R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(31514
+ ,-9
+ ,0
+ ,27071
+ ,-13
+ ,4
+ ,29462
+ ,-18
+ ,5
+ ,26105
+ ,-11
+ ,-7
+ ,22397
+ ,-9
+ ,-2
+ ,23843
+ ,-10
+ ,1
+ ,21705
+ ,-13
+ ,3
+ ,18089
+ ,-11
+ ,-2
+ ,20764
+ ,-5
+ ,-6
+ ,25316
+ ,-15
+ ,10
+ ,17704
+ ,-6
+ ,-9
+ ,15548
+ ,-6
+ ,0
+ ,28029
+ ,-3
+ ,-3
+ ,29383
+ ,-1
+ ,-2
+ ,36438
+ ,-3
+ ,2
+ ,32034
+ ,-4
+ ,1
+ ,22679
+ ,-6
+ ,2
+ ,24319
+ ,0
+ ,-6
+ ,18004
+ ,-4
+ ,4
+ ,17537
+ ,-2
+ ,-2
+ ,20366
+ ,-2
+ ,0
+ ,22782
+ ,-6
+ ,4
+ ,19169
+ ,-7
+ ,1
+ ,13807
+ ,-6
+ ,-1
+ ,29743
+ ,-6
+ ,0
+ ,25591
+ ,-3
+ ,-3
+ ,29096
+ ,-2
+ ,-1
+ ,26482
+ ,-5
+ ,3
+ ,22405
+ ,-11
+ ,6
+ ,27044
+ ,-11
+ ,0
+ ,17970
+ ,-11
+ ,0
+ ,18730
+ ,-10
+ ,-1
+ ,19684
+ ,-14
+ ,4
+ ,19785
+ ,-8
+ ,-6
+ ,18479
+ ,-9
+ ,1
+ ,10698
+ ,-5
+ ,-4
+ ,31956
+ ,-1
+ ,-4
+ ,29506
+ ,-2
+ ,1
+ ,34506
+ ,-5
+ ,3
+ ,27165
+ ,-4
+ ,-1
+ ,26736
+ ,-6
+ ,2
+ ,23691
+ ,-2
+ ,-4
+ ,18157
+ ,-2
+ ,0
+ ,17328
+ ,-2
+ ,0
+ ,18205
+ ,-2
+ ,0
+ ,20995
+ ,2
+ ,-4
+ ,17382
+ ,1
+ ,1
+ ,9367
+ ,-8
+ ,9
+ ,31124
+ ,-1
+ ,-7
+ ,26551
+ ,1
+ ,-2
+ ,30651
+ ,-1
+ ,2
+ ,25859
+ ,2
+ ,-3
+ ,25100
+ ,2
+ ,0
+ ,25778
+ ,1
+ ,1
+ ,20418
+ ,-1
+ ,2
+ ,18688
+ ,-2
+ ,1
+ ,20424
+ ,-2
+ ,0
+ ,24776
+ ,-1
+ ,-1
+ ,19814
+ ,-8
+ ,7
+ ,12738
+ ,-4
+ ,-4
+ ,31566
+ ,-6
+ ,2
+ ,30111
+ ,-3
+ ,-3
+ ,30019
+ ,-3
+ ,0
+ ,31934
+ ,-7
+ ,4
+ ,25826
+ ,-9
+ ,2
+ ,26835
+ ,-11
+ ,2
+ ,20205
+ ,-13
+ ,2
+ ,17789
+ ,-11
+ ,-2
+ ,20520
+ ,-9
+ ,-2
+ ,22518
+ ,-17
+ ,8
+ ,15572
+ ,-22
+ ,5
+ ,11509
+ ,-25
+ ,3
+ ,25447
+ ,-20
+ ,-5
+ ,24090
+ ,-24
+ ,4
+ ,27786
+ ,-24
+ ,0
+ ,26195
+ ,-22
+ ,-2
+ ,20516
+ ,-19
+ ,-3
+ ,22759
+ ,-18
+ ,-1
+ ,19028
+ ,-17
+ ,-1
+ ,16971
+ ,-11
+ ,-6
+ ,20036
+ ,-11
+ ,0
+ ,22485
+ ,-12
+ ,1
+ ,18730
+ ,-10
+ ,-2
+ ,14538
+ ,-15
+ ,5)
+ ,dim=c(3
+ ,84)
+ ,dimnames=list(c('Inschrijvingen'
+ ,'Consumentenvertrouwen'
+ ,'Evolutie_consumentenvertrouwen')
+ ,1:84))
> y <- array(NA,dim=c(3,84),dimnames=list(c('Inschrijvingen','Consumentenvertrouwen','Evolutie_consumentenvertrouwen'),1:84))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Inschrijvingen Consumentenvertrouwen Evolutie_consumentenvertrouwen M1 M2 M3
1 31514 -9 0 1 0 0
2 27071 -13 4 0 1 0
3 29462 -18 5 0 0 1
4 26105 -11 -7 0 0 0
5 22397 -9 -2 0 0 0
6 23843 -10 1 0 0 0
7 21705 -13 3 0 0 0
8 18089 -11 -2 0 0 0
9 20764 -5 -6 0 0 0
10 25316 -15 10 0 0 0
11 17704 -6 -9 0 0 0
12 15548 -6 0 0 0 0
13 28029 -3 -3 1 0 0
14 29383 -1 -2 0 1 0
15 36438 -3 2 0 0 1
16 32034 -4 1 0 0 0
17 22679 -6 2 0 0 0
18 24319 0 -6 0 0 0
19 18004 -4 4 0 0 0
20 17537 -2 -2 0 0 0
21 20366 -2 0 0 0 0
22 22782 -6 4 0 0 0
23 19169 -7 1 0 0 0
24 13807 -6 -1 0 0 0
25 29743 -6 0 1 0 0
26 25591 -3 -3 0 1 0
27 29096 -2 -1 0 0 1
28 26482 -5 3 0 0 0
29 22405 -11 6 0 0 0
30 27044 -11 0 0 0 0
31 17970 -11 0 0 0 0
32 18730 -10 -1 0 0 0
33 19684 -14 4 0 0 0
34 19785 -8 -6 0 0 0
35 18479 -9 1 0 0 0
36 10698 -5 -4 0 0 0
37 31956 -1 -4 1 0 0
38 29506 -2 1 0 1 0
39 34506 -5 3 0 0 1
40 27165 -4 -1 0 0 0
41 26736 -6 2 0 0 0
42 23691 -2 -4 0 0 0
43 18157 -2 0 0 0 0
44 17328 -2 0 0 0 0
45 18205 -2 0 0 0 0
46 20995 2 -4 0 0 0
47 17382 1 1 0 0 0
48 9367 -8 9 0 0 0
49 31124 -1 -7 1 0 0
50 26551 1 -2 0 1 0
51 30651 -1 2 0 0 1
52 25859 2 -3 0 0 0
53 25100 2 0 0 0 0
54 25778 1 1 0 0 0
55 20418 -1 2 0 0 0
56 18688 -2 1 0 0 0
57 20424 -2 0 0 0 0
58 24776 -1 -1 0 0 0
59 19814 -8 7 0 0 0
60 12738 -4 -4 0 0 0
61 31566 -6 2 1 0 0
62 30111 -3 -3 0 1 0
63 30019 -3 0 0 0 1
64 31934 -7 4 0 0 0
65 25826 -9 2 0 0 0
66 26835 -11 2 0 0 0
67 20205 -13 2 0 0 0
68 17789 -11 -2 0 0 0
69 20520 -9 -2 0 0 0
70 22518 -17 8 0 0 0
71 15572 -22 5 0 0 0
72 11509 -25 3 0 0 0
73 25447 -20 -5 1 0 0
74 24090 -24 4 0 1 0
75 27786 -24 0 0 0 1
76 26195 -22 -2 0 0 0
77 20516 -19 -3 0 0 0
78 22759 -18 -1 0 0 0
79 19028 -17 -1 0 0 0
80 16971 -11 -6 0 0 0
81 20036 -11 0 0 0 0
82 22485 -12 1 0 0 0
83 18730 -10 -2 0 0 0
84 14538 -15 5 0 0 0
M4 M5 M6 M7 M8 M9 M10 M11
1 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0
4 1 0 0 0 0 0 0 0
5 0 1 0 0 0 0 0 0
6 0 0 1 0 0 0 0 0
7 0 0 0 1 0 0 0 0
8 0 0 0 0 1 0 0 0
9 0 0 0 0 0 1 0 0
10 0 0 0 0 0 0 1 0
11 0 0 0 0 0 0 0 1
12 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0
16 1 0 0 0 0 0 0 0
17 0 1 0 0 0 0 0 0
18 0 0 1 0 0 0 0 0
19 0 0 0 1 0 0 0 0
20 0 0 0 0 1 0 0 0
21 0 0 0 0 0 1 0 0
22 0 0 0 0 0 0 1 0
23 0 0 0 0 0 0 0 1
24 0 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0 0
28 1 0 0 0 0 0 0 0
29 0 1 0 0 0 0 0 0
30 0 0 1 0 0 0 0 0
31 0 0 0 1 0 0 0 0
32 0 0 0 0 1 0 0 0
33 0 0 0 0 0 1 0 0
34 0 0 0 0 0 0 1 0
35 0 0 0 0 0 0 0 1
36 0 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0 0
38 0 0 0 0 0 0 0 0
39 0 0 0 0 0 0 0 0
40 1 0 0 0 0 0 0 0
41 0 1 0 0 0 0 0 0
42 0 0 1 0 0 0 0 0
43 0 0 0 1 0 0 0 0
44 0 0 0 0 1 0 0 0
45 0 0 0 0 0 1 0 0
46 0 0 0 0 0 0 1 0
47 0 0 0 0 0 0 0 1
48 0 0 0 0 0 0 0 0
49 0 0 0 0 0 0 0 0
50 0 0 0 0 0 0 0 0
51 0 0 0 0 0 0 0 0
52 1 0 0 0 0 0 0 0
53 0 1 0 0 0 0 0 0
54 0 0 1 0 0 0 0 0
55 0 0 0 1 0 0 0 0
56 0 0 0 0 1 0 0 0
57 0 0 0 0 0 1 0 0
58 0 0 0 0 0 0 1 0
59 0 0 0 0 0 0 0 1
60 0 0 0 0 0 0 0 0
61 0 0 0 0 0 0 0 0
62 0 0 0 0 0 0 0 0
63 0 0 0 0 0 0 0 0
64 1 0 0 0 0 0 0 0
65 0 1 0 0 0 0 0 0
66 0 0 1 0 0 0 0 0
67 0 0 0 1 0 0 0 0
68 0 0 0 0 1 0 0 0
69 0 0 0 0 0 1 0 0
70 0 0 0 0 0 0 1 0
71 0 0 0 0 0 0 0 1
72 0 0 0 0 0 0 0 0
73 0 0 0 0 0 0 0 0
74 0 0 0 0 0 0 0 0
75 0 0 0 0 0 0 0 0
76 1 0 0 0 0 0 0 0
77 0 1 0 0 0 0 0 0
78 0 0 1 0 0 0 0 0
79 0 0 0 1 0 0 0 0
80 0 0 0 0 1 0 0 0
81 0 0 0 0 0 1 0 0
82 0 0 0 0 0 0 1 0
83 0 0 0 0 0 0 0 1
84 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Consumentenvertrouwen
13517.2 113.7
Evolutie_consumentenvertrouwen M1
179.0 17576.0
M2 M3
14711.3 18248.2
M4 M5
15406.9 10911.7
M6 M7
12385.9 6573.5
M8 M9
5461.6 7316.0
M10 M11
9767.4 5493.0
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4851.0 -1370.3 -210.8 1219.9 4655.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13517.23 770.19 17.550 < 2e-16 ***
Consumentenvertrouwen 113.73 32.43 3.506 0.000797 ***
Evolutie_consumentenvertrouwen 178.95 62.77 2.851 0.005726 **
M1 17576.01 1022.26 17.193 < 2e-16 ***
M2 14711.30 1005.10 14.637 < 2e-16 ***
M3 18248.24 1000.68 18.236 < 2e-16 ***
M4 15406.90 1005.74 15.319 < 2e-16 ***
M5 10911.71 999.29 10.919 < 2e-16 ***
M6 12385.88 1007.58 12.293 < 2e-16 ***
M7 6573.47 999.10 6.579 7.23e-09 ***
M8 5461.64 1013.92 5.387 9.11e-07 ***
M9 7315.99 1006.83 7.266 4.07e-10 ***
M10 9767.35 1000.79 9.760 1.09e-14 ***
M11 5493.00 999.05 5.498 5.87e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1867 on 70 degrees of freedom
Multiple R-squared: 0.9097, Adjusted R-squared: 0.893
F-statistic: 54.27 on 13 and 70 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,] 0.8348550 0.330290076 0.1651450382
[2,] 0.7248248 0.550350393 0.2751751965
[3,] 0.9604964 0.079007154 0.0395035772
[4,] 0.9477416 0.104516775 0.0522583875
[5,] 0.9263722 0.147255619 0.0736278093
[6,] 0.9148927 0.170214658 0.0851073288
[7,] 0.8719149 0.256170232 0.1280851161
[8,] 0.8371401 0.325719821 0.1628599105
[9,] 0.7759488 0.448102310 0.2240511551
[10,] 0.7651656 0.469668767 0.2348343834
[11,] 0.8207316 0.358536725 0.1792683627
[12,] 0.8757379 0.248524282 0.1242621411
[13,] 0.8598803 0.280239469 0.1401197347
[14,] 0.8740729 0.251854211 0.1259271056
[15,] 0.8367631 0.326473861 0.1632369305
[16,] 0.7940604 0.411879155 0.2059395774
[17,] 0.7457374 0.508525236 0.2542626178
[18,] 0.7130442 0.573911598 0.2869557991
[19,] 0.6517241 0.696551815 0.3482759075
[20,] 0.6578979 0.684204214 0.3421021069
[21,] 0.6475418 0.704916313 0.3524581564
[22,] 0.6053976 0.789204821 0.3946024103
[23,] 0.6790986 0.641802777 0.3209013885
[24,] 0.6308509 0.738298127 0.3691490637
[25,] 0.6938642 0.612271662 0.3061358310
[26,] 0.6519873 0.696025332 0.3480126662
[27,] 0.6356461 0.728707881 0.3643539404
[28,] 0.5982134 0.803573129 0.4017865646
[29,] 0.6296977 0.740604574 0.3703022872
[30,] 0.6285628 0.742874425 0.3714372127
[31,] 0.6282730 0.743453960 0.3717269800
[32,] 0.9254554 0.149089107 0.0745445536
[33,] 0.9170906 0.165818839 0.0829094197
[34,] 0.9101608 0.179678325 0.0898391625
[35,] 0.8822554 0.235489269 0.1177446344
[36,] 0.9787057 0.042588607 0.0212943036
[37,] 0.9718684 0.056263114 0.0281315572
[38,] 0.9739071 0.052185792 0.0260928959
[39,] 0.9819357 0.036128656 0.0180643281
[40,] 0.9816698 0.036660347 0.0183301737
[41,] 0.9834848 0.033030312 0.0165151558
[42,] 0.9733828 0.053234392 0.0266171958
[43,] 0.9571707 0.085658552 0.0428292760
[44,] 0.9778646 0.044270802 0.0221354010
[45,] 0.9618560 0.076287919 0.0381439593
[46,] 0.9600216 0.079956793 0.0399783966
[47,] 0.9992758 0.001448330 0.0007241648
[48,] 0.9985441 0.002911711 0.0014558554
[49,] 0.9963546 0.007290747 0.0036453734
[50,] 0.9982068 0.003586375 0.0017931875
[51,] 0.9922676 0.015464722 0.0077323608
> postscript(file="/var/www/html/rcomp/tmp/1d9j61292678252.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d9j61292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d9j61292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46i0r1292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56i0r1292678252.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 = 84
Frequency = 1
1 2 3 4 5 6
1444.31419 -394.87196 -1151.12392 -315.45535 -650.48161 -1101.78412
7 8 9 10 11 12
2555.91099 719.04197 1573.12655 1947.82154 986.70482 2713.13605
13 14 15 16 17 18
-2186.19977 1626.10012 4655.80812 3385.83277 -1425.47344 -510.40381
19 20 21 22 23 24
-2347.59479 -856.51203 -239.76886 -536.02171 775.91514 1151.08784
25 26 27 28 29 30
-667.87047 -1759.49164 -2263.06473 -2410.34259 -1846.63949 2391.89589
31 32 33 34 35 36
-869.69008 1067.36196 -272.83736 -1516.04736 313.37158 -1534.78501
37 38 39 40 41 42
1692.29558 1325.97297 2772.31278 -1125.26365 2631.52656 -1268.85094
43 44 45 46 47 48
-1706.24407 -1423.41561 -2400.76886 -1801.23316 -1920.91063 -4850.97362
49 50 51 52 53 54
1397.15095 -1433.35632 -1358.64832 -2755.72939 443.60437 -417.79456
55 56 57 58 59 60
83.12412 -242.36740 -181.76886 1784.09613 460.93262 391.48677
61 62 63 64 65 66
797.22594 2760.50836 -1405.28829 3090.16207 2062.71122 1824.99231
67 68 69 70 71 72
1234.86278 419.04197 1068.23227 -264.81843 -1830.96870 298.11689
73 74 75 76 77 78
-2476.91642 -2124.86152 -1249.99564 130.79613 -1215.24761 -918.05477
79 80 81 82 83 84
1049.63104 316.84913 453.78513 386.20299 1214.95517 1831.93109
> postscript(file="/var/www/html/rcomp/tmp/66i0r1292678252.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 1444.31419 NA
1 -394.87196 1444.31419
2 -1151.12392 -394.87196
3 -315.45535 -1151.12392
4 -650.48161 -315.45535
5 -1101.78412 -650.48161
6 2555.91099 -1101.78412
7 719.04197 2555.91099
8 1573.12655 719.04197
9 1947.82154 1573.12655
10 986.70482 1947.82154
11 2713.13605 986.70482
12 -2186.19977 2713.13605
13 1626.10012 -2186.19977
14 4655.80812 1626.10012
15 3385.83277 4655.80812
16 -1425.47344 3385.83277
17 -510.40381 -1425.47344
18 -2347.59479 -510.40381
19 -856.51203 -2347.59479
20 -239.76886 -856.51203
21 -536.02171 -239.76886
22 775.91514 -536.02171
23 1151.08784 775.91514
24 -667.87047 1151.08784
25 -1759.49164 -667.87047
26 -2263.06473 -1759.49164
27 -2410.34259 -2263.06473
28 -1846.63949 -2410.34259
29 2391.89589 -1846.63949
30 -869.69008 2391.89589
31 1067.36196 -869.69008
32 -272.83736 1067.36196
33 -1516.04736 -272.83736
34 313.37158 -1516.04736
35 -1534.78501 313.37158
36 1692.29558 -1534.78501
37 1325.97297 1692.29558
38 2772.31278 1325.97297
39 -1125.26365 2772.31278
40 2631.52656 -1125.26365
41 -1268.85094 2631.52656
42 -1706.24407 -1268.85094
43 -1423.41561 -1706.24407
44 -2400.76886 -1423.41561
45 -1801.23316 -2400.76886
46 -1920.91063 -1801.23316
47 -4850.97362 -1920.91063
48 1397.15095 -4850.97362
49 -1433.35632 1397.15095
50 -1358.64832 -1433.35632
51 -2755.72939 -1358.64832
52 443.60437 -2755.72939
53 -417.79456 443.60437
54 83.12412 -417.79456
55 -242.36740 83.12412
56 -181.76886 -242.36740
57 1784.09613 -181.76886
58 460.93262 1784.09613
59 391.48677 460.93262
60 797.22594 391.48677
61 2760.50836 797.22594
62 -1405.28829 2760.50836
63 3090.16207 -1405.28829
64 2062.71122 3090.16207
65 1824.99231 2062.71122
66 1234.86278 1824.99231
67 419.04197 1234.86278
68 1068.23227 419.04197
69 -264.81843 1068.23227
70 -1830.96870 -264.81843
71 298.11689 -1830.96870
72 -2476.91642 298.11689
73 -2124.86152 -2476.91642
74 -1249.99564 -2124.86152
75 130.79613 -1249.99564
76 -1215.24761 130.79613
77 -918.05477 -1215.24761
78 1049.63104 -918.05477
79 316.84913 1049.63104
80 453.78513 316.84913
81 386.20299 453.78513
82 1214.95517 386.20299
83 1831.93109 1214.95517
84 NA 1831.93109
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -394.87196 1444.31419
[2,] -1151.12392 -394.87196
[3,] -315.45535 -1151.12392
[4,] -650.48161 -315.45535
[5,] -1101.78412 -650.48161
[6,] 2555.91099 -1101.78412
[7,] 719.04197 2555.91099
[8,] 1573.12655 719.04197
[9,] 1947.82154 1573.12655
[10,] 986.70482 1947.82154
[11,] 2713.13605 986.70482
[12,] -2186.19977 2713.13605
[13,] 1626.10012 -2186.19977
[14,] 4655.80812 1626.10012
[15,] 3385.83277 4655.80812
[16,] -1425.47344 3385.83277
[17,] -510.40381 -1425.47344
[18,] -2347.59479 -510.40381
[19,] -856.51203 -2347.59479
[20,] -239.76886 -856.51203
[21,] -536.02171 -239.76886
[22,] 775.91514 -536.02171
[23,] 1151.08784 775.91514
[24,] -667.87047 1151.08784
[25,] -1759.49164 -667.87047
[26,] -2263.06473 -1759.49164
[27,] -2410.34259 -2263.06473
[28,] -1846.63949 -2410.34259
[29,] 2391.89589 -1846.63949
[30,] -869.69008 2391.89589
[31,] 1067.36196 -869.69008
[32,] -272.83736 1067.36196
[33,] -1516.04736 -272.83736
[34,] 313.37158 -1516.04736
[35,] -1534.78501 313.37158
[36,] 1692.29558 -1534.78501
[37,] 1325.97297 1692.29558
[38,] 2772.31278 1325.97297
[39,] -1125.26365 2772.31278
[40,] 2631.52656 -1125.26365
[41,] -1268.85094 2631.52656
[42,] -1706.24407 -1268.85094
[43,] -1423.41561 -1706.24407
[44,] -2400.76886 -1423.41561
[45,] -1801.23316 -2400.76886
[46,] -1920.91063 -1801.23316
[47,] -4850.97362 -1920.91063
[48,] 1397.15095 -4850.97362
[49,] -1433.35632 1397.15095
[50,] -1358.64832 -1433.35632
[51,] -2755.72939 -1358.64832
[52,] 443.60437 -2755.72939
[53,] -417.79456 443.60437
[54,] 83.12412 -417.79456
[55,] -242.36740 83.12412
[56,] -181.76886 -242.36740
[57,] 1784.09613 -181.76886
[58,] 460.93262 1784.09613
[59,] 391.48677 460.93262
[60,] 797.22594 391.48677
[61,] 2760.50836 797.22594
[62,] -1405.28829 2760.50836
[63,] 3090.16207 -1405.28829
[64,] 2062.71122 3090.16207
[65,] 1824.99231 2062.71122
[66,] 1234.86278 1824.99231
[67,] 419.04197 1234.86278
[68,] 1068.23227 419.04197
[69,] -264.81843 1068.23227
[70,] -1830.96870 -264.81843
[71,] 298.11689 -1830.96870
[72,] -2476.91642 298.11689
[73,] -2124.86152 -2476.91642
[74,] -1249.99564 -2124.86152
[75,] 130.79613 -1249.99564
[76,] -1215.24761 130.79613
[77,] -918.05477 -1215.24761
[78,] 1049.63104 -918.05477
[79,] 316.84913 1049.63104
[80,] 453.78513 316.84913
[81,] 386.20299 453.78513
[82,] 1214.95517 386.20299
[83,] 1831.93109 1214.95517
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -394.87196 1444.31419
2 -1151.12392 -394.87196
3 -315.45535 -1151.12392
4 -650.48161 -315.45535
5 -1101.78412 -650.48161
6 2555.91099 -1101.78412
7 719.04197 2555.91099
8 1573.12655 719.04197
9 1947.82154 1573.12655
10 986.70482 1947.82154
11 2713.13605 986.70482
12 -2186.19977 2713.13605
13 1626.10012 -2186.19977
14 4655.80812 1626.10012
15 3385.83277 4655.80812
16 -1425.47344 3385.83277
17 -510.40381 -1425.47344
18 -2347.59479 -510.40381
19 -856.51203 -2347.59479
20 -239.76886 -856.51203
21 -536.02171 -239.76886
22 775.91514 -536.02171
23 1151.08784 775.91514
24 -667.87047 1151.08784
25 -1759.49164 -667.87047
26 -2263.06473 -1759.49164
27 -2410.34259 -2263.06473
28 -1846.63949 -2410.34259
29 2391.89589 -1846.63949
30 -869.69008 2391.89589
31 1067.36196 -869.69008
32 -272.83736 1067.36196
33 -1516.04736 -272.83736
34 313.37158 -1516.04736
35 -1534.78501 313.37158
36 1692.29558 -1534.78501
37 1325.97297 1692.29558
38 2772.31278 1325.97297
39 -1125.26365 2772.31278
40 2631.52656 -1125.26365
41 -1268.85094 2631.52656
42 -1706.24407 -1268.85094
43 -1423.41561 -1706.24407
44 -2400.76886 -1423.41561
45 -1801.23316 -2400.76886
46 -1920.91063 -1801.23316
47 -4850.97362 -1920.91063
48 1397.15095 -4850.97362
49 -1433.35632 1397.15095
50 -1358.64832 -1433.35632
51 -2755.72939 -1358.64832
52 443.60437 -2755.72939
53 -417.79456 443.60437
54 83.12412 -417.79456
55 -242.36740 83.12412
56 -181.76886 -242.36740
57 1784.09613 -181.76886
58 460.93262 1784.09613
59 391.48677 460.93262
60 797.22594 391.48677
61 2760.50836 797.22594
62 -1405.28829 2760.50836
63 3090.16207 -1405.28829
64 2062.71122 3090.16207
65 1824.99231 2062.71122
66 1234.86278 1824.99231
67 419.04197 1234.86278
68 1068.23227 419.04197
69 -264.81843 1068.23227
70 -1830.96870 -264.81843
71 298.11689 -1830.96870
72 -2476.91642 298.11689
73 -2124.86152 -2476.91642
74 -1249.99564 -2124.86152
75 130.79613 -1249.99564
76 -1215.24761 130.79613
77 -918.05477 -1215.24761
78 1049.63104 -918.05477
79 316.84913 1049.63104
80 453.78513 316.84913
81 386.20299 453.78513
82 1214.95517 386.20299
83 1831.93109 1214.95517
> 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/7hrhc1292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89jgf1292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99jgf1292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109jgf1292678252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11v1fl1292678252.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/12ykdq1292678252.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/1353a21292678252.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/14fcan1292678252.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/15juqt1292678252.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/16kpv81292678252.tab")
+ }
>
> try(system("convert tmp/1d9j61292678252.ps tmp/1d9j61292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d9j61292678252.ps tmp/2d9j61292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d9j61292678252.ps tmp/3d9j61292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/46i0r1292678252.ps tmp/46i0r1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/56i0r1292678252.ps tmp/56i0r1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/66i0r1292678252.ps tmp/66i0r1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hrhc1292678252.ps tmp/7hrhc1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/89jgf1292678252.ps tmp/89jgf1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/99jgf1292678252.ps tmp/99jgf1292678252.png",intern=TRUE))
character(0)
> try(system("convert tmp/109jgf1292678252.ps tmp/109jgf1292678252.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.783 1.685 6.851