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(6.1
+ ,110.4
+ ,6.1
+ ,6.3
+ ,6.3
+ ,96.4
+ ,6.1
+ ,6.1
+ ,6.3
+ ,101.9
+ ,6.3
+ ,6.1
+ ,6
+ ,106.2
+ ,6.3
+ ,6.3
+ ,6.2
+ ,81
+ ,6
+ ,6.3
+ ,6.4
+ ,94.7
+ ,6.2
+ ,6
+ ,6.8
+ ,101
+ ,6.4
+ ,6.2
+ ,7.5
+ ,109.4
+ ,6.8
+ ,6.4
+ ,7.5
+ ,102.3
+ ,7.5
+ ,6.8
+ ,7.6
+ ,90.7
+ ,7.5
+ ,7.5
+ ,7.6
+ ,96.2
+ ,7.6
+ ,7.5
+ ,7.4
+ ,96.1
+ ,7.6
+ ,7.6
+ ,7.3
+ ,106
+ ,7.4
+ ,7.6
+ ,7.1
+ ,103.1
+ ,7.3
+ ,7.4
+ ,6.9
+ ,102
+ ,7.1
+ ,7.3
+ ,6.8
+ ,104.7
+ ,6.9
+ ,7.1
+ ,7.5
+ ,86
+ ,6.8
+ ,6.9
+ ,7.6
+ ,92.1
+ ,7.5
+ ,6.8
+ ,7.8
+ ,106.9
+ ,7.6
+ ,7.5
+ ,8
+ ,112.6
+ ,7.8
+ ,7.6
+ ,8.1
+ ,101.7
+ ,8
+ ,7.8
+ ,8.2
+ ,92
+ ,8.1
+ ,8
+ ,8.3
+ ,97.4
+ ,8.2
+ ,8.1
+ ,8.2
+ ,97
+ ,8.3
+ ,8.2
+ ,8
+ ,105.4
+ ,8.2
+ ,8.3
+ ,7.9
+ ,102.7
+ ,8
+ ,8.2
+ ,7.6
+ ,98.1
+ ,7.9
+ ,8
+ ,7.6
+ ,104.5
+ ,7.6
+ ,7.9
+ ,8.3
+ ,87.4
+ ,7.6
+ ,7.6
+ ,8.4
+ ,89.9
+ ,8.3
+ ,7.6
+ ,8.4
+ ,109.8
+ ,8.4
+ ,8.3
+ ,8.4
+ ,111.7
+ ,8.4
+ ,8.4
+ ,8.4
+ ,98.6
+ ,8.4
+ ,8.4
+ ,8.6
+ ,96.9
+ ,8.4
+ ,8.4
+ ,8.9
+ ,95.1
+ ,8.6
+ ,8.4
+ ,8.8
+ ,97
+ ,8.9
+ ,8.6
+ ,8.3
+ ,112.7
+ ,8.8
+ ,8.9
+ ,7.5
+ ,102.9
+ ,8.3
+ ,8.8
+ ,7.2
+ ,97.4
+ ,7.5
+ ,8.3
+ ,7.4
+ ,111.4
+ ,7.2
+ ,7.5
+ ,8.8
+ ,87.4
+ ,7.4
+ ,7.2
+ ,9.3
+ ,96.8
+ ,8.8
+ ,7.4
+ ,9.3
+ ,114.1
+ ,9.3
+ ,8.8
+ ,8.7
+ ,110.3
+ ,9.3
+ ,9.3
+ ,8.2
+ ,103.9
+ ,8.7
+ ,9.3
+ ,8.3
+ ,101.6
+ ,8.2
+ ,8.7
+ ,8.5
+ ,94.6
+ ,8.3
+ ,8.2
+ ,8.6
+ ,95.9
+ ,8.5
+ ,8.3
+ ,8.5
+ ,104.7
+ ,8.6
+ ,8.5
+ ,8.2
+ ,102.8
+ ,8.5
+ ,8.6
+ ,8.1
+ ,98.1
+ ,8.2
+ ,8.5
+ ,7.9
+ ,113.9
+ ,8.1
+ ,8.2
+ ,8.6
+ ,80.9
+ ,7.9
+ ,8.1
+ ,8.7
+ ,95.7
+ ,8.6
+ ,7.9
+ ,8.7
+ ,113.2
+ ,8.7
+ ,8.6
+ ,8.5
+ ,105.9
+ ,8.7
+ ,8.7
+ ,8.4
+ ,108.8
+ ,8.5
+ ,8.7
+ ,8.5
+ ,102.3
+ ,8.4
+ ,8.5
+ ,8.7
+ ,99
+ ,8.5
+ ,8.4
+ ,8.7
+ ,100.7
+ ,8.7
+ ,8.5
+ ,8.6
+ ,115.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,100.7
+ ,8.6
+ ,8.7
+ ,8.3
+ ,109.9
+ ,8.5
+ ,8.6
+ ,8
+ ,114.6
+ ,8.3
+ ,8.5
+ ,8.2
+ ,85.4
+ ,8
+ ,8.3
+ ,8.1
+ ,100.5
+ ,8.2
+ ,8
+ ,8.1
+ ,114.8
+ ,8.1
+ ,8.2
+ ,8
+ ,116.5
+ ,8.1
+ ,8.1
+ ,7.9
+ ,112.9
+ ,8
+ ,8.1
+ ,7.9
+ ,102
+ ,7.9
+ ,8
+ ,8
+ ,106
+ ,7.9
+ ,7.9
+ ,8
+ ,105.3
+ ,8
+ ,7.9
+ ,7.9
+ ,118.8
+ ,8
+ ,8
+ ,8
+ ,106.1
+ ,7.9
+ ,8
+ ,7.7
+ ,109.3
+ ,8
+ ,7.9
+ ,7.2
+ ,117.2
+ ,7.7
+ ,8
+ ,7.5
+ ,92.5
+ ,7.2
+ ,7.7
+ ,7.3
+ ,104.2
+ ,7.5
+ ,7.2
+ ,7
+ ,112.5
+ ,7.3
+ ,7.5
+ ,7
+ ,122.4
+ ,7
+ ,7.3
+ ,7
+ ,113.3
+ ,7
+ ,7
+ ,7.2
+ ,100
+ ,7
+ ,7
+ ,7.3
+ ,110.7
+ ,7.2
+ ,7
+ ,7.1
+ ,112.8
+ ,7.3
+ ,7.2
+ ,6.8
+ ,109.8
+ ,7.1
+ ,7.3
+ ,6.4
+ ,117.3
+ ,6.8
+ ,7.1
+ ,6.1
+ ,109.1
+ ,6.4
+ ,6.8
+ ,6.5
+ ,115.9
+ ,6.1
+ ,6.4
+ ,7.7
+ ,96
+ ,6.5
+ ,6.1
+ ,7.9
+ ,99.8
+ ,7.7
+ ,6.5
+ ,7.5
+ ,116.8
+ ,7.9
+ ,7.7
+ ,6.9
+ ,115.7
+ ,7.5
+ ,7.9
+ ,6.6
+ ,99.4
+ ,6.9
+ ,7.5
+ ,6.9
+ ,94.3
+ ,6.6
+ ,6.9
+ ,7.7
+ ,91
+ ,6.9
+ ,6.6)
+ ,dim=c(4
+ ,95)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:95))
> y <- array(NA,dim=c(4,95),dimnames=list(c('Y','X','Y1','Y2'),1:95))
> 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 Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.1 110.4 6.1 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.3 96.4 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 2
3 6.3 101.9 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.0 106.2 6.3 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.2 81.0 6.0 6.3 0 0 0 0 1 0 0 0 0 0 0 5
6 6.4 94.7 6.2 6.0 0 0 0 0 0 1 0 0 0 0 0 6
7 6.8 101.0 6.4 6.2 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 109.4 6.8 6.4 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 102.3 7.5 6.8 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 90.7 7.5 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.6 96.2 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.4 96.1 7.6 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 7.3 106.0 7.4 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 7.1 103.1 7.3 7.4 0 1 0 0 0 0 0 0 0 0 0 14
15 6.9 102.0 7.1 7.3 0 0 1 0 0 0 0 0 0 0 0 15
16 6.8 104.7 6.9 7.1 0 0 0 1 0 0 0 0 0 0 0 16
17 7.5 86.0 6.8 6.9 0 0 0 0 1 0 0 0 0 0 0 17
18 7.6 92.1 7.5 6.8 0 0 0 0 0 1 0 0 0 0 0 18
19 7.8 106.9 7.6 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.0 112.6 7.8 7.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 101.7 8.0 7.8 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 92.0 8.1 8.0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.3 97.4 8.2 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 97.0 8.3 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 105.4 8.2 8.3 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 102.7 8.0 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 7.6 98.1 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.6 104.5 7.6 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.3 87.4 7.6 7.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.4 89.9 8.3 7.6 0 0 0 0 0 1 0 0 0 0 0 30
31 8.4 109.8 8.4 8.3 0 0 0 0 0 0 1 0 0 0 0 31
32 8.4 111.7 8.4 8.4 0 0 0 0 0 0 0 1 0 0 0 32
33 8.4 98.6 8.4 8.4 0 0 0 0 0 0 0 0 1 0 0 33
34 8.6 96.9 8.4 8.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.9 95.1 8.6 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 8.8 97.0 8.9 8.6 0 0 0 0 0 0 0 0 0 0 0 36
37 8.3 112.7 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 37
38 7.5 102.9 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 38
39 7.2 97.4 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 39
40 7.4 111.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 40
41 8.8 87.4 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 9.3 96.8 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 42
43 9.3 114.1 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 43
44 8.7 110.3 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 44
45 8.2 103.9 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 45
46 8.3 101.6 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 46
47 8.5 94.6 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 8.6 95.9 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 48
49 8.5 104.7 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 49
50 8.2 102.8 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.1 98.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 113.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.6 80.9 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.7 95.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 8.7 113.2 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 55
56 8.5 105.9 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 8.4 108.8 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 57
58 8.5 102.3 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 58
59 8.7 99.0 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 59
60 8.7 100.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 60
61 8.6 115.5 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 61
62 8.5 100.7 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 62
63 8.3 109.9 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 63
64 8.0 114.6 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0 64
65 8.2 85.4 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0 65
66 8.1 100.5 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 8.1 114.8 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 67
68 8.0 116.5 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0 68
69 7.9 112.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 69
70 7.9 102.0 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 70
71 8.0 106.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1 71
72 8.0 105.3 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 72
73 7.9 118.8 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 73
74 8.0 106.1 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 74
75 7.7 109.3 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 75
76 7.2 117.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0 76
77 7.5 92.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0 77
78 7.3 104.2 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0 78
79 7.0 112.5 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 79
80 7.0 122.4 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 80
81 7.0 113.3 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 81
82 7.2 100.0 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0 82
83 7.3 110.7 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1 83
84 7.1 112.8 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 84
85 6.8 109.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0 85
86 6.4 117.3 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0 86
87 6.1 109.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0 87
88 6.5 115.9 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0 88
89 7.7 96.0 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 89
90 7.9 99.8 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0 90
91 7.5 116.8 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 91
92 6.9 115.7 7.5 7.9 0 0 0 0 0 0 0 1 0 0 0 92
93 6.6 99.4 6.9 7.5 0 0 0 0 0 0 0 0 1 0 0 93
94 6.9 94.3 6.6 6.9 0 0 0 0 0 0 0 0 0 1 0 94
95 7.7 91.0 6.9 6.6 0 0 0 0 0 0 0 0 0 0 1 95
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
1.6705854 -0.0094048 1.4664080 -0.5787266 0.0892564 0.0340632
M3 M4 M5 M6 M7 M8
0.0054112 0.1740025 0.6463160 -0.1991109 0.1488553 0.1745287
M9 M10 M11 t
0.0891393 0.2755088 0.2448945 0.0008677
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.511921 -0.118133 0.007142 0.116135 0.584916
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6705854 0.6021832 2.774 0.0069 **
X -0.0094048 0.0055694 -1.689 0.0952 .
Y1 1.4664080 0.0944860 15.520 < 2e-16 ***
Y2 -0.5787266 0.0934879 -6.190 2.51e-08 ***
M1 0.0892564 0.1261000 0.708 0.4811
M2 0.0340632 0.1159003 0.294 0.7696
M3 0.0054112 0.1162264 0.047 0.9630
M4 0.1740025 0.1309258 1.329 0.1877
M5 0.6463160 0.1370400 4.716 1.02e-05 ***
M6 -0.1991109 0.1237505 -1.609 0.1116
M7 0.1488553 0.1232928 1.207 0.2309
M8 0.1745287 0.1295594 1.347 0.1818
M9 0.0891393 0.1134981 0.785 0.4346
M10 0.2755088 0.1134708 2.428 0.0175 *
M11 0.2448945 0.1103753 2.219 0.0294 *
t 0.0008677 0.0010639 0.816 0.4172
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2105 on 79 degrees of freedom
Multiple R-squared: 0.937, Adjusted R-squared: 0.925
F-statistic: 78.3 on 15 and 79 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,] 0.37615287 0.7523057 0.6238471
[2,] 0.45505160 0.9101032 0.5449484
[3,] 0.31413201 0.6282640 0.6858680
[4,] 0.44239962 0.8847992 0.5576004
[5,] 0.35092950 0.7018590 0.6490705
[6,] 0.25990574 0.5198115 0.7400943
[7,] 0.19680493 0.3936099 0.8031951
[8,] 0.12967628 0.2593526 0.8703237
[9,] 0.13347345 0.2669469 0.8665265
[10,] 0.10805721 0.2161144 0.8919428
[11,] 0.08323576 0.1664715 0.9167642
[12,] 0.05747711 0.1149542 0.9425229
[13,] 0.05192364 0.1038473 0.9480764
[14,] 0.11067293 0.2213459 0.8893271
[15,] 0.08867060 0.1773412 0.9113294
[16,] 0.08646869 0.1729374 0.9135313
[17,] 0.05991408 0.1198282 0.9400859
[18,] 0.05710928 0.1142186 0.9428907
[19,] 0.11664897 0.2332979 0.8833510
[20,] 0.58558354 0.8288329 0.4144165
[21,] 0.52309463 0.9538107 0.4769054
[22,] 0.45739616 0.9147923 0.5426038
[23,] 0.52137327 0.9572535 0.4786267
[24,] 0.48138039 0.9627608 0.5186196
[25,] 0.42095422 0.8419084 0.5790458
[26,] 0.71452024 0.5709595 0.2854798
[27,] 0.66881215 0.6623757 0.3311879
[28,] 0.62648071 0.7470386 0.3735193
[29,] 0.72111133 0.5577773 0.2788887
[30,] 0.69172583 0.6165483 0.3082742
[31,] 0.70123205 0.5975359 0.2987679
[32,] 0.75870908 0.4825818 0.2412909
[33,] 0.69984739 0.6003052 0.3001526
[34,] 0.75702892 0.4859422 0.2429711
[35,] 0.71041681 0.5791664 0.2895832
[36,] 0.69474982 0.6105004 0.3052502
[37,] 0.65108631 0.6978274 0.3489137
[38,] 0.76899302 0.4620140 0.2310070
[39,] 0.71177994 0.5764401 0.2882201
[40,] 0.67241761 0.6551648 0.3275824
[41,] 0.62811294 0.7437741 0.3718871
[42,] 0.56763788 0.8647242 0.4323621
[43,] 0.52501306 0.9499739 0.4749869
[44,] 0.45707093 0.9141419 0.5429291
[45,] 0.48410350 0.9682070 0.5158965
[46,] 0.42842428 0.8568486 0.5715757
[47,] 0.67007580 0.6598484 0.3299242
[48,] 0.63918641 0.7216272 0.3608136
[49,] 0.65855382 0.6828924 0.3414462
[50,] 0.66295232 0.6740954 0.3370477
[51,] 0.60524017 0.7895197 0.3947598
[52,] 0.53424202 0.9315160 0.4657580
[53,] 0.43687061 0.8737412 0.5631294
[54,] 0.33118595 0.6623719 0.6688141
[55,] 0.56599708 0.8680058 0.4340029
[56,] 0.60701521 0.7859696 0.3929848
[57,] 0.77632589 0.4473482 0.2236741
[58,] 0.64377604 0.7124479 0.3562240
> postscript(file="/var/www/html/rcomp/tmp/1rbsb1258652872.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jroj1258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30d7o1258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kmg71258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59pc41258652872.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 = 95
Frequency = 1
1 2 3 4 5 6
0.078470165 0.085383040 -0.128387835 -0.441660848 -0.511920736 0.194584780
7 8 9 10 11 12
0.127464881 0.409106290 -0.368141105 -0.159365448 -0.224533091 -0.123574140
13 14 15 16 17 18
0.072691034 -0.069361919 -0.016513987 -0.083043730 -0.001199283 -0.083628993
19 20 21 22 23 24
0.165196075 0.156853444 0.061326460 -0.148032845 -0.056268310 -0.004771600
25 26 27 28 29 30
-0.011381780 0.152959688 -0.131622669 0.141158839 0.033537519 -0.024876841
31 32 33 34 35 36
0.071912749 0.121113447 0.082432166 0.079206810 0.098743266 -0.063537907
37 38 39 40 41 42
-0.185747668 -0.348257938 0.211563001 0.350712437 0.584916339 0.080654874
43 44 45 46 47 48
-0.028462623 -0.401378696 0.002797040 0.279896844 0.007805801 0.128649884
49 50 51 52 53 54
-0.009607314 -0.068637481 0.196993942 -0.050846272 0.101022875 0.042542339
55 56 57 58 59 60
0.116760389 -0.120563150 0.184514103 0.067041148 0.061238533 0.085844539
61 62 63 64 65 66
0.150656991 0.112432138 0.115508795 -0.074338671 -0.297963112 0.121709136
67 68 69 70 71 72
0.169750103 0.001324520 0.098629719 -0.102351733 0.007141552 0.097944160
73 74 75 76 77 78
0.092657702 0.274182946 -0.172450847 -0.269816808 -0.115710670 -0.090400890
79 80 81 82 83 84
-0.194275312 0.196468274 0.021788249 -0.090532884 -0.153436324 -0.120554935
85 86 87 88 89 90
-0.187739129 -0.138700475 -0.075090400 0.427835053 0.207317068 -0.240584404
91 92 93 94 95
-0.428346262 -0.362924129 -0.083346632 0.074138108 0.259308573
> postscript(file="/var/www/html/rcomp/tmp/6x92q1258652872.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 = 95
Frequency = 1
lag(myerror, k = 1) myerror
0 0.078470165 NA
1 0.085383040 0.078470165
2 -0.128387835 0.085383040
3 -0.441660848 -0.128387835
4 -0.511920736 -0.441660848
5 0.194584780 -0.511920736
6 0.127464881 0.194584780
7 0.409106290 0.127464881
8 -0.368141105 0.409106290
9 -0.159365448 -0.368141105
10 -0.224533091 -0.159365448
11 -0.123574140 -0.224533091
12 0.072691034 -0.123574140
13 -0.069361919 0.072691034
14 -0.016513987 -0.069361919
15 -0.083043730 -0.016513987
16 -0.001199283 -0.083043730
17 -0.083628993 -0.001199283
18 0.165196075 -0.083628993
19 0.156853444 0.165196075
20 0.061326460 0.156853444
21 -0.148032845 0.061326460
22 -0.056268310 -0.148032845
23 -0.004771600 -0.056268310
24 -0.011381780 -0.004771600
25 0.152959688 -0.011381780
26 -0.131622669 0.152959688
27 0.141158839 -0.131622669
28 0.033537519 0.141158839
29 -0.024876841 0.033537519
30 0.071912749 -0.024876841
31 0.121113447 0.071912749
32 0.082432166 0.121113447
33 0.079206810 0.082432166
34 0.098743266 0.079206810
35 -0.063537907 0.098743266
36 -0.185747668 -0.063537907
37 -0.348257938 -0.185747668
38 0.211563001 -0.348257938
39 0.350712437 0.211563001
40 0.584916339 0.350712437
41 0.080654874 0.584916339
42 -0.028462623 0.080654874
43 -0.401378696 -0.028462623
44 0.002797040 -0.401378696
45 0.279896844 0.002797040
46 0.007805801 0.279896844
47 0.128649884 0.007805801
48 -0.009607314 0.128649884
49 -0.068637481 -0.009607314
50 0.196993942 -0.068637481
51 -0.050846272 0.196993942
52 0.101022875 -0.050846272
53 0.042542339 0.101022875
54 0.116760389 0.042542339
55 -0.120563150 0.116760389
56 0.184514103 -0.120563150
57 0.067041148 0.184514103
58 0.061238533 0.067041148
59 0.085844539 0.061238533
60 0.150656991 0.085844539
61 0.112432138 0.150656991
62 0.115508795 0.112432138
63 -0.074338671 0.115508795
64 -0.297963112 -0.074338671
65 0.121709136 -0.297963112
66 0.169750103 0.121709136
67 0.001324520 0.169750103
68 0.098629719 0.001324520
69 -0.102351733 0.098629719
70 0.007141552 -0.102351733
71 0.097944160 0.007141552
72 0.092657702 0.097944160
73 0.274182946 0.092657702
74 -0.172450847 0.274182946
75 -0.269816808 -0.172450847
76 -0.115710670 -0.269816808
77 -0.090400890 -0.115710670
78 -0.194275312 -0.090400890
79 0.196468274 -0.194275312
80 0.021788249 0.196468274
81 -0.090532884 0.021788249
82 -0.153436324 -0.090532884
83 -0.120554935 -0.153436324
84 -0.187739129 -0.120554935
85 -0.138700475 -0.187739129
86 -0.075090400 -0.138700475
87 0.427835053 -0.075090400
88 0.207317068 0.427835053
89 -0.240584404 0.207317068
90 -0.428346262 -0.240584404
91 -0.362924129 -0.428346262
92 -0.083346632 -0.362924129
93 0.074138108 -0.083346632
94 0.259308573 0.074138108
95 NA 0.259308573
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.085383040 0.078470165
[2,] -0.128387835 0.085383040
[3,] -0.441660848 -0.128387835
[4,] -0.511920736 -0.441660848
[5,] 0.194584780 -0.511920736
[6,] 0.127464881 0.194584780
[7,] 0.409106290 0.127464881
[8,] -0.368141105 0.409106290
[9,] -0.159365448 -0.368141105
[10,] -0.224533091 -0.159365448
[11,] -0.123574140 -0.224533091
[12,] 0.072691034 -0.123574140
[13,] -0.069361919 0.072691034
[14,] -0.016513987 -0.069361919
[15,] -0.083043730 -0.016513987
[16,] -0.001199283 -0.083043730
[17,] -0.083628993 -0.001199283
[18,] 0.165196075 -0.083628993
[19,] 0.156853444 0.165196075
[20,] 0.061326460 0.156853444
[21,] -0.148032845 0.061326460
[22,] -0.056268310 -0.148032845
[23,] -0.004771600 -0.056268310
[24,] -0.011381780 -0.004771600
[25,] 0.152959688 -0.011381780
[26,] -0.131622669 0.152959688
[27,] 0.141158839 -0.131622669
[28,] 0.033537519 0.141158839
[29,] -0.024876841 0.033537519
[30,] 0.071912749 -0.024876841
[31,] 0.121113447 0.071912749
[32,] 0.082432166 0.121113447
[33,] 0.079206810 0.082432166
[34,] 0.098743266 0.079206810
[35,] -0.063537907 0.098743266
[36,] -0.185747668 -0.063537907
[37,] -0.348257938 -0.185747668
[38,] 0.211563001 -0.348257938
[39,] 0.350712437 0.211563001
[40,] 0.584916339 0.350712437
[41,] 0.080654874 0.584916339
[42,] -0.028462623 0.080654874
[43,] -0.401378696 -0.028462623
[44,] 0.002797040 -0.401378696
[45,] 0.279896844 0.002797040
[46,] 0.007805801 0.279896844
[47,] 0.128649884 0.007805801
[48,] -0.009607314 0.128649884
[49,] -0.068637481 -0.009607314
[50,] 0.196993942 -0.068637481
[51,] -0.050846272 0.196993942
[52,] 0.101022875 -0.050846272
[53,] 0.042542339 0.101022875
[54,] 0.116760389 0.042542339
[55,] -0.120563150 0.116760389
[56,] 0.184514103 -0.120563150
[57,] 0.067041148 0.184514103
[58,] 0.061238533 0.067041148
[59,] 0.085844539 0.061238533
[60,] 0.150656991 0.085844539
[61,] 0.112432138 0.150656991
[62,] 0.115508795 0.112432138
[63,] -0.074338671 0.115508795
[64,] -0.297963112 -0.074338671
[65,] 0.121709136 -0.297963112
[66,] 0.169750103 0.121709136
[67,] 0.001324520 0.169750103
[68,] 0.098629719 0.001324520
[69,] -0.102351733 0.098629719
[70,] 0.007141552 -0.102351733
[71,] 0.097944160 0.007141552
[72,] 0.092657702 0.097944160
[73,] 0.274182946 0.092657702
[74,] -0.172450847 0.274182946
[75,] -0.269816808 -0.172450847
[76,] -0.115710670 -0.269816808
[77,] -0.090400890 -0.115710670
[78,] -0.194275312 -0.090400890
[79,] 0.196468274 -0.194275312
[80,] 0.021788249 0.196468274
[81,] -0.090532884 0.021788249
[82,] -0.153436324 -0.090532884
[83,] -0.120554935 -0.153436324
[84,] -0.187739129 -0.120554935
[85,] -0.138700475 -0.187739129
[86,] -0.075090400 -0.138700475
[87,] 0.427835053 -0.075090400
[88,] 0.207317068 0.427835053
[89,] -0.240584404 0.207317068
[90,] -0.428346262 -0.240584404
[91,] -0.362924129 -0.428346262
[92,] -0.083346632 -0.362924129
[93,] 0.074138108 -0.083346632
[94,] 0.259308573 0.074138108
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.085383040 0.078470165
2 -0.128387835 0.085383040
3 -0.441660848 -0.128387835
4 -0.511920736 -0.441660848
5 0.194584780 -0.511920736
6 0.127464881 0.194584780
7 0.409106290 0.127464881
8 -0.368141105 0.409106290
9 -0.159365448 -0.368141105
10 -0.224533091 -0.159365448
11 -0.123574140 -0.224533091
12 0.072691034 -0.123574140
13 -0.069361919 0.072691034
14 -0.016513987 -0.069361919
15 -0.083043730 -0.016513987
16 -0.001199283 -0.083043730
17 -0.083628993 -0.001199283
18 0.165196075 -0.083628993
19 0.156853444 0.165196075
20 0.061326460 0.156853444
21 -0.148032845 0.061326460
22 -0.056268310 -0.148032845
23 -0.004771600 -0.056268310
24 -0.011381780 -0.004771600
25 0.152959688 -0.011381780
26 -0.131622669 0.152959688
27 0.141158839 -0.131622669
28 0.033537519 0.141158839
29 -0.024876841 0.033537519
30 0.071912749 -0.024876841
31 0.121113447 0.071912749
32 0.082432166 0.121113447
33 0.079206810 0.082432166
34 0.098743266 0.079206810
35 -0.063537907 0.098743266
36 -0.185747668 -0.063537907
37 -0.348257938 -0.185747668
38 0.211563001 -0.348257938
39 0.350712437 0.211563001
40 0.584916339 0.350712437
41 0.080654874 0.584916339
42 -0.028462623 0.080654874
43 -0.401378696 -0.028462623
44 0.002797040 -0.401378696
45 0.279896844 0.002797040
46 0.007805801 0.279896844
47 0.128649884 0.007805801
48 -0.009607314 0.128649884
49 -0.068637481 -0.009607314
50 0.196993942 -0.068637481
51 -0.050846272 0.196993942
52 0.101022875 -0.050846272
53 0.042542339 0.101022875
54 0.116760389 0.042542339
55 -0.120563150 0.116760389
56 0.184514103 -0.120563150
57 0.067041148 0.184514103
58 0.061238533 0.067041148
59 0.085844539 0.061238533
60 0.150656991 0.085844539
61 0.112432138 0.150656991
62 0.115508795 0.112432138
63 -0.074338671 0.115508795
64 -0.297963112 -0.074338671
65 0.121709136 -0.297963112
66 0.169750103 0.121709136
67 0.001324520 0.169750103
68 0.098629719 0.001324520
69 -0.102351733 0.098629719
70 0.007141552 -0.102351733
71 0.097944160 0.007141552
72 0.092657702 0.097944160
73 0.274182946 0.092657702
74 -0.172450847 0.274182946
75 -0.269816808 -0.172450847
76 -0.115710670 -0.269816808
77 -0.090400890 -0.115710670
78 -0.194275312 -0.090400890
79 0.196468274 -0.194275312
80 0.021788249 0.196468274
81 -0.090532884 0.021788249
82 -0.153436324 -0.090532884
83 -0.120554935 -0.153436324
84 -0.187739129 -0.120554935
85 -0.138700475 -0.187739129
86 -0.075090400 -0.138700475
87 0.427835053 -0.075090400
88 0.207317068 0.427835053
89 -0.240584404 0.207317068
90 -0.428346262 -0.240584404
91 -0.362924129 -0.428346262
92 -0.083346632 -0.362924129
93 0.074138108 -0.083346632
94 0.259308573 0.074138108
> 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/7sgkj1258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83v081258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qfyh1258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mt981258652872.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gb991258652872.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/12ts6s1258652872.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/132pyf1258652872.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/140e401258652872.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/15apk21258652873.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/16x2ew1258652873.tab")
+ }
>
> system("convert tmp/1rbsb1258652872.ps tmp/1rbsb1258652872.png")
> system("convert tmp/2jroj1258652872.ps tmp/2jroj1258652872.png")
> system("convert tmp/30d7o1258652872.ps tmp/30d7o1258652872.png")
> system("convert tmp/4kmg71258652872.ps tmp/4kmg71258652872.png")
> system("convert tmp/59pc41258652872.ps tmp/59pc41258652872.png")
> system("convert tmp/6x92q1258652872.ps tmp/6x92q1258652872.png")
> system("convert tmp/7sgkj1258652872.ps tmp/7sgkj1258652872.png")
> system("convert tmp/83v081258652872.ps tmp/83v081258652872.png")
> system("convert tmp/9qfyh1258652872.ps tmp/9qfyh1258652872.png")
> system("convert tmp/10mt981258652872.ps tmp/10mt981258652872.png")
>
>
> proc.time()
user system elapsed
2.942 1.581 3.777