R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
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> x <- array(list(2350.44
+ ,10892.76
+ ,10540.05
+ ,10570
+ ,-4.9
+ ,-3
+ ,1.6
+ ,3.38
+ ,2440.25
+ ,10631.92
+ ,10601.61
+ ,10297
+ ,-4
+ ,-1
+ ,1.3
+ ,3.35
+ ,2408.64
+ ,11441.08
+ ,10323.73
+ ,10635
+ ,-3.1
+ ,-3
+ ,1.1
+ ,3.22
+ ,2472.81
+ ,11950.95
+ ,10418.4
+ ,10872
+ ,-1.3
+ ,-4
+ ,1.9
+ ,3.06
+ ,2407.6
+ ,11037.54
+ ,10092.96
+ ,10296
+ ,0
+ ,-6
+ ,2.6
+ ,3.17
+ ,2454.62
+ ,11527.72
+ ,10364.91
+ ,10383
+ ,-0.4
+ ,0
+ ,2.3
+ ,3.19
+ ,2448.05
+ ,11383.89
+ ,10152.09
+ ,10431
+ ,3
+ ,-4
+ ,2.4
+ ,3.35
+ ,2497.84
+ ,10989.34
+ ,10032.8
+ ,10574
+ ,0.4
+ ,-2
+ ,2.2
+ ,3.24
+ ,2645.64
+ ,11079.42
+ ,10204.59
+ ,10653
+ ,1.2
+ ,-2
+ ,2
+ ,3.23
+ ,2756.76
+ ,11028.93
+ ,10001.6
+ ,10805
+ ,0.6
+ ,-6
+ ,2.9
+ ,3.31
+ ,2849.27
+ ,10973
+ ,10411.75
+ ,10872
+ ,-1.3
+ ,-7
+ ,2.6
+ ,3.25
+ ,2921.44
+ ,11068.05
+ ,10673.38
+ ,10625
+ ,-3.2
+ ,-6
+ ,2.3
+ ,3.2
+ ,2981.85
+ ,11394.84
+ ,10539.51
+ ,10407
+ ,-1.8
+ ,-6
+ ,2.3
+ ,3.1
+ ,3080.58
+ ,11545.71
+ ,10723.78
+ ,10463
+ ,-3.6
+ ,-3
+ ,2.6
+ ,2.93
+ ,3106.22
+ ,11809.38
+ ,10682.06
+ ,10556
+ ,-4.2
+ ,-2
+ ,3.1
+ ,2.92
+ ,3119.31
+ ,11395.64
+ ,10283.19
+ ,10646
+ ,-6.9
+ ,-5
+ ,2.8
+ ,2.9
+ ,3061.26
+ ,11082.38
+ ,10377.18
+ ,10702
+ ,-8
+ ,-11
+ ,2.5
+ ,2.87
+ ,3097.31
+ ,11402.75
+ ,10486.64
+ ,11353
+ ,-7.5
+ ,-11
+ ,2.9
+ ,2.76
+ ,3161.69
+ ,11716.87
+ ,10545.38
+ ,11346
+ ,-8.2
+ ,-11
+ ,3.1
+ ,2.67
+ ,3257.16
+ ,12204.98
+ ,10554.27
+ ,11451
+ ,-7.6
+ ,-10
+ ,3.1
+ ,2.75
+ ,3277.01
+ ,12986.62
+ ,10532.54
+ ,11964
+ ,-3.7
+ ,-14
+ ,3.2
+ ,2.72
+ ,3295.32
+ ,13392.79
+ ,10324.31
+ ,12574
+ ,-1.7
+ ,-8
+ ,2.5
+ ,2.72
+ ,3363.99
+ ,14368.05
+ ,10695.25
+ ,13031
+ ,-0.7
+ ,-9
+ ,2.6
+ ,2.86
+ ,3494.17
+ ,15650.83
+ ,10827.81
+ ,13812
+ ,0.2
+ ,-5
+ ,2.9
+ ,2.99
+ ,3667.03
+ ,16102.64
+ ,10872.48
+ ,14544
+ ,0.6
+ ,-1
+ ,2.6
+ ,3.07
+ ,3813.06
+ ,16187.64
+ ,10971.19
+ ,14931
+ ,2.2
+ ,-2
+ ,2.4
+ ,2.96
+ ,3917.96
+ ,16311.54
+ ,11145.65
+ ,14886
+ ,3.3
+ ,-5
+ ,1.7
+ ,3.04
+ ,3895.51
+ ,17232.97
+ ,11234.68
+ ,16005
+ ,5.3
+ ,-4
+ ,2
+ ,3.3
+ ,3801.06
+ ,16397.83
+ ,11333.88
+ ,17064
+ ,5.5
+ ,-6
+ ,2.2
+ ,3.48
+ ,3570.12
+ ,14990.31
+ ,10997.97
+ ,15168
+ ,6.3
+ ,-2
+ ,1.9
+ ,3.46
+ ,3701.61
+ ,15147.55
+ ,11036.89
+ ,16050
+ ,7.7
+ ,-2
+ ,1.6
+ ,3.57
+ ,3862.27
+ ,15786.78
+ ,11257.35
+ ,15839
+ ,6.5
+ ,-2
+ ,1.6
+ ,3.6
+ ,3970.1
+ ,15934.09
+ ,11533.59
+ ,15137
+ ,5.5
+ ,-2
+ ,1.2
+ ,3.51
+ ,4138.52
+ ,16519.44
+ ,11963.12
+ ,14954
+ ,6.9
+ ,2
+ ,1.2
+ ,3.52
+ ,4199.75
+ ,16101.07
+ ,12185.15
+ ,15648
+ ,5.7
+ ,1
+ ,1.5
+ ,3.49
+ ,4290.89
+ ,16775.08
+ ,12377.62
+ ,15305
+ ,6.9
+ ,-8
+ ,1.6
+ ,3.5
+ ,4443.91
+ ,17286.32
+ ,12512.89
+ ,15579
+ ,6.1
+ ,-1
+ ,1.7
+ ,3.64
+ ,4502.64
+ ,17741.23
+ ,12631.48
+ ,16348
+ ,4.8
+ ,1
+ ,1.8
+ ,3.94
+ ,4356.98
+ ,17128.37
+ ,12268.53
+ ,15928
+ ,3.7
+ ,-1
+ ,1.8
+ ,3.94
+ ,4591.27
+ ,17460.53
+ ,12754.8
+ ,16171
+ ,5.8
+ ,2
+ ,1.8
+ ,3.91
+ ,4696.96
+ ,17611.14
+ ,13407.75
+ ,15937
+ ,6.8
+ ,2
+ ,1.3
+ ,3.88
+ ,4621.4
+ ,18001.37
+ ,13480.21
+ ,15713
+ ,8.5
+ ,1
+ ,1.3
+ ,4.21
+ ,4562.84
+ ,17974.77
+ ,13673.28
+ ,15594
+ ,7.2
+ ,-1
+ ,1.4
+ ,4.39
+ ,4202.52
+ ,16460.95
+ ,13239.71
+ ,15683
+ ,5
+ ,-2
+ ,1.1
+ ,4.33
+ ,4296.49
+ ,16235.39
+ ,13557.69
+ ,16438
+ ,4.7
+ ,-2
+ ,1.5
+ ,4.27
+ ,4435.23
+ ,16903.36
+ ,13901.28
+ ,17032
+ ,2.3
+ ,-1
+ ,2.2
+ ,4.29
+ ,4105.18
+ ,15543.76
+ ,13200.58
+ ,17696
+ ,2.4
+ ,-8
+ ,2.9
+ ,4.18
+ ,4116.68
+ ,15532.18
+ ,13406.97
+ ,17745
+ ,0.1
+ ,-4
+ ,3.1
+ ,4.14
+ ,3844.49
+ ,13731.31
+ ,12538.12
+ ,19394
+ ,1.9
+ ,-6
+ ,3.5
+ ,4.23
+ ,3720.98
+ ,13547.84
+ ,12419.57
+ ,20148
+ ,1.7
+ ,-3
+ ,3.6
+ ,4.07
+ ,3674.4
+ ,12602.93
+ ,12193.88
+ ,20108
+ ,2
+ ,-3
+ ,4.4
+ ,3.74
+ ,3857.62
+ ,13357.7
+ ,12656.63
+ ,18584
+ ,-1.9
+ ,-7
+ ,4.2
+ ,3.66
+ ,3801.06
+ ,13995.33
+ ,12812.48
+ ,18441
+ ,0.5
+ ,-9
+ ,5.2
+ ,3.92
+ ,3504.37
+ ,14084.6
+ ,12056.67
+ ,18391
+ ,-1.3
+ ,-11
+ ,5.8
+ ,4.45
+ ,3032.6
+ ,13168.91
+ ,11322.38
+ ,19178
+ ,-3.3
+ ,-13
+ ,5.9
+ ,4.92
+ ,3047.03
+ ,12989.35
+ ,11530.75
+ ,18079
+ ,-2.8
+ ,-11
+ ,5.4
+ ,4.9
+ ,2962.34
+ ,12123.53
+ ,11114.08
+ ,18483
+ ,-8
+ ,-9
+ ,5.5
+ ,4.54
+ ,2197.82
+ ,9117.03
+ ,9181.73
+ ,19644
+ ,-13.9
+ ,-17
+ ,4.7
+ ,4.53
+ ,2014.45
+ ,8531.45
+ ,8614.55
+ ,19195
+ ,-21.9
+ ,-22
+ ,3.1
+ ,4.14
+ ,1862.83
+ ,8460.94
+ ,8595.56
+ ,19650
+ ,-28.8
+ ,-25
+ ,2.6
+ ,4.05
+ ,1905.41
+ ,8331.49
+ ,8396.2
+ ,20830
+ ,-27.6
+ ,-20
+ ,2.3
+ ,3.92
+ ,1810.99
+ ,7694.78
+ ,7690.5
+ ,23595
+ ,-31.4
+ ,-24
+ ,1.9
+ ,3.68
+ ,1670.07
+ ,7764.58
+ ,7235.47
+ ,22937
+ ,-31.8
+ ,-24
+ ,0.6
+ ,3.35
+ ,1864.44
+ ,8767.96
+ ,7992.12
+ ,21814
+ ,-29.4
+ ,-22
+ ,0.6
+ ,3.38
+ ,2052.02
+ ,9304.43
+ ,8398.37
+ ,21928
+ ,-27.6
+ ,-19
+ ,-0.4
+ ,3.44
+ ,2029.6
+ ,9810.31
+ ,8593
+ ,21777
+ ,-23.6
+ ,-18
+ ,-1.1
+ ,3.5
+ ,2070.83
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,-22.8
+ ,-17
+ ,-1.7
+ ,3.54
+ ,2293.41
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,-18.2
+ ,-11
+ ,-0.8
+ ,3.52
+ ,2443.27
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
+ ,-1.2
+ ,3.53
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,2466.92
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,-8.8
+ ,-10
+ ,-0.1
+ ,3.37
+ ,2502.66
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36)
+ ,dim=c(8
+ ,72)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j')
+ ,1:72))
> y <- array(NA,dim=c(8,72),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j'),1:72))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly 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
BEL_20 Nikkei DJ_Indust Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1 2350.44 10892.76 10540.05 10570 -4.9 -3
2 2440.25 10631.92 10601.61 10297 -4.0 -1
3 2408.64 11441.08 10323.73 10635 -3.1 -3
4 2472.81 11950.95 10418.40 10872 -1.3 -4
5 2407.60 11037.54 10092.96 10296 0.0 -6
6 2454.62 11527.72 10364.91 10383 -0.4 0
7 2448.05 11383.89 10152.09 10431 3.0 -4
8 2497.84 10989.34 10032.80 10574 0.4 -2
9 2645.64 11079.42 10204.59 10653 1.2 -2
10 2756.76 11028.93 10001.60 10805 0.6 -6
11 2849.27 10973.00 10411.75 10872 -1.3 -7
12 2921.44 11068.05 10673.38 10625 -3.2 -6
13 2981.85 11394.84 10539.51 10407 -1.8 -6
14 3080.58 11545.71 10723.78 10463 -3.6 -3
15 3106.22 11809.38 10682.06 10556 -4.2 -2
16 3119.31 11395.64 10283.19 10646 -6.9 -5
17 3061.26 11082.38 10377.18 10702 -8.0 -11
18 3097.31 11402.75 10486.64 11353 -7.5 -11
19 3161.69 11716.87 10545.38 11346 -8.2 -11
20 3257.16 12204.98 10554.27 11451 -7.6 -10
21 3277.01 12986.62 10532.54 11964 -3.7 -14
22 3295.32 13392.79 10324.31 12574 -1.7 -8
23 3363.99 14368.05 10695.25 13031 -0.7 -9
24 3494.17 15650.83 10827.81 13812 0.2 -5
25 3667.03 16102.64 10872.48 14544 0.6 -1
26 3813.06 16187.64 10971.19 14931 2.2 -2
27 3917.96 16311.54 11145.65 14886 3.3 -5
28 3895.51 17232.97 11234.68 16005 5.3 -4
29 3801.06 16397.83 11333.88 17064 5.5 -6
30 3570.12 14990.31 10997.97 15168 6.3 -2
31 3701.61 15147.55 11036.89 16050 7.7 -2
32 3862.27 15786.78 11257.35 15839 6.5 -2
33 3970.10 15934.09 11533.59 15137 5.5 -2
34 4138.52 16519.44 11963.12 14954 6.9 2
35 4199.75 16101.07 12185.15 15648 5.7 1
36 4290.89 16775.08 12377.62 15305 6.9 -8
37 4443.91 17286.32 12512.89 15579 6.1 -1
38 4502.64 17741.23 12631.48 16348 4.8 1
39 4356.98 17128.37 12268.53 15928 3.7 -1
40 4591.27 17460.53 12754.80 16171 5.8 2
41 4696.96 17611.14 13407.75 15937 6.8 2
42 4621.40 18001.37 13480.21 15713 8.5 1
43 4562.84 17974.77 13673.28 15594 7.2 -1
44 4202.52 16460.95 13239.71 15683 5.0 -2
45 4296.49 16235.39 13557.69 16438 4.7 -2
46 4435.23 16903.36 13901.28 17032 2.3 -1
47 4105.18 15543.76 13200.58 17696 2.4 -8
48 4116.68 15532.18 13406.97 17745 0.1 -4
49 3844.49 13731.31 12538.12 19394 1.9 -6
50 3720.98 13547.84 12419.57 20148 1.7 -3
51 3674.40 12602.93 12193.88 20108 2.0 -3
52 3857.62 13357.70 12656.63 18584 -1.9 -7
53 3801.06 13995.33 12812.48 18441 0.5 -9
54 3504.37 14084.60 12056.67 18391 -1.3 -11
55 3032.60 13168.91 11322.38 19178 -3.3 -13
56 3047.03 12989.35 11530.75 18079 -2.8 -11
57 2962.34 12123.53 11114.08 18483 -8.0 -9
58 2197.82 9117.03 9181.73 19644 -13.9 -17
59 2014.45 8531.45 8614.55 19195 -21.9 -22
60 1862.83 8460.94 8595.56 19650 -28.8 -25
61 1905.41 8331.49 8396.20 20830 -27.6 -20
62 1810.99 7694.78 7690.50 23595 -31.4 -24
63 1670.07 7764.58 7235.47 22937 -31.8 -24
64 1864.44 8767.96 7992.12 21814 -29.4 -22
65 2052.02 9304.43 8398.37 21928 -27.6 -19
66 2029.60 9810.31 8593.00 21777 -23.6 -18
67 2070.83 9691.12 8679.75 21383 -22.8 -17
68 2293.41 10430.35 9374.63 21467 -18.2 -11
69 2443.27 10302.87 9634.97 22052 -17.8 -11
70 2513.17 10066.24 9857.34 22680 -14.2 -12
71 2466.92 9633.83 10238.83 24320 -8.8 -10
72 2502.66 10169.02 10433.44 24977 -7.9 -15
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 1.6 3.38 1 0 0 0 0 0 0 0 0 0
2 1.3 3.35 0 1 0 0 0 0 0 0 0 0
3 1.1 3.22 0 0 1 0 0 0 0 0 0 0
4 1.9 3.06 0 0 0 1 0 0 0 0 0 0
5 2.6 3.17 0 0 0 0 1 0 0 0 0 0
6 2.3 3.19 0 0 0 0 0 1 0 0 0 0
7 2.4 3.35 0 0 0 0 0 0 1 0 0 0
8 2.2 3.24 0 0 0 0 0 0 0 1 0 0
9 2.0 3.23 0 0 0 0 0 0 0 0 1 0
10 2.9 3.31 0 0 0 0 0 0 0 0 0 1
11 2.6 3.25 0 0 0 0 0 0 0 0 0 0
12 2.3 3.20 0 0 0 0 0 0 0 0 0 0
13 2.3 3.10 1 0 0 0 0 0 0 0 0 0
14 2.6 2.93 0 1 0 0 0 0 0 0 0 0
15 3.1 2.92 0 0 1 0 0 0 0 0 0 0
16 2.8 2.90 0 0 0 1 0 0 0 0 0 0
17 2.5 2.87 0 0 0 0 1 0 0 0 0 0
18 2.9 2.76 0 0 0 0 0 1 0 0 0 0
19 3.1 2.67 0 0 0 0 0 0 1 0 0 0
20 3.1 2.75 0 0 0 0 0 0 0 1 0 0
21 3.2 2.72 0 0 0 0 0 0 0 0 1 0
22 2.5 2.72 0 0 0 0 0 0 0 0 0 1
23 2.6 2.86 0 0 0 0 0 0 0 0 0 0
24 2.9 2.99 0 0 0 0 0 0 0 0 0 0
25 2.6 3.07 1 0 0 0 0 0 0 0 0 0
26 2.4 2.96 0 1 0 0 0 0 0 0 0 0
27 1.7 3.04 0 0 1 0 0 0 0 0 0 0
28 2.0 3.30 0 0 0 1 0 0 0 0 0 0
29 2.2 3.48 0 0 0 0 1 0 0 0 0 0
30 1.9 3.46 0 0 0 0 0 1 0 0 0 0
31 1.6 3.57 0 0 0 0 0 0 1 0 0 0
32 1.6 3.60 0 0 0 0 0 0 0 1 0 0
33 1.2 3.51 0 0 0 0 0 0 0 0 1 0
34 1.2 3.52 0 0 0 0 0 0 0 0 0 1
35 1.5 3.49 0 0 0 0 0 0 0 0 0 0
36 1.6 3.50 0 0 0 0 0 0 0 0 0 0
37 1.7 3.64 1 0 0 0 0 0 0 0 0 0
38 1.8 3.94 0 1 0 0 0 0 0 0 0 0
39 1.8 3.94 0 0 1 0 0 0 0 0 0 0
40 1.8 3.91 0 0 0 1 0 0 0 0 0 0
41 1.3 3.88 0 0 0 0 1 0 0 0 0 0
42 1.3 4.21 0 0 0 0 0 1 0 0 0 0
43 1.4 4.39 0 0 0 0 0 0 1 0 0 0
44 1.1 4.33 0 0 0 0 0 0 0 1 0 0
45 1.5 4.27 0 0 0 0 0 0 0 0 1 0
46 2.2 4.29 0 0 0 0 0 0 0 0 0 1
47 2.9 4.18 0 0 0 0 0 0 0 0 0 0
48 3.1 4.14 0 0 0 0 0 0 0 0 0 0
49 3.5 4.23 1 0 0 0 0 0 0 0 0 0
50 3.6 4.07 0 1 0 0 0 0 0 0 0 0
51 4.4 3.74 0 0 1 0 0 0 0 0 0 0
52 4.2 3.66 0 0 0 1 0 0 0 0 0 0
53 5.2 3.92 0 0 0 0 1 0 0 0 0 0
54 5.8 4.45 0 0 0 0 0 1 0 0 0 0
55 5.9 4.92 0 0 0 0 0 0 1 0 0 0
56 5.4 4.90 0 0 0 0 0 0 0 1 0 0
57 5.5 4.54 0 0 0 0 0 0 0 0 1 0
58 4.7 4.53 0 0 0 0 0 0 0 0 0 1
59 3.1 4.14 0 0 0 0 0 0 0 0 0 0
60 2.6 4.05 0 0 0 0 0 0 0 0 0 0
61 2.3 3.92 1 0 0 0 0 0 0 0 0 0
62 1.9 3.68 0 1 0 0 0 0 0 0 0 0
63 0.6 3.35 0 0 1 0 0 0 0 0 0 0
64 0.6 3.38 0 0 0 1 0 0 0 0 0 0
65 -0.4 3.44 0 0 0 0 1 0 0 0 0 0
66 -1.1 3.50 0 0 0 0 0 1 0 0 0 0
67 -1.7 3.54 0 0 0 0 0 0 1 0 0 0
68 -0.8 3.52 0 0 0 0 0 0 0 1 0 0
69 -1.2 3.53 0 0 0 0 0 0 0 0 1 0
70 -1.0 3.55 0 0 0 0 0 0 0 0 0 1
71 -0.1 3.37 0 0 0 0 0 0 0 0 0 0
72 0.3 3.36 0 0 0 0 0 0 0 0 0 0
M11
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 1
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 1
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 1
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 1
48 0
49 0
50 0
51 0
52 0
53 0
54 0
55 0
56 0
57 0
58 0
59 1
60 0
61 0
62 0
63 0
64 0
65 0
66 0
67 0
68 0
69 0
70 0
71 1
72 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-2.101e+03 1.924e-01 3.013e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.193e-02 -8.474e+00 -8.597e+00
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1
2.774e+01 -2.598e+02 1.114e+02
M2 M3 M4
1.472e+02 1.433e+02 7.859e+01
M5 M6 M7
6.885e+01 4.606e+01 7.760e+01
M8 M9 M10
9.783e+01 1.175e+02 1.870e+02
M11
1.261e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-375.868 -108.568 7.175 128.876 298.336
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.101e+03 3.104e+02 -6.770 1.06e-08 ***
Nikkei 1.924e-01 1.623e-02 11.856 < 2e-16 ***
DJ_Indust 3.013e-01 3.634e-02 8.291 3.85e-11 ***
Goudprijs 1.193e-02 9.028e-03 1.322 0.191979
Conjunct_Seizoenzuiver -8.474e+00 7.250e+00 -1.169 0.247708
Cons_vertrouw -8.597e+00 9.855e+00 -0.872 0.386953
Alg_consumptie_index_BE 2.774e+01 2.015e+01 1.377 0.174403
Gem_rente_kasbon_5j -2.598e+02 6.440e+01 -4.033 0.000177 ***
M1 1.114e+02 1.038e+02 1.074 0.287874
M2 1.472e+02 1.090e+02 1.351 0.182539
M3 1.433e+02 1.054e+02 1.359 0.179815
M4 7.859e+01 1.037e+02 0.758 0.451868
M5 6.885e+01 9.801e+01 0.702 0.485500
M6 4.606e+01 1.004e+02 0.459 0.648124
M7 7.760e+01 1.008e+02 0.770 0.444861
M8 9.783e+01 1.021e+02 0.958 0.342417
M9 1.175e+02 1.001e+02 1.175 0.245363
M10 1.870e+02 1.011e+02 1.850 0.069938 .
M11 1.261e+02 9.797e+01 1.287 0.203517
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 167.9 on 53 degrees of freedom
Multiple R-squared: 0.9706, Adjusted R-squared: 0.9606
F-statistic: 97.1 on 18 and 53 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,] 0.9690332 6.193351e-02 3.096676e-02
[2,] 0.9774720 4.505598e-02 2.252799e-02
[3,] 0.9721298 5.574042e-02 2.787021e-02
[4,] 0.9937559 1.248828e-02 6.244141e-03
[5,] 0.9956019 8.796225e-03 4.398112e-03
[6,] 0.9995436 9.128595e-04 4.564298e-04
[7,] 0.9998357 3.285746e-04 1.642873e-04
[8,] 0.9999758 4.832472e-05 2.416236e-05
[9,] 0.9999925 1.497552e-05 7.487762e-06
[10,] 0.9999817 3.665725e-05 1.832862e-05
[11,] 0.9999915 1.693510e-05 8.467548e-06
[12,] 0.9999891 2.172546e-05 1.086273e-05
[13,] 0.9999783 4.331699e-05 2.165849e-05
[14,] 0.9999491 1.017885e-04 5.089423e-05
[15,] 0.9998831 2.338893e-04 1.169446e-04
[16,] 0.9996842 6.315573e-04 3.157787e-04
[17,] 0.9996570 6.860725e-04 3.430362e-04
[18,] 0.9998175 3.649062e-04 1.824531e-04
[19,] 0.9994922 1.015600e-03 5.077998e-04
[20,] 0.9987538 2.492362e-03 1.246181e-03
[21,] 0.9975179 4.964155e-03 2.482077e-03
[22,] 0.9949854 1.002920e-02 5.014602e-03
[23,] 0.9929267 1.414653e-02 7.073263e-03
[24,] 0.9936192 1.276158e-02 6.380789e-03
[25,] 0.9939764 1.204722e-02 6.023612e-03
[26,] 0.9880801 2.383979e-02 1.191990e-02
[27,] 0.9703545 5.929099e-02 2.964550e-02
[28,] 0.9795840 4.083205e-02 2.041603e-02
[29,] 0.9630369 7.392622e-02 3.696311e-02
> postscript(file="/var/www/html/rcomp/tmp/1ncjq1291660717.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ncjq1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gm0b1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gm0b1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gm0b1291660717.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 = 72
Frequency = 1
1 2 3 4 5 6
-290.958459 -176.671594 -318.211285 -375.868397 -147.678759 -193.453949
7 8 9 10 11 12
-107.131017 4.712653 72.510390 139.571574 147.380789 239.352589
13 14 15 16 17 18
154.278498 90.078543 67.366266 298.335737 220.951642 141.962556
19 20 21 22 23 24
61.893177 73.740645 -87.996058 -73.899313 -215.761373 -188.073036
25 26 27 28 29 30
-168.882957 -127.403324 -70.758325 -161.196037 -102.010562 128.778374
31 32 33 34 35 36
224.986438 176.140160 140.321032 48.285723 140.829483 107.137909
37 38 39 40 41 42
93.336047 65.186393 129.168252 250.672361 157.766264 102.272967
43 44 45 46 47 48
-23.675645 -17.887532 -34.242535 -230.048947 -141.778719 -65.719120
49 50 51 52 53 54
149.608955 32.104918 134.323279 33.141193 -138.665186 -112.880303
55 56 57 58 59 60
-142.998655 -133.803669 -74.050783 139.582128 138.265892 32.864291
61 62 63 64 65 66
62.617917 116.705065 58.111812 -45.084857 9.636602 -66.679646
67 68 69 70 71 72
-13.074298 -102.902256 -16.542046 -23.491166 -68.936073 -125.562634
> postscript(file="/var/www/html/rcomp/tmp/69v0e1291660717.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 -290.958459 NA
1 -176.671594 -290.958459
2 -318.211285 -176.671594
3 -375.868397 -318.211285
4 -147.678759 -375.868397
5 -193.453949 -147.678759
6 -107.131017 -193.453949
7 4.712653 -107.131017
8 72.510390 4.712653
9 139.571574 72.510390
10 147.380789 139.571574
11 239.352589 147.380789
12 154.278498 239.352589
13 90.078543 154.278498
14 67.366266 90.078543
15 298.335737 67.366266
16 220.951642 298.335737
17 141.962556 220.951642
18 61.893177 141.962556
19 73.740645 61.893177
20 -87.996058 73.740645
21 -73.899313 -87.996058
22 -215.761373 -73.899313
23 -188.073036 -215.761373
24 -168.882957 -188.073036
25 -127.403324 -168.882957
26 -70.758325 -127.403324
27 -161.196037 -70.758325
28 -102.010562 -161.196037
29 128.778374 -102.010562
30 224.986438 128.778374
31 176.140160 224.986438
32 140.321032 176.140160
33 48.285723 140.321032
34 140.829483 48.285723
35 107.137909 140.829483
36 93.336047 107.137909
37 65.186393 93.336047
38 129.168252 65.186393
39 250.672361 129.168252
40 157.766264 250.672361
41 102.272967 157.766264
42 -23.675645 102.272967
43 -17.887532 -23.675645
44 -34.242535 -17.887532
45 -230.048947 -34.242535
46 -141.778719 -230.048947
47 -65.719120 -141.778719
48 149.608955 -65.719120
49 32.104918 149.608955
50 134.323279 32.104918
51 33.141193 134.323279
52 -138.665186 33.141193
53 -112.880303 -138.665186
54 -142.998655 -112.880303
55 -133.803669 -142.998655
56 -74.050783 -133.803669
57 139.582128 -74.050783
58 138.265892 139.582128
59 32.864291 138.265892
60 62.617917 32.864291
61 116.705065 62.617917
62 58.111812 116.705065
63 -45.084857 58.111812
64 9.636602 -45.084857
65 -66.679646 9.636602
66 -13.074298 -66.679646
67 -102.902256 -13.074298
68 -16.542046 -102.902256
69 -23.491166 -16.542046
70 -68.936073 -23.491166
71 -125.562634 -68.936073
72 NA -125.562634
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -176.671594 -290.958459
[2,] -318.211285 -176.671594
[3,] -375.868397 -318.211285
[4,] -147.678759 -375.868397
[5,] -193.453949 -147.678759
[6,] -107.131017 -193.453949
[7,] 4.712653 -107.131017
[8,] 72.510390 4.712653
[9,] 139.571574 72.510390
[10,] 147.380789 139.571574
[11,] 239.352589 147.380789
[12,] 154.278498 239.352589
[13,] 90.078543 154.278498
[14,] 67.366266 90.078543
[15,] 298.335737 67.366266
[16,] 220.951642 298.335737
[17,] 141.962556 220.951642
[18,] 61.893177 141.962556
[19,] 73.740645 61.893177
[20,] -87.996058 73.740645
[21,] -73.899313 -87.996058
[22,] -215.761373 -73.899313
[23,] -188.073036 -215.761373
[24,] -168.882957 -188.073036
[25,] -127.403324 -168.882957
[26,] -70.758325 -127.403324
[27,] -161.196037 -70.758325
[28,] -102.010562 -161.196037
[29,] 128.778374 -102.010562
[30,] 224.986438 128.778374
[31,] 176.140160 224.986438
[32,] 140.321032 176.140160
[33,] 48.285723 140.321032
[34,] 140.829483 48.285723
[35,] 107.137909 140.829483
[36,] 93.336047 107.137909
[37,] 65.186393 93.336047
[38,] 129.168252 65.186393
[39,] 250.672361 129.168252
[40,] 157.766264 250.672361
[41,] 102.272967 157.766264
[42,] -23.675645 102.272967
[43,] -17.887532 -23.675645
[44,] -34.242535 -17.887532
[45,] -230.048947 -34.242535
[46,] -141.778719 -230.048947
[47,] -65.719120 -141.778719
[48,] 149.608955 -65.719120
[49,] 32.104918 149.608955
[50,] 134.323279 32.104918
[51,] 33.141193 134.323279
[52,] -138.665186 33.141193
[53,] -112.880303 -138.665186
[54,] -142.998655 -112.880303
[55,] -133.803669 -142.998655
[56,] -74.050783 -133.803669
[57,] 139.582128 -74.050783
[58,] 138.265892 139.582128
[59,] 32.864291 138.265892
[60,] 62.617917 32.864291
[61,] 116.705065 62.617917
[62,] 58.111812 116.705065
[63,] -45.084857 58.111812
[64,] 9.636602 -45.084857
[65,] -66.679646 9.636602
[66,] -13.074298 -66.679646
[67,] -102.902256 -13.074298
[68,] -16.542046 -102.902256
[69,] -23.491166 -16.542046
[70,] -68.936073 -23.491166
[71,] -125.562634 -68.936073
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -176.671594 -290.958459
2 -318.211285 -176.671594
3 -375.868397 -318.211285
4 -147.678759 -375.868397
5 -193.453949 -147.678759
6 -107.131017 -193.453949
7 4.712653 -107.131017
8 72.510390 4.712653
9 139.571574 72.510390
10 147.380789 139.571574
11 239.352589 147.380789
12 154.278498 239.352589
13 90.078543 154.278498
14 67.366266 90.078543
15 298.335737 67.366266
16 220.951642 298.335737
17 141.962556 220.951642
18 61.893177 141.962556
19 73.740645 61.893177
20 -87.996058 73.740645
21 -73.899313 -87.996058
22 -215.761373 -73.899313
23 -188.073036 -215.761373
24 -168.882957 -188.073036
25 -127.403324 -168.882957
26 -70.758325 -127.403324
27 -161.196037 -70.758325
28 -102.010562 -161.196037
29 128.778374 -102.010562
30 224.986438 128.778374
31 176.140160 224.986438
32 140.321032 176.140160
33 48.285723 140.321032
34 140.829483 48.285723
35 107.137909 140.829483
36 93.336047 107.137909
37 65.186393 93.336047
38 129.168252 65.186393
39 250.672361 129.168252
40 157.766264 250.672361
41 102.272967 157.766264
42 -23.675645 102.272967
43 -17.887532 -23.675645
44 -34.242535 -17.887532
45 -230.048947 -34.242535
46 -141.778719 -230.048947
47 -65.719120 -141.778719
48 149.608955 -65.719120
49 32.104918 149.608955
50 134.323279 32.104918
51 33.141193 134.323279
52 -138.665186 33.141193
53 -112.880303 -138.665186
54 -142.998655 -112.880303
55 -133.803669 -142.998655
56 -74.050783 -133.803669
57 139.582128 -74.050783
58 138.265892 139.582128
59 32.864291 138.265892
60 62.617917 32.864291
61 116.705065 62.617917
62 58.111812 116.705065
63 -45.084857 58.111812
64 9.636602 -45.084857
65 -66.679646 9.636602
66 -13.074298 -66.679646
67 -102.902256 -13.074298
68 -16.542046 -102.902256
69 -23.491166 -16.542046
70 -68.936073 -23.491166
71 -125.562634 -68.936073
> 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/79v0e1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/814zz1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/914zz1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10uvyk1291660717.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xwxq1291660717.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/121wve1291660717.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/13xobn1291660717.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/14i7rs1291660717.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/15tgrd1291660717.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/1678641291660717.tab")
+ }
>
> try(system("convert tmp/1ncjq1291660717.ps tmp/1ncjq1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ncjq1291660717.ps tmp/2ncjq1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gm0b1291660717.ps tmp/3gm0b1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gm0b1291660717.ps tmp/4gm0b1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gm0b1291660717.ps tmp/5gm0b1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/69v0e1291660717.ps tmp/69v0e1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/79v0e1291660717.ps tmp/79v0e1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/814zz1291660717.ps tmp/814zz1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/914zz1291660717.ps tmp/914zz1291660717.png",intern=TRUE))
character(0)
> try(system("convert tmp/10uvyk1291660717.ps tmp/10uvyk1291660717.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.710 1.811 7.035