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
+ ,-0.2643
+ ,0
+ ,0
+ ,24563400
+ ,24.45
+ ,115.7
+ ,9.12
+ ,-0.2643
+ ,0
+ ,0
+ ,14163200
+ ,23.62
+ ,109.2
+ ,11.03
+ ,-0.2643
+ ,0
+ ,0
+ ,18184800
+ ,21.90
+ ,116.9
+ ,12.74
+ ,-0.1918
+ ,0
+ ,0
+ ,20810300
+ ,27.12
+ ,109.9
+ ,9.98
+ ,-0.1918
+ ,0
+ ,0
+ ,12843000
+ ,27.70
+ ,116.1
+ ,11.62
+ ,-0.1918
+ ,0
+ ,0
+ ,13866700
+ ,29.23
+ ,118.9
+ ,9.40
+ ,-0.2246
+ ,0
+ ,0
+ ,15119200
+ ,26.50
+ ,116.3
+ ,9.27
+ ,-0.2246
+ ,0
+ ,0
+ ,8301600
+ ,22.84
+ ,114.0
+ ,7.76
+ ,-0.2246
+ ,0
+ ,0
+ ,14039600
+ ,20.49
+ ,97.0
+ ,8.78
+ ,0.3654
+ ,0
+ ,0
+ ,12139700
+ ,23.28
+ ,85.3
+ ,10.65
+ ,0.3654
+ ,0
+ ,0
+ ,9649000
+ ,25.71
+ ,84.9
+ ,10.95
+ ,0.3654
+ ,0
+ ,0
+ ,8513600
+ ,26.52
+ ,94.6
+ ,12.36
+ ,0.0447
+ ,0
+ ,0
+ ,15278600
+ ,25.51
+ ,97.8
+ ,10.85
+ ,0.0447
+ ,0
+ ,0
+ ,15590900
+ ,23.36
+ ,95.0
+ ,11.84
+ ,0.0447
+ ,0
+ ,0
+ ,9691100
+ ,24.15
+ ,110.7
+ ,12.14
+ ,-0.0312
+ ,0
+ ,0
+ ,10882700
+ ,20.92
+ ,108.5
+ ,11.65
+ ,-0.0312
+ ,0
+ ,0
+ ,10294800
+ ,20.38
+ ,110.3
+ ,8.86
+ ,-0.0312
+ ,0
+ ,0
+ ,16031900
+ ,21.90
+ ,106.3
+ ,7.63
+ ,-0.0048
+ ,0
+ ,0
+ ,13683600
+ ,19.21
+ ,97.4
+ ,7.38
+ ,-0.0048
+ ,0
+ ,0
+ ,8677200
+ ,19.65
+ ,94.5
+ ,7.25
+ ,-0.0048
+ ,0
+ ,0
+ ,9874100
+ ,17.51
+ ,93.7
+ ,8.03
+ ,0.0705
+ ,0
+ ,0
+ ,10725500
+ ,21.41
+ ,79.6
+ ,7.75
+ ,0.0705
+ ,0
+ ,0
+ ,8348400
+ ,23.09
+ ,84.9
+ ,7.16
+ ,0.0705
+ ,0
+ ,0
+ ,8046200
+ ,20.70
+ ,80.7
+ ,7.18
+ ,-0.0134
+ ,0
+ ,0
+ ,10862300
+ ,19.00
+ ,78.8
+ ,7.51
+ ,-0.0134
+ ,0
+ ,0
+ ,8100300
+ ,19.04
+ ,64.8
+ ,7.07
+ ,-0.0134
+ ,0
+ ,0
+ ,7287500
+ ,19.45
+ ,61.4
+ ,7.11
+ ,0.0812
+ ,0
+ ,0
+ ,14002500
+ ,20.54
+ ,81.0
+ ,8.98
+ ,0.0812
+ ,0
+ ,0
+ ,19037900
+ ,19.77
+ ,83.6
+ ,9.53
+ ,0.0812
+ ,0
+ ,0
+ ,10774600
+ ,20.60
+ ,83.5
+ ,10.54
+ ,0.1885
+ ,0
+ ,0
+ ,8960600
+ ,21.21
+ ,77.0
+ ,11.31
+ ,0.1885
+ ,0
+ ,0
+ ,7773300
+ ,21.30
+ ,81.7
+ ,10.36
+ ,0.1885
+ ,0
+ ,0
+ ,9579700
+ ,22.33
+ ,77.0
+ ,11.44
+ ,0.3628
+ ,0
+ ,0
+ ,11270700
+ ,21.12
+ ,81.7
+ ,10.45
+ ,0.3628
+ ,0
+ ,0
+ ,9492800
+ ,20.77
+ ,92.5
+ ,10.69
+ ,0.3628
+ ,0
+ ,0
+ ,9136800
+ ,22.11
+ ,91.7
+ ,11.28
+ ,0.2942
+ ,0
+ ,0
+ ,14487600
+ ,22.34
+ ,96.4
+ ,11.96
+ ,0.2942
+ ,0
+ ,0
+ ,10133200
+ ,21.43
+ ,88.5
+ ,13.52
+ ,0.2942
+ ,0
+ ,0
+ ,18659700
+ ,20.14
+ ,88.5
+ ,12.89
+ ,0.3036
+ ,0
+ ,0
+ ,15980700
+ ,21.11
+ ,93.0
+ ,14.03
+ ,0.3036
+ ,0
+ ,0
+ ,9732100
+ ,21.19
+ ,93.1
+ ,16.27
+ ,0.3036
+ ,0
+ ,0
+ ,14626300
+ ,23.07
+ ,102.8
+ ,16.17
+ ,0.3703
+ ,0
+ ,0
+ ,16904000
+ ,23.01
+ ,105.7
+ ,17.25
+ ,0.3703
+ ,0
+ ,0
+ ,13616700
+ ,22.12
+ ,98.7
+ ,19.38
+ ,0.3703
+ ,0
+ ,0
+ ,13772900
+ ,22.40
+ ,96.7
+ ,26.20
+ ,0.7398
+ ,0
+ ,0
+ ,28749200
+ ,22.66
+ ,92.9
+ ,33.53
+ ,0.7398
+ ,0
+ ,0
+ ,31408300
+ ,24.21
+ ,92.6
+ ,32.20
+ ,0.7398
+ ,0
+ ,0
+ ,26342800
+ ,24.13
+ ,102.7
+ ,38.45
+ ,0.6988
+ ,0
+ ,0
+ ,48909500
+ ,23.73
+ ,105.1
+ ,44.86
+ ,0.6988
+ ,0
+ ,0
+ ,41542400
+ ,22.79
+ ,104.4
+ ,41.67
+ ,0.6988
+ ,0
+ ,0
+ ,24857200
+ ,21.89
+ ,103.0
+ ,36.06
+ ,0.7478
+ ,0
+ ,0
+ ,34093700
+ ,22.92
+ ,97.5
+ ,39.76
+ ,0.7478
+ ,0
+ ,0
+ ,22555200
+ ,23.44
+ ,103.1
+ ,36.81
+ ,0.7478
+ ,0
+ ,0
+ ,19067500
+ ,22.57
+ ,106.2
+ ,42.65
+ ,0.5651
+ ,0
+ ,0
+ ,19029100
+ ,23.27
+ ,103.6
+ ,46.89
+ ,0.5651
+ ,0
+ ,0
+ ,15223200
+ ,24.95
+ ,105.5
+ ,53.61
+ ,0.5651
+ ,0
+ ,0
+ ,21903700
+ ,23.45
+ ,87.5
+ ,57.59
+ ,0.6473
+ ,0
+ ,0
+ ,33306600
+ ,23.42
+ ,85.2
+ ,67.82
+ ,0.6473
+ ,0
+ ,0
+ ,23898100
+ ,25.30
+ ,98.3
+ ,71.89
+ ,0.6473
+ ,0
+ ,0
+ ,23279600
+ ,23.90
+ ,103.8
+ ,75.51
+ ,0.3441
+ ,0
+ ,0
+ ,40699800
+ ,25.73
+ ,106.8
+ ,68.49
+ ,0.3441
+ ,0
+ ,0
+ ,37646000
+ ,24.64
+ ,102.7
+ ,62.72
+ ,0.3441
+ ,0
+ ,0
+ ,37277000
+ ,24.95
+ ,107.5
+ ,70.39
+ ,0.2415
+ ,0
+ ,0
+ ,39246800
+ ,22.15
+ ,109.8
+ ,59.77
+ ,0.2415
+ ,0
+ ,0
+ ,27418400
+ ,20.85
+ ,104.7
+ ,57.27
+ ,0.2415
+ ,0
+ ,0
+ ,30318700
+ ,21.45
+ ,105.7
+ ,67.96
+ ,0.3151
+ ,0
+ ,0
+ ,32808100
+ ,22.15
+ ,107.0
+ ,67.85
+ ,0.3151
+ ,0
+ ,0
+ ,28668200
+ ,23.75
+ ,100.2
+ ,76.98
+ ,0.3151
+ ,0
+ ,0
+ ,32370300
+ ,25.27
+ ,105.9
+ ,81.08
+ ,0.239
+ ,0
+ ,0
+ ,24171100
+ ,26.53
+ ,105.1
+ ,91.66
+ ,0.239
+ ,0
+ ,0
+ ,25009100
+ ,27.22
+ ,105.3
+ ,84.84
+ ,0.239
+ ,0
+ ,0
+ ,32084300
+ ,27.69
+ ,110.0
+ ,85.73
+ ,0.2127
+ ,0.2127
+ ,0
+ ,50117500
+ ,28.61
+ ,110.2
+ ,84.61
+ ,0.2127
+ ,0.2127
+ ,0
+ ,27522200
+ ,26.21
+ ,111.2
+ ,92.91
+ ,0.2127
+ ,0.2127
+ ,0
+ ,26816800
+ ,25.93
+ ,108.2
+ ,99.80
+ ,0.273
+ ,0.273
+ ,0
+ ,25136100
+ ,27.86
+ ,106.3
+ ,121.19
+ ,0.273
+ ,0.273
+ ,0
+ ,30295600
+ ,28.65
+ ,108.5
+ ,122.04
+ ,0.273
+ ,0.273
+ ,0
+ ,41526100
+ ,27.51
+ ,105.3
+ ,131.76
+ ,0.3657
+ ,0.3657
+ ,0
+ ,43845100
+ ,27.06
+ ,111.9
+ ,138.48
+ ,0.3657
+ ,0.3657
+ ,0
+ ,39188900
+ ,26.91
+ ,105.6
+ ,153.47
+ ,0.3657
+ ,0.3657
+ ,0
+ ,40496400
+ ,27.60
+ ,99.5
+ ,189.95
+ ,0.4643
+ ,0.4643
+ ,0
+ ,37438400
+ ,34.48
+ ,95.2
+ ,182.22
+ ,0.4643
+ ,0.4643
+ ,0
+ ,46553700
+ ,31.58
+ ,87.8
+ ,198.08
+ ,0.4643
+ ,0.4643
+ ,0
+ ,31771400
+ ,33.46
+ ,90.6
+ ,135.36
+ ,0.5096
+ ,0.5096
+ ,0
+ ,62108100
+ ,30.64
+ ,87.9
+ ,125.02
+ ,0.5096
+ ,0.5096
+ ,0
+ ,46645400
+ ,25.66
+ ,76.4
+ ,143.50
+ ,0.5096
+ ,0.5096
+ ,0
+ ,42313100
+ ,26.78
+ ,65.9
+ ,173.95
+ ,0.3592
+ ,0.3592
+ ,0
+ ,38841700
+ ,26.91
+ ,62.3
+ ,188.75
+ ,0.3592
+ ,0.3592
+ ,0
+ ,32650300
+ ,26.82
+ ,57.2
+ ,167.44
+ ,0.3592
+ ,0.3592
+ ,0
+ ,34281100
+ ,26.05
+ ,50.4
+ ,158.95
+ ,0.7439
+ ,0.7439
+ ,0
+ ,33096200
+ ,24.36
+ ,51.9
+ ,169.53
+ ,0.7439
+ ,0.7439
+ ,0
+ ,23273800
+ ,25.94
+ ,58.5
+ ,113.66
+ ,0.7439
+ ,0.7439
+ ,0
+ ,43697600
+ ,25.37
+ ,61.4
+ ,107.59
+ ,0.139
+ ,0.139
+ ,0
+ ,66902300
+ ,21.23
+ ,38.8
+ ,92.67
+ ,0.139
+ ,0.139
+ ,0
+ ,44957200
+ ,19.35
+ ,44.9
+ ,85.35
+ ,0.139
+ ,0.139
+ ,0
+ ,33800900
+ ,18.61
+ ,38.6
+ ,90.13
+ ,0.1383
+ ,0.1383
+ ,0
+ ,33487900
+ ,16.37
+ ,4.0
+ ,89.31
+ ,0.1383
+ ,0.1383
+ ,0
+ ,27394900
+ ,15.56
+ ,25.3
+ ,105.12
+ ,0.1383
+ ,0.1383
+ ,0
+ ,25963400
+ ,17.70
+ ,26.9
+ ,125.83
+ ,0.2874
+ ,0.2874
+ ,0
+ ,20952600
+ ,19.52
+ ,40.8
+ ,135.81
+ ,0.2874
+ ,0.2874
+ ,0
+ ,17702900
+ ,20.26
+ ,54.8
+ ,142.43
+ ,0.2874
+ ,0.2874
+ ,0
+ ,21282100
+ ,23.05
+ ,49.3
+ ,163.39
+ ,0.0596
+ ,0.0596
+ ,0
+ ,18449100
+ ,22.81
+ ,47.4
+ ,168.21
+ ,0.0596
+ ,0.0596
+ ,0
+ ,14415700
+ ,24.04
+ ,54.5
+ ,185.35
+ ,0.0596
+ ,0.0596
+ ,0
+ ,17906300
+ ,25.08
+ ,53.4
+ ,188.50
+ ,0.3201
+ ,0.3201
+ ,0
+ ,22197500
+ ,27.04
+ ,48.7
+ ,199.91
+ ,0.3201
+ ,0.3201
+ ,0
+ ,15856500
+ ,28.81
+ ,50.6
+ ,210.73
+ ,0.3201
+ ,0.3201
+ ,0
+ ,19068700
+ ,29.86
+ ,53.6
+ ,192.06
+ ,0.486
+ ,0.486
+ ,0
+ ,30855100
+ ,27.61
+ ,56.5
+ ,204.62
+ ,0.486
+ ,0.486
+ ,0
+ ,21209000
+ ,28.22
+ ,46.4
+ ,235.00
+ ,0.486
+ ,0.486
+ ,0
+ ,19541600
+ ,28.83
+ ,52.3
+ ,261.09
+ ,0.6129
+ ,0.6129
+ ,0.6129
+ ,21955000
+ ,30.06
+ ,57.7
+ ,256.88
+ ,0.6129
+ ,0.6129
+ ,0.6129
+ ,33725900
+ ,25.51
+ ,62.7
+ ,251.53
+ ,0.6129
+ ,0.6129
+ ,0.6129
+ ,28192800
+ ,22.75
+ ,54.3
+ ,257.25
+ ,0.6665
+ ,0.6665
+ ,0.6665
+ ,27377000
+ ,25.52
+ ,51.0
+ ,243.10
+ ,0.6665
+ ,0.6665
+ ,0.6665
+ ,16228100
+ ,23.33
+ ,53.2
+ ,283.75
+ ,0.6665
+ ,0.6665
+ ,0.6665
+ ,21278900
+ ,24.34
+ ,48.6)
+ ,dim=c(7
+ ,117)
+ ,dimnames=list(c('Apple'
+ ,'Omzetgroei'
+ ,'Omzetgroei_iPhone'
+ ,'Omzetgroei_iPad'
+ ,'Volume'
+ ,'Microsoft'
+ ,'Consumentenvertrouwen')
+ ,1:117))
> y <- array(NA,dim=c(7,117),dimnames=list(c('Apple','Omzetgroei','Omzetgroei_iPhone','Omzetgroei_iPad','Volume','Microsoft','Consumentenvertrouwen'),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 Omzetgroei Omzetgroei_iPhone Omzetgroei_iPad Volume Microsoft
1 10.81 -0.2643 0.0000 0.0000 24563400 24.45
2 9.12 -0.2643 0.0000 0.0000 14163200 23.62
3 11.03 -0.2643 0.0000 0.0000 18184800 21.90
4 12.74 -0.1918 0.0000 0.0000 20810300 27.12
5 9.98 -0.1918 0.0000 0.0000 12843000 27.70
6 11.62 -0.1918 0.0000 0.0000 13866700 29.23
7 9.40 -0.2246 0.0000 0.0000 15119200 26.50
8 9.27 -0.2246 0.0000 0.0000 8301600 22.84
9 7.76 -0.2246 0.0000 0.0000 14039600 20.49
10 8.78 0.3654 0.0000 0.0000 12139700 23.28
11 10.65 0.3654 0.0000 0.0000 9649000 25.71
12 10.95 0.3654 0.0000 0.0000 8513600 26.52
13 12.36 0.0447 0.0000 0.0000 15278600 25.51
14 10.85 0.0447 0.0000 0.0000 15590900 23.36
15 11.84 0.0447 0.0000 0.0000 9691100 24.15
16 12.14 -0.0312 0.0000 0.0000 10882700 20.92
17 11.65 -0.0312 0.0000 0.0000 10294800 20.38
18 8.86 -0.0312 0.0000 0.0000 16031900 21.90
19 7.63 -0.0048 0.0000 0.0000 13683600 19.21
20 7.38 -0.0048 0.0000 0.0000 8677200 19.65
21 7.25 -0.0048 0.0000 0.0000 9874100 17.51
22 8.03 0.0705 0.0000 0.0000 10725500 21.41
23 7.75 0.0705 0.0000 0.0000 8348400 23.09
24 7.16 0.0705 0.0000 0.0000 8046200 20.70
25 7.18 -0.0134 0.0000 0.0000 10862300 19.00
26 7.51 -0.0134 0.0000 0.0000 8100300 19.04
27 7.07 -0.0134 0.0000 0.0000 7287500 19.45
28 7.11 0.0812 0.0000 0.0000 14002500 20.54
29 8.98 0.0812 0.0000 0.0000 19037900 19.77
30 9.53 0.0812 0.0000 0.0000 10774600 20.60
31 10.54 0.1885 0.0000 0.0000 8960600 21.21
32 11.31 0.1885 0.0000 0.0000 7773300 21.30
33 10.36 0.1885 0.0000 0.0000 9579700 22.33
34 11.44 0.3628 0.0000 0.0000 11270700 21.12
35 10.45 0.3628 0.0000 0.0000 9492800 20.77
36 10.69 0.3628 0.0000 0.0000 9136800 22.11
37 11.28 0.2942 0.0000 0.0000 14487600 22.34
38 11.96 0.2942 0.0000 0.0000 10133200 21.43
39 13.52 0.2942 0.0000 0.0000 18659700 20.14
40 12.89 0.3036 0.0000 0.0000 15980700 21.11
41 14.03 0.3036 0.0000 0.0000 9732100 21.19
42 16.27 0.3036 0.0000 0.0000 14626300 23.07
43 16.17 0.3703 0.0000 0.0000 16904000 23.01
44 17.25 0.3703 0.0000 0.0000 13616700 22.12
45 19.38 0.3703 0.0000 0.0000 13772900 22.40
46 26.20 0.7398 0.0000 0.0000 28749200 22.66
47 33.53 0.7398 0.0000 0.0000 31408300 24.21
48 32.20 0.7398 0.0000 0.0000 26342800 24.13
49 38.45 0.6988 0.0000 0.0000 48909500 23.73
50 44.86 0.6988 0.0000 0.0000 41542400 22.79
51 41.67 0.6988 0.0000 0.0000 24857200 21.89
52 36.06 0.7478 0.0000 0.0000 34093700 22.92
53 39.76 0.7478 0.0000 0.0000 22555200 23.44
54 36.81 0.7478 0.0000 0.0000 19067500 22.57
55 42.65 0.5651 0.0000 0.0000 19029100 23.27
56 46.89 0.5651 0.0000 0.0000 15223200 24.95
57 53.61 0.5651 0.0000 0.0000 21903700 23.45
58 57.59 0.6473 0.0000 0.0000 33306600 23.42
59 67.82 0.6473 0.0000 0.0000 23898100 25.30
60 71.89 0.6473 0.0000 0.0000 23279600 23.90
61 75.51 0.3441 0.0000 0.0000 40699800 25.73
62 68.49 0.3441 0.0000 0.0000 37646000 24.64
63 62.72 0.3441 0.0000 0.0000 37277000 24.95
64 70.39 0.2415 0.0000 0.0000 39246800 22.15
65 59.77 0.2415 0.0000 0.0000 27418400 20.85
66 57.27 0.2415 0.0000 0.0000 30318700 21.45
67 67.96 0.3151 0.0000 0.0000 32808100 22.15
68 67.85 0.3151 0.0000 0.0000 28668200 23.75
69 76.98 0.3151 0.0000 0.0000 32370300 25.27
70 81.08 0.2390 0.0000 0.0000 24171100 26.53
71 91.66 0.2390 0.0000 0.0000 25009100 27.22
72 84.84 0.2390 0.0000 0.0000 32084300 27.69
73 85.73 0.2127 0.2127 0.0000 50117500 28.61
74 84.61 0.2127 0.2127 0.0000 27522200 26.21
75 92.91 0.2127 0.2127 0.0000 26816800 25.93
76 99.80 0.2730 0.2730 0.0000 25136100 27.86
77 121.19 0.2730 0.2730 0.0000 30295600 28.65
78 122.04 0.2730 0.2730 0.0000 41526100 27.51
79 131.76 0.3657 0.3657 0.0000 43845100 27.06
80 138.48 0.3657 0.3657 0.0000 39188900 26.91
81 153.47 0.3657 0.3657 0.0000 40496400 27.60
82 189.95 0.4643 0.4643 0.0000 37438400 34.48
83 182.22 0.4643 0.4643 0.0000 46553700 31.58
84 198.08 0.4643 0.4643 0.0000 31771400 33.46
85 135.36 0.5096 0.5096 0.0000 62108100 30.64
86 125.02 0.5096 0.5096 0.0000 46645400 25.66
87 143.50 0.5096 0.5096 0.0000 42313100 26.78
88 173.95 0.3592 0.3592 0.0000 38841700 26.91
89 188.75 0.3592 0.3592 0.0000 32650300 26.82
90 167.44 0.3592 0.3592 0.0000 34281100 26.05
91 158.95 0.7439 0.7439 0.0000 33096200 24.36
92 169.53 0.7439 0.7439 0.0000 23273800 25.94
93 113.66 0.7439 0.7439 0.0000 43697600 25.37
94 107.59 0.1390 0.1390 0.0000 66902300 21.23
95 92.67 0.1390 0.1390 0.0000 44957200 19.35
96 85.35 0.1390 0.1390 0.0000 33800900 18.61
97 90.13 0.1383 0.1383 0.0000 33487900 16.37
98 89.31 0.1383 0.1383 0.0000 27394900 15.56
99 105.12 0.1383 0.1383 0.0000 25963400 17.70
100 125.83 0.2874 0.2874 0.0000 20952600 19.52
101 135.81 0.2874 0.2874 0.0000 17702900 20.26
102 142.43 0.2874 0.2874 0.0000 21282100 23.05
103 163.39 0.0596 0.0596 0.0000 18449100 22.81
104 168.21 0.0596 0.0596 0.0000 14415700 24.04
105 185.35 0.0596 0.0596 0.0000 17906300 25.08
106 188.50 0.3201 0.3201 0.0000 22197500 27.04
107 199.91 0.3201 0.3201 0.0000 15856500 28.81
108 210.73 0.3201 0.3201 0.0000 19068700 29.86
109 192.06 0.4860 0.4860 0.0000 30855100 27.61
110 204.62 0.4860 0.4860 0.0000 21209000 28.22
111 235.00 0.4860 0.4860 0.0000 19541600 28.83
112 261.09 0.6129 0.6129 0.6129 21955000 30.06
113 256.88 0.6129 0.6129 0.6129 33725900 25.51
114 251.53 0.6129 0.6129 0.6129 28192800 22.75
115 257.25 0.6665 0.6665 0.6665 27377000 25.52
116 243.10 0.6665 0.6665 0.6665 16228100 23.33
117 283.75 0.6665 0.6665 0.6665 21278900 24.34
Consumentenvertrouwen t
1 115.7 1
2 109.2 2
3 116.9 3
4 109.9 4
5 116.1 5
6 118.9 6
7 116.3 7
8 114.0 8
9 97.0 9
10 85.3 10
11 84.9 11
12 94.6 12
13 97.8 13
14 95.0 14
15 110.7 15
16 108.5 16
17 110.3 17
18 106.3 18
19 97.4 19
20 94.5 20
21 93.7 21
22 79.6 22
23 84.9 23
24 80.7 24
25 78.8 25
26 64.8 26
27 61.4 27
28 81.0 28
29 83.6 29
30 83.5 30
31 77.0 31
32 81.7 32
33 77.0 33
34 81.7 34
35 92.5 35
36 91.7 36
37 96.4 37
38 88.5 38
39 88.5 39
40 93.0 40
41 93.1 41
42 102.8 42
43 105.7 43
44 98.7 44
45 96.7 45
46 92.9 46
47 92.6 47
48 102.7 48
49 105.1 49
50 104.4 50
51 103.0 51
52 97.5 52
53 103.1 53
54 106.2 54
55 103.6 55
56 105.5 56
57 87.5 57
58 85.2 58
59 98.3 59
60 103.8 60
61 106.8 61
62 102.7 62
63 107.5 63
64 109.8 64
65 104.7 65
66 105.7 66
67 107.0 67
68 100.2 68
69 105.9 69
70 105.1 70
71 105.3 71
72 110.0 72
73 110.2 73
74 111.2 74
75 108.2 75
76 106.3 76
77 108.5 77
78 105.3 78
79 111.9 79
80 105.6 80
81 99.5 81
82 95.2 82
83 87.8 83
84 90.6 84
85 87.9 85
86 76.4 86
87 65.9 87
88 62.3 88
89 57.2 89
90 50.4 90
91 51.9 91
92 58.5 92
93 61.4 93
94 38.8 94
95 44.9 95
96 38.6 96
97 4.0 97
98 25.3 98
99 26.9 99
100 40.8 100
101 54.8 101
102 49.3 102
103 47.4 103
104 54.5 104
105 53.4 105
106 48.7 106
107 50.6 107
108 53.6 108
109 56.5 109
110 46.4 110
111 52.3 111
112 57.7 112
113 62.7 113
114 54.3 114
115 51.0 115
116 53.2 116
117 48.6 117
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Omzetgroei Omzetgroei_iPhone
-1.274e+02 -3.127e+01 6.350e+01
Omzetgroei_iPad Volume Microsoft
1.052e+02 -3.079e-07 6.186e+00
Consumentenvertrouwen t
-2.546e-01 1.430e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-43.7520 -9.6857 -0.6614 9.3880 36.0196
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.274e+02 1.141e+01 -11.169 < 2e-16 ***
Omzetgroei -3.127e+01 6.349e+00 -4.926 3.01e-06 ***
Omzetgroei_iPhone 6.350e+01 1.218e+01 5.212 8.93e-07 ***
Omzetgroei_iPad 1.052e+02 1.229e+01 8.564 7.88e-14 ***
Volume -3.079e-07 1.496e-07 -2.058 0.0420 *
Microsoft 6.186e+00 5.943e-01 10.409 < 2e-16 ***
Consumentenvertrouwen -2.546e-01 1.011e-01 -2.519 0.0132 *
t 1.430e+00 8.554e-02 16.723 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.64 on 109 degrees of freedom
Multiple R-squared: 0.9651, Adjusted R-squared: 0.9629
F-statistic: 430.7 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,] 1.724217e-03 3.448434e-03 0.99827578
[2,] 1.590146e-04 3.180291e-04 0.99984099
[3,] 2.197811e-05 4.395623e-05 0.99997802
[4,] 1.807471e-06 3.614943e-06 0.99999819
[5,] 1.480959e-07 2.961917e-07 0.99999985
[6,] 1.624032e-08 3.248064e-08 0.99999998
[7,] 1.335238e-09 2.670475e-09 1.00000000
[8,] 3.134869e-09 6.269738e-09 1.00000000
[9,] 9.651494e-10 1.930299e-09 1.00000000
[10,] 1.372345e-10 2.744689e-10 1.00000000
[11,] 2.508934e-11 5.017868e-11 1.00000000
[12,] 2.807664e-12 5.615328e-12 1.00000000
[13,] 2.832810e-13 5.665620e-13 1.00000000
[14,] 2.697619e-14 5.395237e-14 1.00000000
[15,] 3.281260e-15 6.562520e-15 1.00000000
[16,] 1.314745e-15 2.629490e-15 1.00000000
[17,] 1.945474e-16 3.890948e-16 1.00000000
[18,] 3.565549e-17 7.131097e-17 1.00000000
[19,] 4.542234e-18 9.084467e-18 1.00000000
[20,] 6.633757e-19 1.326751e-18 1.00000000
[21,] 1.783271e-19 3.566542e-19 1.00000000
[22,] 5.166972e-20 1.033394e-19 1.00000000
[23,] 5.532819e-21 1.106564e-20 1.00000000
[24,] 6.298948e-22 1.259790e-21 1.00000000
[25,] 8.280067e-23 1.656013e-22 1.00000000
[26,] 9.133224e-24 1.826645e-23 1.00000000
[27,] 9.394420e-25 1.878884e-24 1.00000000
[28,] 1.159675e-25 2.319350e-25 1.00000000
[29,] 4.019456e-26 8.038912e-26 1.00000000
[30,] 4.231263e-27 8.462527e-27 1.00000000
[31,] 1.360310e-27 2.720619e-27 1.00000000
[32,] 3.321429e-28 6.642858e-28 1.00000000
[33,] 3.360454e-29 6.720908e-29 1.00000000
[34,] 2.085626e-29 4.171253e-29 1.00000000
[35,] 1.449231e-28 2.898461e-28 1.00000000
[36,] 5.680347e-27 1.136069e-26 1.00000000
[37,] 2.718299e-24 5.436598e-24 1.00000000
[38,] 8.204109e-24 1.640822e-23 1.00000000
[39,] 1.404022e-24 2.808045e-24 1.00000000
[40,] 2.923312e-22 5.846624e-22 1.00000000
[41,] 7.247649e-19 1.449530e-18 1.00000000
[42,] 1.749168e-19 3.498335e-19 1.00000000
[43,] 1.673593e-18 3.347187e-18 1.00000000
[44,] 2.121424e-18 4.242848e-18 1.00000000
[45,] 1.739388e-16 3.478776e-16 1.00000000
[46,] 2.310209e-14 4.620419e-14 1.00000000
[47,] 1.056059e-11 2.112119e-11 1.00000000
[48,] 1.222658e-10 2.445315e-10 1.00000000
[49,] 9.817431e-09 1.963486e-08 0.99999999
[50,] 4.132543e-07 8.265085e-07 0.99999959
[51,] 4.450904e-07 8.901808e-07 0.99999955
[52,] 2.564463e-07 5.128925e-07 0.99999974
[53,] 1.236837e-07 2.473674e-07 0.99999988
[54,] 1.852461e-07 3.704921e-07 0.99999981
[55,] 1.610493e-07 3.220986e-07 0.99999984
[56,] 1.007326e-07 2.014651e-07 0.99999990
[57,] 1.364428e-07 2.728856e-07 0.99999986
[58,] 8.002118e-08 1.600424e-07 0.99999992
[59,] 5.784132e-08 1.156826e-07 0.99999994
[60,] 3.568495e-08 7.136990e-08 0.99999996
[61,] 3.864856e-08 7.729712e-08 0.99999996
[62,] 1.735349e-08 3.470697e-08 0.99999998
[63,] 2.527767e-08 5.055534e-08 0.99999997
[64,] 5.049730e-08 1.009946e-07 0.99999995
[65,] 9.671224e-08 1.934245e-07 0.99999990
[66,] 1.338619e-06 2.677238e-06 0.99999866
[67,] 1.577410e-05 3.154820e-05 0.99998423
[68,] 2.462139e-05 4.924279e-05 0.99997538
[69,] 2.458230e-05 4.916461e-05 0.99997542
[70,] 3.408993e-05 6.817987e-05 0.99996591
[71,] 1.272392e-04 2.544785e-04 0.99987276
[72,] 4.900471e-04 9.800943e-04 0.99950995
[73,] 5.381760e-04 1.076352e-03 0.99946182
[74,] 1.106381e-03 2.212762e-03 0.99889362
[75,] 1.027384e-02 2.054769e-02 0.98972616
[76,] 1.313799e-02 2.627598e-02 0.98686201
[77,] 1.080925e-02 2.161849e-02 0.98919075
[78,] 2.268418e-02 4.536836e-02 0.97731582
[79,] 1.228613e-01 2.457225e-01 0.87713873
[80,] 1.989693e-01 3.979386e-01 0.80103072
[81,] 3.352822e-01 6.705643e-01 0.66471784
[82,] 9.199159e-01 1.601681e-01 0.08008405
[83,] 9.736292e-01 5.274151e-02 0.02637076
[84,] 9.724962e-01 5.500766e-02 0.02750383
[85,] 9.588669e-01 8.226622e-02 0.04113311
[86,] 9.503769e-01 9.924612e-02 0.04962306
[87,] 9.258641e-01 1.482719e-01 0.07413593
[88,] 8.883480e-01 2.233040e-01 0.11165201
[89,] 8.520412e-01 2.959177e-01 0.14795885
[90,] 7.946876e-01 4.106248e-01 0.20531239
[91,] 8.064044e-01 3.871911e-01 0.19359557
[92,] 7.659482e-01 4.681037e-01 0.23405184
[93,] 7.127243e-01 5.745514e-01 0.28727569
[94,] 6.047545e-01 7.904910e-01 0.39524551
[95,] 5.144890e-01 9.710221e-01 0.48551104
[96,] 4.088218e-01 8.176436e-01 0.59117820
> postscript(file="/var/www/html/rcomp/tmp/126xj1292318184.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cfw41292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cfw41292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cfw41292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55ow71292318184.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 6
14.3389080 11.4958787 25.8138285 -4.9037538 -13.5561701 -21.7829314
7 8 9 10 11 12
-9.8480999 8.5472795 17.5815460 14.7993826 -0.6614112 -4.6821332
13 14 15 16 17 18
-5.5865498 4.1558401 1.0098110 17.2928307 18.9900800 6.1148027
19 20 21 22 23 24
17.9308759 11.2489508 23.0910615 -2.6575904 -14.1426442 -2.5412946
25 26 27 28 29 30
4.3236452 -1.4392872 -6.9619078 -5.0786840 2.3362038 -6.2479380
31 32 33 34 35 36
-9.2997333 -9.6857140 -19.0782478 -4.7756299 -2.8284400 -12.6212817
37 38 39 40 41 42
-14.1857792 -12.6591536 -1.9248587 -9.3706126 -12.0541719 -18.8975203
43 44 45 46 47 48
-16.5313163 -14.1707303 -15.6643972 3.3151140 0.3687756 -0.8845787
49 50 51 52 53 54
12.6856029 21.0336059 16.4872712 6.0508294 2.9773642 3.6942348
55 56 57 58 59 60
-2.6137149 -10.8843583 1.1574406 9.3880329 6.9971821 19.5069782
61 62 63 64 65 66
7.0213030 3.3293592 -4.6801322 16.8633574 7.9144816 1.4199996
67 68 69 70 71 72
9.7484896 -4.6953326 -3.8072560 -14.0396855 -8.8494946 -16.6324221
73 74 75 76 77 78
-31.5912476 -25.9971399 -18.3765869 -27.8006086 -10.5793443 -1.4652750
79 80 81 82 83 84
9.0144304 12.1942696 20.3348724 7.6111194 17.3117418 16.2738935
85 86 87 88 89 90
-23.2405326 -11.8937984 -5.7797353 25.2980127 36.0196210 16.8129095
91 92 93 94 95 96
4.9640237 2.9964624 -43.7519742 -4.7563261 -14.6801281 -23.8917349
97 98 99 100 101 102
-15.5695847 -9.2617859 -8.1533415 -2.9412858 3.5949778 -8.7726378
103 104 105 106 107 108
18.2280562 14.5750649 24.6458139 5.9688921 3.5310644 8.1782028
109 110 111 112 113 114
1.0156260 2.8304356 28.9955399 -20.4072771 6.9949375 13.4452633
115 116 117
-7.8582494 -12.7637461 20.5917196
> postscript(file="/var/www/html/rcomp/tmp/65ow71292318184.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 14.3389080 NA
1 11.4958787 14.3389080
2 25.8138285 11.4958787
3 -4.9037538 25.8138285
4 -13.5561701 -4.9037538
5 -21.7829314 -13.5561701
6 -9.8480999 -21.7829314
7 8.5472795 -9.8480999
8 17.5815460 8.5472795
9 14.7993826 17.5815460
10 -0.6614112 14.7993826
11 -4.6821332 -0.6614112
12 -5.5865498 -4.6821332
13 4.1558401 -5.5865498
14 1.0098110 4.1558401
15 17.2928307 1.0098110
16 18.9900800 17.2928307
17 6.1148027 18.9900800
18 17.9308759 6.1148027
19 11.2489508 17.9308759
20 23.0910615 11.2489508
21 -2.6575904 23.0910615
22 -14.1426442 -2.6575904
23 -2.5412946 -14.1426442
24 4.3236452 -2.5412946
25 -1.4392872 4.3236452
26 -6.9619078 -1.4392872
27 -5.0786840 -6.9619078
28 2.3362038 -5.0786840
29 -6.2479380 2.3362038
30 -9.2997333 -6.2479380
31 -9.6857140 -9.2997333
32 -19.0782478 -9.6857140
33 -4.7756299 -19.0782478
34 -2.8284400 -4.7756299
35 -12.6212817 -2.8284400
36 -14.1857792 -12.6212817
37 -12.6591536 -14.1857792
38 -1.9248587 -12.6591536
39 -9.3706126 -1.9248587
40 -12.0541719 -9.3706126
41 -18.8975203 -12.0541719
42 -16.5313163 -18.8975203
43 -14.1707303 -16.5313163
44 -15.6643972 -14.1707303
45 3.3151140 -15.6643972
46 0.3687756 3.3151140
47 -0.8845787 0.3687756
48 12.6856029 -0.8845787
49 21.0336059 12.6856029
50 16.4872712 21.0336059
51 6.0508294 16.4872712
52 2.9773642 6.0508294
53 3.6942348 2.9773642
54 -2.6137149 3.6942348
55 -10.8843583 -2.6137149
56 1.1574406 -10.8843583
57 9.3880329 1.1574406
58 6.9971821 9.3880329
59 19.5069782 6.9971821
60 7.0213030 19.5069782
61 3.3293592 7.0213030
62 -4.6801322 3.3293592
63 16.8633574 -4.6801322
64 7.9144816 16.8633574
65 1.4199996 7.9144816
66 9.7484896 1.4199996
67 -4.6953326 9.7484896
68 -3.8072560 -4.6953326
69 -14.0396855 -3.8072560
70 -8.8494946 -14.0396855
71 -16.6324221 -8.8494946
72 -31.5912476 -16.6324221
73 -25.9971399 -31.5912476
74 -18.3765869 -25.9971399
75 -27.8006086 -18.3765869
76 -10.5793443 -27.8006086
77 -1.4652750 -10.5793443
78 9.0144304 -1.4652750
79 12.1942696 9.0144304
80 20.3348724 12.1942696
81 7.6111194 20.3348724
82 17.3117418 7.6111194
83 16.2738935 17.3117418
84 -23.2405326 16.2738935
85 -11.8937984 -23.2405326
86 -5.7797353 -11.8937984
87 25.2980127 -5.7797353
88 36.0196210 25.2980127
89 16.8129095 36.0196210
90 4.9640237 16.8129095
91 2.9964624 4.9640237
92 -43.7519742 2.9964624
93 -4.7563261 -43.7519742
94 -14.6801281 -4.7563261
95 -23.8917349 -14.6801281
96 -15.5695847 -23.8917349
97 -9.2617859 -15.5695847
98 -8.1533415 -9.2617859
99 -2.9412858 -8.1533415
100 3.5949778 -2.9412858
101 -8.7726378 3.5949778
102 18.2280562 -8.7726378
103 14.5750649 18.2280562
104 24.6458139 14.5750649
105 5.9688921 24.6458139
106 3.5310644 5.9688921
107 8.1782028 3.5310644
108 1.0156260 8.1782028
109 2.8304356 1.0156260
110 28.9955399 2.8304356
111 -20.4072771 28.9955399
112 6.9949375 -20.4072771
113 13.4452633 6.9949375
114 -7.8582494 13.4452633
115 -12.7637461 -7.8582494
116 20.5917196 -12.7637461
117 NA 20.5917196
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 11.4958787 14.3389080
[2,] 25.8138285 11.4958787
[3,] -4.9037538 25.8138285
[4,] -13.5561701 -4.9037538
[5,] -21.7829314 -13.5561701
[6,] -9.8480999 -21.7829314
[7,] 8.5472795 -9.8480999
[8,] 17.5815460 8.5472795
[9,] 14.7993826 17.5815460
[10,] -0.6614112 14.7993826
[11,] -4.6821332 -0.6614112
[12,] -5.5865498 -4.6821332
[13,] 4.1558401 -5.5865498
[14,] 1.0098110 4.1558401
[15,] 17.2928307 1.0098110
[16,] 18.9900800 17.2928307
[17,] 6.1148027 18.9900800
[18,] 17.9308759 6.1148027
[19,] 11.2489508 17.9308759
[20,] 23.0910615 11.2489508
[21,] -2.6575904 23.0910615
[22,] -14.1426442 -2.6575904
[23,] -2.5412946 -14.1426442
[24,] 4.3236452 -2.5412946
[25,] -1.4392872 4.3236452
[26,] -6.9619078 -1.4392872
[27,] -5.0786840 -6.9619078
[28,] 2.3362038 -5.0786840
[29,] -6.2479380 2.3362038
[30,] -9.2997333 -6.2479380
[31,] -9.6857140 -9.2997333
[32,] -19.0782478 -9.6857140
[33,] -4.7756299 -19.0782478
[34,] -2.8284400 -4.7756299
[35,] -12.6212817 -2.8284400
[36,] -14.1857792 -12.6212817
[37,] -12.6591536 -14.1857792
[38,] -1.9248587 -12.6591536
[39,] -9.3706126 -1.9248587
[40,] -12.0541719 -9.3706126
[41,] -18.8975203 -12.0541719
[42,] -16.5313163 -18.8975203
[43,] -14.1707303 -16.5313163
[44,] -15.6643972 -14.1707303
[45,] 3.3151140 -15.6643972
[46,] 0.3687756 3.3151140
[47,] -0.8845787 0.3687756
[48,] 12.6856029 -0.8845787
[49,] 21.0336059 12.6856029
[50,] 16.4872712 21.0336059
[51,] 6.0508294 16.4872712
[52,] 2.9773642 6.0508294
[53,] 3.6942348 2.9773642
[54,] -2.6137149 3.6942348
[55,] -10.8843583 -2.6137149
[56,] 1.1574406 -10.8843583
[57,] 9.3880329 1.1574406
[58,] 6.9971821 9.3880329
[59,] 19.5069782 6.9971821
[60,] 7.0213030 19.5069782
[61,] 3.3293592 7.0213030
[62,] -4.6801322 3.3293592
[63,] 16.8633574 -4.6801322
[64,] 7.9144816 16.8633574
[65,] 1.4199996 7.9144816
[66,] 9.7484896 1.4199996
[67,] -4.6953326 9.7484896
[68,] -3.8072560 -4.6953326
[69,] -14.0396855 -3.8072560
[70,] -8.8494946 -14.0396855
[71,] -16.6324221 -8.8494946
[72,] -31.5912476 -16.6324221
[73,] -25.9971399 -31.5912476
[74,] -18.3765869 -25.9971399
[75,] -27.8006086 -18.3765869
[76,] -10.5793443 -27.8006086
[77,] -1.4652750 -10.5793443
[78,] 9.0144304 -1.4652750
[79,] 12.1942696 9.0144304
[80,] 20.3348724 12.1942696
[81,] 7.6111194 20.3348724
[82,] 17.3117418 7.6111194
[83,] 16.2738935 17.3117418
[84,] -23.2405326 16.2738935
[85,] -11.8937984 -23.2405326
[86,] -5.7797353 -11.8937984
[87,] 25.2980127 -5.7797353
[88,] 36.0196210 25.2980127
[89,] 16.8129095 36.0196210
[90,] 4.9640237 16.8129095
[91,] 2.9964624 4.9640237
[92,] -43.7519742 2.9964624
[93,] -4.7563261 -43.7519742
[94,] -14.6801281 -4.7563261
[95,] -23.8917349 -14.6801281
[96,] -15.5695847 -23.8917349
[97,] -9.2617859 -15.5695847
[98,] -8.1533415 -9.2617859
[99,] -2.9412858 -8.1533415
[100,] 3.5949778 -2.9412858
[101,] -8.7726378 3.5949778
[102,] 18.2280562 -8.7726378
[103,] 14.5750649 18.2280562
[104,] 24.6458139 14.5750649
[105,] 5.9688921 24.6458139
[106,] 3.5310644 5.9688921
[107,] 8.1782028 3.5310644
[108,] 1.0156260 8.1782028
[109,] 2.8304356 1.0156260
[110,] 28.9955399 2.8304356
[111,] -20.4072771 28.9955399
[112,] 6.9949375 -20.4072771
[113,] 13.4452633 6.9949375
[114,] -7.8582494 13.4452633
[115,] -12.7637461 -7.8582494
[116,] 20.5917196 -12.7637461
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 11.4958787 14.3389080
2 25.8138285 11.4958787
3 -4.9037538 25.8138285
4 -13.5561701 -4.9037538
5 -21.7829314 -13.5561701
6 -9.8480999 -21.7829314
7 8.5472795 -9.8480999
8 17.5815460 8.5472795
9 14.7993826 17.5815460
10 -0.6614112 14.7993826
11 -4.6821332 -0.6614112
12 -5.5865498 -4.6821332
13 4.1558401 -5.5865498
14 1.0098110 4.1558401
15 17.2928307 1.0098110
16 18.9900800 17.2928307
17 6.1148027 18.9900800
18 17.9308759 6.1148027
19 11.2489508 17.9308759
20 23.0910615 11.2489508
21 -2.6575904 23.0910615
22 -14.1426442 -2.6575904
23 -2.5412946 -14.1426442
24 4.3236452 -2.5412946
25 -1.4392872 4.3236452
26 -6.9619078 -1.4392872
27 -5.0786840 -6.9619078
28 2.3362038 -5.0786840
29 -6.2479380 2.3362038
30 -9.2997333 -6.2479380
31 -9.6857140 -9.2997333
32 -19.0782478 -9.6857140
33 -4.7756299 -19.0782478
34 -2.8284400 -4.7756299
35 -12.6212817 -2.8284400
36 -14.1857792 -12.6212817
37 -12.6591536 -14.1857792
38 -1.9248587 -12.6591536
39 -9.3706126 -1.9248587
40 -12.0541719 -9.3706126
41 -18.8975203 -12.0541719
42 -16.5313163 -18.8975203
43 -14.1707303 -16.5313163
44 -15.6643972 -14.1707303
45 3.3151140 -15.6643972
46 0.3687756 3.3151140
47 -0.8845787 0.3687756
48 12.6856029 -0.8845787
49 21.0336059 12.6856029
50 16.4872712 21.0336059
51 6.0508294 16.4872712
52 2.9773642 6.0508294
53 3.6942348 2.9773642
54 -2.6137149 3.6942348
55 -10.8843583 -2.6137149
56 1.1574406 -10.8843583
57 9.3880329 1.1574406
58 6.9971821 9.3880329
59 19.5069782 6.9971821
60 7.0213030 19.5069782
61 3.3293592 7.0213030
62 -4.6801322 3.3293592
63 16.8633574 -4.6801322
64 7.9144816 16.8633574
65 1.4199996 7.9144816
66 9.7484896 1.4199996
67 -4.6953326 9.7484896
68 -3.8072560 -4.6953326
69 -14.0396855 -3.8072560
70 -8.8494946 -14.0396855
71 -16.6324221 -8.8494946
72 -31.5912476 -16.6324221
73 -25.9971399 -31.5912476
74 -18.3765869 -25.9971399
75 -27.8006086 -18.3765869
76 -10.5793443 -27.8006086
77 -1.4652750 -10.5793443
78 9.0144304 -1.4652750
79 12.1942696 9.0144304
80 20.3348724 12.1942696
81 7.6111194 20.3348724
82 17.3117418 7.6111194
83 16.2738935 17.3117418
84 -23.2405326 16.2738935
85 -11.8937984 -23.2405326
86 -5.7797353 -11.8937984
87 25.2980127 -5.7797353
88 36.0196210 25.2980127
89 16.8129095 36.0196210
90 4.9640237 16.8129095
91 2.9964624 4.9640237
92 -43.7519742 2.9964624
93 -4.7563261 -43.7519742
94 -14.6801281 -4.7563261
95 -23.8917349 -14.6801281
96 -15.5695847 -23.8917349
97 -9.2617859 -15.5695847
98 -8.1533415 -9.2617859
99 -2.9412858 -8.1533415
100 3.5949778 -2.9412858
101 -8.7726378 3.5949778
102 18.2280562 -8.7726378
103 14.5750649 18.2280562
104 24.6458139 14.5750649
105 5.9688921 24.6458139
106 3.5310644 5.9688921
107 8.1782028 3.5310644
108 1.0156260 8.1782028
109 2.8304356 1.0156260
110 28.9955399 2.8304356
111 -20.4072771 28.9955399
112 6.9949375 -20.4072771
113 13.4452633 6.9949375
114 -7.8582494 13.4452633
115 -12.7637461 -7.8582494
116 20.5917196 -12.7637461
> 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/7l02h1292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8l02h1292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/997cd1292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1097cd1292318184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u7bj1292318184.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/12x89p1292318184.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/1349oj1292318184.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/14x05l1292318184.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/1501mr1292318184.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/16eski1292318184.tab")
+ }
>
> try(system("convert tmp/126xj1292318184.ps tmp/126xj1292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cfw41292318184.ps tmp/2cfw41292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cfw41292318184.ps tmp/3cfw41292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cfw41292318184.ps tmp/4cfw41292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/55ow71292318184.ps tmp/55ow71292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/65ow71292318184.ps tmp/65ow71292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l02h1292318184.ps tmp/7l02h1292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l02h1292318184.ps tmp/8l02h1292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/997cd1292318184.ps tmp/997cd1292318184.png",intern=TRUE))
character(0)
> try(system("convert tmp/1097cd1292318184.ps tmp/1097cd1292318184.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.455 1.757 9.108