R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(384
+ ,257.9
+ ,367.6
+ ,275.8
+ ,457.1
+ ,319.4
+ ,429.4
+ ,299.8
+ ,442.2
+ ,331.1
+ ,507.5
+ ,339.3
+ ,348.5
+ ,209.6
+ ,393.2
+ ,280.9
+ ,426.8
+ ,285.5
+ ,403
+ ,247.6
+ ,454.8
+ ,275.1
+ ,413
+ ,262.3
+ ,388.9
+ ,267.8
+ ,406.5
+ ,448.2
+ ,447.4
+ ,563.4
+ ,474.4
+ ,346.6
+ ,428.5
+ ,455.1
+ ,472.8
+ ,424.4
+ ,411
+ ,381.2
+ ,463.9
+ ,382.9
+ ,497.3
+ ,466.6
+ ,474
+ ,400.2
+ ,518.1
+ ,493.6
+ ,566
+ ,367.5
+ ,509.4
+ ,307.1
+ ,445.1
+ ,316.7
+ ,466.6
+ ,314.2
+ ,600.5
+ ,403.7
+ ,538.7
+ ,370.6
+ ,548
+ ,343.7
+ ,591.9
+ ,383
+ ,547.3
+ ,365.4
+ ,610.2
+ ,417.2
+ ,621.6
+ ,411
+ ,582.4
+ ,420.8
+ ,635.8
+ ,493
+ ,663.9
+ ,471.8
+ ,624.2
+ ,452.4
+ ,654.1
+ ,464.8
+ ,723.5
+ ,541.5
+ ,641.2
+ ,484
+ ,565.5
+ ,449.4
+ ,698.6
+ ,436.8
+ ,651
+ ,490
+ ,721.6
+ ,475.4
+ ,643.5
+ ,393.6
+ ,604
+ ,486.8
+ ,618.2
+ ,536.7
+ ,783.5
+ ,467
+ ,672.9
+ ,475.5
+ ,726.7
+ ,532.8
+ ,738.6
+ ,554.1
+ ,692.2
+ ,507.3
+ ,669.5
+ ,455.2
+ ,546.2
+ ,465.3
+ ,715
+ ,563.2
+ ,789.8
+ ,680.1
+ ,684
+ ,518.2
+ ,639
+ ,426.6
+ ,768.5
+ ,612.4
+ ,643.8
+ ,518.1
+ ,623
+ ,540
+ ,692.8
+ ,541.7
+ ,936.5
+ ,627.6
+ ,795.9
+ ,637
+ ,695.7
+ ,564.2
+ ,648.3
+ ,665
+ ,675.2
+ ,703.2
+ ,826.5
+ ,824.4
+ ,742.4
+ ,700.3
+ ,793.9
+ ,1219.6
+ ,685.3
+ ,764.7
+ ,756.1
+ ,479.9
+ ,704
+ ,543.4
+ ,860.6
+ ,593.3
+ ,795.9
+ ,584.3
+ ,816.7
+ ,645.9
+ ,777.9
+ ,548.9
+ ,746.4
+ ,421.8
+ ,694.7
+ ,460.3
+ ,909.2
+ ,553.4
+ ,783.6
+ ,424.4
+ ,730.4
+ ,470.2
+ ,847.7
+ ,547.2
+ ,758.7
+ ,444.8
+ ,839.2
+ ,526.7
+ ,784.8
+ ,598.3
+ ,906.1
+ ,543.5
+ ,838.2
+ ,641.2
+ ,729
+ ,525
+ ,768.1
+ ,521.5
+ ,710.5
+ ,551.8
+ ,863
+ ,543.7
+ ,778.3
+ ,472.1
+ ,827.7
+ ,488
+ ,853.1
+ ,642.8
+ ,859.3
+ ,601.7
+ ,779.2
+ ,553.9
+ ,724.6
+ ,522.5
+ ,829.2
+ ,568.4
+ ,862.9
+ ,675.4
+ ,601.6
+ ,499.1
+ ,964.9
+ ,549.4
+ ,766.3
+ ,531.2
+ ,847.8
+ ,583.3
+ ,992.7
+ ,526.5
+ ,865.3
+ ,513.2
+ ,1054.1
+ ,729.1
+ ,972.5
+ ,753.7
+ ,857.4
+ ,571.7
+ ,1043.3
+ ,680.9
+ ,1061
+ ,757.6
+ ,989.4
+ ,805.4
+ ,963.2
+ ,687.7
+ ,1181.9
+ ,950.8
+ ,1256.4
+ ,1062
+ ,1492.7
+ ,1110.6
+ ,1360.8
+ ,1098.9
+ ,1342.8
+ ,1067
+ ,1464
+ ,1360.1)
+ ,dim=c(2
+ ,120)
+ ,dimnames=list(c('yt'
+ ,'xt')
+ ,1:120))
> y <- array(NA,dim=c(2,120),dimnames=list(c('yt','xt'),1:120))
> 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 = 'Include Monthly 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.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
yt xt M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 384.0 257.9 1 0 0 0 0 0 0 0 0 0 0
2 367.6 275.8 0 1 0 0 0 0 0 0 0 0 0
3 457.1 319.4 0 0 1 0 0 0 0 0 0 0 0
4 429.4 299.8 0 0 0 1 0 0 0 0 0 0 0
5 442.2 331.1 0 0 0 0 1 0 0 0 0 0 0
6 507.5 339.3 0 0 0 0 0 1 0 0 0 0 0
7 348.5 209.6 0 0 0 0 0 0 1 0 0 0 0
8 393.2 280.9 0 0 0 0 0 0 0 1 0 0 0
9 426.8 285.5 0 0 0 0 0 0 0 0 1 0 0
10 403.0 247.6 0 0 0 0 0 0 0 0 0 1 0
11 454.8 275.1 0 0 0 0 0 0 0 0 0 0 1
12 413.0 262.3 0 0 0 0 0 0 0 0 0 0 0
13 388.9 267.8 1 0 0 0 0 0 0 0 0 0 0
14 406.5 448.2 0 1 0 0 0 0 0 0 0 0 0
15 447.4 563.4 0 0 1 0 0 0 0 0 0 0 0
16 474.4 346.6 0 0 0 1 0 0 0 0 0 0 0
17 428.5 455.1 0 0 0 0 1 0 0 0 0 0 0
18 472.8 424.4 0 0 0 0 0 1 0 0 0 0 0
19 411.0 381.2 0 0 0 0 0 0 1 0 0 0 0
20 463.9 382.9 0 0 0 0 0 0 0 1 0 0 0
21 497.3 466.6 0 0 0 0 0 0 0 0 1 0 0
22 474.0 400.2 0 0 0 0 0 0 0 0 0 1 0
23 518.1 493.6 0 0 0 0 0 0 0 0 0 0 1
24 566.0 367.5 0 0 0 0 0 0 0 0 0 0 0
25 509.4 307.1 1 0 0 0 0 0 0 0 0 0 0
26 445.1 316.7 0 1 0 0 0 0 0 0 0 0 0
27 466.6 314.2 0 0 1 0 0 0 0 0 0 0 0
28 600.5 403.7 0 0 0 1 0 0 0 0 0 0 0
29 538.7 370.6 0 0 0 0 1 0 0 0 0 0 0
30 548.0 343.7 0 0 0 0 0 1 0 0 0 0 0
31 591.9 383.0 0 0 0 0 0 0 1 0 0 0 0
32 547.3 365.4 0 0 0 0 0 0 0 1 0 0 0
33 610.2 417.2 0 0 0 0 0 0 0 0 1 0 0
34 621.6 411.0 0 0 0 0 0 0 0 0 0 1 0
35 582.4 420.8 0 0 0 0 0 0 0 0 0 0 1
36 635.8 493.0 0 0 0 0 0 0 0 0 0 0 0
37 663.9 471.8 1 0 0 0 0 0 0 0 0 0 0
38 624.2 452.4 0 1 0 0 0 0 0 0 0 0 0
39 654.1 464.8 0 0 1 0 0 0 0 0 0 0 0
40 723.5 541.5 0 0 0 1 0 0 0 0 0 0 0
41 641.2 484.0 0 0 0 0 1 0 0 0 0 0 0
42 565.5 449.4 0 0 0 0 0 1 0 0 0 0 0
43 698.6 436.8 0 0 0 0 0 0 1 0 0 0 0
44 651.0 490.0 0 0 0 0 0 0 0 1 0 0 0
45 721.6 475.4 0 0 0 0 0 0 0 0 1 0 0
46 643.5 393.6 0 0 0 0 0 0 0 0 0 1 0
47 604.0 486.8 0 0 0 0 0 0 0 0 0 0 1
48 618.2 536.7 0 0 0 0 0 0 0 0 0 0 0
49 783.5 467.0 1 0 0 0 0 0 0 0 0 0 0
50 672.9 475.5 0 1 0 0 0 0 0 0 0 0 0
51 726.7 532.8 0 0 1 0 0 0 0 0 0 0 0
52 738.6 554.1 0 0 0 1 0 0 0 0 0 0 0
53 692.2 507.3 0 0 0 0 1 0 0 0 0 0 0
54 669.5 455.2 0 0 0 0 0 1 0 0 0 0 0
55 546.2 465.3 0 0 0 0 0 0 1 0 0 0 0
56 715.0 563.2 0 0 0 0 0 0 0 1 0 0 0
57 789.8 680.1 0 0 0 0 0 0 0 0 1 0 0
58 684.0 518.2 0 0 0 0 0 0 0 0 0 1 0
59 639.0 426.6 0 0 0 0 0 0 0 0 0 0 1
60 768.5 612.4 0 0 0 0 0 0 0 0 0 0 0
61 643.8 518.1 1 0 0 0 0 0 0 0 0 0 0
62 623.0 540.0 0 1 0 0 0 0 0 0 0 0 0
63 692.8 541.7 0 0 1 0 0 0 0 0 0 0 0
64 936.5 627.6 0 0 0 1 0 0 0 0 0 0 0
65 795.9 637.0 0 0 0 0 1 0 0 0 0 0 0
66 695.7 564.2 0 0 0 0 0 1 0 0 0 0 0
67 648.3 665.0 0 0 0 0 0 0 1 0 0 0 0
68 675.2 703.2 0 0 0 0 0 0 0 1 0 0 0
69 826.5 824.4 0 0 0 0 0 0 0 0 1 0 0
70 742.4 700.3 0 0 0 0 0 0 0 0 0 1 0
71 793.9 1219.6 0 0 0 0 0 0 0 0 0 0 1
72 685.3 764.7 0 0 0 0 0 0 0 0 0 0 0
73 756.1 479.9 1 0 0 0 0 0 0 0 0 0 0
74 704.0 543.4 0 1 0 0 0 0 0 0 0 0 0
75 860.6 593.3 0 0 1 0 0 0 0 0 0 0 0
76 795.9 584.3 0 0 0 1 0 0 0 0 0 0 0
77 816.7 645.9 0 0 0 0 1 0 0 0 0 0 0
78 777.9 548.9 0 0 0 0 0 1 0 0 0 0 0
79 746.4 421.8 0 0 0 0 0 0 1 0 0 0 0
80 694.7 460.3 0 0 0 0 0 0 0 1 0 0 0
81 909.2 553.4 0 0 0 0 0 0 0 0 1 0 0
82 783.6 424.4 0 0 0 0 0 0 0 0 0 1 0
83 730.4 470.2 0 0 0 0 0 0 0 0 0 0 1
84 847.7 547.2 0 0 0 0 0 0 0 0 0 0 0
85 758.7 444.8 1 0 0 0 0 0 0 0 0 0 0
86 839.2 526.7 0 1 0 0 0 0 0 0 0 0 0
87 784.8 598.3 0 0 1 0 0 0 0 0 0 0 0
88 906.1 543.5 0 0 0 1 0 0 0 0 0 0 0
89 838.2 641.2 0 0 0 0 1 0 0 0 0 0 0
90 729.0 525.0 0 0 0 0 0 1 0 0 0 0 0
91 768.1 521.5 0 0 0 0 0 0 1 0 0 0 0
92 710.5 551.8 0 0 0 0 0 0 0 1 0 0 0
93 863.0 543.7 0 0 0 0 0 0 0 0 1 0 0
94 778.3 472.1 0 0 0 0 0 0 0 0 0 1 0
95 827.7 488.0 0 0 0 0 0 0 0 0 0 0 1
96 853.1 642.8 0 0 0 0 0 0 0 0 0 0 0
97 859.3 601.7 1 0 0 0 0 0 0 0 0 0 0
98 779.2 553.9 0 1 0 0 0 0 0 0 0 0 0
99 724.6 522.5 0 0 1 0 0 0 0 0 0 0 0
100 829.2 568.4 0 0 0 1 0 0 0 0 0 0 0
101 862.9 675.4 0 0 0 0 1 0 0 0 0 0 0
102 601.6 499.1 0 0 0 0 0 1 0 0 0 0 0
103 964.9 549.4 0 0 0 0 0 0 1 0 0 0 0
104 766.3 531.2 0 0 0 0 0 0 0 1 0 0 0
105 847.8 583.3 0 0 0 0 0 0 0 0 1 0 0
106 992.7 526.5 0 0 0 0 0 0 0 0 0 1 0
107 865.3 513.2 0 0 0 0 0 0 0 0 0 0 1
108 1054.1 729.1 0 0 0 0 0 0 0 0 0 0 0
109 972.5 753.7 1 0 0 0 0 0 0 0 0 0 0
110 857.4 571.7 0 1 0 0 0 0 0 0 0 0 0
111 1043.3 680.9 0 0 1 0 0 0 0 0 0 0 0
112 1061.0 757.6 0 0 0 1 0 0 0 0 0 0 0
113 989.4 805.4 0 0 0 0 1 0 0 0 0 0 0
114 963.2 687.7 0 0 0 0 0 1 0 0 0 0 0
115 1181.9 950.8 0 0 0 0 0 0 1 0 0 0 0
116 1256.4 1062.0 0 0 0 0 0 0 0 1 0 0 0
117 1492.7 1110.6 0 0 0 0 0 0 0 0 1 0 0
118 1360.8 1098.9 0 0 0 0 0 0 0 0 0 1 0
119 1342.8 1067.0 0 0 0 0 0 0 0 0 0 0 1
120 1464.0 1360.1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) xt M1 M2 M3 M4
169.8037 0.9829 53.0506 -0.2691 11.6519 65.9460
M5 M6 M7 M8 M9 M10
-11.0060 7.8579 30.8704 -12.3136 44.8369 68.1972
M11
-10.0189
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-564.60 -54.95 11.45 60.12 237.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 169.80367 52.29321 3.247 0.00156 **
xt 0.98288 0.05744 17.112 < 2e-16 ***
M1 53.05057 54.20138 0.979 0.32990
M2 -0.26915 54.06377 -0.005 0.99604
M3 11.65194 53.69827 0.217 0.82863
M4 65.94597 53.63133 1.230 0.22154
M5 -11.00604 53.44542 -0.206 0.83724
M6 7.85788 53.93860 0.146 0.88445
M7 30.87043 53.81168 0.574 0.56739
M8 -12.31358 53.52979 -0.230 0.81851
M9 44.83691 53.30921 0.841 0.40218
M10 68.19724 53.65466 1.271 0.20647
M11 -10.01886 53.32958 -0.188 0.85134
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 119.1 on 107 degrees of freedom
Multiple R-squared: 0.7459, Adjusted R-squared: 0.7174
F-statistic: 26.18 on 12 and 107 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,] 9.886082e-03 1.977216e-02 0.99011392
[2,] 2.112279e-03 4.224558e-03 0.99788772
[3,] 7.374830e-04 1.474966e-03 0.99926252
[4,] 4.912365e-04 9.824731e-04 0.99950876
[5,] 3.432214e-04 6.864428e-04 0.99965678
[6,] 1.540196e-04 3.080392e-04 0.99984598
[7,] 6.839042e-05 1.367808e-04 0.99993161
[8,] 1.897549e-05 3.795098e-05 0.99998102
[9,] 1.673668e-04 3.347335e-04 0.99983263
[10,] 4.754340e-04 9.508681e-04 0.99952457
[11,] 3.769726e-04 7.539451e-04 0.99962303
[12,] 2.057150e-04 4.114300e-04 0.99979429
[13,] 5.646665e-04 1.129333e-03 0.99943533
[14,] 7.776014e-04 1.555203e-03 0.99922240
[15,] 5.238651e-04 1.047730e-03 0.99947613
[16,] 2.814622e-03 5.629245e-03 0.99718538
[17,] 2.771000e-03 5.541999e-03 0.99722900
[18,] 3.787555e-03 7.575111e-03 0.99621244
[19,] 5.732741e-03 1.146548e-02 0.99426726
[20,] 4.289108e-03 8.578216e-03 0.99571089
[21,] 2.914008e-03 5.828017e-03 0.99708599
[22,] 3.760553e-03 7.521105e-03 0.99623945
[23,] 5.757451e-03 1.151490e-02 0.99424255
[24,] 8.191245e-03 1.638249e-02 0.99180875
[25,] 6.940636e-03 1.388127e-02 0.99305936
[26,] 5.975985e-03 1.195197e-02 0.99402401
[27,] 3.864547e-03 7.729094e-03 0.99613545
[28,] 5.459255e-03 1.091851e-02 0.99454074
[29,] 3.809115e-03 7.618230e-03 0.99619089
[30,] 4.228175e-03 8.456349e-03 0.99577183
[31,] 3.802193e-03 7.604386e-03 0.99619781
[32,] 2.498593e-03 4.997186e-03 0.99750141
[33,] 1.976640e-03 3.953280e-03 0.99802336
[34,] 3.150863e-03 6.301726e-03 0.99684914
[35,] 2.933856e-03 5.867711e-03 0.99706614
[36,] 2.592779e-03 5.185558e-03 0.99740722
[37,] 1.839924e-03 3.679849e-03 0.99816008
[38,] 1.385754e-03 2.771508e-03 0.99861425
[39,] 1.004279e-03 2.008558e-03 0.99899572
[40,] 1.036439e-03 2.072878e-03 0.99896356
[41,] 6.279257e-04 1.255851e-03 0.99937207
[42,] 5.100488e-04 1.020098e-03 0.99948995
[43,] 3.824774e-04 7.649548e-04 0.99961752
[44,] 2.919553e-04 5.839105e-04 0.99970804
[45,] 1.768323e-04 3.536645e-04 0.99982317
[46,] 1.492703e-04 2.985406e-04 0.99985073
[47,] 1.177815e-04 2.355630e-04 0.99988222
[48,] 8.340905e-05 1.668181e-04 0.99991659
[49,] 9.981521e-05 1.996304e-04 0.99990018
[50,] 5.888631e-05 1.177726e-04 0.99994111
[51,] 3.457835e-05 6.915669e-05 0.99996542
[52,] 1.596214e-04 3.192429e-04 0.99984038
[53,] 3.464691e-04 6.929382e-04 0.99965353
[54,] 9.815887e-04 1.963177e-03 0.99901841
[55,] 2.577460e-03 5.154920e-03 0.99742254
[56,] 8.121355e-01 3.757291e-01 0.18786453
[57,] 9.861508e-01 2.769842e-02 0.01384921
[58,] 9.834558e-01 3.308836e-02 0.01654418
[59,] 9.871150e-01 2.576995e-02 0.01288498
[60,] 9.878713e-01 2.425738e-02 0.01212869
[61,] 9.885725e-01 2.285505e-02 0.01142752
[62,] 9.849127e-01 3.017468e-02 0.01508734
[63,] 9.812301e-01 3.753975e-02 0.01876988
[64,] 9.814131e-01 3.717371e-02 0.01858686
[65,] 9.758219e-01 4.835629e-02 0.02417814
[66,] 9.782545e-01 4.349103e-02 0.02174551
[67,] 9.761907e-01 4.761859e-02 0.02380929
[68,] 9.784645e-01 4.307108e-02 0.02153554
[69,] 9.776011e-01 4.479783e-02 0.02239891
[70,] 9.722910e-01 5.541797e-02 0.02770898
[71,] 9.715955e-01 5.680893e-02 0.02840447
[72,] 9.687553e-01 6.248940e-02 0.03124470
[73,] 9.660665e-01 6.786697e-02 0.03393349
[74,] 9.507133e-01 9.857345e-02 0.04928672
[75,] 9.276717e-01 1.446566e-01 0.07232828
[76,] 9.222863e-01 1.554275e-01 0.07771375
[77,] 9.044963e-01 1.910074e-01 0.09550369
[78,] 8.779149e-01 2.441702e-01 0.12208510
[79,] 8.683151e-01 2.633697e-01 0.13168486
[80,] 8.411004e-01 3.177993e-01 0.15889963
[81,] 7.965359e-01 4.069282e-01 0.20346410
[82,] 7.253290e-01 5.493419e-01 0.27467096
[83,] 6.570008e-01 6.859985e-01 0.34299923
[84,] 7.067566e-01 5.864868e-01 0.29324340
[85,] 6.283758e-01 7.432485e-01 0.37162424
[86,] 5.131755e-01 9.736491e-01 0.48682454
[87,] 6.224176e-01 7.551647e-01 0.37758237
[88,] 5.919574e-01 8.160853e-01 0.40804265
[89,] 4.423954e-01 8.847907e-01 0.55760463
> postscript(file="/var/www/html/rcomp/tmp/1nazs1259328404.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2k9b11259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3r63c1259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zcsq1259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xhbm1259328404.ps",horizontal=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 = 120
Frequency = 1
1 2 3 4 5 6
-92.3385710 -73.0123799 -38.2869704 -101.0165791 -42.0286660 -3.6521899
7 8 9 10 11 12
-58.1854072 -40.3806297 -68.4523606 -78.3616003 24.6253491 -14.6126699
13 14 15 16 17 18
-97.1690671 -203.5606135 -287.8092964 -102.0152876 -177.6055858 -121.9951405
19 20 21 22 23 24
-164.3473381 -69.9342250 -175.9516361 -157.3488419 -126.8335781 34.9885240
25 26 27 28 29 30
-15.2961876 -35.7121058 -23.6760028 -32.0376434 15.6476378 32.5231452
31 32 33 34 35 36
14.7834808 30.6661468 -14.4974439 -20.3639284 9.0199683 -18.5627134
37 38 39 40 41 42
-22.6762577 10.0112973 15.8025124 -44.4782849 6.6892289 -53.8671002
43 44 45 46 47 48
68.6046237 11.8994999 39.6990341 18.6381555 -34.2500051 -79.1144988
49 50 51 52 53 54
101.6415586 36.0068066 21.5667822 -41.7625525 34.7881625 44.4322052
55 56 57 58 59 60
-111.8074103 3.9528021 -93.2961714 -63.3284913 59.9192737 -3.2183926
61 62 63 64 65 66
-88.2835269 -77.2888493 -21.0808355 83.8958862 11.0088359 -36.5015388
67 68 69 70 71 72
-205.9882239 -173.4501718 -198.4255224 -183.9106453 -564.6032859 -236.1107707
73 74 75 76 77 78
61.5624274 0.3693642 96.0026398 -14.1454798 23.0612183 60.7365005
79 80 81 82 83 84
131.1477995 84.7909880 150.6345200 128.4655012 108.4657761 140.0652782
85 86 87 88 89 90
98.6614587 151.9834333 15.2882479 136.1559584 49.1807467 35.3272939
91 92 93 94 95 96
54.8548244 10.6576157 113.9684404 76.2822022 188.2705408 51.5021045
97 98 99 100 101 102
45.0478401 65.2491412 29.5904295 34.7822866 40.2663059 -66.6161559
103 104 105 106 107 108
224.2325175 86.7049105 59.8464563 237.2136181 201.1020055 167.6796999
109 110 111 112 113 114
8.8503255 125.9539059 192.6024933 80.6216961 38.9921158 109.6129806
115 116 117 118 119 120
46.7051336 55.0930635 186.4746837 42.7140303 134.2839557 -42.6165613
> postscript(file="/var/www/html/rcomp/tmp/6n8qw1259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 -92.3385710 NA
1 -73.0123799 -92.3385710
2 -38.2869704 -73.0123799
3 -101.0165791 -38.2869704
4 -42.0286660 -101.0165791
5 -3.6521899 -42.0286660
6 -58.1854072 -3.6521899
7 -40.3806297 -58.1854072
8 -68.4523606 -40.3806297
9 -78.3616003 -68.4523606
10 24.6253491 -78.3616003
11 -14.6126699 24.6253491
12 -97.1690671 -14.6126699
13 -203.5606135 -97.1690671
14 -287.8092964 -203.5606135
15 -102.0152876 -287.8092964
16 -177.6055858 -102.0152876
17 -121.9951405 -177.6055858
18 -164.3473381 -121.9951405
19 -69.9342250 -164.3473381
20 -175.9516361 -69.9342250
21 -157.3488419 -175.9516361
22 -126.8335781 -157.3488419
23 34.9885240 -126.8335781
24 -15.2961876 34.9885240
25 -35.7121058 -15.2961876
26 -23.6760028 -35.7121058
27 -32.0376434 -23.6760028
28 15.6476378 -32.0376434
29 32.5231452 15.6476378
30 14.7834808 32.5231452
31 30.6661468 14.7834808
32 -14.4974439 30.6661468
33 -20.3639284 -14.4974439
34 9.0199683 -20.3639284
35 -18.5627134 9.0199683
36 -22.6762577 -18.5627134
37 10.0112973 -22.6762577
38 15.8025124 10.0112973
39 -44.4782849 15.8025124
40 6.6892289 -44.4782849
41 -53.8671002 6.6892289
42 68.6046237 -53.8671002
43 11.8994999 68.6046237
44 39.6990341 11.8994999
45 18.6381555 39.6990341
46 -34.2500051 18.6381555
47 -79.1144988 -34.2500051
48 101.6415586 -79.1144988
49 36.0068066 101.6415586
50 21.5667822 36.0068066
51 -41.7625525 21.5667822
52 34.7881625 -41.7625525
53 44.4322052 34.7881625
54 -111.8074103 44.4322052
55 3.9528021 -111.8074103
56 -93.2961714 3.9528021
57 -63.3284913 -93.2961714
58 59.9192737 -63.3284913
59 -3.2183926 59.9192737
60 -88.2835269 -3.2183926
61 -77.2888493 -88.2835269
62 -21.0808355 -77.2888493
63 83.8958862 -21.0808355
64 11.0088359 83.8958862
65 -36.5015388 11.0088359
66 -205.9882239 -36.5015388
67 -173.4501718 -205.9882239
68 -198.4255224 -173.4501718
69 -183.9106453 -198.4255224
70 -564.6032859 -183.9106453
71 -236.1107707 -564.6032859
72 61.5624274 -236.1107707
73 0.3693642 61.5624274
74 96.0026398 0.3693642
75 -14.1454798 96.0026398
76 23.0612183 -14.1454798
77 60.7365005 23.0612183
78 131.1477995 60.7365005
79 84.7909880 131.1477995
80 150.6345200 84.7909880
81 128.4655012 150.6345200
82 108.4657761 128.4655012
83 140.0652782 108.4657761
84 98.6614587 140.0652782
85 151.9834333 98.6614587
86 15.2882479 151.9834333
87 136.1559584 15.2882479
88 49.1807467 136.1559584
89 35.3272939 49.1807467
90 54.8548244 35.3272939
91 10.6576157 54.8548244
92 113.9684404 10.6576157
93 76.2822022 113.9684404
94 188.2705408 76.2822022
95 51.5021045 188.2705408
96 45.0478401 51.5021045
97 65.2491412 45.0478401
98 29.5904295 65.2491412
99 34.7822866 29.5904295
100 40.2663059 34.7822866
101 -66.6161559 40.2663059
102 224.2325175 -66.6161559
103 86.7049105 224.2325175
104 59.8464563 86.7049105
105 237.2136181 59.8464563
106 201.1020055 237.2136181
107 167.6796999 201.1020055
108 8.8503255 167.6796999
109 125.9539059 8.8503255
110 192.6024933 125.9539059
111 80.6216961 192.6024933
112 38.9921158 80.6216961
113 109.6129806 38.9921158
114 46.7051336 109.6129806
115 55.0930635 46.7051336
116 186.4746837 55.0930635
117 42.7140303 186.4746837
118 134.2839557 42.7140303
119 -42.6165613 134.2839557
120 NA -42.6165613
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -73.0123799 -92.3385710
[2,] -38.2869704 -73.0123799
[3,] -101.0165791 -38.2869704
[4,] -42.0286660 -101.0165791
[5,] -3.6521899 -42.0286660
[6,] -58.1854072 -3.6521899
[7,] -40.3806297 -58.1854072
[8,] -68.4523606 -40.3806297
[9,] -78.3616003 -68.4523606
[10,] 24.6253491 -78.3616003
[11,] -14.6126699 24.6253491
[12,] -97.1690671 -14.6126699
[13,] -203.5606135 -97.1690671
[14,] -287.8092964 -203.5606135
[15,] -102.0152876 -287.8092964
[16,] -177.6055858 -102.0152876
[17,] -121.9951405 -177.6055858
[18,] -164.3473381 -121.9951405
[19,] -69.9342250 -164.3473381
[20,] -175.9516361 -69.9342250
[21,] -157.3488419 -175.9516361
[22,] -126.8335781 -157.3488419
[23,] 34.9885240 -126.8335781
[24,] -15.2961876 34.9885240
[25,] -35.7121058 -15.2961876
[26,] -23.6760028 -35.7121058
[27,] -32.0376434 -23.6760028
[28,] 15.6476378 -32.0376434
[29,] 32.5231452 15.6476378
[30,] 14.7834808 32.5231452
[31,] 30.6661468 14.7834808
[32,] -14.4974439 30.6661468
[33,] -20.3639284 -14.4974439
[34,] 9.0199683 -20.3639284
[35,] -18.5627134 9.0199683
[36,] -22.6762577 -18.5627134
[37,] 10.0112973 -22.6762577
[38,] 15.8025124 10.0112973
[39,] -44.4782849 15.8025124
[40,] 6.6892289 -44.4782849
[41,] -53.8671002 6.6892289
[42,] 68.6046237 -53.8671002
[43,] 11.8994999 68.6046237
[44,] 39.6990341 11.8994999
[45,] 18.6381555 39.6990341
[46,] -34.2500051 18.6381555
[47,] -79.1144988 -34.2500051
[48,] 101.6415586 -79.1144988
[49,] 36.0068066 101.6415586
[50,] 21.5667822 36.0068066
[51,] -41.7625525 21.5667822
[52,] 34.7881625 -41.7625525
[53,] 44.4322052 34.7881625
[54,] -111.8074103 44.4322052
[55,] 3.9528021 -111.8074103
[56,] -93.2961714 3.9528021
[57,] -63.3284913 -93.2961714
[58,] 59.9192737 -63.3284913
[59,] -3.2183926 59.9192737
[60,] -88.2835269 -3.2183926
[61,] -77.2888493 -88.2835269
[62,] -21.0808355 -77.2888493
[63,] 83.8958862 -21.0808355
[64,] 11.0088359 83.8958862
[65,] -36.5015388 11.0088359
[66,] -205.9882239 -36.5015388
[67,] -173.4501718 -205.9882239
[68,] -198.4255224 -173.4501718
[69,] -183.9106453 -198.4255224
[70,] -564.6032859 -183.9106453
[71,] -236.1107707 -564.6032859
[72,] 61.5624274 -236.1107707
[73,] 0.3693642 61.5624274
[74,] 96.0026398 0.3693642
[75,] -14.1454798 96.0026398
[76,] 23.0612183 -14.1454798
[77,] 60.7365005 23.0612183
[78,] 131.1477995 60.7365005
[79,] 84.7909880 131.1477995
[80,] 150.6345200 84.7909880
[81,] 128.4655012 150.6345200
[82,] 108.4657761 128.4655012
[83,] 140.0652782 108.4657761
[84,] 98.6614587 140.0652782
[85,] 151.9834333 98.6614587
[86,] 15.2882479 151.9834333
[87,] 136.1559584 15.2882479
[88,] 49.1807467 136.1559584
[89,] 35.3272939 49.1807467
[90,] 54.8548244 35.3272939
[91,] 10.6576157 54.8548244
[92,] 113.9684404 10.6576157
[93,] 76.2822022 113.9684404
[94,] 188.2705408 76.2822022
[95,] 51.5021045 188.2705408
[96,] 45.0478401 51.5021045
[97,] 65.2491412 45.0478401
[98,] 29.5904295 65.2491412
[99,] 34.7822866 29.5904295
[100,] 40.2663059 34.7822866
[101,] -66.6161559 40.2663059
[102,] 224.2325175 -66.6161559
[103,] 86.7049105 224.2325175
[104,] 59.8464563 86.7049105
[105,] 237.2136181 59.8464563
[106,] 201.1020055 237.2136181
[107,] 167.6796999 201.1020055
[108,] 8.8503255 167.6796999
[109,] 125.9539059 8.8503255
[110,] 192.6024933 125.9539059
[111,] 80.6216961 192.6024933
[112,] 38.9921158 80.6216961
[113,] 109.6129806 38.9921158
[114,] 46.7051336 109.6129806
[115,] 55.0930635 46.7051336
[116,] 186.4746837 55.0930635
[117,] 42.7140303 186.4746837
[118,] 134.2839557 42.7140303
[119,] -42.6165613 134.2839557
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -73.0123799 -92.3385710
2 -38.2869704 -73.0123799
3 -101.0165791 -38.2869704
4 -42.0286660 -101.0165791
5 -3.6521899 -42.0286660
6 -58.1854072 -3.6521899
7 -40.3806297 -58.1854072
8 -68.4523606 -40.3806297
9 -78.3616003 -68.4523606
10 24.6253491 -78.3616003
11 -14.6126699 24.6253491
12 -97.1690671 -14.6126699
13 -203.5606135 -97.1690671
14 -287.8092964 -203.5606135
15 -102.0152876 -287.8092964
16 -177.6055858 -102.0152876
17 -121.9951405 -177.6055858
18 -164.3473381 -121.9951405
19 -69.9342250 -164.3473381
20 -175.9516361 -69.9342250
21 -157.3488419 -175.9516361
22 -126.8335781 -157.3488419
23 34.9885240 -126.8335781
24 -15.2961876 34.9885240
25 -35.7121058 -15.2961876
26 -23.6760028 -35.7121058
27 -32.0376434 -23.6760028
28 15.6476378 -32.0376434
29 32.5231452 15.6476378
30 14.7834808 32.5231452
31 30.6661468 14.7834808
32 -14.4974439 30.6661468
33 -20.3639284 -14.4974439
34 9.0199683 -20.3639284
35 -18.5627134 9.0199683
36 -22.6762577 -18.5627134
37 10.0112973 -22.6762577
38 15.8025124 10.0112973
39 -44.4782849 15.8025124
40 6.6892289 -44.4782849
41 -53.8671002 6.6892289
42 68.6046237 -53.8671002
43 11.8994999 68.6046237
44 39.6990341 11.8994999
45 18.6381555 39.6990341
46 -34.2500051 18.6381555
47 -79.1144988 -34.2500051
48 101.6415586 -79.1144988
49 36.0068066 101.6415586
50 21.5667822 36.0068066
51 -41.7625525 21.5667822
52 34.7881625 -41.7625525
53 44.4322052 34.7881625
54 -111.8074103 44.4322052
55 3.9528021 -111.8074103
56 -93.2961714 3.9528021
57 -63.3284913 -93.2961714
58 59.9192737 -63.3284913
59 -3.2183926 59.9192737
60 -88.2835269 -3.2183926
61 -77.2888493 -88.2835269
62 -21.0808355 -77.2888493
63 83.8958862 -21.0808355
64 11.0088359 83.8958862
65 -36.5015388 11.0088359
66 -205.9882239 -36.5015388
67 -173.4501718 -205.9882239
68 -198.4255224 -173.4501718
69 -183.9106453 -198.4255224
70 -564.6032859 -183.9106453
71 -236.1107707 -564.6032859
72 61.5624274 -236.1107707
73 0.3693642 61.5624274
74 96.0026398 0.3693642
75 -14.1454798 96.0026398
76 23.0612183 -14.1454798
77 60.7365005 23.0612183
78 131.1477995 60.7365005
79 84.7909880 131.1477995
80 150.6345200 84.7909880
81 128.4655012 150.6345200
82 108.4657761 128.4655012
83 140.0652782 108.4657761
84 98.6614587 140.0652782
85 151.9834333 98.6614587
86 15.2882479 151.9834333
87 136.1559584 15.2882479
88 49.1807467 136.1559584
89 35.3272939 49.1807467
90 54.8548244 35.3272939
91 10.6576157 54.8548244
92 113.9684404 10.6576157
93 76.2822022 113.9684404
94 188.2705408 76.2822022
95 51.5021045 188.2705408
96 45.0478401 51.5021045
97 65.2491412 45.0478401
98 29.5904295 65.2491412
99 34.7822866 29.5904295
100 40.2663059 34.7822866
101 -66.6161559 40.2663059
102 224.2325175 -66.6161559
103 86.7049105 224.2325175
104 59.8464563 86.7049105
105 237.2136181 59.8464563
106 201.1020055 237.2136181
107 167.6796999 201.1020055
108 8.8503255 167.6796999
109 125.9539059 8.8503255
110 192.6024933 125.9539059
111 80.6216961 192.6024933
112 38.9921158 80.6216961
113 109.6129806 38.9921158
114 46.7051336 109.6129806
115 55.0930635 46.7051336
116 186.4746837 55.0930635
117 42.7140303 186.4746837
118 134.2839557 42.7140303
119 -42.6165613 134.2839557
> 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/www/html/rcomp/tmp/7jhax1259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8apy61259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95d711259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100lb71259328404.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11simw1259328404.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/www/html/rcomp/tmp/12qshk1259328404.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/www/html/rcomp/tmp/13fylh1259328404.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/www/html/rcomp/tmp/14ppl91259328404.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/www/html/rcomp/tmp/15eqip1259328404.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/www/html/rcomp/tmp/16xckt1259328404.tab")
+ }
> system("convert tmp/1nazs1259328404.ps tmp/1nazs1259328404.png")
> system("convert tmp/2k9b11259328404.ps tmp/2k9b11259328404.png")
> system("convert tmp/3r63c1259328404.ps tmp/3r63c1259328404.png")
> system("convert tmp/4zcsq1259328404.ps tmp/4zcsq1259328404.png")
> system("convert tmp/5xhbm1259328404.ps tmp/5xhbm1259328404.png")
> system("convert tmp/6n8qw1259328404.ps tmp/6n8qw1259328404.png")
> system("convert tmp/7jhax1259328404.ps tmp/7jhax1259328404.png")
> system("convert tmp/8apy61259328404.ps tmp/8apy61259328404.png")
> system("convert tmp/95d711259328404.ps tmp/95d711259328404.png")
> system("convert tmp/100lb71259328404.ps tmp/100lb71259328404.png")
>
>
> proc.time()
user system elapsed
3.247 1.693 3.769