499
[129,] -0.09578241 2.14455897
[130,] -0.22495989 -0.09578241
[131,] 2.53161092 -0.22495989
[132,] -1.18122590 2.53161092
[133,] 5.03423515 -1.18122590
[134,] 8.35292724 5.03423515
[135,] 0.26717143 8.35292724
[136,] 5.15361246 0.26717143
[137,] 3.35056925 5.15361246
[138,] 4.49761508 3.35056925
[139,] -8.36935132 4.49761508
[140,] 3.70989345 -8.36935132
[141,] -2.98748325 3.70989345
[142,] 4.40308875 -2.98748325
[143,] 4.01202028 4.40308875
[144,] 1.00649828 4.01202028
[145,] 1.67421640 1.00649828
[146,] -3.29006824 1.67421640
[147,] 6.62875623 -3.29006824
[148,] -0.77669239 6.62875623
[149,] -1.48530885 -0.77669239
[150,] 0.90061109 -1.48530885
[151,] -2.25679297 0.90061109
[152,] 6.01604670 -2.25679297
[153,] -0.10709487 6.01604670
[154,] -0.53737885 -0.10709487
[155,] 6.21895547 -0.53737885
[156,] 2.61108726 6.21895547
[157,] -5.58282920 2.61108726
[158,] 0.75238084 -5.58282920
[159,] 3.63444546 0.75238084
[160,] -2.41356126 3.63444546
[161,] -3.10933225 -2.41356126
[162,] -2.47870477 -3.10933225
[163,] -0.20295292 -2.47870477
[164,] 0.28494023 -0.20295292
[165,] 1.26647465 0.28494023
[166,] 4.05540390 1.26647465
[167,] 1.27754676 4.05540390
[168,] -3.16475546 1.27754676
[169,] -3.67495163 -3.16475546
[170,] -8.66149228 -3.67495163
[171,] -7.51931356 -8.66149228
[172,] 3.83959023 -7.51931356
[173,] 0.32257550 3.83959023
[174,] 3.93806198 0.32257550
[175,] -1.25804279 3.93806198
[176,] -2.85467583 -1.25804279
[177,] 1.88092469 -2.85467583
[178,] 1.82498386 1.88092469
[179,] -6.69334583 1.82498386
[180,] 1.55062518 -6.69334583
[181,] -4.48358712 1.55062518
[182,] -4.78576956 -4.48358712
[183,] -4.79868221 -4.78576956
[184,] -0.42626135 -4.79868221
[185,] -2.57263289 -0.42626135
[186,] -3.88821800 -2.57263289
[187,] -1.55668052 -3.88821800
[188,] -2.15467481 -1.55668052
[189,] -2.12497867 -2.15467481
[190,] 1.03246127 -2.12497867
[191,] 7.87549228 1.03246127
[192,] -2.04125942 7.87549228
[193,] -1.94764349 -2.04125942
[194,] 5.99799262 -1.94764349
[195,] 2.75815486 5.99799262
[196,] 4.90169496 2.75815486
[197,] -2.13416993 4.90169496
[198,] 0.81044609 -2.13416993
[199,] 5.61117211 0.81044609
[200,] -3.36883109 5.61117211
[201,] 6.08990344 -3.36883109
[202,] 1.24076212 6.08990344
[203,] -9.08840687 1.24076212
[204,] -0.93658262 -9.08840687
[205,] 1.54090918 -0.93658262
[206,] 10.00677252 1.54090918
[207,] 6.00368701 10.00677252
[208,] -4.20414525 6.00368701
[209,] -3.82188965 -4.20414525
[210,] -5.71466023 -3.82188965
[211,] 1.90189046 -5.71466023
[212,] 4.10242879 1.90189046
[213,] -2.73419337 4.10242879
[214,] 5.25784578 -2.73419337
[215,] 6.36391329 5.25784578
[216,] 2.09645922 6.36391329
[217,] -1.57253141 2.09645922
[218,] 0.70212350 -1.57253141
[219,] -2.89762943 0.70212350
[220,] -8.45703269 -2.89762943
[221,] -4.62496378 -8.45703269
[222,] -5.81422316 -4.62496378
[223,] -11.16865524 -5.81422316
[224,] -9.40671198 -11.16865524
[225,] -0.41753962 -9.40671198
[226,] -4.30494468 -0.41753962
[227,] 3.42863303 -4.30494468
[228,] -2.33677970 3.42863303
[229,] -3.61982793 -2.33677970
[230,] -9.42021978 -3.61982793
[231,] -5.25026020 -9.42021978
[232,] -1.06033120 -5.25026020
[233,] 3.30807495 -1.06033120
[234,] 4.65436673 3.30807495
[235,] -3.83875295 4.65436673
[236,] 3.28730568 -3.83875295
[237,] -7.17810546 3.28730568
[238,] -1.88213706 -7.17810546
[239,] 3.96680349 -1.88213706
[240,] 2.01668075 3.96680349
[241,] -1.76036244 2.01668075
[242,] 2.42426118 -1.76036244
[243,] -0.33786486 2.42426118
[244,] -0.41701823 -0.33786486
[245,] 1.26423963 -0.41701823
[246,] -0.54713168 1.26423963
[247,] -3.72502837 -0.54713168
[248,] 7.74658114 -3.72502837
[249,] -7.61926679 7.74658114
[250,] 1.41883129 -7.61926679
[251,] 4.50054460 1.41883129
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.74370386 -3.85278087
2 6.74323194 -2.74370386
3 -1.52145524 6.74323194
4 1.40892172 -1.52145524
5 3.95966210 1.40892172
6 2.46115045 3.95966210
7 -1.51591458 2.46115045
8 -5.57746578 -1.51591458
9 1.51642818 -5.57746578
10 -1.91723978 1.51642818
11 -5.06741067 -1.91723978
12 2.97464464 -5.06741067
13 -3.85091201 2.97464464
14 -1.98392619 -3.85091201
15 -2.07452297 -1.98392619
16 5.46168396 -2.07452297
17 3.45178803 5.46168396
18 -0.74128899 3.45178803
19 -6.67087455 -0.74128899
20 -2.00905260 -6.67087455
21 -4.68770078 -2.00905260
22 6.04857998 -4.68770078
23 6.57898264 6.04857998
24 5.86727055 6.57898264
25 -4.80909674 5.86727055
26 -1.20335612 -4.80909674
27 4.94504408 -1.20335612
28 -2.66291763 4.94504408
29 -2.82651365 -2.66291763
30 -2.70301698 -2.82651365
31 -5.33116321 -2.70301698
32 5.66487710 -5.33116321
33 -2.51851712 5.66487710
34 0.02974837 -2.51851712
35 2.52041509 0.02974837
36 0.08004236 2.52041509
37 6.49658883 0.08004236
38 -8.83488922 6.49658883
39 0.07467651 -8.83488922
40 -2.17594557 0.07467651
41 0.36516227 -2.17594557
42 -2.63488024 0.36516227
43 5.62472921 -2.63488024
44 -3.20411583 5.62472921
45 3.94345389 -3.20411583
46 3.02869809 3.94345389
47 -3.94087988 3.02869809
48 -4.20872402 -3.94087988
49 -1.52042426 -4.20872402
50 -4.53767109 -1.52042426
51 -2.89173918 -4.53767109
52 -6.22321615 -2.89173918
53 -4.49423589 -6.22321615
54 -3.77501483 -4.49423589
55 -0.95682775 -3.77501483
56 -5.45712319 -0.95682775
57 0.31967653 -5.45712319
58 2.86646987 0.31967653
59 -1.66758680 2.86646987
60 0.74163862 -1.66758680
61 6.28140133 0.74163862
62 3.21806956 6.28140133
63 -1.43822057 3.21806956
64 2.20623005 -1.43822057
65 3.78563402 2.20623005
66 6.06824492 3.78563402
67 -1.89080508 6.06824492
68 -2.42373308 -1.89080508
69 -0.94226659 -2.42373308
70 4.77880706 -0.94226659
71 -3.99740207 4.77880706
72 -2.55938876 -3.99740207
73 1.89475945 -2.55938876
74 -4.99563587 1.89475945
75 6.38867553 -4.99563587
76 -0.16221332 6.38867553
77 2.83098085 -0.16221332
78 -1.68099936 2.83098085
79 -6.01058474 -1.68099936
80 9.22654663 -6.01058474
81 8.88470318 9.22654663
82 -4.56691829 8.88470318
83 5.70358573 -4.56691829
84 -0.17629968 5.70358573
85 5.69086006 -0.17629968
86 -2.66853862 5.69086006
87 1.27555018 -2.66853862
88 -4.37681867 1.27555018
89 -0.07262775 -4.37681867
90 -1.20482625 -0.07262775
91 0.56047139 -1.20482625
92 -2.54098411 0.56047139
93 3.20537655 -2.54098411
94 -5.76198195 3.20537655
95 1.11853064 -5.76198195
96 -7.22865948 1.11853064
97 -4.78864894 -7.22865948
98 -0.03201518 -4.78864894
99 3.06764261 -0.03201518
100 2.70529180 3.06764261
101 0.48184404 2.70529180
102 2.80781511 0.48184404
103 5.12847319 2.80781511
104 1.51676978 5.12847319
105 0.20089741 1.51676978
106 -6.55550552 0.20089741
107 -3.94869164 -6.55550552
108 4.84132018 -3.94869164
109 0.35867218 4.84132018
110 0.46251366 0.35867218
111 -4.44792138 0.46251366
112 1.23522466 -4.44792138
113 1.43454189 1.23522466
114 6.57177812 1.43454189
115 0.27032672 6.57177812
116 3.22990397 0.27032672
117 -0.48701604 3.22990397
118 7.62411008 -0.48701604
119 4.63985912 7.62411008
120 7.54000755 4.63985912
121 2.76459355 7.54000755
122 0.61212986 2.76459355
123 -1.10932506 0.61212986
124 -1.66865976 -1.10932506
125 -3.46492832 -1.66865976
126 5.71852045 -3.46492832
127 8.40431499 5.71852045
128 2.14455897 8.40431499
129 -0.09578241 2.14455897
130 -0.22495989 -0.09578241
131 2.53161092 -0.22495989
132 -1.18122590 2.53161092
133 5.03423515 -1.18122590
134 8.35292724 5.03423515
135 0.26717143 8.35292724
136 5.15361246 0.26717143
137 3.35056925 5.15361246
138 4.49761508 3.35056925
139 -8.36935132 4.49761508
140 3.70989345 -8.36935132
141 -2.98748325 3.70989345
142 4.40308875 -2.98748325
143 4.01202028 4.40308875
144 1.00649828 4.01202028
145 1.67421640 1.00649828
146 -3.29006824 1.67421640
147 6.62875623 -3.29006824
148 -0.77669239 6.62875623
149 -1.48530885 -0.77669239
150 0.90061109 -1.48530885
151 -2.25679297 0.90061109
152 6.01604670 -2.25679297
153 -0.10709487 6.01604670
154 -0.53737885 -0.10709487
155 6.21895547 -0.53737885
156 2.61108726 6.21895547
157 -5.58282920 2.61108726
158 0.75238084 -5.58282920
159 3.63444546 0.75238084
160 -2.41356126 3.63444546
161 -3.10933225 -2.41356126
162 -2.47870477 -3.10933225
163 -0.20295292 -2.47870477
164 0.28494023 -0.20295292
165 1.26647465 0.28494023
166 4.05540390 1.26647465
167 1.27754676 4.05540390
168 -3.16475546 1.27754676
169 -3.67495163 -3.16475546
170 -8.66149228 -3.67495163
171 -7.51931356 -8.66149228
172 3.83959023 -7.51931356
173 0.32257550 3.83959023
174 3.93806198 0.32257550
175 -1.25804279 3.93806198
176 -2.85467583 -1.25804279
177 1.88092469 -2.85467583
178 1.82498386 1.88092469
179 -6.69334583 1.82498386
180 1.55062518 -6.69334583
181 -4.48358712 1.55062518
182 -4.78576956 -4.48358712
183 -4.79868221 -4.78576956
184 -0.42626135 -4.79868221
185 -2.57263289 -0.42626135
186 -3.88821800 -2.57263289
187 -1.55668052 -3.88821800
188 -2.15467481 -1.55668052
189 -2.12497867 -2.15467481
190 1.03246127 -2.12497867
191 7.87549228 1.03246127
192 -2.04125942 7.87549228
193 -1.94764349 -2.04125942
194 5.99799262 -1.94764349
195 2.75815486 5.99799262
196 4.90169496 2.75815486
197 -2.13416993 4.90169496
198 0.81044609 -2.13416993
199 5.61117211 0.81044609
200 -3.36883109 5.61117211
201 6.08990344 -3.36883109
202 1.24076212 6.08990344
203 -9.08840687 1.24076212
204 -0.93658262 -9.08840687
205 1.54090918 -0.93658262
206 10.00677252 1.54090918
207 6.00368701 10.00677252
208 -4.20414525 6.00368701
209 -3.82188965 -4.20414525
210 -5.71466023 -3.82188965
211 1.90189046 -5.71466023
212 4.10242879 1.90189046
213 -2.73419337 4.10242879
214 5.25784578 -2.73419337
215 6.36391329 5.25784578
216 2.09645922 6.36391329
217 -1.57253141 2.09645922
218 0.70212350 -1.57253141
219 -2.89762943 0.70212350
220 -8.45703269 -2.89762943
221 -4.62496378 -8.45703269
222 -5.81422316 -4.62496378
223 -11.16865524 -5.81422316
224 -9.40671198 -11.16865524
225 -0.41753962 -9.40671198
226 -4.30494468 -0.41753962
227 3.42863303 -4.30494468
228 -2.33677970 3.42863303
229 -3.61982793 -2.33677970
230 -9.42021978 -3.61982793
231 -5.25026020 -9.42021978
232 -1.06033120 -5.25026020
233 3.30807495 -1.06033120
234 4.65436673 3.30807495
235 -3.83875295 4.65436673
236 3.28730568 -3.83875295
237 -7.17810546 3.28730568
238 -1.88213706 -7.17810546
239 3.96680349 -1.88213706
240 2.01668075 3.96680349
241 -1.76036244 2.01668075
242 2.42426118 -1.76036244
243 -0.33786486 2.42426118
244 -0.41701823 -0.33786486
245 1.26423963 -0.41701823
246 -0.54713168 1.26423963
247 -3.72502837 -0.54713168
248 7.74658114 -3.72502837
249 -7.61926679 7.74658114
250 1.41883129 -7.61926679
251 4.50054460 1.41883129
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7kit21353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8pete1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xu1a1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10m8a11353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/112d8h1353327359.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12gr3o1353327359.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13esrd1353327359.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14gwtv1353327359.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1544du1353327359.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/165zob1353327359.tab")
+ }
>
> try(system("convert tmp/1mxpn1353327359.ps tmp/1mxpn1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/29to31353327359.ps tmp/29to31353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r7rr1353327359.ps tmp/3r7rr1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bs1s1353327359.ps tmp/4bs1s1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/51qjx1353327359.ps tmp/51qjx1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/631tx1353327359.ps tmp/631tx1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kit21353327359.ps tmp/7kit21353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pete1353327359.ps tmp/8pete1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xu1a1353327359.ps tmp/9xu1a1353327359.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m8a11353327359.ps tmp/10m8a11353327359.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
23.746 2.056 25.954