R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(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 = '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 t
1 25.94 23688100 39.18 3940.35 0.0274 144.7 1
2 28.66 13741000 35.78 4696.69 0.0322 140.8 2
3 33.95 14143500 42.54 4572.83 0.0376 137.1 3
4 31.01 16763800 27.92 3860.66 0.0307 137.7 4
5 21.00 16634600 25.05 3400.91 0.0319 144.7 5
6 26.19 13693300 32.03 3966.11 0.0373 139.2 6
7 25.41 10545800 27.95 3766.99 0.0366 143.0 7
8 30.47 9409900 27.95 4206.35 0.0341 140.8 8
9 12.88 39182200 24.15 3672.82 0.0345 142.5 9
10 9.78 37005800 27.57 3369.63 0.0345 135.8 10
11 8.25 15818500 22.97 2597.93 0.0345 132.6 11
12 7.44 16952000 17.37 2470.52 0.0339 128.6 12
13 10.81 24563400 24.45 2772.73 0.0373 115.7 13
14 9.12 14163200 23.62 2151.83 0.0353 109.2 14
15 11.03 18184800 21.90 1840.26 0.0292 116.9 15
16 12.74 20810300 27.12 2116.24 0.0327 109.9 16
17 9.98 12843000 27.70 2110.49 0.0362 116.1 17
18 11.62 13866700 29.23 2160.54 0.0325 118.9 18
19 9.40 15119200 26.50 2027.13 0.0272 116.3 19
20 9.27 8301600 22.84 1805.43 0.0272 114.0 20
21 7.76 14039600 20.49 1498.80 0.0265 97.0 21
22 8.78 12139700 23.28 1690.20 0.0213 85.3 22
23 10.65 9649000 25.71 1930.58 0.0190 84.9 23
24 10.95 8513600 26.52 1950.40 0.0155 94.6 24
25 12.36 15278600 25.51 1934.03 0.0114 97.8 25
26 10.85 15590900 23.36 1731.49 0.0114 95.0 26
27 11.84 9691100 24.15 1845.35 0.0148 110.7 27
28 12.14 10882700 20.92 1688.23 0.0164 108.5 28
29 11.65 10294800 20.38 1615.73 0.0118 110.3 29
30 8.86 16031900 21.90 1463.21 0.0107 106.3 30
31 7.63 13683600 19.21 1328.26 0.0146 97.4 31
32 7.38 8677200 19.65 1314.85 0.0180 94.5 32
33 7.25 9874100 17.51 1172.06 0.0151 93.7 33
34 8.03 10725500 21.41 1329.75 0.0203 79.6 34
35 7.75 8348400 23.09 1478.78 0.0220 84.9 35
36 7.16 8046200 20.70 1335.51 0.0238 80.7 36
37 7.18 10862300 19.00 1320.91 0.0260 78.8 37
38 7.51 8100300 19.04 1337.52 0.0298 64.8 38
39 7.07 7287500 19.45 1341.17 0.0302 61.4 39
40 7.11 14002500 20.54 1464.31 0.0222 81.0 40
41 8.98 19037900 19.77 1595.91 0.0206 83.6 41
42 9.53 10774600 20.60 1622.80 0.0211 83.5 42
43 10.54 8960600 21.21 1735.02 0.0211 77.0 43
44 11.31 7773300 21.30 1810.45 0.0216 81.7 44
45 10.36 9579700 22.33 1786.94 0.0232 77.0 45
46 11.44 11270700 21.12 1932.21 0.0204 81.7 46
47 10.45 9492800 20.77 1960.26 0.0177 92.5 47
48 10.69 9136800 22.11 2003.37 0.0188 91.7 48
49 11.28 14487600 22.34 2066.15 0.0193 96.4 49
50 11.96 10133200 21.43 2029.82 0.0169 88.5 50
51 13.52 18659700 20.14 1994.22 0.0174 88.5 51
52 12.89 15980700 21.11 1920.15 0.0229 93.0 52
53 14.03 9732100 21.19 1986.74 0.0305 93.1 53
54 16.27 14626300 23.07 2047.79 0.0327 102.8 54
55 16.17 16904000 23.01 1887.36 0.0299 105.7 55
56 17.25 13616700 22.12 1838.10 0.0265 98.7 56
57 19.38 13772900 22.40 1896.84 0.0254 96.7 57
58 26.20 28749200 22.66 1974.99 0.0319 92.9 58
59 33.53 31408300 24.21 2096.81 0.0352 92.6 59
60 32.20 26342800 24.13 2175.44 0.0326 102.7 60
61 38.45 48909500 23.73 2062.41 0.0297 105.1 61
62 44.86 41542400 22.79 2051.72 0.0301 104.4 62
63 41.67 24857200 21.89 1999.23 0.0315 103.0 63
64 36.06 34093700 22.92 1921.65 0.0351 97.5 64
65 39.76 22555200 23.44 2068.22 0.0280 103.1 65
66 36.81 19067500 22.57 2056.96 0.0253 106.2 66
67 42.65 19029100 23.27 2184.83 0.0317 103.6 67
68 46.89 15223200 24.95 2152.09 0.0364 105.5 68
69 53.61 21903700 23.45 2151.69 0.0469 87.5 69
70 57.59 33306600 23.42 2120.30 0.0435 85.2 70
71 67.82 23898100 25.30 2232.82 0.0346 98.3 71
72 71.89 23279600 23.90 2205.32 0.0342 103.8 72
73 75.51 40699800 25.73 2305.82 0.0399 106.8 73
74 68.49 37646000 24.64 2281.39 0.0360 102.7 74
75 62.72 37277000 24.95 2339.79 0.0336 107.5 75
76 70.39 39246800 22.15 2322.57 0.0355 109.8 76
77 59.77 27418400 20.85 2178.88 0.0417 104.7 77
78 57.27 30318700 21.45 2172.09 0.0432 105.7 78
79 67.96 32808100 22.15 2091.47 0.0415 107.0 79
80 67.85 28668200 23.75 2183.75 0.0382 100.2 80
81 76.98 32370300 25.27 2258.43 0.0206 105.9 81
82 81.08 24171100 26.53 2366.71 0.0131 105.1 82
83 91.66 25009100 27.22 2431.77 0.0197 105.3 83
84 84.84 32084300 27.69 2415.29 0.0254 110.0 84
85 85.73 50117500 28.61 2463.93 0.0208 110.2 85
86 84.61 27522200 26.21 2416.15 0.0242 111.2 86
87 92.91 26816800 25.93 2421.64 0.0278 108.2 87
88 99.80 25136100 27.86 2525.09 0.0257 106.3 88
89 121.19 30295600 28.65 2604.52 0.0269 108.5 89
90 122.04 41526100 27.51 2603.23 0.0269 105.3 90
91 131.76 43845100 27.06 2546.27 0.0236 111.9 91
92 138.48 39188900 26.91 2596.36 0.0197 105.6 92
93 153.47 40496400 27.60 2701.50 0.0276 99.5 93
94 189.95 37438400 34.48 2859.12 0.0354 95.2 94
95 182.22 46553700 31.58 2660.96 0.0431 87.8 95
96 198.08 31771400 33.46 2652.28 0.0408 90.6 96
97 135.36 62108100 30.64 2389.86 0.0428 87.9 97
98 125.02 46645400 25.66 2271.48 0.0403 76.4 98
99 143.50 42313100 26.78 2279.10 0.0398 65.9 99
100 173.95 38841700 26.91 2412.80 0.0394 62.3 100
101 188.75 32650300 26.82 2522.66 0.0418 57.2 101
102 167.44 34281100 26.05 2292.98 0.0502 50.4 102
103 158.95 33096200 24.36 2325.55 0.0560 51.9 103
104 169.53 23273800 25.94 2367.52 0.0537 58.5 104
105 113.66 43697600 25.37 2091.88 0.0494 61.4 105
106 107.59 66902300 21.23 1720.95 0.0366 38.8 106
107 92.67 44957200 19.35 1535.57 0.0107 44.9 107
108 85.35 33800900 18.61 1577.03 0.0009 38.6 108
109 90.13 33487900 16.37 1476.42 0.0003 4.0 109
110 89.31 27394900 15.56 1377.84 0.0024 25.3 110
111 105.12 25963400 17.70 1528.59 -0.0038 26.9 111
112 125.83 20952600 19.52 1717.30 -0.0074 40.8 112
113 135.81 17702900 20.26 1774.33 -0.0128 54.8 113
114 142.43 21282100 23.05 1835.04 -0.0143 49.3 114
115 163.39 18449100 22.81 1978.50 -0.0210 47.4 115
116 168.21 14415700 24.04 2009.06 -0.0148 54.5 116
117 185.35 17906300 25.08 2122.42 -0.0129 53.4 117
118 188.50 22197500 27.04 2045.11 -0.0018 48.7 118
119 199.91 15856500 28.81 2144.60 0.0184 50.6 119
120 210.73 19068700 29.86 2269.15 0.0272 53.6 120
121 192.06 30855100 27.61 2147.35 0.0263 56.5 121
122 204.62 21209000 28.22 2238.26 0.0214 46.4 122
123 235.00 19541600 28.83 2397.96 0.0231 52.3 123
124 261.09 21955000 30.06 2461.19 0.0224 57.7 124
125 256.88 33725900 25.51 2257.04 0.0202 62.7 125
126 251.53 28192800 22.75 2109.24 0.0105 54.3 126
127 257.25 27377000 25.52 2254.70 0.0124 51.0 127
128 243.10 16228100 23.33 2114.03 0.0115 53.2 128
129 283.75 21278900 24.34 2368.62 0.0114 48.6 129
130 300.98 21457400 26.51 2507.41 0.0117 49.9 130
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Volume Microsoft
-1.352e+02 -6.929e-07 4.086e+00
NASDAQ Inflatie Consumentenvertrouwen
2.736e-02 2.845e+01 -4.914e-01
t
1.689e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-58.722 -20.770 -3.681 16.340 78.790
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.352e+02 1.926e+01 -7.023 1.30e-10 ***
Volume -6.929e-07 2.685e-07 -2.581 0.01103 *
Microsoft 4.086e+00 9.182e-01 4.450 1.90e-05 ***
NASDAQ 2.736e-02 6.593e-03 4.151 6.14e-05 ***
Inflatie 2.845e+01 2.073e+02 0.137 0.89109
Consumentenvertrouwen -4.914e-01 1.646e-01 -2.985 0.00342 **
t 1.689e+00 1.278e-01 13.214 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.58 on 123 degrees of freedom
Multiple R-squared: 0.8853, Adjusted R-squared: 0.8797
F-statistic: 158.2 on 6 and 123 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,] 5.428209e-03 1.085642e-02 9.945718e-01
[2,] 1.738268e-03 3.476537e-03 9.982617e-01
[3,] 3.109665e-04 6.219330e-04 9.996890e-01
[4,] 5.489867e-05 1.097973e-04 9.999451e-01
[5,] 8.247314e-06 1.649463e-05 9.999918e-01
[6,] 3.480212e-06 6.960424e-06 9.999965e-01
[7,] 6.509822e-07 1.301964e-06 9.999993e-01
[8,] 9.447757e-08 1.889551e-07 9.999999e-01
[9,] 1.339599e-08 2.679197e-08 1.000000e+00
[10,] 2.685989e-09 5.371977e-09 1.000000e+00
[11,] 4.527077e-10 9.054153e-10 1.000000e+00
[12,] 7.292238e-11 1.458448e-10 1.000000e+00
[13,] 1.529455e-11 3.058909e-11 1.000000e+00
[14,] 2.599469e-12 5.198938e-12 1.000000e+00
[15,] 3.584688e-13 7.169376e-13 1.000000e+00
[16,] 5.686854e-14 1.137371e-13 1.000000e+00
[17,] 8.893008e-15 1.778602e-14 1.000000e+00
[18,] 1.139828e-15 2.279656e-15 1.000000e+00
[19,] 3.418388e-16 6.836777e-16 1.000000e+00
[20,] 7.056189e-17 1.411238e-16 1.000000e+00
[21,] 1.232814e-17 2.465629e-17 1.000000e+00
[22,] 3.206142e-18 6.412284e-18 1.000000e+00
[23,] 6.348011e-19 1.269602e-18 1.000000e+00
[24,] 3.125909e-19 6.251817e-19 1.000000e+00
[25,] 6.793591e-20 1.358718e-19 1.000000e+00
[26,] 9.664495e-21 1.932899e-20 1.000000e+00
[27,] 1.967411e-21 3.934822e-21 1.000000e+00
[28,] 9.085263e-22 1.817053e-21 1.000000e+00
[29,] 2.452092e-22 4.904184e-22 1.000000e+00
[30,] 5.380076e-23 1.076015e-22 1.000000e+00
[31,] 2.136773e-23 4.273545e-23 1.000000e+00
[32,] 1.621135e-23 3.242270e-23 1.000000e+00
[33,] 6.175742e-24 1.235148e-23 1.000000e+00
[34,] 1.317809e-24 2.635618e-24 1.000000e+00
[35,] 2.507754e-25 5.015508e-25 1.000000e+00
[36,] 4.504751e-26 9.009502e-26 1.000000e+00
[37,] 6.331731e-27 1.266346e-26 1.000000e+00
[38,] 1.529297e-27 3.058593e-27 1.000000e+00
[39,] 2.918038e-28 5.836075e-28 1.000000e+00
[40,] 3.586814e-29 7.173627e-29 1.000000e+00
[41,] 4.459682e-30 8.919365e-30 1.000000e+00
[42,] 2.656507e-30 5.313014e-30 1.000000e+00
[43,] 1.153464e-30 2.306928e-30 1.000000e+00
[44,] 2.673990e-31 5.347981e-31 1.000000e+00
[45,] 2.228854e-31 4.457709e-31 1.000000e+00
[46,] 2.565837e-31 5.131675e-31 1.000000e+00
[47,] 2.512954e-31 5.025908e-31 1.000000e+00
[48,] 3.775997e-31 7.551993e-31 1.000000e+00
[49,] 7.382515e-27 1.476503e-26 1.000000e+00
[50,] 2.595716e-23 5.191432e-23 1.000000e+00
[51,] 1.185830e-22 2.371660e-22 1.000000e+00
[52,] 1.273931e-20 2.547862e-20 1.000000e+00
[53,] 9.482162e-18 1.896432e-17 1.000000e+00
[54,] 6.386753e-16 1.277351e-15 1.000000e+00
[55,] 4.636135e-15 9.272270e-15 1.000000e+00
[56,] 1.882465e-14 3.764930e-14 1.000000e+00
[57,] 2.978654e-14 5.957309e-14 1.000000e+00
[58,] 4.537335e-14 9.074670e-14 1.000000e+00
[59,] 1.095555e-13 2.191109e-13 1.000000e+00
[60,] 1.405516e-12 2.811033e-12 1.000000e+00
[61,] 3.765021e-11 7.530041e-11 1.000000e+00
[62,] 8.084489e-09 1.616898e-08 1.000000e+00
[63,] 1.961265e-06 3.922530e-06 9.999980e-01
[64,] 3.337980e-05 6.675960e-05 9.999666e-01
[65,] 1.141602e-04 2.283204e-04 9.998858e-01
[66,] 9.345441e-05 1.869088e-04 9.999065e-01
[67,] 1.305361e-04 2.610722e-04 9.998695e-01
[68,] 1.058726e-04 2.117452e-04 9.998941e-01
[69,] 7.164970e-05 1.432994e-04 9.999284e-01
[70,] 4.244390e-04 8.488780e-04 9.995756e-01
[71,] 1.025022e-03 2.050044e-03 9.989750e-01
[72,] 4.942544e-03 9.885088e-03 9.950575e-01
[73,] 1.110371e-02 2.220742e-02 9.888963e-01
[74,] 2.697911e-02 5.395821e-02 9.730209e-01
[75,] 2.489733e-02 4.979466e-02 9.751027e-01
[76,] 2.041647e-02 4.083295e-02 9.795835e-01
[77,] 1.670111e-02 3.340221e-02 9.832989e-01
[78,] 1.812710e-02 3.625420e-02 9.818729e-01
[79,] 2.514051e-02 5.028101e-02 9.748595e-01
[80,] 6.521376e-02 1.304275e-01 9.347862e-01
[81,] 1.558048e-01 3.116096e-01 8.441952e-01
[82,] 2.472782e-01 4.945563e-01 7.527218e-01
[83,] 4.245714e-01 8.491428e-01 5.754286e-01
[84,] 7.997868e-01 4.004264e-01 2.002132e-01
[85,] 9.424037e-01 1.151925e-01 5.759626e-02
[86,] 9.726544e-01 5.469115e-02 2.734558e-02
[87,] 9.995606e-01 8.788989e-04 4.394495e-04
[88,] 9.991879e-01 1.624174e-03 8.120872e-04
[89,] 9.991066e-01 1.786859e-03 8.934297e-04
[90,] 9.986737e-01 2.652680e-03 1.326340e-03
[91,] 9.991671e-01 1.665789e-03 8.328947e-04
[92,] 9.994661e-01 1.067868e-03 5.339341e-04
[93,] 9.998900e-01 2.199706e-04 1.099853e-04
[94,] 9.997872e-01 4.256347e-04 2.128174e-04
[95,] 9.999832e-01 3.366112e-05 1.683056e-05
[96,] 9.999666e-01 6.680869e-05 3.340434e-05
[97,] 9.999289e-01 1.422231e-04 7.111153e-05
[98,] 9.998835e-01 2.330430e-04 1.165215e-04
[99,] 9.997865e-01 4.269428e-04 2.134714e-04
[100,] 9.994774e-01 1.045224e-03 5.226119e-04
[101,] 9.988852e-01 2.229607e-03 1.114804e-03
[102,] 9.979455e-01 4.109033e-03 2.054516e-03
[103,] 9.974293e-01 5.141312e-03 2.570656e-03
[104,] 9.954748e-01 9.050463e-03 4.525231e-03
[105,] 9.901940e-01 1.961196e-02 9.805978e-03
[106,] 9.815162e-01 3.696764e-02 1.848382e-02
[107,] 9.658840e-01 6.823191e-02 3.411595e-02
[108,] 9.854207e-01 2.915867e-02 1.457934e-02
[109,] 9.963886e-01 7.222700e-03 3.611350e-03
[110,] 9.883829e-01 2.323411e-02 1.161706e-02
[111,] 9.966708e-01 6.658417e-03 3.329209e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/1a0yr1292184357.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2layc1292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3layc1292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4layc1292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5djff1292184357.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
-21.6803923 -36.3996023 -58.7216249 18.1777280 34.1013194 -11.2763894
7 8 9 10 11 12
8.0793538 -2.3691024 29.9313926 14.6655009 35.1041185 57.8089657
13 14 15 16 17 18
21.1322649 27.7912489 50.3092666 19.7310909 10.4958541 5.0165929
19 20 21 22 23 24
15.6535625 29.0008611 39.4369893 15.2139593 -2.9680466 -4.1297997
25 26 27 28 29 30
6.5425086 16.5108756 12.9979894 28.8046964 31.4239102 26.9494930
31 32 33 34 35 36
32.6028232 24.2424106 35.5930994 7.9486843 -4.0536590 5.0285249
37 38 39 40 41 42
11.6601316 0.7823497 -5.3667821 -0.3275742 4.2106968 -6.8444419
43 44 45 46 47 48
-17.5372063 -19.4154466 -26.7225160 -22.8020336 -20.6669910 -29.4415114
49 50 51 52 53 54
-27.1951533 -30.3226133 -18.3126786 -22.3696297 -29.5644494 -30.2702217
55 56 57 58 59 60
-24.3411138 -25.5863813 -28.7398247 -18.4840143 -20.9080034 -24.2250523
61 62 63 64 65 66
1.9622037 5.3562212 -6.6985745 -12.4875105 -21.6535449 -23.2464436
67 68 69 70 71 72
-26.9405745 -32.1949906 -25.5382637 -15.3976114 -17.4462215 -6.3072981
73 74 75 76 77 78
-1.2202790 -8.8268883 -16.9793323 3.3540959 -10.5899422 -14.5860952
79 80 81 82 83 84
-3.8268382 -20.8040849 -15.7501804 -27.3113007 -22.5284587 -25.4568514
85 86 87 88 89 90
-18.6207349 -25.5787135 -20.0390899 -27.5927299 -8.6708611 1.3929270
91 92 93 94 95 96
17.7649932 15.8272843 21.1161948 19.0305238 29.3439515 27.2694684
97 98 99 100 101 102
1.2009142 -3.5356358 0.3239222 20.7323887 24.3406217 8.3225625
103 104 105 106 107 108
3.9083417 1.6976211 -30.2902821 -5.6453294 -20.9726954 -38.6399446
109 110 111 112 113 114
-40.8443772 -31.1621775 -29.9390861 -20.0577853 -11.5699340 -19.8789901
115 116 117 118 119 120
-6.2589830 -8.4722080 1.4519489 -2.6311413 -6.8991697 -2.0167080
121 122 123 124 125 126
-0.2320422 -5.8482769 17.6757504 39.6667098 68.6201124 69.2167738
127 128 129 130
55.7091763 46.0485293 75.1589174 78.7900188
> postscript(file="/var/www/html/freestat/rcomp/tmp/6djff1292184357.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 -21.6803923 NA
1 -36.3996023 -21.6803923
2 -58.7216249 -36.3996023
3 18.1777280 -58.7216249
4 34.1013194 18.1777280
5 -11.2763894 34.1013194
6 8.0793538 -11.2763894
7 -2.3691024 8.0793538
8 29.9313926 -2.3691024
9 14.6655009 29.9313926
10 35.1041185 14.6655009
11 57.8089657 35.1041185
12 21.1322649 57.8089657
13 27.7912489 21.1322649
14 50.3092666 27.7912489
15 19.7310909 50.3092666
16 10.4958541 19.7310909
17 5.0165929 10.4958541
18 15.6535625 5.0165929
19 29.0008611 15.6535625
20 39.4369893 29.0008611
21 15.2139593 39.4369893
22 -2.9680466 15.2139593
23 -4.1297997 -2.9680466
24 6.5425086 -4.1297997
25 16.5108756 6.5425086
26 12.9979894 16.5108756
27 28.8046964 12.9979894
28 31.4239102 28.8046964
29 26.9494930 31.4239102
30 32.6028232 26.9494930
31 24.2424106 32.6028232
32 35.5930994 24.2424106
33 7.9486843 35.5930994
34 -4.0536590 7.9486843
35 5.0285249 -4.0536590
36 11.6601316 5.0285249
37 0.7823497 11.6601316
38 -5.3667821 0.7823497
39 -0.3275742 -5.3667821
40 4.2106968 -0.3275742
41 -6.8444419 4.2106968
42 -17.5372063 -6.8444419
43 -19.4154466 -17.5372063
44 -26.7225160 -19.4154466
45 -22.8020336 -26.7225160
46 -20.6669910 -22.8020336
47 -29.4415114 -20.6669910
48 -27.1951533 -29.4415114
49 -30.3226133 -27.1951533
50 -18.3126786 -30.3226133
51 -22.3696297 -18.3126786
52 -29.5644494 -22.3696297
53 -30.2702217 -29.5644494
54 -24.3411138 -30.2702217
55 -25.5863813 -24.3411138
56 -28.7398247 -25.5863813
57 -18.4840143 -28.7398247
58 -20.9080034 -18.4840143
59 -24.2250523 -20.9080034
60 1.9622037 -24.2250523
61 5.3562212 1.9622037
62 -6.6985745 5.3562212
63 -12.4875105 -6.6985745
64 -21.6535449 -12.4875105
65 -23.2464436 -21.6535449
66 -26.9405745 -23.2464436
67 -32.1949906 -26.9405745
68 -25.5382637 -32.1949906
69 -15.3976114 -25.5382637
70 -17.4462215 -15.3976114
71 -6.3072981 -17.4462215
72 -1.2202790 -6.3072981
73 -8.8268883 -1.2202790
74 -16.9793323 -8.8268883
75 3.3540959 -16.9793323
76 -10.5899422 3.3540959
77 -14.5860952 -10.5899422
78 -3.8268382 -14.5860952
79 -20.8040849 -3.8268382
80 -15.7501804 -20.8040849
81 -27.3113007 -15.7501804
82 -22.5284587 -27.3113007
83 -25.4568514 -22.5284587
84 -18.6207349 -25.4568514
85 -25.5787135 -18.6207349
86 -20.0390899 -25.5787135
87 -27.5927299 -20.0390899
88 -8.6708611 -27.5927299
89 1.3929270 -8.6708611
90 17.7649932 1.3929270
91 15.8272843 17.7649932
92 21.1161948 15.8272843
93 19.0305238 21.1161948
94 29.3439515 19.0305238
95 27.2694684 29.3439515
96 1.2009142 27.2694684
97 -3.5356358 1.2009142
98 0.3239222 -3.5356358
99 20.7323887 0.3239222
100 24.3406217 20.7323887
101 8.3225625 24.3406217
102 3.9083417 8.3225625
103 1.6976211 3.9083417
104 -30.2902821 1.6976211
105 -5.6453294 -30.2902821
106 -20.9726954 -5.6453294
107 -38.6399446 -20.9726954
108 -40.8443772 -38.6399446
109 -31.1621775 -40.8443772
110 -29.9390861 -31.1621775
111 -20.0577853 -29.9390861
112 -11.5699340 -20.0577853
113 -19.8789901 -11.5699340
114 -6.2589830 -19.8789901
115 -8.4722080 -6.2589830
116 1.4519489 -8.4722080
117 -2.6311413 1.4519489
118 -6.8991697 -2.6311413
119 -2.0167080 -6.8991697
120 -0.2320422 -2.0167080
121 -5.8482769 -0.2320422
122 17.6757504 -5.8482769
123 39.6667098 17.6757504
124 68.6201124 39.6667098
125 69.2167738 68.6201124
126 55.7091763 69.2167738
127 46.0485293 55.7091763
128 75.1589174 46.0485293
129 78.7900188 75.1589174
130 NA 78.7900188
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -36.3996023 -21.6803923
[2,] -58.7216249 -36.3996023
[3,] 18.1777280 -58.7216249
[4,] 34.1013194 18.1777280
[5,] -11.2763894 34.1013194
[6,] 8.0793538 -11.2763894
[7,] -2.3691024 8.0793538
[8,] 29.9313926 -2.3691024
[9,] 14.6655009 29.9313926
[10,] 35.1041185 14.6655009
[11,] 57.8089657 35.1041185
[12,] 21.1322649 57.8089657
[13,] 27.7912489 21.1322649
[14,] 50.3092666 27.7912489
[15,] 19.7310909 50.3092666
[16,] 10.4958541 19.7310909
[17,] 5.0165929 10.4958541
[18,] 15.6535625 5.0165929
[19,] 29.0008611 15.6535625
[20,] 39.4369893 29.0008611
[21,] 15.2139593 39.4369893
[22,] -2.9680466 15.2139593
[23,] -4.1297997 -2.9680466
[24,] 6.5425086 -4.1297997
[25,] 16.5108756 6.5425086
[26,] 12.9979894 16.5108756
[27,] 28.8046964 12.9979894
[28,] 31.4239102 28.8046964
[29,] 26.9494930 31.4239102
[30,] 32.6028232 26.9494930
[31,] 24.2424106 32.6028232
[32,] 35.5930994 24.2424106
[33,] 7.9486843 35.5930994
[34,] -4.0536590 7.9486843
[35,] 5.0285249 -4.0536590
[36,] 11.6601316 5.0285249
[37,] 0.7823497 11.6601316
[38,] -5.3667821 0.7823497
[39,] -0.3275742 -5.3667821
[40,] 4.2106968 -0.3275742
[41,] -6.8444419 4.2106968
[42,] -17.5372063 -6.8444419
[43,] -19.4154466 -17.5372063
[44,] -26.7225160 -19.4154466
[45,] -22.8020336 -26.7225160
[46,] -20.6669910 -22.8020336
[47,] -29.4415114 -20.6669910
[48,] -27.1951533 -29.4415114
[49,] -30.3226133 -27.1951533
[50,] -18.3126786 -30.3226133
[51,] -22.3696297 -18.3126786
[52,] -29.5644494 -22.3696297
[53,] -30.2702217 -29.5644494
[54,] -24.3411138 -30.2702217
[55,] -25.5863813 -24.3411138
[56,] -28.7398247 -25.5863813
[57,] -18.4840143 -28.7398247
[58,] -20.9080034 -18.4840143
[59,] -24.2250523 -20.9080034
[60,] 1.9622037 -24.2250523
[61,] 5.3562212 1.9622037
[62,] -6.6985745 5.3562212
[63,] -12.4875105 -6.6985745
[64,] -21.6535449 -12.4875105
[65,] -23.2464436 -21.6535449
[66,] -26.9405745 -23.2464436
[67,] -32.1949906 -26.9405745
[68,] -25.5382637 -32.1949906
[69,] -15.3976114 -25.5382637
[70,] -17.4462215 -15.3976114
[71,] -6.3072981 -17.4462215
[72,] -1.2202790 -6.3072981
[73,] -8.8268883 -1.2202790
[74,] -16.9793323 -8.8268883
[75,] 3.3540959 -16.9793323
[76,] -10.5899422 3.3540959
[77,] -14.5860952 -10.5899422
[78,] -3.8268382 -14.5860952
[79,] -20.8040849 -3.8268382
[80,] -15.7501804 -20.8040849
[81,] -27.3113007 -15.7501804
[82,] -22.5284587 -27.3113007
[83,] -25.4568514 -22.5284587
[84,] -18.6207349 -25.4568514
[85,] -25.5787135 -18.6207349
[86,] -20.0390899 -25.5787135
[87,] -27.5927299 -20.0390899
[88,] -8.6708611 -27.5927299
[89,] 1.3929270 -8.6708611
[90,] 17.7649932 1.3929270
[91,] 15.8272843 17.7649932
[92,] 21.1161948 15.8272843
[93,] 19.0305238 21.1161948
[94,] 29.3439515 19.0305238
[95,] 27.2694684 29.3439515
[96,] 1.2009142 27.2694684
[97,] -3.5356358 1.2009142
[98,] 0.3239222 -3.5356358
[99,] 20.7323887 0.3239222
[100,] 24.3406217 20.7323887
[101,] 8.3225625 24.3406217
[102,] 3.9083417 8.3225625
[103,] 1.6976211 3.9083417
[104,] -30.2902821 1.6976211
[105,] -5.6453294 -30.2902821
[106,] -20.9726954 -5.6453294
[107,] -38.6399446 -20.9726954
[108,] -40.8443772 -38.6399446
[109,] -31.1621775 -40.8443772
[110,] -29.9390861 -31.1621775
[111,] -20.0577853 -29.9390861
[112,] -11.5699340 -20.0577853
[113,] -19.8789901 -11.5699340
[114,] -6.2589830 -19.8789901
[115,] -8.4722080 -6.2589830
[116,] 1.4519489 -8.4722080
[117,] -2.6311413 1.4519489
[118,] -6.8991697 -2.6311413
[119,] -2.0167080 -6.8991697
[120,] -0.2320422 -2.0167080
[121,] -5.8482769 -0.2320422
[122,] 17.6757504 -5.8482769
[123,] 39.6667098 17.6757504
[124,] 68.6201124 39.6667098
[125,] 69.2167738 68.6201124
[126,] 55.7091763 69.2167738
[127,] 46.0485293 55.7091763
[128,] 75.1589174 46.0485293
[129,] 78.7900188 75.1589174
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -36.3996023 -21.6803923
2 -58.7216249 -36.3996023
3 18.1777280 -58.7216249
4 34.1013194 18.1777280
5 -11.2763894 34.1013194
6 8.0793538 -11.2763894
7 -2.3691024 8.0793538
8 29.9313926 -2.3691024
9 14.6655009 29.9313926
10 35.1041185 14.6655009
11 57.8089657 35.1041185
12 21.1322649 57.8089657
13 27.7912489 21.1322649
14 50.3092666 27.7912489
15 19.7310909 50.3092666
16 10.4958541 19.7310909
17 5.0165929 10.4958541
18 15.6535625 5.0165929
19 29.0008611 15.6535625
20 39.4369893 29.0008611
21 15.2139593 39.4369893
22 -2.9680466 15.2139593
23 -4.1297997 -2.9680466
24 6.5425086 -4.1297997
25 16.5108756 6.5425086
26 12.9979894 16.5108756
27 28.8046964 12.9979894
28 31.4239102 28.8046964
29 26.9494930 31.4239102
30 32.6028232 26.9494930
31 24.2424106 32.6028232
32 35.5930994 24.2424106
33 7.9486843 35.5930994
34 -4.0536590 7.9486843
35 5.0285249 -4.0536590
36 11.6601316 5.0285249
37 0.7823497 11.6601316
38 -5.3667821 0.7823497
39 -0.3275742 -5.3667821
40 4.2106968 -0.3275742
41 -6.8444419 4.2106968
42 -17.5372063 -6.8444419
43 -19.4154466 -17.5372063
44 -26.7225160 -19.4154466
45 -22.8020336 -26.7225160
46 -20.6669910 -22.8020336
47 -29.4415114 -20.6669910
48 -27.1951533 -29.4415114
49 -30.3226133 -27.1951533
50 -18.3126786 -30.3226133
51 -22.3696297 -18.3126786
52 -29.5644494 -22.3696297
53 -30.2702217 -29.5644494
54 -24.3411138 -30.2702217
55 -25.5863813 -24.3411138
56 -28.7398247 -25.5863813
57 -18.4840143 -28.7398247
58 -20.9080034 -18.4840143
59 -24.2250523 -20.9080034
60 1.9622037 -24.2250523
61 5.3562212 1.9622037
62 -6.6985745 5.3562212
63 -12.4875105 -6.6985745
64 -21.6535449 -12.4875105
65 -23.2464436 -21.6535449
66 -26.9405745 -23.2464436
67 -32.1949906 -26.9405745
68 -25.5382637 -32.1949906
69 -15.3976114 -25.5382637
70 -17.4462215 -15.3976114
71 -6.3072981 -17.4462215
72 -1.2202790 -6.3072981
73 -8.8268883 -1.2202790
74 -16.9793323 -8.8268883
75 3.3540959 -16.9793323
76 -10.5899422 3.3540959
77 -14.5860952 -10.5899422
78 -3.8268382 -14.5860952
79 -20.8040849 -3.8268382
80 -15.7501804 -20.8040849
81 -27.3113007 -15.7501804
82 -22.5284587 -27.3113007
83 -25.4568514 -22.5284587
84 -18.6207349 -25.4568514
85 -25.5787135 -18.6207349
86 -20.0390899 -25.5787135
87 -27.5927299 -20.0390899
88 -8.6708611 -27.5927299
89 1.3929270 -8.6708611
90 17.7649932 1.3929270
91 15.8272843 17.7649932
92 21.1161948 15.8272843
93 19.0305238 21.1161948
94 29.3439515 19.0305238
95 27.2694684 29.3439515
96 1.2009142 27.2694684
97 -3.5356358 1.2009142
98 0.3239222 -3.5356358
99 20.7323887 0.3239222
100 24.3406217 20.7323887
101 8.3225625 24.3406217
102 3.9083417 8.3225625
103 1.6976211 3.9083417
104 -30.2902821 1.6976211
105 -5.6453294 -30.2902821
106 -20.9726954 -5.6453294
107 -38.6399446 -20.9726954
108 -40.8443772 -38.6399446
109 -31.1621775 -40.8443772
110 -29.9390861 -31.1621775
111 -20.0577853 -29.9390861
112 -11.5699340 -20.0577853
113 -19.8789901 -11.5699340
114 -6.2589830 -19.8789901
115 -8.4722080 -6.2589830
116 1.4519489 -8.4722080
117 -2.6311413 1.4519489
118 -6.8991697 -2.6311413
119 -2.0167080 -6.8991697
120 -0.2320422 -2.0167080
121 -5.8482769 -0.2320422
122 17.6757504 -5.8482769
123 39.6667098 17.6757504
124 68.6201124 39.6667098
125 69.2167738 68.6201124
126 55.7091763 69.2167738
127 46.0485293 55.7091763
128 75.1589174 46.0485293
129 78.7900188 75.1589174
> 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/freestat/rcomp/tmp/7oaei1292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8z1v31292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9z1v31292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10z1v31292184357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11vbbu1292184357.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/freestat/rcomp/tmp/12ycsi1292184357.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/freestat/rcomp/tmp/13um7r1292184357.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/freestat/rcomp/tmp/14g46w1292184357.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/freestat/rcomp/tmp/151n421292184357.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/freestat/rcomp/tmp/164nlq1292184357.tab")
+ }
>
> try(system("convert tmp/1a0yr1292184357.ps tmp/1a0yr1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/2layc1292184357.ps tmp/2layc1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/3layc1292184357.ps tmp/3layc1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/4layc1292184357.ps tmp/4layc1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/5djff1292184357.ps tmp/5djff1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/6djff1292184357.ps tmp/6djff1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oaei1292184357.ps tmp/7oaei1292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z1v31292184357.ps tmp/8z1v31292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z1v31292184357.ps tmp/9z1v31292184357.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z1v31292184357.ps tmp/10z1v31292184357.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.347 2.683 5.805