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.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(593530
+ ,3922
+ ,18004
+ ,707169
+ ,610763
+ ,3759
+ ,17537
+ ,703434
+ ,612613
+ ,4138
+ ,20366
+ ,701017
+ ,611324
+ ,4634
+ ,22782
+ ,696968
+ ,594167
+ ,3996
+ ,19169
+ ,688558
+ ,595454
+ ,4308
+ ,13807
+ ,679237
+ ,590865
+ ,4143
+ ,29743
+ ,677362
+ ,589379
+ ,4429
+ ,25591
+ ,676693
+ ,584428
+ ,5219
+ ,29096
+ ,670009
+ ,573100
+ ,4929
+ ,26482
+ ,667209
+ ,567456
+ ,5761
+ ,22405
+ ,662976
+ ,569028
+ ,5592
+ ,27044
+ ,660194
+ ,620735
+ ,4163
+ ,17970
+ ,652270
+ ,628884
+ ,4962
+ ,18730
+ ,648024
+ ,628232
+ ,5208
+ ,19684
+ ,629295
+ ,612117
+ ,4755
+ ,19785
+ ,624961
+ ,595404
+ ,4491
+ ,18479
+ ,617306
+ ,597141
+ ,5732
+ ,10698
+ ,607691
+ ,593408
+ ,5731
+ ,31956
+ ,596219
+ ,590072
+ ,5040
+ ,29506
+ ,591130
+ ,579799
+ ,6102
+ ,34506
+ ,584528
+ ,574205
+ ,4904
+ ,27165
+ ,576798
+ ,572775
+ ,5369
+ ,26736
+ ,575683
+ ,572942
+ ,5578
+ ,23691
+ ,574369
+ ,619567
+ ,4619
+ ,18157
+ ,566815
+ ,625809
+ ,4731
+ ,17328
+ ,573074
+ ,619916
+ ,5011
+ ,18205
+ ,567739
+ ,587625
+ ,5299
+ ,20995
+ ,571942
+ ,565742
+ ,4146
+ ,17382
+ ,570274
+ ,557274
+ ,4625
+ ,9367
+ ,568800
+ ,560576
+ ,4736
+ ,31124
+ ,558115
+ ,548854
+ ,4219
+ ,26551
+ ,550591
+ ,531673
+ ,5116
+ ,30651
+ ,548872
+ ,525919
+ ,4205
+ ,25859
+ ,547009
+ ,511038
+ ,4121
+ ,25100
+ ,545946
+ ,498662
+ ,5103
+ ,25778
+ ,539702
+ ,555362
+ ,4300
+ ,20418
+ ,542427
+ ,564591
+ ,4578
+ ,18688
+ ,542968
+ ,541657
+ ,3809
+ ,20424
+ ,536640
+ ,527070
+ ,5526
+ ,24776
+ ,533653
+ ,509846
+ ,4248
+ ,19814
+ ,540996
+ ,514258
+ ,3830
+ ,12738
+ ,538316
+ ,516922
+ ,4428
+ ,31566
+ ,532646
+ ,507561
+ ,4834
+ ,30111
+ ,533390
+ ,492622
+ ,4406
+ ,30019
+ ,528715
+ ,490243
+ ,4565
+ ,31934
+ ,530664
+ ,469357
+ ,4104
+ ,25826
+ ,528564
+ ,477580
+ ,4798
+ ,26835
+ ,519107
+ ,528379
+ ,3935
+ ,20205
+ ,518703
+ ,533590
+ ,3792
+ ,17789
+ ,519059
+ ,517945
+ ,4387
+ ,20520
+ ,518498
+ ,506174
+ ,4006
+ ,22518
+ ,524575
+ ,501866
+ ,4078
+ ,15572
+ ,536046
+ ,516141
+ ,4724
+ ,11509
+ ,552006
+ ,528222
+ ,3157
+ ,25447
+ ,560687
+ ,532638
+ ,3558
+ ,24090
+ ,578884
+ ,536322
+ ,3899
+ ,27786
+ ,591491
+ ,536535
+ ,4118
+ ,26195
+ ,599228
+ ,523597
+ ,3790
+ ,20516
+ ,633019
+ ,536214
+ ,4278
+ ,22759
+ ,649918
+ ,586570
+ ,4035
+ ,19028
+ ,655509)
+ ,dim=c(4
+ ,61)
+ ,dimnames=list(c('Werk'
+ ,'Bouwvergun'
+ ,'Auto'
+ ,'Hyp')
+ ,1:61))
> y <- array(NA,dim=c(4,61),dimnames=list(c('Werk','Bouwvergun','Auto','Hyp'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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.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
Werk Bouwvergun Auto Hyp M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 593530 3922 18004 707169 1 0 0 0 0 0 0 0 0 0 0 1
2 610763 3759 17537 703434 0 1 0 0 0 0 0 0 0 0 0 2
3 612613 4138 20366 701017 0 0 1 0 0 0 0 0 0 0 0 3
4 611324 4634 22782 696968 0 0 0 1 0 0 0 0 0 0 0 4
5 594167 3996 19169 688558 0 0 0 0 1 0 0 0 0 0 0 5
6 595454 4308 13807 679237 0 0 0 0 0 1 0 0 0 0 0 6
7 590865 4143 29743 677362 0 0 0 0 0 0 1 0 0 0 0 7
8 589379 4429 25591 676693 0 0 0 0 0 0 0 1 0 0 0 8
9 584428 5219 29096 670009 0 0 0 0 0 0 0 0 1 0 0 9
10 573100 4929 26482 667209 0 0 0 0 0 0 0 0 0 1 0 10
11 567456 5761 22405 662976 0 0 0 0 0 0 0 0 0 0 1 11
12 569028 5592 27044 660194 0 0 0 0 0 0 0 0 0 0 0 12
13 620735 4163 17970 652270 1 0 0 0 0 0 0 0 0 0 0 13
14 628884 4962 18730 648024 0 1 0 0 0 0 0 0 0 0 0 14
15 628232 5208 19684 629295 0 0 1 0 0 0 0 0 0 0 0 15
16 612117 4755 19785 624961 0 0 0 1 0 0 0 0 0 0 0 16
17 595404 4491 18479 617306 0 0 0 0 1 0 0 0 0 0 0 17
18 597141 5732 10698 607691 0 0 0 0 0 1 0 0 0 0 0 18
19 593408 5731 31956 596219 0 0 0 0 0 0 1 0 0 0 0 19
20 590072 5040 29506 591130 0 0 0 0 0 0 0 1 0 0 0 20
21 579799 6102 34506 584528 0 0 0 0 0 0 0 0 1 0 0 21
22 574205 4904 27165 576798 0 0 0 0 0 0 0 0 0 1 0 22
23 572775 5369 26736 575683 0 0 0 0 0 0 0 0 0 0 1 23
24 572942 5578 23691 574369 0 0 0 0 0 0 0 0 0 0 0 24
25 619567 4619 18157 566815 1 0 0 0 0 0 0 0 0 0 0 25
26 625809 4731 17328 573074 0 1 0 0 0 0 0 0 0 0 0 26
27 619916 5011 18205 567739 0 0 1 0 0 0 0 0 0 0 0 27
28 587625 5299 20995 571942 0 0 0 1 0 0 0 0 0 0 0 28
29 565742 4146 17382 570274 0 0 0 0 1 0 0 0 0 0 0 29
30 557274 4625 9367 568800 0 0 0 0 0 1 0 0 0 0 0 30
31 560576 4736 31124 558115 0 0 0 0 0 0 1 0 0 0 0 31
32 548854 4219 26551 550591 0 0 0 0 0 0 0 1 0 0 0 32
33 531673 5116 30651 548872 0 0 0 0 0 0 0 0 1 0 0 33
34 525919 4205 25859 547009 0 0 0 0 0 0 0 0 0 1 0 34
35 511038 4121 25100 545946 0 0 0 0 0 0 0 0 0 0 1 35
36 498662 5103 25778 539702 0 0 0 0 0 0 0 0 0 0 0 36
37 555362 4300 20418 542427 1 0 0 0 0 0 0 0 0 0 0 37
38 564591 4578 18688 542968 0 1 0 0 0 0 0 0 0 0 0 38
39 541657 3809 20424 536640 0 0 1 0 0 0 0 0 0 0 0 39
40 527070 5526 24776 533653 0 0 0 1 0 0 0 0 0 0 0 40
41 509846 4248 19814 540996 0 0 0 0 1 0 0 0 0 0 0 41
42 514258 3830 12738 538316 0 0 0 0 0 1 0 0 0 0 0 42
43 516922 4428 31566 532646 0 0 0 0 0 0 1 0 0 0 0 43
44 507561 4834 30111 533390 0 0 0 0 0 0 0 1 0 0 0 44
45 492622 4406 30019 528715 0 0 0 0 0 0 0 0 1 0 0 45
46 490243 4565 31934 530664 0 0 0 0 0 0 0 0 0 1 0 46
47 469357 4104 25826 528564 0 0 0 0 0 0 0 0 0 0 1 47
48 477580 4798 26835 519107 0 0 0 0 0 0 0 0 0 0 0 48
49 528379 3935 20205 518703 1 0 0 0 0 0 0 0 0 0 0 49
50 533590 3792 17789 519059 0 1 0 0 0 0 0 0 0 0 0 50
51 517945 4387 20520 518498 0 0 1 0 0 0 0 0 0 0 0 51
52 506174 4006 22518 524575 0 0 0 1 0 0 0 0 0 0 0 52
53 501866 4078 15572 536046 0 0 0 0 1 0 0 0 0 0 0 53
54 516141 4724 11509 552006 0 0 0 0 0 1 0 0 0 0 0 54
55 528222 3157 25447 560687 0 0 0 0 0 0 1 0 0 0 0 55
56 532638 3558 24090 578884 0 0 0 0 0 0 0 1 0 0 0 56
57 536322 3899 27786 591491 0 0 0 0 0 0 0 0 1 0 0 57
58 536535 4118 26195 599228 0 0 0 0 0 0 0 0 0 1 0 58
59 523597 3790 20516 633019 0 0 0 0 0 0 0 0 0 0 1 59
60 536214 4278 22759 649918 0 0 0 0 0 0 0 0 0 0 0 60
61 586570 4035 19028 655509 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bouwvergun Auto Hyp M1 M2
3.864e+05 2.668e+01 -5.168e+00 2.799e-01 3.633e+04 3.399e+04
M3 M4 M5 M6 M7 M8
3.343e+04 2.214e+04 3.594e+03 -3.810e+04 6.599e+04 4.823e+04
M9 M10 M11 t
4.315e+04 3.491e+04 3.147e+03 -7.005e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-38049.1 -9842.7 -325.3 13392.7 33113.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.864e+05 7.594e+04 5.089 6.84e-06 ***
Bouwvergun 2.668e+01 5.305e+00 5.030 8.34e-06 ***
Auto -5.168e+00 1.492e+00 -3.464 0.001179 **
Hyp 2.799e-01 6.580e-02 4.253 0.000105 ***
M1 3.633e+04 1.451e+04 2.503 0.016014 *
M2 3.399e+04 1.617e+04 2.102 0.041215 *
M3 3.343e+04 1.454e+04 2.300 0.026138 *
M4 2.214e+04 1.267e+04 1.747 0.087434 .
M5 3.594e+03 1.615e+04 0.223 0.824852
M6 -3.810e+04 2.333e+04 -1.633 0.109482
M7 6.599e+04 1.436e+04 4.597 3.47e-05 ***
M8 4.823e+04 1.267e+04 3.806 0.000424 ***
M9 4.315e+04 1.387e+04 3.110 0.003241 **
M10 3.491e+04 1.253e+04 2.785 0.007809 **
M11 3.147e+03 1.180e+04 0.267 0.790854
t -7.005e+02 2.375e+02 -2.949 0.005041 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18170 on 45 degrees of freedom
Multiple R-squared: 0.8627, Adjusted R-squared: 0.8169
F-statistic: 18.85 on 15 and 45 DF, p-value: 1.396e-14
> 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,] 0.20432302 0.40864605 0.79567698
[2,] 0.09211471 0.18422943 0.90788529
[3,] 0.05231621 0.10463243 0.94768379
[4,] 0.04065209 0.08130418 0.95934791
[5,] 0.03183468 0.06366936 0.96816532
[6,] 0.02275188 0.04550376 0.97724812
[7,] 0.01473671 0.02947342 0.98526329
[8,] 0.03626444 0.07252888 0.96373556
[9,] 0.27949702 0.55899404 0.72050298
[10,] 0.89847764 0.20304472 0.10152236
[11,] 0.97014148 0.05971703 0.02985852
[12,] 0.97088712 0.05822576 0.02911288
[13,] 0.97079755 0.05840489 0.02920245
[14,] 0.96814117 0.06371766 0.03185883
[15,] 0.96018771 0.07962459 0.03981229
[16,] 0.95165076 0.09669848 0.04834924
[17,] 0.97951646 0.04096708 0.02048354
[18,] 0.98526089 0.02947823 0.01473911
[19,] 0.96763374 0.06473253 0.03236626
[20,] 0.93888096 0.12223809 0.06111904
[21,] 0.89035744 0.21928512 0.10964256
[22,] 0.87954284 0.24091433 0.12045716
[23,] 0.81168681 0.37662637 0.18831319
[24,] 0.88801863 0.22396274 0.11198137
> postscript(file="/var/www/html/rcomp/tmp/14zzk1261155886.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dcgl1261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dim81261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hn141261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5o80a1261155886.ps",horizontal=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 = 61
Frequency = 1
1 2 3 4 5 6
-38049.0735 -14792.3419 -6502.8957 4590.0895 7380.5198 17635.5048
7 8 9 10 11 12
-3054.7228 -14984.0243 -15240.5252 -22616.2049 -37884.7651 -3201.5689
13 14 15 16 17 18
6320.9683 1312.8044 5523.1319 15223.7005 20192.3152 -6308.0355
19 20 21 22 23 24
-325.2811 21994.9311 16863.4984 16395.2981 33113.8890 16182.1290
25 26 27 28 29 30
26276.0652 26536.9015 20452.4955 5716.2530 15633.6499 -4229.8832
31 32 33 34 35 36
8158.5662 7159.4657 -6496.0965 -3248.9700 12945.4752 -16530.5761
37 38 39 40 41 42
-2501.1678 -6738.5427 2840.3520 -22231.3462 -13814.2136 8324.3734
43 44 45 46 47 48
-9459.9838 -18921.9999 -15824.1326 -4151.1384 -11259.3049 -9842.6919
49 50 51 52 53 54
-5801.7034 -6318.8212 -22313.0837 -3298.6968 -29392.2713 -15421.9595
55 56 57 58 59 60
4681.4216 4751.6274 20697.2558 13621.0152 3084.7058 13392.7080
61
13754.9112
> postscript(file="/var/www/html/rcomp/tmp/6vzkm1261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -38049.0735 NA
1 -14792.3419 -38049.0735
2 -6502.8957 -14792.3419
3 4590.0895 -6502.8957
4 7380.5198 4590.0895
5 17635.5048 7380.5198
6 -3054.7228 17635.5048
7 -14984.0243 -3054.7228
8 -15240.5252 -14984.0243
9 -22616.2049 -15240.5252
10 -37884.7651 -22616.2049
11 -3201.5689 -37884.7651
12 6320.9683 -3201.5689
13 1312.8044 6320.9683
14 5523.1319 1312.8044
15 15223.7005 5523.1319
16 20192.3152 15223.7005
17 -6308.0355 20192.3152
18 -325.2811 -6308.0355
19 21994.9311 -325.2811
20 16863.4984 21994.9311
21 16395.2981 16863.4984
22 33113.8890 16395.2981
23 16182.1290 33113.8890
24 26276.0652 16182.1290
25 26536.9015 26276.0652
26 20452.4955 26536.9015
27 5716.2530 20452.4955
28 15633.6499 5716.2530
29 -4229.8832 15633.6499
30 8158.5662 -4229.8832
31 7159.4657 8158.5662
32 -6496.0965 7159.4657
33 -3248.9700 -6496.0965
34 12945.4752 -3248.9700
35 -16530.5761 12945.4752
36 -2501.1678 -16530.5761
37 -6738.5427 -2501.1678
38 2840.3520 -6738.5427
39 -22231.3462 2840.3520
40 -13814.2136 -22231.3462
41 8324.3734 -13814.2136
42 -9459.9838 8324.3734
43 -18921.9999 -9459.9838
44 -15824.1326 -18921.9999
45 -4151.1384 -15824.1326
46 -11259.3049 -4151.1384
47 -9842.6919 -11259.3049
48 -5801.7034 -9842.6919
49 -6318.8212 -5801.7034
50 -22313.0837 -6318.8212
51 -3298.6968 -22313.0837
52 -29392.2713 -3298.6968
53 -15421.9595 -29392.2713
54 4681.4216 -15421.9595
55 4751.6274 4681.4216
56 20697.2558 4751.6274
57 13621.0152 20697.2558
58 3084.7058 13621.0152
59 13392.7080 3084.7058
60 13754.9112 13392.7080
61 NA 13754.9112
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -14792.3419 -38049.0735
[2,] -6502.8957 -14792.3419
[3,] 4590.0895 -6502.8957
[4,] 7380.5198 4590.0895
[5,] 17635.5048 7380.5198
[6,] -3054.7228 17635.5048
[7,] -14984.0243 -3054.7228
[8,] -15240.5252 -14984.0243
[9,] -22616.2049 -15240.5252
[10,] -37884.7651 -22616.2049
[11,] -3201.5689 -37884.7651
[12,] 6320.9683 -3201.5689
[13,] 1312.8044 6320.9683
[14,] 5523.1319 1312.8044
[15,] 15223.7005 5523.1319
[16,] 20192.3152 15223.7005
[17,] -6308.0355 20192.3152
[18,] -325.2811 -6308.0355
[19,] 21994.9311 -325.2811
[20,] 16863.4984 21994.9311
[21,] 16395.2981 16863.4984
[22,] 33113.8890 16395.2981
[23,] 16182.1290 33113.8890
[24,] 26276.0652 16182.1290
[25,] 26536.9015 26276.0652
[26,] 20452.4955 26536.9015
[27,] 5716.2530 20452.4955
[28,] 15633.6499 5716.2530
[29,] -4229.8832 15633.6499
[30,] 8158.5662 -4229.8832
[31,] 7159.4657 8158.5662
[32,] -6496.0965 7159.4657
[33,] -3248.9700 -6496.0965
[34,] 12945.4752 -3248.9700
[35,] -16530.5761 12945.4752
[36,] -2501.1678 -16530.5761
[37,] -6738.5427 -2501.1678
[38,] 2840.3520 -6738.5427
[39,] -22231.3462 2840.3520
[40,] -13814.2136 -22231.3462
[41,] 8324.3734 -13814.2136
[42,] -9459.9838 8324.3734
[43,] -18921.9999 -9459.9838
[44,] -15824.1326 -18921.9999
[45,] -4151.1384 -15824.1326
[46,] -11259.3049 -4151.1384
[47,] -9842.6919 -11259.3049
[48,] -5801.7034 -9842.6919
[49,] -6318.8212 -5801.7034
[50,] -22313.0837 -6318.8212
[51,] -3298.6968 -22313.0837
[52,] -29392.2713 -3298.6968
[53,] -15421.9595 -29392.2713
[54,] 4681.4216 -15421.9595
[55,] 4751.6274 4681.4216
[56,] 20697.2558 4751.6274
[57,] 13621.0152 20697.2558
[58,] 3084.7058 13621.0152
[59,] 13392.7080 3084.7058
[60,] 13754.9112 13392.7080
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -14792.3419 -38049.0735
2 -6502.8957 -14792.3419
3 4590.0895 -6502.8957
4 7380.5198 4590.0895
5 17635.5048 7380.5198
6 -3054.7228 17635.5048
7 -14984.0243 -3054.7228
8 -15240.5252 -14984.0243
9 -22616.2049 -15240.5252
10 -37884.7651 -22616.2049
11 -3201.5689 -37884.7651
12 6320.9683 -3201.5689
13 1312.8044 6320.9683
14 5523.1319 1312.8044
15 15223.7005 5523.1319
16 20192.3152 15223.7005
17 -6308.0355 20192.3152
18 -325.2811 -6308.0355
19 21994.9311 -325.2811
20 16863.4984 21994.9311
21 16395.2981 16863.4984
22 33113.8890 16395.2981
23 16182.1290 33113.8890
24 26276.0652 16182.1290
25 26536.9015 26276.0652
26 20452.4955 26536.9015
27 5716.2530 20452.4955
28 15633.6499 5716.2530
29 -4229.8832 15633.6499
30 8158.5662 -4229.8832
31 7159.4657 8158.5662
32 -6496.0965 7159.4657
33 -3248.9700 -6496.0965
34 12945.4752 -3248.9700
35 -16530.5761 12945.4752
36 -2501.1678 -16530.5761
37 -6738.5427 -2501.1678
38 2840.3520 -6738.5427
39 -22231.3462 2840.3520
40 -13814.2136 -22231.3462
41 8324.3734 -13814.2136
42 -9459.9838 8324.3734
43 -18921.9999 -9459.9838
44 -15824.1326 -18921.9999
45 -4151.1384 -15824.1326
46 -11259.3049 -4151.1384
47 -9842.6919 -11259.3049
48 -5801.7034 -9842.6919
49 -6318.8212 -5801.7034
50 -22313.0837 -6318.8212
51 -3298.6968 -22313.0837
52 -29392.2713 -3298.6968
53 -15421.9595 -29392.2713
54 4681.4216 -15421.9595
55 4751.6274 4681.4216
56 20697.2558 4751.6274
57 13621.0152 20697.2558
58 3084.7058 13621.0152
59 13392.7080 3084.7058
60 13754.9112 13392.7080
> 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/73k7z1261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/860i41261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w4le1261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nujl1261155886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i1mr1261155886.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/12z3681261155886.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/13g5s91261155886.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/142v2f1261155886.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/15wy8t1261155886.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/16qs0z1261155886.tab")
+ }
>
> try(system("convert tmp/14zzk1261155886.ps tmp/14zzk1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dcgl1261155886.ps tmp/2dcgl1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dim81261155886.ps tmp/3dim81261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hn141261155886.ps tmp/4hn141261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o80a1261155886.ps tmp/5o80a1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vzkm1261155886.ps tmp/6vzkm1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/73k7z1261155886.ps tmp/73k7z1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/860i41261155886.ps tmp/860i41261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w4le1261155886.ps tmp/9w4le1261155886.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nujl1261155886.ps tmp/10nujl1261155886.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.401 1.577 3.340