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(621
+ ,0
+ ,0
+ ,587
+ ,0
+ ,0
+ ,655
+ ,0
+ ,0
+ ,517
+ ,0
+ ,0
+ ,646
+ ,0
+ ,0
+ ,657
+ ,0
+ ,0
+ ,382
+ ,0
+ ,0
+ ,345
+ ,0
+ ,0
+ ,625
+ ,0
+ ,0
+ ,654
+ ,0
+ ,0
+ ,606
+ ,0
+ ,0
+ ,510
+ ,0
+ ,0
+ ,614
+ ,0
+ ,0
+ ,647
+ ,0
+ ,0
+ ,580
+ ,0
+ ,0
+ ,614
+ ,0
+ ,0
+ ,636
+ ,0
+ ,0
+ ,388
+ ,0
+ ,0
+ ,356
+ ,0
+ ,0
+ ,639
+ ,0
+ ,0
+ ,753
+ ,0
+ ,0
+ ,611
+ ,0
+ ,0
+ ,639
+ ,0
+ ,0
+ ,630
+ ,0
+ ,0
+ ,586
+ ,0
+ ,0
+ ,695
+ ,0
+ ,0
+ ,552
+ ,0
+ ,0
+ ,619
+ ,0
+ ,0
+ ,681
+ ,0
+ ,0
+ ,421
+ ,0
+ ,0
+ ,307
+ ,0
+ ,0
+ ,754
+ ,0
+ ,0
+ ,690
+ ,0
+ ,0
+ ,644
+ ,0
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+ ,643
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+ ,651
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+ ,0
+ ,627
+ ,0
+ ,0
+ ,634
+ ,0
+ ,0
+ ,731
+ ,0
+ ,0
+ ,475
+ ,0
+ ,0
+ ,337
+ ,0
+ ,0
+ ,803
+ ,0
+ ,0
+ ,722
+ ,0
+ ,0
+ ,590
+ ,0
+ ,0
+ ,724
+ ,0
+ ,0
+ ,627
+ ,0
+ ,0
+ ,696
+ ,0
+ ,0
+ ,825
+ ,0
+ ,0
+ ,677
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+ ,729
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+ ,695
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+ ,638
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+ ,1
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+ ,1
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+ ,1
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+ ,1
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+ ,858
+ ,1
+ ,0
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+ ,1
+ ,0
+ ,785
+ ,1
+ ,0
+ ,1006
+ ,1
+ ,0
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+ ,1
+ ,0
+ ,734
+ ,1
+ ,0
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+ ,1
+ ,0
+ ,532
+ ,1
+ ,0
+ ,387
+ ,1
+ ,0
+ ,991
+ ,1
+ ,1
+ ,841
+ ,1
+ ,1
+ ,892
+ ,1
+ ,1
+ ,782
+ ,1
+ ,1
+ ,813
+ ,1
+ ,1
+ ,793
+ ,1
+ ,1
+ ,978
+ ,1
+ ,1
+ ,775
+ ,1
+ ,1
+ ,797
+ ,1
+ ,1
+ ,946
+ ,1
+ ,1
+ ,594
+ ,1
+ ,1
+ ,438
+ ,1
+ ,1
+ ,1022
+ ,1
+ ,1
+ ,868
+ ,1
+ ,1
+ ,795
+ ,1
+ ,1)
+ ,dim=c(3
+ ,130)
+ ,dimnames=list(c('Y'
+ ,'X1'
+ ,'X2')
+ ,1:130))
> y <- array(NA,dim=c(3,130),dimnames=list(c('Y','X1','X2'),1:130))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Y X1 X2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 621 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 587 0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 655 0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 517 0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 646 0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 657 0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 382 0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 345 0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 625 0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 654 0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 606 0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 510 0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 614 0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 647 0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 580 0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 614 0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 636 0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 388 0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 356 0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 639 0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 753 0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 611 0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 639 0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 630 0 0 0 0 0 0 0 0 0 0 0 0 0 24
25 586 0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 695 0 0 0 1 0 0 0 0 0 0 0 0 0 26
27 552 0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 619 0 0 0 0 0 1 0 0 0 0 0 0 0 28
29 681 0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 421 0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 307 0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 754 0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 690 0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 644 0 0 0 0 0 0 0 0 0 0 0 1 0 34
35 643 0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 608 0 0 0 0 0 0 0 0 0 0 0 0 0 36
37 651 0 0 1 0 0 0 0 0 0 0 0 0 0 37
38 691 0 0 0 1 0 0 0 0 0 0 0 0 0 38
39 627 0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 634 0 0 0 0 0 1 0 0 0 0 0 0 0 40
41 731 0 0 0 0 0 0 1 0 0 0 0 0 0 41
42 475 0 0 0 0 0 0 0 1 0 0 0 0 0 42
43 337 0 0 0 0 0 0 0 0 1 0 0 0 0 43
44 803 0 0 0 0 0 0 0 0 0 1 0 0 0 44
45 722 0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 590 0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 724 0 0 0 0 0 0 0 0 0 0 0 0 1 47
48 627 0 0 0 0 0 0 0 0 0 0 0 0 0 48
49 696 0 0 1 0 0 0 0 0 0 0 0 0 0 49
50 825 0 0 0 1 0 0 0 0 0 0 0 0 0 50
51 677 0 0 0 0 1 0 0 0 0 0 0 0 0 51
52 656 0 0 0 0 0 1 0 0 0 0 0 0 0 52
53 785 0 0 0 0 0 0 1 0 0 0 0 0 0 53
54 412 0 0 0 0 0 0 0 1 0 0 0 0 0 54
55 352 0 0 0 0 0 0 0 0 1 0 0 0 0 55
56 839 0 0 0 0 0 0 0 0 0 1 0 0 0 56
57 729 0 0 0 0 0 0 0 0 0 0 1 0 0 57
58 696 0 0 0 0 0 0 0 0 0 0 0 1 0 58
59 641 0 0 0 0 0 0 0 0 0 0 0 0 1 59
60 695 0 0 0 0 0 0 0 0 0 0 0 0 0 60
61 638 0 0 1 0 0 0 0 0 0 0 0 0 0 61
62 762 0 0 0 1 0 0 0 0 0 0 0 0 0 62
63 635 0 0 0 0 1 0 0 0 0 0 0 0 0 63
64 721 0 0 0 0 0 1 0 0 0 0 0 0 0 64
65 854 0 0 0 0 0 0 1 0 0 0 0 0 0 65
66 418 0 0 0 0 0 0 0 1 0 0 0 0 0 66
67 367 0 0 0 0 0 0 0 0 1 0 0 0 0 67
68 824 0 0 0 0 0 0 0 0 0 1 0 0 0 68
69 687 0 0 0 0 0 0 0 0 0 0 1 0 0 69
70 601 0 0 0 0 0 0 0 0 0 0 0 1 0 70
71 676 0 0 0 0 0 0 0 0 0 0 0 0 1 71
72 740 0 0 0 0 0 0 0 0 0 0 0 0 0 72
73 691 0 0 1 0 0 0 0 0 0 0 0 0 0 73
74 683 0 0 0 1 0 0 0 0 0 0 0 0 0 74
75 594 0 0 0 0 1 0 0 0 0 0 0 0 0 75
76 729 0 0 0 0 0 1 0 0 0 0 0 0 0 76
77 731 0 0 0 0 0 0 1 0 0 0 0 0 0 77
78 386 0 0 0 0 0 0 0 1 0 0 0 0 0 78
79 331 0 0 0 0 0 0 0 0 1 0 0 0 0 79
80 706 0 0 0 0 0 0 0 0 0 1 0 0 0 80
81 715 0 0 0 0 0 0 0 0 0 0 1 0 0 81
82 657 0 0 0 0 0 0 0 0 0 0 0 1 0 82
83 653 0 0 0 0 0 0 0 0 0 0 0 0 1 83
84 642 0 0 0 0 0 0 0 0 0 0 0 0 0 84
85 643 0 0 1 0 0 0 0 0 0 0 0 0 0 85
86 718 0 0 0 1 0 0 0 0 0 0 0 0 0 86
87 654 0 0 0 0 1 0 0 0 0 0 0 0 0 87
88 632 0 0 0 0 0 1 0 0 0 0 0 0 0 88
89 731 0 0 0 0 0 0 1 0 0 0 0 0 0 89
90 392 0 0 0 0 0 0 0 1 0 0 0 0 0 90
91 344 0 0 0 0 0 0 0 0 1 0 0 0 0 91
92 792 0 0 0 0 0 0 0 0 0 1 0 0 0 92
93 852 0 0 0 0 0 0 0 0 0 0 1 0 0 93
94 649 0 0 0 0 0 0 0 0 0 0 0 1 0 94
95 629 0 0 0 0 0 0 0 0 0 0 0 0 1 95
96 685 0 0 0 0 0 0 0 0 0 0 0 0 0 96
97 617 0 0 1 0 0 0 0 0 0 0 0 0 0 97
98 715 0 0 0 1 0 0 0 0 0 0 0 0 0 98
99 715 0 0 0 0 1 0 0 0 0 0 0 0 0 99
100 629 0 0 0 0 0 1 0 0 0 0 0 0 0 100
101 916 0 0 0 0 0 0 1 0 0 0 0 0 0 101
102 531 1 0 0 0 0 0 0 1 0 0 0 0 0 102
103 357 1 0 0 0 0 0 0 0 1 0 0 0 0 103
104 917 1 0 0 0 0 0 0 0 0 1 0 0 0 104
105 828 1 0 0 0 0 0 0 0 0 0 1 0 0 105
106 708 1 0 0 0 0 0 0 0 0 0 0 1 0 106
107 858 1 0 0 0 0 0 0 0 0 0 0 0 1 107
108 775 1 0 0 0 0 0 0 0 0 0 0 0 0 108
109 785 1 0 1 0 0 0 0 0 0 0 0 0 0 109
110 1006 1 0 0 1 0 0 0 0 0 0 0 0 0 110
111 789 1 0 0 0 1 0 0 0 0 0 0 0 0 111
112 734 1 0 0 0 0 1 0 0 0 0 0 0 0 112
113 906 1 0 0 0 0 0 1 0 0 0 0 0 0 113
114 532 1 0 0 0 0 0 0 1 0 0 0 0 0 114
115 387 1 0 0 0 0 0 0 0 1 0 0 0 0 115
116 991 1 1 0 0 0 0 0 0 0 1 0 0 0 116
117 841 1 1 0 0 0 0 0 0 0 0 1 0 0 117
118 892 1 1 0 0 0 0 0 0 0 0 0 1 0 118
119 782 1 1 0 0 0 0 0 0 0 0 0 0 1 119
120 813 1 1 0 0 0 0 0 0 0 0 0 0 0 120
121 793 1 1 1 0 0 0 0 0 0 0 0 0 0 121
122 978 1 1 0 1 0 0 0 0 0 0 0 0 0 122
123 775 1 1 0 0 1 0 0 0 0 0 0 0 0 123
124 797 1 1 0 0 0 1 0 0 0 0 0 0 0 124
125 946 1 1 0 0 0 0 1 0 0 0 0 0 0 125
126 594 1 1 0 0 0 0 0 1 0 0 0 0 0 126
127 438 1 1 0 0 0 0 0 0 1 0 0 0 0 127
128 1022 1 1 0 0 0 0 0 0 0 1 0 0 0 128
129 868 1 1 0 0 0 0 0 0 0 0 1 0 0 129
130 795 1 1 0 0 0 0 0 0 0 0 0 1 0 130
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 M1 M2 M3
592.9122 79.5172 36.6462 0.6440 88.0983 -8.6293
M4 M5 M6 M7 M8 M9
-6.9023 108.6428 -204.6772 -319.0411 101.6271 71.4450
M10 M11 t
-3.3735 13.5094 0.9094
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-356.814 -38.892 -6.842 30.884 263.309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 592.9122 24.9353 23.778 < 2e-16 ***
X1 79.5172 23.6513 3.362 0.001051 **
X2 36.6462 25.6840 1.427 0.156345
M1 0.6440 29.7285 0.022 0.982753
M2 88.0983 29.7225 2.964 0.003692 **
M3 -8.6293 29.7183 -0.290 0.772057
M4 -6.9023 29.7159 -0.232 0.816736
M5 108.6428 29.7154 3.656 0.000388 ***
M6 -204.6772 29.7713 -6.875 3.40e-10 ***
M7 -319.0411 29.7648 -10.719 < 2e-16 ***
M8 101.6271 29.7643 3.414 0.000884 ***
M9 71.4450 29.7591 2.401 0.017964 *
M10 -3.3735 29.7557 -0.113 0.909932
M11 13.5094 30.4143 0.444 0.657748
t 0.9094 0.2318 3.924 0.000149 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 68.01 on 115 degrees of freedom
Multiple R-squared: 0.8278, Adjusted R-squared: 0.8068
F-statistic: 39.48 on 14 and 115 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,] 0.9985884 2.823267e-03 1.411633e-03
[2,] 0.9965767 6.846606e-03 3.423303e-03
[3,] 0.9999951 9.812307e-06 4.906153e-06
[4,] 0.9999947 1.056106e-05 5.280529e-06
[5,] 0.9999885 2.307688e-05 1.153844e-05
[6,] 0.9999707 5.855470e-05 2.927735e-05
[7,] 0.9999612 7.756707e-05 3.878353e-05
[8,] 0.9999404 1.191362e-04 5.956809e-05
[9,] 0.9999023 1.953523e-04 9.767617e-05
[10,] 0.9999179 1.641151e-04 8.205757e-05
[11,] 0.9998459 3.082575e-04 1.541287e-04
[12,] 0.9998149 3.701111e-04 1.850555e-04
[13,] 0.9998643 2.714545e-04 1.357273e-04
[14,] 0.9998091 3.818127e-04 1.909064e-04
[15,] 0.9999922 1.566910e-05 7.834550e-06
[16,] 0.9999851 2.976110e-05 1.488055e-05
[17,] 0.9999701 5.983492e-05 2.991746e-05
[18,] 0.9999430 1.139704e-04 5.698521e-05
[19,] 0.9999143 1.713887e-04 8.569435e-05
[20,] 0.9998412 3.175974e-04 1.587987e-04
[21,] 0.9998112 3.776424e-04 1.888212e-04
[22,] 0.9996793 6.413518e-04 3.206759e-04
[23,] 0.9994800 1.039920e-03 5.199599e-04
[24,] 0.9994206 1.158795e-03 5.793976e-04
[25,] 0.9992790 1.442049e-03 7.210247e-04
[26,] 0.9989323 2.135301e-03 1.067651e-03
[27,] 0.9996703 6.593167e-04 3.296583e-04
[28,] 0.9994457 1.108523e-03 5.542616e-04
[29,] 0.9995250 9.499146e-04 4.749573e-04
[30,] 0.9994102 1.179598e-03 5.897992e-04
[31,] 0.9992317 1.536601e-03 7.683006e-04
[32,] 0.9988655 2.268915e-03 1.134457e-03
[33,] 0.9990962 1.807615e-03 9.038076e-04
[34,] 0.9985863 2.827405e-03 1.413702e-03
[35,] 0.9977700 4.460029e-03 2.230014e-03
[36,] 0.9969124 6.175169e-03 3.087584e-03
[37,] 0.9976205 4.759013e-03 2.379507e-03
[38,] 0.9969256 6.148765e-03 3.074382e-03
[39,] 0.9978590 4.282054e-03 2.141027e-03
[40,] 0.9967721 6.455796e-03 3.227898e-03
[41,] 0.9957341 8.531896e-03 4.265948e-03
[42,] 0.9946758 1.064836e-02 5.324179e-03
[43,] 0.9925917 1.481654e-02 7.408268e-03
[44,] 0.9904906 1.901887e-02 9.509437e-03
[45,] 0.9861812 2.763758e-02 1.381879e-02
[46,] 0.9817861 3.642772e-02 1.821386e-02
[47,] 0.9809662 3.806754e-02 1.903377e-02
[48,] 0.9827848 3.443035e-02 1.721518e-02
[49,] 0.9836330 3.273409e-02 1.636704e-02
[50,] 0.9854313 2.913737e-02 1.456868e-02
[51,] 0.9841080 3.178395e-02 1.589198e-02
[52,] 0.9812677 3.746466e-02 1.873233e-02
[53,] 0.9798772 4.024569e-02 2.012285e-02
[54,] 0.9736327 5.273465e-02 2.636733e-02
[55,] 0.9796998 4.060044e-02 2.030022e-02
[56,] 0.9797247 4.055059e-02 2.027530e-02
[57,] 0.9809266 3.814678e-02 1.907339e-02
[58,] 0.9793555 4.128908e-02 2.064454e-02
[59,] 0.9896872 2.062554e-02 1.031277e-02
[60,] 0.9867551 2.648979e-02 1.324490e-02
[61,] 0.9866018 2.679643e-02 1.339822e-02
[62,] 0.9884115 2.317697e-02 1.158848e-02
[63,] 0.9906270 1.874600e-02 9.372999e-03
[64,] 0.9862579 2.748420e-02 1.374210e-02
[65,] 0.9807602 3.847955e-02 1.923978e-02
[66,] 0.9732665 5.346694e-02 2.673347e-02
[67,] 0.9627525 7.449508e-02 3.724754e-02
[68,] 0.9510665 9.786695e-02 4.893348e-02
[69,] 0.9489218 1.021564e-01 5.107822e-02
[70,] 0.9293414 1.413173e-01 7.065864e-02
[71,] 0.9093013 1.813974e-01 9.069869e-02
[72,] 0.9201682 1.596636e-01 7.983178e-02
[73,] 0.9123012 1.753977e-01 8.769883e-02
[74,] 0.9091403 1.817195e-01 9.085974e-02
[75,] 0.8947303 2.105394e-01 1.052697e-01
[76,] 0.9636531 7.269381e-02 3.634690e-02
[77,] 0.9468189 1.063621e-01 5.318107e-02
[78,] 0.9400762 1.198477e-01 5.992385e-02
[79,] 0.9157551 1.684899e-01 8.424493e-02
[80,] 0.8980957 2.038085e-01 1.019043e-01
[81,] 0.9882396 2.352088e-02 1.176044e-02
[82,] 0.9802974 3.940513e-02 1.970256e-02
[83,] 0.9875320 2.493604e-02 1.246802e-02
[84,] 0.9819951 3.600988e-02 1.800494e-02
[85,] 0.9685787 6.284250e-02 3.142125e-02
[86,] 0.9503127 9.937469e-02 4.968735e-02
[87,] 0.9438083 1.123834e-01 5.619168e-02
[88,] 0.9078395 1.843211e-01 9.216054e-02
[89,] 0.9633501 7.329971e-02 3.664986e-02
[90,] 0.9811214 3.775730e-02 1.887865e-02
[91,] 0.9602392 7.952160e-02 3.976080e-02
[92,] 0.9231664 1.536671e-01 7.683356e-02
[93,] 0.9155922 1.688155e-01 8.440777e-02
[94,] 0.8923226 2.153548e-01 1.076774e-01
[95,] 0.7765773 4.468454e-01 2.234227e-01
> postscript(file="/var/www/html/rcomp/tmp/16o0n1293556659.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26o0n1293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3zfz81293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zfz81293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zfz81293556659.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 = 130
Frequency = 1
1 2 3 4 5 6
26.534364 -95.829273 67.988909 -72.647455 -60.102000 263.308654
7 8 9 10 11 12
101.763200 -356.814421 -47.541694 55.367397 -10.424881 -93.824881
13 14 15 16 17 18
8.621683 -46.741953 -17.923772 13.439865 -81.014681 -16.604026
19 20 21 22 23 24
64.850519 -73.727102 69.545625 1.454716 11.662439 15.262439
25 26 27 28 29 30
-30.290998 -9.654634 -56.836452 7.527184 -46.927361 5.483293
31 32 33 34 35 36
4.937839 30.360218 -4.367055 23.542036 4.749758 -17.650242
37 38 39 40 41 42
23.796322 -24.567315 7.250867 11.614504 -7.840042 48.570613
43 44 45 46 47 48
24.025158 68.447537 16.720264 -41.370645 74.837078 -9.562922
49 50 51 52 53 54
57.883641 98.520005 46.338187 22.701823 35.247278 -25.342068
55 56 57 58 59 60
28.112477 93.534856 12.807584 53.716675 -19.075603 47.524397
61 62 63 64 65 66
-11.029039 24.607324 -6.574494 76.789142 93.334597 -30.254749
67 68 69 70 71 72
32.199797 67.622176 -40.105097 -52.196006 5.011716 81.611716
73 74 75 76 77 78
31.058280 -65.305356 -58.487175 73.876462 -40.578084 -73.167429
79 80 81 82 83 84
-14.712884 -61.290505 -23.017777 -7.108687 -28.900964 -27.300964
85 86 87 88 89 90
-27.854401 -41.218037 -9.399855 -34.036219 -51.490764 -78.080110
91 92 93 94 95 96
-12.625564 13.796815 103.069542 -26.021367 -63.813645 4.786355
97 98 99 100 101 102
-64.767081 -55.130718 40.687464 -47.948899 122.596555 -29.509989
103 104 105 106 107 108
-90.055444 48.366935 -11.360337 -57.451247 74.756476 4.356476
109 110 111 112 113 114
12.803039 145.439403 24.257585 -33.378779 22.166676 -39.422670
115 116 117 118 119 120
-70.968124 74.808086 -45.919187 78.989904 -48.802374 -5.202374
121 122 123 124 125 126
-26.755810 69.880554 -37.301265 -17.937628 14.607826 -24.981519
127 128 129 130
-67.526974 94.895405 -29.831868 -28.922777
> postscript(file="/var/www/html/rcomp/tmp/6zfz81293556659.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 26.534364 NA
1 -95.829273 26.534364
2 67.988909 -95.829273
3 -72.647455 67.988909
4 -60.102000 -72.647455
5 263.308654 -60.102000
6 101.763200 263.308654
7 -356.814421 101.763200
8 -47.541694 -356.814421
9 55.367397 -47.541694
10 -10.424881 55.367397
11 -93.824881 -10.424881
12 8.621683 -93.824881
13 -46.741953 8.621683
14 -17.923772 -46.741953
15 13.439865 -17.923772
16 -81.014681 13.439865
17 -16.604026 -81.014681
18 64.850519 -16.604026
19 -73.727102 64.850519
20 69.545625 -73.727102
21 1.454716 69.545625
22 11.662439 1.454716
23 15.262439 11.662439
24 -30.290998 15.262439
25 -9.654634 -30.290998
26 -56.836452 -9.654634
27 7.527184 -56.836452
28 -46.927361 7.527184
29 5.483293 -46.927361
30 4.937839 5.483293
31 30.360218 4.937839
32 -4.367055 30.360218
33 23.542036 -4.367055
34 4.749758 23.542036
35 -17.650242 4.749758
36 23.796322 -17.650242
37 -24.567315 23.796322
38 7.250867 -24.567315
39 11.614504 7.250867
40 -7.840042 11.614504
41 48.570613 -7.840042
42 24.025158 48.570613
43 68.447537 24.025158
44 16.720264 68.447537
45 -41.370645 16.720264
46 74.837078 -41.370645
47 -9.562922 74.837078
48 57.883641 -9.562922
49 98.520005 57.883641
50 46.338187 98.520005
51 22.701823 46.338187
52 35.247278 22.701823
53 -25.342068 35.247278
54 28.112477 -25.342068
55 93.534856 28.112477
56 12.807584 93.534856
57 53.716675 12.807584
58 -19.075603 53.716675
59 47.524397 -19.075603
60 -11.029039 47.524397
61 24.607324 -11.029039
62 -6.574494 24.607324
63 76.789142 -6.574494
64 93.334597 76.789142
65 -30.254749 93.334597
66 32.199797 -30.254749
67 67.622176 32.199797
68 -40.105097 67.622176
69 -52.196006 -40.105097
70 5.011716 -52.196006
71 81.611716 5.011716
72 31.058280 81.611716
73 -65.305356 31.058280
74 -58.487175 -65.305356
75 73.876462 -58.487175
76 -40.578084 73.876462
77 -73.167429 -40.578084
78 -14.712884 -73.167429
79 -61.290505 -14.712884
80 -23.017777 -61.290505
81 -7.108687 -23.017777
82 -28.900964 -7.108687
83 -27.300964 -28.900964
84 -27.854401 -27.300964
85 -41.218037 -27.854401
86 -9.399855 -41.218037
87 -34.036219 -9.399855
88 -51.490764 -34.036219
89 -78.080110 -51.490764
90 -12.625564 -78.080110
91 13.796815 -12.625564
92 103.069542 13.796815
93 -26.021367 103.069542
94 -63.813645 -26.021367
95 4.786355 -63.813645
96 -64.767081 4.786355
97 -55.130718 -64.767081
98 40.687464 -55.130718
99 -47.948899 40.687464
100 122.596555 -47.948899
101 -29.509989 122.596555
102 -90.055444 -29.509989
103 48.366935 -90.055444
104 -11.360337 48.366935
105 -57.451247 -11.360337
106 74.756476 -57.451247
107 4.356476 74.756476
108 12.803039 4.356476
109 145.439403 12.803039
110 24.257585 145.439403
111 -33.378779 24.257585
112 22.166676 -33.378779
113 -39.422670 22.166676
114 -70.968124 -39.422670
115 74.808086 -70.968124
116 -45.919187 74.808086
117 78.989904 -45.919187
118 -48.802374 78.989904
119 -5.202374 -48.802374
120 -26.755810 -5.202374
121 69.880554 -26.755810
122 -37.301265 69.880554
123 -17.937628 -37.301265
124 14.607826 -17.937628
125 -24.981519 14.607826
126 -67.526974 -24.981519
127 94.895405 -67.526974
128 -29.831868 94.895405
129 -28.922777 -29.831868
130 NA -28.922777
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -95.829273 26.534364
[2,] 67.988909 -95.829273
[3,] -72.647455 67.988909
[4,] -60.102000 -72.647455
[5,] 263.308654 -60.102000
[6,] 101.763200 263.308654
[7,] -356.814421 101.763200
[8,] -47.541694 -356.814421
[9,] 55.367397 -47.541694
[10,] -10.424881 55.367397
[11,] -93.824881 -10.424881
[12,] 8.621683 -93.824881
[13,] -46.741953 8.621683
[14,] -17.923772 -46.741953
[15,] 13.439865 -17.923772
[16,] -81.014681 13.439865
[17,] -16.604026 -81.014681
[18,] 64.850519 -16.604026
[19,] -73.727102 64.850519
[20,] 69.545625 -73.727102
[21,] 1.454716 69.545625
[22,] 11.662439 1.454716
[23,] 15.262439 11.662439
[24,] -30.290998 15.262439
[25,] -9.654634 -30.290998
[26,] -56.836452 -9.654634
[27,] 7.527184 -56.836452
[28,] -46.927361 7.527184
[29,] 5.483293 -46.927361
[30,] 4.937839 5.483293
[31,] 30.360218 4.937839
[32,] -4.367055 30.360218
[33,] 23.542036 -4.367055
[34,] 4.749758 23.542036
[35,] -17.650242 4.749758
[36,] 23.796322 -17.650242
[37,] -24.567315 23.796322
[38,] 7.250867 -24.567315
[39,] 11.614504 7.250867
[40,] -7.840042 11.614504
[41,] 48.570613 -7.840042
[42,] 24.025158 48.570613
[43,] 68.447537 24.025158
[44,] 16.720264 68.447537
[45,] -41.370645 16.720264
[46,] 74.837078 -41.370645
[47,] -9.562922 74.837078
[48,] 57.883641 -9.562922
[49,] 98.520005 57.883641
[50,] 46.338187 98.520005
[51,] 22.701823 46.338187
[52,] 35.247278 22.701823
[53,] -25.342068 35.247278
[54,] 28.112477 -25.342068
[55,] 93.534856 28.112477
[56,] 12.807584 93.534856
[57,] 53.716675 12.807584
[58,] -19.075603 53.716675
[59,] 47.524397 -19.075603
[60,] -11.029039 47.524397
[61,] 24.607324 -11.029039
[62,] -6.574494 24.607324
[63,] 76.789142 -6.574494
[64,] 93.334597 76.789142
[65,] -30.254749 93.334597
[66,] 32.199797 -30.254749
[67,] 67.622176 32.199797
[68,] -40.105097 67.622176
[69,] -52.196006 -40.105097
[70,] 5.011716 -52.196006
[71,] 81.611716 5.011716
[72,] 31.058280 81.611716
[73,] -65.305356 31.058280
[74,] -58.487175 -65.305356
[75,] 73.876462 -58.487175
[76,] -40.578084 73.876462
[77,] -73.167429 -40.578084
[78,] -14.712884 -73.167429
[79,] -61.290505 -14.712884
[80,] -23.017777 -61.290505
[81,] -7.108687 -23.017777
[82,] -28.900964 -7.108687
[83,] -27.300964 -28.900964
[84,] -27.854401 -27.300964
[85,] -41.218037 -27.854401
[86,] -9.399855 -41.218037
[87,] -34.036219 -9.399855
[88,] -51.490764 -34.036219
[89,] -78.080110 -51.490764
[90,] -12.625564 -78.080110
[91,] 13.796815 -12.625564
[92,] 103.069542 13.796815
[93,] -26.021367 103.069542
[94,] -63.813645 -26.021367
[95,] 4.786355 -63.813645
[96,] -64.767081 4.786355
[97,] -55.130718 -64.767081
[98,] 40.687464 -55.130718
[99,] -47.948899 40.687464
[100,] 122.596555 -47.948899
[101,] -29.509989 122.596555
[102,] -90.055444 -29.509989
[103,] 48.366935 -90.055444
[104,] -11.360337 48.366935
[105,] -57.451247 -11.360337
[106,] 74.756476 -57.451247
[107,] 4.356476 74.756476
[108,] 12.803039 4.356476
[109,] 145.439403 12.803039
[110,] 24.257585 145.439403
[111,] -33.378779 24.257585
[112,] 22.166676 -33.378779
[113,] -39.422670 22.166676
[114,] -70.968124 -39.422670
[115,] 74.808086 -70.968124
[116,] -45.919187 74.808086
[117,] 78.989904 -45.919187
[118,] -48.802374 78.989904
[119,] -5.202374 -48.802374
[120,] -26.755810 -5.202374
[121,] 69.880554 -26.755810
[122,] -37.301265 69.880554
[123,] -17.937628 -37.301265
[124,] 14.607826 -17.937628
[125,] -24.981519 14.607826
[126,] -67.526974 -24.981519
[127,] 94.895405 -67.526974
[128,] -29.831868 94.895405
[129,] -28.922777 -29.831868
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -95.829273 26.534364
2 67.988909 -95.829273
3 -72.647455 67.988909
4 -60.102000 -72.647455
5 263.308654 -60.102000
6 101.763200 263.308654
7 -356.814421 101.763200
8 -47.541694 -356.814421
9 55.367397 -47.541694
10 -10.424881 55.367397
11 -93.824881 -10.424881
12 8.621683 -93.824881
13 -46.741953 8.621683
14 -17.923772 -46.741953
15 13.439865 -17.923772
16 -81.014681 13.439865
17 -16.604026 -81.014681
18 64.850519 -16.604026
19 -73.727102 64.850519
20 69.545625 -73.727102
21 1.454716 69.545625
22 11.662439 1.454716
23 15.262439 11.662439
24 -30.290998 15.262439
25 -9.654634 -30.290998
26 -56.836452 -9.654634
27 7.527184 -56.836452
28 -46.927361 7.527184
29 5.483293 -46.927361
30 4.937839 5.483293
31 30.360218 4.937839
32 -4.367055 30.360218
33 23.542036 -4.367055
34 4.749758 23.542036
35 -17.650242 4.749758
36 23.796322 -17.650242
37 -24.567315 23.796322
38 7.250867 -24.567315
39 11.614504 7.250867
40 -7.840042 11.614504
41 48.570613 -7.840042
42 24.025158 48.570613
43 68.447537 24.025158
44 16.720264 68.447537
45 -41.370645 16.720264
46 74.837078 -41.370645
47 -9.562922 74.837078
48 57.883641 -9.562922
49 98.520005 57.883641
50 46.338187 98.520005
51 22.701823 46.338187
52 35.247278 22.701823
53 -25.342068 35.247278
54 28.112477 -25.342068
55 93.534856 28.112477
56 12.807584 93.534856
57 53.716675 12.807584
58 -19.075603 53.716675
59 47.524397 -19.075603
60 -11.029039 47.524397
61 24.607324 -11.029039
62 -6.574494 24.607324
63 76.789142 -6.574494
64 93.334597 76.789142
65 -30.254749 93.334597
66 32.199797 -30.254749
67 67.622176 32.199797
68 -40.105097 67.622176
69 -52.196006 -40.105097
70 5.011716 -52.196006
71 81.611716 5.011716
72 31.058280 81.611716
73 -65.305356 31.058280
74 -58.487175 -65.305356
75 73.876462 -58.487175
76 -40.578084 73.876462
77 -73.167429 -40.578084
78 -14.712884 -73.167429
79 -61.290505 -14.712884
80 -23.017777 -61.290505
81 -7.108687 -23.017777
82 -28.900964 -7.108687
83 -27.300964 -28.900964
84 -27.854401 -27.300964
85 -41.218037 -27.854401
86 -9.399855 -41.218037
87 -34.036219 -9.399855
88 -51.490764 -34.036219
89 -78.080110 -51.490764
90 -12.625564 -78.080110
91 13.796815 -12.625564
92 103.069542 13.796815
93 -26.021367 103.069542
94 -63.813645 -26.021367
95 4.786355 -63.813645
96 -64.767081 4.786355
97 -55.130718 -64.767081
98 40.687464 -55.130718
99 -47.948899 40.687464
100 122.596555 -47.948899
101 -29.509989 122.596555
102 -90.055444 -29.509989
103 48.366935 -90.055444
104 -11.360337 48.366935
105 -57.451247 -11.360337
106 74.756476 -57.451247
107 4.356476 74.756476
108 12.803039 4.356476
109 145.439403 12.803039
110 24.257585 145.439403
111 -33.378779 24.257585
112 22.166676 -33.378779
113 -39.422670 22.166676
114 -70.968124 -39.422670
115 74.808086 -70.968124
116 -45.919187 74.808086
117 78.989904 -45.919187
118 -48.802374 78.989904
119 -5.202374 -48.802374
120 -26.755810 -5.202374
121 69.880554 -26.755810
122 -37.301265 69.880554
123 -17.937628 -37.301265
124 14.607826 -17.937628
125 -24.981519 14.607826
126 -67.526974 -24.981519
127 94.895405 -67.526974
128 -29.831868 94.895405
129 -28.922777 -29.831868
> 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/7rogt1293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kgxe1293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kgxe1293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10v7xh1293556659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gpd41293556659.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/12rhd71293556659.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/13xhrj1293556659.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/14jiq71293556659.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/15u97a1293556659.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/16q1nj1293556659.tab")
+ }
>
> try(system("convert tmp/16o0n1293556659.ps tmp/16o0n1293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/26o0n1293556659.ps tmp/26o0n1293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zfz81293556659.ps tmp/3zfz81293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zfz81293556659.ps tmp/4zfz81293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zfz81293556659.ps tmp/5zfz81293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zfz81293556659.ps tmp/6zfz81293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rogt1293556659.ps tmp/7rogt1293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kgxe1293556659.ps tmp/8kgxe1293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kgxe1293556659.ps tmp/9kgxe1293556659.png",intern=TRUE))
character(0)
> try(system("convert tmp/10v7xh1293556659.ps tmp/10v7xh1293556659.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.553 1.727 10.024