R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(631.923
+ ,21.454
+ ,97.06
+ ,130.678
+ ,654.294
+ ,23.899
+ ,97.73
+ ,120.877
+ ,671.833
+ ,24.939
+ ,98
+ ,137.114
+ ,586.840
+ ,23.580
+ ,97.76
+ ,134.406
+ ,600.969
+ ,24.562
+ ,97.48
+ ,120.262
+ ,625.568
+ ,24.696
+ ,97.77
+ ,130.846
+ ,558.110
+ ,23.785
+ ,97.96
+ ,120.343
+ ,630.577
+ ,23.812
+ ,98.22
+ ,98.881
+ ,628.654
+ ,21.917
+ ,98.51
+ ,115.678
+ ,603.184
+ ,19.713
+ ,98.19
+ ,120.796
+ ,656.255
+ ,19.282
+ ,98.37
+ ,94.261
+ ,600.730
+ ,18.788
+ ,98.31
+ ,89.151
+ ,670.326
+ ,21.453
+ ,98.6
+ ,119.880
+ ,678.423
+ ,24.482
+ ,98.96
+ ,131.468
+ ,641.502
+ ,27.474
+ ,99.11
+ ,155.089
+ ,625.311
+ ,27.264
+ ,99.64
+ ,149.581
+ ,628.177
+ ,27.349
+ ,100.02
+ ,122.788
+ ,589.767
+ ,30.632
+ ,99.98
+ ,143.900
+ ,582.471
+ ,29.429
+ ,100.32
+ ,112.115
+ ,636.248
+ ,30.084
+ ,100.44
+ ,109.600
+ ,599.885
+ ,26.290
+ ,100.51
+ ,117.446
+ ,621.694
+ ,24.379
+ ,101
+ ,118.456
+ ,637.406
+ ,23.335
+ ,100.88
+ ,101.901
+ ,595.994
+ ,21.346
+ ,100.55
+ ,89.940
+ ,696.308
+ ,21.106
+ ,100.82
+ ,129.143
+ ,674.201
+ ,24.514
+ ,101.5
+ ,126.102
+ ,648.861
+ ,28.353
+ ,102.15
+ ,143.048
+ ,649.605
+ ,30.805
+ ,102.39
+ ,142.258
+ ,672.392
+ ,31.348
+ ,102.54
+ ,131.011
+ ,598.396
+ ,34.556
+ ,102.85
+ ,146.471
+ ,613.177
+ ,33.855
+ ,103.47
+ ,114.073
+ ,638.104
+ ,34.787
+ ,103.56
+ ,114.642
+ ,615.632
+ ,32.529
+ ,103.69
+ ,118.226
+ ,634.465
+ ,29.998
+ ,103.49
+ ,111.338
+ ,638.686
+ ,29.257
+ ,103.47
+ ,108.701
+ ,604.243
+ ,28.155
+ ,103.45
+ ,80.512
+ ,706.669
+ ,30.466
+ ,103.48
+ ,146.865
+ ,677.185
+ ,35.704
+ ,103.93
+ ,137.179
+ ,644.328
+ ,39.327
+ ,103.89
+ ,166.536
+ ,664.825
+ ,39.351
+ ,104.4
+ ,137.070
+ ,605.707
+ ,42.234
+ ,104.79
+ ,127.090
+ ,600.136
+ ,43.630
+ ,104.77
+ ,139.966
+ ,612.166
+ ,43.722
+ ,105.13
+ ,122.243
+ ,599.659
+ ,43.121
+ ,105.26
+ ,109.097
+ ,634.210
+ ,37.985
+ ,104.96
+ ,116.591
+ ,618.234
+ ,37.135
+ ,104.75
+ ,111.964
+ ,613.576
+ ,34.646
+ ,105.01
+ ,109.754
+ ,627.200
+ ,33.026
+ ,105.15
+ ,77.609
+ ,668.973
+ ,35.087
+ ,105.2
+ ,138.445
+ ,651.479
+ ,38.846
+ ,105.77
+ ,127.901
+ ,619.661
+ ,42.013
+ ,105.78
+ ,156.615
+ ,644.260
+ ,43.908
+ ,106.26
+ ,133.264
+ ,579.936
+ ,42.868
+ ,106.13
+ ,143.521
+ ,601.752
+ ,44.423
+ ,106.12
+ ,152.139
+ ,595.376
+ ,44.167
+ ,106.57
+ ,131.523
+ ,588.902
+ ,43.636
+ ,106.44
+ ,113.925
+ ,634.341
+ ,44.382
+ ,106.54
+ ,86.495
+ ,594.305
+ ,42.142
+ ,107.1
+ ,127.877
+ ,606.200
+ ,43.452
+ ,108.1
+ ,107.017
+ ,610.926
+ ,36.912
+ ,108.4
+ ,78.716
+ ,633.685
+ ,42.413
+ ,108.84
+ ,138.278
+ ,639.696
+ ,45.344
+ ,109.62
+ ,144.238
+ ,659.451
+ ,44.873
+ ,110.42
+ ,143.679
+ ,593.248
+ ,47.510
+ ,110.67
+ ,159.932
+ ,606.677
+ ,49.554
+ ,111.66
+ ,136.781
+ ,599.434
+ ,47.369
+ ,112.28
+ ,148.173
+ ,569.578
+ ,45.998
+ ,112.87
+ ,125.673
+ ,629.873
+ ,48.140
+ ,112.18
+ ,105.573
+ ,613.438
+ ,48.441
+ ,112.36
+ ,122.405
+ ,604.172
+ ,44.928
+ ,112.16
+ ,128.045
+ ,658.328
+ ,40.454
+ ,111.49
+ ,94.467
+ ,612.633
+ ,38.661
+ ,111.25
+ ,85.573
+ ,707.372
+ ,37.246
+ ,111.36
+ ,121.501
+ ,739.770
+ ,36.843
+ ,111.74
+ ,125.074
+ ,777.535
+ ,36.424
+ ,111.1
+ ,144.979
+ ,685.030
+ ,37.594
+ ,111.33
+ ,142.120
+ ,730.234
+ ,38.144
+ ,111.25
+ ,124.213
+ ,714.154
+ ,38.737
+ ,111.04
+ ,144.407
+ ,630.872
+ ,34.560
+ ,110.97
+ ,125.170
+ ,719.492
+ ,36.080
+ ,111.31
+ ,109.267
+ ,677.023
+ ,33.508
+ ,111.02
+ ,122.354
+ ,679.272
+ ,35.462
+ ,111.07
+ ,122.589
+ ,718.317
+ ,33.374
+ ,111.36
+ ,104.982
+ ,645.672
+ ,32.110
+ ,111.54
+ ,90.542)
+ ,dim=c(4
+ ,84)
+ ,dimnames=list(c('WERKL'
+ ,'VAC'
+ ,'CPI'
+ ,'INSCHR')
+ ,1:84))
> y <- array(NA,dim=c(4,84),dimnames=list(c('WERKL','VAC','CPI','INSCHR'),1:84))
> 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'
> #'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
WERKL VAC CPI INSCHR
1 631.923 21.454 97.06 130.678
2 654.294 23.899 97.73 120.877
3 671.833 24.939 98.00 137.114
4 586.840 23.580 97.76 134.406
5 600.969 24.562 97.48 120.262
6 625.568 24.696 97.77 130.846
7 558.110 23.785 97.96 120.343
8 630.577 23.812 98.22 98.881
9 628.654 21.917 98.51 115.678
10 603.184 19.713 98.19 120.796
11 656.255 19.282 98.37 94.261
12 600.730 18.788 98.31 89.151
13 670.326 21.453 98.60 119.880
14 678.423 24.482 98.96 131.468
15 641.502 27.474 99.11 155.089
16 625.311 27.264 99.64 149.581
17 628.177 27.349 100.02 122.788
18 589.767 30.632 99.98 143.900
19 582.471 29.429 100.32 112.115
20 636.248 30.084 100.44 109.600
21 599.885 26.290 100.51 117.446
22 621.694 24.379 101.00 118.456
23 637.406 23.335 100.88 101.901
24 595.994 21.346 100.55 89.940
25 696.308 21.106 100.82 129.143
26 674.201 24.514 101.50 126.102
27 648.861 28.353 102.15 143.048
28 649.605 30.805 102.39 142.258
29 672.392 31.348 102.54 131.011
30 598.396 34.556 102.85 146.471
31 613.177 33.855 103.47 114.073
32 638.104 34.787 103.56 114.642
33 615.632 32.529 103.69 118.226
34 634.465 29.998 103.49 111.338
35 638.686 29.257 103.47 108.701
36 604.243 28.155 103.45 80.512
37 706.669 30.466 103.48 146.865
38 677.185 35.704 103.93 137.179
39 644.328 39.327 103.89 166.536
40 664.825 39.351 104.40 137.070
41 605.707 42.234 104.79 127.090
42 600.136 43.630 104.77 139.966
43 612.166 43.722 105.13 122.243
44 599.659 43.121 105.26 109.097
45 634.210 37.985 104.96 116.591
46 618.234 37.135 104.75 111.964
47 613.576 34.646 105.01 109.754
48 627.200 33.026 105.15 77.609
49 668.973 35.087 105.20 138.445
50 651.479 38.846 105.77 127.901
51 619.661 42.013 105.78 156.615
52 644.260 43.908 106.26 133.264
53 579.936 42.868 106.13 143.521
54 601.752 44.423 106.12 152.139
55 595.376 44.167 106.57 131.523
56 588.902 43.636 106.44 113.925
57 634.341 44.382 106.54 86.495
58 594.305 42.142 107.10 127.877
59 606.200 43.452 108.10 107.017
60 610.926 36.912 108.40 78.716
61 633.685 42.413 108.84 138.278
62 639.696 45.344 109.62 144.238
63 659.451 44.873 110.42 143.679
64 593.248 47.510 110.67 159.932
65 606.677 49.554 111.66 136.781
66 599.434 47.369 112.28 148.173
67 569.578 45.998 112.87 125.673
68 629.873 48.140 112.18 105.573
69 613.438 48.441 112.36 122.405
70 604.172 44.928 112.16 128.045
71 658.328 40.454 111.49 94.467
72 612.633 38.661 111.25 85.573
73 707.372 37.246 111.36 121.501
74 739.770 36.843 111.74 125.074
75 777.535 36.424 111.10 144.979
76 685.030 37.594 111.33 142.120
77 730.234 38.144 111.25 124.213
78 714.154 38.737 111.04 144.407
79 630.872 34.560 110.97 125.170
80 719.492 36.080 111.31 109.267
81 677.023 33.508 111.02 122.354
82 679.272 35.462 111.07 122.589
83 718.317 33.374 111.36 104.982
84 645.672 32.110 111.54 90.542
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VAC CPI INSCHR
-229.9598 -5.1149 9.0872 0.7179
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-81.0798 -18.6587 -0.6429 23.0928 80.1316
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -229.9598 121.5184 -1.892 0.062057 .
VAC -5.1149 0.7607 -6.724 2.40e-09 ***
CPI 9.0872 1.2724 7.142 3.79e-10 ***
INSCHR 0.7179 0.1958 3.667 0.000441 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 32.59 on 80 degrees of freedom
Multiple R-squared: 0.4087, Adjusted R-squared: 0.3865
F-statistic: 18.43 on 3 and 80 DF, p-value: 3.49e-09
> 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,] 0.904101303 0.191797394 0.09589870
[2,] 0.922829974 0.154340053 0.07717003
[3,] 0.868359188 0.263281624 0.13164081
[4,] 0.814819350 0.370361299 0.18518065
[5,] 0.810493892 0.379012217 0.18950611
[6,] 0.773785114 0.452429772 0.22621489
[7,] 0.769336017 0.461327967 0.23066398
[8,] 0.717578307 0.564843387 0.28242169
[9,] 0.687440323 0.625119354 0.31255968
[10,] 0.680879657 0.638240687 0.31912034
[11,] 0.615069789 0.769860423 0.38493021
[12,] 0.644545948 0.710908104 0.35545405
[13,] 0.612089565 0.775820871 0.38791044
[14,] 0.599551084 0.800897831 0.40044892
[15,] 0.587755751 0.824488498 0.41224425
[16,] 0.539504058 0.920991885 0.46049594
[17,] 0.474302601 0.948605202 0.52569740
[18,] 0.513389696 0.973220608 0.48661030
[19,] 0.536119016 0.927761967 0.46388098
[20,] 0.491258769 0.982517538 0.50874123
[21,] 0.434062292 0.868124583 0.56593771
[22,] 0.372557659 0.745115317 0.62744234
[23,] 0.375690093 0.751380186 0.62430991
[24,] 0.417403452 0.834806904 0.58259655
[25,] 0.361276028 0.722552057 0.63872397
[26,] 0.325588647 0.651177294 0.67441135
[27,] 0.297272835 0.594545671 0.70272716
[28,] 0.255506891 0.511013782 0.74449311
[29,] 0.219608397 0.439216794 0.78039160
[30,] 0.236294082 0.472588164 0.76370592
[31,] 0.246421912 0.492843825 0.75357809
[32,] 0.257407990 0.514815980 0.74259201
[33,] 0.207720317 0.415440634 0.79227968
[34,] 0.222347612 0.444695224 0.77765239
[35,] 0.177498715 0.354997430 0.82250129
[36,] 0.142643595 0.285287189 0.85735641
[37,] 0.116576941 0.233153883 0.88342306
[38,] 0.089714305 0.179428610 0.91028569
[39,] 0.067947163 0.135894327 0.93205284
[40,] 0.049479032 0.098958064 0.95052097
[41,] 0.044950775 0.089901549 0.95504923
[42,] 0.034184431 0.068368862 0.96581557
[43,] 0.023755833 0.047511667 0.97624417
[44,] 0.016967738 0.033935477 0.98303226
[45,] 0.014266423 0.028532846 0.98573358
[46,] 0.013659163 0.027318326 0.98634084
[47,] 0.023185501 0.046371002 0.97681450
[48,] 0.019463318 0.038926635 0.98053668
[49,] 0.015159241 0.030318481 0.98484076
[50,] 0.011017765 0.022035530 0.98898224
[51,] 0.026943985 0.053887970 0.97305602
[52,] 0.026138578 0.052277157 0.97386142
[53,] 0.017852233 0.035704466 0.98214777
[54,] 0.013389899 0.026779798 0.98661010
[55,] 0.009889800 0.019779600 0.99011020
[56,] 0.006145934 0.012291867 0.99385407
[57,] 0.003804358 0.007608716 0.99619564
[58,] 0.011735446 0.023470892 0.98826455
[59,] 0.009227231 0.018454463 0.99077277
[60,] 0.011979538 0.023959075 0.98802046
[61,] 0.027109898 0.054219796 0.97289010
[62,] 0.020970145 0.041940289 0.97902986
[63,] 0.012790933 0.025581866 0.98720907
[64,] 0.130602667 0.261205335 0.86939733
[65,] 0.095670033 0.191340066 0.90432997
[66,] 0.094096698 0.188193396 0.90590330
[67,] 0.075180258 0.150360516 0.92481974
[68,] 0.062633204 0.125266408 0.93736680
[69,] 0.472613381 0.945226762 0.52738662
[70,] 0.383224403 0.766448806 0.61677560
[71,] 0.257224855 0.514449710 0.74277515
> postscript(file="/var/www/html/rcomp/tmp/13y8j1292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ep741292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ep741292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ep741292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pgop1292760492.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 = 84
Frequency = 1
1 2 3 4 5
-4.199515611 31.625202951 40.373489835 -47.445636307 -15.595309564
6 7 8 9 10
-0.544531968 -66.848586363 18.801578495 -7.508130087 -45.017713763
11 12 13 14 15
23.262748376 -30.575271860 27.956062948 39.955585617 0.017585319
16 17 18 19 20
-18.109518893 0.973012412 -35.437756656 -29.157930895 28.684401532
21 22 23 24 25
-33.353324628 -26.496702930 -3.149233276 -43.149098978 25.339654468
26 27 28 29 30
16.668092101 -7.108152198 4.563797277 36.839414304 -34.663874988
31 32 33 34 35
-5.843706627 22.624041128 -15.151712233 -2.502131961 0.003595199
36 37 38 39 40
-19.657181653 46.681410073 46.853659058 11.815807248 48.954962042
41 42 43 44 45
8.203924207 0.711286865 22.663944705 15.339169522 20.966214947
46 47 48 49 50
5.872620413 -12.292452506 14.850345788 23.036165396 27.158983246
51 52 53 54 55
-9.165006644 37.528731895 -38.297004382 -14.623394582 -11.597661508
56 57 58 59 60
-6.972600083 61.065598574 -25.225048105 -0.741170898 -11.875254114
61 62 63 64 65
-7.737577762 1.898448976 12.375890827 -54.279074099 -22.771264197
66 67 68 69 70
-55.002775282 -81.079821385 10.871391413 -17.743538684 -47.209733389
71 72 73 74 75
14.256533439 -32.043481964 28.665364832 52.983845401 80.131555023
76 77 78 79 80
-6.426571147 55.173168153 29.537186882 -60.663247981 44.058635179
81 82 83 84
-18.325851703 -6.705410238 31.664597602 -38.714741573
> postscript(file="/var/www/html/rcomp/tmp/6pgop1292760492.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.199515611 NA
1 31.625202951 -4.199515611
2 40.373489835 31.625202951
3 -47.445636307 40.373489835
4 -15.595309564 -47.445636307
5 -0.544531968 -15.595309564
6 -66.848586363 -0.544531968
7 18.801578495 -66.848586363
8 -7.508130087 18.801578495
9 -45.017713763 -7.508130087
10 23.262748376 -45.017713763
11 -30.575271860 23.262748376
12 27.956062948 -30.575271860
13 39.955585617 27.956062948
14 0.017585319 39.955585617
15 -18.109518893 0.017585319
16 0.973012412 -18.109518893
17 -35.437756656 0.973012412
18 -29.157930895 -35.437756656
19 28.684401532 -29.157930895
20 -33.353324628 28.684401532
21 -26.496702930 -33.353324628
22 -3.149233276 -26.496702930
23 -43.149098978 -3.149233276
24 25.339654468 -43.149098978
25 16.668092101 25.339654468
26 -7.108152198 16.668092101
27 4.563797277 -7.108152198
28 36.839414304 4.563797277
29 -34.663874988 36.839414304
30 -5.843706627 -34.663874988
31 22.624041128 -5.843706627
32 -15.151712233 22.624041128
33 -2.502131961 -15.151712233
34 0.003595199 -2.502131961
35 -19.657181653 0.003595199
36 46.681410073 -19.657181653
37 46.853659058 46.681410073
38 11.815807248 46.853659058
39 48.954962042 11.815807248
40 8.203924207 48.954962042
41 0.711286865 8.203924207
42 22.663944705 0.711286865
43 15.339169522 22.663944705
44 20.966214947 15.339169522
45 5.872620413 20.966214947
46 -12.292452506 5.872620413
47 14.850345788 -12.292452506
48 23.036165396 14.850345788
49 27.158983246 23.036165396
50 -9.165006644 27.158983246
51 37.528731895 -9.165006644
52 -38.297004382 37.528731895
53 -14.623394582 -38.297004382
54 -11.597661508 -14.623394582
55 -6.972600083 -11.597661508
56 61.065598574 -6.972600083
57 -25.225048105 61.065598574
58 -0.741170898 -25.225048105
59 -11.875254114 -0.741170898
60 -7.737577762 -11.875254114
61 1.898448976 -7.737577762
62 12.375890827 1.898448976
63 -54.279074099 12.375890827
64 -22.771264197 -54.279074099
65 -55.002775282 -22.771264197
66 -81.079821385 -55.002775282
67 10.871391413 -81.079821385
68 -17.743538684 10.871391413
69 -47.209733389 -17.743538684
70 14.256533439 -47.209733389
71 -32.043481964 14.256533439
72 28.665364832 -32.043481964
73 52.983845401 28.665364832
74 80.131555023 52.983845401
75 -6.426571147 80.131555023
76 55.173168153 -6.426571147
77 29.537186882 55.173168153
78 -60.663247981 29.537186882
79 44.058635179 -60.663247981
80 -18.325851703 44.058635179
81 -6.705410238 -18.325851703
82 31.664597602 -6.705410238
83 -38.714741573 31.664597602
84 NA -38.714741573
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 31.625202951 -4.199515611
[2,] 40.373489835 31.625202951
[3,] -47.445636307 40.373489835
[4,] -15.595309564 -47.445636307
[5,] -0.544531968 -15.595309564
[6,] -66.848586363 -0.544531968
[7,] 18.801578495 -66.848586363
[8,] -7.508130087 18.801578495
[9,] -45.017713763 -7.508130087
[10,] 23.262748376 -45.017713763
[11,] -30.575271860 23.262748376
[12,] 27.956062948 -30.575271860
[13,] 39.955585617 27.956062948
[14,] 0.017585319 39.955585617
[15,] -18.109518893 0.017585319
[16,] 0.973012412 -18.109518893
[17,] -35.437756656 0.973012412
[18,] -29.157930895 -35.437756656
[19,] 28.684401532 -29.157930895
[20,] -33.353324628 28.684401532
[21,] -26.496702930 -33.353324628
[22,] -3.149233276 -26.496702930
[23,] -43.149098978 -3.149233276
[24,] 25.339654468 -43.149098978
[25,] 16.668092101 25.339654468
[26,] -7.108152198 16.668092101
[27,] 4.563797277 -7.108152198
[28,] 36.839414304 4.563797277
[29,] -34.663874988 36.839414304
[30,] -5.843706627 -34.663874988
[31,] 22.624041128 -5.843706627
[32,] -15.151712233 22.624041128
[33,] -2.502131961 -15.151712233
[34,] 0.003595199 -2.502131961
[35,] -19.657181653 0.003595199
[36,] 46.681410073 -19.657181653
[37,] 46.853659058 46.681410073
[38,] 11.815807248 46.853659058
[39,] 48.954962042 11.815807248
[40,] 8.203924207 48.954962042
[41,] 0.711286865 8.203924207
[42,] 22.663944705 0.711286865
[43,] 15.339169522 22.663944705
[44,] 20.966214947 15.339169522
[45,] 5.872620413 20.966214947
[46,] -12.292452506 5.872620413
[47,] 14.850345788 -12.292452506
[48,] 23.036165396 14.850345788
[49,] 27.158983246 23.036165396
[50,] -9.165006644 27.158983246
[51,] 37.528731895 -9.165006644
[52,] -38.297004382 37.528731895
[53,] -14.623394582 -38.297004382
[54,] -11.597661508 -14.623394582
[55,] -6.972600083 -11.597661508
[56,] 61.065598574 -6.972600083
[57,] -25.225048105 61.065598574
[58,] -0.741170898 -25.225048105
[59,] -11.875254114 -0.741170898
[60,] -7.737577762 -11.875254114
[61,] 1.898448976 -7.737577762
[62,] 12.375890827 1.898448976
[63,] -54.279074099 12.375890827
[64,] -22.771264197 -54.279074099
[65,] -55.002775282 -22.771264197
[66,] -81.079821385 -55.002775282
[67,] 10.871391413 -81.079821385
[68,] -17.743538684 10.871391413
[69,] -47.209733389 -17.743538684
[70,] 14.256533439 -47.209733389
[71,] -32.043481964 14.256533439
[72,] 28.665364832 -32.043481964
[73,] 52.983845401 28.665364832
[74,] 80.131555023 52.983845401
[75,] -6.426571147 80.131555023
[76,] 55.173168153 -6.426571147
[77,] 29.537186882 55.173168153
[78,] -60.663247981 29.537186882
[79,] 44.058635179 -60.663247981
[80,] -18.325851703 44.058635179
[81,] -6.705410238 -18.325851703
[82,] 31.664597602 -6.705410238
[83,] -38.714741573 31.664597602
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 31.625202951 -4.199515611
2 40.373489835 31.625202951
3 -47.445636307 40.373489835
4 -15.595309564 -47.445636307
5 -0.544531968 -15.595309564
6 -66.848586363 -0.544531968
7 18.801578495 -66.848586363
8 -7.508130087 18.801578495
9 -45.017713763 -7.508130087
10 23.262748376 -45.017713763
11 -30.575271860 23.262748376
12 27.956062948 -30.575271860
13 39.955585617 27.956062948
14 0.017585319 39.955585617
15 -18.109518893 0.017585319
16 0.973012412 -18.109518893
17 -35.437756656 0.973012412
18 -29.157930895 -35.437756656
19 28.684401532 -29.157930895
20 -33.353324628 28.684401532
21 -26.496702930 -33.353324628
22 -3.149233276 -26.496702930
23 -43.149098978 -3.149233276
24 25.339654468 -43.149098978
25 16.668092101 25.339654468
26 -7.108152198 16.668092101
27 4.563797277 -7.108152198
28 36.839414304 4.563797277
29 -34.663874988 36.839414304
30 -5.843706627 -34.663874988
31 22.624041128 -5.843706627
32 -15.151712233 22.624041128
33 -2.502131961 -15.151712233
34 0.003595199 -2.502131961
35 -19.657181653 0.003595199
36 46.681410073 -19.657181653
37 46.853659058 46.681410073
38 11.815807248 46.853659058
39 48.954962042 11.815807248
40 8.203924207 48.954962042
41 0.711286865 8.203924207
42 22.663944705 0.711286865
43 15.339169522 22.663944705
44 20.966214947 15.339169522
45 5.872620413 20.966214947
46 -12.292452506 5.872620413
47 14.850345788 -12.292452506
48 23.036165396 14.850345788
49 27.158983246 23.036165396
50 -9.165006644 27.158983246
51 37.528731895 -9.165006644
52 -38.297004382 37.528731895
53 -14.623394582 -38.297004382
54 -11.597661508 -14.623394582
55 -6.972600083 -11.597661508
56 61.065598574 -6.972600083
57 -25.225048105 61.065598574
58 -0.741170898 -25.225048105
59 -11.875254114 -0.741170898
60 -7.737577762 -11.875254114
61 1.898448976 -7.737577762
62 12.375890827 1.898448976
63 -54.279074099 12.375890827
64 -22.771264197 -54.279074099
65 -55.002775282 -22.771264197
66 -81.079821385 -55.002775282
67 10.871391413 -81.079821385
68 -17.743538684 10.871391413
69 -47.209733389 -17.743538684
70 14.256533439 -47.209733389
71 -32.043481964 14.256533439
72 28.665364832 -32.043481964
73 52.983845401 28.665364832
74 80.131555023 52.983845401
75 -6.426571147 80.131555023
76 55.173168153 -6.426571147
77 29.537186882 55.173168153
78 -60.663247981 29.537186882
79 44.058635179 -60.663247981
80 -18.325851703 44.058635179
81 -6.705410238 -18.325851703
82 31.664597602 -6.705410238
83 -38.714741573 31.664597602
> 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/7hq5a1292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hq5a1292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9shnv1292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10shnv1292760492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11wz311292760492.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/12zi2p1292760492.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/13dszg1292760492.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/14ysg31292760492.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/152bwr1292760492.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/165tdf1292760492.tab")
+ }
>
> try(system("convert tmp/13y8j1292760492.ps tmp/13y8j1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ep741292760492.ps tmp/2ep741292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ep741292760492.ps tmp/3ep741292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ep741292760492.ps tmp/4ep741292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pgop1292760492.ps tmp/5pgop1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pgop1292760492.ps tmp/6pgop1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hq5a1292760492.ps tmp/7hq5a1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hq5a1292760492.ps tmp/8hq5a1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/9shnv1292760492.ps tmp/9shnv1292760492.png",intern=TRUE))
character(0)
> try(system("convert tmp/10shnv1292760492.ps tmp/10shnv1292760492.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.869 1.677 6.451