R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(15
+ ,0
+ ,13.6
+ ,13.7
+ ,13
+ ,14.4
+ ,15
+ ,14.4
+ ,0
+ ,15.2
+ ,13.6
+ ,13.7
+ ,13
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+ ,13
+ ,0
+ ,12.9
+ ,15.2
+ ,13.6
+ ,13.7
+ ,13
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+ ,0
+ ,14
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+ ,13.6
+ ,0
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+ ,15.2
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+ ,0
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+ ,14
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+ ,13.2
+ ,14.1
+ ,14
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+ ,0
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+ ,13.3
+ ,11.3
+ ,13.2
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+ ,0
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+ ,13.3
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+ ,0
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+ ,13.3
+ ,14.4
+ ,13.3
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+ ,13.3
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+ ,13.1
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+ ,15.6
+ ,13.7
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+ ,13.3
+ ,13.2
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+ ,13.7
+ ,12.6
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+ ,14
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+ ,13.3
+ ,13.2
+ ,12.6
+ ,13.2
+ ,0
+ ,13.4
+ ,14
+ ,14.3
+ ,13.3
+ ,13.2
+ ,13.3
+ ,0
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+ ,13.4
+ ,14
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+ ,13.3
+ ,14.3
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+ ,13.9
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+ ,0
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+ ,13.9
+ ,13.4
+ ,14
+ ,13.4
+ ,0
+ ,14.5
+ ,10.5
+ ,13.7
+ ,13.9
+ ,13.4
+ ,13.9
+ ,0
+ ,15
+ ,14.5
+ ,10.5
+ ,13.7
+ ,13.9
+ ,13.7
+ ,0
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+ ,10.5
+ ,13.7
+ ,10.5
+ ,0
+ ,13.5
+ ,13.5
+ ,15
+ ,14.5
+ ,10.5
+ ,14.5
+ ,0
+ ,13.2
+ ,13.5
+ ,13.5
+ ,15
+ ,14.5
+ ,15
+ ,0
+ ,13.8
+ ,13.2
+ ,13.5
+ ,13.5
+ ,15
+ ,13.5
+ ,0
+ ,16.2
+ ,13.8
+ ,13.2
+ ,13.5
+ ,13.5
+ ,13.5
+ ,0
+ ,14.7
+ ,16.2
+ ,13.8
+ ,13.2
+ ,13.5
+ ,13.2
+ ,0
+ ,13.9
+ ,14.7
+ ,16.2
+ ,13.8
+ ,13.2
+ ,13.8
+ ,0
+ ,16
+ ,13.9
+ ,14.7
+ ,16.2
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+ ,16.2
+ ,0
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+ ,16.2
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+ ,0
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+ ,14.7
+ ,13.9
+ ,0
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+ ,12.3
+ ,14.4
+ ,16
+ ,13.9
+ ,16
+ ,0
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+ ,15.9
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+ ,15.9
+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,17.5
+ ,16.2
+ ,17.5
+ ,16.2
+ ,0
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+ ,16.2
+ ,16.6
+ ,17.5
+ ,16.2
+ ,17.5
+ ,0
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+ ,16.6
+ ,16.2
+ ,16.6
+ ,17.5
+ ,16.6
+ ,0
+ ,15.9
+ ,19.6
+ ,16.6
+ ,16.2
+ ,16.6
+ ,16.2
+ ,0
+ ,18
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+ ,19.6
+ ,16.6
+ ,16.2
+ ,16.6
+ ,0
+ ,18.3
+ ,18
+ ,15.9
+ ,19.6
+ ,16.6
+ ,19.6
+ ,0
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+ ,18.3
+ ,18
+ ,15.9
+ ,19.6
+ ,15.9
+ ,0
+ ,14.9
+ ,16.3
+ ,18.3
+ ,18
+ ,15.9
+ ,18
+ ,0
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+ ,14.9
+ ,16.3
+ ,18.3
+ ,18
+ ,18.3
+ ,0
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+ ,18.2
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+ ,16.3
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+ ,0
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+ ,18.2
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+ ,18.2
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+ ,18.2
+ ,18.4
+ ,0
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+ ,18.4
+ ,18.5
+ ,0
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+ ,16
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+ ,17.2
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+ ,16
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+ ,0
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+ ,17.2
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+ ,17.2
+ ,17.4
+ ,17.2
+ ,0
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+ ,18.3
+ ,17.2
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+ ,17.2
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+ ,0
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+ ,18.3
+ ,17.2
+ ,19.6
+ ,17.2
+ ,0
+ ,16.2
+ ,18.1
+ ,19.3
+ ,18.3
+ ,17.2
+ ,18.3
+ ,0
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+ ,18.3
+ ,19.3
+ ,0
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+ ,16.2
+ ,18.4
+ ,0
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+ ,16.5
+ ,19
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+ ,18.4
+ ,20.5
+ ,0
+ ,19
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+ ,19
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+ ,19
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+ ,19
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+ ,16.5
+ ,19
+ ,16.5
+ ,0
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+ ,19
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+ ,16.5
+ ,18.7
+ ,0
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+ ,20.5
+ ,19.2
+ ,19
+ ,18.7
+ ,19
+ ,0
+ ,20.6
+ ,19.3
+ ,20.5
+ ,19.2
+ ,19
+ ,19.2
+ ,0
+ ,20.1
+ ,20.6
+ ,19.3
+ ,20.5
+ ,19.2
+ ,20.5
+ ,0
+ ,16.1
+ ,20.1
+ ,20.6
+ ,19.3
+ ,20.5
+ ,19.3
+ ,0
+ ,20.4
+ ,16.1
+ ,20.1
+ ,20.6
+ ,19.3
+ ,20.6
+ ,0
+ ,19.7
+ ,20.4
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+ ,20.1
+ ,20.6
+ ,20.1
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+ ,16.1
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+ ,15.6
+ ,19.7
+ ,20.4
+ ,16.1
+ ,20.4
+ ,0
+ ,13.7
+ ,14.4
+ ,15.6
+ ,19.7
+ ,20.4
+ ,19.7
+ ,1
+ ,14.1
+ ,13.7
+ ,14.4
+ ,15.6
+ ,19.7
+ ,15.6
+ ,1
+ ,15
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+ ,13.7
+ ,14.4
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+ ,14.4
+ ,1
+ ,14.2
+ ,15
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+ ,13.7
+ ,14.4
+ ,13.7
+ ,1
+ ,13.6
+ ,14.2
+ ,15
+ ,14.1
+ ,13.7
+ ,14.1
+ ,1
+ ,15.4
+ ,13.6
+ ,14.2
+ ,15
+ ,14.1
+ ,15
+ ,1
+ ,14.8
+ ,15.4
+ ,13.6
+ ,14.2
+ ,15
+ ,14.2
+ ,1
+ ,12.5
+ ,14.8
+ ,15.4
+ ,13.6
+ ,14.2
+ ,13.6
+ ,1
+ ,16.2
+ ,12.5
+ ,14.8
+ ,15.4
+ ,13.6
+ ,15.4
+ ,1
+ ,16.1
+ ,16.2
+ ,12.5
+ ,14.8
+ ,15.4
+ ,14.8
+ ,1
+ ,16
+ ,16.1
+ ,16.2
+ ,12.5
+ ,14.8
+ ,12.5
+ ,1
+ ,15.8
+ ,16
+ ,16.1
+ ,16.2
+ ,12.5
+ ,16.2
+ ,1
+ ,15.2
+ ,15.8
+ ,16
+ ,16.1
+ ,16.2
+ ,16.1
+ ,1
+ ,15.7
+ ,15.2
+ ,15.8
+ ,16
+ ,16.1
+ ,16
+ ,1
+ ,18.9
+ ,15.7
+ ,15.2
+ ,15.8
+ ,16
+ ,15.8
+ ,1
+ ,17.4
+ ,18.9
+ ,15.7
+ ,15.2
+ ,15.8
+ ,15.2
+ ,1
+ ,17
+ ,17.4
+ ,18.9
+ ,15.7
+ ,15.2
+ ,15.7
+ ,1
+ ,19.8
+ ,17
+ ,17.4
+ ,18.9
+ ,15.7
+ ,18.9
+ ,1
+ ,17.7
+ ,19.8
+ ,17
+ ,17.4
+ ,18.9
+ ,17.4
+ ,1
+ ,16
+ ,17.7
+ ,19.8
+ ,17
+ ,17.4
+ ,17
+ ,1
+ ,19.6
+ ,16
+ ,17.7
+ ,19.8
+ ,17
+ ,19.8
+ ,1
+ ,19.7
+ ,19.6
+ ,16
+ ,17.7
+ ,19.8)
+ ,dim=c(7
+ ,117)
+ ,dimnames=list(c('uitvoercijfer'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5')
+ ,1:117))
> y <- array(NA,dim=c(7,117),dimnames=list(c('uitvoercijfer','X','Y1','Y2','Y3','Y4','Y5'),1:117))
> 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
> 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
uitvoercijfer X Y1 Y2 Y3 Y4 Y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 15.0 0 13.6 13.7 13.0 14.4 15.0 1 0 0 0 0 0 0 0 0 0 0
2 14.4 0 15.2 13.6 13.7 13.0 14.4 0 1 0 0 0 0 0 0 0 0 0
3 13.0 0 12.9 15.2 13.6 13.7 13.0 0 0 1 0 0 0 0 0 0 0 0
4 13.7 0 14.0 12.9 15.2 13.6 13.7 0 0 0 1 0 0 0 0 0 0 0
5 13.6 0 14.1 14.0 12.9 15.2 13.6 0 0 0 0 1 0 0 0 0 0 0
6 15.2 0 13.2 14.1 14.0 12.9 15.2 0 0 0 0 0 1 0 0 0 0 0
7 12.9 0 11.3 13.2 14.1 14.0 12.9 0 0 0 0 0 0 1 0 0 0 0
8 14.0 0 13.3 11.3 13.2 14.1 14.0 0 0 0 0 0 0 0 1 0 0 0
9 14.1 0 14.4 13.3 11.3 13.2 14.1 0 0 0 0 0 0 0 0 1 0 0
10 13.2 0 13.3 14.4 13.3 11.3 13.2 0 0 0 0 0 0 0 0 0 1 0
11 11.3 0 11.6 13.3 14.4 13.3 11.3 0 0 0 0 0 0 0 0 0 0 1
12 13.3 0 13.2 11.6 13.3 14.4 13.3 0 0 0 0 0 0 0 0 0 0 0
13 14.4 0 13.1 13.2 11.6 13.3 14.4 1 0 0 0 0 0 0 0 0 0 0
14 13.3 0 14.6 13.1 13.2 11.6 13.3 0 1 0 0 0 0 0 0 0 0 0
15 11.6 0 14.0 14.6 13.1 13.2 11.6 0 0 1 0 0 0 0 0 0 0 0
16 13.2 0 14.3 14.0 14.6 13.1 13.2 0 0 0 1 0 0 0 0 0 0 0
17 13.1 0 13.8 14.3 14.0 14.6 13.1 0 0 0 0 1 0 0 0 0 0 0
18 14.6 0 13.7 13.8 14.3 14.0 14.6 0 0 0 0 0 1 0 0 0 0 0
19 14.0 0 11.0 13.7 13.8 14.3 14.0 0 0 0 0 0 0 1 0 0 0 0
20 14.3 0 14.4 11.0 13.7 13.8 14.3 0 0 0 0 0 0 0 1 0 0 0
21 13.8 0 15.6 14.4 11.0 13.7 13.8 0 0 0 0 0 0 0 0 1 0 0
22 13.7 0 13.7 15.6 14.4 11.0 13.7 0 0 0 0 0 0 0 0 0 1 0
23 11.0 0 12.6 13.7 15.6 14.4 11.0 0 0 0 0 0 0 0 0 0 0 1
24 14.4 0 13.2 12.6 13.7 15.6 14.4 0 0 0 0 0 0 0 0 0 0 0
25 15.6 0 13.3 13.2 12.6 13.7 15.6 1 0 0 0 0 0 0 0 0 0 0
26 13.7 0 14.3 13.3 13.2 12.6 13.7 0 1 0 0 0 0 0 0 0 0 0
27 12.6 0 14.0 14.3 13.3 13.2 12.6 0 0 1 0 0 0 0 0 0 0 0
28 13.2 0 13.4 14.0 14.3 13.3 13.2 0 0 0 1 0 0 0 0 0 0 0
29 13.3 0 13.9 13.4 14.0 14.3 13.3 0 0 0 0 1 0 0 0 0 0 0
30 14.3 0 13.7 13.9 13.4 14.0 14.3 0 0 0 0 0 1 0 0 0 0 0
31 14.0 0 10.5 13.7 13.9 13.4 14.0 0 0 0 0 0 0 1 0 0 0 0
32 13.4 0 14.5 10.5 13.7 13.9 13.4 0 0 0 0 0 0 0 1 0 0 0
33 13.9 0 15.0 14.5 10.5 13.7 13.9 0 0 0 0 0 0 0 0 1 0 0
34 13.7 0 13.5 15.0 14.5 10.5 13.7 0 0 0 0 0 0 0 0 0 1 0
35 10.5 0 13.5 13.5 15.0 14.5 10.5 0 0 0 0 0 0 0 0 0 0 1
36 14.5 0 13.2 13.5 13.5 15.0 14.5 0 0 0 0 0 0 0 0 0 0 0
37 15.0 0 13.8 13.2 13.5 13.5 15.0 1 0 0 0 0 0 0 0 0 0 0
38 13.5 0 16.2 13.8 13.2 13.5 13.5 0 1 0 0 0 0 0 0 0 0 0
39 13.5 0 14.7 16.2 13.8 13.2 13.5 0 0 1 0 0 0 0 0 0 0 0
40 13.2 0 13.9 14.7 16.2 13.8 13.2 0 0 0 1 0 0 0 0 0 0 0
41 13.8 0 16.0 13.9 14.7 16.2 13.8 0 0 0 0 1 0 0 0 0 0 0
42 16.2 0 14.4 16.0 13.9 14.7 16.2 0 0 0 0 0 1 0 0 0 0 0
43 14.7 0 12.3 14.4 16.0 13.9 14.7 0 0 0 0 0 0 1 0 0 0 0
44 13.9 0 15.9 12.3 14.4 16.0 13.9 0 0 0 0 0 0 0 1 0 0 0
45 16.0 0 15.9 15.9 12.3 14.4 16.0 0 0 0 0 0 0 0 0 1 0 0
46 14.4 0 15.5 15.9 15.9 12.3 14.4 0 0 0 0 0 0 0 0 0 1 0
47 12.3 0 15.1 15.5 15.9 15.9 12.3 0 0 0 0 0 0 0 0 0 0 1
48 15.9 0 14.5 15.1 15.5 15.9 15.9 0 0 0 0 0 0 0 0 0 0 0
49 15.9 0 15.1 14.5 15.1 15.5 15.9 1 0 0 0 0 0 0 0 0 0 0
50 15.5 0 17.4 15.1 14.5 15.1 15.5 0 1 0 0 0 0 0 0 0 0 0
51 15.1 0 16.2 17.4 15.1 14.5 15.1 0 0 1 0 0 0 0 0 0 0 0
52 14.5 0 15.6 16.2 17.4 15.1 14.5 0 0 0 1 0 0 0 0 0 0 0
53 15.1 0 17.2 15.6 16.2 17.4 15.1 0 0 0 0 1 0 0 0 0 0 0
54 17.4 0 14.9 17.2 15.6 16.2 17.4 0 0 0 0 0 1 0 0 0 0 0
55 16.2 0 13.8 14.9 17.2 15.6 16.2 0 0 0 0 0 0 1 0 0 0 0
56 15.6 0 17.5 13.8 14.9 17.2 15.6 0 0 0 0 0 0 0 1 0 0 0
57 17.2 0 16.2 17.5 13.8 14.9 17.2 0 0 0 0 0 0 0 0 1 0 0
58 14.9 0 17.5 16.2 17.5 13.8 14.9 0 0 0 0 0 0 0 0 0 1 0
59 13.8 0 16.6 17.5 16.2 17.5 13.8 0 0 0 0 0 0 0 0 0 0 1
60 17.5 0 16.2 16.6 17.5 16.2 17.5 0 0 0 0 0 0 0 0 0 0 0
61 16.2 0 16.6 16.2 16.6 17.5 16.2 1 0 0 0 0 0 0 0 0 0 0
62 17.5 0 19.6 16.6 16.2 16.6 17.5 0 1 0 0 0 0 0 0 0 0 0
63 16.6 0 15.9 19.6 16.6 16.2 16.6 0 0 1 0 0 0 0 0 0 0 0
64 16.2 0 18.0 15.9 19.6 16.6 16.2 0 0 0 1 0 0 0 0 0 0 0
65 16.6 0 18.3 18.0 15.9 19.6 16.6 0 0 0 0 1 0 0 0 0 0 0
66 19.6 0 16.3 18.3 18.0 15.9 19.6 0 0 0 0 0 1 0 0 0 0 0
67 15.9 0 14.9 16.3 18.3 18.0 15.9 0 0 0 0 0 0 1 0 0 0 0
68 18.0 0 18.2 14.9 16.3 18.3 18.0 0 0 0 0 0 0 0 1 0 0 0
69 18.3 0 18.4 18.2 14.9 16.3 18.3 0 0 0 0 0 0 0 0 1 0 0
70 16.3 0 18.5 18.4 18.2 14.9 16.3 0 0 0 0 0 0 0 0 0 1 0
71 14.9 0 16.0 18.5 18.4 18.2 14.9 0 0 0 0 0 0 0 0 0 0 1
72 18.2 0 17.4 16.0 18.5 18.4 18.2 0 0 0 0 0 0 0 0 0 0 0
73 18.4 0 17.2 17.4 16.0 18.5 18.4 1 0 0 0 0 0 0 0 0 0 0
74 18.5 0 19.6 17.2 17.4 16.0 18.5 0 1 0 0 0 0 0 0 0 0 0
75 16.0 0 17.2 19.6 17.2 17.4 16.0 0 0 1 0 0 0 0 0 0 0 0
76 17.4 0 18.3 17.2 19.6 17.2 17.4 0 0 0 1 0 0 0 0 0 0 0
77 17.2 0 19.3 18.3 17.2 19.6 17.2 0 0 0 0 1 0 0 0 0 0 0
78 19.6 0 18.1 19.3 18.3 17.2 19.6 0 0 0 0 0 1 0 0 0 0 0
79 17.2 0 16.2 18.1 19.3 18.3 17.2 0 0 0 0 0 0 1 0 0 0 0
80 18.3 0 18.4 16.2 18.1 19.3 18.3 0 0 0 0 0 0 0 1 0 0 0
81 19.3 0 20.5 18.4 16.2 18.1 19.3 0 0 0 0 0 0 0 0 1 0 0
82 18.1 0 19.0 20.5 18.4 16.2 18.1 0 0 0 0 0 0 0 0 0 1 0
83 16.2 0 16.5 19.0 20.5 18.4 16.2 0 0 0 0 0 0 0 0 0 0 1
84 18.4 0 18.7 16.5 19.0 20.5 18.4 0 0 0 0 0 0 0 0 0 0 0
85 20.5 0 19.0 18.7 16.5 19.0 20.5 1 0 0 0 0 0 0 0 0 0 0
86 19.0 0 19.2 19.0 18.7 16.5 19.0 0 1 0 0 0 0 0 0 0 0 0
87 16.5 0 20.5 19.2 19.0 18.7 16.5 0 0 1 0 0 0 0 0 0 0 0
88 18.7 0 19.3 20.5 19.2 19.0 18.7 0 0 0 1 0 0 0 0 0 0 0
89 19.0 0 20.6 19.3 20.5 19.2 19.0 0 0 0 0 1 0 0 0 0 0 0
90 19.2 0 20.1 20.6 19.3 20.5 19.2 0 0 0 0 0 1 0 0 0 0 0
91 20.5 0 16.1 20.1 20.6 19.3 20.5 0 0 0 0 0 0 1 0 0 0 0
92 19.3 0 20.4 16.1 20.1 20.6 19.3 0 0 0 0 0 0 0 1 0 0 0
93 20.6 0 19.7 20.4 16.1 20.1 20.6 0 0 0 0 0 0 0 0 1 0 0
94 20.1 0 15.6 19.7 20.4 16.1 20.1 0 0 0 0 0 0 0 0 0 1 0
95 16.1 0 14.4 15.6 19.7 20.4 16.1 0 0 0 0 0 0 0 0 0 0 1
96 20.4 0 13.7 14.4 15.6 19.7 20.4 0 0 0 0 0 0 0 0 0 0 0
97 19.7 1 14.1 13.7 14.4 15.6 19.7 1 0 0 0 0 0 0 0 0 0 0
98 15.6 1 15.0 14.1 13.7 14.4 15.6 0 1 0 0 0 0 0 0 0 0 0
99 14.4 1 14.2 15.0 14.1 13.7 14.4 0 0 1 0 0 0 0 0 0 0 0
100 13.7 1 13.6 14.2 15.0 14.1 13.7 0 0 0 1 0 0 0 0 0 0 0
101 14.1 1 15.4 13.6 14.2 15.0 14.1 0 0 0 0 1 0 0 0 0 0 0
102 15.0 1 14.8 15.4 13.6 14.2 15.0 0 0 0 0 0 1 0 0 0 0 0
103 14.2 1 12.5 14.8 15.4 13.6 14.2 0 0 0 0 0 0 1 0 0 0 0
104 13.6 1 16.2 12.5 14.8 15.4 13.6 0 0 0 0 0 0 0 1 0 0 0
105 15.4 1 16.1 16.2 12.5 14.8 15.4 0 0 0 0 0 0 0 0 1 0 0
106 14.8 1 16.0 16.1 16.2 12.5 14.8 0 0 0 0 0 0 0 0 0 1 0
107 12.5 1 15.8 16.0 16.1 16.2 12.5 0 0 0 0 0 0 0 0 0 0 1
108 16.2 1 15.2 15.8 16.0 16.1 16.2 0 0 0 0 0 0 0 0 0 0 0
109 16.1 1 15.7 15.2 15.8 16.0 16.1 1 0 0 0 0 0 0 0 0 0 0
110 16.0 1 18.9 15.7 15.2 15.8 16.0 0 1 0 0 0 0 0 0 0 0 0
111 15.8 1 17.4 18.9 15.7 15.2 15.8 0 0 1 0 0 0 0 0 0 0 0
112 15.2 1 17.0 17.4 18.9 15.7 15.2 0 0 0 1 0 0 0 0 0 0 0
113 15.7 1 19.8 17.0 17.4 18.9 15.7 0 0 0 0 1 0 0 0 0 0 0
114 18.9 1 17.7 19.8 17.0 17.4 18.9 0 0 0 0 0 1 0 0 0 0 0
115 17.4 1 16.0 17.7 19.8 17.0 17.4 0 0 0 0 0 0 1 0 0 0 0
116 17.0 1 19.6 16.0 17.7 19.8 17.0 0 0 0 0 0 0 0 1 0 0 0
117 19.8 1 19.7 19.6 16.0 17.7 19.8 0 0 0 0 0 0 0 0 1 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
2.679e-16 1.256e-16 2.220e-17 5.454e-17 -2.949e-17 4.469e-17
Y5 M1 M2 M3 M4 M5
1.000e+00 -7.607e-17 -3.578e-18 4.121e-17 1.085e-16 -4.338e-17
M6 M7 M8 M9 M10 M11
5.990e-17 4.214e-16 -6.425e-17 -2.413e-17 1.785e-16 2.479e-17
t
-2.261e-18
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.750e-16 -6.762e-17 2.640e-18 4.748e-17 2.861e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.679e-16 5.458e-16 4.910e-01 0.6246
X 1.256e-16 2.098e-16 5.990e-01 0.5508
Y1 2.220e-17 4.306e-17 5.160e-01 0.6073
Y2 5.454e-17 4.348e-17 1.255e+00 0.2126
Y3 -2.949e-17 4.416e-17 -6.680e-01 0.5059
Y4 4.469e-17 4.615e-17 9.680e-01 0.3352
Y5 1.000e+00 4.400e-17 2.273e+16 <2e-16 ***
M1 -7.607e-17 1.715e-16 -4.440e-01 0.6583
M2 -3.578e-18 2.160e-16 -1.700e-02 0.9868
M3 4.121e-17 2.193e-16 1.880e-01 0.8513
M4 1.085e-16 1.947e-16 5.570e-01 0.5786
M5 -4.338e-17 1.731e-16 -2.510e-01 0.8027
M6 5.990e-17 1.861e-16 3.220e-01 0.7482
M7 4.214e-16 2.005e-16 2.101e+00 0.0382 *
M8 -6.425e-17 1.806e-16 -3.560e-01 0.7227
M9 -2.413e-17 2.467e-16 -9.800e-02 0.9223
M10 1.785e-16 2.558e-16 6.980e-01 0.4870
M11 2.479e-17 2.503e-16 9.900e-02 0.9213
t -2.261e-18 3.351e-18 -6.750e-01 0.5015
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.269e-16 on 98 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.2e+32 on 18 and 98 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,] 8.127738e-01 3.744525e-01 1.872262e-01
[2,] 4.580354e-03 9.160707e-03 9.954196e-01
[3,] 2.501456e-01 5.002911e-01 7.498544e-01
[4,] 5.148722e-03 1.029744e-02 9.948513e-01
[5,] 1.480263e-07 2.960527e-07 9.999999e-01
[6,] 4.189919e-06 8.379838e-06 9.999958e-01
[7,] 1.261990e-03 2.523979e-03 9.987380e-01
[8,] 1.800704e-01 3.601408e-01 8.199296e-01
[9,] 1.068054e-03 2.136108e-03 9.989319e-01
[10,] 2.448113e-10 4.896227e-10 1.000000e+00
[11,] 9.758537e-01 4.829263e-02 2.414632e-02
[12,] 3.430615e-01 6.861230e-01 6.569385e-01
[13,] 7.261223e-08 1.452245e-07 9.999999e-01
[14,] 1.000000e+00 4.145036e-10 2.072518e-10
[15,] 2.914572e-15 5.829144e-15 1.000000e+00
[16,] 9.999994e-01 1.296817e-06 6.484087e-07
[17,] 6.603430e-01 6.793140e-01 3.396570e-01
[18,] 4.449293e-08 8.898586e-08 1.000000e+00
[19,] 7.659579e-01 4.680842e-01 2.340421e-01
[20,] 2.796164e-10 5.592328e-10 1.000000e+00
[21,] 5.858847e-02 1.171769e-01 9.414115e-01
[22,] 1.659125e-08 3.318249e-08 1.000000e+00
[23,] 7.698331e-01 4.603338e-01 2.301669e-01
[24,] 7.868096e-04 1.573619e-03 9.992132e-01
[25,] 3.669566e-01 7.339131e-01 6.330434e-01
[26,] 3.478802e-07 6.957604e-07 9.999997e-01
[27,] 1.368004e-09 2.736008e-09 1.000000e+00
[28,] 9.973419e-01 5.316178e-03 2.658089e-03
[29,] 9.812582e-03 1.962516e-02 9.901874e-01
[30,] 9.999996e-01 8.074591e-07 4.037295e-07
[31,] 9.999994e-01 1.251111e-06 6.255553e-07
[32,] 8.492831e-02 1.698566e-01 9.150717e-01
[33,] 2.199214e-11 4.398428e-11 1.000000e+00
[34,] 2.136949e-18 4.273898e-18 1.000000e+00
[35,] 2.739214e-01 5.478427e-01 7.260786e-01
[36,] 2.600428e-02 5.200855e-02 9.739957e-01
[37,] 9.999972e-01 5.623181e-06 2.811591e-06
[38,] 5.675924e-04 1.135185e-03 9.994324e-01
[39,] 1.000000e+00 9.612280e-17 4.806140e-17
[40,] 9.555301e-01 8.893984e-02 4.446992e-02
[41,] 3.422778e-10 6.845555e-10 1.000000e+00
[42,] 9.999998e-01 3.671837e-07 1.835919e-07
[43,] 1.179162e-17 2.358323e-17 1.000000e+00
[44,] 1.000000e+00 1.920616e-14 9.603078e-15
[45,] 3.175463e-02 6.350925e-02 9.682454e-01
[46,] 6.074438e-14 1.214888e-13 1.000000e+00
[47,] 9.999709e-01 5.821499e-05 2.910749e-05
[48,] 9.892413e-01 2.151732e-02 1.075866e-02
[49,] 4.633896e-18 9.267792e-18 1.000000e+00
[50,] 1.000000e+00 5.326095e-12 2.663047e-12
[51,] 8.517592e-01 2.964816e-01 1.482408e-01
[52,] 1.551384e-06 3.102768e-06 9.999984e-01
[53,] 8.255157e-23 1.651031e-22 1.000000e+00
[54,] 9.999995e-01 9.353425e-07 4.676713e-07
[55,] 5.198222e-01 9.603557e-01 4.801778e-01
[56,] 9.122739e-02 1.824548e-01 9.087726e-01
[57,] 9.999995e-01 9.247349e-07 4.623675e-07
[58,] 9.999997e-01 5.755110e-07 2.877555e-07
[59,] 9.747423e-03 1.949485e-02 9.902526e-01
[60,] 1.000000e+00 8.645509e-08 4.322755e-08
[61,] 9.970384e-01 5.923290e-03 2.961645e-03
[62,] 9.999998e-01 3.135218e-07 1.567609e-07
[63,] 6.476776e-01 7.046448e-01 3.523224e-01
[64,] 6.199599e-01 7.600802e-01 3.800401e-01
[65,] 6.407414e-01 7.185172e-01 3.592586e-01
[66,] 2.422135e-01 4.844271e-01 7.577865e-01
[67,] 9.897665e-01 2.046706e-02 1.023353e-02
[68,] 8.175534e-08 1.635107e-07 9.999999e-01
[69,] 8.504548e-01 2.990903e-01 1.495452e-01
[70,] 2.186501e-15 4.373001e-15 1.000000e+00
[71,] 1.540041e-25 3.080083e-25 1.000000e+00
[72,] 9.553749e-05 1.910750e-04 9.999045e-01
[73,] 6.869231e-09 1.373846e-08 1.000000e+00
[74,] 4.349876e-06 8.699751e-06 9.999957e-01
> postscript(file="/var/www/rcomp/tmp/1uorf1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2uorf1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/35f901292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/45f901292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/55f901292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 117
Frequency = 1
1 2 3 4 5
-2.892968e-16 -2.579014e-16 -6.749799e-16 -5.995323e-17 1.193760e-16
6 7 8 9 10
1.762469e-16 2.860858e-15 -6.186457e-17 -7.446161e-17 -1.104933e-16
11 12 13 14 15
6.080808e-18 -1.181588e-16 3.699149e-18 2.660700e-17 -1.321214e-16
16 17 18 19 20
-6.762483e-17 -6.317381e-17 -1.246845e-16 -4.519664e-16 3.767563e-17
21 22 23 24 25
-7.066140e-17 -2.326815e-17 -1.719976e-17 -1.242667e-16 -6.863514e-17
26 27 28 29 30
2.468774e-17 8.922540e-17 -6.113808e-17 2.951461e-17 -7.765363e-17
31 32 33 34 35
-3.627758e-16 -1.393141e-17 -6.775578e-17 9.635644e-18 -7.251071e-17
36 37 38 39 40
-5.989202e-17 1.739712e-17 2.640182e-18 1.233901e-16 5.303045e-17
41 42 43 44 45
-1.163220e-16 -8.832207e-17 -2.811574e-16 -2.918593e-17 2.375018e-17
46 47 48 49 50
-1.309115e-17 2.584684e-17 -1.068457e-17 4.833362e-17 -1.239321e-17
51 52 53 54 55
1.058077e-16 6.047475e-17 1.016051e-17 -2.703054e-17 -2.826774e-16
56 57 58 59 60
-5.638469e-17 1.453834e-17 8.397078e-17 -1.712159e-16 6.170889e-17
61 62 63 64 65
6.992927e-17 4.399593e-19 1.371959e-16 2.884177e-17 -1.246761e-16
66 67 68 69 70
1.114019e-16 -3.297269e-16 1.240862e-17 1.098319e-16 -4.536939e-18
71 72 73 74 75
-8.633773e-17 -1.259175e-17 1.502503e-17 9.107760e-18 1.809528e-16
76 77 78 79 80
1.546634e-17 1.023607e-16 -1.777859e-17 -2.919436e-16 6.702062e-17
81 82 83 84 85
6.123488e-17 2.776523e-17 2.946295e-16 -4.183214e-17 1.206044e-17
86 87 88 89 90
1.624581e-16 -6.669979e-17 2.485562e-17 1.316176e-16 4.535701e-19
91 92 93 94 95
-3.390570e-16 8.497419e-17 -2.723956e-18 -1.746523e-17 -2.878372e-17
96 97 98 99 100
2.842992e-16 2.903639e-17 9.016801e-18 6.109152e-17 -1.354064e-17
101 102 103 104 105
3.488966e-17 -1.486328e-17 -2.862913e-16 2.327170e-17 4.747453e-17
106 107 108 109 110
4.748314e-17 4.949066e-17 2.141777e-17 1.624509e-16 3.533701e-17
111 112 113 114 115
1.761376e-16 1.958787e-17 -1.237470e-16 6.223014e-17 -2.352623e-16
116 117
-6.398415e-17 -4.122707e-17
> postscript(file="/var/www/rcomp/tmp/6y6ql1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 117
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.892968e-16 NA
1 -2.579014e-16 -2.892968e-16
2 -6.749799e-16 -2.579014e-16
3 -5.995323e-17 -6.749799e-16
4 1.193760e-16 -5.995323e-17
5 1.762469e-16 1.193760e-16
6 2.860858e-15 1.762469e-16
7 -6.186457e-17 2.860858e-15
8 -7.446161e-17 -6.186457e-17
9 -1.104933e-16 -7.446161e-17
10 6.080808e-18 -1.104933e-16
11 -1.181588e-16 6.080808e-18
12 3.699149e-18 -1.181588e-16
13 2.660700e-17 3.699149e-18
14 -1.321214e-16 2.660700e-17
15 -6.762483e-17 -1.321214e-16
16 -6.317381e-17 -6.762483e-17
17 -1.246845e-16 -6.317381e-17
18 -4.519664e-16 -1.246845e-16
19 3.767563e-17 -4.519664e-16
20 -7.066140e-17 3.767563e-17
21 -2.326815e-17 -7.066140e-17
22 -1.719976e-17 -2.326815e-17
23 -1.242667e-16 -1.719976e-17
24 -6.863514e-17 -1.242667e-16
25 2.468774e-17 -6.863514e-17
26 8.922540e-17 2.468774e-17
27 -6.113808e-17 8.922540e-17
28 2.951461e-17 -6.113808e-17
29 -7.765363e-17 2.951461e-17
30 -3.627758e-16 -7.765363e-17
31 -1.393141e-17 -3.627758e-16
32 -6.775578e-17 -1.393141e-17
33 9.635644e-18 -6.775578e-17
34 -7.251071e-17 9.635644e-18
35 -5.989202e-17 -7.251071e-17
36 1.739712e-17 -5.989202e-17
37 2.640182e-18 1.739712e-17
38 1.233901e-16 2.640182e-18
39 5.303045e-17 1.233901e-16
40 -1.163220e-16 5.303045e-17
41 -8.832207e-17 -1.163220e-16
42 -2.811574e-16 -8.832207e-17
43 -2.918593e-17 -2.811574e-16
44 2.375018e-17 -2.918593e-17
45 -1.309115e-17 2.375018e-17
46 2.584684e-17 -1.309115e-17
47 -1.068457e-17 2.584684e-17
48 4.833362e-17 -1.068457e-17
49 -1.239321e-17 4.833362e-17
50 1.058077e-16 -1.239321e-17
51 6.047475e-17 1.058077e-16
52 1.016051e-17 6.047475e-17
53 -2.703054e-17 1.016051e-17
54 -2.826774e-16 -2.703054e-17
55 -5.638469e-17 -2.826774e-16
56 1.453834e-17 -5.638469e-17
57 8.397078e-17 1.453834e-17
58 -1.712159e-16 8.397078e-17
59 6.170889e-17 -1.712159e-16
60 6.992927e-17 6.170889e-17
61 4.399593e-19 6.992927e-17
62 1.371959e-16 4.399593e-19
63 2.884177e-17 1.371959e-16
64 -1.246761e-16 2.884177e-17
65 1.114019e-16 -1.246761e-16
66 -3.297269e-16 1.114019e-16
67 1.240862e-17 -3.297269e-16
68 1.098319e-16 1.240862e-17
69 -4.536939e-18 1.098319e-16
70 -8.633773e-17 -4.536939e-18
71 -1.259175e-17 -8.633773e-17
72 1.502503e-17 -1.259175e-17
73 9.107760e-18 1.502503e-17
74 1.809528e-16 9.107760e-18
75 1.546634e-17 1.809528e-16
76 1.023607e-16 1.546634e-17
77 -1.777859e-17 1.023607e-16
78 -2.919436e-16 -1.777859e-17
79 6.702062e-17 -2.919436e-16
80 6.123488e-17 6.702062e-17
81 2.776523e-17 6.123488e-17
82 2.946295e-16 2.776523e-17
83 -4.183214e-17 2.946295e-16
84 1.206044e-17 -4.183214e-17
85 1.624581e-16 1.206044e-17
86 -6.669979e-17 1.624581e-16
87 2.485562e-17 -6.669979e-17
88 1.316176e-16 2.485562e-17
89 4.535701e-19 1.316176e-16
90 -3.390570e-16 4.535701e-19
91 8.497419e-17 -3.390570e-16
92 -2.723956e-18 8.497419e-17
93 -1.746523e-17 -2.723956e-18
94 -2.878372e-17 -1.746523e-17
95 2.842992e-16 -2.878372e-17
96 2.903639e-17 2.842992e-16
97 9.016801e-18 2.903639e-17
98 6.109152e-17 9.016801e-18
99 -1.354064e-17 6.109152e-17
100 3.488966e-17 -1.354064e-17
101 -1.486328e-17 3.488966e-17
102 -2.862913e-16 -1.486328e-17
103 2.327170e-17 -2.862913e-16
104 4.747453e-17 2.327170e-17
105 4.748314e-17 4.747453e-17
106 4.949066e-17 4.748314e-17
107 2.141777e-17 4.949066e-17
108 1.624509e-16 2.141777e-17
109 3.533701e-17 1.624509e-16
110 1.761376e-16 3.533701e-17
111 1.958787e-17 1.761376e-16
112 -1.237470e-16 1.958787e-17
113 6.223014e-17 -1.237470e-16
114 -2.352623e-16 6.223014e-17
115 -6.398415e-17 -2.352623e-16
116 -4.122707e-17 -6.398415e-17
117 NA -4.122707e-17
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.579014e-16 -2.892968e-16
[2,] -6.749799e-16 -2.579014e-16
[3,] -5.995323e-17 -6.749799e-16
[4,] 1.193760e-16 -5.995323e-17
[5,] 1.762469e-16 1.193760e-16
[6,] 2.860858e-15 1.762469e-16
[7,] -6.186457e-17 2.860858e-15
[8,] -7.446161e-17 -6.186457e-17
[9,] -1.104933e-16 -7.446161e-17
[10,] 6.080808e-18 -1.104933e-16
[11,] -1.181588e-16 6.080808e-18
[12,] 3.699149e-18 -1.181588e-16
[13,] 2.660700e-17 3.699149e-18
[14,] -1.321214e-16 2.660700e-17
[15,] -6.762483e-17 -1.321214e-16
[16,] -6.317381e-17 -6.762483e-17
[17,] -1.246845e-16 -6.317381e-17
[18,] -4.519664e-16 -1.246845e-16
[19,] 3.767563e-17 -4.519664e-16
[20,] -7.066140e-17 3.767563e-17
[21,] -2.326815e-17 -7.066140e-17
[22,] -1.719976e-17 -2.326815e-17
[23,] -1.242667e-16 -1.719976e-17
[24,] -6.863514e-17 -1.242667e-16
[25,] 2.468774e-17 -6.863514e-17
[26,] 8.922540e-17 2.468774e-17
[27,] -6.113808e-17 8.922540e-17
[28,] 2.951461e-17 -6.113808e-17
[29,] -7.765363e-17 2.951461e-17
[30,] -3.627758e-16 -7.765363e-17
[31,] -1.393141e-17 -3.627758e-16
[32,] -6.775578e-17 -1.393141e-17
[33,] 9.635644e-18 -6.775578e-17
[34,] -7.251071e-17 9.635644e-18
[35,] -5.989202e-17 -7.251071e-17
[36,] 1.739712e-17 -5.989202e-17
[37,] 2.640182e-18 1.739712e-17
[38,] 1.233901e-16 2.640182e-18
[39,] 5.303045e-17 1.233901e-16
[40,] -1.163220e-16 5.303045e-17
[41,] -8.832207e-17 -1.163220e-16
[42,] -2.811574e-16 -8.832207e-17
[43,] -2.918593e-17 -2.811574e-16
[44,] 2.375018e-17 -2.918593e-17
[45,] -1.309115e-17 2.375018e-17
[46,] 2.584684e-17 -1.309115e-17
[47,] -1.068457e-17 2.584684e-17
[48,] 4.833362e-17 -1.068457e-17
[49,] -1.239321e-17 4.833362e-17
[50,] 1.058077e-16 -1.239321e-17
[51,] 6.047475e-17 1.058077e-16
[52,] 1.016051e-17 6.047475e-17
[53,] -2.703054e-17 1.016051e-17
[54,] -2.826774e-16 -2.703054e-17
[55,] -5.638469e-17 -2.826774e-16
[56,] 1.453834e-17 -5.638469e-17
[57,] 8.397078e-17 1.453834e-17
[58,] -1.712159e-16 8.397078e-17
[59,] 6.170889e-17 -1.712159e-16
[60,] 6.992927e-17 6.170889e-17
[61,] 4.399593e-19 6.992927e-17
[62,] 1.371959e-16 4.399593e-19
[63,] 2.884177e-17 1.371959e-16
[64,] -1.246761e-16 2.884177e-17
[65,] 1.114019e-16 -1.246761e-16
[66,] -3.297269e-16 1.114019e-16
[67,] 1.240862e-17 -3.297269e-16
[68,] 1.098319e-16 1.240862e-17
[69,] -4.536939e-18 1.098319e-16
[70,] -8.633773e-17 -4.536939e-18
[71,] -1.259175e-17 -8.633773e-17
[72,] 1.502503e-17 -1.259175e-17
[73,] 9.107760e-18 1.502503e-17
[74,] 1.809528e-16 9.107760e-18
[75,] 1.546634e-17 1.809528e-16
[76,] 1.023607e-16 1.546634e-17
[77,] -1.777859e-17 1.023607e-16
[78,] -2.919436e-16 -1.777859e-17
[79,] 6.702062e-17 -2.919436e-16
[80,] 6.123488e-17 6.702062e-17
[81,] 2.776523e-17 6.123488e-17
[82,] 2.946295e-16 2.776523e-17
[83,] -4.183214e-17 2.946295e-16
[84,] 1.206044e-17 -4.183214e-17
[85,] 1.624581e-16 1.206044e-17
[86,] -6.669979e-17 1.624581e-16
[87,] 2.485562e-17 -6.669979e-17
[88,] 1.316176e-16 2.485562e-17
[89,] 4.535701e-19 1.316176e-16
[90,] -3.390570e-16 4.535701e-19
[91,] 8.497419e-17 -3.390570e-16
[92,] -2.723956e-18 8.497419e-17
[93,] -1.746523e-17 -2.723956e-18
[94,] -2.878372e-17 -1.746523e-17
[95,] 2.842992e-16 -2.878372e-17
[96,] 2.903639e-17 2.842992e-16
[97,] 9.016801e-18 2.903639e-17
[98,] 6.109152e-17 9.016801e-18
[99,] -1.354064e-17 6.109152e-17
[100,] 3.488966e-17 -1.354064e-17
[101,] -1.486328e-17 3.488966e-17
[102,] -2.862913e-16 -1.486328e-17
[103,] 2.327170e-17 -2.862913e-16
[104,] 4.747453e-17 2.327170e-17
[105,] 4.748314e-17 4.747453e-17
[106,] 4.949066e-17 4.748314e-17
[107,] 2.141777e-17 4.949066e-17
[108,] 1.624509e-16 2.141777e-17
[109,] 3.533701e-17 1.624509e-16
[110,] 1.761376e-16 3.533701e-17
[111,] 1.958787e-17 1.761376e-16
[112,] -1.237470e-16 1.958787e-17
[113,] 6.223014e-17 -1.237470e-16
[114,] -2.352623e-16 6.223014e-17
[115,] -6.398415e-17 -2.352623e-16
[116,] -4.122707e-17 -6.398415e-17
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.579014e-16 -2.892968e-16
2 -6.749799e-16 -2.579014e-16
3 -5.995323e-17 -6.749799e-16
4 1.193760e-16 -5.995323e-17
5 1.762469e-16 1.193760e-16
6 2.860858e-15 1.762469e-16
7 -6.186457e-17 2.860858e-15
8 -7.446161e-17 -6.186457e-17
9 -1.104933e-16 -7.446161e-17
10 6.080808e-18 -1.104933e-16
11 -1.181588e-16 6.080808e-18
12 3.699149e-18 -1.181588e-16
13 2.660700e-17 3.699149e-18
14 -1.321214e-16 2.660700e-17
15 -6.762483e-17 -1.321214e-16
16 -6.317381e-17 -6.762483e-17
17 -1.246845e-16 -6.317381e-17
18 -4.519664e-16 -1.246845e-16
19 3.767563e-17 -4.519664e-16
20 -7.066140e-17 3.767563e-17
21 -2.326815e-17 -7.066140e-17
22 -1.719976e-17 -2.326815e-17
23 -1.242667e-16 -1.719976e-17
24 -6.863514e-17 -1.242667e-16
25 2.468774e-17 -6.863514e-17
26 8.922540e-17 2.468774e-17
27 -6.113808e-17 8.922540e-17
28 2.951461e-17 -6.113808e-17
29 -7.765363e-17 2.951461e-17
30 -3.627758e-16 -7.765363e-17
31 -1.393141e-17 -3.627758e-16
32 -6.775578e-17 -1.393141e-17
33 9.635644e-18 -6.775578e-17
34 -7.251071e-17 9.635644e-18
35 -5.989202e-17 -7.251071e-17
36 1.739712e-17 -5.989202e-17
37 2.640182e-18 1.739712e-17
38 1.233901e-16 2.640182e-18
39 5.303045e-17 1.233901e-16
40 -1.163220e-16 5.303045e-17
41 -8.832207e-17 -1.163220e-16
42 -2.811574e-16 -8.832207e-17
43 -2.918593e-17 -2.811574e-16
44 2.375018e-17 -2.918593e-17
45 -1.309115e-17 2.375018e-17
46 2.584684e-17 -1.309115e-17
47 -1.068457e-17 2.584684e-17
48 4.833362e-17 -1.068457e-17
49 -1.239321e-17 4.833362e-17
50 1.058077e-16 -1.239321e-17
51 6.047475e-17 1.058077e-16
52 1.016051e-17 6.047475e-17
53 -2.703054e-17 1.016051e-17
54 -2.826774e-16 -2.703054e-17
55 -5.638469e-17 -2.826774e-16
56 1.453834e-17 -5.638469e-17
57 8.397078e-17 1.453834e-17
58 -1.712159e-16 8.397078e-17
59 6.170889e-17 -1.712159e-16
60 6.992927e-17 6.170889e-17
61 4.399593e-19 6.992927e-17
62 1.371959e-16 4.399593e-19
63 2.884177e-17 1.371959e-16
64 -1.246761e-16 2.884177e-17
65 1.114019e-16 -1.246761e-16
66 -3.297269e-16 1.114019e-16
67 1.240862e-17 -3.297269e-16
68 1.098319e-16 1.240862e-17
69 -4.536939e-18 1.098319e-16
70 -8.633773e-17 -4.536939e-18
71 -1.259175e-17 -8.633773e-17
72 1.502503e-17 -1.259175e-17
73 9.107760e-18 1.502503e-17
74 1.809528e-16 9.107760e-18
75 1.546634e-17 1.809528e-16
76 1.023607e-16 1.546634e-17
77 -1.777859e-17 1.023607e-16
78 -2.919436e-16 -1.777859e-17
79 6.702062e-17 -2.919436e-16
80 6.123488e-17 6.702062e-17
81 2.776523e-17 6.123488e-17
82 2.946295e-16 2.776523e-17
83 -4.183214e-17 2.946295e-16
84 1.206044e-17 -4.183214e-17
85 1.624581e-16 1.206044e-17
86 -6.669979e-17 1.624581e-16
87 2.485562e-17 -6.669979e-17
88 1.316176e-16 2.485562e-17
89 4.535701e-19 1.316176e-16
90 -3.390570e-16 4.535701e-19
91 8.497419e-17 -3.390570e-16
92 -2.723956e-18 8.497419e-17
93 -1.746523e-17 -2.723956e-18
94 -2.878372e-17 -1.746523e-17
95 2.842992e-16 -2.878372e-17
96 2.903639e-17 2.842992e-16
97 9.016801e-18 2.903639e-17
98 6.109152e-17 9.016801e-18
99 -1.354064e-17 6.109152e-17
100 3.488966e-17 -1.354064e-17
101 -1.486328e-17 3.488966e-17
102 -2.862913e-16 -1.486328e-17
103 2.327170e-17 -2.862913e-16
104 4.747453e-17 2.327170e-17
105 4.748314e-17 4.747453e-17
106 4.949066e-17 4.748314e-17
107 2.141777e-17 4.949066e-17
108 1.624509e-16 2.141777e-17
109 3.533701e-17 1.624509e-16
110 1.761376e-16 3.533701e-17
111 1.958787e-17 1.761376e-16
112 -1.237470e-16 1.958787e-17
113 6.223014e-17 -1.237470e-16
114 -2.352623e-16 6.223014e-17
115 -6.398415e-17 -2.352623e-16
116 -4.122707e-17 -6.398415e-17
> 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/7qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1017or1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mpnf1292772458.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/12qq331292772458.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/134z1u1292772458.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/147i001292772458.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/15tjgn1292772458.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/16ejfb1292772458.tab")
+ }
>
> try(system("convert tmp/1uorf1292772458.ps tmp/1uorf1292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uorf1292772458.ps tmp/2uorf1292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/35f901292772458.ps tmp/35f901292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/45f901292772458.ps tmp/45f901292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/55f901292772458.ps tmp/55f901292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y6ql1292772458.ps tmp/6y6ql1292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qfp61292772458.ps tmp/7qfp61292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qfp61292772458.ps tmp/8qfp61292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qfp61292772458.ps tmp/9qfp61292772458.png",intern=TRUE))
character(0)
> try(system("convert tmp/1017or1292772458.ps tmp/1017or1292772458.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.030 1.690 5.743