R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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'
+ ,'USA
')
+ ,1:360))
> y <- array(NA,dim=c(2,360),dimnames=list(c('Colombia','USA
'),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 = 'No 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 USA\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 87.28 255.0 1 0 0 0 0 0 0 0 0 0 0
2 87.28 280.2 0 1 0 0 0 0 0 0 0 0 0
3 87.09 299.9 0 0 1 0 0 0 0 0 0 0 0
4 86.92 339.2 0 0 0 1 0 0 0 0 0 0 0
5 87.59 374.2 0 0 0 0 1 0 0 0 0 0 0
6 90.72 393.5 0 0 0 0 0 1 0 0 0 0 0
7 90.69 389.2 0 0 0 0 0 0 1 0 0 0 0
8 90.30 381.7 0 0 0 0 0 0 0 1 0 0 0
9 89.55 375.2 0 0 0 0 0 0 0 0 1 0 0
10 88.94 369.0 0 0 0 0 0 0 0 0 0 1 0
11 88.41 357.4 0 0 0 0 0 0 0 0 0 0 1
12 87.82 352.1 0 0 0 0 0 0 0 0 0 0 0
13 87.07 346.5 1 0 0 0 0 0 0 0 0 0 0
14 86.82 342.9 0 1 0 0 0 0 0 0 0 0 0
15 86.40 340.3 0 0 1 0 0 0 0 0 0 0 0
16 86.02 328.3 0 0 0 1 0 0 0 0 0 0 0
17 85.66 322.9 0 0 0 0 1 0 0 0 0 0 0
18 85.32 314.3 0 0 0 0 0 1 0 0 0 0 0
19 85.00 308.9 0 0 0 0 0 0 1 0 0 0 0
20 84.67 294.0 0 0 0 0 0 0 0 1 0 0 0
21 83.94 285.6 0 0 0 0 0 0 0 0 1 0 0
22 82.83 281.2 0 0 0 0 0 0 0 0 0 1 0
23 81.95 280.3 0 0 0 0 0 0 0 0 0 0 1
24 81.19 278.8 0 0 0 0 0 0 0 0 0 0 0
25 80.48 274.5 1 0 0 0 0 0 0 0 0 0 0
26 78.86 270.4 0 1 0 0 0 0 0 0 0 0 0
27 69.47 263.4 0 0 1 0 0 0 0 0 0 0 0
28 68.77 259.9 0 0 0 1 0 0 0 0 0 0 0
29 70.06 258.0 0 0 0 0 1 0 0 0 0 0 0
30 73.95 262.7 0 0 0 0 0 1 0 0 0 0 0
31 75.80 284.7 0 0 0 0 0 0 1 0 0 0 0
32 77.79 311.3 0 0 0 0 0 0 0 1 0 0 0
33 81.57 322.1 0 0 0 0 0 0 0 0 1 0 0
34 83.07 327.0 0 0 0 0 0 0 0 0 0 1 0
35 84.34 331.3 0 0 0 0 0 0 0 0 0 0 1
36 85.10 333.3 0 0 0 0 0 0 0 0 0 0 0
37 85.25 321.4 1 0 0 0 0 0 0 0 0 0 0
38 84.26 327.0 0 1 0 0 0 0 0 0 0 0 0
39 83.63 320.0 0 0 1 0 0 0 0 0 0 0 0
40 86.44 314.7 0 0 0 1 0 0 0 0 0 0 0
41 85.30 316.7 0 0 0 0 1 0 0 0 0 0 0
42 84.10 314.4 0 0 0 0 0 1 0 0 0 0 0
43 83.36 321.3 0 0 0 0 0 0 1 0 0 0 0
44 82.48 318.2 0 0 0 0 0 0 0 1 0 0 0
45 81.58 307.2 0 0 0 0 0 0 0 0 1 0 0
46 80.47 301.3 0 0 0 0 0 0 0 0 0 1 0
47 79.34 287.5 0 0 0 0 0 0 0 0 0 0 1
48 82.13 277.7 0 0 0 0 0 0 0 0 0 0 0
49 81.69 274.4 1 0 0 0 0 0 0 0 0 0 0
50 80.70 258.8 0 1 0 0 0 0 0 0 0 0 0
51 79.88 253.3 0 0 1 0 0 0 0 0 0 0 0
52 79.16 251.0 0 0 0 1 0 0 0 0 0 0 0
53 78.38 248.4 0 0 0 0 1 0 0 0 0 0 0
54 77.42 249.5 0 0 0 0 0 1 0 0 0 0 0
55 76.47 246.1 0 0 0 0 0 0 1 0 0 0 0
56 75.46 244.5 0 0 0 0 0 0 0 1 0 0 0
57 74.48 243.6 0 0 0 0 0 0 0 0 1 0 0
58 78.27 244.0 0 0 0 0 0 0 0 0 0 1 0
59 80.70 240.8 0 0 0 0 0 0 0 0 0 0 1
60 79.91 249.8 0 0 0 0 0 0 0 0 0 0 0
61 78.75 248.0 1 0 0 0 0 0 0 0 0 0 0
62 77.78 259.4 0 1 0 0 0 0 0 0 0 0 0
63 81.14 260.5 0 0 1 0 0 0 0 0 0 0 0
64 81.08 260.8 0 0 0 1 0 0 0 0 0 0 0
65 80.03 261.3 0 0 0 0 1 0 0 0 0 0 0
66 78.91 259.5 0 0 0 0 0 1 0 0 0 0 0
67 78.01 256.6 0 0 0 0 0 0 1 0 0 0 0
68 76.90 257.9 0 0 0 0 0 0 0 1 0 0 0
69 75.97 256.5 0 0 0 0 0 0 0 0 1 0 0
70 81.93 254.2 0 0 0 0 0 0 0 0 0 1 0
71 80.27 253.3 0 0 0 0 0 0 0 0 0 0 1
72 78.67 253.8 0 0 0 0 0 0 0 0 0 0 0
73 77.42 255.5 1 0 0 0 0 0 0 0 0 0 0
74 76.16 257.1 0 1 0 0 0 0 0 0 0 0 0
75 74.70 257.3 0 0 1 0 0 0 0 0 0 0 0
76 76.39 253.2 0 0 0 1 0 0 0 0 0 0 0
77 76.04 252.8 0 0 0 0 1 0 0 0 0 0 0
78 74.65 252.0 0 0 0 0 0 1 0 0 0 0 0
79 73.29 250.7 0 0 0 0 0 0 1 0 0 0 0
80 71.79 252.2 0 0 0 0 0 0 0 1 0 0 0
81 74.39 250.0 0 0 0 0 0 0 0 0 1 0 0
82 74.91 251.0 0 0 0 0 0 0 0 0 0 1 0
83 74.54 253.4 0 0 0 0 0 0 0 0 0 0 1
84 73.08 251.2 0 0 0 0 0 0 0 0 0 0 0
85 72.75 255.6 1 0 0 0 0 0 0 0 0 0 0
86 71.32 261.1 0 1 0 0 0 0 0 0 0 0 0
87 70.38 258.9 0 0 1 0 0 0 0 0 0 0 0
88 70.35 259.9 0 0 0 1 0 0 0 0 0 0 0
89 70.01 261.2 0 0 0 0 1 0 0 0 0 0 0
90 69.36 264.7 0 0 0 0 0 1 0 0 0 0 0
91 67.77 267.1 0 0 0 0 0 0 1 0 0 0 0
92 69.26 266.4 0 0 0 0 0 0 0 1 0 0 0
93 69.80 267.7 0 0 0 0 0 0 0 0 1 0 0
94 68.38 268.6 0 0 0 0 0 0 0 0 0 1 0
95 67.62 267.5 0 0 0 0 0 0 0 0 0 0 1
96 68.39 268.5 0 0 0 0 0 0 0 0 0 0 0
97 66.95 268.5 1 0 0 0 0 0 0 0 0 0 0
98 65.21 270.5 0 1 0 0 0 0 0 0 0 0 0
99 66.64 270.9 0 0 1 0 0 0 0 0 0 0 0
100 63.45 270.1 0 0 0 1 0 0 0 0 0 0 0
101 60.66 269.3 0 0 0 0 1 0 0 0 0 0 0
102 62.34 269.8 0 0 0 0 0 1 0 0 0 0 0
103 60.32 270.1 0 0 0 0 0 0 1 0 0 0 0
104 58.64 264.9 0 0 0 0 0 0 0 1 0 0 0
105 60.46 263.7 0 0 0 0 0 0 0 0 1 0 0
106 58.59 264.8 0 0 0 0 0 0 0 0 0 1 0
107 61.87 263.7 0 0 0 0 0 0 0 0 0 0 1
108 61.85 255.9 0 0 0 0 0 0 0 0 0 0 0
109 67.44 276.2 1 0 0 0 0 0 0 0 0 0 0
110 77.06 360.1 0 1 0 0 0 0 0 0 0 0 0
111 91.74 380.5 0 0 1 0 0 0 0 0 0 0 0
112 93.15 373.7 0 0 0 1 0 0 0 0 0 0 0
113 94.15 369.8 0 0 0 0 1 0 0 0 0 0 0
114 93.11 366.6 0 0 0 0 0 1 0 0 0 0 0
115 91.51 359.3 0 0 0 0 0 0 1 0 0 0 0
116 89.96 345.8 0 0 0 0 0 0 0 1 0 0 0
117 88.16 326.2 0 0 0 0 0 0 0 0 1 0 0
118 86.98 324.5 0 0 0 0 0 0 0 0 0 1 0
119 88.03 328.1 0 0 0 0 0 0 0 0 0 0 1
120 86.24 327.5 0 0 0 0 0 0 0 0 0 0 0
121 84.65 324.4 1 0 0 0 0 0 0 0 0 0 0
122 83.23 316.5 0 1 0 0 0 0 0 0 0 0 0
123 81.70 310.9 0 0 1 0 0 0 0 0 0 0 0
124 80.25 301.5 0 0 0 1 0 0 0 0 0 0 0
125 78.80 291.7 0 0 0 0 1 0 0 0 0 0 0
126 77.51 290.4 0 0 0 0 0 1 0 0 0 0 0
127 76.20 287.4 0 0 0 0 0 0 1 0 0 0 0
128 75.04 277.7 0 0 0 0 0 0 0 1 0 0 0
129 74.00 281.6 0 0 0 0 0 0 0 0 1 0 0
130 75.49 288.0 0 0 0 0 0 0 0 0 0 1 0
131 77.14 276.0 0 0 0 0 0 0 0 0 0 0 1
132 76.15 272.9 0 0 0 0 0 0 0 0 0 0 0
133 76.27 283.0 1 0 0 0 0 0 0 0 0 0 0
134 78.19 283.3 0 1 0 0 0 0 0 0 0 0 0
135 76.49 276.8 0 0 1 0 0 0 0 0 0 0 0
136 77.31 284.5 0 0 0 1 0 0 0 0 0 0 0
137 76.65 282.7 0 0 0 0 1 0 0 0 0 0 0
138 74.99 281.2 0 0 0 0 0 1 0 0 0 0 0
139 73.51 287.4 0 0 0 0 0 0 1 0 0 0 0
140 72.07 283.1 0 0 0 0 0 0 0 1 0 0 0
141 70.59 284.0 0 0 0 0 0 0 0 0 1 0 0
142 71.96 285.5 0 0 0 0 0 0 0 0 0 1 0
143 76.29 289.2 0 0 0 0 0 0 0 0 0 0 1
144 74.86 292.5 0 0 0 0 0 0 0 0 0 0 0
145 74.93 296.4 1 0 0 0 0 0 0 0 0 0 0
146 71.90 305.2 0 1 0 0 0 0 0 0 0 0 0
147 71.01 303.9 0 0 1 0 0 0 0 0 0 0 0
148 77.47 311.5 0 0 0 1 0 0 0 0 0 0 0
149 75.78 316.3 0 0 0 0 1 0 0 0 0 0 0
150 76.60 316.7 0 0 0 0 0 1 0 0 0 0 0
151 76.07 322.5 0 0 0 0 0 0 1 0 0 0 0
152 74.57 317.1 0 0 0 0 0 0 0 1 0 0 0
153 73.02 309.8 0 0 0 0 0 0 0 0 1 0 0
154 72.65 303.8 0 0 0 0 0 0 0 0 0 1 0
155 73.16 290.3 0 0 0 0 0 0 0 0 0 0 1
156 71.53 293.7 0 0 0 0 0 0 0 0 0 0 0
157 69.78 291.7 1 0 0 0 0 0 0 0 0 0 0
158 67.98 296.5 0 1 0 0 0 0 0 0 0 0 0
159 69.96 289.1 0 0 1 0 0 0 0 0 0 0 0
160 72.16 288.5 0 0 0 1 0 0 0 0 0 0 0
161 70.47 293.8 0 0 0 0 1 0 0 0 0 0 0
162 68.86 297.7 0 0 0 0 0 1 0 0 0 0 0
163 67.37 305.4 0 0 0 0 0 0 1 0 0 0 0
164 65.87 302.7 0 0 0 0 0 0 0 1 0 0 0
165 72.16 302.5 0 0 0 0 0 0 0 0 1 0 0
166 71.34 303.0 0 0 0 0 0 0 0 0 0 1 0
167 69.93 294.5 0 0 0 0 0 0 0 0 0 0 1
168 68.44 294.1 0 0 0 0 0 0 0 0 0 0 0
169 67.16 294.5 1 0 0 0 0 0 0 0 0 0 0
170 66.01 297.1 0 1 0 0 0 0 0 0 0 0 0
171 67.25 289.4 0 0 1 0 0 0 0 0 0 0 0
172 70.91 292.4 0 0 0 1 0 0 0 0 0 0 0
173 69.75 287.9 0 0 0 0 1 0 0 0 0 0 0
174 68.59 286.6 0 0 0 0 0 1 0 0 0 0 0
175 67.48 280.5 0 0 0 0 0 0 1 0 0 0 0
176 66.31 272.4 0 0 0 0 0 0 0 1 0 0 0
177 64.81 269.2 0 0 0 0 0 0 0 0 1 0 0
178 66.58 270.6 0 0 0 0 0 0 0 0 0 1 0
179 65.97 267.3 0 0 0 0 0 0 0 0 0 0 1
180 64.70 262.5 0 0 0 0 0 0 0 0 0 0 0
181 64.70 266.8 1 0 0 0 0 0 0 0 0 0 0
182 60.94 268.8 0 1 0 0 0 0 0 0 0 0 0
183 59.08 263.1 0 0 1 0 0 0 0 0 0 0 0
184 58.42 261.2 0 0 0 1 0 0 0 0 0 0 0
185 57.77 266.0 0 0 0 0 1 0 0 0 0 0 0
186 57.11 262.5 0 0 0 0 0 1 0 0 0 0 0
187 53.31 265.2 0 0 0 0 0 0 1 0 0 0 0
188 49.96 261.3 0 0 0 0 0 0 0 1 0 0 0
189 49.40 253.7 0 0 0 0 0 0 0 0 1 0 0
190 48.84 249.2 0 0 0 0 0 0 0 0 0 1 0
191 48.30 239.1 0 0 0 0 0 0 0 0 0 0 1
192 47.74 236.4 0 0 0 0 0 0 0 0 0 0 0
193 47.24 235.2 1 0 0 0 0 0 0 0 0 0 0
194 46.76 245.2 0 1 0 0 0 0 0 0 0 0 0
195 46.29 246.2 0 0 1 0 0 0 0 0 0 0 0
196 48.90 247.7 0 0 0 1 0 0 0 0 0 0 0
197 49.23 251.4 0 0 0 0 1 0 0 0 0 0 0
198 48.53 253.3 0 0 0 0 0 1 0 0 0 0 0
199 48.03 254.8 0 0 0 0 0 0 1 0 0 0 0
200 54.34 250.0 0 0 0 0 0 0 0 1 0 0 0
201 53.79 249.3 0 0 0 0 0 0 0 0 1 0 0
202 53.24 241.5 0 0 0 0 0 0 0 0 0 1 0
203 52.96 243.3 0 0 0 0 0 0 0 0 0 0 1
204 52.17 248.0 0 0 0 0 0 0 0 0 0 0 0
205 51.70 253.0 1 0 0 0 0 0 0 0 0 0 0
206 58.55 252.9 0 1 0 0 0 0 0 0 0 0 0
207 78.20 251.5 0 0 1 0 0 0 0 0 0 0 0
208 77.03 251.6 0 0 0 1 0 0 0 0 0 0 0
209 76.19 253.5 0 0 0 0 1 0 0 0 0 0 0
210 77.15 259.8 0 0 0 0 0 1 0 0 0 0 0
211 75.87 334.1 0 0 0 0 0 0 1 0 0 0 0
212 95.47 448.0 0 0 0 0 0 0 0 1 0 0 0
213 109.67 445.8 0 0 0 0 0 0 0 0 1 0 0
214 112.28 445.0 0 0 0 0 0 0 0 0 0 1 0
215 112.01 448.2 0 0 0 0 0 0 0 0 0 0 1
216 107.93 438.2 0 0 0 0 0 0 0 0 0 0 0
217 105.96 439.8 1 0 0 0 0 0 0 0 0 0 0
218 105.06 423.4 0 1 0 0 0 0 0 0 0 0 0
219 102.98 410.8 0 0 1 0 0 0 0 0 0 0 0
220 102.20 408.4 0 0 0 1 0 0 0 0 0 0 0
221 105.23 406.7 0 0 0 0 1 0 0 0 0 0 0
222 101.85 405.9 0 0 0 0 0 1 0 0 0 0 0
223 99.89 402.7 0 0 0 0 0 0 1 0 0 0 0
224 96.23 405.1 0 0 0 0 0 0 0 1 0 0 0
225 94.76 399.6 0 0 0 0 0 0 0 0 1 0 0
226 91.51 386.5 0 0 0 0 0 0 0 0 0 1 0
227 91.63 381.4 0 0 0 0 0 0 0 0 0 0 1
228 91.54 375.2 0 0 0 0 0 0 0 0 0 0 0
229 85.23 357.7 1 0 0 0 0 0 0 0 0 0 0
230 87.83 359.0 0 1 0 0 0 0 0 0 0 0 0
231 87.38 355.0 0 0 1 0 0 0 0 0 0 0 0
232 84.44 352.7 0 0 0 1 0 0 0 0 0 0 0
233 85.19 344.4 0 0 0 0 1 0 0 0 0 0 0
234 84.03 343.8 0 0 0 0 0 1 0 0 0 0 0
235 86.73 338.0 0 0 0 0 0 0 1 0 0 0 0
236 102.52 339.0 0 0 0 0 0 0 0 1 0 0 0
237 104.45 333.3 0 0 0 0 0 0 0 0 1 0 0
238 106.98 334.4 0 0 0 0 0 0 0 0 0 1 0
239 107.02 328.3 0 0 0 0 0 0 0 0 0 0 1
240 99.26 330.7 0 0 0 0 0 0 0 0 0 0 0
241 94.45 330.0 1 0 0 0 0 0 0 0 0 0 0
242 113.44 331.6 0 1 0 0 0 0 0 0 0 0 0
243 157.33 351.2 0 0 1 0 0 0 0 0 0 0 0
244 147.38 389.4 0 0 0 1 0 0 0 0 0 0 0
245 171.89 410.9 0 0 0 0 1 0 0 0 0 0 0
246 171.95 442.8 0 0 0 0 0 1 0 0 0 0 0
247 132.71 462.8 0 0 0 0 0 0 1 0 0 0 0
248 126.02 466.9 0 0 0 0 0 0 0 1 0 0 0
249 121.18 461.7 0 0 0 0 0 0 0 0 1 0 0
250 115.45 439.2 0 0 0 0 0 0 0 0 0 1 0
251 110.48 430.3 0 0 0 0 0 0 0 0 0 0 1
252 117.85 416.1 0 0 0 0 0 0 0 0 0 0 0
253 117.63 402.5 1 0 0 0 0 0 0 0 0 0 0
254 124.65 397.3 0 1 0 0 0 0 0 0 0 0 0
255 109.59 403.3 0 0 1 0 0 0 0 0 0 0 0
256 111.27 395.9 0 0 0 1 0 0 0 0 0 0 0
257 99.78 387.8 0 0 0 0 1 0 0 0 0 0 0
258 98.21 378.6 0 0 0 0 0 1 0 0 0 0 0
259 99.20 377.1 0 0 0 0 0 0 1 0 0 0 0
260 97.97 370.4 0 0 0 0 0 0 0 1 0 0 0
261 89.55 362.0 0 0 0 0 0 0 0 0 1 0 0
262 87.91 350.3 0 0 0 0 0 0 0 0 0 1 0
263 93.34 348.2 0 0 0 0 0 0 0 0 0 0 1
264 94.42 344.6 0 0 0 0 0 0 0 0 0 0 0
265 93.20 343.5 1 0 0 0 0 0 0 0 0 0 0
266 90.29 342.8 0 1 0 0 0 0 0 0 0 0 0
267 91.46 347.6 0 0 1 0 0 0 0 0 0 0 0
268 89.98 346.6 0 0 0 1 0 0 0 0 0 0 0
269 88.35 349.5 0 0 0 0 1 0 0 0 0 0 0
270 88.41 342.1 0 0 0 0 0 1 0 0 0 0 0
271 82.44 342.0 0 0 0 0 0 0 1 0 0 0 0
272 79.89 342.8 0 0 0 0 0 0 0 1 0 0 0
273 75.69 339.3 0 0 0 0 0 0 0 0 1 0 0
274 75.66 348.2 0 0 0 0 0 0 0 0 0 1 0
275 84.50 333.7 0 0 0 0 0 0 0 0 0 0 1
276 96.73 334.7 0 0 0 0 0 0 0 0 0 0 0
277 87.48 354.0 1 0 0 0 0 0 0 0 0 0 0
278 82.39 367.7 0 1 0 0 0 0 0 0 0 0 0
279 83.48 363.3 0 0 1 0 0 0 0 0 0 0 0
280 79.31 358.4 0 0 0 1 0 0 0 0 0 0 0
281 78.16 353.1 0 0 0 0 1 0 0 0 0 0 0
282 72.77 343.1 0 0 0 0 0 1 0 0 0 0 0
283 72.45 344.6 0 0 0 0 0 0 1 0 0 0 0
284 68.46 344.4 0 0 0 0 0 0 0 1 0 0 0
285 67.62 333.9 0 0 0 0 0 0 0 0 1 0 0
286 68.76 331.7 0 0 0 0 0 0 0 0 0 1 0
287 70.07 324.3 0 0 0 0 0 0 0 0 0 0 1
288 68.55 321.2 0 0 0 0 0 0 0 0 0 0 0
289 65.30 322.4 1 0 0 0 0 0 0 0 0 0 0
290 58.96 321.7 0 1 0 0 0 0 0 0 0 0 0
291 59.17 320.5 0 0 1 0 0 0 0 0 0 0 0
292 62.37 312.8 0 0 0 1 0 0 0 0 0 0 0
293 66.28 309.7 0 0 0 0 1 0 0 0 0 0 0
294 55.62 315.6 0 0 0 0 0 1 0 0 0 0 0
295 55.23 309.7 0 0 0 0 0 0 1 0 0 0 0
296 55.85 304.6 0 0 0 0 0 0 0 1 0 0 0
297 56.75 302.5 0 0 0 0 0 0 0 0 1 0 0
298 50.89 301.5 0 0 0 0 0 0 0 0 0 1 0
299 53.88 298.8 0 0 0 0 0 0 0 0 0 0 1
300 52.95 291.3 0 0 0 0 0 0 0 0 0 0 0
301 55.08 293.6 1 0 0 0 0 0 0 0 0 0 0
302 53.61 294.6 0 1 0 0 0 0 0 0 0 0 0
303 58.78 285.9 0 0 1 0 0 0 0 0 0 0 0
304 61.85 297.6 0 0 0 1 0 0 0 0 0 0 0
305 55.91 301.1 0 0 0 0 1 0 0 0 0 0 0
306 53.32 293.8 0 0 0 0 0 1 0 0 0 0 0
307 46.41 297.7 0 0 0 0 0 0 1 0 0 0 0
308 44.57 292.9 0 0 0 0 0 0 0 1 0 0 0
309 50.00 292.1 0 0 0 0 0 0 0 0 1 0 0
310 50.00 287.2 0 0 0 0 0 0 0 0 0 1 0
311 53.36 288.2 0 0 0 0 0 0 0 0 0 0 1
312 46.23 283.8 0 0 0 0 0 0 0 0 0 0 0
313 50.45 299.9 1 0 0 0 0 0 0 0 0 0 0
314 49.07 292.4 0 1 0 0 0 0 0 0 0 0 0
315 45.85 293.3 0 0 1 0 0 0 0 0 0 0 0
316 48.45 300.8 0 0 0 1 0 0 0 0 0 0 0
317 49.96 293.7 0 0 0 0 1 0 0 0 0 0 0
318 46.53 293.1 0 0 0 0 0 1 0 0 0 0 0
319 50.51 294.4 0 0 0 0 0 0 1 0 0 0 0
320 47.58 292.1 0 0 0 0 0 0 0 1 0 0 0
321 48.05 291.9 0 0 0 0 0 0 0 0 1 0 0
322 46.84 282.5 0 0 0 0 0 0 0 0 0 1 0
323 47.67 277.9 0 0 0 0 0 0 0 0 0 0 1
324 49.16 287.5 0 0 0 0 0 0 0 0 0 0 0
325 55.54 289.2 1 0 0 0 0 0 0 0 0 0 0
326 55.82 285.6 0 1 0 0 0 0 0 0 0 0 0
327 58.22 293.2 0 0 1 0 0 0 0 0 0 0 0
328 56.19 290.8 0 0 0 1 0 0 0 0 0 0 0
329 57.77 283.1 0 0 0 0 1 0 0 0 0 0 0
330 63.19 275.0 0 0 0 0 0 1 0 0 0 0 0
331 54.76 287.8 0 0 0 0 0 0 1 0 0 0 0
332 55.74 287.8 0 0 0 0 0 0 0 1 0 0 0
333 62.54 287.4 0 0 0 0 0 0 0 0 1 0 0
334 61.39 284.0 0 0 0 0 0 0 0 0 0 1 0
335 69.60 277.8 0 0 0 0 0 0 0 0 0 0 1
336 79.23 277.6 0 0 0 0 0 0 0 0 0 0 0
337 80.00 304.9 1 0 0 0 0 0 0 0 0 0 0
338 93.68 294.0 0 1 0 0 0 0 0 0 0 0 0
339 107.63 300.9 0 0 1 0 0 0 0 0 0 0 0
340 100.18 324.0 0 0 0 1 0 0 0 0 0 0 0
341 97.30 332.9 0 0 0 0 1 0 0 0 0 0 0
342 90.45 341.6 0 0 0 0 0 1 0 0 0 0 0
343 80.64 333.4 0 0 0 0 0 0 1 0 0 0 0
344 80.58 348.2 0 0 0 0 0 0 0 1 0 0 0
345 75.82 344.7 0 0 0 0 0 0 0 0 1 0 0
346 85.59 344.7 0 0 0 0 0 0 0 0 0 1 0
347 89.35 329.3 0 0 0 0 0 0 0 0 0 0 1
348 89.42 323.5 0 0 0 0 0 0 0 0 0 0 0
349 104.73 323.2 1 0 0 0 0 0 0 0 0 0 0
350 95.32 317.4 0 1 0 0 0 0 0 0 0 0 0
351 89.27 330.1 0 0 1 0 0 0 0 0 0 0 0
352 90.44 329.2 0 0 0 1 0 0 0 0 0 0 0
353 86.97 334.9 0 0 0 0 1 0 0 0 0 0 0
354 79.98 315.8 0 0 0 0 0 1 0 0 0 0 0
355 81.22 315.4 0 0 0 0 0 0 1 0 0 0 0
356 87.35 319.6 0 0 0 0 0 0 0 1 0 0 0
357 83.64 317.3 0 0 0 0 0 0 0 0 1 0 0
358 82.22 313.8 0 0 0 0 0 0 0 0 0 1 0
359 94.40 315.8 0 0 0 0 0 0 0 0 0 0 1
360 102.18 311.3 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `USA\r` M1 M2 M3 M4
-5.4752 0.2752 -0.6002 -1.0911 0.9146 0.1840
M5 M6 M7 M8 M9 M10
-0.1833 -1.5158 -5.3053 -5.5319 -4.4566 -3.6082
M11
-0.8007
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.2942 -7.2074 -0.5488 6.8433 65.2539
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.47520 5.00185 -1.095 0.274
`USA\r` 0.27516 0.01442 19.078 <2e-16 ***
M1 -0.60023 3.44804 -0.174 0.862
M2 -1.09110 3.44835 -0.316 0.752
M3 0.91459 3.44842 0.265 0.791
M4 0.18399 3.44903 0.053 0.957
M5 -0.18333 3.44940 -0.053 0.958
M6 -1.51575 3.44941 -0.439 0.661
M7 -5.30526 3.45131 -1.537 0.125
M8 -5.53187 3.45284 -1.602 0.110
M9 -4.45665 3.45057 -1.292 0.197
M10 -3.60820 3.44927 -1.046 0.296
M11 -0.80066 3.44816 -0.232 0.817
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.35 on 347 degrees of freedom
Multiple R-squared: 0.515, Adjusted R-squared: 0.4982
F-statistic: 30.7 on 12 and 347 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,] 5.189631e-05 1.037926e-04 0.999948104
[2,] 2.511548e-05 5.023097e-05 0.999974885
[3,] 6.425874e-05 1.285175e-04 0.999935741
[4,] 2.266946e-05 4.533891e-05 0.999977331
[5,] 4.673567e-06 9.347133e-06 0.999995326
[6,] 7.917072e-07 1.583414e-06 0.999999208
[7,] 1.443967e-07 2.887935e-07 0.999999856
[8,] 3.261545e-08 6.523091e-08 0.999999967
[9,] 7.673461e-09 1.534692e-08 0.999999992
[10,] 1.175827e-08 2.351653e-08 0.999999988
[11,] 1.600209e-08 3.200418e-08 0.999999984
[12,] 1.557788e-06 3.115577e-06 0.999998442
[13,] 5.423260e-06 1.084652e-05 0.999994577
[14,] 4.344360e-06 8.688720e-06 0.999995656
[15,] 1.877267e-06 3.754535e-06 0.999998123
[16,] 8.681125e-07 1.736225e-06 0.999999132
[17,] 5.387998e-07 1.077600e-06 0.999999461
[18,] 2.229790e-07 4.459580e-07 0.999999777
[19,] 8.023593e-08 1.604719e-07 0.999999920
[20,] 2.668944e-08 5.337888e-08 0.999999973
[21,] 8.300062e-09 1.660012e-08 0.999999992
[22,] 2.937676e-09 5.875353e-09 0.999999997
[23,] 1.069974e-09 2.139947e-09 0.999999999
[24,] 3.091744e-10 6.183488e-10 1.000000000
[25,] 1.581497e-10 3.162994e-10 1.000000000
[26,] 6.765399e-11 1.353080e-10 1.000000000
[27,] 2.056126e-11 4.112251e-11 1.000000000
[28,] 5.771616e-12 1.154323e-11 1.000000000
[29,] 1.604525e-12 3.209050e-12 1.000000000
[30,] 4.517665e-13 9.035330e-13 1.000000000
[31,] 1.306201e-13 2.612403e-13 1.000000000
[32,] 3.675698e-14 7.351396e-14 1.000000000
[33,] 1.136072e-14 2.272143e-14 1.000000000
[34,] 3.095020e-15 6.190040e-15 1.000000000
[35,] 9.213617e-16 1.842723e-15 1.000000000
[36,] 3.996018e-16 7.992037e-16 1.000000000
[37,] 1.567235e-16 3.134470e-16 1.000000000
[38,] 6.164024e-17 1.232805e-16 1.000000000
[39,] 1.778021e-17 3.556041e-17 1.000000000
[40,] 5.292368e-18 1.058474e-17 1.000000000
[41,] 1.531545e-18 3.063090e-18 1.000000000
[42,] 4.693286e-19 9.386572e-19 1.000000000
[43,] 1.603155e-19 3.206311e-19 1.000000000
[44,] 9.285152e-20 1.857030e-19 1.000000000
[45,] 2.887478e-20 5.774957e-20 1.000000000
[46,] 8.594947e-21 1.718989e-20 1.000000000
[47,] 2.746478e-21 5.492956e-21 1.000000000
[48,] 1.082403e-21 2.164806e-21 1.000000000
[49,] 4.354428e-22 8.708857e-22 1.000000000
[50,] 1.535945e-22 3.071891e-22 1.000000000
[51,] 4.398525e-23 8.797050e-23 1.000000000
[52,] 1.345556e-23 2.691113e-23 1.000000000
[53,] 3.935538e-24 7.871077e-24 1.000000000
[54,] 1.204169e-24 2.408337e-24 1.000000000
[55,] 7.278956e-25 1.455791e-24 1.000000000
[56,] 2.385234e-25 4.770468e-25 1.000000000
[57,] 6.673112e-26 1.334622e-25 1.000000000
[58,] 2.756066e-26 5.512132e-26 1.000000000
[59,] 1.200257e-26 2.400513e-26 1.000000000
[60,] 5.033134e-27 1.006627e-26 1.000000000
[61,] 1.469488e-27 2.938975e-27 1.000000000
[62,] 4.142418e-28 8.284836e-28 1.000000000
[63,] 1.502994e-28 3.005988e-28 1.000000000
[64,] 7.398509e-29 1.479702e-28 1.000000000
[65,] 5.413426e-29 1.082685e-28 1.000000000
[66,] 2.035540e-29 4.071080e-29 1.000000000
[67,] 9.813100e-30 1.962620e-29 1.000000000
[68,] 5.570516e-30 1.114103e-29 1.000000000
[69,] 4.925801e-30 9.851602e-30 1.000000000
[70,] 1.059732e-29 2.119464e-29 1.000000000
[71,] 3.127332e-29 6.254664e-29 1.000000000
[72,] 5.492000e-29 1.098400e-28 1.000000000
[73,] 1.049572e-28 2.099144e-28 1.000000000
[74,] 1.672957e-28 3.345914e-28 1.000000000
[75,] 4.319621e-28 8.639242e-28 1.000000000
[76,] 2.082060e-27 4.164119e-27 1.000000000
[77,] 3.364050e-27 6.728100e-27 1.000000000
[78,] 5.520405e-27 1.104081e-26 1.000000000
[79,] 3.311812e-26 6.623625e-26 1.000000000
[80,] 2.449604e-25 4.899208e-25 1.000000000
[81,] 1.001614e-24 2.003228e-24 1.000000000
[82,] 1.174203e-23 2.348406e-23 1.000000000
[83,] 1.384888e-22 2.769777e-22 1.000000000
[84,] 4.098721e-22 8.197441e-22 1.000000000
[85,] 3.892434e-21 7.784869e-21 1.000000000
[86,] 6.672794e-20 1.334559e-19 1.000000000
[87,] 4.162022e-19 8.324043e-19 1.000000000
[88,] 3.465968e-18 6.931937e-18 1.000000000
[89,] 2.964303e-17 5.928606e-17 1.000000000
[90,] 1.322888e-16 2.645777e-16 1.000000000
[91,] 1.277287e-15 2.554574e-15 1.000000000
[92,] 3.969227e-15 7.938454e-15 1.000000000
[93,] 8.862530e-15 1.772506e-14 1.000000000
[94,] 1.327627e-14 2.655255e-14 1.000000000
[95,] 2.481302e-14 4.962605e-14 1.000000000
[96,] 1.315343e-14 2.630686e-14 1.000000000
[97,] 6.955769e-15 1.391154e-14 1.000000000
[98,] 4.186091e-15 8.372183e-15 1.000000000
[99,] 2.323575e-15 4.647150e-15 1.000000000
[100,] 1.316122e-15 2.632243e-15 1.000000000
[101,] 8.100264e-16 1.620053e-15 1.000000000
[102,] 5.379979e-16 1.075996e-15 1.000000000
[103,] 3.039956e-16 6.079913e-16 1.000000000
[104,] 1.602289e-16 3.204579e-16 1.000000000
[105,] 7.583049e-17 1.516610e-16 1.000000000
[106,] 3.537804e-17 7.075608e-17 1.000000000
[107,] 1.649291e-17 3.298581e-17 1.000000000
[108,] 7.469859e-18 1.493972e-17 1.000000000
[109,] 3.458998e-18 6.917996e-18 1.000000000
[110,] 1.639327e-18 3.278655e-18 1.000000000
[111,] 7.923809e-19 1.584762e-18 1.000000000
[112,] 4.358944e-19 8.717888e-19 1.000000000
[113,] 2.642627e-19 5.285254e-19 1.000000000
[114,] 1.524457e-19 3.048914e-19 1.000000000
[115,] 8.348491e-20 1.669698e-19 1.000000000
[116,] 4.455803e-20 8.911607e-20 1.000000000
[117,] 2.301372e-20 4.602743e-20 1.000000000
[118,] 1.250615e-20 2.501229e-20 1.000000000
[119,] 6.433890e-21 1.286778e-20 1.000000000
[120,] 3.101111e-21 6.202222e-21 1.000000000
[121,] 1.478738e-21 2.957476e-21 1.000000000
[122,] 7.030523e-22 1.406105e-21 1.000000000
[123,] 3.521084e-22 7.042167e-22 1.000000000
[124,] 2.038844e-22 4.077688e-22 1.000000000
[125,] 1.281800e-22 2.563599e-22 1.000000000
[126,] 9.195795e-23 1.839159e-22 1.000000000
[127,] 6.109941e-23 1.221988e-22 1.000000000
[128,] 3.057342e-23 6.114684e-23 1.000000000
[129,] 1.622930e-23 3.245860e-23 1.000000000
[130,] 1.015921e-23 2.031841e-23 1.000000000
[131,] 9.712910e-24 1.942582e-23 1.000000000
[132,] 1.105151e-23 2.210302e-23 1.000000000
[133,] 5.598789e-24 1.119758e-23 1.000000000
[134,] 3.433082e-24 6.866164e-24 1.000000000
[135,] 1.861340e-24 3.722679e-24 1.000000000
[136,] 1.062830e-24 2.125660e-24 1.000000000
[137,] 6.491409e-25 1.298282e-24 1.000000000
[138,] 4.432795e-25 8.865590e-25 1.000000000
[139,] 3.115936e-25 6.231873e-25 1.000000000
[140,] 1.878098e-25 3.756195e-25 1.000000000
[141,] 1.376602e-25 2.753203e-25 1.000000000
[142,] 1.509769e-25 3.019539e-25 1.000000000
[143,] 2.016150e-25 4.032300e-25 1.000000000
[144,] 1.546657e-25 3.093314e-25 1.000000000
[145,] 9.322315e-26 1.864463e-25 1.000000000
[146,] 6.839771e-26 1.367954e-25 1.000000000
[147,] 7.027077e-26 1.405415e-25 1.000000000
[148,] 9.704755e-26 1.940951e-25 1.000000000
[149,] 1.567333e-25 3.134666e-25 1.000000000
[150,] 9.804069e-26 1.960814e-25 1.000000000
[151,] 7.143760e-26 1.428752e-25 1.000000000
[152,] 6.277198e-26 1.255440e-25 1.000000000
[153,] 6.520689e-26 1.304138e-25 1.000000000
[154,] 9.887864e-26 1.977573e-25 1.000000000
[155,] 1.519625e-25 3.039250e-25 1.000000000
[156,] 1.560080e-25 3.120160e-25 1.000000000
[157,] 1.050321e-25 2.100642e-25 1.000000000
[158,] 6.777023e-26 1.355405e-25 1.000000000
[159,] 4.823742e-26 9.647484e-26 1.000000000
[160,] 3.642528e-26 7.285056e-26 1.000000000
[161,] 2.981153e-26 5.962306e-26 1.000000000
[162,] 2.868602e-26 5.737204e-26 1.000000000
[163,] 2.386272e-26 4.772544e-26 1.000000000
[164,] 2.061931e-26 4.123862e-26 1.000000000
[165,] 1.735797e-26 3.471594e-26 1.000000000
[166,] 1.794610e-26 3.589220e-26 1.000000000
[167,] 2.611668e-26 5.223335e-26 1.000000000
[168,] 4.949565e-26 9.899130e-26 1.000000000
[169,] 1.129220e-25 2.258440e-25 1.000000000
[170,] 2.707588e-25 5.415177e-25 1.000000000
[171,] 5.901608e-25 1.180322e-24 1.000000000
[172,] 2.485155e-24 4.970311e-24 1.000000000
[173,] 1.704138e-23 3.408276e-23 1.000000000
[174,] 1.023263e-22 2.046527e-22 1.000000000
[175,] 5.855262e-22 1.171052e-21 1.000000000
[176,] 2.825836e-21 5.651672e-21 1.000000000
[177,] 1.093358e-20 2.186715e-20 1.000000000
[178,] 4.569994e-20 9.139987e-20 1.000000000
[179,] 1.782440e-19 3.564879e-19 1.000000000
[180,] 7.997216e-19 1.599443e-18 1.000000000
[181,] 2.190447e-18 4.380895e-18 1.000000000
[182,] 5.128848e-18 1.025770e-17 1.000000000
[183,] 1.246187e-17 2.492373e-17 1.000000000
[184,] 2.598440e-17 5.196880e-17 1.000000000
[185,] 2.697127e-17 5.394254e-17 1.000000000
[186,] 2.948281e-17 5.896562e-17 1.000000000
[187,] 3.191072e-17 6.382145e-17 1.000000000
[188,] 3.521024e-17 7.042048e-17 1.000000000
[189,] 4.169521e-17 8.339041e-17 1.000000000
[190,] 6.217362e-17 1.243472e-16 1.000000000
[191,] 4.312259e-17 8.624518e-17 1.000000000
[192,] 7.615996e-17 1.523199e-16 1.000000000
[193,] 1.396556e-16 2.793111e-16 1.000000000
[194,] 2.259448e-16 4.518895e-16 1.000000000
[195,] 4.067919e-16 8.135839e-16 1.000000000
[196,] 2.405179e-16 4.810358e-16 1.000000000
[197,] 4.577225e-16 9.154450e-16 1.000000000
[198,] 3.365334e-16 6.730668e-16 1.000000000
[199,] 2.556767e-16 5.113533e-16 1.000000000
[200,] 2.645988e-16 5.291975e-16 1.000000000
[201,] 3.419493e-16 6.838986e-16 1.000000000
[202,] 4.481424e-16 8.962849e-16 1.000000000
[203,] 4.849114e-16 9.698228e-16 1.000000000
[204,] 4.871303e-16 9.742605e-16 1.000000000
[205,] 3.949368e-16 7.898736e-16 1.000000000
[206,] 3.080910e-16 6.161821e-16 1.000000000
[207,] 2.300067e-16 4.600134e-16 1.000000000
[208,] 1.418037e-16 2.836074e-16 1.000000000
[209,] 1.000930e-16 2.001860e-16 1.000000000
[210,] 7.156661e-17 1.431332e-16 1.000000000
[211,] 4.988130e-17 9.976260e-17 1.000000000
[212,] 4.247399e-17 8.494799e-17 1.000000000
[213,] 3.648234e-17 7.296467e-17 1.000000000
[214,] 2.452856e-17 4.905712e-17 1.000000000
[215,] 1.555543e-17 3.111087e-17 1.000000000
[216,] 1.014242e-17 2.028485e-17 1.000000000
[217,] 6.468182e-18 1.293636e-17 1.000000000
[218,] 3.395900e-18 6.791799e-18 1.000000000
[219,] 1.748275e-18 3.496550e-18 1.000000000
[220,] 1.109353e-18 2.218707e-18 1.000000000
[221,] 7.813010e-18 1.562602e-17 1.000000000
[222,] 9.998257e-17 1.999651e-16 1.000000000
[223,] 1.472070e-15 2.944140e-15 1.000000000
[224,] 1.356431e-14 2.712863e-14 1.000000000
[225,] 1.955518e-14 3.911037e-14 1.000000000
[226,] 1.949950e-14 3.899901e-14 1.000000000
[227,] 6.022661e-13 1.204532e-12 1.000000000
[228,] 1.545495e-06 3.090990e-06 0.999998455
[229,] 1.076307e-04 2.152613e-04 0.999892369
[230,] 3.559497e-02 7.118995e-02 0.964405026
[231,] 2.869834e-01 5.739667e-01 0.713016641
[232,] 2.731278e-01 5.462555e-01 0.726872243
[233,] 2.549274e-01 5.098547e-01 0.745072628
[234,] 2.440442e-01 4.880884e-01 0.755955799
[235,] 2.285536e-01 4.571071e-01 0.771446429
[236,] 2.474403e-01 4.948806e-01 0.752559703
[237,] 2.387657e-01 4.775313e-01 0.761234329
[238,] 2.204020e-01 4.408040e-01 0.779598011
[239,] 2.303199e-01 4.606398e-01 0.769680079
[240,] 2.107649e-01 4.215299e-01 0.789235066
[241,] 1.879271e-01 3.758541e-01 0.812072933
[242,] 1.707608e-01 3.415215e-01 0.829239236
[243,] 1.505866e-01 3.011732e-01 0.849413418
[244,] 1.320560e-01 2.641121e-01 0.867943963
[245,] 1.172265e-01 2.344530e-01 0.882773518
[246,] 1.014600e-01 2.029201e-01 0.898539964
[247,] 8.782384e-02 1.756477e-01 0.912176156
[248,] 7.489144e-02 1.497829e-01 0.925108559
[249,] 6.357422e-02 1.271484e-01 0.936425781
[250,] 5.514929e-02 1.102986e-01 0.944850710
[251,] 4.670239e-02 9.340477e-02 0.953297613
[252,] 3.894230e-02 7.788461e-02 0.961057696
[253,] 3.253504e-02 6.507007e-02 0.967464965
[254,] 2.691452e-02 5.382904e-02 0.973085478
[255,] 2.292645e-02 4.585289e-02 0.977073555
[256,] 1.902741e-02 3.805483e-02 0.980972587
[257,] 1.566228e-02 3.132455e-02 0.984337724
[258,] 1.321639e-02 2.643278e-02 0.986783610
[259,] 1.218442e-02 2.436883e-02 0.987815585
[260,] 9.644048e-03 1.928810e-02 0.990355952
[261,] 8.585930e-03 1.717186e-02 0.991414070
[262,] 6.898715e-03 1.379743e-02 0.993101285
[263,] 8.103327e-03 1.620665e-02 0.991896673
[264,] 9.148810e-03 1.829762e-02 0.990851190
[265,] 1.070830e-02 2.141660e-02 0.989291700
[266,] 1.177382e-02 2.354764e-02 0.988226180
[267,] 1.259239e-02 2.518478e-02 0.987407612
[268,] 1.229241e-02 2.458482e-02 0.987707589
[269,] 1.411617e-02 2.823234e-02 0.985883828
[270,] 1.453070e-02 2.906139e-02 0.985469304
[271,] 1.481937e-02 2.963873e-02 0.985180633
[272,] 1.579950e-02 3.159901e-02 0.984200497
[273,] 1.883585e-02 3.767171e-02 0.981164147
[274,] 2.256566e-02 4.513133e-02 0.977434335
[275,] 4.394614e-02 8.789228e-02 0.956053860
[276,] 7.930238e-02 1.586048e-01 0.920697616
[277,] 8.176233e-02 1.635247e-01 0.918237674
[278,] 7.240703e-02 1.448141e-01 0.927592968
[279,] 9.630370e-02 1.926074e-01 0.903696300
[280,] 9.673508e-02 1.934702e-01 0.903264924
[281,] 8.877771e-02 1.775554e-01 0.911222291
[282,] 8.046872e-02 1.609374e-01 0.919531276
[283,] 8.812481e-02 1.762496e-01 0.911875194
[284,] 1.040211e-01 2.080422e-01 0.895978892
[285,] 1.164835e-01 2.329669e-01 0.883516534
[286,] 1.122285e-01 2.244571e-01 0.887771452
[287,] 1.275556e-01 2.551113e-01 0.872444353
[288,] 1.118077e-01 2.236154e-01 0.888192304
[289,] 9.682398e-02 1.936480e-01 0.903176022
[290,] 9.363554e-02 1.872711e-01 0.906364464
[291,] 8.716429e-02 1.743286e-01 0.912835708
[292,] 9.159115e-02 1.831823e-01 0.908408849
[293,] 9.023919e-02 1.804784e-01 0.909760814
[294,] 8.056383e-02 1.611277e-01 0.919436168
[295,] 7.154732e-02 1.430946e-01 0.928452680
[296,] 7.078679e-02 1.415736e-01 0.929213213
[297,] 9.514952e-02 1.902990e-01 0.904850476
[298,] 1.362975e-01 2.725951e-01 0.863702461
[299,] 2.048716e-01 4.097433e-01 0.795128352
[300,] 3.163423e-01 6.326846e-01 0.683657715
[301,] 4.046485e-01 8.092969e-01 0.595351530
[302,] 4.203063e-01 8.406125e-01 0.579693730
[303,] 4.721596e-01 9.443192e-01 0.527840375
[304,] 4.522194e-01 9.044388e-01 0.547780595
[305,] 4.345558e-01 8.691116e-01 0.565444194
[306,] 4.186198e-01 8.372395e-01 0.581380243
[307,] 3.998929e-01 7.997858e-01 0.600107124
[308,] 4.409294e-01 8.818588e-01 0.559070583
[309,] 6.396751e-01 7.206499e-01 0.360324939
[310,] 7.125423e-01 5.749153e-01 0.287457664
[311,] 8.306822e-01 3.386356e-01 0.169317822
[312,] 9.310461e-01 1.379077e-01 0.068953852
[313,] 9.661103e-01 6.777945e-02 0.033889724
[314,] 9.686319e-01 6.273616e-02 0.031368079
[315,] 9.529314e-01 9.413711e-02 0.047068553
[316,] 9.530484e-01 9.390312e-02 0.046951558
[317,] 9.558348e-01 8.833045e-02 0.044165226
[318,] 9.371661e-01 1.256679e-01 0.062833928
[319,] 9.335944e-01 1.328112e-01 0.066405610
[320,] 9.470644e-01 1.058712e-01 0.052935584
[321,] 9.601160e-01 7.976794e-02 0.039883972
[322,] 9.933360e-01 1.332796e-02 0.006663982
[323,] 9.876704e-01 2.465926e-02 0.012329629
[324,] 9.905378e-01 1.892433e-02 0.009462165
[325,] 9.862702e-01 2.745954e-02 0.013729770
[326,] 9.818913e-01 3.621744e-02 0.018108719
[327,] 9.882928e-01 2.341435e-02 0.011707177
[328,] 9.657044e-01 6.859117e-02 0.034295585
[329,] 9.036908e-01 1.926185e-01 0.096309229
> postscript(file="/var/www/rcomp/tmp/1sq7o1289591610.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2lips1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3lips1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lips1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5lips1289591610.ps",horizontal=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
23.18928302 16.74608310 9.12971029 -1.12353087 -9.71685452 -10.56504883
7 8 9 10 11 12
-5.62234377 -3.72202779 -3.75870277 -3.51114976 -3.65681574 -3.58912081
13 14 15 16 17 18
-2.19798373 -0.96653576 -2.67680967 0.97572823 2.46892454 5.82773288
19 20 21 22 23 24
10.78311546 14.77962569 15.28575734 14.53801986 11.09812706 9.95020873
25 26 27 28 29 30
11.02363601 11.02266467 1.55310085 2.54676698 4.72689844 8.65606036
31 32 33 34 35 36
8.24202099 3.13933373 2.87236678 2.17562842 -0.54510359 -1.13608676
37 38 39 40 41 42
2.88856704 0.84853027 0.13896645 5.13792307 3.81492513 4.58021674
43 44 45 46 47 48
5.73111429 5.93072017 6.98227142 6.64727602 6.50696509 11.19288625
49 50 51 52 53 54
12.26115215 16.05453674 14.74223084 15.38570331 15.68844774 15.75819064
55 56 57 58 59 60
19.53325046 19.19011426 17.38253552 20.21402339 20.71700178 16.64988890
61 62 63 64 65 66
16.58541272 12.96943991 14.02106887 14.60912173 13.78886587 14.49657679
67 68 69 70 71 72
18.18405591 16.94295170 15.32295365 21.06737726 16.84748446 14.30924336
73 74 75 76 77 78
13.19170233 11.98231109 8.46158530 12.01034826 12.13773764 12.30028718
79 80 81 82 83 84
15.08750808 13.40137159 15.53150266 14.92789370 11.08996832 9.43466296
85 86 87 88 89 90
8.49418619 6.04166555 3.70132708 4.12676698 3.79638200 3.51573759
91 92 93 94 95 96
5.05486137 6.96407992 6.07114614 3.55505332 0.29019279 -0.01562900
97 98 99 100 101 102
-0.85539568 -2.65485147 -3.34060954 -5.57987915 -7.78242522 -4.90758548
103 104 105 106 107 108
-3.22062279 -3.24317800 -2.16820832 -5.18933342 -4.41419395 -3.08859555
109 110 111 112 113 114
-2.48413834 -15.45931158 -8.39829736 -4.38659866 -1.94614443 -0.77320756
115 116 117 118 119 120
3.42498165 5.81626594 8.33420510 6.77353188 4.02541285 1.59984927
121 122 123 124 125 126
1.46308289 2.70772481 0.71293505 2.58005336 4.19395976 4.59408999
127 128 129 130 131 132
7.89908525 9.63475627 6.44640288 5.32692244 7.47132102 6.53366090
133 134 135 136 137 138
4.47476424 6.80308280 4.88593829 4.31779691 4.52041222 4.60557473
139 140 141 142 143 144
5.20908525 5.17888479 2.37601556 2.48482591 2.98919073 -0.14950225
145 146 147 148 149 150
-0.55239833 -5.51295154 -8.05093525 -2.95156050 -5.59501032 -3.55265444
151 152 153 154 155 156
-1.88907937 -1.67660231 -2.29314818 -1.86062744 -0.44348679 -3.80969591
157 158 159 160 161 162
-4.40913982 -7.03904748 -5.02854675 -1.93284864 -4.71387915 -6.06458812
163 164 165 166 167 168
-5.88381969 -6.41427836 -1.14447007 -2.95049834 -4.82916461 -7.00976046
169 170 171 172 173 174
-7.79959169 -9.17414432 -7.82109516 -4.25597804 -3.81042698 -3.28029675
175 176 177 178 179 180
1.07769880 2.36311161 0.66840406 1.20473055 -1.30477493 -2.05466069
181 182 183 184 185 186
-2.63762132 -6.45707711 -8.75435073 -8.16094282 -9.76439264 -8.12890736
187 188 189 190 191 192
-8.88233200 -10.93259701 -10.47659447 -10.64681581 -11.21522387 -11.83294854
193 194 195 196 197 198
-11.40252155 -14.14326842 -16.89412332 -13.96626412 -14.28703642 -14.17742262
199 200 201 202 203 204
-11.30065360 -3.44327336 -4.87588437 -4.12807314 -7.71090169 -10.59482061
205 206 207 208 209 210
-11.84039421 -4.47201109 13.55752134 13.09060648 12.09512467 12.65402838
211 212 213 214 215 216
-5.28095144 -16.79522763 -3.06509657 -1.08341504 -5.04146952 -7.17051607
217 218 219 220 221 222
-8.98054097 -4.87702727 -5.49568733 -4.88469872 -1.01959954 -2.84705000
223 224 225 226 227 228
-0.13702247 -4.23080421 -5.26264057 -5.75647400 -7.04068899 -6.22534880
229 230 231 232 233 234
-7.11979124 -4.38663406 -5.74168203 -7.31820957 -3.91704525 -3.57952798
235 236 237 238 239 240
4.50591915 20.24736336 22.67055927 24.04943417 22.96038057 13.73933284
241 242 243 244 245 246
9.72217913 28.76278789 65.25393123 45.52336760 64.48472264 57.09949488
247 248 249 250 251 252
16.14577828 8.55422219 4.06983741 3.68252100 -1.64608072 8.83055054
253 254 255 256 257 258
12.95297870 21.89468489 3.17802306 7.62481859 -1.26904936 1.02485581
259 260 261 262 263 264
6.21710899 7.05729586 -0.12657249 0.60436814 3.80466900 5.07458958
265 266 267 268 269 270
4.75750043 2.53098038 0.37451222 -0.09972512 -2.16036831 1.26824637
271 272 273 274 275 276
-0.88472639 -3.42824991 -7.74040904 -11.06779295 -1.04549091 10.10868730
277 278 279 280 281 282
-3.85169411 -12.22053811 -11.92552153 -14.01662946 -13.34094930 -14.64691501
283 284 285 286 287 288
-11.59014599 -15.29850813 -14.32453756 -13.42763009 -12.88897389 -14.35663400
289 290 291 292 293 294
-17.33659434 -22.99311439 -24.45861424 -18.40927030 -13.27894518 -24.22997692
295 296 297 298 299 300
-19.20701364 -16.95708499 -16.55447007 -22.98775626 -22.06235857 -21.72930858
301 302 303 304 305 306
-19.63194645 -20.88624085 -15.32803032 -14.74681724 -21.28255727 -20.53145872
307 308 309 310 311 312
-24.72507702 -25.01769679 -20.44279166 -19.94294845 -19.66564788 -26.38559820
313 314 315 316 317 318
-25.99546317 -24.82088580 -30.29422457 -29.02733367 -25.19636301 -27.12884575
319 320 321 322 323 324
-19.71704445 -21.78756768 -22.33775938 -21.80968994 -22.52148562 -24.47369532
325 326 327 328 329 330
-17.96123635 -16.19978839 -17.89670843 -18.53571982 -14.46965233 -5.48842468
331 332 333 334 335 336
-13.65097931 -12.44437372 -6.60953315 -7.67243202 -0.56396948 8.32040239
337 338 339 340 341 342
2.17872990 19.34885598 29.39454891 16.31892219 11.35731068 3.44582707
343 344 345 346 347 348
-0.31833847 -4.22412139 -9.09628052 -0.17472810 5.01521918 5.88049481
349 350 351 352 353 354
21.87327655 14.55007956 2.99983646 5.14808299 0.47698791 0.07499080
355 356 357 358 359 360
5.21456646 10.41549423 6.26314143 4.95775870 13.77989788 21.99746371
> postscript(file="/var/www/rcomp/tmp/6d9ou1289591610.ps",horizontal=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 23.18928302 NA
1 16.74608310 23.18928302
2 9.12971029 16.74608310
3 -1.12353087 9.12971029
4 -9.71685452 -1.12353087
5 -10.56504883 -9.71685452
6 -5.62234377 -10.56504883
7 -3.72202779 -5.62234377
8 -3.75870277 -3.72202779
9 -3.51114976 -3.75870277
10 -3.65681574 -3.51114976
11 -3.58912081 -3.65681574
12 -2.19798373 -3.58912081
13 -0.96653576 -2.19798373
14 -2.67680967 -0.96653576
15 0.97572823 -2.67680967
16 2.46892454 0.97572823
17 5.82773288 2.46892454
18 10.78311546 5.82773288
19 14.77962569 10.78311546
20 15.28575734 14.77962569
21 14.53801986 15.28575734
22 11.09812706 14.53801986
23 9.95020873 11.09812706
24 11.02363601 9.95020873
25 11.02266467 11.02363601
26 1.55310085 11.02266467
27 2.54676698 1.55310085
28 4.72689844 2.54676698
29 8.65606036 4.72689844
30 8.24202099 8.65606036
31 3.13933373 8.24202099
32 2.87236678 3.13933373
33 2.17562842 2.87236678
34 -0.54510359 2.17562842
35 -1.13608676 -0.54510359
36 2.88856704 -1.13608676
37 0.84853027 2.88856704
38 0.13896645 0.84853027
39 5.13792307 0.13896645
40 3.81492513 5.13792307
41 4.58021674 3.81492513
42 5.73111429 4.58021674
43 5.93072017 5.73111429
44 6.98227142 5.93072017
45 6.64727602 6.98227142
46 6.50696509 6.64727602
47 11.19288625 6.50696509
48 12.26115215 11.19288625
49 16.05453674 12.26115215
50 14.74223084 16.05453674
51 15.38570331 14.74223084
52 15.68844774 15.38570331
53 15.75819064 15.68844774
54 19.53325046 15.75819064
55 19.19011426 19.53325046
56 17.38253552 19.19011426
57 20.21402339 17.38253552
58 20.71700178 20.21402339
59 16.64988890 20.71700178
60 16.58541272 16.64988890
61 12.96943991 16.58541272
62 14.02106887 12.96943991
63 14.60912173 14.02106887
64 13.78886587 14.60912173
65 14.49657679 13.78886587
66 18.18405591 14.49657679
67 16.94295170 18.18405591
68 15.32295365 16.94295170
69 21.06737726 15.32295365
70 16.84748446 21.06737726
71 14.30924336 16.84748446
72 13.19170233 14.30924336
73 11.98231109 13.19170233
74 8.46158530 11.98231109
75 12.01034826 8.46158530
76 12.13773764 12.01034826
77 12.30028718 12.13773764
78 15.08750808 12.30028718
79 13.40137159 15.08750808
80 15.53150266 13.40137159
81 14.92789370 15.53150266
82 11.08996832 14.92789370
83 9.43466296 11.08996832
84 8.49418619 9.43466296
85 6.04166555 8.49418619
86 3.70132708 6.04166555
87 4.12676698 3.70132708
88 3.79638200 4.12676698
89 3.51573759 3.79638200
90 5.05486137 3.51573759
91 6.96407992 5.05486137
92 6.07114614 6.96407992
93 3.55505332 6.07114614
94 0.29019279 3.55505332
95 -0.01562900 0.29019279
96 -0.85539568 -0.01562900
97 -2.65485147 -0.85539568
98 -3.34060954 -2.65485147
99 -5.57987915 -3.34060954
100 -7.78242522 -5.57987915
101 -4.90758548 -7.78242522
102 -3.22062279 -4.90758548
103 -3.24317800 -3.22062279
104 -2.16820832 -3.24317800
105 -5.18933342 -2.16820832
106 -4.41419395 -5.18933342
107 -3.08859555 -4.41419395
108 -2.48413834 -3.08859555
109 -15.45931158 -2.48413834
110 -8.39829736 -15.45931158
111 -4.38659866 -8.39829736
112 -1.94614443 -4.38659866
113 -0.77320756 -1.94614443
114 3.42498165 -0.77320756
115 5.81626594 3.42498165
116 8.33420510 5.81626594
117 6.77353188 8.33420510
118 4.02541285 6.77353188
119 1.59984927 4.02541285
120 1.46308289 1.59984927
121 2.70772481 1.46308289
122 0.71293505 2.70772481
123 2.58005336 0.71293505
124 4.19395976 2.58005336
125 4.59408999 4.19395976
126 7.89908525 4.59408999
127 9.63475627 7.89908525
128 6.44640288 9.63475627
129 5.32692244 6.44640288
130 7.47132102 5.32692244
131 6.53366090 7.47132102
132 4.47476424 6.53366090
133 6.80308280 4.47476424
134 4.88593829 6.80308280
135 4.31779691 4.88593829
136 4.52041222 4.31779691
137 4.60557473 4.52041222
138 5.20908525 4.60557473
139 5.17888479 5.20908525
140 2.37601556 5.17888479
141 2.48482591 2.37601556
142 2.98919073 2.48482591
143 -0.14950225 2.98919073
144 -0.55239833 -0.14950225
145 -5.51295154 -0.55239833
146 -8.05093525 -5.51295154
147 -2.95156050 -8.05093525
148 -5.59501032 -2.95156050
149 -3.55265444 -5.59501032
150 -1.88907937 -3.55265444
151 -1.67660231 -1.88907937
152 -2.29314818 -1.67660231
153 -1.86062744 -2.29314818
154 -0.44348679 -1.86062744
155 -3.80969591 -0.44348679
156 -4.40913982 -3.80969591
157 -7.03904748 -4.40913982
158 -5.02854675 -7.03904748
159 -1.93284864 -5.02854675
160 -4.71387915 -1.93284864
161 -6.06458812 -4.71387915
162 -5.88381969 -6.06458812
163 -6.41427836 -5.88381969
164 -1.14447007 -6.41427836
165 -2.95049834 -1.14447007
166 -4.82916461 -2.95049834
167 -7.00976046 -4.82916461
168 -7.79959169 -7.00976046
169 -9.17414432 -7.79959169
170 -7.82109516 -9.17414432
171 -4.25597804 -7.82109516
172 -3.81042698 -4.25597804
173 -3.28029675 -3.81042698
174 1.07769880 -3.28029675
175 2.36311161 1.07769880
176 0.66840406 2.36311161
177 1.20473055 0.66840406
178 -1.30477493 1.20473055
179 -2.05466069 -1.30477493
180 -2.63762132 -2.05466069
181 -6.45707711 -2.63762132
182 -8.75435073 -6.45707711
183 -8.16094282 -8.75435073
184 -9.76439264 -8.16094282
185 -8.12890736 -9.76439264
186 -8.88233200 -8.12890736
187 -10.93259701 -8.88233200
188 -10.47659447 -10.93259701
189 -10.64681581 -10.47659447
190 -11.21522387 -10.64681581
191 -11.83294854 -11.21522387
192 -11.40252155 -11.83294854
193 -14.14326842 -11.40252155
194 -16.89412332 -14.14326842
195 -13.96626412 -16.89412332
196 -14.28703642 -13.96626412
197 -14.17742262 -14.28703642
198 -11.30065360 -14.17742262
199 -3.44327336 -11.30065360
200 -4.87588437 -3.44327336
201 -4.12807314 -4.87588437
202 -7.71090169 -4.12807314
203 -10.59482061 -7.71090169
204 -11.84039421 -10.59482061
205 -4.47201109 -11.84039421
206 13.55752134 -4.47201109
207 13.09060648 13.55752134
208 12.09512467 13.09060648
209 12.65402838 12.09512467
210 -5.28095144 12.65402838
211 -16.79522763 -5.28095144
212 -3.06509657 -16.79522763
213 -1.08341504 -3.06509657
214 -5.04146952 -1.08341504
215 -7.17051607 -5.04146952
216 -8.98054097 -7.17051607
217 -4.87702727 -8.98054097
218 -5.49568733 -4.87702727
219 -4.88469872 -5.49568733
220 -1.01959954 -4.88469872
221 -2.84705000 -1.01959954
222 -0.13702247 -2.84705000
223 -4.23080421 -0.13702247
224 -5.26264057 -4.23080421
225 -5.75647400 -5.26264057
226 -7.04068899 -5.75647400
227 -6.22534880 -7.04068899
228 -7.11979124 -6.22534880
229 -4.38663406 -7.11979124
230 -5.74168203 -4.38663406
231 -7.31820957 -5.74168203
232 -3.91704525 -7.31820957
233 -3.57952798 -3.91704525
234 4.50591915 -3.57952798
235 20.24736336 4.50591915
236 22.67055927 20.24736336
237 24.04943417 22.67055927
238 22.96038057 24.04943417
239 13.73933284 22.96038057
240 9.72217913 13.73933284
241 28.76278789 9.72217913
242 65.25393123 28.76278789
243 45.52336760 65.25393123
244 64.48472264 45.52336760
245 57.09949488 64.48472264
246 16.14577828 57.09949488
247 8.55422219 16.14577828
248 4.06983741 8.55422219
249 3.68252100 4.06983741
250 -1.64608072 3.68252100
251 8.83055054 -1.64608072
252 12.95297870 8.83055054
253 21.89468489 12.95297870
254 3.17802306 21.89468489
255 7.62481859 3.17802306
256 -1.26904936 7.62481859
257 1.02485581 -1.26904936
258 6.21710899 1.02485581
259 7.05729586 6.21710899
260 -0.12657249 7.05729586
261 0.60436814 -0.12657249
262 3.80466900 0.60436814
263 5.07458958 3.80466900
264 4.75750043 5.07458958
265 2.53098038 4.75750043
266 0.37451222 2.53098038
267 -0.09972512 0.37451222
268 -2.16036831 -0.09972512
269 1.26824637 -2.16036831
270 -0.88472639 1.26824637
271 -3.42824991 -0.88472639
272 -7.74040904 -3.42824991
273 -11.06779295 -7.74040904
274 -1.04549091 -11.06779295
275 10.10868730 -1.04549091
276 -3.85169411 10.10868730
277 -12.22053811 -3.85169411
278 -11.92552153 -12.22053811
279 -14.01662946 -11.92552153
280 -13.34094930 -14.01662946
281 -14.64691501 -13.34094930
282 -11.59014599 -14.64691501
283 -15.29850813 -11.59014599
284 -14.32453756 -15.29850813
285 -13.42763009 -14.32453756
286 -12.88897389 -13.42763009
287 -14.35663400 -12.88897389
288 -17.33659434 -14.35663400
289 -22.99311439 -17.33659434
290 -24.45861424 -22.99311439
291 -18.40927030 -24.45861424
292 -13.27894518 -18.40927030
293 -24.22997692 -13.27894518
294 -19.20701364 -24.22997692
295 -16.95708499 -19.20701364
296 -16.55447007 -16.95708499
297 -22.98775626 -16.55447007
298 -22.06235857 -22.98775626
299 -21.72930858 -22.06235857
300 -19.63194645 -21.72930858
301 -20.88624085 -19.63194645
302 -15.32803032 -20.88624085
303 -14.74681724 -15.32803032
304 -21.28255727 -14.74681724
305 -20.53145872 -21.28255727
306 -24.72507702 -20.53145872
307 -25.01769679 -24.72507702
308 -20.44279166 -25.01769679
309 -19.94294845 -20.44279166
310 -19.66564788 -19.94294845
311 -26.38559820 -19.66564788
312 -25.99546317 -26.38559820
313 -24.82088580 -25.99546317
314 -30.29422457 -24.82088580
315 -29.02733367 -30.29422457
316 -25.19636301 -29.02733367
317 -27.12884575 -25.19636301
318 -19.71704445 -27.12884575
319 -21.78756768 -19.71704445
320 -22.33775938 -21.78756768
321 -21.80968994 -22.33775938
322 -22.52148562 -21.80968994
323 -24.47369532 -22.52148562
324 -17.96123635 -24.47369532
325 -16.19978839 -17.96123635
326 -17.89670843 -16.19978839
327 -18.53571982 -17.89670843
328 -14.46965233 -18.53571982
329 -5.48842468 -14.46965233
330 -13.65097931 -5.48842468
331 -12.44437372 -13.65097931
332 -6.60953315 -12.44437372
333 -7.67243202 -6.60953315
334 -0.56396948 -7.67243202
335 8.32040239 -0.56396948
336 2.17872990 8.32040239
337 19.34885598 2.17872990
338 29.39454891 19.34885598
339 16.31892219 29.39454891
340 11.35731068 16.31892219
341 3.44582707 11.35731068
342 -0.31833847 3.44582707
343 -4.22412139 -0.31833847
344 -9.09628052 -4.22412139
345 -0.17472810 -9.09628052
346 5.01521918 -0.17472810
347 5.88049481 5.01521918
348 21.87327655 5.88049481
349 14.55007956 21.87327655
350 2.99983646 14.55007956
351 5.14808299 2.99983646
352 0.47698791 5.14808299
353 0.07499080 0.47698791
354 5.21456646 0.07499080
355 10.41549423 5.21456646
356 6.26314143 10.41549423
357 4.95775870 6.26314143
358 13.77989788 4.95775870
359 21.99746371 13.77989788
360 NA 21.99746371
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 16.74608310 23.18928302
[2,] 9.12971029 16.74608310
[3,] -1.12353087 9.12971029
[4,] -9.71685452 -1.12353087
[5,] -10.56504883 -9.71685452
[6,] -5.62234377 -10.56504883
[7,] -3.72202779 -5.62234377
[8,] -3.75870277 -3.72202779
[9,] -3.51114976 -3.75870277
[10,] -3.65681574 -3.51114976
[11,] -3.58912081 -3.65681574
[12,] -2.19798373 -3.58912081
[13,] -0.96653576 -2.19798373
[14,] -2.67680967 -0.96653576
[15,] 0.97572823 -2.67680967
[16,] 2.46892454 0.97572823
[17,] 5.82773288 2.46892454
[18,] 10.78311546 5.82773288
[19,] 14.77962569 10.78311546
[20,] 15.28575734 14.77962569
[21,] 14.53801986 15.28575734
[22,] 11.09812706 14.53801986
[23,] 9.95020873 11.09812706
[24,] 11.02363601 9.95020873
[25,] 11.02266467 11.02363601
[26,] 1.55310085 11.02266467
[27,] 2.54676698 1.55310085
[28,] 4.72689844 2.54676698
[29,] 8.65606036 4.72689844
[30,] 8.24202099 8.65606036
[31,] 3.13933373 8.24202099
[32,] 2.87236678 3.13933373
[33,] 2.17562842 2.87236678
[34,] -0.54510359 2.17562842
[35,] -1.13608676 -0.54510359
[36,] 2.88856704 -1.13608676
[37,] 0.84853027 2.88856704
[38,] 0.13896645 0.84853027
[39,] 5.13792307 0.13896645
[40,] 3.81492513 5.13792307
[41,] 4.58021674 3.81492513
[42,] 5.73111429 4.58021674
[43,] 5.93072017 5.73111429
[44,] 6.98227142 5.93072017
[45,] 6.64727602 6.98227142
[46,] 6.50696509 6.64727602
[47,] 11.19288625 6.50696509
[48,] 12.26115215 11.19288625
[49,] 16.05453674 12.26115215
[50,] 14.74223084 16.05453674
[51,] 15.38570331 14.74223084
[52,] 15.68844774 15.38570331
[53,] 15.75819064 15.68844774
[54,] 19.53325046 15.75819064
[55,] 19.19011426 19.53325046
[56,] 17.38253552 19.19011426
[57,] 20.21402339 17.38253552
[58,] 20.71700178 20.21402339
[59,] 16.64988890 20.71700178
[60,] 16.58541272 16.64988890
[61,] 12.96943991 16.58541272
[62,] 14.02106887 12.96943991
[63,] 14.60912173 14.02106887
[64,] 13.78886587 14.60912173
[65,] 14.49657679 13.78886587
[66,] 18.18405591 14.49657679
[67,] 16.94295170 18.18405591
[68,] 15.32295365 16.94295170
[69,] 21.06737726 15.32295365
[70,] 16.84748446 21.06737726
[71,] 14.30924336 16.84748446
[72,] 13.19170233 14.30924336
[73,] 11.98231109 13.19170233
[74,] 8.46158530 11.98231109
[75,] 12.01034826 8.46158530
[76,] 12.13773764 12.01034826
[77,] 12.30028718 12.13773764
[78,] 15.08750808 12.30028718
[79,] 13.40137159 15.08750808
[80,] 15.53150266 13.40137159
[81,] 14.92789370 15.53150266
[82,] 11.08996832 14.92789370
[83,] 9.43466296 11.08996832
[84,] 8.49418619 9.43466296
[85,] 6.04166555 8.49418619
[86,] 3.70132708 6.04166555
[87,] 4.12676698 3.70132708
[88,] 3.79638200 4.12676698
[89,] 3.51573759 3.79638200
[90,] 5.05486137 3.51573759
[91,] 6.96407992 5.05486137
[92,] 6.07114614 6.96407992
[93,] 3.55505332 6.07114614
[94,] 0.29019279 3.55505332
[95,] -0.01562900 0.29019279
[96,] -0.85539568 -0.01562900
[97,] -2.65485147 -0.85539568
[98,] -3.34060954 -2.65485147
[99,] -5.57987915 -3.34060954
[100,] -7.78242522 -5.57987915
[101,] -4.90758548 -7.78242522
[102,] -3.22062279 -4.90758548
[103,] -3.24317800 -3.22062279
[104,] -2.16820832 -3.24317800
[105,] -5.18933342 -2.16820832
[106,] -4.41419395 -5.18933342
[107,] -3.08859555 -4.41419395
[108,] -2.48413834 -3.08859555
[109,] -15.45931158 -2.48413834
[110,] -8.39829736 -15.45931158
[111,] -4.38659866 -8.39829736
[112,] -1.94614443 -4.38659866
[113,] -0.77320756 -1.94614443
[114,] 3.42498165 -0.77320756
[115,] 5.81626594 3.42498165
[116,] 8.33420510 5.81626594
[117,] 6.77353188 8.33420510
[118,] 4.02541285 6.77353188
[119,] 1.59984927 4.02541285
[120,] 1.46308289 1.59984927
[121,] 2.70772481 1.46308289
[122,] 0.71293505 2.70772481
[123,] 2.58005336 0.71293505
[124,] 4.19395976 2.58005336
[125,] 4.59408999 4.19395976
[126,] 7.89908525 4.59408999
[127,] 9.63475627 7.89908525
[128,] 6.44640288 9.63475627
[129,] 5.32692244 6.44640288
[130,] 7.47132102 5.32692244
[131,] 6.53366090 7.47132102
[132,] 4.47476424 6.53366090
[133,] 6.80308280 4.47476424
[134,] 4.88593829 6.80308280
[135,] 4.31779691 4.88593829
[136,] 4.52041222 4.31779691
[137,] 4.60557473 4.52041222
[138,] 5.20908525 4.60557473
[139,] 5.17888479 5.20908525
[140,] 2.37601556 5.17888479
[141,] 2.48482591 2.37601556
[142,] 2.98919073 2.48482591
[143,] -0.14950225 2.98919073
[144,] -0.55239833 -0.14950225
[145,] -5.51295154 -0.55239833
[146,] -8.05093525 -5.51295154
[147,] -2.95156050 -8.05093525
[148,] -5.59501032 -2.95156050
[149,] -3.55265444 -5.59501032
[150,] -1.88907937 -3.55265444
[151,] -1.67660231 -1.88907937
[152,] -2.29314818 -1.67660231
[153,] -1.86062744 -2.29314818
[154,] -0.44348679 -1.86062744
[155,] -3.80969591 -0.44348679
[156,] -4.40913982 -3.80969591
[157,] -7.03904748 -4.40913982
[158,] -5.02854675 -7.03904748
[159,] -1.93284864 -5.02854675
[160,] -4.71387915 -1.93284864
[161,] -6.06458812 -4.71387915
[162,] -5.88381969 -6.06458812
[163,] -6.41427836 -5.88381969
[164,] -1.14447007 -6.41427836
[165,] -2.95049834 -1.14447007
[166,] -4.82916461 -2.95049834
[167,] -7.00976046 -4.82916461
[168,] -7.79959169 -7.00976046
[169,] -9.17414432 -7.79959169
[170,] -7.82109516 -9.17414432
[171,] -4.25597804 -7.82109516
[172,] -3.81042698 -4.25597804
[173,] -3.28029675 -3.81042698
[174,] 1.07769880 -3.28029675
[175,] 2.36311161 1.07769880
[176,] 0.66840406 2.36311161
[177,] 1.20473055 0.66840406
[178,] -1.30477493 1.20473055
[179,] -2.05466069 -1.30477493
[180,] -2.63762132 -2.05466069
[181,] -6.45707711 -2.63762132
[182,] -8.75435073 -6.45707711
[183,] -8.16094282 -8.75435073
[184,] -9.76439264 -8.16094282
[185,] -8.12890736 -9.76439264
[186,] -8.88233200 -8.12890736
[187,] -10.93259701 -8.88233200
[188,] -10.47659447 -10.93259701
[189,] -10.64681581 -10.47659447
[190,] -11.21522387 -10.64681581
[191,] -11.83294854 -11.21522387
[192,] -11.40252155 -11.83294854
[193,] -14.14326842 -11.40252155
[194,] -16.89412332 -14.14326842
[195,] -13.96626412 -16.89412332
[196,] -14.28703642 -13.96626412
[197,] -14.17742262 -14.28703642
[198,] -11.30065360 -14.17742262
[199,] -3.44327336 -11.30065360
[200,] -4.87588437 -3.44327336
[201,] -4.12807314 -4.87588437
[202,] -7.71090169 -4.12807314
[203,] -10.59482061 -7.71090169
[204,] -11.84039421 -10.59482061
[205,] -4.47201109 -11.84039421
[206,] 13.55752134 -4.47201109
[207,] 13.09060648 13.55752134
[208,] 12.09512467 13.09060648
[209,] 12.65402838 12.09512467
[210,] -5.28095144 12.65402838
[211,] -16.79522763 -5.28095144
[212,] -3.06509657 -16.79522763
[213,] -1.08341504 -3.06509657
[214,] -5.04146952 -1.08341504
[215,] -7.17051607 -5.04146952
[216,] -8.98054097 -7.17051607
[217,] -4.87702727 -8.98054097
[218,] -5.49568733 -4.87702727
[219,] -4.88469872 -5.49568733
[220,] -1.01959954 -4.88469872
[221,] -2.84705000 -1.01959954
[222,] -0.13702247 -2.84705000
[223,] -4.23080421 -0.13702247
[224,] -5.26264057 -4.23080421
[225,] -5.75647400 -5.26264057
[226,] -7.04068899 -5.75647400
[227,] -6.22534880 -7.04068899
[228,] -7.11979124 -6.22534880
[229,] -4.38663406 -7.11979124
[230,] -5.74168203 -4.38663406
[231,] -7.31820957 -5.74168203
[232,] -3.91704525 -7.31820957
[233,] -3.57952798 -3.91704525
[234,] 4.50591915 -3.57952798
[235,] 20.24736336 4.50591915
[236,] 22.67055927 20.24736336
[237,] 24.04943417 22.67055927
[238,] 22.96038057 24.04943417
[239,] 13.73933284 22.96038057
[240,] 9.72217913 13.73933284
[241,] 28.76278789 9.72217913
[242,] 65.25393123 28.76278789
[243,] 45.52336760 65.25393123
[244,] 64.48472264 45.52336760
[245,] 57.09949488 64.48472264
[246,] 16.14577828 57.09949488
[247,] 8.55422219 16.14577828
[248,] 4.06983741 8.55422219
[249,] 3.68252100 4.06983741
[250,] -1.64608072 3.68252100
[251,] 8.83055054 -1.64608072
[252,] 12.95297870 8.83055054
[253,] 21.89468489 12.95297870
[254,] 3.17802306 21.89468489
[255,] 7.62481859 3.17802306
[256,] -1.26904936 7.62481859
[257,] 1.02485581 -1.26904936
[258,] 6.21710899 1.02485581
[259,] 7.05729586 6.21710899
[260,] -0.12657249 7.05729586
[261,] 0.60436814 -0.12657249
[262,] 3.80466900 0.60436814
[263,] 5.07458958 3.80466900
[264,] 4.75750043 5.07458958
[265,] 2.53098038 4.75750043
[266,] 0.37451222 2.53098038
[267,] -0.09972512 0.37451222
[268,] -2.16036831 -0.09972512
[269,] 1.26824637 -2.16036831
[270,] -0.88472639 1.26824637
[271,] -3.42824991 -0.88472639
[272,] -7.74040904 -3.42824991
[273,] -11.06779295 -7.74040904
[274,] -1.04549091 -11.06779295
[275,] 10.10868730 -1.04549091
[276,] -3.85169411 10.10868730
[277,] -12.22053811 -3.85169411
[278,] -11.92552153 -12.22053811
[279,] -14.01662946 -11.92552153
[280,] -13.34094930 -14.01662946
[281,] -14.64691501 -13.34094930
[282,] -11.59014599 -14.64691501
[283,] -15.29850813 -11.59014599
[284,] -14.32453756 -15.29850813
[285,] -13.42763009 -14.32453756
[286,] -12.88897389 -13.42763009
[287,] -14.35663400 -12.88897389
[288,] -17.33659434 -14.35663400
[289,] -22.99311439 -17.33659434
[290,] -24.45861424 -22.99311439
[291,] -18.40927030 -24.45861424
[292,] -13.27894518 -18.40927030
[293,] -24.22997692 -13.27894518
[294,] -19.20701364 -24.22997692
[295,] -16.95708499 -19.20701364
[296,] -16.55447007 -16.95708499
[297,] -22.98775626 -16.55447007
[298,] -22.06235857 -22.98775626
[299,] -21.72930858 -22.06235857
[300,] -19.63194645 -21.72930858
[301,] -20.88624085 -19.63194645
[302,] -15.32803032 -20.88624085
[303,] -14.74681724 -15.32803032
[304,] -21.28255727 -14.74681724
[305,] -20.53145872 -21.28255727
[306,] -24.72507702 -20.53145872
[307,] -25.01769679 -24.72507702
[308,] -20.44279166 -25.01769679
[309,] -19.94294845 -20.44279166
[310,] -19.66564788 -19.94294845
[311,] -26.38559820 -19.66564788
[312,] -25.99546317 -26.38559820
[313,] -24.82088580 -25.99546317
[314,] -30.29422457 -24.82088580
[315,] -29.02733367 -30.29422457
[316,] -25.19636301 -29.02733367
[317,] -27.12884575 -25.19636301
[318,] -19.71704445 -27.12884575
[319,] -21.78756768 -19.71704445
[320,] -22.33775938 -21.78756768
[321,] -21.80968994 -22.33775938
[322,] -22.52148562 -21.80968994
[323,] -24.47369532 -22.52148562
[324,] -17.96123635 -24.47369532
[325,] -16.19978839 -17.96123635
[326,] -17.89670843 -16.19978839
[327,] -18.53571982 -17.89670843
[328,] -14.46965233 -18.53571982
[329,] -5.48842468 -14.46965233
[330,] -13.65097931 -5.48842468
[331,] -12.44437372 -13.65097931
[332,] -6.60953315 -12.44437372
[333,] -7.67243202 -6.60953315
[334,] -0.56396948 -7.67243202
[335,] 8.32040239 -0.56396948
[336,] 2.17872990 8.32040239
[337,] 19.34885598 2.17872990
[338,] 29.39454891 19.34885598
[339,] 16.31892219 29.39454891
[340,] 11.35731068 16.31892219
[341,] 3.44582707 11.35731068
[342,] -0.31833847 3.44582707
[343,] -4.22412139 -0.31833847
[344,] -9.09628052 -4.22412139
[345,] -0.17472810 -9.09628052
[346,] 5.01521918 -0.17472810
[347,] 5.88049481 5.01521918
[348,] 21.87327655 5.88049481
[349,] 14.55007956 21.87327655
[350,] 2.99983646 14.55007956
[351,] 5.14808299 2.99983646
[352,] 0.47698791 5.14808299
[353,] 0.07499080 0.47698791
[354,] 5.21456646 0.07499080
[355,] 10.41549423 5.21456646
[356,] 6.26314143 10.41549423
[357,] 4.95775870 6.26314143
[358,] 13.77989788 4.95775870
[359,] 21.99746371 13.77989788
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 16.74608310 23.18928302
2 9.12971029 16.74608310
3 -1.12353087 9.12971029
4 -9.71685452 -1.12353087
5 -10.56504883 -9.71685452
6 -5.62234377 -10.56504883
7 -3.72202779 -5.62234377
8 -3.75870277 -3.72202779
9 -3.51114976 -3.75870277
10 -3.65681574 -3.51114976
11 -3.58912081 -3.65681574
12 -2.19798373 -3.58912081
13 -0.96653576 -2.19798373
14 -2.67680967 -0.96653576
15 0.97572823 -2.67680967
16 2.46892454 0.97572823
17 5.82773288 2.46892454
18 10.78311546 5.82773288
19 14.77962569 10.78311546
20 15.28575734 14.77962569
21 14.53801986 15.28575734
22 11.09812706 14.53801986
23 9.95020873 11.09812706
24 11.02363601 9.95020873
25 11.02266467 11.02363601
26 1.55310085 11.02266467
27 2.54676698 1.55310085
28 4.72689844 2.54676698
29 8.65606036 4.72689844
30 8.24202099 8.65606036
31 3.13933373 8.24202099
32 2.87236678 3.13933373
33 2.17562842 2.87236678
34 -0.54510359 2.17562842
35 -1.13608676 -0.54510359
36 2.88856704 -1.13608676
37 0.84853027 2.88856704
38 0.13896645 0.84853027
39 5.13792307 0.13896645
40 3.81492513 5.13792307
41 4.58021674 3.81492513
42 5.73111429 4.58021674
43 5.93072017 5.73111429
44 6.98227142 5.93072017
45 6.64727602 6.98227142
46 6.50696509 6.64727602
47 11.19288625 6.50696509
48 12.26115215 11.19288625
49 16.05453674 12.26115215
50 14.74223084 16.05453674
51 15.38570331 14.74223084
52 15.68844774 15.38570331
53 15.75819064 15.68844774
54 19.53325046 15.75819064
55 19.19011426 19.53325046
56 17.38253552 19.19011426
57 20.21402339 17.38253552
58 20.71700178 20.21402339
59 16.64988890 20.71700178
60 16.58541272 16.64988890
61 12.96943991 16.58541272
62 14.02106887 12.96943991
63 14.60912173 14.02106887
64 13.78886587 14.60912173
65 14.49657679 13.78886587
66 18.18405591 14.49657679
67 16.94295170 18.18405591
68 15.32295365 16.94295170
69 21.06737726 15.32295365
70 16.84748446 21.06737726
71 14.30924336 16.84748446
72 13.19170233 14.30924336
73 11.98231109 13.19170233
74 8.46158530 11.98231109
75 12.01034826 8.46158530
76 12.13773764 12.01034826
77 12.30028718 12.13773764
78 15.08750808 12.30028718
79 13.40137159 15.08750808
80 15.53150266 13.40137159
81 14.92789370 15.53150266
82 11.08996832 14.92789370
83 9.43466296 11.08996832
84 8.49418619 9.43466296
85 6.04166555 8.49418619
86 3.70132708 6.04166555
87 4.12676698 3.70132708
88 3.79638200 4.12676698
89 3.51573759 3.79638200
90 5.05486137 3.51573759
91 6.96407992 5.05486137
92 6.07114614 6.96407992
93 3.55505332 6.07114614
94 0.29019279 3.55505332
95 -0.01562900 0.29019279
96 -0.85539568 -0.01562900
97 -2.65485147 -0.85539568
98 -3.34060954 -2.65485147
99 -5.57987915 -3.34060954
100 -7.78242522 -5.57987915
101 -4.90758548 -7.78242522
102 -3.22062279 -4.90758548
103 -3.24317800 -3.22062279
104 -2.16820832 -3.24317800
105 -5.18933342 -2.16820832
106 -4.41419395 -5.18933342
107 -3.08859555 -4.41419395
108 -2.48413834 -3.08859555
109 -15.45931158 -2.48413834
110 -8.39829736 -15.45931158
111 -4.38659866 -8.39829736
112 -1.94614443 -4.38659866
113 -0.77320756 -1.94614443
114 3.42498165 -0.77320756
115 5.81626594 3.42498165
116 8.33420510 5.81626594
117 6.77353188 8.33420510
118 4.02541285 6.77353188
119 1.59984927 4.02541285
120 1.46308289 1.59984927
121 2.70772481 1.46308289
122 0.71293505 2.70772481
123 2.58005336 0.71293505
124 4.19395976 2.58005336
125 4.59408999 4.19395976
126 7.89908525 4.59408999
127 9.63475627 7.89908525
128 6.44640288 9.63475627
129 5.32692244 6.44640288
130 7.47132102 5.32692244
131 6.53366090 7.47132102
132 4.47476424 6.53366090
133 6.80308280 4.47476424
134 4.88593829 6.80308280
135 4.31779691 4.88593829
136 4.52041222 4.31779691
137 4.60557473 4.52041222
138 5.20908525 4.60557473
139 5.17888479 5.20908525
140 2.37601556 5.17888479
141 2.48482591 2.37601556
142 2.98919073 2.48482591
143 -0.14950225 2.98919073
144 -0.55239833 -0.14950225
145 -5.51295154 -0.55239833
146 -8.05093525 -5.51295154
147 -2.95156050 -8.05093525
148 -5.59501032 -2.95156050
149 -3.55265444 -5.59501032
150 -1.88907937 -3.55265444
151 -1.67660231 -1.88907937
152 -2.29314818 -1.67660231
153 -1.86062744 -2.29314818
154 -0.44348679 -1.86062744
155 -3.80969591 -0.44348679
156 -4.40913982 -3.80969591
157 -7.03904748 -4.40913982
158 -5.02854675 -7.03904748
159 -1.93284864 -5.02854675
160 -4.71387915 -1.93284864
161 -6.06458812 -4.71387915
162 -5.88381969 -6.06458812
163 -6.41427836 -5.88381969
164 -1.14447007 -6.41427836
165 -2.95049834 -1.14447007
166 -4.82916461 -2.95049834
167 -7.00976046 -4.82916461
168 -7.79959169 -7.00976046
169 -9.17414432 -7.79959169
170 -7.82109516 -9.17414432
171 -4.25597804 -7.82109516
172 -3.81042698 -4.25597804
173 -3.28029675 -3.81042698
174 1.07769880 -3.28029675
175 2.36311161 1.07769880
176 0.66840406 2.36311161
177 1.20473055 0.66840406
178 -1.30477493 1.20473055
179 -2.05466069 -1.30477493
180 -2.63762132 -2.05466069
181 -6.45707711 -2.63762132
182 -8.75435073 -6.45707711
183 -8.16094282 -8.75435073
184 -9.76439264 -8.16094282
185 -8.12890736 -9.76439264
186 -8.88233200 -8.12890736
187 -10.93259701 -8.88233200
188 -10.47659447 -10.93259701
189 -10.64681581 -10.47659447
190 -11.21522387 -10.64681581
191 -11.83294854 -11.21522387
192 -11.40252155 -11.83294854
193 -14.14326842 -11.40252155
194 -16.89412332 -14.14326842
195 -13.96626412 -16.89412332
196 -14.28703642 -13.96626412
197 -14.17742262 -14.28703642
198 -11.30065360 -14.17742262
199 -3.44327336 -11.30065360
200 -4.87588437 -3.44327336
201 -4.12807314 -4.87588437
202 -7.71090169 -4.12807314
203 -10.59482061 -7.71090169
204 -11.84039421 -10.59482061
205 -4.47201109 -11.84039421
206 13.55752134 -4.47201109
207 13.09060648 13.55752134
208 12.09512467 13.09060648
209 12.65402838 12.09512467
210 -5.28095144 12.65402838
211 -16.79522763 -5.28095144
212 -3.06509657 -16.79522763
213 -1.08341504 -3.06509657
214 -5.04146952 -1.08341504
215 -7.17051607 -5.04146952
216 -8.98054097 -7.17051607
217 -4.87702727 -8.98054097
218 -5.49568733 -4.87702727
219 -4.88469872 -5.49568733
220 -1.01959954 -4.88469872
221 -2.84705000 -1.01959954
222 -0.13702247 -2.84705000
223 -4.23080421 -0.13702247
224 -5.26264057 -4.23080421
225 -5.75647400 -5.26264057
226 -7.04068899 -5.75647400
227 -6.22534880 -7.04068899
228 -7.11979124 -6.22534880
229 -4.38663406 -7.11979124
230 -5.74168203 -4.38663406
231 -7.31820957 -5.74168203
232 -3.91704525 -7.31820957
233 -3.57952798 -3.91704525
234 4.50591915 -3.57952798
235 20.24736336 4.50591915
236 22.67055927 20.24736336
237 24.04943417 22.67055927
238 22.96038057 24.04943417
239 13.73933284 22.96038057
240 9.72217913 13.73933284
241 28.76278789 9.72217913
242 65.25393123 28.76278789
243 45.52336760 65.25393123
244 64.48472264 45.52336760
245 57.09949488 64.48472264
246 16.14577828 57.09949488
247 8.55422219 16.14577828
248 4.06983741 8.55422219
249 3.68252100 4.06983741
250 -1.64608072 3.68252100
251 8.83055054 -1.64608072
252 12.95297870 8.83055054
253 21.89468489 12.95297870
254 3.17802306 21.89468489
255 7.62481859 3.17802306
256 -1.26904936 7.62481859
257 1.02485581 -1.26904936
258 6.21710899 1.02485581
259 7.05729586 6.21710899
260 -0.12657249 7.05729586
261 0.60436814 -0.12657249
262 3.80466900 0.60436814
263 5.07458958 3.80466900
264 4.75750043 5.07458958
265 2.53098038 4.75750043
266 0.37451222 2.53098038
267 -0.09972512 0.37451222
268 -2.16036831 -0.09972512
269 1.26824637 -2.16036831
270 -0.88472639 1.26824637
271 -3.42824991 -0.88472639
272 -7.74040904 -3.42824991
273 -11.06779295 -7.74040904
274 -1.04549091 -11.06779295
275 10.10868730 -1.04549091
276 -3.85169411 10.10868730
277 -12.22053811 -3.85169411
278 -11.92552153 -12.22053811
279 -14.01662946 -11.92552153
280 -13.34094930 -14.01662946
281 -14.64691501 -13.34094930
282 -11.59014599 -14.64691501
283 -15.29850813 -11.59014599
284 -14.32453756 -15.29850813
285 -13.42763009 -14.32453756
286 -12.88897389 -13.42763009
287 -14.35663400 -12.88897389
288 -17.33659434 -14.35663400
289 -22.99311439 -17.33659434
290 -24.45861424 -22.99311439
291 -18.40927030 -24.45861424
292 -13.27894518 -18.40927030
293 -24.22997692 -13.27894518
294 -19.20701364 -24.22997692
295 -16.95708499 -19.20701364
296 -16.55447007 -16.95708499
297 -22.98775626 -16.55447007
298 -22.06235857 -22.98775626
299 -21.72930858 -22.06235857
300 -19.63194645 -21.72930858
301 -20.88624085 -19.63194645
302 -15.32803032 -20.88624085
303 -14.74681724 -15.32803032
304 -21.28255727 -14.74681724
305 -20.53145872 -21.28255727
306 -24.72507702 -20.53145872
307 -25.01769679 -24.72507702
308 -20.44279166 -25.01769679
309 -19.94294845 -20.44279166
310 -19.66564788 -19.94294845
311 -26.38559820 -19.66564788
312 -25.99546317 -26.38559820
313 -24.82088580 -25.99546317
314 -30.29422457 -24.82088580
315 -29.02733367 -30.29422457
316 -25.19636301 -29.02733367
317 -27.12884575 -25.19636301
318 -19.71704445 -27.12884575
319 -21.78756768 -19.71704445
320 -22.33775938 -21.78756768
321 -21.80968994 -22.33775938
322 -22.52148562 -21.80968994
323 -24.47369532 -22.52148562
324 -17.96123635 -24.47369532
325 -16.19978839 -17.96123635
326 -17.89670843 -16.19978839
327 -18.53571982 -17.89670843
328 -14.46965233 -18.53571982
329 -5.48842468 -14.46965233
330 -13.65097931 -5.48842468
331 -12.44437372 -13.65097931
332 -6.60953315 -12.44437372
333 -7.67243202 -6.60953315
334 -0.56396948 -7.67243202
335 8.32040239 -0.56396948
336 2.17872990 8.32040239
337 19.34885598 2.17872990
338 29.39454891 19.34885598
339 16.31892219 29.39454891
340 11.35731068 16.31892219
341 3.44582707 11.35731068
342 -0.31833847 3.44582707
343 -4.22412139 -0.31833847
344 -9.09628052 -4.22412139
345 -0.17472810 -9.09628052
346 5.01521918 -0.17472810
347 5.88049481 5.01521918
348 21.87327655 5.88049481
349 14.55007956 21.87327655
350 2.99983646 14.55007956
351 5.14808299 2.99983646
352 0.47698791 5.14808299
353 0.07499080 0.47698791
354 5.21456646 0.07499080
355 10.41549423 5.21456646
356 6.26314143 10.41549423
357 4.95775870 6.26314143
358 13.77989788 4.95775870
359 21.99746371 13.77989788
> 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/www/rcomp/tmp/7o0nf1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8o0nf1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9mvup1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10mvup1289591610.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/112slo1289591610.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/www/rcomp/tmp/12na1c1289591610.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/www/rcomp/tmp/13ubh61289591610.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/www/rcomp/tmp/1453y91289591610.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/www/rcomp/tmp/158lww1289591610.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/www/rcomp/tmp/164dun1289591610.tab")
+ }
>
> try(system("convert tmp/1sq7o1289591610.ps tmp/1sq7o1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lips1289591610.ps tmp/2lips1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lips1289591610.ps tmp/3lips1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lips1289591610.ps tmp/4lips1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lips1289591610.ps tmp/5lips1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d9ou1289591610.ps tmp/6d9ou1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/7o0nf1289591610.ps tmp/7o0nf1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/8o0nf1289591610.ps tmp/8o0nf1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mvup1289591610.ps tmp/9mvup1289591610.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mvup1289591610.ps tmp/10mvup1289591610.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.600 0.960 12.573