R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,162556
+ ,162556
+ ,1081
+ ,1081
+ ,213118
+ ,213118
+ ,230380558
+ ,6282929
+ ,1
+ ,29790
+ ,29790
+ ,309
+ ,309
+ ,81767
+ ,81767
+ ,25266003
+ ,4324047
+ ,1
+ ,87550
+ ,87550
+ ,458
+ ,458
+ ,153198
+ ,153198
+ ,70164684
+ ,4108272
+ ,0
+ ,84738
+ ,0
+ ,588
+ ,0
+ ,-26007
+ ,0
+ ,-15292116
+ ,-1212617
+ ,1
+ ,54660
+ ,54660
+ ,299
+ ,299
+ ,126942
+ ,126942
+ ,37955658
+ ,1485329
+ ,1
+ ,42634
+ ,42634
+ ,156
+ ,156
+ ,157214
+ ,157214
+ ,24525384
+ ,1779876
+ ,0
+ ,40949
+ ,0
+ ,481
+ ,0
+ ,129352
+ ,0
+ ,62218312
+ ,1367203
+ ,1
+ ,42312
+ ,42312
+ ,323
+ ,323
+ ,234817
+ ,234817
+ ,75845891
+ ,2519076
+ ,1
+ ,37704
+ ,37704
+ ,452
+ ,452
+ ,60448
+ ,60448
+ ,27322496
+ ,912684
+ ,1
+ ,16275
+ ,16275
+ ,109
+ ,109
+ ,47818
+ ,47818
+ ,5212162
+ ,1443586
+ ,0
+ ,25830
+ ,0
+ ,115
+ ,0
+ ,245546
+ ,0
+ ,28237790
+ ,1220017
+ ,0
+ ,12679
+ ,0
+ ,110
+ ,0
+ ,48020
+ ,0
+ ,5282200
+ ,984885
+ ,1
+ ,18014
+ ,18014
+ ,239
+ ,239
+ ,-1710
+ ,-1710
+ ,-408690
+ ,1457425
+ ,0
+ ,43556
+ ,0
+ ,247
+ ,0
+ ,32648
+ ,0
+ ,8064056
+ ,-572920
+ ,1
+ ,24524
+ ,24524
+ ,497
+ ,497
+ ,95350
+ ,95350
+ ,47388950
+ ,929144
+ ,0
+ ,6532
+ ,0
+ ,103
+ ,0
+ ,151352
+ ,0
+ ,15589256
+ ,1151176
+ ,0
+ ,7123
+ ,0
+ ,109
+ ,0
+ ,288170
+ ,0
+ ,31410530
+ ,790090
+ ,1
+ ,20813
+ ,20813
+ ,502
+ ,502
+ ,114337
+ ,114337
+ ,57397174
+ ,774497
+ ,1
+ ,37597
+ ,37597
+ ,248
+ ,248
+ ,37884
+ ,37884
+ ,9395232
+ ,990576
+ ,0
+ ,17821
+ ,0
+ ,373
+ ,0
+ ,122844
+ ,0
+ ,45820812
+ ,454195
+ ,1
+ ,12988
+ ,12988
+ ,119
+ ,119
+ ,82340
+ ,82340
+ ,9798460
+ ,876607
+ ,1
+ ,22330
+ ,22330
+ ,84
+ ,84
+ ,79801
+ ,79801
+ ,6703284
+ ,711969
+ ,0
+ ,13326
+ ,0
+ ,102
+ ,0
+ ,165548
+ ,0
+ ,16885896
+ ,702380
+ ,0
+ ,16189
+ ,0
+ ,295
+ ,0
+ ,116384
+ ,0
+ ,34333280
+ ,264449
+ ,0
+ ,7146
+ ,0
+ ,105
+ ,0
+ ,134028
+ ,0
+ ,14072940
+ ,450033
+ ,0
+ ,15824
+ ,0
+ ,64
+ ,0
+ ,63838
+ ,0
+ ,4085632
+ ,541063
+ ,1
+ ,26088
+ ,26088
+ ,267
+ ,267
+ ,74996
+ ,74996
+ ,20023932
+ ,588864
+ ,0
+ ,11326
+ ,0
+ ,129
+ ,0
+ ,31080
+ ,0
+ ,4009320
+ ,-37216
+ ,0
+ ,8568
+ ,0
+ ,37
+ ,0
+ ,32168
+ ,0
+ ,1190216
+ ,783310
+ ,0
+ ,14416
+ ,0
+ ,361
+ ,0
+ ,49857
+ ,0
+ ,17998377
+ ,467359
+ ,1
+ ,3369
+ ,3369
+ ,28
+ ,28
+ ,87161
+ ,87161
+ ,2440508
+ ,688779
+ ,1
+ ,11819
+ ,11819
+ ,85
+ ,85
+ ,106113
+ ,106113
+ ,9019605
+ ,608419
+ ,1
+ ,6620
+ ,6620
+ ,44
+ ,44
+ ,80570
+ ,80570
+ ,3545080
+ ,696348
+ ,1
+ ,4519
+ ,4519
+ ,49
+ ,49
+ ,102129
+ ,102129
+ ,5004321
+ ,597793
+ ,0
+ ,2220
+ ,0
+ ,22
+ ,0
+ ,301670
+ ,0
+ ,6636740
+ ,821730
+ ,0
+ ,18562
+ ,0
+ ,155
+ ,0
+ ,102313
+ ,0
+ ,15858515
+ ,377934
+ ,0
+ ,10327
+ ,0
+ ,91
+ ,0
+ ,88577
+ ,0
+ ,8060507
+ ,651939
+ ,1
+ ,5336
+ ,5336
+ ,81
+ ,81
+ ,112477
+ ,112477
+ ,9110637
+ ,697458
+ ,1
+ ,2365
+ ,2365
+ ,79
+ ,79
+ ,191778
+ ,191778
+ ,15150462
+ ,700368
+ ,0
+ ,4069
+ ,0
+ ,145
+ ,0
+ ,79804
+ ,0
+ ,11571580
+ ,225986
+ ,0
+ ,7710
+ ,0
+ ,816
+ ,0
+ ,128294
+ ,0
+ ,104687904
+ ,348695
+ ,0
+ ,13718
+ ,0
+ ,61
+ ,0
+ ,96448
+ ,0
+ ,5883328
+ ,373683
+ ,0
+ ,4525
+ ,0
+ ,226
+ ,0
+ ,93811
+ ,0
+ ,21201286
+ ,501709
+ ,0
+ ,6869
+ ,0
+ ,105
+ ,0
+ ,117520
+ ,0
+ ,12339600
+ ,413743
+ ,0
+ ,4628
+ ,0
+ ,62
+ ,0
+ ,69159
+ ,0
+ ,4287858
+ ,379825
+ ,1
+ ,3653
+ ,3653
+ ,24
+ ,24
+ ,101792
+ ,101792
+ ,2443008
+ ,336260
+ ,1
+ ,1265
+ ,1265
+ ,26
+ ,26
+ ,210568
+ ,210568
+ ,5474768
+ ,636765
+ ,1
+ ,7489
+ ,7489
+ ,322
+ ,322
+ ,136996
+ ,136996
+ ,44112712
+ ,481231
+ ,0
+ ,4901
+ ,0
+ ,84
+ ,0
+ ,121920
+ ,0
+ ,10241280
+ ,469107
+ ,0
+ ,2284
+ ,0
+ ,33
+ ,0
+ ,76403
+ ,0
+ ,2521299
+ ,211928
+ ,1
+ ,3160
+ ,3160
+ ,108
+ ,108
+ ,108094
+ ,108094
+ ,11674152
+ ,563925
+ ,1
+ ,4150
+ ,4150
+ ,150
+ ,150
+ ,134759
+ ,134759
+ ,20213850
+ ,511939
+ ,1
+ ,7285
+ ,7285
+ ,115
+ ,115
+ ,188873
+ ,188873
+ ,21720395
+ ,521016
+ ,1
+ ,1134
+ ,1134
+ ,162
+ ,162
+ ,146216
+ ,146216
+ ,23686992
+ ,543856
+ ,1
+ ,4658
+ ,4658
+ ,158
+ ,158
+ ,156608
+ ,156608
+ ,24744064
+ ,329304
+ ,0
+ ,2384
+ ,0
+ ,97
+ ,0
+ ,61348
+ ,0
+ ,5950756
+ ,423262
+ ,0
+ ,3748
+ ,0
+ ,9
+ ,0
+ ,50350
+ ,0
+ ,453150
+ ,509665
+ ,0
+ ,5371
+ ,0
+ ,66
+ ,0
+ ,87720
+ ,0
+ ,5789520
+ ,455881
+ ,0
+ ,1285
+ ,0
+ ,107
+ ,0
+ ,99489
+ ,0
+ ,10645323
+ ,367772
+ ,1
+ ,9327
+ ,9327
+ ,101
+ ,101
+ ,87419
+ ,87419
+ ,8829319
+ ,406339
+ ,1
+ ,5565
+ ,5565
+ ,47
+ ,47
+ ,94355
+ ,94355
+ ,4434685
+ ,493408
+ ,0
+ ,1528
+ ,0
+ ,38
+ ,0
+ ,60326
+ ,0
+ ,2292388
+ ,232942
+ ,1
+ ,3122
+ ,3122
+ ,34
+ ,34
+ ,94670
+ ,94670
+ ,3218780
+ ,416002
+ ,1
+ ,7317
+ ,7317
+ ,84
+ ,84
+ ,82425
+ ,82425
+ ,6923700
+ ,337430
+ ,0
+ ,2675
+ ,0
+ ,79
+ ,0
+ ,59017
+ ,0
+ ,4662343
+ ,361517
+ ,0
+ ,13253
+ ,0
+ ,947
+ ,0
+ ,90829
+ ,0
+ ,86015063
+ ,360962
+ ,0
+ ,880
+ ,0
+ ,74
+ ,0
+ ,80791
+ ,0
+ ,5978534
+ ,235561
+ ,1
+ ,2053
+ ,2053
+ ,53
+ ,53
+ ,100423
+ ,100423
+ ,5322419
+ ,408247
+ ,0
+ ,1424
+ ,0
+ ,94
+ ,0
+ ,131116
+ ,0
+ ,12324904
+ ,450296
+ ,1
+ ,4036
+ ,4036
+ ,63
+ ,63
+ ,100269
+ ,100269
+ ,6316947
+ ,418799
+ ,1
+ ,3045
+ ,3045
+ ,58
+ ,58
+ ,27330
+ ,27330
+ ,1585140
+ ,247405
+ ,0
+ ,5119
+ ,0
+ ,49
+ ,0
+ ,39039
+ ,0
+ ,1912911
+ ,378519
+ ,0
+ ,1431
+ ,0
+ ,34
+ ,0
+ ,106885
+ ,0
+ ,3634090
+ ,326638
+ ,0
+ ,554
+ ,0
+ ,11
+ ,0
+ ,79285
+ ,0
+ ,872135
+ ,328233
+ ,0
+ ,1975
+ ,0
+ ,35
+ ,0
+ ,118881
+ ,0
+ ,4160835
+ ,386225
+ ,1
+ ,1286
+ ,1286
+ ,17
+ ,17
+ ,77623
+ ,77623
+ ,1319591
+ ,283662
+ ,0
+ ,1012
+ ,0
+ ,47
+ ,0
+ ,114768
+ ,0
+ ,5394096
+ ,370225
+ ,0
+ ,810
+ ,0
+ ,43
+ ,0
+ ,74015
+ ,0
+ ,3182645
+ ,269236
+ ,0
+ ,1280
+ ,0
+ ,117
+ ,0
+ ,69465
+ ,0
+ ,8127405
+ ,365732
+ ,1
+ ,666
+ ,666
+ ,171
+ ,171
+ ,117869
+ ,117869
+ ,20155599
+ ,420383
+ ,0
+ ,1380
+ ,0
+ ,26
+ ,0
+ ,60982
+ ,0
+ ,1585532
+ ,345811
+ ,1
+ ,4608
+ ,4608
+ ,73
+ ,73
+ ,90131
+ ,90131
+ ,6579563
+ ,431809
+ ,0
+ ,876
+ ,0
+ ,59
+ ,0
+ ,138971
+ ,0
+ ,8199289
+ ,418876
+ ,0
+ ,814
+ ,0
+ ,18
+ ,0
+ ,39625
+ ,0
+ ,713250
+ ,297476
+ ,0
+ ,514
+ ,0
+ ,15
+ ,0
+ ,102725
+ ,0
+ ,1540875
+ ,416776
+ ,1
+ ,5692
+ ,5692
+ ,72
+ ,72
+ ,64239
+ ,64239
+ ,4625208
+ ,357257
+ ,0
+ ,3642
+ ,0
+ ,86
+ ,0
+ ,90262
+ ,0
+ ,7762532
+ ,458343
+ ,0
+ ,540
+ ,0
+ ,14
+ ,0
+ ,103960
+ ,0
+ ,1455440
+ ,388386
+ ,0
+ ,2099
+ ,0
+ ,64
+ ,0
+ ,106611
+ ,0
+ ,6823104
+ ,358934
+ ,0
+ ,567
+ ,0
+ ,11
+ ,0
+ ,103345
+ ,0
+ ,1136795
+ ,407560
+ ,0
+ ,2001
+ ,0
+ ,52
+ ,0
+ ,95551
+ ,0
+ ,4968652
+ ,392558
+ ,1
+ ,2949
+ ,2949
+ ,41
+ ,41
+ ,82903
+ ,82903
+ ,3399023
+ ,373177
+ ,0
+ ,2253
+ ,0
+ ,99
+ ,0
+ ,63593
+ ,0
+ ,6295707
+ ,428370
+ ,1
+ ,6533
+ ,6533
+ ,75
+ ,75
+ ,126910
+ ,126910
+ ,9518250
+ ,369419
+ ,0
+ ,1889
+ ,0
+ ,45
+ ,0
+ ,37527
+ ,0
+ ,1688715
+ ,358649
+ ,1
+ ,3055
+ ,3055
+ ,43
+ ,43
+ ,60247
+ ,60247
+ ,2590621
+ ,376641
+ ,0
+ ,272
+ ,0
+ ,8
+ ,0
+ ,112995
+ ,0
+ ,903960
+ ,467427
+ ,1
+ ,1414
+ ,1414
+ ,198
+ ,198
+ ,70184
+ ,70184
+ ,13896432
+ ,364885
+ ,0
+ ,2564
+ ,0
+ ,22
+ ,0
+ ,130140
+ ,0
+ ,2863080
+ ,436230
+ ,1
+ ,1383
+ ,1383
+ ,11
+ ,11
+ ,73221
+ ,73221
+ ,805431
+ ,329118)
+ ,dim=c(9
+ ,100)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'GrCosts'
+ ,'Trades'
+ ,'GrTrades'
+ ,'Dividends'
+ ,'GrDiv'
+ ,'TrDiv'
+ ,'Wealth
')
+ ,1:100))
> y <- array(NA,dim=c(9,100),dimnames=list(c('Group','Costs','GrCosts','Trades','GrTrades','Dividends','GrDiv','TrDiv','Wealth
'),1:100))
> 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 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Trades Group Costs GrCosts GrTrades Dividends GrDiv TrDiv Wealth\r
1 1081 1 162556 162556 1081 213118 213118 230380558 6282929
2 309 1 29790 29790 309 81767 81767 25266003 4324047
3 458 1 87550 87550 458 153198 153198 70164684 4108272
4 588 0 84738 0 0 -26007 0 -15292116 -1212617
5 299 1 54660 54660 299 126942 126942 37955658 1485329
6 156 1 42634 42634 156 157214 157214 24525384 1779876
7 481 0 40949 0 0 129352 0 62218312 1367203
8 323 1 42312 42312 323 234817 234817 75845891 2519076
9 452 1 37704 37704 452 60448 60448 27322496 912684
10 109 1 16275 16275 109 47818 47818 5212162 1443586
11 115 0 25830 0 0 245546 0 28237790 1220017
12 110 0 12679 0 0 48020 0 5282200 984885
13 239 1 18014 18014 239 -1710 -1710 -408690 1457425
14 247 0 43556 0 0 32648 0 8064056 -572920
15 497 1 24524 24524 497 95350 95350 47388950 929144
16 103 0 6532 0 0 151352 0 15589256 1151176
17 109 0 7123 0 0 288170 0 31410530 790090
18 502 1 20813 20813 502 114337 114337 57397174 774497
19 248 1 37597 37597 248 37884 37884 9395232 990576
20 373 0 17821 0 0 122844 0 45820812 454195
21 119 1 12988 12988 119 82340 82340 9798460 876607
22 84 1 22330 22330 84 79801 79801 6703284 711969
23 102 0 13326 0 0 165548 0 16885896 702380
24 295 0 16189 0 0 116384 0 34333280 264449
25 105 0 7146 0 0 134028 0 14072940 450033
26 64 0 15824 0 0 63838 0 4085632 541063
27 267 1 26088 26088 267 74996 74996 20023932 588864
28 129 0 11326 0 0 31080 0 4009320 -37216
29 37 0 8568 0 0 32168 0 1190216 783310
30 361 0 14416 0 0 49857 0 17998377 467359
31 28 1 3369 3369 28 87161 87161 2440508 688779
32 85 1 11819 11819 85 106113 106113 9019605 608419
33 44 1 6620 6620 44 80570 80570 3545080 696348
34 49 1 4519 4519 49 102129 102129 5004321 597793
35 22 0 2220 0 0 301670 0 6636740 821730
36 155 0 18562 0 0 102313 0 15858515 377934
37 91 0 10327 0 0 88577 0 8060507 651939
38 81 1 5336 5336 81 112477 112477 9110637 697458
39 79 1 2365 2365 79 191778 191778 15150462 700368
40 145 0 4069 0 0 79804 0 11571580 225986
41 816 0 7710 0 0 128294 0 104687904 348695
42 61 0 13718 0 0 96448 0 5883328 373683
43 226 0 4525 0 0 93811 0 21201286 501709
44 105 0 6869 0 0 117520 0 12339600 413743
45 62 0 4628 0 0 69159 0 4287858 379825
46 24 1 3653 3653 24 101792 101792 2443008 336260
47 26 1 1265 1265 26 210568 210568 5474768 636765
48 322 1 7489 7489 322 136996 136996 44112712 481231
49 84 0 4901 0 0 121920 0 10241280 469107
50 33 0 2284 0 0 76403 0 2521299 211928
51 108 1 3160 3160 108 108094 108094 11674152 563925
52 150 1 4150 4150 150 134759 134759 20213850 511939
53 115 1 7285 7285 115 188873 188873 21720395 521016
54 162 1 1134 1134 162 146216 146216 23686992 543856
55 158 1 4658 4658 158 156608 156608 24744064 329304
56 97 0 2384 0 0 61348 0 5950756 423262
57 9 0 3748 0 0 50350 0 453150 509665
58 66 0 5371 0 0 87720 0 5789520 455881
59 107 0 1285 0 0 99489 0 10645323 367772
60 101 1 9327 9327 101 87419 87419 8829319 406339
61 47 1 5565 5565 47 94355 94355 4434685 493408
62 38 0 1528 0 0 60326 0 2292388 232942
63 34 1 3122 3122 34 94670 94670 3218780 416002
64 84 1 7317 7317 84 82425 82425 6923700 337430
65 79 0 2675 0 0 59017 0 4662343 361517
66 947 0 13253 0 0 90829 0 86015063 360962
67 74 0 880 0 0 80791 0 5978534 235561
68 53 1 2053 2053 53 100423 100423 5322419 408247
69 94 0 1424 0 0 131116 0 12324904 450296
70 63 1 4036 4036 63 100269 100269 6316947 418799
71 58 1 3045 3045 58 27330 27330 1585140 247405
72 49 0 5119 0 0 39039 0 1912911 378519
73 34 0 1431 0 0 106885 0 3634090 326638
74 11 0 554 0 0 79285 0 872135 328233
75 35 0 1975 0 0 118881 0 4160835 386225
76 17 1 1286 1286 17 77623 77623 1319591 283662
77 47 0 1012 0 0 114768 0 5394096 370225
78 43 0 810 0 0 74015 0 3182645 269236
79 117 0 1280 0 0 69465 0 8127405 365732
80 171 1 666 666 171 117869 117869 20155599 420383
81 26 0 1380 0 0 60982 0 1585532 345811
82 73 1 4608 4608 73 90131 90131 6579563 431809
83 59 0 876 0 0 138971 0 8199289 418876
84 18 0 814 0 0 39625 0 713250 297476
85 15 0 514 0 0 102725 0 1540875 416776
86 72 1 5692 5692 72 64239 64239 4625208 357257
87 86 0 3642 0 0 90262 0 7762532 458343
88 14 0 540 0 0 103960 0 1455440 388386
89 64 0 2099 0 0 106611 0 6823104 358934
90 11 0 567 0 0 103345 0 1136795 407560
91 52 0 2001 0 0 95551 0 4968652 392558
92 41 1 2949 2949 41 82903 82903 3399023 373177
93 99 0 2253 0 0 63593 0 6295707 428370
94 75 1 6533 6533 75 126910 126910 9518250 369419
95 45 0 1889 0 0 37527 0 1688715 358649
96 43 1 3055 3055 43 60247 60247 2590621 376641
97 8 0 272 0 0 112995 0 903960 467427
98 198 1 1414 1414 198 70184 70184 13896432 364885
99 22 0 2564 0 0 130140 0 2863080 436230
100 11 1 1383 1383 11 73221 73221 805431 329118
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Group Costs GrCosts GrTrades Dividends
7.812e+01 8.523e+01 5.286e-03 -7.245e-03 3.280e-01 -6.164e-04
GrDiv TrDiv `Wealth\r`
-6.034e-04 6.470e-06 -2.224e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-209.270 -25.213 -9.376 10.042 306.335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.812e+01 1.982e+01 3.942 0.000158 ***
Group 8.523e+01 3.277e+01 2.601 0.010842 *
Costs 5.286e-03 6.817e-04 7.754 1.24e-11 ***
GrCosts -7.245e-03 1.195e-03 -6.062 3.01e-08 ***
GrTrades 3.280e-01 1.111e-01 2.951 0.004025 **
Dividends -6.164e-04 1.743e-04 -3.537 0.000640 ***
GrDiv -6.034e-04 2.700e-04 -2.235 0.027891 *
TrDiv 6.470e-06 4.305e-07 15.029 < 2e-16 ***
`Wealth\r` -2.224e-05 1.586e-05 -1.402 0.164291
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 65.37 on 91 degrees of freedom
Multiple R-squared: 0.891, Adjusted R-squared: 0.8814
F-statistic: 92.98 on 8 and 91 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,] 0.9798082 4.038353e-02 2.019177e-02
[2,] 0.9763917 4.721660e-02 2.360830e-02
[3,] 0.9937059 1.258817e-02 6.294087e-03
[4,] 0.9886936 2.261281e-02 1.130641e-02
[5,] 0.9912597 1.748061e-02 8.740306e-03
[6,] 0.9934661 1.306784e-02 6.533921e-03
[7,] 0.9883523 2.329550e-02 1.164775e-02
[8,] 0.9825813 3.483734e-02 1.741867e-02
[9,] 0.9993152 1.369557e-03 6.847785e-04
[10,] 0.9991795 1.641051e-03 8.205254e-04
[11,] 0.9988585 2.283029e-03 1.141514e-03
[12,] 0.9986934 2.613191e-03 1.306595e-03
[13,] 0.9992857 1.428594e-03 7.142972e-04
[14,] 0.9988235 2.352906e-03 1.176453e-03
[15,] 0.9996061 7.877591e-04 3.938795e-04
[16,] 0.9995155 9.689392e-04 4.844696e-04
[17,] 0.9991551 1.689787e-03 8.448934e-04
[18,] 0.9993376 1.324782e-03 6.623910e-04
[19,] 0.9999998 3.821535e-07 1.910767e-07
[20,] 0.9999998 4.224996e-07 2.112498e-07
[21,] 0.9999996 8.324443e-07 4.162222e-07
[22,] 0.9999993 1.305581e-06 6.527907e-07
[23,] 0.9999987 2.550945e-06 1.275473e-06
[24,] 0.9999988 2.463957e-06 1.231979e-06
[25,] 0.9999985 2.961012e-06 1.480506e-06
[26,] 0.9999979 4.294429e-06 2.147215e-06
[27,] 0.9999956 8.814713e-06 4.407356e-06
[28,] 0.9999914 1.725805e-05 8.629025e-06
[29,] 0.9999884 2.329434e-05 1.164717e-05
[30,] 1.0000000 2.542167e-18 1.271084e-18
[31,] 1.0000000 7.047155e-18 3.523578e-18
[32,] 1.0000000 1.864497e-17 9.322484e-18
[33,] 1.0000000 4.575671e-17 2.287835e-17
[34,] 1.0000000 1.823203e-16 9.116016e-17
[35,] 1.0000000 5.484943e-16 2.742471e-16
[36,] 1.0000000 3.751680e-18 1.875840e-18
[37,] 1.0000000 5.889555e-22 2.944777e-22
[38,] 1.0000000 1.709432e-21 8.547162e-22
[39,] 1.0000000 9.297388e-21 4.648694e-21
[40,] 1.0000000 3.951417e-20 1.975709e-20
[41,] 1.0000000 1.467965e-19 7.339823e-20
[42,] 1.0000000 7.960425e-19 3.980213e-19
[43,] 1.0000000 6.385502e-19 3.192751e-19
[44,] 1.0000000 8.681936e-21 4.340968e-21
[45,] 1.0000000 1.898619e-20 9.493094e-21
[46,] 1.0000000 1.163031e-20 5.815156e-21
[47,] 1.0000000 5.726598e-20 2.863299e-20
[48,] 1.0000000 3.473322e-19 1.736661e-19
[49,] 1.0000000 1.643172e-18 8.215860e-19
[50,] 1.0000000 9.273029e-18 4.636514e-18
[51,] 1.0000000 5.785979e-17 2.892989e-17
[52,] 1.0000000 2.345003e-16 1.172502e-16
[53,] 1.0000000 1.205333e-15 6.026663e-16
[54,] 1.0000000 5.398794e-15 2.699397e-15
[55,] 1.0000000 8.533023e-17 4.266511e-17
[56,] 1.0000000 3.289369e-16 1.644685e-16
[57,] 1.0000000 1.488169e-15 7.440843e-16
[58,] 1.0000000 2.898803e-15 1.449401e-15
[59,] 1.0000000 1.850099e-14 9.250494e-15
[60,] 1.0000000 3.237977e-14 1.618989e-14
[61,] 1.0000000 2.288831e-13 1.144416e-13
[62,] 1.0000000 1.727394e-12 8.636969e-13
[63,] 1.0000000 1.153408e-11 5.767038e-12
[64,] 1.0000000 8.232247e-11 4.116123e-11
[65,] 1.0000000 3.312736e-10 1.656368e-10
[66,] 1.0000000 2.270815e-09 1.135408e-09
[67,] 1.0000000 1.390759e-08 6.953794e-09
[68,] 1.0000000 6.774287e-09 3.387144e-09
[69,] 1.0000000 2.072890e-10 1.036445e-10
[70,] 1.0000000 2.070647e-09 1.035323e-09
[71,] 1.0000000 1.235282e-08 6.176408e-09
[72,] 1.0000000 7.743279e-08 3.871640e-08
[73,] 0.9999998 3.412117e-07 1.706058e-07
[74,] 0.9999980 4.097205e-06 2.048603e-06
[75,] 0.9999855 2.903454e-05 1.451727e-05
[76,] 0.9999759 4.811164e-05 2.405582e-05
[77,] 0.9998255 3.490717e-04 1.745359e-04
> postscript(file="/var/www/html/rcomp/tmp/13v361293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23v361293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3dmkr1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dmkr1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dmkr1293220838.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 = 100
Frequency = 1
1 2 3 4 5 6
-209.2702864 135.1009848 140.2323221 117.9096450 86.9756071 97.6827075
7 8 9 10 11 12
-105.9735603 -11.6540456 131.5321439 -1.5072821 -103.8558510 -17.8132414
13 14 15 16 17 18
65.5246457 -106.1399366 49.0669351 8.3883652 -14.7882605 0.1267077
19 20 21 22 23 24
84.4209165 -9.9502801 -1.3975703 6.6487816 -38.1421129 -13.2029125
25 26 27 28 29 30
-9.3183571 -72.8147119 42.2132122 -16.5988371 -56.8632557 131.3565874
31 32 33 34 35 36
-32.0886728 1.5362077 -29.9851004 -16.0757660 93.4365889 -52.3667753
37 38 39 40 41 42
-24.7600593 -4.6981676 45.8572643 24.7215302 106.6451343 -59.9342050
43 44 45 46 47 48
55.7743657 -7.6228127 -17.2492094 -24.2292704 92.1901748 -39.8766515
49 50 51 52 53 54
-0.7010143 -21.6983264 -15.7173659 -9.4327646 29.6504458 -15.0622274
55 56 57 58 59 60
-9.7811533 15.0043800 -49.4946447 -13.7591185 22.7175079 -18.6588476
61 62 63 64 65 66
-23.4894413 -20.6643022 -30.4822914 -29.3162611 0.9920482 306.3351088
67 68 69 70 71 72
7.5855162 -26.5727920 19.4472349 -22.3552984 -89.8257778 -36.0748980
73 74 75 76 77 78
-2.0480864 -19.5210151 1.3883359 -56.9533963 7.6082975 -8.3834912
79 80 81 82 83 84
30.4814236 -24.4067230 -24.3942933 -28.2904638 18.1793662 -37.9990365
85 86 87 88 89 90
-3.2179956 -47.4357058 4.2365826 -3.6727378 4.3378270 -4.7065614
91 92 93 94 95 96
-1.2160823 -42.5888709 16.9625138 1.2875663 -22.9252427 -63.3657733
97 98 99 100
2.6390912 -23.7008580 1.7237745 -61.8277982
> postscript(file="/var/www/html/rcomp/tmp/6oekc1293220838.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 = 100
Frequency = 1
lag(myerror, k = 1) myerror
0 -209.2702864 NA
1 135.1009848 -209.2702864
2 140.2323221 135.1009848
3 117.9096450 140.2323221
4 86.9756071 117.9096450
5 97.6827075 86.9756071
6 -105.9735603 97.6827075
7 -11.6540456 -105.9735603
8 131.5321439 -11.6540456
9 -1.5072821 131.5321439
10 -103.8558510 -1.5072821
11 -17.8132414 -103.8558510
12 65.5246457 -17.8132414
13 -106.1399366 65.5246457
14 49.0669351 -106.1399366
15 8.3883652 49.0669351
16 -14.7882605 8.3883652
17 0.1267077 -14.7882605
18 84.4209165 0.1267077
19 -9.9502801 84.4209165
20 -1.3975703 -9.9502801
21 6.6487816 -1.3975703
22 -38.1421129 6.6487816
23 -13.2029125 -38.1421129
24 -9.3183571 -13.2029125
25 -72.8147119 -9.3183571
26 42.2132122 -72.8147119
27 -16.5988371 42.2132122
28 -56.8632557 -16.5988371
29 131.3565874 -56.8632557
30 -32.0886728 131.3565874
31 1.5362077 -32.0886728
32 -29.9851004 1.5362077
33 -16.0757660 -29.9851004
34 93.4365889 -16.0757660
35 -52.3667753 93.4365889
36 -24.7600593 -52.3667753
37 -4.6981676 -24.7600593
38 45.8572643 -4.6981676
39 24.7215302 45.8572643
40 106.6451343 24.7215302
41 -59.9342050 106.6451343
42 55.7743657 -59.9342050
43 -7.6228127 55.7743657
44 -17.2492094 -7.6228127
45 -24.2292704 -17.2492094
46 92.1901748 -24.2292704
47 -39.8766515 92.1901748
48 -0.7010143 -39.8766515
49 -21.6983264 -0.7010143
50 -15.7173659 -21.6983264
51 -9.4327646 -15.7173659
52 29.6504458 -9.4327646
53 -15.0622274 29.6504458
54 -9.7811533 -15.0622274
55 15.0043800 -9.7811533
56 -49.4946447 15.0043800
57 -13.7591185 -49.4946447
58 22.7175079 -13.7591185
59 -18.6588476 22.7175079
60 -23.4894413 -18.6588476
61 -20.6643022 -23.4894413
62 -30.4822914 -20.6643022
63 -29.3162611 -30.4822914
64 0.9920482 -29.3162611
65 306.3351088 0.9920482
66 7.5855162 306.3351088
67 -26.5727920 7.5855162
68 19.4472349 -26.5727920
69 -22.3552984 19.4472349
70 -89.8257778 -22.3552984
71 -36.0748980 -89.8257778
72 -2.0480864 -36.0748980
73 -19.5210151 -2.0480864
74 1.3883359 -19.5210151
75 -56.9533963 1.3883359
76 7.6082975 -56.9533963
77 -8.3834912 7.6082975
78 30.4814236 -8.3834912
79 -24.4067230 30.4814236
80 -24.3942933 -24.4067230
81 -28.2904638 -24.3942933
82 18.1793662 -28.2904638
83 -37.9990365 18.1793662
84 -3.2179956 -37.9990365
85 -47.4357058 -3.2179956
86 4.2365826 -47.4357058
87 -3.6727378 4.2365826
88 4.3378270 -3.6727378
89 -4.7065614 4.3378270
90 -1.2160823 -4.7065614
91 -42.5888709 -1.2160823
92 16.9625138 -42.5888709
93 1.2875663 16.9625138
94 -22.9252427 1.2875663
95 -63.3657733 -22.9252427
96 2.6390912 -63.3657733
97 -23.7008580 2.6390912
98 1.7237745 -23.7008580
99 -61.8277982 1.7237745
100 NA -61.8277982
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 135.1009848 -209.2702864
[2,] 140.2323221 135.1009848
[3,] 117.9096450 140.2323221
[4,] 86.9756071 117.9096450
[5,] 97.6827075 86.9756071
[6,] -105.9735603 97.6827075
[7,] -11.6540456 -105.9735603
[8,] 131.5321439 -11.6540456
[9,] -1.5072821 131.5321439
[10,] -103.8558510 -1.5072821
[11,] -17.8132414 -103.8558510
[12,] 65.5246457 -17.8132414
[13,] -106.1399366 65.5246457
[14,] 49.0669351 -106.1399366
[15,] 8.3883652 49.0669351
[16,] -14.7882605 8.3883652
[17,] 0.1267077 -14.7882605
[18,] 84.4209165 0.1267077
[19,] -9.9502801 84.4209165
[20,] -1.3975703 -9.9502801
[21,] 6.6487816 -1.3975703
[22,] -38.1421129 6.6487816
[23,] -13.2029125 -38.1421129
[24,] -9.3183571 -13.2029125
[25,] -72.8147119 -9.3183571
[26,] 42.2132122 -72.8147119
[27,] -16.5988371 42.2132122
[28,] -56.8632557 -16.5988371
[29,] 131.3565874 -56.8632557
[30,] -32.0886728 131.3565874
[31,] 1.5362077 -32.0886728
[32,] -29.9851004 1.5362077
[33,] -16.0757660 -29.9851004
[34,] 93.4365889 -16.0757660
[35,] -52.3667753 93.4365889
[36,] -24.7600593 -52.3667753
[37,] -4.6981676 -24.7600593
[38,] 45.8572643 -4.6981676
[39,] 24.7215302 45.8572643
[40,] 106.6451343 24.7215302
[41,] -59.9342050 106.6451343
[42,] 55.7743657 -59.9342050
[43,] -7.6228127 55.7743657
[44,] -17.2492094 -7.6228127
[45,] -24.2292704 -17.2492094
[46,] 92.1901748 -24.2292704
[47,] -39.8766515 92.1901748
[48,] -0.7010143 -39.8766515
[49,] -21.6983264 -0.7010143
[50,] -15.7173659 -21.6983264
[51,] -9.4327646 -15.7173659
[52,] 29.6504458 -9.4327646
[53,] -15.0622274 29.6504458
[54,] -9.7811533 -15.0622274
[55,] 15.0043800 -9.7811533
[56,] -49.4946447 15.0043800
[57,] -13.7591185 -49.4946447
[58,] 22.7175079 -13.7591185
[59,] -18.6588476 22.7175079
[60,] -23.4894413 -18.6588476
[61,] -20.6643022 -23.4894413
[62,] -30.4822914 -20.6643022
[63,] -29.3162611 -30.4822914
[64,] 0.9920482 -29.3162611
[65,] 306.3351088 0.9920482
[66,] 7.5855162 306.3351088
[67,] -26.5727920 7.5855162
[68,] 19.4472349 -26.5727920
[69,] -22.3552984 19.4472349
[70,] -89.8257778 -22.3552984
[71,] -36.0748980 -89.8257778
[72,] -2.0480864 -36.0748980
[73,] -19.5210151 -2.0480864
[74,] 1.3883359 -19.5210151
[75,] -56.9533963 1.3883359
[76,] 7.6082975 -56.9533963
[77,] -8.3834912 7.6082975
[78,] 30.4814236 -8.3834912
[79,] -24.4067230 30.4814236
[80,] -24.3942933 -24.4067230
[81,] -28.2904638 -24.3942933
[82,] 18.1793662 -28.2904638
[83,] -37.9990365 18.1793662
[84,] -3.2179956 -37.9990365
[85,] -47.4357058 -3.2179956
[86,] 4.2365826 -47.4357058
[87,] -3.6727378 4.2365826
[88,] 4.3378270 -3.6727378
[89,] -4.7065614 4.3378270
[90,] -1.2160823 -4.7065614
[91,] -42.5888709 -1.2160823
[92,] 16.9625138 -42.5888709
[93,] 1.2875663 16.9625138
[94,] -22.9252427 1.2875663
[95,] -63.3657733 -22.9252427
[96,] 2.6390912 -63.3657733
[97,] -23.7008580 2.6390912
[98,] 1.7237745 -23.7008580
[99,] -61.8277982 1.7237745
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 135.1009848 -209.2702864
2 140.2323221 135.1009848
3 117.9096450 140.2323221
4 86.9756071 117.9096450
5 97.6827075 86.9756071
6 -105.9735603 97.6827075
7 -11.6540456 -105.9735603
8 131.5321439 -11.6540456
9 -1.5072821 131.5321439
10 -103.8558510 -1.5072821
11 -17.8132414 -103.8558510
12 65.5246457 -17.8132414
13 -106.1399366 65.5246457
14 49.0669351 -106.1399366
15 8.3883652 49.0669351
16 -14.7882605 8.3883652
17 0.1267077 -14.7882605
18 84.4209165 0.1267077
19 -9.9502801 84.4209165
20 -1.3975703 -9.9502801
21 6.6487816 -1.3975703
22 -38.1421129 6.6487816
23 -13.2029125 -38.1421129
24 -9.3183571 -13.2029125
25 -72.8147119 -9.3183571
26 42.2132122 -72.8147119
27 -16.5988371 42.2132122
28 -56.8632557 -16.5988371
29 131.3565874 -56.8632557
30 -32.0886728 131.3565874
31 1.5362077 -32.0886728
32 -29.9851004 1.5362077
33 -16.0757660 -29.9851004
34 93.4365889 -16.0757660
35 -52.3667753 93.4365889
36 -24.7600593 -52.3667753
37 -4.6981676 -24.7600593
38 45.8572643 -4.6981676
39 24.7215302 45.8572643
40 106.6451343 24.7215302
41 -59.9342050 106.6451343
42 55.7743657 -59.9342050
43 -7.6228127 55.7743657
44 -17.2492094 -7.6228127
45 -24.2292704 -17.2492094
46 92.1901748 -24.2292704
47 -39.8766515 92.1901748
48 -0.7010143 -39.8766515
49 -21.6983264 -0.7010143
50 -15.7173659 -21.6983264
51 -9.4327646 -15.7173659
52 29.6504458 -9.4327646
53 -15.0622274 29.6504458
54 -9.7811533 -15.0622274
55 15.0043800 -9.7811533
56 -49.4946447 15.0043800
57 -13.7591185 -49.4946447
58 22.7175079 -13.7591185
59 -18.6588476 22.7175079
60 -23.4894413 -18.6588476
61 -20.6643022 -23.4894413
62 -30.4822914 -20.6643022
63 -29.3162611 -30.4822914
64 0.9920482 -29.3162611
65 306.3351088 0.9920482
66 7.5855162 306.3351088
67 -26.5727920 7.5855162
68 19.4472349 -26.5727920
69 -22.3552984 19.4472349
70 -89.8257778 -22.3552984
71 -36.0748980 -89.8257778
72 -2.0480864 -36.0748980
73 -19.5210151 -2.0480864
74 1.3883359 -19.5210151
75 -56.9533963 1.3883359
76 7.6082975 -56.9533963
77 -8.3834912 7.6082975
78 30.4814236 -8.3834912
79 -24.4067230 30.4814236
80 -24.3942933 -24.4067230
81 -28.2904638 -24.3942933
82 18.1793662 -28.2904638
83 -37.9990365 18.1793662
84 -3.2179956 -37.9990365
85 -47.4357058 -3.2179956
86 4.2365826 -47.4357058
87 -3.6727378 4.2365826
88 4.3378270 -3.6727378
89 -4.7065614 4.3378270
90 -1.2160823 -4.7065614
91 -42.5888709 -1.2160823
92 16.9625138 -42.5888709
93 1.2875663 16.9625138
94 -22.9252427 1.2875663
95 -63.3657733 -22.9252427
96 2.6390912 -63.3657733
97 -23.7008580 2.6390912
98 1.7237745 -23.7008580
99 -61.8277982 1.7237745
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7oekc1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z51f1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z51f1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109eih1293220838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11dfg51293220838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12n6g81293220838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13u7v21293220838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/145ycn1293220838.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/158zbb1293220838.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16mr8k1293220838.tab")
+ }
>
> try(system("convert tmp/13v361293220838.ps tmp/13v361293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/23v361293220838.ps tmp/23v361293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dmkr1293220838.ps tmp/3dmkr1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dmkr1293220838.ps tmp/4dmkr1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dmkr1293220838.ps tmp/5dmkr1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oekc1293220838.ps tmp/6oekc1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oekc1293220838.ps tmp/7oekc1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z51f1293220838.ps tmp/8z51f1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z51f1293220838.ps tmp/9z51f1293220838.png",intern=TRUE))
character(0)
> try(system("convert tmp/109eih1293220838.ps tmp/109eih1293220838.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.199 1.676 7.142