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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> x <- array(list(14
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+ ,-16)
+ ,dim=c(8
+ ,143)
+ ,dimnames=list(c('i'
+ ,'w'
+ ,'f'
+ ,'s'
+ ,'c'
+ ,'b'
+ ,'h'
+ ,'a')
+ ,1:143))
> y <- array(NA,dim=c(8,143),dimnames=list(c('i','w','f','s','c','b','h','a'),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 a
1 14 501 11 20 91.81 77585 1303.2 13
2 14 485 11 19 91.98 77585 -58.7 15
3 15 464 11 18 91.72 77585 -378.9 3
4 13 460 11 13 90.27 78302 175.6 2
5 8 467 11 17 91.89 78302 233.7 -2
6 7 460 9 17 92.07 78302 706.8 1
7 3 448 8 13 92.92 78224 -23.6 1
8 3 443 6 14 93.34 78224 420.9 -1
9 4 436 7 13 93.60 78224 722.1 -6
10 4 431 8 17 92.41 78178 1401.3 -13
11 0 484 6 17 93.60 78178 -94.9 -25
12 -4 510 5 15 93.77 78178 1043.6 -26
13 -14 513 2 9 93.60 77988 1300.1 -9
14 -18 503 3 10 93.60 77988 721.1 1
15 -8 471 3 9 93.51 77988 -45.6 3
16 -1 471 7 14 92.66 77876 787.5 6
17 1 476 8 18 94.20 77876 694.3 2
18 2 475 7 18 94.37 77876 1054.7 5
19 0 470 7 12 94.45 78432 821.9 5
20 1 461 6 16 94.62 78432 1100.7 0
21 0 455 6 12 94.37 78432 862.4 -5
22 -1 456 7 19 93.43 79025 1656.1 -4
23 -3 517 5 13 94.79 79025 -174.0 -2
24 -3 525 5 12 94.88 79025 1337.6 -1
25 -3 523 5 13 94.79 79407 1394.9 -8
26 -4 519 4 11 94.62 79407 915.7 -16
27 -8 509 4 10 94.71 79407 -481.1 -19
28 -9 512 4 16 93.77 79644 167.9 -28
29 -13 519 1 12 95.73 79644 208.2 -11
30 -18 517 -1 6 95.99 79644 382.2 -4
31 -11 510 3 8 95.82 79381 1004.0 -9
32 -9 509 4 6 95.47 79381 864.7 -12
33 -10 501 3 8 95.82 79381 1052.9 -10
34 -13 507 2 8 94.71 79536 1417.6 -2
35 -11 569 1 9 96.33 79536 -197.7 -13
36 -5 580 4 13 96.50 79536 1262.1 0
37 -15 578 3 8 96.16 79813 1147.2 0
38 -6 565 5 11 96.33 79813 700.2 4
39 -6 547 6 8 96.33 79813 45.3 7
40 -3 555 6 10 95.05 80332 458.5 5
41 -1 562 6 15 96.84 80332 610.2 2
42 -3 561 6 12 96.92 80332 786.4 -2
43 -4 555 6 13 97.44 81434 787.2 6
44 -6 544 5 12 97.78 81434 1040.0 -3
45 0 537 6 15 97.69 81434 324.1 1
46 -4 543 5 13 96.67 82167 1343.0 0
47 -2 594 6 13 98.29 82167 -501.2 -7
48 -2 611 5 16 98.20 82167 800.4 -6
49 -6 613 7 14 98.71 82816 916.7 -4
50 -7 611 4 12 98.54 82816 695.8 -4
51 -6 594 5 15 98.20 82816 28.0 -2
52 -6 595 6 14 96.92 83000 495.6 2
53 -3 591 6 19 99.06 83000 366.2 -5
54 -2 589 5 16 99.65 83000 633.0 -15
55 -5 584 3 16 99.82 83251 848.3 -16
56 -11 573 2 11 99.99 83251 472.2 -18
57 -11 567 3 13 100.33 83251 357.8 -13
58 -11 569 3 12 99.31 83591 824.3 -23
59 -10 621 2 11 101.10 83591 -880.1 -10
60 -14 629 0 6 101.10 83591 1066.8 -10
61 -8 628 4 9 100.93 83910 1052.8 -6
62 -9 612 4 6 100.85 83910 -32.1 -3
63 -5 595 5 15 100.93 83910 -1331.4 -4
64 -1 597 6 17 99.60 84599 -767.1 -7
65 -2 593 6 13 101.88 84599 -236.7 -7
66 -5 590 5 12 101.81 84599 -184.9 -7
67 -4 580 5 13 102.38 85275 -143.4 -3
68 -6 574 3 10 102.74 85275 493.9 0
69 -2 573 5 14 102.82 85275 549.7 -5
70 -2 573 5 13 101.72 85608 982.7 -3
71 -2 620 5 10 103.47 85608 -856.3 3
72 -2 626 3 11 102.98 85608 967.0 2
73 2 620 6 12 102.68 86303 659.4 -7
74 1 588 6 7 102.90 86303 577.2 -1
75 -8 566 4 11 103.03 86303 -213.1 0
76 -1 557 6 9 101.29 87115 17.7 -3
77 1 561 5 13 103.69 87115 390.1 4
78 -1 549 4 12 103.68 87115 509.3 2
79 2 532 5 5 104.20 87931 410.0 3
80 2 526 5 13 104.08 87931 212.5 0
81 1 511 4 11 104.16 87931 818.0 -10
82 -1 499 3 8 103.05 88164 422.7 -10
83 -2 555 2 8 104.66 88164 -158.0 -9
84 -2 565 3 8 104.46 88164 427.2 -22
85 -1 542 2 8 104.95 88792 243.4 -16
86 -8 527 -1 0 105.85 88792 -419.3 -18
87 -4 510 0 3 106.23 88792 -1459.8 -14
88 -6 514 -2 0 104.86 89263 -1389.8 -12
89 -3 517 1 -1 107.44 89263 -2.1 -17
90 -3 508 -2 -1 108.23 89263 -938.6 -23
91 -7 493 -2 -4 108.45 89881 -839.9 -28
92 -9 490 -2 1 109.39 89881 -297.6 -31
93 -11 469 -6 -1 110.15 89881 -376.3 -21
94 -13 478 -4 0 109.13 90120 -79.4 -19
95 -11 528 -2 -1 110.28 90120 -2091.3 -22
96 -9 534 0 6 110.17 90120 -1023.0 -22
97 -17 518 -5 0 109.99 89703 -765.6 -25
98 -22 506 -4 -3 109.26 89703 -1592.3 -16
99 -25 502 -5 -3 109.11 89703 -1588.8 -22
100 -20 516 -1 4 107.06 87818 -1318.0 -21
101 -24 528 -2 1 109.53 87818 -402.4 -10
102 -24 533 -4 0 108.92 87818 -814.5 -7
103 -22 536 -1 -4 109.24 86273 -98.4 -5
104 -19 537 1 -2 109.12 86273 -305.9 -4
105 -18 524 1 3 109.00 86273 -18.4 7
106 -17 536 -2 2 107.23 86316 610.3 6
107 -11 587 1 5 109.49 86316 -917.3 3
108 -11 597 1 6 109.04 86316 88.4 10
109 -12 581 3 6 109.02 87234 -740.2 0
110 -10 564 3 3 109.23 87234 29.3 -2
111 -15 558 1 4 109.46 87234 -893.2 -1
112 -15 575 1 7 107.90 87885 -1030.2 2
113 -15 580 0 5 110.42 87885 -403.4 8
114 -13 575 2 6 110.98 87885 -46.9 -6
115 -8 563 2 1 111.48 88003 -321.2 -4
116 -13 552 -1 3 111.88 88003 -239.9 4
117 -9 537 1 6 111.89 88003 640.9 7
118 -7 545 0 0 109.85 88910 511.6 3
119 -4 601 1 3 112.10 88910 -665.1 3
120 -4 604 1 4 112.24 88910 657.7 8
121 -2 586 3 7 112.39 89397 -207.7 3
122 0 564 2 6 112.52 89397 -885.2 -3
123 -2 549 0 6 113.16 89397 -1595.8 4
124 -3 551 0 6 111.84 89813 -1374.9 -5
125 1 556 3 6 114.33 89813 -316.6 -1
126 -2 548 -2 2 114.82 89813 -283.4 5
127 -1 540 0 2 115.20 90539 -175.8 0
128 1 531 1 2 115.40 90539 -694.2 -6
129 -3 521 -1 3 115.74 90539 -249.9 -13
130 -4 519 -2 -1 114.19 90688 268.2 -15
131 -9 572 -1 -4 115.94 90688 -2105.1 -8
132 -9 581 -1 4 116.03 90688 -762.8 -20
133 -7 563 1 5 116.24 90691 -117.1 -10
134 -14 548 -2 3 116.66 90691 -1094.4 -22
135 -12 539 -5 -1 116.79 90691 -2095.2 -25
136 -16 541 -5 -4 115.48 90645 -1587.6 -10
137 -20 562 -6 0 118.16 90645 -528.0 -8
138 -12 559 -4 -1 118.38 90645 -324.2 -9
139 -12 546 -3 -1 118.51 90861 -276.1 -5
140 -10 536 -3 3 118.42 90861 -139.1 -7
141 -10 528 -1 2 118.24 90861 268.0 -11
142 -13 530 -2 -4 116.47 90401 570.5 -11
143 -16 582 -3 -3 118.96 90401 -316.5 -16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) w f s c b
-8.411e+01 -5.391e-02 2.061e+00 3.182e-01 8.315e-02 1.068e-03
h a
8.043e-05 -5.053e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.5150 -1.8303 0.1509 2.3993 9.7411
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.411e+01 1.089e+01 -7.722 2.30e-12 ***
w -5.391e-02 8.285e-03 -6.507 1.38e-09 ***
f 2.061e+00 2.157e-01 9.555 < 2e-16 ***
s 3.182e-01 1.250e-01 2.545 0.012 *
c 8.315e-02 1.518e-01 0.548 0.585
b 1.068e-03 2.448e-04 4.362 2.54e-05 ***
h 8.043e-05 5.138e-04 0.157 0.876
a -5.053e-03 4.406e-02 -0.115 0.909
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.857 on 135 degrees of freedom
Multiple R-squared: 0.7483, Adjusted R-squared: 0.7353
F-statistic: 57.34 on 7 and 135 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,] 0.1128309880 0.225661976 0.8871690
[2,] 0.0598477512 0.119695502 0.9401522
[3,] 0.1889222114 0.377844423 0.8110778
[4,] 0.5187608043 0.962478391 0.4812392
[5,] 0.4433671681 0.886734336 0.5566328
[6,] 0.3795404399 0.759080880 0.6204596
[7,] 0.3204893335 0.640978667 0.6795107
[8,] 0.2541986535 0.508397307 0.7458013
[9,] 0.2190393037 0.438078607 0.7809607
[10,] 0.1993412710 0.398682542 0.8006587
[11,] 0.1603114252 0.320622850 0.8396886
[12,] 0.1188977105 0.237795421 0.8811023
[13,] 0.1234209925 0.246841985 0.8765790
[14,] 0.1246956566 0.249391313 0.8753043
[15,] 0.0994164249 0.198832850 0.9005836
[16,] 0.0888199985 0.177639997 0.9111800
[17,] 0.0769746143 0.153949229 0.9230254
[18,] 0.0603786145 0.120757229 0.9396214
[19,] 0.0481914791 0.096382958 0.9518085
[20,] 0.0386943553 0.077388711 0.9613056
[21,] 0.0294376615 0.058875323 0.9705623
[22,] 0.0220409020 0.044081804 0.9779591
[23,] 0.0146136295 0.029227259 0.9853864
[24,] 0.0093308279 0.018661656 0.9906692
[25,] 0.0111916962 0.022383392 0.9888083
[26,] 0.0090065684 0.018013137 0.9909934
[27,] 0.0140143025 0.028028605 0.9859857
[28,] 0.0113990176 0.022798035 0.9886010
[29,] 0.0134552253 0.026910451 0.9865448
[30,] 0.0106791952 0.021358390 0.9893208
[31,] 0.0092247100 0.018449420 0.9907753
[32,] 0.0080406132 0.016081226 0.9919594
[33,] 0.0064453399 0.012890680 0.9935547
[34,] 0.0046863194 0.009372639 0.9953137
[35,] 0.0053840204 0.010768041 0.9946160
[36,] 0.0042633301 0.008526660 0.9957367
[37,] 0.0040530018 0.008106004 0.9959470
[38,] 0.0042134603 0.008426921 0.9957865
[39,] 0.0076792887 0.015358577 0.9923207
[40,] 0.0057111793 0.011422359 0.9942888
[41,] 0.0044233557 0.008846711 0.9955766
[42,] 0.0038068742 0.007613748 0.9961931
[43,] 0.0027414347 0.005482869 0.9972586
[44,] 0.0029920471 0.005984094 0.9970080
[45,] 0.0041392466 0.008278493 0.9958608
[46,] 0.0032164096 0.006432819 0.9967836
[47,] 0.0026982097 0.005396419 0.9973018
[48,] 0.0018296163 0.003659233 0.9981704
[49,] 0.0016694761 0.003338952 0.9983305
[50,] 0.0033956775 0.006791355 0.9966043
[51,] 0.0024229134 0.004845827 0.9975771
[52,] 0.0017819106 0.003563821 0.9982181
[53,] 0.0017759946 0.003551989 0.9982240
[54,] 0.0012959477 0.002591895 0.9987041
[55,] 0.0011000307 0.002200061 0.9989000
[56,] 0.0009001624 0.001800325 0.9990998
[57,] 0.0006255360 0.001251072 0.9993745
[58,] 0.0007335361 0.001467072 0.9992665
[59,] 0.0007009831 0.001401966 0.9992990
[60,] 0.0005547449 0.001109490 0.9994453
[61,] 0.0005962930 0.001192586 0.9994037
[62,] 0.0036618263 0.007323653 0.9963382
[63,] 0.0048519002 0.009703800 0.9951481
[64,] 0.0047462792 0.009492558 0.9952537
[65,] 0.0046310952 0.009262190 0.9953689
[66,] 0.0036745646 0.007349129 0.9963254
[67,] 0.0028588175 0.005717635 0.9971412
[68,] 0.0021974534 0.004394907 0.9978025
[69,] 0.0019885372 0.003977074 0.9980115
[70,] 0.0014740816 0.002948163 0.9985259
[71,] 0.0010897124 0.002179425 0.9989103
[72,] 0.0009116591 0.001823318 0.9990883
[73,] 0.0011250534 0.002250107 0.9988749
[74,] 0.0009878048 0.001975610 0.9990122
[75,] 0.0010294777 0.002058955 0.9989705
[76,] 0.0010331312 0.002066262 0.9989669
[77,] 0.0010578020 0.002115604 0.9989422
[78,] 0.0015197341 0.003039468 0.9984803
[79,] 0.0011241765 0.002248353 0.9988758
[80,] 0.0105280347 0.021056069 0.9894720
[81,] 0.0144634162 0.028926832 0.9855366
[82,] 0.0170792358 0.034158472 0.9829208
[83,] 0.0505575610 0.101115122 0.9494424
[84,] 0.0458117652 0.091623530 0.9541882
[85,] 0.0455551768 0.091110354 0.9544448
[86,] 0.0451291936 0.090258387 0.9548708
[87,] 0.0638178103 0.127635621 0.9361822
[88,] 0.1271541407 0.254308281 0.8728459
[89,] 0.2218274807 0.443654961 0.7781725
[90,] 0.3100871837 0.620174367 0.6899128
[91,] 0.4894760411 0.978952082 0.5105240
[92,] 0.5088961930 0.982207614 0.4911038
[93,] 0.4835207634 0.967041527 0.5164792
[94,] 0.4595522058 0.919104412 0.5404478
[95,] 0.5108035466 0.978392907 0.4891965
[96,] 0.4652691399 0.930538280 0.5347309
[97,] 0.5640921260 0.871815748 0.4359079
[98,] 0.6788041133 0.642391773 0.3211959
[99,] 0.6409914733 0.718017053 0.3590085
[100,] 0.5838116982 0.832376604 0.4161883
[101,] 0.5337846586 0.932430683 0.4662153
[102,] 0.7338010920 0.532397816 0.2661989
[103,] 0.8046462222 0.390707556 0.1953538
[104,] 0.8076820613 0.384635877 0.1923179
[105,] 0.7702079091 0.459584182 0.2297921
[106,] 0.7416532323 0.516693535 0.2583468
[107,] 0.7995638122 0.400872376 0.2004362
[108,] 0.8700736134 0.259852773 0.1299264
[109,] 0.8597163937 0.280567213 0.1402836
[110,] 0.8426583326 0.314683335 0.1573417
[111,] 0.8188809176 0.362238165 0.1811191
[112,] 0.7787147612 0.442570478 0.2212852
[113,] 0.7599964385 0.480007123 0.2400036
[114,] 0.7790336958 0.441932608 0.2209663
[115,] 0.7741020290 0.451795942 0.2258980
[116,] 0.7409987183 0.518002563 0.2590013
[117,] 0.6807822112 0.638435578 0.3192178
[118,] 0.6549865630 0.690026874 0.3450134
[119,] 0.8023558906 0.395288219 0.1976441
[120,] 0.8046389566 0.390722087 0.1953610
[121,] 0.6810597403 0.637880519 0.3189403
[122,] 0.7223266536 0.555346693 0.2776733
> postscript(file="/var/wessaorg/rcomp/tmp/1m6uy1351893223.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2z2th1351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38bv11351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4uhbw1351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5oevf1351893223.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
5.56753904 5.12869523 5.30152111 3.98240501 -2.07276335 0.63416904
7 8 9 10 11 12
-0.60736625 2.84622965 1.65488740 -1.89066119 1.04962520 1.03815522
13 14 15 16 17 18
-0.42511306 -7.24646891 1.42583806 -1.27130916 -2.47657009 0.50264220
19 20 21 22 23 24
-0.43907714 0.80201283 0.76619658 -5.08248344 2.28200851 2.90753613
25 26 27 28 29 30
2.04108789 3.53526835 -0.59590673 -3.61619404 0.13710612 1.06066473
31 32 33 34 35 36
-0.97787899 -0.43124903 -0.47207470 -1.14963093 5.87541174 4.94634680
37 38 39 40 41 42
-1.76743990 1.49682613 -0.51212556 1.79160926 2.40158810 1.26136545
43 44 45 46 47 48
-1.55991231 -1.86769437 0.82438166 -0.93943897 1.72725365 3.65797231
49 50 51 52 53 54
-4.45453730 1.28929287 -1.55093978 -3.34731164 -2.35707125 1.42987322
55 56 57 58 59 60
1.97797576 -0.95675197 -3.97160392 -3.91181491 2.32488502 4.31297664
61 62 63 64 65 66
0.75483574 -0.04396429 -1.79293476 -1.06833200 -1.24325879 -2.02401019
67 68 69 70 71 72
-2.63370888 0.05368631 -1.43177480 -1.40235662 2.11895887 6.13540928
73 74 75 76 77 78
2.57253745 1.45718920 -5.82185797 -3.54883288 -0.73920545 -1.02568230
79 80 81 82 83 84
0.32291264 -2.53578765 -1.75277892 -1.50860995 2.48945043 0.87135895
85 86 87 88 89 90
2.02626434 2.91514333 3.05512475 5.96314901 2.90845157 8.58582948
91 92 93 94 95 96
4.02049395 0.13061192 5.87295961 -0.26640936 0.67630307 -3.42687219
97 98 99 100 101 102
0.34979499 -6.23084910 -7.40353370 -9.95433550 -10.51501993 -5.70601242
103 104 105 106 107 108
-6.87897400 -8.55200351 -9.80163597 -1.60753741 -0.07621852 0.13655850
109 110 111 112 113 114
-6.81064686 -4.86188913 -6.32129837 -6.89874148 -4.16124531 -7.01720326
115 116 117 118 119 120
-1.20834626 -1.25397251 -3.19607692 2.39710815 5.30793326 5.05865570
121 122 123 124 125 126
0.52319801 3.72982717 5.08263075 3.79277965 1.60711740 9.74113576
127 128 129 130 131 132
5.34694300 4.79539304 3.96083240 6.10507195 2.93679861 0.69999640
133 134 135 136 137 138
-2.73291639 -3.73879773 5.28675552 2.54231267 0.16462913 4.15921716
139 140 141 142 143
1.17216326 1.34643402 -2.92678877 -1.23440595 0.15091160
> postscript(file="/var/wessaorg/rcomp/tmp/6rhtq1351893223.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 5.56753904 NA
1 5.12869523 5.56753904
2 5.30152111 5.12869523
3 3.98240501 5.30152111
4 -2.07276335 3.98240501
5 0.63416904 -2.07276335
6 -0.60736625 0.63416904
7 2.84622965 -0.60736625
8 1.65488740 2.84622965
9 -1.89066119 1.65488740
10 1.04962520 -1.89066119
11 1.03815522 1.04962520
12 -0.42511306 1.03815522
13 -7.24646891 -0.42511306
14 1.42583806 -7.24646891
15 -1.27130916 1.42583806
16 -2.47657009 -1.27130916
17 0.50264220 -2.47657009
18 -0.43907714 0.50264220
19 0.80201283 -0.43907714
20 0.76619658 0.80201283
21 -5.08248344 0.76619658
22 2.28200851 -5.08248344
23 2.90753613 2.28200851
24 2.04108789 2.90753613
25 3.53526835 2.04108789
26 -0.59590673 3.53526835
27 -3.61619404 -0.59590673
28 0.13710612 -3.61619404
29 1.06066473 0.13710612
30 -0.97787899 1.06066473
31 -0.43124903 -0.97787899
32 -0.47207470 -0.43124903
33 -1.14963093 -0.47207470
34 5.87541174 -1.14963093
35 4.94634680 5.87541174
36 -1.76743990 4.94634680
37 1.49682613 -1.76743990
38 -0.51212556 1.49682613
39 1.79160926 -0.51212556
40 2.40158810 1.79160926
41 1.26136545 2.40158810
42 -1.55991231 1.26136545
43 -1.86769437 -1.55991231
44 0.82438166 -1.86769437
45 -0.93943897 0.82438166
46 1.72725365 -0.93943897
47 3.65797231 1.72725365
48 -4.45453730 3.65797231
49 1.28929287 -4.45453730
50 -1.55093978 1.28929287
51 -3.34731164 -1.55093978
52 -2.35707125 -3.34731164
53 1.42987322 -2.35707125
54 1.97797576 1.42987322
55 -0.95675197 1.97797576
56 -3.97160392 -0.95675197
57 -3.91181491 -3.97160392
58 2.32488502 -3.91181491
59 4.31297664 2.32488502
60 0.75483574 4.31297664
61 -0.04396429 0.75483574
62 -1.79293476 -0.04396429
63 -1.06833200 -1.79293476
64 -1.24325879 -1.06833200
65 -2.02401019 -1.24325879
66 -2.63370888 -2.02401019
67 0.05368631 -2.63370888
68 -1.43177480 0.05368631
69 -1.40235662 -1.43177480
70 2.11895887 -1.40235662
71 6.13540928 2.11895887
72 2.57253745 6.13540928
73 1.45718920 2.57253745
74 -5.82185797 1.45718920
75 -3.54883288 -5.82185797
76 -0.73920545 -3.54883288
77 -1.02568230 -0.73920545
78 0.32291264 -1.02568230
79 -2.53578765 0.32291264
80 -1.75277892 -2.53578765
81 -1.50860995 -1.75277892
82 2.48945043 -1.50860995
83 0.87135895 2.48945043
84 2.02626434 0.87135895
85 2.91514333 2.02626434
86 3.05512475 2.91514333
87 5.96314901 3.05512475
88 2.90845157 5.96314901
89 8.58582948 2.90845157
90 4.02049395 8.58582948
91 0.13061192 4.02049395
92 5.87295961 0.13061192
93 -0.26640936 5.87295961
94 0.67630307 -0.26640936
95 -3.42687219 0.67630307
96 0.34979499 -3.42687219
97 -6.23084910 0.34979499
98 -7.40353370 -6.23084910
99 -9.95433550 -7.40353370
100 -10.51501993 -9.95433550
101 -5.70601242 -10.51501993
102 -6.87897400 -5.70601242
103 -8.55200351 -6.87897400
104 -9.80163597 -8.55200351
105 -1.60753741 -9.80163597
106 -0.07621852 -1.60753741
107 0.13655850 -0.07621852
108 -6.81064686 0.13655850
109 -4.86188913 -6.81064686
110 -6.32129837 -4.86188913
111 -6.89874148 -6.32129837
112 -4.16124531 -6.89874148
113 -7.01720326 -4.16124531
114 -1.20834626 -7.01720326
115 -1.25397251 -1.20834626
116 -3.19607692 -1.25397251
117 2.39710815 -3.19607692
118 5.30793326 2.39710815
119 5.05865570 5.30793326
120 0.52319801 5.05865570
121 3.72982717 0.52319801
122 5.08263075 3.72982717
123 3.79277965 5.08263075
124 1.60711740 3.79277965
125 9.74113576 1.60711740
126 5.34694300 9.74113576
127 4.79539304 5.34694300
128 3.96083240 4.79539304
129 6.10507195 3.96083240
130 2.93679861 6.10507195
131 0.69999640 2.93679861
132 -2.73291639 0.69999640
133 -3.73879773 -2.73291639
134 5.28675552 -3.73879773
135 2.54231267 5.28675552
136 0.16462913 2.54231267
137 4.15921716 0.16462913
138 1.17216326 4.15921716
139 1.34643402 1.17216326
140 -2.92678877 1.34643402
141 -1.23440595 -2.92678877
142 0.15091160 -1.23440595
143 NA 0.15091160
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.12869523 5.56753904
[2,] 5.30152111 5.12869523
[3,] 3.98240501 5.30152111
[4,] -2.07276335 3.98240501
[5,] 0.63416904 -2.07276335
[6,] -0.60736625 0.63416904
[7,] 2.84622965 -0.60736625
[8,] 1.65488740 2.84622965
[9,] -1.89066119 1.65488740
[10,] 1.04962520 -1.89066119
[11,] 1.03815522 1.04962520
[12,] -0.42511306 1.03815522
[13,] -7.24646891 -0.42511306
[14,] 1.42583806 -7.24646891
[15,] -1.27130916 1.42583806
[16,] -2.47657009 -1.27130916
[17,] 0.50264220 -2.47657009
[18,] -0.43907714 0.50264220
[19,] 0.80201283 -0.43907714
[20,] 0.76619658 0.80201283
[21,] -5.08248344 0.76619658
[22,] 2.28200851 -5.08248344
[23,] 2.90753613 2.28200851
[24,] 2.04108789 2.90753613
[25,] 3.53526835 2.04108789
[26,] -0.59590673 3.53526835
[27,] -3.61619404 -0.59590673
[28,] 0.13710612 -3.61619404
[29,] 1.06066473 0.13710612
[30,] -0.97787899 1.06066473
[31,] -0.43124903 -0.97787899
[32,] -0.47207470 -0.43124903
[33,] -1.14963093 -0.47207470
[34,] 5.87541174 -1.14963093
[35,] 4.94634680 5.87541174
[36,] -1.76743990 4.94634680
[37,] 1.49682613 -1.76743990
[38,] -0.51212556 1.49682613
[39,] 1.79160926 -0.51212556
[40,] 2.40158810 1.79160926
[41,] 1.26136545 2.40158810
[42,] -1.55991231 1.26136545
[43,] -1.86769437 -1.55991231
[44,] 0.82438166 -1.86769437
[45,] -0.93943897 0.82438166
[46,] 1.72725365 -0.93943897
[47,] 3.65797231 1.72725365
[48,] -4.45453730 3.65797231
[49,] 1.28929287 -4.45453730
[50,] -1.55093978 1.28929287
[51,] -3.34731164 -1.55093978
[52,] -2.35707125 -3.34731164
[53,] 1.42987322 -2.35707125
[54,] 1.97797576 1.42987322
[55,] -0.95675197 1.97797576
[56,] -3.97160392 -0.95675197
[57,] -3.91181491 -3.97160392
[58,] 2.32488502 -3.91181491
[59,] 4.31297664 2.32488502
[60,] 0.75483574 4.31297664
[61,] -0.04396429 0.75483574
[62,] -1.79293476 -0.04396429
[63,] -1.06833200 -1.79293476
[64,] -1.24325879 -1.06833200
[65,] -2.02401019 -1.24325879
[66,] -2.63370888 -2.02401019
[67,] 0.05368631 -2.63370888
[68,] -1.43177480 0.05368631
[69,] -1.40235662 -1.43177480
[70,] 2.11895887 -1.40235662
[71,] 6.13540928 2.11895887
[72,] 2.57253745 6.13540928
[73,] 1.45718920 2.57253745
[74,] -5.82185797 1.45718920
[75,] -3.54883288 -5.82185797
[76,] -0.73920545 -3.54883288
[77,] -1.02568230 -0.73920545
[78,] 0.32291264 -1.02568230
[79,] -2.53578765 0.32291264
[80,] -1.75277892 -2.53578765
[81,] -1.50860995 -1.75277892
[82,] 2.48945043 -1.50860995
[83,] 0.87135895 2.48945043
[84,] 2.02626434 0.87135895
[85,] 2.91514333 2.02626434
[86,] 3.05512475 2.91514333
[87,] 5.96314901 3.05512475
[88,] 2.90845157 5.96314901
[89,] 8.58582948 2.90845157
[90,] 4.02049395 8.58582948
[91,] 0.13061192 4.02049395
[92,] 5.87295961 0.13061192
[93,] -0.26640936 5.87295961
[94,] 0.67630307 -0.26640936
[95,] -3.42687219 0.67630307
[96,] 0.34979499 -3.42687219
[97,] -6.23084910 0.34979499
[98,] -7.40353370 -6.23084910
[99,] -9.95433550 -7.40353370
[100,] -10.51501993 -9.95433550
[101,] -5.70601242 -10.51501993
[102,] -6.87897400 -5.70601242
[103,] -8.55200351 -6.87897400
[104,] -9.80163597 -8.55200351
[105,] -1.60753741 -9.80163597
[106,] -0.07621852 -1.60753741
[107,] 0.13655850 -0.07621852
[108,] -6.81064686 0.13655850
[109,] -4.86188913 -6.81064686
[110,] -6.32129837 -4.86188913
[111,] -6.89874148 -6.32129837
[112,] -4.16124531 -6.89874148
[113,] -7.01720326 -4.16124531
[114,] -1.20834626 -7.01720326
[115,] -1.25397251 -1.20834626
[116,] -3.19607692 -1.25397251
[117,] 2.39710815 -3.19607692
[118,] 5.30793326 2.39710815
[119,] 5.05865570 5.30793326
[120,] 0.52319801 5.05865570
[121,] 3.72982717 0.52319801
[122,] 5.08263075 3.72982717
[123,] 3.79277965 5.08263075
[124,] 1.60711740 3.79277965
[125,] 9.74113576 1.60711740
[126,] 5.34694300 9.74113576
[127,] 4.79539304 5.34694300
[128,] 3.96083240 4.79539304
[129,] 6.10507195 3.96083240
[130,] 2.93679861 6.10507195
[131,] 0.69999640 2.93679861
[132,] -2.73291639 0.69999640
[133,] -3.73879773 -2.73291639
[134,] 5.28675552 -3.73879773
[135,] 2.54231267 5.28675552
[136,] 0.16462913 2.54231267
[137,] 4.15921716 0.16462913
[138,] 1.17216326 4.15921716
[139,] 1.34643402 1.17216326
[140,] -2.92678877 1.34643402
[141,] -1.23440595 -2.92678877
[142,] 0.15091160 -1.23440595
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.12869523 5.56753904
2 5.30152111 5.12869523
3 3.98240501 5.30152111
4 -2.07276335 3.98240501
5 0.63416904 -2.07276335
6 -0.60736625 0.63416904
7 2.84622965 -0.60736625
8 1.65488740 2.84622965
9 -1.89066119 1.65488740
10 1.04962520 -1.89066119
11 1.03815522 1.04962520
12 -0.42511306 1.03815522
13 -7.24646891 -0.42511306
14 1.42583806 -7.24646891
15 -1.27130916 1.42583806
16 -2.47657009 -1.27130916
17 0.50264220 -2.47657009
18 -0.43907714 0.50264220
19 0.80201283 -0.43907714
20 0.76619658 0.80201283
21 -5.08248344 0.76619658
22 2.28200851 -5.08248344
23 2.90753613 2.28200851
24 2.04108789 2.90753613
25 3.53526835 2.04108789
26 -0.59590673 3.53526835
27 -3.61619404 -0.59590673
28 0.13710612 -3.61619404
29 1.06066473 0.13710612
30 -0.97787899 1.06066473
31 -0.43124903 -0.97787899
32 -0.47207470 -0.43124903
33 -1.14963093 -0.47207470
34 5.87541174 -1.14963093
35 4.94634680 5.87541174
36 -1.76743990 4.94634680
37 1.49682613 -1.76743990
38 -0.51212556 1.49682613
39 1.79160926 -0.51212556
40 2.40158810 1.79160926
41 1.26136545 2.40158810
42 -1.55991231 1.26136545
43 -1.86769437 -1.55991231
44 0.82438166 -1.86769437
45 -0.93943897 0.82438166
46 1.72725365 -0.93943897
47 3.65797231 1.72725365
48 -4.45453730 3.65797231
49 1.28929287 -4.45453730
50 -1.55093978 1.28929287
51 -3.34731164 -1.55093978
52 -2.35707125 -3.34731164
53 1.42987322 -2.35707125
54 1.97797576 1.42987322
55 -0.95675197 1.97797576
56 -3.97160392 -0.95675197
57 -3.91181491 -3.97160392
58 2.32488502 -3.91181491
59 4.31297664 2.32488502
60 0.75483574 4.31297664
61 -0.04396429 0.75483574
62 -1.79293476 -0.04396429
63 -1.06833200 -1.79293476
64 -1.24325879 -1.06833200
65 -2.02401019 -1.24325879
66 -2.63370888 -2.02401019
67 0.05368631 -2.63370888
68 -1.43177480 0.05368631
69 -1.40235662 -1.43177480
70 2.11895887 -1.40235662
71 6.13540928 2.11895887
72 2.57253745 6.13540928
73 1.45718920 2.57253745
74 -5.82185797 1.45718920
75 -3.54883288 -5.82185797
76 -0.73920545 -3.54883288
77 -1.02568230 -0.73920545
78 0.32291264 -1.02568230
79 -2.53578765 0.32291264
80 -1.75277892 -2.53578765
81 -1.50860995 -1.75277892
82 2.48945043 -1.50860995
83 0.87135895 2.48945043
84 2.02626434 0.87135895
85 2.91514333 2.02626434
86 3.05512475 2.91514333
87 5.96314901 3.05512475
88 2.90845157 5.96314901
89 8.58582948 2.90845157
90 4.02049395 8.58582948
91 0.13061192 4.02049395
92 5.87295961 0.13061192
93 -0.26640936 5.87295961
94 0.67630307 -0.26640936
95 -3.42687219 0.67630307
96 0.34979499 -3.42687219
97 -6.23084910 0.34979499
98 -7.40353370 -6.23084910
99 -9.95433550 -7.40353370
100 -10.51501993 -9.95433550
101 -5.70601242 -10.51501993
102 -6.87897400 -5.70601242
103 -8.55200351 -6.87897400
104 -9.80163597 -8.55200351
105 -1.60753741 -9.80163597
106 -0.07621852 -1.60753741
107 0.13655850 -0.07621852
108 -6.81064686 0.13655850
109 -4.86188913 -6.81064686
110 -6.32129837 -4.86188913
111 -6.89874148 -6.32129837
112 -4.16124531 -6.89874148
113 -7.01720326 -4.16124531
114 -1.20834626 -7.01720326
115 -1.25397251 -1.20834626
116 -3.19607692 -1.25397251
117 2.39710815 -3.19607692
118 5.30793326 2.39710815
119 5.05865570 5.30793326
120 0.52319801 5.05865570
121 3.72982717 0.52319801
122 5.08263075 3.72982717
123 3.79277965 5.08263075
124 1.60711740 3.79277965
125 9.74113576 1.60711740
126 5.34694300 9.74113576
127 4.79539304 5.34694300
128 3.96083240 4.79539304
129 6.10507195 3.96083240
130 2.93679861 6.10507195
131 0.69999640 2.93679861
132 -2.73291639 0.69999640
133 -3.73879773 -2.73291639
134 5.28675552 -3.73879773
135 2.54231267 5.28675552
136 0.16462913 2.54231267
137 4.15921716 0.16462913
138 1.17216326 4.15921716
139 1.34643402 1.17216326
140 -2.92678877 1.34643402
141 -1.23440595 -2.92678877
142 0.15091160 -1.23440595
> 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/wessaorg/rcomp/tmp/79jvi1351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8j50v1351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9uvdf1351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10unm21351893223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/113s3i1351893223.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/wessaorg/rcomp/tmp/12bggy1351893223.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/wessaorg/rcomp/tmp/13phvr1351893223.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/wessaorg/rcomp/tmp/14lynk1351893223.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/wessaorg/rcomp/tmp/153dne1351893223.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/wessaorg/rcomp/tmp/16dael1351893223.tab")
+ }
>
> try(system("convert tmp/1m6uy1351893223.ps tmp/1m6uy1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z2th1351893223.ps tmp/2z2th1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/38bv11351893223.ps tmp/38bv11351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uhbw1351893223.ps tmp/4uhbw1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oevf1351893223.ps tmp/5oevf1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rhtq1351893223.ps tmp/6rhtq1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/79jvi1351893223.ps tmp/79jvi1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j50v1351893223.ps tmp/8j50v1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uvdf1351893223.ps tmp/9uvdf1351893223.png",intern=TRUE))
character(0)
> try(system("convert tmp/10unm21351893223.ps tmp/10unm21351893223.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.195 0.847 8.048