R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.0
+ ,2.77
+ ,8.0
+ ,7.8
+ ,7.6
+ ,7.6
+ ,8.0
+ ,2.93
+ ,8.0
+ ,8.0
+ ,7.8
+ ,7.6
+ ,7.9
+ ,2.91
+ ,8.0
+ ,8.0
+ ,8.0
+ ,7.8
+ ,7.9
+ ,2.69
+ ,7.9
+ ,8.0
+ ,8.0
+ ,8.0
+ ,8.0
+ ,2.38
+ ,7.9
+ ,7.9
+ ,8.0
+ ,8.0
+ ,8.5
+ ,2.58
+ ,8.0
+ ,7.9
+ ,7.9
+ ,8.0
+ ,9.2
+ ,3.19
+ ,8.5
+ ,8.0
+ ,7.9
+ ,7.9
+ ,9.4
+ ,2.82
+ ,9.2
+ ,8.5
+ ,8.0
+ ,7.9
+ ,9.5
+ ,2.72
+ ,9.4
+ ,9.2
+ ,8.5
+ ,8.0
+ ,9.5
+ ,2.53
+ ,9.5
+ ,9.4
+ ,9.2
+ ,8.5
+ ,9.6
+ ,2.70
+ ,9.5
+ ,9.5
+ ,9.4
+ ,9.2
+ ,9.7
+ ,2.42
+ ,9.6
+ ,9.5
+ ,9.5
+ ,9.4
+ ,9.7
+ ,2.50
+ ,9.7
+ ,9.6
+ ,9.5
+ ,9.5
+ ,9.6
+ ,2.31
+ ,9.7
+ ,9.7
+ ,9.6
+ ,9.5
+ ,9.5
+ ,2.41
+ ,9.6
+ ,9.7
+ ,9.7
+ ,9.6
+ ,9.4
+ ,2.56
+ ,9.5
+ ,9.6
+ ,9.7
+ ,9.7
+ ,9.3
+ ,2.76
+ ,9.4
+ ,9.5
+ ,9.6
+ ,9.7
+ ,9.6
+ ,2.71
+ ,9.3
+ ,9.4
+ ,9.5
+ ,9.6
+ ,10.2
+ ,2.44
+ ,9.6
+ ,9.3
+ ,9.4
+ ,9.5
+ ,10.2
+ ,2.46
+ ,10.2
+ ,9.6
+ ,9.3
+ ,9.4
+ ,10.1
+ ,2.12
+ ,10.2
+ ,10.2
+ ,9.6
+ ,9.3
+ ,9.9
+ ,1.99
+ ,10.1
+ ,10.2
+ ,10.2
+ ,9.6
+ ,9.8
+ ,1.86
+ ,9.9
+ ,10.1
+ ,10.2
+ ,10.2
+ ,9.8
+ ,1.88
+ ,9.8
+ ,9.9
+ ,10.1
+ ,10.2
+ ,9.7
+ ,1.82
+ ,9.8
+ ,9.8
+ ,9.9
+ ,10.1
+ ,9.5
+ ,1.74
+ ,9.7
+ ,9.8
+ ,9.8
+ ,9.9
+ ,9.3
+ ,1.71
+ ,9.5
+ ,9.7
+ ,9.8
+ ,9.8
+ ,9.1
+ ,1.38
+ ,9.3
+ ,9.5
+ ,9.7
+ ,9.8
+ ,9.0
+ ,1.27
+ ,9.1
+ ,9.3
+ ,9.5
+ ,9.7
+ ,9.5
+ ,1.19
+ ,9.0
+ ,9.1
+ ,9.3
+ ,9.5
+ ,10.0
+ ,1.28
+ ,9.5
+ ,9.0
+ ,9.1
+ ,9.3
+ ,10.2
+ ,1.19
+ ,10.0
+ ,9.5
+ ,9.0
+ ,9.1
+ ,10.1
+ ,1.22
+ ,10.2
+ ,10.0
+ ,9.5
+ ,9.0
+ ,10.0
+ ,1.47
+ ,10.1
+ ,10.2
+ ,10.0
+ ,9.5
+ ,9.9
+ ,1.46
+ ,10.0
+ ,10.1
+ ,10.2
+ ,10.0
+ ,10.0
+ ,1.96
+ ,9.9
+ ,10.0
+ ,10.1
+ ,10.2
+ ,9.9
+ ,1.88
+ ,10.0
+ ,9.9
+ ,10.0
+ ,10.1
+ ,9.7
+ ,2.03
+ ,9.9
+ ,10.0
+ ,9.9
+ ,10.0
+ ,9.5
+ ,2.04
+ ,9.7
+ ,9.9
+ ,10.0
+ ,9.9
+ ,9.2
+ ,1.90
+ ,9.5
+ ,9.7
+ ,9.9
+ ,10.0
+ ,9.0
+ ,1.80
+ ,9.2
+ ,9.5
+ ,9.7
+ ,9.9
+ ,9.3
+ ,1.92
+ ,9.0
+ ,9.2
+ ,9.5
+ ,9.7
+ ,9.8
+ ,1.92
+ ,9.3
+ ,9.0
+ ,9.2
+ ,9.5
+ ,9.8
+ ,1.97
+ ,9.8
+ ,9.3
+ ,9.0
+ ,9.2
+ ,9.6
+ ,2.46
+ ,9.8
+ ,9.8
+ ,9.3
+ ,9.0
+ ,9.4
+ ,2.36
+ ,9.6
+ ,9.8
+ ,9.8
+ ,9.3
+ ,9.3
+ ,2.53
+ ,9.4
+ ,9.6
+ ,9.8
+ ,9.8
+ ,9.2
+ ,2.31
+ ,9.3
+ ,9.4
+ ,9.6
+ ,9.8
+ ,9.2
+ ,1.98
+ ,9.2
+ ,9.3
+ ,9.4
+ ,9.6
+ ,9.0
+ ,1.46
+ ,9.2
+ ,9.2
+ ,9.3
+ ,9.4
+ ,8.8
+ ,1.26
+ ,9.0
+ ,9.2
+ ,9.2
+ ,9.3
+ ,8.7
+ ,1.58
+ ,8.8
+ ,9.0
+ ,9.2
+ ,9.2
+ ,8.7
+ ,1.74
+ ,8.7
+ ,8.8
+ ,9.0
+ ,9.2
+ ,9.1
+ ,1.89
+ ,8.7
+ ,8.7
+ ,8.8
+ ,9.0
+ ,9.7
+ ,1.85
+ ,9.1
+ ,8.7
+ ,8.7
+ ,8.8
+ ,9.8
+ ,1.62
+ ,9.7
+ ,9.1
+ ,8.7
+ ,8.7
+ ,9.6
+ ,1.30
+ ,9.8
+ ,9.7
+ ,9.1
+ ,8.7
+ ,9.4
+ ,1.42
+ ,9.6
+ ,9.8
+ ,9.7
+ ,9.1
+ ,9.4
+ ,1.15
+ ,9.4
+ ,9.6
+ ,9.8
+ ,9.7
+ ,9.5
+ ,0.42
+ ,9.4
+ ,9.4
+ ,9.6
+ ,9.8
+ ,9.4
+ ,0.74
+ ,9.5
+ ,9.4
+ ,9.4
+ ,9.6
+ ,9.3
+ ,1.02
+ ,9.4
+ ,9.5
+ ,9.4
+ ,9.4
+ ,9.2
+ ,1.51
+ ,9.3
+ ,9.4
+ ,9.5
+ ,9.4
+ ,9.0
+ ,1.86
+ ,9.2
+ ,9.3
+ ,9.4
+ ,9.5
+ ,8.9
+ ,1.59
+ ,9.0
+ ,9.2
+ ,9.3
+ ,9.4
+ ,9.2
+ ,1.03
+ ,8.9
+ ,9.0
+ ,9.2
+ ,9.3
+ ,9.8
+ ,0.44
+ ,9.2
+ ,8.9
+ ,9.0
+ ,9.2
+ ,9.9
+ ,0.82
+ ,9.8
+ ,9.2
+ ,8.9
+ ,9.0
+ ,9.6
+ ,0.86
+ ,9.9
+ ,9.8
+ ,9.2
+ ,8.9
+ ,9.2
+ ,0.58
+ ,9.6
+ ,9.9
+ ,9.8
+ ,9.2
+ ,9.1
+ ,0.59
+ ,9.2
+ ,9.6
+ ,9.9
+ ,9.8
+ ,9.1
+ ,0.95
+ ,9.1
+ ,9.2
+ ,9.6
+ ,9.9
+ ,9.0
+ ,0.98
+ ,9.1
+ ,9.1
+ ,9.2
+ ,9.6
+ ,8.9
+ ,1.23
+ ,9.0
+ ,9.1
+ ,9.1
+ ,9.2
+ ,8.7
+ ,1.17
+ ,8.9
+ ,9.0
+ ,9.1
+ ,9.1
+ ,8.5
+ ,0.84
+ ,8.7
+ ,8.9
+ ,9.0
+ ,9.1
+ ,8.3
+ ,0.74
+ ,8.5
+ ,8.7
+ ,8.9
+ ,9.0
+ ,8.5
+ ,0.65
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.9
+ ,8.7
+ ,0.91
+ ,8.5
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.4
+ ,1.19
+ ,8.7
+ ,8.5
+ ,8.3
+ ,8.5
+ ,8.1
+ ,1.30
+ ,8.4
+ ,8.7
+ ,8.5
+ ,8.3
+ ,7.8
+ ,1.53
+ ,8.1
+ ,8.4
+ ,8.7
+ ,8.5
+ ,7.7
+ ,1.94
+ ,7.8
+ ,8.1
+ ,8.4
+ ,8.7
+ ,7.5
+ ,1.79
+ ,7.7
+ ,7.8
+ ,8.1
+ ,8.4
+ ,7.2
+ ,1.95
+ ,7.5
+ ,7.7
+ ,7.8
+ ,8.1
+ ,6.8
+ ,2.26
+ ,7.2
+ ,7.5
+ ,7.7
+ ,7.8
+ ,6.7
+ ,2.04
+ ,6.8
+ ,7.2
+ ,7.5
+ ,7.7
+ ,6.4
+ ,2.16
+ ,6.7
+ ,6.8
+ ,7.2
+ ,7.5
+ ,6.3
+ ,2.75
+ ,6.4
+ ,6.7
+ ,6.8
+ ,7.2
+ ,6.8
+ ,2.79
+ ,6.3
+ ,6.4
+ ,6.7
+ ,6.8
+ ,7.3
+ ,2.88
+ ,6.8
+ ,6.3
+ ,6.4
+ ,6.7
+ ,7.1
+ ,3.36
+ ,7.3
+ ,6.8
+ ,6.3
+ ,6.4
+ ,7.0
+ ,2.97
+ ,7.1
+ ,7.3
+ ,6.8
+ ,6.3
+ ,6.8
+ ,3.10
+ ,7.0
+ ,7.1
+ ,7.3
+ ,6.8
+ ,6.6
+ ,2.49
+ ,6.8
+ ,7.0
+ ,7.1
+ ,7.3
+ ,6.3
+ ,2.20
+ ,6.6
+ ,6.8
+ ,7.0
+ ,7.1
+ ,6.1
+ ,2.25
+ ,6.3
+ ,6.6
+ ,6.8
+ ,7.0
+ ,6.1
+ ,2.09
+ ,6.1
+ ,6.3
+ ,6.6
+ ,6.8
+ ,6.3
+ ,2.79
+ ,6.1
+ ,6.1
+ ,6.3
+ ,6.6
+ ,6.3
+ ,3.14
+ ,6.3
+ ,6.1
+ ,6.1
+ ,6.3
+ ,6.0
+ ,2.93
+ ,6.3
+ ,6.3
+ ,6.1
+ ,6.1
+ ,6.2
+ ,2.65
+ ,6.0
+ ,6.3
+ ,6.3
+ ,6.1
+ ,6.4
+ ,2.67
+ ,6.2
+ ,6.0
+ ,6.3
+ ,6.3
+ ,6.8
+ ,2.26
+ ,6.4
+ ,6.2
+ ,6.0
+ ,6.3
+ ,7.5
+ ,2.35
+ ,6.8
+ ,6.4
+ ,6.2
+ ,6.0
+ ,7.5
+ ,2.13
+ ,7.5
+ ,6.8
+ ,6.4
+ ,6.2
+ ,7.6
+ ,2.18
+ ,7.5
+ ,7.5
+ ,6.8
+ ,6.4
+ ,7.6
+ ,2.90
+ ,7.6
+ ,7.5
+ ,7.5
+ ,6.8
+ ,7.4
+ ,2.63
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.3
+ ,2.67
+ ,7.4
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.1
+ ,1.81
+ ,7.3
+ ,7.4
+ ,7.6
+ ,7.6
+ ,6.9
+ ,1.33
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.6
+ ,6.8
+ ,0.88
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.5
+ ,1.28
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.6
+ ,1.26
+ ,7.5
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.8
+ ,1.26
+ ,7.6
+ ,7.5
+ ,6.8
+ ,6.9
+ ,8.0
+ ,1.29
+ ,7.8
+ ,7.6
+ ,7.5
+ ,6.8
+ ,8.1
+ ,1.10
+ ,8.0
+ ,7.8
+ ,7.6
+ ,7.5
+ ,8.2
+ ,1.37
+ ,8.1
+ ,8.0
+ ,7.8
+ ,7.6
+ ,8.3
+ ,1.21
+ ,8.2
+ ,8.1
+ ,8.0
+ ,7.8
+ ,8.2
+ ,1.74
+ ,8.3
+ ,8.2
+ ,8.1
+ ,8.0
+ ,8.0
+ ,1.76
+ ,8.2
+ ,8.3
+ ,8.2
+ ,8.1
+ ,7.9
+ ,1.48
+ ,8.0
+ ,8.2
+ ,8.3
+ ,8.2
+ ,7.6
+ ,1.04
+ ,7.9
+ ,8.0
+ ,8.2
+ ,8.3
+ ,7.6
+ ,1.62
+ ,7.6
+ ,7.9
+ ,8.0
+ ,8.2
+ ,8.3
+ ,1.49
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8.0
+ ,8.4
+ ,1.79
+ ,8.3
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8.4
+ ,1.80
+ ,8.4
+ ,8.3
+ ,7.6
+ ,7.6
+ ,8.4
+ ,1.58
+ ,8.4
+ ,8.4
+ ,8.3
+ ,7.6
+ ,8.4
+ ,1.86
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.3
+ ,8.6
+ ,1.74
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.9
+ ,1.59
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.8
+ ,1.26
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.3
+ ,1.13
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,7.5
+ ,1.92
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,7.2
+ ,2.61
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,2.26
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,2.41
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,2.26
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,2.03
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,2.86
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,2.55
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,2.27
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,2.26
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,2.57
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,3.07
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,2.76
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,2.51
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,2.87
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,3.14
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,3.11
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,3.16
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,2.47
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,2.57
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.89
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,2.63
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2.38
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,1.69
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,1.96
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,2.19
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.0
+ ,1.87
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,1.6
+ ,8.0
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,1.63
+ ,8.2
+ ,8.0
+ ,8.3
+ ,8.5
+ ,8.1
+ ,1.22
+ ,8.1
+ ,8.2
+ ,8.0
+ ,8.3)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:164))
> 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 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.0 2.77 8.0 7.8 7.6 7.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.0 2.93 8.0 8.0 7.8 7.6 0 1 0 0 0 0 0 0 0 0 0 2
3 7.9 2.91 8.0 8.0 8.0 7.8 0 0 1 0 0 0 0 0 0 0 0 3
4 7.9 2.69 7.9 8.0 8.0 8.0 0 0 0 1 0 0 0 0 0 0 0 4
5 8.0 2.38 7.9 7.9 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 5
6 8.5 2.58 8.0 7.9 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 6
7 9.2 3.19 8.5 8.0 7.9 7.9 0 0 0 0 0 0 1 0 0 0 0 7
8 9.4 2.82 9.2 8.5 8.0 7.9 0 0 0 0 0 0 0 1 0 0 0 8
9 9.5 2.72 9.4 9.2 8.5 8.0 0 0 0 0 0 0 0 0 1 0 0 9
10 9.5 2.53 9.5 9.4 9.2 8.5 0 0 0 0 0 0 0 0 0 1 0 10
11 9.6 2.70 9.5 9.5 9.4 9.2 0 0 0 0 0 0 0 0 0 0 1 11
12 9.7 2.42 9.6 9.5 9.5 9.4 0 0 0 0 0 0 0 0 0 0 0 12
13 9.7 2.50 9.7 9.6 9.5 9.5 1 0 0 0 0 0 0 0 0 0 0 13
14 9.6 2.31 9.7 9.7 9.6 9.5 0 1 0 0 0 0 0 0 0 0 0 14
15 9.5 2.41 9.6 9.7 9.7 9.6 0 0 1 0 0 0 0 0 0 0 0 15
16 9.4 2.56 9.5 9.6 9.7 9.7 0 0 0 1 0 0 0 0 0 0 0 16
17 9.3 2.76 9.4 9.5 9.6 9.7 0 0 0 0 1 0 0 0 0 0 0 17
18 9.6 2.71 9.3 9.4 9.5 9.6 0 0 0 0 0 1 0 0 0 0 0 18
19 10.2 2.44 9.6 9.3 9.4 9.5 0 0 0 0 0 0 1 0 0 0 0 19
20 10.2 2.46 10.2 9.6 9.3 9.4 0 0 0 0 0 0 0 1 0 0 0 20
21 10.1 2.12 10.2 10.2 9.6 9.3 0 0 0 0 0 0 0 0 1 0 0 21
22 9.9 1.99 10.1 10.2 10.2 9.6 0 0 0 0 0 0 0 0 0 1 0 22
23 9.8 1.86 9.9 10.1 10.2 10.2 0 0 0 0 0 0 0 0 0 0 1 23
24 9.8 1.88 9.8 9.9 10.1 10.2 0 0 0 0 0 0 0 0 0 0 0 24
25 9.7 1.82 9.8 9.8 9.9 10.1 1 0 0 0 0 0 0 0 0 0 0 25
26 9.5 1.74 9.7 9.8 9.8 9.9 0 1 0 0 0 0 0 0 0 0 0 26
27 9.3 1.71 9.5 9.7 9.8 9.8 0 0 1 0 0 0 0 0 0 0 0 27
28 9.1 1.38 9.3 9.5 9.7 9.8 0 0 0 1 0 0 0 0 0 0 0 28
29 9.0 1.27 9.1 9.3 9.5 9.7 0 0 0 0 1 0 0 0 0 0 0 29
30 9.5 1.19 9.0 9.1 9.3 9.5 0 0 0 0 0 1 0 0 0 0 0 30
31 10.0 1.28 9.5 9.0 9.1 9.3 0 0 0 0 0 0 1 0 0 0 0 31
32 10.2 1.19 10.0 9.5 9.0 9.1 0 0 0 0 0 0 0 1 0 0 0 32
33 10.1 1.22 10.2 10.0 9.5 9.0 0 0 0 0 0 0 0 0 1 0 0 33
34 10.0 1.47 10.1 10.2 10.0 9.5 0 0 0 0 0 0 0 0 0 1 0 34
35 9.9 1.46 10.0 10.1 10.2 10.0 0 0 0 0 0 0 0 0 0 0 1 35
36 10.0 1.96 9.9 10.0 10.1 10.2 0 0 0 0 0 0 0 0 0 0 0 36
37 9.9 1.88 10.0 9.9 10.0 10.1 1 0 0 0 0 0 0 0 0 0 0 37
38 9.7 2.03 9.9 10.0 9.9 10.0 0 1 0 0 0 0 0 0 0 0 0 38
39 9.5 2.04 9.7 9.9 10.0 9.9 0 0 1 0 0 0 0 0 0 0 0 39
40 9.2 1.90 9.5 9.7 9.9 10.0 0 0 0 1 0 0 0 0 0 0 0 40
41 9.0 1.80 9.2 9.5 9.7 9.9 0 0 0 0 1 0 0 0 0 0 0 41
42 9.3 1.92 9.0 9.2 9.5 9.7 0 0 0 0 0 1 0 0 0 0 0 42
43 9.8 1.92 9.3 9.0 9.2 9.5 0 0 0 0 0 0 1 0 0 0 0 43
44 9.8 1.97 9.8 9.3 9.0 9.2 0 0 0 0 0 0 0 1 0 0 0 44
45 9.6 2.46 9.8 9.8 9.3 9.0 0 0 0 0 0 0 0 0 1 0 0 45
46 9.4 2.36 9.6 9.8 9.8 9.3 0 0 0 0 0 0 0 0 0 1 0 46
47 9.3 2.53 9.4 9.6 9.8 9.8 0 0 0 0 0 0 0 0 0 0 1 47
48 9.2 2.31 9.3 9.4 9.6 9.8 0 0 0 0 0 0 0 0 0 0 0 48
49 9.2 1.98 9.2 9.3 9.4 9.6 1 0 0 0 0 0 0 0 0 0 0 49
50 9.0 1.46 9.2 9.2 9.3 9.4 0 1 0 0 0 0 0 0 0 0 0 50
51 8.8 1.26 9.0 9.2 9.2 9.3 0 0 1 0 0 0 0 0 0 0 0 51
52 8.7 1.58 8.8 9.0 9.2 9.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.7 1.74 8.7 8.8 9.0 9.2 0 0 0 0 1 0 0 0 0 0 0 53
54 9.1 1.89 8.7 8.7 8.8 9.0 0 0 0 0 0 1 0 0 0 0 0 54
55 9.7 1.85 9.1 8.7 8.7 8.8 0 0 0 0 0 0 1 0 0 0 0 55
56 9.8 1.62 9.7 9.1 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 9.6 1.30 9.8 9.7 9.1 8.7 0 0 0 0 0 0 0 0 1 0 0 57
58 9.4 1.42 9.6 9.8 9.7 9.1 0 0 0 0 0 0 0 0 0 1 0 58
59 9.4 1.15 9.4 9.6 9.8 9.7 0 0 0 0 0 0 0 0 0 0 1 59
60 9.5 0.42 9.4 9.4 9.6 9.8 0 0 0 0 0 0 0 0 0 0 0 60
61 9.4 0.74 9.5 9.4 9.4 9.6 1 0 0 0 0 0 0 0 0 0 0 61
62 9.3 1.02 9.4 9.5 9.4 9.4 0 1 0 0 0 0 0 0 0 0 0 62
63 9.2 1.51 9.3 9.4 9.5 9.4 0 0 1 0 0 0 0 0 0 0 0 63
64 9.0 1.86 9.2 9.3 9.4 9.5 0 0 0 1 0 0 0 0 0 0 0 64
65 8.9 1.59 9.0 9.2 9.3 9.4 0 0 0 0 1 0 0 0 0 0 0 65
66 9.2 1.03 8.9 9.0 9.2 9.3 0 0 0 0 0 1 0 0 0 0 0 66
67 9.8 0.44 9.2 8.9 9.0 9.2 0 0 0 0 0 0 1 0 0 0 0 67
68 9.9 0.82 9.8 9.2 8.9 9.0 0 0 0 0 0 0 0 1 0 0 0 68
69 9.6 0.86 9.9 9.8 9.2 8.9 0 0 0 0 0 0 0 0 1 0 0 69
70 9.2 0.58 9.6 9.9 9.8 9.2 0 0 0 0 0 0 0 0 0 1 0 70
71 9.1 0.59 9.2 9.6 9.9 9.8 0 0 0 0 0 0 0 0 0 0 1 71
72 9.1 0.95 9.1 9.2 9.6 9.9 0 0 0 0 0 0 0 0 0 0 0 72
73 9.0 0.98 9.1 9.1 9.2 9.6 1 0 0 0 0 0 0 0 0 0 0 73
74 8.9 1.23 9.0 9.1 9.1 9.2 0 1 0 0 0 0 0 0 0 0 0 74
75 8.7 1.17 8.9 9.0 9.1 9.1 0 0 1 0 0 0 0 0 0 0 0 75
76 8.5 0.84 8.7 8.9 9.0 9.1 0 0 0 1 0 0 0 0 0 0 0 76
77 8.3 0.74 8.5 8.7 8.9 9.0 0 0 0 0 1 0 0 0 0 0 0 77
78 8.5 0.65 8.3 8.5 8.7 8.9 0 0 0 0 0 1 0 0 0 0 0 78
79 8.7 0.91 8.5 8.3 8.5 8.7 0 0 0 0 0 0 1 0 0 0 0 79
80 8.4 1.19 8.7 8.5 8.3 8.5 0 0 0 0 0 0 0 1 0 0 0 80
81 8.1 1.30 8.4 8.7 8.5 8.3 0 0 0 0 0 0 0 0 1 0 0 81
82 7.8 1.53 8.1 8.4 8.7 8.5 0 0 0 0 0 0 0 0 0 1 0 82
83 7.7 1.94 7.8 8.1 8.4 8.7 0 0 0 0 0 0 0 0 0 0 1 83
84 7.5 1.79 7.7 7.8 8.1 8.4 0 0 0 0 0 0 0 0 0 0 0 84
85 7.2 1.95 7.5 7.7 7.8 8.1 1 0 0 0 0 0 0 0 0 0 0 85
86 6.8 2.26 7.2 7.5 7.7 7.8 0 1 0 0 0 0 0 0 0 0 0 86
87 6.7 2.04 6.8 7.2 7.5 7.7 0 0 1 0 0 0 0 0 0 0 0 87
88 6.4 2.16 6.7 6.8 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 88
89 6.3 2.75 6.4 6.7 6.8 7.2 0 0 0 0 1 0 0 0 0 0 0 89
90 6.8 2.79 6.3 6.4 6.7 6.8 0 0 0 0 0 1 0 0 0 0 0 90
91 7.3 2.88 6.8 6.3 6.4 6.7 0 0 0 0 0 0 1 0 0 0 0 91
92 7.1 3.36 7.3 6.8 6.3 6.4 0 0 0 0 0 0 0 1 0 0 0 92
93 7.0 2.97 7.1 7.3 6.8 6.3 0 0 0 0 0 0 0 0 1 0 0 93
94 6.8 3.10 7.0 7.1 7.3 6.8 0 0 0 0 0 0 0 0 0 1 0 94
95 6.6 2.49 6.8 7.0 7.1 7.3 0 0 0 0 0 0 0 0 0 0 1 95
96 6.3 2.20 6.6 6.8 7.0 7.1 0 0 0 0 0 0 0 0 0 0 0 96
97 6.1 2.25 6.3 6.6 6.8 7.0 1 0 0 0 0 0 0 0 0 0 0 97
98 6.1 2.09 6.1 6.3 6.6 6.8 0 1 0 0 0 0 0 0 0 0 0 98
99 6.3 2.79 6.1 6.1 6.3 6.6 0 0 1 0 0 0 0 0 0 0 0 99
100 6.3 3.14 6.3 6.1 6.1 6.3 0 0 0 1 0 0 0 0 0 0 0 100
101 6.0 2.93 6.3 6.3 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 101
102 6.2 2.65 6.0 6.3 6.3 6.1 0 0 0 0 0 1 0 0 0 0 0 102
103 6.4 2.67 6.2 6.0 6.3 6.3 0 0 0 0 0 0 1 0 0 0 0 103
104 6.8 2.26 6.4 6.2 6.0 6.3 0 0 0 0 0 0 0 1 0 0 0 104
105 7.5 2.35 6.8 6.4 6.2 6.0 0 0 0 0 0 0 0 0 1 0 0 105
106 7.5 2.13 7.5 6.8 6.4 6.2 0 0 0 0 0 0 0 0 0 1 0 106
107 7.6 2.18 7.5 7.5 6.8 6.4 0 0 0 0 0 0 0 0 0 0 1 107
108 7.6 2.90 7.6 7.5 7.5 6.8 0 0 0 0 0 0 0 0 0 0 0 108
109 7.4 2.63 7.6 7.6 7.5 7.5 1 0 0 0 0 0 0 0 0 0 0 109
110 7.3 2.67 7.4 7.6 7.6 7.5 0 1 0 0 0 0 0 0 0 0 0 110
111 7.1 1.81 7.3 7.4 7.6 7.6 0 0 1 0 0 0 0 0 0 0 0 111
112 6.9 1.33 7.1 7.3 7.4 7.6 0 0 0 1 0 0 0 0 0 0 0 112
113 6.8 0.88 6.9 7.1 7.3 7.4 0 0 0 0 1 0 0 0 0 0 0 113
114 7.5 1.28 6.8 6.9 7.1 7.3 0 0 0 0 0 1 0 0 0 0 0 114
115 7.6 1.26 7.5 6.8 6.9 7.1 0 0 0 0 0 0 1 0 0 0 0 115
116 7.8 1.26 7.6 7.5 6.8 6.9 0 0 0 0 0 0 0 1 0 0 0 116
117 8.0 1.29 7.8 7.6 7.5 6.8 0 0 0 0 0 0 0 0 1 0 0 117
118 8.1 1.10 8.0 7.8 7.6 7.5 0 0 0 0 0 0 0 0 0 1 0 118
119 8.2 1.37 8.1 8.0 7.8 7.6 0 0 0 0 0 0 0 0 0 0 1 119
120 8.3 1.21 8.2 8.1 8.0 7.8 0 0 0 0 0 0 0 0 0 0 0 120
121 8.2 1.74 8.3 8.2 8.1 8.0 1 0 0 0 0 0 0 0 0 0 0 121
122 8.0 1.76 8.2 8.3 8.2 8.1 0 1 0 0 0 0 0 0 0 0 0 122
123 7.9 1.48 8.0 8.2 8.3 8.2 0 0 1 0 0 0 0 0 0 0 0 123
124 7.6 1.04 7.9 8.0 8.2 8.3 0 0 0 1 0 0 0 0 0 0 0 124
125 7.6 1.62 7.6 7.9 8.0 8.2 0 0 0 0 1 0 0 0 0 0 0 125
126 8.3 1.49 7.6 7.6 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 126
127 8.4 1.79 8.3 7.6 7.6 7.9 0 0 0 0 0 0 1 0 0 0 0 127
128 8.4 1.80 8.4 8.3 7.6 7.6 0 0 0 0 0 0 0 1 0 0 0 128
129 8.4 1.58 8.4 8.4 8.3 7.6 0 0 0 0 0 0 0 0 1 0 0 129
130 8.4 1.86 8.4 8.4 8.4 8.3 0 0 0 0 0 0 0 0 0 1 0 130
131 8.6 1.74 8.4 8.4 8.4 8.4 0 0 0 0 0 0 0 0 0 0 1 131
132 8.9 1.59 8.6 8.4 8.4 8.4 0 0 0 0 0 0 0 0 0 0 0 132
133 8.8 1.26 8.9 8.6 8.4 8.4 1 0 0 0 0 0 0 0 0 0 0 133
134 8.3 1.13 8.8 8.9 8.6 8.4 0 1 0 0 0 0 0 0 0 0 0 134
135 7.5 1.92 8.3 8.8 8.9 8.6 0 0 1 0 0 0 0 0 0 0 0 135
136 7.2 2.61 7.5 8.3 8.8 8.9 0 0 0 1 0 0 0 0 0 0 0 136
137 7.4 2.26 7.2 7.5 8.3 8.8 0 0 0 0 1 0 0 0 0 0 0 137
138 8.8 2.41 7.4 7.2 7.5 8.3 0 0 0 0 0 1 0 0 0 0 0 138
139 9.3 2.26 8.8 7.4 7.2 7.5 0 0 0 0 0 0 1 0 0 0 0 139
140 9.3 2.03 9.3 8.8 7.4 7.2 0 0 0 0 0 0 0 1 0 0 0 140
141 8.7 2.86 9.3 9.3 8.8 7.4 0 0 0 0 0 0 0 0 1 0 0 141
142 8.2 2.55 8.7 9.3 9.3 8.8 0 0 0 0 0 0 0 0 0 1 0 142
143 8.3 2.27 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 0 0 0 1 143
144 8.5 2.26 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 0 0 0 144
145 8.6 2.57 8.5 8.3 8.2 8.7 1 0 0 0 0 0 0 0 0 0 0 145
146 8.5 3.07 8.6 8.5 8.3 8.2 0 1 0 0 0 0 0 0 0 0 0 146
147 8.2 2.76 8.5 8.6 8.5 8.3 0 0 1 0 0 0 0 0 0 0 0 147
148 8.1 2.51 8.2 8.5 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 148
149 7.9 2.87 8.1 8.2 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 149
150 8.6 3.14 7.9 8.1 8.2 8.5 0 0 0 0 0 1 0 0 0 0 0 150
151 8.7 3.11 8.6 7.9 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 151
152 8.7 3.16 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 1 0 0 0 152
153 8.5 2.47 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 1 0 0 153
154 8.4 2.57 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 154
155 8.5 2.89 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 155
156 8.7 2.63 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 0 0 0 156
157 8.7 2.38 8.7 8.5 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 157
158 8.6 1.69 8.7 8.7 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 158
159 8.5 1.96 8.6 8.7 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 159
160 8.3 2.19 8.5 8.6 8.7 8.7 0 0 0 1 0 0 0 0 0 0 0 160
161 8.0 1.87 8.3 8.5 8.6 8.7 0 0 0 0 1 0 0 0 0 0 0 161
162 8.2 1.60 8.0 8.3 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 162
163 8.1 1.63 8.2 8.0 8.3 8.5 0 0 0 0 0 0 1 0 0 0 0 163
164 8.1 1.22 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 1 0 0 0 164
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
0.2186968 0.0214034 1.3829422 -0.4405702 -0.2552298 0.2890960
M1 M2 M3 M4 M5 M6
-0.1619912 -0.1380757 -0.1186297 -0.1584563 -0.1062896 0.4488373
M7 M8 M9 M10 M11 t
0.0935945 -0.1157012 0.0501367 -0.0165215 0.0682680 -0.0005324
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.38537 -0.09896 0.01087 0.10332 0.60742
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2186968 0.1896275 1.153 0.25067
X 0.0214034 0.0216471 0.989 0.32443
Y1 1.3829422 0.0795471 17.385 < 2e-16 ***
Y2 -0.4405702 0.1392151 -3.165 0.00189 **
Y3 -0.2552298 0.1398827 -1.825 0.07011 .
Y4 0.2890960 0.0807906 3.578 0.00047 ***
M1 -0.1619912 0.0682837 -2.372 0.01898 *
M2 -0.1380757 0.0692958 -1.993 0.04817 *
M3 -0.1186297 0.0685675 -1.730 0.08572 .
M4 -0.1584563 0.0679698 -2.331 0.02111 *
M5 -0.1062896 0.0685470 -1.551 0.12316
M6 0.4488373 0.0678494 6.615 6.56e-10 ***
M7 0.0935945 0.0757132 1.236 0.21838
M8 -0.1157012 0.0840642 -1.376 0.17082
M9 0.0501367 0.0848894 0.591 0.55569
M10 -0.0165215 0.0765156 -0.216 0.82935
M11 0.0682680 0.0694986 0.982 0.32758
t -0.0005324 0.0003333 -1.598 0.11231
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1718 on 146 degrees of freedom
Multiple R-squared: 0.9763, Adjusted R-squared: 0.9735
F-statistic: 353.7 on 17 and 146 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,] 1.633577e-02 3.267154e-02 0.9836642
[2,] 3.348292e-03 6.696584e-03 0.9966517
[3,] 6.279854e-04 1.255971e-03 0.9993720
[4,] 2.166750e-03 4.333499e-03 0.9978333
[5,] 8.579206e-04 1.715841e-03 0.9991421
[6,] 2.984303e-04 5.968607e-04 0.9997016
[7,] 8.847322e-05 1.769464e-04 0.9999115
[8,] 7.336841e-05 1.467368e-04 0.9999266
[9,] 1.923466e-05 3.846932e-05 0.9999808
[10,] 4.113741e-05 8.227482e-05 0.9999589
[11,] 7.000455e-05 1.400091e-04 0.9999300
[12,] 2.948327e-05 5.896654e-05 0.9999705
[13,] 1.272625e-05 2.545251e-05 0.9999873
[14,] 4.973291e-06 9.946582e-06 0.9999950
[15,] 1.602528e-06 3.205057e-06 0.9999984
[16,] 3.546220e-06 7.092440e-06 0.9999965
[17,] 1.997213e-06 3.994426e-06 0.9999980
[18,] 6.918886e-07 1.383777e-06 0.9999993
[19,] 2.265921e-07 4.531841e-07 0.9999998
[20,] 1.431588e-07 2.863176e-07 0.9999999
[21,] 6.537626e-08 1.307525e-07 0.9999999
[22,] 2.516178e-08 5.032357e-08 1.0000000
[23,] 1.521834e-08 3.043669e-08 1.0000000
[24,] 4.943934e-09 9.887867e-09 1.0000000
[25,] 1.639975e-09 3.279951e-09 1.0000000
[26,] 6.248525e-10 1.249705e-09 1.0000000
[27,] 4.093571e-10 8.187142e-10 1.0000000
[28,] 1.709139e-10 3.418277e-10 1.0000000
[29,] 5.849103e-10 1.169821e-09 1.0000000
[30,] 2.391781e-10 4.783563e-10 1.0000000
[31,] 8.585545e-11 1.717109e-10 1.0000000
[32,] 4.420510e-11 8.841020e-11 1.0000000
[33,] 5.421199e-11 1.084240e-10 1.0000000
[34,] 1.856632e-11 3.713264e-11 1.0000000
[35,] 2.239248e-11 4.478496e-11 1.0000000
[36,] 7.688119e-12 1.537624e-11 1.0000000
[37,] 1.724360e-11 3.448719e-11 1.0000000
[38,] 1.227492e-11 2.454983e-11 1.0000000
[39,] 7.667657e-12 1.533531e-11 1.0000000
[40,] 4.544973e-12 9.089947e-12 1.0000000
[41,] 1.863111e-12 3.726222e-12 1.0000000
[42,] 7.980457e-13 1.596091e-12 1.0000000
[43,] 6.885919e-13 1.377184e-12 1.0000000
[44,] 2.533744e-13 5.067488e-13 1.0000000
[45,] 1.258649e-13 2.517298e-13 1.0000000
[46,] 8.693485e-14 1.738697e-13 1.0000000
[47,] 5.868632e-13 1.173726e-12 1.0000000
[48,] 2.268044e-13 4.536088e-13 1.0000000
[49,] 1.732245e-12 3.464491e-12 1.0000000
[50,] 4.881951e-11 9.763901e-11 1.0000000
[51,] 2.386194e-11 4.772387e-11 1.0000000
[52,] 1.125067e-11 2.250135e-11 1.0000000
[53,] 4.983515e-12 9.967029e-12 1.0000000
[54,] 4.823838e-12 9.647676e-12 1.0000000
[55,] 2.356884e-12 4.713768e-12 1.0000000
[56,] 1.646219e-12 3.292438e-12 1.0000000
[57,] 2.249121e-12 4.498243e-12 1.0000000
[58,] 2.904364e-12 5.808728e-12 1.0000000
[59,] 8.671045e-10 1.734209e-09 1.0000000
[60,] 1.988153e-09 3.976306e-09 1.0000000
[61,] 1.567921e-09 3.135842e-09 1.0000000
[62,] 8.274607e-10 1.654921e-09 1.0000000
[63,] 4.085076e-10 8.170152e-10 1.0000000
[64,] 1.044668e-09 2.089337e-09 1.0000000
[65,] 6.283864e-10 1.256773e-09 1.0000000
[66,] 4.518351e-10 9.036702e-10 1.0000000
[67,] 2.698934e-09 5.397868e-09 1.0000000
[68,] 3.430941e-09 6.861883e-09 1.0000000
[69,] 1.909953e-09 3.819906e-09 1.0000000
[70,] 4.693101e-09 9.386201e-09 1.0000000
[71,] 1.259777e-08 2.519554e-08 1.0000000
[72,] 2.645274e-07 5.290547e-07 0.9999997
[73,] 3.556240e-07 7.112480e-07 0.9999996
[74,] 1.906914e-07 3.813827e-07 0.9999998
[75,] 1.951135e-06 3.902271e-06 0.9999980
[76,] 4.778968e-05 9.557937e-05 0.9999522
[77,] 3.867349e-05 7.734697e-05 0.9999613
[78,] 9.103594e-05 1.820719e-04 0.9999090
[79,] 3.229856e-04 6.459712e-04 0.9996770
[80,] 3.046847e-04 6.093694e-04 0.9996953
[81,] 1.241048e-03 2.482096e-03 0.9987590
[82,] 3.542250e-03 7.084500e-03 0.9964578
[83,] 4.872074e-03 9.744148e-03 0.9951279
[84,] 3.721217e-02 7.442435e-02 0.9627878
[85,] 1.864700e-01 3.729400e-01 0.8135300
[86,] 3.959831e-01 7.919662e-01 0.6040169
[87,] 4.235122e-01 8.470244e-01 0.5764878
[88,] 3.760433e-01 7.520865e-01 0.6239567
[89,] 3.650540e-01 7.301080e-01 0.6349460
[90,] 3.372505e-01 6.745011e-01 0.6627495
[91,] 3.599408e-01 7.198817e-01 0.6400592
[92,] 4.152104e-01 8.304207e-01 0.5847896
[93,] 3.799964e-01 7.599928e-01 0.6200036
[94,] 4.594269e-01 9.188539e-01 0.5405731
[95,] 6.678991e-01 6.642017e-01 0.3321009
[96,] 6.834973e-01 6.330054e-01 0.3165027
[97,] 6.518419e-01 6.963163e-01 0.3481581
[98,] 6.591988e-01 6.816023e-01 0.3408012
[99,] 7.129720e-01 5.740559e-01 0.2870280
[100,] 6.741537e-01 6.516925e-01 0.3258463
[101,] 6.239107e-01 7.521786e-01 0.3760893
[102,] 5.729403e-01 8.541193e-01 0.4270597
[103,] 5.403739e-01 9.192523e-01 0.4596261
[104,] 6.910562e-01 6.178877e-01 0.3089438
[105,] 6.661675e-01 6.676651e-01 0.3338325
[106,] 6.617742e-01 6.764515e-01 0.3382258
[107,] 6.714931e-01 6.570139e-01 0.3285069
[108,] 6.142745e-01 7.714510e-01 0.3857255
[109,] 5.701019e-01 8.597963e-01 0.4298981
[110,] 5.433824e-01 9.132352e-01 0.4566176
[111,] 5.053335e-01 9.893329e-01 0.4946665
[112,] 5.501418e-01 8.997164e-01 0.4498582
[113,] 4.840876e-01 9.681753e-01 0.5159124
[114,] 4.403531e-01 8.807061e-01 0.5596469
[115,] 6.990384e-01 6.019232e-01 0.3009616
[116,] 7.121075e-01 5.757851e-01 0.2878925
[117,] 6.447277e-01 7.105447e-01 0.3552723
[118,] 8.042239e-01 3.915522e-01 0.1957761
[119,] 7.362085e-01 5.275830e-01 0.2637915
[120,] 7.180947e-01 5.638106e-01 0.2819053
[121,] 6.199942e-01 7.600116e-01 0.3800058
[122,] 4.983992e-01 9.967984e-01 0.5016008
[123,] 4.116999e-01 8.233997e-01 0.5883001
> postscript(file="/var/www/html/rcomp/tmp/1n58j1258727480.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/242qr1258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e8x11258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lwkn1258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z2bs1258727480.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 = 164
Frequency = 1
1 2 3 4 5
6.565136e-05 1.124181e-01 -1.284071e-02 1.127021e-01 1.236459e-01
6 7 8 9 10
-9.904647e-02 3.251683e-01 2.066418e-02 8.801504e-02 1.432049e-01
11 12 13 14 15
4.804512e-02 5.224801e-02 8.991254e-02 4.017621e-02 5.402980e-02
16 17 18 19 20
5.650596e-02 -3.069477e-02 -1.865952e-01 3.194060e-01 -1.654017e-01
21 22 23 24 25
-5.360932e-02 2.106700e-02 -1.013338e-01 -8.304219e-03 -1.068978e-02
26 27 28 29 30
-6.177003e-02 -1.860056e-02 -8.226943e-03 8.831202e-03 1.290245e-02
31 32 33 34 35
1.379966e-01 1.108612e-01 -5.486519e-02 1.164497e-01 -6.685812e-02
36 37 38 39 40
1.021356e-01 -1.259314e-02 -5.344876e-02 1.438756e-02 -1.082151e-01
41 42 43 44 45
-5.307675e-02 -1.590490e-01 1.749799e-01 -1.398794e-01 -1.609992e-01
46 47 48 49 50
2.580624e-02 -1.181630e-01 -1.455196e-01 1.250776e-01 -9.893639e-02
51 52 53 54 55
-3.359430e-02 1.172997e-01 6.137509e-02 -1.337136e-01 3.020372e-01
56 57 58 59 60
-7.839610e-03 -1.381562e-01 8.461094e-02 4.667262e-02 6.302790e-02
61 62 63 64 65
-1.281855e-02 9.797598e-02 8.833484e-02 -3.899266e-02 5.107002e-02
66 67 68 69 70
-1.379717e-01 3.493556e-01 -1.424768e-02 -2.488828e-01 -5.035054e-02
71 72 73 74 75
3.824960e-02 -4.406761e-02 -4.160622e-02 5.806962e-02 -3.641302e-02
76 77 78 79 80
1.801761e-02 -3.961530e-02 -2.259454e-01 -3.366418e-02 -3.115302e-01
81 82 83 84 85
-1.673282e-01 -1.291220e-01 -1.738309e-01 -2.855370e-01 -1.837466e-01
86 87 88 89 90
-2.257902e-01 5.887439e-02 -2.600185e-01 -6.881825e-02 -2.803025e-02
91 92 93 94 95
4.263134e-02 -3.677944e-01 2.864550e-02 -7.369927e-02 -3.079627e-01
96 97 98 99 100
-3.121847e-01 -4.609904e-02 8.513317e-02 1.443734e-01 -6.366429e-02
101 102 103 104 105
-2.648706e-01 -1.475435e-01 -5.877497e-02 2.947851e-01 5.002653e-01
106 107 108 109 110
-2.264400e-01 1.409047e-01 1.190228e-01 -7.098488e-02 1.068874e-01
111 112 113 114 115
-7.234873e-02 -4.023057e-02 3.853731e-02 2.034253e-01 -3.457146e-01
116 117 118 119 120
2.665146e-01 2.756061e-01 8.154473e-02 6.346499e-02 1.346795e-01
121 122 123 124 125
5.932585e-02 1.447941e-02 1.307035e-01 -1.237723e-01 1.608687e-01
126 127 128 129 130
2.091819e-01 -3.571827e-01 1.092651e-01 1.713862e-01 5.573964e-02
131 132 133 134 135
1.451415e-01 2.405639e-01 -1.661796e-02 -2.157073e-01 -3.853658e-01
136 137 138 139 140
1.140418e-01 2.336199e-01 6.074197e-01 -2.268916e-01 5.096118e-02
141 142 143 144 145
-2.123215e-01 -8.585014e-02 2.184668e-01 -2.423596e-02 4.496392e-02
146 147 148 149 150
3.077007e-02 -7.702094e-02 2.072184e-01 -1.004305e-01 2.240682e-01
151 152 153 154 155
-3.144821e-01 4.224433e-02 -2.775572e-02 3.703875e-02 6.720325e-02
156 157 158 159 160
1.081713e-01 7.581061e-02 1.097426e-01 1.454806e-01 1.733488e-02
161 162 163 164
-1.204419e-01 -1.391022e-01 -3.148648e-01 1.113972e-01
> postscript(file="/var/www/html/rcomp/tmp/66mrq1258727480.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 6.565136e-05 NA
1 1.124181e-01 6.565136e-05
2 -1.284071e-02 1.124181e-01
3 1.127021e-01 -1.284071e-02
4 1.236459e-01 1.127021e-01
5 -9.904647e-02 1.236459e-01
6 3.251683e-01 -9.904647e-02
7 2.066418e-02 3.251683e-01
8 8.801504e-02 2.066418e-02
9 1.432049e-01 8.801504e-02
10 4.804512e-02 1.432049e-01
11 5.224801e-02 4.804512e-02
12 8.991254e-02 5.224801e-02
13 4.017621e-02 8.991254e-02
14 5.402980e-02 4.017621e-02
15 5.650596e-02 5.402980e-02
16 -3.069477e-02 5.650596e-02
17 -1.865952e-01 -3.069477e-02
18 3.194060e-01 -1.865952e-01
19 -1.654017e-01 3.194060e-01
20 -5.360932e-02 -1.654017e-01
21 2.106700e-02 -5.360932e-02
22 -1.013338e-01 2.106700e-02
23 -8.304219e-03 -1.013338e-01
24 -1.068978e-02 -8.304219e-03
25 -6.177003e-02 -1.068978e-02
26 -1.860056e-02 -6.177003e-02
27 -8.226943e-03 -1.860056e-02
28 8.831202e-03 -8.226943e-03
29 1.290245e-02 8.831202e-03
30 1.379966e-01 1.290245e-02
31 1.108612e-01 1.379966e-01
32 -5.486519e-02 1.108612e-01
33 1.164497e-01 -5.486519e-02
34 -6.685812e-02 1.164497e-01
35 1.021356e-01 -6.685812e-02
36 -1.259314e-02 1.021356e-01
37 -5.344876e-02 -1.259314e-02
38 1.438756e-02 -5.344876e-02
39 -1.082151e-01 1.438756e-02
40 -5.307675e-02 -1.082151e-01
41 -1.590490e-01 -5.307675e-02
42 1.749799e-01 -1.590490e-01
43 -1.398794e-01 1.749799e-01
44 -1.609992e-01 -1.398794e-01
45 2.580624e-02 -1.609992e-01
46 -1.181630e-01 2.580624e-02
47 -1.455196e-01 -1.181630e-01
48 1.250776e-01 -1.455196e-01
49 -9.893639e-02 1.250776e-01
50 -3.359430e-02 -9.893639e-02
51 1.172997e-01 -3.359430e-02
52 6.137509e-02 1.172997e-01
53 -1.337136e-01 6.137509e-02
54 3.020372e-01 -1.337136e-01
55 -7.839610e-03 3.020372e-01
56 -1.381562e-01 -7.839610e-03
57 8.461094e-02 -1.381562e-01
58 4.667262e-02 8.461094e-02
59 6.302790e-02 4.667262e-02
60 -1.281855e-02 6.302790e-02
61 9.797598e-02 -1.281855e-02
62 8.833484e-02 9.797598e-02
63 -3.899266e-02 8.833484e-02
64 5.107002e-02 -3.899266e-02
65 -1.379717e-01 5.107002e-02
66 3.493556e-01 -1.379717e-01
67 -1.424768e-02 3.493556e-01
68 -2.488828e-01 -1.424768e-02
69 -5.035054e-02 -2.488828e-01
70 3.824960e-02 -5.035054e-02
71 -4.406761e-02 3.824960e-02
72 -4.160622e-02 -4.406761e-02
73 5.806962e-02 -4.160622e-02
74 -3.641302e-02 5.806962e-02
75 1.801761e-02 -3.641302e-02
76 -3.961530e-02 1.801761e-02
77 -2.259454e-01 -3.961530e-02
78 -3.366418e-02 -2.259454e-01
79 -3.115302e-01 -3.366418e-02
80 -1.673282e-01 -3.115302e-01
81 -1.291220e-01 -1.673282e-01
82 -1.738309e-01 -1.291220e-01
83 -2.855370e-01 -1.738309e-01
84 -1.837466e-01 -2.855370e-01
85 -2.257902e-01 -1.837466e-01
86 5.887439e-02 -2.257902e-01
87 -2.600185e-01 5.887439e-02
88 -6.881825e-02 -2.600185e-01
89 -2.803025e-02 -6.881825e-02
90 4.263134e-02 -2.803025e-02
91 -3.677944e-01 4.263134e-02
92 2.864550e-02 -3.677944e-01
93 -7.369927e-02 2.864550e-02
94 -3.079627e-01 -7.369927e-02
95 -3.121847e-01 -3.079627e-01
96 -4.609904e-02 -3.121847e-01
97 8.513317e-02 -4.609904e-02
98 1.443734e-01 8.513317e-02
99 -6.366429e-02 1.443734e-01
100 -2.648706e-01 -6.366429e-02
101 -1.475435e-01 -2.648706e-01
102 -5.877497e-02 -1.475435e-01
103 2.947851e-01 -5.877497e-02
104 5.002653e-01 2.947851e-01
105 -2.264400e-01 5.002653e-01
106 1.409047e-01 -2.264400e-01
107 1.190228e-01 1.409047e-01
108 -7.098488e-02 1.190228e-01
109 1.068874e-01 -7.098488e-02
110 -7.234873e-02 1.068874e-01
111 -4.023057e-02 -7.234873e-02
112 3.853731e-02 -4.023057e-02
113 2.034253e-01 3.853731e-02
114 -3.457146e-01 2.034253e-01
115 2.665146e-01 -3.457146e-01
116 2.756061e-01 2.665146e-01
117 8.154473e-02 2.756061e-01
118 6.346499e-02 8.154473e-02
119 1.346795e-01 6.346499e-02
120 5.932585e-02 1.346795e-01
121 1.447941e-02 5.932585e-02
122 1.307035e-01 1.447941e-02
123 -1.237723e-01 1.307035e-01
124 1.608687e-01 -1.237723e-01
125 2.091819e-01 1.608687e-01
126 -3.571827e-01 2.091819e-01
127 1.092651e-01 -3.571827e-01
128 1.713862e-01 1.092651e-01
129 5.573964e-02 1.713862e-01
130 1.451415e-01 5.573964e-02
131 2.405639e-01 1.451415e-01
132 -1.661796e-02 2.405639e-01
133 -2.157073e-01 -1.661796e-02
134 -3.853658e-01 -2.157073e-01
135 1.140418e-01 -3.853658e-01
136 2.336199e-01 1.140418e-01
137 6.074197e-01 2.336199e-01
138 -2.268916e-01 6.074197e-01
139 5.096118e-02 -2.268916e-01
140 -2.123215e-01 5.096118e-02
141 -8.585014e-02 -2.123215e-01
142 2.184668e-01 -8.585014e-02
143 -2.423596e-02 2.184668e-01
144 4.496392e-02 -2.423596e-02
145 3.077007e-02 4.496392e-02
146 -7.702094e-02 3.077007e-02
147 2.072184e-01 -7.702094e-02
148 -1.004305e-01 2.072184e-01
149 2.240682e-01 -1.004305e-01
150 -3.144821e-01 2.240682e-01
151 4.224433e-02 -3.144821e-01
152 -2.775572e-02 4.224433e-02
153 3.703875e-02 -2.775572e-02
154 6.720325e-02 3.703875e-02
155 1.081713e-01 6.720325e-02
156 7.581061e-02 1.081713e-01
157 1.097426e-01 7.581061e-02
158 1.454806e-01 1.097426e-01
159 1.733488e-02 1.454806e-01
160 -1.204419e-01 1.733488e-02
161 -1.391022e-01 -1.204419e-01
162 -3.148648e-01 -1.391022e-01
163 1.113972e-01 -3.148648e-01
164 NA 1.113972e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.112418126 6.565136e-05
[2,] -0.012840715 1.124181e-01
[3,] 0.112702112 -1.284071e-02
[4,] 0.123645865 1.127021e-01
[5,] -0.099046472 1.236459e-01
[6,] 0.325168320 -9.904647e-02
[7,] 0.020664175 3.251683e-01
[8,] 0.088015040 2.066418e-02
[9,] 0.143204942 8.801504e-02
[10,] 0.048045117 1.432049e-01
[11,] 0.052248012 4.804512e-02
[12,] 0.089912538 5.224801e-02
[13,] 0.040176206 8.991254e-02
[14,] 0.054029804 4.017621e-02
[15,] 0.056505961 5.402980e-02
[16,] -0.030694773 5.650596e-02
[17,] -0.186595242 -3.069477e-02
[18,] 0.319405956 -1.865952e-01
[19,] -0.165401678 3.194060e-01
[20,] -0.053609324 -1.654017e-01
[21,] 0.021067005 -5.360932e-02
[22,] -0.101333752 2.106700e-02
[23,] -0.008304219 -1.013338e-01
[24,] -0.010689780 -8.304219e-03
[25,] -0.061770028 -1.068978e-02
[26,] -0.018600556 -6.177003e-02
[27,] -0.008226943 -1.860056e-02
[28,] 0.008831202 -8.226943e-03
[29,] 0.012902445 8.831202e-03
[30,] 0.137996617 1.290245e-02
[31,] 0.110861216 1.379966e-01
[32,] -0.054865188 1.108612e-01
[33,] 0.116449705 -5.486519e-02
[34,] -0.066858121 1.164497e-01
[35,] 0.102135593 -6.685812e-02
[36,] -0.012593142 1.021356e-01
[37,] -0.053448756 -1.259314e-02
[38,] 0.014387556 -5.344876e-02
[39,] -0.108215081 1.438756e-02
[40,] -0.053076752 -1.082151e-01
[41,] -0.159048989 -5.307675e-02
[42,] 0.174979929 -1.590490e-01
[43,] -0.139879356 1.749799e-01
[44,] -0.160999232 -1.398794e-01
[45,] 0.025806237 -1.609992e-01
[46,] -0.118162952 2.580624e-02
[47,] -0.145519580 -1.181630e-01
[48,] 0.125077597 -1.455196e-01
[49,] -0.098936395 1.250776e-01
[50,] -0.033594302 -9.893639e-02
[51,] 0.117299686 -3.359430e-02
[52,] 0.061375093 1.172997e-01
[53,] -0.133713643 6.137509e-02
[54,] 0.302037182 -1.337136e-01
[55,] -0.007839610 3.020372e-01
[56,] -0.138156170 -7.839610e-03
[57,] 0.084610941 -1.381562e-01
[58,] 0.046672616 8.461094e-02
[59,] 0.063027896 4.667262e-02
[60,] -0.012818547 6.302790e-02
[61,] 0.097975977 -1.281855e-02
[62,] 0.088334839 9.797598e-02
[63,] -0.038992658 8.833484e-02
[64,] 0.051070024 -3.899266e-02
[65,] -0.137971734 5.107002e-02
[66,] 0.349355575 -1.379717e-01
[67,] -0.014247676 3.493556e-01
[68,] -0.248882828 -1.424768e-02
[69,] -0.050350538 -2.488828e-01
[70,] 0.038249603 -5.035054e-02
[71,] -0.044067611 3.824960e-02
[72,] -0.041606218 -4.406761e-02
[73,] 0.058069624 -4.160622e-02
[74,] -0.036413019 5.806962e-02
[75,] 0.018017612 -3.641302e-02
[76,] -0.039615301 1.801761e-02
[77,] -0.225945412 -3.961530e-02
[78,] -0.033664183 -2.259454e-01
[79,] -0.311530220 -3.366418e-02
[80,] -0.167328187 -3.115302e-01
[81,] -0.129121998 -1.673282e-01
[82,] -0.173830914 -1.291220e-01
[83,] -0.285536961 -1.738309e-01
[84,] -0.183746599 -2.855370e-01
[85,] -0.225790169 -1.837466e-01
[86,] 0.058874394 -2.257902e-01
[87,] -0.260018516 5.887439e-02
[88,] -0.068818250 -2.600185e-01
[89,] -0.028030249 -6.881825e-02
[90,] 0.042631343 -2.803025e-02
[91,] -0.367794387 4.263134e-02
[92,] 0.028645503 -3.677944e-01
[93,] -0.073699273 2.864550e-02
[94,] -0.307962747 -7.369927e-02
[95,] -0.312184736 -3.079627e-01
[96,] -0.046099037 -3.121847e-01
[97,] 0.085133171 -4.609904e-02
[98,] 0.144373393 8.513317e-02
[99,] -0.063664294 1.443734e-01
[100,] -0.264870613 -6.366429e-02
[101,] -0.147543528 -2.648706e-01
[102,] -0.058774972 -1.475435e-01
[103,] 0.294785148 -5.877497e-02
[104,] 0.500265332 2.947851e-01
[105,] -0.226439993 5.002653e-01
[106,] 0.140904676 -2.264400e-01
[107,] 0.119022821 1.409047e-01
[108,] -0.070984876 1.190228e-01
[109,] 0.106887427 -7.098488e-02
[110,] -0.072348731 1.068874e-01
[111,] -0.040230565 -7.234873e-02
[112,] 0.038537313 -4.023057e-02
[113,] 0.203425323 3.853731e-02
[114,] -0.345714566 2.034253e-01
[115,] 0.266514635 -3.457146e-01
[116,] 0.275606105 2.665146e-01
[117,] 0.081544729 2.756061e-01
[118,] 0.063464994 8.154473e-02
[119,] 0.134679476 6.346499e-02
[120,] 0.059325846 1.346795e-01
[121,] 0.014479414 5.932585e-02
[122,] 0.130703500 1.447941e-02
[123,] -0.123772336 1.307035e-01
[124,] 0.160868705 -1.237723e-01
[125,] 0.209181856 1.608687e-01
[126,] -0.357182679 2.091819e-01
[127,] 0.109265068 -3.571827e-01
[128,] 0.171386216 1.092651e-01
[129,] 0.055739641 1.713862e-01
[130,] 0.145141459 5.573964e-02
[131,] 0.240563929 1.451415e-01
[132,] -0.016617965 2.405639e-01
[133,] -0.215707270 -1.661796e-02
[134,] -0.385365816 -2.157073e-01
[135,] 0.114041770 -3.853658e-01
[136,] 0.233619907 1.140418e-01
[137,] 0.607419661 2.336199e-01
[138,] -0.226891598 6.074197e-01
[139,] 0.050961177 -2.268916e-01
[140,] -0.212321544 5.096118e-02
[141,] -0.085850145 -2.123215e-01
[142,] 0.218466769 -8.585014e-02
[143,] -0.024235964 2.184668e-01
[144,] 0.044963921 -2.423596e-02
[145,] 0.030770073 4.496392e-02
[146,] -0.077020943 3.077007e-02
[147,] 0.207218376 -7.702094e-02
[148,] -0.100430544 2.072184e-01
[149,] 0.224068168 -1.004305e-01
[150,] -0.314482126 2.240682e-01
[151,] 0.042244325 -3.144821e-01
[152,] -0.027755723 4.224433e-02
[153,] 0.037038748 -2.775572e-02
[154,] 0.067203252 3.703875e-02
[155,] 0.108171344 6.720325e-02
[156,] 0.075810611 1.081713e-01
[157,] 0.109742598 7.581061e-02
[158,] 0.145480596 1.097426e-01
[159,] 0.017334876 1.454806e-01
[160,] -0.120441877 1.733488e-02
[161,] -0.139102184 -1.204419e-01
[162,] -0.314864799 -1.391022e-01
[163,] 0.111397182 -3.148648e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.112418126 6.565136e-05
2 -0.012840715 1.124181e-01
3 0.112702112 -1.284071e-02
4 0.123645865 1.127021e-01
5 -0.099046472 1.236459e-01
6 0.325168320 -9.904647e-02
7 0.020664175 3.251683e-01
8 0.088015040 2.066418e-02
9 0.143204942 8.801504e-02
10 0.048045117 1.432049e-01
11 0.052248012 4.804512e-02
12 0.089912538 5.224801e-02
13 0.040176206 8.991254e-02
14 0.054029804 4.017621e-02
15 0.056505961 5.402980e-02
16 -0.030694773 5.650596e-02
17 -0.186595242 -3.069477e-02
18 0.319405956 -1.865952e-01
19 -0.165401678 3.194060e-01
20 -0.053609324 -1.654017e-01
21 0.021067005 -5.360932e-02
22 -0.101333752 2.106700e-02
23 -0.008304219 -1.013338e-01
24 -0.010689780 -8.304219e-03
25 -0.061770028 -1.068978e-02
26 -0.018600556 -6.177003e-02
27 -0.008226943 -1.860056e-02
28 0.008831202 -8.226943e-03
29 0.012902445 8.831202e-03
30 0.137996617 1.290245e-02
31 0.110861216 1.379966e-01
32 -0.054865188 1.108612e-01
33 0.116449705 -5.486519e-02
34 -0.066858121 1.164497e-01
35 0.102135593 -6.685812e-02
36 -0.012593142 1.021356e-01
37 -0.053448756 -1.259314e-02
38 0.014387556 -5.344876e-02
39 -0.108215081 1.438756e-02
40 -0.053076752 -1.082151e-01
41 -0.159048989 -5.307675e-02
42 0.174979929 -1.590490e-01
43 -0.139879356 1.749799e-01
44 -0.160999232 -1.398794e-01
45 0.025806237 -1.609992e-01
46 -0.118162952 2.580624e-02
47 -0.145519580 -1.181630e-01
48 0.125077597 -1.455196e-01
49 -0.098936395 1.250776e-01
50 -0.033594302 -9.893639e-02
51 0.117299686 -3.359430e-02
52 0.061375093 1.172997e-01
53 -0.133713643 6.137509e-02
54 0.302037182 -1.337136e-01
55 -0.007839610 3.020372e-01
56 -0.138156170 -7.839610e-03
57 0.084610941 -1.381562e-01
58 0.046672616 8.461094e-02
59 0.063027896 4.667262e-02
60 -0.012818547 6.302790e-02
61 0.097975977 -1.281855e-02
62 0.088334839 9.797598e-02
63 -0.038992658 8.833484e-02
64 0.051070024 -3.899266e-02
65 -0.137971734 5.107002e-02
66 0.349355575 -1.379717e-01
67 -0.014247676 3.493556e-01
68 -0.248882828 -1.424768e-02
69 -0.050350538 -2.488828e-01
70 0.038249603 -5.035054e-02
71 -0.044067611 3.824960e-02
72 -0.041606218 -4.406761e-02
73 0.058069624 -4.160622e-02
74 -0.036413019 5.806962e-02
75 0.018017612 -3.641302e-02
76 -0.039615301 1.801761e-02
77 -0.225945412 -3.961530e-02
78 -0.033664183 -2.259454e-01
79 -0.311530220 -3.366418e-02
80 -0.167328187 -3.115302e-01
81 -0.129121998 -1.673282e-01
82 -0.173830914 -1.291220e-01
83 -0.285536961 -1.738309e-01
84 -0.183746599 -2.855370e-01
85 -0.225790169 -1.837466e-01
86 0.058874394 -2.257902e-01
87 -0.260018516 5.887439e-02
88 -0.068818250 -2.600185e-01
89 -0.028030249 -6.881825e-02
90 0.042631343 -2.803025e-02
91 -0.367794387 4.263134e-02
92 0.028645503 -3.677944e-01
93 -0.073699273 2.864550e-02
94 -0.307962747 -7.369927e-02
95 -0.312184736 -3.079627e-01
96 -0.046099037 -3.121847e-01
97 0.085133171 -4.609904e-02
98 0.144373393 8.513317e-02
99 -0.063664294 1.443734e-01
100 -0.264870613 -6.366429e-02
101 -0.147543528 -2.648706e-01
102 -0.058774972 -1.475435e-01
103 0.294785148 -5.877497e-02
104 0.500265332 2.947851e-01
105 -0.226439993 5.002653e-01
106 0.140904676 -2.264400e-01
107 0.119022821 1.409047e-01
108 -0.070984876 1.190228e-01
109 0.106887427 -7.098488e-02
110 -0.072348731 1.068874e-01
111 -0.040230565 -7.234873e-02
112 0.038537313 -4.023057e-02
113 0.203425323 3.853731e-02
114 -0.345714566 2.034253e-01
115 0.266514635 -3.457146e-01
116 0.275606105 2.665146e-01
117 0.081544729 2.756061e-01
118 0.063464994 8.154473e-02
119 0.134679476 6.346499e-02
120 0.059325846 1.346795e-01
121 0.014479414 5.932585e-02
122 0.130703500 1.447941e-02
123 -0.123772336 1.307035e-01
124 0.160868705 -1.237723e-01
125 0.209181856 1.608687e-01
126 -0.357182679 2.091819e-01
127 0.109265068 -3.571827e-01
128 0.171386216 1.092651e-01
129 0.055739641 1.713862e-01
130 0.145141459 5.573964e-02
131 0.240563929 1.451415e-01
132 -0.016617965 2.405639e-01
133 -0.215707270 -1.661796e-02
134 -0.385365816 -2.157073e-01
135 0.114041770 -3.853658e-01
136 0.233619907 1.140418e-01
137 0.607419661 2.336199e-01
138 -0.226891598 6.074197e-01
139 0.050961177 -2.268916e-01
140 -0.212321544 5.096118e-02
141 -0.085850145 -2.123215e-01
142 0.218466769 -8.585014e-02
143 -0.024235964 2.184668e-01
144 0.044963921 -2.423596e-02
145 0.030770073 4.496392e-02
146 -0.077020943 3.077007e-02
147 0.207218376 -7.702094e-02
148 -0.100430544 2.072184e-01
149 0.224068168 -1.004305e-01
150 -0.314482126 2.240682e-01
151 0.042244325 -3.144821e-01
152 -0.027755723 4.224433e-02
153 0.037038748 -2.775572e-02
154 0.067203252 3.703875e-02
155 0.108171344 6.720325e-02
156 0.075810611 1.081713e-01
157 0.109742598 7.581061e-02
158 0.145480596 1.097426e-01
159 0.017334876 1.454806e-01
160 -0.120441877 1.733488e-02
161 -0.139102184 -1.204419e-01
162 -0.314864799 -1.391022e-01
163 0.111397182 -3.148648e-01
> 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/7lk5b1258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/809pv1258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9st4n1258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ltd41258727480.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z5yf1258727480.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/12y15n1258727480.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/13qg5l1258727480.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/144db61258727480.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/15d8kg1258727480.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/16jlfe1258727480.tab")
+ }
> system("convert tmp/1n58j1258727480.ps tmp/1n58j1258727480.png")
> system("convert tmp/242qr1258727480.ps tmp/242qr1258727480.png")
> system("convert tmp/3e8x11258727480.ps tmp/3e8x11258727480.png")
> system("convert tmp/4lwkn1258727480.ps tmp/4lwkn1258727480.png")
> system("convert tmp/5z2bs1258727480.ps tmp/5z2bs1258727480.png")
> system("convert tmp/66mrq1258727480.ps tmp/66mrq1258727480.png")
> system("convert tmp/7lk5b1258727480.ps tmp/7lk5b1258727480.png")
> system("convert tmp/809pv1258727480.ps tmp/809pv1258727480.png")
> system("convert tmp/9st4n1258727480.ps tmp/9st4n1258727480.png")
> system("convert tmp/10ltd41258727480.ps tmp/10ltd41258727480.png")
>
>
> proc.time()
user system elapsed
4.497 1.753 4.928