R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(25.94
+ ,23688100
+ ,39.18
+ ,3940.35
+ ,144.7
+ ,28.66
+ ,13741000
+ ,35.78
+ ,4696.69
+ ,140.8
+ ,33.95
+ ,14143500
+ ,42.54
+ ,4572.83
+ ,137.1
+ ,31.01
+ ,16763800
+ ,27.92
+ ,3860.66
+ ,137.7
+ ,21.00
+ ,16634600
+ ,25.05
+ ,3400.91
+ ,144.7
+ ,26.19
+ ,13693300
+ ,32.03
+ ,3966.11
+ ,139.2
+ ,25.41
+ ,10545800
+ ,27.95
+ ,3766.99
+ ,143.0
+ ,30.47
+ ,9409900
+ ,27.95
+ ,4206.35
+ ,140.8
+ ,12.88
+ ,39182200
+ ,24.15
+ ,3672.82
+ ,142.5
+ ,9.78
+ ,37005800
+ ,27.57
+ ,3369.63
+ ,135.8
+ ,8.25
+ ,15818500
+ ,22.97
+ ,2597.93
+ ,132.6
+ ,7.44
+ ,16952000
+ ,17.37
+ ,2470.52
+ ,128.6
+ ,10.81
+ ,24563400
+ ,24.45
+ ,2772.73
+ ,115.7
+ ,9.12
+ ,14163200
+ ,23.62
+ ,2151.83
+ ,109.2
+ ,11.03
+ ,18184800
+ ,21.90
+ ,1840.26
+ ,116.9
+ ,12.74
+ ,20810300
+ ,27.12
+ ,2116.24
+ ,109.9
+ ,9.98
+ ,12843000
+ ,27.70
+ ,2110.49
+ ,116.1
+ ,11.62
+ ,13866700
+ ,29.23
+ ,2160.54
+ ,118.9
+ ,9.40
+ ,15119200
+ ,26.50
+ ,2027.13
+ ,116.3
+ ,9.27
+ ,8301600
+ ,22.84
+ ,1805.43
+ ,114.0
+ ,7.76
+ ,14039600
+ ,20.49
+ ,1498.80
+ ,97.0
+ ,8.78
+ ,12139700
+ ,23.28
+ ,1690.20
+ ,85.3
+ ,10.65
+ ,9649000
+ ,25.71
+ ,1930.58
+ ,84.9
+ ,10.95
+ ,8513600
+ ,26.52
+ ,1950.40
+ ,94.6
+ ,12.36
+ ,15278600
+ ,25.51
+ ,1934.03
+ ,97.8
+ ,10.85
+ ,15590900
+ ,23.36
+ ,1731.49
+ ,95.0
+ ,11.84
+ ,9691100
+ ,24.15
+ ,1845.35
+ ,110.7
+ ,12.14
+ ,10882700
+ ,20.92
+ ,1688.23
+ ,108.5
+ ,11.65
+ ,10294800
+ ,20.38
+ ,1615.73
+ ,110.3
+ ,8.86
+ ,16031900
+ ,21.90
+ ,1463.21
+ ,106.3
+ ,7.63
+ ,13683600
+ ,19.21
+ ,1328.26
+ ,97.4
+ ,7.38
+ ,8677200
+ ,19.65
+ ,1314.85
+ ,94.5
+ ,7.25
+ ,9874100
+ ,17.51
+ ,1172.06
+ ,93.7
+ ,8.03
+ ,10725500
+ ,21.41
+ ,1329.75
+ ,79.6
+ ,7.75
+ ,8348400
+ ,23.09
+ ,1478.78
+ ,84.9
+ ,7.16
+ ,8046200
+ ,20.70
+ ,1335.51
+ ,80.7
+ ,7.18
+ ,10862300
+ ,19.00
+ ,1320.91
+ ,78.8
+ ,7.51
+ ,8100300
+ ,19.04
+ ,1337.52
+ ,64.8
+ ,7.07
+ ,7287500
+ ,19.45
+ ,1341.17
+ ,61.4
+ ,7.11
+ ,14002500
+ ,20.54
+ ,1464.31
+ ,81.0
+ ,8.98
+ ,19037900
+ ,19.77
+ ,1595.91
+ ,83.6
+ ,9.53
+ ,10774600
+ ,20.60
+ ,1622.80
+ ,83.5
+ ,10.54
+ ,8960600
+ ,21.21
+ ,1735.02
+ ,77.0
+ ,11.31
+ ,7773300
+ ,21.30
+ ,1810.45
+ ,81.7
+ ,10.36
+ ,9579700
+ ,22.33
+ ,1786.94
+ ,77.0
+ ,11.44
+ ,11270700
+ ,21.12
+ ,1932.21
+ ,81.7
+ ,10.45
+ ,9492800
+ ,20.77
+ ,1960.26
+ ,92.5
+ ,10.69
+ ,9136800
+ ,22.11
+ ,2003.37
+ ,91.7
+ ,11.28
+ ,14487600
+ ,22.34
+ ,2066.15
+ ,96.4
+ ,11.96
+ ,10133200
+ ,21.43
+ ,2029.82
+ ,88.5
+ ,13.52
+ ,18659700
+ ,20.14
+ ,1994.22
+ ,88.5
+ ,12.89
+ ,15980700
+ ,21.11
+ ,1920.15
+ ,93.0
+ ,14.03
+ ,9732100
+ ,21.19
+ ,1986.74
+ ,93.1
+ ,16.27
+ ,14626300
+ ,23.07
+ ,2047.79
+ ,102.8
+ ,16.17
+ ,16904000
+ ,23.01
+ ,1887.36
+ ,105.7
+ ,17.25
+ ,13616700
+ ,22.12
+ ,1838.10
+ ,98.7
+ ,19.38
+ ,13772900
+ ,22.40
+ ,1896.84
+ ,96.7
+ ,26.20
+ ,28749200
+ ,22.66
+ ,1974.99
+ ,92.9
+ ,33.53
+ ,31408300
+ ,24.21
+ ,2096.81
+ ,92.6
+ ,32.20
+ ,26342800
+ ,24.13
+ ,2175.44
+ ,102.7
+ ,38.45
+ ,48909500
+ ,23.73
+ ,2062.41
+ ,105.1
+ ,44.86
+ ,41542400
+ ,22.79
+ ,2051.72
+ ,104.4
+ ,41.67
+ ,24857200
+ ,21.89
+ ,1999.23
+ ,103.0
+ ,36.06
+ ,34093700
+ ,22.92
+ ,1921.65
+ ,97.5
+ ,39.76
+ ,22555200
+ ,23.44
+ ,2068.22
+ ,103.1
+ ,36.81
+ ,19067500
+ ,22.57
+ ,2056.96
+ ,106.2
+ ,42.65
+ ,19029100
+ ,23.27
+ ,2184.83
+ ,103.6
+ ,46.89
+ ,15223200
+ ,24.95
+ ,2152.09
+ ,105.5
+ ,53.61
+ ,21903700
+ ,23.45
+ ,2151.69
+ ,87.5
+ ,57.59
+ ,33306600
+ ,23.42
+ ,2120.30
+ ,85.2
+ ,67.82
+ ,23898100
+ ,25.30
+ ,2232.82
+ ,98.3
+ ,71.89
+ ,23279600
+ ,23.90
+ ,2205.32
+ ,103.8
+ ,75.51
+ ,40699800
+ ,25.73
+ ,2305.82
+ ,106.8
+ ,68.49
+ ,37646000
+ ,24.64
+ ,2281.39
+ ,102.7
+ ,62.72
+ ,37277000
+ ,24.95
+ ,2339.79
+ ,107.5
+ ,70.39
+ ,39246800
+ ,22.15
+ ,2322.57
+ ,109.8
+ ,59.77
+ ,27418400
+ ,20.85
+ ,2178.88
+ ,104.7
+ ,57.27
+ ,30318700
+ ,21.45
+ ,2172.09
+ ,105.7
+ ,67.96
+ ,32808100
+ ,22.15
+ ,2091.47
+ ,107.0
+ ,67.85
+ ,28668200
+ ,23.75
+ ,2183.75
+ ,100.2
+ ,76.98
+ ,32370300
+ ,25.27
+ ,2258.43
+ ,105.9
+ ,81.08
+ ,24171100
+ ,26.53
+ ,2366.71
+ ,105.1
+ ,91.66
+ ,25009100
+ ,27.22
+ ,2431.77
+ ,105.3
+ ,84.84
+ ,32084300
+ ,27.69
+ ,2415.29
+ ,110.0
+ ,85.73
+ ,50117500
+ ,28.61
+ ,2463.93
+ ,110.2
+ ,84.61
+ ,27522200
+ ,26.21
+ ,2416.15
+ ,111.2
+ ,92.91
+ ,26816800
+ ,25.93
+ ,2421.64
+ ,108.2
+ ,99.80
+ ,25136100
+ ,27.86
+ ,2525.09
+ ,106.3
+ ,121.19
+ ,30295600
+ ,28.65
+ ,2604.52
+ ,108.5
+ ,122.04
+ ,41526100
+ ,27.51
+ ,2603.23
+ ,105.3
+ ,131.76
+ ,43845100
+ ,27.06
+ ,2546.27
+ ,111.9
+ ,138.48
+ ,39188900
+ ,26.91
+ ,2596.36
+ ,105.6
+ ,153.47
+ ,40496400
+ ,27.60
+ ,2701.50
+ ,99.5
+ ,189.95
+ ,37438400
+ ,34.48
+ ,2859.12
+ ,95.2
+ ,182.22
+ ,46553700
+ ,31.58
+ ,2660.96
+ ,87.8
+ ,198.08
+ ,31771400
+ ,33.46
+ ,2652.28
+ ,90.6
+ ,135.36
+ ,62108100
+ ,30.64
+ ,2389.86
+ ,87.9
+ ,125.02
+ ,46645400
+ ,25.66
+ ,2271.48
+ ,76.4
+ ,143.50
+ ,42313100
+ ,26.78
+ ,2279.10
+ ,65.9
+ ,173.95
+ ,38841700
+ ,26.91
+ ,2412.80
+ ,62.3
+ ,188.75
+ ,32650300
+ ,26.82
+ ,2522.66
+ ,57.2
+ ,167.44
+ ,34281100
+ ,26.05
+ ,2292.98
+ ,50.4
+ ,158.95
+ ,33096200
+ ,24.36
+ ,2325.55
+ ,51.9
+ ,169.53
+ ,23273800
+ ,25.94
+ ,2367.52
+ ,58.5
+ ,113.66
+ ,43697600
+ ,25.37
+ ,2091.88
+ ,61.4
+ ,107.59
+ ,66902300
+ ,21.23
+ ,1720.95
+ ,38.8
+ ,92.67
+ ,44957200
+ ,19.35
+ ,1535.57
+ ,44.9
+ ,85.35
+ ,33800900
+ ,18.61
+ ,1577.03
+ ,38.6
+ ,90.13
+ ,33487900
+ ,16.37
+ ,1476.42
+ ,4.0
+ ,89.31
+ ,27394900
+ ,15.56
+ ,1377.84
+ ,25.3
+ ,105.12
+ ,25963400
+ ,17.70
+ ,1528.59
+ ,26.9
+ ,125.83
+ ,20952600
+ ,19.52
+ ,1717.30
+ ,40.8
+ ,135.81
+ ,17702900
+ ,20.26
+ ,1774.33
+ ,54.8
+ ,142.43
+ ,21282100
+ ,23.05
+ ,1835.04
+ ,49.3
+ ,163.39
+ ,18449100
+ ,22.81
+ ,1978.50
+ ,47.4
+ ,168.21
+ ,14415700
+ ,24.04
+ ,2009.06
+ ,54.5
+ ,185.35
+ ,17906300
+ ,25.08
+ ,2122.42
+ ,53.4
+ ,188.50
+ ,22197500
+ ,27.04
+ ,2045.11
+ ,48.7
+ ,199.91
+ ,15856500
+ ,28.81
+ ,2144.60
+ ,50.6
+ ,210.73
+ ,19068700
+ ,29.86
+ ,2269.15
+ ,53.6
+ ,192.06
+ ,30855100
+ ,27.61
+ ,2147.35
+ ,56.5
+ ,204.62
+ ,21209000
+ ,28.22
+ ,2238.26
+ ,46.4
+ ,235.00
+ ,19541600
+ ,28.83
+ ,2397.96
+ ,52.3
+ ,261.09
+ ,21955000
+ ,30.06
+ ,2461.19
+ ,57.7
+ ,256.88
+ ,33725900
+ ,25.51
+ ,2257.04
+ ,62.7
+ ,251.53
+ ,28192800
+ ,22.75
+ ,2109.24
+ ,54.3
+ ,257.25
+ ,27377000
+ ,25.52
+ ,2254.70
+ ,51.0
+ ,243.10
+ ,16228100
+ ,23.33
+ ,2114.03
+ ,53.2
+ ,283.75
+ ,21278900
+ ,24.34
+ ,2368.62
+ ,48.6
+ ,300.98
+ ,21457400
+ ,26.51
+ ,2507.41
+ ,49.9)
+ ,dim=c(5
+ ,130)
+ ,dimnames=list(c('Apple'
+ ,'Volume'
+ ,'Microsoft'
+ ,'NASDAQ'
+ ,'Consumentenvertrouwen')
+ ,1:130))
> y <- array(NA,dim=c(5,130),dimnames=list(c('Apple','Volume','Microsoft','NASDAQ','Consumentenvertrouwen'),1:130))
> 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 = 'Do not include Seasonal 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Apple Volume Microsoft NASDAQ Consumentenvertrouwen t
1 25.94 23688100 39.18 3940.35 144.7 1
2 28.66 13741000 35.78 4696.69 140.8 2
3 33.95 14143500 42.54 4572.83 137.1 3
4 31.01 16763800 27.92 3860.66 137.7 4
5 21.00 16634600 25.05 3400.91 144.7 5
6 26.19 13693300 32.03 3966.11 139.2 6
7 25.41 10545800 27.95 3766.99 143.0 7
8 30.47 9409900 27.95 4206.35 140.8 8
9 12.88 39182200 24.15 3672.82 142.5 9
10 9.78 37005800 27.57 3369.63 135.8 10
11 8.25 15818500 22.97 2597.93 132.6 11
12 7.44 16952000 17.37 2470.52 128.6 12
13 10.81 24563400 24.45 2772.73 115.7 13
14 9.12 14163200 23.62 2151.83 109.2 14
15 11.03 18184800 21.90 1840.26 116.9 15
16 12.74 20810300 27.12 2116.24 109.9 16
17 9.98 12843000 27.70 2110.49 116.1 17
18 11.62 13866700 29.23 2160.54 118.9 18
19 9.40 15119200 26.50 2027.13 116.3 19
20 9.27 8301600 22.84 1805.43 114.0 20
21 7.76 14039600 20.49 1498.80 97.0 21
22 8.78 12139700 23.28 1690.20 85.3 22
23 10.65 9649000 25.71 1930.58 84.9 23
24 10.95 8513600 26.52 1950.40 94.6 24
25 12.36 15278600 25.51 1934.03 97.8 25
26 10.85 15590900 23.36 1731.49 95.0 26
27 11.84 9691100 24.15 1845.35 110.7 27
28 12.14 10882700 20.92 1688.23 108.5 28
29 11.65 10294800 20.38 1615.73 110.3 29
30 8.86 16031900 21.90 1463.21 106.3 30
31 7.63 13683600 19.21 1328.26 97.4 31
32 7.38 8677200 19.65 1314.85 94.5 32
33 7.25 9874100 17.51 1172.06 93.7 33
34 8.03 10725500 21.41 1329.75 79.6 34
35 7.75 8348400 23.09 1478.78 84.9 35
36 7.16 8046200 20.70 1335.51 80.7 36
37 7.18 10862300 19.00 1320.91 78.8 37
38 7.51 8100300 19.04 1337.52 64.8 38
39 7.07 7287500 19.45 1341.17 61.4 39
40 7.11 14002500 20.54 1464.31 81.0 40
41 8.98 19037900 19.77 1595.91 83.6 41
42 9.53 10774600 20.60 1622.80 83.5 42
43 10.54 8960600 21.21 1735.02 77.0 43
44 11.31 7773300 21.30 1810.45 81.7 44
45 10.36 9579700 22.33 1786.94 77.0 45
46 11.44 11270700 21.12 1932.21 81.7 46
47 10.45 9492800 20.77 1960.26 92.5 47
48 10.69 9136800 22.11 2003.37 91.7 48
49 11.28 14487600 22.34 2066.15 96.4 49
50 11.96 10133200 21.43 2029.82 88.5 50
51 13.52 18659700 20.14 1994.22 88.5 51
52 12.89 15980700 21.11 1920.15 93.0 52
53 14.03 9732100 21.19 1986.74 93.1 53
54 16.27 14626300 23.07 2047.79 102.8 54
55 16.17 16904000 23.01 1887.36 105.7 55
56 17.25 13616700 22.12 1838.10 98.7 56
57 19.38 13772900 22.40 1896.84 96.7 57
58 26.20 28749200 22.66 1974.99 92.9 58
59 33.53 31408300 24.21 2096.81 92.6 59
60 32.20 26342800 24.13 2175.44 102.7 60
61 38.45 48909500 23.73 2062.41 105.1 61
62 44.86 41542400 22.79 2051.72 104.4 62
63 41.67 24857200 21.89 1999.23 103.0 63
64 36.06 34093700 22.92 1921.65 97.5 64
65 39.76 22555200 23.44 2068.22 103.1 65
66 36.81 19067500 22.57 2056.96 106.2 66
67 42.65 19029100 23.27 2184.83 103.6 67
68 46.89 15223200 24.95 2152.09 105.5 68
69 53.61 21903700 23.45 2151.69 87.5 69
70 57.59 33306600 23.42 2120.30 85.2 70
71 67.82 23898100 25.30 2232.82 98.3 71
72 71.89 23279600 23.90 2205.32 103.8 72
73 75.51 40699800 25.73 2305.82 106.8 73
74 68.49 37646000 24.64 2281.39 102.7 74
75 62.72 37277000 24.95 2339.79 107.5 75
76 70.39 39246800 22.15 2322.57 109.8 76
77 59.77 27418400 20.85 2178.88 104.7 77
78 57.27 30318700 21.45 2172.09 105.7 78
79 67.96 32808100 22.15 2091.47 107.0 79
80 67.85 28668200 23.75 2183.75 100.2 80
81 76.98 32370300 25.27 2258.43 105.9 81
82 81.08 24171100 26.53 2366.71 105.1 82
83 91.66 25009100 27.22 2431.77 105.3 83
84 84.84 32084300 27.69 2415.29 110.0 84
85 85.73 50117500 28.61 2463.93 110.2 85
86 84.61 27522200 26.21 2416.15 111.2 86
87 92.91 26816800 25.93 2421.64 108.2 87
88 99.80 25136100 27.86 2525.09 106.3 88
89 121.19 30295600 28.65 2604.52 108.5 89
90 122.04 41526100 27.51 2603.23 105.3 90
91 131.76 43845100 27.06 2546.27 111.9 91
92 138.48 39188900 26.91 2596.36 105.6 92
93 153.47 40496400 27.60 2701.50 99.5 93
94 189.95 37438400 34.48 2859.12 95.2 94
95 182.22 46553700 31.58 2660.96 87.8 95
96 198.08 31771400 33.46 2652.28 90.6 96
97 135.36 62108100 30.64 2389.86 87.9 97
98 125.02 46645400 25.66 2271.48 76.4 98
99 143.50 42313100 26.78 2279.10 65.9 99
100 173.95 38841700 26.91 2412.80 62.3 100
101 188.75 32650300 26.82 2522.66 57.2 101
102 167.44 34281100 26.05 2292.98 50.4 102
103 158.95 33096200 24.36 2325.55 51.9 103
104 169.53 23273800 25.94 2367.52 58.5 104
105 113.66 43697600 25.37 2091.88 61.4 105
106 107.59 66902300 21.23 1720.95 38.8 106
107 92.67 44957200 19.35 1535.57 44.9 107
108 85.35 33800900 18.61 1577.03 38.6 108
109 90.13 33487900 16.37 1476.42 4.0 109
110 89.31 27394900 15.56 1377.84 25.3 110
111 105.12 25963400 17.70 1528.59 26.9 111
112 125.83 20952600 19.52 1717.30 40.8 112
113 135.81 17702900 20.26 1774.33 54.8 113
114 142.43 21282100 23.05 1835.04 49.3 114
115 163.39 18449100 22.81 1978.50 47.4 115
116 168.21 14415700 24.04 2009.06 54.5 116
117 185.35 17906300 25.08 2122.42 53.4 117
118 188.50 22197500 27.04 2045.11 48.7 118
119 199.91 15856500 28.81 2144.60 50.6 119
120 210.73 19068700 29.86 2269.15 53.6 120
121 192.06 30855100 27.61 2147.35 56.5 121
122 204.62 21209000 28.22 2238.26 46.4 122
123 235.00 19541600 28.83 2397.96 52.3 123
124 261.09 21955000 30.06 2461.19 57.7 124
125 256.88 33725900 25.51 2257.04 62.7 125
126 251.53 28192800 22.75 2109.24 54.3 126
127 257.25 27377000 25.52 2254.70 51.0 127
128 243.10 16228100 23.33 2114.03 53.2 128
129 283.75 21278900 24.34 2368.62 48.6 129
130 300.98 21457400 26.51 2507.41 49.9 130
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Volume Microsoft
-1.353e+02 -6.778e-07 4.103e+00
NASDAQ Consumentenvertrouwen t
2.735e-02 -4.888e-01 1.685e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-58.868 -20.678 -3.389 15.956 78.750
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.353e+02 1.918e+01 -7.053 1.09e-10 ***
Volume -6.778e-07 2.439e-07 -2.779 0.00630 **
Microsoft 4.103e+00 9.056e-01 4.531 1.36e-05 ***
NASDAQ 2.735e-02 6.566e-03 4.165 5.78e-05 ***
Consumentenvertrouwen -4.888e-01 1.628e-01 -3.001 0.00325 **
t 1.685e+00 1.244e-01 13.548 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.48 on 124 degrees of freedom
Multiple R-squared: 0.8853, Adjusted R-squared: 0.8807
F-statistic: 191.4 on 5 and 124 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,] 2.270654e-03 4.541309e-03 9.977293e-01
[2,] 7.836686e-04 1.567337e-03 9.992163e-01
[3,] 2.622332e-04 5.244663e-04 9.997378e-01
[4,] 4.540894e-05 9.081789e-05 9.999546e-01
[5,] 7.845400e-06 1.569080e-05 9.999922e-01
[6,] 1.129215e-06 2.258431e-06 9.999989e-01
[7,] 1.188929e-06 2.377857e-06 9.999988e-01
[8,] 3.376023e-07 6.752045e-07 9.999997e-01
[9,] 5.357655e-08 1.071531e-07 9.999999e-01
[10,] 7.909768e-09 1.581954e-08 1.000000e+00
[11,] 1.120259e-09 2.240518e-09 1.000000e+00
[12,] 1.703089e-10 3.406179e-10 1.000000e+00
[13,] 3.549435e-11 7.098870e-11 1.000000e+00
[14,] 4.883249e-12 9.766498e-12 1.000000e+00
[15,] 6.472378e-13 1.294476e-12 1.000000e+00
[16,] 7.888008e-14 1.577602e-13 1.000000e+00
[17,] 2.419247e-14 4.838494e-14 1.000000e+00
[18,] 6.456102e-15 1.291220e-14 1.000000e+00
[19,] 9.794224e-16 1.958845e-15 1.000000e+00
[20,] 3.111408e-16 6.222816e-16 1.000000e+00
[21,] 8.213833e-17 1.642767e-16 1.000000e+00
[22,] 1.705280e-17 3.410559e-17 1.000000e+00
[23,] 4.400473e-18 8.800946e-18 1.000000e+00
[24,] 1.010989e-18 2.021978e-18 1.000000e+00
[25,] 5.188629e-19 1.037726e-18 1.000000e+00
[26,] 9.674729e-20 1.934946e-19 1.000000e+00
[27,] 2.604995e-20 5.209990e-20 1.000000e+00
[28,] 6.960002e-21 1.392000e-20 1.000000e+00
[29,] 2.579224e-21 5.158448e-21 1.000000e+00
[30,] 6.669079e-22 1.333816e-21 1.000000e+00
[31,] 1.802711e-22 3.605422e-22 1.000000e+00
[32,] 8.298640e-23 1.659728e-22 1.000000e+00
[33,] 5.751107e-23 1.150221e-22 1.000000e+00
[34,] 2.637401e-23 5.274802e-23 1.000000e+00
[35,] 6.467098e-24 1.293420e-23 1.000000e+00
[36,] 1.490124e-24 2.980248e-24 1.000000e+00
[37,] 3.362501e-25 6.725002e-25 1.000000e+00
[38,] 5.166536e-26 1.033307e-25 1.000000e+00
[39,] 1.290054e-26 2.580107e-26 1.000000e+00
[40,] 2.880241e-27 5.760482e-27 1.000000e+00
[41,] 3.862704e-28 7.725407e-28 1.000000e+00
[42,] 5.165804e-29 1.033161e-28 1.000000e+00
[43,] 2.788829e-29 5.577658e-29 1.000000e+00
[44,] 6.456055e-30 1.291211e-29 1.000000e+00
[45,] 8.665588e-31 1.733118e-30 1.000000e+00
[46,] 2.165920e-31 4.331840e-31 1.000000e+00
[47,] 1.340294e-31 2.680588e-31 1.000000e+00
[48,] 1.025724e-31 2.051447e-31 1.000000e+00
[49,] 1.276992e-31 2.553984e-31 1.000000e+00
[50,] 1.700206e-27 3.400412e-27 1.000000e+00
[51,] 7.375909e-24 1.475182e-23 1.000000e+00
[52,] 3.887957e-23 7.775915e-23 1.000000e+00
[53,] 2.912708e-21 5.825417e-21 1.000000e+00
[54,] 1.617869e-18 3.235738e-18 1.000000e+00
[55,] 1.288482e-16 2.576964e-16 1.000000e+00
[56,] 1.003826e-15 2.007652e-15 1.000000e+00
[57,] 3.741995e-15 7.483989e-15 1.000000e+00
[58,] 5.480478e-15 1.096096e-14 1.000000e+00
[59,] 9.191653e-15 1.838331e-14 1.000000e+00
[60,] 3.646221e-14 7.292441e-14 1.000000e+00
[61,] 2.085401e-12 4.170803e-12 1.000000e+00
[62,] 1.238892e-10 2.477785e-10 1.000000e+00
[63,] 2.096185e-08 4.192370e-08 1.000000e+00
[64,] 4.510887e-06 9.021774e-06 9.999955e-01
[65,] 7.073937e-05 1.414787e-04 9.999293e-01
[66,] 2.058254e-04 4.116508e-04 9.997942e-01
[67,] 1.578685e-04 3.157370e-04 9.998421e-01
[68,] 2.004713e-04 4.009427e-04 9.997995e-01
[69,] 2.206154e-04 4.412307e-04 9.997794e-01
[70,] 1.857581e-04 3.715161e-04 9.998142e-01
[71,] 1.517036e-03 3.034072e-03 9.984830e-01
[72,] 3.356932e-03 6.713864e-03 9.966431e-01
[73,] 9.001954e-03 1.800391e-02 9.909980e-01
[74,] 1.367167e-02 2.734334e-02 9.863283e-01
[75,] 2.844413e-02 5.688826e-02 9.715559e-01
[76,] 2.552149e-02 5.104297e-02 9.744785e-01
[77,] 2.002965e-02 4.005930e-02 9.799703e-01
[78,] 1.636575e-02 3.273149e-02 9.836343e-01
[79,] 1.768749e-02 3.537499e-02 9.823125e-01
[80,] 2.569354e-02 5.138707e-02 9.743065e-01
[81,] 6.818205e-02 1.363641e-01 9.318180e-01
[82,] 1.642046e-01 3.284093e-01 8.357954e-01
[83,] 2.699825e-01 5.399650e-01 7.300175e-01
[84,] 4.757242e-01 9.514485e-01 5.242758e-01
[85,] 8.249461e-01 3.501078e-01 1.750539e-01
[86,] 9.460227e-01 1.079547e-01 5.397733e-02
[87,] 9.750740e-01 4.985204e-02 2.492602e-02
[88,] 9.996694e-01 6.611062e-04 3.305531e-04
[89,] 9.993989e-01 1.202122e-03 6.010609e-04
[90,] 9.993059e-01 1.388109e-03 6.940546e-04
[91,] 9.990126e-01 1.974753e-03 9.873767e-04
[92,] 9.994725e-01 1.054906e-03 5.274529e-04
[93,] 9.996914e-01 6.171353e-04 3.085677e-04
[94,] 9.999436e-01 1.128242e-04 5.641209e-05
[95,] 9.998929e-01 2.141378e-04 1.070689e-04
[96,] 9.999859e-01 2.813946e-05 1.406973e-05
[97,] 9.999831e-01 3.371412e-05 1.685706e-05
[98,] 9.999653e-01 6.930672e-05 3.465336e-05
[99,] 9.999445e-01 1.110323e-04 5.551615e-05
[100,] 9.999164e-01 1.672432e-04 8.362162e-05
[101,] 9.998023e-01 3.954726e-04 1.977363e-04
[102,] 9.995341e-01 9.317760e-04 4.658880e-04
[103,] 9.990029e-01 1.994230e-03 9.971150e-04
[104,] 9.978122e-01 4.375527e-03 2.187764e-03
[105,] 9.954363e-01 9.127367e-03 4.563683e-03
[106,] 9.901597e-01 1.968062e-02 9.840310e-03
[107,] 9.806656e-01 3.866879e-02 1.933440e-02
[108,] 9.661982e-01 6.760368e-02 3.380184e-02
[109,] 9.709557e-01 5.808855e-02 2.904428e-02
[110,] 9.559822e-01 8.803564e-02 4.401782e-02
[111,] 9.684314e-01 6.313727e-02 3.156863e-02
[112,] 9.658415e-01 6.831702e-02 3.415851e-02
[113,] 9.575494e-01 8.490125e-02 4.245063e-02
> postscript(file="/var/www/html/rcomp/tmp/17ry01292184662.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27ry01292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/37ry01292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4z0fk1292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z0fk1292184662.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 = 130
Frequency = 1
1 2 3 4 5 6
-22.23848555 -36.58558664 -58.86799387 18.04533233 34.03438739 -11.24210478
7 8 9 10 11 12
8.20426195 -2.28213834 29.63822553 14.36146321 35.20216456 57.98399290
13 14 15 16 17 18
21.20568565 27.99104219 50.28419354 19.69973431 10.66175273 5.03192895
19 20 21 22 23 24
15.55594571 29.07737179 39.49178280 15.13728973 -3.10707189 -4.38674970
25 26 27 28 29 30
6.07968189 16.08931711 12.71293999 28.61133423 31.11609117 26.50868463
31 32 33 34 35 36
32.38086016 24.19614485 35.48774503 7.95225642 -4.00331765 5.18939218
37 38 39 40 41 42
11.87951076 1.19128061 -4.92879407 -0.28334991 4.14582826 -6.78045607
43 44 45 46 47 48
-17.43425807 -19.28929268 -26.58073365 -22.75041688 -20.68307654 -29.43814083
49 50 51 52 53 54
-27.27006242 -30.36014810 -18.43887396 -22.32509341 -29.20622066 -29.97720044
55 56 57 58 59 60
-24.16731191 -25.42266881 -28.60489726 -18.38035887 -20.77172213 -24.10613172
61 62 63 64 65 66
1.66040555 5.19915913 -6.54111835 -12.36856262 -21.58000481 -23.18612257
67 68 69 70 71 72
-26.69757854 -31.79214902 -24.86063969 -14.97927806 -17.20074645 -6.05010742
73 74 75 76 77 78
-1.09915752 -8.73725243 -16.96575449 3.43890843 -10.11214219 -14.11903330
79 80 81 82 83 84
-3.45897910 -20.47298697 -16.01253728 -27.67790632 -22.72799219 -25.61822828
85 86 87 88 89 90
-19.19785295 -25.67476418 -20.00550166 -27.61735347 -8.75410003 1.17212176
91 92 93 94 95 96
17.40892365 15.45417745 20.95703565 19.03519028 29.50115219 27.54778121
97 98 99 100 101 102
1.13425348 -3.31981312 0.60238980 21.06474508 24.85506494 9.08291899
103 104 105 106 107 108
4.88177010 2.71335825 -29.70341816 -5.64315370 -21.35726749 -39.10084350
109 110 111 112 113 114
-41.18576125 -31.39058225 -30.35821333 -20.56536351 -12.22692655 -20.66309189
115 116 117 118 119 120
-7.17579945 -9.18770989 0.72764581 -3.12436469 -6.75293114 -1.68946219
121 122 123 124 125 126
-0.07442548 -5.66365149 17.91391918 39.81738004 68.59835042 69.07486876
127 128 129 130
55.59920065 46.11607262 75.14892603 78.74994379
> postscript(file="/var/www/html/rcomp/tmp/6z0fk1292184662.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 -22.23848555 NA
1 -36.58558664 -22.23848555
2 -58.86799387 -36.58558664
3 18.04533233 -58.86799387
4 34.03438739 18.04533233
5 -11.24210478 34.03438739
6 8.20426195 -11.24210478
7 -2.28213834 8.20426195
8 29.63822553 -2.28213834
9 14.36146321 29.63822553
10 35.20216456 14.36146321
11 57.98399290 35.20216456
12 21.20568565 57.98399290
13 27.99104219 21.20568565
14 50.28419354 27.99104219
15 19.69973431 50.28419354
16 10.66175273 19.69973431
17 5.03192895 10.66175273
18 15.55594571 5.03192895
19 29.07737179 15.55594571
20 39.49178280 29.07737179
21 15.13728973 39.49178280
22 -3.10707189 15.13728973
23 -4.38674970 -3.10707189
24 6.07968189 -4.38674970
25 16.08931711 6.07968189
26 12.71293999 16.08931711
27 28.61133423 12.71293999
28 31.11609117 28.61133423
29 26.50868463 31.11609117
30 32.38086016 26.50868463
31 24.19614485 32.38086016
32 35.48774503 24.19614485
33 7.95225642 35.48774503
34 -4.00331765 7.95225642
35 5.18939218 -4.00331765
36 11.87951076 5.18939218
37 1.19128061 11.87951076
38 -4.92879407 1.19128061
39 -0.28334991 -4.92879407
40 4.14582826 -0.28334991
41 -6.78045607 4.14582826
42 -17.43425807 -6.78045607
43 -19.28929268 -17.43425807
44 -26.58073365 -19.28929268
45 -22.75041688 -26.58073365
46 -20.68307654 -22.75041688
47 -29.43814083 -20.68307654
48 -27.27006242 -29.43814083
49 -30.36014810 -27.27006242
50 -18.43887396 -30.36014810
51 -22.32509341 -18.43887396
52 -29.20622066 -22.32509341
53 -29.97720044 -29.20622066
54 -24.16731191 -29.97720044
55 -25.42266881 -24.16731191
56 -28.60489726 -25.42266881
57 -18.38035887 -28.60489726
58 -20.77172213 -18.38035887
59 -24.10613172 -20.77172213
60 1.66040555 -24.10613172
61 5.19915913 1.66040555
62 -6.54111835 5.19915913
63 -12.36856262 -6.54111835
64 -21.58000481 -12.36856262
65 -23.18612257 -21.58000481
66 -26.69757854 -23.18612257
67 -31.79214902 -26.69757854
68 -24.86063969 -31.79214902
69 -14.97927806 -24.86063969
70 -17.20074645 -14.97927806
71 -6.05010742 -17.20074645
72 -1.09915752 -6.05010742
73 -8.73725243 -1.09915752
74 -16.96575449 -8.73725243
75 3.43890843 -16.96575449
76 -10.11214219 3.43890843
77 -14.11903330 -10.11214219
78 -3.45897910 -14.11903330
79 -20.47298697 -3.45897910
80 -16.01253728 -20.47298697
81 -27.67790632 -16.01253728
82 -22.72799219 -27.67790632
83 -25.61822828 -22.72799219
84 -19.19785295 -25.61822828
85 -25.67476418 -19.19785295
86 -20.00550166 -25.67476418
87 -27.61735347 -20.00550166
88 -8.75410003 -27.61735347
89 1.17212176 -8.75410003
90 17.40892365 1.17212176
91 15.45417745 17.40892365
92 20.95703565 15.45417745
93 19.03519028 20.95703565
94 29.50115219 19.03519028
95 27.54778121 29.50115219
96 1.13425348 27.54778121
97 -3.31981312 1.13425348
98 0.60238980 -3.31981312
99 21.06474508 0.60238980
100 24.85506494 21.06474508
101 9.08291899 24.85506494
102 4.88177010 9.08291899
103 2.71335825 4.88177010
104 -29.70341816 2.71335825
105 -5.64315370 -29.70341816
106 -21.35726749 -5.64315370
107 -39.10084350 -21.35726749
108 -41.18576125 -39.10084350
109 -31.39058225 -41.18576125
110 -30.35821333 -31.39058225
111 -20.56536351 -30.35821333
112 -12.22692655 -20.56536351
113 -20.66309189 -12.22692655
114 -7.17579945 -20.66309189
115 -9.18770989 -7.17579945
116 0.72764581 -9.18770989
117 -3.12436469 0.72764581
118 -6.75293114 -3.12436469
119 -1.68946219 -6.75293114
120 -0.07442548 -1.68946219
121 -5.66365149 -0.07442548
122 17.91391918 -5.66365149
123 39.81738004 17.91391918
124 68.59835042 39.81738004
125 69.07486876 68.59835042
126 55.59920065 69.07486876
127 46.11607262 55.59920065
128 75.14892603 46.11607262
129 78.74994379 75.14892603
130 NA 78.74994379
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -36.58558664 -22.23848555
[2,] -58.86799387 -36.58558664
[3,] 18.04533233 -58.86799387
[4,] 34.03438739 18.04533233
[5,] -11.24210478 34.03438739
[6,] 8.20426195 -11.24210478
[7,] -2.28213834 8.20426195
[8,] 29.63822553 -2.28213834
[9,] 14.36146321 29.63822553
[10,] 35.20216456 14.36146321
[11,] 57.98399290 35.20216456
[12,] 21.20568565 57.98399290
[13,] 27.99104219 21.20568565
[14,] 50.28419354 27.99104219
[15,] 19.69973431 50.28419354
[16,] 10.66175273 19.69973431
[17,] 5.03192895 10.66175273
[18,] 15.55594571 5.03192895
[19,] 29.07737179 15.55594571
[20,] 39.49178280 29.07737179
[21,] 15.13728973 39.49178280
[22,] -3.10707189 15.13728973
[23,] -4.38674970 -3.10707189
[24,] 6.07968189 -4.38674970
[25,] 16.08931711 6.07968189
[26,] 12.71293999 16.08931711
[27,] 28.61133423 12.71293999
[28,] 31.11609117 28.61133423
[29,] 26.50868463 31.11609117
[30,] 32.38086016 26.50868463
[31,] 24.19614485 32.38086016
[32,] 35.48774503 24.19614485
[33,] 7.95225642 35.48774503
[34,] -4.00331765 7.95225642
[35,] 5.18939218 -4.00331765
[36,] 11.87951076 5.18939218
[37,] 1.19128061 11.87951076
[38,] -4.92879407 1.19128061
[39,] -0.28334991 -4.92879407
[40,] 4.14582826 -0.28334991
[41,] -6.78045607 4.14582826
[42,] -17.43425807 -6.78045607
[43,] -19.28929268 -17.43425807
[44,] -26.58073365 -19.28929268
[45,] -22.75041688 -26.58073365
[46,] -20.68307654 -22.75041688
[47,] -29.43814083 -20.68307654
[48,] -27.27006242 -29.43814083
[49,] -30.36014810 -27.27006242
[50,] -18.43887396 -30.36014810
[51,] -22.32509341 -18.43887396
[52,] -29.20622066 -22.32509341
[53,] -29.97720044 -29.20622066
[54,] -24.16731191 -29.97720044
[55,] -25.42266881 -24.16731191
[56,] -28.60489726 -25.42266881
[57,] -18.38035887 -28.60489726
[58,] -20.77172213 -18.38035887
[59,] -24.10613172 -20.77172213
[60,] 1.66040555 -24.10613172
[61,] 5.19915913 1.66040555
[62,] -6.54111835 5.19915913
[63,] -12.36856262 -6.54111835
[64,] -21.58000481 -12.36856262
[65,] -23.18612257 -21.58000481
[66,] -26.69757854 -23.18612257
[67,] -31.79214902 -26.69757854
[68,] -24.86063969 -31.79214902
[69,] -14.97927806 -24.86063969
[70,] -17.20074645 -14.97927806
[71,] -6.05010742 -17.20074645
[72,] -1.09915752 -6.05010742
[73,] -8.73725243 -1.09915752
[74,] -16.96575449 -8.73725243
[75,] 3.43890843 -16.96575449
[76,] -10.11214219 3.43890843
[77,] -14.11903330 -10.11214219
[78,] -3.45897910 -14.11903330
[79,] -20.47298697 -3.45897910
[80,] -16.01253728 -20.47298697
[81,] -27.67790632 -16.01253728
[82,] -22.72799219 -27.67790632
[83,] -25.61822828 -22.72799219
[84,] -19.19785295 -25.61822828
[85,] -25.67476418 -19.19785295
[86,] -20.00550166 -25.67476418
[87,] -27.61735347 -20.00550166
[88,] -8.75410003 -27.61735347
[89,] 1.17212176 -8.75410003
[90,] 17.40892365 1.17212176
[91,] 15.45417745 17.40892365
[92,] 20.95703565 15.45417745
[93,] 19.03519028 20.95703565
[94,] 29.50115219 19.03519028
[95,] 27.54778121 29.50115219
[96,] 1.13425348 27.54778121
[97,] -3.31981312 1.13425348
[98,] 0.60238980 -3.31981312
[99,] 21.06474508 0.60238980
[100,] 24.85506494 21.06474508
[101,] 9.08291899 24.85506494
[102,] 4.88177010 9.08291899
[103,] 2.71335825 4.88177010
[104,] -29.70341816 2.71335825
[105,] -5.64315370 -29.70341816
[106,] -21.35726749 -5.64315370
[107,] -39.10084350 -21.35726749
[108,] -41.18576125 -39.10084350
[109,] -31.39058225 -41.18576125
[110,] -30.35821333 -31.39058225
[111,] -20.56536351 -30.35821333
[112,] -12.22692655 -20.56536351
[113,] -20.66309189 -12.22692655
[114,] -7.17579945 -20.66309189
[115,] -9.18770989 -7.17579945
[116,] 0.72764581 -9.18770989
[117,] -3.12436469 0.72764581
[118,] -6.75293114 -3.12436469
[119,] -1.68946219 -6.75293114
[120,] -0.07442548 -1.68946219
[121,] -5.66365149 -0.07442548
[122,] 17.91391918 -5.66365149
[123,] 39.81738004 17.91391918
[124,] 68.59835042 39.81738004
[125,] 69.07486876 68.59835042
[126,] 55.59920065 69.07486876
[127,] 46.11607262 55.59920065
[128,] 75.14892603 46.11607262
[129,] 78.74994379 75.14892603
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -36.58558664 -22.23848555
2 -58.86799387 -36.58558664
3 18.04533233 -58.86799387
4 34.03438739 18.04533233
5 -11.24210478 34.03438739
6 8.20426195 -11.24210478
7 -2.28213834 8.20426195
8 29.63822553 -2.28213834
9 14.36146321 29.63822553
10 35.20216456 14.36146321
11 57.98399290 35.20216456
12 21.20568565 57.98399290
13 27.99104219 21.20568565
14 50.28419354 27.99104219
15 19.69973431 50.28419354
16 10.66175273 19.69973431
17 5.03192895 10.66175273
18 15.55594571 5.03192895
19 29.07737179 15.55594571
20 39.49178280 29.07737179
21 15.13728973 39.49178280
22 -3.10707189 15.13728973
23 -4.38674970 -3.10707189
24 6.07968189 -4.38674970
25 16.08931711 6.07968189
26 12.71293999 16.08931711
27 28.61133423 12.71293999
28 31.11609117 28.61133423
29 26.50868463 31.11609117
30 32.38086016 26.50868463
31 24.19614485 32.38086016
32 35.48774503 24.19614485
33 7.95225642 35.48774503
34 -4.00331765 7.95225642
35 5.18939218 -4.00331765
36 11.87951076 5.18939218
37 1.19128061 11.87951076
38 -4.92879407 1.19128061
39 -0.28334991 -4.92879407
40 4.14582826 -0.28334991
41 -6.78045607 4.14582826
42 -17.43425807 -6.78045607
43 -19.28929268 -17.43425807
44 -26.58073365 -19.28929268
45 -22.75041688 -26.58073365
46 -20.68307654 -22.75041688
47 -29.43814083 -20.68307654
48 -27.27006242 -29.43814083
49 -30.36014810 -27.27006242
50 -18.43887396 -30.36014810
51 -22.32509341 -18.43887396
52 -29.20622066 -22.32509341
53 -29.97720044 -29.20622066
54 -24.16731191 -29.97720044
55 -25.42266881 -24.16731191
56 -28.60489726 -25.42266881
57 -18.38035887 -28.60489726
58 -20.77172213 -18.38035887
59 -24.10613172 -20.77172213
60 1.66040555 -24.10613172
61 5.19915913 1.66040555
62 -6.54111835 5.19915913
63 -12.36856262 -6.54111835
64 -21.58000481 -12.36856262
65 -23.18612257 -21.58000481
66 -26.69757854 -23.18612257
67 -31.79214902 -26.69757854
68 -24.86063969 -31.79214902
69 -14.97927806 -24.86063969
70 -17.20074645 -14.97927806
71 -6.05010742 -17.20074645
72 -1.09915752 -6.05010742
73 -8.73725243 -1.09915752
74 -16.96575449 -8.73725243
75 3.43890843 -16.96575449
76 -10.11214219 3.43890843
77 -14.11903330 -10.11214219
78 -3.45897910 -14.11903330
79 -20.47298697 -3.45897910
80 -16.01253728 -20.47298697
81 -27.67790632 -16.01253728
82 -22.72799219 -27.67790632
83 -25.61822828 -22.72799219
84 -19.19785295 -25.61822828
85 -25.67476418 -19.19785295
86 -20.00550166 -25.67476418
87 -27.61735347 -20.00550166
88 -8.75410003 -27.61735347
89 1.17212176 -8.75410003
90 17.40892365 1.17212176
91 15.45417745 17.40892365
92 20.95703565 15.45417745
93 19.03519028 20.95703565
94 29.50115219 19.03519028
95 27.54778121 29.50115219
96 1.13425348 27.54778121
97 -3.31981312 1.13425348
98 0.60238980 -3.31981312
99 21.06474508 0.60238980
100 24.85506494 21.06474508
101 9.08291899 24.85506494
102 4.88177010 9.08291899
103 2.71335825 4.88177010
104 -29.70341816 2.71335825
105 -5.64315370 -29.70341816
106 -21.35726749 -5.64315370
107 -39.10084350 -21.35726749
108 -41.18576125 -39.10084350
109 -31.39058225 -41.18576125
110 -30.35821333 -31.39058225
111 -20.56536351 -30.35821333
112 -12.22692655 -20.56536351
113 -20.66309189 -12.22692655
114 -7.17579945 -20.66309189
115 -9.18770989 -7.17579945
116 0.72764581 -9.18770989
117 -3.12436469 0.72764581
118 -6.75293114 -3.12436469
119 -1.68946219 -6.75293114
120 -0.07442548 -1.68946219
121 -5.66365149 -0.07442548
122 17.91391918 -5.66365149
123 39.81738004 17.91391918
124 68.59835042 39.81738004
125 69.07486876 68.59835042
126 55.59920065 69.07486876
127 46.11607262 55.59920065
128 75.14892603 46.11607262
129 78.74994379 75.14892603
> 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/html/rcomp/tmp/7sawn1292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/831vq1292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/931vq1292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vsub1292184662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11ztbz1292184662.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/html/rcomp/tmp/122t9n1292184662.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/html/rcomp/tmp/139c6g1292184662.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/html/rcomp/tmp/14k3o11292184662.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/html/rcomp/tmp/155mmp1292184662.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/html/rcomp/tmp/161wkg1292184662.tab")
+ }
>
> try(system("convert tmp/17ry01292184662.ps tmp/17ry01292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/27ry01292184662.ps tmp/27ry01292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/37ry01292184662.ps tmp/37ry01292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z0fk1292184662.ps tmp/4z0fk1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z0fk1292184662.ps tmp/5z0fk1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z0fk1292184662.ps tmp/6z0fk1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sawn1292184662.ps tmp/7sawn1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/831vq1292184662.ps tmp/831vq1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/931vq1292184662.ps tmp/931vq1292184662.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vsub1292184662.ps tmp/10vsub1292184662.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.512 1.796 8.337