R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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.
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Type 'license()' or 'licence()' for distribution details.
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'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(493
+ ,0.3
+ ,9
+ ,3
+ ,481
+ ,2.1
+ ,11
+ ,3.21
+ ,462
+ ,2.5
+ ,13
+ ,3.37
+ ,457
+ ,2.3
+ ,12
+ ,3.51
+ ,442
+ ,2.4
+ ,13
+ ,3.75
+ ,439
+ ,3
+ ,15
+ ,4.11
+ ,488
+ ,1.7
+ ,13
+ ,4.25
+ ,521
+ ,3.5
+ ,16
+ ,4.25
+ ,501
+ ,4
+ ,10
+ ,4.5
+ ,485
+ ,3.7
+ ,14
+ ,4.7
+ ,464
+ ,3.7
+ ,14
+ ,4.75
+ ,460
+ ,3
+ ,15
+ ,4.75
+ ,467
+ ,2.7
+ ,13
+ ,4.75
+ ,460
+ ,2.5
+ ,8
+ ,4.75
+ ,448
+ ,2.2
+ ,7
+ ,4.75
+ ,443
+ ,2.9
+ ,3
+ ,4.75
+ ,436
+ ,3.1
+ ,3
+ ,4.58
+ ,431
+ ,3
+ ,4
+ ,4.5
+ ,484
+ ,2.8
+ ,4
+ ,4.5
+ ,510
+ ,2.5
+ ,0
+ ,4.49
+ ,513
+ ,1.9
+ ,-4
+ ,4.03
+ ,503
+ ,1.9
+ ,-14
+ ,3.75
+ ,471
+ ,1.8
+ ,-18
+ ,3.39
+ ,471
+ ,2
+ ,-8
+ ,3.25
+ ,476
+ ,2.6
+ ,-1
+ ,3.25
+ ,475
+ ,2.5
+ ,1
+ ,3.25
+ ,470
+ ,2.5
+ ,2
+ ,3.25
+ ,461
+ ,1.6
+ ,0
+ ,3.25
+ ,455
+ ,1.4
+ ,1
+ ,3.25
+ ,456
+ ,0.8
+ ,0
+ ,3.25
+ ,517
+ ,1.1
+ ,-1
+ ,3.25
+ ,525
+ ,1.3
+ ,-3
+ ,3.25
+ ,523
+ ,1.2
+ ,-3
+ ,3.25
+ ,519
+ ,1.3
+ ,-3
+ ,3.25
+ ,509
+ ,1.1
+ ,-4
+ ,3.25
+ ,512
+ ,1.3
+ ,-8
+ ,2.85
+ ,519
+ ,1.2
+ ,-9
+ ,2.75
+ ,517
+ ,1.6
+ ,-13
+ ,2.75
+ ,510
+ ,1.7
+ ,-18
+ ,2.55
+ ,509
+ ,1.5
+ ,-11
+ ,2.5
+ ,501
+ ,0.9
+ ,-9
+ ,2.5
+ ,507
+ ,1.5
+ ,-10
+ ,2.1
+ ,569
+ ,1.4
+ ,-13
+ ,2
+ ,580
+ ,1.6
+ ,-11
+ ,2
+ ,578
+ ,1.7
+ ,-5
+ ,2
+ ,565
+ ,1.4
+ ,-15
+ ,2
+ ,547
+ ,1.8
+ ,-6
+ ,2
+ ,555
+ ,1.7
+ ,-6
+ ,2
+ ,562
+ ,1.4
+ ,-3
+ ,2
+ ,561
+ ,1.2
+ ,-1
+ ,2
+ ,555
+ ,1
+ ,-3
+ ,2
+ ,544
+ ,1.7
+ ,-4
+ ,2
+ ,537
+ ,2.4
+ ,-6
+ ,2
+ ,543
+ ,2
+ ,0
+ ,2
+ ,594
+ ,2.1
+ ,-4
+ ,2
+ ,611
+ ,2
+ ,-2
+ ,2
+ ,613
+ ,1.8
+ ,-2
+ ,2
+ ,611
+ ,2.7
+ ,-6
+ ,2
+ ,594
+ ,2.3
+ ,-7
+ ,2
+ ,595
+ ,1.9
+ ,-6
+ ,2
+ ,591
+ ,2
+ ,-6
+ ,2
+ ,589
+ ,2.3
+ ,-3
+ ,2
+ ,584
+ ,2.8
+ ,-2
+ ,2
+ ,573
+ ,2.4
+ ,-5
+ ,2
+ ,567
+ ,2.3
+ ,-11
+ ,2
+ ,569
+ ,2.7
+ ,-11
+ ,2
+ ,621
+ ,2.7
+ ,-11
+ ,2
+ ,629
+ ,2.9
+ ,-10
+ ,2
+ ,628
+ ,3
+ ,-14
+ ,2
+ ,612
+ ,2.2
+ ,-8
+ ,2
+ ,595
+ ,2.3
+ ,-9
+ ,2
+ ,597
+ ,2.8
+ ,-5
+ ,2.21
+ ,593
+ ,2.8
+ ,-1
+ ,2.25
+ ,590
+ ,2.8
+ ,-2
+ ,2.25
+ ,580
+ ,2.2
+ ,-5
+ ,2.45
+ ,574
+ ,2.6
+ ,-4
+ ,2.5
+ ,573
+ ,2.8
+ ,-6
+ ,2.5
+ ,573
+ ,2.5
+ ,-2
+ ,2.64
+ ,620
+ ,2.4
+ ,-2
+ ,2.75
+ ,626
+ ,2.3
+ ,-2
+ ,2.93
+ ,620
+ ,1.9
+ ,-2
+ ,3
+ ,588
+ ,1.7
+ ,2
+ ,3.17
+ ,566
+ ,2
+ ,1
+ ,3.25
+ ,557
+ ,2.1
+ ,-8
+ ,3.39
+ ,561
+ ,1.7
+ ,-1
+ ,3.5
+ ,549
+ ,1.8
+ ,1
+ ,3.5
+ ,532
+ ,1.8
+ ,-1
+ ,3.65
+ ,526
+ ,1.8
+ ,2
+ ,3.75
+ ,511
+ ,1.3
+ ,2
+ ,3.75
+ ,499
+ ,1.3
+ ,1
+ ,3.9
+ ,555
+ ,1.3
+ ,-1
+ ,4
+ ,565
+ ,1.2
+ ,-2
+ ,4
+ ,542
+ ,1.4
+ ,-2
+ ,4
+ ,527
+ ,2.2
+ ,-1
+ ,4
+ ,510
+ ,2.9
+ ,-8
+ ,4
+ ,514
+ ,3.1
+ ,-4
+ ,4
+ ,517
+ ,3.5
+ ,-6
+ ,4
+ ,508
+ ,3.6
+ ,-3
+ ,4
+ ,493
+ ,4.4
+ ,-3
+ ,4
+ ,490
+ ,4.1
+ ,-7
+ ,4
+ ,469
+ ,5.1
+ ,-9
+ ,4
+ ,478
+ ,5.8
+ ,-11
+ ,4
+ ,528
+ ,5.9
+ ,-13
+ ,4.18
+ ,534
+ ,5.4
+ ,-11
+ ,4.25
+ ,518
+ ,5.5
+ ,-9
+ ,4.25
+ ,506
+ ,4.8
+ ,-17
+ ,3.97
+ ,502
+ ,3.2
+ ,-22
+ ,3.42
+ ,516
+ ,2.7
+ ,-25
+ ,2.75
+ ,528
+ ,2.1
+ ,-20
+ ,2.31
+ ,533
+ ,1.9
+ ,-24
+ ,2
+ ,536
+ ,0.6
+ ,-24
+ ,1.66
+ ,537
+ ,0.7
+ ,-22
+ ,1.31
+ ,524
+ ,-0.2
+ ,-19
+ ,1.09
+ ,536
+ ,-1
+ ,-18
+ ,1
+ ,587
+ ,-1.7
+ ,-17
+ ,1
+ ,597
+ ,-0.7
+ ,-11
+ ,1
+ ,581
+ ,-1
+ ,-11
+ ,1
+ ,564
+ ,-0.9
+ ,-12
+ ,1
+ ,558
+ ,0
+ ,-10
+ ,1
+ ,575
+ ,0.3
+ ,-15
+ ,1
+ ,580
+ ,0.8
+ ,-15
+ ,1
+ ,575
+ ,0.8
+ ,-15
+ ,1
+ ,563
+ ,1.9
+ ,-13
+ ,1
+ ,552
+ ,2.1
+ ,-8
+ ,1
+ ,537
+ ,2.5
+ ,-13
+ ,1
+ ,545
+ ,2.7
+ ,-9
+ ,1
+ ,601
+ ,2.4
+ ,-7
+ ,1
+ ,604
+ ,2.4
+ ,-4
+ ,1
+ ,586
+ ,2.9
+ ,-4
+ ,1
+ ,564
+ ,3.1
+ ,-2
+ ,1
+ ,549
+ ,3
+ ,0
+ ,1)
+ ,dim=c(4
+ ,131)
+ ,dimnames=list(c('Werkl'
+ ,'HICP'
+ ,'Consvertr'
+ ,'Rente')
+ ,1:131))
> y <- array(NA,dim=c(4,131),dimnames=list(c('Werkl','HICP','Consvertr','Rente'),1:131))
> 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
Werkl HICP Consvertr Rente
1 493 0.3 9 3.00
2 481 2.1 11 3.21
3 462 2.5 13 3.37
4 457 2.3 12 3.51
5 442 2.4 13 3.75
6 439 3.0 15 4.11
7 488 1.7 13 4.25
8 521 3.5 16 4.25
9 501 4.0 10 4.50
10 485 3.7 14 4.70
11 464 3.7 14 4.75
12 460 3.0 15 4.75
13 467 2.7 13 4.75
14 460 2.5 8 4.75
15 448 2.2 7 4.75
16 443 2.9 3 4.75
17 436 3.1 3 4.58
18 431 3.0 4 4.50
19 484 2.8 4 4.50
20 510 2.5 0 4.49
21 513 1.9 -4 4.03
22 503 1.9 -14 3.75
23 471 1.8 -18 3.39
24 471 2.0 -8 3.25
25 476 2.6 -1 3.25
26 475 2.5 1 3.25
27 470 2.5 2 3.25
28 461 1.6 0 3.25
29 455 1.4 1 3.25
30 456 0.8 0 3.25
31 517 1.1 -1 3.25
32 525 1.3 -3 3.25
33 523 1.2 -3 3.25
34 519 1.3 -3 3.25
35 509 1.1 -4 3.25
36 512 1.3 -8 2.85
37 519 1.2 -9 2.75
38 517 1.6 -13 2.75
39 510 1.7 -18 2.55
40 509 1.5 -11 2.50
41 501 0.9 -9 2.50
42 507 1.5 -10 2.10
43 569 1.4 -13 2.00
44 580 1.6 -11 2.00
45 578 1.7 -5 2.00
46 565 1.4 -15 2.00
47 547 1.8 -6 2.00
48 555 1.7 -6 2.00
49 562 1.4 -3 2.00
50 561 1.2 -1 2.00
51 555 1.0 -3 2.00
52 544 1.7 -4 2.00
53 537 2.4 -6 2.00
54 543 2.0 0 2.00
55 594 2.1 -4 2.00
56 611 2.0 -2 2.00
57 613 1.8 -2 2.00
58 611 2.7 -6 2.00
59 594 2.3 -7 2.00
60 595 1.9 -6 2.00
61 591 2.0 -6 2.00
62 589 2.3 -3 2.00
63 584 2.8 -2 2.00
64 573 2.4 -5 2.00
65 567 2.3 -11 2.00
66 569 2.7 -11 2.00
67 621 2.7 -11 2.00
68 629 2.9 -10 2.00
69 628 3.0 -14 2.00
70 612 2.2 -8 2.00
71 595 2.3 -9 2.00
72 597 2.8 -5 2.21
73 593 2.8 -1 2.25
74 590 2.8 -2 2.25
75 580 2.2 -5 2.45
76 574 2.6 -4 2.50
77 573 2.8 -6 2.50
78 573 2.5 -2 2.64
79 620 2.4 -2 2.75
80 626 2.3 -2 2.93
81 620 1.9 -2 3.00
82 588 1.7 2 3.17
83 566 2.0 1 3.25
84 557 2.1 -8 3.39
85 561 1.7 -1 3.50
86 549 1.8 1 3.50
87 532 1.8 -1 3.65
88 526 1.8 2 3.75
89 511 1.3 2 3.75
90 499 1.3 1 3.90
91 555 1.3 -1 4.00
92 565 1.2 -2 4.00
93 542 1.4 -2 4.00
94 527 2.2 -1 4.00
95 510 2.9 -8 4.00
96 514 3.1 -4 4.00
97 517 3.5 -6 4.00
98 508 3.6 -3 4.00
99 493 4.4 -3 4.00
100 490 4.1 -7 4.00
101 469 5.1 -9 4.00
102 478 5.8 -11 4.00
103 528 5.9 -13 4.18
104 534 5.4 -11 4.25
105 518 5.5 -9 4.25
106 506 4.8 -17 3.97
107 502 3.2 -22 3.42
108 516 2.7 -25 2.75
109 528 2.1 -20 2.31
110 533 1.9 -24 2.00
111 536 0.6 -24 1.66
112 537 0.7 -22 1.31
113 524 -0.2 -19 1.09
114 536 -1.0 -18 1.00
115 587 -1.7 -17 1.00
116 597 -0.7 -11 1.00
117 581 -1.0 -11 1.00
118 564 -0.9 -12 1.00
119 558 0.0 -10 1.00
120 575 0.3 -15 1.00
121 580 0.8 -15 1.00
122 575 0.8 -15 1.00
123 563 1.9 -13 1.00
124 552 2.1 -8 1.00
125 537 2.5 -13 1.00
126 545 2.7 -9 1.00
127 601 2.4 -7 1.00
128 604 2.4 -4 1.00
129 586 2.9 -4 1.00
130 564 3.1 -2 1.00
131 549 3.0 0 1.00
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) HICP Consvertr Rente
613.155042 6.442345 0.003955 -32.632383
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-64.297 -26.542 2.529 25.240 93.648
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 613.155042 11.172997 54.878 < 2e-16 ***
HICP 6.442345 2.983358 2.159 0.0327 *
Consvertr 0.003955 0.441388 0.009 0.9929
Rente -32.632383 3.806714 -8.572 2.96e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 36.68 on 127 degrees of freedom
Multiple R-squared: 0.4696, Adjusted R-squared: 0.4571
F-statistic: 37.49 on 3 and 127 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,] 0.11827548 0.2365509684 8.817245e-01
[2,] 0.59709735 0.8058053057 4.029027e-01
[3,] 0.47127939 0.9425587748 5.287206e-01
[4,] 0.34242462 0.6848492479 6.575754e-01
[5,] 0.27990743 0.5598148555 7.200926e-01
[6,] 0.21473383 0.4294676614 7.852662e-01
[7,] 0.15410405 0.3082081066 8.458959e-01
[8,] 0.13179549 0.2635909800 8.682045e-01
[9,] 0.10836341 0.2167268253 8.916366e-01
[10,] 0.08362209 0.1672441732 9.163779e-01
[11,] 0.06754311 0.1350862153 9.324569e-01
[12,] 0.06018668 0.1203733527 9.398133e-01
[13,] 0.07076572 0.1415314339 9.292343e-01
[14,] 0.15342165 0.3068432904 8.465784e-01
[15,] 0.17873668 0.3574733560 8.212633e-01
[16,] 0.13539401 0.2707880245 8.646060e-01
[17,] 0.12262686 0.2452537221 8.773731e-01
[18,] 0.10436536 0.2087307113 8.956346e-01
[19,] 0.08857099 0.1771419880 9.114290e-01
[20,] 0.07796961 0.1559392220 9.220304e-01
[21,] 0.07511484 0.1502296895 9.248852e-01
[22,] 0.08218815 0.1643763002 9.178118e-01
[23,] 0.10625705 0.2125140924 8.937430e-01
[24,] 0.13440486 0.2688097202 8.655951e-01
[25,] 0.18083622 0.3616724358 8.191638e-01
[26,] 0.23627535 0.4725506964 7.637247e-01
[27,] 0.26804236 0.5360847212 7.319576e-01
[28,] 0.27804367 0.5560873371 7.219563e-01
[29,] 0.26092724 0.5218544875 7.390728e-01
[30,] 0.24085586 0.4817117227 7.591441e-01
[31,] 0.22267847 0.4453569488 7.773215e-01
[32,] 0.19474522 0.3894904342 8.052548e-01
[33,] 0.16180208 0.3236041686 8.381979e-01
[34,] 0.14407102 0.2881420354 8.559290e-01
[35,] 0.13971918 0.2794383607 8.602808e-01
[36,] 0.13822445 0.2764489080 8.617755e-01
[37,] 0.20797589 0.4159517795 7.920241e-01
[38,] 0.30631453 0.6126290695 6.936855e-01
[39,] 0.37393793 0.7478758506 6.260621e-01
[40,] 0.35891548 0.7178309667 6.410845e-01
[41,] 0.32706558 0.6541311617 6.729344e-01
[42,] 0.29922926 0.5984585123 7.007707e-01
[43,] 0.28771710 0.5754342070 7.122829e-01
[44,] 0.27823983 0.5564796656 7.217602e-01
[45,] 0.25831749 0.5166349738 7.416825e-01
[46,] 0.23668073 0.4733614679 7.633193e-01
[47,] 0.22243384 0.4448676817 7.775662e-01
[48,] 0.22016153 0.4403230591 7.798385e-01
[49,] 0.25676170 0.5135234061 7.432383e-01
[50,] 0.35475292 0.7095058360 6.452471e-01
[51,] 0.45407582 0.9081516308 5.459242e-01
[52,] 0.50235441 0.9952911849 4.976456e-01
[53,] 0.49125871 0.9825174122 5.087413e-01
[54,] 0.48916105 0.9783220902 5.108390e-01
[55,] 0.46955660 0.9391131918 5.304434e-01
[56,] 0.43345201 0.8669040222 5.665480e-01
[57,] 0.38492563 0.7698512578 6.150744e-01
[58,] 0.33685886 0.6737177234 6.631411e-01
[59,] 0.29297601 0.5859520181 7.070240e-01
[60,] 0.25423772 0.5084754440 7.457623e-01
[61,] 0.34240682 0.6848136362 6.575932e-01
[62,] 0.47902398 0.9580479655 5.209760e-01
[63,] 0.66438519 0.6712296121 3.356148e-01
[64,] 0.72850234 0.5429953182 2.714977e-01
[65,] 0.73266703 0.5346659377 2.673330e-01
[66,] 0.73719227 0.5256154600 2.628077e-01
[67,] 0.71999361 0.5600127722 2.800064e-01
[68,] 0.70017493 0.5996501464 2.998251e-01
[69,] 0.68738525 0.6252295033 3.126148e-01
[70,] 0.65521560 0.6895688079 3.447844e-01
[71,] 0.62601886 0.7479622827 3.739811e-01
[72,] 0.59628189 0.8074362145 4.037181e-01
[73,] 0.81987375 0.3602524961 1.801262e-01
[74,] 0.97354159 0.0529168236 2.645841e-02
[75,] 0.99853222 0.0029355592 1.467780e-03
[76,] 0.99954692 0.0009061666 4.530833e-04
[77,] 0.99960902 0.0007819563 3.909781e-04
[78,] 0.99968451 0.0006309771 3.154886e-04
[79,] 0.99978202 0.0004359506 2.179753e-04
[80,] 0.99974540 0.0005091942 2.545971e-04
[81,] 0.99962991 0.0007401825 3.700912e-04
[82,] 0.99948335 0.0010333099 5.166549e-04
[83,] 0.99950319 0.0009936244 4.968122e-04
[84,] 0.99973592 0.0005281592 2.640796e-04
[85,] 0.99980764 0.0003847183 1.923592e-04
[86,] 0.99992903 0.0001419380 7.096902e-05
[87,] 0.99992263 0.0001547450 7.737251e-05
[88,] 0.99986983 0.0002603498 1.301749e-04
[89,] 0.99975861 0.0004827800 2.413900e-04
[90,] 0.99956092 0.0008781695 4.390848e-04
[91,] 0.99923511 0.0015297790 7.648895e-04
[92,] 0.99870498 0.0025900392 1.295020e-03
[93,] 0.99854418 0.0029116409 1.455820e-03
[94,] 0.99862288 0.0027542355 1.377118e-03
[95,] 0.99970297 0.0005940536 2.970268e-04
[96,] 0.99989433 0.0002113338 1.056669e-04
[97,] 0.99982505 0.0003498978 1.749489e-04
[98,] 0.99974637 0.0005072582 2.536291e-04
[99,] 0.99948965 0.0010206958 5.103479e-04
[100,] 0.99900897 0.0019820513 9.910256e-04
[101,] 0.99831540 0.0033691966 1.684598e-03
[102,] 0.99705170 0.0058966043 2.948302e-03
[103,] 0.99503972 0.0099205690 4.960284e-03
[104,] 0.99204180 0.0159163969 7.958198e-03
[105,] 0.98802568 0.0239486457 1.197432e-02
[106,] 0.99022927 0.0195414552 9.770728e-03
[107,] 0.98446128 0.0310774322 1.553872e-02
[108,] 0.98970573 0.0205885327 1.029427e-02
[109,] 0.98145262 0.0370947622 1.854738e-02
[110,] 0.97363223 0.0527355363 2.636777e-02
[111,] 0.95306822 0.0938635501 4.693178e-02
[112,] 0.92832012 0.1433597534 7.167988e-02
[113,] 0.94235807 0.1152838647 5.764193e-02
[114,] 0.90945651 0.1810869849 9.054349e-02
[115,] 0.84212030 0.3157593951 1.578797e-01
[116,] 0.76156766 0.4768646722 2.384323e-01
[117,] 0.63263567 0.7347286514 3.673643e-01
[118,] 0.74170617 0.5165876691 2.582938e-01
> postscript(file="/var/www/rcomp/tmp/1q1kh1293443341.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/21a2k1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/31a2k1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/41a2k1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5tkj51293443341.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 = 131
Frequency = 1
1 2 3 4 5 6
-24.22619030 -40.97752161 -57.34118846 -56.48023070 -64.29664823 -59.42230750
7 8 9 10 11 12
2.52918526 23.92109845 8.88175155 1.32511186 -18.04326897 -17.53758215
13 14 15 16 17 18
-8.59696851 -14.28872443 -24.35206579 -33.84588761 -47.68186188 -54.65217300
19 20 21 22 23 24
-0.36370391 27.25849589 19.12882682 0.03130947 -43.05629400 -48.95284675
25 26 27 28 29 30
-47.84593902 -48.20961447 -53.21356947 -56.40754856 -61.12303447 -56.25367220
31 32 33 34 35 36
2.81757917 9.53702007 8.18125462 3.53702007 -5.17055584 -16.49615828
37 38 39 40 41 42
-12.11120707 -16.67232525 -30.82326148 -32.19409655 -36.33659927 -47.25100489
43 44 45 46 47 48
12.14185632 21.84547723 19.17751269 8.14976632 -12.46276686 -3.81853231
49 50 51 52 53 54
5.10230633 5.38286542 0.67924451 -14.82644231 -26.32817413 -17.77496594
55 56 57 58 59 60
32.59661951 50.23294405 53.52141315 45.73912223 31.32001541 34.89299860
61 62 63 64 65 66
30.24876405 26.30419542 18.07906769 9.66787087 4.33583541 3.75889723
67 68 69 70 71 72
55.75889723 62.46647314 60.83805859 49.96820496 32.32792541 37.94373319
73 74 75 76 77 78
35.23320853 32.23716353 32.64091247 25.69163846 23.41107937 29.89649668
79 80 81 82 83 84
81.13029339 93.64835694 92.50956196 67.32971623 46.01155826 40.97145237
85 86 87 88 89 90
51.11026773 38.45812319 26.36089069 23.61226403 11.83343676 4.73224926
91 92 93 94 95 96
64.00339759 74.65158714 50.36311805 30.20528668 8.72332986 11.41904077
97 98 99 100 101 102
11.85001259 2.19391304 -17.95996332 -19.01143969 -46.44587515 -41.94760697
103 104 105 106 107 108
13.28989749 24.78742705 8.13528251 -8.46050302 -20.08078614 -24.71144527
109 110 111 112 113 114
-23.22406167 -27.03581142 -26.75577267 -37.82925139 -52.22212981 -38.00912295
115 116 117 118 119 120
17.49656387 21.03048842 6.96319206 -10.67708749 -22.48310840 -7.39603704
121 122 123 124 125 126
-5.61720977 -10.61720977 -29.71169977 -42.01994386 -59.57710704 -52.88139613
127 128 129 130 131
5.04339751 8.03153251 -13.18964022 -36.48601931 -50.84969476
> postscript(file="/var/www/rcomp/tmp/6tkj51293443341.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 = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 -24.22619030 NA
1 -40.97752161 -24.22619030
2 -57.34118846 -40.97752161
3 -56.48023070 -57.34118846
4 -64.29664823 -56.48023070
5 -59.42230750 -64.29664823
6 2.52918526 -59.42230750
7 23.92109845 2.52918526
8 8.88175155 23.92109845
9 1.32511186 8.88175155
10 -18.04326897 1.32511186
11 -17.53758215 -18.04326897
12 -8.59696851 -17.53758215
13 -14.28872443 -8.59696851
14 -24.35206579 -14.28872443
15 -33.84588761 -24.35206579
16 -47.68186188 -33.84588761
17 -54.65217300 -47.68186188
18 -0.36370391 -54.65217300
19 27.25849589 -0.36370391
20 19.12882682 27.25849589
21 0.03130947 19.12882682
22 -43.05629400 0.03130947
23 -48.95284675 -43.05629400
24 -47.84593902 -48.95284675
25 -48.20961447 -47.84593902
26 -53.21356947 -48.20961447
27 -56.40754856 -53.21356947
28 -61.12303447 -56.40754856
29 -56.25367220 -61.12303447
30 2.81757917 -56.25367220
31 9.53702007 2.81757917
32 8.18125462 9.53702007
33 3.53702007 8.18125462
34 -5.17055584 3.53702007
35 -16.49615828 -5.17055584
36 -12.11120707 -16.49615828
37 -16.67232525 -12.11120707
38 -30.82326148 -16.67232525
39 -32.19409655 -30.82326148
40 -36.33659927 -32.19409655
41 -47.25100489 -36.33659927
42 12.14185632 -47.25100489
43 21.84547723 12.14185632
44 19.17751269 21.84547723
45 8.14976632 19.17751269
46 -12.46276686 8.14976632
47 -3.81853231 -12.46276686
48 5.10230633 -3.81853231
49 5.38286542 5.10230633
50 0.67924451 5.38286542
51 -14.82644231 0.67924451
52 -26.32817413 -14.82644231
53 -17.77496594 -26.32817413
54 32.59661951 -17.77496594
55 50.23294405 32.59661951
56 53.52141315 50.23294405
57 45.73912223 53.52141315
58 31.32001541 45.73912223
59 34.89299860 31.32001541
60 30.24876405 34.89299860
61 26.30419542 30.24876405
62 18.07906769 26.30419542
63 9.66787087 18.07906769
64 4.33583541 9.66787087
65 3.75889723 4.33583541
66 55.75889723 3.75889723
67 62.46647314 55.75889723
68 60.83805859 62.46647314
69 49.96820496 60.83805859
70 32.32792541 49.96820496
71 37.94373319 32.32792541
72 35.23320853 37.94373319
73 32.23716353 35.23320853
74 32.64091247 32.23716353
75 25.69163846 32.64091247
76 23.41107937 25.69163846
77 29.89649668 23.41107937
78 81.13029339 29.89649668
79 93.64835694 81.13029339
80 92.50956196 93.64835694
81 67.32971623 92.50956196
82 46.01155826 67.32971623
83 40.97145237 46.01155826
84 51.11026773 40.97145237
85 38.45812319 51.11026773
86 26.36089069 38.45812319
87 23.61226403 26.36089069
88 11.83343676 23.61226403
89 4.73224926 11.83343676
90 64.00339759 4.73224926
91 74.65158714 64.00339759
92 50.36311805 74.65158714
93 30.20528668 50.36311805
94 8.72332986 30.20528668
95 11.41904077 8.72332986
96 11.85001259 11.41904077
97 2.19391304 11.85001259
98 -17.95996332 2.19391304
99 -19.01143969 -17.95996332
100 -46.44587515 -19.01143969
101 -41.94760697 -46.44587515
102 13.28989749 -41.94760697
103 24.78742705 13.28989749
104 8.13528251 24.78742705
105 -8.46050302 8.13528251
106 -20.08078614 -8.46050302
107 -24.71144527 -20.08078614
108 -23.22406167 -24.71144527
109 -27.03581142 -23.22406167
110 -26.75577267 -27.03581142
111 -37.82925139 -26.75577267
112 -52.22212981 -37.82925139
113 -38.00912295 -52.22212981
114 17.49656387 -38.00912295
115 21.03048842 17.49656387
116 6.96319206 21.03048842
117 -10.67708749 6.96319206
118 -22.48310840 -10.67708749
119 -7.39603704 -22.48310840
120 -5.61720977 -7.39603704
121 -10.61720977 -5.61720977
122 -29.71169977 -10.61720977
123 -42.01994386 -29.71169977
124 -59.57710704 -42.01994386
125 -52.88139613 -59.57710704
126 5.04339751 -52.88139613
127 8.03153251 5.04339751
128 -13.18964022 8.03153251
129 -36.48601931 -13.18964022
130 -50.84969476 -36.48601931
131 NA -50.84969476
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -40.97752161 -24.22619030
[2,] -57.34118846 -40.97752161
[3,] -56.48023070 -57.34118846
[4,] -64.29664823 -56.48023070
[5,] -59.42230750 -64.29664823
[6,] 2.52918526 -59.42230750
[7,] 23.92109845 2.52918526
[8,] 8.88175155 23.92109845
[9,] 1.32511186 8.88175155
[10,] -18.04326897 1.32511186
[11,] -17.53758215 -18.04326897
[12,] -8.59696851 -17.53758215
[13,] -14.28872443 -8.59696851
[14,] -24.35206579 -14.28872443
[15,] -33.84588761 -24.35206579
[16,] -47.68186188 -33.84588761
[17,] -54.65217300 -47.68186188
[18,] -0.36370391 -54.65217300
[19,] 27.25849589 -0.36370391
[20,] 19.12882682 27.25849589
[21,] 0.03130947 19.12882682
[22,] -43.05629400 0.03130947
[23,] -48.95284675 -43.05629400
[24,] -47.84593902 -48.95284675
[25,] -48.20961447 -47.84593902
[26,] -53.21356947 -48.20961447
[27,] -56.40754856 -53.21356947
[28,] -61.12303447 -56.40754856
[29,] -56.25367220 -61.12303447
[30,] 2.81757917 -56.25367220
[31,] 9.53702007 2.81757917
[32,] 8.18125462 9.53702007
[33,] 3.53702007 8.18125462
[34,] -5.17055584 3.53702007
[35,] -16.49615828 -5.17055584
[36,] -12.11120707 -16.49615828
[37,] -16.67232525 -12.11120707
[38,] -30.82326148 -16.67232525
[39,] -32.19409655 -30.82326148
[40,] -36.33659927 -32.19409655
[41,] -47.25100489 -36.33659927
[42,] 12.14185632 -47.25100489
[43,] 21.84547723 12.14185632
[44,] 19.17751269 21.84547723
[45,] 8.14976632 19.17751269
[46,] -12.46276686 8.14976632
[47,] -3.81853231 -12.46276686
[48,] 5.10230633 -3.81853231
[49,] 5.38286542 5.10230633
[50,] 0.67924451 5.38286542
[51,] -14.82644231 0.67924451
[52,] -26.32817413 -14.82644231
[53,] -17.77496594 -26.32817413
[54,] 32.59661951 -17.77496594
[55,] 50.23294405 32.59661951
[56,] 53.52141315 50.23294405
[57,] 45.73912223 53.52141315
[58,] 31.32001541 45.73912223
[59,] 34.89299860 31.32001541
[60,] 30.24876405 34.89299860
[61,] 26.30419542 30.24876405
[62,] 18.07906769 26.30419542
[63,] 9.66787087 18.07906769
[64,] 4.33583541 9.66787087
[65,] 3.75889723 4.33583541
[66,] 55.75889723 3.75889723
[67,] 62.46647314 55.75889723
[68,] 60.83805859 62.46647314
[69,] 49.96820496 60.83805859
[70,] 32.32792541 49.96820496
[71,] 37.94373319 32.32792541
[72,] 35.23320853 37.94373319
[73,] 32.23716353 35.23320853
[74,] 32.64091247 32.23716353
[75,] 25.69163846 32.64091247
[76,] 23.41107937 25.69163846
[77,] 29.89649668 23.41107937
[78,] 81.13029339 29.89649668
[79,] 93.64835694 81.13029339
[80,] 92.50956196 93.64835694
[81,] 67.32971623 92.50956196
[82,] 46.01155826 67.32971623
[83,] 40.97145237 46.01155826
[84,] 51.11026773 40.97145237
[85,] 38.45812319 51.11026773
[86,] 26.36089069 38.45812319
[87,] 23.61226403 26.36089069
[88,] 11.83343676 23.61226403
[89,] 4.73224926 11.83343676
[90,] 64.00339759 4.73224926
[91,] 74.65158714 64.00339759
[92,] 50.36311805 74.65158714
[93,] 30.20528668 50.36311805
[94,] 8.72332986 30.20528668
[95,] 11.41904077 8.72332986
[96,] 11.85001259 11.41904077
[97,] 2.19391304 11.85001259
[98,] -17.95996332 2.19391304
[99,] -19.01143969 -17.95996332
[100,] -46.44587515 -19.01143969
[101,] -41.94760697 -46.44587515
[102,] 13.28989749 -41.94760697
[103,] 24.78742705 13.28989749
[104,] 8.13528251 24.78742705
[105,] -8.46050302 8.13528251
[106,] -20.08078614 -8.46050302
[107,] -24.71144527 -20.08078614
[108,] -23.22406167 -24.71144527
[109,] -27.03581142 -23.22406167
[110,] -26.75577267 -27.03581142
[111,] -37.82925139 -26.75577267
[112,] -52.22212981 -37.82925139
[113,] -38.00912295 -52.22212981
[114,] 17.49656387 -38.00912295
[115,] 21.03048842 17.49656387
[116,] 6.96319206 21.03048842
[117,] -10.67708749 6.96319206
[118,] -22.48310840 -10.67708749
[119,] -7.39603704 -22.48310840
[120,] -5.61720977 -7.39603704
[121,] -10.61720977 -5.61720977
[122,] -29.71169977 -10.61720977
[123,] -42.01994386 -29.71169977
[124,] -59.57710704 -42.01994386
[125,] -52.88139613 -59.57710704
[126,] 5.04339751 -52.88139613
[127,] 8.03153251 5.04339751
[128,] -13.18964022 8.03153251
[129,] -36.48601931 -13.18964022
[130,] -50.84969476 -36.48601931
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -40.97752161 -24.22619030
2 -57.34118846 -40.97752161
3 -56.48023070 -57.34118846
4 -64.29664823 -56.48023070
5 -59.42230750 -64.29664823
6 2.52918526 -59.42230750
7 23.92109845 2.52918526
8 8.88175155 23.92109845
9 1.32511186 8.88175155
10 -18.04326897 1.32511186
11 -17.53758215 -18.04326897
12 -8.59696851 -17.53758215
13 -14.28872443 -8.59696851
14 -24.35206579 -14.28872443
15 -33.84588761 -24.35206579
16 -47.68186188 -33.84588761
17 -54.65217300 -47.68186188
18 -0.36370391 -54.65217300
19 27.25849589 -0.36370391
20 19.12882682 27.25849589
21 0.03130947 19.12882682
22 -43.05629400 0.03130947
23 -48.95284675 -43.05629400
24 -47.84593902 -48.95284675
25 -48.20961447 -47.84593902
26 -53.21356947 -48.20961447
27 -56.40754856 -53.21356947
28 -61.12303447 -56.40754856
29 -56.25367220 -61.12303447
30 2.81757917 -56.25367220
31 9.53702007 2.81757917
32 8.18125462 9.53702007
33 3.53702007 8.18125462
34 -5.17055584 3.53702007
35 -16.49615828 -5.17055584
36 -12.11120707 -16.49615828
37 -16.67232525 -12.11120707
38 -30.82326148 -16.67232525
39 -32.19409655 -30.82326148
40 -36.33659927 -32.19409655
41 -47.25100489 -36.33659927
42 12.14185632 -47.25100489
43 21.84547723 12.14185632
44 19.17751269 21.84547723
45 8.14976632 19.17751269
46 -12.46276686 8.14976632
47 -3.81853231 -12.46276686
48 5.10230633 -3.81853231
49 5.38286542 5.10230633
50 0.67924451 5.38286542
51 -14.82644231 0.67924451
52 -26.32817413 -14.82644231
53 -17.77496594 -26.32817413
54 32.59661951 -17.77496594
55 50.23294405 32.59661951
56 53.52141315 50.23294405
57 45.73912223 53.52141315
58 31.32001541 45.73912223
59 34.89299860 31.32001541
60 30.24876405 34.89299860
61 26.30419542 30.24876405
62 18.07906769 26.30419542
63 9.66787087 18.07906769
64 4.33583541 9.66787087
65 3.75889723 4.33583541
66 55.75889723 3.75889723
67 62.46647314 55.75889723
68 60.83805859 62.46647314
69 49.96820496 60.83805859
70 32.32792541 49.96820496
71 37.94373319 32.32792541
72 35.23320853 37.94373319
73 32.23716353 35.23320853
74 32.64091247 32.23716353
75 25.69163846 32.64091247
76 23.41107937 25.69163846
77 29.89649668 23.41107937
78 81.13029339 29.89649668
79 93.64835694 81.13029339
80 92.50956196 93.64835694
81 67.32971623 92.50956196
82 46.01155826 67.32971623
83 40.97145237 46.01155826
84 51.11026773 40.97145237
85 38.45812319 51.11026773
86 26.36089069 38.45812319
87 23.61226403 26.36089069
88 11.83343676 23.61226403
89 4.73224926 11.83343676
90 64.00339759 4.73224926
91 74.65158714 64.00339759
92 50.36311805 74.65158714
93 30.20528668 50.36311805
94 8.72332986 30.20528668
95 11.41904077 8.72332986
96 11.85001259 11.41904077
97 2.19391304 11.85001259
98 -17.95996332 2.19391304
99 -19.01143969 -17.95996332
100 -46.44587515 -19.01143969
101 -41.94760697 -46.44587515
102 13.28989749 -41.94760697
103 24.78742705 13.28989749
104 8.13528251 24.78742705
105 -8.46050302 8.13528251
106 -20.08078614 -8.46050302
107 -24.71144527 -20.08078614
108 -23.22406167 -24.71144527
109 -27.03581142 -23.22406167
110 -26.75577267 -27.03581142
111 -37.82925139 -26.75577267
112 -52.22212981 -37.82925139
113 -38.00912295 -52.22212981
114 17.49656387 -38.00912295
115 21.03048842 17.49656387
116 6.96319206 21.03048842
117 -10.67708749 6.96319206
118 -22.48310840 -10.67708749
119 -7.39603704 -22.48310840
120 -5.61720977 -7.39603704
121 -10.61720977 -5.61720977
122 -29.71169977 -10.61720977
123 -42.01994386 -29.71169977
124 -59.57710704 -42.01994386
125 -52.88139613 -59.57710704
126 5.04339751 -52.88139613
127 8.03153251 5.04339751
128 -13.18964022 8.03153251
129 -36.48601931 -13.18964022
130 -50.84969476 -36.48601931
> 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/rcomp/tmp/7mb0q1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8f2it1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9f2it1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10f2it1293443341.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11i2yh1293443341.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/rcomp/tmp/12l3x51293443341.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/rcomp/tmp/13lewb1293443342.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/rcomp/tmp/14doex1293443342.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/rcomp/tmp/15hoc21293443342.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/rcomp/tmp/16dyst1293443342.tab")
+ }
> try(system("convert tmp/1q1kh1293443341.ps tmp/1q1kh1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/21a2k1293443341.ps tmp/21a2k1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/31a2k1293443341.ps tmp/31a2k1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/41a2k1293443341.ps tmp/41a2k1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tkj51293443341.ps tmp/5tkj51293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tkj51293443341.ps tmp/6tkj51293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mb0q1293443341.ps tmp/7mb0q1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f2it1293443341.ps tmp/8f2it1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f2it1293443341.ps tmp/9f2it1293443341.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f2it1293443341.ps tmp/10f2it1293443341.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.980 1.630 5.665