R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(239
+ ,202
+ ,12
+ ,26
+ ,503
+ ,171
+ ,39
+ ,293
+ ,598
+ ,299
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+ ,1843
+ ,11507
+ ,983
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+ ,871
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+ ,993
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+ ,1551
+ ,703
+ ,1119
+ ,10931
+ ,8889
+ ,652
+ ,1390)
+ ,dim=c(4
+ ,115)
+ ,dimnames=list(c('Totaal'
+ ,'TerugbetalingAanDeAandeelhouders'
+ ,'AanzuiveringVanVerliezen'
+ ,'Andere')
+ ,1:115))
> y <- array(NA,dim=c(4,115),dimnames=list(c('Totaal','TerugbetalingAanDeAandeelhouders','AanzuiveringVanVerliezen','Andere'),1:115))
> 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 TerugbetalingAanDeAandeelhouders AanzuiveringVanVerliezen Andere
1 239 202 12 26
2 503 171 39 293
3 598 299 146 154
4 2999 2857 85 58
5 1673 1231 87 354
6 14333 1843 11507 983
7 4438 4135 94 210
8 157 47 69 40
9 3126 2679 112 335
10 2379 1133 317 929
11 469 209 135 125
12 10171 1265 1640 7266
13 2698 2228 266 204
14 2381 1865 34 482
15 3136 919 52 2165
16 830 748 52 30
17 681 339 211 130
18 1730 871 497 362
19 3780 307 477 2996
20 1196 594 161 441
21 4870 1485 240 3144
22 3144 2732 15 398
23 1908 1695 56 157
24 5807 426 744 4637
25 324 228 65 31
26 337 300 19 18
27 1125 150 91 883
28 2121 1584 137 400
29 7910 118 7426 365
30 3551 1899 369 1283
31 1842 745 87 1011
32 175 100 50 25
33 2846 1844 97 905
34 5934 160 52 5722
35 2214 925 232 1056
36 11672 1864 427 9381
37 1012 183 63 765
38 222 72 100 50
39 1494 1107 204 183
40 1022 845 111 65
41 881 587 54 240
42 11267 9242 611 1414
43 1248 246 701 301
44 924 256 571 97
45 8451 4807 131 3512
46 2274 1993 164 117
47 1504 228 62 1214
48 8090 7235 294 561
49 2221 2089 21 111
50 305 144 7 154
51 971 465 296 210
52 850 326 45 479
53 1986 1314 208 464
54 3128 1238 1247 643
55 3571 2417 148 1006
56 2842 2435 249 159
57 1352 951 211 191
58 5806 4695 763 348
59 4049 1991 308 1749
60 19550 11173 561 7816
61 58941 22003 92 36845
62 1621 1312 210 99
63 1067 302 83 683
64 393 86 33 274
65 7059 6891 38 130
66 7278 1673 5195 410
67 1433 592 160 682
68 2410 2285 35 90
69 902 420 177 305
70 3679 3542 39 98
71 607 211 17 380
72 4527 1552 278 2697
73 2352 1653 13 686
74 524 111 339 74
75 5784 5569 63 153
76 11475 969 10056 450
77 2940 499 1367 1074
78 36980 473 35687 820
79 1576 489 86 1002
80 607 353 21 232
81 1190 432 296 463
82 1731 681 247 804
83 617 120 306 191
84 6107 3067 1179 1860
85 3524 2863 66 595
86 1432 94 52 1286
87 1150 560 184 406
88 879 585 84 210
89 7430 117 7171 143
90 3404 169 478 2756
91 4945 642 115 4188
92 602 420 81 101
93 3590 2114 437 1039
94 5262 4200 145 917
95 3349 2550 106 694
96 44336 38503 1757 4075
97 947 385 13 548
98 1311 263 117 932
99 1006 588 331 87
100 6224 5858 79 287
101 6890 786 5853 251
102 3014 1114 391 1510
103 3288 1782 82 1423
104 1787 551 1076 160
105 12518 993 2264 9261
106 5500 4486 709 305
107 27519 27188 215 116
108 14607 4179 2663 7766
109 815 594 52 169
110 851 427 95 330
111 1152 869 123 160
112 3179 949 88 2141
113 25090 2163 22199 728
114 3373 1551 703 1119
115 10931 8889 652 1390
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TerugbetalingAanDeAandeelhouders
-0.07928 1.00001
AanzuiveringVanVerliezen Andere
1.00000 1.00002
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.14830 -0.04004 0.04667 0.07128 1.07769
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.928e-02 6.370e-02 -1.245 0.216
TerugbetalingAanDeAandeelhouders 1.000e+00 1.208e-05 82770.987 <2e-16 ***
AanzuiveringVanVerliezen 1.000e+00 1.303e-05 76762.128 <2e-16 ***
Andere 1.000e+00 1.577e-05 63418.147 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5873 on 111 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 7.782e+09 on 3 and 111 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,] 0.53748879 0.92502243 0.46251121
[2,] 0.97485035 0.05029930 0.02514965
[3,] 0.95070723 0.09858553 0.04929277
[4,] 0.96213420 0.07573160 0.03786580
[5,] 0.93491464 0.13017071 0.06508536
[6,] 0.92508449 0.14983102 0.07491551
[7,] 0.89607234 0.20785532 0.10392766
[8,] 0.85497925 0.29004150 0.14502075
[9,] 0.79778893 0.40442215 0.20221107
[10,] 0.73139816 0.53720368 0.26860184
[11,] 0.81124810 0.37750380 0.18875190
[12,] 0.75166190 0.49667620 0.24833810
[13,] 0.68886633 0.62226735 0.31113367
[14,] 0.61776729 0.76446542 0.38223271
[15,] 0.73810856 0.52378288 0.26189144
[16,] 0.75610066 0.48779869 0.24389934
[17,] 0.70122709 0.59754583 0.29877291
[18,] 0.65062881 0.69874237 0.34937119
[19,] 0.58694546 0.82610908 0.41305454
[20,] 0.52075621 0.95848759 0.47924379
[21,] 0.59702711 0.80594578 0.40297289
[22,] 0.53415634 0.93168733 0.46584366
[23,] 0.56709659 0.86580682 0.43290341
[24,] 0.50721552 0.98556897 0.49278448
[25,] 0.64458363 0.71083275 0.35541637
[26,] 0.58947331 0.82105337 0.41052669
[27,] 0.53349126 0.93301748 0.46650874
[28,] 0.48267172 0.96534344 0.51732828
[29,] 0.59910707 0.80178586 0.40089293
[30,] 0.54051296 0.91897408 0.45948704
[31,] 0.61884516 0.76230969 0.38115484
[32,] 0.57014768 0.85970464 0.42985232
[33,] 0.51243844 0.97512312 0.48756156
[34,] 0.62583204 0.74833592 0.37416796
[35,] 0.57377030 0.85245941 0.42622970
[36,] 0.65363840 0.69272320 0.34636160
[37,] 0.60326179 0.79347641 0.39673821
[38,] 0.55104989 0.89790023 0.44895011
[39,] 0.68464348 0.63071304 0.31535652
[40,] 0.63422339 0.73155322 0.36577661
[41,] 0.58328180 0.83343641 0.41671820
[42,] 0.53272080 0.93455840 0.46727920
[43,] 0.47837666 0.95675331 0.52162334
[44,] 0.42623012 0.85246024 0.57376988
[45,] 0.37524695 0.75049391 0.62475305
[46,] 0.32651146 0.65302292 0.67348854
[47,] 0.27989614 0.55979228 0.72010386
[48,] 0.23720047 0.47440095 0.76279953
[49,] 0.19751618 0.39503236 0.80248382
[50,] 0.26158277 0.52316553 0.73841723
[51,] 0.33803109 0.67606218 0.66196891
[52,] 0.29073154 0.58146309 0.70926846
[53,] 0.39704058 0.79408115 0.60295942
[54,] 0.34782613 0.69565227 0.65217387
[55,] 0.30017487 0.60034974 0.69982513
[56,] 0.25568573 0.51137146 0.74431427
[57,] 0.32980642 0.65961284 0.67019358
[58,] 0.28335180 0.56670359 0.71664820
[59,] 0.24042354 0.48084707 0.75957646
[60,] 0.20169903 0.40339805 0.79830097
[61,] 0.26374480 0.52748961 0.73625520
[62,] 0.22121591 0.44243182 0.77878409
[63,] 0.18319468 0.36638936 0.81680532
[64,] 0.14912716 0.29825432 0.85087284
[65,] 0.19744375 0.39488751 0.80255625
[66,] 0.16104993 0.32209986 0.83895007
[67,] 0.12933672 0.25867344 0.87066328
[68,] 0.10248075 0.20496150 0.89751925
[69,] 0.14052465 0.28104930 0.85947535
[70,] 0.11381127 0.22762255 0.88618873
[71,] 0.08876251 0.17752502 0.91123749
[72,] 0.07312331 0.14624662 0.92687669
[73,] 0.10685550 0.21371100 0.89314450
[74,] 0.17462033 0.34924066 0.82537967
[75,] 0.23119327 0.46238653 0.76880673
[76,] 0.30502980 0.61005960 0.69497020
[77,] 0.25353171 0.50706341 0.74646829
[78,] 0.35429797 0.70859594 0.64570203
[79,] 0.29760702 0.59521404 0.70239298
[80,] 0.24543493 0.49086985 0.75456507
[81,] 0.19816304 0.39632608 0.80183696
[82,] 0.15652059 0.31304117 0.84347941
[83,] 0.20679040 0.41358080 0.79320960
[84,] 0.31978911 0.63957822 0.68021089
[85,] 0.26520519 0.53041039 0.73479481
[86,] 0.21216084 0.42432167 0.78783916
[87,] 0.16511912 0.33023824 0.83488088
[88,] 0.12455883 0.24911766 0.87544117
[89,] 0.17773643 0.35547286 0.82226357
[90,] 0.19601297 0.39202594 0.80398703
[91,] 0.31141245 0.62282491 0.68858755
[92,] 0.41365385 0.82730769 0.58634615
[93,] 0.33250682 0.66501365 0.66749318
[94,] 0.25663061 0.51326122 0.74336939
[95,] 0.18922878 0.37845755 0.81077122
[96,] 0.29562199 0.59124399 0.70437801
[97,] 0.45476621 0.90953242 0.54523379
[98,] 0.34850220 0.69700441 0.65149780
[99,] 0.31560908 0.63121816 0.68439092
[100,] 0.21390926 0.42781852 0.78609074
[101,] 0.13063009 0.26126019 0.86936991
[102,] 0.47054682 0.94109365 0.52945318
> postscript(file="/var/fisher/rcomp/tmp/12thk1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ngdw1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3yf3h1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4h22x1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5m0w01353056031.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 = 115
Frequency = 1
1 2 3 4 5
-0.9237165819 0.0706661397 -0.9279883619 -0.9560898826 1.0566311830
6 7 8 9 10
0.0117012508 -0.9746520739 1.0776938777 0.0398154605 0.0445481955
11 12 13 14 15
0.0737466341 -0.1004874628 0.0477577072 0.0463740063 0.0201905761
16 17 18 19 20
0.0696313154 1.0719349672 0.0598754186 0.0081185024 0.0621059567
21 22 23 24 25
0.9913424902 -0.9620113051 0.0555631814 -0.0302890499 0.0757538170
26 27 28 29 30
0.0752834199 1.0577055471 0.0513162940 1.0543670772 0.0274877647
31 32 33 34 35
-0.9521904361 0.0774373684 0.0370990980 -0.0497902021 1.0443730135
36 37 38 39 40
-0.1520466695 1.0599923721 0.0771107655 0.0616574363 1.0675803913
41 42 43 44 45
0.0668746167 -0.0630748139 0.0682245824 0.0729058245 0.9439690602
46 47 48 49 50
0.0526904796 0.0494895719 -0.0196543809 0.0519810796 0.0741397226
51 52 53 54 55
0.0684865994 0.0646836143 0.0529522606 0.0477231921 0.0279496703
56 57 58 59 60
-0.9536645211 -0.9366832375 0.0142486944 1.0161741357 -0.2280563649
61 62 63 64 65
-0.0002572619 0.0610772759 -0.9396405520 0.0721093503 -0.0054692647
66 67 68 69 70
0.0395417511 -0.9432200343 0.0500921164 0.0671579998 0.0349873002
71 72 73 74 75
-0.9316949305 0.0003948101 0.0444035761 0.0756189657 -0.9903415737
76 77 78 79 80
0.0369216356 0.0466740195 -0.0185964615 -0.9489501329 1.0698980086
81 82 83 84 85
-0.9367400192 -0.9471660927 0.0729824500 0.9991310901 0.0319533610
86 87 88 89 90
0.0495018403 0.0632389941 0.0675022987 -0.9401620109 1.0150838944
91 92 93 94 95
-0.0215766090 0.0718873726 0.0302133225 0.0087705285 -0.9666132243
96 97 98 99 100
0.5281672929 1.0625175115 -0.9447776878 0.0696855461 0.0032194993
101 102 103 104 105
0.0522346233 -0.9682819280 1.0263630339 0.0669576040 -0.1428563706
106 107 108 109 110
0.0177961819 -0.2464231692 -1.1482988550 0.0683723590 -0.9333100837
111 112 113 114 115
0.0651610103 1.0202927712 -0.0086216435 0.0345668335 -0.0584373110
> postscript(file="/var/fisher/rcomp/tmp/6a5tg1353056031.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 = 115
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.9237165819 NA
1 0.0706661397 -0.9237165819
2 -0.9279883619 0.0706661397
3 -0.9560898826 -0.9279883619
4 1.0566311830 -0.9560898826
5 0.0117012508 1.0566311830
6 -0.9746520739 0.0117012508
7 1.0776938777 -0.9746520739
8 0.0398154605 1.0776938777
9 0.0445481955 0.0398154605
10 0.0737466341 0.0445481955
11 -0.1004874628 0.0737466341
12 0.0477577072 -0.1004874628
13 0.0463740063 0.0477577072
14 0.0201905761 0.0463740063
15 0.0696313154 0.0201905761
16 1.0719349672 0.0696313154
17 0.0598754186 1.0719349672
18 0.0081185024 0.0598754186
19 0.0621059567 0.0081185024
20 0.9913424902 0.0621059567
21 -0.9620113051 0.9913424902
22 0.0555631814 -0.9620113051
23 -0.0302890499 0.0555631814
24 0.0757538170 -0.0302890499
25 0.0752834199 0.0757538170
26 1.0577055471 0.0752834199
27 0.0513162940 1.0577055471
28 1.0543670772 0.0513162940
29 0.0274877647 1.0543670772
30 -0.9521904361 0.0274877647
31 0.0774373684 -0.9521904361
32 0.0370990980 0.0774373684
33 -0.0497902021 0.0370990980
34 1.0443730135 -0.0497902021
35 -0.1520466695 1.0443730135
36 1.0599923721 -0.1520466695
37 0.0771107655 1.0599923721
38 0.0616574363 0.0771107655
39 1.0675803913 0.0616574363
40 0.0668746167 1.0675803913
41 -0.0630748139 0.0668746167
42 0.0682245824 -0.0630748139
43 0.0729058245 0.0682245824
44 0.9439690602 0.0729058245
45 0.0526904796 0.9439690602
46 0.0494895719 0.0526904796
47 -0.0196543809 0.0494895719
48 0.0519810796 -0.0196543809
49 0.0741397226 0.0519810796
50 0.0684865994 0.0741397226
51 0.0646836143 0.0684865994
52 0.0529522606 0.0646836143
53 0.0477231921 0.0529522606
54 0.0279496703 0.0477231921
55 -0.9536645211 0.0279496703
56 -0.9366832375 -0.9536645211
57 0.0142486944 -0.9366832375
58 1.0161741357 0.0142486944
59 -0.2280563649 1.0161741357
60 -0.0002572619 -0.2280563649
61 0.0610772759 -0.0002572619
62 -0.9396405520 0.0610772759
63 0.0721093503 -0.9396405520
64 -0.0054692647 0.0721093503
65 0.0395417511 -0.0054692647
66 -0.9432200343 0.0395417511
67 0.0500921164 -0.9432200343
68 0.0671579998 0.0500921164
69 0.0349873002 0.0671579998
70 -0.9316949305 0.0349873002
71 0.0003948101 -0.9316949305
72 0.0444035761 0.0003948101
73 0.0756189657 0.0444035761
74 -0.9903415737 0.0756189657
75 0.0369216356 -0.9903415737
76 0.0466740195 0.0369216356
77 -0.0185964615 0.0466740195
78 -0.9489501329 -0.0185964615
79 1.0698980086 -0.9489501329
80 -0.9367400192 1.0698980086
81 -0.9471660927 -0.9367400192
82 0.0729824500 -0.9471660927
83 0.9991310901 0.0729824500
84 0.0319533610 0.9991310901
85 0.0495018403 0.0319533610
86 0.0632389941 0.0495018403
87 0.0675022987 0.0632389941
88 -0.9401620109 0.0675022987
89 1.0150838944 -0.9401620109
90 -0.0215766090 1.0150838944
91 0.0718873726 -0.0215766090
92 0.0302133225 0.0718873726
93 0.0087705285 0.0302133225
94 -0.9666132243 0.0087705285
95 0.5281672929 -0.9666132243
96 1.0625175115 0.5281672929
97 -0.9447776878 1.0625175115
98 0.0696855461 -0.9447776878
99 0.0032194993 0.0696855461
100 0.0522346233 0.0032194993
101 -0.9682819280 0.0522346233
102 1.0263630339 -0.9682819280
103 0.0669576040 1.0263630339
104 -0.1428563706 0.0669576040
105 0.0177961819 -0.1428563706
106 -0.2464231692 0.0177961819
107 -1.1482988550 -0.2464231692
108 0.0683723590 -1.1482988550
109 -0.9333100837 0.0683723590
110 0.0651610103 -0.9333100837
111 1.0202927712 0.0651610103
112 -0.0086216435 1.0202927712
113 0.0345668335 -0.0086216435
114 -0.0584373110 0.0345668335
115 NA -0.0584373110
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0706661397 -0.9237165819
[2,] -0.9279883619 0.0706661397
[3,] -0.9560898826 -0.9279883619
[4,] 1.0566311830 -0.9560898826
[5,] 0.0117012508 1.0566311830
[6,] -0.9746520739 0.0117012508
[7,] 1.0776938777 -0.9746520739
[8,] 0.0398154605 1.0776938777
[9,] 0.0445481955 0.0398154605
[10,] 0.0737466341 0.0445481955
[11,] -0.1004874628 0.0737466341
[12,] 0.0477577072 -0.1004874628
[13,] 0.0463740063 0.0477577072
[14,] 0.0201905761 0.0463740063
[15,] 0.0696313154 0.0201905761
[16,] 1.0719349672 0.0696313154
[17,] 0.0598754186 1.0719349672
[18,] 0.0081185024 0.0598754186
[19,] 0.0621059567 0.0081185024
[20,] 0.9913424902 0.0621059567
[21,] -0.9620113051 0.9913424902
[22,] 0.0555631814 -0.9620113051
[23,] -0.0302890499 0.0555631814
[24,] 0.0757538170 -0.0302890499
[25,] 0.0752834199 0.0757538170
[26,] 1.0577055471 0.0752834199
[27,] 0.0513162940 1.0577055471
[28,] 1.0543670772 0.0513162940
[29,] 0.0274877647 1.0543670772
[30,] -0.9521904361 0.0274877647
[31,] 0.0774373684 -0.9521904361
[32,] 0.0370990980 0.0774373684
[33,] -0.0497902021 0.0370990980
[34,] 1.0443730135 -0.0497902021
[35,] -0.1520466695 1.0443730135
[36,] 1.0599923721 -0.1520466695
[37,] 0.0771107655 1.0599923721
[38,] 0.0616574363 0.0771107655
[39,] 1.0675803913 0.0616574363
[40,] 0.0668746167 1.0675803913
[41,] -0.0630748139 0.0668746167
[42,] 0.0682245824 -0.0630748139
[43,] 0.0729058245 0.0682245824
[44,] 0.9439690602 0.0729058245
[45,] 0.0526904796 0.9439690602
[46,] 0.0494895719 0.0526904796
[47,] -0.0196543809 0.0494895719
[48,] 0.0519810796 -0.0196543809
[49,] 0.0741397226 0.0519810796
[50,] 0.0684865994 0.0741397226
[51,] 0.0646836143 0.0684865994
[52,] 0.0529522606 0.0646836143
[53,] 0.0477231921 0.0529522606
[54,] 0.0279496703 0.0477231921
[55,] -0.9536645211 0.0279496703
[56,] -0.9366832375 -0.9536645211
[57,] 0.0142486944 -0.9366832375
[58,] 1.0161741357 0.0142486944
[59,] -0.2280563649 1.0161741357
[60,] -0.0002572619 -0.2280563649
[61,] 0.0610772759 -0.0002572619
[62,] -0.9396405520 0.0610772759
[63,] 0.0721093503 -0.9396405520
[64,] -0.0054692647 0.0721093503
[65,] 0.0395417511 -0.0054692647
[66,] -0.9432200343 0.0395417511
[67,] 0.0500921164 -0.9432200343
[68,] 0.0671579998 0.0500921164
[69,] 0.0349873002 0.0671579998
[70,] -0.9316949305 0.0349873002
[71,] 0.0003948101 -0.9316949305
[72,] 0.0444035761 0.0003948101
[73,] 0.0756189657 0.0444035761
[74,] -0.9903415737 0.0756189657
[75,] 0.0369216356 -0.9903415737
[76,] 0.0466740195 0.0369216356
[77,] -0.0185964615 0.0466740195
[78,] -0.9489501329 -0.0185964615
[79,] 1.0698980086 -0.9489501329
[80,] -0.9367400192 1.0698980086
[81,] -0.9471660927 -0.9367400192
[82,] 0.0729824500 -0.9471660927
[83,] 0.9991310901 0.0729824500
[84,] 0.0319533610 0.9991310901
[85,] 0.0495018403 0.0319533610
[86,] 0.0632389941 0.0495018403
[87,] 0.0675022987 0.0632389941
[88,] -0.9401620109 0.0675022987
[89,] 1.0150838944 -0.9401620109
[90,] -0.0215766090 1.0150838944
[91,] 0.0718873726 -0.0215766090
[92,] 0.0302133225 0.0718873726
[93,] 0.0087705285 0.0302133225
[94,] -0.9666132243 0.0087705285
[95,] 0.5281672929 -0.9666132243
[96,] 1.0625175115 0.5281672929
[97,] -0.9447776878 1.0625175115
[98,] 0.0696855461 -0.9447776878
[99,] 0.0032194993 0.0696855461
[100,] 0.0522346233 0.0032194993
[101,] -0.9682819280 0.0522346233
[102,] 1.0263630339 -0.9682819280
[103,] 0.0669576040 1.0263630339
[104,] -0.1428563706 0.0669576040
[105,] 0.0177961819 -0.1428563706
[106,] -0.2464231692 0.0177961819
[107,] -1.1482988550 -0.2464231692
[108,] 0.0683723590 -1.1482988550
[109,] -0.9333100837 0.0683723590
[110,] 0.0651610103 -0.9333100837
[111,] 1.0202927712 0.0651610103
[112,] -0.0086216435 1.0202927712
[113,] 0.0345668335 -0.0086216435
[114,] -0.0584373110 0.0345668335
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0706661397 -0.9237165819
2 -0.9279883619 0.0706661397
3 -0.9560898826 -0.9279883619
4 1.0566311830 -0.9560898826
5 0.0117012508 1.0566311830
6 -0.9746520739 0.0117012508
7 1.0776938777 -0.9746520739
8 0.0398154605 1.0776938777
9 0.0445481955 0.0398154605
10 0.0737466341 0.0445481955
11 -0.1004874628 0.0737466341
12 0.0477577072 -0.1004874628
13 0.0463740063 0.0477577072
14 0.0201905761 0.0463740063
15 0.0696313154 0.0201905761
16 1.0719349672 0.0696313154
17 0.0598754186 1.0719349672
18 0.0081185024 0.0598754186
19 0.0621059567 0.0081185024
20 0.9913424902 0.0621059567
21 -0.9620113051 0.9913424902
22 0.0555631814 -0.9620113051
23 -0.0302890499 0.0555631814
24 0.0757538170 -0.0302890499
25 0.0752834199 0.0757538170
26 1.0577055471 0.0752834199
27 0.0513162940 1.0577055471
28 1.0543670772 0.0513162940
29 0.0274877647 1.0543670772
30 -0.9521904361 0.0274877647
31 0.0774373684 -0.9521904361
32 0.0370990980 0.0774373684
33 -0.0497902021 0.0370990980
34 1.0443730135 -0.0497902021
35 -0.1520466695 1.0443730135
36 1.0599923721 -0.1520466695
37 0.0771107655 1.0599923721
38 0.0616574363 0.0771107655
39 1.0675803913 0.0616574363
40 0.0668746167 1.0675803913
41 -0.0630748139 0.0668746167
42 0.0682245824 -0.0630748139
43 0.0729058245 0.0682245824
44 0.9439690602 0.0729058245
45 0.0526904796 0.9439690602
46 0.0494895719 0.0526904796
47 -0.0196543809 0.0494895719
48 0.0519810796 -0.0196543809
49 0.0741397226 0.0519810796
50 0.0684865994 0.0741397226
51 0.0646836143 0.0684865994
52 0.0529522606 0.0646836143
53 0.0477231921 0.0529522606
54 0.0279496703 0.0477231921
55 -0.9536645211 0.0279496703
56 -0.9366832375 -0.9536645211
57 0.0142486944 -0.9366832375
58 1.0161741357 0.0142486944
59 -0.2280563649 1.0161741357
60 -0.0002572619 -0.2280563649
61 0.0610772759 -0.0002572619
62 -0.9396405520 0.0610772759
63 0.0721093503 -0.9396405520
64 -0.0054692647 0.0721093503
65 0.0395417511 -0.0054692647
66 -0.9432200343 0.0395417511
67 0.0500921164 -0.9432200343
68 0.0671579998 0.0500921164
69 0.0349873002 0.0671579998
70 -0.9316949305 0.0349873002
71 0.0003948101 -0.9316949305
72 0.0444035761 0.0003948101
73 0.0756189657 0.0444035761
74 -0.9903415737 0.0756189657
75 0.0369216356 -0.9903415737
76 0.0466740195 0.0369216356
77 -0.0185964615 0.0466740195
78 -0.9489501329 -0.0185964615
79 1.0698980086 -0.9489501329
80 -0.9367400192 1.0698980086
81 -0.9471660927 -0.9367400192
82 0.0729824500 -0.9471660927
83 0.9991310901 0.0729824500
84 0.0319533610 0.9991310901
85 0.0495018403 0.0319533610
86 0.0632389941 0.0495018403
87 0.0675022987 0.0632389941
88 -0.9401620109 0.0675022987
89 1.0150838944 -0.9401620109
90 -0.0215766090 1.0150838944
91 0.0718873726 -0.0215766090
92 0.0302133225 0.0718873726
93 0.0087705285 0.0302133225
94 -0.9666132243 0.0087705285
95 0.5281672929 -0.9666132243
96 1.0625175115 0.5281672929
97 -0.9447776878 1.0625175115
98 0.0696855461 -0.9447776878
99 0.0032194993 0.0696855461
100 0.0522346233 0.0032194993
101 -0.9682819280 0.0522346233
102 1.0263630339 -0.9682819280
103 0.0669576040 1.0263630339
104 -0.1428563706 0.0669576040
105 0.0177961819 -0.1428563706
106 -0.2464231692 0.0177961819
107 -1.1482988550 -0.2464231692
108 0.0683723590 -1.1482988550
109 -0.9333100837 0.0683723590
110 0.0651610103 -0.9333100837
111 1.0202927712 0.0651610103
112 -0.0086216435 1.0202927712
113 0.0345668335 -0.0086216435
114 -0.0584373110 0.0345668335
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7ely11353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8tozo1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/98p751353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10y19w1353056031.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11zy3q1353056031.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12bpj81353056031.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13n2tz1353056031.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14uioq1353056031.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15q87p1353056031.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16lowp1353056031.tab")
+ }
>
> try(system("convert tmp/12thk1353056031.ps tmp/12thk1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ngdw1353056031.ps tmp/2ngdw1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yf3h1353056031.ps tmp/3yf3h1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h22x1353056031.ps tmp/4h22x1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m0w01353056031.ps tmp/5m0w01353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a5tg1353056031.ps tmp/6a5tg1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ely11353056031.ps tmp/7ely11353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tozo1353056031.ps tmp/8tozo1353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/98p751353056031.ps tmp/98p751353056031.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y19w1353056031.ps tmp/10y19w1353056031.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.562 1.425 8.978