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)
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> x <- array(list(1
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+ ,dim=c(9
+ ,100)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'GrCosts'
+ ,'Trades'
+ ,'GrTrades'
+ ,'Dividends'
+ ,'GrDiv'
+ ,'TrDiv'
+ ,'Wealth
')
+ ,1:100))
> y <- array(NA,dim=c(9,100),dimnames=list(c('Group','Costs','GrCosts','Trades','GrTrades','Dividends','GrDiv','TrDiv','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 = '9'
> #'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
Wealth\r Group Costs GrCosts Trades GrTrades Dividends GrDiv TrDiv
1 6282929 1 162556 162556 1081 1081 213118 213118 230380558
2 4324047 1 29790 29790 309 309 81767 81767 25266003
3 4108272 1 87550 87550 458 458 153198 153198 70164684
4 -1212617 0 84738 0 588 0 -26007 0 -15292116
5 1485329 1 54660 54660 299 299 126942 126942 37955658
6 1779876 1 42634 42634 156 156 157214 157214 24525384
7 1367203 0 40949 0 481 0 129352 0 62218312
8 2519076 1 42312 42312 323 323 234817 234817 75845891
9 912684 1 37704 37704 452 452 60448 60448 27322496
10 1443586 1 16275 16275 109 109 47818 47818 5212162
11 1220017 0 25830 0 115 0 245546 0 28237790
12 984885 0 12679 0 110 0 48020 0 5282200
13 1457425 1 18014 18014 239 239 -1710 -1710 -408690
14 -572920 0 43556 0 247 0 32648 0 8064056
15 929144 1 24524 24524 497 497 95350 95350 47388950
16 1151176 0 6532 0 103 0 151352 0 15589256
17 790090 0 7123 0 109 0 288170 0 31410530
18 774497 1 20813 20813 502 502 114337 114337 57397174
19 990576 1 37597 37597 248 248 37884 37884 9395232
20 454195 0 17821 0 373 0 122844 0 45820812
21 876607 1 12988 12988 119 119 82340 82340 9798460
22 711969 1 22330 22330 84 84 79801 79801 6703284
23 702380 0 13326 0 102 0 165548 0 16885896
24 264449 0 16189 0 295 0 116384 0 34333280
25 450033 0 7146 0 105 0 134028 0 14072940
26 541063 0 15824 0 64 0 63838 0 4085632
27 588864 1 26088 26088 267 267 74996 74996 20023932
28 -37216 0 11326 0 129 0 31080 0 4009320
29 783310 0 8568 0 37 0 32168 0 1190216
30 467359 0 14416 0 361 0 49857 0 17998377
31 688779 1 3369 3369 28 28 87161 87161 2440508
32 608419 1 11819 11819 85 85 106113 106113 9019605
33 696348 1 6620 6620 44 44 80570 80570 3545080
34 597793 1 4519 4519 49 49 102129 102129 5004321
35 821730 0 2220 0 22 0 301670 0 6636740
36 377934 0 18562 0 155 0 102313 0 15858515
37 651939 0 10327 0 91 0 88577 0 8060507
38 697458 1 5336 5336 81 81 112477 112477 9110637
39 700368 1 2365 2365 79 79 191778 191778 15150462
40 225986 0 4069 0 145 0 79804 0 11571580
41 348695 0 7710 0 816 0 128294 0 104687904
42 373683 0 13718 0 61 0 96448 0 5883328
43 501709 0 4525 0 226 0 93811 0 21201286
44 413743 0 6869 0 105 0 117520 0 12339600
45 379825 0 4628 0 62 0 69159 0 4287858
46 336260 1 3653 3653 24 24 101792 101792 2443008
47 636765 1 1265 1265 26 26 210568 210568 5474768
48 481231 1 7489 7489 322 322 136996 136996 44112712
49 469107 0 4901 0 84 0 121920 0 10241280
50 211928 0 2284 0 33 0 76403 0 2521299
51 563925 1 3160 3160 108 108 108094 108094 11674152
52 511939 1 4150 4150 150 150 134759 134759 20213850
53 521016 1 7285 7285 115 115 188873 188873 21720395
54 543856 1 1134 1134 162 162 146216 146216 23686992
55 329304 1 4658 4658 158 158 156608 156608 24744064
56 423262 0 2384 0 97 0 61348 0 5950756
57 509665 0 3748 0 9 0 50350 0 453150
58 455881 0 5371 0 66 0 87720 0 5789520
59 367772 0 1285 0 107 0 99489 0 10645323
60 406339 1 9327 9327 101 101 87419 87419 8829319
61 493408 1 5565 5565 47 47 94355 94355 4434685
62 232942 0 1528 0 38 0 60326 0 2292388
63 416002 1 3122 3122 34 34 94670 94670 3218780
64 337430 1 7317 7317 84 84 82425 82425 6923700
65 361517 0 2675 0 79 0 59017 0 4662343
66 360962 0 13253 0 947 0 90829 0 86015063
67 235561 0 880 0 74 0 80791 0 5978534
68 408247 1 2053 2053 53 53 100423 100423 5322419
69 450296 0 1424 0 94 0 131116 0 12324904
70 418799 1 4036 4036 63 63 100269 100269 6316947
71 247405 1 3045 3045 58 58 27330 27330 1585140
72 378519 0 5119 0 49 0 39039 0 1912911
73 326638 0 1431 0 34 0 106885 0 3634090
74 328233 0 554 0 11 0 79285 0 872135
75 386225 0 1975 0 35 0 118881 0 4160835
76 283662 1 1286 1286 17 17 77623 77623 1319591
77 370225 0 1012 0 47 0 114768 0 5394096
78 269236 0 810 0 43 0 74015 0 3182645
79 365732 0 1280 0 117 0 69465 0 8127405
80 420383 1 666 666 171 171 117869 117869 20155599
81 345811 0 1380 0 26 0 60982 0 1585532
82 431809 1 4608 4608 73 73 90131 90131 6579563
83 418876 0 876 0 59 0 138971 0 8199289
84 297476 0 814 0 18 0 39625 0 713250
85 416776 0 514 0 15 0 102725 0 1540875
86 357257 1 5692 5692 72 72 64239 64239 4625208
87 458343 0 3642 0 86 0 90262 0 7762532
88 388386 0 540 0 14 0 103960 0 1455440
89 358934 0 2099 0 64 0 106611 0 6823104
90 407560 0 567 0 11 0 103345 0 1136795
91 392558 0 2001 0 52 0 95551 0 4968652
92 373177 1 2949 2949 41 41 82903 82903 3399023
93 428370 0 2253 0 99 0 63593 0 6295707
94 369419 1 6533 6533 75 75 126910 126910 9518250
95 358649 0 1889 0 45 0 37527 0 1688715
96 376641 1 3055 3055 43 43 60247 60247 2590621
97 467427 0 272 0 8 0 112995 0 903960
98 364885 1 1414 1414 198 198 70184 70184 13896432
99 436230 0 2564 0 22 0 130140 0 2863080
100 329118 1 1383 1383 11 11 73221 73221 805431
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Group Costs GrCosts Trades GrTrades
2.472e+05 1.824e+05 -3.390e+00 3.661e+01 -9.509e+02 -6.481e+01
Dividends GrDiv TrDiv
2.256e+00 -2.694e+00 8.814e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-735059 -157487 -70014 84406 3031952
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.472e+05 1.378e+05 1.794 0.0762 .
Group 1.824e+05 2.212e+05 0.824 0.4119
Costs -3.390e+00 5.733e+00 -0.591 0.5558
GrCosts 3.661e+01 8.427e+00 4.344 3.63e-05 ***
Trades -9.509e+02 6.782e+02 -1.402 0.1643
GrTrades -6.481e+01 7.606e+02 -0.085 0.9323
Dividends 2.256e+00 1.192e+00 1.892 0.0616 .
GrDiv -2.694e+00 1.791e+00 -1.504 0.1361
TrDiv 8.814e-03 5.171e-03 1.705 0.0917 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 427500 on 91 degrees of freedom
Multiple R-squared: 0.7807, Adjusted R-squared: 0.7614
F-statistic: 40.5 on 8 and 91 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 1.033658e-23 5.168288e-24
[2,] 1.0000000 4.397215e-26 2.198608e-26
[3,] 1.0000000 1.143513e-29 5.717566e-30
[4,] 1.0000000 7.555857e-30 3.777929e-30
[5,] 1.0000000 6.450887e-33 3.225443e-33
[6,] 1.0000000 1.016877e-32 5.084385e-33
[7,] 1.0000000 2.891019e-32 1.445510e-32
[8,] 1.0000000 9.960913e-33 4.980457e-33
[9,] 1.0000000 2.217084e-32 1.108542e-32
[10,] 1.0000000 2.956038e-33 1.478019e-33
[11,] 1.0000000 3.071784e-33 1.535892e-33
[12,] 1.0000000 1.239624e-32 6.198118e-33
[13,] 1.0000000 1.474330e-32 7.371650e-33
[14,] 1.0000000 9.695610e-32 4.847805e-32
[15,] 1.0000000 5.561865e-31 2.780933e-31
[16,] 1.0000000 1.089926e-30 5.449632e-31
[17,] 1.0000000 1.644172e-32 8.220858e-33
[18,] 1.0000000 5.494118e-35 2.747059e-35
[19,] 1.0000000 3.920701e-34 1.960350e-34
[20,] 1.0000000 1.044335e-34 5.221673e-35
[21,] 1.0000000 4.471712e-34 2.235856e-34
[22,] 1.0000000 3.682900e-35 1.841450e-35
[23,] 1.0000000 5.396272e-35 2.698136e-35
[24,] 1.0000000 1.522614e-34 7.613068e-35
[25,] 1.0000000 3.570169e-34 1.785085e-34
[26,] 1.0000000 1.509830e-34 7.549152e-35
[27,] 1.0000000 5.165974e-36 2.582987e-36
[28,] 1.0000000 1.108873e-35 5.544364e-36
[29,] 1.0000000 9.072300e-36 4.536150e-36
[30,] 1.0000000 5.308463e-35 2.654231e-35
[31,] 1.0000000 5.995773e-35 2.997887e-35
[32,] 1.0000000 1.315342e-34 6.576711e-35
[33,] 1.0000000 7.240782e-34 3.620391e-34
[34,] 1.0000000 5.862044e-33 2.931022e-33
[35,] 1.0000000 2.812520e-32 1.406260e-32
[36,] 1.0000000 2.635542e-31 1.317771e-31
[37,] 1.0000000 1.523154e-30 7.615768e-31
[38,] 1.0000000 1.413838e-29 7.069189e-30
[39,] 1.0000000 4.123533e-30 2.061766e-30
[40,] 1.0000000 3.230425e-30 1.615213e-30
[41,] 1.0000000 1.450701e-29 7.253506e-30
[42,] 1.0000000 1.349946e-28 6.749730e-29
[43,] 1.0000000 7.755483e-29 3.877741e-29
[44,] 1.0000000 2.834822e-28 1.417411e-28
[45,] 1.0000000 1.507356e-27 7.536782e-28
[46,] 1.0000000 9.289898e-28 4.644949e-28
[47,] 1.0000000 1.024469e-26 5.122345e-27
[48,] 1.0000000 1.070649e-25 5.353243e-26
[49,] 1.0000000 9.611115e-25 4.805557e-25
[50,] 1.0000000 1.428948e-24 7.144740e-25
[51,] 1.0000000 1.798383e-24 8.991914e-25
[52,] 1.0000000 1.596682e-23 7.983410e-24
[53,] 1.0000000 1.320938e-22 6.604692e-23
[54,] 1.0000000 1.478523e-21 7.392613e-22
[55,] 1.0000000 1.293218e-20 6.466089e-21
[56,] 1.0000000 4.557225e-21 2.278613e-21
[57,] 1.0000000 4.839985e-20 2.419992e-20
[58,] 1.0000000 3.417178e-19 1.708589e-19
[59,] 1.0000000 3.241690e-18 1.620845e-18
[60,] 1.0000000 1.933736e-17 9.668682e-18
[61,] 1.0000000 2.138713e-16 1.069357e-16
[62,] 1.0000000 7.481749e-16 3.740874e-16
[63,] 1.0000000 7.177123e-15 3.588561e-15
[64,] 1.0000000 5.537067e-14 2.768533e-14
[65,] 1.0000000 1.980990e-13 9.904948e-14
[66,] 1.0000000 1.738649e-12 8.693246e-13
[67,] 1.0000000 1.691614e-12 8.458072e-13
[68,] 1.0000000 1.707892e-11 8.539462e-12
[69,] 1.0000000 1.896702e-11 9.483511e-12
[70,] 1.0000000 2.810031e-10 1.405015e-10
[71,] 1.0000000 7.951780e-10 3.975890e-10
[72,] 1.0000000 1.102868e-08 5.514338e-09
[73,] 0.9999999 1.650593e-07 8.252964e-08
[74,] 0.9999989 2.202584e-06 1.101292e-06
[75,] 0.9999920 1.606091e-05 8.030457e-06
[76,] 0.9999911 1.775782e-05 8.878910e-06
[77,] 0.9999348 1.303868e-04 6.519338e-05
> postscript(file="/var/www/rcomp/tmp/1dmkr1293221008.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5z51f1293221008.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
-385354.276 3031951.861 684521.405 -419917.413 -735058.574 -54673.022
7 8 9 10 11 12
876008.912 446465.076 -524497.192 559157.609 366925.180 730410.155
13 14 15 16 17 18
675130.635 -582281.337 -186143.292 545236.147 -256257.720 -292351.579
19 20 21 22 23 24
-502111.672 -58892.047 86204.203 -398109.843 75068.519 -212510.435
25 26 27 28 29 30
-99474.932 228372.697 -579685.791 -228775.340 517314.639 341210.922
31 32 33 34 35 36
192414.104 -160414.226 95629.064 68514.934 -136059.940 -29514.627
37 38 39 40 41 42
255437.451 141881.057 222926.524 -151541.907 -308606.418 -38414.301
43 44 45 46 47 48
86268.023 -84183.615 13486.942 -167216.843 235540.637 -198863.700
49 50 51 52 53 54
-46891.407 -190703.858 83533.225 -22273.982 -142461.650 96411.623
55 56 57 58 59 60
-244021.200 85560.439 166181.431 40755.448 -91572.997 -269978.532
61 62 63 64 65 66
-71027.436 -129211.004 -69636.892 -274781.047 24301.778 96124.721
67 68 69 70 71 72
-173218.164 -38610.124 -107094.288 -92600.863 -226384.569 90369.202
73 74 75 76 77 78
-156511.488 -93148.911 -125835.323 -148983.735 -135281.252 -129326.659
79 80 81 82 83 84
5806.934 16339.721 -23503.635 -95186.842 -155009.175 -25494.132
85 86 87 88 89 90
-59713.673 -200870.123 33247.147 -90999.522 -120915.516 -70390.488
91 92 93 94 95 96
-57736.898 -106346.630 84020.761 -229284.273 61131.895 -107166.188
97 98 99 100
-34094.584 -2288.539 -100156.337 -110239.042
> postscript(file="/var/www/rcomp/tmp/6z51f1293221008.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 -385354.276 NA
1 3031951.861 -385354.276
2 684521.405 3031951.861
3 -419917.413 684521.405
4 -735058.574 -419917.413
5 -54673.022 -735058.574
6 876008.912 -54673.022
7 446465.076 876008.912
8 -524497.192 446465.076
9 559157.609 -524497.192
10 366925.180 559157.609
11 730410.155 366925.180
12 675130.635 730410.155
13 -582281.337 675130.635
14 -186143.292 -582281.337
15 545236.147 -186143.292
16 -256257.720 545236.147
17 -292351.579 -256257.720
18 -502111.672 -292351.579
19 -58892.047 -502111.672
20 86204.203 -58892.047
21 -398109.843 86204.203
22 75068.519 -398109.843
23 -212510.435 75068.519
24 -99474.932 -212510.435
25 228372.697 -99474.932
26 -579685.791 228372.697
27 -228775.340 -579685.791
28 517314.639 -228775.340
29 341210.922 517314.639
30 192414.104 341210.922
31 -160414.226 192414.104
32 95629.064 -160414.226
33 68514.934 95629.064
34 -136059.940 68514.934
35 -29514.627 -136059.940
36 255437.451 -29514.627
37 141881.057 255437.451
38 222926.524 141881.057
39 -151541.907 222926.524
40 -308606.418 -151541.907
41 -38414.301 -308606.418
42 86268.023 -38414.301
43 -84183.615 86268.023
44 13486.942 -84183.615
45 -167216.843 13486.942
46 235540.637 -167216.843
47 -198863.700 235540.637
48 -46891.407 -198863.700
49 -190703.858 -46891.407
50 83533.225 -190703.858
51 -22273.982 83533.225
52 -142461.650 -22273.982
53 96411.623 -142461.650
54 -244021.200 96411.623
55 85560.439 -244021.200
56 166181.431 85560.439
57 40755.448 166181.431
58 -91572.997 40755.448
59 -269978.532 -91572.997
60 -71027.436 -269978.532
61 -129211.004 -71027.436
62 -69636.892 -129211.004
63 -274781.047 -69636.892
64 24301.778 -274781.047
65 96124.721 24301.778
66 -173218.164 96124.721
67 -38610.124 -173218.164
68 -107094.288 -38610.124
69 -92600.863 -107094.288
70 -226384.569 -92600.863
71 90369.202 -226384.569
72 -156511.488 90369.202
73 -93148.911 -156511.488
74 -125835.323 -93148.911
75 -148983.735 -125835.323
76 -135281.252 -148983.735
77 -129326.659 -135281.252
78 5806.934 -129326.659
79 16339.721 5806.934
80 -23503.635 16339.721
81 -95186.842 -23503.635
82 -155009.175 -95186.842
83 -25494.132 -155009.175
84 -59713.673 -25494.132
85 -200870.123 -59713.673
86 33247.147 -200870.123
87 -90999.522 33247.147
88 -120915.516 -90999.522
89 -70390.488 -120915.516
90 -57736.898 -70390.488
91 -106346.630 -57736.898
92 84020.761 -106346.630
93 -229284.273 84020.761
94 61131.895 -229284.273
95 -107166.188 61131.895
96 -34094.584 -107166.188
97 -2288.539 -34094.584
98 -100156.337 -2288.539
99 -110239.042 -100156.337
100 NA -110239.042
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3031951.861 -385354.276
[2,] 684521.405 3031951.861
[3,] -419917.413 684521.405
[4,] -735058.574 -419917.413
[5,] -54673.022 -735058.574
[6,] 876008.912 -54673.022
[7,] 446465.076 876008.912
[8,] -524497.192 446465.076
[9,] 559157.609 -524497.192
[10,] 366925.180 559157.609
[11,] 730410.155 366925.180
[12,] 675130.635 730410.155
[13,] -582281.337 675130.635
[14,] -186143.292 -582281.337
[15,] 545236.147 -186143.292
[16,] -256257.720 545236.147
[17,] -292351.579 -256257.720
[18,] -502111.672 -292351.579
[19,] -58892.047 -502111.672
[20,] 86204.203 -58892.047
[21,] -398109.843 86204.203
[22,] 75068.519 -398109.843
[23,] -212510.435 75068.519
[24,] -99474.932 -212510.435
[25,] 228372.697 -99474.932
[26,] -579685.791 228372.697
[27,] -228775.340 -579685.791
[28,] 517314.639 -228775.340
[29,] 341210.922 517314.639
[30,] 192414.104 341210.922
[31,] -160414.226 192414.104
[32,] 95629.064 -160414.226
[33,] 68514.934 95629.064
[34,] -136059.940 68514.934
[35,] -29514.627 -136059.940
[36,] 255437.451 -29514.627
[37,] 141881.057 255437.451
[38,] 222926.524 141881.057
[39,] -151541.907 222926.524
[40,] -308606.418 -151541.907
[41,] -38414.301 -308606.418
[42,] 86268.023 -38414.301
[43,] -84183.615 86268.023
[44,] 13486.942 -84183.615
[45,] -167216.843 13486.942
[46,] 235540.637 -167216.843
[47,] -198863.700 235540.637
[48,] -46891.407 -198863.700
[49,] -190703.858 -46891.407
[50,] 83533.225 -190703.858
[51,] -22273.982 83533.225
[52,] -142461.650 -22273.982
[53,] 96411.623 -142461.650
[54,] -244021.200 96411.623
[55,] 85560.439 -244021.200
[56,] 166181.431 85560.439
[57,] 40755.448 166181.431
[58,] -91572.997 40755.448
[59,] -269978.532 -91572.997
[60,] -71027.436 -269978.532
[61,] -129211.004 -71027.436
[62,] -69636.892 -129211.004
[63,] -274781.047 -69636.892
[64,] 24301.778 -274781.047
[65,] 96124.721 24301.778
[66,] -173218.164 96124.721
[67,] -38610.124 -173218.164
[68,] -107094.288 -38610.124
[69,] -92600.863 -107094.288
[70,] -226384.569 -92600.863
[71,] 90369.202 -226384.569
[72,] -156511.488 90369.202
[73,] -93148.911 -156511.488
[74,] -125835.323 -93148.911
[75,] -148983.735 -125835.323
[76,] -135281.252 -148983.735
[77,] -129326.659 -135281.252
[78,] 5806.934 -129326.659
[79,] 16339.721 5806.934
[80,] -23503.635 16339.721
[81,] -95186.842 -23503.635
[82,] -155009.175 -95186.842
[83,] -25494.132 -155009.175
[84,] -59713.673 -25494.132
[85,] -200870.123 -59713.673
[86,] 33247.147 -200870.123
[87,] -90999.522 33247.147
[88,] -120915.516 -90999.522
[89,] -70390.488 -120915.516
[90,] -57736.898 -70390.488
[91,] -106346.630 -57736.898
[92,] 84020.761 -106346.630
[93,] -229284.273 84020.761
[94,] 61131.895 -229284.273
[95,] -107166.188 61131.895
[96,] -34094.584 -107166.188
[97,] -2288.539 -34094.584
[98,] -100156.337 -2288.539
[99,] -110239.042 -100156.337
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3031951.861 -385354.276
2 684521.405 3031951.861
3 -419917.413 684521.405
4 -735058.574 -419917.413
5 -54673.022 -735058.574
6 876008.912 -54673.022
7 446465.076 876008.912
8 -524497.192 446465.076
9 559157.609 -524497.192
10 366925.180 559157.609
11 730410.155 366925.180
12 675130.635 730410.155
13 -582281.337 675130.635
14 -186143.292 -582281.337
15 545236.147 -186143.292
16 -256257.720 545236.147
17 -292351.579 -256257.720
18 -502111.672 -292351.579
19 -58892.047 -502111.672
20 86204.203 -58892.047
21 -398109.843 86204.203
22 75068.519 -398109.843
23 -212510.435 75068.519
24 -99474.932 -212510.435
25 228372.697 -99474.932
26 -579685.791 228372.697
27 -228775.340 -579685.791
28 517314.639 -228775.340
29 341210.922 517314.639
30 192414.104 341210.922
31 -160414.226 192414.104
32 95629.064 -160414.226
33 68514.934 95629.064
34 -136059.940 68514.934
35 -29514.627 -136059.940
36 255437.451 -29514.627
37 141881.057 255437.451
38 222926.524 141881.057
39 -151541.907 222926.524
40 -308606.418 -151541.907
41 -38414.301 -308606.418
42 86268.023 -38414.301
43 -84183.615 86268.023
44 13486.942 -84183.615
45 -167216.843 13486.942
46 235540.637 -167216.843
47 -198863.700 235540.637
48 -46891.407 -198863.700
49 -190703.858 -46891.407
50 83533.225 -190703.858
51 -22273.982 83533.225
52 -142461.650 -22273.982
53 96411.623 -142461.650
54 -244021.200 96411.623
55 85560.439 -244021.200
56 166181.431 85560.439
57 40755.448 166181.431
58 -91572.997 40755.448
59 -269978.532 -91572.997
60 -71027.436 -269978.532
61 -129211.004 -71027.436
62 -69636.892 -129211.004
63 -274781.047 -69636.892
64 24301.778 -274781.047
65 96124.721 24301.778
66 -173218.164 96124.721
67 -38610.124 -173218.164
68 -107094.288 -38610.124
69 -92600.863 -107094.288
70 -226384.569 -92600.863
71 90369.202 -226384.569
72 -156511.488 90369.202
73 -93148.911 -156511.488
74 -125835.323 -93148.911
75 -148983.735 -125835.323
76 -135281.252 -148983.735
77 -129326.659 -135281.252
78 5806.934 -129326.659
79 16339.721 5806.934
80 -23503.635 16339.721
81 -95186.842 -23503.635
82 -155009.175 -95186.842
83 -25494.132 -155009.175
84 -59713.673 -25494.132
85 -200870.123 -59713.673
86 33247.147 -200870.123
87 -90999.522 33247.147
88 -120915.516 -90999.522
89 -70390.488 -120915.516
90 -57736.898 -70390.488
91 -106346.630 -57736.898
92 84020.761 -106346.630
93 -229284.273 84020.761
94 61131.895 -229284.273
95 -107166.188 61131.895
96 -34094.584 -107166.188
97 -2288.539 -34094.584
98 -100156.337 -2288.539
99 -110239.042 -100156.337
> 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/79eih1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/89eih1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9k5zk1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10k5zk1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n6g81293221008.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/1296ew1293221008.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/13yquq1293221008.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/148zbb1293221008.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/15uzrh1293221008.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/1689pp1293221008.tab")
+ }
>
> try(system("convert tmp/1dmkr1293221008.ps tmp/1dmkr1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oekc1293221008.ps tmp/2oekc1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/3oekc1293221008.ps tmp/3oekc1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/4oekc1293221008.ps tmp/4oekc1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z51f1293221008.ps tmp/5z51f1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z51f1293221008.ps tmp/6z51f1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/79eih1293221008.ps tmp/79eih1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/89eih1293221008.ps tmp/89eih1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k5zk1293221008.ps tmp/9k5zk1293221008.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k5zk1293221008.ps tmp/10k5zk1293221008.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.770 0.810 4.618