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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(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 = '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
> 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
1 1.6 3.38
2 1.3 3.35
3 1.1 3.22
4 1.9 3.06
5 2.6 3.17
6 2.3 3.19
7 2.4 3.35
8 2.2 3.24
9 2.0 3.23
10 2.9 3.31
11 2.6 3.25
12 2.3 3.20
13 2.3 3.10
14 2.6 2.93
15 3.1 2.92
16 2.8 2.90
17 2.5 2.87
18 2.9 2.76
19 3.1 2.67
20 3.1 2.75
21 3.2 2.72
22 2.5 2.72
23 2.6 2.86
24 2.9 2.99
25 2.6 3.07
26 2.4 2.96
27 1.7 3.04
28 2.0 3.30
29 2.2 3.48
30 1.9 3.46
31 1.6 3.57
32 1.6 3.60
33 1.2 3.51
34 1.2 3.52
35 1.5 3.49
36 1.6 3.50
37 1.7 3.64
38 1.8 3.94
39 1.8 3.94
40 1.8 3.91
41 1.3 3.88
42 1.3 4.21
43 1.4 4.39
44 1.1 4.33
45 1.5 4.27
46 2.2 4.29
47 2.9 4.18
48 3.1 4.14
49 3.5 4.23
50 3.6 4.07
51 4.4 3.74
52 4.2 3.66
53 5.2 3.92
54 5.8 4.45
55 5.9 4.92
56 5.4 4.90
57 5.5 4.54
58 4.7 4.53
59 3.1 4.14
60 2.6 4.05
61 2.3 3.92
62 1.9 3.68
63 0.6 3.35
64 0.6 3.38
65 -0.4 3.44
66 -1.1 3.50
67 -1.7 3.54
68 -0.8 3.52
69 -1.2 3.53
70 -1.0 3.55
71 -0.1 3.37
72 0.3 3.36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.886e+03 1.918e-01 2.883e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.477e-02 -9.983e+00 -2.508e+00
Alg_consumptie_index_BE Gem_rente_kasbon_5j
3.396e+01 -2.557e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-402.63 -126.02 7.44 123.86 262.81
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.886e+03 2.702e+02 -6.980 2.02e-09 ***
Nikkei 1.918e-01 1.495e-02 12.826 < 2e-16 ***
DJ_Indust 2.883e-01 3.319e-02 8.686 2.01e-12 ***
Goudprijs 1.477e-02 8.199e-03 1.802 0.0763 .
Conjunct_Seizoenzuiver -9.983e+00 6.033e+00 -1.655 0.1029
Cons_vertrouw -2.508e+00 7.649e+00 -0.328 0.7441
Alg_consumptie_index_BE 3.396e+01 1.724e+01 1.969 0.0532 .
Gem_rente_kasbon_5j -2.557e+02 5.619e+01 -4.551 2.45e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 160.1 on 64 degrees of freedom
Multiple R-squared: 0.9677, Adjusted R-squared: 0.9641
F-statistic: 273.7 on 7 and 64 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,] 0.5058541 0.988291819 0.4941459094
[2,] 0.7009749 0.598050181 0.2990250907
[3,] 0.9185313 0.162937427 0.0814687135
[4,] 0.8927598 0.214480442 0.1072402210
[5,] 0.8660782 0.267843524 0.1339217620
[6,] 0.8664803 0.267039303 0.1335196513
[7,] 0.8375413 0.324917319 0.1624586596
[8,] 0.9166633 0.166673404 0.0833367022
[9,] 0.9050493 0.189901394 0.0949506968
[10,] 0.8802318 0.239536439 0.1197682197
[11,] 0.8444991 0.311001757 0.1555008786
[12,] 0.8800631 0.239873801 0.1199369004
[13,] 0.8537381 0.292523891 0.1462619457
[14,] 0.8850713 0.229857302 0.1149286511
[15,] 0.9204767 0.159046593 0.0795232964
[16,] 0.9270636 0.145872819 0.0729364097
[17,] 0.9549976 0.090004756 0.0450023780
[18,] 0.9652589 0.069482118 0.0347410590
[19,] 0.9874513 0.025097313 0.0125486567
[20,] 0.9901481 0.019703792 0.0098518958
[21,] 0.9866579 0.026684108 0.0133420540
[22,] 0.9884751 0.023049847 0.0115249235
[23,] 0.9919375 0.016125080 0.0080625402
[24,] 0.9931542 0.013691655 0.0068458275
[25,] 0.9931002 0.013799506 0.0068997529
[26,] 0.9900011 0.019997704 0.0099988520
[27,] 0.9865799 0.026840177 0.0134200885
[28,] 0.9790220 0.041955917 0.0209779585
[29,] 0.9722265 0.055547018 0.0277735088
[30,] 0.9614006 0.077198799 0.0385993996
[31,] 0.9547835 0.090432998 0.0452164989
[32,] 0.9425011 0.114997773 0.0574988867
[33,] 0.9349967 0.130006616 0.0650033081
[34,] 0.9249851 0.150029719 0.0750148596
[35,] 0.9560449 0.087910222 0.0439551112
[36,] 0.9710690 0.057861947 0.0289309735
[37,] 0.9795759 0.040848186 0.0204240928
[38,] 0.9730742 0.053851521 0.0269257603
[39,] 0.9967080 0.006583988 0.0032919941
[40,] 0.9972003 0.005599384 0.0027996920
[41,] 0.9954777 0.009044639 0.0045223197
[42,] 0.9960781 0.007843850 0.0039219248
[43,] 0.9961583 0.007683427 0.0038417137
[44,] 0.9992866 0.001426858 0.0007134289
[45,] 0.9979998 0.004000440 0.0020002200
[46,] 0.9945378 0.010924349 0.0054621744
[47,] 0.9920447 0.015910549 0.0079552744
[48,] 0.9810246 0.037950875 0.0189754376
[49,] 0.9960579 0.007884167 0.0039420837
[50,] 0.9859927 0.028014544 0.0140072721
[51,] 0.9515035 0.096993066 0.0484965329
> postscript(file="/var/www/rcomp/tmp/1fo241291658575.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2fo241291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qx271291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4qx271291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5qx271291658575.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
-294.08226974 -151.44350857 -285.60239617 -402.63001921 -178.00087290
6 7 8 9 10
-278.32972929 -135.23819996 -19.77342567 72.27644805 223.23092640
11 12 13 14 15
180.59964668 143.70096836 171.65817469 123.39748123 86.10393778
16 17 18 19 20
262.80734456 213.40192557 110.11596103 60.62635082 87.31939692
21 22 23 24 25
-26.21816216 23.99681813 -168.19819246 -291.73185802 -184.55081048
26 27 28 29 30
-96.86717344 -17.67905871 -180.28262324 -122.58733262 64.36712358
31 32 33 34 35
193.74013246 167.04836990 157.94115063 119.52783066 154.38879347
36 37 38 39 40
54.40781905 108.29737665 99.57807831 166.30707385 213.92573084
41 42 43 44 45
125.22965499 56.08731171 -26.64099026 -2.56179279 -0.07786313
46 47 48 49 50
-137.39116297 -82.92491509 -159.38118970 162.34121756 58.27982658
51 52 53 54 55
150.03619545 14.96614952 -155.23842765 -158.25139313 -162.52832183
56 57 58 59 60
-135.62793009 -82.44372345 215.25972228 176.55732377 -45.22860030
61 62 63 64 65
63.69011483 158.24713379 100.61893360 -62.36195762 -21.68878135
66 67 68 69 70
-113.46199948 -27.46620424 -122.94750332 -12.20321046 61.44928422
71 72
-53.73788344 -206.14824494
> postscript(file="/var/www/rcomp/tmp/6161a1291658575.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 -294.08226974 NA
1 -151.44350857 -294.08226974
2 -285.60239617 -151.44350857
3 -402.63001921 -285.60239617
4 -178.00087290 -402.63001921
5 -278.32972929 -178.00087290
6 -135.23819996 -278.32972929
7 -19.77342567 -135.23819996
8 72.27644805 -19.77342567
9 223.23092640 72.27644805
10 180.59964668 223.23092640
11 143.70096836 180.59964668
12 171.65817469 143.70096836
13 123.39748123 171.65817469
14 86.10393778 123.39748123
15 262.80734456 86.10393778
16 213.40192557 262.80734456
17 110.11596103 213.40192557
18 60.62635082 110.11596103
19 87.31939692 60.62635082
20 -26.21816216 87.31939692
21 23.99681813 -26.21816216
22 -168.19819246 23.99681813
23 -291.73185802 -168.19819246
24 -184.55081048 -291.73185802
25 -96.86717344 -184.55081048
26 -17.67905871 -96.86717344
27 -180.28262324 -17.67905871
28 -122.58733262 -180.28262324
29 64.36712358 -122.58733262
30 193.74013246 64.36712358
31 167.04836990 193.74013246
32 157.94115063 167.04836990
33 119.52783066 157.94115063
34 154.38879347 119.52783066
35 54.40781905 154.38879347
36 108.29737665 54.40781905
37 99.57807831 108.29737665
38 166.30707385 99.57807831
39 213.92573084 166.30707385
40 125.22965499 213.92573084
41 56.08731171 125.22965499
42 -26.64099026 56.08731171
43 -2.56179279 -26.64099026
44 -0.07786313 -2.56179279
45 -137.39116297 -0.07786313
46 -82.92491509 -137.39116297
47 -159.38118970 -82.92491509
48 162.34121756 -159.38118970
49 58.27982658 162.34121756
50 150.03619545 58.27982658
51 14.96614952 150.03619545
52 -155.23842765 14.96614952
53 -158.25139313 -155.23842765
54 -162.52832183 -158.25139313
55 -135.62793009 -162.52832183
56 -82.44372345 -135.62793009
57 215.25972228 -82.44372345
58 176.55732377 215.25972228
59 -45.22860030 176.55732377
60 63.69011483 -45.22860030
61 158.24713379 63.69011483
62 100.61893360 158.24713379
63 -62.36195762 100.61893360
64 -21.68878135 -62.36195762
65 -113.46199948 -21.68878135
66 -27.46620424 -113.46199948
67 -122.94750332 -27.46620424
68 -12.20321046 -122.94750332
69 61.44928422 -12.20321046
70 -53.73788344 61.44928422
71 -206.14824494 -53.73788344
72 NA -206.14824494
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -151.44350857 -294.08226974
[2,] -285.60239617 -151.44350857
[3,] -402.63001921 -285.60239617
[4,] -178.00087290 -402.63001921
[5,] -278.32972929 -178.00087290
[6,] -135.23819996 -278.32972929
[7,] -19.77342567 -135.23819996
[8,] 72.27644805 -19.77342567
[9,] 223.23092640 72.27644805
[10,] 180.59964668 223.23092640
[11,] 143.70096836 180.59964668
[12,] 171.65817469 143.70096836
[13,] 123.39748123 171.65817469
[14,] 86.10393778 123.39748123
[15,] 262.80734456 86.10393778
[16,] 213.40192557 262.80734456
[17,] 110.11596103 213.40192557
[18,] 60.62635082 110.11596103
[19,] 87.31939692 60.62635082
[20,] -26.21816216 87.31939692
[21,] 23.99681813 -26.21816216
[22,] -168.19819246 23.99681813
[23,] -291.73185802 -168.19819246
[24,] -184.55081048 -291.73185802
[25,] -96.86717344 -184.55081048
[26,] -17.67905871 -96.86717344
[27,] -180.28262324 -17.67905871
[28,] -122.58733262 -180.28262324
[29,] 64.36712358 -122.58733262
[30,] 193.74013246 64.36712358
[31,] 167.04836990 193.74013246
[32,] 157.94115063 167.04836990
[33,] 119.52783066 157.94115063
[34,] 154.38879347 119.52783066
[35,] 54.40781905 154.38879347
[36,] 108.29737665 54.40781905
[37,] 99.57807831 108.29737665
[38,] 166.30707385 99.57807831
[39,] 213.92573084 166.30707385
[40,] 125.22965499 213.92573084
[41,] 56.08731171 125.22965499
[42,] -26.64099026 56.08731171
[43,] -2.56179279 -26.64099026
[44,] -0.07786313 -2.56179279
[45,] -137.39116297 -0.07786313
[46,] -82.92491509 -137.39116297
[47,] -159.38118970 -82.92491509
[48,] 162.34121756 -159.38118970
[49,] 58.27982658 162.34121756
[50,] 150.03619545 58.27982658
[51,] 14.96614952 150.03619545
[52,] -155.23842765 14.96614952
[53,] -158.25139313 -155.23842765
[54,] -162.52832183 -158.25139313
[55,] -135.62793009 -162.52832183
[56,] -82.44372345 -135.62793009
[57,] 215.25972228 -82.44372345
[58,] 176.55732377 215.25972228
[59,] -45.22860030 176.55732377
[60,] 63.69011483 -45.22860030
[61,] 158.24713379 63.69011483
[62,] 100.61893360 158.24713379
[63,] -62.36195762 100.61893360
[64,] -21.68878135 -62.36195762
[65,] -113.46199948 -21.68878135
[66,] -27.46620424 -113.46199948
[67,] -122.94750332 -27.46620424
[68,] -12.20321046 -122.94750332
[69,] 61.44928422 -12.20321046
[70,] -53.73788344 61.44928422
[71,] -206.14824494 -53.73788344
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -151.44350857 -294.08226974
2 -285.60239617 -151.44350857
3 -402.63001921 -285.60239617
4 -178.00087290 -402.63001921
5 -278.32972929 -178.00087290
6 -135.23819996 -278.32972929
7 -19.77342567 -135.23819996
8 72.27644805 -19.77342567
9 223.23092640 72.27644805
10 180.59964668 223.23092640
11 143.70096836 180.59964668
12 171.65817469 143.70096836
13 123.39748123 171.65817469
14 86.10393778 123.39748123
15 262.80734456 86.10393778
16 213.40192557 262.80734456
17 110.11596103 213.40192557
18 60.62635082 110.11596103
19 87.31939692 60.62635082
20 -26.21816216 87.31939692
21 23.99681813 -26.21816216
22 -168.19819246 23.99681813
23 -291.73185802 -168.19819246
24 -184.55081048 -291.73185802
25 -96.86717344 -184.55081048
26 -17.67905871 -96.86717344
27 -180.28262324 -17.67905871
28 -122.58733262 -180.28262324
29 64.36712358 -122.58733262
30 193.74013246 64.36712358
31 167.04836990 193.74013246
32 157.94115063 167.04836990
33 119.52783066 157.94115063
34 154.38879347 119.52783066
35 54.40781905 154.38879347
36 108.29737665 54.40781905
37 99.57807831 108.29737665
38 166.30707385 99.57807831
39 213.92573084 166.30707385
40 125.22965499 213.92573084
41 56.08731171 125.22965499
42 -26.64099026 56.08731171
43 -2.56179279 -26.64099026
44 -0.07786313 -2.56179279
45 -137.39116297 -0.07786313
46 -82.92491509 -137.39116297
47 -159.38118970 -82.92491509
48 162.34121756 -159.38118970
49 58.27982658 162.34121756
50 150.03619545 58.27982658
51 14.96614952 150.03619545
52 -155.23842765 14.96614952
53 -158.25139313 -155.23842765
54 -162.52832183 -158.25139313
55 -135.62793009 -162.52832183
56 -82.44372345 -135.62793009
57 215.25972228 -82.44372345
58 176.55732377 215.25972228
59 -45.22860030 176.55732377
60 63.69011483 -45.22860030
61 158.24713379 63.69011483
62 100.61893360 158.24713379
63 -62.36195762 100.61893360
64 -21.68878135 -62.36195762
65 -113.46199948 -21.68878135
66 -27.46620424 -113.46199948
67 -122.94750332 -27.46620424
68 -12.20321046 -122.94750332
69 61.44928422 -12.20321046
70 -53.73788344 61.44928422
71 -206.14824494 -53.73788344
> 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/7tg0d1291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8tg0d1291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9tg0d1291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10m7ig1291658575.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p7ym1291658575.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/12t8x91291658575.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/137zu01291658575.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/14s0t61291658575.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/15e19u1291658575.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/16zjqi1291658575.tab")
+ }
>
> try(system("convert tmp/1fo241291658575.ps tmp/1fo241291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fo241291658575.ps tmp/2fo241291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qx271291658575.ps tmp/3qx271291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qx271291658575.ps tmp/4qx271291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qx271291658575.ps tmp/5qx271291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/6161a1291658575.ps tmp/6161a1291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tg0d1291658575.ps tmp/7tg0d1291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tg0d1291658575.ps tmp/8tg0d1291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tg0d1291658575.ps tmp/9tg0d1291658575.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m7ig1291658575.ps tmp/10m7ig1291658575.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.240 1.900 5.198