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)
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> x <- array(list(6217
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+ ,dim=c(7
+ ,121)
+ ,dimnames=list(c('Totaal'
+ ,'InbrengInContanten'
+ ,'InbrengInNatura'
+ ,'TeStortenBedrag'
+ ,'ConversieVanEigenMiddelen'
+ ,'Schuldconversie'
+ ,'Uitgiftepremies')
+ ,1:121))
> y <- array(NA,dim=c(7,121),dimnames=list(c('Totaal','InbrengInContanten','InbrengInNatura','TeStortenBedrag','ConversieVanEigenMiddelen','Schuldconversie','Uitgiftepremies'),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
Totaal InbrengInContanten InbrengInNatura TeStortenBedrag
1 6217 1148 4753 78
2 5884 1457 4057 3
3 1431 374 894 15
4 2610 178 2232 13
5 3395 1445 1821 5
6 14135 2870 9878 297
7 8611 1339 7182 2
8 255 155 33 7
9 1722 392 725 8
10 3736 988 1846 426
11 2241 600 430 15
12 1871 837 688 2
13 6911 779 3268 4
14 1515 298 1069 1
15 2289 616 307 131
16 1299 606 273 16
17 774 314 156 4
18 9485 5281 2199 228
19 2107 1047 249 5
20 1720 343 1210 9
21 2643 1422 1024 32
22 12106 371 6734 15
23 962 491 72 6
24 2309 895 535 19
25 7083 912 5911 70
26 4895 466 921 86
27 5256 2834 743 4
28 3856 997 164 811
29 3742 920 2391 14
30 23692 14367 5798 2517
31 3198 472 906 18
32 1993 643 173 6
33 5442 1932 1547 106
34 2245 815 176 5
35 1239 478 374 4
36 6388 1083 1629 1255
37 1679 185 1040 9
38 830 224 130 7
39 2505 1148 346 2
40 4387 501 2614 1
41 2162 882 1051 3
42 11993 4115 7092 7
43 18864 11544 1324 433
44 1979 1533 290 19
45 19220 16061 422 204
46 4410 3057 565 33
47 6942 4858 760 11
48 7762 3417 3497 118
49 17814 4783 9768 11
50 2523 1631 458 32
51 12586 4622 6225 49
52 2244 1292 449 151
53 7931 3167 2963 56
54 15720 4019 6676 122
55 3029 1432 354 677
56 8217 2339 358 54
57 14346 8323 1902 37
58 7944 6085 761 77
59 6745 2291 3466 209
60 10650 3023 3415 43
61 17682 6288 2152 3709
62 6789 6005 307 9
63 10109 5006 2237 49
64 11981 6187 1628 168
65 24259 2127 19327 1578
66 68744 17503 31561 830
67 85056 3661 76825 11
68 3134 2026 101 120
69 6751 3231 1096 24
70 7098 3226 906 86
71 6142 1805 3666 343
72 3974 1290 447 179
73 14614 6500 5219 35
74 13438 2539 643 4
75 9746 6710 529 881
76 23024 10028 2608 76
77 12102 5223 1402 147
78 41056 20553 3504 2593
79 2495 746 188 5
80 7056 3947 1383 36
81 7708 2218 649 58
82 8229 4053 470 44
83 4714 1548 896 8
84 14317 6280 986 369
85 5267 1674 1315 777
86 4087 3700 126 11
87 3823 843 932 13
88 2137 1449 310 45
89 4241 2098 548 73
90 13654 4027 4649 1876
91 1913 1343 70 10
92 2380 1763 314 17
93 5223 731 4038 24
94 2337 1923 127 125
95 10031 2334 276 89
96 4588 2647 624 51
97 9479 3400 4929 782
98 18171 2434 14635 7
99 14015 2237 9832 14
100 4919 1700 1148 244
101 4573 513 2482 22
102 82257 22476 47568 6098
103 2375 385 728 5
104 3772 1961 512 431
105 3954 1135 574 24
106 4861 698 834 18
107 2652 308 918 19
108 13527 2432 7258 115
109 28039 810 23428 3
110 2874 456 418 311
111 11152 765 9300 156
112 2727 1018 363 40
113 3056 1682 290 6
114 47201 4177 33868 639
115 2370 1137 205 22
116 2439 1870 218 6
117 10484 6845 1048 1750
118 3107 636 1742 7
119 14931 1375 377 51
120 8929 1418 401 23
121 3814 1479 959 15
ConversieVanEigenMiddelen Schuldconversie Uitgiftepremies
1 14 103 121
2 4 115 248
3 16 123 9
4 11 152 24
5 29 68 27
6 129 442 519
7 4 30 55
8 14 7 39
9 131 241 225
10 7 370 101
11 156 101 939
12 69 144 130
13 13 2760 86
14 8 76 62
15 28 372 835
16 213 174 17
17 6 178 116
18 73 977 727
19 32 29 745
20 9 52 98
21 37 72 55
22 137 3812 1036
23 148 237 8
24 162 535 162
25 31 38 121
26 27 42 3353
27 1017 110 546
28 1613 121 150
29 130 103 184
30 316 325 369
31 72 49 1681
32 254 829 88
33 25 323 1508
34 165 64 1020
35 97 56 229
36 907 1298 215
37 20 16 409
38 6 54 408
39 804 53 152
40 381 296 593
41 13 42 170
42 152 239 389
43 23 293 5246
44 10 76 51
45 41 759 1733
46 37 55 664
47 182 220 911
48 111 242 376
49 82 114 3057
50 47 219 136
51 254 237 1199
52 106 58 188
53 94 1467 185
54 152 578 4173
55 14 25 527
56 55 88 5323
57 489 484 3110
58 408 48 565
59 119 491 170
60 1195 202 2774
61 1979 1270 2284
62 127 160 182
63 1162 296 1360
64 523 335 3139
65 89 233 906
66 725 571 17553
67 62 60 4436
68 440 412 35
69 62 186 2151
70 60 195 2625
71 74 185 69
72 323 422 1313
73 236 427 2198
74 9 9159 1084
75 105 863 658
76 1095 4707 4509
77 40 507 4782
78 142 958 13306
79 608 13 935
80 19 70 1601
81 1833 474 2475
82 217 179 3266
83 207 247 1807
84 4304 1989 389
85 14 321 1165
86 74 158 18
87 161 340 1532
88 60 154 118
89 174 963 384
90 584 1770 748
91 307 112 70
92 22 102 162
93 188 99 142
94 24 129 10
95 467 4178 2687
96 49 315 900
97 123 182 62
98 237 852 6
99 755 1122 55
100 539 177 1112
101 107 114 1334
102 186 974 4954
103 284 92 880
104 99 61 707
105 123 779 1318
106 2869 254 189
107 483 161 764
108 912 306 2504
109 730 282 2786
110 1126 350 212
111 36 605 290
112 30 71 1204
113 199 225 655
114 998 4298 3221
115 145 302 560
116 24 88 233
117 30 220 591
118 335 58 329
119 11986 379 762
120 857 2859 3371
121 173 311 878
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InbrengInContanten
0.1137 1.0000
InbrengInNatura TeStortenBedrag
1.0000 1.0000
ConversieVanEigenMiddelen Schuldconversie
1.0000 1.0000
Uitgiftepremies
1.0001
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.2641 -0.2629 -0.1116 0.7957 1.8780
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.137e-01 9.812e-02 1.159e+00 0.249
InbrengInContanten 1.000e+00 3.169e-05 3.155e+04 <2e-16 ***
InbrengInNatura 1.000e+00 8.522e-06 1.173e+05 <2e-16 ***
TeStortenBedrag 1.000e+00 1.282e-04 7.799e+03 <2e-16 ***
ConversieVanEigenMiddelen 1.000e+00 5.935e-05 1.685e+04 <2e-16 ***
Schuldconversie 1.000e+00 6.253e-05 1.599e+04 <2e-16 ***
Uitgiftepremies 1.000e+00 4.504e-05 2.220e+04 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7812 on 114 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 5.873e+09 on 6 and 114 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.40757836 0.81515672 0.5924216
[2,] 0.29641786 0.59283572 0.7035821
[3,] 0.28860408 0.57720816 0.7113959
[4,] 0.22257803 0.44515606 0.7774220
[5,] 0.37935223 0.75870446 0.6206478
[6,] 0.29735880 0.59471759 0.7026412
[7,] 0.22920297 0.45840594 0.7707970
[8,] 0.15858637 0.31717274 0.8414136
[9,] 0.12142000 0.24284000 0.8785800
[10,] 0.08300133 0.16600265 0.9169987
[11,] 0.12282178 0.24564355 0.8771782
[12,] 0.16546839 0.33093679 0.8345316
[13,] 0.12458112 0.24916225 0.8754189
[14,] 0.09125524 0.18251048 0.9087448
[15,] 0.08378953 0.16757905 0.9162105
[16,] 0.06050222 0.12100444 0.9394978
[17,] 0.04195488 0.08390977 0.9580451
[18,] 0.03818002 0.07636004 0.9618200
[19,] 0.02616697 0.05233395 0.9738330
[20,] 0.01759911 0.03519821 0.9824009
[21,] 0.07671156 0.15342312 0.9232884
[22,] 0.05469144 0.10938289 0.9453086
[23,] 0.04532576 0.09065152 0.9546742
[24,] 0.04552791 0.09105581 0.9544721
[25,] 0.03348370 0.06696740 0.9665163
[26,] 0.04310940 0.08621879 0.9568906
[27,] 0.08425058 0.16850116 0.9157494
[28,] 0.06227886 0.12455772 0.9377211
[29,] 0.07638120 0.15276240 0.9236188
[30,] 0.07482177 0.14964355 0.9251782
[31,] 0.07500241 0.15000482 0.9249976
[32,] 0.08581535 0.17163071 0.9141846
[33,] 0.11942592 0.23885184 0.8805741
[34,] 0.10005388 0.20010776 0.8999461
[35,] 0.07810074 0.15620148 0.9218993
[36,] 0.08424774 0.16849547 0.9157523
[37,] 0.11654061 0.23308121 0.8834594
[38,] 0.09281070 0.18562141 0.9071893
[39,] 0.10922557 0.21845114 0.8907744
[40,] 0.12168631 0.24337261 0.8783137
[41,] 0.09633714 0.19267429 0.9036629
[42,] 0.07449897 0.14899793 0.9255010
[43,] 0.05697423 0.11394846 0.9430258
[44,] 0.09271283 0.18542565 0.9072872
[45,] 0.07293973 0.14587945 0.9270603
[46,] 0.05567328 0.11134656 0.9443267
[47,] 0.04570205 0.09140409 0.9542980
[48,] 0.04203153 0.08406305 0.9579685
[49,] 0.03220898 0.06441797 0.9677910
[50,] 0.04173919 0.08347837 0.9582608
[51,] 0.26372175 0.52744349 0.7362783
[52,] 0.23316676 0.46633352 0.7668332
[53,] 0.27571229 0.55142458 0.7242877
[54,] 0.32034501 0.64069002 0.6796550
[55,] 0.32192111 0.64384222 0.6780789
[56,] 0.36620894 0.73241787 0.6337911
[57,] 0.37244060 0.74488120 0.6275594
[58,] 0.44962481 0.89924961 0.5503752
[59,] 0.39750755 0.79501510 0.6024925
[60,] 0.40047907 0.80095814 0.5995209
[61,] 0.35179748 0.70359496 0.6482025
[62,] 0.30420584 0.60841168 0.6957942
[63,] 0.26267364 0.52534728 0.7373264
[64,] 0.30623638 0.61247276 0.6937636
[65,] 0.28727506 0.57455012 0.7127249
[66,] 0.24405738 0.48811476 0.7559426
[67,] 0.26024949 0.52049897 0.7397505
[68,] 0.25257618 0.50515236 0.7474238
[69,] 0.22594963 0.45189926 0.7740504
[70,] 0.19106320 0.38212640 0.8089368
[71,] 0.15672295 0.31344590 0.8432771
[72,] 0.13767920 0.27535840 0.8623208
[73,] 0.11683750 0.23367500 0.8831625
[74,] 0.10700587 0.21401174 0.8929941
[75,] 0.08482567 0.16965134 0.9151743
[76,] 0.07945755 0.15891511 0.9205424
[77,] 0.06025901 0.12051802 0.9397410
[78,] 0.15075322 0.30150644 0.8492468
[79,] 0.15225688 0.30451377 0.8477431
[80,] 0.15762648 0.31525297 0.8423735
[81,] 0.12636684 0.25273368 0.8736332
[82,] 0.13260678 0.26521356 0.8673932
[83,] 0.10070178 0.20140355 0.8992982
[84,] 0.10629680 0.21259360 0.8937032
[85,] 0.12464372 0.24928744 0.8753563
[86,] 0.09535692 0.19071385 0.9046431
[87,] 0.36327364 0.72654729 0.6367264
[88,] 0.38026620 0.76053241 0.6197338
[89,] 0.34545515 0.69091030 0.6545449
[90,] 0.34089885 0.68179771 0.6591011
[91,] 0.41653517 0.83307034 0.5834648
[92,] 0.39119822 0.78239645 0.6088018
[93,] 0.33602680 0.67205360 0.6639732
[94,] 0.33815296 0.67630592 0.6618470
[95,] 0.35671757 0.71343513 0.6432824
[96,] 0.46290959 0.92581919 0.5370904
[97,] 0.51932840 0.96134319 0.4806716
[98,] 0.65512168 0.68975665 0.3448783
[99,] 0.56599476 0.86801048 0.4340052
[100,] 0.53331023 0.93337953 0.4666898
[101,] 0.46037875 0.92075750 0.5396213
[102,] 0.31063688 0.62127376 0.6893631
> postscript(file="/var/wessaorg/rcomp/tmp/12giu1353055832.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2yuwt1353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3g3x01353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tncc1353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51mt71353055832.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
-0.10975799 -0.10592255 -0.11162829 -0.11911869 -0.08970735 -0.12589385
7 8 9 10 11 12
-1.09971518 -0.11369703 -0.12998689 -2.13023505 -0.16504660 0.88985948
13 14 15 16 17 18
0.82862347 0.88538162 -0.16587450 -0.11406522 -0.11853648 -0.08208947
19 20 21 22 23 24
-0.13741058 -1.11583786 0.90768175 0.72971941 -0.11449957 0.87645105
25 26 27 28 29 30
-0.11524029 -0.30843892 1.87694564 -0.19722133 -0.11573940 0.02700613
31 32 33 34 35 36
-0.20829375 -0.13336923 0.82340791 -0.16378396 0.87776846 0.77407097
37 38 39 40 41 42
-0.13683309 0.86510185 -0.12694181 0.83851494 0.89213843 -1.06958780
43 44 45 46 47 48
0.79567110 -0.08716859 0.10053252 -1.09196675 -0.07571236 0.91751425
49 50 51 52 53 54
-1.21118854 -0.09549128 -0.11047879 -0.10976683 -1.10065543 -0.30713700
55 56 57 58 59 60
-0.14775749 -0.38377018 0.84997502 -0.03573484 -1.10490734 -2.26411269
61 62 63 64 65 66
-0.38895101 -1.00410272 -1.13780110 0.79877606 -1.23349832 0.10548224
67 68 69 70 71 72
0.58603058 -0.10244218 0.81999851 -0.21082741 -0.10749563 -0.19312312
73 74 75 76 77 78
-1.13051781 -0.33634935 -0.07435163 0.68319093 0.69496419 -0.61053372
79 80 81 82 83 84
-0.17405569 -0.12907610 0.71172232 -0.23305373 0.79879092 -0.21186035
85 86 87 88 89 90
0.80684965 -0.04203423 1.79892073 0.90231824 0.87636228 -0.22804207
91 92 93 94 95 96
0.89752063 -0.08970193 0.87788423 -1.08287033 -0.33880869 1.87794965
97 98 99 100 101 102
0.90286755 -0.11144481 -0.13551880 -1.17822355 0.80780526 0.68843688
103 104 105 106 107 108
0.82972313 0.86080858 0.80893055 -1.21464663 -1.17436734 -0.26291221
109 110 111 112 113 114
-0.32634375 0.82206727 -0.15076428 0.83220196 -1.12873146 -0.42727619
115 116 117 118 119 120
-1.13546046 -0.09065691 -0.09225035 -0.13496982 0.45464274 -0.37846356
121
-1.14876102
> postscript(file="/var/wessaorg/rcomp/tmp/6wk961353055832.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 -0.10975799 NA
1 -0.10592255 -0.10975799
2 -0.11162829 -0.10592255
3 -0.11911869 -0.11162829
4 -0.08970735 -0.11911869
5 -0.12589385 -0.08970735
6 -1.09971518 -0.12589385
7 -0.11369703 -1.09971518
8 -0.12998689 -0.11369703
9 -2.13023505 -0.12998689
10 -0.16504660 -2.13023505
11 0.88985948 -0.16504660
12 0.82862347 0.88985948
13 0.88538162 0.82862347
14 -0.16587450 0.88538162
15 -0.11406522 -0.16587450
16 -0.11853648 -0.11406522
17 -0.08208947 -0.11853648
18 -0.13741058 -0.08208947
19 -1.11583786 -0.13741058
20 0.90768175 -1.11583786
21 0.72971941 0.90768175
22 -0.11449957 0.72971941
23 0.87645105 -0.11449957
24 -0.11524029 0.87645105
25 -0.30843892 -0.11524029
26 1.87694564 -0.30843892
27 -0.19722133 1.87694564
28 -0.11573940 -0.19722133
29 0.02700613 -0.11573940
30 -0.20829375 0.02700613
31 -0.13336923 -0.20829375
32 0.82340791 -0.13336923
33 -0.16378396 0.82340791
34 0.87776846 -0.16378396
35 0.77407097 0.87776846
36 -0.13683309 0.77407097
37 0.86510185 -0.13683309
38 -0.12694181 0.86510185
39 0.83851494 -0.12694181
40 0.89213843 0.83851494
41 -1.06958780 0.89213843
42 0.79567110 -1.06958780
43 -0.08716859 0.79567110
44 0.10053252 -0.08716859
45 -1.09196675 0.10053252
46 -0.07571236 -1.09196675
47 0.91751425 -0.07571236
48 -1.21118854 0.91751425
49 -0.09549128 -1.21118854
50 -0.11047879 -0.09549128
51 -0.10976683 -0.11047879
52 -1.10065543 -0.10976683
53 -0.30713700 -1.10065543
54 -0.14775749 -0.30713700
55 -0.38377018 -0.14775749
56 0.84997502 -0.38377018
57 -0.03573484 0.84997502
58 -1.10490734 -0.03573484
59 -2.26411269 -1.10490734
60 -0.38895101 -2.26411269
61 -1.00410272 -0.38895101
62 -1.13780110 -1.00410272
63 0.79877606 -1.13780110
64 -1.23349832 0.79877606
65 0.10548224 -1.23349832
66 0.58603058 0.10548224
67 -0.10244218 0.58603058
68 0.81999851 -0.10244218
69 -0.21082741 0.81999851
70 -0.10749563 -0.21082741
71 -0.19312312 -0.10749563
72 -1.13051781 -0.19312312
73 -0.33634935 -1.13051781
74 -0.07435163 -0.33634935
75 0.68319093 -0.07435163
76 0.69496419 0.68319093
77 -0.61053372 0.69496419
78 -0.17405569 -0.61053372
79 -0.12907610 -0.17405569
80 0.71172232 -0.12907610
81 -0.23305373 0.71172232
82 0.79879092 -0.23305373
83 -0.21186035 0.79879092
84 0.80684965 -0.21186035
85 -0.04203423 0.80684965
86 1.79892073 -0.04203423
87 0.90231824 1.79892073
88 0.87636228 0.90231824
89 -0.22804207 0.87636228
90 0.89752063 -0.22804207
91 -0.08970193 0.89752063
92 0.87788423 -0.08970193
93 -1.08287033 0.87788423
94 -0.33880869 -1.08287033
95 1.87794965 -0.33880869
96 0.90286755 1.87794965
97 -0.11144481 0.90286755
98 -0.13551880 -0.11144481
99 -1.17822355 -0.13551880
100 0.80780526 -1.17822355
101 0.68843688 0.80780526
102 0.82972313 0.68843688
103 0.86080858 0.82972313
104 0.80893055 0.86080858
105 -1.21464663 0.80893055
106 -1.17436734 -1.21464663
107 -0.26291221 -1.17436734
108 -0.32634375 -0.26291221
109 0.82206727 -0.32634375
110 -0.15076428 0.82206727
111 0.83220196 -0.15076428
112 -1.12873146 0.83220196
113 -0.42727619 -1.12873146
114 -1.13546046 -0.42727619
115 -0.09065691 -1.13546046
116 -0.09225035 -0.09065691
117 -0.13496982 -0.09225035
118 0.45464274 -0.13496982
119 -0.37846356 0.45464274
120 -1.14876102 -0.37846356
121 NA -1.14876102
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.10592255 -0.10975799
[2,] -0.11162829 -0.10592255
[3,] -0.11911869 -0.11162829
[4,] -0.08970735 -0.11911869
[5,] -0.12589385 -0.08970735
[6,] -1.09971518 -0.12589385
[7,] -0.11369703 -1.09971518
[8,] -0.12998689 -0.11369703
[9,] -2.13023505 -0.12998689
[10,] -0.16504660 -2.13023505
[11,] 0.88985948 -0.16504660
[12,] 0.82862347 0.88985948
[13,] 0.88538162 0.82862347
[14,] -0.16587450 0.88538162
[15,] -0.11406522 -0.16587450
[16,] -0.11853648 -0.11406522
[17,] -0.08208947 -0.11853648
[18,] -0.13741058 -0.08208947
[19,] -1.11583786 -0.13741058
[20,] 0.90768175 -1.11583786
[21,] 0.72971941 0.90768175
[22,] -0.11449957 0.72971941
[23,] 0.87645105 -0.11449957
[24,] -0.11524029 0.87645105
[25,] -0.30843892 -0.11524029
[26,] 1.87694564 -0.30843892
[27,] -0.19722133 1.87694564
[28,] -0.11573940 -0.19722133
[29,] 0.02700613 -0.11573940
[30,] -0.20829375 0.02700613
[31,] -0.13336923 -0.20829375
[32,] 0.82340791 -0.13336923
[33,] -0.16378396 0.82340791
[34,] 0.87776846 -0.16378396
[35,] 0.77407097 0.87776846
[36,] -0.13683309 0.77407097
[37,] 0.86510185 -0.13683309
[38,] -0.12694181 0.86510185
[39,] 0.83851494 -0.12694181
[40,] 0.89213843 0.83851494
[41,] -1.06958780 0.89213843
[42,] 0.79567110 -1.06958780
[43,] -0.08716859 0.79567110
[44,] 0.10053252 -0.08716859
[45,] -1.09196675 0.10053252
[46,] -0.07571236 -1.09196675
[47,] 0.91751425 -0.07571236
[48,] -1.21118854 0.91751425
[49,] -0.09549128 -1.21118854
[50,] -0.11047879 -0.09549128
[51,] -0.10976683 -0.11047879
[52,] -1.10065543 -0.10976683
[53,] -0.30713700 -1.10065543
[54,] -0.14775749 -0.30713700
[55,] -0.38377018 -0.14775749
[56,] 0.84997502 -0.38377018
[57,] -0.03573484 0.84997502
[58,] -1.10490734 -0.03573484
[59,] -2.26411269 -1.10490734
[60,] -0.38895101 -2.26411269
[61,] -1.00410272 -0.38895101
[62,] -1.13780110 -1.00410272
[63,] 0.79877606 -1.13780110
[64,] -1.23349832 0.79877606
[65,] 0.10548224 -1.23349832
[66,] 0.58603058 0.10548224
[67,] -0.10244218 0.58603058
[68,] 0.81999851 -0.10244218
[69,] -0.21082741 0.81999851
[70,] -0.10749563 -0.21082741
[71,] -0.19312312 -0.10749563
[72,] -1.13051781 -0.19312312
[73,] -0.33634935 -1.13051781
[74,] -0.07435163 -0.33634935
[75,] 0.68319093 -0.07435163
[76,] 0.69496419 0.68319093
[77,] -0.61053372 0.69496419
[78,] -0.17405569 -0.61053372
[79,] -0.12907610 -0.17405569
[80,] 0.71172232 -0.12907610
[81,] -0.23305373 0.71172232
[82,] 0.79879092 -0.23305373
[83,] -0.21186035 0.79879092
[84,] 0.80684965 -0.21186035
[85,] -0.04203423 0.80684965
[86,] 1.79892073 -0.04203423
[87,] 0.90231824 1.79892073
[88,] 0.87636228 0.90231824
[89,] -0.22804207 0.87636228
[90,] 0.89752063 -0.22804207
[91,] -0.08970193 0.89752063
[92,] 0.87788423 -0.08970193
[93,] -1.08287033 0.87788423
[94,] -0.33880869 -1.08287033
[95,] 1.87794965 -0.33880869
[96,] 0.90286755 1.87794965
[97,] -0.11144481 0.90286755
[98,] -0.13551880 -0.11144481
[99,] -1.17822355 -0.13551880
[100,] 0.80780526 -1.17822355
[101,] 0.68843688 0.80780526
[102,] 0.82972313 0.68843688
[103,] 0.86080858 0.82972313
[104,] 0.80893055 0.86080858
[105,] -1.21464663 0.80893055
[106,] -1.17436734 -1.21464663
[107,] -0.26291221 -1.17436734
[108,] -0.32634375 -0.26291221
[109,] 0.82206727 -0.32634375
[110,] -0.15076428 0.82206727
[111,] 0.83220196 -0.15076428
[112,] -1.12873146 0.83220196
[113,] -0.42727619 -1.12873146
[114,] -1.13546046 -0.42727619
[115,] -0.09065691 -1.13546046
[116,] -0.09225035 -0.09065691
[117,] -0.13496982 -0.09225035
[118,] 0.45464274 -0.13496982
[119,] -0.37846356 0.45464274
[120,] -1.14876102 -0.37846356
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.10592255 -0.10975799
2 -0.11162829 -0.10592255
3 -0.11911869 -0.11162829
4 -0.08970735 -0.11911869
5 -0.12589385 -0.08970735
6 -1.09971518 -0.12589385
7 -0.11369703 -1.09971518
8 -0.12998689 -0.11369703
9 -2.13023505 -0.12998689
10 -0.16504660 -2.13023505
11 0.88985948 -0.16504660
12 0.82862347 0.88985948
13 0.88538162 0.82862347
14 -0.16587450 0.88538162
15 -0.11406522 -0.16587450
16 -0.11853648 -0.11406522
17 -0.08208947 -0.11853648
18 -0.13741058 -0.08208947
19 -1.11583786 -0.13741058
20 0.90768175 -1.11583786
21 0.72971941 0.90768175
22 -0.11449957 0.72971941
23 0.87645105 -0.11449957
24 -0.11524029 0.87645105
25 -0.30843892 -0.11524029
26 1.87694564 -0.30843892
27 -0.19722133 1.87694564
28 -0.11573940 -0.19722133
29 0.02700613 -0.11573940
30 -0.20829375 0.02700613
31 -0.13336923 -0.20829375
32 0.82340791 -0.13336923
33 -0.16378396 0.82340791
34 0.87776846 -0.16378396
35 0.77407097 0.87776846
36 -0.13683309 0.77407097
37 0.86510185 -0.13683309
38 -0.12694181 0.86510185
39 0.83851494 -0.12694181
40 0.89213843 0.83851494
41 -1.06958780 0.89213843
42 0.79567110 -1.06958780
43 -0.08716859 0.79567110
44 0.10053252 -0.08716859
45 -1.09196675 0.10053252
46 -0.07571236 -1.09196675
47 0.91751425 -0.07571236
48 -1.21118854 0.91751425
49 -0.09549128 -1.21118854
50 -0.11047879 -0.09549128
51 -0.10976683 -0.11047879
52 -1.10065543 -0.10976683
53 -0.30713700 -1.10065543
54 -0.14775749 -0.30713700
55 -0.38377018 -0.14775749
56 0.84997502 -0.38377018
57 -0.03573484 0.84997502
58 -1.10490734 -0.03573484
59 -2.26411269 -1.10490734
60 -0.38895101 -2.26411269
61 -1.00410272 -0.38895101
62 -1.13780110 -1.00410272
63 0.79877606 -1.13780110
64 -1.23349832 0.79877606
65 0.10548224 -1.23349832
66 0.58603058 0.10548224
67 -0.10244218 0.58603058
68 0.81999851 -0.10244218
69 -0.21082741 0.81999851
70 -0.10749563 -0.21082741
71 -0.19312312 -0.10749563
72 -1.13051781 -0.19312312
73 -0.33634935 -1.13051781
74 -0.07435163 -0.33634935
75 0.68319093 -0.07435163
76 0.69496419 0.68319093
77 -0.61053372 0.69496419
78 -0.17405569 -0.61053372
79 -0.12907610 -0.17405569
80 0.71172232 -0.12907610
81 -0.23305373 0.71172232
82 0.79879092 -0.23305373
83 -0.21186035 0.79879092
84 0.80684965 -0.21186035
85 -0.04203423 0.80684965
86 1.79892073 -0.04203423
87 0.90231824 1.79892073
88 0.87636228 0.90231824
89 -0.22804207 0.87636228
90 0.89752063 -0.22804207
91 -0.08970193 0.89752063
92 0.87788423 -0.08970193
93 -1.08287033 0.87788423
94 -0.33880869 -1.08287033
95 1.87794965 -0.33880869
96 0.90286755 1.87794965
97 -0.11144481 0.90286755
98 -0.13551880 -0.11144481
99 -1.17822355 -0.13551880
100 0.80780526 -1.17822355
101 0.68843688 0.80780526
102 0.82972313 0.68843688
103 0.86080858 0.82972313
104 0.80893055 0.86080858
105 -1.21464663 0.80893055
106 -1.17436734 -1.21464663
107 -0.26291221 -1.17436734
108 -0.32634375 -0.26291221
109 0.82206727 -0.32634375
110 -0.15076428 0.82206727
111 0.83220196 -0.15076428
112 -1.12873146 0.83220196
113 -0.42727619 -1.12873146
114 -1.13546046 -0.42727619
115 -0.09065691 -1.13546046
116 -0.09225035 -0.09065691
117 -0.13496982 -0.09225035
118 0.45464274 -0.13496982
119 -0.37846356 0.45464274
120 -1.14876102 -0.37846356
> 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/wessaorg/rcomp/tmp/7torw1353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8szbx1353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xfye1353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10mke91353055832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11za491353055832.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/wessaorg/rcomp/tmp/12uy8d1353055833.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/wessaorg/rcomp/tmp/13aiw11353055833.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/wessaorg/rcomp/tmp/14ov191353055833.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/wessaorg/rcomp/tmp/15j5xk1353055833.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/wessaorg/rcomp/tmp/16rcoy1353055833.tab")
+ }
>
> try(system("convert tmp/12giu1353055832.ps tmp/12giu1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yuwt1353055832.ps tmp/2yuwt1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g3x01353055832.ps tmp/3g3x01353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tncc1353055832.ps tmp/4tncc1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/51mt71353055832.ps tmp/51mt71353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wk961353055832.ps tmp/6wk961353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/7torw1353055832.ps tmp/7torw1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/8szbx1353055832.ps tmp/8szbx1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xfye1353055832.ps tmp/9xfye1353055832.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mke91353055832.ps tmp/10mke91353055832.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.987 2.144 14.164