R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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(1
+ ,162556
+ ,1081
+ ,213118
+ ,6282929
+ ,1
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+ ,1
+ ,1383
+ ,11
+ ,73221
+ ,329118)
+ ,dim=c(5
+ ,100)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'Trades'
+ ,'Dividends'
+ ,'Wealth')
+ ,1:100))
> y <- array(NA,dim=c(5,100),dimnames=list(c('Group','Costs','Trades','Dividends','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 = '5'
> #'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 Trades Dividends
1 6282929 1 162556 1081 213118
2 4324047 1 29790 309 81767
3 4108272 1 87550 458 153198
4 -1212617 0 84738 588 -26007
5 1485329 1 54660 299 126942
6 1779876 1 42634 156 157214
7 1367203 0 40949 481 129352
8 2519076 1 42312 323 234817
9 912684 1 37704 452 60448
10 1443586 1 16275 109 47818
11 1220017 0 25830 115 245546
12 984885 0 12679 110 48020
13 1457425 1 18014 239 -1710
14 -572920 0 43556 247 32648
15 929144 1 24524 497 95350
16 1151176 0 6532 103 151352
17 790090 0 7123 109 288170
18 774497 1 20813 502 114337
19 990576 1 37597 248 37884
20 454195 0 17821 373 122844
21 876607 1 12988 119 82340
22 711969 1 22330 84 79801
23 702380 0 13326 102 165548
24 264449 0 16189 295 116384
25 450033 0 7146 105 134028
26 541063 0 15824 64 63838
27 588864 1 26088 267 74996
28 -37216 0 11326 129 31080
29 783310 0 8568 37 32168
30 467359 0 14416 361 49857
31 688779 1 3369 28 87161
32 608419 1 11819 85 106113
33 696348 1 6620 44 80570
34 597793 1 4519 49 102129
35 821730 0 2220 22 301670
36 377934 0 18562 155 102313
37 651939 0 10327 91 88577
38 697458 1 5336 81 112477
39 700368 1 2365 79 191778
40 225986 0 4069 145 79804
41 348695 0 7710 816 128294
42 373683 0 13718 61 96448
43 501709 0 4525 226 93811
44 413743 0 6869 105 117520
45 379825 0 4628 62 69159
46 336260 1 3653 24 101792
47 636765 1 1265 26 210568
48 481231 1 7489 322 136996
49 469107 0 4901 84 121920
50 211928 0 2284 33 76403
51 563925 1 3160 108 108094
52 511939 1 4150 150 134759
53 521016 1 7285 115 188873
54 543856 1 1134 162 146216
55 329304 1 4658 158 156608
56 423262 0 2384 97 61348
57 509665 0 3748 9 50350
58 455881 0 5371 66 87720
59 367772 0 1285 107 99489
60 406339 1 9327 101 87419
61 493408 1 5565 47 94355
62 232942 0 1528 38 60326
63 416002 1 3122 34 94670
64 337430 1 7317 84 82425
65 361517 0 2675 79 59017
66 360962 0 13253 947 90829
67 235561 0 880 74 80791
68 408247 1 2053 53 100423
69 450296 0 1424 94 131116
70 418799 1 4036 63 100269
71 247405 1 3045 58 27330
72 378519 0 5119 49 39039
73 326638 0 1431 34 106885
74 328233 0 554 11 79285
75 386225 0 1975 35 118881
76 283662 1 1286 17 77623
77 370225 0 1012 47 114768
78 269236 0 810 43 74015
79 365732 0 1280 117 69465
80 420383 1 666 171 117869
81 345811 0 1380 26 60982
82 431809 1 4608 73 90131
83 418876 0 876 59 138971
84 297476 0 814 18 39625
85 416776 0 514 15 102725
86 357257 1 5692 72 64239
87 458343 0 3642 86 90262
88 388386 0 540 14 103960
89 358934 0 2099 64 106611
90 407560 0 567 11 103345
91 392558 0 2001 52 95551
92 373177 1 2949 41 82903
93 428370 0 2253 99 63593
94 369419 1 6533 75 126910
95 358649 0 1889 45 37527
96 376641 1 3055 43 60247
97 467427 0 272 8 112995
98 364885 1 1414 198 70184
99 436230 0 2564 22 130140
100 329118 1 1383 11 73221
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Group Costs Trades Dividends
-1.913e+05 2.851e+05 2.684e+01 -2.658e+02 4.282e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3028137 -214095 2702 155211 3162664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.913e+05 1.368e+05 -1.398 0.165407
Group 2.851e+05 1.190e+05 2.395 0.018579 *
Costs 2.684e+01 3.834e+00 7.001 3.59e-10 ***
Trades -2.658e+02 4.337e+02 -0.613 0.541459
Dividends 4.282e+00 1.099e+00 3.895 0.000183 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 579100 on 95 degrees of freedom
Multiple R-squared: 0.5799, Adjusted R-squared: 0.5622
F-statistic: 32.78 on 4 and 95 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 3.164570e-12 1.582285e-12
[2,] 1.0000000 2.364351e-19 1.182175e-19
[3,] 1.0000000 1.051133e-20 5.255663e-21
[4,] 1.0000000 1.048183e-20 5.240916e-21
[5,] 1.0000000 3.323243e-24 1.661621e-24
[6,] 1.0000000 1.317159e-27 6.585797e-28
[7,] 1.0000000 9.085710e-36 4.542855e-36
[8,] 1.0000000 2.118988e-37 1.059494e-37
[9,] 1.0000000 5.214214e-41 2.607107e-41
[10,] 1.0000000 1.410454e-40 7.052269e-41
[11,] 1.0000000 8.718609e-41 4.359304e-41
[12,] 1.0000000 7.899861e-41 3.949931e-41
[13,] 1.0000000 5.791592e-40 2.895796e-40
[14,] 1.0000000 6.976624e-41 3.488312e-41
[15,] 1.0000000 1.619499e-40 8.097496e-41
[16,] 1.0000000 8.917351e-40 4.458675e-40
[17,] 1.0000000 1.345051e-39 6.725257e-40
[18,] 1.0000000 1.012936e-38 5.064680e-39
[19,] 1.0000000 3.392844e-38 1.696422e-38
[20,] 1.0000000 1.043643e-37 5.218214e-38
[21,] 1.0000000 1.582697e-39 7.913483e-40
[22,] 1.0000000 1.112115e-42 5.560574e-43
[23,] 1.0000000 4.249026e-42 2.124513e-42
[24,] 1.0000000 1.078682e-42 5.393410e-43
[25,] 1.0000000 3.424021e-42 1.712010e-42
[26,] 1.0000000 3.598034e-43 1.799017e-43
[27,] 1.0000000 5.616123e-43 2.808061e-43
[28,] 1.0000000 2.794489e-42 1.397244e-42
[29,] 1.0000000 1.250188e-41 6.250938e-42
[30,] 1.0000000 2.380352e-42 1.190176e-42
[31,] 1.0000000 1.078540e-43 5.392699e-44
[32,] 1.0000000 6.480481e-44 3.240240e-44
[33,] 1.0000000 1.041254e-43 5.206269e-44
[34,] 1.0000000 7.913408e-43 3.956704e-43
[35,] 1.0000000 6.640030e-42 3.320015e-42
[36,] 1.0000000 1.886089e-41 9.430443e-42
[37,] 1.0000000 2.103871e-40 1.051936e-40
[38,] 1.0000000 2.087557e-39 1.043778e-39
[39,] 1.0000000 1.011865e-38 5.059327e-39
[40,] 1.0000000 2.683087e-38 1.341543e-38
[41,] 1.0000000 2.185421e-37 1.092711e-37
[42,] 1.0000000 2.145281e-36 1.072640e-36
[43,] 1.0000000 1.734324e-36 8.671620e-37
[44,] 1.0000000 9.747034e-37 4.873517e-37
[45,] 1.0000000 4.353363e-36 2.176681e-36
[46,] 1.0000000 3.507115e-35 1.753558e-35
[47,] 1.0000000 2.192313e-35 1.096156e-35
[48,] 1.0000000 4.314225e-35 2.157112e-35
[49,] 1.0000000 2.232823e-34 1.116412e-34
[50,] 1.0000000 4.959510e-35 2.479755e-35
[51,] 1.0000000 3.650404e-34 1.825202e-34
[52,] 1.0000000 5.054141e-33 2.527070e-33
[53,] 1.0000000 6.564166e-32 3.282083e-32
[54,] 1.0000000 1.807638e-31 9.038190e-32
[55,] 1.0000000 2.779396e-31 1.389698e-31
[56,] 1.0000000 3.066572e-30 1.533286e-30
[57,] 1.0000000 2.687789e-29 1.343894e-29
[58,] 1.0000000 3.497279e-28 1.748639e-28
[59,] 1.0000000 1.553617e-27 7.768085e-28
[60,] 1.0000000 3.413088e-28 1.706544e-28
[61,] 1.0000000 3.559568e-27 1.779784e-27
[62,] 1.0000000 5.476736e-26 2.738368e-26
[63,] 1.0000000 6.117324e-25 3.058662e-25
[64,] 1.0000000 3.879072e-24 1.939536e-24
[65,] 1.0000000 5.623347e-23 2.811673e-23
[66,] 1.0000000 2.805737e-22 1.402868e-22
[67,] 1.0000000 3.391584e-21 1.695792e-21
[68,] 1.0000000 4.438249e-20 2.219125e-20
[69,] 1.0000000 2.824922e-19 1.412461e-19
[70,] 1.0000000 2.959433e-18 1.479716e-18
[71,] 1.0000000 1.436308e-18 7.181542e-19
[72,] 1.0000000 2.012527e-17 1.006263e-17
[73,] 1.0000000 3.501721e-16 1.750861e-16
[74,] 1.0000000 4.675948e-15 2.337974e-15
[75,] 1.0000000 2.841764e-14 1.420882e-14
[76,] 1.0000000 4.235281e-13 2.117640e-13
[77,] 1.0000000 1.099104e-12 5.495521e-13
[78,] 1.0000000 2.067292e-11 1.033646e-11
[79,] 1.0000000 3.766652e-10 1.883326e-10
[80,] 1.0000000 2.605812e-09 1.302906e-09
[81,] 1.0000000 3.727675e-08 1.863838e-08
[82,] 0.9999999 1.430624e-07 7.153118e-08
[83,] 0.9999988 2.462329e-06 1.231165e-06
[84,] 0.9999846 3.086108e-05 1.543054e-05
[85,] 0.9997112 5.776100e-04 2.888050e-04
> postscript(file="/var/www/html/rcomp/tmp/1ghef1291317430.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29qvi1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39qvi1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49qvi1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jzcl1291317430.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
1200693.3847 3162663.9890 1130276.1738 -3028136.5026 -539693.6871
6 7 8 9 10
-89993.1298 33314.0915 369927.4372 -331820.5097 737177.3415
11 12 13 14 15
-302899.8543 659461.7758 950978.0809 -1624875.1811 -99091.6719
16 17 18 19 20
546400.4421 -414829.5333 -234108.0652 -208653.6802 -259751.6213
21 22 23 24 25
113254.0285 -300562.5645 -145808.5165 -398761.4531 -96507.3415
26 27 28 29 30
51263.7402 -455322.0550 -248733.8097 616708.6703 154158.0978
31 32 33 34 35
138779.0885 -234393.0864 91564.2043 -41587.8911 -332520.2890
36 37 38 39 40
-325925.5314 210924.6393 341.3384 -257114.6615 4862.1880
41 42 43 44 45
541.6755 -200027.4870 229893.9687 -54672.5998 167220.6314
46 47 48 49 50
-285078.0692 -385740.7061 -314614.8206 29091.5707 23511.3114
51 52 53 54 55
-48840.8443 -230420.1750 -546516.5321 -163421.9681 -518124.9061
56 57 58 59 60
313639.1192 387137.2475 144915.8555 126979.5938 -285282.2284
61 62 63 64 65
-141290.6792 134990.1956 -157928.1782 -283374.0579 249281.0006
66 67 68 69 70
59277.4332 76936.0708 -156575.5102 66885.8600 -195931.9276
71 72 73 74 75
-29719.4381 278258.9834 30853.8687 168062.8507 24736.4390
76 77 78 79 80
-172508.5480 55386.2067 133266.5484 256298.9944 -150555.1399
81 82 83 84 85
245833.1785 -152204.6812 7236.0191 302017.8680 158368.9491
86 87 88 89 90
-145244.5920 188216.3577 123726.8413 54366.8730 144012.3107
91 92 93 94 95
134792.5136 -143861.1100 313181.4210 -423225.3479 350496.8863
96 97 98 99 100
-45694.2264 169677.2778 -14759.5958 7264.1543 -112400.7820
> postscript(file="/var/www/html/rcomp/tmp/6jzcl1291317430.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 1200693.3847 NA
1 3162663.9890 1200693.3847
2 1130276.1738 3162663.9890
3 -3028136.5026 1130276.1738
4 -539693.6871 -3028136.5026
5 -89993.1298 -539693.6871
6 33314.0915 -89993.1298
7 369927.4372 33314.0915
8 -331820.5097 369927.4372
9 737177.3415 -331820.5097
10 -302899.8543 737177.3415
11 659461.7758 -302899.8543
12 950978.0809 659461.7758
13 -1624875.1811 950978.0809
14 -99091.6719 -1624875.1811
15 546400.4421 -99091.6719
16 -414829.5333 546400.4421
17 -234108.0652 -414829.5333
18 -208653.6802 -234108.0652
19 -259751.6213 -208653.6802
20 113254.0285 -259751.6213
21 -300562.5645 113254.0285
22 -145808.5165 -300562.5645
23 -398761.4531 -145808.5165
24 -96507.3415 -398761.4531
25 51263.7402 -96507.3415
26 -455322.0550 51263.7402
27 -248733.8097 -455322.0550
28 616708.6703 -248733.8097
29 154158.0978 616708.6703
30 138779.0885 154158.0978
31 -234393.0864 138779.0885
32 91564.2043 -234393.0864
33 -41587.8911 91564.2043
34 -332520.2890 -41587.8911
35 -325925.5314 -332520.2890
36 210924.6393 -325925.5314
37 341.3384 210924.6393
38 -257114.6615 341.3384
39 4862.1880 -257114.6615
40 541.6755 4862.1880
41 -200027.4870 541.6755
42 229893.9687 -200027.4870
43 -54672.5998 229893.9687
44 167220.6314 -54672.5998
45 -285078.0692 167220.6314
46 -385740.7061 -285078.0692
47 -314614.8206 -385740.7061
48 29091.5707 -314614.8206
49 23511.3114 29091.5707
50 -48840.8443 23511.3114
51 -230420.1750 -48840.8443
52 -546516.5321 -230420.1750
53 -163421.9681 -546516.5321
54 -518124.9061 -163421.9681
55 313639.1192 -518124.9061
56 387137.2475 313639.1192
57 144915.8555 387137.2475
58 126979.5938 144915.8555
59 -285282.2284 126979.5938
60 -141290.6792 -285282.2284
61 134990.1956 -141290.6792
62 -157928.1782 134990.1956
63 -283374.0579 -157928.1782
64 249281.0006 -283374.0579
65 59277.4332 249281.0006
66 76936.0708 59277.4332
67 -156575.5102 76936.0708
68 66885.8600 -156575.5102
69 -195931.9276 66885.8600
70 -29719.4381 -195931.9276
71 278258.9834 -29719.4381
72 30853.8687 278258.9834
73 168062.8507 30853.8687
74 24736.4390 168062.8507
75 -172508.5480 24736.4390
76 55386.2067 -172508.5480
77 133266.5484 55386.2067
78 256298.9944 133266.5484
79 -150555.1399 256298.9944
80 245833.1785 -150555.1399
81 -152204.6812 245833.1785
82 7236.0191 -152204.6812
83 302017.8680 7236.0191
84 158368.9491 302017.8680
85 -145244.5920 158368.9491
86 188216.3577 -145244.5920
87 123726.8413 188216.3577
88 54366.8730 123726.8413
89 144012.3107 54366.8730
90 134792.5136 144012.3107
91 -143861.1100 134792.5136
92 313181.4210 -143861.1100
93 -423225.3479 313181.4210
94 350496.8863 -423225.3479
95 -45694.2264 350496.8863
96 169677.2778 -45694.2264
97 -14759.5958 169677.2778
98 7264.1543 -14759.5958
99 -112400.7820 7264.1543
100 NA -112400.7820
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3162663.9890 1200693.3847
[2,] 1130276.1738 3162663.9890
[3,] -3028136.5026 1130276.1738
[4,] -539693.6871 -3028136.5026
[5,] -89993.1298 -539693.6871
[6,] 33314.0915 -89993.1298
[7,] 369927.4372 33314.0915
[8,] -331820.5097 369927.4372
[9,] 737177.3415 -331820.5097
[10,] -302899.8543 737177.3415
[11,] 659461.7758 -302899.8543
[12,] 950978.0809 659461.7758
[13,] -1624875.1811 950978.0809
[14,] -99091.6719 -1624875.1811
[15,] 546400.4421 -99091.6719
[16,] -414829.5333 546400.4421
[17,] -234108.0652 -414829.5333
[18,] -208653.6802 -234108.0652
[19,] -259751.6213 -208653.6802
[20,] 113254.0285 -259751.6213
[21,] -300562.5645 113254.0285
[22,] -145808.5165 -300562.5645
[23,] -398761.4531 -145808.5165
[24,] -96507.3415 -398761.4531
[25,] 51263.7402 -96507.3415
[26,] -455322.0550 51263.7402
[27,] -248733.8097 -455322.0550
[28,] 616708.6703 -248733.8097
[29,] 154158.0978 616708.6703
[30,] 138779.0885 154158.0978
[31,] -234393.0864 138779.0885
[32,] 91564.2043 -234393.0864
[33,] -41587.8911 91564.2043
[34,] -332520.2890 -41587.8911
[35,] -325925.5314 -332520.2890
[36,] 210924.6393 -325925.5314
[37,] 341.3384 210924.6393
[38,] -257114.6615 341.3384
[39,] 4862.1880 -257114.6615
[40,] 541.6755 4862.1880
[41,] -200027.4870 541.6755
[42,] 229893.9687 -200027.4870
[43,] -54672.5998 229893.9687
[44,] 167220.6314 -54672.5998
[45,] -285078.0692 167220.6314
[46,] -385740.7061 -285078.0692
[47,] -314614.8206 -385740.7061
[48,] 29091.5707 -314614.8206
[49,] 23511.3114 29091.5707
[50,] -48840.8443 23511.3114
[51,] -230420.1750 -48840.8443
[52,] -546516.5321 -230420.1750
[53,] -163421.9681 -546516.5321
[54,] -518124.9061 -163421.9681
[55,] 313639.1192 -518124.9061
[56,] 387137.2475 313639.1192
[57,] 144915.8555 387137.2475
[58,] 126979.5938 144915.8555
[59,] -285282.2284 126979.5938
[60,] -141290.6792 -285282.2284
[61,] 134990.1956 -141290.6792
[62,] -157928.1782 134990.1956
[63,] -283374.0579 -157928.1782
[64,] 249281.0006 -283374.0579
[65,] 59277.4332 249281.0006
[66,] 76936.0708 59277.4332
[67,] -156575.5102 76936.0708
[68,] 66885.8600 -156575.5102
[69,] -195931.9276 66885.8600
[70,] -29719.4381 -195931.9276
[71,] 278258.9834 -29719.4381
[72,] 30853.8687 278258.9834
[73,] 168062.8507 30853.8687
[74,] 24736.4390 168062.8507
[75,] -172508.5480 24736.4390
[76,] 55386.2067 -172508.5480
[77,] 133266.5484 55386.2067
[78,] 256298.9944 133266.5484
[79,] -150555.1399 256298.9944
[80,] 245833.1785 -150555.1399
[81,] -152204.6812 245833.1785
[82,] 7236.0191 -152204.6812
[83,] 302017.8680 7236.0191
[84,] 158368.9491 302017.8680
[85,] -145244.5920 158368.9491
[86,] 188216.3577 -145244.5920
[87,] 123726.8413 188216.3577
[88,] 54366.8730 123726.8413
[89,] 144012.3107 54366.8730
[90,] 134792.5136 144012.3107
[91,] -143861.1100 134792.5136
[92,] 313181.4210 -143861.1100
[93,] -423225.3479 313181.4210
[94,] 350496.8863 -423225.3479
[95,] -45694.2264 350496.8863
[96,] 169677.2778 -45694.2264
[97,] -14759.5958 169677.2778
[98,] 7264.1543 -14759.5958
[99,] -112400.7820 7264.1543
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3162663.9890 1200693.3847
2 1130276.1738 3162663.9890
3 -3028136.5026 1130276.1738
4 -539693.6871 -3028136.5026
5 -89993.1298 -539693.6871
6 33314.0915 -89993.1298
7 369927.4372 33314.0915
8 -331820.5097 369927.4372
9 737177.3415 -331820.5097
10 -302899.8543 737177.3415
11 659461.7758 -302899.8543
12 950978.0809 659461.7758
13 -1624875.1811 950978.0809
14 -99091.6719 -1624875.1811
15 546400.4421 -99091.6719
16 -414829.5333 546400.4421
17 -234108.0652 -414829.5333
18 -208653.6802 -234108.0652
19 -259751.6213 -208653.6802
20 113254.0285 -259751.6213
21 -300562.5645 113254.0285
22 -145808.5165 -300562.5645
23 -398761.4531 -145808.5165
24 -96507.3415 -398761.4531
25 51263.7402 -96507.3415
26 -455322.0550 51263.7402
27 -248733.8097 -455322.0550
28 616708.6703 -248733.8097
29 154158.0978 616708.6703
30 138779.0885 154158.0978
31 -234393.0864 138779.0885
32 91564.2043 -234393.0864
33 -41587.8911 91564.2043
34 -332520.2890 -41587.8911
35 -325925.5314 -332520.2890
36 210924.6393 -325925.5314
37 341.3384 210924.6393
38 -257114.6615 341.3384
39 4862.1880 -257114.6615
40 541.6755 4862.1880
41 -200027.4870 541.6755
42 229893.9687 -200027.4870
43 -54672.5998 229893.9687
44 167220.6314 -54672.5998
45 -285078.0692 167220.6314
46 -385740.7061 -285078.0692
47 -314614.8206 -385740.7061
48 29091.5707 -314614.8206
49 23511.3114 29091.5707
50 -48840.8443 23511.3114
51 -230420.1750 -48840.8443
52 -546516.5321 -230420.1750
53 -163421.9681 -546516.5321
54 -518124.9061 -163421.9681
55 313639.1192 -518124.9061
56 387137.2475 313639.1192
57 144915.8555 387137.2475
58 126979.5938 144915.8555
59 -285282.2284 126979.5938
60 -141290.6792 -285282.2284
61 134990.1956 -141290.6792
62 -157928.1782 134990.1956
63 -283374.0579 -157928.1782
64 249281.0006 -283374.0579
65 59277.4332 249281.0006
66 76936.0708 59277.4332
67 -156575.5102 76936.0708
68 66885.8600 -156575.5102
69 -195931.9276 66885.8600
70 -29719.4381 -195931.9276
71 278258.9834 -29719.4381
72 30853.8687 278258.9834
73 168062.8507 30853.8687
74 24736.4390 168062.8507
75 -172508.5480 24736.4390
76 55386.2067 -172508.5480
77 133266.5484 55386.2067
78 256298.9944 133266.5484
79 -150555.1399 256298.9944
80 245833.1785 -150555.1399
81 -152204.6812 245833.1785
82 7236.0191 -152204.6812
83 302017.8680 7236.0191
84 158368.9491 302017.8680
85 -145244.5920 158368.9491
86 188216.3577 -145244.5920
87 123726.8413 188216.3577
88 54366.8730 123726.8413
89 144012.3107 54366.8730
90 134792.5136 144012.3107
91 -143861.1100 134792.5136
92 313181.4210 -143861.1100
93 -423225.3479 313181.4210
94 350496.8863 -423225.3479
95 -45694.2264 350496.8863
96 169677.2778 -45694.2264
97 -14759.5958 169677.2778
98 7264.1543 -14759.5958
99 -112400.7820 7264.1543
> 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/7cquo1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cquo1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nibq1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nibq1291317430.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11q09w1291317430.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/12cj821291317430.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/13i25w1291317430.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/14tbmz1291317430.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/15fcl51291317430.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/16b3iv1291317430.tab")
+ }
>
> try(system("convert tmp/1ghef1291317430.ps tmp/1ghef1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/29qvi1291317430.ps tmp/29qvi1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/39qvi1291317430.ps tmp/39qvi1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/49qvi1291317430.ps tmp/49qvi1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jzcl1291317430.ps tmp/5jzcl1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jzcl1291317430.ps tmp/6jzcl1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cquo1291317430.ps tmp/7cquo1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cquo1291317430.ps tmp/8cquo1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nibq1291317430.ps tmp/9nibq1291317430.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nibq1291317430.ps tmp/10nibq1291317430.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.971 1.667 8.131