R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(140824
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+ ,34)
+ ,dim=c(4
+ ,164)
+ ,dimnames=list(c('Writing'
+ ,'Pageviews'
+ ,'TimeRFC'
+ ,'Reviews')
+ ,1:164))
> y <- array(NA,dim=c(4,164),dimnames=list(c('Writing','Pageviews','TimeRFC','Reviews'),1:164))
> 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
> 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
Writing Pageviews TimeRFC Reviews
1 140824 1818 279055 42
2 110459 1439 212450 38
3 105079 2059 233939 46
4 112098 2733 222117 42
5 43929 1399 189911 30
6 76173 631 70849 35
7 187326 5460 605767 40
8 22807 381 33186 18
9 144408 2150 227332 38
10 66485 2042 267925 37
11 79089 2541 372083 46
12 81625 2429 276291 60
13 68788 2100 212638 37
14 103297 3020 368577 55
15 69446 2265 269455 44
16 114948 5148 400733 63
17 167949 2363 335567 40
18 125081 3564 432711 43
19 125818 1516 185822 32
20 136588 2398 267365 52
21 112431 2546 279428 49
22 103037 3253 527853 41
23 82317 1761 227252 25
24 118906 1787 200004 57
25 83515 3792 257139 45
26 104581 3108 270941 42
27 103129 3230 324969 45
28 83243 2348 329962 43
29 37110 1826 196752 36
30 113344 3257 399591 45
31 139165 2692 327660 50
32 86652 2187 269239 50
33 112302 2593 397689 51
34 69652 1293 130446 42
35 119442 3567 430118 44
36 69867 2764 273950 42
37 101629 3755 428077 44
38 70168 2075 254312 40
39 31081 995 120351 17
40 103925 3750 395658 43
41 92622 3413 345875 41
42 79011 2053 216827 41
43 93487 2038 234780 40
44 64520 1825 182485 49
45 93473 2879 176781 52
46 114360 5572 459455 42
47 33032 918 78800 26
48 96125 2685 255072 59
49 151911 4145 368086 50
50 89256 2841 230299 50
51 95676 2175 244782 47
52 5950 496 24188 4
53 149695 2699 400109 51
54 32551 744 65029 18
55 31701 1161 101097 14
56 100087 3333 309810 41
57 169707 2970 375638 61
58 150491 3970 367230 40
59 120192 2919 387748 44
60 95893 2399 280106 40
61 151715 4121 400971 51
62 176225 3330 322780 29
63 59900 3132 291391 43
64 104767 2868 295075 42
65 114799 1778 280018 41
66 72128 2109 267432 30
67 143592 2148 217181 39
68 89626 3009 258166 51
69 131072 2582 270167 40
70 126817 1737 182961 29
71 81351 2680 256967 47
72 22618 893 73566 23
73 88977 2395 272823 48
74 92059 2197 229056 38
75 81897 2227 229851 42
76 108146 2370 371391 46
77 126372 3231 398312 40
78 249771 1978 220419 45
79 71154 2516 231884 42
80 71571 2147 219381 41
81 55918 2150 206169 37
82 160141 4229 483074 47
83 38692 1380 146100 26
84 102812 2449 295224 48
85 56622 870 80953 8
86 15986 2700 217384 27
87 123534 1574 179344 38
88 108535 4046 415550 41
89 93879 3319 393492 61
90 144551 3098 180679 45
91 56750 2615 299505 41
92 127654 2404 292260 42
93 65594 1932 199481 35
94 59938 3147 282361 36
95 146975 2598 329281 40
96 165904 2108 234577 40
97 169265 2193 297995 38
98 183500 2506 352078 43
99 165986 4198 416463 65
100 184923 4165 429565 33
101 140358 2842 297080 51
102 149959 2562 331792 45
103 57224 2497 237763 36
104 43750 602 43287 19
105 48029 2579 238089 25
106 104978 2591 263322 44
107 100046 2957 302082 45
108 101047 2786 321797 44
109 197426 1477 193926 35
110 160902 3358 175737 46
111 147172 2107 354041 44
112 109432 2338 303566 45
113 1168 400 23668 1
114 83248 2233 196743 40
115 25162 530 61857 11
116 45724 2033 217543 51
117 110529 3246 440711 38
118 855 387 21054 0
119 101382 2137 252805 30
120 14116 492 31961 8
121 89506 3838 360436 43
122 135356 2193 251948 48
123 116066 1796 187320 49
124 144244 1907 180842 32
125 8773 568 38214 8
126 102153 2644 289276 43
127 117440 2819 358276 52
128 104128 1464 211775 53
129 134238 3946 447335 49
130 134047 2554 348017 48
131 279488 3506 441946 56
132 79756 1552 215177 45
133 66089 1476 140328 40
134 102070 3101 318037 48
135 146760 4541 466139 50
136 154771 1872 162279 43
137 165933 4469 417354 46
138 64593 2113 178322 40
139 92280 2046 292443 45
140 67150 2564 283913 46
141 128692 2209 253950 37
142 124089 4112 387072 45
143 125386 2340 246963 39
144 37238 2035 173260 21
145 140015 3241 346748 50
146 150047 1991 178402 55
147 154451 2864 277892 40
148 156349 2749 314070 48
149 0 2 1 0
150 6023 207 14688 0
151 0 5 98 0
152 0 8 455 0
153 0 0 0 0
154 0 0 0 0
155 84601 2449 291847 46
156 68946 3497 415839 52
157 0 0 0 0
158 0 4 203 0
159 1644 151 7199 0
160 6179 475 46660 5
161 3926 141 17547 1
162 52789 1145 121550 48
163 0 29 969 0
164 100350 2080 242774 34
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pageviews TimeRFC Reviews
4158.0590 -1.0801 0.1802 1287.0452
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-73299 -21318 -6024 15724 150111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4158.05902 7282.77488 0.571 0.56884
Pageviews -1.08009 5.94635 -0.182 0.85610
TimeRFC 0.18021 0.05555 3.244 0.00144 **
Reviews 1287.04520 276.92734 4.648 6.98e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35140 on 160 degrees of freedom
Multiple R-squared: 0.5549, Adjusted R-squared: 0.5465
F-statistic: 66.48 on 3 and 160 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,] 0.2843643948 0.5687287897 0.7156356052
[2,] 0.1848370537 0.3696741075 0.8151629463
[3,] 0.2673477127 0.5346954254 0.7326522873
[4,] 0.3525439442 0.7050878883 0.6474560558
[5,] 0.4986969589 0.9973939179 0.5013030411
[6,] 0.5843517747 0.8312964507 0.4156482253
[7,] 0.5357238464 0.9285523072 0.4642761536
[8,] 0.4715759644 0.9431519287 0.5284240356
[9,] 0.4438498154 0.8876996307 0.5561501846
[10,] 0.4187831025 0.8375662050 0.5812168975
[11,] 0.5498485686 0.9003028628 0.4501514314
[12,] 0.4766759102 0.9533518205 0.5233240898
[13,] 0.5091311237 0.9817377525 0.4908688763
[14,] 0.5122035250 0.9755929500 0.4877964750
[15,] 0.4404180923 0.8808361846 0.5595819077
[16,] 0.4774663713 0.9549327426 0.5225336287
[17,] 0.4117300832 0.8234601663 0.5882699168
[18,] 0.3729029788 0.7458059577 0.6270970212
[19,] 0.3372010989 0.6744021978 0.6627989011
[20,] 0.2775160698 0.5550321396 0.7224839302
[21,] 0.2282779165 0.4565558330 0.7717220835
[22,] 0.2121375081 0.4242750163 0.7878624919
[23,] 0.2657004132 0.5314008264 0.7342995868
[24,] 0.2199570118 0.4399140236 0.7800429882
[25,] 0.2001568284 0.4003136569 0.7998431716
[26,] 0.1723401711 0.3446803422 0.8276598289
[27,] 0.1426002472 0.2852004945 0.8573997528
[28,] 0.1129507858 0.2259015717 0.8870492142
[29,] 0.0883264887 0.1766529775 0.9116735113
[30,] 0.0852441423 0.1704882845 0.9147558577
[31,] 0.0750722667 0.1501445334 0.9249277333
[32,] 0.0666986457 0.1333972914 0.9333013543
[33,] 0.0588154168 0.1176308337 0.9411845832
[34,] 0.0468017900 0.0936035801 0.9531982100
[35,] 0.0372713345 0.0745426689 0.9627286655
[36,] 0.0280439953 0.0560879907 0.9719560047
[37,] 0.0203955562 0.0407911123 0.9796044438
[38,] 0.0180537554 0.0361075109 0.9819462446
[39,] 0.0129604827 0.0259209654 0.9870395173
[40,] 0.0095878703 0.0191757407 0.9904121297
[41,] 0.0076179319 0.0152358638 0.9923820681
[42,] 0.0057629713 0.0115259426 0.9942370287
[43,] 0.0061060015 0.0122120030 0.9938939985
[44,] 0.0043756189 0.0087512378 0.9956243811
[45,] 0.0030156558 0.0060313116 0.9969843442
[46,] 0.0021948802 0.0043897604 0.9978051198
[47,] 0.0018147442 0.0036294884 0.9981852558
[48,] 0.0012059336 0.0024118672 0.9987940664
[49,] 0.0007989884 0.0015979767 0.9992010116
[50,] 0.0005213281 0.0010426562 0.9994786719
[51,] 0.0005655253 0.0011310506 0.9994344747
[52,] 0.0007507083 0.0015014166 0.9992492917
[53,] 0.0004917446 0.0009834892 0.9995082554
[54,] 0.0003176999 0.0006353997 0.9996823001
[55,] 0.0002508413 0.0005016825 0.9997491587
[56,] 0.0025227152 0.0050454304 0.9974772848
[57,] 0.0035813397 0.0071626793 0.9964186603
[58,] 0.0024967473 0.0049934945 0.9975032527
[59,] 0.0018615893 0.0037231785 0.9981384107
[60,] 0.0013789263 0.0027578526 0.9986210737
[61,] 0.0028952736 0.0057905473 0.9971047264
[62,] 0.0022848848 0.0045697695 0.9977151152
[63,] 0.0022683009 0.0045366019 0.9977316991
[64,] 0.0041169161 0.0082338321 0.9958830839
[65,] 0.0034771675 0.0069543350 0.9965228325
[66,] 0.0029880452 0.0059760904 0.9970119548
[67,] 0.0024027656 0.0048055313 0.9975972344
[68,] 0.0016745469 0.0033490938 0.9983254531
[69,] 0.0012283291 0.0024566582 0.9987716709
[70,] 0.0009284204 0.0018568408 0.9990715796
[71,] 0.0006283686 0.0012567373 0.9993716314
[72,] 0.1480581713 0.2961163425 0.8519418287
[73,] 0.1368042281 0.2736084563 0.8631957719
[74,] 0.1231486233 0.2462972466 0.8768513767
[75,] 0.1187837638 0.2375675277 0.8812162362
[76,] 0.1012049683 0.2024099366 0.8987950317
[77,] 0.0918632679 0.1837265358 0.9081367321
[78,] 0.0776055755 0.1552111509 0.9223944245
[79,] 0.0702200106 0.1404400211 0.9297799894
[80,] 0.1069911522 0.2139823044 0.8930088478
[81,] 0.1129484884 0.2258969769 0.8870515116
[82,] 0.0996429651 0.1992859303 0.9003570349
[83,] 0.1370643210 0.2741286420 0.8629356790
[84,] 0.1710967091 0.3421934182 0.8289032909
[85,] 0.2135070875 0.4270141751 0.7864929125
[86,] 0.1908431594 0.3816863188 0.8091568406
[87,] 0.1706517301 0.3413034603 0.8293482699
[88,] 0.1822974138 0.3645948277 0.8177025862
[89,] 0.1789251034 0.3578502068 0.8210748966
[90,] 0.2738007994 0.5476015988 0.7261992006
[91,] 0.3623247241 0.7246494482 0.6376752759
[92,] 0.4568329599 0.9136659197 0.5431670401
[93,] 0.4182936844 0.8365873688 0.5817063156
[94,] 0.5174215999 0.9651568002 0.4825784001
[95,] 0.4832503164 0.9665006328 0.5167496836
[96,] 0.4711216641 0.9422433281 0.5288783359
[97,] 0.4752789519 0.9505579039 0.5247210481
[98,] 0.4291983011 0.8583966022 0.5708016989
[99,] 0.4195634338 0.8391268676 0.5804365662
[100,] 0.3756415027 0.7512830055 0.6243584973
[101,] 0.3432057335 0.6864114670 0.6567942665
[102,] 0.3106837542 0.6213675084 0.6893162458
[103,] 0.7471091189 0.5057817622 0.2528908811
[104,] 0.8109855236 0.3780289528 0.1890144764
[105,] 0.7971118816 0.4057762369 0.2028881184
[106,] 0.7607518323 0.4784963353 0.2392481677
[107,] 0.7239126214 0.5521747573 0.2760873786
[108,] 0.6841062160 0.6317875679 0.3158937840
[109,] 0.6390119671 0.7219760657 0.3609880329
[110,] 0.7521086424 0.4957827152 0.2478913576
[111,] 0.7179927116 0.5640145768 0.2820072884
[112,] 0.6750642025 0.6498715950 0.3249357975
[113,] 0.6355463927 0.7289072146 0.3644536073
[114,] 0.5871147819 0.8257704362 0.4128852181
[115,] 0.6025376749 0.7949246503 0.3974623251
[116,] 0.5720613402 0.8558773195 0.4279386598
[117,] 0.5255584742 0.9488830516 0.4744415258
[118,] 0.6484214868 0.7031570263 0.3515785132
[119,] 0.6022566066 0.7954867867 0.3977433934
[120,] 0.5531214668 0.8937570665 0.4468785332
[121,] 0.5118723826 0.9762552348 0.4881276174
[122,] 0.4574979141 0.9149958282 0.5425020859
[123,] 0.4163454830 0.8326909660 0.5836545170
[124,] 0.3626252475 0.7252504950 0.6373747525
[125,] 0.9678470925 0.0643058150 0.0321529075
[126,] 0.9554532850 0.0890934300 0.0445467150
[127,] 0.9481924057 0.1036151885 0.0518075943
[128,] 0.9394657414 0.1210685171 0.0605342586
[129,] 0.9174091980 0.1651816039 0.0825908020
[130,] 0.9530969157 0.0938061685 0.0469030843
[131,] 0.9412708744 0.1174582511 0.0587291256
[132,] 0.9482468517 0.1035062965 0.0517531483
[133,] 0.9293768770 0.1412462461 0.0706231230
[134,] 0.9482606229 0.1034787542 0.0517393771
[135,] 0.9618874248 0.0762251504 0.0381125752
[136,] 0.9621103861 0.0757792279 0.0378896139
[137,] 0.9567582213 0.0864835574 0.0432417787
[138,] 0.9967354544 0.0065290913 0.0032645456
[139,] 0.9940314808 0.0119370385 0.0059685192
[140,] 0.9897253398 0.0205493204 0.0102746602
[141,] 0.9834794371 0.0330411257 0.0165205629
[142,] 0.9994628288 0.0010743423 0.0005371712
[143,] 0.9986095889 0.0027808222 0.0013904111
[144,] 0.9965628677 0.0068742646 0.0034371323
[145,] 0.9918505003 0.0162989993 0.0081494997
[146,] 0.9816156194 0.0367687613 0.0183843806
[147,] 0.9606971597 0.0786056806 0.0393028403
[148,] 0.9208272705 0.1583454590 0.0791727295
[149,] 0.8626778580 0.2746442840 0.1373221420
[150,] 0.9993854266 0.0012291469 0.0006145734
[151,] 0.9951013825 0.0097972349 0.0048986175
> postscript(file="/var/wessaorg/rcomp/tmp/1w0n11324663934.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ixxq1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39gyt1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44efr1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58teb1324663934.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 = 164
Frequency = 1
1 2 3 4 5 6
34285.57930 20662.18917 1782.98323 16808.56749 -31552.93493 14882.30744
7 8 9 10 11 12
28419.09415 -10086.75605 52697.26997 -31370.53513 -48581.12760 -46922.20442
13 14 15 16 17 18
-19041.70656 -34807.36147 -37453.71080 -36949.07785 54389.37895 -8548.73853
19 20 21 22 23 24
48625.21724 19912.21492 -2397.65072 -45499.94594 6932.12001 7274.07509
25 26 27 28 29 30
-20803.01443 898.10417 -14019.56173 -33183.88973 -46865.81439 -17222.91279
31 32 33 34 35 36
14515.18396 -28015.30387 -26361.60609 -10672.87244 -15004.26301 -34729.69331
37 38 39 40 41 42
-32246.40104 -29059.85047 -15570.40452 -22826.57919 -22948.16223 -14772.54463
43 44 45 46 47 48
-2260.98269 -33617.44845 -6359.26231 -20633.37063 -17798.13767 -27034.81399
49 50 51 52 53 54
21545.44602 -17687.60999 -10735.77175 -7179.39762 10709.77885 -5689.06109
55 56 57 58 59 60
-7440.24117 -9070.35226 22553.91118 32961.14104 -7318.72894 -7633.19816
61 62 63 64 65 66
14110.32449 80171.64658 -48729.28131 -3524.26722 9330.88026 -16557.01004
67 68 69 70 71 72
52421.36036 -23445.06870 29534.54955 54239.62864 -26711.16697 -23434.79334
73 74 75 76 77 78
-23537.42215 88.35478 -15332.68909 -19584.11844 2442.71751 150110.95927
79 80 81 82 83 84
-26129.90736 -22571.26868 -30691.93383 13005.50211 -23767.16428 -13680.94642
85 86 87 88 89 90
28518.84301 -59180.47087 39848.98134 -18907.48879 -56114.57937 53262.14096
91 92 93 94 95 96
-51325.80766 19368.85862 -17472.06865 -38038.48144 34801.98985 70268.20579
97 98 99 100 101 102
64866.64550 63258.27456 7654.06943 65379.78218 20093.92575 30859.37729
103 104 105 106 107 108
-33417.60317 7987.61287 -28425.28666 -464.38377 -13272.99546 -14722.45451
109 110 111 112 113 114
114869.54908 69497.50872 24858.52505 -4822.99931 -8110.24209 -5434.77746
115 116 117 118 119 120
-3728.26239 -61080.62766 -18450.64797 -6679.17319 15363.14112 -5566.65891
121 122 123 124 125 126
-30803.23013 26385.25102 17025.92123 68370.96968 -11954.41552 -6622.22348
127 128 129 130 131 132
-15163.99654 -4825.84598 -9336.78416 8153.71920 127399.80387 -19419.50568
133 134 135 136 137 138
-13244.94561 -17829.82387 -847.81625 68047.87844 32187.06646 -20899.76855
139 140 141 142 143 144
-20285.92668 -44606.30833 33535.25239 -3298.40812 29055.76983 -22972.94149
145 146 147 148 149 150
12518.33383 45102.36597 51826.02428 36783.87195 -4156.07905 -558.38219
151 152 153 154 155 156
-4170.31900 -4231.41315 -4158.05902 -4158.05902 -28709.29215 -73299.03463
157 158 159 160 161 162
-4158.05902 -4190.32098 -3648.28620 -12309.76851 -4528.92911 -33814.86251
163 164
-4301.35843 10929.06605
> postscript(file="/var/wessaorg/rcomp/tmp/61cos1324663934.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 34285.57930 NA
1 20662.18917 34285.57930
2 1782.98323 20662.18917
3 16808.56749 1782.98323
4 -31552.93493 16808.56749
5 14882.30744 -31552.93493
6 28419.09415 14882.30744
7 -10086.75605 28419.09415
8 52697.26997 -10086.75605
9 -31370.53513 52697.26997
10 -48581.12760 -31370.53513
11 -46922.20442 -48581.12760
12 -19041.70656 -46922.20442
13 -34807.36147 -19041.70656
14 -37453.71080 -34807.36147
15 -36949.07785 -37453.71080
16 54389.37895 -36949.07785
17 -8548.73853 54389.37895
18 48625.21724 -8548.73853
19 19912.21492 48625.21724
20 -2397.65072 19912.21492
21 -45499.94594 -2397.65072
22 6932.12001 -45499.94594
23 7274.07509 6932.12001
24 -20803.01443 7274.07509
25 898.10417 -20803.01443
26 -14019.56173 898.10417
27 -33183.88973 -14019.56173
28 -46865.81439 -33183.88973
29 -17222.91279 -46865.81439
30 14515.18396 -17222.91279
31 -28015.30387 14515.18396
32 -26361.60609 -28015.30387
33 -10672.87244 -26361.60609
34 -15004.26301 -10672.87244
35 -34729.69331 -15004.26301
36 -32246.40104 -34729.69331
37 -29059.85047 -32246.40104
38 -15570.40452 -29059.85047
39 -22826.57919 -15570.40452
40 -22948.16223 -22826.57919
41 -14772.54463 -22948.16223
42 -2260.98269 -14772.54463
43 -33617.44845 -2260.98269
44 -6359.26231 -33617.44845
45 -20633.37063 -6359.26231
46 -17798.13767 -20633.37063
47 -27034.81399 -17798.13767
48 21545.44602 -27034.81399
49 -17687.60999 21545.44602
50 -10735.77175 -17687.60999
51 -7179.39762 -10735.77175
52 10709.77885 -7179.39762
53 -5689.06109 10709.77885
54 -7440.24117 -5689.06109
55 -9070.35226 -7440.24117
56 22553.91118 -9070.35226
57 32961.14104 22553.91118
58 -7318.72894 32961.14104
59 -7633.19816 -7318.72894
60 14110.32449 -7633.19816
61 80171.64658 14110.32449
62 -48729.28131 80171.64658
63 -3524.26722 -48729.28131
64 9330.88026 -3524.26722
65 -16557.01004 9330.88026
66 52421.36036 -16557.01004
67 -23445.06870 52421.36036
68 29534.54955 -23445.06870
69 54239.62864 29534.54955
70 -26711.16697 54239.62864
71 -23434.79334 -26711.16697
72 -23537.42215 -23434.79334
73 88.35478 -23537.42215
74 -15332.68909 88.35478
75 -19584.11844 -15332.68909
76 2442.71751 -19584.11844
77 150110.95927 2442.71751
78 -26129.90736 150110.95927
79 -22571.26868 -26129.90736
80 -30691.93383 -22571.26868
81 13005.50211 -30691.93383
82 -23767.16428 13005.50211
83 -13680.94642 -23767.16428
84 28518.84301 -13680.94642
85 -59180.47087 28518.84301
86 39848.98134 -59180.47087
87 -18907.48879 39848.98134
88 -56114.57937 -18907.48879
89 53262.14096 -56114.57937
90 -51325.80766 53262.14096
91 19368.85862 -51325.80766
92 -17472.06865 19368.85862
93 -38038.48144 -17472.06865
94 34801.98985 -38038.48144
95 70268.20579 34801.98985
96 64866.64550 70268.20579
97 63258.27456 64866.64550
98 7654.06943 63258.27456
99 65379.78218 7654.06943
100 20093.92575 65379.78218
101 30859.37729 20093.92575
102 -33417.60317 30859.37729
103 7987.61287 -33417.60317
104 -28425.28666 7987.61287
105 -464.38377 -28425.28666
106 -13272.99546 -464.38377
107 -14722.45451 -13272.99546
108 114869.54908 -14722.45451
109 69497.50872 114869.54908
110 24858.52505 69497.50872
111 -4822.99931 24858.52505
112 -8110.24209 -4822.99931
113 -5434.77746 -8110.24209
114 -3728.26239 -5434.77746
115 -61080.62766 -3728.26239
116 -18450.64797 -61080.62766
117 -6679.17319 -18450.64797
118 15363.14112 -6679.17319
119 -5566.65891 15363.14112
120 -30803.23013 -5566.65891
121 26385.25102 -30803.23013
122 17025.92123 26385.25102
123 68370.96968 17025.92123
124 -11954.41552 68370.96968
125 -6622.22348 -11954.41552
126 -15163.99654 -6622.22348
127 -4825.84598 -15163.99654
128 -9336.78416 -4825.84598
129 8153.71920 -9336.78416
130 127399.80387 8153.71920
131 -19419.50568 127399.80387
132 -13244.94561 -19419.50568
133 -17829.82387 -13244.94561
134 -847.81625 -17829.82387
135 68047.87844 -847.81625
136 32187.06646 68047.87844
137 -20899.76855 32187.06646
138 -20285.92668 -20899.76855
139 -44606.30833 -20285.92668
140 33535.25239 -44606.30833
141 -3298.40812 33535.25239
142 29055.76983 -3298.40812
143 -22972.94149 29055.76983
144 12518.33383 -22972.94149
145 45102.36597 12518.33383
146 51826.02428 45102.36597
147 36783.87195 51826.02428
148 -4156.07905 36783.87195
149 -558.38219 -4156.07905
150 -4170.31900 -558.38219
151 -4231.41315 -4170.31900
152 -4158.05902 -4231.41315
153 -4158.05902 -4158.05902
154 -28709.29215 -4158.05902
155 -73299.03463 -28709.29215
156 -4158.05902 -73299.03463
157 -4190.32098 -4158.05902
158 -3648.28620 -4190.32098
159 -12309.76851 -3648.28620
160 -4528.92911 -12309.76851
161 -33814.86251 -4528.92911
162 -4301.35843 -33814.86251
163 10929.06605 -4301.35843
164 NA 10929.06605
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 20662.18917 34285.57930
[2,] 1782.98323 20662.18917
[3,] 16808.56749 1782.98323
[4,] -31552.93493 16808.56749
[5,] 14882.30744 -31552.93493
[6,] 28419.09415 14882.30744
[7,] -10086.75605 28419.09415
[8,] 52697.26997 -10086.75605
[9,] -31370.53513 52697.26997
[10,] -48581.12760 -31370.53513
[11,] -46922.20442 -48581.12760
[12,] -19041.70656 -46922.20442
[13,] -34807.36147 -19041.70656
[14,] -37453.71080 -34807.36147
[15,] -36949.07785 -37453.71080
[16,] 54389.37895 -36949.07785
[17,] -8548.73853 54389.37895
[18,] 48625.21724 -8548.73853
[19,] 19912.21492 48625.21724
[20,] -2397.65072 19912.21492
[21,] -45499.94594 -2397.65072
[22,] 6932.12001 -45499.94594
[23,] 7274.07509 6932.12001
[24,] -20803.01443 7274.07509
[25,] 898.10417 -20803.01443
[26,] -14019.56173 898.10417
[27,] -33183.88973 -14019.56173
[28,] -46865.81439 -33183.88973
[29,] -17222.91279 -46865.81439
[30,] 14515.18396 -17222.91279
[31,] -28015.30387 14515.18396
[32,] -26361.60609 -28015.30387
[33,] -10672.87244 -26361.60609
[34,] -15004.26301 -10672.87244
[35,] -34729.69331 -15004.26301
[36,] -32246.40104 -34729.69331
[37,] -29059.85047 -32246.40104
[38,] -15570.40452 -29059.85047
[39,] -22826.57919 -15570.40452
[40,] -22948.16223 -22826.57919
[41,] -14772.54463 -22948.16223
[42,] -2260.98269 -14772.54463
[43,] -33617.44845 -2260.98269
[44,] -6359.26231 -33617.44845
[45,] -20633.37063 -6359.26231
[46,] -17798.13767 -20633.37063
[47,] -27034.81399 -17798.13767
[48,] 21545.44602 -27034.81399
[49,] -17687.60999 21545.44602
[50,] -10735.77175 -17687.60999
[51,] -7179.39762 -10735.77175
[52,] 10709.77885 -7179.39762
[53,] -5689.06109 10709.77885
[54,] -7440.24117 -5689.06109
[55,] -9070.35226 -7440.24117
[56,] 22553.91118 -9070.35226
[57,] 32961.14104 22553.91118
[58,] -7318.72894 32961.14104
[59,] -7633.19816 -7318.72894
[60,] 14110.32449 -7633.19816
[61,] 80171.64658 14110.32449
[62,] -48729.28131 80171.64658
[63,] -3524.26722 -48729.28131
[64,] 9330.88026 -3524.26722
[65,] -16557.01004 9330.88026
[66,] 52421.36036 -16557.01004
[67,] -23445.06870 52421.36036
[68,] 29534.54955 -23445.06870
[69,] 54239.62864 29534.54955
[70,] -26711.16697 54239.62864
[71,] -23434.79334 -26711.16697
[72,] -23537.42215 -23434.79334
[73,] 88.35478 -23537.42215
[74,] -15332.68909 88.35478
[75,] -19584.11844 -15332.68909
[76,] 2442.71751 -19584.11844
[77,] 150110.95927 2442.71751
[78,] -26129.90736 150110.95927
[79,] -22571.26868 -26129.90736
[80,] -30691.93383 -22571.26868
[81,] 13005.50211 -30691.93383
[82,] -23767.16428 13005.50211
[83,] -13680.94642 -23767.16428
[84,] 28518.84301 -13680.94642
[85,] -59180.47087 28518.84301
[86,] 39848.98134 -59180.47087
[87,] -18907.48879 39848.98134
[88,] -56114.57937 -18907.48879
[89,] 53262.14096 -56114.57937
[90,] -51325.80766 53262.14096
[91,] 19368.85862 -51325.80766
[92,] -17472.06865 19368.85862
[93,] -38038.48144 -17472.06865
[94,] 34801.98985 -38038.48144
[95,] 70268.20579 34801.98985
[96,] 64866.64550 70268.20579
[97,] 63258.27456 64866.64550
[98,] 7654.06943 63258.27456
[99,] 65379.78218 7654.06943
[100,] 20093.92575 65379.78218
[101,] 30859.37729 20093.92575
[102,] -33417.60317 30859.37729
[103,] 7987.61287 -33417.60317
[104,] -28425.28666 7987.61287
[105,] -464.38377 -28425.28666
[106,] -13272.99546 -464.38377
[107,] -14722.45451 -13272.99546
[108,] 114869.54908 -14722.45451
[109,] 69497.50872 114869.54908
[110,] 24858.52505 69497.50872
[111,] -4822.99931 24858.52505
[112,] -8110.24209 -4822.99931
[113,] -5434.77746 -8110.24209
[114,] -3728.26239 -5434.77746
[115,] -61080.62766 -3728.26239
[116,] -18450.64797 -61080.62766
[117,] -6679.17319 -18450.64797
[118,] 15363.14112 -6679.17319
[119,] -5566.65891 15363.14112
[120,] -30803.23013 -5566.65891
[121,] 26385.25102 -30803.23013
[122,] 17025.92123 26385.25102
[123,] 68370.96968 17025.92123
[124,] -11954.41552 68370.96968
[125,] -6622.22348 -11954.41552
[126,] -15163.99654 -6622.22348
[127,] -4825.84598 -15163.99654
[128,] -9336.78416 -4825.84598
[129,] 8153.71920 -9336.78416
[130,] 127399.80387 8153.71920
[131,] -19419.50568 127399.80387
[132,] -13244.94561 -19419.50568
[133,] -17829.82387 -13244.94561
[134,] -847.81625 -17829.82387
[135,] 68047.87844 -847.81625
[136,] 32187.06646 68047.87844
[137,] -20899.76855 32187.06646
[138,] -20285.92668 -20899.76855
[139,] -44606.30833 -20285.92668
[140,] 33535.25239 -44606.30833
[141,] -3298.40812 33535.25239
[142,] 29055.76983 -3298.40812
[143,] -22972.94149 29055.76983
[144,] 12518.33383 -22972.94149
[145,] 45102.36597 12518.33383
[146,] 51826.02428 45102.36597
[147,] 36783.87195 51826.02428
[148,] -4156.07905 36783.87195
[149,] -558.38219 -4156.07905
[150,] -4170.31900 -558.38219
[151,] -4231.41315 -4170.31900
[152,] -4158.05902 -4231.41315
[153,] -4158.05902 -4158.05902
[154,] -28709.29215 -4158.05902
[155,] -73299.03463 -28709.29215
[156,] -4158.05902 -73299.03463
[157,] -4190.32098 -4158.05902
[158,] -3648.28620 -4190.32098
[159,] -12309.76851 -3648.28620
[160,] -4528.92911 -12309.76851
[161,] -33814.86251 -4528.92911
[162,] -4301.35843 -33814.86251
[163,] 10929.06605 -4301.35843
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 20662.18917 34285.57930
2 1782.98323 20662.18917
3 16808.56749 1782.98323
4 -31552.93493 16808.56749
5 14882.30744 -31552.93493
6 28419.09415 14882.30744
7 -10086.75605 28419.09415
8 52697.26997 -10086.75605
9 -31370.53513 52697.26997
10 -48581.12760 -31370.53513
11 -46922.20442 -48581.12760
12 -19041.70656 -46922.20442
13 -34807.36147 -19041.70656
14 -37453.71080 -34807.36147
15 -36949.07785 -37453.71080
16 54389.37895 -36949.07785
17 -8548.73853 54389.37895
18 48625.21724 -8548.73853
19 19912.21492 48625.21724
20 -2397.65072 19912.21492
21 -45499.94594 -2397.65072
22 6932.12001 -45499.94594
23 7274.07509 6932.12001
24 -20803.01443 7274.07509
25 898.10417 -20803.01443
26 -14019.56173 898.10417
27 -33183.88973 -14019.56173
28 -46865.81439 -33183.88973
29 -17222.91279 -46865.81439
30 14515.18396 -17222.91279
31 -28015.30387 14515.18396
32 -26361.60609 -28015.30387
33 -10672.87244 -26361.60609
34 -15004.26301 -10672.87244
35 -34729.69331 -15004.26301
36 -32246.40104 -34729.69331
37 -29059.85047 -32246.40104
38 -15570.40452 -29059.85047
39 -22826.57919 -15570.40452
40 -22948.16223 -22826.57919
41 -14772.54463 -22948.16223
42 -2260.98269 -14772.54463
43 -33617.44845 -2260.98269
44 -6359.26231 -33617.44845
45 -20633.37063 -6359.26231
46 -17798.13767 -20633.37063
47 -27034.81399 -17798.13767
48 21545.44602 -27034.81399
49 -17687.60999 21545.44602
50 -10735.77175 -17687.60999
51 -7179.39762 -10735.77175
52 10709.77885 -7179.39762
53 -5689.06109 10709.77885
54 -7440.24117 -5689.06109
55 -9070.35226 -7440.24117
56 22553.91118 -9070.35226
57 32961.14104 22553.91118
58 -7318.72894 32961.14104
59 -7633.19816 -7318.72894
60 14110.32449 -7633.19816
61 80171.64658 14110.32449
62 -48729.28131 80171.64658
63 -3524.26722 -48729.28131
64 9330.88026 -3524.26722
65 -16557.01004 9330.88026
66 52421.36036 -16557.01004
67 -23445.06870 52421.36036
68 29534.54955 -23445.06870
69 54239.62864 29534.54955
70 -26711.16697 54239.62864
71 -23434.79334 -26711.16697
72 -23537.42215 -23434.79334
73 88.35478 -23537.42215
74 -15332.68909 88.35478
75 -19584.11844 -15332.68909
76 2442.71751 -19584.11844
77 150110.95927 2442.71751
78 -26129.90736 150110.95927
79 -22571.26868 -26129.90736
80 -30691.93383 -22571.26868
81 13005.50211 -30691.93383
82 -23767.16428 13005.50211
83 -13680.94642 -23767.16428
84 28518.84301 -13680.94642
85 -59180.47087 28518.84301
86 39848.98134 -59180.47087
87 -18907.48879 39848.98134
88 -56114.57937 -18907.48879
89 53262.14096 -56114.57937
90 -51325.80766 53262.14096
91 19368.85862 -51325.80766
92 -17472.06865 19368.85862
93 -38038.48144 -17472.06865
94 34801.98985 -38038.48144
95 70268.20579 34801.98985
96 64866.64550 70268.20579
97 63258.27456 64866.64550
98 7654.06943 63258.27456
99 65379.78218 7654.06943
100 20093.92575 65379.78218
101 30859.37729 20093.92575
102 -33417.60317 30859.37729
103 7987.61287 -33417.60317
104 -28425.28666 7987.61287
105 -464.38377 -28425.28666
106 -13272.99546 -464.38377
107 -14722.45451 -13272.99546
108 114869.54908 -14722.45451
109 69497.50872 114869.54908
110 24858.52505 69497.50872
111 -4822.99931 24858.52505
112 -8110.24209 -4822.99931
113 -5434.77746 -8110.24209
114 -3728.26239 -5434.77746
115 -61080.62766 -3728.26239
116 -18450.64797 -61080.62766
117 -6679.17319 -18450.64797
118 15363.14112 -6679.17319
119 -5566.65891 15363.14112
120 -30803.23013 -5566.65891
121 26385.25102 -30803.23013
122 17025.92123 26385.25102
123 68370.96968 17025.92123
124 -11954.41552 68370.96968
125 -6622.22348 -11954.41552
126 -15163.99654 -6622.22348
127 -4825.84598 -15163.99654
128 -9336.78416 -4825.84598
129 8153.71920 -9336.78416
130 127399.80387 8153.71920
131 -19419.50568 127399.80387
132 -13244.94561 -19419.50568
133 -17829.82387 -13244.94561
134 -847.81625 -17829.82387
135 68047.87844 -847.81625
136 32187.06646 68047.87844
137 -20899.76855 32187.06646
138 -20285.92668 -20899.76855
139 -44606.30833 -20285.92668
140 33535.25239 -44606.30833
141 -3298.40812 33535.25239
142 29055.76983 -3298.40812
143 -22972.94149 29055.76983
144 12518.33383 -22972.94149
145 45102.36597 12518.33383
146 51826.02428 45102.36597
147 36783.87195 51826.02428
148 -4156.07905 36783.87195
149 -558.38219 -4156.07905
150 -4170.31900 -558.38219
151 -4231.41315 -4170.31900
152 -4158.05902 -4231.41315
153 -4158.05902 -4158.05902
154 -28709.29215 -4158.05902
155 -73299.03463 -28709.29215
156 -4158.05902 -73299.03463
157 -4190.32098 -4158.05902
158 -3648.28620 -4190.32098
159 -12309.76851 -3648.28620
160 -4528.92911 -12309.76851
161 -33814.86251 -4528.92911
162 -4301.35843 -33814.86251
163 10929.06605 -4301.35843
> 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/70ae91324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ydiv1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9e6xw1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/103vmd1324663934.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bl7g1324663934.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/120n791324663934.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/13hmki1324663934.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/14y9by1324663934.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/15jp8i1324663934.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/163rdh1324663934.tab")
+ }
>
> try(system("convert tmp/1w0n11324663934.ps tmp/1w0n11324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ixxq1324663934.ps tmp/2ixxq1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/39gyt1324663934.ps tmp/39gyt1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/44efr1324663934.ps tmp/44efr1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/58teb1324663934.ps tmp/58teb1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/61cos1324663934.ps tmp/61cos1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/70ae91324663934.ps tmp/70ae91324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ydiv1324663934.ps tmp/8ydiv1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/9e6xw1324663934.ps tmp/9e6xw1324663934.png",intern=TRUE))
character(0)
> try(system("convert tmp/103vmd1324663934.ps tmp/103vmd1324663934.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.644 0.583 5.243