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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+ ,-3
+ ,-3
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+ ,-316.5)
+ ,dim=c(6
+ ,143)
+ ,dimnames=list(c('i'
+ ,'w'
+ ,'f'
+ ,'s'
+ ,'c'
+ ,'h')
+ ,1:143))
> y <- array(NA,dim=c(6,143),dimnames=list(c('i','w','f','s','c','h'),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 h
1 14 501 11 20 91.81 1303.2
2 14 485 11 19 91.98 -58.7
3 15 464 11 18 91.72 -378.9
4 13 460 11 13 90.27 175.6
5 8 467 11 17 91.89 233.7
6 7 460 9 17 92.07 706.8
7 3 448 8 13 92.92 -23.6
8 3 443 6 14 93.34 420.9
9 4 436 7 13 93.60 722.1
10 4 431 8 17 92.41 1401.3
11 0 484 6 17 93.60 -94.9
12 -4 510 5 15 93.77 1043.6
13 -14 513 2 9 93.60 1300.1
14 -18 503 3 10 93.60 721.1
15 -8 471 3 9 93.51 -45.6
16 -1 471 7 14 92.66 787.5
17 1 476 8 18 94.20 694.3
18 2 475 7 18 94.37 1054.7
19 0 470 7 12 94.45 821.9
20 1 461 6 16 94.62 1100.7
21 0 455 6 12 94.37 862.4
22 -1 456 7 19 93.43 1656.1
23 -3 517 5 13 94.79 -174.0
24 -3 525 5 12 94.88 1337.6
25 -3 523 5 13 94.79 1394.9
26 -4 519 4 11 94.62 915.7
27 -8 509 4 10 94.71 -481.1
28 -9 512 4 16 93.77 167.9
29 -13 519 1 12 95.73 208.2
30 -18 517 -1 6 95.99 382.2
31 -11 510 3 8 95.82 1004.0
32 -9 509 4 6 95.47 864.7
33 -10 501 3 8 95.82 1052.9
34 -13 507 2 8 94.71 1417.6
35 -11 569 1 9 96.33 -197.7
36 -5 580 4 13 96.50 1262.1
37 -15 578 3 8 96.16 1147.2
38 -6 565 5 11 96.33 700.2
39 -6 547 6 8 96.33 45.3
40 -3 555 6 10 95.05 458.5
41 -1 562 6 15 96.84 610.2
42 -3 561 6 12 96.92 786.4
43 -4 555 6 13 97.44 787.2
44 -6 544 5 12 97.78 1040.0
45 0 537 6 15 97.69 324.1
46 -4 543 5 13 96.67 1343.0
47 -2 594 6 13 98.29 -501.2
48 -2 611 5 16 98.20 800.4
49 -6 613 7 14 98.71 916.7
50 -7 611 4 12 98.54 695.8
51 -6 594 5 15 98.20 28.0
52 -6 595 6 14 96.92 495.6
53 -3 591 6 19 99.06 366.2
54 -2 589 5 16 99.65 633.0
55 -5 584 3 16 99.82 848.3
56 -11 573 2 11 99.99 472.2
57 -11 567 3 13 100.33 357.8
58 -11 569 3 12 99.31 824.3
59 -10 621 2 11 101.10 -880.1
60 -14 629 0 6 101.10 1066.8
61 -8 628 4 9 100.93 1052.8
62 -9 612 4 6 100.85 -32.1
63 -5 595 5 15 100.93 -1331.4
64 -1 597 6 17 99.60 -767.1
65 -2 593 6 13 101.88 -236.7
66 -5 590 5 12 101.81 -184.9
67 -4 580 5 13 102.38 -143.4
68 -6 574 3 10 102.74 493.9
69 -2 573 5 14 102.82 549.7
70 -2 573 5 13 101.72 982.7
71 -2 620 5 10 103.47 -856.3
72 -2 626 3 11 102.98 967.0
73 2 620 6 12 102.68 659.4
74 1 588 6 7 102.90 577.2
75 -8 566 4 11 103.03 -213.1
76 -1 557 6 9 101.29 17.7
77 1 561 5 13 103.69 390.1
78 -1 549 4 12 103.68 509.3
79 2 532 5 5 104.20 410.0
80 2 526 5 13 104.08 212.5
81 1 511 4 11 104.16 818.0
82 -1 499 3 8 103.05 422.7
83 -2 555 2 8 104.66 -158.0
84 -2 565 3 8 104.46 427.2
85 -1 542 2 8 104.95 243.4
86 -8 527 -1 0 105.85 -419.3
87 -4 510 0 3 106.23 -1459.8
88 -6 514 -2 0 104.86 -1389.8
89 -3 517 1 -1 107.44 -2.1
90 -3 508 -2 -1 108.23 -938.6
91 -7 493 -2 -4 108.45 -839.9
92 -9 490 -2 1 109.39 -297.6
93 -11 469 -6 -1 110.15 -376.3
94 -13 478 -4 0 109.13 -79.4
95 -11 528 -2 -1 110.28 -2091.3
96 -9 534 0 6 110.17 -1023.0
97 -17 518 -5 0 109.99 -765.6
98 -22 506 -4 -3 109.26 -1592.3
99 -25 502 -5 -3 109.11 -1588.8
100 -20 516 -1 4 107.06 -1318.0
101 -24 528 -2 1 109.53 -402.4
102 -24 533 -4 0 108.92 -814.5
103 -22 536 -1 -4 109.24 -98.4
104 -19 537 1 -2 109.12 -305.9
105 -18 524 1 3 109.00 -18.4
106 -17 536 -2 2 107.23 610.3
107 -11 587 1 5 109.49 -917.3
108 -11 597 1 6 109.04 88.4
109 -12 581 3 6 109.02 -740.2
110 -10 564 3 3 109.23 29.3
111 -15 558 1 4 109.46 -893.2
112 -15 575 1 7 107.90 -1030.2
113 -15 580 0 5 110.42 -403.4
114 -13 575 2 6 110.98 -46.9
115 -8 563 2 1 111.48 -321.2
116 -13 552 -1 3 111.88 -239.9
117 -9 537 1 6 111.89 640.9
118 -7 545 0 0 109.85 511.6
119 -4 601 1 3 112.10 -665.1
120 -4 604 1 4 112.24 657.7
121 -2 586 3 7 112.39 -207.7
122 0 564 2 6 112.52 -885.2
123 -2 549 0 6 113.16 -1595.8
124 -3 551 0 6 111.84 -1374.9
125 1 556 3 6 114.33 -316.6
126 -2 548 -2 2 114.82 -283.4
127 -1 540 0 2 115.20 -175.8
128 1 531 1 2 115.40 -694.2
129 -3 521 -1 3 115.74 -249.9
130 -4 519 -2 -1 114.19 268.2
131 -9 572 -1 -4 115.94 -2105.1
132 -9 581 -1 4 116.03 -762.8
133 -7 563 1 5 116.24 -117.1
134 -14 548 -2 3 116.66 -1094.4
135 -12 539 -5 -1 116.79 -2095.2
136 -16 541 -5 -4 115.48 -1587.6
137 -20 562 -6 0 118.16 -528.0
138 -12 559 -4 -1 118.38 -324.2
139 -12 546 -3 -1 118.51 -276.1
140 -10 536 -3 3 118.42 -139.1
141 -10 528 -1 2 118.24 268.0
142 -13 530 -2 -4 116.47 570.5
143 -16 582 -3 -3 118.96 -316.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) w f s c h
-5.075e+01 -4.773e-02 2.066e+00 2.904e-01 6.061e-01 -3.975e-04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.2542 -2.0710 -0.0187 2.6995 10.4429
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.075e+01 8.803e+00 -5.764 5.19e-08 ***
w -4.773e-02 8.559e-03 -5.576 1.27e-07 ***
f 2.066e+00 2.023e-01 10.214 < 2e-16 ***
s 2.904e-01 1.312e-01 2.213 0.0286 *
c 6.061e-01 8.808e-02 6.881 1.94e-10 ***
h -3.975e-04 5.358e-04 -0.742 0.4594
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.127 on 137 degrees of freedom
Multiple R-squared: 0.7076, Adjusted R-squared: 0.6969
F-statistic: 66.3 on 5 and 137 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 3.802037e-01 7.604074e-01 0.61979628
[2,] 2.265508e-01 4.531016e-01 0.77344919
[3,] 2.253569e-01 4.507139e-01 0.77464305
[4,] 1.488803e-01 2.977606e-01 0.85111972
[5,] 9.279931e-02 1.855986e-01 0.90720069
[6,] 2.741804e-01 5.483609e-01 0.72581955
[7,] 2.298826e-01 4.597653e-01 0.77011737
[8,] 1.752033e-01 3.504066e-01 0.82479670
[9,] 1.456539e-01 2.913077e-01 0.85434613
[10,] 1.021614e-01 2.043227e-01 0.89783864
[11,] 7.449286e-02 1.489857e-01 0.92550714
[12,] 5.543676e-02 1.108735e-01 0.94456324
[13,] 4.017918e-02 8.035836e-02 0.95982082
[14,] 3.935591e-02 7.871182e-02 0.96064409
[15,] 2.780579e-02 5.561158e-02 0.97219421
[16,] 2.619088e-02 5.238175e-02 0.97380912
[17,] 1.963690e-02 3.927381e-02 0.98036310
[18,] 1.881993e-02 3.763986e-02 0.98118007
[19,] 1.329519e-02 2.659038e-02 0.98670481
[20,] 9.254116e-03 1.850823e-02 0.99074588
[21,] 6.824292e-03 1.364858e-02 0.99317571
[22,] 5.242293e-03 1.048459e-02 0.99475771
[23,] 3.760537e-03 7.521074e-03 0.99623946
[24,] 2.648845e-03 5.297689e-03 0.99735116
[25,] 1.584418e-03 3.168836e-03 0.99841558
[26,] 9.498122e-04 1.899624e-03 0.99905019
[27,] 9.044834e-04 1.808967e-03 0.99909552
[28,] 5.543897e-04 1.108779e-03 0.99944561
[29,] 9.533024e-04 1.906605e-03 0.99904670
[30,] 6.235636e-04 1.247127e-03 0.99937644
[31,] 6.795452e-04 1.359090e-03 0.99932045
[32,] 3.914690e-04 7.829380e-04 0.99960853
[33,] 2.223911e-04 4.447822e-04 0.99977761
[34,] 1.252092e-04 2.504184e-04 0.99987479
[35,] 8.331697e-05 1.666339e-04 0.99991668
[36,] 4.962464e-05 9.924929e-05 0.99995038
[37,] 2.780248e-05 5.560496e-05 0.99997220
[38,] 1.558925e-05 3.117851e-05 0.99998441
[39,] 8.246713e-06 1.649343e-05 0.99999175
[40,] 6.046154e-06 1.209231e-05 0.99999395
[41,] 1.530177e-05 3.060354e-05 0.99998470
[42,] 8.842883e-06 1.768577e-05 0.99999116
[43,] 5.546311e-06 1.109262e-05 0.99999445
[44,] 4.733194e-06 9.466388e-06 0.99999527
[45,] 2.921193e-06 5.842386e-06 0.99999708
[46,] 2.133938e-06 4.267876e-06 0.99999787
[47,] 2.178719e-06 4.357438e-06 0.99999782
[48,] 1.166899e-06 2.333798e-06 0.99999883
[49,] 9.747336e-07 1.949467e-06 0.99999903
[50,] 6.326520e-07 1.265304e-06 0.99999937
[51,] 3.684443e-07 7.368887e-07 0.99999963
[52,] 8.149888e-07 1.629978e-06 0.99999919
[53,] 4.370814e-07 8.741628e-07 0.99999956
[54,] 2.197064e-07 4.394128e-07 0.99999978
[55,] 1.453372e-07 2.906745e-07 0.99999985
[56,] 7.113903e-08 1.422781e-07 0.99999993
[57,] 3.462040e-08 6.924080e-08 0.99999997
[58,] 1.743663e-08 3.487326e-08 0.99999998
[59,] 8.711257e-09 1.742251e-08 0.99999999
[60,] 6.964605e-09 1.392921e-08 0.99999999
[61,] 3.576642e-09 7.153284e-09 1.00000000
[62,] 1.952292e-09 3.904584e-09 1.00000000
[63,] 1.250974e-09 2.501949e-09 1.00000000
[64,] 1.666753e-08 3.333506e-08 0.99999998
[65,] 2.225015e-08 4.450031e-08 0.99999998
[66,] 1.867125e-08 3.734250e-08 0.99999998
[67,] 1.885697e-08 3.771395e-08 0.99999998
[68,] 9.088288e-09 1.817658e-08 0.99999999
[69,] 5.751615e-09 1.150323e-08 0.99999999
[70,] 3.559044e-09 7.118088e-09 1.00000000
[71,] 3.018527e-09 6.037053e-09 1.00000000
[72,] 1.490176e-09 2.980352e-09 1.00000000
[73,] 8.403267e-10 1.680653e-09 1.00000000
[74,] 5.931647e-10 1.186329e-09 1.00000000
[75,] 1.432534e-09 2.865069e-09 1.00000000
[76,] 1.688402e-09 3.376804e-09 1.00000000
[77,] 4.518361e-09 9.036721e-09 1.00000000
[78,] 8.429384e-09 1.685877e-08 0.99999999
[79,] 9.074476e-09 1.814895e-08 0.99999999
[80,] 1.348397e-07 2.696793e-07 0.99999987
[81,] 1.879622e-07 3.759243e-07 0.99999981
[82,] 2.890891e-06 5.781782e-06 0.99999711
[83,] 7.522034e-06 1.504407e-05 0.99999248
[84,] 7.368936e-06 1.473787e-05 0.99999263
[85,] 2.471220e-05 4.942439e-05 0.99997529
[86,] 4.117348e-05 8.234695e-05 0.99995883
[87,] 6.216283e-05 1.243257e-04 0.99993784
[88,] 8.132228e-05 1.626446e-04 0.99991868
[89,] 1.085105e-04 2.170210e-04 0.99989149
[90,] 3.426591e-04 6.853182e-04 0.99965734
[91,] 7.824078e-04 1.564816e-03 0.99921759
[92,] 2.770812e-03 5.541624e-03 0.99722919
[93,] 1.878461e-02 3.756921e-02 0.98121539
[94,] 2.063393e-02 4.126787e-02 0.97936607
[95,] 4.504239e-02 9.008477e-02 0.95495761
[96,] 1.213340e-01 2.426679e-01 0.87866604
[97,] 3.209101e-01 6.418203e-01 0.67908986
[98,] 2.735572e-01 5.471143e-01 0.72644284
[99,] 2.328965e-01 4.657929e-01 0.76710353
[100,] 1.987015e-01 3.974029e-01 0.80129854
[101,] 2.515773e-01 5.031547e-01 0.74842266
[102,] 2.661201e-01 5.322403e-01 0.73387985
[103,] 3.843985e-01 7.687971e-01 0.61560145
[104,] 4.934248e-01 9.868496e-01 0.50657521
[105,] 5.543257e-01 8.913486e-01 0.44567428
[106,] 7.875602e-01 4.248795e-01 0.21243976
[107,] 8.232555e-01 3.534890e-01 0.17674451
[108,] 8.780061e-01 2.439878e-01 0.12199389
[109,] 9.584895e-01 8.302095e-02 0.04151048
[110,] 9.777528e-01 4.449446e-02 0.02224723
[111,] 9.681359e-01 6.372823e-02 0.03186412
[112,] 9.568755e-01 8.624908e-02 0.04312454
[113,] 9.426702e-01 1.146596e-01 0.05732981
[114,] 9.184646e-01 1.630708e-01 0.08153541
[115,] 8.862580e-01 2.274840e-01 0.11374200
[116,] 8.774620e-01 2.450760e-01 0.12253801
[117,] 8.311265e-01 3.377470e-01 0.16887350
[118,] 8.888556e-01 2.222887e-01 0.11114436
[119,] 8.828785e-01 2.342431e-01 0.11712153
[120,] 8.778486e-01 2.443027e-01 0.12215137
[121,] 8.584128e-01 2.831745e-01 0.14158723
[122,] 9.416853e-01 1.166295e-01 0.05831473
[123,] 8.911737e-01 2.176525e-01 0.10882626
[124,] 8.582943e-01 2.834115e-01 0.14170575
[125,] 9.227970e-01 1.544059e-01 0.07720296
[126,] 8.242873e-01 3.514254e-01 0.17571268
> postscript(file="/var/fisher/rcomp/tmp/1wax81351677347.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2b0301351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3onl81351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4zrc91351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5rawz1351677347.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
4.99431007 3.87668608 4.19515051 4.55548637 -2.23078594 0.64629042
7 8 9 10 11 12
-1.50424524 2.02105428 0.87344114 -1.60163692 -0.25603429 -0.01874661
13 14 15 16 17 18
-1.72991182 -8.79381745 -0.28085736 -2.15086456 -4.11033534 -1.05173739
19 20 21 22 23 24
-1.68899962 -0.20623633 -0.27420159 -4.44012888 0.79390174 2.01245081
25 26 27 28 29 30
1.70392687 3.07245368 -1.72420658 -3.49571703 -0.97367346 -0.28296359
31 32 33 34 35 36
-2.11198099 -1.48824477 -1.52206848 -1.35190323 3.75877839 3.40116264
37 38 39 40 41 42
-3.01581234 0.07965908 -2.23462424 1.50641769 1.36392114 0.20894987
43 44 45 46 47 48
-1.68264331 -1.95670503 0.54189326 0.49836882 1.15130106 3.72948467
49 50 51 52 53 54
-3.98931442 1.70952148 -1.09849099 -1.86479247 -1.85613734 1.73417050
55 56 57 58 59 60
2.61027648 -0.64915853 -3.83394020 -2.64444523 1.43132977 4.17124193
61 62 63 64 65 66
1.08543129 -0.18976110 -2.24575622 0.23321528 -0.96710495 -1.69077499
67 68 69 70 71 72
-1.78739356 0.96478361 -0.40302018 0.72619382 2.04878813 7.19870181
73 74 75 76 77 78
4.48322995 3.24200557 -4.23031777 -0.06489804 1.72394292 1.56117479
79 80 81 82 83 84
3.36191468 0.74659140 1.86981509 2.75000659 5.28207451 4.04708329
85 86 87 88 89 90
5.64545027 5.64212155 5.24957486 9.30200632 5.52527052 10.44292854
91 92 93 94 95 96
6.50414593 2.55483808 7.90585911 2.64903392 1.69674338 -1.69053358
97 98 99 100 101 102
1.83012039 -4.82366840 -5.85617648 -9.13506111 -10.75811769 -5.89102530
103 104 105 106 107 108
-8.69380116 -10.36880882 -11.25421275 -1.87016050 -2.48263853 -1.62326095
109 110 111 112 113 114
-7.83630808 -5.59782161 -7.54850638 -6.71735195 -5.10999217 -7.96888288
115 116 117 118 119 120
-2.50167072 -2.61929058 -3.99446538 3.38082103 4.28469899 4.57846816
121 122 123 124 125 126
0.28111168 3.23953413 3.98546440 3.96875266 0.92067129 8.74713068
127 128 129 130 131 132
5.04561419 4.22270659 3.55778721 6.83540186 1.16586116 -0.24875526
133 134 135 136 137 138
-3.40098138 -4.98084601 3.47285933 1.43525336 -1.86110241 2.10162022
139 140 141 142 143
-0.64456551 -0.17440472 -4.12705955 -2.03011501 -2.63445799
> postscript(file="/var/fisher/rcomp/tmp/6e3rj1351677347.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 4.99431007 NA
1 3.87668608 4.99431007
2 4.19515051 3.87668608
3 4.55548637 4.19515051
4 -2.23078594 4.55548637
5 0.64629042 -2.23078594
6 -1.50424524 0.64629042
7 2.02105428 -1.50424524
8 0.87344114 2.02105428
9 -1.60163692 0.87344114
10 -0.25603429 -1.60163692
11 -0.01874661 -0.25603429
12 -1.72991182 -0.01874661
13 -8.79381745 -1.72991182
14 -0.28085736 -8.79381745
15 -2.15086456 -0.28085736
16 -4.11033534 -2.15086456
17 -1.05173739 -4.11033534
18 -1.68899962 -1.05173739
19 -0.20623633 -1.68899962
20 -0.27420159 -0.20623633
21 -4.44012888 -0.27420159
22 0.79390174 -4.44012888
23 2.01245081 0.79390174
24 1.70392687 2.01245081
25 3.07245368 1.70392687
26 -1.72420658 3.07245368
27 -3.49571703 -1.72420658
28 -0.97367346 -3.49571703
29 -0.28296359 -0.97367346
30 -2.11198099 -0.28296359
31 -1.48824477 -2.11198099
32 -1.52206848 -1.48824477
33 -1.35190323 -1.52206848
34 3.75877839 -1.35190323
35 3.40116264 3.75877839
36 -3.01581234 3.40116264
37 0.07965908 -3.01581234
38 -2.23462424 0.07965908
39 1.50641769 -2.23462424
40 1.36392114 1.50641769
41 0.20894987 1.36392114
42 -1.68264331 0.20894987
43 -1.95670503 -1.68264331
44 0.54189326 -1.95670503
45 0.49836882 0.54189326
46 1.15130106 0.49836882
47 3.72948467 1.15130106
48 -3.98931442 3.72948467
49 1.70952148 -3.98931442
50 -1.09849099 1.70952148
51 -1.86479247 -1.09849099
52 -1.85613734 -1.86479247
53 1.73417050 -1.85613734
54 2.61027648 1.73417050
55 -0.64915853 2.61027648
56 -3.83394020 -0.64915853
57 -2.64444523 -3.83394020
58 1.43132977 -2.64444523
59 4.17124193 1.43132977
60 1.08543129 4.17124193
61 -0.18976110 1.08543129
62 -2.24575622 -0.18976110
63 0.23321528 -2.24575622
64 -0.96710495 0.23321528
65 -1.69077499 -0.96710495
66 -1.78739356 -1.69077499
67 0.96478361 -1.78739356
68 -0.40302018 0.96478361
69 0.72619382 -0.40302018
70 2.04878813 0.72619382
71 7.19870181 2.04878813
72 4.48322995 7.19870181
73 3.24200557 4.48322995
74 -4.23031777 3.24200557
75 -0.06489804 -4.23031777
76 1.72394292 -0.06489804
77 1.56117479 1.72394292
78 3.36191468 1.56117479
79 0.74659140 3.36191468
80 1.86981509 0.74659140
81 2.75000659 1.86981509
82 5.28207451 2.75000659
83 4.04708329 5.28207451
84 5.64545027 4.04708329
85 5.64212155 5.64545027
86 5.24957486 5.64212155
87 9.30200632 5.24957486
88 5.52527052 9.30200632
89 10.44292854 5.52527052
90 6.50414593 10.44292854
91 2.55483808 6.50414593
92 7.90585911 2.55483808
93 2.64903392 7.90585911
94 1.69674338 2.64903392
95 -1.69053358 1.69674338
96 1.83012039 -1.69053358
97 -4.82366840 1.83012039
98 -5.85617648 -4.82366840
99 -9.13506111 -5.85617648
100 -10.75811769 -9.13506111
101 -5.89102530 -10.75811769
102 -8.69380116 -5.89102530
103 -10.36880882 -8.69380116
104 -11.25421275 -10.36880882
105 -1.87016050 -11.25421275
106 -2.48263853 -1.87016050
107 -1.62326095 -2.48263853
108 -7.83630808 -1.62326095
109 -5.59782161 -7.83630808
110 -7.54850638 -5.59782161
111 -6.71735195 -7.54850638
112 -5.10999217 -6.71735195
113 -7.96888288 -5.10999217
114 -2.50167072 -7.96888288
115 -2.61929058 -2.50167072
116 -3.99446538 -2.61929058
117 3.38082103 -3.99446538
118 4.28469899 3.38082103
119 4.57846816 4.28469899
120 0.28111168 4.57846816
121 3.23953413 0.28111168
122 3.98546440 3.23953413
123 3.96875266 3.98546440
124 0.92067129 3.96875266
125 8.74713068 0.92067129
126 5.04561419 8.74713068
127 4.22270659 5.04561419
128 3.55778721 4.22270659
129 6.83540186 3.55778721
130 1.16586116 6.83540186
131 -0.24875526 1.16586116
132 -3.40098138 -0.24875526
133 -4.98084601 -3.40098138
134 3.47285933 -4.98084601
135 1.43525336 3.47285933
136 -1.86110241 1.43525336
137 2.10162022 -1.86110241
138 -0.64456551 2.10162022
139 -0.17440472 -0.64456551
140 -4.12705955 -0.17440472
141 -2.03011501 -4.12705955
142 -2.63445799 -2.03011501
143 NA -2.63445799
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.87668608 4.99431007
[2,] 4.19515051 3.87668608
[3,] 4.55548637 4.19515051
[4,] -2.23078594 4.55548637
[5,] 0.64629042 -2.23078594
[6,] -1.50424524 0.64629042
[7,] 2.02105428 -1.50424524
[8,] 0.87344114 2.02105428
[9,] -1.60163692 0.87344114
[10,] -0.25603429 -1.60163692
[11,] -0.01874661 -0.25603429
[12,] -1.72991182 -0.01874661
[13,] -8.79381745 -1.72991182
[14,] -0.28085736 -8.79381745
[15,] -2.15086456 -0.28085736
[16,] -4.11033534 -2.15086456
[17,] -1.05173739 -4.11033534
[18,] -1.68899962 -1.05173739
[19,] -0.20623633 -1.68899962
[20,] -0.27420159 -0.20623633
[21,] -4.44012888 -0.27420159
[22,] 0.79390174 -4.44012888
[23,] 2.01245081 0.79390174
[24,] 1.70392687 2.01245081
[25,] 3.07245368 1.70392687
[26,] -1.72420658 3.07245368
[27,] -3.49571703 -1.72420658
[28,] -0.97367346 -3.49571703
[29,] -0.28296359 -0.97367346
[30,] -2.11198099 -0.28296359
[31,] -1.48824477 -2.11198099
[32,] -1.52206848 -1.48824477
[33,] -1.35190323 -1.52206848
[34,] 3.75877839 -1.35190323
[35,] 3.40116264 3.75877839
[36,] -3.01581234 3.40116264
[37,] 0.07965908 -3.01581234
[38,] -2.23462424 0.07965908
[39,] 1.50641769 -2.23462424
[40,] 1.36392114 1.50641769
[41,] 0.20894987 1.36392114
[42,] -1.68264331 0.20894987
[43,] -1.95670503 -1.68264331
[44,] 0.54189326 -1.95670503
[45,] 0.49836882 0.54189326
[46,] 1.15130106 0.49836882
[47,] 3.72948467 1.15130106
[48,] -3.98931442 3.72948467
[49,] 1.70952148 -3.98931442
[50,] -1.09849099 1.70952148
[51,] -1.86479247 -1.09849099
[52,] -1.85613734 -1.86479247
[53,] 1.73417050 -1.85613734
[54,] 2.61027648 1.73417050
[55,] -0.64915853 2.61027648
[56,] -3.83394020 -0.64915853
[57,] -2.64444523 -3.83394020
[58,] 1.43132977 -2.64444523
[59,] 4.17124193 1.43132977
[60,] 1.08543129 4.17124193
[61,] -0.18976110 1.08543129
[62,] -2.24575622 -0.18976110
[63,] 0.23321528 -2.24575622
[64,] -0.96710495 0.23321528
[65,] -1.69077499 -0.96710495
[66,] -1.78739356 -1.69077499
[67,] 0.96478361 -1.78739356
[68,] -0.40302018 0.96478361
[69,] 0.72619382 -0.40302018
[70,] 2.04878813 0.72619382
[71,] 7.19870181 2.04878813
[72,] 4.48322995 7.19870181
[73,] 3.24200557 4.48322995
[74,] -4.23031777 3.24200557
[75,] -0.06489804 -4.23031777
[76,] 1.72394292 -0.06489804
[77,] 1.56117479 1.72394292
[78,] 3.36191468 1.56117479
[79,] 0.74659140 3.36191468
[80,] 1.86981509 0.74659140
[81,] 2.75000659 1.86981509
[82,] 5.28207451 2.75000659
[83,] 4.04708329 5.28207451
[84,] 5.64545027 4.04708329
[85,] 5.64212155 5.64545027
[86,] 5.24957486 5.64212155
[87,] 9.30200632 5.24957486
[88,] 5.52527052 9.30200632
[89,] 10.44292854 5.52527052
[90,] 6.50414593 10.44292854
[91,] 2.55483808 6.50414593
[92,] 7.90585911 2.55483808
[93,] 2.64903392 7.90585911
[94,] 1.69674338 2.64903392
[95,] -1.69053358 1.69674338
[96,] 1.83012039 -1.69053358
[97,] -4.82366840 1.83012039
[98,] -5.85617648 -4.82366840
[99,] -9.13506111 -5.85617648
[100,] -10.75811769 -9.13506111
[101,] -5.89102530 -10.75811769
[102,] -8.69380116 -5.89102530
[103,] -10.36880882 -8.69380116
[104,] -11.25421275 -10.36880882
[105,] -1.87016050 -11.25421275
[106,] -2.48263853 -1.87016050
[107,] -1.62326095 -2.48263853
[108,] -7.83630808 -1.62326095
[109,] -5.59782161 -7.83630808
[110,] -7.54850638 -5.59782161
[111,] -6.71735195 -7.54850638
[112,] -5.10999217 -6.71735195
[113,] -7.96888288 -5.10999217
[114,] -2.50167072 -7.96888288
[115,] -2.61929058 -2.50167072
[116,] -3.99446538 -2.61929058
[117,] 3.38082103 -3.99446538
[118,] 4.28469899 3.38082103
[119,] 4.57846816 4.28469899
[120,] 0.28111168 4.57846816
[121,] 3.23953413 0.28111168
[122,] 3.98546440 3.23953413
[123,] 3.96875266 3.98546440
[124,] 0.92067129 3.96875266
[125,] 8.74713068 0.92067129
[126,] 5.04561419 8.74713068
[127,] 4.22270659 5.04561419
[128,] 3.55778721 4.22270659
[129,] 6.83540186 3.55778721
[130,] 1.16586116 6.83540186
[131,] -0.24875526 1.16586116
[132,] -3.40098138 -0.24875526
[133,] -4.98084601 -3.40098138
[134,] 3.47285933 -4.98084601
[135,] 1.43525336 3.47285933
[136,] -1.86110241 1.43525336
[137,] 2.10162022 -1.86110241
[138,] -0.64456551 2.10162022
[139,] -0.17440472 -0.64456551
[140,] -4.12705955 -0.17440472
[141,] -2.03011501 -4.12705955
[142,] -2.63445799 -2.03011501
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.87668608 4.99431007
2 4.19515051 3.87668608
3 4.55548637 4.19515051
4 -2.23078594 4.55548637
5 0.64629042 -2.23078594
6 -1.50424524 0.64629042
7 2.02105428 -1.50424524
8 0.87344114 2.02105428
9 -1.60163692 0.87344114
10 -0.25603429 -1.60163692
11 -0.01874661 -0.25603429
12 -1.72991182 -0.01874661
13 -8.79381745 -1.72991182
14 -0.28085736 -8.79381745
15 -2.15086456 -0.28085736
16 -4.11033534 -2.15086456
17 -1.05173739 -4.11033534
18 -1.68899962 -1.05173739
19 -0.20623633 -1.68899962
20 -0.27420159 -0.20623633
21 -4.44012888 -0.27420159
22 0.79390174 -4.44012888
23 2.01245081 0.79390174
24 1.70392687 2.01245081
25 3.07245368 1.70392687
26 -1.72420658 3.07245368
27 -3.49571703 -1.72420658
28 -0.97367346 -3.49571703
29 -0.28296359 -0.97367346
30 -2.11198099 -0.28296359
31 -1.48824477 -2.11198099
32 -1.52206848 -1.48824477
33 -1.35190323 -1.52206848
34 3.75877839 -1.35190323
35 3.40116264 3.75877839
36 -3.01581234 3.40116264
37 0.07965908 -3.01581234
38 -2.23462424 0.07965908
39 1.50641769 -2.23462424
40 1.36392114 1.50641769
41 0.20894987 1.36392114
42 -1.68264331 0.20894987
43 -1.95670503 -1.68264331
44 0.54189326 -1.95670503
45 0.49836882 0.54189326
46 1.15130106 0.49836882
47 3.72948467 1.15130106
48 -3.98931442 3.72948467
49 1.70952148 -3.98931442
50 -1.09849099 1.70952148
51 -1.86479247 -1.09849099
52 -1.85613734 -1.86479247
53 1.73417050 -1.85613734
54 2.61027648 1.73417050
55 -0.64915853 2.61027648
56 -3.83394020 -0.64915853
57 -2.64444523 -3.83394020
58 1.43132977 -2.64444523
59 4.17124193 1.43132977
60 1.08543129 4.17124193
61 -0.18976110 1.08543129
62 -2.24575622 -0.18976110
63 0.23321528 -2.24575622
64 -0.96710495 0.23321528
65 -1.69077499 -0.96710495
66 -1.78739356 -1.69077499
67 0.96478361 -1.78739356
68 -0.40302018 0.96478361
69 0.72619382 -0.40302018
70 2.04878813 0.72619382
71 7.19870181 2.04878813
72 4.48322995 7.19870181
73 3.24200557 4.48322995
74 -4.23031777 3.24200557
75 -0.06489804 -4.23031777
76 1.72394292 -0.06489804
77 1.56117479 1.72394292
78 3.36191468 1.56117479
79 0.74659140 3.36191468
80 1.86981509 0.74659140
81 2.75000659 1.86981509
82 5.28207451 2.75000659
83 4.04708329 5.28207451
84 5.64545027 4.04708329
85 5.64212155 5.64545027
86 5.24957486 5.64212155
87 9.30200632 5.24957486
88 5.52527052 9.30200632
89 10.44292854 5.52527052
90 6.50414593 10.44292854
91 2.55483808 6.50414593
92 7.90585911 2.55483808
93 2.64903392 7.90585911
94 1.69674338 2.64903392
95 -1.69053358 1.69674338
96 1.83012039 -1.69053358
97 -4.82366840 1.83012039
98 -5.85617648 -4.82366840
99 -9.13506111 -5.85617648
100 -10.75811769 -9.13506111
101 -5.89102530 -10.75811769
102 -8.69380116 -5.89102530
103 -10.36880882 -8.69380116
104 -11.25421275 -10.36880882
105 -1.87016050 -11.25421275
106 -2.48263853 -1.87016050
107 -1.62326095 -2.48263853
108 -7.83630808 -1.62326095
109 -5.59782161 -7.83630808
110 -7.54850638 -5.59782161
111 -6.71735195 -7.54850638
112 -5.10999217 -6.71735195
113 -7.96888288 -5.10999217
114 -2.50167072 -7.96888288
115 -2.61929058 -2.50167072
116 -3.99446538 -2.61929058
117 3.38082103 -3.99446538
118 4.28469899 3.38082103
119 4.57846816 4.28469899
120 0.28111168 4.57846816
121 3.23953413 0.28111168
122 3.98546440 3.23953413
123 3.96875266 3.98546440
124 0.92067129 3.96875266
125 8.74713068 0.92067129
126 5.04561419 8.74713068
127 4.22270659 5.04561419
128 3.55778721 4.22270659
129 6.83540186 3.55778721
130 1.16586116 6.83540186
131 -0.24875526 1.16586116
132 -3.40098138 -0.24875526
133 -4.98084601 -3.40098138
134 3.47285933 -4.98084601
135 1.43525336 3.47285933
136 -1.86110241 1.43525336
137 2.10162022 -1.86110241
138 -0.64456551 2.10162022
139 -0.17440472 -0.64456551
140 -4.12705955 -0.17440472
141 -2.03011501 -4.12705955
142 -2.63445799 -2.03011501
> 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/7aoin1351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/87sw51351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9mti81351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/107ub61351677347.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uzuj1351677347.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/12eazn1351677347.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/13cydk1351677347.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/14886r1351677347.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/15d7xw1351677347.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/16yrns1351677347.tab")
+ }
>
> try(system("convert tmp/1wax81351677347.ps tmp/1wax81351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b0301351677347.ps tmp/2b0301351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/3onl81351677347.ps tmp/3onl81351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zrc91351677347.ps tmp/4zrc91351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rawz1351677347.ps tmp/5rawz1351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e3rj1351677347.ps tmp/6e3rj1351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aoin1351677347.ps tmp/7aoin1351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/87sw51351677347.ps tmp/87sw51351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mti81351677347.ps tmp/9mti81351677347.png",intern=TRUE))
character(0)
> try(system("convert tmp/107ub61351677347.ps tmp/107ub61351677347.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.477 1.094 8.567