R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(87.28
+ ,255
+ ,87.28
+ ,280.2
+ ,87.09
+ ,299.9
+ ,86.92
+ ,339.2
+ ,87.59
+ ,374.2
+ ,90.72
+ ,393.5
+ ,90.69
+ ,389.2
+ ,90.3
+ ,381.7
+ ,89.55
+ ,375.2
+ ,88.94
+ ,369
+ ,88.41
+ ,357.4
+ ,87.82
+ ,352.1
+ ,87.07
+ ,346.5
+ ,86.82
+ ,342.9
+ ,86.4
+ ,340.3
+ ,86.02
+ ,328.3
+ ,85.66
+ ,322.9
+ ,85.32
+ ,314.3
+ ,85
+ ,308.9
+ ,84.67
+ ,294
+ ,83.94
+ ,285.6
+ ,82.83
+ ,281.2
+ ,81.95
+ ,280.3
+ ,81.19
+ ,278.8
+ ,80.48
+ ,274.5
+ ,78.86
+ ,270.4
+ ,69.47
+ ,263.4
+ ,68.77
+ ,259.9
+ ,70.06
+ ,258
+ ,73.95
+ ,262.7
+ ,75.8
+ ,284.7
+ ,77.79
+ ,311.3
+ ,81.57
+ ,322.1
+ ,83.07
+ ,327
+ ,84.34
+ ,331.3
+ ,85.1
+ ,333.3
+ ,85.25
+ ,321.4
+ ,84.26
+ ,327
+ ,83.63
+ ,320
+ ,86.44
+ ,314.7
+ ,85.3
+ ,316.7
+ ,84.1
+ ,314.4
+ ,83.36
+ ,321.3
+ ,82.48
+ ,318.2
+ ,81.58
+ ,307.2
+ ,80.47
+ ,301.3
+ ,79.34
+ ,287.5
+ ,82.13
+ ,277.7
+ ,81.69
+ ,274.4
+ ,80.7
+ ,258.8
+ ,79.88
+ ,253.3
+ ,79.16
+ ,251
+ ,78.38
+ ,248.4
+ ,77.42
+ ,249.5
+ ,76.47
+ ,246.1
+ ,75.46
+ ,244.5
+ ,74.48
+ ,243.6
+ ,78.27
+ ,244
+ ,80.7
+ ,240.8
+ ,79.91
+ ,249.8
+ ,78.75
+ ,248
+ ,77.78
+ ,259.4
+ ,81.14
+ ,260.5
+ ,81.08
+ ,260.8
+ ,80.03
+ ,261.3
+ ,78.91
+ ,259.5
+ ,78.01
+ ,256.6
+ ,76.9
+ ,257.9
+ ,75.97
+ ,256.5
+ ,81.93
+ ,254.2
+ ,80.27
+ ,253.3
+ ,78.67
+ ,253.8
+ ,77.42
+ ,255.5
+ ,76.16
+ ,257.1
+ ,74.7
+ ,257.3
+ ,76.39
+ ,253.2
+ ,76.04
+ ,252.8
+ ,74.65
+ ,252
+ ,73.29
+ ,250.7
+ ,71.79
+ ,252.2
+ ,74.39
+ ,250
+ ,74.91
+ ,251
+ ,74.54
+ ,253.4
+ ,73.08
+ ,251.2
+ ,72.75
+ ,255.6
+ ,71.32
+ ,261.1
+ ,70.38
+ ,258.9
+ ,70.35
+ ,259.9
+ ,70.01
+ ,261.2
+ ,69.36
+ ,264.7
+ ,67.77
+ ,267.1
+ ,69.26
+ ,266.4
+ ,69.8
+ ,267.7
+ ,68.38
+ ,268.6
+ ,67.62
+ ,267.5
+ ,68.39
+ ,268.5
+ ,66.95
+ ,268.5
+ ,65.21
+ ,270.5
+ ,66.64
+ ,270.9
+ ,63.45
+ ,270.1
+ ,60.66
+ ,269.3
+ ,62.34
+ ,269.8
+ ,60.32
+ ,270.1
+ ,58.64
+ ,264.9
+ ,60.46
+ ,263.7
+ ,58.59
+ ,264.8
+ ,61.87
+ ,263.7
+ ,61.85
+ ,255.9
+ ,67.44
+ ,276.2
+ ,77.06
+ ,360.1
+ ,91.74
+ ,380.5
+ ,93.15
+ ,373.7
+ ,94.15
+ ,369.8
+ ,93.11
+ ,366.6
+ ,91.51
+ ,359.3
+ ,89.96
+ ,345.8
+ ,88.16
+ ,326.2
+ ,86.98
+ ,324.5
+ ,88.03
+ ,328.1
+ ,86.24
+ ,327.5
+ ,84.65
+ ,324.4
+ ,83.23
+ ,316.5
+ ,81.7
+ ,310.9
+ ,80.25
+ ,301.5
+ ,78.8
+ ,291.7
+ ,77.51
+ ,290.4
+ ,76.2
+ ,287.4
+ ,75.04
+ ,277.7
+ ,74
+ ,281.6
+ ,75.49
+ ,288
+ ,77.14
+ ,276
+ ,76.15
+ ,272.9
+ ,76.27
+ ,283
+ ,78.19
+ ,283.3
+ ,76.49
+ ,276.8
+ ,77.31
+ ,284.5
+ ,76.65
+ ,282.7
+ ,74.99
+ ,281.2
+ ,73.51
+ ,287.4
+ ,72.07
+ ,283.1
+ ,70.59
+ ,284
+ ,71.96
+ ,285.5
+ ,76.29
+ ,289.2
+ ,74.86
+ ,292.5
+ ,74.93
+ ,296.4
+ ,71.9
+ ,305.2
+ ,71.01
+ ,303.9
+ ,77.47
+ ,311.5
+ ,75.78
+ ,316.3
+ ,76.6
+ ,316.7
+ ,76.07
+ ,322.5
+ ,74.57
+ ,317.1
+ ,73.02
+ ,309.8
+ ,72.65
+ ,303.8
+ ,73.16
+ ,290.3
+ ,71.53
+ ,293.7
+ ,69.78
+ ,291.7
+ ,67.98
+ ,296.5
+ ,69.96
+ ,289.1
+ ,72.16
+ ,288.5
+ ,70.47
+ ,293.8
+ ,68.86
+ ,297.7
+ ,67.37
+ ,305.4
+ ,65.87
+ ,302.7
+ ,72.16
+ ,302.5
+ ,71.34
+ ,303
+ ,69.93
+ ,294.5
+ ,68.44
+ ,294.1
+ ,67.16
+ ,294.5
+ ,66.01
+ ,297.1
+ ,67.25
+ ,289.4
+ ,70.91
+ ,292.4
+ ,69.75
+ ,287.9
+ ,68.59
+ ,286.6
+ ,67.48
+ ,280.5
+ ,66.31
+ ,272.4
+ ,64.81
+ ,269.2
+ ,66.58
+ ,270.6
+ ,65.97
+ ,267.3
+ ,64.7
+ ,262.5
+ ,64.7
+ ,266.8
+ ,60.94
+ ,268.8
+ ,59.08
+ ,263.1
+ ,58.42
+ ,261.2
+ ,57.77
+ ,266
+ ,57.11
+ ,262.5
+ ,53.31
+ ,265.2
+ ,49.96
+ ,261.3
+ ,49.4
+ ,253.7
+ ,48.84
+ ,249.2
+ ,48.3
+ ,239.1
+ ,47.74
+ ,236.4
+ ,47.24
+ ,235.2
+ ,46.76
+ ,245.2
+ ,46.29
+ ,246.2
+ ,48.9
+ ,247.7
+ ,49.23
+ ,251.4
+ ,48.53
+ ,253.3
+ ,48.03
+ ,254.8
+ ,54.34
+ ,250
+ ,53.79
+ ,249.3
+ ,53.24
+ ,241.5
+ ,52.96
+ ,243.3
+ ,52.17
+ ,248
+ ,51.7
+ ,253
+ ,58.55
+ ,252.9
+ ,78.2
+ ,251.5
+ ,77.03
+ ,251.6
+ ,76.19
+ ,253.5
+ ,77.15
+ ,259.8
+ ,75.87
+ ,334.1
+ ,95.47
+ ,448
+ ,109.67
+ ,445.8
+ ,112.28
+ ,445
+ ,112.01
+ ,448.2
+ ,107.93
+ ,438.2
+ ,105.96
+ ,439.8
+ ,105.06
+ ,423.4
+ ,102.98
+ ,410.8
+ ,102.2
+ ,408.4
+ ,105.23
+ ,406.7
+ ,101.85
+ ,405.9
+ ,99.89
+ ,402.7
+ ,96.23
+ ,405.1
+ ,94.76
+ ,399.6
+ ,91.51
+ ,386.5
+ ,91.63
+ ,381.4
+ ,91.54
+ ,375.2
+ ,85.23
+ ,357.7
+ ,87.83
+ ,359
+ ,87.38
+ ,355
+ ,84.44
+ ,352.7
+ ,85.19
+ ,344.4
+ ,84.03
+ ,343.8
+ ,86.73
+ ,338
+ ,102.52
+ ,339
+ ,104.45
+ ,333.3
+ ,106.98
+ ,334.4
+ ,107.02
+ ,328.3
+ ,99.26
+ ,330.7
+ ,94.45
+ ,330
+ ,113.44
+ ,331.6
+ ,157.33
+ ,351.2
+ ,147.38
+ ,389.4
+ ,171.89
+ ,410.9
+ ,171.95
+ ,442.8
+ ,132.71
+ ,462.8
+ ,126.02
+ ,466.9
+ ,121.18
+ ,461.7
+ ,115.45
+ ,439.2
+ ,110.48
+ ,430.3
+ ,117.85
+ ,416.1
+ ,117.63
+ ,402.5
+ ,124.65
+ ,397.3
+ ,109.59
+ ,403.3
+ ,111.27
+ ,395.9
+ ,99.78
+ ,387.8
+ ,98.21
+ ,378.6
+ ,99.2
+ ,377.1
+ ,97.97
+ ,370.4
+ ,89.55
+ ,362
+ ,87.91
+ ,350.3
+ ,93.34
+ ,348.2
+ ,94.42
+ ,344.6
+ ,93.2
+ ,343.5
+ ,90.29
+ ,342.8
+ ,91.46
+ ,347.6
+ ,89.98
+ ,346.6
+ ,88.35
+ ,349.5
+ ,88.41
+ ,342.1
+ ,82.44
+ ,342
+ ,79.89
+ ,342.8
+ ,75.69
+ ,339.3
+ ,75.66
+ ,348.2
+ ,84.5
+ ,333.7
+ ,96.73
+ ,334.7
+ ,87.48
+ ,354
+ ,82.39
+ ,367.7
+ ,83.48
+ ,363.3
+ ,79.31
+ ,358.4
+ ,78.16
+ ,353.1
+ ,72.77
+ ,343.1
+ ,72.45
+ ,344.6
+ ,68.46
+ ,344.4
+ ,67.62
+ ,333.9
+ ,68.76
+ ,331.7
+ ,70.07
+ ,324.3
+ ,68.55
+ ,321.2
+ ,65.3
+ ,322.4
+ ,58.96
+ ,321.7
+ ,59.17
+ ,320.5
+ ,62.37
+ ,312.8
+ ,66.28
+ ,309.7
+ ,55.62
+ ,315.6
+ ,55.23
+ ,309.7
+ ,55.85
+ ,304.6
+ ,56.75
+ ,302.5
+ ,50.89
+ ,301.5
+ ,53.88
+ ,298.8
+ ,52.95
+ ,291.3
+ ,55.08
+ ,293.6
+ ,53.61
+ ,294.6
+ ,58.78
+ ,285.9
+ ,61.85
+ ,297.6
+ ,55.91
+ ,301.1
+ ,53.32
+ ,293.8
+ ,46.41
+ ,297.7
+ ,44.57
+ ,292.9
+ ,50
+ ,292.1
+ ,50
+ ,287.2
+ ,53.36
+ ,288.2
+ ,46.23
+ ,283.8
+ ,50.45
+ ,299.9
+ ,49.07
+ ,292.4
+ ,45.85
+ ,293.3
+ ,48.45
+ ,300.8
+ ,49.96
+ ,293.7
+ ,46.53
+ ,293.1
+ ,50.51
+ ,294.4
+ ,47.58
+ ,292.1
+ ,48.05
+ ,291.9
+ ,46.84
+ ,282.5
+ ,47.67
+ ,277.9
+ ,49.16
+ ,287.5
+ ,55.54
+ ,289.2
+ ,55.82
+ ,285.6
+ ,58.22
+ ,293.2
+ ,56.19
+ ,290.8
+ ,57.77
+ ,283.1
+ ,63.19
+ ,275
+ ,54.76
+ ,287.8
+ ,55.74
+ ,287.8
+ ,62.54
+ ,287.4
+ ,61.39
+ ,284
+ ,69.6
+ ,277.8
+ ,79.23
+ ,277.6
+ ,80
+ ,304.9
+ ,93.68
+ ,294
+ ,107.63
+ ,300.9
+ ,100.18
+ ,324
+ ,97.3
+ ,332.9
+ ,90.45
+ ,341.6
+ ,80.64
+ ,333.4
+ ,80.58
+ ,348.2
+ ,75.82
+ ,344.7
+ ,85.59
+ ,344.7
+ ,89.35
+ ,329.3
+ ,89.42
+ ,323.5
+ ,104.73
+ ,323.2
+ ,95.32
+ ,317.4
+ ,89.27
+ ,330.1
+ ,90.44
+ ,329.2
+ ,86.97
+ ,334.9
+ ,79.98
+ ,315.8
+ ,81.22
+ ,315.4
+ ,87.35
+ ,319.6
+ ,83.64
+ ,317.3
+ ,82.22
+ ,313.8
+ ,94.4
+ ,315.8
+ ,102.18
+ ,311.3)
+ ,dim=c(2
+ ,360)
+ ,dimnames=list(c('Colombia'
+ ,'US')
+ ,1:360))
> y <- array(NA,dim=c(2,360),dimnames=list(c('Colombia','US'),1:360))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Colombia US M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 87.28 255.0 1 0 0 0 0 0 0 0 0 0 0 1
2 87.28 280.2 0 1 0 0 0 0 0 0 0 0 0 2
3 87.09 299.9 0 0 1 0 0 0 0 0 0 0 0 3
4 86.92 339.2 0 0 0 1 0 0 0 0 0 0 0 4
5 87.59 374.2 0 0 0 0 1 0 0 0 0 0 0 5
6 90.72 393.5 0 0 0 0 0 1 0 0 0 0 0 6
7 90.69 389.2 0 0 0 0 0 0 1 0 0 0 0 7
8 90.30 381.7 0 0 0 0 0 0 0 1 0 0 0 8
9 89.55 375.2 0 0 0 0 0 0 0 0 1 0 0 9
10 88.94 369.0 0 0 0 0 0 0 0 0 0 1 0 10
11 88.41 357.4 0 0 0 0 0 0 0 0 0 0 1 11
12 87.82 352.1 0 0 0 0 0 0 0 0 0 0 0 12
13 87.07 346.5 1 0 0 0 0 0 0 0 0 0 0 13
14 86.82 342.9 0 1 0 0 0 0 0 0 0 0 0 14
15 86.40 340.3 0 0 1 0 0 0 0 0 0 0 0 15
16 86.02 328.3 0 0 0 1 0 0 0 0 0 0 0 16
17 85.66 322.9 0 0 0 0 1 0 0 0 0 0 0 17
18 85.32 314.3 0 0 0 0 0 1 0 0 0 0 0 18
19 85.00 308.9 0 0 0 0 0 0 1 0 0 0 0 19
20 84.67 294.0 0 0 0 0 0 0 0 1 0 0 0 20
21 83.94 285.6 0 0 0 0 0 0 0 0 1 0 0 21
22 82.83 281.2 0 0 0 0 0 0 0 0 0 1 0 22
23 81.95 280.3 0 0 0 0 0 0 0 0 0 0 1 23
24 81.19 278.8 0 0 0 0 0 0 0 0 0 0 0 24
25 80.48 274.5 1 0 0 0 0 0 0 0 0 0 0 25
26 78.86 270.4 0 1 0 0 0 0 0 0 0 0 0 26
27 69.47 263.4 0 0 1 0 0 0 0 0 0 0 0 27
28 68.77 259.9 0 0 0 1 0 0 0 0 0 0 0 28
29 70.06 258.0 0 0 0 0 1 0 0 0 0 0 0 29
30 73.95 262.7 0 0 0 0 0 1 0 0 0 0 0 30
31 75.80 284.7 0 0 0 0 0 0 1 0 0 0 0 31
32 77.79 311.3 0 0 0 0 0 0 0 1 0 0 0 32
33 81.57 322.1 0 0 0 0 0 0 0 0 1 0 0 33
34 83.07 327.0 0 0 0 0 0 0 0 0 0 1 0 34
35 84.34 331.3 0 0 0 0 0 0 0 0 0 0 1 35
36 85.10 333.3 0 0 0 0 0 0 0 0 0 0 0 36
37 85.25 321.4 1 0 0 0 0 0 0 0 0 0 0 37
38 84.26 327.0 0 1 0 0 0 0 0 0 0 0 0 38
39 83.63 320.0 0 0 1 0 0 0 0 0 0 0 0 39
40 86.44 314.7 0 0 0 1 0 0 0 0 0 0 0 40
41 85.30 316.7 0 0 0 0 1 0 0 0 0 0 0 41
42 84.10 314.4 0 0 0 0 0 1 0 0 0 0 0 42
43 83.36 321.3 0 0 0 0 0 0 1 0 0 0 0 43
44 82.48 318.2 0 0 0 0 0 0 0 1 0 0 0 44
45 81.58 307.2 0 0 0 0 0 0 0 0 1 0 0 45
46 80.47 301.3 0 0 0 0 0 0 0 0 0 1 0 46
47 79.34 287.5 0 0 0 0 0 0 0 0 0 0 1 47
48 82.13 277.7 0 0 0 0 0 0 0 0 0 0 0 48
49 81.69 274.4 1 0 0 0 0 0 0 0 0 0 0 49
50 80.70 258.8 0 1 0 0 0 0 0 0 0 0 0 50
51 79.88 253.3 0 0 1 0 0 0 0 0 0 0 0 51
52 79.16 251.0 0 0 0 1 0 0 0 0 0 0 0 52
53 78.38 248.4 0 0 0 0 1 0 0 0 0 0 0 53
54 77.42 249.5 0 0 0 0 0 1 0 0 0 0 0 54
55 76.47 246.1 0 0 0 0 0 0 1 0 0 0 0 55
56 75.46 244.5 0 0 0 0 0 0 0 1 0 0 0 56
57 74.48 243.6 0 0 0 0 0 0 0 0 1 0 0 57
58 78.27 244.0 0 0 0 0 0 0 0 0 0 1 0 58
59 80.70 240.8 0 0 0 0 0 0 0 0 0 0 1 59
60 79.91 249.8 0 0 0 0 0 0 0 0 0 0 0 60
61 78.75 248.0 1 0 0 0 0 0 0 0 0 0 0 61
62 77.78 259.4 0 1 0 0 0 0 0 0 0 0 0 62
63 81.14 260.5 0 0 1 0 0 0 0 0 0 0 0 63
64 81.08 260.8 0 0 0 1 0 0 0 0 0 0 0 64
65 80.03 261.3 0 0 0 0 1 0 0 0 0 0 0 65
66 78.91 259.5 0 0 0 0 0 1 0 0 0 0 0 66
67 78.01 256.6 0 0 0 0 0 0 1 0 0 0 0 67
68 76.90 257.9 0 0 0 0 0 0 0 1 0 0 0 68
69 75.97 256.5 0 0 0 0 0 0 0 0 1 0 0 69
70 81.93 254.2 0 0 0 0 0 0 0 0 0 1 0 70
71 80.27 253.3 0 0 0 0 0 0 0 0 0 0 1 71
72 78.67 253.8 0 0 0 0 0 0 0 0 0 0 0 72
73 77.42 255.5 1 0 0 0 0 0 0 0 0 0 0 73
74 76.16 257.1 0 1 0 0 0 0 0 0 0 0 0 74
75 74.70 257.3 0 0 1 0 0 0 0 0 0 0 0 75
76 76.39 253.2 0 0 0 1 0 0 0 0 0 0 0 76
77 76.04 252.8 0 0 0 0 1 0 0 0 0 0 0 77
78 74.65 252.0 0 0 0 0 0 1 0 0 0 0 0 78
79 73.29 250.7 0 0 0 0 0 0 1 0 0 0 0 79
80 71.79 252.2 0 0 0 0 0 0 0 1 0 0 0 80
81 74.39 250.0 0 0 0 0 0 0 0 0 1 0 0 81
82 74.91 251.0 0 0 0 0 0 0 0 0 0 1 0 82
83 74.54 253.4 0 0 0 0 0 0 0 0 0 0 1 83
84 73.08 251.2 0 0 0 0 0 0 0 0 0 0 0 84
85 72.75 255.6 1 0 0 0 0 0 0 0 0 0 0 85
86 71.32 261.1 0 1 0 0 0 0 0 0 0 0 0 86
87 70.38 258.9 0 0 1 0 0 0 0 0 0 0 0 87
88 70.35 259.9 0 0 0 1 0 0 0 0 0 0 0 88
89 70.01 261.2 0 0 0 0 1 0 0 0 0 0 0 89
90 69.36 264.7 0 0 0 0 0 1 0 0 0 0 0 90
91 67.77 267.1 0 0 0 0 0 0 1 0 0 0 0 91
92 69.26 266.4 0 0 0 0 0 0 0 1 0 0 0 92
93 69.80 267.7 0 0 0 0 0 0 0 0 1 0 0 93
94 68.38 268.6 0 0 0 0 0 0 0 0 0 1 0 94
95 67.62 267.5 0 0 0 0 0 0 0 0 0 0 1 95
96 68.39 268.5 0 0 0 0 0 0 0 0 0 0 0 96
97 66.95 268.5 1 0 0 0 0 0 0 0 0 0 0 97
98 65.21 270.5 0 1 0 0 0 0 0 0 0 0 0 98
99 66.64 270.9 0 0 1 0 0 0 0 0 0 0 0 99
100 63.45 270.1 0 0 0 1 0 0 0 0 0 0 0 100
101 60.66 269.3 0 0 0 0 1 0 0 0 0 0 0 101
102 62.34 269.8 0 0 0 0 0 1 0 0 0 0 0 102
103 60.32 270.1 0 0 0 0 0 0 1 0 0 0 0 103
104 58.64 264.9 0 0 0 0 0 0 0 1 0 0 0 104
105 60.46 263.7 0 0 0 0 0 0 0 0 1 0 0 105
106 58.59 264.8 0 0 0 0 0 0 0 0 0 1 0 106
107 61.87 263.7 0 0 0 0 0 0 0 0 0 0 1 107
108 61.85 255.9 0 0 0 0 0 0 0 0 0 0 0 108
109 67.44 276.2 1 0 0 0 0 0 0 0 0 0 0 109
110 77.06 360.1 0 1 0 0 0 0 0 0 0 0 0 110
111 91.74 380.5 0 0 1 0 0 0 0 0 0 0 0 111
112 93.15 373.7 0 0 0 1 0 0 0 0 0 0 0 112
113 94.15 369.8 0 0 0 0 1 0 0 0 0 0 0 113
114 93.11 366.6 0 0 0 0 0 1 0 0 0 0 0 114
115 91.51 359.3 0 0 0 0 0 0 1 0 0 0 0 115
116 89.96 345.8 0 0 0 0 0 0 0 1 0 0 0 116
117 88.16 326.2 0 0 0 0 0 0 0 0 1 0 0 117
118 86.98 324.5 0 0 0 0 0 0 0 0 0 1 0 118
119 88.03 328.1 0 0 0 0 0 0 0 0 0 0 1 119
120 86.24 327.5 0 0 0 0 0 0 0 0 0 0 0 120
121 84.65 324.4 1 0 0 0 0 0 0 0 0 0 0 121
122 83.23 316.5 0 1 0 0 0 0 0 0 0 0 0 122
123 81.70 310.9 0 0 1 0 0 0 0 0 0 0 0 123
124 80.25 301.5 0 0 0 1 0 0 0 0 0 0 0 124
125 78.80 291.7 0 0 0 0 1 0 0 0 0 0 0 125
126 77.51 290.4 0 0 0 0 0 1 0 0 0 0 0 126
127 76.20 287.4 0 0 0 0 0 0 1 0 0 0 0 127
128 75.04 277.7 0 0 0 0 0 0 0 1 0 0 0 128
129 74.00 281.6 0 0 0 0 0 0 0 0 1 0 0 129
130 75.49 288.0 0 0 0 0 0 0 0 0 0 1 0 130
131 77.14 276.0 0 0 0 0 0 0 0 0 0 0 1 131
132 76.15 272.9 0 0 0 0 0 0 0 0 0 0 0 132
133 76.27 283.0 1 0 0 0 0 0 0 0 0 0 0 133
134 78.19 283.3 0 1 0 0 0 0 0 0 0 0 0 134
135 76.49 276.8 0 0 1 0 0 0 0 0 0 0 0 135
136 77.31 284.5 0 0 0 1 0 0 0 0 0 0 0 136
137 76.65 282.7 0 0 0 0 1 0 0 0 0 0 0 137
138 74.99 281.2 0 0 0 0 0 1 0 0 0 0 0 138
139 73.51 287.4 0 0 0 0 0 0 1 0 0 0 0 139
140 72.07 283.1 0 0 0 0 0 0 0 1 0 0 0 140
141 70.59 284.0 0 0 0 0 0 0 0 0 1 0 0 141
142 71.96 285.5 0 0 0 0 0 0 0 0 0 1 0 142
143 76.29 289.2 0 0 0 0 0 0 0 0 0 0 1 143
144 74.86 292.5 0 0 0 0 0 0 0 0 0 0 0 144
145 74.93 296.4 1 0 0 0 0 0 0 0 0 0 0 145
146 71.90 305.2 0 1 0 0 0 0 0 0 0 0 0 146
147 71.01 303.9 0 0 1 0 0 0 0 0 0 0 0 147
148 77.47 311.5 0 0 0 1 0 0 0 0 0 0 0 148
149 75.78 316.3 0 0 0 0 1 0 0 0 0 0 0 149
150 76.60 316.7 0 0 0 0 0 1 0 0 0 0 0 150
151 76.07 322.5 0 0 0 0 0 0 1 0 0 0 0 151
152 74.57 317.1 0 0 0 0 0 0 0 1 0 0 0 152
153 73.02 309.8 0 0 0 0 0 0 0 0 1 0 0 153
154 72.65 303.8 0 0 0 0 0 0 0 0 0 1 0 154
155 73.16 290.3 0 0 0 0 0 0 0 0 0 0 1 155
156 71.53 293.7 0 0 0 0 0 0 0 0 0 0 0 156
157 69.78 291.7 1 0 0 0 0 0 0 0 0 0 0 157
158 67.98 296.5 0 1 0 0 0 0 0 0 0 0 0 158
159 69.96 289.1 0 0 1 0 0 0 0 0 0 0 0 159
160 72.16 288.5 0 0 0 1 0 0 0 0 0 0 0 160
161 70.47 293.8 0 0 0 0 1 0 0 0 0 0 0 161
162 68.86 297.7 0 0 0 0 0 1 0 0 0 0 0 162
163 67.37 305.4 0 0 0 0 0 0 1 0 0 0 0 163
164 65.87 302.7 0 0 0 0 0 0 0 1 0 0 0 164
165 72.16 302.5 0 0 0 0 0 0 0 0 1 0 0 165
166 71.34 303.0 0 0 0 0 0 0 0 0 0 1 0 166
167 69.93 294.5 0 0 0 0 0 0 0 0 0 0 1 167
168 68.44 294.1 0 0 0 0 0 0 0 0 0 0 0 168
169 67.16 294.5 1 0 0 0 0 0 0 0 0 0 0 169
170 66.01 297.1 0 1 0 0 0 0 0 0 0 0 0 170
171 67.25 289.4 0 0 1 0 0 0 0 0 0 0 0 171
172 70.91 292.4 0 0 0 1 0 0 0 0 0 0 0 172
173 69.75 287.9 0 0 0 0 1 0 0 0 0 0 0 173
174 68.59 286.6 0 0 0 0 0 1 0 0 0 0 0 174
175 67.48 280.5 0 0 0 0 0 0 1 0 0 0 0 175
176 66.31 272.4 0 0 0 0 0 0 0 1 0 0 0 176
177 64.81 269.2 0 0 0 0 0 0 0 0 1 0 0 177
178 66.58 270.6 0 0 0 0 0 0 0 0 0 1 0 178
179 65.97 267.3 0 0 0 0 0 0 0 0 0 0 1 179
180 64.70 262.5 0 0 0 0 0 0 0 0 0 0 0 180
181 64.70 266.8 1 0 0 0 0 0 0 0 0 0 0 181
182 60.94 268.8 0 1 0 0 0 0 0 0 0 0 0 182
183 59.08 263.1 0 0 1 0 0 0 0 0 0 0 0 183
184 58.42 261.2 0 0 0 1 0 0 0 0 0 0 0 184
185 57.77 266.0 0 0 0 0 1 0 0 0 0 0 0 185
186 57.11 262.5 0 0 0 0 0 1 0 0 0 0 0 186
187 53.31 265.2 0 0 0 0 0 0 1 0 0 0 0 187
188 49.96 261.3 0 0 0 0 0 0 0 1 0 0 0 188
189 49.40 253.7 0 0 0 0 0 0 0 0 1 0 0 189
190 48.84 249.2 0 0 0 0 0 0 0 0 0 1 0 190
191 48.30 239.1 0 0 0 0 0 0 0 0 0 0 1 191
192 47.74 236.4 0 0 0 0 0 0 0 0 0 0 0 192
193 47.24 235.2 1 0 0 0 0 0 0 0 0 0 0 193
194 46.76 245.2 0 1 0 0 0 0 0 0 0 0 0 194
195 46.29 246.2 0 0 1 0 0 0 0 0 0 0 0 195
196 48.90 247.7 0 0 0 1 0 0 0 0 0 0 0 196
197 49.23 251.4 0 0 0 0 1 0 0 0 0 0 0 197
198 48.53 253.3 0 0 0 0 0 1 0 0 0 0 0 198
199 48.03 254.8 0 0 0 0 0 0 1 0 0 0 0 199
200 54.34 250.0 0 0 0 0 0 0 0 1 0 0 0 200
201 53.79 249.3 0 0 0 0 0 0 0 0 1 0 0 201
202 53.24 241.5 0 0 0 0 0 0 0 0 0 1 0 202
203 52.96 243.3 0 0 0 0 0 0 0 0 0 0 1 203
204 52.17 248.0 0 0 0 0 0 0 0 0 0 0 0 204
205 51.70 253.0 1 0 0 0 0 0 0 0 0 0 0 205
206 58.55 252.9 0 1 0 0 0 0 0 0 0 0 0 206
207 78.20 251.5 0 0 1 0 0 0 0 0 0 0 0 207
208 77.03 251.6 0 0 0 1 0 0 0 0 0 0 0 208
209 76.19 253.5 0 0 0 0 1 0 0 0 0 0 0 209
210 77.15 259.8 0 0 0 0 0 1 0 0 0 0 0 210
211 75.87 334.1 0 0 0 0 0 0 1 0 0 0 0 211
212 95.47 448.0 0 0 0 0 0 0 0 1 0 0 0 212
213 109.67 445.8 0 0 0 0 0 0 0 0 1 0 0 213
214 112.28 445.0 0 0 0 0 0 0 0 0 0 1 0 214
215 112.01 448.2 0 0 0 0 0 0 0 0 0 0 1 215
216 107.93 438.2 0 0 0 0 0 0 0 0 0 0 0 216
217 105.96 439.8 1 0 0 0 0 0 0 0 0 0 0 217
218 105.06 423.4 0 1 0 0 0 0 0 0 0 0 0 218
219 102.98 410.8 0 0 1 0 0 0 0 0 0 0 0 219
220 102.20 408.4 0 0 0 1 0 0 0 0 0 0 0 220
221 105.23 406.7 0 0 0 0 1 0 0 0 0 0 0 221
222 101.85 405.9 0 0 0 0 0 1 0 0 0 0 0 222
223 99.89 402.7 0 0 0 0 0 0 1 0 0 0 0 223
224 96.23 405.1 0 0 0 0 0 0 0 1 0 0 0 224
225 94.76 399.6 0 0 0 0 0 0 0 0 1 0 0 225
226 91.51 386.5 0 0 0 0 0 0 0 0 0 1 0 226
227 91.63 381.4 0 0 0 0 0 0 0 0 0 0 1 227
228 91.54 375.2 0 0 0 0 0 0 0 0 0 0 0 228
229 85.23 357.7 1 0 0 0 0 0 0 0 0 0 0 229
230 87.83 359.0 0 1 0 0 0 0 0 0 0 0 0 230
231 87.38 355.0 0 0 1 0 0 0 0 0 0 0 0 231
232 84.44 352.7 0 0 0 1 0 0 0 0 0 0 0 232
233 85.19 344.4 0 0 0 0 1 0 0 0 0 0 0 233
234 84.03 343.8 0 0 0 0 0 1 0 0 0 0 0 234
235 86.73 338.0 0 0 0 0 0 0 1 0 0 0 0 235
236 102.52 339.0 0 0 0 0 0 0 0 1 0 0 0 236
237 104.45 333.3 0 0 0 0 0 0 0 0 1 0 0 237
238 106.98 334.4 0 0 0 0 0 0 0 0 0 1 0 238
239 107.02 328.3 0 0 0 0 0 0 0 0 0 0 1 239
240 99.26 330.7 0 0 0 0 0 0 0 0 0 0 0 240
241 94.45 330.0 1 0 0 0 0 0 0 0 0 0 0 241
242 113.44 331.6 0 1 0 0 0 0 0 0 0 0 0 242
243 157.33 351.2 0 0 1 0 0 0 0 0 0 0 0 243
244 147.38 389.4 0 0 0 1 0 0 0 0 0 0 0 244
245 171.89 410.9 0 0 0 0 1 0 0 0 0 0 0 245
246 171.95 442.8 0 0 0 0 0 1 0 0 0 0 0 246
247 132.71 462.8 0 0 0 0 0 0 1 0 0 0 0 247
248 126.02 466.9 0 0 0 0 0 0 0 1 0 0 0 248
249 121.18 461.7 0 0 0 0 0 0 0 0 1 0 0 249
250 115.45 439.2 0 0 0 0 0 0 0 0 0 1 0 250
251 110.48 430.3 0 0 0 0 0 0 0 0 0 0 1 251
252 117.85 416.1 0 0 0 0 0 0 0 0 0 0 0 252
253 117.63 402.5 1 0 0 0 0 0 0 0 0 0 0 253
254 124.65 397.3 0 1 0 0 0 0 0 0 0 0 0 254
255 109.59 403.3 0 0 1 0 0 0 0 0 0 0 0 255
256 111.27 395.9 0 0 0 1 0 0 0 0 0 0 0 256
257 99.78 387.8 0 0 0 0 1 0 0 0 0 0 0 257
258 98.21 378.6 0 0 0 0 0 1 0 0 0 0 0 258
259 99.20 377.1 0 0 0 0 0 0 1 0 0 0 0 259
260 97.97 370.4 0 0 0 0 0 0 0 1 0 0 0 260
261 89.55 362.0 0 0 0 0 0 0 0 0 1 0 0 261
262 87.91 350.3 0 0 0 0 0 0 0 0 0 1 0 262
263 93.34 348.2 0 0 0 0 0 0 0 0 0 0 1 263
264 94.42 344.6 0 0 0 0 0 0 0 0 0 0 0 264
265 93.20 343.5 1 0 0 0 0 0 0 0 0 0 0 265
266 90.29 342.8 0 1 0 0 0 0 0 0 0 0 0 266
267 91.46 347.6 0 0 1 0 0 0 0 0 0 0 0 267
268 89.98 346.6 0 0 0 1 0 0 0 0 0 0 0 268
269 88.35 349.5 0 0 0 0 1 0 0 0 0 0 0 269
270 88.41 342.1 0 0 0 0 0 1 0 0 0 0 0 270
271 82.44 342.0 0 0 0 0 0 0 1 0 0 0 0 271
272 79.89 342.8 0 0 0 0 0 0 0 1 0 0 0 272
273 75.69 339.3 0 0 0 0 0 0 0 0 1 0 0 273
274 75.66 348.2 0 0 0 0 0 0 0 0 0 1 0 274
275 84.50 333.7 0 0 0 0 0 0 0 0 0 0 1 275
276 96.73 334.7 0 0 0 0 0 0 0 0 0 0 0 276
277 87.48 354.0 1 0 0 0 0 0 0 0 0 0 0 277
278 82.39 367.7 0 1 0 0 0 0 0 0 0 0 0 278
279 83.48 363.3 0 0 1 0 0 0 0 0 0 0 0 279
280 79.31 358.4 0 0 0 1 0 0 0 0 0 0 0 280
281 78.16 353.1 0 0 0 0 1 0 0 0 0 0 0 281
282 72.77 343.1 0 0 0 0 0 1 0 0 0 0 0 282
283 72.45 344.6 0 0 0 0 0 0 1 0 0 0 0 283
284 68.46 344.4 0 0 0 0 0 0 0 1 0 0 0 284
285 67.62 333.9 0 0 0 0 0 0 0 0 1 0 0 285
286 68.76 331.7 0 0 0 0 0 0 0 0 0 1 0 286
287 70.07 324.3 0 0 0 0 0 0 0 0 0 0 1 287
288 68.55 321.2 0 0 0 0 0 0 0 0 0 0 0 288
289 65.30 322.4 1 0 0 0 0 0 0 0 0 0 0 289
290 58.96 321.7 0 1 0 0 0 0 0 0 0 0 0 290
291 59.17 320.5 0 0 1 0 0 0 0 0 0 0 0 291
292 62.37 312.8 0 0 0 1 0 0 0 0 0 0 0 292
293 66.28 309.7 0 0 0 0 1 0 0 0 0 0 0 293
294 55.62 315.6 0 0 0 0 0 1 0 0 0 0 0 294
295 55.23 309.7 0 0 0 0 0 0 1 0 0 0 0 295
296 55.85 304.6 0 0 0 0 0 0 0 1 0 0 0 296
297 56.75 302.5 0 0 0 0 0 0 0 0 1 0 0 297
298 50.89 301.5 0 0 0 0 0 0 0 0 0 1 0 298
299 53.88 298.8 0 0 0 0 0 0 0 0 0 0 1 299
300 52.95 291.3 0 0 0 0 0 0 0 0 0 0 0 300
301 55.08 293.6 1 0 0 0 0 0 0 0 0 0 0 301
302 53.61 294.6 0 1 0 0 0 0 0 0 0 0 0 302
303 58.78 285.9 0 0 1 0 0 0 0 0 0 0 0 303
304 61.85 297.6 0 0 0 1 0 0 0 0 0 0 0 304
305 55.91 301.1 0 0 0 0 1 0 0 0 0 0 0 305
306 53.32 293.8 0 0 0 0 0 1 0 0 0 0 0 306
307 46.41 297.7 0 0 0 0 0 0 1 0 0 0 0 307
308 44.57 292.9 0 0 0 0 0 0 0 1 0 0 0 308
309 50.00 292.1 0 0 0 0 0 0 0 0 1 0 0 309
310 50.00 287.2 0 0 0 0 0 0 0 0 0 1 0 310
311 53.36 288.2 0 0 0 0 0 0 0 0 0 0 1 311
312 46.23 283.8 0 0 0 0 0 0 0 0 0 0 0 312
313 50.45 299.9 1 0 0 0 0 0 0 0 0 0 0 313
314 49.07 292.4 0 1 0 0 0 0 0 0 0 0 0 314
315 45.85 293.3 0 0 1 0 0 0 0 0 0 0 0 315
316 48.45 300.8 0 0 0 1 0 0 0 0 0 0 0 316
317 49.96 293.7 0 0 0 0 1 0 0 0 0 0 0 317
318 46.53 293.1 0 0 0 0 0 1 0 0 0 0 0 318
319 50.51 294.4 0 0 0 0 0 0 1 0 0 0 0 319
320 47.58 292.1 0 0 0 0 0 0 0 1 0 0 0 320
321 48.05 291.9 0 0 0 0 0 0 0 0 1 0 0 321
322 46.84 282.5 0 0 0 0 0 0 0 0 0 1 0 322
323 47.67 277.9 0 0 0 0 0 0 0 0 0 0 1 323
324 49.16 287.5 0 0 0 0 0 0 0 0 0 0 0 324
325 55.54 289.2 1 0 0 0 0 0 0 0 0 0 0 325
326 55.82 285.6 0 1 0 0 0 0 0 0 0 0 0 326
327 58.22 293.2 0 0 1 0 0 0 0 0 0 0 0 327
328 56.19 290.8 0 0 0 1 0 0 0 0 0 0 0 328
329 57.77 283.1 0 0 0 0 1 0 0 0 0 0 0 329
330 63.19 275.0 0 0 0 0 0 1 0 0 0 0 0 330
331 54.76 287.8 0 0 0 0 0 0 1 0 0 0 0 331
332 55.74 287.8 0 0 0 0 0 0 0 1 0 0 0 332
333 62.54 287.4 0 0 0 0 0 0 0 0 1 0 0 333
334 61.39 284.0 0 0 0 0 0 0 0 0 0 1 0 334
335 69.60 277.8 0 0 0 0 0 0 0 0 0 0 1 335
336 79.23 277.6 0 0 0 0 0 0 0 0 0 0 0 336
337 80.00 304.9 1 0 0 0 0 0 0 0 0 0 0 337
338 93.68 294.0 0 1 0 0 0 0 0 0 0 0 0 338
339 107.63 300.9 0 0 1 0 0 0 0 0 0 0 0 339
340 100.18 324.0 0 0 0 1 0 0 0 0 0 0 0 340
341 97.30 332.9 0 0 0 0 1 0 0 0 0 0 0 341
342 90.45 341.6 0 0 0 0 0 1 0 0 0 0 0 342
343 80.64 333.4 0 0 0 0 0 0 1 0 0 0 0 343
344 80.58 348.2 0 0 0 0 0 0 0 1 0 0 0 344
345 75.82 344.7 0 0 0 0 0 0 0 0 1 0 0 345
346 85.59 344.7 0 0 0 0 0 0 0 0 0 1 0 346
347 89.35 329.3 0 0 0 0 0 0 0 0 0 0 1 347
348 89.42 323.5 0 0 0 0 0 0 0 0 0 0 0 348
349 104.73 323.2 1 0 0 0 0 0 0 0 0 0 0 349
350 95.32 317.4 0 1 0 0 0 0 0 0 0 0 0 350
351 89.27 330.1 0 0 1 0 0 0 0 0 0 0 0 351
352 90.44 329.2 0 0 0 1 0 0 0 0 0 0 0 352
353 86.97 334.9 0 0 0 0 1 0 0 0 0 0 0 353
354 79.98 315.8 0 0 0 0 0 1 0 0 0 0 0 354
355 81.22 315.4 0 0 0 0 0 0 1 0 0 0 0 355
356 87.35 319.6 0 0 0 0 0 0 0 1 0 0 0 356
357 83.64 317.3 0 0 0 0 0 0 0 0 1 0 0 357
358 82.22 313.8 0 0 0 0 0 0 0 0 0 1 0 358
359 94.40 315.8 0 0 0 0 0 0 0 0 0 0 1 359
360 102.18 311.3 0 0 0 0 0 0 0 0 0 0 0 360
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) US M1 M2 M3 M4
-5.23226 0.30427 -1.13280 -1.67122 0.37298 -0.37228
M5 M6 M7 M8 M9 M10
-0.71944 -2.00385 -5.85187 -6.09392 -4.86950 -3.89165
M11 t
-0.90833 -0.04869
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-23.195 -7.770 -1.816 6.481 67.163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.232260 4.653327 -1.124 0.2616
US 0.304265 0.013980 21.764 < 2e-16 ***
M1 -1.132799 3.208509 -0.353 0.7243
M2 -1.671215 3.208947 -0.521 0.6028
M3 0.372980 3.208890 0.116 0.9075
M4 -0.372282 3.209503 -0.116 0.9077
M5 -0.719441 3.209782 -0.224 0.8228
M6 -2.003852 3.209652 -0.624 0.5328
M7 -5.851875 3.211594 -1.822 0.0693 .
M8 -6.093918 3.213065 -1.897 0.0587 .
M9 -4.869499 3.210544 -1.517 0.1303
M10 -3.891648 3.209079 -1.213 0.2261
M11 -0.908333 3.207852 -0.283 0.7772
t -0.048688 0.006568 -7.413 9.61e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.42 on 346 degrees of freedom
Multiple R-squared: 0.5814, Adjusted R-squared: 0.5657
F-statistic: 36.97 on 13 and 346 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,] 4.066097e-06 8.132193e-06 0.99999593
[2,] 4.682797e-05 9.365595e-05 0.99995317
[3,] 6.592907e-06 1.318581e-05 0.99999341
[4,] 5.453701e-07 1.090740e-06 0.99999945
[5,] 3.861495e-08 7.722990e-08 0.99999996
[6,] 3.359928e-09 6.719856e-09 1.00000000
[7,] 4.698709e-10 9.397419e-10 1.00000000
[8,] 7.237685e-11 1.447537e-10 1.00000000
[9,] 1.915322e-11 3.830645e-11 1.00000000
[10,] 9.288095e-12 1.857619e-11 1.00000000
[11,] 6.250179e-08 1.250036e-07 0.99999994
[12,] 2.565165e-07 5.130331e-07 0.99999974
[13,] 1.652040e-07 3.304080e-07 0.99999983
[14,] 4.708379e-08 9.416757e-08 0.99999995
[15,] 1.171288e-08 2.342576e-08 0.99999999
[16,] 2.729902e-09 5.459805e-09 1.00000000
[17,] 8.026064e-10 1.605213e-09 1.00000000
[18,] 3.377398e-10 6.754796e-10 1.00000000
[19,] 1.840317e-10 3.680633e-10 1.00000000
[20,] 1.142657e-10 2.285313e-10 1.00000000
[21,] 6.878978e-11 1.375796e-10 1.00000000
[22,] 2.736935e-11 5.473870e-11 1.00000000
[23,] 2.901916e-11 5.803833e-11 1.00000000
[24,] 1.388609e-10 2.777218e-10 1.00000000
[25,] 1.971074e-10 3.942148e-10 1.00000000
[26,] 9.683292e-11 1.936658e-10 1.00000000
[27,] 3.403697e-11 6.807395e-11 1.00000000
[28,] 1.052240e-11 2.104479e-11 1.00000000
[29,] 3.020104e-12 6.040208e-12 1.00000000
[30,] 8.339393e-13 1.667879e-12 1.00000000
[31,] 2.264631e-13 4.529262e-13 1.00000000
[32,] 9.526172e-14 1.905234e-13 1.00000000
[33,] 2.803939e-14 5.607879e-14 1.00000000
[34,] 1.088558e-14 2.177116e-14 1.00000000
[35,] 7.467464e-15 1.493493e-14 1.00000000
[36,] 3.960740e-15 7.921480e-15 1.00000000
[37,] 1.939394e-15 3.878789e-15 1.00000000
[38,] 6.056736e-16 1.211347e-15 1.00000000
[39,] 1.863048e-16 3.726096e-16 1.00000000
[40,] 5.372569e-17 1.074514e-16 1.00000000
[41,] 1.560778e-17 3.121556e-17 1.00000000
[42,] 5.645697e-18 1.129139e-17 1.00000000
[43,] 3.705249e-18 7.410499e-18 1.00000000
[44,] 1.222785e-18 2.445570e-18 1.00000000
[45,] 3.424058e-19 6.848116e-19 1.00000000
[46,] 9.515137e-20 1.903027e-19 1.00000000
[47,] 4.840943e-20 9.681887e-20 1.00000000
[48,] 2.330401e-20 4.660802e-20 1.00000000
[49,] 9.139532e-21 1.827906e-20 1.00000000
[50,] 2.680817e-21 5.361633e-21 1.00000000
[51,] 8.074432e-22 1.614886e-21 1.00000000
[52,] 2.262980e-22 4.525961e-22 1.00000000
[53,] 6.436194e-23 1.287239e-22 1.00000000
[54,] 3.965739e-23 7.931478e-23 1.00000000
[55,] 1.276956e-23 2.553911e-23 1.00000000
[56,] 3.409973e-24 6.819946e-24 1.00000000
[57,] 1.206737e-24 2.413473e-24 1.00000000
[58,] 4.342211e-25 8.684422e-25 1.00000000
[59,] 1.482924e-25 2.965847e-25 1.00000000
[60,] 3.924013e-26 7.848026e-26 1.00000000
[61,] 1.021821e-26 2.043643e-26 1.00000000
[62,] 3.101611e-27 6.203223e-27 1.00000000
[63,] 1.193033e-27 2.386066e-27 1.00000000
[64,] 6.264361e-28 1.252872e-27 1.00000000
[65,] 1.930835e-28 3.861670e-28 1.00000000
[66,] 6.946074e-29 1.389215e-28 1.00000000
[67,] 2.702333e-29 5.404666e-29 1.00000000
[68,] 1.485528e-29 2.971056e-29 1.00000000
[69,] 1.265860e-29 2.531720e-29 1.00000000
[70,] 1.252635e-29 2.505271e-29 1.00000000
[71,] 8.058201e-30 1.611640e-29 1.00000000
[72,] 5.340039e-30 1.068008e-29 1.00000000
[73,] 3.092646e-30 6.185292e-30 1.00000000
[74,] 2.482645e-30 4.965291e-30 1.00000000
[75,] 3.223530e-30 6.447059e-30 1.00000000
[76,] 1.715067e-30 3.430133e-30 1.00000000
[77,] 8.564356e-31 1.712871e-30 1.00000000
[78,] 1.176965e-30 2.353930e-30 1.00000000
[79,] 1.905282e-30 3.810564e-30 1.00000000
[80,] 1.737520e-30 3.475040e-30 1.00000000
[81,] 2.391259e-30 4.782518e-30 1.00000000
[82,] 3.652478e-30 7.304955e-30 1.00000000
[83,] 1.782014e-30 3.564027e-30 1.00000000
[84,] 2.700356e-30 5.400712e-30 1.00000000
[85,] 1.051107e-29 2.102215e-29 1.00000000
[86,] 1.446526e-29 2.893052e-29 1.00000000
[87,] 3.127989e-29 6.255977e-29 1.00000000
[88,] 9.822991e-29 1.964598e-28 1.00000000
[89,] 1.340004e-28 2.680008e-28 1.00000000
[90,] 5.364191e-28 1.072838e-27 1.00000000
[91,] 4.630704e-28 9.261408e-28 1.00000000
[92,] 3.429917e-28 6.859834e-28 1.00000000
[93,] 1.059199e-28 2.118397e-28 1.00000000
[94,] 1.936624e-28 3.873247e-28 1.00000000
[95,] 2.416730e-25 4.833461e-25 1.00000000
[96,] 3.062188e-23 6.124376e-23 1.00000000
[97,] 1.456636e-21 2.913271e-21 1.00000000
[98,] 1.279798e-20 2.559597e-20 1.00000000
[99,] 5.395168e-20 1.079034e-19 1.00000000
[100,] 1.628640e-19 3.257280e-19 1.00000000
[101,] 3.589478e-19 7.178956e-19 1.00000000
[102,] 4.406090e-19 8.812180e-19 1.00000000
[103,] 4.836280e-19 9.672560e-19 1.00000000
[104,] 3.609219e-19 7.218438e-19 1.00000000
[105,] 1.999249e-19 3.998498e-19 1.00000000
[106,] 1.208301e-19 2.416603e-19 1.00000000
[107,] 6.347795e-20 1.269559e-19 1.00000000
[108,] 3.213561e-20 6.427122e-20 1.00000000
[109,] 1.706803e-20 3.413605e-20 1.00000000
[110,] 8.202287e-21 1.640457e-20 1.00000000
[111,] 4.260608e-21 8.521217e-21 1.00000000
[112,] 2.468285e-21 4.936571e-21 1.00000000
[113,] 1.150278e-21 2.300556e-21 1.00000000
[114,] 5.131082e-22 1.026216e-21 1.00000000
[115,] 2.743078e-22 5.486157e-22 1.00000000
[116,] 1.367283e-22 2.734566e-22 1.00000000
[117,] 6.066515e-23 1.213303e-22 1.00000000
[118,] 3.373473e-23 6.746946e-23 1.00000000
[119,] 1.616984e-23 3.233968e-23 1.00000000
[120,] 7.531893e-24 1.506379e-23 1.00000000
[121,] 3.553478e-24 7.106956e-24 1.00000000
[122,] 1.562844e-24 3.125689e-24 1.00000000
[123,] 6.965662e-25 1.393132e-24 1.00000000
[124,] 3.201064e-25 6.402128e-25 1.00000000
[125,] 1.459791e-25 2.919582e-25 1.00000000
[126,] 6.370638e-26 1.274128e-25 1.00000000
[127,] 2.634443e-26 5.268886e-26 1.00000000
[128,] 1.020827e-26 2.041654e-26 1.00000000
[129,] 4.081771e-27 8.163541e-27 1.00000000
[130,] 2.050004e-27 4.100007e-27 1.00000000
[131,] 1.189058e-27 2.378116e-27 1.00000000
[132,] 4.497947e-28 8.995895e-28 1.00000000
[133,] 1.798538e-28 3.597076e-28 1.00000000
[134,] 6.775364e-29 1.355073e-28 1.00000000
[135,] 2.522818e-29 5.045635e-29 1.00000000
[136,] 9.514747e-30 1.902949e-29 1.00000000
[137,] 3.714620e-30 7.429241e-30 1.00000000
[138,] 1.464509e-30 2.929019e-30 1.00000000
[139,] 5.479948e-31 1.095990e-30 1.00000000
[140,] 2.205301e-31 4.410601e-31 1.00000000
[141,] 1.068790e-31 2.137580e-31 1.00000000
[142,] 6.118580e-32 1.223716e-31 1.00000000
[143,] 2.443575e-32 4.887149e-32 1.00000000
[144,] 8.743477e-33 1.748695e-32 1.00000000
[145,] 3.360324e-33 6.720648e-33 1.00000000
[146,] 1.560983e-33 3.121965e-33 1.00000000
[147,] 8.683329e-34 1.736666e-33 1.00000000
[148,] 5.418133e-34 1.083627e-33 1.00000000
[149,] 1.931764e-34 3.863528e-34 1.00000000
[150,] 7.030871e-35 1.406174e-34 1.00000000
[151,] 2.814878e-35 5.629757e-35 1.00000000
[152,] 1.279317e-35 2.558634e-35 1.00000000
[153,] 7.048978e-36 1.409796e-35 1.00000000
[154,] 4.107821e-36 8.215642e-36 1.00000000
[155,] 1.792336e-36 3.584673e-36 1.00000000
[156,] 6.131289e-37 1.226258e-36 1.00000000
[157,] 2.069978e-37 4.139956e-37 1.00000000
[158,] 7.082144e-38 1.416429e-37 1.00000000
[159,] 2.651834e-38 5.303667e-38 1.00000000
[160,] 1.101105e-38 2.202209e-38 1.00000000
[161,] 4.602299e-39 9.204597e-39 1.00000000
[162,] 1.824220e-39 3.648440e-39 1.00000000
[163,] 6.868607e-40 1.373721e-39 1.00000000
[164,] 2.522990e-40 5.045980e-40 1.00000000
[165,] 9.998451e-41 1.999690e-40 1.00000000
[166,] 5.124945e-41 1.024989e-40 1.00000000
[167,] 3.494259e-41 6.988518e-41 1.00000000
[168,] 2.909181e-41 5.818363e-41 1.00000000
[169,] 2.616220e-41 5.232441e-41 1.00000000
[170,] 2.153238e-41 4.306476e-41 1.00000000
[171,] 3.953763e-41 7.907526e-41 1.00000000
[172,] 1.504404e-40 3.008808e-40 1.00000000
[173,] 5.220057e-40 1.044011e-39 1.00000000
[174,] 1.879695e-39 3.759389e-39 1.00000000
[175,] 6.037830e-39 1.207566e-38 1.00000000
[176,] 1.527624e-38 3.055248e-38 1.00000000
[177,] 3.771940e-38 7.543879e-38 1.00000000
[178,] 9.233288e-38 1.846658e-37 1.00000000
[179,] 3.024163e-37 6.048326e-37 1.00000000
[180,] 4.787141e-37 9.574282e-37 1.00000000
[181,] 6.246015e-37 1.249203e-36 1.00000000
[182,] 8.830479e-37 1.766096e-36 1.00000000
[183,] 9.563080e-37 1.912616e-36 1.00000000
[184,] 4.256282e-37 8.512565e-37 1.00000000
[185,] 1.955083e-37 3.910165e-37 1.00000000
[186,] 9.347595e-38 1.869519e-37 1.00000000
[187,] 4.468626e-38 8.937252e-38 1.00000000
[188,] 2.313739e-38 4.627478e-38 1.00000000
[189,] 1.391413e-38 2.782827e-38 1.00000000
[190,] 5.160275e-39 1.032055e-38 1.00000000
[191,] 4.869894e-37 9.739788e-37 1.00000000
[192,] 2.293822e-35 4.587643e-35 1.00000000
[193,] 8.697850e-34 1.739570e-33 1.00000000
[194,] 5.242154e-32 1.048431e-31 1.00000000
[195,] 3.356966e-32 6.713931e-32 1.00000000
[196,] 9.431553e-32 1.886311e-31 1.00000000
[197,] 1.676301e-30 3.352602e-30 1.00000000
[198,] 2.217291e-29 4.434581e-29 1.00000000
[199,] 1.561691e-28 3.123381e-28 1.00000000
[200,] 7.259067e-28 1.451813e-27 1.00000000
[201,] 2.059541e-27 4.119081e-27 1.00000000
[202,] 6.563514e-27 1.312703e-26 1.00000000
[203,] 1.376156e-26 2.752311e-26 1.00000000
[204,] 1.809085e-26 3.618169e-26 1.00000000
[205,] 3.619053e-26 7.238107e-26 1.00000000
[206,] 4.445075e-26 8.890149e-26 1.00000000
[207,] 4.643309e-26 9.286618e-26 1.00000000
[208,] 3.586474e-26 7.172949e-26 1.00000000
[209,] 2.376173e-26 4.752347e-26 1.00000000
[210,] 1.381308e-26 2.762615e-26 1.00000000
[211,] 1.009282e-26 2.018564e-26 1.00000000
[212,] 8.472291e-27 1.694458e-26 1.00000000
[213,] 4.454875e-27 8.909750e-27 1.00000000
[214,] 2.978648e-27 5.957295e-27 1.00000000
[215,] 1.874485e-27 3.748971e-27 1.00000000
[216,] 9.272590e-28 1.854518e-27 1.00000000
[217,] 4.863016e-28 9.726032e-28 1.00000000
[218,] 2.412437e-28 4.824874e-28 1.00000000
[219,] 3.106476e-28 6.212952e-28 1.00000000
[220,] 6.395365e-26 1.279073e-25 1.00000000
[221,] 2.802651e-23 5.605302e-23 1.00000000
[222,] 1.296895e-20 2.593790e-20 1.00000000
[223,] 2.574782e-18 5.149565e-18 1.00000000
[224,] 1.835107e-17 3.670215e-17 1.00000000
[225,] 6.509827e-17 1.301965e-16 1.00000000
[226,] 5.916249e-14 1.183250e-13 1.00000000
[227,] 2.890618e-05 5.781236e-05 0.99997109
[228,] 4.210161e-03 8.420322e-03 0.99578984
[229,] 4.299874e-01 8.599748e-01 0.57001261
[230,] 9.008768e-01 1.982463e-01 0.09912317
[231,] 8.956251e-01 2.087498e-01 0.10437490
[232,] 8.960430e-01 2.079141e-01 0.10395703
[233,] 9.068540e-01 1.862920e-01 0.09314601
[234,] 9.064362e-01 1.871275e-01 0.09356376
[235,] 9.333309e-01 1.333381e-01 0.06666907
[236,] 9.345633e-01 1.308734e-01 0.06543669
[237,] 9.272098e-01 1.455803e-01 0.07279017
[238,] 9.368747e-01 1.262507e-01 0.06312533
[239,] 9.285760e-01 1.428480e-01 0.07142400
[240,] 9.171929e-01 1.656141e-01 0.08280706
[241,] 9.036889e-01 1.926223e-01 0.09631114
[242,] 8.878910e-01 2.242180e-01 0.11210900
[243,] 8.775925e-01 2.448150e-01 0.12240748
[244,] 8.777047e-01 2.445906e-01 0.12229528
[245,] 8.667673e-01 2.664654e-01 0.13323270
[246,] 8.677084e-01 2.645831e-01 0.13229156
[247,] 8.643257e-01 2.713486e-01 0.13567429
[248,] 8.617039e-01 2.765921e-01 0.13829606
[249,] 8.766804e-01 2.466392e-01 0.12331959
[250,] 8.844787e-01 2.310425e-01 0.11552127
[251,] 8.860316e-01 2.279369e-01 0.11396844
[252,] 8.955953e-01 2.088095e-01 0.10440474
[253,] 8.982102e-01 2.035795e-01 0.10178976
[254,] 9.232476e-01 1.535049e-01 0.07675245
[255,] 9.393780e-01 1.212441e-01 0.06062204
[256,] 9.499926e-01 1.000149e-01 0.05000743
[257,] 9.532578e-01 9.348430e-02 0.04674215
[258,] 9.485521e-01 1.028959e-01 0.05144793
[259,] 9.577212e-01 8.455759e-02 0.04227879
[260,] 9.848012e-01 3.039764e-02 0.01519882
[261,] 9.841478e-01 3.170439e-02 0.01585219
[262,] 9.810014e-01 3.799720e-02 0.01899860
[263,] 9.768563e-01 4.628748e-02 0.02314374
[264,] 9.724297e-01 5.514058e-02 0.02757029
[265,] 9.676975e-01 6.460506e-02 0.03230253
[266,] 9.643096e-01 7.138086e-02 0.03569043
[267,] 9.627964e-01 7.440724e-02 0.03720362
[268,] 9.592628e-01 8.147440e-02 0.04073720
[269,] 9.590559e-01 8.188820e-02 0.04094410
[270,] 9.606095e-01 7.878107e-02 0.03939053
[271,] 9.599438e-01 8.011240e-02 0.04005620
[272,] 9.571462e-01 8.570759e-02 0.04285380
[273,] 9.520877e-01 9.582469e-02 0.04791234
[274,] 9.462712e-01 1.074576e-01 0.05372882
[275,] 9.406903e-01 1.186195e-01 0.05930973
[276,] 9.361595e-01 1.276811e-01 0.06384055
[277,] 9.467640e-01 1.064720e-01 0.05323601
[278,] 9.415852e-01 1.168297e-01 0.05841483
[279,] 9.439732e-01 1.120537e-01 0.05602684
[280,] 9.544831e-01 9.103373e-02 0.04551687
[281,] 9.666839e-01 6.663219e-02 0.03331610
[282,] 9.672664e-01 6.546711e-02 0.03273355
[283,] 9.644314e-01 7.113713e-02 0.03556856
[284,] 9.604679e-01 7.906413e-02 0.03953206
[285,] 9.557419e-01 8.851612e-02 0.04425806
[286,] 9.464790e-01 1.070420e-01 0.05352100
[287,] 9.465196e-01 1.069608e-01 0.05348040
[288,] 9.556351e-01 8.872978e-02 0.04436489
[289,] 9.546857e-01 9.062866e-02 0.04531433
[290,] 9.568142e-01 8.637162e-02 0.04318581
[291,] 9.539261e-01 9.214771e-02 0.04607385
[292,] 9.505720e-01 9.885600e-02 0.04942800
[293,] 9.591153e-01 8.176948e-02 0.04088474
[294,] 9.662517e-01 6.749668e-02 0.03374834
[295,] 9.668035e-01 6.639295e-02 0.03319647
[296,] 9.576411e-01 8.471788e-02 0.04235894
[297,] 9.469361e-01 1.061277e-01 0.05306385
[298,] 9.365188e-01 1.269623e-01 0.06348117
[299,] 9.384999e-01 1.230001e-01 0.06150005
[300,] 9.299735e-01 1.400529e-01 0.07002646
[301,] 9.117748e-01 1.764505e-01 0.08822524
[302,] 8.932598e-01 2.134804e-01 0.10674018
[303,] 8.693914e-01 2.612171e-01 0.13060855
[304,] 8.374328e-01 3.251343e-01 0.16256717
[305,] 8.015126e-01 3.969748e-01 0.19848739
[306,] 7.578947e-01 4.842105e-01 0.24210527
[307,] 7.226992e-01 5.546017e-01 0.27730083
[308,] 7.469302e-01 5.061395e-01 0.25306977
[309,] 7.643331e-01 4.713337e-01 0.23566686
[310,] 8.232539e-01 3.534923e-01 0.17674614
[311,] 9.008666e-01 1.982668e-01 0.09913341
[312,] 9.401869e-01 1.196261e-01 0.05981305
[313,] 9.420764e-01 1.158471e-01 0.05792357
[314,] 9.159286e-01 1.681428e-01 0.08407138
[315,] 9.024365e-01 1.951270e-01 0.09756350
[316,] 8.958287e-01 2.083425e-01 0.10417126
[317,] 8.529312e-01 2.941375e-01 0.14706876
[318,] 8.237949e-01 3.524103e-01 0.17620513
[319,] 8.219234e-01 3.561532e-01 0.17807660
[320,] 8.384593e-01 3.230815e-01 0.16154073
[321,] 9.773542e-01 4.529168e-02 0.02264584
[322,] 9.815832e-01 3.683369e-02 0.01841684
[323,] 9.676266e-01 6.474690e-02 0.03237345
[324,] 9.437234e-01 1.125532e-01 0.05627658
[325,] 9.626465e-01 7.470710e-02 0.03735355
[326,] 9.794824e-01 4.103515e-02 0.02051758
[327,] 9.596574e-01 8.068515e-02 0.04034258
> postscript(file="/var/wessaorg/rcomp/tmp/1bxpr1320923671.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ve7n1320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b3iq1320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ugwi1320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59e7t1320923671.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 = 360
Frequency = 1
1 2 3 4 5 6
16.1060456 9.0256596 0.8461223 -10.4875605 -20.0710061 -21.4802307
7 8 9 10 11 12
-16.3051778 -14.1224549 -14.0704593 -13.7231762 -13.6583225 -13.4953601
13 14 15 16 17 18
-11.3599857 -9.9275253 -11.5519421 -7.4868049 -5.8079244 -2.1981416
19 20 21 22 23 24
3.0216034 7.5158909 8.1659909 7.4655961 3.9248091 2.7615626
25 26 27 28 29 30
4.5413918 4.7559849 -4.4996637 -3.3407832 -1.0768319 2.7162198
31 32 33 34 35 36
1.7690901 -4.0436405 -4.7254380 -5.6455020 -8.6184696 -9.3266453
37 38 39 40 41 42
-4.3743983 -6.4811805 -6.9768291 -1.7602708 -3.1129549 -2.2800447
43 44 45 46 47 48
-1.2227654 -0.8688107 0.4023796 0.1583830 0.2926209 5.2047780
49 50 51 52 53 54
6.9503417 11.2939881 10.1519412 10.9257031 11.3326403 11.3710477
55 56 57 58 59 60
15.3522617 15.1198181 13.2379269 15.9770577 16.4460813 12.0580471
61 62 63 64 65 66
12.6272126 8.7756905 9.8054914 10.4481629 9.6418770 10.4026545
67 68 69 70 71 72
14.2817357 13.0669222 11.3871637 17.1178113 12.7970243 10.1852468
73 74 75 76 77 78
9.5994830 8.4397628 4.9234026 8.6548424 8.8223955 9.0089074
79 80 81 82 83 84
11.9411638 10.2754972 12.3691511 11.6557226 7.6208594 5.9705988
85 86 87 88 89 90
5.4833182 2.9669625 0.7008395 1.1605253 0.8208270 0.4389973
91 92 93 94 95 96
2.0154713 4.0091888 2.9779135 0.3549116 -3.0050224 -3.3989326
97 98 99 100 101 102
-3.6574450 -5.4188715 -6.1060847 -8.2587211 -10.4094618 -7.5484951
103 104 105 106 107 108
-5.7630635 -5.5701512 -4.5607628 -7.6946178 -7.0145518 -5.5209257
109 110 111 112 113 114
-4.9260277 -20.2467983 -13.7693215 -9.4963649 -6.9138826 -5.6471335
115 116 117 118 119 120
-1.1292841 1.7190317 4.7069053 3.1149937 0.1350119 -2.3320736
121 122 123 124 125 126
-1.7973629 -0.2265609 -2.0481812 0.1558657 2.0835144 2.5221591
127 128 129 130 131 132
6.0216668 8.1037738 4.7014082 3.3149460 5.6815060 4.7750843
133 134 135 136 137 138
3.0034903 5.4193153 3.7015339 2.9726408 3.2561656 3.3856634
139 140 141 142 143 144
3.9159285 4.0750018 1.1454327 1.1298715 1.3994631 -1.8942578
145 146 147 148 149 150
-1.8294056 -6.9498374 -9.4397993 -4.4982659 -7.2528934 -5.2215001
151 152 153 154 155 156
-3.6195287 -3.1857634 -3.6903555 -3.1639255 -1.4809672 -5.0051147
157 158 159 160 161 162
-4.9650961 -7.6384659 -5.4024083 -2.2258978 -5.1326580 -6.5961940
163 164 165 166 167 168
-6.5323270 -6.9200785 -1.7449556 -3.6462514 -5.4046206 -7.6325592
169 170 171 172 173 174
-7.8527778 -9.2067635 -7.6194262 -4.0782715 -3.4732299 -2.9045852
175 176 177 178 179 180
1.7381455 3.3234277 1.6213471 2.0362125 -0.5043374 -1.1735078
181 182 183 184 185 186
-1.3003618 -5.0817882 -7.2029820 -6.4909263 -8.2055538 -6.4675251
187 188 189 190 191 192
-7.1923306 -9.0649636 -8.4882760 -8.6082442 -9.0097887 -9.6079166
193 194 195 196 197 198
-8.5613104 -11.4968608 -14.2666334 -11.3190804 -11.7190159 -11.6640208
199 200 201 202 203 204
-8.7237078 -0.6625017 -2.1752461 -1.2811382 -5.0434421 -8.1231347
205 206 207 208 209 210
-8.9329746 -1.4654434 16.6150212 16.2085459 15.1862883 15.5625152
211 212 213 214 215 216
-4.4277000 -19.1928085 -5.4991546 -3.5749052 -7.7531808 -9.6501706
217 218 219 220 221 222
-10.9255078 -6.2484490 -6.4902108 -5.7460224 -1.8029242 -3.6064123
223 224 225 226 227 228
-0.6960514 -4.7955570 -5.7678269 -5.9611119 -7.2239839 -6.2871825
229 230 231 232 233 234
-6.0910487 -3.2994893 -4.5279344 -5.9741725 -2.3029220 -1.9472632
235 236 237 238 239 240
6.4141879 22.1906541 24.6792372 25.9453822 24.9067757 15.5568938
241 242 243 244 245 246
12.1413672 31.2316470 67.1625362 46.3835454 64.7476841 56.4347143
247 248 249 250 251 252
15.0061156 7.3593587 2.9258091 3.1126198 -2.0840433 8.7468820
253 254 255 256 257 258
13.8463804 23.0356655 4.1545655 8.8800814 0.2504788 2.8128209
259 260 261 262 263 264
8.1559304 9.2552409 2.2153409 3.2060842 6.3404157 7.6561268
265 266 267 268 269 270
7.9523064 5.8423969 3.5564154 3.1746321 1.0581090 4.7027732
271 272 273 274 275 276
2.6599110 0.1572303 -4.1535707 -7.8206966 2.4965271 13.5626169
277 278 279 280 281 282
-0.3782196 -9.0495523 -8.6162912 -10.5014391 -9.6429851 -10.6572306
283 284 285 286 287 288
-7.5369176 -11.1753328 -9.9962753 -9.1160542 -8.4891155 -9.9255372
289 290 291 292 293 294
-12.3591682 -17.8990778 -19.3194662 -12.9826707 -7.7336008 -18.8556677
295 296 297 298 299 300
-13.5537900 -11.0913043 -10.7280770 -17.2129744 -16.3360836 -15.8437371
301 302 303 304 305 306
-13.2320602 -14.4192211 -8.5976183 -8.2935734 -14.9026558 -13.9384182
307 308 309 310 311 312
-18.1383423 -18.2271363 -13.7294541 -13.1677161 -13.0466076 -19.6974842
313 314 315 316 317 318
-19.1946711 -17.7055753 -23.1949213 -22.0829613 -18.0168294 -19.9311706
319 320 321 322 323 324
-12.4500045 -14.3894622 -15.0343393 -14.3134066 -15.0184113 -17.3090048
325 326 327 328 329 330
-10.2647686 -8.3023082 -10.2102331 -10.7160446 -6.3973535 2.8202966
331 332 333 334 335 336
-5.6075905 -4.3368589 1.4091171 0.3644568 7.5262769 16.3574853
337 338 339 340 341 342
10.0025248 27.5861233 37.4411843 23.7566025 18.5644865 10.4004762
343 344 345 346 347 348
6.9821645 2.7097668 -2.1610342 6.6798029 12.1908655 13.1659607
349 350 351 352 353 354
29.7487279 22.6905724 10.7808935 13.0186837 8.2102172 8.3647877
355 356 357 358 359 360
13.6232051 18.7660217 14.5801022 13.2958684 21.9327114 30.2222614
> postscript(file="/var/wessaorg/rcomp/tmp/6zjew1320923671.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 = 360
Frequency = 1
lag(myerror, k = 1) myerror
0 16.1060456 NA
1 9.0256596 16.1060456
2 0.8461223 9.0256596
3 -10.4875605 0.8461223
4 -20.0710061 -10.4875605
5 -21.4802307 -20.0710061
6 -16.3051778 -21.4802307
7 -14.1224549 -16.3051778
8 -14.0704593 -14.1224549
9 -13.7231762 -14.0704593
10 -13.6583225 -13.7231762
11 -13.4953601 -13.6583225
12 -11.3599857 -13.4953601
13 -9.9275253 -11.3599857
14 -11.5519421 -9.9275253
15 -7.4868049 -11.5519421
16 -5.8079244 -7.4868049
17 -2.1981416 -5.8079244
18 3.0216034 -2.1981416
19 7.5158909 3.0216034
20 8.1659909 7.5158909
21 7.4655961 8.1659909
22 3.9248091 7.4655961
23 2.7615626 3.9248091
24 4.5413918 2.7615626
25 4.7559849 4.5413918
26 -4.4996637 4.7559849
27 -3.3407832 -4.4996637
28 -1.0768319 -3.3407832
29 2.7162198 -1.0768319
30 1.7690901 2.7162198
31 -4.0436405 1.7690901
32 -4.7254380 -4.0436405
33 -5.6455020 -4.7254380
34 -8.6184696 -5.6455020
35 -9.3266453 -8.6184696
36 -4.3743983 -9.3266453
37 -6.4811805 -4.3743983
38 -6.9768291 -6.4811805
39 -1.7602708 -6.9768291
40 -3.1129549 -1.7602708
41 -2.2800447 -3.1129549
42 -1.2227654 -2.2800447
43 -0.8688107 -1.2227654
44 0.4023796 -0.8688107
45 0.1583830 0.4023796
46 0.2926209 0.1583830
47 5.2047780 0.2926209
48 6.9503417 5.2047780
49 11.2939881 6.9503417
50 10.1519412 11.2939881
51 10.9257031 10.1519412
52 11.3326403 10.9257031
53 11.3710477 11.3326403
54 15.3522617 11.3710477
55 15.1198181 15.3522617
56 13.2379269 15.1198181
57 15.9770577 13.2379269
58 16.4460813 15.9770577
59 12.0580471 16.4460813
60 12.6272126 12.0580471
61 8.7756905 12.6272126
62 9.8054914 8.7756905
63 10.4481629 9.8054914
64 9.6418770 10.4481629
65 10.4026545 9.6418770
66 14.2817357 10.4026545
67 13.0669222 14.2817357
68 11.3871637 13.0669222
69 17.1178113 11.3871637
70 12.7970243 17.1178113
71 10.1852468 12.7970243
72 9.5994830 10.1852468
73 8.4397628 9.5994830
74 4.9234026 8.4397628
75 8.6548424 4.9234026
76 8.8223955 8.6548424
77 9.0089074 8.8223955
78 11.9411638 9.0089074
79 10.2754972 11.9411638
80 12.3691511 10.2754972
81 11.6557226 12.3691511
82 7.6208594 11.6557226
83 5.9705988 7.6208594
84 5.4833182 5.9705988
85 2.9669625 5.4833182
86 0.7008395 2.9669625
87 1.1605253 0.7008395
88 0.8208270 1.1605253
89 0.4389973 0.8208270
90 2.0154713 0.4389973
91 4.0091888 2.0154713
92 2.9779135 4.0091888
93 0.3549116 2.9779135
94 -3.0050224 0.3549116
95 -3.3989326 -3.0050224
96 -3.6574450 -3.3989326
97 -5.4188715 -3.6574450
98 -6.1060847 -5.4188715
99 -8.2587211 -6.1060847
100 -10.4094618 -8.2587211
101 -7.5484951 -10.4094618
102 -5.7630635 -7.5484951
103 -5.5701512 -5.7630635
104 -4.5607628 -5.5701512
105 -7.6946178 -4.5607628
106 -7.0145518 -7.6946178
107 -5.5209257 -7.0145518
108 -4.9260277 -5.5209257
109 -20.2467983 -4.9260277
110 -13.7693215 -20.2467983
111 -9.4963649 -13.7693215
112 -6.9138826 -9.4963649
113 -5.6471335 -6.9138826
114 -1.1292841 -5.6471335
115 1.7190317 -1.1292841
116 4.7069053 1.7190317
117 3.1149937 4.7069053
118 0.1350119 3.1149937
119 -2.3320736 0.1350119
120 -1.7973629 -2.3320736
121 -0.2265609 -1.7973629
122 -2.0481812 -0.2265609
123 0.1558657 -2.0481812
124 2.0835144 0.1558657
125 2.5221591 2.0835144
126 6.0216668 2.5221591
127 8.1037738 6.0216668
128 4.7014082 8.1037738
129 3.3149460 4.7014082
130 5.6815060 3.3149460
131 4.7750843 5.6815060
132 3.0034903 4.7750843
133 5.4193153 3.0034903
134 3.7015339 5.4193153
135 2.9726408 3.7015339
136 3.2561656 2.9726408
137 3.3856634 3.2561656
138 3.9159285 3.3856634
139 4.0750018 3.9159285
140 1.1454327 4.0750018
141 1.1298715 1.1454327
142 1.3994631 1.1298715
143 -1.8942578 1.3994631
144 -1.8294056 -1.8942578
145 -6.9498374 -1.8294056
146 -9.4397993 -6.9498374
147 -4.4982659 -9.4397993
148 -7.2528934 -4.4982659
149 -5.2215001 -7.2528934
150 -3.6195287 -5.2215001
151 -3.1857634 -3.6195287
152 -3.6903555 -3.1857634
153 -3.1639255 -3.6903555
154 -1.4809672 -3.1639255
155 -5.0051147 -1.4809672
156 -4.9650961 -5.0051147
157 -7.6384659 -4.9650961
158 -5.4024083 -7.6384659
159 -2.2258978 -5.4024083
160 -5.1326580 -2.2258978
161 -6.5961940 -5.1326580
162 -6.5323270 -6.5961940
163 -6.9200785 -6.5323270
164 -1.7449556 -6.9200785
165 -3.6462514 -1.7449556
166 -5.4046206 -3.6462514
167 -7.6325592 -5.4046206
168 -7.8527778 -7.6325592
169 -9.2067635 -7.8527778
170 -7.6194262 -9.2067635
171 -4.0782715 -7.6194262
172 -3.4732299 -4.0782715
173 -2.9045852 -3.4732299
174 1.7381455 -2.9045852
175 3.3234277 1.7381455
176 1.6213471 3.3234277
177 2.0362125 1.6213471
178 -0.5043374 2.0362125
179 -1.1735078 -0.5043374
180 -1.3003618 -1.1735078
181 -5.0817882 -1.3003618
182 -7.2029820 -5.0817882
183 -6.4909263 -7.2029820
184 -8.2055538 -6.4909263
185 -6.4675251 -8.2055538
186 -7.1923306 -6.4675251
187 -9.0649636 -7.1923306
188 -8.4882760 -9.0649636
189 -8.6082442 -8.4882760
190 -9.0097887 -8.6082442
191 -9.6079166 -9.0097887
192 -8.5613104 -9.6079166
193 -11.4968608 -8.5613104
194 -14.2666334 -11.4968608
195 -11.3190804 -14.2666334
196 -11.7190159 -11.3190804
197 -11.6640208 -11.7190159
198 -8.7237078 -11.6640208
199 -0.6625017 -8.7237078
200 -2.1752461 -0.6625017
201 -1.2811382 -2.1752461
202 -5.0434421 -1.2811382
203 -8.1231347 -5.0434421
204 -8.9329746 -8.1231347
205 -1.4654434 -8.9329746
206 16.6150212 -1.4654434
207 16.2085459 16.6150212
208 15.1862883 16.2085459
209 15.5625152 15.1862883
210 -4.4277000 15.5625152
211 -19.1928085 -4.4277000
212 -5.4991546 -19.1928085
213 -3.5749052 -5.4991546
214 -7.7531808 -3.5749052
215 -9.6501706 -7.7531808
216 -10.9255078 -9.6501706
217 -6.2484490 -10.9255078
218 -6.4902108 -6.2484490
219 -5.7460224 -6.4902108
220 -1.8029242 -5.7460224
221 -3.6064123 -1.8029242
222 -0.6960514 -3.6064123
223 -4.7955570 -0.6960514
224 -5.7678269 -4.7955570
225 -5.9611119 -5.7678269
226 -7.2239839 -5.9611119
227 -6.2871825 -7.2239839
228 -6.0910487 -6.2871825
229 -3.2994893 -6.0910487
230 -4.5279344 -3.2994893
231 -5.9741725 -4.5279344
232 -2.3029220 -5.9741725
233 -1.9472632 -2.3029220
234 6.4141879 -1.9472632
235 22.1906541 6.4141879
236 24.6792372 22.1906541
237 25.9453822 24.6792372
238 24.9067757 25.9453822
239 15.5568938 24.9067757
240 12.1413672 15.5568938
241 31.2316470 12.1413672
242 67.1625362 31.2316470
243 46.3835454 67.1625362
244 64.7476841 46.3835454
245 56.4347143 64.7476841
246 15.0061156 56.4347143
247 7.3593587 15.0061156
248 2.9258091 7.3593587
249 3.1126198 2.9258091
250 -2.0840433 3.1126198
251 8.7468820 -2.0840433
252 13.8463804 8.7468820
253 23.0356655 13.8463804
254 4.1545655 23.0356655
255 8.8800814 4.1545655
256 0.2504788 8.8800814
257 2.8128209 0.2504788
258 8.1559304 2.8128209
259 9.2552409 8.1559304
260 2.2153409 9.2552409
261 3.2060842 2.2153409
262 6.3404157 3.2060842
263 7.6561268 6.3404157
264 7.9523064 7.6561268
265 5.8423969 7.9523064
266 3.5564154 5.8423969
267 3.1746321 3.5564154
268 1.0581090 3.1746321
269 4.7027732 1.0581090
270 2.6599110 4.7027732
271 0.1572303 2.6599110
272 -4.1535707 0.1572303
273 -7.8206966 -4.1535707
274 2.4965271 -7.8206966
275 13.5626169 2.4965271
276 -0.3782196 13.5626169
277 -9.0495523 -0.3782196
278 -8.6162912 -9.0495523
279 -10.5014391 -8.6162912
280 -9.6429851 -10.5014391
281 -10.6572306 -9.6429851
282 -7.5369176 -10.6572306
283 -11.1753328 -7.5369176
284 -9.9962753 -11.1753328
285 -9.1160542 -9.9962753
286 -8.4891155 -9.1160542
287 -9.9255372 -8.4891155
288 -12.3591682 -9.9255372
289 -17.8990778 -12.3591682
290 -19.3194662 -17.8990778
291 -12.9826707 -19.3194662
292 -7.7336008 -12.9826707
293 -18.8556677 -7.7336008
294 -13.5537900 -18.8556677
295 -11.0913043 -13.5537900
296 -10.7280770 -11.0913043
297 -17.2129744 -10.7280770
298 -16.3360836 -17.2129744
299 -15.8437371 -16.3360836
300 -13.2320602 -15.8437371
301 -14.4192211 -13.2320602
302 -8.5976183 -14.4192211
303 -8.2935734 -8.5976183
304 -14.9026558 -8.2935734
305 -13.9384182 -14.9026558
306 -18.1383423 -13.9384182
307 -18.2271363 -18.1383423
308 -13.7294541 -18.2271363
309 -13.1677161 -13.7294541
310 -13.0466076 -13.1677161
311 -19.6974842 -13.0466076
312 -19.1946711 -19.6974842
313 -17.7055753 -19.1946711
314 -23.1949213 -17.7055753
315 -22.0829613 -23.1949213
316 -18.0168294 -22.0829613
317 -19.9311706 -18.0168294
318 -12.4500045 -19.9311706
319 -14.3894622 -12.4500045
320 -15.0343393 -14.3894622
321 -14.3134066 -15.0343393
322 -15.0184113 -14.3134066
323 -17.3090048 -15.0184113
324 -10.2647686 -17.3090048
325 -8.3023082 -10.2647686
326 -10.2102331 -8.3023082
327 -10.7160446 -10.2102331
328 -6.3973535 -10.7160446
329 2.8202966 -6.3973535
330 -5.6075905 2.8202966
331 -4.3368589 -5.6075905
332 1.4091171 -4.3368589
333 0.3644568 1.4091171
334 7.5262769 0.3644568
335 16.3574853 7.5262769
336 10.0025248 16.3574853
337 27.5861233 10.0025248
338 37.4411843 27.5861233
339 23.7566025 37.4411843
340 18.5644865 23.7566025
341 10.4004762 18.5644865
342 6.9821645 10.4004762
343 2.7097668 6.9821645
344 -2.1610342 2.7097668
345 6.6798029 -2.1610342
346 12.1908655 6.6798029
347 13.1659607 12.1908655
348 29.7487279 13.1659607
349 22.6905724 29.7487279
350 10.7808935 22.6905724
351 13.0186837 10.7808935
352 8.2102172 13.0186837
353 8.3647877 8.2102172
354 13.6232051 8.3647877
355 18.7660217 13.6232051
356 14.5801022 18.7660217
357 13.2958684 14.5801022
358 21.9327114 13.2958684
359 30.2222614 21.9327114
360 NA 30.2222614
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.0256596 16.1060456
[2,] 0.8461223 9.0256596
[3,] -10.4875605 0.8461223
[4,] -20.0710061 -10.4875605
[5,] -21.4802307 -20.0710061
[6,] -16.3051778 -21.4802307
[7,] -14.1224549 -16.3051778
[8,] -14.0704593 -14.1224549
[9,] -13.7231762 -14.0704593
[10,] -13.6583225 -13.7231762
[11,] -13.4953601 -13.6583225
[12,] -11.3599857 -13.4953601
[13,] -9.9275253 -11.3599857
[14,] -11.5519421 -9.9275253
[15,] -7.4868049 -11.5519421
[16,] -5.8079244 -7.4868049
[17,] -2.1981416 -5.8079244
[18,] 3.0216034 -2.1981416
[19,] 7.5158909 3.0216034
[20,] 8.1659909 7.5158909
[21,] 7.4655961 8.1659909
[22,] 3.9248091 7.4655961
[23,] 2.7615626 3.9248091
[24,] 4.5413918 2.7615626
[25,] 4.7559849 4.5413918
[26,] -4.4996637 4.7559849
[27,] -3.3407832 -4.4996637
[28,] -1.0768319 -3.3407832
[29,] 2.7162198 -1.0768319
[30,] 1.7690901 2.7162198
[31,] -4.0436405 1.7690901
[32,] -4.7254380 -4.0436405
[33,] -5.6455020 -4.7254380
[34,] -8.6184696 -5.6455020
[35,] -9.3266453 -8.6184696
[36,] -4.3743983 -9.3266453
[37,] -6.4811805 -4.3743983
[38,] -6.9768291 -6.4811805
[39,] -1.7602708 -6.9768291
[40,] -3.1129549 -1.7602708
[41,] -2.2800447 -3.1129549
[42,] -1.2227654 -2.2800447
[43,] -0.8688107 -1.2227654
[44,] 0.4023796 -0.8688107
[45,] 0.1583830 0.4023796
[46,] 0.2926209 0.1583830
[47,] 5.2047780 0.2926209
[48,] 6.9503417 5.2047780
[49,] 11.2939881 6.9503417
[50,] 10.1519412 11.2939881
[51,] 10.9257031 10.1519412
[52,] 11.3326403 10.9257031
[53,] 11.3710477 11.3326403
[54,] 15.3522617 11.3710477
[55,] 15.1198181 15.3522617
[56,] 13.2379269 15.1198181
[57,] 15.9770577 13.2379269
[58,] 16.4460813 15.9770577
[59,] 12.0580471 16.4460813
[60,] 12.6272126 12.0580471
[61,] 8.7756905 12.6272126
[62,] 9.8054914 8.7756905
[63,] 10.4481629 9.8054914
[64,] 9.6418770 10.4481629
[65,] 10.4026545 9.6418770
[66,] 14.2817357 10.4026545
[67,] 13.0669222 14.2817357
[68,] 11.3871637 13.0669222
[69,] 17.1178113 11.3871637
[70,] 12.7970243 17.1178113
[71,] 10.1852468 12.7970243
[72,] 9.5994830 10.1852468
[73,] 8.4397628 9.5994830
[74,] 4.9234026 8.4397628
[75,] 8.6548424 4.9234026
[76,] 8.8223955 8.6548424
[77,] 9.0089074 8.8223955
[78,] 11.9411638 9.0089074
[79,] 10.2754972 11.9411638
[80,] 12.3691511 10.2754972
[81,] 11.6557226 12.3691511
[82,] 7.6208594 11.6557226
[83,] 5.9705988 7.6208594
[84,] 5.4833182 5.9705988
[85,] 2.9669625 5.4833182
[86,] 0.7008395 2.9669625
[87,] 1.1605253 0.7008395
[88,] 0.8208270 1.1605253
[89,] 0.4389973 0.8208270
[90,] 2.0154713 0.4389973
[91,] 4.0091888 2.0154713
[92,] 2.9779135 4.0091888
[93,] 0.3549116 2.9779135
[94,] -3.0050224 0.3549116
[95,] -3.3989326 -3.0050224
[96,] -3.6574450 -3.3989326
[97,] -5.4188715 -3.6574450
[98,] -6.1060847 -5.4188715
[99,] -8.2587211 -6.1060847
[100,] -10.4094618 -8.2587211
[101,] -7.5484951 -10.4094618
[102,] -5.7630635 -7.5484951
[103,] -5.5701512 -5.7630635
[104,] -4.5607628 -5.5701512
[105,] -7.6946178 -4.5607628
[106,] -7.0145518 -7.6946178
[107,] -5.5209257 -7.0145518
[108,] -4.9260277 -5.5209257
[109,] -20.2467983 -4.9260277
[110,] -13.7693215 -20.2467983
[111,] -9.4963649 -13.7693215
[112,] -6.9138826 -9.4963649
[113,] -5.6471335 -6.9138826
[114,] -1.1292841 -5.6471335
[115,] 1.7190317 -1.1292841
[116,] 4.7069053 1.7190317
[117,] 3.1149937 4.7069053
[118,] 0.1350119 3.1149937
[119,] -2.3320736 0.1350119
[120,] -1.7973629 -2.3320736
[121,] -0.2265609 -1.7973629
[122,] -2.0481812 -0.2265609
[123,] 0.1558657 -2.0481812
[124,] 2.0835144 0.1558657
[125,] 2.5221591 2.0835144
[126,] 6.0216668 2.5221591
[127,] 8.1037738 6.0216668
[128,] 4.7014082 8.1037738
[129,] 3.3149460 4.7014082
[130,] 5.6815060 3.3149460
[131,] 4.7750843 5.6815060
[132,] 3.0034903 4.7750843
[133,] 5.4193153 3.0034903
[134,] 3.7015339 5.4193153
[135,] 2.9726408 3.7015339
[136,] 3.2561656 2.9726408
[137,] 3.3856634 3.2561656
[138,] 3.9159285 3.3856634
[139,] 4.0750018 3.9159285
[140,] 1.1454327 4.0750018
[141,] 1.1298715 1.1454327
[142,] 1.3994631 1.1298715
[143,] -1.8942578 1.3994631
[144,] -1.8294056 -1.8942578
[145,] -6.9498374 -1.8294056
[146,] -9.4397993 -6.9498374
[147,] -4.4982659 -9.4397993
[148,] -7.2528934 -4.4982659
[149,] -5.2215001 -7.2528934
[150,] -3.6195287 -5.2215001
[151,] -3.1857634 -3.6195287
[152,] -3.6903555 -3.1857634
[153,] -3.1639255 -3.6903555
[154,] -1.4809672 -3.1639255
[155,] -5.0051147 -1.4809672
[156,] -4.9650961 -5.0051147
[157,] -7.6384659 -4.9650961
[158,] -5.4024083 -7.6384659
[159,] -2.2258978 -5.4024083
[160,] -5.1326580 -2.2258978
[161,] -6.5961940 -5.1326580
[162,] -6.5323270 -6.5961940
[163,] -6.9200785 -6.5323270
[164,] -1.7449556 -6.9200785
[165,] -3.6462514 -1.7449556
[166,] -5.4046206 -3.6462514
[167,] -7.6325592 -5.4046206
[168,] -7.8527778 -7.6325592
[169,] -9.2067635 -7.8527778
[170,] -7.6194262 -9.2067635
[171,] -4.0782715 -7.6194262
[172,] -3.4732299 -4.0782715
[173,] -2.9045852 -3.4732299
[174,] 1.7381455 -2.9045852
[175,] 3.3234277 1.7381455
[176,] 1.6213471 3.3234277
[177,] 2.0362125 1.6213471
[178,] -0.5043374 2.0362125
[179,] -1.1735078 -0.5043374
[180,] -1.3003618 -1.1735078
[181,] -5.0817882 -1.3003618
[182,] -7.2029820 -5.0817882
[183,] -6.4909263 -7.2029820
[184,] -8.2055538 -6.4909263
[185,] -6.4675251 -8.2055538
[186,] -7.1923306 -6.4675251
[187,] -9.0649636 -7.1923306
[188,] -8.4882760 -9.0649636
[189,] -8.6082442 -8.4882760
[190,] -9.0097887 -8.6082442
[191,] -9.6079166 -9.0097887
[192,] -8.5613104 -9.6079166
[193,] -11.4968608 -8.5613104
[194,] -14.2666334 -11.4968608
[195,] -11.3190804 -14.2666334
[196,] -11.7190159 -11.3190804
[197,] -11.6640208 -11.7190159
[198,] -8.7237078 -11.6640208
[199,] -0.6625017 -8.7237078
[200,] -2.1752461 -0.6625017
[201,] -1.2811382 -2.1752461
[202,] -5.0434421 -1.2811382
[203,] -8.1231347 -5.0434421
[204,] -8.9329746 -8.1231347
[205,] -1.4654434 -8.9329746
[206,] 16.6150212 -1.4654434
[207,] 16.2085459 16.6150212
[208,] 15.1862883 16.2085459
[209,] 15.5625152 15.1862883
[210,] -4.4277000 15.5625152
[211,] -19.1928085 -4.4277000
[212,] -5.4991546 -19.1928085
[213,] -3.5749052 -5.4991546
[214,] -7.7531808 -3.5749052
[215,] -9.6501706 -7.7531808
[216,] -10.9255078 -9.6501706
[217,] -6.2484490 -10.9255078
[218,] -6.4902108 -6.2484490
[219,] -5.7460224 -6.4902108
[220,] -1.8029242 -5.7460224
[221,] -3.6064123 -1.8029242
[222,] -0.6960514 -3.6064123
[223,] -4.7955570 -0.6960514
[224,] -5.7678269 -4.7955570
[225,] -5.9611119 -5.7678269
[226,] -7.2239839 -5.9611119
[227,] -6.2871825 -7.2239839
[228,] -6.0910487 -6.2871825
[229,] -3.2994893 -6.0910487
[230,] -4.5279344 -3.2994893
[231,] -5.9741725 -4.5279344
[232,] -2.3029220 -5.9741725
[233,] -1.9472632 -2.3029220
[234,] 6.4141879 -1.9472632
[235,] 22.1906541 6.4141879
[236,] 24.6792372 22.1906541
[237,] 25.9453822 24.6792372
[238,] 24.9067757 25.9453822
[239,] 15.5568938 24.9067757
[240,] 12.1413672 15.5568938
[241,] 31.2316470 12.1413672
[242,] 67.1625362 31.2316470
[243,] 46.3835454 67.1625362
[244,] 64.7476841 46.3835454
[245,] 56.4347143 64.7476841
[246,] 15.0061156 56.4347143
[247,] 7.3593587 15.0061156
[248,] 2.9258091 7.3593587
[249,] 3.1126198 2.9258091
[250,] -2.0840433 3.1126198
[251,] 8.7468820 -2.0840433
[252,] 13.8463804 8.7468820
[253,] 23.0356655 13.8463804
[254,] 4.1545655 23.0356655
[255,] 8.8800814 4.1545655
[256,] 0.2504788 8.8800814
[257,] 2.8128209 0.2504788
[258,] 8.1559304 2.8128209
[259,] 9.2552409 8.1559304
[260,] 2.2153409 9.2552409
[261,] 3.2060842 2.2153409
[262,] 6.3404157 3.2060842
[263,] 7.6561268 6.3404157
[264,] 7.9523064 7.6561268
[265,] 5.8423969 7.9523064
[266,] 3.5564154 5.8423969
[267,] 3.1746321 3.5564154
[268,] 1.0581090 3.1746321
[269,] 4.7027732 1.0581090
[270,] 2.6599110 4.7027732
[271,] 0.1572303 2.6599110
[272,] -4.1535707 0.1572303
[273,] -7.8206966 -4.1535707
[274,] 2.4965271 -7.8206966
[275,] 13.5626169 2.4965271
[276,] -0.3782196 13.5626169
[277,] -9.0495523 -0.3782196
[278,] -8.6162912 -9.0495523
[279,] -10.5014391 -8.6162912
[280,] -9.6429851 -10.5014391
[281,] -10.6572306 -9.6429851
[282,] -7.5369176 -10.6572306
[283,] -11.1753328 -7.5369176
[284,] -9.9962753 -11.1753328
[285,] -9.1160542 -9.9962753
[286,] -8.4891155 -9.1160542
[287,] -9.9255372 -8.4891155
[288,] -12.3591682 -9.9255372
[289,] -17.8990778 -12.3591682
[290,] -19.3194662 -17.8990778
[291,] -12.9826707 -19.3194662
[292,] -7.7336008 -12.9826707
[293,] -18.8556677 -7.7336008
[294,] -13.5537900 -18.8556677
[295,] -11.0913043 -13.5537900
[296,] -10.7280770 -11.0913043
[297,] -17.2129744 -10.7280770
[298,] -16.3360836 -17.2129744
[299,] -15.8437371 -16.3360836
[300,] -13.2320602 -15.8437371
[301,] -14.4192211 -13.2320602
[302,] -8.5976183 -14.4192211
[303,] -8.2935734 -8.5976183
[304,] -14.9026558 -8.2935734
[305,] -13.9384182 -14.9026558
[306,] -18.1383423 -13.9384182
[307,] -18.2271363 -18.1383423
[308,] -13.7294541 -18.2271363
[309,] -13.1677161 -13.7294541
[310,] -13.0466076 -13.1677161
[311,] -19.6974842 -13.0466076
[312,] -19.1946711 -19.6974842
[313,] -17.7055753 -19.1946711
[314,] -23.1949213 -17.7055753
[315,] -22.0829613 -23.1949213
[316,] -18.0168294 -22.0829613
[317,] -19.9311706 -18.0168294
[318,] -12.4500045 -19.9311706
[319,] -14.3894622 -12.4500045
[320,] -15.0343393 -14.3894622
[321,] -14.3134066 -15.0343393
[322,] -15.0184113 -14.3134066
[323,] -17.3090048 -15.0184113
[324,] -10.2647686 -17.3090048
[325,] -8.3023082 -10.2647686
[326,] -10.2102331 -8.3023082
[327,] -10.7160446 -10.2102331
[328,] -6.3973535 -10.7160446
[329,] 2.8202966 -6.3973535
[330,] -5.6075905 2.8202966
[331,] -4.3368589 -5.6075905
[332,] 1.4091171 -4.3368589
[333,] 0.3644568 1.4091171
[334,] 7.5262769 0.3644568
[335,] 16.3574853 7.5262769
[336,] 10.0025248 16.3574853
[337,] 27.5861233 10.0025248
[338,] 37.4411843 27.5861233
[339,] 23.7566025 37.4411843
[340,] 18.5644865 23.7566025
[341,] 10.4004762 18.5644865
[342,] 6.9821645 10.4004762
[343,] 2.7097668 6.9821645
[344,] -2.1610342 2.7097668
[345,] 6.6798029 -2.1610342
[346,] 12.1908655 6.6798029
[347,] 13.1659607 12.1908655
[348,] 29.7487279 13.1659607
[349,] 22.6905724 29.7487279
[350,] 10.7808935 22.6905724
[351,] 13.0186837 10.7808935
[352,] 8.2102172 13.0186837
[353,] 8.3647877 8.2102172
[354,] 13.6232051 8.3647877
[355,] 18.7660217 13.6232051
[356,] 14.5801022 18.7660217
[357,] 13.2958684 14.5801022
[358,] 21.9327114 13.2958684
[359,] 30.2222614 21.9327114
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.0256596 16.1060456
2 0.8461223 9.0256596
3 -10.4875605 0.8461223
4 -20.0710061 -10.4875605
5 -21.4802307 -20.0710061
6 -16.3051778 -21.4802307
7 -14.1224549 -16.3051778
8 -14.0704593 -14.1224549
9 -13.7231762 -14.0704593
10 -13.6583225 -13.7231762
11 -13.4953601 -13.6583225
12 -11.3599857 -13.4953601
13 -9.9275253 -11.3599857
14 -11.5519421 -9.9275253
15 -7.4868049 -11.5519421
16 -5.8079244 -7.4868049
17 -2.1981416 -5.8079244
18 3.0216034 -2.1981416
19 7.5158909 3.0216034
20 8.1659909 7.5158909
21 7.4655961 8.1659909
22 3.9248091 7.4655961
23 2.7615626 3.9248091
24 4.5413918 2.7615626
25 4.7559849 4.5413918
26 -4.4996637 4.7559849
27 -3.3407832 -4.4996637
28 -1.0768319 -3.3407832
29 2.7162198 -1.0768319
30 1.7690901 2.7162198
31 -4.0436405 1.7690901
32 -4.7254380 -4.0436405
33 -5.6455020 -4.7254380
34 -8.6184696 -5.6455020
35 -9.3266453 -8.6184696
36 -4.3743983 -9.3266453
37 -6.4811805 -4.3743983
38 -6.9768291 -6.4811805
39 -1.7602708 -6.9768291
40 -3.1129549 -1.7602708
41 -2.2800447 -3.1129549
42 -1.2227654 -2.2800447
43 -0.8688107 -1.2227654
44 0.4023796 -0.8688107
45 0.1583830 0.4023796
46 0.2926209 0.1583830
47 5.2047780 0.2926209
48 6.9503417 5.2047780
49 11.2939881 6.9503417
50 10.1519412 11.2939881
51 10.9257031 10.1519412
52 11.3326403 10.9257031
53 11.3710477 11.3326403
54 15.3522617 11.3710477
55 15.1198181 15.3522617
56 13.2379269 15.1198181
57 15.9770577 13.2379269
58 16.4460813 15.9770577
59 12.0580471 16.4460813
60 12.6272126 12.0580471
61 8.7756905 12.6272126
62 9.8054914 8.7756905
63 10.4481629 9.8054914
64 9.6418770 10.4481629
65 10.4026545 9.6418770
66 14.2817357 10.4026545
67 13.0669222 14.2817357
68 11.3871637 13.0669222
69 17.1178113 11.3871637
70 12.7970243 17.1178113
71 10.1852468 12.7970243
72 9.5994830 10.1852468
73 8.4397628 9.5994830
74 4.9234026 8.4397628
75 8.6548424 4.9234026
76 8.8223955 8.6548424
77 9.0089074 8.8223955
78 11.9411638 9.0089074
79 10.2754972 11.9411638
80 12.3691511 10.2754972
81 11.6557226 12.3691511
82 7.6208594 11.6557226
83 5.9705988 7.6208594
84 5.4833182 5.9705988
85 2.9669625 5.4833182
86 0.7008395 2.9669625
87 1.1605253 0.7008395
88 0.8208270 1.1605253
89 0.4389973 0.8208270
90 2.0154713 0.4389973
91 4.0091888 2.0154713
92 2.9779135 4.0091888
93 0.3549116 2.9779135
94 -3.0050224 0.3549116
95 -3.3989326 -3.0050224
96 -3.6574450 -3.3989326
97 -5.4188715 -3.6574450
98 -6.1060847 -5.4188715
99 -8.2587211 -6.1060847
100 -10.4094618 -8.2587211
101 -7.5484951 -10.4094618
102 -5.7630635 -7.5484951
103 -5.5701512 -5.7630635
104 -4.5607628 -5.5701512
105 -7.6946178 -4.5607628
106 -7.0145518 -7.6946178
107 -5.5209257 -7.0145518
108 -4.9260277 -5.5209257
109 -20.2467983 -4.9260277
110 -13.7693215 -20.2467983
111 -9.4963649 -13.7693215
112 -6.9138826 -9.4963649
113 -5.6471335 -6.9138826
114 -1.1292841 -5.6471335
115 1.7190317 -1.1292841
116 4.7069053 1.7190317
117 3.1149937 4.7069053
118 0.1350119 3.1149937
119 -2.3320736 0.1350119
120 -1.7973629 -2.3320736
121 -0.2265609 -1.7973629
122 -2.0481812 -0.2265609
123 0.1558657 -2.0481812
124 2.0835144 0.1558657
125 2.5221591 2.0835144
126 6.0216668 2.5221591
127 8.1037738 6.0216668
128 4.7014082 8.1037738
129 3.3149460 4.7014082
130 5.6815060 3.3149460
131 4.7750843 5.6815060
132 3.0034903 4.7750843
133 5.4193153 3.0034903
134 3.7015339 5.4193153
135 2.9726408 3.7015339
136 3.2561656 2.9726408
137 3.3856634 3.2561656
138 3.9159285 3.3856634
139 4.0750018 3.9159285
140 1.1454327 4.0750018
141 1.1298715 1.1454327
142 1.3994631 1.1298715
143 -1.8942578 1.3994631
144 -1.8294056 -1.8942578
145 -6.9498374 -1.8294056
146 -9.4397993 -6.9498374
147 -4.4982659 -9.4397993
148 -7.2528934 -4.4982659
149 -5.2215001 -7.2528934
150 -3.6195287 -5.2215001
151 -3.1857634 -3.6195287
152 -3.6903555 -3.1857634
153 -3.1639255 -3.6903555
154 -1.4809672 -3.1639255
155 -5.0051147 -1.4809672
156 -4.9650961 -5.0051147
157 -7.6384659 -4.9650961
158 -5.4024083 -7.6384659
159 -2.2258978 -5.4024083
160 -5.1326580 -2.2258978
161 -6.5961940 -5.1326580
162 -6.5323270 -6.5961940
163 -6.9200785 -6.5323270
164 -1.7449556 -6.9200785
165 -3.6462514 -1.7449556
166 -5.4046206 -3.6462514
167 -7.6325592 -5.4046206
168 -7.8527778 -7.6325592
169 -9.2067635 -7.8527778
170 -7.6194262 -9.2067635
171 -4.0782715 -7.6194262
172 -3.4732299 -4.0782715
173 -2.9045852 -3.4732299
174 1.7381455 -2.9045852
175 3.3234277 1.7381455
176 1.6213471 3.3234277
177 2.0362125 1.6213471
178 -0.5043374 2.0362125
179 -1.1735078 -0.5043374
180 -1.3003618 -1.1735078
181 -5.0817882 -1.3003618
182 -7.2029820 -5.0817882
183 -6.4909263 -7.2029820
184 -8.2055538 -6.4909263
185 -6.4675251 -8.2055538
186 -7.1923306 -6.4675251
187 -9.0649636 -7.1923306
188 -8.4882760 -9.0649636
189 -8.6082442 -8.4882760
190 -9.0097887 -8.6082442
191 -9.6079166 -9.0097887
192 -8.5613104 -9.6079166
193 -11.4968608 -8.5613104
194 -14.2666334 -11.4968608
195 -11.3190804 -14.2666334
196 -11.7190159 -11.3190804
197 -11.6640208 -11.7190159
198 -8.7237078 -11.6640208
199 -0.6625017 -8.7237078
200 -2.1752461 -0.6625017
201 -1.2811382 -2.1752461
202 -5.0434421 -1.2811382
203 -8.1231347 -5.0434421
204 -8.9329746 -8.1231347
205 -1.4654434 -8.9329746
206 16.6150212 -1.4654434
207 16.2085459 16.6150212
208 15.1862883 16.2085459
209 15.5625152 15.1862883
210 -4.4277000 15.5625152
211 -19.1928085 -4.4277000
212 -5.4991546 -19.1928085
213 -3.5749052 -5.4991546
214 -7.7531808 -3.5749052
215 -9.6501706 -7.7531808
216 -10.9255078 -9.6501706
217 -6.2484490 -10.9255078
218 -6.4902108 -6.2484490
219 -5.7460224 -6.4902108
220 -1.8029242 -5.7460224
221 -3.6064123 -1.8029242
222 -0.6960514 -3.6064123
223 -4.7955570 -0.6960514
224 -5.7678269 -4.7955570
225 -5.9611119 -5.7678269
226 -7.2239839 -5.9611119
227 -6.2871825 -7.2239839
228 -6.0910487 -6.2871825
229 -3.2994893 -6.0910487
230 -4.5279344 -3.2994893
231 -5.9741725 -4.5279344
232 -2.3029220 -5.9741725
233 -1.9472632 -2.3029220
234 6.4141879 -1.9472632
235 22.1906541 6.4141879
236 24.6792372 22.1906541
237 25.9453822 24.6792372
238 24.9067757 25.9453822
239 15.5568938 24.9067757
240 12.1413672 15.5568938
241 31.2316470 12.1413672
242 67.1625362 31.2316470
243 46.3835454 67.1625362
244 64.7476841 46.3835454
245 56.4347143 64.7476841
246 15.0061156 56.4347143
247 7.3593587 15.0061156
248 2.9258091 7.3593587
249 3.1126198 2.9258091
250 -2.0840433 3.1126198
251 8.7468820 -2.0840433
252 13.8463804 8.7468820
253 23.0356655 13.8463804
254 4.1545655 23.0356655
255 8.8800814 4.1545655
256 0.2504788 8.8800814
257 2.8128209 0.2504788
258 8.1559304 2.8128209
259 9.2552409 8.1559304
260 2.2153409 9.2552409
261 3.2060842 2.2153409
262 6.3404157 3.2060842
263 7.6561268 6.3404157
264 7.9523064 7.6561268
265 5.8423969 7.9523064
266 3.5564154 5.8423969
267 3.1746321 3.5564154
268 1.0581090 3.1746321
269 4.7027732 1.0581090
270 2.6599110 4.7027732
271 0.1572303 2.6599110
272 -4.1535707 0.1572303
273 -7.8206966 -4.1535707
274 2.4965271 -7.8206966
275 13.5626169 2.4965271
276 -0.3782196 13.5626169
277 -9.0495523 -0.3782196
278 -8.6162912 -9.0495523
279 -10.5014391 -8.6162912
280 -9.6429851 -10.5014391
281 -10.6572306 -9.6429851
282 -7.5369176 -10.6572306
283 -11.1753328 -7.5369176
284 -9.9962753 -11.1753328
285 -9.1160542 -9.9962753
286 -8.4891155 -9.1160542
287 -9.9255372 -8.4891155
288 -12.3591682 -9.9255372
289 -17.8990778 -12.3591682
290 -19.3194662 -17.8990778
291 -12.9826707 -19.3194662
292 -7.7336008 -12.9826707
293 -18.8556677 -7.7336008
294 -13.5537900 -18.8556677
295 -11.0913043 -13.5537900
296 -10.7280770 -11.0913043
297 -17.2129744 -10.7280770
298 -16.3360836 -17.2129744
299 -15.8437371 -16.3360836
300 -13.2320602 -15.8437371
301 -14.4192211 -13.2320602
302 -8.5976183 -14.4192211
303 -8.2935734 -8.5976183
304 -14.9026558 -8.2935734
305 -13.9384182 -14.9026558
306 -18.1383423 -13.9384182
307 -18.2271363 -18.1383423
308 -13.7294541 -18.2271363
309 -13.1677161 -13.7294541
310 -13.0466076 -13.1677161
311 -19.6974842 -13.0466076
312 -19.1946711 -19.6974842
313 -17.7055753 -19.1946711
314 -23.1949213 -17.7055753
315 -22.0829613 -23.1949213
316 -18.0168294 -22.0829613
317 -19.9311706 -18.0168294
318 -12.4500045 -19.9311706
319 -14.3894622 -12.4500045
320 -15.0343393 -14.3894622
321 -14.3134066 -15.0343393
322 -15.0184113 -14.3134066
323 -17.3090048 -15.0184113
324 -10.2647686 -17.3090048
325 -8.3023082 -10.2647686
326 -10.2102331 -8.3023082
327 -10.7160446 -10.2102331
328 -6.3973535 -10.7160446
329 2.8202966 -6.3973535
330 -5.6075905 2.8202966
331 -4.3368589 -5.6075905
332 1.4091171 -4.3368589
333 0.3644568 1.4091171
334 7.5262769 0.3644568
335 16.3574853 7.5262769
336 10.0025248 16.3574853
337 27.5861233 10.0025248
338 37.4411843 27.5861233
339 23.7566025 37.4411843
340 18.5644865 23.7566025
341 10.4004762 18.5644865
342 6.9821645 10.4004762
343 2.7097668 6.9821645
344 -2.1610342 2.7097668
345 6.6798029 -2.1610342
346 12.1908655 6.6798029
347 13.1659607 12.1908655
348 29.7487279 13.1659607
349 22.6905724 29.7487279
350 10.7808935 22.6905724
351 13.0186837 10.7808935
352 8.2102172 13.0186837
353 8.3647877 8.2102172
354 13.6232051 8.3647877
355 18.7660217 13.6232051
356 14.5801022 18.7660217
357 13.2958684 14.5801022
358 21.9327114 13.2958684
359 30.2222614 21.9327114
> 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/73hp11320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/838yf1320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/96o611320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/102epd1320923671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h74c1320923671.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/12n0ux1320923671.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/13f1qf1320923671.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/14l5bo1320923671.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/15m0pq1320923671.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/169o5f1320923671.tab")
+ }
>
> try(system("convert tmp/1bxpr1320923671.ps tmp/1bxpr1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ve7n1320923671.ps tmp/2ve7n1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b3iq1320923671.ps tmp/3b3iq1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ugwi1320923671.ps tmp/4ugwi1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/59e7t1320923671.ps tmp/59e7t1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zjew1320923671.ps tmp/6zjew1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/73hp11320923671.ps tmp/73hp11320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/838yf1320923671.ps tmp/838yf1320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/96o611320923671.ps tmp/96o611320923671.png",intern=TRUE))
character(0)
> try(system("convert tmp/102epd1320923671.ps tmp/102epd1320923671.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.925 0.633 10.691