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
+ ,0.0274
+ ,144.7
+ ,28.66
+ ,13741000
+ ,35.78
+ ,4696.69
+ ,0.0322
+ ,140.8
+ ,33.95
+ ,14143500
+ ,42.54
+ ,4572.83
+ ,0.0376
+ ,137.1
+ ,31.01
+ ,16763800
+ ,27.92
+ ,3860.66
+ ,0.0307
+ ,137.7
+ ,21.00
+ ,16634600
+ ,25.05
+ ,3400.91
+ ,0.0319
+ ,144.7
+ ,26.19
+ ,13693300
+ ,32.03
+ ,3966.11
+ ,0.0373
+ ,139.2
+ ,25.41
+ ,10545800
+ ,27.95
+ ,3766.99
+ ,0.0366
+ ,143.0
+ ,30.47
+ ,9409900
+ ,27.95
+ ,4206.35
+ ,0.0341
+ ,140.8
+ ,12.88
+ ,39182200
+ ,24.15
+ ,3672.82
+ ,0.0345
+ ,142.5
+ ,9.78
+ ,37005800
+ ,27.57
+ ,3369.63
+ ,0.0345
+ ,135.8
+ ,8.25
+ ,15818500
+ ,22.97
+ ,2597.93
+ ,0.0345
+ ,132.6
+ ,7.44
+ ,16952000
+ ,17.37
+ ,2470.52
+ ,0.0339
+ ,128.6
+ ,10.81
+ ,24563400
+ ,24.45
+ ,2772.73
+ ,0.0373
+ ,115.7
+ ,9.12
+ ,14163200
+ ,23.62
+ ,2151.83
+ ,0.0353
+ ,109.2
+ ,11.03
+ ,18184800
+ ,21.90
+ ,1840.26
+ ,0.0292
+ ,116.9
+ ,12.74
+ ,20810300
+ ,27.12
+ ,2116.24
+ ,0.0327
+ ,109.9
+ ,9.98
+ ,12843000
+ ,27.70
+ ,2110.49
+ ,0.0362
+ ,116.1
+ ,11.62
+ ,13866700
+ ,29.23
+ ,2160.54
+ ,0.0325
+ ,118.9
+ ,9.40
+ ,15119200
+ ,26.50
+ ,2027.13
+ ,0.0272
+ ,116.3
+ ,9.27
+ ,8301600
+ ,22.84
+ ,1805.43
+ ,0.0272
+ ,114.0
+ ,7.76
+ ,14039600
+ ,20.49
+ ,1498.80
+ ,0.0265
+ ,97.0
+ ,8.78
+ ,12139700
+ ,23.28
+ ,1690.20
+ ,0.0213
+ ,85.3
+ ,10.65
+ ,9649000
+ ,25.71
+ ,1930.58
+ ,0.019
+ ,84.9
+ ,10.95
+ ,8513600
+ ,26.52
+ ,1950.40
+ ,0.0155
+ ,94.6
+ ,12.36
+ ,15278600
+ ,25.51
+ ,1934.03
+ ,0.0114
+ ,97.8
+ ,10.85
+ ,15590900
+ ,23.36
+ ,1731.49
+ ,0.0114
+ ,95.0
+ ,11.84
+ ,9691100
+ ,24.15
+ ,1845.35
+ ,0.0148
+ ,110.7
+ ,12.14
+ ,10882700
+ ,20.92
+ ,1688.23
+ ,0.0164
+ ,108.5
+ ,11.65
+ ,10294800
+ ,20.38
+ ,1615.73
+ ,0.0118
+ ,110.3
+ ,8.86
+ ,16031900
+ ,21.90
+ ,1463.21
+ ,0.0107
+ ,106.3
+ ,7.63
+ ,13683600
+ ,19.21
+ ,1328.26
+ ,0.0146
+ ,97.4
+ ,7.38
+ ,8677200
+ ,19.65
+ ,1314.85
+ ,0.018
+ ,94.5
+ ,7.25
+ ,9874100
+ ,17.51
+ ,1172.06
+ ,0.0151
+ ,93.7
+ ,8.03
+ ,10725500
+ ,21.41
+ ,1329.75
+ ,0.0203
+ ,79.6
+ ,7.75
+ ,8348400
+ ,23.09
+ ,1478.78
+ ,0.022
+ ,84.9
+ ,7.16
+ ,8046200
+ ,20.70
+ ,1335.51
+ ,0.0238
+ ,80.7
+ ,7.18
+ ,10862300
+ ,19.00
+ ,1320.91
+ ,0.026
+ ,78.8
+ ,7.51
+ ,8100300
+ ,19.04
+ ,1337.52
+ ,0.0298
+ ,64.8
+ ,7.07
+ ,7287500
+ ,19.45
+ ,1341.17
+ ,0.0302
+ ,61.4
+ ,7.11
+ ,14002500
+ ,20.54
+ ,1464.31
+ ,0.0222
+ ,81.0
+ ,8.98
+ ,19037900
+ ,19.77
+ ,1595.91
+ ,0.0206
+ ,83.6
+ ,9.53
+ ,10774600
+ ,20.60
+ ,1622.80
+ ,0.0211
+ ,83.5
+ ,10.54
+ ,8960600
+ ,21.21
+ ,1735.02
+ ,0.0211
+ ,77.0
+ ,11.31
+ ,7773300
+ ,21.30
+ ,1810.45
+ ,0.0216
+ ,81.7
+ ,10.36
+ ,9579700
+ ,22.33
+ ,1786.94
+ ,0.0232
+ ,77.0
+ ,11.44
+ ,11270700
+ ,21.12
+ ,1932.21
+ ,0.0204
+ ,81.7
+ ,10.45
+ ,9492800
+ ,20.77
+ ,1960.26
+ ,0.0177
+ ,92.5
+ ,10.69
+ ,9136800
+ ,22.11
+ ,2003.37
+ ,0.0188
+ ,91.7
+ ,11.28
+ ,14487600
+ ,22.34
+ ,2066.15
+ ,0.0193
+ ,96.4
+ ,11.96
+ ,10133200
+ ,21.43
+ ,2029.82
+ ,0.0169
+ ,88.5
+ ,13.52
+ ,18659700
+ ,20.14
+ ,1994.22
+ ,0.0174
+ ,88.5
+ ,12.89
+ ,15980700
+ ,21.11
+ ,1920.15
+ ,0.0229
+ ,93.0
+ ,14.03
+ ,9732100
+ ,21.19
+ ,1986.74
+ ,0.0305
+ ,93.1
+ ,16.27
+ ,14626300
+ ,23.07
+ ,2047.79
+ ,0.0327
+ ,102.8
+ ,16.17
+ ,16904000
+ ,23.01
+ ,1887.36
+ ,0.0299
+ ,105.7
+ ,17.25
+ ,13616700
+ ,22.12
+ ,1838.10
+ ,0.0265
+ ,98.7
+ ,19.38
+ ,13772900
+ ,22.40
+ ,1896.84
+ ,0.0254
+ ,96.7
+ ,26.20
+ ,28749200
+ ,22.66
+ ,1974.99
+ ,0.0319
+ ,92.9
+ ,33.53
+ ,31408300
+ ,24.21
+ ,2096.81
+ ,0.0352
+ ,92.6
+ ,32.20
+ ,26342800
+ ,24.13
+ ,2175.44
+ ,0.0326
+ ,102.7
+ ,38.45
+ ,48909500
+ ,23.73
+ ,2062.41
+ ,0.0297
+ ,105.1
+ ,44.86
+ ,41542400
+ ,22.79
+ ,2051.72
+ ,0.0301
+ ,104.4
+ ,41.67
+ ,24857200
+ ,21.89
+ ,1999.23
+ ,0.0315
+ ,103.0
+ ,36.06
+ ,34093700
+ ,22.92
+ ,1921.65
+ ,0.0351
+ ,97.5
+ ,39.76
+ ,22555200
+ ,23.44
+ ,2068.22
+ ,0.028
+ ,103.1
+ ,36.81
+ ,19067500
+ ,22.57
+ ,2056.96
+ ,0.0253
+ ,106.2
+ ,42.65
+ ,19029100
+ ,23.27
+ ,2184.83
+ ,0.0317
+ ,103.6
+ ,46.89
+ ,15223200
+ ,24.95
+ ,2152.09
+ ,0.0364
+ ,105.5
+ ,53.61
+ ,21903700
+ ,23.45
+ ,2151.69
+ ,0.0469
+ ,87.5
+ ,57.59
+ ,33306600
+ ,23.42
+ ,2120.30
+ ,0.0435
+ ,85.2
+ ,67.82
+ ,23898100
+ ,25.30
+ ,2232.82
+ ,0.0346
+ ,98.3
+ ,71.89
+ ,23279600
+ ,23.90
+ ,2205.32
+ ,0.0342
+ ,103.8
+ ,75.51
+ ,40699800
+ ,25.73
+ ,2305.82
+ ,0.0399
+ ,106.8
+ ,68.49
+ ,37646000
+ ,24.64
+ ,2281.39
+ ,0.036
+ ,102.7
+ ,62.72
+ ,37277000
+ ,24.95
+ ,2339.79
+ ,0.0336
+ ,107.5
+ ,70.39
+ ,39246800
+ ,22.15
+ ,2322.57
+ ,0.0355
+ ,109.8
+ ,59.77
+ ,27418400
+ ,20.85
+ ,2178.88
+ ,0.0417
+ ,104.7
+ ,57.27
+ ,30318700
+ ,21.45
+ ,2172.09
+ ,0.0432
+ ,105.7
+ ,67.96
+ ,32808100
+ ,22.15
+ ,2091.47
+ ,0.0415
+ ,107.0
+ ,67.85
+ ,28668200
+ ,23.75
+ ,2183.75
+ ,0.0382
+ ,100.2
+ ,76.98
+ ,32370300
+ ,25.27
+ ,2258.43
+ ,0.0206
+ ,105.9
+ ,81.08
+ ,24171100
+ ,26.53
+ ,2366.71
+ ,0.0131
+ ,105.1
+ ,91.66
+ ,25009100
+ ,27.22
+ ,2431.77
+ ,0.0197
+ ,105.3
+ ,84.84
+ ,32084300
+ ,27.69
+ ,2415.29
+ ,0.0254
+ ,110.0
+ ,85.73
+ ,50117500
+ ,28.61
+ ,2463.93
+ ,0.0208
+ ,110.2
+ ,84.61
+ ,27522200
+ ,26.21
+ ,2416.15
+ ,0.0242
+ ,111.2
+ ,92.91
+ ,26816800
+ ,25.93
+ ,2421.64
+ ,0.0278
+ ,108.2
+ ,99.80
+ ,25136100
+ ,27.86
+ ,2525.09
+ ,0.0257
+ ,106.3
+ ,121.19
+ ,30295600
+ ,28.65
+ ,2604.52
+ ,0.0269
+ ,108.5
+ ,122.04
+ ,41526100
+ ,27.51
+ ,2603.23
+ ,0.0269
+ ,105.3
+ ,131.76
+ ,43845100
+ ,27.06
+ ,2546.27
+ ,0.0236
+ ,111.9
+ ,138.48
+ ,39188900
+ ,26.91
+ ,2596.36
+ ,0.0197
+ ,105.6
+ ,153.47
+ ,40496400
+ ,27.60
+ ,2701.50
+ ,0.0276
+ ,99.5
+ ,189.95
+ ,37438400
+ ,34.48
+ ,2859.12
+ ,0.0354
+ ,95.2
+ ,182.22
+ ,46553700
+ ,31.58
+ ,2660.96
+ ,0.0431
+ ,87.8
+ ,198.08
+ ,31771400
+ ,33.46
+ ,2652.28
+ ,0.0408
+ ,90.6
+ ,135.36
+ ,62108100
+ ,30.64
+ ,2389.86
+ ,0.0428
+ ,87.9
+ ,125.02
+ ,46645400
+ ,25.66
+ ,2271.48
+ ,0.0403
+ ,76.4
+ ,143.50
+ ,42313100
+ ,26.78
+ ,2279.10
+ ,0.0398
+ ,65.9
+ ,173.95
+ ,38841700
+ ,26.91
+ ,2412.80
+ ,0.0394
+ ,62.3
+ ,188.75
+ ,32650300
+ ,26.82
+ ,2522.66
+ ,0.0418
+ ,57.2
+ ,167.44
+ ,34281100
+ ,26.05
+ ,2292.98
+ ,0.0502
+ ,50.4
+ ,158.95
+ ,33096200
+ ,24.36
+ ,2325.55
+ ,0.056
+ ,51.9
+ ,169.53
+ ,23273800
+ ,25.94
+ ,2367.52
+ ,0.0537
+ ,58.5
+ ,113.66
+ ,43697600
+ ,25.37
+ ,2091.88
+ ,0.0494
+ ,61.4
+ ,107.59
+ ,66902300
+ ,21.23
+ ,1720.95
+ ,0.0366
+ ,38.8
+ ,92.67
+ ,44957200
+ ,19.35
+ ,1535.57
+ ,0.0107
+ ,44.9
+ ,85.35
+ ,33800900
+ ,18.61
+ ,1577.03
+ ,0.0009
+ ,38.6
+ ,90.13
+ ,33487900
+ ,16.37
+ ,1476.42
+ ,0.0003
+ ,4.0
+ ,89.31
+ ,27394900
+ ,15.56
+ ,1377.84
+ ,0.0024
+ ,25.3
+ ,105.12
+ ,25963400
+ ,17.70
+ ,1528.59
+ ,-0.0038
+ ,26.9
+ ,125.83
+ ,20952600
+ ,19.52
+ ,1717.30
+ ,-0.0074
+ ,40.8
+ ,135.81
+ ,17702900
+ ,20.26
+ ,1774.33
+ ,-0.0128
+ ,54.8
+ ,142.43
+ ,21282100
+ ,23.05
+ ,1835.04
+ ,-0.0143
+ ,49.3
+ ,163.39
+ ,18449100
+ ,22.81
+ ,1978.50
+ ,-0.021
+ ,47.4
+ ,168.21
+ ,14415700
+ ,24.04
+ ,2009.06
+ ,-0.0148
+ ,54.5
+ ,185.35
+ ,17906300
+ ,25.08
+ ,2122.42
+ ,-0.0129
+ ,53.4
+ ,188.50
+ ,22197500
+ ,27.04
+ ,2045.11
+ ,-0.0018
+ ,48.7
+ ,199.91
+ ,15856500
+ ,28.81
+ ,2144.60
+ ,0.0184
+ ,50.6
+ ,210.73
+ ,19068700
+ ,29.86
+ ,2269.15
+ ,0.0272
+ ,53.6
+ ,192.06
+ ,30855100
+ ,27.61
+ ,2147.35
+ ,0.0263
+ ,56.5
+ ,204.62
+ ,21209000
+ ,28.22
+ ,2238.26
+ ,0.0214
+ ,46.4
+ ,235.00
+ ,19541600
+ ,28.83
+ ,2397.96
+ ,0.0231
+ ,52.3
+ ,261.09
+ ,21955000
+ ,30.06
+ ,2461.19
+ ,0.0224
+ ,57.7
+ ,256.88
+ ,33725900
+ ,25.51
+ ,2257.04
+ ,0.0202
+ ,62.7
+ ,251.53
+ ,28192800
+ ,22.75
+ ,2109.24
+ ,0.0105
+ ,54.3
+ ,257.25
+ ,27377000
+ ,25.52
+ ,2254.70
+ ,0.0124
+ ,51.0
+ ,243.10
+ ,16228100
+ ,23.33
+ ,2114.03
+ ,0.0115
+ ,53.2
+ ,283.75
+ ,21278900
+ ,24.34
+ ,2368.62
+ ,0.0114
+ ,48.6
+ ,300.98
+ ,21457400
+ ,26.51
+ ,2507.41
+ ,0.0117
+ ,49.9)
+ ,dim=c(6
+ ,130)
+ ,dimnames=list(c('Apple'
+ ,'Volume'
+ ,'Microsoft'
+ ,'NASDAQ'
+ ,'Inflatie'
+ ,'Consumentenvertrouwen')
+ ,1:130))
> y <- array(NA,dim=c(6,130),dimnames=list(c('Apple','Volume','Microsoft','NASDAQ','Inflatie','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 = 'No 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 Inflatie Consumentenvertrouwen
1 25.94 23688100 39.18 3940.35 0.0274 144.7
2 28.66 13741000 35.78 4696.69 0.0322 140.8
3 33.95 14143500 42.54 4572.83 0.0376 137.1
4 31.01 16763800 27.92 3860.66 0.0307 137.7
5 21.00 16634600 25.05 3400.91 0.0319 144.7
6 26.19 13693300 32.03 3966.11 0.0373 139.2
7 25.41 10545800 27.95 3766.99 0.0366 143.0
8 30.47 9409900 27.95 4206.35 0.0341 140.8
9 12.88 39182200 24.15 3672.82 0.0345 142.5
10 9.78 37005800 27.57 3369.63 0.0345 135.8
11 8.25 15818500 22.97 2597.93 0.0345 132.6
12 7.44 16952000 17.37 2470.52 0.0339 128.6
13 10.81 24563400 24.45 2772.73 0.0373 115.7
14 9.12 14163200 23.62 2151.83 0.0353 109.2
15 11.03 18184800 21.90 1840.26 0.0292 116.9
16 12.74 20810300 27.12 2116.24 0.0327 109.9
17 9.98 12843000 27.70 2110.49 0.0362 116.1
18 11.62 13866700 29.23 2160.54 0.0325 118.9
19 9.40 15119200 26.50 2027.13 0.0272 116.3
20 9.27 8301600 22.84 1805.43 0.0272 114.0
21 7.76 14039600 20.49 1498.80 0.0265 97.0
22 8.78 12139700 23.28 1690.20 0.0213 85.3
23 10.65 9649000 25.71 1930.58 0.0190 84.9
24 10.95 8513600 26.52 1950.40 0.0155 94.6
25 12.36 15278600 25.51 1934.03 0.0114 97.8
26 10.85 15590900 23.36 1731.49 0.0114 95.0
27 11.84 9691100 24.15 1845.35 0.0148 110.7
28 12.14 10882700 20.92 1688.23 0.0164 108.5
29 11.65 10294800 20.38 1615.73 0.0118 110.3
30 8.86 16031900 21.90 1463.21 0.0107 106.3
31 7.63 13683600 19.21 1328.26 0.0146 97.4
32 7.38 8677200 19.65 1314.85 0.0180 94.5
33 7.25 9874100 17.51 1172.06 0.0151 93.7
34 8.03 10725500 21.41 1329.75 0.0203 79.6
35 7.75 8348400 23.09 1478.78 0.0220 84.9
36 7.16 8046200 20.70 1335.51 0.0238 80.7
37 7.18 10862300 19.00 1320.91 0.0260 78.8
38 7.51 8100300 19.04 1337.52 0.0298 64.8
39 7.07 7287500 19.45 1341.17 0.0302 61.4
40 7.11 14002500 20.54 1464.31 0.0222 81.0
41 8.98 19037900 19.77 1595.91 0.0206 83.6
42 9.53 10774600 20.60 1622.80 0.0211 83.5
43 10.54 8960600 21.21 1735.02 0.0211 77.0
44 11.31 7773300 21.30 1810.45 0.0216 81.7
45 10.36 9579700 22.33 1786.94 0.0232 77.0
46 11.44 11270700 21.12 1932.21 0.0204 81.7
47 10.45 9492800 20.77 1960.26 0.0177 92.5
48 10.69 9136800 22.11 2003.37 0.0188 91.7
49 11.28 14487600 22.34 2066.15 0.0193 96.4
50 11.96 10133200 21.43 2029.82 0.0169 88.5
51 13.52 18659700 20.14 1994.22 0.0174 88.5
52 12.89 15980700 21.11 1920.15 0.0229 93.0
53 14.03 9732100 21.19 1986.74 0.0305 93.1
54 16.27 14626300 23.07 2047.79 0.0327 102.8
55 16.17 16904000 23.01 1887.36 0.0299 105.7
56 17.25 13616700 22.12 1838.10 0.0265 98.7
57 19.38 13772900 22.40 1896.84 0.0254 96.7
58 26.20 28749200 22.66 1974.99 0.0319 92.9
59 33.53 31408300 24.21 2096.81 0.0352 92.6
60 32.20 26342800 24.13 2175.44 0.0326 102.7
61 38.45 48909500 23.73 2062.41 0.0297 105.1
62 44.86 41542400 22.79 2051.72 0.0301 104.4
63 41.67 24857200 21.89 1999.23 0.0315 103.0
64 36.06 34093700 22.92 1921.65 0.0351 97.5
65 39.76 22555200 23.44 2068.22 0.0280 103.1
66 36.81 19067500 22.57 2056.96 0.0253 106.2
67 42.65 19029100 23.27 2184.83 0.0317 103.6
68 46.89 15223200 24.95 2152.09 0.0364 105.5
69 53.61 21903700 23.45 2151.69 0.0469 87.5
70 57.59 33306600 23.42 2120.30 0.0435 85.2
71 67.82 23898100 25.30 2232.82 0.0346 98.3
72 71.89 23279600 23.90 2205.32 0.0342 103.8
73 75.51 40699800 25.73 2305.82 0.0399 106.8
74 68.49 37646000 24.64 2281.39 0.0360 102.7
75 62.72 37277000 24.95 2339.79 0.0336 107.5
76 70.39 39246800 22.15 2322.57 0.0355 109.8
77 59.77 27418400 20.85 2178.88 0.0417 104.7
78 57.27 30318700 21.45 2172.09 0.0432 105.7
79 67.96 32808100 22.15 2091.47 0.0415 107.0
80 67.85 28668200 23.75 2183.75 0.0382 100.2
81 76.98 32370300 25.27 2258.43 0.0206 105.9
82 81.08 24171100 26.53 2366.71 0.0131 105.1
83 91.66 25009100 27.22 2431.77 0.0197 105.3
84 84.84 32084300 27.69 2415.29 0.0254 110.0
85 85.73 50117500 28.61 2463.93 0.0208 110.2
86 84.61 27522200 26.21 2416.15 0.0242 111.2
87 92.91 26816800 25.93 2421.64 0.0278 108.2
88 99.80 25136100 27.86 2525.09 0.0257 106.3
89 121.19 30295600 28.65 2604.52 0.0269 108.5
90 122.04 41526100 27.51 2603.23 0.0269 105.3
91 131.76 43845100 27.06 2546.27 0.0236 111.9
92 138.48 39188900 26.91 2596.36 0.0197 105.6
93 153.47 40496400 27.60 2701.50 0.0276 99.5
94 189.95 37438400 34.48 2859.12 0.0354 95.2
95 182.22 46553700 31.58 2660.96 0.0431 87.8
96 198.08 31771400 33.46 2652.28 0.0408 90.6
97 135.36 62108100 30.64 2389.86 0.0428 87.9
98 125.02 46645400 25.66 2271.48 0.0403 76.4
99 143.50 42313100 26.78 2279.10 0.0398 65.9
100 173.95 38841700 26.91 2412.80 0.0394 62.3
101 188.75 32650300 26.82 2522.66 0.0418 57.2
102 167.44 34281100 26.05 2292.98 0.0502 50.4
103 158.95 33096200 24.36 2325.55 0.0560 51.9
104 169.53 23273800 25.94 2367.52 0.0537 58.5
105 113.66 43697600 25.37 2091.88 0.0494 61.4
106 107.59 66902300 21.23 1720.95 0.0366 38.8
107 92.67 44957200 19.35 1535.57 0.0107 44.9
108 85.35 33800900 18.61 1577.03 0.0009 38.6
109 90.13 33487900 16.37 1476.42 0.0003 4.0
110 89.31 27394900 15.56 1377.84 0.0024 25.3
111 105.12 25963400 17.70 1528.59 -0.0038 26.9
112 125.83 20952600 19.52 1717.30 -0.0074 40.8
113 135.81 17702900 20.26 1774.33 -0.0128 54.8
114 142.43 21282100 23.05 1835.04 -0.0143 49.3
115 163.39 18449100 22.81 1978.50 -0.0210 47.4
116 168.21 14415700 24.04 2009.06 -0.0148 54.5
117 185.35 17906300 25.08 2122.42 -0.0129 53.4
118 188.50 22197500 27.04 2045.11 -0.0018 48.7
119 199.91 15856500 28.81 2144.60 0.0184 50.6
120 210.73 19068700 29.86 2269.15 0.0272 53.6
121 192.06 30855100 27.61 2147.35 0.0263 56.5
122 204.62 21209000 28.22 2238.26 0.0214 46.4
123 235.00 19541600 28.83 2397.96 0.0231 52.3
124 261.09 21955000 30.06 2461.19 0.0224 57.7
125 256.88 33725900 25.51 2257.04 0.0202 62.7
126 251.53 28192800 22.75 2109.24 0.0105 54.3
127 257.25 27377000 25.52 2254.70 0.0124 51.0
128 243.10 16228100 23.33 2114.03 0.0115 53.2
129 283.75 21278900 24.34 2368.62 0.0114 48.6
130 300.98 21457400 26.51 2507.41 0.0117 49.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Volume Microsoft
9.489e+00 1.394e-06 6.788e+00
NASDAQ Inflatie Consumentenvertrouwen
3.178e-02 -5.549e+02 -2.094e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-121.3289 -25.7809 0.9295 20.7535 112.9450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.489e+00 2.454e+01 0.387 0.69961
Volume 1.394e-06 3.364e-07 4.145 6.25e-05 ***
Microsoft 6.788e+00 1.387e+00 4.895 3.00e-06 ***
NASDAQ 3.178e-02 1.020e-02 3.115 0.00228 **
Inflatie -5.549e+02 3.138e+02 -1.768 0.07949 .
Consumentenvertrouwen -2.094e+00 1.723e-01 -12.157 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 41.18 on 124 degrees of freedom
Multiple R-squared: 0.7225, Adjusted R-squared: 0.7113
F-statistic: 64.56 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,] 2.603375e-04 5.206749e-04 9.997397e-01
[2,] 2.953718e-04 5.907437e-04 9.997046e-01
[3,] 9.969252e-05 1.993850e-04 9.999003e-01
[4,] 1.133017e-05 2.266033e-05 9.999887e-01
[5,] 1.906588e-06 3.813177e-06 9.999981e-01
[6,] 2.052907e-07 4.105814e-07 9.999998e-01
[7,] 4.128346e-08 8.256692e-08 1.000000e+00
[8,] 6.339102e-09 1.267820e-08 1.000000e+00
[9,] 6.578830e-10 1.315766e-09 1.000000e+00
[10,] 6.990776e-11 1.398155e-10 1.000000e+00
[11,] 1.290734e-11 2.581469e-11 1.000000e+00
[12,] 1.820512e-12 3.641025e-12 1.000000e+00
[13,] 1.869666e-13 3.739331e-13 1.000000e+00
[14,] 3.081523e-14 6.163046e-14 1.000000e+00
[15,] 6.951117e-15 1.390223e-14 1.000000e+00
[16,] 1.420215e-15 2.840431e-15 1.000000e+00
[17,] 1.894613e-16 3.789225e-16 1.000000e+00
[18,] 2.097325e-17 4.194651e-17 1.000000e+00
[19,] 1.962342e-18 3.924684e-18 1.000000e+00
[20,] 2.604056e-19 5.208112e-19 1.000000e+00
[21,] 3.004058e-20 6.008115e-20 1.000000e+00
[22,] 3.220847e-21 6.441693e-21 1.000000e+00
[23,] 4.902611e-22 9.805223e-22 1.000000e+00
[24,] 7.092760e-23 1.418552e-22 1.000000e+00
[25,] 2.930559e-23 5.861119e-23 1.000000e+00
[26,] 3.594116e-24 7.188231e-24 1.000000e+00
[27,] 3.525395e-25 7.050791e-25 1.000000e+00
[28,] 4.539854e-26 9.079708e-26 1.000000e+00
[29,] 1.124357e-26 2.248713e-26 1.000000e+00
[30,] 1.483877e-27 2.967755e-27 1.000000e+00
[31,] 1.483827e-28 2.967654e-28 1.000000e+00
[32,] 1.925037e-29 3.850075e-29 1.000000e+00
[33,] 3.626834e-30 7.253668e-30 1.000000e+00
[34,] 3.575701e-31 7.151403e-31 1.000000e+00
[35,] 3.214303e-32 6.428605e-32 1.000000e+00
[36,] 2.937679e-33 5.875358e-33 1.000000e+00
[37,] 3.052273e-34 6.104547e-34 1.000000e+00
[38,] 4.497529e-35 8.995058e-35 1.000000e+00
[39,] 8.314197e-36 1.662839e-35 1.000000e+00
[40,] 2.294801e-36 4.589603e-36 1.000000e+00
[41,] 3.806580e-37 7.613160e-37 1.000000e+00
[42,] 1.300057e-37 2.600113e-37 1.000000e+00
[43,] 9.695251e-38 1.939050e-37 1.000000e+00
[44,] 2.732846e-38 5.465692e-38 1.000000e+00
[45,] 8.658445e-39 1.731689e-38 1.000000e+00
[46,] 2.730162e-38 5.460324e-38 1.000000e+00
[47,] 1.918175e-37 3.836351e-37 1.000000e+00
[48,] 6.894574e-37 1.378915e-36 1.000000e+00
[49,] 5.555534e-36 1.111107e-35 1.000000e+00
[50,] 2.901028e-31 5.802055e-31 1.000000e+00
[51,] 2.370945e-27 4.741890e-27 1.000000e+00
[52,] 1.262505e-25 2.525011e-25 1.000000e+00
[53,] 1.767438e-24 3.534876e-24 1.000000e+00
[54,] 1.047884e-22 2.095768e-22 1.000000e+00
[55,] 9.419698e-21 1.883940e-20 1.000000e+00
[56,] 9.100173e-21 1.820035e-20 1.000000e+00
[57,] 8.623347e-20 1.724669e-19 1.000000e+00
[58,] 5.403425e-19 1.080685e-18 1.000000e+00
[59,] 7.358246e-18 1.471649e-17 1.000000e+00
[60,] 1.744942e-16 3.489884e-16 1.000000e+00
[61,] 1.041479e-15 2.082958e-15 1.000000e+00
[62,] 1.831956e-15 3.663912e-15 1.000000e+00
[63,] 1.729144e-13 3.458289e-13 1.000000e+00
[64,] 1.603913e-11 3.207826e-11 1.000000e+00
[65,] 4.498391e-11 8.996782e-11 1.000000e+00
[66,] 6.315789e-11 1.263158e-10 1.000000e+00
[67,] 6.154369e-11 1.230874e-10 1.000000e+00
[68,] 8.998252e-11 1.799650e-10 1.000000e+00
[69,] 8.810115e-11 1.762023e-10 1.000000e+00
[70,] 5.937417e-11 1.187483e-10 1.000000e+00
[71,] 3.651015e-10 7.302030e-10 1.000000e+00
[72,] 5.909334e-10 1.181867e-09 1.000000e+00
[73,] 5.359316e-09 1.071863e-08 1.000000e+00
[74,] 1.620562e-07 3.241124e-07 9.999998e-01
[75,] 2.220997e-06 4.441993e-06 9.999978e-01
[76,] 3.437634e-06 6.875267e-06 9.999966e-01
[77,] 2.223661e-06 4.447322e-06 9.999978e-01
[78,] 4.832136e-06 9.664273e-06 9.999952e-01
[79,] 1.404172e-05 2.808343e-05 9.999860e-01
[80,] 6.393723e-05 1.278745e-04 9.999361e-01
[81,] 3.177023e-04 6.354047e-04 9.996823e-01
[82,] 5.283350e-04 1.056670e-03 9.994717e-01
[83,] 7.341625e-04 1.468325e-03 9.992658e-01
[84,] 1.471875e-03 2.943751e-03 9.985281e-01
[85,] 5.272988e-03 1.054598e-02 9.947270e-01
[86,] 3.072056e-02 6.144111e-02 9.692794e-01
[87,] 3.839640e-02 7.679281e-02 9.616036e-01
[88,] 7.369753e-02 1.473951e-01 9.263025e-01
[89,] 7.449243e-02 1.489849e-01 9.255076e-01
[90,] 7.619830e-02 1.523966e-01 9.238017e-01
[91,] 8.138216e-02 1.627643e-01 9.186178e-01
[92,] 1.212521e-01 2.425043e-01 8.787479e-01
[93,] 2.924199e-01 5.848398e-01 7.075801e-01
[94,] 2.904673e-01 5.809347e-01 7.095327e-01
[95,] 4.192183e-01 8.384365e-01 5.807817e-01
[96,] 7.594814e-01 4.810371e-01 2.405186e-01
[97,] 9.964392e-01 7.121608e-03 3.560804e-03
[98,] 9.980587e-01 3.882604e-03 1.941302e-03
[99,] 9.964781e-01 7.043796e-03 3.521898e-03
[100,] 9.981879e-01 3.624259e-03 1.812130e-03
[101,] 9.967133e-01 6.573389e-03 3.286694e-03
[102,] 9.935744e-01 1.285120e-02 6.425599e-03
[103,] 9.880820e-01 2.383609e-02 1.191805e-02
[104,] 9.897126e-01 2.057478e-02 1.028739e-02
[105,] 9.924204e-01 1.515926e-02 7.579630e-03
[106,] 9.859182e-01 2.816358e-02 1.408179e-02
[107,] 9.847719e-01 3.045617e-02 1.522809e-02
[108,] 9.814286e-01 3.714283e-02 1.857142e-02
[109,] 9.994614e-01 1.077181e-03 5.385903e-04
[110,] 9.999330e-01 1.339601e-04 6.698003e-05
[111,] 9.996583e-01 6.833906e-04 3.416953e-04
[112,] 9.999397e-01 1.205975e-04 6.029875e-05
[113,] 9.992140e-01 1.571933e-03 7.859667e-04
> postscript(file="/var/www/html/rcomp/tmp/1al4a1292183066.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2al4a1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3al4a1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43v3v1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53v3v1292183066.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
-89.4815496 -79.3539008 -121.3288885 -8.6215938 30.9675956 -33.6062861
7 8 9 10 11 12
11.5956334 -1.7181077 -14.2870669 -41.9652113 35.0924860 66.0531811
13 14 15 16 17 18
-23.9840595 -0.5312021 30.0907096 -28.7827843 -9.2607164 -17.2129184
19 20 21 22 23 24
-6.7948511 29.6531472 9.8450005 -38.8975586 -59.8027929 -45.6738897
25 26 27 28 29 30
-41.8929584 -28.6720722 6.3324742 28.1695122 35.6861998 10.4380619
31 32 33 34 35 36
18.5541333 18.5366684 32.5171765 -26.0206300 -27.0822917 -14.2724543
37 38 39 40 41 42
-8.9339775 -32.7659691 -41.8709301 -25.8938019 -25.4422232 -19.7914220
43 44 45 46 47 48
-37.5730305 -28.0342746 -46.7034918 -36.0941067 -11.9990182 -22.7937552
49 50 51 52 53 54
-23.0992826 -26.8942629 -27.0573059 -15.7055661 -4.0857926 -1.8343741
55 56 57 58 59 60
4.9156541 1.6380814 -5.0163342 -27.6780333 -37.2454973 -13.7573350
61 62 63 64 65 66
-29.2470549 -7.0889299 18.6069056 -13.9294447 5.4599922 18.6307977
67 68 69 70 71 72
13.8149160 19.5854781 -4.6879121 -22.1094818 7.4000449 34.0070435
73 74 75 76 77 78
7.1687278 1.8305601 1.3364523 31.6851876 43.7067842 36.2327086
79 80 81 82 83 84
43.0416594 18.8370669 12.2863941 9.9871819 16.7287057 10.3840336
85 86 87 88 89 90
-23.7937137 28.3812798 35.1052761 22.8055055 34.3886097 20.6572036
91 92 93 94 95 96
44.0007849 41.2801256 38.0296827 22.3849466 16.7020247 45.2753169
97 98 99 100 101 102
-36.8061955 -13.4938656 -19.0872678 3.3095728 14.5118868 -6.1272042
103 104 105 106 107 108
3.8316470 28.5951139 -39.4348602 -92.4057646 -59.6711087 -66.3634971
109 110 111 112 113 114
-115.5454089 -53.4619793 -55.0623597 -18.6019352 15.3993736 -16.1907061
115 116 117 118 119 120
-1.9078724 17.5263387 17.8878514 0.5225107 20.7855543 27.2077667
121 122 123 124 125 126
16.8223860 11.9291603 48.7187302 72.0066970 98.0093333 100.8300618
127 128 129 130
78.4047699 103.2439902 112.2152578 112.9450176
> postscript(file="/var/www/html/rcomp/tmp/6vmky1292183066.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 -89.4815496 NA
1 -79.3539008 -89.4815496
2 -121.3288885 -79.3539008
3 -8.6215938 -121.3288885
4 30.9675956 -8.6215938
5 -33.6062861 30.9675956
6 11.5956334 -33.6062861
7 -1.7181077 11.5956334
8 -14.2870669 -1.7181077
9 -41.9652113 -14.2870669
10 35.0924860 -41.9652113
11 66.0531811 35.0924860
12 -23.9840595 66.0531811
13 -0.5312021 -23.9840595
14 30.0907096 -0.5312021
15 -28.7827843 30.0907096
16 -9.2607164 -28.7827843
17 -17.2129184 -9.2607164
18 -6.7948511 -17.2129184
19 29.6531472 -6.7948511
20 9.8450005 29.6531472
21 -38.8975586 9.8450005
22 -59.8027929 -38.8975586
23 -45.6738897 -59.8027929
24 -41.8929584 -45.6738897
25 -28.6720722 -41.8929584
26 6.3324742 -28.6720722
27 28.1695122 6.3324742
28 35.6861998 28.1695122
29 10.4380619 35.6861998
30 18.5541333 10.4380619
31 18.5366684 18.5541333
32 32.5171765 18.5366684
33 -26.0206300 32.5171765
34 -27.0822917 -26.0206300
35 -14.2724543 -27.0822917
36 -8.9339775 -14.2724543
37 -32.7659691 -8.9339775
38 -41.8709301 -32.7659691
39 -25.8938019 -41.8709301
40 -25.4422232 -25.8938019
41 -19.7914220 -25.4422232
42 -37.5730305 -19.7914220
43 -28.0342746 -37.5730305
44 -46.7034918 -28.0342746
45 -36.0941067 -46.7034918
46 -11.9990182 -36.0941067
47 -22.7937552 -11.9990182
48 -23.0992826 -22.7937552
49 -26.8942629 -23.0992826
50 -27.0573059 -26.8942629
51 -15.7055661 -27.0573059
52 -4.0857926 -15.7055661
53 -1.8343741 -4.0857926
54 4.9156541 -1.8343741
55 1.6380814 4.9156541
56 -5.0163342 1.6380814
57 -27.6780333 -5.0163342
58 -37.2454973 -27.6780333
59 -13.7573350 -37.2454973
60 -29.2470549 -13.7573350
61 -7.0889299 -29.2470549
62 18.6069056 -7.0889299
63 -13.9294447 18.6069056
64 5.4599922 -13.9294447
65 18.6307977 5.4599922
66 13.8149160 18.6307977
67 19.5854781 13.8149160
68 -4.6879121 19.5854781
69 -22.1094818 -4.6879121
70 7.4000449 -22.1094818
71 34.0070435 7.4000449
72 7.1687278 34.0070435
73 1.8305601 7.1687278
74 1.3364523 1.8305601
75 31.6851876 1.3364523
76 43.7067842 31.6851876
77 36.2327086 43.7067842
78 43.0416594 36.2327086
79 18.8370669 43.0416594
80 12.2863941 18.8370669
81 9.9871819 12.2863941
82 16.7287057 9.9871819
83 10.3840336 16.7287057
84 -23.7937137 10.3840336
85 28.3812798 -23.7937137
86 35.1052761 28.3812798
87 22.8055055 35.1052761
88 34.3886097 22.8055055
89 20.6572036 34.3886097
90 44.0007849 20.6572036
91 41.2801256 44.0007849
92 38.0296827 41.2801256
93 22.3849466 38.0296827
94 16.7020247 22.3849466
95 45.2753169 16.7020247
96 -36.8061955 45.2753169
97 -13.4938656 -36.8061955
98 -19.0872678 -13.4938656
99 3.3095728 -19.0872678
100 14.5118868 3.3095728
101 -6.1272042 14.5118868
102 3.8316470 -6.1272042
103 28.5951139 3.8316470
104 -39.4348602 28.5951139
105 -92.4057646 -39.4348602
106 -59.6711087 -92.4057646
107 -66.3634971 -59.6711087
108 -115.5454089 -66.3634971
109 -53.4619793 -115.5454089
110 -55.0623597 -53.4619793
111 -18.6019352 -55.0623597
112 15.3993736 -18.6019352
113 -16.1907061 15.3993736
114 -1.9078724 -16.1907061
115 17.5263387 -1.9078724
116 17.8878514 17.5263387
117 0.5225107 17.8878514
118 20.7855543 0.5225107
119 27.2077667 20.7855543
120 16.8223860 27.2077667
121 11.9291603 16.8223860
122 48.7187302 11.9291603
123 72.0066970 48.7187302
124 98.0093333 72.0066970
125 100.8300618 98.0093333
126 78.4047699 100.8300618
127 103.2439902 78.4047699
128 112.2152578 103.2439902
129 112.9450176 112.2152578
130 NA 112.9450176
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -79.3539008 -89.4815496
[2,] -121.3288885 -79.3539008
[3,] -8.6215938 -121.3288885
[4,] 30.9675956 -8.6215938
[5,] -33.6062861 30.9675956
[6,] 11.5956334 -33.6062861
[7,] -1.7181077 11.5956334
[8,] -14.2870669 -1.7181077
[9,] -41.9652113 -14.2870669
[10,] 35.0924860 -41.9652113
[11,] 66.0531811 35.0924860
[12,] -23.9840595 66.0531811
[13,] -0.5312021 -23.9840595
[14,] 30.0907096 -0.5312021
[15,] -28.7827843 30.0907096
[16,] -9.2607164 -28.7827843
[17,] -17.2129184 -9.2607164
[18,] -6.7948511 -17.2129184
[19,] 29.6531472 -6.7948511
[20,] 9.8450005 29.6531472
[21,] -38.8975586 9.8450005
[22,] -59.8027929 -38.8975586
[23,] -45.6738897 -59.8027929
[24,] -41.8929584 -45.6738897
[25,] -28.6720722 -41.8929584
[26,] 6.3324742 -28.6720722
[27,] 28.1695122 6.3324742
[28,] 35.6861998 28.1695122
[29,] 10.4380619 35.6861998
[30,] 18.5541333 10.4380619
[31,] 18.5366684 18.5541333
[32,] 32.5171765 18.5366684
[33,] -26.0206300 32.5171765
[34,] -27.0822917 -26.0206300
[35,] -14.2724543 -27.0822917
[36,] -8.9339775 -14.2724543
[37,] -32.7659691 -8.9339775
[38,] -41.8709301 -32.7659691
[39,] -25.8938019 -41.8709301
[40,] -25.4422232 -25.8938019
[41,] -19.7914220 -25.4422232
[42,] -37.5730305 -19.7914220
[43,] -28.0342746 -37.5730305
[44,] -46.7034918 -28.0342746
[45,] -36.0941067 -46.7034918
[46,] -11.9990182 -36.0941067
[47,] -22.7937552 -11.9990182
[48,] -23.0992826 -22.7937552
[49,] -26.8942629 -23.0992826
[50,] -27.0573059 -26.8942629
[51,] -15.7055661 -27.0573059
[52,] -4.0857926 -15.7055661
[53,] -1.8343741 -4.0857926
[54,] 4.9156541 -1.8343741
[55,] 1.6380814 4.9156541
[56,] -5.0163342 1.6380814
[57,] -27.6780333 -5.0163342
[58,] -37.2454973 -27.6780333
[59,] -13.7573350 -37.2454973
[60,] -29.2470549 -13.7573350
[61,] -7.0889299 -29.2470549
[62,] 18.6069056 -7.0889299
[63,] -13.9294447 18.6069056
[64,] 5.4599922 -13.9294447
[65,] 18.6307977 5.4599922
[66,] 13.8149160 18.6307977
[67,] 19.5854781 13.8149160
[68,] -4.6879121 19.5854781
[69,] -22.1094818 -4.6879121
[70,] 7.4000449 -22.1094818
[71,] 34.0070435 7.4000449
[72,] 7.1687278 34.0070435
[73,] 1.8305601 7.1687278
[74,] 1.3364523 1.8305601
[75,] 31.6851876 1.3364523
[76,] 43.7067842 31.6851876
[77,] 36.2327086 43.7067842
[78,] 43.0416594 36.2327086
[79,] 18.8370669 43.0416594
[80,] 12.2863941 18.8370669
[81,] 9.9871819 12.2863941
[82,] 16.7287057 9.9871819
[83,] 10.3840336 16.7287057
[84,] -23.7937137 10.3840336
[85,] 28.3812798 -23.7937137
[86,] 35.1052761 28.3812798
[87,] 22.8055055 35.1052761
[88,] 34.3886097 22.8055055
[89,] 20.6572036 34.3886097
[90,] 44.0007849 20.6572036
[91,] 41.2801256 44.0007849
[92,] 38.0296827 41.2801256
[93,] 22.3849466 38.0296827
[94,] 16.7020247 22.3849466
[95,] 45.2753169 16.7020247
[96,] -36.8061955 45.2753169
[97,] -13.4938656 -36.8061955
[98,] -19.0872678 -13.4938656
[99,] 3.3095728 -19.0872678
[100,] 14.5118868 3.3095728
[101,] -6.1272042 14.5118868
[102,] 3.8316470 -6.1272042
[103,] 28.5951139 3.8316470
[104,] -39.4348602 28.5951139
[105,] -92.4057646 -39.4348602
[106,] -59.6711087 -92.4057646
[107,] -66.3634971 -59.6711087
[108,] -115.5454089 -66.3634971
[109,] -53.4619793 -115.5454089
[110,] -55.0623597 -53.4619793
[111,] -18.6019352 -55.0623597
[112,] 15.3993736 -18.6019352
[113,] -16.1907061 15.3993736
[114,] -1.9078724 -16.1907061
[115,] 17.5263387 -1.9078724
[116,] 17.8878514 17.5263387
[117,] 0.5225107 17.8878514
[118,] 20.7855543 0.5225107
[119,] 27.2077667 20.7855543
[120,] 16.8223860 27.2077667
[121,] 11.9291603 16.8223860
[122,] 48.7187302 11.9291603
[123,] 72.0066970 48.7187302
[124,] 98.0093333 72.0066970
[125,] 100.8300618 98.0093333
[126,] 78.4047699 100.8300618
[127,] 103.2439902 78.4047699
[128,] 112.2152578 103.2439902
[129,] 112.9450176 112.2152578
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -79.3539008 -89.4815496
2 -121.3288885 -79.3539008
3 -8.6215938 -121.3288885
4 30.9675956 -8.6215938
5 -33.6062861 30.9675956
6 11.5956334 -33.6062861
7 -1.7181077 11.5956334
8 -14.2870669 -1.7181077
9 -41.9652113 -14.2870669
10 35.0924860 -41.9652113
11 66.0531811 35.0924860
12 -23.9840595 66.0531811
13 -0.5312021 -23.9840595
14 30.0907096 -0.5312021
15 -28.7827843 30.0907096
16 -9.2607164 -28.7827843
17 -17.2129184 -9.2607164
18 -6.7948511 -17.2129184
19 29.6531472 -6.7948511
20 9.8450005 29.6531472
21 -38.8975586 9.8450005
22 -59.8027929 -38.8975586
23 -45.6738897 -59.8027929
24 -41.8929584 -45.6738897
25 -28.6720722 -41.8929584
26 6.3324742 -28.6720722
27 28.1695122 6.3324742
28 35.6861998 28.1695122
29 10.4380619 35.6861998
30 18.5541333 10.4380619
31 18.5366684 18.5541333
32 32.5171765 18.5366684
33 -26.0206300 32.5171765
34 -27.0822917 -26.0206300
35 -14.2724543 -27.0822917
36 -8.9339775 -14.2724543
37 -32.7659691 -8.9339775
38 -41.8709301 -32.7659691
39 -25.8938019 -41.8709301
40 -25.4422232 -25.8938019
41 -19.7914220 -25.4422232
42 -37.5730305 -19.7914220
43 -28.0342746 -37.5730305
44 -46.7034918 -28.0342746
45 -36.0941067 -46.7034918
46 -11.9990182 -36.0941067
47 -22.7937552 -11.9990182
48 -23.0992826 -22.7937552
49 -26.8942629 -23.0992826
50 -27.0573059 -26.8942629
51 -15.7055661 -27.0573059
52 -4.0857926 -15.7055661
53 -1.8343741 -4.0857926
54 4.9156541 -1.8343741
55 1.6380814 4.9156541
56 -5.0163342 1.6380814
57 -27.6780333 -5.0163342
58 -37.2454973 -27.6780333
59 -13.7573350 -37.2454973
60 -29.2470549 -13.7573350
61 -7.0889299 -29.2470549
62 18.6069056 -7.0889299
63 -13.9294447 18.6069056
64 5.4599922 -13.9294447
65 18.6307977 5.4599922
66 13.8149160 18.6307977
67 19.5854781 13.8149160
68 -4.6879121 19.5854781
69 -22.1094818 -4.6879121
70 7.4000449 -22.1094818
71 34.0070435 7.4000449
72 7.1687278 34.0070435
73 1.8305601 7.1687278
74 1.3364523 1.8305601
75 31.6851876 1.3364523
76 43.7067842 31.6851876
77 36.2327086 43.7067842
78 43.0416594 36.2327086
79 18.8370669 43.0416594
80 12.2863941 18.8370669
81 9.9871819 12.2863941
82 16.7287057 9.9871819
83 10.3840336 16.7287057
84 -23.7937137 10.3840336
85 28.3812798 -23.7937137
86 35.1052761 28.3812798
87 22.8055055 35.1052761
88 34.3886097 22.8055055
89 20.6572036 34.3886097
90 44.0007849 20.6572036
91 41.2801256 44.0007849
92 38.0296827 41.2801256
93 22.3849466 38.0296827
94 16.7020247 22.3849466
95 45.2753169 16.7020247
96 -36.8061955 45.2753169
97 -13.4938656 -36.8061955
98 -19.0872678 -13.4938656
99 3.3095728 -19.0872678
100 14.5118868 3.3095728
101 -6.1272042 14.5118868
102 3.8316470 -6.1272042
103 28.5951139 3.8316470
104 -39.4348602 28.5951139
105 -92.4057646 -39.4348602
106 -59.6711087 -92.4057646
107 -66.3634971 -59.6711087
108 -115.5454089 -66.3634971
109 -53.4619793 -115.5454089
110 -55.0623597 -53.4619793
111 -18.6019352 -55.0623597
112 15.3993736 -18.6019352
113 -16.1907061 15.3993736
114 -1.9078724 -16.1907061
115 17.5263387 -1.9078724
116 17.8878514 17.5263387
117 0.5225107 17.8878514
118 20.7855543 0.5225107
119 27.2077667 20.7855543
120 16.8223860 27.2077667
121 11.9291603 16.8223860
122 48.7187302 11.9291603
123 72.0066970 48.7187302
124 98.0093333 72.0066970
125 100.8300618 98.0093333
126 78.4047699 100.8300618
127 103.2439902 78.4047699
128 112.2152578 103.2439902
129 112.9450176 112.2152578
> 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/7vmky1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ovkj1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ovkj1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mp8b1292183066.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11k5ia1292183066.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/12n5yg1292183066.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/13kfe71292183066.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/14ngvd1292183066.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/15g7uf1292183066.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/16ch961292183066.tab")
+ }
>
> try(system("convert tmp/1al4a1292183066.ps tmp/1al4a1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/2al4a1292183066.ps tmp/2al4a1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/3al4a1292183066.ps tmp/3al4a1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/43v3v1292183066.ps tmp/43v3v1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/53v3v1292183066.ps tmp/53v3v1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vmky1292183066.ps tmp/6vmky1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vmky1292183066.ps tmp/7vmky1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ovkj1292183066.ps tmp/8ovkj1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ovkj1292183066.ps tmp/9ovkj1292183066.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mp8b1292183066.ps tmp/10mp8b1292183066.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.558 1.771 8.246