R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,dim=c(8
+ ,100)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'GrCosts'
+ ,'Trades'
+ ,'GrTrades'
+ ,'Dividends'
+ ,'GrDiv'
+ ,'Wealth')
+ ,1:100))
> y <- array(NA,dim=c(8,100),dimnames=list(c('Group','Costs','GrCosts','Trades','GrTrades','Dividends','GrDiv','Wealth'),1:100))
> 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 = '8'
> #'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
Wealth Group Costs GrCosts Trades GrTrades Dividends GrDiv
1 6282929 1 162556 162556 1081 1081 213118 213118
2 4324047 1 29790 29790 309 309 81767 81767
3 4108272 1 87550 87550 458 458 153198 153198
4 -1212617 0 84738 0 588 0 -26007 0
5 1485329 1 54660 54660 299 299 126942 126942
6 1779876 1 42634 42634 156 156 157214 157214
7 1367203 0 40949 0 481 0 129352 0
8 2519076 1 42312 42312 323 323 234817 234817
9 912684 1 37704 37704 452 452 60448 60448
10 1443586 1 16275 16275 109 109 47818 47818
11 1220017 0 25830 0 115 0 245546 0
12 984885 0 12679 0 110 0 48020 0
13 1457425 1 18014 18014 239 239 -1710 -1710
14 -572920 0 43556 0 247 0 32648 0
15 929144 1 24524 24524 497 497 95350 95350
16 1151176 0 6532 0 103 0 151352 0
17 790090 0 7123 0 109 0 288170 0
18 774497 1 20813 20813 502 502 114337 114337
19 990576 1 37597 37597 248 248 37884 37884
20 454195 0 17821 0 373 0 122844 0
21 876607 1 12988 12988 119 119 82340 82340
22 711969 1 22330 22330 84 84 79801 79801
23 702380 0 13326 0 102 0 165548 0
24 264449 0 16189 0 295 0 116384 0
25 450033 0 7146 0 105 0 134028 0
26 541063 0 15824 0 64 0 63838 0
27 588864 1 26088 26088 267 267 74996 74996
28 -37216 0 11326 0 129 0 31080 0
29 783310 0 8568 0 37 0 32168 0
30 467359 0 14416 0 361 0 49857 0
31 688779 1 3369 3369 28 28 87161 87161
32 608419 1 11819 11819 85 85 106113 106113
33 696348 1 6620 6620 44 44 80570 80570
34 597793 1 4519 4519 49 49 102129 102129
35 821730 0 2220 0 22 0 301670 0
36 377934 0 18562 0 155 0 102313 0
37 651939 0 10327 0 91 0 88577 0
38 697458 1 5336 5336 81 81 112477 112477
39 700368 1 2365 2365 79 79 191778 191778
40 225986 0 4069 0 145 0 79804 0
41 348695 0 7710 0 816 0 128294 0
42 373683 0 13718 0 61 0 96448 0
43 501709 0 4525 0 226 0 93811 0
44 413743 0 6869 0 105 0 117520 0
45 379825 0 4628 0 62 0 69159 0
46 336260 1 3653 3653 24 24 101792 101792
47 636765 1 1265 1265 26 26 210568 210568
48 481231 1 7489 7489 322 322 136996 136996
49 469107 0 4901 0 84 0 121920 0
50 211928 0 2284 0 33 0 76403 0
51 563925 1 3160 3160 108 108 108094 108094
52 511939 1 4150 4150 150 150 134759 134759
53 521016 1 7285 7285 115 115 188873 188873
54 543856 1 1134 1134 162 162 146216 146216
55 329304 1 4658 4658 158 158 156608 156608
56 423262 0 2384 0 97 0 61348 0
57 509665 0 3748 0 9 0 50350 0
58 455881 0 5371 0 66 0 87720 0
59 367772 0 1285 0 107 0 99489 0
60 406339 1 9327 9327 101 101 87419 87419
61 493408 1 5565 5565 47 47 94355 94355
62 232942 0 1528 0 38 0 60326 0
63 416002 1 3122 3122 34 34 94670 94670
64 337430 1 7317 7317 84 84 82425 82425
65 361517 0 2675 0 79 0 59017 0
66 360962 0 13253 0 947 0 90829 0
67 235561 0 880 0 74 0 80791 0
68 408247 1 2053 2053 53 53 100423 100423
69 450296 0 1424 0 94 0 131116 0
70 418799 1 4036 4036 63 63 100269 100269
71 247405 1 3045 3045 58 58 27330 27330
72 378519 0 5119 0 49 0 39039 0
73 326638 0 1431 0 34 0 106885 0
74 328233 0 554 0 11 0 79285 0
75 386225 0 1975 0 35 0 118881 0
76 283662 1 1286 1286 17 17 77623 77623
77 370225 0 1012 0 47 0 114768 0
78 269236 0 810 0 43 0 74015 0
79 365732 0 1280 0 117 0 69465 0
80 420383 1 666 666 171 171 117869 117869
81 345811 0 1380 0 26 0 60982 0
82 431809 1 4608 4608 73 73 90131 90131
83 418876 0 876 0 59 0 138971 0
84 297476 0 814 0 18 0 39625 0
85 416776 0 514 0 15 0 102725 0
86 357257 1 5692 5692 72 72 64239 64239
87 458343 0 3642 0 86 0 90262 0
88 388386 0 540 0 14 0 103960 0
89 358934 0 2099 0 64 0 106611 0
90 407560 0 567 0 11 0 103345 0
91 392558 0 2001 0 52 0 95551 0
92 373177 1 2949 2949 41 41 82903 82903
93 428370 0 2253 0 99 0 63593 0
94 369419 1 6533 6533 75 75 126910 126910
95 358649 0 1889 0 45 0 37527 0
96 376641 1 3055 3055 43 43 60247 60247
97 467427 0 272 0 8 0 112995 0
98 364885 1 1414 1414 198 198 70184 70184
99 436230 0 2564 0 22 0 130140 0
100 329118 1 1383 1383 11 11 73221 73221
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Group Costs GrCosts Trades GrTrades
169088.919 44386.189 -7.976 44.982 20.889 -124.124
Dividends GrDiv
3.244 -1.982
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-880218 -164971 -58294 84173 2936875
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 169088.919 131306.033 1.288 0.20106
Group 44386.189 208024.502 0.213 0.83151
Costs -7.976 5.115 -1.559 0.12234
GrCosts 44.982 6.917 6.503 4.01e-09 ***
Trades 20.889 371.116 0.056 0.95524
GrTrades -124.124 767.621 -0.162 0.87190
Dividends 3.244 1.053 3.082 0.00271 **
GrDiv -1.982 1.760 -1.126 0.26300
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 431900 on 92 degrees of freedom
Multiple R-squared: 0.7737, Adjusted R-squared: 0.7565
F-statistic: 44.94 on 7 and 92 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,] 1.0000000 1.190379e-22 5.951896e-23
[2,] 1.0000000 7.096225e-24 3.548113e-24
[3,] 1.0000000 1.752406e-26 8.762028e-27
[4,] 1.0000000 8.647713e-30 4.323857e-30
[5,] 1.0000000 3.484822e-30 1.742411e-30
[6,] 1.0000000 1.549031e-33 7.745156e-34
[7,] 1.0000000 5.028888e-34 2.514444e-34
[8,] 1.0000000 1.792619e-33 8.963094e-34
[9,] 1.0000000 5.797992e-34 2.898996e-34
[10,] 1.0000000 8.329440e-34 4.164720e-34
[11,] 1.0000000 2.121156e-34 1.060578e-34
[12,] 1.0000000 5.132892e-34 2.566446e-34
[13,] 1.0000000 2.201123e-33 1.100561e-33
[14,] 1.0000000 1.923062e-33 9.615310e-34
[15,] 1.0000000 1.115834e-32 5.579170e-33
[16,] 1.0000000 6.119109e-32 3.059554e-32
[17,] 1.0000000 1.289984e-31 6.449919e-32
[18,] 1.0000000 2.084033e-33 1.042016e-33
[19,] 1.0000000 9.709997e-36 4.854999e-36
[20,] 1.0000000 5.242918e-35 2.621459e-35
[21,] 1.0000000 1.714046e-35 8.570230e-36
[22,] 1.0000000 8.169510e-35 4.084755e-35
[23,] 1.0000000 7.538136e-36 3.769068e-36
[24,] 1.0000000 1.153997e-35 5.769983e-36
[25,] 1.0000000 1.421873e-35 7.109365e-36
[26,] 1.0000000 3.082032e-35 1.541016e-35
[27,] 1.0000000 1.416904e-35 7.084521e-36
[28,] 1.0000000 4.309244e-37 2.154622e-37
[29,] 1.0000000 9.504762e-37 4.752381e-37
[30,] 1.0000000 8.310026e-37 4.155013e-37
[31,] 1.0000000 6.099214e-36 3.049607e-36
[32,] 1.0000000 9.236479e-36 4.618240e-36
[33,] 1.0000000 2.298622e-35 1.149311e-35
[34,] 1.0000000 1.105845e-34 5.529227e-35
[35,] 1.0000000 9.334162e-34 4.667081e-34
[36,] 1.0000000 4.847147e-33 2.423573e-33
[37,] 1.0000000 3.138925e-32 1.569463e-32
[38,] 1.0000000 2.872243e-31 1.436121e-31
[39,] 1.0000000 2.593747e-30 1.296874e-30
[40,] 1.0000000 7.222995e-31 3.611497e-31
[41,] 1.0000000 4.096923e-31 2.048462e-31
[42,] 1.0000000 2.256225e-30 1.128113e-30
[43,] 1.0000000 2.076865e-29 1.038433e-29
[44,] 1.0000000 3.244515e-29 1.622258e-29
[45,] 1.0000000 2.635715e-29 1.317857e-29
[46,] 1.0000000 1.146314e-28 5.731569e-29
[47,] 1.0000000 1.209813e-28 6.049063e-29
[48,] 1.0000000 1.357057e-27 6.785287e-28
[49,] 1.0000000 1.427485e-26 7.137427e-27
[50,] 1.0000000 1.405641e-25 7.028206e-26
[51,] 1.0000000 1.982275e-25 9.911375e-26
[52,] 1.0000000 2.158152e-25 1.079076e-25
[53,] 1.0000000 1.756903e-24 8.784516e-25
[54,] 1.0000000 1.472448e-23 7.362241e-24
[55,] 1.0000000 1.655217e-22 8.276087e-23
[56,] 1.0000000 1.455170e-21 7.275848e-22
[57,] 1.0000000 5.470071e-22 2.735036e-22
[58,] 1.0000000 5.209189e-21 2.604594e-21
[59,] 1.0000000 5.413749e-20 2.706875e-20
[60,] 1.0000000 4.575651e-19 2.287825e-19
[61,] 1.0000000 1.866055e-18 9.330277e-19
[62,] 1.0000000 2.132988e-17 1.066494e-17
[63,] 1.0000000 7.315406e-17 3.657703e-17
[64,] 1.0000000 6.997643e-16 3.498821e-16
[65,] 1.0000000 5.493112e-15 2.746556e-15
[66,] 1.0000000 2.449696e-14 1.224848e-14
[67,] 1.0000000 1.997491e-13 9.987453e-14
[68,] 1.0000000 1.678485e-13 8.392425e-14
[69,] 1.0000000 1.955483e-12 9.777413e-13
[70,] 1.0000000 1.919404e-11 9.597020e-12
[71,] 1.0000000 2.136631e-10 1.068315e-10
[72,] 1.0000000 8.795079e-10 4.397539e-10
[73,] 1.0000000 7.089977e-09 3.544989e-09
[74,] 1.0000000 4.317546e-08 2.158773e-08
[75,] 0.9999997 5.436366e-07 2.718183e-07
[76,] 0.9999974 5.290244e-06 2.645122e-06
[77,] 0.9999849 3.011961e-05 1.505981e-05
[78,] 0.9998661 2.678675e-04 1.339337e-04
[79,] 0.9996477 7.046941e-04 3.523471e-04
> postscript(file="/var/www/html/rcomp/tmp/1fnlq1291409893.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fnlq1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pwkb1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pwkb1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pwkb1291409893.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 = 100
Frequency = 1
1 2 3 4 5 6
-103387.0397 2936875.4516 508893.7371 -633713.8167 -880217.8319 -193617.1624
7 8 9 10 11 12
1095053.2091 476787.1991 -725675.9287 578745.3061 457965.3572 758846.4051
13 14 15 16 17 18
604167.5452 -505663.9308 -260893.9792 541027.0438 -259329.8960 -301663.7065
19 20 21 22 23 24
-636403.7071 20936.1128 90860.9998 -419889.2020 100389.4601 -159240.7445
25 26 27 28 29 30
-99058.3766 289754.9236 -657103.2585 -219487.4587 577431.7775 243972.7477
31 32 33 34 35 36
243507.6887 -167584.3847 140743.3944 93240.6806 -308777.0058 21745.2962
37 38 39 40 41 42
275963.3616 152914.4601 165465.9636 -172572.9085 -192147.7346 -152.8003
43 44 45 46 47 48
59654.3540 -84003.2243 21992.7796 -138401.2187 113380.3590 -149053.5866
49 50 51 52 53 54
-58172.4144 -187495.8185 108224.6759 -9717.6635 -188569.0372 120585.0268
55 56 57 58 59 60
-237903.6047 72139.7742 206940.0957 43676.8093 -116060.7308 -252199.5252
61 62 63 64 65 66
-40245.9057 -120460.0388 -28987.5520 -242179.3221 20654.0473 -16861.5212
67 68 69 70 71 72
-190153.5077 -2483.9129 -134759.5433 -64086.7390 -107260.0004 122588.8847
73 74 75 76 77 78
-178499.0979 -93880.1004 -153510.7855 -73623.8082 -164099.4883 -134406.8603
79 80 81 82 83 84
-20946.8047 51139.6183 -10649.0475 -58415.1070 -195302.3812 5953.9759
85 86 87 88 89 90
-81782.8523 -140502.8645 23683.1814 -113951.1162 -140612.6340 -92503.9273
91 92 93 94 95 96
-71639.2295 -49835.5118 68877.9462 -238256.8598 81943.8504 -21490.8823
97 98 99 100
-66233.4537 30937.6637 -135062.1427 -26820.4682
> postscript(file="/var/www/html/rcomp/tmp/6in2e1291409893.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 = 100
Frequency = 1
lag(myerror, k = 1) myerror
0 -103387.0397 NA
1 2936875.4516 -103387.0397
2 508893.7371 2936875.4516
3 -633713.8167 508893.7371
4 -880217.8319 -633713.8167
5 -193617.1624 -880217.8319
6 1095053.2091 -193617.1624
7 476787.1991 1095053.2091
8 -725675.9287 476787.1991
9 578745.3061 -725675.9287
10 457965.3572 578745.3061
11 758846.4051 457965.3572
12 604167.5452 758846.4051
13 -505663.9308 604167.5452
14 -260893.9792 -505663.9308
15 541027.0438 -260893.9792
16 -259329.8960 541027.0438
17 -301663.7065 -259329.8960
18 -636403.7071 -301663.7065
19 20936.1128 -636403.7071
20 90860.9998 20936.1128
21 -419889.2020 90860.9998
22 100389.4601 -419889.2020
23 -159240.7445 100389.4601
24 -99058.3766 -159240.7445
25 289754.9236 -99058.3766
26 -657103.2585 289754.9236
27 -219487.4587 -657103.2585
28 577431.7775 -219487.4587
29 243972.7477 577431.7775
30 243507.6887 243972.7477
31 -167584.3847 243507.6887
32 140743.3944 -167584.3847
33 93240.6806 140743.3944
34 -308777.0058 93240.6806
35 21745.2962 -308777.0058
36 275963.3616 21745.2962
37 152914.4601 275963.3616
38 165465.9636 152914.4601
39 -172572.9085 165465.9636
40 -192147.7346 -172572.9085
41 -152.8003 -192147.7346
42 59654.3540 -152.8003
43 -84003.2243 59654.3540
44 21992.7796 -84003.2243
45 -138401.2187 21992.7796
46 113380.3590 -138401.2187
47 -149053.5866 113380.3590
48 -58172.4144 -149053.5866
49 -187495.8185 -58172.4144
50 108224.6759 -187495.8185
51 -9717.6635 108224.6759
52 -188569.0372 -9717.6635
53 120585.0268 -188569.0372
54 -237903.6047 120585.0268
55 72139.7742 -237903.6047
56 206940.0957 72139.7742
57 43676.8093 206940.0957
58 -116060.7308 43676.8093
59 -252199.5252 -116060.7308
60 -40245.9057 -252199.5252
61 -120460.0388 -40245.9057
62 -28987.5520 -120460.0388
63 -242179.3221 -28987.5520
64 20654.0473 -242179.3221
65 -16861.5212 20654.0473
66 -190153.5077 -16861.5212
67 -2483.9129 -190153.5077
68 -134759.5433 -2483.9129
69 -64086.7390 -134759.5433
70 -107260.0004 -64086.7390
71 122588.8847 -107260.0004
72 -178499.0979 122588.8847
73 -93880.1004 -178499.0979
74 -153510.7855 -93880.1004
75 -73623.8082 -153510.7855
76 -164099.4883 -73623.8082
77 -134406.8603 -164099.4883
78 -20946.8047 -134406.8603
79 51139.6183 -20946.8047
80 -10649.0475 51139.6183
81 -58415.1070 -10649.0475
82 -195302.3812 -58415.1070
83 5953.9759 -195302.3812
84 -81782.8523 5953.9759
85 -140502.8645 -81782.8523
86 23683.1814 -140502.8645
87 -113951.1162 23683.1814
88 -140612.6340 -113951.1162
89 -92503.9273 -140612.6340
90 -71639.2295 -92503.9273
91 -49835.5118 -71639.2295
92 68877.9462 -49835.5118
93 -238256.8598 68877.9462
94 81943.8504 -238256.8598
95 -21490.8823 81943.8504
96 -66233.4537 -21490.8823
97 30937.6637 -66233.4537
98 -135062.1427 30937.6637
99 -26820.4682 -135062.1427
100 NA -26820.4682
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2936875.4516 -103387.0397
[2,] 508893.7371 2936875.4516
[3,] -633713.8167 508893.7371
[4,] -880217.8319 -633713.8167
[5,] -193617.1624 -880217.8319
[6,] 1095053.2091 -193617.1624
[7,] 476787.1991 1095053.2091
[8,] -725675.9287 476787.1991
[9,] 578745.3061 -725675.9287
[10,] 457965.3572 578745.3061
[11,] 758846.4051 457965.3572
[12,] 604167.5452 758846.4051
[13,] -505663.9308 604167.5452
[14,] -260893.9792 -505663.9308
[15,] 541027.0438 -260893.9792
[16,] -259329.8960 541027.0438
[17,] -301663.7065 -259329.8960
[18,] -636403.7071 -301663.7065
[19,] 20936.1128 -636403.7071
[20,] 90860.9998 20936.1128
[21,] -419889.2020 90860.9998
[22,] 100389.4601 -419889.2020
[23,] -159240.7445 100389.4601
[24,] -99058.3766 -159240.7445
[25,] 289754.9236 -99058.3766
[26,] -657103.2585 289754.9236
[27,] -219487.4587 -657103.2585
[28,] 577431.7775 -219487.4587
[29,] 243972.7477 577431.7775
[30,] 243507.6887 243972.7477
[31,] -167584.3847 243507.6887
[32,] 140743.3944 -167584.3847
[33,] 93240.6806 140743.3944
[34,] -308777.0058 93240.6806
[35,] 21745.2962 -308777.0058
[36,] 275963.3616 21745.2962
[37,] 152914.4601 275963.3616
[38,] 165465.9636 152914.4601
[39,] -172572.9085 165465.9636
[40,] -192147.7346 -172572.9085
[41,] -152.8003 -192147.7346
[42,] 59654.3540 -152.8003
[43,] -84003.2243 59654.3540
[44,] 21992.7796 -84003.2243
[45,] -138401.2187 21992.7796
[46,] 113380.3590 -138401.2187
[47,] -149053.5866 113380.3590
[48,] -58172.4144 -149053.5866
[49,] -187495.8185 -58172.4144
[50,] 108224.6759 -187495.8185
[51,] -9717.6635 108224.6759
[52,] -188569.0372 -9717.6635
[53,] 120585.0268 -188569.0372
[54,] -237903.6047 120585.0268
[55,] 72139.7742 -237903.6047
[56,] 206940.0957 72139.7742
[57,] 43676.8093 206940.0957
[58,] -116060.7308 43676.8093
[59,] -252199.5252 -116060.7308
[60,] -40245.9057 -252199.5252
[61,] -120460.0388 -40245.9057
[62,] -28987.5520 -120460.0388
[63,] -242179.3221 -28987.5520
[64,] 20654.0473 -242179.3221
[65,] -16861.5212 20654.0473
[66,] -190153.5077 -16861.5212
[67,] -2483.9129 -190153.5077
[68,] -134759.5433 -2483.9129
[69,] -64086.7390 -134759.5433
[70,] -107260.0004 -64086.7390
[71,] 122588.8847 -107260.0004
[72,] -178499.0979 122588.8847
[73,] -93880.1004 -178499.0979
[74,] -153510.7855 -93880.1004
[75,] -73623.8082 -153510.7855
[76,] -164099.4883 -73623.8082
[77,] -134406.8603 -164099.4883
[78,] -20946.8047 -134406.8603
[79,] 51139.6183 -20946.8047
[80,] -10649.0475 51139.6183
[81,] -58415.1070 -10649.0475
[82,] -195302.3812 -58415.1070
[83,] 5953.9759 -195302.3812
[84,] -81782.8523 5953.9759
[85,] -140502.8645 -81782.8523
[86,] 23683.1814 -140502.8645
[87,] -113951.1162 23683.1814
[88,] -140612.6340 -113951.1162
[89,] -92503.9273 -140612.6340
[90,] -71639.2295 -92503.9273
[91,] -49835.5118 -71639.2295
[92,] 68877.9462 -49835.5118
[93,] -238256.8598 68877.9462
[94,] 81943.8504 -238256.8598
[95,] -21490.8823 81943.8504
[96,] -66233.4537 -21490.8823
[97,] 30937.6637 -66233.4537
[98,] -135062.1427 30937.6637
[99,] -26820.4682 -135062.1427
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2936875.4516 -103387.0397
2 508893.7371 2936875.4516
3 -633713.8167 508893.7371
4 -880217.8319 -633713.8167
5 -193617.1624 -880217.8319
6 1095053.2091 -193617.1624
7 476787.1991 1095053.2091
8 -725675.9287 476787.1991
9 578745.3061 -725675.9287
10 457965.3572 578745.3061
11 758846.4051 457965.3572
12 604167.5452 758846.4051
13 -505663.9308 604167.5452
14 -260893.9792 -505663.9308
15 541027.0438 -260893.9792
16 -259329.8960 541027.0438
17 -301663.7065 -259329.8960
18 -636403.7071 -301663.7065
19 20936.1128 -636403.7071
20 90860.9998 20936.1128
21 -419889.2020 90860.9998
22 100389.4601 -419889.2020
23 -159240.7445 100389.4601
24 -99058.3766 -159240.7445
25 289754.9236 -99058.3766
26 -657103.2585 289754.9236
27 -219487.4587 -657103.2585
28 577431.7775 -219487.4587
29 243972.7477 577431.7775
30 243507.6887 243972.7477
31 -167584.3847 243507.6887
32 140743.3944 -167584.3847
33 93240.6806 140743.3944
34 -308777.0058 93240.6806
35 21745.2962 -308777.0058
36 275963.3616 21745.2962
37 152914.4601 275963.3616
38 165465.9636 152914.4601
39 -172572.9085 165465.9636
40 -192147.7346 -172572.9085
41 -152.8003 -192147.7346
42 59654.3540 -152.8003
43 -84003.2243 59654.3540
44 21992.7796 -84003.2243
45 -138401.2187 21992.7796
46 113380.3590 -138401.2187
47 -149053.5866 113380.3590
48 -58172.4144 -149053.5866
49 -187495.8185 -58172.4144
50 108224.6759 -187495.8185
51 -9717.6635 108224.6759
52 -188569.0372 -9717.6635
53 120585.0268 -188569.0372
54 -237903.6047 120585.0268
55 72139.7742 -237903.6047
56 206940.0957 72139.7742
57 43676.8093 206940.0957
58 -116060.7308 43676.8093
59 -252199.5252 -116060.7308
60 -40245.9057 -252199.5252
61 -120460.0388 -40245.9057
62 -28987.5520 -120460.0388
63 -242179.3221 -28987.5520
64 20654.0473 -242179.3221
65 -16861.5212 20654.0473
66 -190153.5077 -16861.5212
67 -2483.9129 -190153.5077
68 -134759.5433 -2483.9129
69 -64086.7390 -134759.5433
70 -107260.0004 -64086.7390
71 122588.8847 -107260.0004
72 -178499.0979 122588.8847
73 -93880.1004 -178499.0979
74 -153510.7855 -93880.1004
75 -73623.8082 -153510.7855
76 -164099.4883 -73623.8082
77 -134406.8603 -164099.4883
78 -20946.8047 -134406.8603
79 51139.6183 -20946.8047
80 -10649.0475 51139.6183
81 -58415.1070 -10649.0475
82 -195302.3812 -58415.1070
83 5953.9759 -195302.3812
84 -81782.8523 5953.9759
85 -140502.8645 -81782.8523
86 23683.1814 -140502.8645
87 -113951.1162 23683.1814
88 -140612.6340 -113951.1162
89 -92503.9273 -140612.6340
90 -71639.2295 -92503.9273
91 -49835.5118 -71639.2295
92 68877.9462 -49835.5118
93 -238256.8598 68877.9462
94 81943.8504 -238256.8598
95 -21490.8823 81943.8504
96 -66233.4537 -21490.8823
97 30937.6637 -66233.4537
98 -135062.1427 30937.6637
99 -26820.4682 -135062.1427
> 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/7tfjz1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tfjz1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tfjz1291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m6i21291409893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p6g81291409893.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/12spxe1291409893.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/13hqu81291409893.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/14lrbw1291409893.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/1569r11291409893.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/169sq71291409893.tab")
+ }
>
> try(system("convert tmp/1fnlq1291409893.ps tmp/1fnlq1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fnlq1291409893.ps tmp/2fnlq1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pwkb1291409893.ps tmp/3pwkb1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pwkb1291409893.ps tmp/4pwkb1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pwkb1291409893.ps tmp/5pwkb1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/6in2e1291409893.ps tmp/6in2e1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tfjz1291409893.ps tmp/7tfjz1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tfjz1291409893.ps tmp/8tfjz1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tfjz1291409893.ps tmp/9tfjz1291409893.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m6i21291409893.ps tmp/10m6i21291409893.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.114 1.660 7.118