R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,476000
+ ,113000
+ ,363000
+ ,2
+ ,475000
+ ,110000
+ ,364000
+ ,3
+ ,470000
+ ,107000
+ ,363000
+ ,4
+ ,461000
+ ,103000
+ ,358000
+ ,5
+ ,455000
+ ,98000
+ ,357000
+ ,6
+ ,456000
+ ,98000
+ ,357000
+ ,7
+ ,517000
+ ,137000
+ ,380000
+ ,8
+ ,525000
+ ,148000
+ ,378000
+ ,9
+ ,523000
+ ,147000
+ ,376000
+ ,10
+ ,519000
+ ,139000
+ ,380000
+ ,11
+ ,509000
+ ,130000
+ ,379000
+ ,12
+ ,512000
+ ,128000
+ ,384000
+ ,1
+ ,519000
+ ,127000
+ ,392000
+ ,2
+ ,517000
+ ,123000
+ ,394000
+ ,3
+ ,510000
+ ,118000
+ ,392000
+ ,4
+ ,509000
+ ,114000
+ ,396000
+ ,5
+ ,501000
+ ,108000
+ ,392000
+ ,6
+ ,507000
+ ,111000
+ ,396000
+ ,7
+ ,569000
+ ,151000
+ ,419000
+ ,8
+ ,580000
+ ,159000
+ ,421000
+ ,9
+ ,578000
+ ,158000
+ ,420000
+ ,10
+ ,565000
+ ,148000
+ ,418000
+ ,11
+ ,547000
+ ,138000
+ ,410000
+ ,12
+ ,555000
+ ,137000
+ ,418000
+ ,1
+ ,562000
+ ,136000
+ ,426000
+ ,2
+ ,561000
+ ,133000
+ ,428000
+ ,3
+ ,555000
+ ,126000
+ ,430000
+ ,4
+ ,544000
+ ,120000
+ ,424000
+ ,5
+ ,537000
+ ,114000
+ ,423000
+ ,6
+ ,543000
+ ,116000
+ ,427000
+ ,7
+ ,594000
+ ,153000
+ ,441000
+ ,8
+ ,611000
+ ,162000
+ ,449000
+ ,9
+ ,613000
+ ,161000
+ ,452000
+ ,10
+ ,611000
+ ,149000
+ ,462000
+ ,11
+ ,594000
+ ,139000
+ ,455000
+ ,12
+ ,595000
+ ,135000
+ ,461000
+ ,1
+ ,591000
+ ,130000
+ ,461000
+ ,2
+ ,589000
+ ,127000
+ ,463000
+ ,3
+ ,584000
+ ,122000
+ ,462000
+ ,4
+ ,573000
+ ,117000
+ ,456000
+ ,5
+ ,567000
+ ,112000
+ ,455000
+ ,6
+ ,569000
+ ,113000
+ ,456000
+ ,7
+ ,621000
+ ,149000
+ ,472000
+ ,8
+ ,629000
+ ,157000
+ ,472000
+ ,9
+ ,628000
+ ,157000
+ ,471000
+ ,10
+ ,612000
+ ,147000
+ ,465000
+ ,11
+ ,595000
+ ,137000
+ ,459000
+ ,12
+ ,597000
+ ,132000
+ ,465000
+ ,1
+ ,593000
+ ,125000
+ ,468000
+ ,2
+ ,590000
+ ,123000
+ ,467000
+ ,3
+ ,580000
+ ,117000
+ ,463000
+ ,4
+ ,574000
+ ,114000
+ ,460000
+ ,5
+ ,573000
+ ,111000
+ ,462000
+ ,6
+ ,573000
+ ,112000
+ ,461000
+ ,7
+ ,620000
+ ,144000
+ ,476000
+ ,8
+ ,626000
+ ,150000
+ ,476000
+ ,9
+ ,620000
+ ,149000
+ ,471000
+ ,10
+ ,588000
+ ,134000
+ ,453000
+ ,11
+ ,566000
+ ,123000
+ ,443000
+ ,12
+ ,557000
+ ,116000
+ ,442000
+ ,1
+ ,561000
+ ,117000
+ ,444000
+ ,2
+ ,549000
+ ,111000
+ ,438000
+ ,3
+ ,532000
+ ,105000
+ ,427000
+ ,4
+ ,526000
+ ,102000
+ ,424000
+ ,5
+ ,511000
+ ,95000
+ ,416000
+ ,6
+ ,499000
+ ,93000
+ ,406000
+ ,7
+ ,555000
+ ,124000
+ ,431000
+ ,8
+ ,565000
+ ,130000
+ ,434000
+ ,9
+ ,542000
+ ,124000
+ ,418000
+ ,10
+ ,527000
+ ,115000
+ ,412000
+ ,11
+ ,510000
+ ,106000
+ ,404000
+ ,12
+ ,514000
+ ,105000
+ ,409000
+ ,1
+ ,517000
+ ,105000
+ ,412000
+ ,2
+ ,508000
+ ,101000
+ ,406000
+ ,3
+ ,493000
+ ,95000
+ ,398000
+ ,4
+ ,490000
+ ,93000
+ ,397000
+ ,5
+ ,469000
+ ,84000
+ ,385000
+ ,6
+ ,478000
+ ,87000
+ ,390000
+ ,7
+ ,528000
+ ,116000
+ ,413000
+ ,8
+ ,534000
+ ,120000
+ ,413000
+ ,9
+ ,518000
+ ,117000
+ ,401000
+ ,10
+ ,506000
+ ,109000
+ ,397000
+ ,11
+ ,502000
+ ,105000
+ ,397000
+ ,12
+ ,516000
+ ,107000
+ ,409000
+ ,1
+ ,528000
+ ,109000
+ ,419000
+ ,2
+ ,533000
+ ,109000
+ ,424000
+ ,3
+ ,536000
+ ,108000
+ ,428000
+ ,4
+ ,537000
+ ,107000
+ ,430000
+ ,5
+ ,524000
+ ,99000
+ ,424000
+ ,6
+ ,536000
+ ,103000
+ ,433000
+ ,7
+ ,587000
+ ,131000
+ ,456000
+ ,8
+ ,597000
+ ,137000
+ ,459000
+ ,9
+ ,581000
+ ,135000
+ ,446000
+ ,10
+ ,564000
+ ,124000
+ ,441000
+ ,11
+ ,558000
+ ,118000
+ ,439000
+ ,12
+ ,575000
+ ,121000
+ ,454000
+ ,1
+ ,580000
+ ,121000
+ ,460000
+ ,2
+ ,575000
+ ,118000
+ ,457000
+ ,3
+ ,563000
+ ,113000
+ ,451000
+ ,4
+ ,552000
+ ,107000
+ ,444000
+ ,5
+ ,537000
+ ,100000
+ ,437000
+ ,6
+ ,545000
+ ,102000
+ ,443000
+ ,7
+ ,601000
+ ,130000
+ ,471000
+ ,8
+ ,604000
+ ,136000
+ ,469000
+ ,9
+ ,586000
+ ,133000
+ ,454000
+ ,10
+ ,564000
+ ,120000
+ ,444000
+ ,11
+ ,549000
+ ,112000
+ ,436000
+ ,12
+ ,551000
+ ,109000
+ ,442000
+ ,1
+ ,556000
+ ,110000
+ ,446000
+ ,2
+ ,548000
+ ,106000
+ ,442000
+ ,3
+ ,540000
+ ,102000
+ ,438000
+ ,4
+ ,531000
+ ,98000
+ ,433000
+ ,5
+ ,521000
+ ,92000
+ ,428000
+ ,6
+ ,519000
+ ,92000
+ ,426000
+ ,7
+ ,572000
+ ,120000
+ ,452000
+ ,8
+ ,581000
+ ,127000
+ ,455000
+ ,9
+ ,563000
+ ,124000
+ ,439000
+ ,10
+ ,548000
+ ,114000
+ ,434000
+ ,11
+ ,539000
+ ,108000
+ ,431000
+ ,12
+ ,541000
+ ,106000
+ ,435000
+ ,1
+ ,562000
+ ,111000
+ ,450000
+ ,2
+ ,559000
+ ,110000
+ ,449000
+ ,3
+ ,546000
+ ,104000
+ ,442000
+ ,4
+ ,536000
+ ,100000
+ ,437000
+ ,5
+ ,528000
+ ,96000
+ ,431000
+ ,6
+ ,530000
+ ,98000
+ ,433000
+ ,7
+ ,582000
+ ,122000
+ ,460000
+ ,8
+ ,599000
+ ,134000
+ ,465000
+ ,9
+ ,584000
+ ,133000
+ ,451000
+ ,10
+ ,571000
+ ,125000
+ ,447000)
+ ,dim=c(4
+ ,130)
+ ,dimnames=list(c('Maanden'
+ ,'Totaal'
+ ,'jongerdan25jaar'
+ ,'vanaf25jaar')
+ ,1:130))
> y <- array(NA,dim=c(4,130),dimnames=list(c('Maanden','Totaal','jongerdan25jaar','vanaf25jaar'),1:130))
> 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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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 Maanden jongerdan25jaar vanaf25jaar
1 476000 1 113000 363000
2 475000 2 110000 364000
3 470000 3 107000 363000
4 461000 4 103000 358000
5 455000 5 98000 357000
6 456000 6 98000 357000
7 517000 7 137000 380000
8 525000 8 148000 378000
9 523000 9 147000 376000
10 519000 10 139000 380000
11 509000 11 130000 379000
12 512000 12 128000 384000
13 519000 1 127000 392000
14 517000 2 123000 394000
15 510000 3 118000 392000
16 509000 4 114000 396000
17 501000 5 108000 392000
18 507000 6 111000 396000
19 569000 7 151000 419000
20 580000 8 159000 421000
21 578000 9 158000 420000
22 565000 10 148000 418000
23 547000 11 138000 410000
24 555000 12 137000 418000
25 562000 1 136000 426000
26 561000 2 133000 428000
27 555000 3 126000 430000
28 544000 4 120000 424000
29 537000 5 114000 423000
30 543000 6 116000 427000
31 594000 7 153000 441000
32 611000 8 162000 449000
33 613000 9 161000 452000
34 611000 10 149000 462000
35 594000 11 139000 455000
36 595000 12 135000 461000
37 591000 1 130000 461000
38 589000 2 127000 463000
39 584000 3 122000 462000
40 573000 4 117000 456000
41 567000 5 112000 455000
42 569000 6 113000 456000
43 621000 7 149000 472000
44 629000 8 157000 472000
45 628000 9 157000 471000
46 612000 10 147000 465000
47 595000 11 137000 459000
48 597000 12 132000 465000
49 593000 1 125000 468000
50 590000 2 123000 467000
51 580000 3 117000 463000
52 574000 4 114000 460000
53 573000 5 111000 462000
54 573000 6 112000 461000
55 620000 7 144000 476000
56 626000 8 150000 476000
57 620000 9 149000 471000
58 588000 10 134000 453000
59 566000 11 123000 443000
60 557000 12 116000 442000
61 561000 1 117000 444000
62 549000 2 111000 438000
63 532000 3 105000 427000
64 526000 4 102000 424000
65 511000 5 95000 416000
66 499000 6 93000 406000
67 555000 7 124000 431000
68 565000 8 130000 434000
69 542000 9 124000 418000
70 527000 10 115000 412000
71 510000 11 106000 404000
72 514000 12 105000 409000
73 517000 1 105000 412000
74 508000 2 101000 406000
75 493000 3 95000 398000
76 490000 4 93000 397000
77 469000 5 84000 385000
78 478000 6 87000 390000
79 528000 7 116000 413000
80 534000 8 120000 413000
81 518000 9 117000 401000
82 506000 10 109000 397000
83 502000 11 105000 397000
84 516000 12 107000 409000
85 528000 1 109000 419000
86 533000 2 109000 424000
87 536000 3 108000 428000
88 537000 4 107000 430000
89 524000 5 99000 424000
90 536000 6 103000 433000
91 587000 7 131000 456000
92 597000 8 137000 459000
93 581000 9 135000 446000
94 564000 10 124000 441000
95 558000 11 118000 439000
96 575000 12 121000 454000
97 580000 1 121000 460000
98 575000 2 118000 457000
99 563000 3 113000 451000
100 552000 4 107000 444000
101 537000 5 100000 437000
102 545000 6 102000 443000
103 601000 7 130000 471000
104 604000 8 136000 469000
105 586000 9 133000 454000
106 564000 10 120000 444000
107 549000 11 112000 436000
108 551000 12 109000 442000
109 556000 1 110000 446000
110 548000 2 106000 442000
111 540000 3 102000 438000
112 531000 4 98000 433000
113 521000 5 92000 428000
114 519000 6 92000 426000
115 572000 7 120000 452000
116 581000 8 127000 455000
117 563000 9 124000 439000
118 548000 10 114000 434000
119 539000 11 108000 431000
120 541000 12 106000 435000
121 562000 1 111000 450000
122 559000 2 110000 449000
123 546000 3 104000 442000
124 536000 4 100000 437000
125 528000 5 96000 431000
126 530000 6 98000 433000
127 582000 7 122000 460000
128 599000 8 134000 465000
129 584000 9 133000 451000
130 571000 10 125000 447000
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maanden jongerdan25jaar vanaf25jaar
1347.6807 -1.4324 0.9928 0.9988
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1133.62 -114.33 0.24 157.54 1180.12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.348e+03 6.760e+02 1.994 0.0484 *
Maanden -1.432e+00 1.437e+01 -0.100 0.9208
jongerdan25jaar 9.928e-01 2.972e-03 334.076 <2e-16 ***
vanaf25jaar 9.988e-01 1.660e-03 601.755 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 521.3 on 126 degrees of freedom
Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
F-statistic: 2.429e+05 on 3 and 126 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,] 0.762914145 0.474171710 0.2370859
[2,] 0.635156436 0.729687129 0.3648436
[3,] 0.732840187 0.534319626 0.2671598
[4,] 0.619990992 0.760018016 0.3800090
[5,] 0.521575576 0.956848849 0.4784244
[6,] 0.430402197 0.860804394 0.5695978
[7,] 0.334197066 0.668394132 0.6658029
[8,] 0.250139647 0.500279293 0.7498604
[9,] 0.183870142 0.367740284 0.8161299
[10,] 0.405254795 0.810509590 0.5947452
[11,] 0.590769745 0.818460509 0.4092303
[12,] 0.513700584 0.972598831 0.4862994
[13,] 0.479037102 0.958074204 0.5209629
[14,] 0.536197597 0.927604807 0.4638024
[15,] 0.521651257 0.956697487 0.4783487
[16,] 0.564705499 0.870589001 0.4352945
[17,] 0.622416421 0.755167158 0.3775836
[18,] 0.587138799 0.825722403 0.4128612
[19,] 0.540931536 0.918136928 0.4590685
[20,] 0.483114135 0.966228270 0.5168859
[21,] 0.579999707 0.840000585 0.4200003
[22,] 0.524227458 0.951545084 0.4757725
[23,] 0.463078174 0.926156348 0.5369218
[24,] 0.404216003 0.808432007 0.5957840
[25,] 0.400202549 0.800405098 0.5997975
[26,] 0.400837249 0.801674498 0.5991628
[27,] 0.384814701 0.769629402 0.6151853
[28,] 0.348805482 0.697610963 0.6511945
[29,] 0.301396619 0.602793239 0.6986034
[30,] 0.367933269 0.735866537 0.6320667
[31,] 0.317547211 0.635094421 0.6824528
[32,] 0.399679891 0.799359782 0.6003201
[33,] 0.353386161 0.706772322 0.6466138
[34,] 0.305449554 0.610899108 0.6945504
[35,] 0.259164079 0.518328158 0.7408359
[36,] 0.217038949 0.434077897 0.7829611
[37,] 0.195425331 0.390850662 0.8045747
[38,] 0.177552004 0.355104008 0.8224480
[39,] 0.158773722 0.317547443 0.8412263
[40,] 0.135922450 0.271844900 0.8640775
[41,] 0.176903831 0.353807662 0.8230962
[42,] 0.150610934 0.301221868 0.8493891
[43,] 0.122139154 0.244278308 0.8778608
[44,] 0.097582672 0.195165344 0.9024173
[45,] 0.076552012 0.153104025 0.9234480
[46,] 0.059120260 0.118240519 0.9408797
[47,] 0.044987175 0.089974351 0.9550128
[48,] 0.033775475 0.067550951 0.9662245
[49,] 0.026852471 0.053704942 0.9731475
[50,] 0.021668722 0.043337444 0.9783313
[51,] 0.017282537 0.034565074 0.9827175
[52,] 0.058750011 0.117500021 0.9412500
[53,] 0.045169123 0.090338246 0.9548309
[54,] 0.082407196 0.164814392 0.9175928
[55,] 0.064441016 0.128882032 0.9355590
[56,] 0.049649279 0.099298559 0.9503507
[57,] 0.037894724 0.075789448 0.9621053
[58,] 0.028642014 0.057284028 0.9713580
[59,] 0.021718386 0.043436773 0.9782816
[60,] 0.016511587 0.033023175 0.9834884
[61,] 0.011967058 0.023934116 0.9880329
[62,] 0.038587468 0.077174937 0.9614125
[63,] 0.029216459 0.058432919 0.9707835
[64,] 0.021651556 0.043303111 0.9783484
[65,] 0.016046912 0.032093824 0.9839531
[66,] 0.011821297 0.023642595 0.9881787
[67,] 0.008471621 0.016943242 0.9915284
[68,] 0.015456433 0.030912865 0.9845436
[69,] 0.011575688 0.023151376 0.9884243
[70,] 0.008746566 0.017493132 0.9912534
[71,] 0.007348525 0.014697050 0.9926515
[72,] 0.009733381 0.019466762 0.9902666
[73,] 0.023843293 0.047686586 0.9761567
[74,] 0.050724680 0.101449360 0.9492753
[75,] 0.038426788 0.076853576 0.9615732
[76,] 0.029063455 0.058126911 0.9709365
[77,] 0.022568006 0.045136012 0.9774320
[78,] 0.017635220 0.035270440 0.9823648
[79,] 0.012884529 0.025769059 0.9871155
[80,] 0.009314527 0.018629053 0.9906855
[81,] 0.006676862 0.013353724 0.9933231
[82,] 0.004755225 0.009510450 0.9952448
[83,] 0.006996436 0.013992872 0.9930036
[84,] 0.004907107 0.009814214 0.9950929
[85,] 0.003485006 0.006970013 0.9965150
[86,] 0.021267470 0.042534940 0.9787325
[87,] 0.017730980 0.035461959 0.9822690
[88,] 0.029979206 0.059958411 0.9700208
[89,] 0.064621999 0.129243997 0.9353780
[90,] 0.049297585 0.098595169 0.9507024
[91,] 0.069892848 0.139785695 0.9301072
[92,] 0.053393262 0.106786524 0.9466067
[93,] 0.098262424 0.196524848 0.9017376
[94,] 0.154738430 0.309476860 0.8452616
[95,] 0.124838572 0.249677144 0.8751614
[96,] 0.097865054 0.195730109 0.9021349
[97,] 0.087040003 0.174080007 0.9129600
[98,] 0.086800394 0.173600788 0.9131996
[99,] 0.102584266 0.205168532 0.8974157
[100,] 0.076579052 0.153158104 0.9234209
[101,] 0.149518753 0.299037506 0.8504812
[102,] 0.115109717 0.230219434 0.8848903
[103,] 0.086305603 0.172611206 0.9136944
[104,] 0.064182229 0.128364459 0.9358178
[105,] 0.047529322 0.095058644 0.9524707
[106,] 0.035559439 0.071118879 0.9644406
[107,] 0.042865486 0.085730972 0.9571345
[108,] 0.071249219 0.142498438 0.9287508
[109,] 0.048635181 0.097270361 0.9513648
[110,] 0.075986019 0.151972038 0.9240140
[111,] 0.049021302 0.098042604 0.9509787
[112,] 0.031868657 0.063737313 0.9681313
[113,] 0.023461393 0.046922786 0.9765386
[114,] 0.036926836 0.073853672 0.9630732
[115,] 0.033048641 0.066097282 0.9669514
[116,] 0.017495885 0.034991770 0.9825041
[117,] 0.007573144 0.015146288 0.9924269
> postscript(file="/var/fisher/rcomp/tmp/1c5wh1353451392.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2re6y1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/382i91353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4hxnq1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5xhku1353451392.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 = 130
Frequency = 1
1 2 3 4 5
-113.5322295 867.4487755 -153.8782698 -187.0232295 -222.7539029
6 7 8 9 10
778.6785190 87.5242649 -834.1314093 157.7911483 106.2251585
11 12 13 14 15
41.6872291 34.4861484 20.7598947 -4.3068893 -41.1915877
16 17 18 19 20
-1063.9503215 889.6551159 -82.6909198 -766.6433599 294.7116243
21 22 23 24 25
287.7882070 -785.1055615 -864.9234806 138.5393280 124.8130743
26 27 28 29 30
106.9481044 -939.7241217 11.5732655 -31.3592219 -10.9070716
31 32 33 34 35
273.1488203 348.6297692 346.3224523 272.8733570 194.2094630
36 37 38 39 40
-826.2412206 121.9930688 -895.8719012 68.3974255 26.8966267
41 42 43 44 45
-8.8340467 0.9542143 280.1163424 339.1632764 339.4416732
46 47 48 49 50
261.9318043 -815.5780646 156.7694378 94.0621744 79.9369432
51 52 53 54 55
33.5423805 9.9072850 -7.9576850 -0.4774742 248.7233727
56 57 58 59 60
293.3666787 281.8271610 1154.4599208 65.1321375 -985.0021639
61 62 63 64 65
8.7510599 -39.9515530 -94.4242913 -118.0593868 -176.2718639
66 67 68 69 70
-200.7833211 52.7559628 1100.8613441 40.6184802 -29.6895747
71 72 73 74 75
-102.3056797 -102.3049464 -114.5995116 851.1015036 -199.9091595
76 77 78 79 80
-214.0343907 -291.2665962 737.5413932 -1025.6310011 1004.6086768
81 82 83 84 85
-29.4126446 -90.2108353 -117.5856695 -87.9013184 -77.7140798
86 87 88 89 90
-70.5115325 -71.6648242 -75.1261661 861.7675930 -97.6065032
91 92 93 94 95
132.0192885 1180.1246698 152.1511373 -931.4065206 1024.5069670
96 97 98 99 100
64.8552075 -943.9772825 32.3876221 -1009.1131768 941.0301853
101 102 103 104 105
-116.0282667 -93.2680662 142.1278510 -815.5368932 -853.0202899
106 107 108 109 110
43.2482987 977.8340076 -35.4148620 -39.3535881 -71.3445227
111 112 113 114 115
-103.3354573 -136.4804170 815.9709953 815.0953669 48.1832339
116 117 118 119 120
-896.5095708 64.8530074 -11.5028364 -56.7433740 -65.0984797
121 122 123 124 125
972.4643264 -34.4590909 -84.3157288 -1117.4606885 848.2403266
126 127 128 129 130
-1133.6155733 71.8190628 165.4433783 143.5176347 -917.2805559
> postscript(file="/var/fisher/rcomp/tmp/6wae11353451392.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 -113.5322295 NA
1 867.4487755 -113.5322295
2 -153.8782698 867.4487755
3 -187.0232295 -153.8782698
4 -222.7539029 -187.0232295
5 778.6785190 -222.7539029
6 87.5242649 778.6785190
7 -834.1314093 87.5242649
8 157.7911483 -834.1314093
9 106.2251585 157.7911483
10 41.6872291 106.2251585
11 34.4861484 41.6872291
12 20.7598947 34.4861484
13 -4.3068893 20.7598947
14 -41.1915877 -4.3068893
15 -1063.9503215 -41.1915877
16 889.6551159 -1063.9503215
17 -82.6909198 889.6551159
18 -766.6433599 -82.6909198
19 294.7116243 -766.6433599
20 287.7882070 294.7116243
21 -785.1055615 287.7882070
22 -864.9234806 -785.1055615
23 138.5393280 -864.9234806
24 124.8130743 138.5393280
25 106.9481044 124.8130743
26 -939.7241217 106.9481044
27 11.5732655 -939.7241217
28 -31.3592219 11.5732655
29 -10.9070716 -31.3592219
30 273.1488203 -10.9070716
31 348.6297692 273.1488203
32 346.3224523 348.6297692
33 272.8733570 346.3224523
34 194.2094630 272.8733570
35 -826.2412206 194.2094630
36 121.9930688 -826.2412206
37 -895.8719012 121.9930688
38 68.3974255 -895.8719012
39 26.8966267 68.3974255
40 -8.8340467 26.8966267
41 0.9542143 -8.8340467
42 280.1163424 0.9542143
43 339.1632764 280.1163424
44 339.4416732 339.1632764
45 261.9318043 339.4416732
46 -815.5780646 261.9318043
47 156.7694378 -815.5780646
48 94.0621744 156.7694378
49 79.9369432 94.0621744
50 33.5423805 79.9369432
51 9.9072850 33.5423805
52 -7.9576850 9.9072850
53 -0.4774742 -7.9576850
54 248.7233727 -0.4774742
55 293.3666787 248.7233727
56 281.8271610 293.3666787
57 1154.4599208 281.8271610
58 65.1321375 1154.4599208
59 -985.0021639 65.1321375
60 8.7510599 -985.0021639
61 -39.9515530 8.7510599
62 -94.4242913 -39.9515530
63 -118.0593868 -94.4242913
64 -176.2718639 -118.0593868
65 -200.7833211 -176.2718639
66 52.7559628 -200.7833211
67 1100.8613441 52.7559628
68 40.6184802 1100.8613441
69 -29.6895747 40.6184802
70 -102.3056797 -29.6895747
71 -102.3049464 -102.3056797
72 -114.5995116 -102.3049464
73 851.1015036 -114.5995116
74 -199.9091595 851.1015036
75 -214.0343907 -199.9091595
76 -291.2665962 -214.0343907
77 737.5413932 -291.2665962
78 -1025.6310011 737.5413932
79 1004.6086768 -1025.6310011
80 -29.4126446 1004.6086768
81 -90.2108353 -29.4126446
82 -117.5856695 -90.2108353
83 -87.9013184 -117.5856695
84 -77.7140798 -87.9013184
85 -70.5115325 -77.7140798
86 -71.6648242 -70.5115325
87 -75.1261661 -71.6648242
88 861.7675930 -75.1261661
89 -97.6065032 861.7675930
90 132.0192885 -97.6065032
91 1180.1246698 132.0192885
92 152.1511373 1180.1246698
93 -931.4065206 152.1511373
94 1024.5069670 -931.4065206
95 64.8552075 1024.5069670
96 -943.9772825 64.8552075
97 32.3876221 -943.9772825
98 -1009.1131768 32.3876221
99 941.0301853 -1009.1131768
100 -116.0282667 941.0301853
101 -93.2680662 -116.0282667
102 142.1278510 -93.2680662
103 -815.5368932 142.1278510
104 -853.0202899 -815.5368932
105 43.2482987 -853.0202899
106 977.8340076 43.2482987
107 -35.4148620 977.8340076
108 -39.3535881 -35.4148620
109 -71.3445227 -39.3535881
110 -103.3354573 -71.3445227
111 -136.4804170 -103.3354573
112 815.9709953 -136.4804170
113 815.0953669 815.9709953
114 48.1832339 815.0953669
115 -896.5095708 48.1832339
116 64.8530074 -896.5095708
117 -11.5028364 64.8530074
118 -56.7433740 -11.5028364
119 -65.0984797 -56.7433740
120 972.4643264 -65.0984797
121 -34.4590909 972.4643264
122 -84.3157288 -34.4590909
123 -1117.4606885 -84.3157288
124 848.2403266 -1117.4606885
125 -1133.6155733 848.2403266
126 71.8190628 -1133.6155733
127 165.4433783 71.8190628
128 143.5176347 165.4433783
129 -917.2805559 143.5176347
130 NA -917.2805559
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 867.4487755 -113.5322295
[2,] -153.8782698 867.4487755
[3,] -187.0232295 -153.8782698
[4,] -222.7539029 -187.0232295
[5,] 778.6785190 -222.7539029
[6,] 87.5242649 778.6785190
[7,] -834.1314093 87.5242649
[8,] 157.7911483 -834.1314093
[9,] 106.2251585 157.7911483
[10,] 41.6872291 106.2251585
[11,] 34.4861484 41.6872291
[12,] 20.7598947 34.4861484
[13,] -4.3068893 20.7598947
[14,] -41.1915877 -4.3068893
[15,] -1063.9503215 -41.1915877
[16,] 889.6551159 -1063.9503215
[17,] -82.6909198 889.6551159
[18,] -766.6433599 -82.6909198
[19,] 294.7116243 -766.6433599
[20,] 287.7882070 294.7116243
[21,] -785.1055615 287.7882070
[22,] -864.9234806 -785.1055615
[23,] 138.5393280 -864.9234806
[24,] 124.8130743 138.5393280
[25,] 106.9481044 124.8130743
[26,] -939.7241217 106.9481044
[27,] 11.5732655 -939.7241217
[28,] -31.3592219 11.5732655
[29,] -10.9070716 -31.3592219
[30,] 273.1488203 -10.9070716
[31,] 348.6297692 273.1488203
[32,] 346.3224523 348.6297692
[33,] 272.8733570 346.3224523
[34,] 194.2094630 272.8733570
[35,] -826.2412206 194.2094630
[36,] 121.9930688 -826.2412206
[37,] -895.8719012 121.9930688
[38,] 68.3974255 -895.8719012
[39,] 26.8966267 68.3974255
[40,] -8.8340467 26.8966267
[41,] 0.9542143 -8.8340467
[42,] 280.1163424 0.9542143
[43,] 339.1632764 280.1163424
[44,] 339.4416732 339.1632764
[45,] 261.9318043 339.4416732
[46,] -815.5780646 261.9318043
[47,] 156.7694378 -815.5780646
[48,] 94.0621744 156.7694378
[49,] 79.9369432 94.0621744
[50,] 33.5423805 79.9369432
[51,] 9.9072850 33.5423805
[52,] -7.9576850 9.9072850
[53,] -0.4774742 -7.9576850
[54,] 248.7233727 -0.4774742
[55,] 293.3666787 248.7233727
[56,] 281.8271610 293.3666787
[57,] 1154.4599208 281.8271610
[58,] 65.1321375 1154.4599208
[59,] -985.0021639 65.1321375
[60,] 8.7510599 -985.0021639
[61,] -39.9515530 8.7510599
[62,] -94.4242913 -39.9515530
[63,] -118.0593868 -94.4242913
[64,] -176.2718639 -118.0593868
[65,] -200.7833211 -176.2718639
[66,] 52.7559628 -200.7833211
[67,] 1100.8613441 52.7559628
[68,] 40.6184802 1100.8613441
[69,] -29.6895747 40.6184802
[70,] -102.3056797 -29.6895747
[71,] -102.3049464 -102.3056797
[72,] -114.5995116 -102.3049464
[73,] 851.1015036 -114.5995116
[74,] -199.9091595 851.1015036
[75,] -214.0343907 -199.9091595
[76,] -291.2665962 -214.0343907
[77,] 737.5413932 -291.2665962
[78,] -1025.6310011 737.5413932
[79,] 1004.6086768 -1025.6310011
[80,] -29.4126446 1004.6086768
[81,] -90.2108353 -29.4126446
[82,] -117.5856695 -90.2108353
[83,] -87.9013184 -117.5856695
[84,] -77.7140798 -87.9013184
[85,] -70.5115325 -77.7140798
[86,] -71.6648242 -70.5115325
[87,] -75.1261661 -71.6648242
[88,] 861.7675930 -75.1261661
[89,] -97.6065032 861.7675930
[90,] 132.0192885 -97.6065032
[91,] 1180.1246698 132.0192885
[92,] 152.1511373 1180.1246698
[93,] -931.4065206 152.1511373
[94,] 1024.5069670 -931.4065206
[95,] 64.8552075 1024.5069670
[96,] -943.9772825 64.8552075
[97,] 32.3876221 -943.9772825
[98,] -1009.1131768 32.3876221
[99,] 941.0301853 -1009.1131768
[100,] -116.0282667 941.0301853
[101,] -93.2680662 -116.0282667
[102,] 142.1278510 -93.2680662
[103,] -815.5368932 142.1278510
[104,] -853.0202899 -815.5368932
[105,] 43.2482987 -853.0202899
[106,] 977.8340076 43.2482987
[107,] -35.4148620 977.8340076
[108,] -39.3535881 -35.4148620
[109,] -71.3445227 -39.3535881
[110,] -103.3354573 -71.3445227
[111,] -136.4804170 -103.3354573
[112,] 815.9709953 -136.4804170
[113,] 815.0953669 815.9709953
[114,] 48.1832339 815.0953669
[115,] -896.5095708 48.1832339
[116,] 64.8530074 -896.5095708
[117,] -11.5028364 64.8530074
[118,] -56.7433740 -11.5028364
[119,] -65.0984797 -56.7433740
[120,] 972.4643264 -65.0984797
[121,] -34.4590909 972.4643264
[122,] -84.3157288 -34.4590909
[123,] -1117.4606885 -84.3157288
[124,] 848.2403266 -1117.4606885
[125,] -1133.6155733 848.2403266
[126,] 71.8190628 -1133.6155733
[127,] 165.4433783 71.8190628
[128,] 143.5176347 165.4433783
[129,] -917.2805559 143.5176347
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 867.4487755 -113.5322295
2 -153.8782698 867.4487755
3 -187.0232295 -153.8782698
4 -222.7539029 -187.0232295
5 778.6785190 -222.7539029
6 87.5242649 778.6785190
7 -834.1314093 87.5242649
8 157.7911483 -834.1314093
9 106.2251585 157.7911483
10 41.6872291 106.2251585
11 34.4861484 41.6872291
12 20.7598947 34.4861484
13 -4.3068893 20.7598947
14 -41.1915877 -4.3068893
15 -1063.9503215 -41.1915877
16 889.6551159 -1063.9503215
17 -82.6909198 889.6551159
18 -766.6433599 -82.6909198
19 294.7116243 -766.6433599
20 287.7882070 294.7116243
21 -785.1055615 287.7882070
22 -864.9234806 -785.1055615
23 138.5393280 -864.9234806
24 124.8130743 138.5393280
25 106.9481044 124.8130743
26 -939.7241217 106.9481044
27 11.5732655 -939.7241217
28 -31.3592219 11.5732655
29 -10.9070716 -31.3592219
30 273.1488203 -10.9070716
31 348.6297692 273.1488203
32 346.3224523 348.6297692
33 272.8733570 346.3224523
34 194.2094630 272.8733570
35 -826.2412206 194.2094630
36 121.9930688 -826.2412206
37 -895.8719012 121.9930688
38 68.3974255 -895.8719012
39 26.8966267 68.3974255
40 -8.8340467 26.8966267
41 0.9542143 -8.8340467
42 280.1163424 0.9542143
43 339.1632764 280.1163424
44 339.4416732 339.1632764
45 261.9318043 339.4416732
46 -815.5780646 261.9318043
47 156.7694378 -815.5780646
48 94.0621744 156.7694378
49 79.9369432 94.0621744
50 33.5423805 79.9369432
51 9.9072850 33.5423805
52 -7.9576850 9.9072850
53 -0.4774742 -7.9576850
54 248.7233727 -0.4774742
55 293.3666787 248.7233727
56 281.8271610 293.3666787
57 1154.4599208 281.8271610
58 65.1321375 1154.4599208
59 -985.0021639 65.1321375
60 8.7510599 -985.0021639
61 -39.9515530 8.7510599
62 -94.4242913 -39.9515530
63 -118.0593868 -94.4242913
64 -176.2718639 -118.0593868
65 -200.7833211 -176.2718639
66 52.7559628 -200.7833211
67 1100.8613441 52.7559628
68 40.6184802 1100.8613441
69 -29.6895747 40.6184802
70 -102.3056797 -29.6895747
71 -102.3049464 -102.3056797
72 -114.5995116 -102.3049464
73 851.1015036 -114.5995116
74 -199.9091595 851.1015036
75 -214.0343907 -199.9091595
76 -291.2665962 -214.0343907
77 737.5413932 -291.2665962
78 -1025.6310011 737.5413932
79 1004.6086768 -1025.6310011
80 -29.4126446 1004.6086768
81 -90.2108353 -29.4126446
82 -117.5856695 -90.2108353
83 -87.9013184 -117.5856695
84 -77.7140798 -87.9013184
85 -70.5115325 -77.7140798
86 -71.6648242 -70.5115325
87 -75.1261661 -71.6648242
88 861.7675930 -75.1261661
89 -97.6065032 861.7675930
90 132.0192885 -97.6065032
91 1180.1246698 132.0192885
92 152.1511373 1180.1246698
93 -931.4065206 152.1511373
94 1024.5069670 -931.4065206
95 64.8552075 1024.5069670
96 -943.9772825 64.8552075
97 32.3876221 -943.9772825
98 -1009.1131768 32.3876221
99 941.0301853 -1009.1131768
100 -116.0282667 941.0301853
101 -93.2680662 -116.0282667
102 142.1278510 -93.2680662
103 -815.5368932 142.1278510
104 -853.0202899 -815.5368932
105 43.2482987 -853.0202899
106 977.8340076 43.2482987
107 -35.4148620 977.8340076
108 -39.3535881 -35.4148620
109 -71.3445227 -39.3535881
110 -103.3354573 -71.3445227
111 -136.4804170 -103.3354573
112 815.9709953 -136.4804170
113 815.0953669 815.9709953
114 48.1832339 815.0953669
115 -896.5095708 48.1832339
116 64.8530074 -896.5095708
117 -11.5028364 64.8530074
118 -56.7433740 -11.5028364
119 -65.0984797 -56.7433740
120 972.4643264 -65.0984797
121 -34.4590909 972.4643264
122 -84.3157288 -34.4590909
123 -1117.4606885 -84.3157288
124 848.2403266 -1117.4606885
125 -1133.6155733 848.2403266
126 71.8190628 -1133.6155733
127 165.4433783 71.8190628
128 143.5176347 165.4433783
129 -917.2805559 143.5176347
> 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/7ka1l1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8bebu1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9454s1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10m6yq1353451392.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lmw61353451392.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/121tsc1353451392.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/138gkt1353451392.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/14ksmx1353451392.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/15uq9e1353451392.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/16lp4m1353451392.tab")
+ }
>
> try(system("convert tmp/1c5wh1353451392.ps tmp/1c5wh1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/2re6y1353451392.ps tmp/2re6y1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/382i91353451392.ps tmp/382i91353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hxnq1353451392.ps tmp/4hxnq1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xhku1353451392.ps tmp/5xhku1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wae11353451392.ps tmp/6wae11353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ka1l1353451392.ps tmp/7ka1l1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bebu1353451392.ps tmp/8bebu1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/9454s1353451392.ps tmp/9454s1353451392.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m6yq1353451392.ps tmp/10m6yq1353451392.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.926 1.328 8.253