R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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(3.50
+ ,2.20
+ ,3.00
+ ,3.43
+ ,4.33
+ ,2.67
+ ,2.75
+ ,1.40
+ ,2.00
+ ,3.57
+ ,3.83
+ ,2.78
+ ,1.50
+ ,3.40
+ ,2.00
+ ,4.29
+ ,4.17
+ ,1.89
+ ,3.00
+ ,2.00
+ ,2.00
+ ,2.71
+ ,3.83
+ ,2.00
+ ,2.00
+ ,2.40
+ ,2.25
+ ,3.14
+ ,3.17
+ ,2.00
+ ,2.50
+ ,2.40
+ ,1.75
+ ,3.14
+ ,4.83
+ ,1.78
+ ,2.50
+ ,2.20
+ ,1.00
+ ,3.57
+ ,4.17
+ ,2.22
+ ,2.75
+ ,2.20
+ ,2.75
+ ,3.29
+ ,3.50
+ ,1.78
+ ,4.00
+ ,2.40
+ ,1.75
+ ,2.43
+ ,3.67
+ ,2.00
+ ,2.75
+ ,2.60
+ ,1.75
+ ,3.00
+ ,4.17
+ ,1.89
+ ,3.25
+ ,2.80
+ ,3.00
+ ,2.71
+ ,4.00
+ ,2.56
+ ,3.00
+ ,3.20
+ ,2.50
+ ,2.71
+ ,3.00
+ ,3.33
+ ,2.00
+ ,2.20
+ ,2.50
+ ,2.14
+ ,3.67
+ ,2.56
+ ,3.00
+ ,2.00
+ ,2.00
+ ,2.29
+ ,2.50
+ ,2.00
+ ,2.75
+ ,2.20
+ ,2.00
+ ,3.29
+ ,3.67
+ ,1.67
+ ,1.00
+ ,3.00
+ ,1.00
+ ,3.86
+ ,4.67
+ ,1.33
+ ,2.25
+ ,1.80
+ ,2.25
+ ,3.14
+ ,3.33
+ ,2.33
+ ,2.00
+ ,2.20
+ ,2.00
+ ,2.00
+ ,2.00
+ ,1.67
+ ,2.00
+ ,3.40
+ ,1.75
+ ,3.14
+ ,4.00
+ ,2.22
+ ,3.50
+ ,3.40
+ ,2.75
+ ,3.29
+ ,3.33
+ ,3.44
+ ,3.75
+ ,2.20
+ ,2.25
+ ,3.29
+ ,3.50
+ ,3.00
+ ,4.00
+ ,3.60
+ ,2.75
+ ,3.00
+ ,3.33
+ ,3.78
+ ,2.25
+ ,2.80
+ ,3.25
+ ,2.71
+ ,3.50
+ ,2.33
+ ,3.50
+ ,2.00
+ ,2.00
+ ,2.57
+ ,3.83
+ ,3.44
+ ,2.75
+ ,2.20
+ ,2.00
+ ,2.86
+ ,4.67
+ ,2.11
+ ,2.00
+ ,3.00
+ ,2.25
+ ,3.29
+ ,4.00
+ ,1.78
+ ,2.25
+ ,3.00
+ ,1.50
+ ,3.57
+ ,4.00
+ ,2.22
+ ,2.25
+ ,2.60
+ ,2.25
+ ,2.71
+ ,4.00
+ ,2.33
+ ,2.25
+ ,3.20
+ ,2.25
+ ,3.43
+ ,3.83
+ ,2.44
+ ,2.25
+ ,2.60
+ ,1.50
+ ,3.14
+ ,3.83
+ ,1.89
+ ,2.50
+ ,1.80
+ ,1.50
+ ,3.57
+ ,4.83
+ ,2.67
+ ,4.00
+ ,3.60
+ ,4.00
+ ,3.71
+ ,4.00
+ ,2.78
+ ,2.75
+ ,3.60
+ ,1.25
+ ,4.14
+ ,3.00
+ ,2.89
+ ,2.00
+ ,2.40
+ ,1.75
+ ,4.57
+ ,4.17
+ ,2.78
+ ,2.25
+ ,3.40
+ ,2.25
+ ,3.57
+ ,3.50
+ ,1.89
+ ,4.00
+ ,1.80
+ ,1.50
+ ,4.14
+ ,4.33
+ ,3.56
+ ,2.75
+ ,1.80
+ ,1.50
+ ,4.00
+ ,3.67
+ ,3.67
+ ,4.00
+ ,2.40
+ ,1.25
+ ,2.43
+ ,3.67
+ ,1.44
+ ,3.00
+ ,3.60
+ ,3.00
+ ,4.00
+ ,3.67
+ ,3.56
+ ,3.00
+ ,2.40
+ ,1.75
+ ,4.14
+ ,3.83
+ ,2.78
+ ,3.50
+ ,3.60
+ ,2.50
+ ,3.71
+ ,5.00
+ ,3.22
+ ,2.25
+ ,2.80
+ ,2.25
+ ,3.57
+ ,3.83
+ ,2.44
+ ,2.50
+ ,3.00
+ ,2.00
+ ,2.00
+ ,2.83
+ ,2.00
+ ,2.25
+ ,3.20
+ ,1.25
+ ,3.57
+ ,3.83
+ ,1.89
+ ,2.50
+ ,2.00
+ ,2.00
+ ,3.71
+ ,3.83
+ ,2.22
+ ,3.00
+ ,2.20
+ ,2.00
+ ,2.86
+ ,4.17
+ ,1.67
+ ,3.50
+ ,2.80
+ ,2.50
+ ,2.57
+ ,4.00
+ ,2.22
+ ,3.50
+ ,1.80
+ ,1.50
+ ,4.57
+ ,4.00
+ ,3.67
+ ,2.50
+ ,2.40
+ ,2.00
+ ,3.57
+ ,3.83
+ ,3.22
+ ,3.50
+ ,3.40
+ ,1.75
+ ,3.57
+ ,3.50
+ ,2.56
+ ,4.00
+ ,1.00
+ ,1.00
+ ,3.29
+ ,4.00
+ ,2.89
+ ,2.25
+ ,2.40
+ ,2.00
+ ,3.00
+ ,4.00
+ ,2.00
+ ,2.50
+ ,2.40
+ ,2.00
+ ,2.86
+ ,4.67
+ ,2.22
+ ,1.50
+ ,1.20
+ ,1.00
+ ,2.14
+ ,2.67
+ ,1.22
+ ,2.00
+ ,4.80
+ ,5.00
+ ,4.29
+ ,3.33
+ ,3.11
+ ,3.25
+ ,2.40
+ ,2.00
+ ,3.43
+ ,4.83
+ ,2.89
+ ,2.50
+ ,2.40
+ ,2.00
+ ,3.71
+ ,4.50
+ ,2.44
+ ,2.00
+ ,2.80
+ ,1.50
+ ,3.43
+ ,3.67
+ ,1.89
+ ,1.75
+ ,1.40
+ ,1.00
+ ,3.14
+ ,4.67
+ ,1.33
+ ,3.75
+ ,2.60
+ ,2.00
+ ,2.00
+ ,2.67
+ ,1.56
+ ,2.25
+ ,2.40
+ ,2.25
+ ,3.43
+ ,4.17
+ ,1.89
+ ,2.50
+ ,2.60
+ ,1.50
+ ,3.43
+ ,4.00
+ ,2.33
+ ,3.00
+ ,2.80
+ ,1.75
+ ,3.43
+ ,4.67
+ ,2.11
+ ,3.25
+ ,1.60
+ ,2.25
+ ,3.43
+ ,4.00
+ ,2.00
+ ,2.50
+ ,2.20
+ ,1.25
+ ,2.71
+ ,3.83
+ ,1.11
+ ,2.75
+ ,1.80
+ ,1.25
+ ,4.43
+ ,5.00
+ ,3.22
+ ,2.00
+ ,2.20
+ ,2.00
+ ,3.14
+ ,4.00
+ ,3.44
+ ,2.25
+ ,2.60
+ ,2.00
+ ,3.86
+ ,3.50
+ ,2.11
+ ,3.25
+ ,2.00
+ ,1.50
+ ,2.71
+ ,4.17
+ ,1.00
+ ,2.75
+ ,2.20
+ ,2.00
+ ,3.57
+ ,4.17
+ ,2.22
+ ,2.00
+ ,2.40
+ ,1.75
+ ,2.86
+ ,3.67
+ ,3.11
+ ,2.25
+ ,1.80
+ ,1.75
+ ,3.00
+ ,3.83
+ ,2.11
+ ,2.25
+ ,3.00
+ ,2.25
+ ,3.86
+ ,4.33
+ ,3.33
+ ,3.75
+ ,3.60
+ ,2.75
+ ,3.29
+ ,3.83
+ ,3.22
+ ,2.25
+ ,3.00
+ ,1.50
+ ,3.57
+ ,4.17
+ ,2.89
+ ,2.50
+ ,2.40
+ ,2.00
+ ,2.86
+ ,3.50
+ ,2.56
+ ,3.50
+ ,2.60
+ ,1.50
+ ,3.00
+ ,4.17
+ ,1.44
+ ,3.00
+ ,2.80
+ ,2.25
+ ,3.14
+ ,4.00
+ ,2.33
+ ,3.00
+ ,2.00
+ ,2.00
+ ,3.29
+ ,4.83
+ ,2.11
+ ,2.75
+ ,2.60
+ ,1.50
+ ,3.57
+ ,3.67
+ ,3.11
+ ,3.50
+ ,2.60
+ ,2.50
+ ,3.57
+ ,4.50
+ ,2.56
+ ,1.50
+ ,2.20
+ ,2.00
+ ,2.43
+ ,4.33
+ ,2.00
+ ,3.00
+ ,2.60
+ ,2.00
+ ,2.71
+ ,3.67
+ ,2.33
+ ,2.00
+ ,3.20
+ ,2.50
+ ,3.57
+ ,4.00
+ ,2.22
+ ,3.50
+ ,1.60
+ ,1.25
+ ,2.71
+ ,4.50
+ ,2.56
+ ,2.75
+ ,3.20
+ ,1.75
+ ,2.86
+ ,4.00
+ ,2.33
+ ,2.50
+ ,2.20
+ ,1.25
+ ,3.71
+ ,4.00
+ ,2.33
+ ,3.50
+ ,1.80
+ ,2.00
+ ,3.29
+ ,4.83
+ ,1.67
+ ,3.00
+ ,3.20
+ ,3.50
+ ,3.86
+ ,3.67
+ ,3.11
+ ,2.50
+ ,2.40
+ ,1.75
+ ,2.43
+ ,3.50
+ ,2.11
+ ,3.50
+ ,2.80
+ ,2.00
+ ,2.43
+ ,4.00
+ ,2.89
+ ,1.25
+ ,1.60
+ ,1.50
+ ,2.71
+ ,4.00
+ ,1.11
+ ,2.75
+ ,1.80
+ ,1.25
+ ,2.43
+ ,3.83
+ ,1.78
+ ,2.50
+ ,3.00
+ ,1.50
+ ,3.14
+ ,3.33
+ ,2.44
+ ,2.25
+ ,2.20
+ ,2.50
+ ,3.00
+ ,4.50
+ ,2.11
+ ,2.50
+ ,4.20
+ ,3.00
+ ,4.57
+ ,4.33
+ ,3.44
+ ,4.00
+ ,2.80
+ ,2.25
+ ,3.00
+ ,4.17
+ ,3.44
+ ,3.25
+ ,3.60
+ ,3.00
+ ,3.00
+ ,3.50
+ ,3.22
+ ,2.25
+ ,2.40
+ ,1.75
+ ,2.57
+ ,3.50
+ ,2.11
+ ,2.50
+ ,2.60
+ ,2.00
+ ,2.57
+ ,3.17
+ ,2.44
+ ,2.50
+ ,3.00
+ ,2.50
+ ,3.29
+ ,3.50
+ ,2.56
+ ,1.75
+ ,2.40
+ ,1.50
+ ,2.71
+ ,3.50
+ ,1.67
+ ,2.25
+ ,3.80
+ ,2.50
+ ,2.86
+ ,2.67
+ ,2.22
+ ,2.00
+ ,3.00
+ ,2.50
+ ,3.00
+ ,3.67
+ ,2.00
+ ,3.50
+ ,2.20
+ ,2.50
+ ,2.86
+ ,4.83
+ ,2.56
+ ,3.50
+ ,2.20
+ ,1.25
+ ,2.43
+ ,2.50
+ ,2.78
+ ,2.00
+ ,2.00
+ ,1.75
+ ,2.57
+ ,2.83
+ ,2.33
+ ,2.25
+ ,2.60
+ ,2.50
+ ,2.71
+ ,2.50
+ ,2.67
+ ,3.50
+ ,3.00
+ ,2.75
+ ,3.14
+ ,3.50
+ ,2.78
+ ,3.50
+ ,2.40
+ ,1.50
+ ,2.14
+ ,3.50
+ ,1.89
+ ,2.00
+ ,2.40
+ ,1.75
+ ,2.00
+ ,3.17
+ ,1.44
+ ,2.00
+ ,3.20
+ ,3.00
+ ,2.57
+ ,4.00
+ ,3.11
+ ,2.00
+ ,1.80
+ ,2.75
+ ,3.43
+ ,3.33
+ ,2.33
+ ,1.75
+ ,3.60
+ ,2.75
+ ,5.00
+ ,2.83
+ ,2.78
+ ,1.50
+ ,1.60
+ ,2.75
+ ,4.14
+ ,3.83
+ ,1.00
+ ,2.00
+ ,2.60
+ ,1.25
+ ,3.00
+ ,4.00
+ ,1.78
+ ,1.50
+ ,3.40
+ ,2.00
+ ,3.57
+ ,2.33
+ ,2.11
+ ,2.75
+ ,1.80
+ ,1.50
+ ,2.86
+ ,3.17
+ ,1.89
+ ,3.50
+ ,3.00
+ ,2.25
+ ,3.14
+ ,4.00
+ ,2.78
+ ,2.75
+ ,1.60
+ ,1.00
+ ,1.86
+ ,2.17
+ ,2.22
+ ,2.75
+ ,1.40
+ ,1.00
+ ,3.71
+ ,3.67
+ ,3.22
+ ,2.75
+ ,2.40
+ ,1.75
+ ,2.43
+ ,2.67
+ ,1.56
+ ,3.50
+ ,2.80
+ ,2.75
+ ,3.57
+ ,3.17
+ ,2.44
+ ,2.00
+ ,1.20
+ ,1.50
+ ,2.86
+ ,4.17
+ ,1.67
+ ,5.00
+ ,1.60
+ ,1.75
+ ,2.71
+ ,4.17
+ ,2.11
+ ,2.75
+ ,3.40
+ ,2.00
+ ,3.00
+ ,3.83
+ ,2.22
+ ,2.00
+ ,2.00
+ ,1.00
+ ,3.14
+ ,4.00
+ ,1.67
+ ,2.75
+ ,2.20
+ ,2.00
+ ,3.43
+ ,4.33
+ ,2.22
+ ,2.50
+ ,2.80
+ ,2.25
+ ,3.00
+ ,4.33
+ ,2.00
+ ,3.50
+ ,2.20
+ ,2.00
+ ,3.71
+ ,4.17
+ ,3.67
+ ,2.75
+ ,2.60
+ ,2.75
+ ,3.43
+ ,3.00
+ ,2.44
+ ,2.25
+ ,2.40
+ ,2.00
+ ,2.29
+ ,3.50
+ ,1.78
+ ,2.25
+ ,2.20
+ ,1.25
+ ,3.29
+ ,4.33
+ ,1.89
+ ,2.00
+ ,1.80
+ ,1.00
+ ,2.57
+ ,3.83
+ ,1.78
+ ,2.50
+ ,2.40
+ ,2.00
+ ,2.29
+ ,3.83
+ ,2.33
+ ,3.25
+ ,4.00
+ ,2.50
+ ,3.71
+ ,3.67
+ ,2.89
+ ,3.25
+ ,2.40
+ ,1.50
+ ,2.71
+ ,3.33
+ ,2.00
+ ,3.00
+ ,2.60
+ ,2.25
+ ,3.00
+ ,2.17
+ ,2.00
+ ,2.00
+ ,2.40
+ ,2.25
+ ,3.00
+ ,4.00
+ ,1.89
+ ,3.25
+ ,2.40
+ ,3.25
+ ,3.14
+ ,2.50
+ ,2.44
+ ,3.50
+ ,1.80
+ ,2.25
+ ,3.29
+ ,2.33
+ ,3.33
+ ,3.00
+ ,3.00
+ ,2.50
+ ,4.14
+ ,3.67
+ ,3.33
+ ,3.50
+ ,4.80
+ ,5.00
+ ,3.00
+ ,1.67
+ ,2.67
+ ,3.75
+ ,1.40
+ ,1.25
+ ,3.00
+ ,4.00
+ ,2.33
+ ,3.25
+ ,3.40
+ ,2.75
+ ,3.29
+ ,3.67
+ ,2.33
+ ,4.00
+ ,2.20
+ ,1.50
+ ,3.86
+ ,4.00
+ ,3.22
+ ,2.25
+ ,3.40
+ ,2.25
+ ,3.57
+ ,3.17
+ ,3.44
+ ,2.25
+ ,2.20
+ ,1.75
+ ,3.00
+ ,3.33
+ ,2.22
+ ,2.25
+ ,2.40
+ ,2.25
+ ,1.43
+ ,2.17
+ ,1.78
+ ,2.00
+ ,2.80
+ ,2.50
+ ,2.86
+ ,3.33
+ ,2.44
+ ,1.75
+ ,2.20
+ ,2.25
+ ,3.71
+ ,3.67
+ ,2.22
+ ,4.00
+ ,3.20
+ ,2.00
+ ,3.43
+ ,4.00
+ ,3.11
+ ,2.75
+ ,4.20
+ ,1.75
+ ,4.14
+ ,4.83
+ ,4.22
+ ,2.25
+ ,2.80
+ ,1.50
+ ,2.71
+ ,2.00
+ ,2.44
+ ,2.75
+ ,4.00
+ ,3.25
+ ,3.43
+ ,3.33
+ ,2.22
+ ,2.25
+ ,2.60
+ ,1.50
+ ,2.71
+ ,3.50
+ ,1.89
+ ,3.50
+ ,2.20
+ ,2.00
+ ,3.43
+ ,4.00
+ ,3.11
+ ,3.25
+ ,3.00
+ ,2.50
+ ,3.14
+ ,3.67
+ ,2.44
+ ,4.00
+ ,3.80
+ ,4.00
+ ,2.43
+ ,3.33
+ ,3.44)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5'
+ ,'Y')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('X1','X2','X3','X4','X5','Y'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> #'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
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X1 X2 X3 X4 X5
1 2.67 3.50 2.2 3.00 3.43 4.33
2 2.78 2.75 1.4 2.00 3.57 3.83
3 1.89 1.50 3.4 2.00 4.29 4.17
4 2.00 3.00 2.0 2.00 2.71 3.83
5 2.00 2.00 2.4 2.25 3.14 3.17
6 1.78 2.50 2.4 1.75 3.14 4.83
7 2.22 2.50 2.2 1.00 3.57 4.17
8 1.78 2.75 2.2 2.75 3.29 3.50
9 2.00 4.00 2.4 1.75 2.43 3.67
10 1.89 2.75 2.6 1.75 3.00 4.17
11 2.56 3.25 2.8 3.00 2.71 4.00
12 3.33 3.00 3.2 2.50 2.71 3.00
13 2.56 2.00 2.2 2.50 2.14 3.67
14 2.00 3.00 2.0 2.00 2.29 2.50
15 1.67 2.75 2.2 2.00 3.29 3.67
16 1.33 1.00 3.0 1.00 3.86 4.67
17 2.33 2.25 1.8 2.25 3.14 3.33
18 1.67 2.00 2.2 2.00 2.00 2.00
19 2.22 2.00 3.4 1.75 3.14 4.00
20 3.44 3.50 3.4 2.75 3.29 3.33
21 3.00 3.75 2.2 2.25 3.29 3.50
22 3.78 4.00 3.6 2.75 3.00 3.33
23 2.33 2.25 2.8 3.25 2.71 3.50
24 3.44 3.50 2.0 2.00 2.57 3.83
25 2.11 2.75 2.2 2.00 2.86 4.67
26 1.78 2.00 3.0 2.25 3.29 4.00
27 2.22 2.25 3.0 1.50 3.57 4.00
28 2.33 2.25 2.6 2.25 2.71 4.00
29 2.44 2.25 3.2 2.25 3.43 3.83
30 1.89 2.25 2.6 1.50 3.14 3.83
31 2.67 2.50 1.8 1.50 3.57 4.83
32 2.78 4.00 3.6 4.00 3.71 4.00
33 2.89 2.75 3.6 1.25 4.14 3.00
34 2.78 2.00 2.4 1.75 4.57 4.17
35 1.89 2.25 3.4 2.25 3.57 3.50
36 3.56 4.00 1.8 1.50 4.14 4.33
37 3.67 2.75 1.8 1.50 4.00 3.67
38 1.44 4.00 2.4 1.25 2.43 3.67
39 3.56 3.00 3.6 3.00 4.00 3.67
40 2.78 3.00 2.4 1.75 4.14 3.83
41 3.22 3.50 3.6 2.50 3.71 5.00
42 2.44 2.25 2.8 2.25 3.57 3.83
43 2.00 2.50 3.0 2.00 2.00 2.83
44 1.89 2.25 3.2 1.25 3.57 3.83
45 2.22 2.50 2.0 2.00 3.71 3.83
46 1.67 3.00 2.2 2.00 2.86 4.17
47 2.22 3.50 2.8 2.50 2.57 4.00
48 3.67 3.50 1.8 1.50 4.57 4.00
49 3.22 2.50 2.4 2.00 3.57 3.83
50 2.56 3.50 3.4 1.75 3.57 3.50
51 2.89 4.00 1.0 1.00 3.29 4.00
52 2.00 2.25 2.4 2.00 3.00 4.00
53 2.22 2.50 2.4 2.00 2.86 4.67
54 1.22 1.50 1.2 1.00 2.14 2.67
55 3.11 2.00 4.8 5.00 4.29 3.33
56 2.89 3.25 2.4 2.00 3.43 4.83
57 2.44 2.50 2.4 2.00 3.71 4.50
58 1.89 2.00 2.8 1.50 3.43 3.67
59 1.33 1.75 1.4 1.00 3.14 4.67
60 1.56 3.75 2.6 2.00 2.00 2.67
61 1.89 2.25 2.4 2.25 3.43 4.17
62 2.33 2.50 2.6 1.50 3.43 4.00
63 2.11 3.00 2.8 1.75 3.43 4.67
64 2.00 3.25 1.6 2.25 3.43 4.00
65 1.11 2.50 2.2 1.25 2.71 3.83
66 3.22 2.75 1.8 1.25 4.43 5.00
67 3.44 2.00 2.2 2.00 3.14 4.00
68 2.11 2.25 2.6 2.00 3.86 3.50
69 1.00 3.25 2.0 1.50 2.71 4.17
70 2.22 2.75 2.2 2.00 3.57 4.17
71 3.11 2.00 2.4 1.75 2.86 3.67
72 2.11 2.25 1.8 1.75 3.00 3.83
73 3.33 2.25 3.0 2.25 3.86 4.33
74 3.22 3.75 3.6 2.75 3.29 3.83
75 2.89 2.25 3.0 1.50 3.57 4.17
76 2.56 2.50 2.4 2.00 2.86 3.50
77 1.44 3.50 2.6 1.50 3.00 4.17
78 2.33 3.00 2.8 2.25 3.14 4.00
79 2.11 3.00 2.0 2.00 3.29 4.83
80 3.11 2.75 2.6 1.50 3.57 3.67
81 2.56 3.50 2.6 2.50 3.57 4.50
82 2.00 1.50 2.2 2.00 2.43 4.33
83 2.33 3.00 2.6 2.00 2.71 3.67
84 2.22 2.00 3.2 2.50 3.57 4.00
85 2.56 3.50 1.6 1.25 2.71 4.50
86 2.33 2.75 3.2 1.75 2.86 4.00
87 2.33 2.50 2.2 1.25 3.71 4.00
88 1.67 3.50 1.8 2.00 3.29 4.83
89 3.11 3.00 3.2 3.50 3.86 3.67
90 2.11 2.50 2.4 1.75 2.43 3.50
91 2.89 3.50 2.8 2.00 2.43 4.00
92 1.11 1.25 1.6 1.50 2.71 4.00
93 1.78 2.75 1.8 1.25 2.43 3.83
94 2.44 2.50 3.0 1.50 3.14 3.33
95 2.11 2.25 2.2 2.50 3.00 4.50
96 3.44 2.50 4.2 3.00 4.57 4.33
97 3.44 4.00 2.8 2.25 3.00 4.17
98 3.22 3.25 3.6 3.00 3.00 3.50
99 2.11 2.25 2.4 1.75 2.57 3.50
100 2.44 2.50 2.6 2.00 2.57 3.17
101 2.56 2.50 3.0 2.50 3.29 3.50
102 1.67 1.75 2.4 1.50 2.71 3.50
103 2.22 2.25 3.8 2.50 2.86 2.67
104 2.00 2.00 3.0 2.50 3.00 3.67
105 2.56 3.50 2.2 2.50 2.86 4.83
106 2.78 3.50 2.2 1.25 2.43 2.50
107 2.33 2.00 2.0 1.75 2.57 2.83
108 2.67 2.25 2.6 2.50 2.71 2.50
109 2.78 3.50 3.0 2.75 3.14 3.50
110 1.89 3.50 2.4 1.50 2.14 3.50
111 1.44 2.00 2.4 1.75 2.00 3.17
112 3.11 2.00 3.2 3.00 2.57 4.00
113 2.33 2.00 1.8 2.75 3.43 3.33
114 2.78 1.75 3.6 2.75 5.00 2.83
115 1.00 1.50 1.6 2.75 4.14 3.83
116 1.78 2.00 2.6 1.25 3.00 4.00
117 2.11 1.50 3.4 2.00 3.57 2.33
118 1.89 2.75 1.8 1.50 2.86 3.17
119 2.78 3.50 3.0 2.25 3.14 4.00
120 2.22 2.75 1.6 1.00 1.86 2.17
121 3.22 2.75 1.4 1.00 3.71 3.67
122 1.56 2.75 2.4 1.75 2.43 2.67
123 2.44 3.50 2.8 2.75 3.57 3.17
124 1.67 2.00 1.2 1.50 2.86 4.17
125 2.11 5.00 1.6 1.75 2.71 4.17
126 2.22 2.75 3.4 2.00 3.00 3.83
127 1.67 2.00 2.0 1.00 3.14 4.00
128 2.22 2.75 2.2 2.00 3.43 4.33
129 2.00 2.50 2.8 2.25 3.00 4.33
130 3.67 3.50 2.2 2.00 3.71 4.17
131 2.44 2.75 2.6 2.75 3.43 3.00
132 1.78 2.25 2.4 2.00 2.29 3.50
133 1.89 2.25 2.2 1.25 3.29 4.33
134 1.78 2.00 1.8 1.00 2.57 3.83
135 2.33 2.50 2.4 2.00 2.29 3.83
136 2.89 3.25 4.0 2.50 3.71 3.67
137 2.00 3.25 2.4 1.50 2.71 3.33
138 2.00 3.00 2.6 2.25 3.00 2.17
139 1.89 2.00 2.4 2.25 3.00 4.00
140 2.44 3.25 2.4 3.25 3.14 2.50
141 3.33 3.50 1.8 2.25 3.29 2.33
142 3.33 3.00 3.0 2.50 4.14 3.67
143 2.67 3.50 4.8 5.00 3.00 1.67
144 2.33 3.75 1.4 1.25 3.00 4.00
145 2.33 3.25 3.4 2.75 3.29 3.67
146 3.22 4.00 2.2 1.50 3.86 4.00
147 3.44 2.25 3.4 2.25 3.57 3.17
148 2.22 2.25 2.2 1.75 3.00 3.33
149 1.78 2.25 2.4 2.25 1.43 2.17
150 2.44 2.00 2.8 2.50 2.86 3.33
151 2.22 1.75 2.2 2.25 3.71 3.67
152 3.11 4.00 3.2 2.00 3.43 4.00
153 4.22 2.75 4.2 1.75 4.14 4.83
154 2.44 2.25 2.8 1.50 2.71 2.00
155 2.22 2.75 4.0 3.25 3.43 3.33
156 1.89 2.25 2.6 1.50 2.71 3.50
157 3.11 3.50 2.2 2.00 3.43 4.00
158 2.44 3.25 3.0 2.50 3.14 3.67
159 3.44 4.00 3.8 4.00 2.43 3.33
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 X3 X4 X5
-0.21697 0.36037 0.13903 0.08356 0.44041 -0.07763
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.30178 -0.27590 -0.04085 0.30407 1.39089
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.21697 0.33907 -0.640 0.5232
X1 0.36037 0.05789 6.225 4.41e-09 ***
X2 0.13903 0.07370 1.886 0.0611 .
X3 0.08356 0.07474 1.118 0.2653
X4 0.44041 0.07449 5.913 2.12e-08 ***
X5 -0.07763 0.06868 -1.130 0.2601
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4972 on 153 degrees of freedom
Multiple R-squared: 0.4074, Adjusted R-squared: 0.3881
F-statistic: 21.04 on 5 and 153 DF, p-value: 5.682e-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,] 0.21391453 0.42782907 0.78608547
[2,] 0.10367434 0.20734868 0.89632566
[3,] 0.23040844 0.46081688 0.76959156
[4,] 0.65240676 0.69518648 0.34759324
[5,] 0.66681301 0.66637398 0.33318699
[6,] 0.64170714 0.71658571 0.35829286
[7,] 0.64593287 0.70813426 0.35406713
[8,] 0.58101335 0.83797331 0.41898665
[9,] 0.49595235 0.99190471 0.50404765
[10,] 0.46511199 0.93022398 0.53488801
[11,] 0.40116978 0.80233956 0.59883022
[12,] 0.43500926 0.87001852 0.56499074
[13,] 0.39481370 0.78962740 0.60518630
[14,] 0.40000907 0.80001814 0.59999093
[15,] 0.33865994 0.67731989 0.66134006
[16,] 0.60011385 0.79977231 0.39988615
[17,] 0.53021199 0.93957602 0.46978801
[18,] 0.49995869 0.99991739 0.50004131
[19,] 0.43820638 0.87641277 0.56179362
[20,] 0.39123676 0.78247351 0.60876324
[21,] 0.33108632 0.66217265 0.66891368
[22,] 0.27916987 0.55833975 0.72083013
[23,] 0.34063540 0.68127080 0.65936460
[24,] 0.40073436 0.80146873 0.59926564
[25,] 0.35746685 0.71493371 0.64253315
[26,] 0.36888896 0.73777791 0.63111104
[27,] 0.38316683 0.76633366 0.61683317
[28,] 0.37496049 0.74992097 0.62503951
[29,] 0.55584627 0.88830745 0.44415373
[30,] 0.76122577 0.47754846 0.23877423
[31,] 0.75816710 0.48366579 0.24183290
[32,] 0.71645993 0.56708013 0.28354007
[33,] 0.69167441 0.61665118 0.30832559
[34,] 0.64244969 0.71510062 0.35755031
[35,] 0.59400651 0.81198699 0.40599349
[36,] 0.58041156 0.83917689 0.41958844
[37,] 0.54621716 0.90756567 0.45378284
[38,] 0.56780139 0.86439723 0.43219861
[39,] 0.52700472 0.94599056 0.47299528
[40,] 0.51809807 0.96380387 0.48190193
[41,] 0.58228769 0.83542463 0.41771231
[42,] 0.56065885 0.87868230 0.43934115
[43,] 0.52524409 0.94951182 0.47475591
[44,] 0.47592759 0.95185519 0.52407241
[45,] 0.43752514 0.87505028 0.56247486
[46,] 0.39068852 0.78137704 0.60931148
[47,] 0.34797979 0.69595957 0.65202021
[48,] 0.32080909 0.64161817 0.67919091
[49,] 0.27770148 0.55540296 0.72229852
[50,] 0.25385381 0.50770763 0.74614619
[51,] 0.23424644 0.46849288 0.76575356
[52,] 0.29239159 0.58478318 0.70760841
[53,] 0.28058158 0.56116316 0.71941842
[54,] 0.24315733 0.48631466 0.75684267
[55,] 0.23067021 0.46134042 0.76932979
[56,] 0.26507517 0.53015035 0.73492483
[57,] 0.34169735 0.68339470 0.65830265
[58,] 0.34401571 0.68803143 0.65598429
[59,] 0.67606006 0.64787989 0.32393994
[60,] 0.66928455 0.66143090 0.33071545
[61,] 0.84515348 0.30969305 0.15484652
[62,] 0.82558689 0.34882623 0.17441311
[63,] 0.93093817 0.13812366 0.06906183
[64,] 0.91470052 0.17059896 0.08529948
[65,] 0.93747137 0.12505726 0.06252863
[66,] 0.92550461 0.14899078 0.07449539
[67,] 0.92747514 0.14504973 0.07252486
[68,] 0.92135980 0.15728041 0.07864020
[69,] 0.97061822 0.05876356 0.02938178
[70,] 0.96339098 0.07321804 0.03660902
[71,] 0.95615491 0.08769018 0.04384509
[72,] 0.95880978 0.08238044 0.04119022
[73,] 0.95110447 0.09779107 0.04889553
[74,] 0.95079759 0.09840482 0.04920241
[75,] 0.93813518 0.12372965 0.06186482
[76,] 0.92554709 0.14890582 0.07445291
[77,] 0.91750227 0.16499546 0.08249773
[78,] 0.90243841 0.19512318 0.09756159
[79,] 0.88179375 0.23641251 0.11820625
[80,] 0.92513240 0.14973520 0.07486760
[81,] 0.90941944 0.18116111 0.09058056
[82,] 0.89034524 0.21930952 0.10965476
[83,] 0.89225218 0.21549563 0.10774782
[84,] 0.88172763 0.23654474 0.11827237
[85,] 0.86068062 0.27863876 0.13931938
[86,] 0.83424465 0.33151070 0.16575535
[87,] 0.80260690 0.39478620 0.19739310
[88,] 0.77413845 0.45172310 0.22586155
[89,] 0.79254215 0.41491571 0.20745785
[90,] 0.78725432 0.42549136 0.21274568
[91,] 0.75347256 0.49305488 0.24652744
[92,] 0.72969623 0.54060754 0.27030377
[93,] 0.68855320 0.62289359 0.31144680
[94,] 0.65058283 0.69883435 0.34941717
[95,] 0.61291221 0.77417559 0.38708779
[96,] 0.57114198 0.85771603 0.42885802
[97,] 0.52584182 0.94831636 0.47415818
[98,] 0.50645537 0.98708926 0.49354463
[99,] 0.50284530 0.99430939 0.49715470
[100,] 0.51572271 0.96855457 0.48427729
[101,] 0.46561243 0.93122485 0.53438757
[102,] 0.44215144 0.88430288 0.55784856
[103,] 0.40286206 0.80572412 0.59713794
[104,] 0.62701642 0.74596717 0.37298358
[105,] 0.61870508 0.76258984 0.38129492
[106,] 0.59019977 0.81960047 0.40980023
[107,] 0.79085639 0.41828721 0.20914361
[108,] 0.76886294 0.46227412 0.23113706
[109,] 0.75817854 0.48364292 0.24182146
[110,] 0.73164739 0.53670521 0.26835261
[111,] 0.68424321 0.63151357 0.31575679
[112,] 0.69262614 0.61474773 0.30737386
[113,] 0.74406044 0.51187912 0.25593956
[114,] 0.74848408 0.50303183 0.25151592
[115,] 0.75480040 0.49039920 0.24519960
[116,] 0.70564641 0.58870718 0.29435359
[117,] 0.74869489 0.50261022 0.25130511
[118,] 0.72218375 0.55563249 0.27781625
[119,] 0.71137322 0.57725357 0.28862678
[120,] 0.67795517 0.64408966 0.32204483
[121,] 0.65264604 0.69470793 0.34735396
[122,] 0.72503875 0.54992251 0.27496125
[123,] 0.67217538 0.65564924 0.32782462
[124,] 0.61160596 0.77678808 0.38839404
[125,] 0.61127422 0.77745157 0.38872578
[126,] 0.55205602 0.89588797 0.44794398
[127,] 0.51095787 0.97808426 0.48904213
[128,] 0.48280557 0.96561114 0.51719443
[129,] 0.48816562 0.97633124 0.51183438
[130,] 0.53179488 0.93641025 0.46820512
[131,] 0.46421279 0.92842558 0.53578721
[132,] 0.39008035 0.78016070 0.60991965
[133,] 0.50150220 0.99699560 0.49849780
[134,] 0.44883293 0.89766586 0.55116707
[135,] 0.37835813 0.75671626 0.62164187
[136,] 0.29858003 0.59716006 0.70141997
[137,] 0.35698624 0.71397247 0.64301376
[138,] 0.26438258 0.52876517 0.73561742
[139,] 0.34158689 0.68317379 0.65841311
[140,] 0.23573931 0.47147862 0.76426069
[141,] 0.14676544 0.29353087 0.85323456
[142,] 0.09219834 0.18439668 0.90780166
> postscript(file="/var/www/rcomp/tmp/1juft1290539179.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2u3ww1290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3u3ww1290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4u3ww1290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/54ceh1290539179.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 = 159
Frequency = 1
1 2 3 4 5
-1.053337e-01 3.692543e-01 -6.390297e-01 -2.055047e-01 -1.622422e-01
6 7 8 9 10
-3.917828e-01 -1.019204e-01 -7.069398e-01 -4.897070e-01 -3.892611e-01
11 12 13 14 15
8.281923e-02 8.514505e-01 8.838965e-01 -1.237827e-01 -7.410738e-01
16 17 18 19 20
-6.514827e-01 1.735011e-01 -3.231082e-02 2.494415e-02 5.027502e-01
21 22 23 24 25
1.944649e-01 7.624758e-01 1.534886e-01 1.115965e+00 -3.406759e-02
26 27 28 29 30
-4.672856e-01 -1.780245e-01 3.036678e-01 -3.881296e-05 -2.762359e-01
31 32 33 34 35
4.131475e-01 -6.026488e-01 -7.940126e-02 1.073852e-01 -6.651194e-01
36 37 38 39 40
4.727380e-01 1.043627e+00 -1.007928e+00 4.679476e-01 -9.000854e-02
41 42 43 44 45
2.205070e-01 -6.085244e-03 7.071437e-02 -5.281374e-01 -2.457251e-01
46 47 48 49 50
-6.029767e-01 -2.438381e-01 5.479318e-01 7.603214e-01 -4.038082e-01
51 52 53 54 55
3.044667e-01 -1.053555e-01 1.382207e-01 -8.918294e-02 -1.097393e-01
56 57 58 59 60
2.993285e-01 -2.932296e-02 -3.540867e-01 -3.822276e-01 -7.765640e-01
61 62 63 64 65
-4.124230e-01 -4.085041e-02 -4.377198e-01 -5.647735e-01 -8.804540e-01
66 67 68 69 70
5.283902e-01 1.390886e+00 -4.407267e-01 -1.227425e+00 -2.755724e-01
71 72 73 74 75
1.151667e+00 9.575284e-02 7.672067e-01 2.036667e-01 5.051728e-01
76 77 78 79 80
3.873925e-01 -1.088652e+00 -1.837936e-01 -2.733102e-01 5.617807e-01
81 82 83 84 85
-2.776249e-01 4.693812e-01 2.865838e-02 -1.992946e-01 3.446002e-01
86 87 88 89 90
1.578274e-02 -8.766429e-02 -8.656921e-01 9.343611e-02 1.476574e-01
91 92 93 94 95
5.295982e-01 -3.542623e-01 -1.216229e-01 8.924444e-02 2.948611e-02
96 97 98 99 100
2.449230e-01 6.406863e-01 4.650643e-01 1.760939e-01 3.416872e-01
101 102 103 104 105
7.282213e-02 -1.244863e-01 -1.633639e-01 -1.660752e-01 1.162933e-01
106 107 108 109 110
4.492365e-01 4.897855e-01 5.063318e-01 -2.238080e-02 -2.841093e-01
111 112 113 114 115
-1.783983e-01 1.089334e+00 9.409731e-02 -3.463122e-01 -1.301784e+00
116 117 118 119 120
-2.003983e-01 -2.447771e-01 -2.731241e-01 5.821388e-02 4.892370e-01
121 122 123 124 125
8.187348e-01 -5.568699e-01 -5.495689e-01 -6.179643e-02 -7.133589e-01
126 127 128 129 130
-2.177665e-01 -2.677498e-01 -2.014944e-01 -2.463311e-01 8.424897e-01
131 132 133 134 135
-2.030230e-01 -5.148155e-02 -2.269813e-01 1.078905e-01 4.340430e-01
136 137 138 139 140
-1.782592e-01 -3.482452e-01 -5.663958e-01 -1.461514e-01 -3.082814e-01
141 142 143 144 145
5.793410e-01 3.014857e-01 -6.510426e-01 -1.142217e-01 -4.907617e-01
146 147 148 149 150
1.748233e-01 8.592624e-01 1.113268e-01 2.031302e-01 3.369927e-01
151 152 153 154 155
-3.656008e-02 7.339276e-02 1.267468e+00 2.932695e-01 -6.338212e-01
156 157 158 159
-1.124788e-01 3.926066e-01 -2.382003e-01 5.412548e-01
> postscript(file="/var/www/rcomp/tmp/64ceh1290539179.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.053337e-01 NA
1 3.692543e-01 -1.053337e-01
2 -6.390297e-01 3.692543e-01
3 -2.055047e-01 -6.390297e-01
4 -1.622422e-01 -2.055047e-01
5 -3.917828e-01 -1.622422e-01
6 -1.019204e-01 -3.917828e-01
7 -7.069398e-01 -1.019204e-01
8 -4.897070e-01 -7.069398e-01
9 -3.892611e-01 -4.897070e-01
10 8.281923e-02 -3.892611e-01
11 8.514505e-01 8.281923e-02
12 8.838965e-01 8.514505e-01
13 -1.237827e-01 8.838965e-01
14 -7.410738e-01 -1.237827e-01
15 -6.514827e-01 -7.410738e-01
16 1.735011e-01 -6.514827e-01
17 -3.231082e-02 1.735011e-01
18 2.494415e-02 -3.231082e-02
19 5.027502e-01 2.494415e-02
20 1.944649e-01 5.027502e-01
21 7.624758e-01 1.944649e-01
22 1.534886e-01 7.624758e-01
23 1.115965e+00 1.534886e-01
24 -3.406759e-02 1.115965e+00
25 -4.672856e-01 -3.406759e-02
26 -1.780245e-01 -4.672856e-01
27 3.036678e-01 -1.780245e-01
28 -3.881296e-05 3.036678e-01
29 -2.762359e-01 -3.881296e-05
30 4.131475e-01 -2.762359e-01
31 -6.026488e-01 4.131475e-01
32 -7.940126e-02 -6.026488e-01
33 1.073852e-01 -7.940126e-02
34 -6.651194e-01 1.073852e-01
35 4.727380e-01 -6.651194e-01
36 1.043627e+00 4.727380e-01
37 -1.007928e+00 1.043627e+00
38 4.679476e-01 -1.007928e+00
39 -9.000854e-02 4.679476e-01
40 2.205070e-01 -9.000854e-02
41 -6.085244e-03 2.205070e-01
42 7.071437e-02 -6.085244e-03
43 -5.281374e-01 7.071437e-02
44 -2.457251e-01 -5.281374e-01
45 -6.029767e-01 -2.457251e-01
46 -2.438381e-01 -6.029767e-01
47 5.479318e-01 -2.438381e-01
48 7.603214e-01 5.479318e-01
49 -4.038082e-01 7.603214e-01
50 3.044667e-01 -4.038082e-01
51 -1.053555e-01 3.044667e-01
52 1.382207e-01 -1.053555e-01
53 -8.918294e-02 1.382207e-01
54 -1.097393e-01 -8.918294e-02
55 2.993285e-01 -1.097393e-01
56 -2.932296e-02 2.993285e-01
57 -3.540867e-01 -2.932296e-02
58 -3.822276e-01 -3.540867e-01
59 -7.765640e-01 -3.822276e-01
60 -4.124230e-01 -7.765640e-01
61 -4.085041e-02 -4.124230e-01
62 -4.377198e-01 -4.085041e-02
63 -5.647735e-01 -4.377198e-01
64 -8.804540e-01 -5.647735e-01
65 5.283902e-01 -8.804540e-01
66 1.390886e+00 5.283902e-01
67 -4.407267e-01 1.390886e+00
68 -1.227425e+00 -4.407267e-01
69 -2.755724e-01 -1.227425e+00
70 1.151667e+00 -2.755724e-01
71 9.575284e-02 1.151667e+00
72 7.672067e-01 9.575284e-02
73 2.036667e-01 7.672067e-01
74 5.051728e-01 2.036667e-01
75 3.873925e-01 5.051728e-01
76 -1.088652e+00 3.873925e-01
77 -1.837936e-01 -1.088652e+00
78 -2.733102e-01 -1.837936e-01
79 5.617807e-01 -2.733102e-01
80 -2.776249e-01 5.617807e-01
81 4.693812e-01 -2.776249e-01
82 2.865838e-02 4.693812e-01
83 -1.992946e-01 2.865838e-02
84 3.446002e-01 -1.992946e-01
85 1.578274e-02 3.446002e-01
86 -8.766429e-02 1.578274e-02
87 -8.656921e-01 -8.766429e-02
88 9.343611e-02 -8.656921e-01
89 1.476574e-01 9.343611e-02
90 5.295982e-01 1.476574e-01
91 -3.542623e-01 5.295982e-01
92 -1.216229e-01 -3.542623e-01
93 8.924444e-02 -1.216229e-01
94 2.948611e-02 8.924444e-02
95 2.449230e-01 2.948611e-02
96 6.406863e-01 2.449230e-01
97 4.650643e-01 6.406863e-01
98 1.760939e-01 4.650643e-01
99 3.416872e-01 1.760939e-01
100 7.282213e-02 3.416872e-01
101 -1.244863e-01 7.282213e-02
102 -1.633639e-01 -1.244863e-01
103 -1.660752e-01 -1.633639e-01
104 1.162933e-01 -1.660752e-01
105 4.492365e-01 1.162933e-01
106 4.897855e-01 4.492365e-01
107 5.063318e-01 4.897855e-01
108 -2.238080e-02 5.063318e-01
109 -2.841093e-01 -2.238080e-02
110 -1.783983e-01 -2.841093e-01
111 1.089334e+00 -1.783983e-01
112 9.409731e-02 1.089334e+00
113 -3.463122e-01 9.409731e-02
114 -1.301784e+00 -3.463122e-01
115 -2.003983e-01 -1.301784e+00
116 -2.447771e-01 -2.003983e-01
117 -2.731241e-01 -2.447771e-01
118 5.821388e-02 -2.731241e-01
119 4.892370e-01 5.821388e-02
120 8.187348e-01 4.892370e-01
121 -5.568699e-01 8.187348e-01
122 -5.495689e-01 -5.568699e-01
123 -6.179643e-02 -5.495689e-01
124 -7.133589e-01 -6.179643e-02
125 -2.177665e-01 -7.133589e-01
126 -2.677498e-01 -2.177665e-01
127 -2.014944e-01 -2.677498e-01
128 -2.463311e-01 -2.014944e-01
129 8.424897e-01 -2.463311e-01
130 -2.030230e-01 8.424897e-01
131 -5.148155e-02 -2.030230e-01
132 -2.269813e-01 -5.148155e-02
133 1.078905e-01 -2.269813e-01
134 4.340430e-01 1.078905e-01
135 -1.782592e-01 4.340430e-01
136 -3.482452e-01 -1.782592e-01
137 -5.663958e-01 -3.482452e-01
138 -1.461514e-01 -5.663958e-01
139 -3.082814e-01 -1.461514e-01
140 5.793410e-01 -3.082814e-01
141 3.014857e-01 5.793410e-01
142 -6.510426e-01 3.014857e-01
143 -1.142217e-01 -6.510426e-01
144 -4.907617e-01 -1.142217e-01
145 1.748233e-01 -4.907617e-01
146 8.592624e-01 1.748233e-01
147 1.113268e-01 8.592624e-01
148 2.031302e-01 1.113268e-01
149 3.369927e-01 2.031302e-01
150 -3.656008e-02 3.369927e-01
151 7.339276e-02 -3.656008e-02
152 1.267468e+00 7.339276e-02
153 2.932695e-01 1.267468e+00
154 -6.338212e-01 2.932695e-01
155 -1.124788e-01 -6.338212e-01
156 3.926066e-01 -1.124788e-01
157 -2.382003e-01 3.926066e-01
158 5.412548e-01 -2.382003e-01
159 NA 5.412548e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.692543e-01 -1.053337e-01
[2,] -6.390297e-01 3.692543e-01
[3,] -2.055047e-01 -6.390297e-01
[4,] -1.622422e-01 -2.055047e-01
[5,] -3.917828e-01 -1.622422e-01
[6,] -1.019204e-01 -3.917828e-01
[7,] -7.069398e-01 -1.019204e-01
[8,] -4.897070e-01 -7.069398e-01
[9,] -3.892611e-01 -4.897070e-01
[10,] 8.281923e-02 -3.892611e-01
[11,] 8.514505e-01 8.281923e-02
[12,] 8.838965e-01 8.514505e-01
[13,] -1.237827e-01 8.838965e-01
[14,] -7.410738e-01 -1.237827e-01
[15,] -6.514827e-01 -7.410738e-01
[16,] 1.735011e-01 -6.514827e-01
[17,] -3.231082e-02 1.735011e-01
[18,] 2.494415e-02 -3.231082e-02
[19,] 5.027502e-01 2.494415e-02
[20,] 1.944649e-01 5.027502e-01
[21,] 7.624758e-01 1.944649e-01
[22,] 1.534886e-01 7.624758e-01
[23,] 1.115965e+00 1.534886e-01
[24,] -3.406759e-02 1.115965e+00
[25,] -4.672856e-01 -3.406759e-02
[26,] -1.780245e-01 -4.672856e-01
[27,] 3.036678e-01 -1.780245e-01
[28,] -3.881296e-05 3.036678e-01
[29,] -2.762359e-01 -3.881296e-05
[30,] 4.131475e-01 -2.762359e-01
[31,] -6.026488e-01 4.131475e-01
[32,] -7.940126e-02 -6.026488e-01
[33,] 1.073852e-01 -7.940126e-02
[34,] -6.651194e-01 1.073852e-01
[35,] 4.727380e-01 -6.651194e-01
[36,] 1.043627e+00 4.727380e-01
[37,] -1.007928e+00 1.043627e+00
[38,] 4.679476e-01 -1.007928e+00
[39,] -9.000854e-02 4.679476e-01
[40,] 2.205070e-01 -9.000854e-02
[41,] -6.085244e-03 2.205070e-01
[42,] 7.071437e-02 -6.085244e-03
[43,] -5.281374e-01 7.071437e-02
[44,] -2.457251e-01 -5.281374e-01
[45,] -6.029767e-01 -2.457251e-01
[46,] -2.438381e-01 -6.029767e-01
[47,] 5.479318e-01 -2.438381e-01
[48,] 7.603214e-01 5.479318e-01
[49,] -4.038082e-01 7.603214e-01
[50,] 3.044667e-01 -4.038082e-01
[51,] -1.053555e-01 3.044667e-01
[52,] 1.382207e-01 -1.053555e-01
[53,] -8.918294e-02 1.382207e-01
[54,] -1.097393e-01 -8.918294e-02
[55,] 2.993285e-01 -1.097393e-01
[56,] -2.932296e-02 2.993285e-01
[57,] -3.540867e-01 -2.932296e-02
[58,] -3.822276e-01 -3.540867e-01
[59,] -7.765640e-01 -3.822276e-01
[60,] -4.124230e-01 -7.765640e-01
[61,] -4.085041e-02 -4.124230e-01
[62,] -4.377198e-01 -4.085041e-02
[63,] -5.647735e-01 -4.377198e-01
[64,] -8.804540e-01 -5.647735e-01
[65,] 5.283902e-01 -8.804540e-01
[66,] 1.390886e+00 5.283902e-01
[67,] -4.407267e-01 1.390886e+00
[68,] -1.227425e+00 -4.407267e-01
[69,] -2.755724e-01 -1.227425e+00
[70,] 1.151667e+00 -2.755724e-01
[71,] 9.575284e-02 1.151667e+00
[72,] 7.672067e-01 9.575284e-02
[73,] 2.036667e-01 7.672067e-01
[74,] 5.051728e-01 2.036667e-01
[75,] 3.873925e-01 5.051728e-01
[76,] -1.088652e+00 3.873925e-01
[77,] -1.837936e-01 -1.088652e+00
[78,] -2.733102e-01 -1.837936e-01
[79,] 5.617807e-01 -2.733102e-01
[80,] -2.776249e-01 5.617807e-01
[81,] 4.693812e-01 -2.776249e-01
[82,] 2.865838e-02 4.693812e-01
[83,] -1.992946e-01 2.865838e-02
[84,] 3.446002e-01 -1.992946e-01
[85,] 1.578274e-02 3.446002e-01
[86,] -8.766429e-02 1.578274e-02
[87,] -8.656921e-01 -8.766429e-02
[88,] 9.343611e-02 -8.656921e-01
[89,] 1.476574e-01 9.343611e-02
[90,] 5.295982e-01 1.476574e-01
[91,] -3.542623e-01 5.295982e-01
[92,] -1.216229e-01 -3.542623e-01
[93,] 8.924444e-02 -1.216229e-01
[94,] 2.948611e-02 8.924444e-02
[95,] 2.449230e-01 2.948611e-02
[96,] 6.406863e-01 2.449230e-01
[97,] 4.650643e-01 6.406863e-01
[98,] 1.760939e-01 4.650643e-01
[99,] 3.416872e-01 1.760939e-01
[100,] 7.282213e-02 3.416872e-01
[101,] -1.244863e-01 7.282213e-02
[102,] -1.633639e-01 -1.244863e-01
[103,] -1.660752e-01 -1.633639e-01
[104,] 1.162933e-01 -1.660752e-01
[105,] 4.492365e-01 1.162933e-01
[106,] 4.897855e-01 4.492365e-01
[107,] 5.063318e-01 4.897855e-01
[108,] -2.238080e-02 5.063318e-01
[109,] -2.841093e-01 -2.238080e-02
[110,] -1.783983e-01 -2.841093e-01
[111,] 1.089334e+00 -1.783983e-01
[112,] 9.409731e-02 1.089334e+00
[113,] -3.463122e-01 9.409731e-02
[114,] -1.301784e+00 -3.463122e-01
[115,] -2.003983e-01 -1.301784e+00
[116,] -2.447771e-01 -2.003983e-01
[117,] -2.731241e-01 -2.447771e-01
[118,] 5.821388e-02 -2.731241e-01
[119,] 4.892370e-01 5.821388e-02
[120,] 8.187348e-01 4.892370e-01
[121,] -5.568699e-01 8.187348e-01
[122,] -5.495689e-01 -5.568699e-01
[123,] -6.179643e-02 -5.495689e-01
[124,] -7.133589e-01 -6.179643e-02
[125,] -2.177665e-01 -7.133589e-01
[126,] -2.677498e-01 -2.177665e-01
[127,] -2.014944e-01 -2.677498e-01
[128,] -2.463311e-01 -2.014944e-01
[129,] 8.424897e-01 -2.463311e-01
[130,] -2.030230e-01 8.424897e-01
[131,] -5.148155e-02 -2.030230e-01
[132,] -2.269813e-01 -5.148155e-02
[133,] 1.078905e-01 -2.269813e-01
[134,] 4.340430e-01 1.078905e-01
[135,] -1.782592e-01 4.340430e-01
[136,] -3.482452e-01 -1.782592e-01
[137,] -5.663958e-01 -3.482452e-01
[138,] -1.461514e-01 -5.663958e-01
[139,] -3.082814e-01 -1.461514e-01
[140,] 5.793410e-01 -3.082814e-01
[141,] 3.014857e-01 5.793410e-01
[142,] -6.510426e-01 3.014857e-01
[143,] -1.142217e-01 -6.510426e-01
[144,] -4.907617e-01 -1.142217e-01
[145,] 1.748233e-01 -4.907617e-01
[146,] 8.592624e-01 1.748233e-01
[147,] 1.113268e-01 8.592624e-01
[148,] 2.031302e-01 1.113268e-01
[149,] 3.369927e-01 2.031302e-01
[150,] -3.656008e-02 3.369927e-01
[151,] 7.339276e-02 -3.656008e-02
[152,] 1.267468e+00 7.339276e-02
[153,] 2.932695e-01 1.267468e+00
[154,] -6.338212e-01 2.932695e-01
[155,] -1.124788e-01 -6.338212e-01
[156,] 3.926066e-01 -1.124788e-01
[157,] -2.382003e-01 3.926066e-01
[158,] 5.412548e-01 -2.382003e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.692543e-01 -1.053337e-01
2 -6.390297e-01 3.692543e-01
3 -2.055047e-01 -6.390297e-01
4 -1.622422e-01 -2.055047e-01
5 -3.917828e-01 -1.622422e-01
6 -1.019204e-01 -3.917828e-01
7 -7.069398e-01 -1.019204e-01
8 -4.897070e-01 -7.069398e-01
9 -3.892611e-01 -4.897070e-01
10 8.281923e-02 -3.892611e-01
11 8.514505e-01 8.281923e-02
12 8.838965e-01 8.514505e-01
13 -1.237827e-01 8.838965e-01
14 -7.410738e-01 -1.237827e-01
15 -6.514827e-01 -7.410738e-01
16 1.735011e-01 -6.514827e-01
17 -3.231082e-02 1.735011e-01
18 2.494415e-02 -3.231082e-02
19 5.027502e-01 2.494415e-02
20 1.944649e-01 5.027502e-01
21 7.624758e-01 1.944649e-01
22 1.534886e-01 7.624758e-01
23 1.115965e+00 1.534886e-01
24 -3.406759e-02 1.115965e+00
25 -4.672856e-01 -3.406759e-02
26 -1.780245e-01 -4.672856e-01
27 3.036678e-01 -1.780245e-01
28 -3.881296e-05 3.036678e-01
29 -2.762359e-01 -3.881296e-05
30 4.131475e-01 -2.762359e-01
31 -6.026488e-01 4.131475e-01
32 -7.940126e-02 -6.026488e-01
33 1.073852e-01 -7.940126e-02
34 -6.651194e-01 1.073852e-01
35 4.727380e-01 -6.651194e-01
36 1.043627e+00 4.727380e-01
37 -1.007928e+00 1.043627e+00
38 4.679476e-01 -1.007928e+00
39 -9.000854e-02 4.679476e-01
40 2.205070e-01 -9.000854e-02
41 -6.085244e-03 2.205070e-01
42 7.071437e-02 -6.085244e-03
43 -5.281374e-01 7.071437e-02
44 -2.457251e-01 -5.281374e-01
45 -6.029767e-01 -2.457251e-01
46 -2.438381e-01 -6.029767e-01
47 5.479318e-01 -2.438381e-01
48 7.603214e-01 5.479318e-01
49 -4.038082e-01 7.603214e-01
50 3.044667e-01 -4.038082e-01
51 -1.053555e-01 3.044667e-01
52 1.382207e-01 -1.053555e-01
53 -8.918294e-02 1.382207e-01
54 -1.097393e-01 -8.918294e-02
55 2.993285e-01 -1.097393e-01
56 -2.932296e-02 2.993285e-01
57 -3.540867e-01 -2.932296e-02
58 -3.822276e-01 -3.540867e-01
59 -7.765640e-01 -3.822276e-01
60 -4.124230e-01 -7.765640e-01
61 -4.085041e-02 -4.124230e-01
62 -4.377198e-01 -4.085041e-02
63 -5.647735e-01 -4.377198e-01
64 -8.804540e-01 -5.647735e-01
65 5.283902e-01 -8.804540e-01
66 1.390886e+00 5.283902e-01
67 -4.407267e-01 1.390886e+00
68 -1.227425e+00 -4.407267e-01
69 -2.755724e-01 -1.227425e+00
70 1.151667e+00 -2.755724e-01
71 9.575284e-02 1.151667e+00
72 7.672067e-01 9.575284e-02
73 2.036667e-01 7.672067e-01
74 5.051728e-01 2.036667e-01
75 3.873925e-01 5.051728e-01
76 -1.088652e+00 3.873925e-01
77 -1.837936e-01 -1.088652e+00
78 -2.733102e-01 -1.837936e-01
79 5.617807e-01 -2.733102e-01
80 -2.776249e-01 5.617807e-01
81 4.693812e-01 -2.776249e-01
82 2.865838e-02 4.693812e-01
83 -1.992946e-01 2.865838e-02
84 3.446002e-01 -1.992946e-01
85 1.578274e-02 3.446002e-01
86 -8.766429e-02 1.578274e-02
87 -8.656921e-01 -8.766429e-02
88 9.343611e-02 -8.656921e-01
89 1.476574e-01 9.343611e-02
90 5.295982e-01 1.476574e-01
91 -3.542623e-01 5.295982e-01
92 -1.216229e-01 -3.542623e-01
93 8.924444e-02 -1.216229e-01
94 2.948611e-02 8.924444e-02
95 2.449230e-01 2.948611e-02
96 6.406863e-01 2.449230e-01
97 4.650643e-01 6.406863e-01
98 1.760939e-01 4.650643e-01
99 3.416872e-01 1.760939e-01
100 7.282213e-02 3.416872e-01
101 -1.244863e-01 7.282213e-02
102 -1.633639e-01 -1.244863e-01
103 -1.660752e-01 -1.633639e-01
104 1.162933e-01 -1.660752e-01
105 4.492365e-01 1.162933e-01
106 4.897855e-01 4.492365e-01
107 5.063318e-01 4.897855e-01
108 -2.238080e-02 5.063318e-01
109 -2.841093e-01 -2.238080e-02
110 -1.783983e-01 -2.841093e-01
111 1.089334e+00 -1.783983e-01
112 9.409731e-02 1.089334e+00
113 -3.463122e-01 9.409731e-02
114 -1.301784e+00 -3.463122e-01
115 -2.003983e-01 -1.301784e+00
116 -2.447771e-01 -2.003983e-01
117 -2.731241e-01 -2.447771e-01
118 5.821388e-02 -2.731241e-01
119 4.892370e-01 5.821388e-02
120 8.187348e-01 4.892370e-01
121 -5.568699e-01 8.187348e-01
122 -5.495689e-01 -5.568699e-01
123 -6.179643e-02 -5.495689e-01
124 -7.133589e-01 -6.179643e-02
125 -2.177665e-01 -7.133589e-01
126 -2.677498e-01 -2.177665e-01
127 -2.014944e-01 -2.677498e-01
128 -2.463311e-01 -2.014944e-01
129 8.424897e-01 -2.463311e-01
130 -2.030230e-01 8.424897e-01
131 -5.148155e-02 -2.030230e-01
132 -2.269813e-01 -5.148155e-02
133 1.078905e-01 -2.269813e-01
134 4.340430e-01 1.078905e-01
135 -1.782592e-01 4.340430e-01
136 -3.482452e-01 -1.782592e-01
137 -5.663958e-01 -3.482452e-01
138 -1.461514e-01 -5.663958e-01
139 -3.082814e-01 -1.461514e-01
140 5.793410e-01 -3.082814e-01
141 3.014857e-01 5.793410e-01
142 -6.510426e-01 3.014857e-01
143 -1.142217e-01 -6.510426e-01
144 -4.907617e-01 -1.142217e-01
145 1.748233e-01 -4.907617e-01
146 8.592624e-01 1.748233e-01
147 1.113268e-01 8.592624e-01
148 2.031302e-01 1.113268e-01
149 3.369927e-01 2.031302e-01
150 -3.656008e-02 3.369927e-01
151 7.339276e-02 -3.656008e-02
152 1.267468e+00 7.339276e-02
153 2.932695e-01 1.267468e+00
154 -6.338212e-01 2.932695e-01
155 -1.124788e-01 -6.338212e-01
156 3.926066e-01 -1.124788e-01
157 -2.382003e-01 3.926066e-01
158 5.412548e-01 -2.382003e-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/rcomp/tmp/7f3d21290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8f3d21290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9qvc51290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10qvc51290539179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11bvbb1290539179.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/rcomp/tmp/12we9z1290539179.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/rcomp/tmp/133x6s1290539179.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/rcomp/tmp/14e6nv1290539179.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/rcomp/tmp/15sz7e1290539180.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/rcomp/tmp/166q451290539180.tab")
+ }
>
> try(system("convert tmp/1juft1290539179.ps tmp/1juft1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u3ww1290539179.ps tmp/2u3ww1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u3ww1290539179.ps tmp/3u3ww1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u3ww1290539179.ps tmp/4u3ww1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/54ceh1290539179.ps tmp/54ceh1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/64ceh1290539179.ps tmp/64ceh1290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f3d21290539179.ps tmp/7f3d21290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f3d21290539179.ps tmp/8f3d21290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qvc51290539179.ps tmp/9qvc51290539179.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qvc51290539179.ps tmp/10qvc51290539179.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.650 2.180 7.789