R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(6.392
+ ,0
+ ,8.686
+ ,0
+ ,9.245
+ ,0
+ ,8.183
+ ,0
+ ,7.451
+ ,0
+ ,7.989
+ ,0
+ ,8.244
+ ,0
+ ,8.843
+ ,0
+ ,9.093
+ ,0
+ ,8.247
+ ,0
+ ,9.312
+ ,0
+ ,8.341
+ ,0
+ ,7.117
+ ,0
+ ,9.636
+ ,0
+ ,9.815
+ ,0
+ ,8.611
+ ,0
+ ,8.298
+ ,0
+ ,8.715
+ ,0
+ ,8.920
+ ,0
+ ,10.086
+ ,0
+ ,9.512
+ ,0
+ ,8.991
+ ,0
+ ,10.311
+ ,0
+ ,8.895
+ ,0
+ ,7.450
+ ,0
+ ,10.084
+ ,0
+ ,9.859
+ ,0
+ ,9.100
+ ,0
+ ,8.921
+ ,0
+ ,8.503
+ ,0
+ ,8.600
+ ,0
+ ,10.394
+ ,0
+ ,9.290
+ ,0
+ ,8.742
+ ,0
+ ,10.217
+ ,0
+ ,8.639
+ ,0
+ ,8.140
+ ,0
+ ,10.779
+ ,0
+ ,10.428
+ ,0
+ ,10.349
+ ,0
+ ,10.036
+ ,0
+ ,9.492
+ ,0
+ ,10.639
+ ,0
+ ,12.055
+ ,0
+ ,10.325
+ ,0
+ ,11.817
+ ,0
+ ,11.009
+ ,0
+ ,9.997
+ ,0
+ ,9.420
+ ,0
+ ,11.959
+ ,0
+ ,12.595
+ ,0
+ ,11.891
+ ,0
+ ,10.872
+ ,0
+ ,11.836
+ ,0
+ ,11.542
+ ,0
+ ,13.094
+ ,0
+ ,11.180
+ ,0
+ ,12.036
+ ,0
+ ,12.112
+ ,0
+ ,10.875
+ ,0
+ ,9.897
+ ,0
+ ,11.672
+ ,0
+ ,12.386
+ ,0
+ ,11.406
+ ,0
+ ,9.831
+ ,0
+ ,11.025
+ ,1
+ ,10.854
+ ,1
+ ,12.253
+ ,1
+ ,11.839
+ ,1
+ ,11.669
+ ,1
+ ,11.601
+ ,1
+ ,11.178
+ ,1
+ ,9.516
+ ,1
+ ,12.103
+ ,1
+ ,12.989
+ ,1
+ ,11.610
+ ,1
+ ,10.206
+ ,1
+ ,11.356
+ ,1
+ ,11.307
+ ,1
+ ,12.649
+ ,1
+ ,11.947
+ ,1
+ ,11.714
+ ,1
+ ,12.193
+ ,1
+ ,11.269
+ ,1
+ ,9.097
+ ,1
+ ,12.640
+ ,1
+ ,13.040
+ ,1
+ ,11.687
+ ,1
+ ,11.192
+ ,1
+ ,11.392
+ ,1
+ ,11.793
+ ,1
+ ,13.933
+ ,1
+ ,12.778
+ ,1
+ ,11.810
+ ,1
+ ,13.698
+ ,1
+ ,11.957
+ ,1
+ ,10.724
+ ,1
+ ,13.939
+ ,1
+ ,13.980
+ ,1
+ ,13.807
+ ,1
+ ,12.974
+ ,1
+ ,12.510
+ ,1
+ ,12.934
+ ,1
+ ,14.908
+ ,1
+ ,13.772
+ ,1
+ ,13.013
+ ,1
+ ,14.050
+ ,1
+ ,11.817
+ ,1
+ ,11.593
+ ,1
+ ,14.466
+ ,1
+ ,13.616
+ ,1
+ ,14.734
+ ,1
+ ,13.881
+ ,1
+ ,13.528
+ ,1
+ ,13.584
+ ,1
+ ,16.170
+ ,1
+ ,13.261
+ ,1
+ ,14.742
+ ,1
+ ,15.487
+ ,1
+ ,13.155
+ ,1
+ ,12.621
+ ,1)
+ ,dim=c(2
+ ,121)
+ ,dimnames=list(c('y'
+ ,'x')
+ ,1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('y','x'),1:121))
> 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 x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.392 0 1 0 0 0 0 0 0 0 0 0 0 1
2 8.686 0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.245 0 0 0 1 0 0 0 0 0 0 0 0 3
4 8.183 0 0 0 0 1 0 0 0 0 0 0 0 4
5 7.451 0 0 0 0 0 1 0 0 0 0 0 0 5
6 7.989 0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.244 0 0 0 0 0 0 0 1 0 0 0 0 7
8 8.843 0 0 0 0 0 0 0 0 1 0 0 0 8
9 9.093 0 0 0 0 0 0 0 0 0 1 0 0 9
10 8.247 0 0 0 0 0 0 0 0 0 0 1 0 10
11 9.312 0 0 0 0 0 0 0 0 0 0 0 1 11
12 8.341 0 0 0 0 0 0 0 0 0 0 0 0 12
13 7.117 0 1 0 0 0 0 0 0 0 0 0 0 13
14 9.636 0 0 1 0 0 0 0 0 0 0 0 0 14
15 9.815 0 0 0 1 0 0 0 0 0 0 0 0 15
16 8.611 0 0 0 0 1 0 0 0 0 0 0 0 16
17 8.298 0 0 0 0 0 1 0 0 0 0 0 0 17
18 8.715 0 0 0 0 0 0 1 0 0 0 0 0 18
19 8.920 0 0 0 0 0 0 0 1 0 0 0 0 19
20 10.086 0 0 0 0 0 0 0 0 1 0 0 0 20
21 9.512 0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.991 0 0 0 0 0 0 0 0 0 0 1 0 22
23 10.311 0 0 0 0 0 0 0 0 0 0 0 1 23
24 8.895 0 0 0 0 0 0 0 0 0 0 0 0 24
25 7.450 0 1 0 0 0 0 0 0 0 0 0 0 25
26 10.084 0 0 1 0 0 0 0 0 0 0 0 0 26
27 9.859 0 0 0 1 0 0 0 0 0 0 0 0 27
28 9.100 0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.921 0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.503 0 0 0 0 0 0 1 0 0 0 0 0 30
31 8.600 0 0 0 0 0 0 0 1 0 0 0 0 31
32 10.394 0 0 0 0 0 0 0 0 1 0 0 0 32
33 9.290 0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.742 0 0 0 0 0 0 0 0 0 0 1 0 34
35 10.217 0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.639 0 0 0 0 0 0 0 0 0 0 0 0 36
37 8.140 0 1 0 0 0 0 0 0 0 0 0 0 37
38 10.779 0 0 1 0 0 0 0 0 0 0 0 0 38
39 10.428 0 0 0 1 0 0 0 0 0 0 0 0 39
40 10.349 0 0 0 0 1 0 0 0 0 0 0 0 40
41 10.036 0 0 0 0 0 1 0 0 0 0 0 0 41
42 9.492 0 0 0 0 0 0 1 0 0 0 0 0 42
43 10.639 0 0 0 0 0 0 0 1 0 0 0 0 43
44 12.055 0 0 0 0 0 0 0 0 1 0 0 0 44
45 10.325 0 0 0 0 0 0 0 0 0 1 0 0 45
46 11.817 0 0 0 0 0 0 0 0 0 0 1 0 46
47 11.009 0 0 0 0 0 0 0 0 0 0 0 1 47
48 9.997 0 0 0 0 0 0 0 0 0 0 0 0 48
49 9.420 0 1 0 0 0 0 0 0 0 0 0 0 49
50 11.959 0 0 1 0 0 0 0 0 0 0 0 0 50
51 12.595 0 0 0 1 0 0 0 0 0 0 0 0 51
52 11.891 0 0 0 0 1 0 0 0 0 0 0 0 52
53 10.872 0 0 0 0 0 1 0 0 0 0 0 0 53
54 11.836 0 0 0 0 0 0 1 0 0 0 0 0 54
55 11.542 0 0 0 0 0 0 0 1 0 0 0 0 55
56 13.094 0 0 0 0 0 0 0 0 1 0 0 0 56
57 11.180 0 0 0 0 0 0 0 0 0 1 0 0 57
58 12.036 0 0 0 0 0 0 0 0 0 0 1 0 58
59 12.112 0 0 0 0 0 0 0 0 0 0 0 1 59
60 10.875 0 0 0 0 0 0 0 0 0 0 0 0 60
61 9.897 0 1 0 0 0 0 0 0 0 0 0 0 61
62 11.672 0 0 1 0 0 0 0 0 0 0 0 0 62
63 12.386 0 0 0 1 0 0 0 0 0 0 0 0 63
64 11.406 0 0 0 0 1 0 0 0 0 0 0 0 64
65 9.831 0 0 0 0 0 1 0 0 0 0 0 0 65
66 11.025 1 0 0 0 0 0 1 0 0 0 0 0 66
67 10.854 1 0 0 0 0 0 0 1 0 0 0 0 67
68 12.253 1 0 0 0 0 0 0 0 1 0 0 0 68
69 11.839 1 0 0 0 0 0 0 0 0 1 0 0 69
70 11.669 1 0 0 0 0 0 0 0 0 0 1 0 70
71 11.601 1 0 0 0 0 0 0 0 0 0 0 1 71
72 11.178 1 0 0 0 0 0 0 0 0 0 0 0 72
73 9.516 1 1 0 0 0 0 0 0 0 0 0 0 73
74 12.103 1 0 1 0 0 0 0 0 0 0 0 0 74
75 12.989 1 0 0 1 0 0 0 0 0 0 0 0 75
76 11.610 1 0 0 0 1 0 0 0 0 0 0 0 76
77 10.206 1 0 0 0 0 1 0 0 0 0 0 0 77
78 11.356 1 0 0 0 0 0 1 0 0 0 0 0 78
79 11.307 1 0 0 0 0 0 0 1 0 0 0 0 79
80 12.649 1 0 0 0 0 0 0 0 1 0 0 0 80
81 11.947 1 0 0 0 0 0 0 0 0 1 0 0 81
82 11.714 1 0 0 0 0 0 0 0 0 0 1 0 82
83 12.193 1 0 0 0 0 0 0 0 0 0 0 1 83
84 11.269 1 0 0 0 0 0 0 0 0 0 0 0 84
85 9.097 1 1 0 0 0 0 0 0 0 0 0 0 85
86 12.640 1 0 1 0 0 0 0 0 0 0 0 0 86
87 13.040 1 0 0 1 0 0 0 0 0 0 0 0 87
88 11.687 1 0 0 0 1 0 0 0 0 0 0 0 88
89 11.192 1 0 0 0 0 1 0 0 0 0 0 0 89
90 11.392 1 0 0 0 0 0 1 0 0 0 0 0 90
91 11.793 1 0 0 0 0 0 0 1 0 0 0 0 91
92 13.933 1 0 0 0 0 0 0 0 1 0 0 0 92
93 12.778 1 0 0 0 0 0 0 0 0 1 0 0 93
94 11.810 1 0 0 0 0 0 0 0 0 0 1 0 94
95 13.698 1 0 0 0 0 0 0 0 0 0 0 1 95
96 11.957 1 0 0 0 0 0 0 0 0 0 0 0 96
97 10.724 1 1 0 0 0 0 0 0 0 0 0 0 97
98 13.939 1 0 1 0 0 0 0 0 0 0 0 0 98
99 13.980 1 0 0 1 0 0 0 0 0 0 0 0 99
100 13.807 1 0 0 0 1 0 0 0 0 0 0 0 100
101 12.974 1 0 0 0 0 1 0 0 0 0 0 0 101
102 12.510 1 0 0 0 0 0 1 0 0 0 0 0 102
103 12.934 1 0 0 0 0 0 0 1 0 0 0 0 103
104 14.908 1 0 0 0 0 0 0 0 1 0 0 0 104
105 13.772 1 0 0 0 0 0 0 0 0 1 0 0 105
106 13.013 1 0 0 0 0 0 0 0 0 0 1 0 106
107 14.050 1 0 0 0 0 0 0 0 0 0 0 1 107
108 11.817 1 0 0 0 0 0 0 0 0 0 0 0 108
109 11.593 1 1 0 0 0 0 0 0 0 0 0 0 109
110 14.466 1 0 1 0 0 0 0 0 0 0 0 0 110
111 13.616 1 0 0 1 0 0 0 0 0 0 0 0 111
112 14.734 1 0 0 0 1 0 0 0 0 0 0 0 112
113 13.881 1 0 0 0 0 1 0 0 0 0 0 0 113
114 13.528 1 0 0 0 0 0 1 0 0 0 0 0 114
115 13.584 1 0 0 0 0 0 0 1 0 0 0 0 115
116 16.170 1 0 0 0 0 0 0 0 1 0 0 0 116
117 13.261 1 0 0 0 0 0 0 0 0 1 0 0 117
118 14.742 1 0 0 0 0 0 0 0 0 0 1 0 118
119 15.487 1 0 0 0 0 0 0 0 0 0 0 1 119
120 13.155 1 0 0 0 0 0 0 0 0 0 0 0 120
121 12.621 1 1 0 0 0 0 0 0 0 0 0 0 121
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
7.03122 -0.98365 -1.07873 1.50284 1.64003 0.92082
M5 M6 M7 M8 M9 M10
0.08751 0.39256 0.53795 2.07304 0.87253 0.78922
M11 t
1.44841 0.06171
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.29892 -0.32841 0.01266 0.36947 1.15787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.031217 0.199371 35.267 < 2e-16 ***
x -0.983645 0.191853 -5.127 1.32e-06 ***
M1 -1.078730 0.230709 -4.676 8.59e-06 ***
M2 1.502842 0.236456 6.356 5.19e-09 ***
M3 1.640032 0.236347 6.939 3.15e-10 ***
M4 0.920821 0.236270 3.897 0.000170 ***
M5 0.087510 0.236224 0.370 0.711776
M6 0.392564 0.236583 1.659 0.099981 .
M7 0.537953 0.236408 2.276 0.024867 *
M8 2.073043 0.236266 8.774 2.99e-14 ***
M9 0.872532 0.236155 3.695 0.000349 ***
M10 0.789221 0.236075 3.343 0.001142 **
M11 1.448411 0.236028 6.137 1.45e-08 ***
t 0.061711 0.002737 22.544 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5277 on 107 degrees of freedom
Multiple R-squared: 0.9378, Adjusted R-squared: 0.9302
F-statistic: 124 on 13 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,] 3.863603e-02 0.0772720550 0.9613640
[2,] 9.737204e-03 0.0194744080 0.9902628
[3,] 2.244402e-03 0.0044888032 0.9977556
[4,] 5.810261e-03 0.0116205229 0.9941897
[5,] 3.696153e-03 0.0073923067 0.9963038
[6,] 1.166064e-03 0.0023321281 0.9988339
[7,] 5.785079e-04 0.0011570158 0.9994215
[8,] 2.439447e-04 0.0004878895 0.9997561
[9,] 2.513103e-04 0.0005026205 0.9997487
[10,] 8.692806e-05 0.0001738561 0.9999131
[11,] 4.376923e-04 0.0008753846 0.9995623
[12,] 2.181279e-04 0.0004362558 0.9997819
[13,] 9.309648e-05 0.0001861930 0.9999069
[14,] 4.821890e-04 0.0009643780 0.9995178
[15,] 1.929740e-03 0.0038594802 0.9980703
[16,] 1.222118e-03 0.0024442361 0.9987779
[17,] 2.952458e-03 0.0059049156 0.9970475
[18,] 5.314308e-03 0.0106286163 0.9946857
[19,] 3.566669e-03 0.0071333385 0.9964333
[20,] 4.752023e-03 0.0095040455 0.9952480
[21,] 3.382115e-03 0.0067642293 0.9966179
[22,] 2.908941e-03 0.0058178823 0.9970911
[23,] 2.079575e-03 0.0041591501 0.9979204
[24,] 4.504518e-03 0.0090090369 0.9954955
[25,] 8.836904e-03 0.0176738090 0.9911631
[26,] 6.829025e-03 0.0136580503 0.9931710
[27,] 1.720637e-02 0.0344127397 0.9827936
[28,] 4.579066e-02 0.0915813183 0.9542093
[29,] 3.544150e-02 0.0708830097 0.9645585
[30,] 2.929273e-01 0.5858546241 0.7070727
[31,] 2.598854e-01 0.5197708192 0.7401146
[32,] 2.133866e-01 0.4267732565 0.7866134
[33,] 2.037566e-01 0.4075131673 0.7962434
[34,] 1.871133e-01 0.3742265343 0.8128867
[35,] 2.603226e-01 0.5206451540 0.7396774
[36,] 3.297545e-01 0.6595090749 0.6702455
[37,] 3.134590e-01 0.6269180094 0.6865410
[38,] 4.934729e-01 0.9869457552 0.5065271
[39,] 5.043852e-01 0.9912295994 0.4956148
[40,] 5.212918e-01 0.9574163065 0.4787082
[41,] 4.725601e-01 0.9451202947 0.5274399
[42,] 5.138972e-01 0.9722056967 0.4861028
[43,] 4.623322e-01 0.9246643681 0.5376678
[44,] 4.403465e-01 0.8806930936 0.5596535
[45,] 4.752013e-01 0.9504025396 0.5247987
[46,] 4.836057e-01 0.9672113946 0.5163943
[47,] 4.752143e-01 0.9504285239 0.5247857
[48,] 4.566261e-01 0.9132522103 0.5433739
[49,] 5.701020e-01 0.8597960797 0.4298980
[50,] 5.603037e-01 0.8793925756 0.4396963
[51,] 5.225507e-01 0.9548985992 0.4774493
[52,] 4.675675e-01 0.9351349550 0.5324325
[53,] 4.924463e-01 0.9848925114 0.5075537
[54,] 5.088348e-01 0.9823303428 0.4911652
[55,] 4.722903e-01 0.9445805347 0.5277097
[56,] 5.742660e-01 0.8514680291 0.4257340
[57,] 5.765913e-01 0.8468174887 0.4234087
[58,] 5.216290e-01 0.9567420812 0.4783710
[59,] 6.433405e-01 0.7133189010 0.3566595
[60,] 5.852526e-01 0.8294947698 0.4147474
[61,] 6.207420e-01 0.7585159843 0.3792580
[62,] 6.189279e-01 0.7621442403 0.3810721
[63,] 5.835385e-01 0.8329230993 0.4164615
[64,] 5.608485e-01 0.8783029236 0.4391515
[65,] 5.324722e-01 0.9350556477 0.4675278
[66,] 4.971109e-01 0.9942218943 0.5028891
[67,] 4.546005e-01 0.9092009863 0.5453995
[68,] 4.979308e-01 0.9958616942 0.5020692
[69,] 5.776649e-01 0.8446702402 0.4223351
[70,] 5.117418e-01 0.9765164087 0.4882582
[71,] 5.020653e-01 0.9958693408 0.4979347
[72,] 6.498199e-01 0.7003601626 0.3501801
[73,] 7.115607e-01 0.5768785638 0.2884393
[74,] 6.778793e-01 0.6442414803 0.3221207
[75,] 6.175543e-01 0.7648914372 0.3824457
[76,] 5.736904e-01 0.8526191773 0.4263096
[77,] 5.357826e-01 0.9284348873 0.4642174
[78,] 6.230292e-01 0.7539416190 0.3769708
[79,] 5.493346e-01 0.9013307961 0.4506654
[80,] 5.553750e-01 0.8892500100 0.4446250
[81,] 4.637527e-01 0.9275054033 0.5362473
[82,] 3.913438e-01 0.7826876914 0.6086562
[83,] 5.900823e-01 0.8198354567 0.4099177
[84,] 5.029206e-01 0.9941588357 0.4970794
[85,] 4.035913e-01 0.8071825663 0.5964087
[86,] 2.869312e-01 0.5738624401 0.7130688
[87,] 1.997650e-01 0.3995299151 0.8002350
[88,] 1.190806e-01 0.2381611995 0.8809194
> postscript(file="/var/www/html/rcomp/tmp/14q531229175079.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26zso1229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jwd11229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pvgu1229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vo751229175079.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 = 121
Frequency = 1
1 2 3 4 5 6
0.377803068 0.028519536 0.388619536 -0.015880464 0.023719536 0.194955011
7 8 9 10 11 12
0.242855011 -0.754944989 0.633855011 -0.190544989 0.153555011 0.569255011
13 14 15 16 17 18
0.362274705 0.237991173 0.218091173 -0.328408827 0.130191173 0.180426648
19 20 21 22 23 24
0.178326648 -0.252473352 0.312326648 -0.187073352 0.412026648 0.382726648
25 26 27 28 29 30
-0.045253659 -0.054537190 -0.478437190 -0.579937190 0.012662810 -0.772101715
31 32 33 34 35 36
-0.882201715 -0.685001715 -0.650201715 -1.176601715 -0.422501715 -0.613801715
37 38 39 40 41 42
-0.095782022 -0.100065554 -0.649965554 -0.071465554 0.387134446 -0.523630078
43 44 45 46 47 48
0.416269922 0.235469922 -0.355730078 1.157869922 -0.371030078 0.003669922
49 50 51 52 53 54
0.443689615 0.339406083 0.776506083 0.730006083 0.482606083 1.079841559
55 56 57 58 59 60
0.578741559 0.533941559 -0.241258441 0.636341559 -0.008558441 0.141141559
61 62 63 64 65 66
0.180161252 -0.688122280 -0.173022280 -0.495522280 -1.298922280 0.511958441
67 68 69 70 71 72
0.133858441 -0.063941559 0.660858441 0.512458441 -0.276441559 0.687258441
73 74 75 76 77 78
0.042278135 -0.014005397 0.673094603 -0.048405397 -0.680805397 0.102430078
79 80 81 82 83 84
-0.153669922 -0.408469922 0.028330078 -0.183069922 -0.424969922 0.037730078
85 86 87 88 89 90
-1.117250228 -0.217533760 -0.016433760 -0.711933760 -0.435333760 -0.602098285
91 92 93 94 95 96
-0.408198285 0.135001715 0.118801715 -0.827598285 0.339501715 -0.014798285
97 98 99 100 101 102
-0.230778592 0.340937877 0.183037877 0.667537877 0.606137877 -0.224626648
103 104 105 106 107 108
-0.007726648 0.369473352 0.372273352 -0.365126648 -0.049026648 -0.895326648
109 110 111 112 113 114
-0.102306955 0.127409513 -0.921490487 0.854009513 0.772609513 0.052844989
115 116 117 118 119 120
-0.098255011 0.890944989 -0.879255011 0.623344989 0.647444989 -0.297855011
121
0.185164682
> postscript(file="/var/www/html/rcomp/tmp/66mcv1229175079.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 0.377803068 NA
1 0.028519536 0.377803068
2 0.388619536 0.028519536
3 -0.015880464 0.388619536
4 0.023719536 -0.015880464
5 0.194955011 0.023719536
6 0.242855011 0.194955011
7 -0.754944989 0.242855011
8 0.633855011 -0.754944989
9 -0.190544989 0.633855011
10 0.153555011 -0.190544989
11 0.569255011 0.153555011
12 0.362274705 0.569255011
13 0.237991173 0.362274705
14 0.218091173 0.237991173
15 -0.328408827 0.218091173
16 0.130191173 -0.328408827
17 0.180426648 0.130191173
18 0.178326648 0.180426648
19 -0.252473352 0.178326648
20 0.312326648 -0.252473352
21 -0.187073352 0.312326648
22 0.412026648 -0.187073352
23 0.382726648 0.412026648
24 -0.045253659 0.382726648
25 -0.054537190 -0.045253659
26 -0.478437190 -0.054537190
27 -0.579937190 -0.478437190
28 0.012662810 -0.579937190
29 -0.772101715 0.012662810
30 -0.882201715 -0.772101715
31 -0.685001715 -0.882201715
32 -0.650201715 -0.685001715
33 -1.176601715 -0.650201715
34 -0.422501715 -1.176601715
35 -0.613801715 -0.422501715
36 -0.095782022 -0.613801715
37 -0.100065554 -0.095782022
38 -0.649965554 -0.100065554
39 -0.071465554 -0.649965554
40 0.387134446 -0.071465554
41 -0.523630078 0.387134446
42 0.416269922 -0.523630078
43 0.235469922 0.416269922
44 -0.355730078 0.235469922
45 1.157869922 -0.355730078
46 -0.371030078 1.157869922
47 0.003669922 -0.371030078
48 0.443689615 0.003669922
49 0.339406083 0.443689615
50 0.776506083 0.339406083
51 0.730006083 0.776506083
52 0.482606083 0.730006083
53 1.079841559 0.482606083
54 0.578741559 1.079841559
55 0.533941559 0.578741559
56 -0.241258441 0.533941559
57 0.636341559 -0.241258441
58 -0.008558441 0.636341559
59 0.141141559 -0.008558441
60 0.180161252 0.141141559
61 -0.688122280 0.180161252
62 -0.173022280 -0.688122280
63 -0.495522280 -0.173022280
64 -1.298922280 -0.495522280
65 0.511958441 -1.298922280
66 0.133858441 0.511958441
67 -0.063941559 0.133858441
68 0.660858441 -0.063941559
69 0.512458441 0.660858441
70 -0.276441559 0.512458441
71 0.687258441 -0.276441559
72 0.042278135 0.687258441
73 -0.014005397 0.042278135
74 0.673094603 -0.014005397
75 -0.048405397 0.673094603
76 -0.680805397 -0.048405397
77 0.102430078 -0.680805397
78 -0.153669922 0.102430078
79 -0.408469922 -0.153669922
80 0.028330078 -0.408469922
81 -0.183069922 0.028330078
82 -0.424969922 -0.183069922
83 0.037730078 -0.424969922
84 -1.117250228 0.037730078
85 -0.217533760 -1.117250228
86 -0.016433760 -0.217533760
87 -0.711933760 -0.016433760
88 -0.435333760 -0.711933760
89 -0.602098285 -0.435333760
90 -0.408198285 -0.602098285
91 0.135001715 -0.408198285
92 0.118801715 0.135001715
93 -0.827598285 0.118801715
94 0.339501715 -0.827598285
95 -0.014798285 0.339501715
96 -0.230778592 -0.014798285
97 0.340937877 -0.230778592
98 0.183037877 0.340937877
99 0.667537877 0.183037877
100 0.606137877 0.667537877
101 -0.224626648 0.606137877
102 -0.007726648 -0.224626648
103 0.369473352 -0.007726648
104 0.372273352 0.369473352
105 -0.365126648 0.372273352
106 -0.049026648 -0.365126648
107 -0.895326648 -0.049026648
108 -0.102306955 -0.895326648
109 0.127409513 -0.102306955
110 -0.921490487 0.127409513
111 0.854009513 -0.921490487
112 0.772609513 0.854009513
113 0.052844989 0.772609513
114 -0.098255011 0.052844989
115 0.890944989 -0.098255011
116 -0.879255011 0.890944989
117 0.623344989 -0.879255011
118 0.647444989 0.623344989
119 -0.297855011 0.647444989
120 0.185164682 -0.297855011
121 NA 0.185164682
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.028519536 0.377803068
[2,] 0.388619536 0.028519536
[3,] -0.015880464 0.388619536
[4,] 0.023719536 -0.015880464
[5,] 0.194955011 0.023719536
[6,] 0.242855011 0.194955011
[7,] -0.754944989 0.242855011
[8,] 0.633855011 -0.754944989
[9,] -0.190544989 0.633855011
[10,] 0.153555011 -0.190544989
[11,] 0.569255011 0.153555011
[12,] 0.362274705 0.569255011
[13,] 0.237991173 0.362274705
[14,] 0.218091173 0.237991173
[15,] -0.328408827 0.218091173
[16,] 0.130191173 -0.328408827
[17,] 0.180426648 0.130191173
[18,] 0.178326648 0.180426648
[19,] -0.252473352 0.178326648
[20,] 0.312326648 -0.252473352
[21,] -0.187073352 0.312326648
[22,] 0.412026648 -0.187073352
[23,] 0.382726648 0.412026648
[24,] -0.045253659 0.382726648
[25,] -0.054537190 -0.045253659
[26,] -0.478437190 -0.054537190
[27,] -0.579937190 -0.478437190
[28,] 0.012662810 -0.579937190
[29,] -0.772101715 0.012662810
[30,] -0.882201715 -0.772101715
[31,] -0.685001715 -0.882201715
[32,] -0.650201715 -0.685001715
[33,] -1.176601715 -0.650201715
[34,] -0.422501715 -1.176601715
[35,] -0.613801715 -0.422501715
[36,] -0.095782022 -0.613801715
[37,] -0.100065554 -0.095782022
[38,] -0.649965554 -0.100065554
[39,] -0.071465554 -0.649965554
[40,] 0.387134446 -0.071465554
[41,] -0.523630078 0.387134446
[42,] 0.416269922 -0.523630078
[43,] 0.235469922 0.416269922
[44,] -0.355730078 0.235469922
[45,] 1.157869922 -0.355730078
[46,] -0.371030078 1.157869922
[47,] 0.003669922 -0.371030078
[48,] 0.443689615 0.003669922
[49,] 0.339406083 0.443689615
[50,] 0.776506083 0.339406083
[51,] 0.730006083 0.776506083
[52,] 0.482606083 0.730006083
[53,] 1.079841559 0.482606083
[54,] 0.578741559 1.079841559
[55,] 0.533941559 0.578741559
[56,] -0.241258441 0.533941559
[57,] 0.636341559 -0.241258441
[58,] -0.008558441 0.636341559
[59,] 0.141141559 -0.008558441
[60,] 0.180161252 0.141141559
[61,] -0.688122280 0.180161252
[62,] -0.173022280 -0.688122280
[63,] -0.495522280 -0.173022280
[64,] -1.298922280 -0.495522280
[65,] 0.511958441 -1.298922280
[66,] 0.133858441 0.511958441
[67,] -0.063941559 0.133858441
[68,] 0.660858441 -0.063941559
[69,] 0.512458441 0.660858441
[70,] -0.276441559 0.512458441
[71,] 0.687258441 -0.276441559
[72,] 0.042278135 0.687258441
[73,] -0.014005397 0.042278135
[74,] 0.673094603 -0.014005397
[75,] -0.048405397 0.673094603
[76,] -0.680805397 -0.048405397
[77,] 0.102430078 -0.680805397
[78,] -0.153669922 0.102430078
[79,] -0.408469922 -0.153669922
[80,] 0.028330078 -0.408469922
[81,] -0.183069922 0.028330078
[82,] -0.424969922 -0.183069922
[83,] 0.037730078 -0.424969922
[84,] -1.117250228 0.037730078
[85,] -0.217533760 -1.117250228
[86,] -0.016433760 -0.217533760
[87,] -0.711933760 -0.016433760
[88,] -0.435333760 -0.711933760
[89,] -0.602098285 -0.435333760
[90,] -0.408198285 -0.602098285
[91,] 0.135001715 -0.408198285
[92,] 0.118801715 0.135001715
[93,] -0.827598285 0.118801715
[94,] 0.339501715 -0.827598285
[95,] -0.014798285 0.339501715
[96,] -0.230778592 -0.014798285
[97,] 0.340937877 -0.230778592
[98,] 0.183037877 0.340937877
[99,] 0.667537877 0.183037877
[100,] 0.606137877 0.667537877
[101,] -0.224626648 0.606137877
[102,] -0.007726648 -0.224626648
[103,] 0.369473352 -0.007726648
[104,] 0.372273352 0.369473352
[105,] -0.365126648 0.372273352
[106,] -0.049026648 -0.365126648
[107,] -0.895326648 -0.049026648
[108,] -0.102306955 -0.895326648
[109,] 0.127409513 -0.102306955
[110,] -0.921490487 0.127409513
[111,] 0.854009513 -0.921490487
[112,] 0.772609513 0.854009513
[113,] 0.052844989 0.772609513
[114,] -0.098255011 0.052844989
[115,] 0.890944989 -0.098255011
[116,] -0.879255011 0.890944989
[117,] 0.623344989 -0.879255011
[118,] 0.647444989 0.623344989
[119,] -0.297855011 0.647444989
[120,] 0.185164682 -0.297855011
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.028519536 0.377803068
2 0.388619536 0.028519536
3 -0.015880464 0.388619536
4 0.023719536 -0.015880464
5 0.194955011 0.023719536
6 0.242855011 0.194955011
7 -0.754944989 0.242855011
8 0.633855011 -0.754944989
9 -0.190544989 0.633855011
10 0.153555011 -0.190544989
11 0.569255011 0.153555011
12 0.362274705 0.569255011
13 0.237991173 0.362274705
14 0.218091173 0.237991173
15 -0.328408827 0.218091173
16 0.130191173 -0.328408827
17 0.180426648 0.130191173
18 0.178326648 0.180426648
19 -0.252473352 0.178326648
20 0.312326648 -0.252473352
21 -0.187073352 0.312326648
22 0.412026648 -0.187073352
23 0.382726648 0.412026648
24 -0.045253659 0.382726648
25 -0.054537190 -0.045253659
26 -0.478437190 -0.054537190
27 -0.579937190 -0.478437190
28 0.012662810 -0.579937190
29 -0.772101715 0.012662810
30 -0.882201715 -0.772101715
31 -0.685001715 -0.882201715
32 -0.650201715 -0.685001715
33 -1.176601715 -0.650201715
34 -0.422501715 -1.176601715
35 -0.613801715 -0.422501715
36 -0.095782022 -0.613801715
37 -0.100065554 -0.095782022
38 -0.649965554 -0.100065554
39 -0.071465554 -0.649965554
40 0.387134446 -0.071465554
41 -0.523630078 0.387134446
42 0.416269922 -0.523630078
43 0.235469922 0.416269922
44 -0.355730078 0.235469922
45 1.157869922 -0.355730078
46 -0.371030078 1.157869922
47 0.003669922 -0.371030078
48 0.443689615 0.003669922
49 0.339406083 0.443689615
50 0.776506083 0.339406083
51 0.730006083 0.776506083
52 0.482606083 0.730006083
53 1.079841559 0.482606083
54 0.578741559 1.079841559
55 0.533941559 0.578741559
56 -0.241258441 0.533941559
57 0.636341559 -0.241258441
58 -0.008558441 0.636341559
59 0.141141559 -0.008558441
60 0.180161252 0.141141559
61 -0.688122280 0.180161252
62 -0.173022280 -0.688122280
63 -0.495522280 -0.173022280
64 -1.298922280 -0.495522280
65 0.511958441 -1.298922280
66 0.133858441 0.511958441
67 -0.063941559 0.133858441
68 0.660858441 -0.063941559
69 0.512458441 0.660858441
70 -0.276441559 0.512458441
71 0.687258441 -0.276441559
72 0.042278135 0.687258441
73 -0.014005397 0.042278135
74 0.673094603 -0.014005397
75 -0.048405397 0.673094603
76 -0.680805397 -0.048405397
77 0.102430078 -0.680805397
78 -0.153669922 0.102430078
79 -0.408469922 -0.153669922
80 0.028330078 -0.408469922
81 -0.183069922 0.028330078
82 -0.424969922 -0.183069922
83 0.037730078 -0.424969922
84 -1.117250228 0.037730078
85 -0.217533760 -1.117250228
86 -0.016433760 -0.217533760
87 -0.711933760 -0.016433760
88 -0.435333760 -0.711933760
89 -0.602098285 -0.435333760
90 -0.408198285 -0.602098285
91 0.135001715 -0.408198285
92 0.118801715 0.135001715
93 -0.827598285 0.118801715
94 0.339501715 -0.827598285
95 -0.014798285 0.339501715
96 -0.230778592 -0.014798285
97 0.340937877 -0.230778592
98 0.183037877 0.340937877
99 0.667537877 0.183037877
100 0.606137877 0.667537877
101 -0.224626648 0.606137877
102 -0.007726648 -0.224626648
103 0.369473352 -0.007726648
104 0.372273352 0.369473352
105 -0.365126648 0.372273352
106 -0.049026648 -0.365126648
107 -0.895326648 -0.049026648
108 -0.102306955 -0.895326648
109 0.127409513 -0.102306955
110 -0.921490487 0.127409513
111 0.854009513 -0.921490487
112 0.772609513 0.854009513
113 0.052844989 0.772609513
114 -0.098255011 0.052844989
115 0.890944989 -0.098255011
116 -0.879255011 0.890944989
117 0.623344989 -0.879255011
118 0.647444989 0.623344989
119 -0.297855011 0.647444989
120 0.185164682 -0.297855011
> 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/7itr11229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a6i01229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9epkd1229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gz641229175079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o6ya1229175079.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/12w81a1229175079.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/13vecj1229175079.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/14l0q31229175079.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/15aakx1229175079.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/16e36x1229175079.tab")
+ }
>
> system("convert tmp/14q531229175079.ps tmp/14q531229175079.png")
> system("convert tmp/26zso1229175079.ps tmp/26zso1229175079.png")
> system("convert tmp/3jwd11229175079.ps tmp/3jwd11229175079.png")
> system("convert tmp/4pvgu1229175079.ps tmp/4pvgu1229175079.png")
> system("convert tmp/5vo751229175079.ps tmp/5vo751229175079.png")
> system("convert tmp/66mcv1229175079.ps tmp/66mcv1229175079.png")
> system("convert tmp/7itr11229175079.ps tmp/7itr11229175079.png")
> system("convert tmp/8a6i01229175079.ps tmp/8a6i01229175079.png")
> system("convert tmp/9epkd1229175079.ps tmp/9epkd1229175079.png")
> system("convert tmp/10gz641229175079.ps tmp/10gz641229175079.png")
>
>
> proc.time()
user system elapsed
3.181 1.684 4.075