R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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(14
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+ ,'c'
+ ,'b'
+ ,'h'
+ ,'t'
+ ,'c_t')
+ ,1:143))
> y <- array(NA,dim=c(9,143),dimnames=list(c('i','w','f','s','c','b','h','t','c_t'),1:143))
> 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'
> 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
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
i w f s c b h t c_t
1 14 501 11 20 91.81 77585 1303.2 2000 183620.0
2 14 485 11 19 91.98 77585 -58.7 2000 183960.0
3 15 464 11 18 91.72 77585 -378.9 2000 183440.0
4 13 460 11 13 90.27 78302 175.6 2001 180630.3
5 8 467 11 17 91.89 78302 233.7 2001 183871.9
6 7 460 9 17 92.07 78302 706.8 2001 184232.1
7 3 448 8 13 92.92 78224 -23.6 2001 185932.9
8 3 443 6 14 93.34 78224 420.9 2001 186773.3
9 4 436 7 13 93.60 78224 722.1 2001 187293.6
10 4 431 8 17 92.41 78178 1401.3 2001 184912.4
11 0 484 6 17 93.60 78178 -94.9 2001 187293.6
12 -4 510 5 15 93.77 78178 1043.6 2001 187633.8
13 -14 513 2 9 93.60 77988 1300.1 2001 187293.6
14 -18 503 3 10 93.60 77988 721.1 2001 187293.6
15 -8 471 3 9 93.51 77988 -45.6 2001 187113.5
16 -1 471 7 14 92.66 77876 787.5 2002 185505.3
17 1 476 8 18 94.20 77876 694.3 2002 188588.4
18 2 475 7 18 94.37 77876 1054.7 2002 188928.7
19 0 470 7 12 94.45 78432 821.9 2002 189088.9
20 1 461 6 16 94.62 78432 1100.7 2002 189429.2
21 0 455 6 12 94.37 78432 862.4 2002 188928.7
22 -1 456 7 19 93.43 79025 1656.1 2002 187046.9
23 -3 517 5 13 94.79 79025 -174.0 2002 189769.6
24 -3 525 5 12 94.88 79025 1337.6 2002 189949.8
25 -3 523 5 13 94.79 79407 1394.9 2002 189769.6
26 -4 519 4 11 94.62 79407 915.7 2002 189429.2
27 -8 509 4 10 94.71 79407 -481.1 2002 189609.4
28 -9 512 4 16 93.77 79644 167.9 2003 187821.3
29 -13 519 1 12 95.73 79644 208.2 2003 191747.2
30 -18 517 -1 6 95.99 79644 382.2 2003 192268.0
31 -11 510 3 8 95.82 79381 1004.0 2003 191927.5
32 -9 509 4 6 95.47 79381 864.7 2003 191226.4
33 -10 501 3 8 95.82 79381 1052.9 2003 191927.5
34 -13 507 2 8 94.71 79536 1417.6 2003 189704.1
35 -11 569 1 9 96.33 79536 -197.7 2003 192949.0
36 -5 580 4 13 96.50 79536 1262.1 2003 193289.5
37 -15 578 3 8 96.16 79813 1147.2 2003 192608.5
38 -6 565 5 11 96.33 79813 700.2 2003 192949.0
39 -6 547 6 8 96.33 79813 45.3 2003 192949.0
40 -3 555 6 10 95.05 80332 458.5 2004 190480.2
41 -1 562 6 15 96.84 80332 610.2 2004 194067.4
42 -3 561 6 12 96.92 80332 786.4 2004 194227.7
43 -4 555 6 13 97.44 81434 787.2 2004 195269.8
44 -6 544 5 12 97.78 81434 1040.0 2004 195951.1
45 0 537 6 15 97.69 81434 324.1 2004 195770.8
46 -4 543 5 13 96.67 82167 1343.0 2004 193726.7
47 -2 594 6 13 98.29 82167 -501.2 2004 196973.2
48 -2 611 5 16 98.20 82167 800.4 2004 196792.8
49 -6 613 7 14 98.71 82816 916.7 2004 197814.8
50 -7 611 4 12 98.54 82816 695.8 2004 197474.2
51 -6 594 5 15 98.20 82816 28.0 2004 196792.8
52 -6 595 6 14 96.92 83000 495.6 2005 194324.6
53 -3 591 6 19 99.06 83000 366.2 2005 198615.3
54 -2 589 5 16 99.65 83000 633.0 2005 199798.2
55 -5 584 3 16 99.82 83251 848.3 2005 200139.1
56 -11 573 2 11 99.99 83251 472.2 2005 200480.0
57 -11 567 3 13 100.33 83251 357.8 2005 201161.6
58 -11 569 3 12 99.31 83591 824.3 2005 199116.5
59 -10 621 2 11 101.10 83591 -880.1 2005 202705.5
60 -14 629 0 6 101.10 83591 1066.8 2005 202705.5
61 -8 628 4 9 100.93 83910 1052.8 2005 202364.6
62 -9 612 4 6 100.85 83910 -32.1 2005 202204.2
63 -5 595 5 15 100.93 83910 -1331.4 2005 202364.6
64 -1 597 6 17 99.60 84599 -767.1 2006 199797.6
65 -2 593 6 13 101.88 84599 -236.7 2006 204371.3
66 -5 590 5 12 101.81 84599 -184.9 2006 204230.9
67 -4 580 5 13 102.38 85275 -143.4 2006 205374.3
68 -6 574 3 10 102.74 85275 493.9 2006 206096.4
69 -2 573 5 14 102.82 85275 549.7 2006 206256.9
70 -2 573 5 13 101.72 85608 982.7 2006 204050.3
71 -2 620 5 10 103.47 85608 -856.3 2006 207560.8
72 -2 626 3 11 102.98 85608 967.0 2006 206577.9
73 2 620 6 12 102.68 86303 659.4 2006 205976.1
74 1 588 6 7 102.90 86303 577.2 2006 206417.4
75 -8 566 4 11 103.03 86303 -213.1 2006 206678.2
76 -1 557 6 9 101.29 87115 17.7 2007 203289.0
77 1 561 5 13 103.69 87115 390.1 2007 208105.8
78 -1 549 4 12 103.68 87115 509.3 2007 208085.8
79 2 532 5 5 104.20 87931 410.0 2007 209129.4
80 2 526 5 13 104.08 87931 212.5 2007 208888.6
81 1 511 4 11 104.16 87931 818.0 2007 209049.1
82 -1 499 3 8 103.05 88164 422.7 2007 206821.4
83 -2 555 2 8 104.66 88164 -158.0 2007 210052.6
84 -2 565 3 8 104.46 88164 427.2 2007 209651.2
85 -1 542 2 8 104.95 88792 243.4 2007 210634.6
86 -8 527 -1 0 105.85 88792 -419.3 2007 212441.0
87 -4 510 0 3 106.23 88792 -1459.8 2007 213203.6
88 -6 514 -2 0 104.86 89263 -1389.8 2008 210558.9
89 -3 517 1 -1 107.44 89263 -2.1 2008 215739.5
90 -3 508 -2 -1 108.23 89263 -938.6 2008 217325.8
91 -7 493 -2 -4 108.45 89881 -839.9 2008 217767.6
92 -9 490 -2 1 109.39 89881 -297.6 2008 219655.1
93 -11 469 -6 -1 110.15 89881 -376.3 2008 221181.2
94 -13 478 -4 0 109.13 90120 -79.4 2008 219133.0
95 -11 528 -2 -1 110.28 90120 -2091.3 2008 221442.2
96 -9 534 0 6 110.17 90120 -1023.0 2008 221221.4
97 -17 518 -5 0 109.99 89703 -765.6 2008 220859.9
98 -22 506 -4 -3 109.26 89703 -1592.3 2008 219394.1
99 -25 502 -5 -3 109.11 89703 -1588.8 2008 219092.9
100 -20 516 -1 4 107.06 87818 -1318.0 2009 215083.5
101 -24 528 -2 1 109.53 87818 -402.4 2009 220045.8
102 -24 533 -4 0 108.92 87818 -814.5 2009 218820.3
103 -22 536 -1 -4 109.24 86273 -98.4 2009 219463.2
104 -19 537 1 -2 109.12 86273 -305.9 2009 219222.1
105 -18 524 1 3 109.00 86273 -18.4 2009 218981.0
106 -17 536 -2 2 107.23 86316 610.3 2009 215425.1
107 -11 587 1 5 109.49 86316 -917.3 2009 219965.4
108 -11 597 1 6 109.04 86316 88.4 2009 219061.4
109 -12 581 3 6 109.02 87234 -740.2 2009 219021.2
110 -10 564 3 3 109.23 87234 29.3 2009 219443.1
111 -15 558 1 4 109.46 87234 -893.2 2009 219905.1
112 -15 575 1 7 107.90 87885 -1030.2 2010 216879.0
113 -15 580 0 5 110.42 87885 -403.4 2010 221944.2
114 -13 575 2 6 110.98 87885 -46.9 2010 223069.8
115 -8 563 2 1 111.48 88003 -321.2 2010 224074.8
116 -13 552 -1 3 111.88 88003 -239.9 2010 224878.8
117 -9 537 1 6 111.89 88003 640.9 2010 224898.9
118 -7 545 0 0 109.85 88910 511.6 2010 220798.5
119 -4 601 1 3 112.10 88910 -665.1 2010 225321.0
120 -4 604 1 4 112.24 88910 657.7 2010 225602.4
121 -2 586 3 7 112.39 89397 -207.7 2010 225903.9
122 0 564 2 6 112.52 89397 -885.2 2010 226165.2
123 -2 549 0 6 113.16 89397 -1595.8 2010 227451.6
124 -3 551 0 6 111.84 89813 -1374.9 2011 224910.2
125 1 556 3 6 114.33 89813 -316.6 2011 229917.6
126 -2 548 -2 2 114.82 89813 -283.4 2011 230903.0
127 -1 540 0 2 115.20 90539 -175.8 2011 231667.2
128 1 531 1 2 115.40 90539 -694.2 2011 232069.4
129 -3 521 -1 3 115.74 90539 -249.9 2011 232753.1
130 -4 519 -2 -1 114.19 90688 268.2 2011 229636.1
131 -9 572 -1 -4 115.94 90688 -2105.1 2011 233155.3
132 -9 581 -1 4 116.03 90688 -762.8 2011 233336.3
133 -7 563 1 5 116.24 90691 -117.1 2011 233758.6
134 -14 548 -2 3 116.66 90691 -1094.4 2011 234603.3
135 -12 539 -5 -1 116.79 90691 -2095.2 2011 234864.7
136 -16 541 -5 -4 115.48 90645 -1587.6 2012 232345.8
137 -20 562 -6 0 118.16 90645 -528.0 2012 237737.9
138 -12 559 -4 -1 118.38 90645 -324.2 2012 238180.6
139 -12 546 -3 -1 118.51 90861 -276.1 2012 238442.1
140 -10 536 -3 3 118.42 90861 -139.1 2012 238261.0
141 -10 528 -1 2 118.24 90861 268.0 2012 237898.9
142 -13 530 -2 -4 116.47 90401 570.5 2012 234337.6
143 -16 582 -3 -3 118.96 90401 -316.5 2012 239347.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) w f s c b
2.377e+04 -2.092e-02 1.959e+00 2.365e-01 -1.999e+02 2.387e-03
h t c_t
3.718e-04 -1.195e+01 9.957e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.9487 -1.7621 0.0617 2.1474 9.3271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.377e+04 4.450e+03 5.343 3.82e-07 ***
w -2.092e-02 9.077e-03 -2.305 0.0227 *
f 1.959e+00 1.719e-01 11.394 < 2e-16 ***
s 2.365e-01 1.091e-01 2.168 0.0320 *
c -1.999e+02 4.491e+01 -4.451 1.79e-05 ***
b 2.387e-03 3.077e-04 7.757 1.96e-12 ***
h 3.718e-04 4.545e-04 0.818 0.4148
t -1.195e+01 2.220e+00 -5.381 3.21e-07 ***
c_t 9.957e-02 2.227e-02 4.470 1.65e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.4 on 134 degrees of freedom
Multiple R-squared: 0.8058, Adjusted R-squared: 0.7942
F-statistic: 69.51 on 8 and 134 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,] 8.580678e-02 1.716136e-01 0.91419322
[2,] 6.093765e-02 1.218753e-01 0.93906235
[3,] 1.735370e-01 3.470739e-01 0.82646305
[4,] 2.431141e-01 4.862281e-01 0.75688594
[5,] 5.171063e-01 9.657874e-01 0.48289372
[6,] 4.740605e-01 9.481210e-01 0.52593952
[7,] 3.981352e-01 7.962704e-01 0.60186481
[8,] 3.059538e-01 6.119075e-01 0.69404625
[9,] 2.291162e-01 4.582324e-01 0.77088378
[10,] 1.692270e-01 3.384541e-01 0.83077296
[11,] 1.668239e-01 3.336477e-01 0.83317613
[12,] 1.324900e-01 2.649801e-01 0.86750996
[13,] 1.127205e-01 2.254409e-01 0.88727955
[14,] 7.942654e-02 1.588531e-01 0.92057346
[15,] 6.019131e-02 1.203826e-01 0.93980869
[16,] 6.264975e-02 1.252995e-01 0.93735025
[17,] 4.574405e-02 9.148810e-02 0.95425595
[18,] 3.858926e-02 7.717851e-02 0.96141074
[19,] 2.612695e-02 5.225390e-02 0.97387305
[20,] 2.709292e-02 5.418584e-02 0.97290708
[21,] 2.027990e-02 4.055980e-02 0.97972010
[22,] 1.386564e-02 2.773127e-02 0.98613436
[23,] 9.188526e-03 1.837705e-02 0.99081147
[24,] 1.031654e-02 2.063308e-02 0.98968346
[25,] 7.485984e-03 1.497197e-02 0.99251402
[26,] 1.586559e-02 3.173119e-02 0.98413441
[27,] 1.258248e-02 2.516496e-02 0.98741752
[28,] 1.431650e-02 2.863301e-02 0.98568350
[29,] 1.182264e-02 2.364527e-02 0.98817736
[30,] 1.101389e-02 2.202777e-02 0.98898611
[31,] 1.034893e-02 2.069786e-02 0.98965107
[32,] 1.428586e-02 2.857173e-02 0.98571414
[33,] 1.244980e-02 2.489959e-02 0.98755020
[34,] 1.111970e-02 2.223940e-02 0.98888030
[35,] 7.726380e-03 1.545276e-02 0.99227362
[36,] 6.388965e-03 1.277793e-02 0.99361104
[37,] 5.258078e-03 1.051616e-02 0.99474192
[38,] 1.560630e-02 3.121259e-02 0.98439370
[39,] 1.097765e-02 2.195531e-02 0.98902235
[40,] 8.564174e-03 1.712835e-02 0.99143583
[41,] 7.315094e-03 1.463019e-02 0.99268491
[42,] 6.185150e-03 1.237030e-02 0.99381485
[43,] 5.177842e-03 1.035568e-02 0.99482216
[44,] 4.697656e-03 9.395312e-03 0.99530234
[45,] 3.507175e-03 7.014349e-03 0.99649283
[46,] 3.873497e-03 7.746994e-03 0.99612650
[47,] 2.862421e-03 5.724842e-03 0.99713758
[48,] 2.111752e-03 4.223504e-03 0.99788825
[49,] 2.047659e-03 4.095318e-03 0.99795234
[50,] 1.355951e-03 2.711901e-03 0.99864405
[51,] 9.306569e-04 1.861314e-03 0.99906934
[52,] 8.022609e-04 1.604522e-03 0.99919774
[53,] 5.340503e-04 1.068101e-03 0.99946595
[54,] 4.115870e-04 8.231739e-04 0.99958841
[55,] 3.043634e-04 6.087268e-04 0.99969564
[56,] 1.967684e-04 3.935368e-04 0.99980323
[57,] 1.530261e-04 3.060522e-04 0.99984697
[58,] 1.019031e-04 2.038062e-04 0.99989810
[59,] 6.629227e-05 1.325845e-04 0.99993371
[60,] 5.102222e-05 1.020444e-04 0.99994898
[61,] 1.558561e-04 3.117122e-04 0.99984414
[62,] 1.291770e-04 2.583541e-04 0.99987082
[63,] 1.053074e-04 2.106148e-04 0.99989469
[64,] 1.198724e-04 2.397448e-04 0.99988013
[65,] 8.282981e-05 1.656596e-04 0.99991717
[66,] 5.077212e-05 1.015442e-04 0.99994923
[67,] 3.132510e-05 6.265021e-05 0.99996867
[68,] 2.316603e-05 4.633205e-05 0.99997683
[69,] 1.353802e-05 2.707603e-05 0.99998646
[70,] 8.384693e-06 1.676939e-05 0.99999162
[71,] 7.337972e-06 1.467594e-05 0.99999266
[72,] 6.049339e-06 1.209868e-05 0.99999395
[73,] 3.464665e-06 6.929331e-06 0.99999654
[74,] 2.571495e-06 5.142990e-06 0.99999743
[75,] 1.721058e-06 3.442116e-06 0.99999828
[76,] 2.263436e-06 4.526871e-06 0.99999774
[77,] 8.738681e-06 1.747736e-05 0.99999126
[78,] 1.019519e-05 2.039039e-05 0.99998980
[79,] 2.232292e-04 4.464584e-04 0.99977677
[80,] 7.716340e-04 1.543268e-03 0.99922837
[81,] 1.393686e-03 2.787372e-03 0.99860631
[82,] 2.292166e-03 4.584333e-03 0.99770783
[83,] 3.297683e-03 6.595365e-03 0.99670232
[84,] 6.378330e-03 1.275666e-02 0.99362167
[85,] 1.183870e-02 2.367740e-02 0.98816130
[86,] 1.223983e-02 2.447966e-02 0.98776017
[87,] 2.900663e-02 5.801325e-02 0.97099337
[88,] 5.458447e-02 1.091689e-01 0.94541553
[89,] 7.136260e-02 1.427252e-01 0.92863740
[90,] 1.664708e-01 3.329417e-01 0.83352916
[91,] 1.950716e-01 3.901432e-01 0.80492842
[92,] 1.731409e-01 3.462819e-01 0.82685905
[93,] 1.579877e-01 3.159754e-01 0.84201231
[94,] 1.647729e-01 3.295458e-01 0.83522712
[95,] 1.714423e-01 3.428845e-01 0.82855775
[96,] 2.516031e-01 5.032061e-01 0.74839693
[97,] 4.057170e-01 8.114340e-01 0.59428301
[98,] 3.529866e-01 7.059732e-01 0.64701342
[99,] 3.014382e-01 6.028763e-01 0.69856185
[100,] 2.500868e-01 5.001737e-01 0.74991317
[101,] 2.455062e-01 4.910125e-01 0.75449376
[102,] 2.431511e-01 4.863021e-01 0.75684894
[103,] 3.424148e-01 6.848295e-01 0.65758524
[104,] 3.079155e-01 6.158309e-01 0.69208453
[105,] 3.170710e-01 6.341419e-01 0.68292903
[106,] 6.380416e-01 7.239168e-01 0.36195838
[107,] 6.900152e-01 6.199696e-01 0.30998481
[108,] 6.573706e-01 6.852587e-01 0.34262936
[109,] 5.999266e-01 8.001469e-01 0.40007344
[110,] 5.524180e-01 8.951639e-01 0.44758197
[111,] 5.096831e-01 9.806337e-01 0.49031687
[112,] 7.019345e-01 5.961309e-01 0.29806547
[113,] 8.281225e-01 3.437551e-01 0.17187754
[114,] 9.133691e-01 1.732617e-01 0.08663087
[115,] 8.775512e-01 2.448975e-01 0.12244876
[116,] 8.309383e-01 3.381233e-01 0.16906165
[117,] 7.441836e-01 5.116327e-01 0.25581637
[118,] 7.053629e-01 5.892741e-01 0.29463707
[119,] 5.810804e-01 8.378392e-01 0.41891962
[120,] 6.294064e-01 7.411872e-01 0.37059358
> postscript(file="/var/fisher/rcomp/tmp/1todv1351688701.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/23w3o1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3u6wm1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/46rfi1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5pyga1351688701.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 = 143
Frequency = 1
1 2 3 4 5 6
0.78511034 1.31707160 2.04384857 1.12853361 -3.67387936 -0.96492955
7 8 9 10 11 12
-1.31847601 2.35736127 1.53991344 -2.36108305 -0.02928039 -1.36950706
13 14 15 16 17 18
-3.75913262 -9.94869030 -0.15325913 -0.02762840 0.02166005 2.91578501
19 20 21 22 23 24
1.03223156 2.84315081 2.62002013 -4.18196698 1.83210880 1.72166264
25 26 27 28 29 30
0.46256535 1.89917726 -1.50654464 -2.55654240 1.24066406 1.58320971
31 32 33 34 35 36
0.45092603 0.84538707 1.24442961 -0.65360206 5.66304535 4.60019712
37 38 39 40 41 42
-3.06446327 1.27506350 -0.10751193 3.12786804 4.62635287 3.27595073
43 44 45 46 47 48
-0.54497998 -0.56126371 2.86005278 -1.04747425 1.28124447 2.37272963
49 50 51 52 53 54
-6.45432194 -1.11993664 -3.00830059 -3.44821557 -1.17287532 2.49083996
55 56 57 58 59 60
2.66440204 -0.24496631 -2.68168182 -3.62353469 1.70668928 2.25101675
61 62 63 64 65 66
-1.11114253 -1.35139272 -1.29346166 0.18570783 0.14985434 -0.74576033
67 68 69 70 71 72
-1.74573193 0.56669214 -0.32875387 -1.19221922 1.41398112 4.47894945
73 74 75 76 77 78
0.65590540 0.22843513 -5.94905640 -2.97212567 0.06167181 -0.03845636
79 80 81 82 83 84
0.40822283 -1.53997963 -0.64431213 -0.67087622 1.72657970 -0.24712057
85 86 87 88 89 90
0.81554867 1.54592842 2.92039368 5.94523688 2.67587954 8.65916868
91 92 93 94 95 96
3.52820647 0.01715291 5.86459442 -0.71317174 -0.67876419 -4.51681231
97 98 99 100 101 102
-0.72524356 -6.86872814 -7.98446399 -6.35778605 -8.19245315 -3.67772250
103 104 105 106 107 108
-3.17821502 -4.45121452 -4.99269768 2.33237758 3.00188386 2.67608645
109 110 111 112 113 114
-4.45647343 -2.42386627 -3.56340030 -3.95457376 -2.32416242 -4.86559846
115 116 117 118 119 120
0.75275831 0.78958287 -0.48215706 3.49179823 4.83121472 4.12822059
121 122 123 124 125 126
0.24316852 4.19559934 5.89305884 5.02437925 1.94515201 9.32713715
127 128 129 130 131 132
4.32943783 4.30146154 3.48390647 5.36745923 0.46758035 -1.76849149
133 134 135 136 137 138
-4.62402097 -5.37830905 3.58111749 1.18349985 -3.00788843 1.06940568
139 140 141 142 143
-1.75571696 -0.92007837 -4.83646500 -2.60548744 -3.62626552
> postscript(file="/var/fisher/rcomp/tmp/6wvae1351688701.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 0.78511034 NA
1 1.31707160 0.78511034
2 2.04384857 1.31707160
3 1.12853361 2.04384857
4 -3.67387936 1.12853361
5 -0.96492955 -3.67387936
6 -1.31847601 -0.96492955
7 2.35736127 -1.31847601
8 1.53991344 2.35736127
9 -2.36108305 1.53991344
10 -0.02928039 -2.36108305
11 -1.36950706 -0.02928039
12 -3.75913262 -1.36950706
13 -9.94869030 -3.75913262
14 -0.15325913 -9.94869030
15 -0.02762840 -0.15325913
16 0.02166005 -0.02762840
17 2.91578501 0.02166005
18 1.03223156 2.91578501
19 2.84315081 1.03223156
20 2.62002013 2.84315081
21 -4.18196698 2.62002013
22 1.83210880 -4.18196698
23 1.72166264 1.83210880
24 0.46256535 1.72166264
25 1.89917726 0.46256535
26 -1.50654464 1.89917726
27 -2.55654240 -1.50654464
28 1.24066406 -2.55654240
29 1.58320971 1.24066406
30 0.45092603 1.58320971
31 0.84538707 0.45092603
32 1.24442961 0.84538707
33 -0.65360206 1.24442961
34 5.66304535 -0.65360206
35 4.60019712 5.66304535
36 -3.06446327 4.60019712
37 1.27506350 -3.06446327
38 -0.10751193 1.27506350
39 3.12786804 -0.10751193
40 4.62635287 3.12786804
41 3.27595073 4.62635287
42 -0.54497998 3.27595073
43 -0.56126371 -0.54497998
44 2.86005278 -0.56126371
45 -1.04747425 2.86005278
46 1.28124447 -1.04747425
47 2.37272963 1.28124447
48 -6.45432194 2.37272963
49 -1.11993664 -6.45432194
50 -3.00830059 -1.11993664
51 -3.44821557 -3.00830059
52 -1.17287532 -3.44821557
53 2.49083996 -1.17287532
54 2.66440204 2.49083996
55 -0.24496631 2.66440204
56 -2.68168182 -0.24496631
57 -3.62353469 -2.68168182
58 1.70668928 -3.62353469
59 2.25101675 1.70668928
60 -1.11114253 2.25101675
61 -1.35139272 -1.11114253
62 -1.29346166 -1.35139272
63 0.18570783 -1.29346166
64 0.14985434 0.18570783
65 -0.74576033 0.14985434
66 -1.74573193 -0.74576033
67 0.56669214 -1.74573193
68 -0.32875387 0.56669214
69 -1.19221922 -0.32875387
70 1.41398112 -1.19221922
71 4.47894945 1.41398112
72 0.65590540 4.47894945
73 0.22843513 0.65590540
74 -5.94905640 0.22843513
75 -2.97212567 -5.94905640
76 0.06167181 -2.97212567
77 -0.03845636 0.06167181
78 0.40822283 -0.03845636
79 -1.53997963 0.40822283
80 -0.64431213 -1.53997963
81 -0.67087622 -0.64431213
82 1.72657970 -0.67087622
83 -0.24712057 1.72657970
84 0.81554867 -0.24712057
85 1.54592842 0.81554867
86 2.92039368 1.54592842
87 5.94523688 2.92039368
88 2.67587954 5.94523688
89 8.65916868 2.67587954
90 3.52820647 8.65916868
91 0.01715291 3.52820647
92 5.86459442 0.01715291
93 -0.71317174 5.86459442
94 -0.67876419 -0.71317174
95 -4.51681231 -0.67876419
96 -0.72524356 -4.51681231
97 -6.86872814 -0.72524356
98 -7.98446399 -6.86872814
99 -6.35778605 -7.98446399
100 -8.19245315 -6.35778605
101 -3.67772250 -8.19245315
102 -3.17821502 -3.67772250
103 -4.45121452 -3.17821502
104 -4.99269768 -4.45121452
105 2.33237758 -4.99269768
106 3.00188386 2.33237758
107 2.67608645 3.00188386
108 -4.45647343 2.67608645
109 -2.42386627 -4.45647343
110 -3.56340030 -2.42386627
111 -3.95457376 -3.56340030
112 -2.32416242 -3.95457376
113 -4.86559846 -2.32416242
114 0.75275831 -4.86559846
115 0.78958287 0.75275831
116 -0.48215706 0.78958287
117 3.49179823 -0.48215706
118 4.83121472 3.49179823
119 4.12822059 4.83121472
120 0.24316852 4.12822059
121 4.19559934 0.24316852
122 5.89305884 4.19559934
123 5.02437925 5.89305884
124 1.94515201 5.02437925
125 9.32713715 1.94515201
126 4.32943783 9.32713715
127 4.30146154 4.32943783
128 3.48390647 4.30146154
129 5.36745923 3.48390647
130 0.46758035 5.36745923
131 -1.76849149 0.46758035
132 -4.62402097 -1.76849149
133 -5.37830905 -4.62402097
134 3.58111749 -5.37830905
135 1.18349985 3.58111749
136 -3.00788843 1.18349985
137 1.06940568 -3.00788843
138 -1.75571696 1.06940568
139 -0.92007837 -1.75571696
140 -4.83646500 -0.92007837
141 -2.60548744 -4.83646500
142 -3.62626552 -2.60548744
143 NA -3.62626552
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.31707160 0.78511034
[2,] 2.04384857 1.31707160
[3,] 1.12853361 2.04384857
[4,] -3.67387936 1.12853361
[5,] -0.96492955 -3.67387936
[6,] -1.31847601 -0.96492955
[7,] 2.35736127 -1.31847601
[8,] 1.53991344 2.35736127
[9,] -2.36108305 1.53991344
[10,] -0.02928039 -2.36108305
[11,] -1.36950706 -0.02928039
[12,] -3.75913262 -1.36950706
[13,] -9.94869030 -3.75913262
[14,] -0.15325913 -9.94869030
[15,] -0.02762840 -0.15325913
[16,] 0.02166005 -0.02762840
[17,] 2.91578501 0.02166005
[18,] 1.03223156 2.91578501
[19,] 2.84315081 1.03223156
[20,] 2.62002013 2.84315081
[21,] -4.18196698 2.62002013
[22,] 1.83210880 -4.18196698
[23,] 1.72166264 1.83210880
[24,] 0.46256535 1.72166264
[25,] 1.89917726 0.46256535
[26,] -1.50654464 1.89917726
[27,] -2.55654240 -1.50654464
[28,] 1.24066406 -2.55654240
[29,] 1.58320971 1.24066406
[30,] 0.45092603 1.58320971
[31,] 0.84538707 0.45092603
[32,] 1.24442961 0.84538707
[33,] -0.65360206 1.24442961
[34,] 5.66304535 -0.65360206
[35,] 4.60019712 5.66304535
[36,] -3.06446327 4.60019712
[37,] 1.27506350 -3.06446327
[38,] -0.10751193 1.27506350
[39,] 3.12786804 -0.10751193
[40,] 4.62635287 3.12786804
[41,] 3.27595073 4.62635287
[42,] -0.54497998 3.27595073
[43,] -0.56126371 -0.54497998
[44,] 2.86005278 -0.56126371
[45,] -1.04747425 2.86005278
[46,] 1.28124447 -1.04747425
[47,] 2.37272963 1.28124447
[48,] -6.45432194 2.37272963
[49,] -1.11993664 -6.45432194
[50,] -3.00830059 -1.11993664
[51,] -3.44821557 -3.00830059
[52,] -1.17287532 -3.44821557
[53,] 2.49083996 -1.17287532
[54,] 2.66440204 2.49083996
[55,] -0.24496631 2.66440204
[56,] -2.68168182 -0.24496631
[57,] -3.62353469 -2.68168182
[58,] 1.70668928 -3.62353469
[59,] 2.25101675 1.70668928
[60,] -1.11114253 2.25101675
[61,] -1.35139272 -1.11114253
[62,] -1.29346166 -1.35139272
[63,] 0.18570783 -1.29346166
[64,] 0.14985434 0.18570783
[65,] -0.74576033 0.14985434
[66,] -1.74573193 -0.74576033
[67,] 0.56669214 -1.74573193
[68,] -0.32875387 0.56669214
[69,] -1.19221922 -0.32875387
[70,] 1.41398112 -1.19221922
[71,] 4.47894945 1.41398112
[72,] 0.65590540 4.47894945
[73,] 0.22843513 0.65590540
[74,] -5.94905640 0.22843513
[75,] -2.97212567 -5.94905640
[76,] 0.06167181 -2.97212567
[77,] -0.03845636 0.06167181
[78,] 0.40822283 -0.03845636
[79,] -1.53997963 0.40822283
[80,] -0.64431213 -1.53997963
[81,] -0.67087622 -0.64431213
[82,] 1.72657970 -0.67087622
[83,] -0.24712057 1.72657970
[84,] 0.81554867 -0.24712057
[85,] 1.54592842 0.81554867
[86,] 2.92039368 1.54592842
[87,] 5.94523688 2.92039368
[88,] 2.67587954 5.94523688
[89,] 8.65916868 2.67587954
[90,] 3.52820647 8.65916868
[91,] 0.01715291 3.52820647
[92,] 5.86459442 0.01715291
[93,] -0.71317174 5.86459442
[94,] -0.67876419 -0.71317174
[95,] -4.51681231 -0.67876419
[96,] -0.72524356 -4.51681231
[97,] -6.86872814 -0.72524356
[98,] -7.98446399 -6.86872814
[99,] -6.35778605 -7.98446399
[100,] -8.19245315 -6.35778605
[101,] -3.67772250 -8.19245315
[102,] -3.17821502 -3.67772250
[103,] -4.45121452 -3.17821502
[104,] -4.99269768 -4.45121452
[105,] 2.33237758 -4.99269768
[106,] 3.00188386 2.33237758
[107,] 2.67608645 3.00188386
[108,] -4.45647343 2.67608645
[109,] -2.42386627 -4.45647343
[110,] -3.56340030 -2.42386627
[111,] -3.95457376 -3.56340030
[112,] -2.32416242 -3.95457376
[113,] -4.86559846 -2.32416242
[114,] 0.75275831 -4.86559846
[115,] 0.78958287 0.75275831
[116,] -0.48215706 0.78958287
[117,] 3.49179823 -0.48215706
[118,] 4.83121472 3.49179823
[119,] 4.12822059 4.83121472
[120,] 0.24316852 4.12822059
[121,] 4.19559934 0.24316852
[122,] 5.89305884 4.19559934
[123,] 5.02437925 5.89305884
[124,] 1.94515201 5.02437925
[125,] 9.32713715 1.94515201
[126,] 4.32943783 9.32713715
[127,] 4.30146154 4.32943783
[128,] 3.48390647 4.30146154
[129,] 5.36745923 3.48390647
[130,] 0.46758035 5.36745923
[131,] -1.76849149 0.46758035
[132,] -4.62402097 -1.76849149
[133,] -5.37830905 -4.62402097
[134,] 3.58111749 -5.37830905
[135,] 1.18349985 3.58111749
[136,] -3.00788843 1.18349985
[137,] 1.06940568 -3.00788843
[138,] -1.75571696 1.06940568
[139,] -0.92007837 -1.75571696
[140,] -4.83646500 -0.92007837
[141,] -2.60548744 -4.83646500
[142,] -3.62626552 -2.60548744
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.31707160 0.78511034
2 2.04384857 1.31707160
3 1.12853361 2.04384857
4 -3.67387936 1.12853361
5 -0.96492955 -3.67387936
6 -1.31847601 -0.96492955
7 2.35736127 -1.31847601
8 1.53991344 2.35736127
9 -2.36108305 1.53991344
10 -0.02928039 -2.36108305
11 -1.36950706 -0.02928039
12 -3.75913262 -1.36950706
13 -9.94869030 -3.75913262
14 -0.15325913 -9.94869030
15 -0.02762840 -0.15325913
16 0.02166005 -0.02762840
17 2.91578501 0.02166005
18 1.03223156 2.91578501
19 2.84315081 1.03223156
20 2.62002013 2.84315081
21 -4.18196698 2.62002013
22 1.83210880 -4.18196698
23 1.72166264 1.83210880
24 0.46256535 1.72166264
25 1.89917726 0.46256535
26 -1.50654464 1.89917726
27 -2.55654240 -1.50654464
28 1.24066406 -2.55654240
29 1.58320971 1.24066406
30 0.45092603 1.58320971
31 0.84538707 0.45092603
32 1.24442961 0.84538707
33 -0.65360206 1.24442961
34 5.66304535 -0.65360206
35 4.60019712 5.66304535
36 -3.06446327 4.60019712
37 1.27506350 -3.06446327
38 -0.10751193 1.27506350
39 3.12786804 -0.10751193
40 4.62635287 3.12786804
41 3.27595073 4.62635287
42 -0.54497998 3.27595073
43 -0.56126371 -0.54497998
44 2.86005278 -0.56126371
45 -1.04747425 2.86005278
46 1.28124447 -1.04747425
47 2.37272963 1.28124447
48 -6.45432194 2.37272963
49 -1.11993664 -6.45432194
50 -3.00830059 -1.11993664
51 -3.44821557 -3.00830059
52 -1.17287532 -3.44821557
53 2.49083996 -1.17287532
54 2.66440204 2.49083996
55 -0.24496631 2.66440204
56 -2.68168182 -0.24496631
57 -3.62353469 -2.68168182
58 1.70668928 -3.62353469
59 2.25101675 1.70668928
60 -1.11114253 2.25101675
61 -1.35139272 -1.11114253
62 -1.29346166 -1.35139272
63 0.18570783 -1.29346166
64 0.14985434 0.18570783
65 -0.74576033 0.14985434
66 -1.74573193 -0.74576033
67 0.56669214 -1.74573193
68 -0.32875387 0.56669214
69 -1.19221922 -0.32875387
70 1.41398112 -1.19221922
71 4.47894945 1.41398112
72 0.65590540 4.47894945
73 0.22843513 0.65590540
74 -5.94905640 0.22843513
75 -2.97212567 -5.94905640
76 0.06167181 -2.97212567
77 -0.03845636 0.06167181
78 0.40822283 -0.03845636
79 -1.53997963 0.40822283
80 -0.64431213 -1.53997963
81 -0.67087622 -0.64431213
82 1.72657970 -0.67087622
83 -0.24712057 1.72657970
84 0.81554867 -0.24712057
85 1.54592842 0.81554867
86 2.92039368 1.54592842
87 5.94523688 2.92039368
88 2.67587954 5.94523688
89 8.65916868 2.67587954
90 3.52820647 8.65916868
91 0.01715291 3.52820647
92 5.86459442 0.01715291
93 -0.71317174 5.86459442
94 -0.67876419 -0.71317174
95 -4.51681231 -0.67876419
96 -0.72524356 -4.51681231
97 -6.86872814 -0.72524356
98 -7.98446399 -6.86872814
99 -6.35778605 -7.98446399
100 -8.19245315 -6.35778605
101 -3.67772250 -8.19245315
102 -3.17821502 -3.67772250
103 -4.45121452 -3.17821502
104 -4.99269768 -4.45121452
105 2.33237758 -4.99269768
106 3.00188386 2.33237758
107 2.67608645 3.00188386
108 -4.45647343 2.67608645
109 -2.42386627 -4.45647343
110 -3.56340030 -2.42386627
111 -3.95457376 -3.56340030
112 -2.32416242 -3.95457376
113 -4.86559846 -2.32416242
114 0.75275831 -4.86559846
115 0.78958287 0.75275831
116 -0.48215706 0.78958287
117 3.49179823 -0.48215706
118 4.83121472 3.49179823
119 4.12822059 4.83121472
120 0.24316852 4.12822059
121 4.19559934 0.24316852
122 5.89305884 4.19559934
123 5.02437925 5.89305884
124 1.94515201 5.02437925
125 9.32713715 1.94515201
126 4.32943783 9.32713715
127 4.30146154 4.32943783
128 3.48390647 4.30146154
129 5.36745923 3.48390647
130 0.46758035 5.36745923
131 -1.76849149 0.46758035
132 -4.62402097 -1.76849149
133 -5.37830905 -4.62402097
134 3.58111749 -5.37830905
135 1.18349985 3.58111749
136 -3.00788843 1.18349985
137 1.06940568 -3.00788843
138 -1.75571696 1.06940568
139 -0.92007837 -1.75571696
140 -4.83646500 -0.92007837
141 -2.60548744 -4.83646500
142 -3.62626552 -2.60548744
> 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/7729h1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8dz9z1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9pdze1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/104xol1351688701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113m1r1351688701.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/121m101351688701.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/13rqhq1351688701.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/14ej8c1351688701.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/155gwx1351688701.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/16tp6k1351688701.tab")
+ }
>
> try(system("convert tmp/1todv1351688701.ps tmp/1todv1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/23w3o1351688701.ps tmp/23w3o1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u6wm1351688701.ps tmp/3u6wm1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/46rfi1351688701.ps tmp/46rfi1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pyga1351688701.ps tmp/5pyga1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wvae1351688701.ps tmp/6wvae1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/7729h1351688701.ps tmp/7729h1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dz9z1351688701.ps tmp/8dz9z1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pdze1351688701.ps tmp/9pdze1351688701.png",intern=TRUE))
character(0)
> try(system("convert tmp/104xol1351688701.ps tmp/104xol1351688701.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.500 1.180 9.677