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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> x <- array(list(2502.66
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36
+ ,12
+ ,2466.92
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,-8.8
+ ,-10
+ ,-0.1
+ ,3.37
+ ,11
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,10
+ ,2443.27
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
+ ,-1.2
+ ,3.53
+ ,09
+ ,2293.41
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,-18.2
+ ,-11
+ ,-0.8
+ ,3.52
+ ,08
+ ,2070.83
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,-22.8
+ ,-17
+ ,-1.7
+ ,3.54
+ ,07
+ ,2029.6
+ ,9810.31
+ ,8593
+ ,21777
+ ,-23.6
+ ,-18
+ ,-1.1
+ ,3.5
+ ,06
+ ,2052.02
+ ,9304.43
+ ,8398.37
+ ,21928
+ ,-27.6
+ ,-19
+ ,-0.4
+ ,3.44
+ ,05
+ ,1864.44
+ ,8767.96
+ ,7992.12
+ ,21814
+ ,-29.4
+ ,-22
+ ,0.6
+ ,3.38
+ ,04
+ ,1670.07
+ ,7764.58
+ ,7235.47
+ ,22937
+ ,-31.8
+ ,-24
+ ,0.6
+ ,3.35
+ ,03
+ ,1810.99
+ ,7694.78
+ ,7690.5
+ ,23595
+ ,-31.4
+ ,-24
+ ,1.9
+ ,3.68
+ ,02
+ ,1905.41
+ ,8331.49
+ ,8396.2
+ ,20830
+ ,-27.6
+ ,-20
+ ,2.3
+ ,3.92
+ ,01
+ ,1862.83
+ ,8460.94
+ ,8595.56
+ ,19650
+ ,-28.8
+ ,-25
+ ,2.6
+ ,4.05
+ ,12
+ ,2014.45
+ ,8531.45
+ ,8614.55
+ ,19195
+ ,-21.9
+ ,-22
+ ,3.1
+ ,4.14
+ ,11
+ ,2197.82
+ ,9117.03
+ ,9181.73
+ ,19644
+ ,-13.9
+ ,-17
+ ,4.7
+ ,4.53
+ ,10
+ ,2962.34
+ ,12123.53
+ ,11114.08
+ ,18483
+ ,-8
+ ,-9
+ ,5.5
+ ,4.54
+ ,09
+ ,3047.03
+ ,12989.35
+ ,11530.75
+ ,18079
+ ,-2.8
+ ,-11
+ ,5.4
+ ,4.9
+ ,08
+ ,3032.6
+ ,13168.91
+ ,11322.38
+ ,19178
+ ,-3.3
+ ,-13
+ ,5.9
+ ,4.92
+ ,07
+ ,3504.37
+ ,14084.6
+ ,12056.67
+ ,18391
+ ,-1.3
+ ,-11
+ ,5.8
+ ,4.45
+ ,06
+ ,3801.06
+ ,13995.33
+ ,12812.48
+ ,18441
+ ,0.5
+ ,-9
+ ,5.2
+ ,3.92
+ ,05
+ ,3857.62
+ ,13357.7
+ ,12656.63
+ ,18584
+ ,-1.9
+ ,-7
+ ,4.2
+ ,3.66
+ ,04
+ ,3674.4
+ ,12602.93
+ ,12193.88
+ ,20108
+ ,2
+ ,-3
+ ,4.4
+ ,3.74
+ ,03
+ ,3720.98
+ ,13547.84
+ ,12419.57
+ ,20148
+ ,1.7
+ ,-3
+ ,3.6
+ ,4.07
+ ,02
+ ,3844.49
+ ,13731.31
+ ,12538.12
+ ,19394
+ ,1.9
+ ,-6
+ ,3.5
+ ,4.23
+ ,01
+ ,4116.68
+ ,15532.18
+ ,13406.97
+ ,17745
+ ,0.1
+ ,-4
+ ,3.1
+ ,4.14
+ ,12
+ ,4105.18
+ ,15543.76
+ ,13200.58
+ ,17696
+ ,2.4
+ ,-8
+ ,2.9
+ ,4.18
+ ,11
+ ,4435.23
+ ,16903.36
+ ,13901.28
+ ,17032
+ ,2.3
+ ,-1
+ ,2.2
+ ,4.29
+ ,10
+ ,4296.49
+ ,16235.39
+ ,13557.69
+ ,16438
+ ,4.7
+ ,-2
+ ,1.5
+ ,4.27
+ ,09
+ ,4202.52
+ ,16460.95
+ ,13239.71
+ ,15683
+ ,5
+ ,-2
+ ,1.1
+ ,4.33
+ ,08
+ ,4562.84
+ ,17974.77
+ ,13673.28
+ ,15594
+ ,7.2
+ ,-1
+ ,1.4
+ ,4.39
+ ,07
+ ,4621.4
+ ,18001.37
+ ,13480.21
+ ,15713
+ ,8.5
+ ,1
+ ,1.3
+ ,4.21
+ ,06
+ ,4696.96
+ ,17611.14
+ ,13407.75
+ ,15937
+ ,6.8
+ ,2
+ ,1.3
+ ,3.88
+ ,05
+ ,4591.27
+ ,17460.53
+ ,12754.8
+ ,16171
+ ,5.8
+ ,2
+ ,1.8
+ ,3.91
+ ,04
+ ,4356.98
+ ,17128.37
+ ,12268.53
+ ,15928
+ ,3.7
+ ,-1
+ ,1.8
+ ,3.94
+ ,03
+ ,4502.64
+ ,17741.23
+ ,12631.48
+ ,16348
+ ,4.8
+ ,1
+ ,1.8
+ ,3.94
+ ,02
+ ,4443.91
+ ,17286.32
+ ,12512.89
+ ,15579
+ ,6.1
+ ,-1
+ ,1.7
+ ,3.64
+ ,01
+ ,4290.89
+ ,16775.08
+ ,12377.62
+ ,15305
+ ,6.9
+ ,-8
+ ,1.6
+ ,3.5
+ ,12
+ ,4199.75
+ ,16101.07
+ ,12185.15
+ ,15648
+ ,5.7
+ ,1
+ ,1.5
+ ,3.49
+ ,11
+ ,4138.52
+ ,16519.44
+ ,11963.12
+ ,14954
+ ,6.9
+ ,2
+ ,1.2
+ ,3.52
+ ,10
+ ,3970.1
+ ,15934.09
+ ,11533.59
+ ,15137
+ ,5.5
+ ,-2
+ ,1.2
+ ,3.51
+ ,09
+ ,3862.27
+ ,15786.78
+ ,11257.35
+ ,15839
+ ,6.5
+ ,-2
+ ,1.6
+ ,3.6
+ ,08
+ ,3701.61
+ ,15147.55
+ ,11036.89
+ ,16050
+ ,7.7
+ ,-2
+ ,1.6
+ ,3.57
+ ,07
+ ,3570.12
+ ,14990.31
+ ,10997.97
+ ,15168
+ ,6.3
+ ,-2
+ ,1.9
+ ,3.46
+ ,06
+ ,3801.06
+ ,16397.83
+ ,11333.88
+ ,17064
+ ,5.5
+ ,-6
+ ,2.2
+ ,3.48
+ ,05
+ ,3895.51
+ ,17232.97
+ ,11234.68
+ ,16005
+ ,5.3
+ ,-4
+ ,2
+ ,3.3
+ ,04
+ ,3917.96
+ ,16311.54
+ ,11145.65
+ ,14886
+ ,3.3
+ ,-5
+ ,1.7
+ ,3.04
+ ,03
+ ,3813.06
+ ,16187.64
+ ,10971.19
+ ,14931
+ ,2.2
+ ,-2
+ ,2.4
+ ,2.96
+ ,02
+ ,3667.03
+ ,16102.64
+ ,10872.48
+ ,14544
+ ,0.6
+ ,-1
+ ,2.6
+ ,3.07
+ ,01
+ ,3494.17
+ ,15650.83
+ ,10827.81
+ ,13812
+ ,0.2
+ ,-5
+ ,2.9
+ ,2.99
+ ,12
+ ,3363.99
+ ,14368.05
+ ,10695.25
+ ,13031
+ ,-0.7
+ ,-9
+ ,2.6
+ ,2.86
+ ,11
+ ,3295.32
+ ,13392.79
+ ,10324.31
+ ,12574
+ ,-1.7
+ ,-8
+ ,2.5
+ ,2.72
+ ,10
+ ,3277.01
+ ,12986.62
+ ,10532.54
+ ,11964
+ ,-3.7
+ ,-14
+ ,3.2
+ ,2.72
+ ,09
+ ,3257.16
+ ,12204.98
+ ,10554.27
+ ,11451
+ ,-7.6
+ ,-10
+ ,3.1
+ ,2.75
+ ,08
+ ,3161.69
+ ,11716.87
+ ,10545.38
+ ,11346
+ ,-8.2
+ ,-11
+ ,3.1
+ ,2.67
+ ,07
+ ,3097.31
+ ,11402.75
+ ,10486.64
+ ,11353
+ ,-7.5
+ ,-11
+ ,2.9
+ ,2.76
+ ,06
+ ,3061.26
+ ,11082.38
+ ,10377.18
+ ,10702
+ ,-8
+ ,-11
+ ,2.5
+ ,2.87
+ ,05
+ ,3119.31
+ ,11395.64
+ ,10283.19
+ ,10646
+ ,-6.9
+ ,-5
+ ,2.8
+ ,2.9
+ ,04
+ ,3106.22
+ ,11809.38
+ ,10682.06
+ ,10556
+ ,-4.2
+ ,-2
+ ,3.1
+ ,2.92
+ ,03
+ ,3080.58
+ ,11545.71
+ ,10723.78
+ ,10463
+ ,-3.6
+ ,-3
+ ,2.6
+ ,2.93
+ ,02
+ ,2981.85
+ ,11394.84
+ ,10539.51
+ ,10407
+ ,-1.8
+ ,-6
+ ,2.3
+ ,3.1
+ ,01
+ ,2921.44
+ ,11068.05
+ ,10673.38
+ ,10625
+ ,-3.2
+ ,-6
+ ,2.3
+ ,3.2
+ ,12
+ ,2849.27
+ ,10973
+ ,10411.75
+ ,10872
+ ,-1.3
+ ,-7
+ ,2.6
+ ,3.25
+ ,11
+ ,2756.76
+ ,11028.93
+ ,10001.6
+ ,10805
+ ,0.6
+ ,-6
+ ,2.9
+ ,3.31
+ ,10
+ ,2645.64
+ ,11079.42
+ ,10204.59
+ ,10653
+ ,1.2
+ ,-2
+ ,2
+ ,3.23
+ ,09
+ ,2497.84
+ ,10989.34
+ ,10032.8
+ ,10574
+ ,0.4
+ ,-2
+ ,2.2
+ ,3.24
+ ,08
+ ,2448.05
+ ,11383.89
+ ,10152.09
+ ,10431
+ ,3
+ ,-4
+ ,2.4
+ ,3.35
+ ,07
+ ,2454.62
+ ,11527.72
+ ,10364.91
+ ,10383
+ ,-0.4
+ ,0
+ ,2.3
+ ,3.19
+ ,06
+ ,2407.6
+ ,11037.54
+ ,10092.96
+ ,10296
+ ,0
+ ,-6
+ ,2.6
+ ,3.17
+ ,05
+ ,2472.81
+ ,11950.95
+ ,10418.4
+ ,10872
+ ,-1.3
+ ,-4
+ ,1.9
+ ,3.06
+ ,04
+ ,2408.64
+ ,11441.08
+ ,10323.73
+ ,10635
+ ,-3.1
+ ,-3
+ ,1.1
+ ,3.22
+ ,03
+ ,2440.25
+ ,10631.92
+ ,10601.61
+ ,10297
+ ,-4
+ ,-1
+ ,1.3
+ ,3.35
+ ,02
+ ,2350.44
+ ,10892.76
+ ,10540.05
+ ,10570
+ ,-4.9
+ ,-3
+ ,1.6
+ ,3.38
+ ,01)
+ ,dim=c(9
+ ,72)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j'
+ ,'Maand')
+ ,1:72))
> y <- array(NA,dim=c(9,72),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j','Maand'),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 = '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
BEL_20 Nikkei DJ_Indust Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1 2502.66 10169.02 10433.44 24977 -7.9 -15
2 2466.92 9633.83 10238.83 24320 -8.8 -10
3 2513.17 10066.24 9857.34 22680 -14.2 -12
4 2443.27 10302.87 9634.97 22052 -17.8 -11
5 2293.41 10430.35 9374.63 21467 -18.2 -11
6 2070.83 9691.12 8679.75 21383 -22.8 -17
7 2029.60 9810.31 8593.00 21777 -23.6 -18
8 2052.02 9304.43 8398.37 21928 -27.6 -19
9 1864.44 8767.96 7992.12 21814 -29.4 -22
10 1670.07 7764.58 7235.47 22937 -31.8 -24
11 1810.99 7694.78 7690.50 23595 -31.4 -24
12 1905.41 8331.49 8396.20 20830 -27.6 -20
13 1862.83 8460.94 8595.56 19650 -28.8 -25
14 2014.45 8531.45 8614.55 19195 -21.9 -22
15 2197.82 9117.03 9181.73 19644 -13.9 -17
16 2962.34 12123.53 11114.08 18483 -8.0 -9
17 3047.03 12989.35 11530.75 18079 -2.8 -11
18 3032.60 13168.91 11322.38 19178 -3.3 -13
19 3504.37 14084.60 12056.67 18391 -1.3 -11
20 3801.06 13995.33 12812.48 18441 0.5 -9
21 3857.62 13357.70 12656.63 18584 -1.9 -7
22 3674.40 12602.93 12193.88 20108 2.0 -3
23 3720.98 13547.84 12419.57 20148 1.7 -3
24 3844.49 13731.31 12538.12 19394 1.9 -6
25 4116.68 15532.18 13406.97 17745 0.1 -4
26 4105.18 15543.76 13200.58 17696 2.4 -8
27 4435.23 16903.36 13901.28 17032 2.3 -1
28 4296.49 16235.39 13557.69 16438 4.7 -2
29 4202.52 16460.95 13239.71 15683 5.0 -2
30 4562.84 17974.77 13673.28 15594 7.2 -1
31 4621.40 18001.37 13480.21 15713 8.5 1
32 4696.96 17611.14 13407.75 15937 6.8 2
33 4591.27 17460.53 12754.80 16171 5.8 2
34 4356.98 17128.37 12268.53 15928 3.7 -1
35 4502.64 17741.23 12631.48 16348 4.8 1
36 4443.91 17286.32 12512.89 15579 6.1 -1
37 4290.89 16775.08 12377.62 15305 6.9 -8
38 4199.75 16101.07 12185.15 15648 5.7 1
39 4138.52 16519.44 11963.12 14954 6.9 2
40 3970.10 15934.09 11533.59 15137 5.5 -2
41 3862.27 15786.78 11257.35 15839 6.5 -2
42 3701.61 15147.55 11036.89 16050 7.7 -2
43 3570.12 14990.31 10997.97 15168 6.3 -2
44 3801.06 16397.83 11333.88 17064 5.5 -6
45 3895.51 17232.97 11234.68 16005 5.3 -4
46 3917.96 16311.54 11145.65 14886 3.3 -5
47 3813.06 16187.64 10971.19 14931 2.2 -2
48 3667.03 16102.64 10872.48 14544 0.6 -1
49 3494.17 15650.83 10827.81 13812 0.2 -5
50 3363.99 14368.05 10695.25 13031 -0.7 -9
51 3295.32 13392.79 10324.31 12574 -1.7 -8
52 3277.01 12986.62 10532.54 11964 -3.7 -14
53 3257.16 12204.98 10554.27 11451 -7.6 -10
54 3161.69 11716.87 10545.38 11346 -8.2 -11
55 3097.31 11402.75 10486.64 11353 -7.5 -11
56 3061.26 11082.38 10377.18 10702 -8.0 -11
57 3119.31 11395.64 10283.19 10646 -6.9 -5
58 3106.22 11809.38 10682.06 10556 -4.2 -2
59 3080.58 11545.71 10723.78 10463 -3.6 -3
60 2981.85 11394.84 10539.51 10407 -1.8 -6
61 2921.44 11068.05 10673.38 10625 -3.2 -6
62 2849.27 10973.00 10411.75 10872 -1.3 -7
63 2756.76 11028.93 10001.60 10805 0.6 -6
64 2645.64 11079.42 10204.59 10653 1.2 -2
65 2497.84 10989.34 10032.80 10574 0.4 -2
66 2448.05 11383.89 10152.09 10431 3.0 -4
67 2454.62 11527.72 10364.91 10383 -0.4 0
68 2407.60 11037.54 10092.96 10296 0.0 -6
69 2472.81 11950.95 10418.40 10872 -1.3 -4
70 2408.64 11441.08 10323.73 10635 -3.1 -3
71 2440.25 10631.92 10601.61 10297 -4.0 -1
72 2350.44 10892.76 10540.05 10570 -4.9 -3
Alg_consumptie_index_BE Gem_rente_kasbon_5j Maand
1 0.3 3.36 12
2 -0.1 3.37 11
3 -1.0 3.55 10
4 -1.2 3.53 9
5 -0.8 3.52 8
6 -1.7 3.54 7
7 -1.1 3.50 6
8 -0.4 3.44 5
9 0.6 3.38 4
10 0.6 3.35 3
11 1.9 3.68 2
12 2.3 3.92 1
13 2.6 4.05 12
14 3.1 4.14 11
15 4.7 4.53 10
16 5.5 4.54 9
17 5.4 4.90 8
18 5.9 4.92 7
19 5.8 4.45 6
20 5.2 3.92 5
21 4.2 3.66 4
22 4.4 3.74 3
23 3.6 4.07 2
24 3.5 4.23 1
25 3.1 4.14 12
26 2.9 4.18 11
27 2.2 4.29 10
28 1.5 4.27 9
29 1.1 4.33 8
30 1.4 4.39 7
31 1.3 4.21 6
32 1.3 3.88 5
33 1.8 3.91 4
34 1.8 3.94 3
35 1.8 3.94 2
36 1.7 3.64 1
37 1.6 3.50 12
38 1.5 3.49 11
39 1.2 3.52 10
40 1.2 3.51 9
41 1.6 3.60 8
42 1.6 3.57 7
43 1.9 3.46 6
44 2.2 3.48 5
45 2.0 3.30 4
46 1.7 3.04 3
47 2.4 2.96 2
48 2.6 3.07 1
49 2.9 2.99 12
50 2.6 2.86 11
51 2.5 2.72 10
52 3.2 2.72 9
53 3.1 2.75 8
54 3.1 2.67 7
55 2.9 2.76 6
56 2.5 2.87 5
57 2.8 2.90 4
58 3.1 2.92 3
59 2.6 2.93 2
60 2.3 3.10 1
61 2.3 3.20 12
62 2.6 3.25 11
63 2.9 3.31 10
64 2.0 3.23 9
65 2.2 3.24 8
66 2.4 3.35 7
67 2.3 3.19 6
68 2.6 3.17 5
69 1.9 3.06 4
70 1.1 3.22 3
71 1.3 3.35 2
72 1.6 3.38 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.882e+03 1.911e-01 2.893e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.456e-02 -9.428e+00 -3.355e+00
Alg_consumptie_index_BE Gem_rente_kasbon_5j Maand
3.264e+01 -2.542e+02 -1.322e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-405.552 -127.761 6.604 124.752 263.266
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.882e+03 2.730e+02 -6.894 3.06e-09 ***
Nikkei 1.911e-01 1.543e-02 12.383 < 2e-16 ***
DJ_Indust 2.893e-01 3.376e-02 8.568 3.64e-12 ***
Goudprijs 1.456e-02 8.322e-03 1.750 0.0850 .
Conjunct_Seizoenzuiver -9.428e+00 6.643e+00 -1.419 0.1607
Cons_vertrouw -3.355e+00 8.725e+00 -0.385 0.7019
Alg_consumptie_index_BE 3.264e+01 1.850e+01 1.764 0.0825 .
Gem_rente_kasbon_5j -2.542e+02 5.706e+01 -4.455 3.50e-05 ***
Maand -1.322e+00 6.377e+00 -0.207 0.8365
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 161.3 on 63 degrees of freedom
Multiple R-squared: 0.9677, Adjusted R-squared: 0.9636
F-statistic: 235.9 on 8 and 63 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,] 0.0181248920 0.036249784 0.9818751
[2,] 0.0145253859 0.029050772 0.9854746
[3,] 0.0402136461 0.080427292 0.9597864
[4,] 0.0172033280 0.034406656 0.9827967
[5,] 0.0120453351 0.024090670 0.9879547
[6,] 0.0045428030 0.009085606 0.9954572
[7,] 0.0016700532 0.003340106 0.9983299
[8,] 0.0065193063 0.013038613 0.9934807
[9,] 0.0043627130 0.008725426 0.9956373
[10,] 0.0033954545 0.006790909 0.9966045
[11,] 0.0016466516 0.003293303 0.9983533
[12,] 0.0009667237 0.001933447 0.9990333
[13,] 0.0009790197 0.001958039 0.9990210
[14,] 0.0031613402 0.006322680 0.9968387
[15,] 0.0065900414 0.013180083 0.9934100
[16,] 0.0119068436 0.023813687 0.9880932
[17,] 0.0180566012 0.036113202 0.9819434
[18,] 0.0136772448 0.027354490 0.9863228
[19,] 0.0121186342 0.024237268 0.9878814
[20,] 0.0111202289 0.022240458 0.9888798
[21,] 0.0112564764 0.022512953 0.9887435
[22,] 0.0104560215 0.020912043 0.9895440
[23,] 0.0073811902 0.014762380 0.9926188
[24,] 0.0043388394 0.008677679 0.9956612
[25,] 0.0037387086 0.007477417 0.9962613
[26,] 0.0021629203 0.004325841 0.9978371
[27,] 0.0016380597 0.003276119 0.9983619
[28,] 0.0012410860 0.002482172 0.9987589
[29,] 0.0015411812 0.003082362 0.9984588
[30,] 0.0017438306 0.003487661 0.9982562
[31,] 0.0013274429 0.002654886 0.9986726
[32,] 0.0031844260 0.006368852 0.9968156
[33,] 0.0112412326 0.022482465 0.9887588
[34,] 0.0149128352 0.029825670 0.9850872
[35,] 0.0283909735 0.056781947 0.9716090
[36,] 0.0323875488 0.064775098 0.9676125
[37,] 0.0601679197 0.120335839 0.9398321
[38,] 0.0521190785 0.104238157 0.9478809
[39,] 0.0419667549 0.083933510 0.9580332
[40,] 0.0659438230 0.131887646 0.9340562
[41,] 0.0484730248 0.096946050 0.9515270
[42,] 0.0328086404 0.065617281 0.9671914
[43,] 0.0295480117 0.059096023 0.9704520
[44,] 0.0552847884 0.110569577 0.9447152
[45,] 0.0424297703 0.084859541 0.9575702
[46,] 0.0420662167 0.084132433 0.9579338
[47,] 0.0348674606 0.069734921 0.9651325
[48,] 0.0270167406 0.054033481 0.9729833
[49,] 0.8322769107 0.335446179 0.1677231
> postscript(file="/var/www/html/rcomp/tmp/13oqh1291294595.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23oqh1291294595.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/367sf1291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/467sf1291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/567sf1291294596.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
-205.0199455 -50.0570692 63.9838362 -8.1322629 -119.2191311 -28.6713149
7 8 9 10 11 12
-115.2955410 -22.5929412 -64.7321858 96.8701612 153.8348106 58.6014223
13 14 15 16 17 18
-39.4899040 180.1476363 218.8141861 -75.6734539 -135.3065546 -163.7650608
19 20 21 22 23 24
-159.8962077 -158.2932383 12.4195671 147.7741877 53.7694446 153.3449388
25 26 27 28 29 30
-151.4622261 -81.1225013 -131.8727428 0.7878717 -3.4968196 -28.3495506
31 32 33 34 35 36
54.3910209 124.3345205 213.4468426 163.2697122 96.4679551 101.3969346
37 38 39 40 41 42
55.4641297 162.0887231 126.0052364 160.5417514 168.4741252 193.0336650
43 44 45 46 47 48
63.4183917 -126.3902269 -183.1467001 -22.3999944 -98.6262343 -185.8392654
49 50 51 52 53 54
-281.5565740 -163.3630181 28.5714022 -26.6248515 90.3018816 61.5390274
55 56 57 58 59 60
108.7620202 210.0621059 263.2662001 86.5367167 120.4123415 163.2233501
61 62 63 64 65 66
150.1283462 184.3597082 226.1852188 75.7071873 -16.8862300 -136.5806643
67 68 69 70 71 72
-275.7258827 -181.6991825 -405.5523233 -289.6011715 -155.3934602 -299.9021469
> postscript(file="/var/www/html/rcomp/tmp/667sf1291294596.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 -205.0199455 NA
1 -50.0570692 -205.0199455
2 63.9838362 -50.0570692
3 -8.1322629 63.9838362
4 -119.2191311 -8.1322629
5 -28.6713149 -119.2191311
6 -115.2955410 -28.6713149
7 -22.5929412 -115.2955410
8 -64.7321858 -22.5929412
9 96.8701612 -64.7321858
10 153.8348106 96.8701612
11 58.6014223 153.8348106
12 -39.4899040 58.6014223
13 180.1476363 -39.4899040
14 218.8141861 180.1476363
15 -75.6734539 218.8141861
16 -135.3065546 -75.6734539
17 -163.7650608 -135.3065546
18 -159.8962077 -163.7650608
19 -158.2932383 -159.8962077
20 12.4195671 -158.2932383
21 147.7741877 12.4195671
22 53.7694446 147.7741877
23 153.3449388 53.7694446
24 -151.4622261 153.3449388
25 -81.1225013 -151.4622261
26 -131.8727428 -81.1225013
27 0.7878717 -131.8727428
28 -3.4968196 0.7878717
29 -28.3495506 -3.4968196
30 54.3910209 -28.3495506
31 124.3345205 54.3910209
32 213.4468426 124.3345205
33 163.2697122 213.4468426
34 96.4679551 163.2697122
35 101.3969346 96.4679551
36 55.4641297 101.3969346
37 162.0887231 55.4641297
38 126.0052364 162.0887231
39 160.5417514 126.0052364
40 168.4741252 160.5417514
41 193.0336650 168.4741252
42 63.4183917 193.0336650
43 -126.3902269 63.4183917
44 -183.1467001 -126.3902269
45 -22.3999944 -183.1467001
46 -98.6262343 -22.3999944
47 -185.8392654 -98.6262343
48 -281.5565740 -185.8392654
49 -163.3630181 -281.5565740
50 28.5714022 -163.3630181
51 -26.6248515 28.5714022
52 90.3018816 -26.6248515
53 61.5390274 90.3018816
54 108.7620202 61.5390274
55 210.0621059 108.7620202
56 263.2662001 210.0621059
57 86.5367167 263.2662001
58 120.4123415 86.5367167
59 163.2233501 120.4123415
60 150.1283462 163.2233501
61 184.3597082 150.1283462
62 226.1852188 184.3597082
63 75.7071873 226.1852188
64 -16.8862300 75.7071873
65 -136.5806643 -16.8862300
66 -275.7258827 -136.5806643
67 -181.6991825 -275.7258827
68 -405.5523233 -181.6991825
69 -289.6011715 -405.5523233
70 -155.3934602 -289.6011715
71 -299.9021469 -155.3934602
72 NA -299.9021469
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -50.0570692 -205.0199455
[2,] 63.9838362 -50.0570692
[3,] -8.1322629 63.9838362
[4,] -119.2191311 -8.1322629
[5,] -28.6713149 -119.2191311
[6,] -115.2955410 -28.6713149
[7,] -22.5929412 -115.2955410
[8,] -64.7321858 -22.5929412
[9,] 96.8701612 -64.7321858
[10,] 153.8348106 96.8701612
[11,] 58.6014223 153.8348106
[12,] -39.4899040 58.6014223
[13,] 180.1476363 -39.4899040
[14,] 218.8141861 180.1476363
[15,] -75.6734539 218.8141861
[16,] -135.3065546 -75.6734539
[17,] -163.7650608 -135.3065546
[18,] -159.8962077 -163.7650608
[19,] -158.2932383 -159.8962077
[20,] 12.4195671 -158.2932383
[21,] 147.7741877 12.4195671
[22,] 53.7694446 147.7741877
[23,] 153.3449388 53.7694446
[24,] -151.4622261 153.3449388
[25,] -81.1225013 -151.4622261
[26,] -131.8727428 -81.1225013
[27,] 0.7878717 -131.8727428
[28,] -3.4968196 0.7878717
[29,] -28.3495506 -3.4968196
[30,] 54.3910209 -28.3495506
[31,] 124.3345205 54.3910209
[32,] 213.4468426 124.3345205
[33,] 163.2697122 213.4468426
[34,] 96.4679551 163.2697122
[35,] 101.3969346 96.4679551
[36,] 55.4641297 101.3969346
[37,] 162.0887231 55.4641297
[38,] 126.0052364 162.0887231
[39,] 160.5417514 126.0052364
[40,] 168.4741252 160.5417514
[41,] 193.0336650 168.4741252
[42,] 63.4183917 193.0336650
[43,] -126.3902269 63.4183917
[44,] -183.1467001 -126.3902269
[45,] -22.3999944 -183.1467001
[46,] -98.6262343 -22.3999944
[47,] -185.8392654 -98.6262343
[48,] -281.5565740 -185.8392654
[49,] -163.3630181 -281.5565740
[50,] 28.5714022 -163.3630181
[51,] -26.6248515 28.5714022
[52,] 90.3018816 -26.6248515
[53,] 61.5390274 90.3018816
[54,] 108.7620202 61.5390274
[55,] 210.0621059 108.7620202
[56,] 263.2662001 210.0621059
[57,] 86.5367167 263.2662001
[58,] 120.4123415 86.5367167
[59,] 163.2233501 120.4123415
[60,] 150.1283462 163.2233501
[61,] 184.3597082 150.1283462
[62,] 226.1852188 184.3597082
[63,] 75.7071873 226.1852188
[64,] -16.8862300 75.7071873
[65,] -136.5806643 -16.8862300
[66,] -275.7258827 -136.5806643
[67,] -181.6991825 -275.7258827
[68,] -405.5523233 -181.6991825
[69,] -289.6011715 -405.5523233
[70,] -155.3934602 -289.6011715
[71,] -299.9021469 -155.3934602
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -50.0570692 -205.0199455
2 63.9838362 -50.0570692
3 -8.1322629 63.9838362
4 -119.2191311 -8.1322629
5 -28.6713149 -119.2191311
6 -115.2955410 -28.6713149
7 -22.5929412 -115.2955410
8 -64.7321858 -22.5929412
9 96.8701612 -64.7321858
10 153.8348106 96.8701612
11 58.6014223 153.8348106
12 -39.4899040 58.6014223
13 180.1476363 -39.4899040
14 218.8141861 180.1476363
15 -75.6734539 218.8141861
16 -135.3065546 -75.6734539
17 -163.7650608 -135.3065546
18 -159.8962077 -163.7650608
19 -158.2932383 -159.8962077
20 12.4195671 -158.2932383
21 147.7741877 12.4195671
22 53.7694446 147.7741877
23 153.3449388 53.7694446
24 -151.4622261 153.3449388
25 -81.1225013 -151.4622261
26 -131.8727428 -81.1225013
27 0.7878717 -131.8727428
28 -3.4968196 0.7878717
29 -28.3495506 -3.4968196
30 54.3910209 -28.3495506
31 124.3345205 54.3910209
32 213.4468426 124.3345205
33 163.2697122 213.4468426
34 96.4679551 163.2697122
35 101.3969346 96.4679551
36 55.4641297 101.3969346
37 162.0887231 55.4641297
38 126.0052364 162.0887231
39 160.5417514 126.0052364
40 168.4741252 160.5417514
41 193.0336650 168.4741252
42 63.4183917 193.0336650
43 -126.3902269 63.4183917
44 -183.1467001 -126.3902269
45 -22.3999944 -183.1467001
46 -98.6262343 -22.3999944
47 -185.8392654 -98.6262343
48 -281.5565740 -185.8392654
49 -163.3630181 -281.5565740
50 28.5714022 -163.3630181
51 -26.6248515 28.5714022
52 90.3018816 -26.6248515
53 61.5390274 90.3018816
54 108.7620202 61.5390274
55 210.0621059 108.7620202
56 263.2662001 210.0621059
57 86.5367167 263.2662001
58 120.4123415 86.5367167
59 163.2233501 120.4123415
60 150.1283462 163.2233501
61 184.3597082 150.1283462
62 226.1852188 184.3597082
63 75.7071873 226.1852188
64 -16.8862300 75.7071873
65 -136.5806643 -16.8862300
66 -275.7258827 -136.5806643
67 -181.6991825 -275.7258827
68 -405.5523233 -181.6991825
69 -289.6011715 -405.5523233
70 -155.3934602 -289.6011715
71 -299.9021469 -155.3934602
> 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/7zyr01291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8aqr31291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9aqr31291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103hqo1291294596.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oioc1291294596.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/12rini1291294596.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/135ak91291294596.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/14g1ku1291294596.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/15kk001291294596.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/16gcy81291294596.tab")
+ }
>
> try(system("convert tmp/13oqh1291294595.ps tmp/13oqh1291294595.png",intern=TRUE))
character(0)
> try(system("convert tmp/23oqh1291294595.ps tmp/23oqh1291294595.png",intern=TRUE))
character(0)
> try(system("convert tmp/367sf1291294596.ps tmp/367sf1291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/467sf1291294596.ps tmp/467sf1291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/567sf1291294596.ps tmp/567sf1291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/667sf1291294596.ps tmp/667sf1291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zyr01291294596.ps tmp/7zyr01291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/8aqr31291294596.ps tmp/8aqr31291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/9aqr31291294596.ps tmp/9aqr31291294596.png",intern=TRUE))
character(0)
> try(system("convert tmp/103hqo1291294596.ps tmp/103hqo1291294596.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.745 1.648 6.115