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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(33024
+ ,31086
+ ,19828
+ ,18932
+ ,32526
+ ,30839
+ ,19967
+ ,18927
+ ,31455
+ ,30051
+ ,19814
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+ ,31524
+ ,29976
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+ ,19066
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+ ,30463
+ ,20719
+ ,19971
+ ,32696
+ ,31422
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+ ,35906
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+ ,22404
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+ ,35824
+ ,23031
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+ ,21127
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+ ,21216
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+ ,34594
+ ,21031
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+ ,20968
+ ,19791
+ ,37635
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+ ,21049
+ ,20672
+ ,38875
+ ,37416
+ ,21033
+ ,20938
+ ,38372
+ ,37953
+ ,21078
+ ,20675
+ ,38897
+ ,37517
+ ,20702
+ ,19992
+ ,38018
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+ ,20309
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+ ,37325
+ ,36963
+ ,20449
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+ ,147
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+ ,4
+ ,75
+ ,11
+ ,80
+ ,4
+ ,54
+ ,3
+ ,53
+ ,1
+ ,23
+ ,0
+ ,32
+ ,0
+ ,16
+ ,2
+ ,11
+ ,0
+ ,6
+ ,1
+ ,6
+ ,0
+ ,7
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0)
+ ,dim=c(4
+ ,111)
+ ,dimnames=list(c('MVG'
+ ,'VVG'
+ ,'MWG'
+ ,'VWG')
+ ,1:111))
> y <- array(NA,dim=c(4,111),dimnames=list(c('MVG','VVG','MWG','VWG'),1:111))
> 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
MVG VVG MWG VWG
1 33024 31086 19828 18932
2 32526 30839 19967 18927
3 31455 30051 19814 19124
4 31524 29976 20053 19066
5 31856 30463 20719 19971
6 32696 31422 21174 20165
7 32584 31588 20648 19705
8 33498 31900 20659 19718
9 34175 32878 20733 19938
10 34172 33010 21069 20039
11 34379 32954 20566 19721
12 34988 33076 20839 19777
13 36158 35057 21615 20505
14 37411 35906 22739 21763
15 38015 36100 23222 22404
16 37577 35824 23031 22038
17 36354 34579 23014 22038
18 36030 34484 22868 21874
19 35636 33920 22182 21269
20 35669 34059 22177 21127
21 34635 33812 21216 20609
22 35496 34594 21031 20565
23 36376 36083 20968 19791
24 37635 36563 21049 20672
25 38875 37416 21033 20938
26 38372 37953 21078 20675
27 38897 37517 20702 19992
28 38018 37467 20309 19801
29 37325 36963 20449 20050
30 36893 36019 20737 20427
31 36117 35232 20849 20815
32 37599 36857 21966 21666
33 39037 37978 23100 22720
34 40809 40160 23975 23650
35 42508 42165 24350 24244
36 44021 43069 24020 23669
37 44088 43021 24005 23881
38 44510 43376 23602 23857
39 45786 43978 24120 23999
40 47349 45911 24847 24780
41 48696 47107 25702 25426
42 50598 49168 26312 26229
43 50066 48390 25891 25973
44 49367 47678 25172 25375
45 48784 47822 25698 25966
46 47841 46695 25833 25391
47 48300 47185 25658 26046
48 47518 45684 25269 25572
49 46504 44884 24846 24900
50 45147 44256 24390 24744
51 44404 43637 23954 24526
52 43455 42368 23828 24274
53 42299 40892 23507 23774
54 42105 40616 23144 23414
55 40152 39026 22302 23002
56 39519 38921 23028 23137
57 39633 38512 22741 22947
58 39376 38884 23129 23733
59 38850 38406 22911 23234
60 39657 38804 22071 22969
61 34804 34871 16466 17708
62 34372 34660 16370 17377
63 32678 33104 15049 16273
64 28420 28952 13174 14342
65 25420 26488 12231 13522
66 27683 29418 13620 15210
67 29904 32315 14317 16493
68 30546 32885 14039 16701
69 29142 31565 13526 15662
70 27724 30782 12826 15526
71 27069 30442 12360 15413
72 26665 30851 12592 15805
73 26004 30432 12381 15802
74 25767 31260 12554 16753
75 24915 30737 12338 16906
76 23689 30129 11768 16891
77 20915 27672 10687 15703
78 19414 26469 9964 15429
79 17824 24895 9338 14762
80 16348 24427 8697 14426
81 15571 23252 8068 14250
82 13929 21815 7295 13267
83 12480 20837 6372 12397
84 10837 18537 5649 11586
85 9473 17237 4926 10888
86 8051 15476 4199 9841
87 5278 10709 2568 6443
88 3008 6776 1461 4019
89 2404 5810 1173 3449
90 2298 5765 1084 3179
91 2260 5775 978 3341
92 1938 5589 947 3325
93 1371 4687 679 2478
94 1009 3630 457 1982
95 686 2552 262 1405
96 493 1928 218 1059
97 285 1323 132 740
98 192 1005 70 533
99 129 678 44 366
100 60 397 24 224
101 54 286 20 147
102 26 166 4 75
103 11 80 4 54
104 3 53 1 23
105 0 32 0 16
106 2 11 0 6
107 1 6 0 7
108 0 4 0 2
109 0 2 0 0
110 0 0 0 0
111 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VVG MWG VWG
-351.344 1.085 1.350 -1.447
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2092.11 -292.89 6.33 340.48 1346.70
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -351.34356 116.05303 -3.027 0.00309 **
VVG 1.08526 0.02422 44.816 < 2e-16 ***
MWG 1.34992 0.02643 51.084 < 2e-16 ***
VWG -1.44737 0.05499 -26.322 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 529.4 on 107 degrees of freedom
Multiple R-squared: 0.999, Adjusted R-squared: 0.999
F-statistic: 3.599e+04 on 3 and 107 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,] 0.182755549 3.655111e-01 8.172445e-01
[2,] 0.100451329 2.009027e-01 8.995487e-01
[3,] 0.044826341 8.965268e-02 9.551737e-01
[4,] 0.021389027 4.277805e-02 9.786110e-01
[5,] 0.008302752 1.660550e-02 9.916972e-01
[6,] 0.014492908 2.898582e-02 9.855071e-01
[7,] 0.009305866 1.861173e-02 9.906941e-01
[8,] 0.019957225 3.991445e-02 9.800428e-01
[9,] 0.038336440 7.667288e-02 9.616636e-01
[10,] 0.025764066 5.152813e-02 9.742359e-01
[11,] 0.016082728 3.216546e-02 9.839173e-01
[12,] 0.009104535 1.820907e-02 9.908955e-01
[13,] 0.005172749 1.034550e-02 9.948273e-01
[14,] 0.002855944 5.711888e-03 9.971441e-01
[15,] 0.006084675 1.216935e-02 9.939153e-01
[16,] 0.003836454 7.672908e-03 9.961635e-01
[17,] 0.045575554 9.115111e-02 9.544244e-01
[18,] 0.042398448 8.479690e-02 9.576016e-01
[19,] 0.055842362 1.116847e-01 9.441576e-01
[20,] 0.076727866 1.534557e-01 9.232721e-01
[21,] 0.116870217 2.337404e-01 8.831298e-01
[22,] 0.137354507 2.747090e-01 8.626455e-01
[23,] 0.211332679 4.226654e-01 7.886673e-01
[24,] 0.188440325 3.768807e-01 8.115597e-01
[25,] 0.156555277 3.131106e-01 8.434447e-01
[26,] 0.180656613 3.613132e-01 8.193434e-01
[27,] 0.173824875 3.476498e-01 8.261751e-01
[28,] 0.272192041 5.443841e-01 7.278080e-01
[29,] 0.437871773 8.757435e-01 5.621282e-01
[30,] 0.558221184 8.835576e-01 4.417788e-01
[31,] 0.612755770 7.744885e-01 3.872442e-01
[32,] 0.653257706 6.934846e-01 3.467423e-01
[33,] 0.827036892 3.459262e-01 1.729631e-01
[34,] 0.839634566 3.207309e-01 1.603654e-01
[35,] 0.853587359 2.928253e-01 1.464126e-01
[36,] 0.845456936 3.090861e-01 1.545431e-01
[37,] 0.848564905 3.028702e-01 1.514351e-01
[38,] 0.871140827 2.577183e-01 1.288592e-01
[39,] 0.856660162 2.866797e-01 1.433398e-01
[40,] 0.955075593 8.984881e-02 4.492441e-02
[41,] 0.940974496 1.180510e-01 5.902550e-02
[42,] 0.961029716 7.794057e-02 3.897028e-02
[43,] 0.953674539 9.265092e-02 4.632546e-02
[44,] 0.947044472 1.059111e-01 5.295553e-02
[45,] 0.934680771 1.306385e-01 6.531923e-02
[46,] 0.915560485 1.688790e-01 8.443951e-02
[47,] 0.900930490 1.981390e-01 9.906951e-02
[48,] 0.899384947 2.012301e-01 1.006151e-01
[49,] 0.929379984 1.412400e-01 7.062002e-02
[50,] 0.967166263 6.566747e-02 3.283374e-02
[51,] 0.956163485 8.767303e-02 4.383651e-02
[52,] 0.961783254 7.643349e-02 3.821675e-02
[53,] 0.997627354 4.745291e-03 2.372646e-03
[54,] 0.998283685 3.432629e-03 1.716315e-03
[55,] 0.998599034 2.801933e-03 1.400966e-03
[56,] 0.997820220 4.359560e-03 2.179780e-03
[57,] 0.998314395 3.371211e-03 1.685605e-03
[58,] 0.998568350 2.863301e-03 1.431650e-03
[59,] 0.997996906 4.006187e-03 2.003094e-03
[60,] 0.999406117 1.187767e-03 5.938834e-04
[61,] 0.999813683 3.726339e-04 1.863169e-04
[62,] 0.999986957 2.608547e-05 1.304273e-05
[63,] 0.999987034 2.593117e-05 1.296558e-05
[64,] 0.999994541 1.091888e-05 5.459439e-06
[65,] 0.999999973 5.421982e-08 2.710991e-08
[66,] 0.999999994 1.101873e-08 5.509365e-09
[67,] 0.999999999 1.572877e-09 7.864386e-10
[68,] 1.000000000 2.998267e-10 1.499133e-10
[69,] 1.000000000 7.141289e-11 3.570644e-11
[70,] 1.000000000 2.664307e-14 1.332153e-14
[71,] 1.000000000 1.192203e-14 5.961016e-15
[72,] 1.000000000 1.689641e-16 8.448204e-17
[73,] 1.000000000 4.390272e-16 2.195136e-16
[74,] 1.000000000 1.686104e-18 8.430521e-19
[75,] 1.000000000 1.604939e-18 8.024696e-19
[76,] 1.000000000 5.775829e-18 2.887915e-18
[77,] 1.000000000 3.246062e-17 1.623031e-17
[78,] 1.000000000 2.413370e-16 1.206685e-16
[79,] 1.000000000 1.561923e-15 7.809616e-16
[80,] 1.000000000 2.743796e-17 1.371898e-17
[81,] 1.000000000 1.994345e-16 9.971724e-17
[82,] 1.000000000 1.482143e-15 7.410716e-16
[83,] 1.000000000 6.238684e-15 3.119342e-15
[84,] 1.000000000 4.706000e-14 2.353000e-14
[85,] 1.000000000 3.167916e-21 1.583958e-21
[86,] 1.000000000 9.069873e-20 4.534937e-20
[87,] 1.000000000 1.692671e-18 8.463353e-19
[88,] 1.000000000 2.373040e-17 1.186520e-17
[89,] 1.000000000 1.909824e-22 9.549122e-23
[90,] 1.000000000 8.387104e-23 4.193552e-23
[91,] 1.000000000 8.564219e-21 4.282109e-21
[92,] 1.000000000 1.032814e-18 5.164070e-19
[93,] 1.000000000 5.812301e-18 2.906150e-18
[94,] 1.000000000 3.252246e-16 1.626123e-16
[95,] 1.000000000 5.655225e-14 2.827613e-14
[96,] 1.000000000 4.774995e-14 2.387498e-14
[97,] 1.000000000 2.511597e-11 1.255799e-11
[98,] 0.999999992 1.676296e-08 8.381482e-09
> postscript(file="/var/www/rcomp/tmp/1ot2q1292077746.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ot2q1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3h2jb1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4h2jb1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5h2jb1292077746.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 = 111
Frequency = 1
1 2 3 4 5 6
274.531775 -150.285458 125.567413 -130.616801 83.690368 -450.493804
7 8 9 10 11 12
-698.380925 -119.014332 -284.867300 -738.509384 -251.990475 -62.866900
13 14 15 16 17 18
-1036.609955 -401.505830 267.710087 -142.663351 8.430137 -252.751581
19 20 21 22 23 24
15.716705 -300.911555 -519.320270 -320.940524 -2092.110819 -188.240890
25 26 27 28 29 30
532.635233 -994.553542 -477.368399 -1048.035651 -1022.658713 -273.292922
31 32 33 34 35 36
215.194493 -342.492637 -126.341938 -557.494067 -680.913696 -535.752694
37 38 39 40 41 42
-89.568224 456.446173 585.390325 199.596497 29.451782 33.527891
43 44 45 46 47 48
543.646092 722.411365 128.474873 -605.919932 505.570251 1191.604182
49 50 51 52 53 54
644.190177 358.504453 560.315820 793.858390 949.334637 1023.831609
55 56 57 58 59 60
1336.704172 33.010072 703.305952 656.457767 221.253275 1346.699274
61 62 63 64 65 66
713.677824 161.178427 341.180731 325.386895 85.587173 -263.086085
67 68 69 70 71 72
-269.988107 429.746939 -353.027067 -173.170112 6.326461 -587.354240
73 74 75 76 77 78
-513.140689 -505.816567 -277.196313 -95.616574 -463.357926 -79.382567
79 80 81 82 83 84
-81.537179 -670.656260 421.882145 -39.884757 -440.743355 214.518854
85 86 87 88 89 90
227.077443 182.205527 -133.833734 -149.592572 -141.460895 -469.272545
91 92 93 94 95 96
-140.559009 -242.011705 -694.257534 -327.356473 -52.350160 -9.544883
97 98 99 100 101 102
93.416239 129.616476 214.881892 272.310330 280.725706 300.344301
103 104 105 106 107 108
348.281536 328.764629 339.773324 350.089976 355.963635 349.897278
109 110 111
349.173043 351.343557 350.258300
> postscript(file="/var/www/rcomp/tmp/6rc0w1292077746.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 = 111
Frequency = 1
lag(myerror, k = 1) myerror
0 274.531775 NA
1 -150.285458 274.531775
2 125.567413 -150.285458
3 -130.616801 125.567413
4 83.690368 -130.616801
5 -450.493804 83.690368
6 -698.380925 -450.493804
7 -119.014332 -698.380925
8 -284.867300 -119.014332
9 -738.509384 -284.867300
10 -251.990475 -738.509384
11 -62.866900 -251.990475
12 -1036.609955 -62.866900
13 -401.505830 -1036.609955
14 267.710087 -401.505830
15 -142.663351 267.710087
16 8.430137 -142.663351
17 -252.751581 8.430137
18 15.716705 -252.751581
19 -300.911555 15.716705
20 -519.320270 -300.911555
21 -320.940524 -519.320270
22 -2092.110819 -320.940524
23 -188.240890 -2092.110819
24 532.635233 -188.240890
25 -994.553542 532.635233
26 -477.368399 -994.553542
27 -1048.035651 -477.368399
28 -1022.658713 -1048.035651
29 -273.292922 -1022.658713
30 215.194493 -273.292922
31 -342.492637 215.194493
32 -126.341938 -342.492637
33 -557.494067 -126.341938
34 -680.913696 -557.494067
35 -535.752694 -680.913696
36 -89.568224 -535.752694
37 456.446173 -89.568224
38 585.390325 456.446173
39 199.596497 585.390325
40 29.451782 199.596497
41 33.527891 29.451782
42 543.646092 33.527891
43 722.411365 543.646092
44 128.474873 722.411365
45 -605.919932 128.474873
46 505.570251 -605.919932
47 1191.604182 505.570251
48 644.190177 1191.604182
49 358.504453 644.190177
50 560.315820 358.504453
51 793.858390 560.315820
52 949.334637 793.858390
53 1023.831609 949.334637
54 1336.704172 1023.831609
55 33.010072 1336.704172
56 703.305952 33.010072
57 656.457767 703.305952
58 221.253275 656.457767
59 1346.699274 221.253275
60 713.677824 1346.699274
61 161.178427 713.677824
62 341.180731 161.178427
63 325.386895 341.180731
64 85.587173 325.386895
65 -263.086085 85.587173
66 -269.988107 -263.086085
67 429.746939 -269.988107
68 -353.027067 429.746939
69 -173.170112 -353.027067
70 6.326461 -173.170112
71 -587.354240 6.326461
72 -513.140689 -587.354240
73 -505.816567 -513.140689
74 -277.196313 -505.816567
75 -95.616574 -277.196313
76 -463.357926 -95.616574
77 -79.382567 -463.357926
78 -81.537179 -79.382567
79 -670.656260 -81.537179
80 421.882145 -670.656260
81 -39.884757 421.882145
82 -440.743355 -39.884757
83 214.518854 -440.743355
84 227.077443 214.518854
85 182.205527 227.077443
86 -133.833734 182.205527
87 -149.592572 -133.833734
88 -141.460895 -149.592572
89 -469.272545 -141.460895
90 -140.559009 -469.272545
91 -242.011705 -140.559009
92 -694.257534 -242.011705
93 -327.356473 -694.257534
94 -52.350160 -327.356473
95 -9.544883 -52.350160
96 93.416239 -9.544883
97 129.616476 93.416239
98 214.881892 129.616476
99 272.310330 214.881892
100 280.725706 272.310330
101 300.344301 280.725706
102 348.281536 300.344301
103 328.764629 348.281536
104 339.773324 328.764629
105 350.089976 339.773324
106 355.963635 350.089976
107 349.897278 355.963635
108 349.173043 349.897278
109 351.343557 349.173043
110 350.258300 351.343557
111 NA 350.258300
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -150.285458 274.531775
[2,] 125.567413 -150.285458
[3,] -130.616801 125.567413
[4,] 83.690368 -130.616801
[5,] -450.493804 83.690368
[6,] -698.380925 -450.493804
[7,] -119.014332 -698.380925
[8,] -284.867300 -119.014332
[9,] -738.509384 -284.867300
[10,] -251.990475 -738.509384
[11,] -62.866900 -251.990475
[12,] -1036.609955 -62.866900
[13,] -401.505830 -1036.609955
[14,] 267.710087 -401.505830
[15,] -142.663351 267.710087
[16,] 8.430137 -142.663351
[17,] -252.751581 8.430137
[18,] 15.716705 -252.751581
[19,] -300.911555 15.716705
[20,] -519.320270 -300.911555
[21,] -320.940524 -519.320270
[22,] -2092.110819 -320.940524
[23,] -188.240890 -2092.110819
[24,] 532.635233 -188.240890
[25,] -994.553542 532.635233
[26,] -477.368399 -994.553542
[27,] -1048.035651 -477.368399
[28,] -1022.658713 -1048.035651
[29,] -273.292922 -1022.658713
[30,] 215.194493 -273.292922
[31,] -342.492637 215.194493
[32,] -126.341938 -342.492637
[33,] -557.494067 -126.341938
[34,] -680.913696 -557.494067
[35,] -535.752694 -680.913696
[36,] -89.568224 -535.752694
[37,] 456.446173 -89.568224
[38,] 585.390325 456.446173
[39,] 199.596497 585.390325
[40,] 29.451782 199.596497
[41,] 33.527891 29.451782
[42,] 543.646092 33.527891
[43,] 722.411365 543.646092
[44,] 128.474873 722.411365
[45,] -605.919932 128.474873
[46,] 505.570251 -605.919932
[47,] 1191.604182 505.570251
[48,] 644.190177 1191.604182
[49,] 358.504453 644.190177
[50,] 560.315820 358.504453
[51,] 793.858390 560.315820
[52,] 949.334637 793.858390
[53,] 1023.831609 949.334637
[54,] 1336.704172 1023.831609
[55,] 33.010072 1336.704172
[56,] 703.305952 33.010072
[57,] 656.457767 703.305952
[58,] 221.253275 656.457767
[59,] 1346.699274 221.253275
[60,] 713.677824 1346.699274
[61,] 161.178427 713.677824
[62,] 341.180731 161.178427
[63,] 325.386895 341.180731
[64,] 85.587173 325.386895
[65,] -263.086085 85.587173
[66,] -269.988107 -263.086085
[67,] 429.746939 -269.988107
[68,] -353.027067 429.746939
[69,] -173.170112 -353.027067
[70,] 6.326461 -173.170112
[71,] -587.354240 6.326461
[72,] -513.140689 -587.354240
[73,] -505.816567 -513.140689
[74,] -277.196313 -505.816567
[75,] -95.616574 -277.196313
[76,] -463.357926 -95.616574
[77,] -79.382567 -463.357926
[78,] -81.537179 -79.382567
[79,] -670.656260 -81.537179
[80,] 421.882145 -670.656260
[81,] -39.884757 421.882145
[82,] -440.743355 -39.884757
[83,] 214.518854 -440.743355
[84,] 227.077443 214.518854
[85,] 182.205527 227.077443
[86,] -133.833734 182.205527
[87,] -149.592572 -133.833734
[88,] -141.460895 -149.592572
[89,] -469.272545 -141.460895
[90,] -140.559009 -469.272545
[91,] -242.011705 -140.559009
[92,] -694.257534 -242.011705
[93,] -327.356473 -694.257534
[94,] -52.350160 -327.356473
[95,] -9.544883 -52.350160
[96,] 93.416239 -9.544883
[97,] 129.616476 93.416239
[98,] 214.881892 129.616476
[99,] 272.310330 214.881892
[100,] 280.725706 272.310330
[101,] 300.344301 280.725706
[102,] 348.281536 300.344301
[103,] 328.764629 348.281536
[104,] 339.773324 328.764629
[105,] 350.089976 339.773324
[106,] 355.963635 350.089976
[107,] 349.897278 355.963635
[108,] 349.173043 349.897278
[109,] 351.343557 349.173043
[110,] 350.258300 351.343557
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -150.285458 274.531775
2 125.567413 -150.285458
3 -130.616801 125.567413
4 83.690368 -130.616801
5 -450.493804 83.690368
6 -698.380925 -450.493804
7 -119.014332 -698.380925
8 -284.867300 -119.014332
9 -738.509384 -284.867300
10 -251.990475 -738.509384
11 -62.866900 -251.990475
12 -1036.609955 -62.866900
13 -401.505830 -1036.609955
14 267.710087 -401.505830
15 -142.663351 267.710087
16 8.430137 -142.663351
17 -252.751581 8.430137
18 15.716705 -252.751581
19 -300.911555 15.716705
20 -519.320270 -300.911555
21 -320.940524 -519.320270
22 -2092.110819 -320.940524
23 -188.240890 -2092.110819
24 532.635233 -188.240890
25 -994.553542 532.635233
26 -477.368399 -994.553542
27 -1048.035651 -477.368399
28 -1022.658713 -1048.035651
29 -273.292922 -1022.658713
30 215.194493 -273.292922
31 -342.492637 215.194493
32 -126.341938 -342.492637
33 -557.494067 -126.341938
34 -680.913696 -557.494067
35 -535.752694 -680.913696
36 -89.568224 -535.752694
37 456.446173 -89.568224
38 585.390325 456.446173
39 199.596497 585.390325
40 29.451782 199.596497
41 33.527891 29.451782
42 543.646092 33.527891
43 722.411365 543.646092
44 128.474873 722.411365
45 -605.919932 128.474873
46 505.570251 -605.919932
47 1191.604182 505.570251
48 644.190177 1191.604182
49 358.504453 644.190177
50 560.315820 358.504453
51 793.858390 560.315820
52 949.334637 793.858390
53 1023.831609 949.334637
54 1336.704172 1023.831609
55 33.010072 1336.704172
56 703.305952 33.010072
57 656.457767 703.305952
58 221.253275 656.457767
59 1346.699274 221.253275
60 713.677824 1346.699274
61 161.178427 713.677824
62 341.180731 161.178427
63 325.386895 341.180731
64 85.587173 325.386895
65 -263.086085 85.587173
66 -269.988107 -263.086085
67 429.746939 -269.988107
68 -353.027067 429.746939
69 -173.170112 -353.027067
70 6.326461 -173.170112
71 -587.354240 6.326461
72 -513.140689 -587.354240
73 -505.816567 -513.140689
74 -277.196313 -505.816567
75 -95.616574 -277.196313
76 -463.357926 -95.616574
77 -79.382567 -463.357926
78 -81.537179 -79.382567
79 -670.656260 -81.537179
80 421.882145 -670.656260
81 -39.884757 421.882145
82 -440.743355 -39.884757
83 214.518854 -440.743355
84 227.077443 214.518854
85 182.205527 227.077443
86 -133.833734 182.205527
87 -149.592572 -133.833734
88 -141.460895 -149.592572
89 -469.272545 -141.460895
90 -140.559009 -469.272545
91 -242.011705 -140.559009
92 -694.257534 -242.011705
93 -327.356473 -694.257534
94 -52.350160 -327.356473
95 -9.544883 -52.350160
96 93.416239 -9.544883
97 129.616476 93.416239
98 214.881892 129.616476
99 272.310330 214.881892
100 280.725706 272.310330
101 300.344301 280.725706
102 348.281536 300.344301
103 328.764629 348.281536
104 339.773324 328.764629
105 350.089976 339.773324
106 355.963635 350.089976
107 349.897278 355.963635
108 349.173043 349.897278
109 351.343557 349.173043
110 350.258300 351.343557
> 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/7k3hy1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8k3hy1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9k3hy1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10dczk1292077746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ydfp1292077746.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/12c5y81292077747.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/13qxwh1292077747.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/141ov21292077747.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/15m7cq1292077747.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/160y9g1292077747.tab")
+ }
>
> try(system("convert tmp/1ot2q1292077746.ps tmp/1ot2q1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ot2q1292077746.ps tmp/2ot2q1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h2jb1292077746.ps tmp/3h2jb1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h2jb1292077746.ps tmp/4h2jb1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h2jb1292077746.ps tmp/5h2jb1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rc0w1292077746.ps tmp/6rc0w1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k3hy1292077746.ps tmp/7k3hy1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k3hy1292077746.ps tmp/8k3hy1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k3hy1292077746.ps tmp/9k3hy1292077746.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dczk1292077746.ps tmp/10dczk1292077746.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.780 1.180 5.287