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.
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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(621,0,587,0,655,0,517,0,646,0,657,0,382,0,345,0,625,0,654,0,606,0,510,0,614,0,647,0,580,0,614,0,636,0,388,0,356,0,639,0,753,0,611,0,639,0,630,0,586,0,695,0,552,0,619,0,681,0,421,0,307,0,754,0,690,0,644,0,643,0,608,0,651,0,691,0,627,0,634,0,731,0,475,0,337,0,803,0,722,0,590,0,724,0,627,0,696,0,825,0,677,0,656,0,785,0,412,0,352,0,839,0,729,0,696,0,641,0,695,0,638,0,762,0,635,0,721,0,854,0,418,0,367,0,824,0,687,0,601,0,676,0,740,0,691,0,683,0,594,0,729,0,731,0,386,0,331,0,706,0,715,0,657,0,653,0,642,0,643,0,718,0,654,0,632,0,731,0,392,0,344,0,792,0,852,0,649,0,629,0,685,0,617,0,715,0,715,0,629,0,916,0,531,0,357,0,917,0,828,0,708,0,858,0,775,0,785,0,1006,0,789,0,734,0,906,0,532,0,387,0,991,1,841,1,892,1,782,1,813,1,793,1,978,1,775,1,797,1,946,1,594,1,438,1,1022,1,868,1,795,1),dim=c(2,130),dimnames=list(c('Y','X2'),1:130))
> y <- array(NA,dim=c(2,130),dimnames=list(c('Y','X2'),1:130))
> 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
Y X2
1 621 0
2 587 0
3 655 0
4 517 0
5 646 0
6 657 0
7 382 0
8 345 0
9 625 0
10 654 0
11 606 0
12 510 0
13 614 0
14 647 0
15 580 0
16 614 0
17 636 0
18 388 0
19 356 0
20 639 0
21 753 0
22 611 0
23 639 0
24 630 0
25 586 0
26 695 0
27 552 0
28 619 0
29 681 0
30 421 0
31 307 0
32 754 0
33 690 0
34 644 0
35 643 0
36 608 0
37 651 0
38 691 0
39 627 0
40 634 0
41 731 0
42 475 0
43 337 0
44 803 0
45 722 0
46 590 0
47 724 0
48 627 0
49 696 0
50 825 0
51 677 0
52 656 0
53 785 0
54 412 0
55 352 0
56 839 0
57 729 0
58 696 0
59 641 0
60 695 0
61 638 0
62 762 0
63 635 0
64 721 0
65 854 0
66 418 0
67 367 0
68 824 0
69 687 0
70 601 0
71 676 0
72 740 0
73 691 0
74 683 0
75 594 0
76 729 0
77 731 0
78 386 0
79 331 0
80 706 0
81 715 0
82 657 0
83 653 0
84 642 0
85 643 0
86 718 0
87 654 0
88 632 0
89 731 0
90 392 0
91 344 0
92 792 0
93 852 0
94 649 0
95 629 0
96 685 0
97 617 0
98 715 0
99 715 0
100 629 0
101 916 0
102 531 0
103 357 0
104 917 0
105 828 0
106 708 0
107 858 0
108 775 0
109 785 0
110 1006 0
111 789 0
112 734 0
113 906 0
114 532 0
115 387 0
116 991 1
117 841 1
118 892 1
119 782 1
120 813 1
121 793 1
122 978 1
123 775 1
124 797 1
125 946 1
126 594 1
127 438 1
128 1022 1
129 868 1
130 795 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X2
639.9 181.7
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-383.67 -37.70 8.05 81.80 366.05
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 639.95 13.42 47.69 <2e-16 ***
X2 181.72 39.50 4.60 1e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 143.9 on 128 degrees of freedom
Multiple R-squared: 0.1419, Adjusted R-squared: 0.1352
F-statistic: 21.16 on 1 and 128 DF, p-value: 1.001e-05
> 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,] 0.100255008 0.20051002 0.8997450
[2,] 0.047096123 0.09419225 0.9529039
[3,] 0.280284214 0.56056843 0.7197158
[4,] 0.491711927 0.98342385 0.5082881
[5,] 0.399303046 0.79860609 0.6006970
[6,] 0.334887527 0.66977505 0.6651125
[7,] 0.247911145 0.49582229 0.7520889
[8,] 0.190018756 0.38003751 0.8099812
[9,] 0.135707926 0.27141585 0.8642921
[10,] 0.102944217 0.20588843 0.8970558
[11,] 0.067178242 0.13435648 0.9328218
[12,] 0.043959226 0.08791845 0.9560408
[13,] 0.029734602 0.05946920 0.9702654
[14,] 0.060184867 0.12036973 0.9398151
[15,] 0.124320788 0.24864158 0.8756792
[16,] 0.099249154 0.19849831 0.9007508
[17,] 0.132128988 0.26425798 0.8678710
[18,] 0.098974859 0.19794972 0.9010251
[19,] 0.076087505 0.15217501 0.9239125
[20,] 0.056368321 0.11273664 0.9436317
[21,] 0.039354175 0.07870835 0.9606458
[22,] 0.035340877 0.07068175 0.9646591
[23,] 0.025142085 0.05028417 0.9748579
[24,] 0.017237845 0.03447569 0.9827622
[25,] 0.014060750 0.02812150 0.9859393
[26,] 0.020667426 0.04133485 0.9793326
[27,] 0.078817884 0.15763577 0.9211821
[28,] 0.093710944 0.18742189 0.9062891
[29,] 0.083119840 0.16623968 0.9168802
[30,] 0.065513003 0.13102601 0.9344870
[31,] 0.050839753 0.10167951 0.9491602
[32,] 0.037631251 0.07526250 0.9623687
[33,] 0.028811483 0.05762297 0.9711885
[34,] 0.024313953 0.04862791 0.9756860
[35,] 0.017538752 0.03507750 0.9824612
[36,] 0.012561910 0.02512382 0.9874381
[37,] 0.012332524 0.02466505 0.9876675
[38,] 0.012795782 0.02559156 0.9872042
[39,] 0.038714242 0.07742848 0.9612858
[40,] 0.054864559 0.10972912 0.9451354
[41,] 0.050791870 0.10158374 0.9492081
[42,] 0.038989667 0.07797933 0.9610103
[43,] 0.035848134 0.07169627 0.9641519
[44,] 0.026877492 0.05375498 0.9731225
[45,] 0.022190790 0.04438158 0.9778092
[46,] 0.033662203 0.06732441 0.9663378
[47,] 0.026424375 0.05284875 0.9735756
[48,] 0.019874026 0.03974805 0.9801260
[49,] 0.022670258 0.04534052 0.9773297
[50,] 0.035347051 0.07069410 0.9646529
[51,] 0.078675170 0.15735034 0.9213248
[52,] 0.108062584 0.21612517 0.8919374
[53,] 0.097599613 0.19519923 0.9024004
[54,] 0.082102230 0.16420446 0.9178978
[55,] 0.065039391 0.13007878 0.9349606
[56,] 0.053536383 0.10707277 0.9464636
[57,] 0.041414738 0.08282948 0.9585853
[58,] 0.039950245 0.07990049 0.9600498
[59,] 0.030419197 0.06083839 0.9695808
[60,] 0.025528022 0.05105604 0.9744720
[61,] 0.038031543 0.07606309 0.9619685
[62,] 0.055844862 0.11168972 0.9441551
[63,] 0.106998034 0.21399607 0.8930020
[64,] 0.123789667 0.24757933 0.8762103
[65,] 0.102738887 0.20547777 0.8972611
[66,] 0.084232100 0.16846420 0.9157679
[67,] 0.067753348 0.13550670 0.9322467
[68,] 0.059575473 0.11915095 0.9404245
[69,] 0.047656124 0.09531225 0.9523439
[70,] 0.037343226 0.07468645 0.9626568
[71,] 0.029423368 0.05884674 0.9705766
[72,] 0.024388924 0.04877785 0.9756111
[73,] 0.020147869 0.04029574 0.9798521
[74,] 0.039718273 0.07943655 0.9602817
[75,] 0.108240546 0.21648109 0.8917595
[76,] 0.089853780 0.17970756 0.9101462
[77,] 0.074610840 0.14922168 0.9253892
[78,] 0.058806340 0.11761268 0.9411937
[79,] 0.045747566 0.09149513 0.9542524
[80,] 0.035234824 0.07046965 0.9647652
[81,] 0.026784227 0.05356845 0.9732158
[82,] 0.021017642 0.04203528 0.9789824
[83,] 0.015498742 0.03099748 0.9845013
[84,] 0.011422204 0.02284441 0.9885778
[85,] 0.008845025 0.01769005 0.9911550
[86,] 0.021090793 0.04218159 0.9789092
[87,] 0.073636608 0.14727322 0.9263634
[88,] 0.067868373 0.13573675 0.9321316
[89,] 0.076727792 0.15345558 0.9232722
[90,] 0.060898733 0.12179747 0.9391013
[91,] 0.049018850 0.09803770 0.9509812
[92,] 0.037459602 0.07491920 0.9625404
[93,] 0.030432777 0.06086555 0.9695672
[94,] 0.022885493 0.04577099 0.9771145
[95,] 0.016917669 0.03383534 0.9830823
[96,] 0.013124570 0.02624914 0.9868754
[97,] 0.020655146 0.04131029 0.9793449
[98,] 0.022912416 0.04582483 0.9770876
[99,] 0.106112336 0.21222467 0.8938877
[100,] 0.131132763 0.26226553 0.8688672
[101,] 0.119221036 0.23844207 0.8807790
[102,] 0.093552771 0.18710554 0.9064472
[103,] 0.091918963 0.18383793 0.9080810
[104,] 0.072995138 0.14599028 0.9270049
[105,] 0.058275652 0.11655130 0.9417243
[106,] 0.157097318 0.31419464 0.8429027
[107,] 0.147865745 0.29573149 0.8521343
[108,] 0.126576315 0.25315263 0.8734237
[109,] 0.374011003 0.74802201 0.6259890
[110,] 0.336304553 0.67260911 0.6636954
[111,] 0.289407177 0.57881435 0.7105928
[112,] 0.302378143 0.60475629 0.6976219
[113,] 0.237323786 0.47464757 0.7626762
[114,] 0.190852203 0.38170441 0.8091478
[115,] 0.137276512 0.27455302 0.8627235
[116,] 0.091826164 0.18365233 0.9081738
[117,] 0.057464080 0.11492816 0.9425359
[118,] 0.061382195 0.12276439 0.9386178
[119,] 0.034279797 0.06855959 0.9657202
[120,] 0.016944140 0.03388828 0.9830559
[121,] 0.015071171 0.03014234 0.9849288
> postscript(file="/var/www/rcomp/tmp/14hst1293562447.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/24hst1293562447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3f89e1293562447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4f89e1293562447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5f89e1293562447.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 = 130
Frequency = 1
1 2 3 4 5 6
-18.947826 -52.947826 15.052174 -122.947826 6.052174 17.052174
7 8 9 10 11 12
-257.947826 -294.947826 -14.947826 14.052174 -33.947826 -129.947826
13 14 15 16 17 18
-25.947826 7.052174 -59.947826 -25.947826 -3.947826 -251.947826
19 20 21 22 23 24
-283.947826 -0.947826 113.052174 -28.947826 -0.947826 -9.947826
25 26 27 28 29 30
-53.947826 55.052174 -87.947826 -20.947826 41.052174 -218.947826
31 32 33 34 35 36
-332.947826 114.052174 50.052174 4.052174 3.052174 -31.947826
37 38 39 40 41 42
11.052174 51.052174 -12.947826 -5.947826 91.052174 -164.947826
43 44 45 46 47 48
-302.947826 163.052174 82.052174 -49.947826 84.052174 -12.947826
49 50 51 52 53 54
56.052174 185.052174 37.052174 16.052174 145.052174 -227.947826
55 56 57 58 59 60
-287.947826 199.052174 89.052174 56.052174 1.052174 55.052174
61 62 63 64 65 66
-1.947826 122.052174 -4.947826 81.052174 214.052174 -221.947826
67 68 69 70 71 72
-272.947826 184.052174 47.052174 -38.947826 36.052174 100.052174
73 74 75 76 77 78
51.052174 43.052174 -45.947826 89.052174 91.052174 -253.947826
79 80 81 82 83 84
-308.947826 66.052174 75.052174 17.052174 13.052174 2.052174
85 86 87 88 89 90
3.052174 78.052174 14.052174 -7.947826 91.052174 -247.947826
91 92 93 94 95 96
-295.947826 152.052174 212.052174 9.052174 -10.947826 45.052174
97 98 99 100 101 102
-22.947826 75.052174 75.052174 -10.947826 276.052174 -108.947826
103 104 105 106 107 108
-282.947826 277.052174 188.052174 68.052174 218.052174 135.052174
109 110 111 112 113 114
145.052174 366.052174 149.052174 94.052174 266.052174 -107.947826
115 116 117 118 119 120
-252.947826 169.333333 19.333333 70.333333 -39.666667 -8.666667
121 122 123 124 125 126
-28.666667 156.333333 -46.666667 -24.666667 124.333333 -227.666667
127 128 129 130
-383.666667 200.333333 46.333333 -26.666667
> postscript(file="/var/www/rcomp/tmp/6i9tu1293562448.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 -18.947826 NA
1 -52.947826 -18.947826
2 15.052174 -52.947826
3 -122.947826 15.052174
4 6.052174 -122.947826
5 17.052174 6.052174
6 -257.947826 17.052174
7 -294.947826 -257.947826
8 -14.947826 -294.947826
9 14.052174 -14.947826
10 -33.947826 14.052174
11 -129.947826 -33.947826
12 -25.947826 -129.947826
13 7.052174 -25.947826
14 -59.947826 7.052174
15 -25.947826 -59.947826
16 -3.947826 -25.947826
17 -251.947826 -3.947826
18 -283.947826 -251.947826
19 -0.947826 -283.947826
20 113.052174 -0.947826
21 -28.947826 113.052174
22 -0.947826 -28.947826
23 -9.947826 -0.947826
24 -53.947826 -9.947826
25 55.052174 -53.947826
26 -87.947826 55.052174
27 -20.947826 -87.947826
28 41.052174 -20.947826
29 -218.947826 41.052174
30 -332.947826 -218.947826
31 114.052174 -332.947826
32 50.052174 114.052174
33 4.052174 50.052174
34 3.052174 4.052174
35 -31.947826 3.052174
36 11.052174 -31.947826
37 51.052174 11.052174
38 -12.947826 51.052174
39 -5.947826 -12.947826
40 91.052174 -5.947826
41 -164.947826 91.052174
42 -302.947826 -164.947826
43 163.052174 -302.947826
44 82.052174 163.052174
45 -49.947826 82.052174
46 84.052174 -49.947826
47 -12.947826 84.052174
48 56.052174 -12.947826
49 185.052174 56.052174
50 37.052174 185.052174
51 16.052174 37.052174
52 145.052174 16.052174
53 -227.947826 145.052174
54 -287.947826 -227.947826
55 199.052174 -287.947826
56 89.052174 199.052174
57 56.052174 89.052174
58 1.052174 56.052174
59 55.052174 1.052174
60 -1.947826 55.052174
61 122.052174 -1.947826
62 -4.947826 122.052174
63 81.052174 -4.947826
64 214.052174 81.052174
65 -221.947826 214.052174
66 -272.947826 -221.947826
67 184.052174 -272.947826
68 47.052174 184.052174
69 -38.947826 47.052174
70 36.052174 -38.947826
71 100.052174 36.052174
72 51.052174 100.052174
73 43.052174 51.052174
74 -45.947826 43.052174
75 89.052174 -45.947826
76 91.052174 89.052174
77 -253.947826 91.052174
78 -308.947826 -253.947826
79 66.052174 -308.947826
80 75.052174 66.052174
81 17.052174 75.052174
82 13.052174 17.052174
83 2.052174 13.052174
84 3.052174 2.052174
85 78.052174 3.052174
86 14.052174 78.052174
87 -7.947826 14.052174
88 91.052174 -7.947826
89 -247.947826 91.052174
90 -295.947826 -247.947826
91 152.052174 -295.947826
92 212.052174 152.052174
93 9.052174 212.052174
94 -10.947826 9.052174
95 45.052174 -10.947826
96 -22.947826 45.052174
97 75.052174 -22.947826
98 75.052174 75.052174
99 -10.947826 75.052174
100 276.052174 -10.947826
101 -108.947826 276.052174
102 -282.947826 -108.947826
103 277.052174 -282.947826
104 188.052174 277.052174
105 68.052174 188.052174
106 218.052174 68.052174
107 135.052174 218.052174
108 145.052174 135.052174
109 366.052174 145.052174
110 149.052174 366.052174
111 94.052174 149.052174
112 266.052174 94.052174
113 -107.947826 266.052174
114 -252.947826 -107.947826
115 169.333333 -252.947826
116 19.333333 169.333333
117 70.333333 19.333333
118 -39.666667 70.333333
119 -8.666667 -39.666667
120 -28.666667 -8.666667
121 156.333333 -28.666667
122 -46.666667 156.333333
123 -24.666667 -46.666667
124 124.333333 -24.666667
125 -227.666667 124.333333
126 -383.666667 -227.666667
127 200.333333 -383.666667
128 46.333333 200.333333
129 -26.666667 46.333333
130 NA -26.666667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -52.947826 -18.947826
[2,] 15.052174 -52.947826
[3,] -122.947826 15.052174
[4,] 6.052174 -122.947826
[5,] 17.052174 6.052174
[6,] -257.947826 17.052174
[7,] -294.947826 -257.947826
[8,] -14.947826 -294.947826
[9,] 14.052174 -14.947826
[10,] -33.947826 14.052174
[11,] -129.947826 -33.947826
[12,] -25.947826 -129.947826
[13,] 7.052174 -25.947826
[14,] -59.947826 7.052174
[15,] -25.947826 -59.947826
[16,] -3.947826 -25.947826
[17,] -251.947826 -3.947826
[18,] -283.947826 -251.947826
[19,] -0.947826 -283.947826
[20,] 113.052174 -0.947826
[21,] -28.947826 113.052174
[22,] -0.947826 -28.947826
[23,] -9.947826 -0.947826
[24,] -53.947826 -9.947826
[25,] 55.052174 -53.947826
[26,] -87.947826 55.052174
[27,] -20.947826 -87.947826
[28,] 41.052174 -20.947826
[29,] -218.947826 41.052174
[30,] -332.947826 -218.947826
[31,] 114.052174 -332.947826
[32,] 50.052174 114.052174
[33,] 4.052174 50.052174
[34,] 3.052174 4.052174
[35,] -31.947826 3.052174
[36,] 11.052174 -31.947826
[37,] 51.052174 11.052174
[38,] -12.947826 51.052174
[39,] -5.947826 -12.947826
[40,] 91.052174 -5.947826
[41,] -164.947826 91.052174
[42,] -302.947826 -164.947826
[43,] 163.052174 -302.947826
[44,] 82.052174 163.052174
[45,] -49.947826 82.052174
[46,] 84.052174 -49.947826
[47,] -12.947826 84.052174
[48,] 56.052174 -12.947826
[49,] 185.052174 56.052174
[50,] 37.052174 185.052174
[51,] 16.052174 37.052174
[52,] 145.052174 16.052174
[53,] -227.947826 145.052174
[54,] -287.947826 -227.947826
[55,] 199.052174 -287.947826
[56,] 89.052174 199.052174
[57,] 56.052174 89.052174
[58,] 1.052174 56.052174
[59,] 55.052174 1.052174
[60,] -1.947826 55.052174
[61,] 122.052174 -1.947826
[62,] -4.947826 122.052174
[63,] 81.052174 -4.947826
[64,] 214.052174 81.052174
[65,] -221.947826 214.052174
[66,] -272.947826 -221.947826
[67,] 184.052174 -272.947826
[68,] 47.052174 184.052174
[69,] -38.947826 47.052174
[70,] 36.052174 -38.947826
[71,] 100.052174 36.052174
[72,] 51.052174 100.052174
[73,] 43.052174 51.052174
[74,] -45.947826 43.052174
[75,] 89.052174 -45.947826
[76,] 91.052174 89.052174
[77,] -253.947826 91.052174
[78,] -308.947826 -253.947826
[79,] 66.052174 -308.947826
[80,] 75.052174 66.052174
[81,] 17.052174 75.052174
[82,] 13.052174 17.052174
[83,] 2.052174 13.052174
[84,] 3.052174 2.052174
[85,] 78.052174 3.052174
[86,] 14.052174 78.052174
[87,] -7.947826 14.052174
[88,] 91.052174 -7.947826
[89,] -247.947826 91.052174
[90,] -295.947826 -247.947826
[91,] 152.052174 -295.947826
[92,] 212.052174 152.052174
[93,] 9.052174 212.052174
[94,] -10.947826 9.052174
[95,] 45.052174 -10.947826
[96,] -22.947826 45.052174
[97,] 75.052174 -22.947826
[98,] 75.052174 75.052174
[99,] -10.947826 75.052174
[100,] 276.052174 -10.947826
[101,] -108.947826 276.052174
[102,] -282.947826 -108.947826
[103,] 277.052174 -282.947826
[104,] 188.052174 277.052174
[105,] 68.052174 188.052174
[106,] 218.052174 68.052174
[107,] 135.052174 218.052174
[108,] 145.052174 135.052174
[109,] 366.052174 145.052174
[110,] 149.052174 366.052174
[111,] 94.052174 149.052174
[112,] 266.052174 94.052174
[113,] -107.947826 266.052174
[114,] -252.947826 -107.947826
[115,] 169.333333 -252.947826
[116,] 19.333333 169.333333
[117,] 70.333333 19.333333
[118,] -39.666667 70.333333
[119,] -8.666667 -39.666667
[120,] -28.666667 -8.666667
[121,] 156.333333 -28.666667
[122,] -46.666667 156.333333
[123,] -24.666667 -46.666667
[124,] 124.333333 -24.666667
[125,] -227.666667 124.333333
[126,] -383.666667 -227.666667
[127,] 200.333333 -383.666667
[128,] 46.333333 200.333333
[129,] -26.666667 46.333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -52.947826 -18.947826
2 15.052174 -52.947826
3 -122.947826 15.052174
4 6.052174 -122.947826
5 17.052174 6.052174
6 -257.947826 17.052174
7 -294.947826 -257.947826
8 -14.947826 -294.947826
9 14.052174 -14.947826
10 -33.947826 14.052174
11 -129.947826 -33.947826
12 -25.947826 -129.947826
13 7.052174 -25.947826
14 -59.947826 7.052174
15 -25.947826 -59.947826
16 -3.947826 -25.947826
17 -251.947826 -3.947826
18 -283.947826 -251.947826
19 -0.947826 -283.947826
20 113.052174 -0.947826
21 -28.947826 113.052174
22 -0.947826 -28.947826
23 -9.947826 -0.947826
24 -53.947826 -9.947826
25 55.052174 -53.947826
26 -87.947826 55.052174
27 -20.947826 -87.947826
28 41.052174 -20.947826
29 -218.947826 41.052174
30 -332.947826 -218.947826
31 114.052174 -332.947826
32 50.052174 114.052174
33 4.052174 50.052174
34 3.052174 4.052174
35 -31.947826 3.052174
36 11.052174 -31.947826
37 51.052174 11.052174
38 -12.947826 51.052174
39 -5.947826 -12.947826
40 91.052174 -5.947826
41 -164.947826 91.052174
42 -302.947826 -164.947826
43 163.052174 -302.947826
44 82.052174 163.052174
45 -49.947826 82.052174
46 84.052174 -49.947826
47 -12.947826 84.052174
48 56.052174 -12.947826
49 185.052174 56.052174
50 37.052174 185.052174
51 16.052174 37.052174
52 145.052174 16.052174
53 -227.947826 145.052174
54 -287.947826 -227.947826
55 199.052174 -287.947826
56 89.052174 199.052174
57 56.052174 89.052174
58 1.052174 56.052174
59 55.052174 1.052174
60 -1.947826 55.052174
61 122.052174 -1.947826
62 -4.947826 122.052174
63 81.052174 -4.947826
64 214.052174 81.052174
65 -221.947826 214.052174
66 -272.947826 -221.947826
67 184.052174 -272.947826
68 47.052174 184.052174
69 -38.947826 47.052174
70 36.052174 -38.947826
71 100.052174 36.052174
72 51.052174 100.052174
73 43.052174 51.052174
74 -45.947826 43.052174
75 89.052174 -45.947826
76 91.052174 89.052174
77 -253.947826 91.052174
78 -308.947826 -253.947826
79 66.052174 -308.947826
80 75.052174 66.052174
81 17.052174 75.052174
82 13.052174 17.052174
83 2.052174 13.052174
84 3.052174 2.052174
85 78.052174 3.052174
86 14.052174 78.052174
87 -7.947826 14.052174
88 91.052174 -7.947826
89 -247.947826 91.052174
90 -295.947826 -247.947826
91 152.052174 -295.947826
92 212.052174 152.052174
93 9.052174 212.052174
94 -10.947826 9.052174
95 45.052174 -10.947826
96 -22.947826 45.052174
97 75.052174 -22.947826
98 75.052174 75.052174
99 -10.947826 75.052174
100 276.052174 -10.947826
101 -108.947826 276.052174
102 -282.947826 -108.947826
103 277.052174 -282.947826
104 188.052174 277.052174
105 68.052174 188.052174
106 218.052174 68.052174
107 135.052174 218.052174
108 145.052174 135.052174
109 366.052174 145.052174
110 149.052174 366.052174
111 94.052174 149.052174
112 266.052174 94.052174
113 -107.947826 266.052174
114 -252.947826 -107.947826
115 169.333333 -252.947826
116 19.333333 169.333333
117 70.333333 19.333333
118 -39.666667 70.333333
119 -8.666667 -39.666667
120 -28.666667 -8.666667
121 156.333333 -28.666667
122 -46.666667 156.333333
123 -24.666667 -46.666667
124 124.333333 -24.666667
125 -227.666667 124.333333
126 -383.666667 -227.666667
127 200.333333 -383.666667
128 46.333333 200.333333
129 -26.666667 46.333333
> 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/7t0sx1293562448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8t0sx1293562448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9t0sx1293562448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103aai1293562448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pa861293562448.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/12abpt1293562448.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/13o2521293562448.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/149l3q1293562448.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/15v31w1293562448.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/16ymi21293562448.tab")
+ }
>
> try(system("convert tmp/14hst1293562447.ps tmp/14hst1293562447.png",intern=TRUE))
character(0)
> try(system("convert tmp/24hst1293562447.ps tmp/24hst1293562447.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f89e1293562447.ps tmp/3f89e1293562447.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f89e1293562447.ps tmp/4f89e1293562447.png",intern=TRUE))
character(0)
> try(system("convert tmp/5f89e1293562447.ps tmp/5f89e1293562447.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i9tu1293562448.ps tmp/6i9tu1293562448.png",intern=TRUE))
character(0)
> try(system("convert tmp/7t0sx1293562448.ps tmp/7t0sx1293562448.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t0sx1293562448.ps tmp/8t0sx1293562448.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t0sx1293562448.ps tmp/9t0sx1293562448.png",intern=TRUE))
character(0)
> try(system("convert tmp/103aai1293562448.ps tmp/103aai1293562448.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.02 1.60 5.62