R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(1635.25
+ ,8169.75
+ ,7977.64
+ ,10171
+ ,-14.9
+ ,-18
+ ,1.8
+ ,2.05
+ ,1833.42
+ ,7905.84
+ ,8334.59
+ ,9721
+ ,-16.2
+ ,-11
+ ,1.5
+ ,2.05
+ ,1910.43
+ ,8145.82
+ ,8623.36
+ ,9897
+ ,-14.4
+ ,-9
+ ,1
+ ,1.81
+ ,1959.67
+ ,8895.71
+ ,9098.03
+ ,9828
+ ,-17.3
+ ,-10
+ ,1.6
+ ,1.58
+ ,1969.6
+ ,9676.31
+ ,9154.34
+ ,9924
+ ,-15.7
+ ,-13
+ ,1.5
+ ,1.57
+ ,2061.41
+ ,9884.59
+ ,9284.73
+ ,10371
+ ,-12.6
+ ,-11
+ ,1.8
+ ,1.76
+ ,2093.48
+ ,10637.44
+ ,9492.49
+ ,10846
+ ,-9.4
+ ,-5
+ ,1.8
+ ,1.76
+ ,2120.88
+ ,10717.13
+ ,9682.35
+ ,10413
+ ,-8.1
+ ,-15
+ ,1.6
+ ,1.89
+ ,2174.56
+ ,10205.29
+ ,9762.12
+ ,10709
+ ,-5.4
+ ,-6
+ ,1.9
+ ,1.9
+ ,2196.72
+ ,10295.98
+ ,10124.63
+ ,10662
+ ,-4.6
+ ,-6
+ ,1.7
+ ,1.9
+ ,2350.44
+ ,10892.76
+ ,10540.05
+ ,10570
+ ,-4.9
+ ,-3
+ ,1.6
+ ,1.92
+ ,2440.25
+ ,10631.92
+ ,10601.61
+ ,10297
+ ,-4
+ ,-1
+ ,1.3
+ ,1.76
+ ,2408.64
+ ,11441.08
+ ,10323.73
+ ,10635
+ ,-3.1
+ ,-3
+ ,1.1
+ ,1.64
+ ,2472.81
+ ,11950.95
+ ,10418.4
+ ,10872
+ ,-1.3
+ ,-4
+ ,1.9
+ ,1.57
+ ,2407.6
+ ,11037.54
+ ,10092.96
+ ,10296
+ ,0
+ ,-6
+ ,2.6
+ ,1.69
+ ,2454.62
+ ,11527.72
+ ,10364.91
+ ,10383
+ ,-0.4
+ ,0
+ ,2.3
+ ,1.76
+ ,2448.05
+ ,11383.89
+ ,10152.09
+ ,10431
+ ,3
+ ,-4
+ ,2.4
+ ,1.89
+ ,2497.84
+ ,10989.34
+ ,10032.8
+ ,10574
+ ,0.4
+ ,-2
+ ,2.2
+ ,1.78
+ ,2645.64
+ ,11079.42
+ ,10204.59
+ ,10653
+ ,1.2
+ ,-2
+ ,2
+ ,1.88
+ ,2756.76
+ ,11028.93
+ ,10001.6
+ ,10805
+ ,0.6
+ ,-6
+ ,2.9
+ ,1.86
+ ,2849.27
+ ,10973
+ ,10411.75
+ ,10872
+ ,-1.3
+ ,-7
+ ,2.6
+ ,1.88
+ ,2921.44
+ ,11068.05
+ ,10673.38
+ ,10625
+ ,-3.2
+ ,-6
+ ,2.3
+ ,1.87
+ ,2981.85
+ ,11394.84
+ ,10539.51
+ ,10407
+ ,-1.8
+ ,-6
+ ,2.3
+ ,1.86
+ ,3080.58
+ ,11545.71
+ ,10723.78
+ ,10463
+ ,-3.6
+ ,-3
+ ,2.6
+ ,1.89
+ ,3106.22
+ ,11809.38
+ ,10682.06
+ ,10556
+ ,-4.2
+ ,-2
+ ,3.1
+ ,1.9
+ ,3119.31
+ ,11395.64
+ ,10283.19
+ ,10646
+ ,-6.9
+ ,-5
+ ,2.8
+ ,1.89
+ ,3061.26
+ ,11082.38
+ ,10377.18
+ ,10702
+ ,-8
+ ,-11
+ ,2.5
+ ,1.85
+ ,3097.31
+ ,11402.75
+ ,10486.64
+ ,11353
+ ,-7.5
+ ,-11
+ ,2.9
+ ,1.78
+ ,3161.69
+ ,11716.87
+ ,10545.38
+ ,11346
+ ,-8.2
+ ,-11
+ ,3.1
+ ,1.71
+ ,3257.16
+ ,12204.98
+ ,10554.27
+ ,11451
+ ,-7.6
+ ,-10
+ ,3.1
+ ,1.69
+ ,3277.01
+ ,12986.62
+ ,10532.54
+ ,11964
+ ,-3.7
+ ,-14
+ ,3.2
+ ,1.72
+ ,3295.32
+ ,13392.79
+ ,10324.31
+ ,12574
+ ,-1.7
+ ,-8
+ ,2.5
+ ,1.77
+ ,3363.99
+ ,14368.05
+ ,10695.25
+ ,13031
+ ,-0.7
+ ,-9
+ ,2.6
+ ,1.98
+ ,3494.17
+ ,15650.83
+ ,10827.81
+ ,13812
+ ,0.2
+ ,-5
+ ,2.9
+ ,2.2
+ ,3667.03
+ ,16102.64
+ ,10872.48
+ ,14544
+ ,0.6
+ ,-1
+ ,2.6
+ ,2.25
+ ,3813.06
+ ,16187.64
+ ,10971.19
+ ,14931
+ ,2.2
+ ,-2
+ ,2.4
+ ,2.24
+ ,3917.96
+ ,16311.54
+ ,11145.65
+ ,14886
+ ,3.3
+ ,-5
+ ,1.7
+ ,2.51
+ ,3895.51
+ ,17232.97
+ ,11234.68
+ ,16005
+ ,5.3
+ ,-4
+ ,2
+ ,2.79
+ ,3801.06
+ ,16397.83
+ ,11333.88
+ ,17064
+ ,5.5
+ ,-6
+ ,2.2
+ ,3.07
+ ,3570.12
+ ,14990.31
+ ,10997.97
+ ,15168
+ ,6.3
+ ,-2
+ ,1.9
+ ,3.08
+ ,3701.61
+ ,15147.55
+ ,11036.89
+ ,16050
+ ,7.7
+ ,-2
+ ,1.6
+ ,3.05
+ ,3862.27
+ ,15786.78
+ ,11257.35
+ ,15839
+ ,6.5
+ ,-2
+ ,1.6
+ ,3.08
+ ,3970.1
+ ,15934.09
+ ,11533.59
+ ,15137
+ ,5.5
+ ,-2
+ ,1.2
+ ,3.15
+ ,4138.52
+ ,16519.44
+ ,11963.12
+ ,14954
+ ,6.9
+ ,2
+ ,1.2
+ ,3.16
+ ,4199.75
+ ,16101.07
+ ,12185.15
+ ,15648
+ ,5.7
+ ,1
+ ,1.5
+ ,3.16
+ ,4290.89
+ ,16775.08
+ ,12377.62
+ ,15305
+ ,6.9
+ ,-8
+ ,1.6
+ ,3.19
+ ,4443.91
+ ,17286.32
+ ,12512.89
+ ,15579
+ ,6.1
+ ,-1
+ ,1.7
+ ,3.44
+ ,4502.64
+ ,17741.23
+ ,12631.48
+ ,16348
+ ,4.8
+ ,1
+ ,1.8
+ ,3.55
+ ,4356.98
+ ,17128.37
+ ,12268.53
+ ,15928
+ ,3.7
+ ,-1
+ ,1.8
+ ,3.6
+ ,4591.27
+ ,17460.53
+ ,12754.8
+ ,16171
+ ,5.8
+ ,2
+ ,1.8
+ ,3.62
+ ,4696.96
+ ,17611.14
+ ,13407.75
+ ,15937
+ ,6.8
+ ,2
+ ,1.3
+ ,3.69)
+ ,dim=c(8
+ ,51)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_1j')
+ ,1:51))
> y <- array(NA,dim=c(8,51),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_1j'),1:51))
> 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 1635.25 8169.75 7977.64 10171 -14.9 -18
2 1833.42 7905.84 8334.59 9721 -16.2 -11
3 1910.43 8145.82 8623.36 9897 -14.4 -9
4 1959.67 8895.71 9098.03 9828 -17.3 -10
5 1969.60 9676.31 9154.34 9924 -15.7 -13
6 2061.41 9884.59 9284.73 10371 -12.6 -11
7 2093.48 10637.44 9492.49 10846 -9.4 -5
8 2120.88 10717.13 9682.35 10413 -8.1 -15
9 2174.56 10205.29 9762.12 10709 -5.4 -6
10 2196.72 10295.98 10124.63 10662 -4.6 -6
11 2350.44 10892.76 10540.05 10570 -4.9 -3
12 2440.25 10631.92 10601.61 10297 -4.0 -1
13 2408.64 11441.08 10323.73 10635 -3.1 -3
14 2472.81 11950.95 10418.40 10872 -1.3 -4
15 2407.60 11037.54 10092.96 10296 0.0 -6
16 2454.62 11527.72 10364.91 10383 -0.4 0
17 2448.05 11383.89 10152.09 10431 3.0 -4
18 2497.84 10989.34 10032.80 10574 0.4 -2
19 2645.64 11079.42 10204.59 10653 1.2 -2
20 2756.76 11028.93 10001.60 10805 0.6 -6
21 2849.27 10973.00 10411.75 10872 -1.3 -7
22 2921.44 11068.05 10673.38 10625 -3.2 -6
23 2981.85 11394.84 10539.51 10407 -1.8 -6
24 3080.58 11545.71 10723.78 10463 -3.6 -3
25 3106.22 11809.38 10682.06 10556 -4.2 -2
26 3119.31 11395.64 10283.19 10646 -6.9 -5
27 3061.26 11082.38 10377.18 10702 -8.0 -11
28 3097.31 11402.75 10486.64 11353 -7.5 -11
29 3161.69 11716.87 10545.38 11346 -8.2 -11
30 3257.16 12204.98 10554.27 11451 -7.6 -10
31 3277.01 12986.62 10532.54 11964 -3.7 -14
32 3295.32 13392.79 10324.31 12574 -1.7 -8
33 3363.99 14368.05 10695.25 13031 -0.7 -9
34 3494.17 15650.83 10827.81 13812 0.2 -5
35 3667.03 16102.64 10872.48 14544 0.6 -1
36 3813.06 16187.64 10971.19 14931 2.2 -2
37 3917.96 16311.54 11145.65 14886 3.3 -5
38 3895.51 17232.97 11234.68 16005 5.3 -4
39 3801.06 16397.83 11333.88 17064 5.5 -6
40 3570.12 14990.31 10997.97 15168 6.3 -2
41 3701.61 15147.55 11036.89 16050 7.7 -2
42 3862.27 15786.78 11257.35 15839 6.5 -2
43 3970.10 15934.09 11533.59 15137 5.5 -2
44 4138.52 16519.44 11963.12 14954 6.9 2
45 4199.75 16101.07 12185.15 15648 5.7 1
46 4290.89 16775.08 12377.62 15305 6.9 -8
47 4443.91 17286.32 12512.89 15579 6.1 -1
48 4502.64 17741.23 12631.48 16348 4.8 1
49 4356.98 17128.37 12268.53 15928 3.7 -1
50 4591.27 17460.53 12754.80 16171 5.8 2
51 4696.96 17611.14 13407.75 15937 6.8 2
Alg_consumptie_index_BE Gem_rente_kasbon_1j
1 1.8 2.05
2 1.5 2.05
3 1.0 1.81
4 1.6 1.58
5 1.5 1.57
6 1.8 1.76
7 1.8 1.76
8 1.6 1.89
9 1.9 1.90
10 1.7 1.90
11 1.6 1.92
12 1.3 1.76
13 1.1 1.64
14 1.9 1.57
15 2.6 1.69
16 2.3 1.76
17 2.4 1.89
18 2.2 1.78
19 2.0 1.88
20 2.9 1.86
21 2.6 1.88
22 2.3 1.87
23 2.3 1.86
24 2.6 1.89
25 3.1 1.90
26 2.8 1.89
27 2.5 1.85
28 2.9 1.78
29 3.1 1.71
30 3.1 1.69
31 3.2 1.72
32 2.5 1.77
33 2.6 1.98
34 2.9 2.20
35 2.6 2.25
36 2.4 2.24
37 1.7 2.51
38 2.0 2.79
39 2.2 3.07
40 1.9 3.08
41 1.6 3.05
42 1.6 3.08
43 1.2 3.15
44 1.2 3.16
45 1.5 3.16
46 1.6 3.19
47 1.7 3.44
48 1.8 3.55
49 1.8 3.60
50 1.8 3.62
51 1.3 3.69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-3.817e+03 9.453e-02 3.604e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
6.537e-02 -1.461e+01 -3.862e+00
Alg_consumptie_index_BE Gem_rente_kasbon_1j
2.466e+02 2.230e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-302.418 -120.773 7.501 98.352 307.226
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.817e+03 6.162e+02 -6.195 1.90e-07 ***
Nikkei 9.453e-02 5.050e-02 1.872 0.0680 .
DJ_Indust 3.604e-01 6.999e-02 5.150 6.18e-06 ***
Goudprijs 6.537e-02 5.824e-02 1.122 0.2679
Conjunct_Seizoenzuiver -1.461e+01 7.768e+00 -1.880 0.0669 .
Cons_vertrouw -3.862e+00 8.350e+00 -0.463 0.6461
Alg_consumptie_index_BE 2.466e+02 5.289e+01 4.662 3.04e-05 ***
Gem_rente_kasbon_1j 2.230e+02 1.185e+02 1.882 0.0666 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 163.4 on 43 degrees of freedom
Multiple R-squared: 0.9673, Adjusted R-squared: 0.962
F-statistic: 181.9 on 7 and 43 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,] 0.049742577 9.948515e-02 9.502574e-01
[2,] 0.014832143 2.966429e-02 9.851679e-01
[3,] 0.005098960 1.019792e-02 9.949010e-01
[4,] 0.004268944 8.537888e-03 9.957311e-01
[5,] 0.002487970 4.975940e-03 9.975120e-01
[6,] 0.007529687 1.505937e-02 9.924703e-01
[7,] 0.005465010 1.093002e-02 9.945350e-01
[8,] 0.044318420 8.863684e-02 9.556816e-01
[9,] 0.597273941 8.054521e-01 4.027261e-01
[10,] 0.979489691 4.102062e-02 2.051031e-02
[11,] 0.994398110 1.120378e-02 5.601890e-03
[12,] 0.999591096 8.178084e-04 4.089042e-04
[13,] 0.999939825 1.203509e-04 6.017543e-05
[14,] 0.999959841 8.031894e-05 4.015947e-05
[15,] 0.999916746 1.665070e-04 8.325351e-05
[16,] 0.999932954 1.340915e-04 6.704577e-05
[17,] 0.999909031 1.819387e-04 9.096936e-05
[18,] 0.999806962 3.860754e-04 1.930377e-04
[19,] 0.999584411 8.311774e-04 4.155887e-04
[20,] 0.999018873 1.962253e-03 9.811267e-04
[21,] 0.998688432 2.623135e-03 1.311568e-03
[22,] 0.999068370 1.863260e-03 9.316302e-04
[23,] 0.997551003 4.897993e-03 2.448997e-03
[24,] 0.997244734 5.510531e-03 2.755266e-03
[25,] 0.996554919 6.890162e-03 3.445081e-03
[26,] 0.990968239 1.806352e-02 9.031761e-03
[27,] 0.987201599 2.559680e-02 1.279840e-02
[28,] 0.968003728 6.399254e-02 3.199627e-02
[29,] 0.980317125 3.936575e-02 1.968288e-02
[30,] 0.990179486 1.964103e-02 9.820514e-03
> postscript(file="/var/www/html/rcomp/tmp/120ki1291647280.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cr1k1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cr1k1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cr1k1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/550j51291647280.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 = 51
Frequency = 1
1 2 3 4 5 6
-48.226307 157.661625 307.226008 -23.869117 -75.630561 -143.078181
7 8 9 10 11 12
-218.205072 -237.777967 -185.826866 -238.834527 -257.853495 -17.200629
13 14 15 16 17 18
34.267073 -158.594886 -170.603323 -197.946018 -136.785973 27.549135
19 20 21 22 23 24
138.442730 75.901311 59.378350 96.717935 211.418916 130.436696
25 26 27 28 29 30
9.690697 224.962783 202.643849 50.684621 20.729380 77.075403
31 32 33 34 35 36
7.501319 236.427162 -11.428130 -195.797403 -45.491332 102.678412
37 38 39 40 41 42
252.784215 -65.361923 -302.418356 -56.409152 89.643355 99.985571
43 44 45 46 47 48
208.619650 212.508951 92.529590 24.424067 -2.626171 -140.372172
49 50 51
-104.759029 -55.232774 -61.559439
> postscript(file="/var/www/html/rcomp/tmp/650j51291647280.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 = 51
Frequency = 1
lag(myerror, k = 1) myerror
0 -48.226307 NA
1 157.661625 -48.226307
2 307.226008 157.661625
3 -23.869117 307.226008
4 -75.630561 -23.869117
5 -143.078181 -75.630561
6 -218.205072 -143.078181
7 -237.777967 -218.205072
8 -185.826866 -237.777967
9 -238.834527 -185.826866
10 -257.853495 -238.834527
11 -17.200629 -257.853495
12 34.267073 -17.200629
13 -158.594886 34.267073
14 -170.603323 -158.594886
15 -197.946018 -170.603323
16 -136.785973 -197.946018
17 27.549135 -136.785973
18 138.442730 27.549135
19 75.901311 138.442730
20 59.378350 75.901311
21 96.717935 59.378350
22 211.418916 96.717935
23 130.436696 211.418916
24 9.690697 130.436696
25 224.962783 9.690697
26 202.643849 224.962783
27 50.684621 202.643849
28 20.729380 50.684621
29 77.075403 20.729380
30 7.501319 77.075403
31 236.427162 7.501319
32 -11.428130 236.427162
33 -195.797403 -11.428130
34 -45.491332 -195.797403
35 102.678412 -45.491332
36 252.784215 102.678412
37 -65.361923 252.784215
38 -302.418356 -65.361923
39 -56.409152 -302.418356
40 89.643355 -56.409152
41 99.985571 89.643355
42 208.619650 99.985571
43 212.508951 208.619650
44 92.529590 212.508951
45 24.424067 92.529590
46 -2.626171 24.424067
47 -140.372172 -2.626171
48 -104.759029 -140.372172
49 -55.232774 -104.759029
50 -61.559439 -55.232774
51 NA -61.559439
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 157.661625 -48.226307
[2,] 307.226008 157.661625
[3,] -23.869117 307.226008
[4,] -75.630561 -23.869117
[5,] -143.078181 -75.630561
[6,] -218.205072 -143.078181
[7,] -237.777967 -218.205072
[8,] -185.826866 -237.777967
[9,] -238.834527 -185.826866
[10,] -257.853495 -238.834527
[11,] -17.200629 -257.853495
[12,] 34.267073 -17.200629
[13,] -158.594886 34.267073
[14,] -170.603323 -158.594886
[15,] -197.946018 -170.603323
[16,] -136.785973 -197.946018
[17,] 27.549135 -136.785973
[18,] 138.442730 27.549135
[19,] 75.901311 138.442730
[20,] 59.378350 75.901311
[21,] 96.717935 59.378350
[22,] 211.418916 96.717935
[23,] 130.436696 211.418916
[24,] 9.690697 130.436696
[25,] 224.962783 9.690697
[26,] 202.643849 224.962783
[27,] 50.684621 202.643849
[28,] 20.729380 50.684621
[29,] 77.075403 20.729380
[30,] 7.501319 77.075403
[31,] 236.427162 7.501319
[32,] -11.428130 236.427162
[33,] -195.797403 -11.428130
[34,] -45.491332 -195.797403
[35,] 102.678412 -45.491332
[36,] 252.784215 102.678412
[37,] -65.361923 252.784215
[38,] -302.418356 -65.361923
[39,] -56.409152 -302.418356
[40,] 89.643355 -56.409152
[41,] 99.985571 89.643355
[42,] 208.619650 99.985571
[43,] 212.508951 208.619650
[44,] 92.529590 212.508951
[45,] 24.424067 92.529590
[46,] -2.626171 24.424067
[47,] -140.372172 -2.626171
[48,] -104.759029 -140.372172
[49,] -55.232774 -104.759029
[50,] -61.559439 -55.232774
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 157.661625 -48.226307
2 307.226008 157.661625
3 -23.869117 307.226008
4 -75.630561 -23.869117
5 -143.078181 -75.630561
6 -218.205072 -143.078181
7 -237.777967 -218.205072
8 -185.826866 -237.777967
9 -238.834527 -185.826866
10 -257.853495 -238.834527
11 -17.200629 -257.853495
12 34.267073 -17.200629
13 -158.594886 34.267073
14 -170.603323 -158.594886
15 -197.946018 -170.603323
16 -136.785973 -197.946018
17 27.549135 -136.785973
18 138.442730 27.549135
19 75.901311 138.442730
20 59.378350 75.901311
21 96.717935 59.378350
22 211.418916 96.717935
23 130.436696 211.418916
24 9.690697 130.436696
25 224.962783 9.690697
26 202.643849 224.962783
27 50.684621 202.643849
28 20.729380 50.684621
29 77.075403 20.729380
30 7.501319 77.075403
31 236.427162 7.501319
32 -11.428130 236.427162
33 -195.797403 -11.428130
34 -45.491332 -195.797403
35 102.678412 -45.491332
36 252.784215 102.678412
37 -65.361923 252.784215
38 -302.418356 -65.361923
39 -56.409152 -302.418356
40 89.643355 -56.409152
41 99.985571 89.643355
42 208.619650 99.985571
43 212.508951 208.619650
44 92.529590 212.508951
45 24.424067 92.529590
46 -2.626171 24.424067
47 -140.372172 -2.626171
48 -104.759029 -140.372172
49 -55.232774 -104.759029
50 -61.559439 -55.232774
> 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/7ya0q1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ya0q1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rjhb1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rjhb1291647280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c1yz1291647280.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/12xkw51291647280.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/1343bh1291647280.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/14fus11291647280.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/150v971291647280.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/16e46y1291647280.tab")
+ }
>
> try(system("convert tmp/120ki1291647280.ps tmp/120ki1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cr1k1291647280.ps tmp/2cr1k1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cr1k1291647280.ps tmp/3cr1k1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cr1k1291647280.ps tmp/4cr1k1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/550j51291647280.ps tmp/550j51291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/650j51291647280.ps tmp/650j51291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ya0q1291647280.ps tmp/7ya0q1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ya0q1291647280.ps tmp/8ya0q1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rjhb1291647280.ps tmp/9rjhb1291647280.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rjhb1291647280.ps tmp/10rjhb1291647280.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.466 1.642 6.527