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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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(10.81
+ ,24563400
+ ,-0.2643
+ ,24.45
+ ,2772.73
+ ,0.0373
+ ,115.7
+ ,9.12
+ ,14163200
+ ,-0.2643
+ ,23.62
+ ,2151.83
+ ,0.0353
+ ,109.2
+ ,11.03
+ ,18184800
+ ,-0.2643
+ ,21.90
+ ,1840.26
+ ,0.0292
+ ,116.9
+ ,12.74
+ ,20810300
+ ,-0.1918
+ ,27.12
+ ,2116.24
+ ,0.0327
+ ,109.9
+ ,9.98
+ ,12843000
+ ,-0.1918
+ ,27.70
+ ,2110.49
+ ,0.0362
+ ,116.1
+ ,11.62
+ ,13866700
+ ,-0.1918
+ ,29.23
+ ,2160.54
+ ,0.0325
+ ,118.9
+ ,9.40
+ ,15119200
+ ,-0.2246
+ ,26.50
+ ,2027.13
+ ,0.0272
+ ,116.3
+ ,9.27
+ ,8301600
+ ,-0.2246
+ ,22.84
+ ,1805.43
+ ,0.0272
+ ,114.0
+ ,7.76
+ ,14039600
+ ,-0.2246
+ ,20.49
+ ,1498.80
+ ,0.0265
+ ,97.0
+ ,8.78
+ ,12139700
+ ,0.3654
+ ,23.28
+ ,1690.20
+ ,0.0213
+ ,85.3
+ ,10.65
+ ,9649000
+ ,0.3654
+ ,25.71
+ ,1930.58
+ ,0.019
+ ,84.9
+ ,10.95
+ ,8513600
+ ,0.3654
+ ,26.52
+ ,1950.40
+ ,0.0155
+ ,94.6
+ ,12.36
+ ,15278600
+ ,0.0447
+ ,25.51
+ ,1934.03
+ ,0.0114
+ ,97.8
+ ,10.85
+ ,15590900
+ ,0.0447
+ ,23.36
+ ,1731.49
+ ,0.0114
+ ,95.0
+ ,11.84
+ ,9691100
+ ,0.0447
+ ,24.15
+ ,1845.35
+ ,0.0148
+ ,110.7
+ ,12.14
+ ,10882700
+ ,-0.0312
+ ,20.92
+ ,1688.23
+ ,0.0164
+ ,108.5
+ ,11.65
+ ,10294800
+ ,-0.0312
+ ,20.38
+ ,1615.73
+ ,0.0118
+ ,110.3
+ ,8.86
+ ,16031900
+ ,-0.0312
+ ,21.90
+ ,1463.21
+ ,0.0107
+ ,106.3
+ ,7.63
+ ,13683600
+ ,-0.0048
+ ,19.21
+ ,1328.26
+ ,0.0146
+ ,97.4
+ ,7.38
+ ,8677200
+ ,-0.0048
+ ,19.65
+ ,1314.85
+ ,0.018
+ ,94.5
+ ,7.25
+ ,9874100
+ ,-0.0048
+ ,17.51
+ ,1172.06
+ ,0.0151
+ ,93.7
+ ,8.03
+ ,10725500
+ ,0.0705
+ ,21.41
+ ,1329.75
+ ,0.0203
+ ,79.6
+ ,7.75
+ ,8348400
+ ,0.0705
+ ,23.09
+ ,1478.78
+ ,0.022
+ ,84.9
+ ,7.16
+ ,8046200
+ ,0.0705
+ ,20.70
+ ,1335.51
+ ,0.0238
+ ,80.7
+ ,7.18
+ ,10862300
+ ,-0.0134
+ ,19.00
+ ,1320.91
+ ,0.026
+ ,78.8
+ ,7.51
+ ,8100300
+ ,-0.0134
+ ,19.04
+ ,1337.52
+ ,0.0298
+ ,64.8
+ ,7.07
+ ,7287500
+ ,-0.0134
+ ,19.45
+ ,1341.17
+ ,0.0302
+ ,61.4
+ ,7.11
+ ,14002500
+ ,0.0812
+ ,20.54
+ ,1464.31
+ ,0.0222
+ ,81.0
+ ,8.98
+ ,19037900
+ ,0.0812
+ ,19.77
+ ,1595.91
+ ,0.0206
+ ,83.6
+ ,9.53
+ ,10774600
+ ,0.0812
+ ,20.60
+ ,1622.80
+ ,0.0211
+ ,83.5
+ ,10.54
+ ,8960600
+ ,0.1885
+ ,21.21
+ ,1735.02
+ ,0.0211
+ ,77.0
+ ,11.31
+ ,7773300
+ ,0.1885
+ ,21.30
+ ,1810.45
+ ,0.0216
+ ,81.7
+ ,10.36
+ ,9579700
+ ,0.1885
+ ,22.33
+ ,1786.94
+ ,0.0232
+ ,77.0
+ ,11.44
+ ,11270700
+ ,0.3628
+ ,21.12
+ ,1932.21
+ ,0.0204
+ ,81.7
+ ,10.45
+ ,9492800
+ ,0.3628
+ ,20.77
+ ,1960.26
+ ,0.0177
+ ,92.5
+ ,10.69
+ ,9136800
+ ,0.3628
+ ,22.11
+ ,2003.37
+ ,0.0188
+ ,91.7
+ ,11.28
+ ,14487600
+ ,0.2942
+ ,22.34
+ ,2066.15
+ ,0.0193
+ ,96.4
+ ,11.96
+ ,10133200
+ ,0.2942
+ ,21.43
+ ,2029.82
+ ,0.0169
+ ,88.5
+ ,13.52
+ ,18659700
+ ,0.2942
+ ,20.14
+ ,1994.22
+ ,0.0174
+ ,88.5
+ ,12.89
+ ,15980700
+ ,0.3036
+ ,21.11
+ ,1920.15
+ ,0.0229
+ ,93.0
+ ,14.03
+ ,9732100
+ ,0.3036
+ ,21.19
+ ,1986.74
+ ,0.0305
+ ,93.1
+ ,16.27
+ ,14626300
+ ,0.3036
+ ,23.07
+ ,2047.79
+ ,0.0327
+ ,102.8
+ ,16.17
+ ,16904000
+ ,0.3703
+ ,23.01
+ ,1887.36
+ ,0.0299
+ ,105.7
+ ,17.25
+ ,13616700
+ ,0.3703
+ ,22.12
+ ,1838.10
+ ,0.0265
+ ,98.7
+ ,19.38
+ ,13772900
+ ,0.3703
+ ,22.40
+ ,1896.84
+ ,0.0254
+ ,96.7
+ ,26.20
+ ,28749200
+ ,0.7398
+ ,22.66
+ ,1974.99
+ ,0.0319
+ ,92.9
+ ,33.53
+ ,31408300
+ ,0.7398
+ ,24.21
+ ,2096.81
+ ,0.0352
+ ,92.6
+ ,32.20
+ ,26342800
+ ,0.7398
+ ,24.13
+ ,2175.44
+ ,0.0326
+ ,102.7
+ ,38.45
+ ,48909500
+ ,0.6988
+ ,23.73
+ ,2062.41
+ ,0.0297
+ ,105.1
+ ,44.86
+ ,41542400
+ ,0.6988
+ ,22.79
+ ,2051.72
+ ,0.0301
+ ,104.4
+ ,41.67
+ ,24857200
+ ,0.6988
+ ,21.89
+ ,1999.23
+ ,0.0315
+ ,103.0
+ ,36.06
+ ,34093700
+ ,0.7478
+ ,22.92
+ ,1921.65
+ ,0.0351
+ ,97.5
+ ,39.76
+ ,22555200
+ ,0.7478
+ ,23.44
+ ,2068.22
+ ,0.028
+ ,103.1
+ ,36.81
+ ,19067500
+ ,0.7478
+ ,22.57
+ ,2056.96
+ ,0.0253
+ ,106.2
+ ,42.65
+ ,19029100
+ ,0.5651
+ ,23.27
+ ,2184.83
+ ,0.0317
+ ,103.6
+ ,46.89
+ ,15223200
+ ,0.5651
+ ,24.95
+ ,2152.09
+ ,0.0364
+ ,105.5
+ ,53.61
+ ,21903700
+ ,0.5651
+ ,23.45
+ ,2151.69
+ ,0.0469
+ ,87.5
+ ,57.59
+ ,33306600
+ ,0.6473
+ ,23.42
+ ,2120.30
+ ,0.0435
+ ,85.2
+ ,67.82
+ ,23898100
+ ,0.6473
+ ,25.30
+ ,2232.82
+ ,0.0346
+ ,98.3
+ ,71.89
+ ,23279600
+ ,0.6473
+ ,23.90
+ ,2205.32
+ ,0.0342
+ ,103.8
+ ,75.51
+ ,40699800
+ ,0.3441
+ ,25.73
+ ,2305.82
+ ,0.0399
+ ,106.8
+ ,68.49
+ ,37646000
+ ,0.3441
+ ,24.64
+ ,2281.39
+ ,0.036
+ ,102.7
+ ,62.72
+ ,37277000
+ ,0.3441
+ ,24.95
+ ,2339.79
+ ,0.0336
+ ,107.5
+ ,70.39
+ ,39246800
+ ,0.2415
+ ,22.15
+ ,2322.57
+ ,0.0355
+ ,109.8
+ ,59.77
+ ,27418400
+ ,0.2415
+ ,20.85
+ ,2178.88
+ ,0.0417
+ ,104.7
+ ,57.27
+ ,30318700
+ ,0.2415
+ ,21.45
+ ,2172.09
+ ,0.0432
+ ,105.7
+ ,67.96
+ ,32808100
+ ,0.3151
+ ,22.15
+ ,2091.47
+ ,0.0415
+ ,107.0
+ ,67.85
+ ,28668200
+ ,0.3151
+ ,23.75
+ ,2183.75
+ ,0.0382
+ ,100.2
+ ,76.98
+ ,32370300
+ ,0.3151
+ ,25.27
+ ,2258.43
+ ,0.0206
+ ,105.9
+ ,81.08
+ ,24171100
+ ,0.239
+ ,26.53
+ ,2366.71
+ ,0.0131
+ ,105.1
+ ,91.66
+ ,25009100
+ ,0.239
+ ,27.22
+ ,2431.77
+ ,0.0197
+ ,105.3
+ ,84.84
+ ,32084300
+ ,0.239
+ ,27.69
+ ,2415.29
+ ,0.0254
+ ,110.0
+ ,85.73
+ ,50117500
+ ,0.2127
+ ,28.61
+ ,2463.93
+ ,0.0208
+ ,110.2
+ ,84.61
+ ,27522200
+ ,0.2127
+ ,26.21
+ ,2416.15
+ ,0.0242
+ ,111.2
+ ,92.91
+ ,26816800
+ ,0.2127
+ ,25.93
+ ,2421.64
+ ,0.0278
+ ,108.2
+ ,99.80
+ ,25136100
+ ,0.273
+ ,27.86
+ ,2525.09
+ ,0.0257
+ ,106.3
+ ,121.19
+ ,30295600
+ ,0.273
+ ,28.65
+ ,2604.52
+ ,0.0269
+ ,108.5
+ ,122.04
+ ,41526100
+ ,0.273
+ ,27.51
+ ,2603.23
+ ,0.0269
+ ,105.3
+ ,131.76
+ ,43845100
+ ,0.3657
+ ,27.06
+ ,2546.27
+ ,0.0236
+ ,111.9
+ ,138.48
+ ,39188900
+ ,0.3657
+ ,26.91
+ ,2596.36
+ ,0.0197
+ ,105.6
+ ,153.47
+ ,40496400
+ ,0.3657
+ ,27.60
+ ,2701.50
+ ,0.0276
+ ,99.5
+ ,189.95
+ ,37438400
+ ,0.4643
+ ,34.48
+ ,2859.12
+ ,0.0354
+ ,95.2
+ ,182.22
+ ,46553700
+ ,0.4643
+ ,31.58
+ ,2660.96
+ ,0.0431
+ ,87.8
+ ,198.08
+ ,31771400
+ ,0.4643
+ ,33.46
+ ,2652.28
+ ,0.0408
+ ,90.6
+ ,135.36
+ ,62108100
+ ,0.5096
+ ,30.64
+ ,2389.86
+ ,0.0428
+ ,87.9
+ ,125.02
+ ,46645400
+ ,0.5096
+ ,25.66
+ ,2271.48
+ ,0.0403
+ ,76.4
+ ,143.50
+ ,42313100
+ ,0.5096
+ ,26.78
+ ,2279.10
+ ,0.0398
+ ,65.9
+ ,173.95
+ ,38841700
+ ,0.3592
+ ,26.91
+ ,2412.80
+ ,0.0394
+ ,62.3
+ ,188.75
+ ,32650300
+ ,0.3592
+ ,26.82
+ ,2522.66
+ ,0.0418
+ ,57.2
+ ,167.44
+ ,34281100
+ ,0.3592
+ ,26.05
+ ,2292.98
+ ,0.0502
+ ,50.4
+ ,158.95
+ ,33096200
+ ,0.7439
+ ,24.36
+ ,2325.55
+ ,0.056
+ ,51.9
+ ,169.53
+ ,23273800
+ ,0.7439
+ ,25.94
+ ,2367.52
+ ,0.0537
+ ,58.5
+ ,113.66
+ ,43697600
+ ,0.7439
+ ,25.37
+ ,2091.88
+ ,0.0494
+ ,61.4
+ ,107.59
+ ,66902300
+ ,0.139
+ ,21.23
+ ,1720.95
+ ,0.0366
+ ,38.8
+ ,92.67
+ ,44957200
+ ,0.139
+ ,19.35
+ ,1535.57
+ ,0.0107
+ ,44.9
+ ,85.35
+ ,33800900
+ ,0.139
+ ,18.61
+ ,1577.03
+ ,0.0009
+ ,38.6
+ ,90.13
+ ,33487900
+ ,0.1383
+ ,16.37
+ ,1476.42
+ ,0.0003
+ ,4.0
+ ,89.31
+ ,27394900
+ ,0.1383
+ ,15.56
+ ,1377.84
+ ,0.0024
+ ,25.3
+ ,105.12
+ ,25963400
+ ,0.1383
+ ,17.70
+ ,1528.59
+ ,-0.0038
+ ,26.9
+ ,125.83
+ ,20952600
+ ,0.2874
+ ,19.52
+ ,1717.30
+ ,-0.0074
+ ,40.8
+ ,135.81
+ ,17702900
+ ,0.2874
+ ,20.26
+ ,1774.33
+ ,-0.0128
+ ,54.8
+ ,142.43
+ ,21282100
+ ,0.2874
+ ,23.05
+ ,1835.04
+ ,-0.0143
+ ,49.3
+ ,163.39
+ ,18449100
+ ,0.0596
+ ,22.81
+ ,1978.50
+ ,-0.021
+ ,47.4
+ ,168.21
+ ,14415700
+ ,0.0596
+ ,24.04
+ ,2009.06
+ ,-0.0148
+ ,54.5
+ ,185.35
+ ,17906300
+ ,0.0596
+ ,25.08
+ ,2122.42
+ ,-0.0129
+ ,53.4
+ ,188.50
+ ,22197500
+ ,0.3201
+ ,27.04
+ ,2045.11
+ ,-0.0018
+ ,48.7
+ ,199.91
+ ,15856500
+ ,0.3201
+ ,28.81
+ ,2144.60
+ ,0.0184
+ ,50.6
+ ,210.73
+ ,19068700
+ ,0.3201
+ ,29.86
+ ,2269.15
+ ,0.0272
+ ,53.6
+ ,192.06
+ ,30855100
+ ,0.486
+ ,27.61
+ ,2147.35
+ ,0.0263
+ ,56.5
+ ,204.62
+ ,21209000
+ ,0.486
+ ,28.22
+ ,2238.26
+ ,0.0214
+ ,46.4
+ ,235.00
+ ,19541600
+ ,0.486
+ ,28.83
+ ,2397.96
+ ,0.0231
+ ,52.3
+ ,261.09
+ ,21955000
+ ,0.6129
+ ,30.06
+ ,2461.19
+ ,0.0224
+ ,57.7
+ ,256.88
+ ,33725900
+ ,0.6129
+ ,25.51
+ ,2257.04
+ ,0.0202
+ ,62.7
+ ,251.53
+ ,28192800
+ ,0.6129
+ ,22.75
+ ,2109.24
+ ,0.0105
+ ,54.3
+ ,257.25
+ ,27377000
+ ,0.6665
+ ,25.52
+ ,2254.70
+ ,0.0124
+ ,51.0
+ ,243.10
+ ,16228100
+ ,0.6665
+ ,23.33
+ ,2114.03
+ ,0.0115
+ ,53.2
+ ,283.75
+ ,21278900
+ ,0.6665
+ ,24.34
+ ,2368.62
+ ,0.0114
+ ,48.6)
+ ,dim=c(7
+ ,117)
+ ,dimnames=list(c('APPLE'
+ ,'VOLUME'
+ ,'REV.GROWTH'
+ ,'MICROSOFT'
+ ,'NASDAQ'
+ ,'INFLATION'
+ ,'CONS.CONF')
+ ,1:117))
> y <- array(NA,dim=c(7,117),dimnames=list(c('APPLE','VOLUME','REV.GROWTH','MICROSOFT','NASDAQ','INFLATION','CONS.CONF'),1:117))
> 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 REV.GROWTH MICROSOFT NASDAQ INFLATION CONS.CONF t
1 10.81 24563400 -0.2643 24.45 2772.73 0.0373 115.7 1
2 9.12 14163200 -0.2643 23.62 2151.83 0.0353 109.2 2
3 11.03 18184800 -0.2643 21.90 1840.26 0.0292 116.9 3
4 12.74 20810300 -0.1918 27.12 2116.24 0.0327 109.9 4
5 9.98 12843000 -0.1918 27.70 2110.49 0.0362 116.1 5
6 11.62 13866700 -0.1918 29.23 2160.54 0.0325 118.9 6
7 9.40 15119200 -0.2246 26.50 2027.13 0.0272 116.3 7
8 9.27 8301600 -0.2246 22.84 1805.43 0.0272 114.0 8
9 7.76 14039600 -0.2246 20.49 1498.80 0.0265 97.0 9
10 8.78 12139700 0.3654 23.28 1690.20 0.0213 85.3 10
11 10.65 9649000 0.3654 25.71 1930.58 0.0190 84.9 11
12 10.95 8513600 0.3654 26.52 1950.40 0.0155 94.6 12
13 12.36 15278600 0.0447 25.51 1934.03 0.0114 97.8 13
14 10.85 15590900 0.0447 23.36 1731.49 0.0114 95.0 14
15 11.84 9691100 0.0447 24.15 1845.35 0.0148 110.7 15
16 12.14 10882700 -0.0312 20.92 1688.23 0.0164 108.5 16
17 11.65 10294800 -0.0312 20.38 1615.73 0.0118 110.3 17
18 8.86 16031900 -0.0312 21.90 1463.21 0.0107 106.3 18
19 7.63 13683600 -0.0048 19.21 1328.26 0.0146 97.4 19
20 7.38 8677200 -0.0048 19.65 1314.85 0.0180 94.5 20
21 7.25 9874100 -0.0048 17.51 1172.06 0.0151 93.7 21
22 8.03 10725500 0.0705 21.41 1329.75 0.0203 79.6 22
23 7.75 8348400 0.0705 23.09 1478.78 0.0220 84.9 23
24 7.16 8046200 0.0705 20.70 1335.51 0.0238 80.7 24
25 7.18 10862300 -0.0134 19.00 1320.91 0.0260 78.8 25
26 7.51 8100300 -0.0134 19.04 1337.52 0.0298 64.8 26
27 7.07 7287500 -0.0134 19.45 1341.17 0.0302 61.4 27
28 7.11 14002500 0.0812 20.54 1464.31 0.0222 81.0 28
29 8.98 19037900 0.0812 19.77 1595.91 0.0206 83.6 29
30 9.53 10774600 0.0812 20.60 1622.80 0.0211 83.5 30
31 10.54 8960600 0.1885 21.21 1735.02 0.0211 77.0 31
32 11.31 7773300 0.1885 21.30 1810.45 0.0216 81.7 32
33 10.36 9579700 0.1885 22.33 1786.94 0.0232 77.0 33
34 11.44 11270700 0.3628 21.12 1932.21 0.0204 81.7 34
35 10.45 9492800 0.3628 20.77 1960.26 0.0177 92.5 35
36 10.69 9136800 0.3628 22.11 2003.37 0.0188 91.7 36
37 11.28 14487600 0.2942 22.34 2066.15 0.0193 96.4 37
38 11.96 10133200 0.2942 21.43 2029.82 0.0169 88.5 38
39 13.52 18659700 0.2942 20.14 1994.22 0.0174 88.5 39
40 12.89 15980700 0.3036 21.11 1920.15 0.0229 93.0 40
41 14.03 9732100 0.3036 21.19 1986.74 0.0305 93.1 41
42 16.27 14626300 0.3036 23.07 2047.79 0.0327 102.8 42
43 16.17 16904000 0.3703 23.01 1887.36 0.0299 105.7 43
44 17.25 13616700 0.3703 22.12 1838.10 0.0265 98.7 44
45 19.38 13772900 0.3703 22.40 1896.84 0.0254 96.7 45
46 26.20 28749200 0.7398 22.66 1974.99 0.0319 92.9 46
47 33.53 31408300 0.7398 24.21 2096.81 0.0352 92.6 47
48 32.20 26342800 0.7398 24.13 2175.44 0.0326 102.7 48
49 38.45 48909500 0.6988 23.73 2062.41 0.0297 105.1 49
50 44.86 41542400 0.6988 22.79 2051.72 0.0301 104.4 50
51 41.67 24857200 0.6988 21.89 1999.23 0.0315 103.0 51
52 36.06 34093700 0.7478 22.92 1921.65 0.0351 97.5 52
53 39.76 22555200 0.7478 23.44 2068.22 0.0280 103.1 53
54 36.81 19067500 0.7478 22.57 2056.96 0.0253 106.2 54
55 42.65 19029100 0.5651 23.27 2184.83 0.0317 103.6 55
56 46.89 15223200 0.5651 24.95 2152.09 0.0364 105.5 56
57 53.61 21903700 0.5651 23.45 2151.69 0.0469 87.5 57
58 57.59 33306600 0.6473 23.42 2120.30 0.0435 85.2 58
59 67.82 23898100 0.6473 25.30 2232.82 0.0346 98.3 59
60 71.89 23279600 0.6473 23.90 2205.32 0.0342 103.8 60
61 75.51 40699800 0.3441 25.73 2305.82 0.0399 106.8 61
62 68.49 37646000 0.3441 24.64 2281.39 0.0360 102.7 62
63 62.72 37277000 0.3441 24.95 2339.79 0.0336 107.5 63
64 70.39 39246800 0.2415 22.15 2322.57 0.0355 109.8 64
65 59.77 27418400 0.2415 20.85 2178.88 0.0417 104.7 65
66 57.27 30318700 0.2415 21.45 2172.09 0.0432 105.7 66
67 67.96 32808100 0.3151 22.15 2091.47 0.0415 107.0 67
68 67.85 28668200 0.3151 23.75 2183.75 0.0382 100.2 68
69 76.98 32370300 0.3151 25.27 2258.43 0.0206 105.9 69
70 81.08 24171100 0.2390 26.53 2366.71 0.0131 105.1 70
71 91.66 25009100 0.2390 27.22 2431.77 0.0197 105.3 71
72 84.84 32084300 0.2390 27.69 2415.29 0.0254 110.0 72
73 85.73 50117500 0.2127 28.61 2463.93 0.0208 110.2 73
74 84.61 27522200 0.2127 26.21 2416.15 0.0242 111.2 74
75 92.91 26816800 0.2127 25.93 2421.64 0.0278 108.2 75
76 99.80 25136100 0.2730 27.86 2525.09 0.0257 106.3 76
77 121.19 30295600 0.2730 28.65 2604.52 0.0269 108.5 77
78 122.04 41526100 0.2730 27.51 2603.23 0.0269 105.3 78
79 131.76 43845100 0.3657 27.06 2546.27 0.0236 111.9 79
80 138.48 39188900 0.3657 26.91 2596.36 0.0197 105.6 80
81 153.47 40496400 0.3657 27.60 2701.50 0.0276 99.5 81
82 189.95 37438400 0.4643 34.48 2859.12 0.0354 95.2 82
83 182.22 46553700 0.4643 31.58 2660.96 0.0431 87.8 83
84 198.08 31771400 0.4643 33.46 2652.28 0.0408 90.6 84
85 135.36 62108100 0.5096 30.64 2389.86 0.0428 87.9 85
86 125.02 46645400 0.5096 25.66 2271.48 0.0403 76.4 86
87 143.50 42313100 0.5096 26.78 2279.10 0.0398 65.9 87
88 173.95 38841700 0.3592 26.91 2412.80 0.0394 62.3 88
89 188.75 32650300 0.3592 26.82 2522.66 0.0418 57.2 89
90 167.44 34281100 0.3592 26.05 2292.98 0.0502 50.4 90
91 158.95 33096200 0.7439 24.36 2325.55 0.0560 51.9 91
92 169.53 23273800 0.7439 25.94 2367.52 0.0537 58.5 92
93 113.66 43697600 0.7439 25.37 2091.88 0.0494 61.4 93
94 107.59 66902300 0.1390 21.23 1720.95 0.0366 38.8 94
95 92.67 44957200 0.1390 19.35 1535.57 0.0107 44.9 95
96 85.35 33800900 0.1390 18.61 1577.03 0.0009 38.6 96
97 90.13 33487900 0.1383 16.37 1476.42 0.0003 4.0 97
98 89.31 27394900 0.1383 15.56 1377.84 0.0024 25.3 98
99 105.12 25963400 0.1383 17.70 1528.59 -0.0038 26.9 99
100 125.83 20952600 0.2874 19.52 1717.30 -0.0074 40.8 100
101 135.81 17702900 0.2874 20.26 1774.33 -0.0128 54.8 101
102 142.43 21282100 0.2874 23.05 1835.04 -0.0143 49.3 102
103 163.39 18449100 0.0596 22.81 1978.50 -0.0210 47.4 103
104 168.21 14415700 0.0596 24.04 2009.06 -0.0148 54.5 104
105 185.35 17906300 0.0596 25.08 2122.42 -0.0129 53.4 105
106 188.50 22197500 0.3201 27.04 2045.11 -0.0018 48.7 106
107 199.91 15856500 0.3201 28.81 2144.60 0.0184 50.6 107
108 210.73 19068700 0.3201 29.86 2269.15 0.0272 53.6 108
109 192.06 30855100 0.4860 27.61 2147.35 0.0263 56.5 109
110 204.62 21209000 0.4860 28.22 2238.26 0.0214 46.4 110
111 235.00 19541600 0.4860 28.83 2397.96 0.0231 52.3 111
112 261.09 21955000 0.6129 30.06 2461.19 0.0224 57.7 112
113 256.88 33725900 0.6129 25.51 2257.04 0.0202 62.7 113
114 251.53 28192800 0.6129 22.75 2109.24 0.0105 54.3 114
115 257.25 27377000 0.6665 25.52 2254.70 0.0124 51.0 115
116 243.10 16228100 0.6665 23.33 2114.03 0.0115 53.2 116
117 283.75 21278900 0.6665 24.34 2368.62 0.0114 48.6 117
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VOLUME REV.GROWTH MICROSOFT NASDAQ INFLATION
-1.484e+02 -7.063e-07 -1.821e+01 6.833e+00 1.897e-02 8.227e+01
CONS.CONF t
-6.112e-01 1.658e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.588 -16.131 -5.664 11.160 82.828
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.484e+02 1.763e+01 -8.418 1.67e-13 ***
VOLUME -7.063e-07 2.573e-07 -2.745 0.00708 **
REV.GROWTH -1.821e+01 1.110e+01 -1.640 0.10381
MICROSOFT 6.833e+00 1.128e+00 6.060 1.98e-08 ***
NASDAQ 1.897e-02 1.442e-02 1.315 0.19120
INFLATION 8.227e+01 2.181e+02 0.377 0.70673
CONS.CONF -6.112e-01 1.778e-01 -3.438 0.00083 ***
t 1.658e+00 1.753e-01 9.459 7.34e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23.67 on 109 degrees of freedom
Multiple R-squared: 0.9088, Adjusted R-squared: 0.903
F-statistic: 155.2 on 7 and 109 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,] 2.766377e-04 5.532754e-04 0.999723362
[2,] 1.321181e-05 2.642361e-05 0.999986788
[3,] 6.182891e-07 1.236578e-06 0.999999382
[4,] 2.526976e-08 5.053952e-08 0.999999975
[5,] 2.625173e-09 5.250346e-09 0.999999997
[6,] 3.524603e-10 7.049206e-10 1.000000000
[7,] 1.904919e-11 3.809838e-11 1.000000000
[8,] 1.648140e-11 3.296280e-11 1.000000000
[9,] 1.575053e-12 3.150106e-12 1.000000000
[10,] 1.138525e-13 2.277049e-13 1.000000000
[11,] 1.229241e-14 2.458481e-14 1.000000000
[12,] 1.629934e-15 3.259867e-15 1.000000000
[13,] 9.852056e-17 1.970411e-16 1.000000000
[14,] 8.059649e-18 1.611930e-17 1.000000000
[15,] 1.192837e-18 2.385674e-18 1.000000000
[16,] 3.065549e-19 6.131099e-19 1.000000000
[17,] 3.653192e-20 7.306385e-20 1.000000000
[18,] 7.628915e-21 1.525783e-20 1.000000000
[19,] 1.983395e-21 3.966791e-21 1.000000000
[20,] 5.452571e-22 1.090514e-21 1.000000000
[21,] 9.935438e-23 1.987088e-22 1.000000000
[22,] 1.811638e-23 3.623275e-23 1.000000000
[23,] 2.369781e-24 4.739563e-24 1.000000000
[24,] 1.953227e-25 3.906453e-25 1.000000000
[25,] 2.724525e-26 5.449050e-26 1.000000000
[26,] 2.551970e-27 5.103940e-27 1.000000000
[27,] 1.870016e-28 3.740032e-28 1.000000000
[28,] 1.260375e-29 2.520749e-29 1.000000000
[29,] 7.980551e-30 1.596110e-29 1.000000000
[30,] 3.587119e-30 7.174239e-30 1.000000000
[31,] 5.956260e-30 1.191252e-29 1.000000000
[32,] 9.456410e-30 1.891282e-29 1.000000000
[33,] 4.523612e-30 9.047223e-30 1.000000000
[34,] 2.120700e-29 4.241400e-29 1.000000000
[35,] 1.000465e-27 2.000930e-27 1.000000000
[36,] 1.681716e-26 3.363432e-26 1.000000000
[37,] 3.261263e-24 6.522527e-24 1.000000000
[38,] 5.596357e-24 1.119271e-23 1.000000000
[39,] 1.423662e-24 2.847324e-24 1.000000000
[40,] 9.207405e-22 1.841481e-21 1.000000000
[41,] 1.663905e-18 3.327810e-18 1.000000000
[42,] 7.710904e-19 1.542181e-18 1.000000000
[43,] 8.614082e-18 1.722816e-17 1.000000000
[44,] 1.139452e-17 2.278904e-17 1.000000000
[45,] 1.588700e-15 3.177399e-15 1.000000000
[46,] 8.126930e-14 1.625386e-13 1.000000000
[47,] 1.092447e-11 2.184893e-11 1.000000000
[48,] 1.753614e-10 3.507229e-10 1.000000000
[49,] 6.809011e-08 1.361802e-07 0.999999932
[50,] 8.148286e-06 1.629657e-05 0.999991852
[51,] 4.957806e-05 9.915611e-05 0.999950422
[52,] 8.154638e-05 1.630928e-04 0.999918454
[53,] 5.133413e-05 1.026683e-04 0.999948666
[54,] 5.165800e-05 1.033160e-04 0.999948342
[55,] 3.925786e-05 7.851572e-05 0.999960742
[56,] 2.671718e-05 5.343435e-05 0.999973283
[57,] 1.335744e-04 2.671488e-04 0.999866426
[58,] 2.598860e-04 5.197721e-04 0.999740114
[59,] 5.885936e-04 1.177187e-03 0.999411406
[60,] 7.025688e-04 1.405138e-03 0.999297431
[61,] 1.097868e-03 2.195736e-03 0.998902132
[62,] 7.722079e-04 1.544416e-03 0.999227792
[63,] 8.703011e-04 1.740602e-03 0.999129699
[64,] 5.489023e-04 1.097805e-03 0.999451098
[65,] 4.924666e-04 9.849331e-04 0.999507533
[66,] 5.929318e-04 1.185864e-03 0.999407068
[67,] 2.150175e-03 4.300350e-03 0.997849825
[68,] 6.660317e-03 1.332063e-02 0.993339683
[69,] 1.876643e-02 3.753285e-02 0.981233574
[70,] 6.786316e-02 1.357263e-01 0.932136836
[71,] 3.694165e-01 7.388330e-01 0.630583476
[72,] 5.920643e-01 8.158713e-01 0.407935653
[73,] 6.691349e-01 6.617301e-01 0.330865067
[74,] 9.732414e-01 5.351729e-02 0.026758647
[75,] 9.695298e-01 6.094043e-02 0.030470217
[76,] 9.661251e-01 6.774980e-02 0.033874899
[77,] 9.590627e-01 8.187452e-02 0.040937261
[78,] 9.679140e-01 6.417209e-02 0.032086045
[79,] 9.725229e-01 5.495412e-02 0.027477059
[80,] 9.869054e-01 2.618924e-02 0.013094620
[81,] 9.821822e-01 3.563553e-02 0.017817763
[82,] 9.977107e-01 4.578620e-03 0.002289310
[83,] 9.965454e-01 6.909120e-03 0.003454560
[84,] 9.957312e-01 8.537571e-03 0.004268786
[85,] 9.942663e-01 1.146746e-02 0.005733732
[86,] 9.917216e-01 1.655671e-02 0.008278354
[87,] 9.844404e-01 3.111930e-02 0.015559648
[88,] 9.736777e-01 5.264464e-02 0.026322319
[89,] 9.638682e-01 7.226355e-02 0.036131775
[90,] 9.583274e-01 8.334513e-02 0.041672566
[91,] 9.341197e-01 1.317605e-01 0.065880267
[92,] 8.849632e-01 2.300736e-01 0.115036798
[93,] 8.247107e-01 3.505786e-01 0.175289311
[94,] 7.292689e-01 5.414621e-01 0.270731074
[95,] 8.721238e-01 2.557525e-01 0.127876248
[96,] 9.712315e-01 5.753709e-02 0.028768544
> postscript(file="/var/www/html/rcomp/tmp/1jlr81292323666.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bc8t1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bc8t1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bc8t1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bc8t1292323666.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 = 117
Frequency = 1
1 2 3 4 5
18.066297200 21.013817266 46.977156068 4.734078365 -5.663511639
6 7 8 9 10
-14.346490890 2.094161134 23.298981671 35.724162534 15.068137275
11 12 13 14 15
-7.698086753 -9.551908392 -1.355946743 12.517833209 9.441441973
16 17 18 19 20
31.117377140 35.097738279 24.854463609 35.968082547 25.719733734
21 22 23 24 25
41.857566413 4.266222984 -10.557405589 3.314140027 12.687953109
26 27 28 29 30
-0.048996794 -7.702937332 -0.001713365 8.252289237 -4.975597136
31 32 33 34 35
-15.221056133 -16.161951003 -27.090505679 -14.685730510 -9.906799467
36 37 38 39 40
-22.129637905 -20.598452112 -22.376063890 -7.003559459 -13.937474919
41 42 43 44 45
-21.242769547 -25.460290055 -18.939208957 -18.822167587 -22.399362430
46 47 48 49 50
-6.049238775 -11.855971960 -12.979413638 13.387048225 19.100670352
51 52 53 54 55
8.642647052 -0.433957325 -8.867862971 -7.664128301 -16.160039912
56 57 58 59 60
-26.349690736 -18.178482528 -6.632093211 -10.946191396 4.511162774
61 62 63 64 65
0.210381072 -4.898425386 -12.681841763 13.561452475 0.910386244
66 67 68 69 70
-4.682431680 5.128408942 -16.131479282 -12.915514674 -28.185356798
71 72 73 74 75
-25.041207255 -29.017358290 -24.236140920 -25.335971682 -19.513071767
76 77 78 79 80
-30.508730431 -12.791578404 0.190152144 20.038474758 18.356754442
81 82 83 84 85
21.524565278 2.711741945 18.179289054 11.160341012 -8.535211763
86 87 88 89 90
-2.004258860 -2.416186264 15.593726064 19.579096377 2.533584041
91 92 93 94 95
9.922002702 4.537793587 -27.315753105 -7.104156140 -16.959811992
96 97 98 99 100
-32.591860679 -33.588469445 -20.119071954 -22.972141566 -11.968060083
101 102 103 104 105
-3.078140749 -19.041995143 -7.579958456 -12.421328481 -4.559342095
106 107 108 109 110
-11.005779171 -20.214438931 -17.211368014 -6.663587232 -14.237212909
111 112 113 114 115
9.575899778 31.777024127 72.421651195 78.831878330 59.433771173
116 117
54.802634859 82.827764625
> postscript(file="/var/www/html/rcomp/tmp/6m3qe1292323666.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 = 117
Frequency = 1
lag(myerror, k = 1) myerror
0 18.066297200 NA
1 21.013817266 18.066297200
2 46.977156068 21.013817266
3 4.734078365 46.977156068
4 -5.663511639 4.734078365
5 -14.346490890 -5.663511639
6 2.094161134 -14.346490890
7 23.298981671 2.094161134
8 35.724162534 23.298981671
9 15.068137275 35.724162534
10 -7.698086753 15.068137275
11 -9.551908392 -7.698086753
12 -1.355946743 -9.551908392
13 12.517833209 -1.355946743
14 9.441441973 12.517833209
15 31.117377140 9.441441973
16 35.097738279 31.117377140
17 24.854463609 35.097738279
18 35.968082547 24.854463609
19 25.719733734 35.968082547
20 41.857566413 25.719733734
21 4.266222984 41.857566413
22 -10.557405589 4.266222984
23 3.314140027 -10.557405589
24 12.687953109 3.314140027
25 -0.048996794 12.687953109
26 -7.702937332 -0.048996794
27 -0.001713365 -7.702937332
28 8.252289237 -0.001713365
29 -4.975597136 8.252289237
30 -15.221056133 -4.975597136
31 -16.161951003 -15.221056133
32 -27.090505679 -16.161951003
33 -14.685730510 -27.090505679
34 -9.906799467 -14.685730510
35 -22.129637905 -9.906799467
36 -20.598452112 -22.129637905
37 -22.376063890 -20.598452112
38 -7.003559459 -22.376063890
39 -13.937474919 -7.003559459
40 -21.242769547 -13.937474919
41 -25.460290055 -21.242769547
42 -18.939208957 -25.460290055
43 -18.822167587 -18.939208957
44 -22.399362430 -18.822167587
45 -6.049238775 -22.399362430
46 -11.855971960 -6.049238775
47 -12.979413638 -11.855971960
48 13.387048225 -12.979413638
49 19.100670352 13.387048225
50 8.642647052 19.100670352
51 -0.433957325 8.642647052
52 -8.867862971 -0.433957325
53 -7.664128301 -8.867862971
54 -16.160039912 -7.664128301
55 -26.349690736 -16.160039912
56 -18.178482528 -26.349690736
57 -6.632093211 -18.178482528
58 -10.946191396 -6.632093211
59 4.511162774 -10.946191396
60 0.210381072 4.511162774
61 -4.898425386 0.210381072
62 -12.681841763 -4.898425386
63 13.561452475 -12.681841763
64 0.910386244 13.561452475
65 -4.682431680 0.910386244
66 5.128408942 -4.682431680
67 -16.131479282 5.128408942
68 -12.915514674 -16.131479282
69 -28.185356798 -12.915514674
70 -25.041207255 -28.185356798
71 -29.017358290 -25.041207255
72 -24.236140920 -29.017358290
73 -25.335971682 -24.236140920
74 -19.513071767 -25.335971682
75 -30.508730431 -19.513071767
76 -12.791578404 -30.508730431
77 0.190152144 -12.791578404
78 20.038474758 0.190152144
79 18.356754442 20.038474758
80 21.524565278 18.356754442
81 2.711741945 21.524565278
82 18.179289054 2.711741945
83 11.160341012 18.179289054
84 -8.535211763 11.160341012
85 -2.004258860 -8.535211763
86 -2.416186264 -2.004258860
87 15.593726064 -2.416186264
88 19.579096377 15.593726064
89 2.533584041 19.579096377
90 9.922002702 2.533584041
91 4.537793587 9.922002702
92 -27.315753105 4.537793587
93 -7.104156140 -27.315753105
94 -16.959811992 -7.104156140
95 -32.591860679 -16.959811992
96 -33.588469445 -32.591860679
97 -20.119071954 -33.588469445
98 -22.972141566 -20.119071954
99 -11.968060083 -22.972141566
100 -3.078140749 -11.968060083
101 -19.041995143 -3.078140749
102 -7.579958456 -19.041995143
103 -12.421328481 -7.579958456
104 -4.559342095 -12.421328481
105 -11.005779171 -4.559342095
106 -20.214438931 -11.005779171
107 -17.211368014 -20.214438931
108 -6.663587232 -17.211368014
109 -14.237212909 -6.663587232
110 9.575899778 -14.237212909
111 31.777024127 9.575899778
112 72.421651195 31.777024127
113 78.831878330 72.421651195
114 59.433771173 78.831878330
115 54.802634859 59.433771173
116 82.827764625 54.802634859
117 NA 82.827764625
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 21.013817266 18.066297200
[2,] 46.977156068 21.013817266
[3,] 4.734078365 46.977156068
[4,] -5.663511639 4.734078365
[5,] -14.346490890 -5.663511639
[6,] 2.094161134 -14.346490890
[7,] 23.298981671 2.094161134
[8,] 35.724162534 23.298981671
[9,] 15.068137275 35.724162534
[10,] -7.698086753 15.068137275
[11,] -9.551908392 -7.698086753
[12,] -1.355946743 -9.551908392
[13,] 12.517833209 -1.355946743
[14,] 9.441441973 12.517833209
[15,] 31.117377140 9.441441973
[16,] 35.097738279 31.117377140
[17,] 24.854463609 35.097738279
[18,] 35.968082547 24.854463609
[19,] 25.719733734 35.968082547
[20,] 41.857566413 25.719733734
[21,] 4.266222984 41.857566413
[22,] -10.557405589 4.266222984
[23,] 3.314140027 -10.557405589
[24,] 12.687953109 3.314140027
[25,] -0.048996794 12.687953109
[26,] -7.702937332 -0.048996794
[27,] -0.001713365 -7.702937332
[28,] 8.252289237 -0.001713365
[29,] -4.975597136 8.252289237
[30,] -15.221056133 -4.975597136
[31,] -16.161951003 -15.221056133
[32,] -27.090505679 -16.161951003
[33,] -14.685730510 -27.090505679
[34,] -9.906799467 -14.685730510
[35,] -22.129637905 -9.906799467
[36,] -20.598452112 -22.129637905
[37,] -22.376063890 -20.598452112
[38,] -7.003559459 -22.376063890
[39,] -13.937474919 -7.003559459
[40,] -21.242769547 -13.937474919
[41,] -25.460290055 -21.242769547
[42,] -18.939208957 -25.460290055
[43,] -18.822167587 -18.939208957
[44,] -22.399362430 -18.822167587
[45,] -6.049238775 -22.399362430
[46,] -11.855971960 -6.049238775
[47,] -12.979413638 -11.855971960
[48,] 13.387048225 -12.979413638
[49,] 19.100670352 13.387048225
[50,] 8.642647052 19.100670352
[51,] -0.433957325 8.642647052
[52,] -8.867862971 -0.433957325
[53,] -7.664128301 -8.867862971
[54,] -16.160039912 -7.664128301
[55,] -26.349690736 -16.160039912
[56,] -18.178482528 -26.349690736
[57,] -6.632093211 -18.178482528
[58,] -10.946191396 -6.632093211
[59,] 4.511162774 -10.946191396
[60,] 0.210381072 4.511162774
[61,] -4.898425386 0.210381072
[62,] -12.681841763 -4.898425386
[63,] 13.561452475 -12.681841763
[64,] 0.910386244 13.561452475
[65,] -4.682431680 0.910386244
[66,] 5.128408942 -4.682431680
[67,] -16.131479282 5.128408942
[68,] -12.915514674 -16.131479282
[69,] -28.185356798 -12.915514674
[70,] -25.041207255 -28.185356798
[71,] -29.017358290 -25.041207255
[72,] -24.236140920 -29.017358290
[73,] -25.335971682 -24.236140920
[74,] -19.513071767 -25.335971682
[75,] -30.508730431 -19.513071767
[76,] -12.791578404 -30.508730431
[77,] 0.190152144 -12.791578404
[78,] 20.038474758 0.190152144
[79,] 18.356754442 20.038474758
[80,] 21.524565278 18.356754442
[81,] 2.711741945 21.524565278
[82,] 18.179289054 2.711741945
[83,] 11.160341012 18.179289054
[84,] -8.535211763 11.160341012
[85,] -2.004258860 -8.535211763
[86,] -2.416186264 -2.004258860
[87,] 15.593726064 -2.416186264
[88,] 19.579096377 15.593726064
[89,] 2.533584041 19.579096377
[90,] 9.922002702 2.533584041
[91,] 4.537793587 9.922002702
[92,] -27.315753105 4.537793587
[93,] -7.104156140 -27.315753105
[94,] -16.959811992 -7.104156140
[95,] -32.591860679 -16.959811992
[96,] -33.588469445 -32.591860679
[97,] -20.119071954 -33.588469445
[98,] -22.972141566 -20.119071954
[99,] -11.968060083 -22.972141566
[100,] -3.078140749 -11.968060083
[101,] -19.041995143 -3.078140749
[102,] -7.579958456 -19.041995143
[103,] -12.421328481 -7.579958456
[104,] -4.559342095 -12.421328481
[105,] -11.005779171 -4.559342095
[106,] -20.214438931 -11.005779171
[107,] -17.211368014 -20.214438931
[108,] -6.663587232 -17.211368014
[109,] -14.237212909 -6.663587232
[110,] 9.575899778 -14.237212909
[111,] 31.777024127 9.575899778
[112,] 72.421651195 31.777024127
[113,] 78.831878330 72.421651195
[114,] 59.433771173 78.831878330
[115,] 54.802634859 59.433771173
[116,] 82.827764625 54.802634859
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 21.013817266 18.066297200
2 46.977156068 21.013817266
3 4.734078365 46.977156068
4 -5.663511639 4.734078365
5 -14.346490890 -5.663511639
6 2.094161134 -14.346490890
7 23.298981671 2.094161134
8 35.724162534 23.298981671
9 15.068137275 35.724162534
10 -7.698086753 15.068137275
11 -9.551908392 -7.698086753
12 -1.355946743 -9.551908392
13 12.517833209 -1.355946743
14 9.441441973 12.517833209
15 31.117377140 9.441441973
16 35.097738279 31.117377140
17 24.854463609 35.097738279
18 35.968082547 24.854463609
19 25.719733734 35.968082547
20 41.857566413 25.719733734
21 4.266222984 41.857566413
22 -10.557405589 4.266222984
23 3.314140027 -10.557405589
24 12.687953109 3.314140027
25 -0.048996794 12.687953109
26 -7.702937332 -0.048996794
27 -0.001713365 -7.702937332
28 8.252289237 -0.001713365
29 -4.975597136 8.252289237
30 -15.221056133 -4.975597136
31 -16.161951003 -15.221056133
32 -27.090505679 -16.161951003
33 -14.685730510 -27.090505679
34 -9.906799467 -14.685730510
35 -22.129637905 -9.906799467
36 -20.598452112 -22.129637905
37 -22.376063890 -20.598452112
38 -7.003559459 -22.376063890
39 -13.937474919 -7.003559459
40 -21.242769547 -13.937474919
41 -25.460290055 -21.242769547
42 -18.939208957 -25.460290055
43 -18.822167587 -18.939208957
44 -22.399362430 -18.822167587
45 -6.049238775 -22.399362430
46 -11.855971960 -6.049238775
47 -12.979413638 -11.855971960
48 13.387048225 -12.979413638
49 19.100670352 13.387048225
50 8.642647052 19.100670352
51 -0.433957325 8.642647052
52 -8.867862971 -0.433957325
53 -7.664128301 -8.867862971
54 -16.160039912 -7.664128301
55 -26.349690736 -16.160039912
56 -18.178482528 -26.349690736
57 -6.632093211 -18.178482528
58 -10.946191396 -6.632093211
59 4.511162774 -10.946191396
60 0.210381072 4.511162774
61 -4.898425386 0.210381072
62 -12.681841763 -4.898425386
63 13.561452475 -12.681841763
64 0.910386244 13.561452475
65 -4.682431680 0.910386244
66 5.128408942 -4.682431680
67 -16.131479282 5.128408942
68 -12.915514674 -16.131479282
69 -28.185356798 -12.915514674
70 -25.041207255 -28.185356798
71 -29.017358290 -25.041207255
72 -24.236140920 -29.017358290
73 -25.335971682 -24.236140920
74 -19.513071767 -25.335971682
75 -30.508730431 -19.513071767
76 -12.791578404 -30.508730431
77 0.190152144 -12.791578404
78 20.038474758 0.190152144
79 18.356754442 20.038474758
80 21.524565278 18.356754442
81 2.711741945 21.524565278
82 18.179289054 2.711741945
83 11.160341012 18.179289054
84 -8.535211763 11.160341012
85 -2.004258860 -8.535211763
86 -2.416186264 -2.004258860
87 15.593726064 -2.416186264
88 19.579096377 15.593726064
89 2.533584041 19.579096377
90 9.922002702 2.533584041
91 4.537793587 9.922002702
92 -27.315753105 4.537793587
93 -7.104156140 -27.315753105
94 -16.959811992 -7.104156140
95 -32.591860679 -16.959811992
96 -33.588469445 -32.591860679
97 -20.119071954 -33.588469445
98 -22.972141566 -20.119071954
99 -11.968060083 -22.972141566
100 -3.078140749 -11.968060083
101 -19.041995143 -3.078140749
102 -7.579958456 -19.041995143
103 -12.421328481 -7.579958456
104 -4.559342095 -12.421328481
105 -11.005779171 -4.559342095
106 -20.214438931 -11.005779171
107 -17.211368014 -20.214438931
108 -6.663587232 -17.211368014
109 -14.237212909 -6.663587232
110 9.575899778 -14.237212909
111 31.777024127 9.575899778
112 72.421651195 31.777024127
113 78.831878330 72.421651195
114 59.433771173 78.831878330
115 54.802634859 59.433771173
116 82.827764625 54.802634859
> 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/7fvpz1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fvpz1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pm6k1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pm6k1292323666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bnn81292323666.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/12w53w1292323666.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/13l6i71292323666.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/14wfzs1292323666.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/15zyyy1292323666.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/162yem1292323666.tab")
+ }
> try(system("convert tmp/1jlr81292323666.ps tmp/1jlr81292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bc8t1292323666.ps tmp/2bc8t1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bc8t1292323666.ps tmp/3bc8t1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bc8t1292323666.ps tmp/4bc8t1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bc8t1292323666.ps tmp/5bc8t1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m3qe1292323666.ps tmp/6m3qe1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fvpz1292323666.ps tmp/7fvpz1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fvpz1292323666.ps tmp/8fvpz1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pm6k1292323666.ps tmp/9pm6k1292323666.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pm6k1292323666.ps tmp/10pm6k1292323666.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.405 1.845 11.085