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 '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(14
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+ ,-3
+ ,118.96
+ ,90401
+ ,-316.5)
+ ,dim=c(7
+ ,143)
+ ,dimnames=list(c('i'
+ ,'w'
+ ,'f'
+ ,'s'
+ ,'c'
+ ,'b'
+ ,'h')
+ ,1:143))
> y <- array(NA,dim=c(7,143),dimnames=list(c('i','w','f','s','c','b','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 b h
1 14 501 11 20 91.81 77585 1303.2
2 14 485 11 19 91.98 77585 -58.7
3 15 464 11 18 91.72 77585 -378.9
4 13 460 11 13 90.27 78302 175.6
5 8 467 11 17 91.89 78302 233.7
6 7 460 9 17 92.07 78302 706.8
7 3 448 8 13 92.92 78224 -23.6
8 3 443 6 14 93.34 78224 420.9
9 4 436 7 13 93.60 78224 722.1
10 4 431 8 17 92.41 78178 1401.3
11 0 484 6 17 93.60 78178 -94.9
12 -4 510 5 15 93.77 78178 1043.6
13 -14 513 2 9 93.60 77988 1300.1
14 -18 503 3 10 93.60 77988 721.1
15 -8 471 3 9 93.51 77988 -45.6
16 -1 471 7 14 92.66 77876 787.5
17 1 476 8 18 94.20 77876 694.3
18 2 475 7 18 94.37 77876 1054.7
19 0 470 7 12 94.45 78432 821.9
20 1 461 6 16 94.62 78432 1100.7
21 0 455 6 12 94.37 78432 862.4
22 -1 456 7 19 93.43 79025 1656.1
23 -3 517 5 13 94.79 79025 -174.0
24 -3 525 5 12 94.88 79025 1337.6
25 -3 523 5 13 94.79 79407 1394.9
26 -4 519 4 11 94.62 79407 915.7
27 -8 509 4 10 94.71 79407 -481.1
28 -9 512 4 16 93.77 79644 167.9
29 -13 519 1 12 95.73 79644 208.2
30 -18 517 -1 6 95.99 79644 382.2
31 -11 510 3 8 95.82 79381 1004.0
32 -9 509 4 6 95.47 79381 864.7
33 -10 501 3 8 95.82 79381 1052.9
34 -13 507 2 8 94.71 79536 1417.6
35 -11 569 1 9 96.33 79536 -197.7
36 -5 580 4 13 96.50 79536 1262.1
37 -15 578 3 8 96.16 79813 1147.2
38 -6 565 5 11 96.33 79813 700.2
39 -6 547 6 8 96.33 79813 45.3
40 -3 555 6 10 95.05 80332 458.5
41 -1 562 6 15 96.84 80332 610.2
42 -3 561 6 12 96.92 80332 786.4
43 -4 555 6 13 97.44 81434 787.2
44 -6 544 5 12 97.78 81434 1040.0
45 0 537 6 15 97.69 81434 324.1
46 -4 543 5 13 96.67 82167 1343.0
47 -2 594 6 13 98.29 82167 -501.2
48 -2 611 5 16 98.20 82167 800.4
49 -6 613 7 14 98.71 82816 916.7
50 -7 611 4 12 98.54 82816 695.8
51 -6 594 5 15 98.20 82816 28.0
52 -6 595 6 14 96.92 83000 495.6
53 -3 591 6 19 99.06 83000 366.2
54 -2 589 5 16 99.65 83000 633.0
55 -5 584 3 16 99.82 83251 848.3
56 -11 573 2 11 99.99 83251 472.2
57 -11 567 3 13 100.33 83251 357.8
58 -11 569 3 12 99.31 83591 824.3
59 -10 621 2 11 101.10 83591 -880.1
60 -14 629 0 6 101.10 83591 1066.8
61 -8 628 4 9 100.93 83910 1052.8
62 -9 612 4 6 100.85 83910 -32.1
63 -5 595 5 15 100.93 83910 -1331.4
64 -1 597 6 17 99.60 84599 -767.1
65 -2 593 6 13 101.88 84599 -236.7
66 -5 590 5 12 101.81 84599 -184.9
67 -4 580 5 13 102.38 85275 -143.4
68 -6 574 3 10 102.74 85275 493.9
69 -2 573 5 14 102.82 85275 549.7
70 -2 573 5 13 101.72 85608 982.7
71 -2 620 5 10 103.47 85608 -856.3
72 -2 626 3 11 102.98 85608 967.0
73 2 620 6 12 102.68 86303 659.4
74 1 588 6 7 102.90 86303 577.2
75 -8 566 4 11 103.03 86303 -213.1
76 -1 557 6 9 101.29 87115 17.7
77 1 561 5 13 103.69 87115 390.1
78 -1 549 4 12 103.68 87115 509.3
79 2 532 5 5 104.20 87931 410.0
80 2 526 5 13 104.08 87931 212.5
81 1 511 4 11 104.16 87931 818.0
82 -1 499 3 8 103.05 88164 422.7
83 -2 555 2 8 104.66 88164 -158.0
84 -2 565 3 8 104.46 88164 427.2
85 -1 542 2 8 104.95 88792 243.4
86 -8 527 -1 0 105.85 88792 -419.3
87 -4 510 0 3 106.23 88792 -1459.8
88 -6 514 -2 0 104.86 89263 -1389.8
89 -3 517 1 -1 107.44 89263 -2.1
90 -3 508 -2 -1 108.23 89263 -938.6
91 -7 493 -2 -4 108.45 89881 -839.9
92 -9 490 -2 1 109.39 89881 -297.6
93 -11 469 -6 -1 110.15 89881 -376.3
94 -13 478 -4 0 109.13 90120 -79.4
95 -11 528 -2 -1 110.28 90120 -2091.3
96 -9 534 0 6 110.17 90120 -1023.0
97 -17 518 -5 0 109.99 89703 -765.6
98 -22 506 -4 -3 109.26 89703 -1592.3
99 -25 502 -5 -3 109.11 89703 -1588.8
100 -20 516 -1 4 107.06 87818 -1318.0
101 -24 528 -2 1 109.53 87818 -402.4
102 -24 533 -4 0 108.92 87818 -814.5
103 -22 536 -1 -4 109.24 86273 -98.4
104 -19 537 1 -2 109.12 86273 -305.9
105 -18 524 1 3 109.00 86273 -18.4
106 -17 536 -2 2 107.23 86316 610.3
107 -11 587 1 5 109.49 86316 -917.3
108 -11 597 1 6 109.04 86316 88.4
109 -12 581 3 6 109.02 87234 -740.2
110 -10 564 3 3 109.23 87234 29.3
111 -15 558 1 4 109.46 87234 -893.2
112 -15 575 1 7 107.90 87885 -1030.2
113 -15 580 0 5 110.42 87885 -403.4
114 -13 575 2 6 110.98 87885 -46.9
115 -8 563 2 1 111.48 88003 -321.2
116 -13 552 -1 3 111.88 88003 -239.9
117 -9 537 1 6 111.89 88003 640.9
118 -7 545 0 0 109.85 88910 511.6
119 -4 601 1 3 112.10 88910 -665.1
120 -4 604 1 4 112.24 88910 657.7
121 -2 586 3 7 112.39 89397 -207.7
122 0 564 2 6 112.52 89397 -885.2
123 -2 549 0 6 113.16 89397 -1595.8
124 -3 551 0 6 111.84 89813 -1374.9
125 1 556 3 6 114.33 89813 -316.6
126 -2 548 -2 2 114.82 89813 -283.4
127 -1 540 0 2 115.20 90539 -175.8
128 1 531 1 2 115.40 90539 -694.2
129 -3 521 -1 3 115.74 90539 -249.9
130 -4 519 -2 -1 114.19 90688 268.2
131 -9 572 -1 -4 115.94 90688 -2105.1
132 -9 581 -1 4 116.03 90688 -762.8
133 -7 563 1 5 116.24 90691 -117.1
134 -14 548 -2 3 116.66 90691 -1094.4
135 -12 539 -5 -1 116.79 90691 -2095.2
136 -16 541 -5 -4 115.48 90645 -1587.6
137 -20 562 -6 0 118.16 90645 -528.0
138 -12 559 -4 -1 118.38 90645 -324.2
139 -12 546 -3 -1 118.51 90861 -276.1
140 -10 536 -3 3 118.42 90861 -139.1
141 -10 528 -1 2 118.24 90861 268.0
142 -13 530 -2 -4 116.47 90401 570.5
143 -16 582 -3 -3 118.96 90401 -316.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) w f s c b
-8.407e+01 -5.410e-02 2.049e+00 3.209e-01 7.643e-02 1.077e-03
h
7.412e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.5242 -1.8201 0.2005 2.3687 9.6748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.407e+01 1.085e+01 -7.750 1.91e-12 ***
w -5.410e-02 8.085e-03 -6.692 5.29e-10 ***
f 2.049e+00 1.884e-01 10.878 < 2e-16 ***
s 3.209e-01 1.224e-01 2.622 0.00973 **
c 7.643e-02 1.396e-01 0.548 0.58484
b 1.077e-03 2.296e-04 4.691 6.55e-06 ***
h 7.412e-05 5.090e-04 0.146 0.88444
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.843 on 136 degrees of freedom
Multiple R-squared: 0.7483, Adjusted R-squared: 0.7372
F-statistic: 67.39 on 6 and 136 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,] 0.1106556789 0.2213113579 0.8893443
[2,] 0.0535156151 0.1070312302 0.9464844
[3,] 0.0430459103 0.0860918207 0.9569541
[4,] 0.1126470521 0.2252941042 0.8873529
[5,] 0.4469998207 0.8939996415 0.5530002
[6,] 0.3659196711 0.7318393422 0.6340803
[7,] 0.3173344791 0.6346689582 0.6826655
[8,] 0.2795495894 0.5590991789 0.7204504
[9,] 0.2106759552 0.4213519105 0.7893240
[10,] 0.1762755759 0.3525511517 0.8237244
[11,] 0.1500050874 0.3000101748 0.8499949
[12,] 0.1210117008 0.2420234016 0.8789883
[13,] 0.0956656616 0.1913313232 0.9043343
[14,] 0.0934697917 0.1869395835 0.9065302
[15,] 0.0953616835 0.1907233671 0.9046383
[16,] 0.0757631446 0.1515262892 0.9242369
[17,] 0.0698272796 0.1396545591 0.9301727
[18,] 0.0594096195 0.1188192391 0.9405904
[19,] 0.0464088959 0.0928177919 0.9535911
[20,] 0.0359397422 0.0718794845 0.9640603
[21,] 0.0290105617 0.0580211234 0.9709894
[22,] 0.0222298045 0.0444596091 0.9777702
[23,] 0.0171894540 0.0343789080 0.9828105
[24,] 0.0113454851 0.0226909703 0.9886545
[25,] 0.0072062958 0.0144125917 0.9927937
[26,] 0.0088574472 0.0177148944 0.9911426
[27,] 0.0072643665 0.0145287329 0.9927356
[28,] 0.0110465855 0.0220931711 0.9889534
[29,] 0.0088483156 0.0176966312 0.9911517
[30,] 0.0104703610 0.0209407221 0.9895296
[31,] 0.0082813482 0.0165626964 0.9917187
[32,] 0.0071724543 0.0143449085 0.9928275
[33,] 0.0063062066 0.0126124132 0.9936938
[34,] 0.0050011441 0.0100022882 0.9949989
[35,] 0.0036280246 0.0072560492 0.9963720
[36,] 0.0042142672 0.0084285344 0.9957857
[37,] 0.0033334239 0.0066668477 0.9966666
[38,] 0.0032278636 0.0064557272 0.9967721
[39,] 0.0033943828 0.0067887655 0.9966056
[40,] 0.0065678275 0.0131356551 0.9934322
[41,] 0.0049110024 0.0098220049 0.9950890
[42,] 0.0037317217 0.0074634434 0.9962683
[43,] 0.0031585835 0.0063171669 0.9968414
[44,] 0.0022701367 0.0045402735 0.9977299
[45,] 0.0024886350 0.0049772701 0.9975114
[46,] 0.0034736731 0.0069473461 0.9965263
[47,] 0.0026574948 0.0053149896 0.9973425
[48,] 0.0022013766 0.0044027532 0.9977986
[49,] 0.0014742811 0.0029485621 0.9985257
[50,] 0.0013069592 0.0026139184 0.9986930
[51,] 0.0026250675 0.0052501351 0.9973749
[52,] 0.0018252687 0.0036505375 0.9981747
[53,] 0.0012999996 0.0025999991 0.9987000
[54,] 0.0012528580 0.0025057161 0.9987471
[55,] 0.0008726267 0.0017452534 0.9991274
[56,] 0.0006560263 0.0013120527 0.9993440
[57,] 0.0004699595 0.0009399190 0.9995300
[58,] 0.0003058200 0.0006116400 0.9996942
[59,] 0.0003440566 0.0006881131 0.9996559
[60,] 0.0002680114 0.0005360227 0.9997320
[61,] 0.0001969029 0.0003938058 0.9998031
[62,] 0.0001970479 0.0003940958 0.9998030
[63,] 0.0012646006 0.0025292012 0.9987354
[64,] 0.0012825773 0.0025651546 0.9987174
[65,] 0.0010914698 0.0021829395 0.9989085
[66,] 0.0010556012 0.0021112025 0.9989444
[67,] 0.0007950589 0.0015901177 0.9992049
[68,] 0.0006135628 0.0012271256 0.9993864
[69,] 0.0004711551 0.0009423103 0.9995288
[70,] 0.0003939756 0.0007879511 0.9996060
[71,] 0.0002605862 0.0005211724 0.9997394
[72,] 0.0001834776 0.0003669552 0.9998165
[73,] 0.0001426297 0.0002852593 0.9998574
[74,] 0.0001808735 0.0003617471 0.9998191
[75,] 0.0001385646 0.0002771293 0.9998614
[76,] 0.0001412619 0.0002825238 0.9998587
[77,] 0.0001326486 0.0002652973 0.9998674
[78,] 0.0001446822 0.0002893644 0.9998553
[79,] 0.0002940081 0.0005880162 0.9997060
[80,] 0.0002007843 0.0004015686 0.9997992
[81,] 0.0014865661 0.0029731322 0.9985134
[82,] 0.0015219898 0.0030439796 0.9984780
[83,] 0.0011188880 0.0022377761 0.9988811
[84,] 0.0035461521 0.0070923041 0.9964538
[85,] 0.0029894735 0.0059789469 0.9970105
[86,] 0.0027313612 0.0054627225 0.9972686
[87,] 0.0032946547 0.0065893093 0.9967053
[88,] 0.0025706361 0.0051412722 0.9974294
[89,] 0.0067782160 0.0135564320 0.9932218
[90,] 0.0201352660 0.0402705319 0.9798647
[91,] 0.0628209588 0.1256419176 0.9371790
[92,] 0.1920073501 0.3840147002 0.8079926
[93,] 0.2300435305 0.4600870610 0.7699565
[94,] 0.2147218504 0.4294437008 0.7852781
[95,] 0.2079610953 0.4159221905 0.7920389
[96,] 0.2195630060 0.4391260121 0.7804370
[97,] 0.1832581113 0.3665162225 0.8167419
[98,] 0.2293266537 0.4586533073 0.7706733
[99,] 0.3164030798 0.6328061596 0.6835969
[100,] 0.2971442693 0.5942885386 0.7028557
[101,] 0.2519242066 0.5038484133 0.7480758
[102,] 0.2315020702 0.4630041403 0.7684979
[103,] 0.4365025026 0.8730050052 0.5634975
[104,] 0.4605812885 0.9211625770 0.5394187
[105,] 0.6086875547 0.7826248906 0.3913124
[106,] 0.5705097771 0.8589804458 0.4294902
[107,] 0.5600632579 0.8798734842 0.4399367
[108,] 0.7136444887 0.5727110227 0.2863555
[109,] 0.8263589068 0.3472821863 0.1736411
[110,] 0.8163018230 0.3673963541 0.1836982
[111,] 0.7994905592 0.4010188816 0.2005094
[112,] 0.7685203951 0.4629592099 0.2314796
[113,] 0.7340750870 0.5318498260 0.2659249
[114,] 0.7088487062 0.5823025875 0.2911513
[115,] 0.7257358824 0.5485282353 0.2742641
[116,] 0.7283339288 0.5433321424 0.2716661
[117,] 0.7370792802 0.5258414396 0.2629207
[118,] 0.7347420964 0.5305158072 0.2652579
[119,] 0.7461769308 0.5076461383 0.2538231
[120,] 0.8244813043 0.3510373914 0.1755187
[121,] 0.8846262410 0.2307475180 0.1153738
[122,] 0.8021513746 0.3956972507 0.1978486
[123,] 0.7167924543 0.5664150914 0.2832075
[124,] 0.7934722796 0.4130554408 0.2065277
> postscript(file="/var/fisher/rcomp/tmp/191xj1351894379.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2zfi61351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yo471351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ce4v1351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59ozp1351894379.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
5.52919952 5.07241366 5.30076623 3.98633361 -2.04676693 0.62405370
7 8 9 10 11 12
-0.61910451 2.82277377 1.67359563 -1.83965205 1.14615542 1.14651466
13 14 15 16 17 18
-0.41940590 -7.28764016 1.36567205 -1.31183064 -2.48499450 0.47038150
19 20 21 22 23 24
-0.46237659 0.78251066 0.77836453 -5.08898518 2.26696724 2.90181067
25 26 27 28 29 30
2.06380593 3.58693803 -0.53652479 -3.53133751 0.12586194 1.00881570
31 32 33 34 35 36
-0.95828076 -0.38264071 -0.44883827 -1.18419829 5.89439494 4.93708860
37 38 39 40 41 42
-1.78119242 1.47444859 -0.53728266 1.76181321 2.38785586 1.27735896
43 44 45 46 47 48
-1.59510019 -1.86485214 0.80439615 -0.96711524 1.75586145 3.67243643
49 50 51 52 53 54
-4.42259139 1.28797178 -1.56827489 -3.37746809 -2.35248166 1.48640859
55 56 57 58 59 60
2.01492314 -0.91151713 -3.94468337 -3.83842663 2.33459429 4.32612842
61 62 63 64 65 66
0.78290216 -0.03345343 -1.80055594 -1.06577307 -1.21204754 -2.00273714
67 68 69 70 71 72
-2.63954862 0.02222911 -1.42419958 -1.41001248 2.09819274 6.10256924
73 74 75 76 77 78
2.60651945 1.46911346 -5.85786832 -3.56014907 -0.78928521 -1.07648915
79 80 81 82 83 84
0.28964843 -2.57857862 -1.75008817 -1.52422982 2.47475147 0.93851704
85 86 87 88 89 90
2.04298806 2.92673077 3.04307925 5.91278455 2.94843528 8.61808604
91 92 93 94 95 96
4.07945288 0.20047241 5.85063218 -0.28323630 0.70572991 -3.38527487
97 98 99 100 101 102
0.36443138 -6.25415219 -7.41017821 -9.92876331 -10.52419245 -5.75721489
103 104 105 106 107 108
-6.87195857 -8.53352590 -9.85364492 -1.69356254 -0.10411323 0.07585055
109 110 111 112 113 114
-6.81413303 -4.84419788 -6.34058485 -6.95549837 -4.23299670 -6.99203200
115 116 117 118 119 120
-1.18163943 -1.30767845 -3.24642815 2.34959365 5.28268621 5.01532941
121 122 123 124 125 126
0.50837938 3.72848719 5.01905309 3.76364485 1.61786863 9.67475582
127 128 129 130 131 132
5.32447118 4.81149036 3.98897924 6.13321898 2.95646277 0.76961678
133 134 135 136 137 138
-2.69068292 -3.67249850 5.33606743 2.51910675 0.13740077 4.16572946
139 140 141 142 143
1.16701499 1.33899551 -2.88769099 -1.19635567 0.22072944
> postscript(file="/var/fisher/rcomp/tmp/6kffo1351894379.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 5.52919952 NA
1 5.07241366 5.52919952
2 5.30076623 5.07241366
3 3.98633361 5.30076623
4 -2.04676693 3.98633361
5 0.62405370 -2.04676693
6 -0.61910451 0.62405370
7 2.82277377 -0.61910451
8 1.67359563 2.82277377
9 -1.83965205 1.67359563
10 1.14615542 -1.83965205
11 1.14651466 1.14615542
12 -0.41940590 1.14651466
13 -7.28764016 -0.41940590
14 1.36567205 -7.28764016
15 -1.31183064 1.36567205
16 -2.48499450 -1.31183064
17 0.47038150 -2.48499450
18 -0.46237659 0.47038150
19 0.78251066 -0.46237659
20 0.77836453 0.78251066
21 -5.08898518 0.77836453
22 2.26696724 -5.08898518
23 2.90181067 2.26696724
24 2.06380593 2.90181067
25 3.58693803 2.06380593
26 -0.53652479 3.58693803
27 -3.53133751 -0.53652479
28 0.12586194 -3.53133751
29 1.00881570 0.12586194
30 -0.95828076 1.00881570
31 -0.38264071 -0.95828076
32 -0.44883827 -0.38264071
33 -1.18419829 -0.44883827
34 5.89439494 -1.18419829
35 4.93708860 5.89439494
36 -1.78119242 4.93708860
37 1.47444859 -1.78119242
38 -0.53728266 1.47444859
39 1.76181321 -0.53728266
40 2.38785586 1.76181321
41 1.27735896 2.38785586
42 -1.59510019 1.27735896
43 -1.86485214 -1.59510019
44 0.80439615 -1.86485214
45 -0.96711524 0.80439615
46 1.75586145 -0.96711524
47 3.67243643 1.75586145
48 -4.42259139 3.67243643
49 1.28797178 -4.42259139
50 -1.56827489 1.28797178
51 -3.37746809 -1.56827489
52 -2.35248166 -3.37746809
53 1.48640859 -2.35248166
54 2.01492314 1.48640859
55 -0.91151713 2.01492314
56 -3.94468337 -0.91151713
57 -3.83842663 -3.94468337
58 2.33459429 -3.83842663
59 4.32612842 2.33459429
60 0.78290216 4.32612842
61 -0.03345343 0.78290216
62 -1.80055594 -0.03345343
63 -1.06577307 -1.80055594
64 -1.21204754 -1.06577307
65 -2.00273714 -1.21204754
66 -2.63954862 -2.00273714
67 0.02222911 -2.63954862
68 -1.42419958 0.02222911
69 -1.41001248 -1.42419958
70 2.09819274 -1.41001248
71 6.10256924 2.09819274
72 2.60651945 6.10256924
73 1.46911346 2.60651945
74 -5.85786832 1.46911346
75 -3.56014907 -5.85786832
76 -0.78928521 -3.56014907
77 -1.07648915 -0.78928521
78 0.28964843 -1.07648915
79 -2.57857862 0.28964843
80 -1.75008817 -2.57857862
81 -1.52422982 -1.75008817
82 2.47475147 -1.52422982
83 0.93851704 2.47475147
84 2.04298806 0.93851704
85 2.92673077 2.04298806
86 3.04307925 2.92673077
87 5.91278455 3.04307925
88 2.94843528 5.91278455
89 8.61808604 2.94843528
90 4.07945288 8.61808604
91 0.20047241 4.07945288
92 5.85063218 0.20047241
93 -0.28323630 5.85063218
94 0.70572991 -0.28323630
95 -3.38527487 0.70572991
96 0.36443138 -3.38527487
97 -6.25415219 0.36443138
98 -7.41017821 -6.25415219
99 -9.92876331 -7.41017821
100 -10.52419245 -9.92876331
101 -5.75721489 -10.52419245
102 -6.87195857 -5.75721489
103 -8.53352590 -6.87195857
104 -9.85364492 -8.53352590
105 -1.69356254 -9.85364492
106 -0.10411323 -1.69356254
107 0.07585055 -0.10411323
108 -6.81413303 0.07585055
109 -4.84419788 -6.81413303
110 -6.34058485 -4.84419788
111 -6.95549837 -6.34058485
112 -4.23299670 -6.95549837
113 -6.99203200 -4.23299670
114 -1.18163943 -6.99203200
115 -1.30767845 -1.18163943
116 -3.24642815 -1.30767845
117 2.34959365 -3.24642815
118 5.28268621 2.34959365
119 5.01532941 5.28268621
120 0.50837938 5.01532941
121 3.72848719 0.50837938
122 5.01905309 3.72848719
123 3.76364485 5.01905309
124 1.61786863 3.76364485
125 9.67475582 1.61786863
126 5.32447118 9.67475582
127 4.81149036 5.32447118
128 3.98897924 4.81149036
129 6.13321898 3.98897924
130 2.95646277 6.13321898
131 0.76961678 2.95646277
132 -2.69068292 0.76961678
133 -3.67249850 -2.69068292
134 5.33606743 -3.67249850
135 2.51910675 5.33606743
136 0.13740077 2.51910675
137 4.16572946 0.13740077
138 1.16701499 4.16572946
139 1.33899551 1.16701499
140 -2.88769099 1.33899551
141 -1.19635567 -2.88769099
142 0.22072944 -1.19635567
143 NA 0.22072944
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.07241366 5.52919952
[2,] 5.30076623 5.07241366
[3,] 3.98633361 5.30076623
[4,] -2.04676693 3.98633361
[5,] 0.62405370 -2.04676693
[6,] -0.61910451 0.62405370
[7,] 2.82277377 -0.61910451
[8,] 1.67359563 2.82277377
[9,] -1.83965205 1.67359563
[10,] 1.14615542 -1.83965205
[11,] 1.14651466 1.14615542
[12,] -0.41940590 1.14651466
[13,] -7.28764016 -0.41940590
[14,] 1.36567205 -7.28764016
[15,] -1.31183064 1.36567205
[16,] -2.48499450 -1.31183064
[17,] 0.47038150 -2.48499450
[18,] -0.46237659 0.47038150
[19,] 0.78251066 -0.46237659
[20,] 0.77836453 0.78251066
[21,] -5.08898518 0.77836453
[22,] 2.26696724 -5.08898518
[23,] 2.90181067 2.26696724
[24,] 2.06380593 2.90181067
[25,] 3.58693803 2.06380593
[26,] -0.53652479 3.58693803
[27,] -3.53133751 -0.53652479
[28,] 0.12586194 -3.53133751
[29,] 1.00881570 0.12586194
[30,] -0.95828076 1.00881570
[31,] -0.38264071 -0.95828076
[32,] -0.44883827 -0.38264071
[33,] -1.18419829 -0.44883827
[34,] 5.89439494 -1.18419829
[35,] 4.93708860 5.89439494
[36,] -1.78119242 4.93708860
[37,] 1.47444859 -1.78119242
[38,] -0.53728266 1.47444859
[39,] 1.76181321 -0.53728266
[40,] 2.38785586 1.76181321
[41,] 1.27735896 2.38785586
[42,] -1.59510019 1.27735896
[43,] -1.86485214 -1.59510019
[44,] 0.80439615 -1.86485214
[45,] -0.96711524 0.80439615
[46,] 1.75586145 -0.96711524
[47,] 3.67243643 1.75586145
[48,] -4.42259139 3.67243643
[49,] 1.28797178 -4.42259139
[50,] -1.56827489 1.28797178
[51,] -3.37746809 -1.56827489
[52,] -2.35248166 -3.37746809
[53,] 1.48640859 -2.35248166
[54,] 2.01492314 1.48640859
[55,] -0.91151713 2.01492314
[56,] -3.94468337 -0.91151713
[57,] -3.83842663 -3.94468337
[58,] 2.33459429 -3.83842663
[59,] 4.32612842 2.33459429
[60,] 0.78290216 4.32612842
[61,] -0.03345343 0.78290216
[62,] -1.80055594 -0.03345343
[63,] -1.06577307 -1.80055594
[64,] -1.21204754 -1.06577307
[65,] -2.00273714 -1.21204754
[66,] -2.63954862 -2.00273714
[67,] 0.02222911 -2.63954862
[68,] -1.42419958 0.02222911
[69,] -1.41001248 -1.42419958
[70,] 2.09819274 -1.41001248
[71,] 6.10256924 2.09819274
[72,] 2.60651945 6.10256924
[73,] 1.46911346 2.60651945
[74,] -5.85786832 1.46911346
[75,] -3.56014907 -5.85786832
[76,] -0.78928521 -3.56014907
[77,] -1.07648915 -0.78928521
[78,] 0.28964843 -1.07648915
[79,] -2.57857862 0.28964843
[80,] -1.75008817 -2.57857862
[81,] -1.52422982 -1.75008817
[82,] 2.47475147 -1.52422982
[83,] 0.93851704 2.47475147
[84,] 2.04298806 0.93851704
[85,] 2.92673077 2.04298806
[86,] 3.04307925 2.92673077
[87,] 5.91278455 3.04307925
[88,] 2.94843528 5.91278455
[89,] 8.61808604 2.94843528
[90,] 4.07945288 8.61808604
[91,] 0.20047241 4.07945288
[92,] 5.85063218 0.20047241
[93,] -0.28323630 5.85063218
[94,] 0.70572991 -0.28323630
[95,] -3.38527487 0.70572991
[96,] 0.36443138 -3.38527487
[97,] -6.25415219 0.36443138
[98,] -7.41017821 -6.25415219
[99,] -9.92876331 -7.41017821
[100,] -10.52419245 -9.92876331
[101,] -5.75721489 -10.52419245
[102,] -6.87195857 -5.75721489
[103,] -8.53352590 -6.87195857
[104,] -9.85364492 -8.53352590
[105,] -1.69356254 -9.85364492
[106,] -0.10411323 -1.69356254
[107,] 0.07585055 -0.10411323
[108,] -6.81413303 0.07585055
[109,] -4.84419788 -6.81413303
[110,] -6.34058485 -4.84419788
[111,] -6.95549837 -6.34058485
[112,] -4.23299670 -6.95549837
[113,] -6.99203200 -4.23299670
[114,] -1.18163943 -6.99203200
[115,] -1.30767845 -1.18163943
[116,] -3.24642815 -1.30767845
[117,] 2.34959365 -3.24642815
[118,] 5.28268621 2.34959365
[119,] 5.01532941 5.28268621
[120,] 0.50837938 5.01532941
[121,] 3.72848719 0.50837938
[122,] 5.01905309 3.72848719
[123,] 3.76364485 5.01905309
[124,] 1.61786863 3.76364485
[125,] 9.67475582 1.61786863
[126,] 5.32447118 9.67475582
[127,] 4.81149036 5.32447118
[128,] 3.98897924 4.81149036
[129,] 6.13321898 3.98897924
[130,] 2.95646277 6.13321898
[131,] 0.76961678 2.95646277
[132,] -2.69068292 0.76961678
[133,] -3.67249850 -2.69068292
[134,] 5.33606743 -3.67249850
[135,] 2.51910675 5.33606743
[136,] 0.13740077 2.51910675
[137,] 4.16572946 0.13740077
[138,] 1.16701499 4.16572946
[139,] 1.33899551 1.16701499
[140,] -2.88769099 1.33899551
[141,] -1.19635567 -2.88769099
[142,] 0.22072944 -1.19635567
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.07241366 5.52919952
2 5.30076623 5.07241366
3 3.98633361 5.30076623
4 -2.04676693 3.98633361
5 0.62405370 -2.04676693
6 -0.61910451 0.62405370
7 2.82277377 -0.61910451
8 1.67359563 2.82277377
9 -1.83965205 1.67359563
10 1.14615542 -1.83965205
11 1.14651466 1.14615542
12 -0.41940590 1.14651466
13 -7.28764016 -0.41940590
14 1.36567205 -7.28764016
15 -1.31183064 1.36567205
16 -2.48499450 -1.31183064
17 0.47038150 -2.48499450
18 -0.46237659 0.47038150
19 0.78251066 -0.46237659
20 0.77836453 0.78251066
21 -5.08898518 0.77836453
22 2.26696724 -5.08898518
23 2.90181067 2.26696724
24 2.06380593 2.90181067
25 3.58693803 2.06380593
26 -0.53652479 3.58693803
27 -3.53133751 -0.53652479
28 0.12586194 -3.53133751
29 1.00881570 0.12586194
30 -0.95828076 1.00881570
31 -0.38264071 -0.95828076
32 -0.44883827 -0.38264071
33 -1.18419829 -0.44883827
34 5.89439494 -1.18419829
35 4.93708860 5.89439494
36 -1.78119242 4.93708860
37 1.47444859 -1.78119242
38 -0.53728266 1.47444859
39 1.76181321 -0.53728266
40 2.38785586 1.76181321
41 1.27735896 2.38785586
42 -1.59510019 1.27735896
43 -1.86485214 -1.59510019
44 0.80439615 -1.86485214
45 -0.96711524 0.80439615
46 1.75586145 -0.96711524
47 3.67243643 1.75586145
48 -4.42259139 3.67243643
49 1.28797178 -4.42259139
50 -1.56827489 1.28797178
51 -3.37746809 -1.56827489
52 -2.35248166 -3.37746809
53 1.48640859 -2.35248166
54 2.01492314 1.48640859
55 -0.91151713 2.01492314
56 -3.94468337 -0.91151713
57 -3.83842663 -3.94468337
58 2.33459429 -3.83842663
59 4.32612842 2.33459429
60 0.78290216 4.32612842
61 -0.03345343 0.78290216
62 -1.80055594 -0.03345343
63 -1.06577307 -1.80055594
64 -1.21204754 -1.06577307
65 -2.00273714 -1.21204754
66 -2.63954862 -2.00273714
67 0.02222911 -2.63954862
68 -1.42419958 0.02222911
69 -1.41001248 -1.42419958
70 2.09819274 -1.41001248
71 6.10256924 2.09819274
72 2.60651945 6.10256924
73 1.46911346 2.60651945
74 -5.85786832 1.46911346
75 -3.56014907 -5.85786832
76 -0.78928521 -3.56014907
77 -1.07648915 -0.78928521
78 0.28964843 -1.07648915
79 -2.57857862 0.28964843
80 -1.75008817 -2.57857862
81 -1.52422982 -1.75008817
82 2.47475147 -1.52422982
83 0.93851704 2.47475147
84 2.04298806 0.93851704
85 2.92673077 2.04298806
86 3.04307925 2.92673077
87 5.91278455 3.04307925
88 2.94843528 5.91278455
89 8.61808604 2.94843528
90 4.07945288 8.61808604
91 0.20047241 4.07945288
92 5.85063218 0.20047241
93 -0.28323630 5.85063218
94 0.70572991 -0.28323630
95 -3.38527487 0.70572991
96 0.36443138 -3.38527487
97 -6.25415219 0.36443138
98 -7.41017821 -6.25415219
99 -9.92876331 -7.41017821
100 -10.52419245 -9.92876331
101 -5.75721489 -10.52419245
102 -6.87195857 -5.75721489
103 -8.53352590 -6.87195857
104 -9.85364492 -8.53352590
105 -1.69356254 -9.85364492
106 -0.10411323 -1.69356254
107 0.07585055 -0.10411323
108 -6.81413303 0.07585055
109 -4.84419788 -6.81413303
110 -6.34058485 -4.84419788
111 -6.95549837 -6.34058485
112 -4.23299670 -6.95549837
113 -6.99203200 -4.23299670
114 -1.18163943 -6.99203200
115 -1.30767845 -1.18163943
116 -3.24642815 -1.30767845
117 2.34959365 -3.24642815
118 5.28268621 2.34959365
119 5.01532941 5.28268621
120 0.50837938 5.01532941
121 3.72848719 0.50837938
122 5.01905309 3.72848719
123 3.76364485 5.01905309
124 1.61786863 3.76364485
125 9.67475582 1.61786863
126 5.32447118 9.67475582
127 4.81149036 5.32447118
128 3.98897924 4.81149036
129 6.13321898 3.98897924
130 2.95646277 6.13321898
131 0.76961678 2.95646277
132 -2.69068292 0.76961678
133 -3.67249850 -2.69068292
134 5.33606743 -3.67249850
135 2.51910675 5.33606743
136 0.13740077 2.51910675
137 4.16572946 0.13740077
138 1.16701499 4.16572946
139 1.33899551 1.16701499
140 -2.88769099 1.33899551
141 -1.19635567 -2.88769099
142 0.22072944 -1.19635567
> 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/7aras1351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8u1051351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9vp1z1351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10c2k81351894379.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rvdi1351894379.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/1201qf1351894379.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/13m1ns1351894379.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/14szfy1351894379.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/15mdtc1351894379.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/16zujz1351894379.tab")
+ }
>
> try(system("convert tmp/191xj1351894379.ps tmp/191xj1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zfi61351894379.ps tmp/2zfi61351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yo471351894379.ps tmp/3yo471351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ce4v1351894379.ps tmp/4ce4v1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/59ozp1351894379.ps tmp/59ozp1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kffo1351894379.ps tmp/6kffo1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aras1351894379.ps tmp/7aras1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u1051351894379.ps tmp/8u1051351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vp1z1351894379.ps tmp/9vp1z1351894379.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c2k81351894379.ps tmp/10c2k81351894379.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.585 1.150 8.737