R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(101.5
+ ,467
+ ,99.2
+ ,460
+ ,107.8
+ ,448
+ ,92.3
+ ,443
+ ,99.2
+ ,436
+ ,101.6
+ ,431
+ ,87
+ ,484
+ ,71.4
+ ,510
+ ,104.7
+ ,513
+ ,115.1
+ ,503
+ ,102.5
+ ,471
+ ,75.3
+ ,471
+ ,96.7
+ ,476
+ ,94.6
+ ,475
+ ,98.6
+ ,470
+ ,99.5
+ ,461
+ ,92
+ ,455
+ ,93.6
+ ,456
+ ,89.3
+ ,517
+ ,66.9
+ ,525
+ ,108.8
+ ,523
+ ,113.2
+ ,519
+ ,105.5
+ ,509
+ ,77.8
+ ,512
+ ,102.1
+ ,519
+ ,97
+ ,517
+ ,95.5
+ ,510
+ ,99.3
+ ,509
+ ,86.4
+ ,501
+ ,92.4
+ ,507
+ ,85.7
+ ,569
+ ,61.9
+ ,580
+ ,104.9
+ ,578
+ ,107.9
+ ,565
+ ,95.6
+ ,547
+ ,79.8
+ ,555
+ ,94.8
+ ,562
+ ,93.7
+ ,561
+ ,108.1
+ ,555
+ ,96.9
+ ,544
+ ,88.8
+ ,537
+ ,106.7
+ ,543
+ ,86.8
+ ,594
+ ,69.8
+ ,611
+ ,110.9
+ ,613
+ ,105.4
+ ,611
+ ,99.2
+ ,594
+ ,84.4
+ ,595
+ ,87.2
+ ,591
+ ,91.9
+ ,589
+ ,97.9
+ ,584
+ ,94.5
+ ,573
+ ,85
+ ,567
+ ,100.3
+ ,569
+ ,78.7
+ ,621
+ ,65.8
+ ,629
+ ,104.8
+ ,628
+ ,96
+ ,612
+ ,103.3
+ ,595
+ ,82.9
+ ,597
+ ,91.4
+ ,593
+ ,94.5
+ ,590
+ ,109.3
+ ,580
+ ,92.1
+ ,574
+ ,99.3
+ ,573
+ ,109.6
+ ,573
+ ,87.5
+ ,620
+ ,73.1
+ ,626
+ ,110.7
+ ,620
+ ,111.6
+ ,588
+ ,110.7
+ ,566
+ ,84
+ ,557
+ ,101.6
+ ,561
+ ,102.1
+ ,549
+ ,113.9
+ ,532
+ ,99
+ ,526
+ ,100.4
+ ,511
+ ,109.5
+ ,499
+ ,93.1
+ ,555
+ ,77
+ ,565
+ ,108
+ ,542
+ ,119.9
+ ,527
+ ,105.9
+ ,510
+ ,78.2
+ ,514
+ ,100.3
+ ,517
+ ,102.2
+ ,508
+ ,97
+ ,493
+ ,101.3
+ ,490
+ ,89.2
+ ,469
+ ,93.3
+ ,478
+ ,88.5
+ ,528
+ ,61.5
+ ,534
+ ,96.3
+ ,518
+ ,95.4
+ ,506
+ ,79.9
+ ,502
+ ,66.7
+ ,516
+ ,71.2
+ ,528
+ ,73.1
+ ,533
+ ,81
+ ,536
+ ,77.2
+ ,537
+ ,67.7
+ ,524
+ ,76.7
+ ,536
+ ,73.3
+ ,587
+ ,54.1
+ ,597
+ ,85
+ ,581
+ ,85.9
+ ,564
+ ,79.3
+ ,558
+ ,67.2
+ ,575
+ ,72.4
+ ,580
+ ,76.1
+ ,575
+ ,89.8
+ ,563
+ ,84
+ ,552
+ ,75.4
+ ,537
+ ,90
+ ,545
+ ,76.8
+ ,601
+ ,59.6
+ ,604
+ ,92.1
+ ,586
+ ,88.4
+ ,564
+ ,82.8
+ ,549
+ ,69.4
+ ,551
+ ,73.4
+ ,556)
+ ,dim=c(2
+ ,121)
+ ,dimnames=list(c('Textiel'
+ ,'werkloosheid')
+ ,1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('Textiel','werkloosheid'),1:121))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Textiel werkloosheid
1 101.5 467
2 99.2 460
3 107.8 448
4 92.3 443
5 99.2 436
6 101.6 431
7 87.0 484
8 71.4 510
9 104.7 513
10 115.1 503
11 102.5 471
12 75.3 471
13 96.7 476
14 94.6 475
15 98.6 470
16 99.5 461
17 92.0 455
18 93.6 456
19 89.3 517
20 66.9 525
21 108.8 523
22 113.2 519
23 105.5 509
24 77.8 512
25 102.1 519
26 97.0 517
27 95.5 510
28 99.3 509
29 86.4 501
30 92.4 507
31 85.7 569
32 61.9 580
33 104.9 578
34 107.9 565
35 95.6 547
36 79.8 555
37 94.8 562
38 93.7 561
39 108.1 555
40 96.9 544
41 88.8 537
42 106.7 543
43 86.8 594
44 69.8 611
45 110.9 613
46 105.4 611
47 99.2 594
48 84.4 595
49 87.2 591
50 91.9 589
51 97.9 584
52 94.5 573
53 85.0 567
54 100.3 569
55 78.7 621
56 65.8 629
57 104.8 628
58 96.0 612
59 103.3 595
60 82.9 597
61 91.4 593
62 94.5 590
63 109.3 580
64 92.1 574
65 99.3 573
66 109.6 573
67 87.5 620
68 73.1 626
69 110.7 620
70 111.6 588
71 110.7 566
72 84.0 557
73 101.6 561
74 102.1 549
75 113.9 532
76 99.0 526
77 100.4 511
78 109.5 499
79 93.1 555
80 77.0 565
81 108.0 542
82 119.9 527
83 105.9 510
84 78.2 514
85 100.3 517
86 102.2 508
87 97.0 493
88 101.3 490
89 89.2 469
90 93.3 478
91 88.5 528
92 61.5 534
93 96.3 518
94 95.4 506
95 79.9 502
96 66.7 516
97 71.2 528
98 73.1 533
99 81.0 536
100 77.2 537
101 67.7 524
102 76.7 536
103 73.3 587
104 54.1 597
105 85.0 581
106 85.9 564
107 79.3 558
108 67.2 575
109 72.4 580
110 76.1 575
111 89.8 563
112 84.0 552
113 75.4 537
114 90.0 545
115 76.8 601
116 59.6 604
117 92.1 586
118 88.4 564
119 82.8 549
120 69.4 551
121 73.4 556
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) werkloosheid
124.08610 -0.06101
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.562 -10.384 1.715 9.445 27.967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.08610 14.51219 8.550 4.91e-14 ***
werkloosheid -0.06101 0.02667 -2.288 0.0239 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.76 on 119 degrees of freedom
Multiple R-squared: 0.04213, Adjusted R-squared: 0.03408
F-statistic: 5.233 on 1 and 119 DF, p-value: 0.02392
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.10273642 0.20547283 0.89726358
[2,] 0.04061215 0.08122430 0.95938785
[3,] 0.04292083 0.08584165 0.95707917
[4,] 0.04860007 0.09720015 0.95139993
[5,] 0.21902186 0.43804371 0.78097814
[6,] 0.41522666 0.83045333 0.58477334
[7,] 0.32629132 0.65258263 0.67370868
[8,] 0.48149905 0.96299811 0.51850095
[9,] 0.38790357 0.77580714 0.61209643
[10,] 0.30344351 0.60688702 0.69655649
[11,] 0.23166625 0.46333251 0.76833375
[12,] 0.17221050 0.34442101 0.82778950
[13,] 0.13117889 0.26235778 0.86882111
[14,] 0.09426996 0.18853991 0.90573004
[15,] 0.06607238 0.13214477 0.93392762
[16,] 0.13662645 0.27325290 0.86337355
[17,] 0.20371653 0.40743307 0.79628347
[18,] 0.28920881 0.57841762 0.71079119
[19,] 0.27041331 0.54082662 0.72958669
[20,] 0.29731621 0.59463242 0.70268379
[21,] 0.26411718 0.52823436 0.73588282
[22,] 0.21473234 0.42946468 0.78526766
[23,] 0.16972588 0.33945176 0.83027412
[24,] 0.13634983 0.27269967 0.86365017
[25,] 0.11620221 0.23240443 0.88379779
[26,] 0.08816272 0.17632543 0.91183728
[27,] 0.06832824 0.13665648 0.93167176
[28,] 0.14130691 0.28261381 0.85869309
[29,] 0.17722732 0.35445464 0.82277268
[30,] 0.21250609 0.42501217 0.78749391
[31,] 0.17508641 0.35017282 0.82491359
[32,] 0.16322451 0.32644902 0.83677549
[33,] 0.13304620 0.26609240 0.86695380
[34,] 0.10553945 0.21107889 0.89446055
[35,] 0.12210849 0.24421698 0.87789151
[36,] 0.09854787 0.19709575 0.90145213
[37,] 0.07770926 0.15541853 0.92229074
[38,] 0.08156964 0.16313927 0.91843036
[39,] 0.06366351 0.12732703 0.93633649
[40,] 0.08227499 0.16454998 0.91772501
[41,] 0.12867282 0.25734565 0.87132718
[42,] 0.14036833 0.28073667 0.85963167
[43,] 0.12363625 0.24727250 0.87636375
[44,] 0.10420124 0.20840249 0.89579876
[45,] 0.08360943 0.16721885 0.91639057
[46,] 0.06537477 0.13074953 0.93462523
[47,] 0.05454117 0.10908235 0.94545883
[48,] 0.04234613 0.08469225 0.95765387
[49,] 0.03412562 0.06825123 0.96587438
[50,] 0.02939580 0.05879161 0.97060420
[51,] 0.02622774 0.05245547 0.97377226
[52,] 0.04329619 0.08659238 0.95670381
[53,] 0.05242612 0.10485224 0.94757388
[54,] 0.04412205 0.08824411 0.95587795
[55,] 0.04637281 0.09274562 0.95362719
[56,] 0.03757355 0.07514710 0.96242645
[57,] 0.02875427 0.05750853 0.97124573
[58,] 0.02272463 0.04544926 0.97727537
[59,] 0.03355170 0.06710340 0.96644830
[60,] 0.02572383 0.05144766 0.97427617
[61,] 0.02285588 0.04571176 0.97714412
[62,] 0.03505053 0.07010107 0.96494947
[63,] 0.02821091 0.05642183 0.97178909
[64,] 0.02853319 0.05706639 0.97146681
[65,] 0.07126648 0.14253297 0.92873352
[66,] 0.15135378 0.30270755 0.84864622
[67,] 0.24865331 0.49730662 0.75134669
[68,] 0.22001054 0.44002108 0.77998946
[69,] 0.24423248 0.48846496 0.75576752
[70,] 0.26242734 0.52485468 0.73757266
[71,] 0.39519858 0.79039716 0.60480142
[72,] 0.37280007 0.74560014 0.62719993
[73,] 0.34520797 0.69041595 0.65479203
[74,] 0.37850291 0.75700583 0.62149709
[75,] 0.36215892 0.72431784 0.63784108
[76,] 0.34767861 0.69535723 0.65232139
[77,] 0.46669696 0.93339391 0.53330304
[78,] 0.79502725 0.40994550 0.20497275
[79,] 0.84059344 0.31881312 0.15940656
[80,] 0.83966134 0.32067732 0.16033866
[81,] 0.85247594 0.29504812 0.14752406
[82,] 0.87429734 0.25140532 0.12570266
[83,] 0.86087255 0.27825491 0.13912745
[84,] 0.87533609 0.24932783 0.12466391
[85,] 0.84598317 0.30803367 0.15401683
[86,] 0.82205739 0.35588523 0.17794261
[87,] 0.80371945 0.39256110 0.19628055
[88,] 0.90367300 0.19265400 0.09632700
[89,] 0.92043325 0.15913351 0.07956675
[90,] 0.93767979 0.12464041 0.06232021
[91,] 0.92211736 0.15576529 0.07788264
[92,] 0.94439959 0.11120081 0.05560041
[93,] 0.94593576 0.10812848 0.05406424
[94,] 0.94138899 0.11722203 0.05861101
[95,] 0.92056361 0.15887277 0.07943639
[96,] 0.89906273 0.20187455 0.10093727
[97,] 0.93916102 0.12167796 0.06083898
[98,] 0.93009024 0.13981951 0.06990976
[99,] 0.90601539 0.18796923 0.09398461
[100,] 0.96613022 0.06773956 0.03386978
[101,] 0.95715476 0.08569047 0.04284524
[102,] 0.94217191 0.11565619 0.05782809
[103,] 0.91174126 0.17651748 0.08825874
[104,] 0.91141717 0.17716567 0.08858283
[105,] 0.87893708 0.24212584 0.12106292
[106,] 0.82405318 0.35189364 0.17594682
[107,] 0.79720871 0.40558258 0.20279129
[108,] 0.71294773 0.57410454 0.28705227
[109,] 0.64755272 0.70489457 0.35244728
[110,] 0.56528154 0.86943691 0.43471846
[111,] 0.42866601 0.85733201 0.57133399
[112,] 0.83131165 0.33737670 0.16868835
> postscript(file="/var/fisher/rcomp/tmp/1zpyq1353236351.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/249ol1353236351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/35g9d1353236351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4k1s71353236351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5rh0s1353236351.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 = 121
Frequency = 1
1 2 3 4 5 6
5.90632549 3.17924415 11.04710471 -4.75795339 1.71496526 3.80990716
7 8 9 10 11 12
-7.55647697 -21.57017484 11.91286002 21.70274382 7.15037197 -20.04962803
13 14 15 16 17 18
1.65543007 -0.50558155 3.18936035 3.54025577 -4.32581395 -2.66480233
19 20 21 22 23 24
-3.24309350 -25.15500054 16.62297622 20.77892974 12.46881354 -15.04815160
25 26 27 28 29 30
9.67892974 4.45690650 2.52982516 6.26881354 -7.11927942 -0.75320970
31 32 33 34 35 36
-3.67048925 -26.79936143 16.07861533 18.28546427 4.88725510 -10.42465194
37 38 39 40 41 42
5.00242941 3.84141779 17.87534806 6.00422024 -2.52286110 15.74320862
43 44 45 46 47 48
-1.04519875 -17.00800121 24.21402203 18.59199879 11.35480125 -3.38418713
49 50 51 52 53 54
-0.82823361 3.74974315 9.44468505 5.37355723 -4.49251249 10.92951075
55 56 57 58 59 60
-7.49788500 -19.90979204 19.02919634 9.25301041 15.51581287 -4.76216389
61 62 63 64 65 66
3.49378963 6.41075477 20.60063857 3.03456885 10.17355723 20.47355723
67 68 69 70 71 72
1.24110338 -12.79282690 24.44110338 23.38873153 21.14647589 -6.10262869
73 74 75 76 77 78
11.74141779 11.50927834 22.27208080 7.00601108 7.49083678 15.85869734
79 80 81 82 83 84
2.87534806 -12.61453573 16.98219700 27.96702270 12.92982516 -14.52612836
85 86 87 88 89 90
7.75690650 9.10780192 2.99262761 7.10959275 -6.27165127 -1.62254669
91 92 93 94 95 96
-3.37196568 -30.00589596 3.81791812 2.18577868 -13.55826780 -25.90410512
97 98 99 100 101 102
-20.67196568 -18.46690758 -10.38387272 -14.12286110 -24.41601216 -14.68387272
103 104 105 106 107 108
-14.97228009 -33.56216389 -3.63834981 -3.77554735 -10.74161707 -21.80441953
109 110 111 112 113 114
-16.29936143 -12.90441953 0.06344103 -6.40768680 -15.92286110 -0.83476814
115 116 117 118 119 120
-10.61811741 -27.63508255 3.76670829 -1.27554735 -7.79072166 -21.06869842
121
-16.76364032
> postscript(file="/var/fisher/rcomp/tmp/6x5c31353236351.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 5.90632549 NA
1 3.17924415 5.90632549
2 11.04710471 3.17924415
3 -4.75795339 11.04710471
4 1.71496526 -4.75795339
5 3.80990716 1.71496526
6 -7.55647697 3.80990716
7 -21.57017484 -7.55647697
8 11.91286002 -21.57017484
9 21.70274382 11.91286002
10 7.15037197 21.70274382
11 -20.04962803 7.15037197
12 1.65543007 -20.04962803
13 -0.50558155 1.65543007
14 3.18936035 -0.50558155
15 3.54025577 3.18936035
16 -4.32581395 3.54025577
17 -2.66480233 -4.32581395
18 -3.24309350 -2.66480233
19 -25.15500054 -3.24309350
20 16.62297622 -25.15500054
21 20.77892974 16.62297622
22 12.46881354 20.77892974
23 -15.04815160 12.46881354
24 9.67892974 -15.04815160
25 4.45690650 9.67892974
26 2.52982516 4.45690650
27 6.26881354 2.52982516
28 -7.11927942 6.26881354
29 -0.75320970 -7.11927942
30 -3.67048925 -0.75320970
31 -26.79936143 -3.67048925
32 16.07861533 -26.79936143
33 18.28546427 16.07861533
34 4.88725510 18.28546427
35 -10.42465194 4.88725510
36 5.00242941 -10.42465194
37 3.84141779 5.00242941
38 17.87534806 3.84141779
39 6.00422024 17.87534806
40 -2.52286110 6.00422024
41 15.74320862 -2.52286110
42 -1.04519875 15.74320862
43 -17.00800121 -1.04519875
44 24.21402203 -17.00800121
45 18.59199879 24.21402203
46 11.35480125 18.59199879
47 -3.38418713 11.35480125
48 -0.82823361 -3.38418713
49 3.74974315 -0.82823361
50 9.44468505 3.74974315
51 5.37355723 9.44468505
52 -4.49251249 5.37355723
53 10.92951075 -4.49251249
54 -7.49788500 10.92951075
55 -19.90979204 -7.49788500
56 19.02919634 -19.90979204
57 9.25301041 19.02919634
58 15.51581287 9.25301041
59 -4.76216389 15.51581287
60 3.49378963 -4.76216389
61 6.41075477 3.49378963
62 20.60063857 6.41075477
63 3.03456885 20.60063857
64 10.17355723 3.03456885
65 20.47355723 10.17355723
66 1.24110338 20.47355723
67 -12.79282690 1.24110338
68 24.44110338 -12.79282690
69 23.38873153 24.44110338
70 21.14647589 23.38873153
71 -6.10262869 21.14647589
72 11.74141779 -6.10262869
73 11.50927834 11.74141779
74 22.27208080 11.50927834
75 7.00601108 22.27208080
76 7.49083678 7.00601108
77 15.85869734 7.49083678
78 2.87534806 15.85869734
79 -12.61453573 2.87534806
80 16.98219700 -12.61453573
81 27.96702270 16.98219700
82 12.92982516 27.96702270
83 -14.52612836 12.92982516
84 7.75690650 -14.52612836
85 9.10780192 7.75690650
86 2.99262761 9.10780192
87 7.10959275 2.99262761
88 -6.27165127 7.10959275
89 -1.62254669 -6.27165127
90 -3.37196568 -1.62254669
91 -30.00589596 -3.37196568
92 3.81791812 -30.00589596
93 2.18577868 3.81791812
94 -13.55826780 2.18577868
95 -25.90410512 -13.55826780
96 -20.67196568 -25.90410512
97 -18.46690758 -20.67196568
98 -10.38387272 -18.46690758
99 -14.12286110 -10.38387272
100 -24.41601216 -14.12286110
101 -14.68387272 -24.41601216
102 -14.97228009 -14.68387272
103 -33.56216389 -14.97228009
104 -3.63834981 -33.56216389
105 -3.77554735 -3.63834981
106 -10.74161707 -3.77554735
107 -21.80441953 -10.74161707
108 -16.29936143 -21.80441953
109 -12.90441953 -16.29936143
110 0.06344103 -12.90441953
111 -6.40768680 0.06344103
112 -15.92286110 -6.40768680
113 -0.83476814 -15.92286110
114 -10.61811741 -0.83476814
115 -27.63508255 -10.61811741
116 3.76670829 -27.63508255
117 -1.27554735 3.76670829
118 -7.79072166 -1.27554735
119 -21.06869842 -7.79072166
120 -16.76364032 -21.06869842
121 NA -16.76364032
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.17924415 5.90632549
[2,] 11.04710471 3.17924415
[3,] -4.75795339 11.04710471
[4,] 1.71496526 -4.75795339
[5,] 3.80990716 1.71496526
[6,] -7.55647697 3.80990716
[7,] -21.57017484 -7.55647697
[8,] 11.91286002 -21.57017484
[9,] 21.70274382 11.91286002
[10,] 7.15037197 21.70274382
[11,] -20.04962803 7.15037197
[12,] 1.65543007 -20.04962803
[13,] -0.50558155 1.65543007
[14,] 3.18936035 -0.50558155
[15,] 3.54025577 3.18936035
[16,] -4.32581395 3.54025577
[17,] -2.66480233 -4.32581395
[18,] -3.24309350 -2.66480233
[19,] -25.15500054 -3.24309350
[20,] 16.62297622 -25.15500054
[21,] 20.77892974 16.62297622
[22,] 12.46881354 20.77892974
[23,] -15.04815160 12.46881354
[24,] 9.67892974 -15.04815160
[25,] 4.45690650 9.67892974
[26,] 2.52982516 4.45690650
[27,] 6.26881354 2.52982516
[28,] -7.11927942 6.26881354
[29,] -0.75320970 -7.11927942
[30,] -3.67048925 -0.75320970
[31,] -26.79936143 -3.67048925
[32,] 16.07861533 -26.79936143
[33,] 18.28546427 16.07861533
[34,] 4.88725510 18.28546427
[35,] -10.42465194 4.88725510
[36,] 5.00242941 -10.42465194
[37,] 3.84141779 5.00242941
[38,] 17.87534806 3.84141779
[39,] 6.00422024 17.87534806
[40,] -2.52286110 6.00422024
[41,] 15.74320862 -2.52286110
[42,] -1.04519875 15.74320862
[43,] -17.00800121 -1.04519875
[44,] 24.21402203 -17.00800121
[45,] 18.59199879 24.21402203
[46,] 11.35480125 18.59199879
[47,] -3.38418713 11.35480125
[48,] -0.82823361 -3.38418713
[49,] 3.74974315 -0.82823361
[50,] 9.44468505 3.74974315
[51,] 5.37355723 9.44468505
[52,] -4.49251249 5.37355723
[53,] 10.92951075 -4.49251249
[54,] -7.49788500 10.92951075
[55,] -19.90979204 -7.49788500
[56,] 19.02919634 -19.90979204
[57,] 9.25301041 19.02919634
[58,] 15.51581287 9.25301041
[59,] -4.76216389 15.51581287
[60,] 3.49378963 -4.76216389
[61,] 6.41075477 3.49378963
[62,] 20.60063857 6.41075477
[63,] 3.03456885 20.60063857
[64,] 10.17355723 3.03456885
[65,] 20.47355723 10.17355723
[66,] 1.24110338 20.47355723
[67,] -12.79282690 1.24110338
[68,] 24.44110338 -12.79282690
[69,] 23.38873153 24.44110338
[70,] 21.14647589 23.38873153
[71,] -6.10262869 21.14647589
[72,] 11.74141779 -6.10262869
[73,] 11.50927834 11.74141779
[74,] 22.27208080 11.50927834
[75,] 7.00601108 22.27208080
[76,] 7.49083678 7.00601108
[77,] 15.85869734 7.49083678
[78,] 2.87534806 15.85869734
[79,] -12.61453573 2.87534806
[80,] 16.98219700 -12.61453573
[81,] 27.96702270 16.98219700
[82,] 12.92982516 27.96702270
[83,] -14.52612836 12.92982516
[84,] 7.75690650 -14.52612836
[85,] 9.10780192 7.75690650
[86,] 2.99262761 9.10780192
[87,] 7.10959275 2.99262761
[88,] -6.27165127 7.10959275
[89,] -1.62254669 -6.27165127
[90,] -3.37196568 -1.62254669
[91,] -30.00589596 -3.37196568
[92,] 3.81791812 -30.00589596
[93,] 2.18577868 3.81791812
[94,] -13.55826780 2.18577868
[95,] -25.90410512 -13.55826780
[96,] -20.67196568 -25.90410512
[97,] -18.46690758 -20.67196568
[98,] -10.38387272 -18.46690758
[99,] -14.12286110 -10.38387272
[100,] -24.41601216 -14.12286110
[101,] -14.68387272 -24.41601216
[102,] -14.97228009 -14.68387272
[103,] -33.56216389 -14.97228009
[104,] -3.63834981 -33.56216389
[105,] -3.77554735 -3.63834981
[106,] -10.74161707 -3.77554735
[107,] -21.80441953 -10.74161707
[108,] -16.29936143 -21.80441953
[109,] -12.90441953 -16.29936143
[110,] 0.06344103 -12.90441953
[111,] -6.40768680 0.06344103
[112,] -15.92286110 -6.40768680
[113,] -0.83476814 -15.92286110
[114,] -10.61811741 -0.83476814
[115,] -27.63508255 -10.61811741
[116,] 3.76670829 -27.63508255
[117,] -1.27554735 3.76670829
[118,] -7.79072166 -1.27554735
[119,] -21.06869842 -7.79072166
[120,] -16.76364032 -21.06869842
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.17924415 5.90632549
2 11.04710471 3.17924415
3 -4.75795339 11.04710471
4 1.71496526 -4.75795339
5 3.80990716 1.71496526
6 -7.55647697 3.80990716
7 -21.57017484 -7.55647697
8 11.91286002 -21.57017484
9 21.70274382 11.91286002
10 7.15037197 21.70274382
11 -20.04962803 7.15037197
12 1.65543007 -20.04962803
13 -0.50558155 1.65543007
14 3.18936035 -0.50558155
15 3.54025577 3.18936035
16 -4.32581395 3.54025577
17 -2.66480233 -4.32581395
18 -3.24309350 -2.66480233
19 -25.15500054 -3.24309350
20 16.62297622 -25.15500054
21 20.77892974 16.62297622
22 12.46881354 20.77892974
23 -15.04815160 12.46881354
24 9.67892974 -15.04815160
25 4.45690650 9.67892974
26 2.52982516 4.45690650
27 6.26881354 2.52982516
28 -7.11927942 6.26881354
29 -0.75320970 -7.11927942
30 -3.67048925 -0.75320970
31 -26.79936143 -3.67048925
32 16.07861533 -26.79936143
33 18.28546427 16.07861533
34 4.88725510 18.28546427
35 -10.42465194 4.88725510
36 5.00242941 -10.42465194
37 3.84141779 5.00242941
38 17.87534806 3.84141779
39 6.00422024 17.87534806
40 -2.52286110 6.00422024
41 15.74320862 -2.52286110
42 -1.04519875 15.74320862
43 -17.00800121 -1.04519875
44 24.21402203 -17.00800121
45 18.59199879 24.21402203
46 11.35480125 18.59199879
47 -3.38418713 11.35480125
48 -0.82823361 -3.38418713
49 3.74974315 -0.82823361
50 9.44468505 3.74974315
51 5.37355723 9.44468505
52 -4.49251249 5.37355723
53 10.92951075 -4.49251249
54 -7.49788500 10.92951075
55 -19.90979204 -7.49788500
56 19.02919634 -19.90979204
57 9.25301041 19.02919634
58 15.51581287 9.25301041
59 -4.76216389 15.51581287
60 3.49378963 -4.76216389
61 6.41075477 3.49378963
62 20.60063857 6.41075477
63 3.03456885 20.60063857
64 10.17355723 3.03456885
65 20.47355723 10.17355723
66 1.24110338 20.47355723
67 -12.79282690 1.24110338
68 24.44110338 -12.79282690
69 23.38873153 24.44110338
70 21.14647589 23.38873153
71 -6.10262869 21.14647589
72 11.74141779 -6.10262869
73 11.50927834 11.74141779
74 22.27208080 11.50927834
75 7.00601108 22.27208080
76 7.49083678 7.00601108
77 15.85869734 7.49083678
78 2.87534806 15.85869734
79 -12.61453573 2.87534806
80 16.98219700 -12.61453573
81 27.96702270 16.98219700
82 12.92982516 27.96702270
83 -14.52612836 12.92982516
84 7.75690650 -14.52612836
85 9.10780192 7.75690650
86 2.99262761 9.10780192
87 7.10959275 2.99262761
88 -6.27165127 7.10959275
89 -1.62254669 -6.27165127
90 -3.37196568 -1.62254669
91 -30.00589596 -3.37196568
92 3.81791812 -30.00589596
93 2.18577868 3.81791812
94 -13.55826780 2.18577868
95 -25.90410512 -13.55826780
96 -20.67196568 -25.90410512
97 -18.46690758 -20.67196568
98 -10.38387272 -18.46690758
99 -14.12286110 -10.38387272
100 -24.41601216 -14.12286110
101 -14.68387272 -24.41601216
102 -14.97228009 -14.68387272
103 -33.56216389 -14.97228009
104 -3.63834981 -33.56216389
105 -3.77554735 -3.63834981
106 -10.74161707 -3.77554735
107 -21.80441953 -10.74161707
108 -16.29936143 -21.80441953
109 -12.90441953 -16.29936143
110 0.06344103 -12.90441953
111 -6.40768680 0.06344103
112 -15.92286110 -6.40768680
113 -0.83476814 -15.92286110
114 -10.61811741 -0.83476814
115 -27.63508255 -10.61811741
116 3.76670829 -27.63508255
117 -1.27554735 3.76670829
118 -7.79072166 -1.27554735
119 -21.06869842 -7.79072166
120 -16.76364032 -21.06869842
> 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/fisher/rcomp/tmp/7ujwv1353236352.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8h8wa1353236352.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/905w91353236352.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/103siq1353236352.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11l1wx1353236352.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/fisher/rcomp/tmp/1259lc1353236352.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/fisher/rcomp/tmp/13rou01353236352.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/fisher/rcomp/tmp/14ne191353236352.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/fisher/rcomp/tmp/150msv1353236352.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/fisher/rcomp/tmp/1679mr1353236352.tab")
+ }
>
> try(system("convert tmp/1zpyq1353236351.ps tmp/1zpyq1353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/249ol1353236351.ps tmp/249ol1353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/35g9d1353236351.ps tmp/35g9d1353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k1s71353236351.ps tmp/4k1s71353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rh0s1353236351.ps tmp/5rh0s1353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/6x5c31353236351.ps tmp/6x5c31353236351.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ujwv1353236352.ps tmp/7ujwv1353236352.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h8wa1353236352.ps tmp/8h8wa1353236352.png",intern=TRUE))
character(0)
> try(system("convert tmp/905w91353236352.ps tmp/905w91353236352.png",intern=TRUE))
character(0)
> try(system("convert tmp/103siq1353236352.ps tmp/103siq1353236352.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.944 1.235 8.191