R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,2012
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+ ,133
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+ ,11.60
+ ,10.60)
+ ,dim=c(8
+ ,145)
+ ,dimnames=list(c('t'
+ ,'jaartal'
+ ,'Totaal'
+ ,'jongerdan25jaar'
+ ,'vanaf25jaar'
+ ,'België'
+ ,'Eurogebied'
+ ,'EU-27
')
+ ,1:145))
> y <- array(NA,dim=c(8,145),dimnames=list(c('t','jaartal','Totaal','jongerdan25jaar','vanaf25jaar','België','Eurogebied','EU-27
'),1:145))
> 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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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 t jaartal jongerdan25jaar vanaf25jaar Belgi\303\253 Eurogebied
1 501 1 2000 134 368 6.7 8.5
2 485 2 2000 124 361 6.8 8.4
3 464 3 2000 113 351 6.7 8.4
4 460 4 2000 109 351 6.6 8.3
5 467 5 2001 109 358 6.4 8.2
6 460 6 2001 106 354 6.3 8.2
7 448 7 2001 101 347 6.3 8.1
8 443 8 2001 98 345 6.5 8.1
9 436 9 2001 93 343 6.5 8.1
10 431 10 2001 91 340 6.4 8.1
11 484 11 2001 122 362 6.2 8.1
12 510 12 2001 139 370 6.2 8.1
13 513 13 2001 140 373 6.5 8.1
14 503 14 2001 132 371 7.0 8.2
15 471 15 2001 117 354 7.2 8.2
16 471 16 2001 114 357 7.3 8.3
17 476 17 2002 113 363 7.4 8.2
18 475 18 2002 110 364 7.4 8.3
19 470 19 2002 107 363 7.4 8.3
20 461 20 2002 103 358 7.3 8.4
21 455 21 2002 98 357 7.4 8.5
22 456 22 2002 98 357 7.4 8.5
23 517 23 2002 137 380 7.6 8.6
24 525 24 2002 148 378 7.6 8.6
25 523 25 2002 147 376 7.7 8.7
26 519 26 2002 139 380 7.7 8.7
27 509 27 2002 130 379 7.8 8.8
28 512 28 2002 128 384 7.8 8.8
29 519 29 2003 127 392 8.0 8.9
30 517 30 2003 123 394 8.1 9.0
31 510 31 2003 118 392 8.1 9.0
32 509 32 2003 114 396 8.2 9.0
33 501 33 2003 108 392 8.1 9.0
34 507 34 2003 111 396 8.1 9.1
35 569 35 2003 151 419 8.1 9.1
36 580 36 2003 159 421 8.1 9.0
37 578 37 2003 158 420 8.2 9.1
38 565 38 2003 148 418 8.2 9.0
39 547 39 2003 138 410 8.3 9.1
40 555 40 2003 137 418 8.4 9.1
41 562 41 2004 136 426 8.6 9.2
42 561 42 2004 133 428 8.6 9.2
43 555 43 2004 126 430 8.4 9.2
44 544 44 2004 120 424 8.0 9.2
45 537 45 2004 114 423 7.9 9.2
46 543 46 2004 116 427 8.1 9.3
47 594 47 2004 153 441 8.5 9.3
48 611 48 2004 162 449 8.8 9.3
49 613 49 2004 161 452 8.8 9.3
50 611 50 2004 149 462 8.5 9.3
51 594 51 2004 139 455 8.3 9.4
52 595 52 2004 135 461 8.3 9.4
53 591 53 2005 130 461 8.3 9.3
54 589 54 2005 127 463 8.4 9.3
55 584 55 2005 122 462 8.5 9.3
56 573 56 2005 117 456 8.5 9.3
57 567 57 2005 112 455 8.6 9.2
58 569 58 2005 113 456 8.5 9.2
59 621 59 2005 149 472 8.6 9.2
60 629 60 2005 157 472 8.6 9.1
61 628 61 2005 157 471 8.6 9.1
62 612 62 2005 147 465 8.5 9.1
63 595 63 2005 137 459 8.4 9.1
64 597 64 2005 132 465 8.4 9.0
65 593 65 2006 125 468 8.5 8.9
66 590 66 2006 123 467 8.5 8.8
67 580 67 2006 117 463 8.5 8.7
68 574 68 2006 114 460 8.6 8.6
69 573 69 2006 111 462 8.6 8.6
70 573 70 2006 112 461 8.4 8.5
71 620 71 2006 144 476 8.2 8.4
72 626 72 2006 150 476 8.0 8.4
73 620 73 2006 149 471 8.0 8.3
74 588 74 2006 134 453 8.0 8.2
75 566 75 2006 123 443 8.0 8.2
76 557 76 2006 116 442 7.9 8.0
77 561 77 2007 117 444 7.9 7.9
78 549 78 2007 111 438 7.9 7.8
79 532 79 2007 105 427 7.9 7.7
80 526 80 2007 102 424 8.0 7.6
81 511 81 2007 95 416 7.9 7.6
82 499 82 2007 93 406 7.4 7.6
83 555 83 2007 124 431 7.2 7.6
84 565 84 2007 130 434 7.0 7.6
85 542 85 2007 124 418 6.9 7.5
86 527 86 2007 115 412 7.1 7.5
87 510 87 2007 106 404 7.2 7.4
88 514 88 2007 105 409 7.2 7.4
89 517 89 2008 105 412 7.1 7.4
90 508 90 2008 101 406 6.9 7.3
91 493 91 2008 95 398 6.8 7.3
92 490 92 2008 93 397 6.8 7.4
93 469 93 2008 84 385 6.8 7.5
94 478 94 2008 87 390 6.9 7.6
95 528 95 2008 116 413 7.1 7.6
96 534 96 2008 120 413 7.2 7.7
97 518 97 2008 117 401 7.2 7.7
98 506 98 2008 109 397 7.1 7.9
99 502 99 2008 105 397 7.1 8.1
100 516 100 2008 107 409 7.2 8.4
101 528 101 2009 109 419 7.5 8.7
102 533 102 2009 109 424 7.7 9.0
103 536 103 2009 108 428 7.8 9.3
104 537 104 2009 107 430 7.7 9.4
105 524 105 2009 99 424 7.7 9.5
106 536 106 2009 103 433 7.8 9.6
107 587 107 2009 131 456 8.0 9.8
108 597 108 2009 137 459 8.1 9.8
109 581 109 2009 135 446 8.1 9.9
110 564 110 2009 124 441 8.0 10.0
111 558 111 2009 118 439 8.1 10.0
112 575 112 2010 121 454 8.2 10.1
113 580 113 2010 121 460 8.4 10.1
114 575 114 2010 118 457 8.5 10.1
115 563 115 2010 113 451 8.5 10.1
116 552 116 2010 107 444 8.5 10.2
117 537 117 2010 100 437 8.5 10.2
118 545 118 2010 102 443 8.5 10.1
119 601 119 2010 130 471 8.4 10.1
120 604 120 2010 136 469 8.3 10.1
121 586 121 2010 133 454 8.2 10.1
122 564 122 2010 120 444 8.1 10.1
123 549 123 2010 112 436 7.9 10.1
124 551 124 2010 109 442 7.6 10.1
125 556 125 2011 110 446 7.3 10.0
126 548 126 2011 106 442 7.1 9.9
127 540 127 2011 102 438 7.0 9.9
128 531 128 2011 98 433 7.1 9.9
129 521 129 2011 92 428 7.1 9.9
130 519 130 2011 92 426 7.1 10.0
131 572 131 2011 120 452 7.3 10.1
132 581 132 2011 127 455 7.3 10.2
133 563 133 2011 124 439 7.3 10.3
134 548 134 2011 114 434 7.2 10.5
135 539 135 2011 108 431 7.2 10.6
136 541 136 2011 106 435 7.1 10.7
137 562 137 2012 111 450 7.1 10.8
138 559 138 2012 110 449 7.1 10.9
139 546 139 2012 104 442 7.2 11.0
140 536 140 2012 100 437 7.3 11.2
141 528 141 2012 96 431 7.4 11.3
142 530 142 2012 98 433 7.4 11.4
143 582 143 2012 122 460 7.5 11.5
144 599 144 2012 134 465 7.4 11.5
145 584 145 2012 133 451 7.4 11.6
EU-27\r
1 8.7
2 8.6
3 8.6
4 8.5
5 8.5
6 8.5
7 8.5
8 8.5
9 8.5
10 8.5
11 8.5
12 8.6
13 8.6
14 8.6
15 8.7
16 8.7
17 8.7
18 8.8
19 8.8
20 8.9
21 8.9
22 8.9
23 9.0
24 9.0
25 9.0
26 9.0
27 9.0
28 9.0
29 9.1
30 9.1
31 9.1
32 9.1
33 9.1
34 9.1
35 9.1
36 9.1
37 9.1
38 9.1
39 9.1
40 9.2
41 9.3
42 9.3
43 9.3
44 9.2
45 9.2
46 9.2
47 9.2
48 9.2
49 9.2
50 9.2
51 9.2
52 9.2
53 9.2
54 9.2
55 9.2
56 9.2
57 9.1
58 9.1
59 9.0
60 8.9
61 8.9
62 9.0
63 8.9
64 8.8
65 8.7
66 8.6
67 8.5
68 8.5
69 8.4
70 8.3
71 8.2
72 8.2
73 8.1
74 8.0
75 7.9
76 7.8
77 7.6
78 7.5
79 7.4
80 7.3
81 7.3
82 7.2
83 7.2
84 7.2
85 7.1
86 7.0
87 7.0
88 6.9
89 6.9
90 6.8
91 6.8
92 6.8
93 6.9
94 7.0
95 7.0
96 7.1
97 7.2
98 7.3
99 7.5
100 7.7
101 8.1
102 8.4
103 8.6
104 8.8
105 8.9
106 9.1
107 9.2
108 9.3
109 9.4
110 9.4
111 9.5
112 9.5
113 9.7
114 9.7
115 9.7
116 9.7
117 9.7
118 9.6
119 9.6
120 9.6
121 9.6
122 9.6
123 9.6
124 9.6
125 9.5
126 9.5
127 9.4
128 9.4
129 9.5
130 9.5
131 9.6
132 9.7
133 9.8
134 9.9
135 10.0
136 10.0
137 10.1
138 10.2
139 10.3
140 10.3
141 10.4
142 10.5
143 10.5
144 10.6
145 10.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) t jaartal jongerdan25jaar
-84.859846 0.001109 0.043127 0.995879
vanaf25jaar `Belgi\\303\\253` Eurogebied `EU-27\\r`
1.000234 -0.076660 -0.461901 0.385382
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1065 -0.1462 -0.0059 0.1496 1.1182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -84.859846 336.990516 -0.252 0.802
t 0.001109 0.014254 0.078 0.938
jaartal 0.043127 0.168533 0.256 0.798
jongerdan25jaar 0.995879 0.003930 253.403 <2e-16 ***
vanaf25jaar 1.000234 0.003058 327.134 <2e-16 ***
`Belgi\\303\\253` -0.076660 0.113462 -0.676 0.500
Eurogebied -0.461901 0.372345 -1.241 0.217
`EU-27\\r` 0.385382 0.350866 1.098 0.274
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5042 on 137 degrees of freedom
Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
F-statistic: 1.661e+05 on 7 and 137 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,] 0.0441230367 0.088246073 0.9558770
[2,] 0.3573185288 0.714637058 0.6426815
[3,] 0.2968624315 0.593724863 0.7031376
[4,] 0.1934032779 0.386806556 0.8065967
[5,] 0.1157020034 0.231404007 0.8842980
[6,] 0.1047585833 0.209517167 0.8952414
[7,] 0.0663808915 0.132761783 0.9336191
[8,] 0.2389261690 0.477852338 0.7610738
[9,] 0.2425732441 0.485146488 0.7574268
[10,] 0.2040925088 0.408185018 0.7959075
[11,] 0.1472493216 0.294498643 0.8527507
[12,] 0.2223422871 0.444684574 0.7776577
[13,] 0.1841944158 0.368388832 0.8158056
[14,] 0.1821302240 0.364260448 0.8178698
[15,] 0.2417708528 0.483541706 0.7582291
[16,] 0.1867755290 0.373551058 0.8132245
[17,] 0.1452078133 0.290415627 0.8547922
[18,] 0.1207206855 0.241441371 0.8792793
[19,] 0.0883050196 0.176610039 0.9116950
[20,] 0.0632875752 0.126575150 0.9367124
[21,] 0.0452865733 0.090573147 0.9547134
[22,] 0.1479019044 0.295803809 0.8520981
[23,] 0.2637105374 0.527421075 0.7362895
[24,] 0.2188830133 0.437766027 0.7811170
[25,] 0.2636168466 0.527233693 0.7363832
[26,] 0.2346543120 0.469308624 0.7653457
[27,] 0.2133129031 0.426625806 0.7866871
[28,] 0.3093186181 0.618637236 0.6906814
[29,] 0.3645665795 0.729133159 0.6354334
[30,] 0.3134188357 0.626837671 0.6865812
[31,] 0.2658351750 0.531670350 0.7341648
[32,] 0.2208755724 0.441751145 0.7791244
[33,] 0.3618166032 0.723633206 0.6381834
[34,] 0.3121495474 0.624299095 0.6878505
[35,] 0.2649296144 0.529859229 0.7350704
[36,] 0.2236140440 0.447228088 0.7763860
[37,] 0.2086492044 0.417298409 0.7913508
[38,] 0.1959938038 0.391987608 0.8040062
[39,] 0.1734772637 0.346954527 0.8265227
[40,] 0.1456633976 0.291326795 0.8543366
[41,] 0.1194010095 0.238802019 0.8805990
[42,] 0.1682530021 0.336506004 0.8317470
[43,] 0.1371067244 0.274213449 0.8628933
[44,] 0.2123592540 0.424718508 0.7876407
[45,] 0.1800091922 0.360018384 0.8199908
[46,] 0.1489512916 0.297902583 0.8510487
[47,] 0.1210496124 0.242099225 0.8789504
[48,] 0.0973733494 0.194746699 0.9026267
[49,] 0.0772537227 0.154507445 0.9227463
[50,] 0.0604042694 0.120808539 0.9395957
[51,] 0.0468166912 0.093633382 0.9531833
[52,] 0.0357297953 0.071459591 0.9642702
[53,] 0.0853567861 0.170713572 0.9146432
[54,] 0.0680704161 0.136140832 0.9319296
[55,] 0.0537999153 0.107599831 0.9462001
[56,] 0.0419611809 0.083922362 0.9580388
[57,] 0.0324296373 0.064859275 0.9675704
[58,] 0.0251521359 0.050304272 0.9748479
[59,] 0.0187285749 0.037457150 0.9812714
[60,] 0.0138277202 0.027655440 0.9861723
[61,] 0.0099413130 0.019882626 0.9900587
[62,] 0.0070506769 0.014101354 0.9929493
[63,] 0.0049350625 0.009870125 0.9950649
[64,] 0.0094021867 0.018804373 0.9905978
[65,] 0.0073446888 0.014689378 0.9926553
[66,] 0.0309156180 0.061831236 0.9690844
[67,] 0.0236197171 0.047239434 0.9763803
[68,] 0.0177937217 0.035587443 0.9822063
[69,] 0.0132957379 0.026591476 0.9867043
[70,] 0.0097026420 0.019405284 0.9902974
[71,] 0.0071932105 0.014386421 0.9928068
[72,] 0.0057800557 0.011560111 0.9942199
[73,] 0.0041756246 0.008351249 0.9958244
[74,] 0.0080388545 0.016077709 0.9919611
[75,] 0.0060116980 0.012023396 0.9939883
[76,] 0.0042729007 0.008545801 0.9957271
[77,] 0.0031472113 0.006294423 0.9968528
[78,] 0.0022416102 0.004483220 0.9977584
[79,] 0.0015517449 0.003103490 0.9984483
[80,] 0.0027181261 0.005436252 0.9972819
[81,] 0.0020589649 0.004117930 0.9979410
[82,] 0.0014764021 0.002952804 0.9985236
[83,] 0.0011870150 0.002374030 0.9988130
[84,] 0.0016869014 0.003373803 0.9983131
[85,] 0.0058806148 0.011761230 0.9941194
[86,] 0.0151041295 0.030208259 0.9848959
[87,] 0.0113876456 0.022775291 0.9886124
[88,] 0.0081214915 0.016242983 0.9918785
[89,] 0.0057841877 0.011568375 0.9942158
[90,] 0.0041947247 0.008389449 0.9958053
[91,] 0.0028804626 0.005760925 0.9971195
[92,] 0.0019347063 0.003869413 0.9980653
[93,] 0.0013286283 0.002657257 0.9986714
[94,] 0.0010604722 0.002120944 0.9989395
[95,] 0.0012145448 0.002429090 0.9987855
[96,] 0.0010136277 0.002027255 0.9989864
[97,] 0.0006436949 0.001287390 0.9993563
[98,] 0.0023534575 0.004706915 0.9976465
[99,] 0.0017502910 0.003500582 0.9982497
[100,] 0.0076184378 0.015236876 0.9923816
[101,] 0.0119480600 0.023896120 0.9880519
[102,] 0.0093772916 0.018754583 0.9906227
[103,] 0.0164155658 0.032831132 0.9835844
[104,] 0.0119509657 0.023901931 0.9880490
[105,] 0.0299097473 0.059819495 0.9700903
[106,] 0.0574369466 0.114873893 0.9425631
[107,] 0.0410948859 0.082189772 0.9589051
[108,] 0.0288160627 0.057632125 0.9711839
[109,] 0.0249079026 0.049815805 0.9750921
[110,] 0.0260044945 0.052008989 0.9739955
[111,] 0.0427253266 0.085450653 0.9572747
[112,] 0.0327664504 0.065532901 0.9672335
[113,] 0.0555698401 0.111139680 0.9444302
[114,] 0.0528936703 0.105787341 0.9471063
[115,] 0.0489782356 0.097956471 0.9510218
[116,] 0.0494801822 0.098960364 0.9505198
[117,] 0.0359346922 0.071869384 0.9640653
[118,] 0.0223799436 0.044759887 0.9776201
[119,] 0.0189573541 0.037914708 0.9810426
[120,] 0.0168841898 0.033768380 0.9831158
[121,] 0.0123757382 0.024751476 0.9876243
[122,] 0.0225683386 0.045136677 0.9774317
[123,] 0.1930831920 0.386166384 0.8069168
[124,] 0.1301768929 0.260353786 0.8698231
> postscript(file="/var/fisher/rcomp/tmp/163p11352144485.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ukho1352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i2b21352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4utc21352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5ywsu1352144485.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 = 145
Frequency = 1
1 2 3 4 5 6
-0.842685591 0.116645547 0.064877508 0.031965796 -0.075430892 -0.095632975
7 8 9 10 11 12
-0.161899462 -0.159571713 -0.180818658 -0.197133525 -0.090963373 0.937579539
13 14 15 16 17 18
-0.037111940 0.013797322 -0.068357438 -0.028676167 -0.116962016 0.876983213
19 20 21 22 23 24
-0.136255188 -0.152692635 -0.120317573 0.878573900 0.055796494 -0.899509199
25 26 27 28 29 30
0.149585288 0.114569687 0.130459435 0.119937692 0.092691591 0.128485645
31 32 33 34 35 36
0.107238700 -0.903625569 1.063808363 0.120317473 -0.721322495 0.263881280
37 38 39 40 41 42
0.312741655 -0.775302032 -0.761894829 0.200130193 0.172884092 0.158943356
43 44 45 46 47 48
-0.886814730 0.096627617 0.063359213 0.131079032 0.309846160 0.366954748
49 50 51 52 53 54
0.361022556 0.285118916 0.275293952 -0.743704560 0.145262931 -0.861011787
55 56 57 58 59 60
0.125173175 0.104862677 0.083395785 0.078508456 0.268226223 0.292436469
61 62 63 64 65 66
0.291562053 0.204440648 -0.805604262 0.163624045 0.089850808 0.073081883
67 68 69 70 71 72
0.040529980 -0.010764278 0.013833233 -0.005903744 0.098374666 0.106662071
73 74 75 76 77 78
0.094950922 1.028584630 0.023020856 -1.068210829 -0.077907099 -0.109990778
79 80 81 82 83 84
-0.140903898 -0.153659910 -0.189410859 -0.196212770 -0.090744953 0.916840117
85 86 87 88 89 90
-0.120568463 -0.103493990 -0.178345659 -0.146207827 -0.198811949 0.762014991
91 92 93 94 95 96
-0.269614630 -0.232541602 -0.260280889 0.765121881 -1.106520661 0.924173997
97 98 99 100 101 102
-0.125027421 -0.111994192 -0.114284327 -0.040799714 -0.071718576 -0.035710066
103 104 105 106 107 108
0.027283455 -0.016967036 0.958010334 -0.051940262 0.126137821 1.118182700
109 110 111 112 113 114
0.119526822 -0.887221675 1.056537823 0.075010481 -0.989247175 0.005648667
115 116 117 118 119 120
-1.014661831 1.007330555 -0.020988488 -0.022910882 0.077156633 -0.906421720
121 122 123 124 125 126
-0.924048573 0.015940731 0.968402435 -0.069472803 -0.141173599 -0.219353129
127 128 129 130 131 132
-0.205138294 -0.213895557 0.722900258 0.768450054 -0.100364298 -1.065674010
133 134 135 136 137 138
-0.067748880 -0.062724196 -0.080206505 -0.051970054 0.928540995 -0.068802896
139 140 141 142 143 144
-0.077682739 -0.994059802 1.005068900 -0.980613341 0.164725110 0.165697698
145
0.209935507
> postscript(file="/var/fisher/rcomp/tmp/6d9aj1352144485.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.842685591 NA
1 0.116645547 -0.842685591
2 0.064877508 0.116645547
3 0.031965796 0.064877508
4 -0.075430892 0.031965796
5 -0.095632975 -0.075430892
6 -0.161899462 -0.095632975
7 -0.159571713 -0.161899462
8 -0.180818658 -0.159571713
9 -0.197133525 -0.180818658
10 -0.090963373 -0.197133525
11 0.937579539 -0.090963373
12 -0.037111940 0.937579539
13 0.013797322 -0.037111940
14 -0.068357438 0.013797322
15 -0.028676167 -0.068357438
16 -0.116962016 -0.028676167
17 0.876983213 -0.116962016
18 -0.136255188 0.876983213
19 -0.152692635 -0.136255188
20 -0.120317573 -0.152692635
21 0.878573900 -0.120317573
22 0.055796494 0.878573900
23 -0.899509199 0.055796494
24 0.149585288 -0.899509199
25 0.114569687 0.149585288
26 0.130459435 0.114569687
27 0.119937692 0.130459435
28 0.092691591 0.119937692
29 0.128485645 0.092691591
30 0.107238700 0.128485645
31 -0.903625569 0.107238700
32 1.063808363 -0.903625569
33 0.120317473 1.063808363
34 -0.721322495 0.120317473
35 0.263881280 -0.721322495
36 0.312741655 0.263881280
37 -0.775302032 0.312741655
38 -0.761894829 -0.775302032
39 0.200130193 -0.761894829
40 0.172884092 0.200130193
41 0.158943356 0.172884092
42 -0.886814730 0.158943356
43 0.096627617 -0.886814730
44 0.063359213 0.096627617
45 0.131079032 0.063359213
46 0.309846160 0.131079032
47 0.366954748 0.309846160
48 0.361022556 0.366954748
49 0.285118916 0.361022556
50 0.275293952 0.285118916
51 -0.743704560 0.275293952
52 0.145262931 -0.743704560
53 -0.861011787 0.145262931
54 0.125173175 -0.861011787
55 0.104862677 0.125173175
56 0.083395785 0.104862677
57 0.078508456 0.083395785
58 0.268226223 0.078508456
59 0.292436469 0.268226223
60 0.291562053 0.292436469
61 0.204440648 0.291562053
62 -0.805604262 0.204440648
63 0.163624045 -0.805604262
64 0.089850808 0.163624045
65 0.073081883 0.089850808
66 0.040529980 0.073081883
67 -0.010764278 0.040529980
68 0.013833233 -0.010764278
69 -0.005903744 0.013833233
70 0.098374666 -0.005903744
71 0.106662071 0.098374666
72 0.094950922 0.106662071
73 1.028584630 0.094950922
74 0.023020856 1.028584630
75 -1.068210829 0.023020856
76 -0.077907099 -1.068210829
77 -0.109990778 -0.077907099
78 -0.140903898 -0.109990778
79 -0.153659910 -0.140903898
80 -0.189410859 -0.153659910
81 -0.196212770 -0.189410859
82 -0.090744953 -0.196212770
83 0.916840117 -0.090744953
84 -0.120568463 0.916840117
85 -0.103493990 -0.120568463
86 -0.178345659 -0.103493990
87 -0.146207827 -0.178345659
88 -0.198811949 -0.146207827
89 0.762014991 -0.198811949
90 -0.269614630 0.762014991
91 -0.232541602 -0.269614630
92 -0.260280889 -0.232541602
93 0.765121881 -0.260280889
94 -1.106520661 0.765121881
95 0.924173997 -1.106520661
96 -0.125027421 0.924173997
97 -0.111994192 -0.125027421
98 -0.114284327 -0.111994192
99 -0.040799714 -0.114284327
100 -0.071718576 -0.040799714
101 -0.035710066 -0.071718576
102 0.027283455 -0.035710066
103 -0.016967036 0.027283455
104 0.958010334 -0.016967036
105 -0.051940262 0.958010334
106 0.126137821 -0.051940262
107 1.118182700 0.126137821
108 0.119526822 1.118182700
109 -0.887221675 0.119526822
110 1.056537823 -0.887221675
111 0.075010481 1.056537823
112 -0.989247175 0.075010481
113 0.005648667 -0.989247175
114 -1.014661831 0.005648667
115 1.007330555 -1.014661831
116 -0.020988488 1.007330555
117 -0.022910882 -0.020988488
118 0.077156633 -0.022910882
119 -0.906421720 0.077156633
120 -0.924048573 -0.906421720
121 0.015940731 -0.924048573
122 0.968402435 0.015940731
123 -0.069472803 0.968402435
124 -0.141173599 -0.069472803
125 -0.219353129 -0.141173599
126 -0.205138294 -0.219353129
127 -0.213895557 -0.205138294
128 0.722900258 -0.213895557
129 0.768450054 0.722900258
130 -0.100364298 0.768450054
131 -1.065674010 -0.100364298
132 -0.067748880 -1.065674010
133 -0.062724196 -0.067748880
134 -0.080206505 -0.062724196
135 -0.051970054 -0.080206505
136 0.928540995 -0.051970054
137 -0.068802896 0.928540995
138 -0.077682739 -0.068802896
139 -0.994059802 -0.077682739
140 1.005068900 -0.994059802
141 -0.980613341 1.005068900
142 0.164725110 -0.980613341
143 0.165697698 0.164725110
144 0.209935507 0.165697698
145 NA 0.209935507
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.116645547 -0.842685591
[2,] 0.064877508 0.116645547
[3,] 0.031965796 0.064877508
[4,] -0.075430892 0.031965796
[5,] -0.095632975 -0.075430892
[6,] -0.161899462 -0.095632975
[7,] -0.159571713 -0.161899462
[8,] -0.180818658 -0.159571713
[9,] -0.197133525 -0.180818658
[10,] -0.090963373 -0.197133525
[11,] 0.937579539 -0.090963373
[12,] -0.037111940 0.937579539
[13,] 0.013797322 -0.037111940
[14,] -0.068357438 0.013797322
[15,] -0.028676167 -0.068357438
[16,] -0.116962016 -0.028676167
[17,] 0.876983213 -0.116962016
[18,] -0.136255188 0.876983213
[19,] -0.152692635 -0.136255188
[20,] -0.120317573 -0.152692635
[21,] 0.878573900 -0.120317573
[22,] 0.055796494 0.878573900
[23,] -0.899509199 0.055796494
[24,] 0.149585288 -0.899509199
[25,] 0.114569687 0.149585288
[26,] 0.130459435 0.114569687
[27,] 0.119937692 0.130459435
[28,] 0.092691591 0.119937692
[29,] 0.128485645 0.092691591
[30,] 0.107238700 0.128485645
[31,] -0.903625569 0.107238700
[32,] 1.063808363 -0.903625569
[33,] 0.120317473 1.063808363
[34,] -0.721322495 0.120317473
[35,] 0.263881280 -0.721322495
[36,] 0.312741655 0.263881280
[37,] -0.775302032 0.312741655
[38,] -0.761894829 -0.775302032
[39,] 0.200130193 -0.761894829
[40,] 0.172884092 0.200130193
[41,] 0.158943356 0.172884092
[42,] -0.886814730 0.158943356
[43,] 0.096627617 -0.886814730
[44,] 0.063359213 0.096627617
[45,] 0.131079032 0.063359213
[46,] 0.309846160 0.131079032
[47,] 0.366954748 0.309846160
[48,] 0.361022556 0.366954748
[49,] 0.285118916 0.361022556
[50,] 0.275293952 0.285118916
[51,] -0.743704560 0.275293952
[52,] 0.145262931 -0.743704560
[53,] -0.861011787 0.145262931
[54,] 0.125173175 -0.861011787
[55,] 0.104862677 0.125173175
[56,] 0.083395785 0.104862677
[57,] 0.078508456 0.083395785
[58,] 0.268226223 0.078508456
[59,] 0.292436469 0.268226223
[60,] 0.291562053 0.292436469
[61,] 0.204440648 0.291562053
[62,] -0.805604262 0.204440648
[63,] 0.163624045 -0.805604262
[64,] 0.089850808 0.163624045
[65,] 0.073081883 0.089850808
[66,] 0.040529980 0.073081883
[67,] -0.010764278 0.040529980
[68,] 0.013833233 -0.010764278
[69,] -0.005903744 0.013833233
[70,] 0.098374666 -0.005903744
[71,] 0.106662071 0.098374666
[72,] 0.094950922 0.106662071
[73,] 1.028584630 0.094950922
[74,] 0.023020856 1.028584630
[75,] -1.068210829 0.023020856
[76,] -0.077907099 -1.068210829
[77,] -0.109990778 -0.077907099
[78,] -0.140903898 -0.109990778
[79,] -0.153659910 -0.140903898
[80,] -0.189410859 -0.153659910
[81,] -0.196212770 -0.189410859
[82,] -0.090744953 -0.196212770
[83,] 0.916840117 -0.090744953
[84,] -0.120568463 0.916840117
[85,] -0.103493990 -0.120568463
[86,] -0.178345659 -0.103493990
[87,] -0.146207827 -0.178345659
[88,] -0.198811949 -0.146207827
[89,] 0.762014991 -0.198811949
[90,] -0.269614630 0.762014991
[91,] -0.232541602 -0.269614630
[92,] -0.260280889 -0.232541602
[93,] 0.765121881 -0.260280889
[94,] -1.106520661 0.765121881
[95,] 0.924173997 -1.106520661
[96,] -0.125027421 0.924173997
[97,] -0.111994192 -0.125027421
[98,] -0.114284327 -0.111994192
[99,] -0.040799714 -0.114284327
[100,] -0.071718576 -0.040799714
[101,] -0.035710066 -0.071718576
[102,] 0.027283455 -0.035710066
[103,] -0.016967036 0.027283455
[104,] 0.958010334 -0.016967036
[105,] -0.051940262 0.958010334
[106,] 0.126137821 -0.051940262
[107,] 1.118182700 0.126137821
[108,] 0.119526822 1.118182700
[109,] -0.887221675 0.119526822
[110,] 1.056537823 -0.887221675
[111,] 0.075010481 1.056537823
[112,] -0.989247175 0.075010481
[113,] 0.005648667 -0.989247175
[114,] -1.014661831 0.005648667
[115,] 1.007330555 -1.014661831
[116,] -0.020988488 1.007330555
[117,] -0.022910882 -0.020988488
[118,] 0.077156633 -0.022910882
[119,] -0.906421720 0.077156633
[120,] -0.924048573 -0.906421720
[121,] 0.015940731 -0.924048573
[122,] 0.968402435 0.015940731
[123,] -0.069472803 0.968402435
[124,] -0.141173599 -0.069472803
[125,] -0.219353129 -0.141173599
[126,] -0.205138294 -0.219353129
[127,] -0.213895557 -0.205138294
[128,] 0.722900258 -0.213895557
[129,] 0.768450054 0.722900258
[130,] -0.100364298 0.768450054
[131,] -1.065674010 -0.100364298
[132,] -0.067748880 -1.065674010
[133,] -0.062724196 -0.067748880
[134,] -0.080206505 -0.062724196
[135,] -0.051970054 -0.080206505
[136,] 0.928540995 -0.051970054
[137,] -0.068802896 0.928540995
[138,] -0.077682739 -0.068802896
[139,] -0.994059802 -0.077682739
[140,] 1.005068900 -0.994059802
[141,] -0.980613341 1.005068900
[142,] 0.164725110 -0.980613341
[143,] 0.165697698 0.164725110
[144,] 0.209935507 0.165697698
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.116645547 -0.842685591
2 0.064877508 0.116645547
3 0.031965796 0.064877508
4 -0.075430892 0.031965796
5 -0.095632975 -0.075430892
6 -0.161899462 -0.095632975
7 -0.159571713 -0.161899462
8 -0.180818658 -0.159571713
9 -0.197133525 -0.180818658
10 -0.090963373 -0.197133525
11 0.937579539 -0.090963373
12 -0.037111940 0.937579539
13 0.013797322 -0.037111940
14 -0.068357438 0.013797322
15 -0.028676167 -0.068357438
16 -0.116962016 -0.028676167
17 0.876983213 -0.116962016
18 -0.136255188 0.876983213
19 -0.152692635 -0.136255188
20 -0.120317573 -0.152692635
21 0.878573900 -0.120317573
22 0.055796494 0.878573900
23 -0.899509199 0.055796494
24 0.149585288 -0.899509199
25 0.114569687 0.149585288
26 0.130459435 0.114569687
27 0.119937692 0.130459435
28 0.092691591 0.119937692
29 0.128485645 0.092691591
30 0.107238700 0.128485645
31 -0.903625569 0.107238700
32 1.063808363 -0.903625569
33 0.120317473 1.063808363
34 -0.721322495 0.120317473
35 0.263881280 -0.721322495
36 0.312741655 0.263881280
37 -0.775302032 0.312741655
38 -0.761894829 -0.775302032
39 0.200130193 -0.761894829
40 0.172884092 0.200130193
41 0.158943356 0.172884092
42 -0.886814730 0.158943356
43 0.096627617 -0.886814730
44 0.063359213 0.096627617
45 0.131079032 0.063359213
46 0.309846160 0.131079032
47 0.366954748 0.309846160
48 0.361022556 0.366954748
49 0.285118916 0.361022556
50 0.275293952 0.285118916
51 -0.743704560 0.275293952
52 0.145262931 -0.743704560
53 -0.861011787 0.145262931
54 0.125173175 -0.861011787
55 0.104862677 0.125173175
56 0.083395785 0.104862677
57 0.078508456 0.083395785
58 0.268226223 0.078508456
59 0.292436469 0.268226223
60 0.291562053 0.292436469
61 0.204440648 0.291562053
62 -0.805604262 0.204440648
63 0.163624045 -0.805604262
64 0.089850808 0.163624045
65 0.073081883 0.089850808
66 0.040529980 0.073081883
67 -0.010764278 0.040529980
68 0.013833233 -0.010764278
69 -0.005903744 0.013833233
70 0.098374666 -0.005903744
71 0.106662071 0.098374666
72 0.094950922 0.106662071
73 1.028584630 0.094950922
74 0.023020856 1.028584630
75 -1.068210829 0.023020856
76 -0.077907099 -1.068210829
77 -0.109990778 -0.077907099
78 -0.140903898 -0.109990778
79 -0.153659910 -0.140903898
80 -0.189410859 -0.153659910
81 -0.196212770 -0.189410859
82 -0.090744953 -0.196212770
83 0.916840117 -0.090744953
84 -0.120568463 0.916840117
85 -0.103493990 -0.120568463
86 -0.178345659 -0.103493990
87 -0.146207827 -0.178345659
88 -0.198811949 -0.146207827
89 0.762014991 -0.198811949
90 -0.269614630 0.762014991
91 -0.232541602 -0.269614630
92 -0.260280889 -0.232541602
93 0.765121881 -0.260280889
94 -1.106520661 0.765121881
95 0.924173997 -1.106520661
96 -0.125027421 0.924173997
97 -0.111994192 -0.125027421
98 -0.114284327 -0.111994192
99 -0.040799714 -0.114284327
100 -0.071718576 -0.040799714
101 -0.035710066 -0.071718576
102 0.027283455 -0.035710066
103 -0.016967036 0.027283455
104 0.958010334 -0.016967036
105 -0.051940262 0.958010334
106 0.126137821 -0.051940262
107 1.118182700 0.126137821
108 0.119526822 1.118182700
109 -0.887221675 0.119526822
110 1.056537823 -0.887221675
111 0.075010481 1.056537823
112 -0.989247175 0.075010481
113 0.005648667 -0.989247175
114 -1.014661831 0.005648667
115 1.007330555 -1.014661831
116 -0.020988488 1.007330555
117 -0.022910882 -0.020988488
118 0.077156633 -0.022910882
119 -0.906421720 0.077156633
120 -0.924048573 -0.906421720
121 0.015940731 -0.924048573
122 0.968402435 0.015940731
123 -0.069472803 0.968402435
124 -0.141173599 -0.069472803
125 -0.219353129 -0.141173599
126 -0.205138294 -0.219353129
127 -0.213895557 -0.205138294
128 0.722900258 -0.213895557
129 0.768450054 0.722900258
130 -0.100364298 0.768450054
131 -1.065674010 -0.100364298
132 -0.067748880 -1.065674010
133 -0.062724196 -0.067748880
134 -0.080206505 -0.062724196
135 -0.051970054 -0.080206505
136 0.928540995 -0.051970054
137 -0.068802896 0.928540995
138 -0.077682739 -0.068802896
139 -0.994059802 -0.077682739
140 1.005068900 -0.994059802
141 -0.980613341 1.005068900
142 0.164725110 -0.980613341
143 0.165697698 0.164725110
144 0.209935507 0.165697698
> 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/7d4v81352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8t9p11352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9ozkf1352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10abtk1352144485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yc5d1352144485.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/122o9z1352144485.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/13beya1352144485.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/14pw071352144485.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/15ua2v1352144485.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/16y1ky1352144485.tab")
+ }
>
> try(system("convert tmp/163p11352144485.ps tmp/163p11352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ukho1352144485.ps tmp/2ukho1352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i2b21352144485.ps tmp/3i2b21352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/4utc21352144485.ps tmp/4utc21352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ywsu1352144485.ps tmp/5ywsu1352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d9aj1352144485.ps tmp/6d9aj1352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d4v81352144485.ps tmp/7d4v81352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t9p11352144485.ps tmp/8t9p11352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ozkf1352144485.ps tmp/9ozkf1352144485.png",intern=TRUE))
character(0)
> try(system("convert tmp/10abtk1352144485.ps tmp/10abtk1352144485.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.809 1.156 8.961