R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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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 = '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
> 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 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-204.72246 0.17740 0.22014
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
-0.07931 8.83568 -8.23573
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1
18.53284 -280.15537 161.09041
M2 M3 M4
247.44557 189.27261 111.72679
M5 M6 M7
83.06017 -3.16898 12.86255
M8 M9 M10
4.30907 35.70057 112.03753
M11 t
84.53766 22.31861
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-274.22 -94.18 11.01 95.21 273.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -204.72246 567.98052 -0.360 0.719979
Nikkei 0.17740 0.01500 11.827 2.32e-16 ***
DJ_Indust 0.22014 0.03873 5.683 6.06e-07 ***
Goudprijs -0.07931 0.02519 -3.149 0.002713 **
Conjunct_Seizoenzuiver 8.83568 7.89396 1.119 0.268158
Cons_vertrouw -8.23573 8.79061 -0.937 0.353153
Alg_consumptie_index_BE 18.53284 18.13143 1.022 0.311447
Gem_rente_kasbon_5j -280.15537 57.69116 -4.856 1.14e-05 ***
M1 161.09041 93.47611 1.723 0.090771 .
M2 247.44557 100.67728 2.458 0.017348 *
M3 189.27261 94.80828 1.996 0.051142 .
M4 111.72679 92.89767 1.203 0.234545
M5 83.06017 87.50212 0.949 0.346892
M6 -3.16898 90.43885 -0.035 0.972182
M7 12.86255 91.49801 0.141 0.888747
M8 4.30907 94.31335 0.046 0.963733
M9 35.70057 91.77300 0.389 0.698858
M10 112.03753 92.27560 1.214 0.230171
M11 84.53766 88.05574 0.960 0.341474
t 22.31861 5.83697 3.824 0.000354 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 149.8 on 52 degrees of freedom
Multiple R-squared: 0.977, Adjusted R-squared: 0.9686
F-statistic: 116.4 on 19 and 52 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,] 0.01990774 0.03981548 0.98009226
[2,] 0.02874061 0.05748122 0.97125939
[3,] 0.02619620 0.05239240 0.97380380
[4,] 0.02105503 0.04211006 0.97894497
[5,] 0.01306470 0.02612940 0.98693530
[6,] 0.09850107 0.19700214 0.90149893
[7,] 0.37792728 0.75585456 0.62207272
[8,] 0.89464735 0.21070529 0.10535265
[9,] 0.86114190 0.27771621 0.13885810
[10,] 0.81181131 0.37637738 0.18818869
[11,] 0.74115388 0.51769223 0.25884612
[12,] 0.75644276 0.48711448 0.24355724
[13,] 0.70969293 0.58061414 0.29030707
[14,] 0.62791585 0.74416830 0.37208415
[15,] 0.59267116 0.81465768 0.40732884
[16,] 0.63387046 0.73225909 0.36612954
[17,] 0.69713869 0.60572263 0.30286131
[18,] 0.60861731 0.78276539 0.39138269
[19,] 0.53663904 0.92672191 0.46336096
[20,] 0.51331934 0.97336132 0.48668066
[21,] 0.53523089 0.92953822 0.46476911
[22,] 0.86084784 0.27830431 0.13915216
[23,] 0.90797838 0.18404325 0.09202162
[24,] 0.91036257 0.17927487 0.08963743
[25,] 0.94523889 0.10952221 0.05476111
[26,] 0.89200170 0.21599661 0.10799830
[27,] 0.77609566 0.44780867 0.22390434
> postscript(file="/var/www/rcomp/tmp/1gmrt1291659512.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qwqx1291659512.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3qwqx1291659512.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4qwqx1291659512.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5jnph1291659512.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
-106.754132 -108.874487 -217.331781 -274.220732 -155.197680 -120.081388
7 8 9 10 11 12
-108.840868 47.113505 87.506935 143.761960 163.700101 220.616728
13 14 15 16 17 18
11.440023 -73.967272 -41.218529 194.548744 139.629519 167.643614
19 20 21 22 23 24
101.726504 128.557920 -76.154576 -89.618078 -213.895414 -160.450235
25 26 27 28 29 30
-145.546887 -163.789528 -86.031283 -89.720191 60.968186 93.036742
31 32 33 34 35 36
243.674531 230.909638 133.442064 13.667550 148.864693 29.303036
37 38 39 40 41 42
2.259727 16.650223 55.403735 195.967165 111.011575 65.612572
43 44 45 46 47 48
-34.930257 -38.003685 10.578507 -74.291202 -53.372655 19.203857
49 50 51 52 53 54
190.562014 56.621387 150.106449 14.724655 -177.623604 -127.023533
55 56 57 58 59 60
-119.612813 -204.412291 -105.738752 80.184045 45.025709 28.712268
61 62 63 64 65 66
48.039255 273.359676 139.071409 -41.299641 21.212003 -79.188006
67 68 69 70 71 72
-82.017098 -164.165086 -49.634178 -73.704275 -90.322435 -137.385653
> postscript(file="/var/www/rcomp/tmp/6jnph1291659512.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 -106.754132 NA
1 -108.874487 -106.754132
2 -217.331781 -108.874487
3 -274.220732 -217.331781
4 -155.197680 -274.220732
5 -120.081388 -155.197680
6 -108.840868 -120.081388
7 47.113505 -108.840868
8 87.506935 47.113505
9 143.761960 87.506935
10 163.700101 143.761960
11 220.616728 163.700101
12 11.440023 220.616728
13 -73.967272 11.440023
14 -41.218529 -73.967272
15 194.548744 -41.218529
16 139.629519 194.548744
17 167.643614 139.629519
18 101.726504 167.643614
19 128.557920 101.726504
20 -76.154576 128.557920
21 -89.618078 -76.154576
22 -213.895414 -89.618078
23 -160.450235 -213.895414
24 -145.546887 -160.450235
25 -163.789528 -145.546887
26 -86.031283 -163.789528
27 -89.720191 -86.031283
28 60.968186 -89.720191
29 93.036742 60.968186
30 243.674531 93.036742
31 230.909638 243.674531
32 133.442064 230.909638
33 13.667550 133.442064
34 148.864693 13.667550
35 29.303036 148.864693
36 2.259727 29.303036
37 16.650223 2.259727
38 55.403735 16.650223
39 195.967165 55.403735
40 111.011575 195.967165
41 65.612572 111.011575
42 -34.930257 65.612572
43 -38.003685 -34.930257
44 10.578507 -38.003685
45 -74.291202 10.578507
46 -53.372655 -74.291202
47 19.203857 -53.372655
48 190.562014 19.203857
49 56.621387 190.562014
50 150.106449 56.621387
51 14.724655 150.106449
52 -177.623604 14.724655
53 -127.023533 -177.623604
54 -119.612813 -127.023533
55 -204.412291 -119.612813
56 -105.738752 -204.412291
57 80.184045 -105.738752
58 45.025709 80.184045
59 28.712268 45.025709
60 48.039255 28.712268
61 273.359676 48.039255
62 139.071409 273.359676
63 -41.299641 139.071409
64 21.212003 -41.299641
65 -79.188006 21.212003
66 -82.017098 -79.188006
67 -164.165086 -82.017098
68 -49.634178 -164.165086
69 -73.704275 -49.634178
70 -90.322435 -73.704275
71 -137.385653 -90.322435
72 NA -137.385653
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -108.874487 -106.754132
[2,] -217.331781 -108.874487
[3,] -274.220732 -217.331781
[4,] -155.197680 -274.220732
[5,] -120.081388 -155.197680
[6,] -108.840868 -120.081388
[7,] 47.113505 -108.840868
[8,] 87.506935 47.113505
[9,] 143.761960 87.506935
[10,] 163.700101 143.761960
[11,] 220.616728 163.700101
[12,] 11.440023 220.616728
[13,] -73.967272 11.440023
[14,] -41.218529 -73.967272
[15,] 194.548744 -41.218529
[16,] 139.629519 194.548744
[17,] 167.643614 139.629519
[18,] 101.726504 167.643614
[19,] 128.557920 101.726504
[20,] -76.154576 128.557920
[21,] -89.618078 -76.154576
[22,] -213.895414 -89.618078
[23,] -160.450235 -213.895414
[24,] -145.546887 -160.450235
[25,] -163.789528 -145.546887
[26,] -86.031283 -163.789528
[27,] -89.720191 -86.031283
[28,] 60.968186 -89.720191
[29,] 93.036742 60.968186
[30,] 243.674531 93.036742
[31,] 230.909638 243.674531
[32,] 133.442064 230.909638
[33,] 13.667550 133.442064
[34,] 148.864693 13.667550
[35,] 29.303036 148.864693
[36,] 2.259727 29.303036
[37,] 16.650223 2.259727
[38,] 55.403735 16.650223
[39,] 195.967165 55.403735
[40,] 111.011575 195.967165
[41,] 65.612572 111.011575
[42,] -34.930257 65.612572
[43,] -38.003685 -34.930257
[44,] 10.578507 -38.003685
[45,] -74.291202 10.578507
[46,] -53.372655 -74.291202
[47,] 19.203857 -53.372655
[48,] 190.562014 19.203857
[49,] 56.621387 190.562014
[50,] 150.106449 56.621387
[51,] 14.724655 150.106449
[52,] -177.623604 14.724655
[53,] -127.023533 -177.623604
[54,] -119.612813 -127.023533
[55,] -204.412291 -119.612813
[56,] -105.738752 -204.412291
[57,] 80.184045 -105.738752
[58,] 45.025709 80.184045
[59,] 28.712268 45.025709
[60,] 48.039255 28.712268
[61,] 273.359676 48.039255
[62,] 139.071409 273.359676
[63,] -41.299641 139.071409
[64,] 21.212003 -41.299641
[65,] -79.188006 21.212003
[66,] -82.017098 -79.188006
[67,] -164.165086 -82.017098
[68,] -49.634178 -164.165086
[69,] -73.704275 -49.634178
[70,] -90.322435 -73.704275
[71,] -137.385653 -90.322435
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -108.874487 -106.754132
2 -217.331781 -108.874487
3 -274.220732 -217.331781
4 -155.197680 -274.220732
5 -120.081388 -155.197680
6 -108.840868 -120.081388
7 47.113505 -108.840868
8 87.506935 47.113505
9 143.761960 87.506935
10 163.700101 143.761960
11 220.616728 163.700101
12 11.440023 220.616728
13 -73.967272 11.440023
14 -41.218529 -73.967272
15 194.548744 -41.218529
16 139.629519 194.548744
17 167.643614 139.629519
18 101.726504 167.643614
19 128.557920 101.726504
20 -76.154576 128.557920
21 -89.618078 -76.154576
22 -213.895414 -89.618078
23 -160.450235 -213.895414
24 -145.546887 -160.450235
25 -163.789528 -145.546887
26 -86.031283 -163.789528
27 -89.720191 -86.031283
28 60.968186 -89.720191
29 93.036742 60.968186
30 243.674531 93.036742
31 230.909638 243.674531
32 133.442064 230.909638
33 13.667550 133.442064
34 148.864693 13.667550
35 29.303036 148.864693
36 2.259727 29.303036
37 16.650223 2.259727
38 55.403735 16.650223
39 195.967165 55.403735
40 111.011575 195.967165
41 65.612572 111.011575
42 -34.930257 65.612572
43 -38.003685 -34.930257
44 10.578507 -38.003685
45 -74.291202 10.578507
46 -53.372655 -74.291202
47 19.203857 -53.372655
48 190.562014 19.203857
49 56.621387 190.562014
50 150.106449 56.621387
51 14.724655 150.106449
52 -177.623604 14.724655
53 -127.023533 -177.623604
54 -119.612813 -127.023533
55 -204.412291 -119.612813
56 -105.738752 -204.412291
57 80.184045 -105.738752
58 45.025709 80.184045
59 28.712268 45.025709
60 48.039255 28.712268
61 273.359676 48.039255
62 139.071409 273.359676
63 -41.299641 139.071409
64 21.212003 -41.299641
65 -79.188006 21.212003
66 -82.017098 -79.188006
67 -164.165086 -82.017098
68 -49.634178 -164.165086
69 -73.704275 -49.634178
70 -90.322435 -73.704275
71 -137.385653 -90.322435
> 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/rcomp/tmp/7uwo21291659512.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8uwo21291659512.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9xx801291659513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10xx801291659513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/110ypo1291659513.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/rcomp/tmp/12mg5u1291659513.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/rcomp/tmp/13t0lo1291659513.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/rcomp/tmp/143r2r1291659513.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/rcomp/tmp/15pr0e1291659513.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/rcomp/tmp/16l1g51291659513.tab")
+ }
>
> try(system("convert tmp/1gmrt1291659512.ps tmp/1gmrt1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qwqx1291659512.ps tmp/2qwqx1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qwqx1291659512.ps tmp/3qwqx1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qwqx1291659512.ps tmp/4qwqx1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jnph1291659512.ps tmp/5jnph1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jnph1291659512.ps tmp/6jnph1291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uwo21291659512.ps tmp/7uwo21291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uwo21291659512.ps tmp/8uwo21291659512.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xx801291659513.ps tmp/9xx801291659513.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xx801291659513.ps tmp/10xx801291659513.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.350 1.610 4.936