R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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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(32.5
+ ,15.5
+ ,-2.2
+ ,3.1
+ ,-6.6
+ ,7.7
+ ,14.4
+ ,18.2
+ ,-1.3
+ ,3.2
+ ,-6.2
+ ,10.9
+ ,11.5
+ ,22.1
+ ,-6.4
+ ,6.0
+ ,-4.0
+ ,10.0
+ ,17.4
+ ,15.8
+ ,3.0
+ ,9.7
+ ,-7.8
+ ,0.4
+ ,12.1
+ ,18.7
+ ,10.1
+ ,4.4
+ ,-6.1
+ ,9.6
+ ,5.0
+ ,20.5
+ ,12.8
+ ,1.3
+ ,-6.9
+ ,2.4
+ ,5.2
+ ,15.9
+ ,8.2
+ ,-2.6
+ ,-3.3
+ ,1.6
+ ,-1.0
+ ,19.1
+ ,-5.4
+ ,2.3
+ ,-6.9
+ ,1.1
+ ,-14.1
+ ,12.9
+ ,-9.4
+ ,-6.1
+ ,1.5
+ ,2.9
+ ,-13.9
+ ,13.2
+ ,3.6
+ ,-4.2
+ ,0.1
+ ,4.4
+ ,-5.8
+ ,13.4
+ ,-0.4
+ ,-14.3
+ ,-7.2
+ ,-3.8
+ ,-6.4
+ ,14.3
+ ,-1.8
+ ,-5.7
+ ,-10.7
+ ,-0.4
+ ,-17.0
+ ,12.3
+ ,-0.1
+ ,-7.5
+ ,-5.9
+ ,-23.6
+ ,-33.0
+ ,8.7
+ ,-0.9
+ ,-12.2
+ ,-23.8
+ ,-7.9
+ ,-7.8
+ ,15.0
+ ,5.2
+ ,-15.9
+ ,-15.3
+ ,-4.6
+ ,11.7
+ ,16.7
+ ,-1.9
+ ,-11.2
+ ,-13.5
+ ,4.4
+ ,6.1
+ ,16.6
+ ,-1.9
+ ,-14.8
+ ,-8.3
+ ,-7.4
+ ,-14.2
+ ,17.0
+ ,2.7
+ ,-12.6
+ ,-8.9
+ ,-4.2
+ ,7.7
+ ,23.7
+ ,6.0
+ ,-12.0
+ ,1.4
+ ,-3.5
+ ,-7.4
+ ,15.6
+ ,3.0
+ ,-8.8
+ ,-6.0
+ ,-7.4
+ ,8.2
+ ,17.2
+ ,6.6
+ ,-2.8
+ ,-13.3
+ ,-3.3
+ ,28.4
+ ,18.5
+ ,2.9
+ ,-3.5
+ ,-13.6
+ ,-1.8
+ ,-1.7
+ ,18.9
+ ,1.5
+ ,3.0
+ ,-9.4
+ ,-11.3
+ ,-31.6
+ ,9.4
+ ,-6.5
+ ,-2.3
+ ,-9.4
+ ,10.7
+ ,-0.4
+ ,17.0
+ ,-7.8
+ ,-18.1
+ ,-11.2
+ ,1.9
+ ,-8.6
+ ,17.8
+ ,-5.4
+ ,1.1
+ ,-2.3
+ ,5.1
+ ,-2.5
+ ,7.4
+ ,4.1
+ ,-0.3
+ ,-7.3
+ ,3.8
+ ,-28.5
+ ,9.4
+ ,-6.1
+ ,-4.2
+ ,-11.9
+ ,-8.3
+ ,-35.3
+ ,2.3
+ ,-19.9
+ ,-10.1
+ ,-13.3
+ ,-13.1
+ ,-16.1
+ ,6.2
+ ,-12.9
+ ,-15.0
+ ,-11.2
+ ,-16.5
+ ,-15.3
+ ,3.7
+ ,-10.6
+ ,-8.0
+ ,-25.2
+ ,-21.1
+ ,-14.4
+ ,10.8
+ ,-5.1
+ ,-2.0
+ ,-24.8
+ ,-12.9
+ ,-0.6
+ ,1.6
+ ,3.0
+ ,-4.7
+ ,-28.6
+ ,-13.5
+ ,-16.0
+ ,8.0
+ ,-9.2
+ ,-6.8
+ ,-25.3
+ ,-28.0
+ ,-20.4
+ ,5.9
+ ,-3.3
+ ,-14.5
+ ,-24.0
+ ,-11.5
+ ,-19.9
+ ,11.1
+ ,9.2
+ ,-6.3
+ ,-20.1
+ ,-22.2
+ ,-5.6
+ ,12.2
+ ,6.3
+ ,-8.0
+ ,-25.4
+ ,-6.2
+ ,1.9
+ ,13.2
+ ,5.8
+ ,-9.1
+ ,-22.8
+ ,4.9
+ ,1.2
+ ,26.6
+ ,-2.2
+ ,-9.3
+ ,-25.7
+ ,-28.0
+ ,8.3
+ ,20.2
+ ,1.8
+ ,-3.8
+ ,-13.2
+ ,-23.2
+ ,17.0
+ ,16.0
+ ,1.6
+ ,6.5
+ ,-20.3
+ ,-3.7
+ ,24.0
+ ,12.5
+ ,4.0
+ ,9.6
+ ,-21.1
+ ,-3.3
+ ,8.1
+ ,21.3
+ ,-5.3
+ ,3.8
+ ,0.1
+ ,-2.4
+ ,15.1
+ ,16.0
+ ,-7.3
+ ,0.2
+ ,-4.1
+ ,-3.6
+ ,3.9
+ ,12.7
+ ,-8.1
+ ,-5.7
+ ,-11.6
+ ,-11.1
+ ,12.9
+ ,9.5
+ ,-12.8
+ ,3.2
+ ,-9.1
+ ,-12.3
+ ,21.7
+ ,10.5
+ ,2.7
+ ,8.1
+ ,-9.3
+ ,-9.4
+ ,24.3
+ ,16.0
+ ,5.8
+ ,0.4
+ ,-6.5
+ ,-6.6
+ ,16.3
+ ,9.3
+ ,-0.4
+ ,-7.9
+ ,-6.3
+ ,-8.8
+ ,24.3
+ ,12.3
+ ,7.3
+ ,0.2
+ ,-7.3
+ ,-17.1
+ ,12.8
+ ,7.7
+ ,-1.2
+ ,1.6
+ ,-0.9
+ ,-7.3
+ ,26.1
+ ,9.2
+ ,4.9
+ ,-3.4
+ ,-3.5
+ ,-16.9
+ ,0.8
+ ,13.0
+ ,2.7
+ ,1.7
+ ,-4.6
+ ,-16.3
+ ,0.7
+ ,6.0
+ ,1.3
+ ,-5.4
+ ,-8.7
+ ,-8.9
+ ,5.2
+ ,10.2
+ ,6.7
+ ,-2.5
+ ,-7.1
+ ,-17.3
+ ,0.4
+ ,5.7
+ ,-0.4
+ ,-2.8
+ ,-2.5
+ ,-19.2
+ ,0.4
+ ,9.9
+ ,-8.5
+ ,-3.4
+ ,-2.5
+ ,-35.5
+ ,5.7
+ ,4.9
+ ,-0.9
+ ,-5.4
+ ,-3.9
+ ,-6.5
+ ,1.8
+ ,3.2
+ ,-5.3
+ ,1.8
+ ,-20.5
+ ,-10.7
+ ,-1.4
+ ,8.6
+ ,-4.7
+ ,-0.4
+ ,-5.9
+ ,-11.5
+ ,12.8
+ ,8.5
+ ,4.7
+ ,3.7
+ ,-0.8
+ ,-11.8
+ ,14.4
+ ,8.3
+ ,3.2
+ ,1.0
+ ,-10.6
+ ,-11.4
+ ,9.8
+ ,4.9
+ ,13.4
+ ,-7.1
+ ,-8.1
+ ,-2.2
+ ,14.2
+ ,7.5
+ ,11.1
+ ,1.6
+ ,-4.1
+ ,-3.6
+ ,22.0
+ ,8.6
+ ,17.4
+ ,-12.5
+ ,5.3
+ ,-3.7
+ ,13.5
+ ,5.1
+ ,5.8
+ ,-0.6
+ ,0.9
+ ,-0.2
+ ,7.6
+ ,-7.2
+ ,-0.2
+ ,3.9
+ ,-7.8
+ ,5.9
+ ,15.2
+ ,3.5
+ ,11.2
+ ,3.6
+ ,-3.7
+ ,-8.3
+ ,21.4
+ ,14.6
+ ,17.3
+ ,8.0
+ ,-8.0
+ ,5.1
+ ,7.2
+ ,15.9
+ ,4.9
+ ,-11.3
+ ,2.2
+ ,-3.7
+ ,21.3
+ ,15.7
+ ,7.9
+ ,-7.4
+ ,6.1
+ ,-10.3
+ ,22.3
+ ,10.3
+ ,9.2
+ ,1.1
+ ,5.4
+ ,2.0
+ ,8.5
+ ,3.5
+ ,9.7
+ ,0.0
+ ,7.4
+ ,1.3
+ ,0.3
+ ,12.0
+ ,4.7
+ ,2.1
+ ,7.5
+ ,-0.1
+ ,10.4
+ ,9.3
+ ,3.5
+ ,3.3
+ ,5.5
+ ,-11.4
+ ,-3.0
+ ,15.0
+ ,-3.6
+ ,1.6
+ ,6.0
+ ,33.7
+ ,22.5
+ ,11.5
+ ,10.6
+ ,1.5
+ ,1.5
+ ,22.4
+ ,18.3
+ ,11.2
+ ,6.1
+ ,3.5
+ ,5.7
+ ,-3.8
+ ,14.8
+ ,17.8
+ ,7.9
+ ,-1.2
+ ,16.7
+ ,9.9
+ ,15.9
+ ,11.7
+ ,11.5
+ ,0.7
+ ,11.1
+ ,15.8
+ ,13.1
+ ,5.6
+ ,12.7
+ ,2.4
+ ,6.7
+ ,13.5
+ ,10.0
+ ,-4.2
+ ,17.8
+ ,6.5
+ ,12.0
+ ,12.8
+ ,7.0
+ ,9.3
+ ,14.3
+ ,6.0
+ ,11.2
+ ,13.7
+ ,2.8
+ ,11.1
+ ,-2.9
+ ,2.0
+ ,11.3
+ ,12.6
+ ,12.7
+ ,6.7
+ ,2.3
+ ,0.9
+ ,15.7
+ ,9.0
+ ,13.2
+ ,2.4
+ ,-3.8
+ ,-1.5
+ ,8.8
+ ,44.7
+ ,8.6
+ ,13.3
+ ,-0.9
+ ,2.4
+ ,4.7
+ ,7.2
+ ,30.4
+ ,8.1
+ ,-3.5
+ ,-1.2
+ ,3.4
+ ,-7.7
+ ,-18.5
+ ,14.0
+ ,-9.2
+ ,-0.1
+ ,-2.8
+ ,-12.5
+ ,0.9
+ ,18.1
+ ,-4.5
+ ,-0.1
+ ,3.0
+ ,-23.6
+ ,9.8
+ ,17.0
+ ,-8.0
+ ,7.7
+ ,-6.7
+ ,-25.1
+ ,-6.4
+ ,17.7
+ ,-10.7
+ ,-1.3
+ ,-10.4
+ ,-11.8
+ ,-5.3
+ ,10.6
+ ,-10.2
+ ,-3.1
+ ,-8.7
+ ,-26.6
+ ,27.0
+ ,13.3
+ ,-14.1
+ ,-2.4
+ ,-14.7
+ ,-18.5
+ ,-22.0
+ ,5.3
+ ,-27.6
+ ,-3.8
+ ,-21.8
+ ,-28.6
+ ,-5.7
+ ,4.4
+ ,-15.6
+ ,-11.1
+ ,-26.0
+ ,-45.5
+ ,-36.0
+ ,14.0
+ ,-23.6
+ ,-11.2
+ ,-28.4
+ ,-43.2
+ ,-38.1
+ ,7.6
+ ,-15.9
+ ,8.3
+ ,-26.7
+ ,-42.3
+ ,-48.0
+ ,4.5
+ ,-18.8
+ ,-3.1
+ ,-26.2
+ ,-50.0
+ ,-55.5
+ ,-0.3
+ ,-25.4
+ ,-40.9
+ ,-32.7
+ ,-39.6
+ ,-65.5
+ ,1.9
+ ,-20.0
+ ,-44.2
+ ,-32.8
+ ,-44.6
+ ,-44.6
+ ,3.3
+ ,-20.2
+ ,-45.2
+ ,-37.1
+ ,-40.9
+ ,-40.4
+ ,-1.4
+ ,-9.2
+ ,-53.0
+ ,-36.3
+ ,-46.1
+ ,-13.2
+ ,-5.0
+ ,-9.6
+ ,-44.3
+ ,-32.2
+ ,-46.3
+ ,-28.6
+ ,3.4
+ ,-9.6
+ ,-41.6
+ ,-36.0
+ ,-45.5
+ ,-30.6
+ ,8.3
+ ,-16.4
+ ,-44.3
+ ,-30.6
+ ,-34.4
+ ,-34.2
+ ,7.9
+ ,-11.4
+ ,-41.3
+ ,-23.6
+ ,-35.1
+ ,-37.4
+ ,-2.6
+ ,-17.3
+ ,-43.9
+ ,-16.3
+ ,-41.1
+ ,-7.5
+ ,7.1
+ ,-12.0
+ ,-33.3
+ ,-32.4
+ ,-35.8
+ ,5.8
+ ,10.2
+ ,1.6
+ ,-3.6
+ ,-25.8
+ ,-35.4
+ ,13.3
+ ,5.4
+ ,8.8
+ ,-4.7
+ ,-23.1
+ ,-12.8
+ ,2.4
+ ,-1.4
+ ,10.5
+ ,-4.8
+ ,-17.4
+ ,-23.5
+ ,0.9
+ ,-5.8
+ ,2.2
+ ,-1.1
+ ,-10.5
+ ,-7.8
+ ,-5.3
+ ,6.0
+ ,1.4
+ ,7.7
+ ,-7.8
+ ,-18.4
+ ,-1.7
+ ,6.2
+ ,11.5
+ ,6.1
+ ,1.7
+ ,-6.9
+ ,-4.4
+ ,5.1
+ ,2.8
+ ,1.6
+ ,4.6
+ ,-16.6
+ ,-5.2
+ ,-4.0
+ ,4.3
+ ,2.1
+ ,-6.4
+ ,0.5
+ ,2.9
+ ,-2.1
+ ,12.0
+ ,-7.8
+ ,-0.1
+ ,4.7
+ ,2.0
+ ,-1.0
+ ,11.4
+ ,25.1
+ ,-3.3
+ ,-8.3
+ ,-18.2
+ ,11.2
+ ,4.9
+ ,1.7
+ ,1.9
+ ,8.6
+ ,-13.5
+ ,3.4
+ ,10.5
+ ,0.2
+ ,2.5
+ ,3.5
+ ,12.5
+ ,25.7
+ ,6.2
+ ,-9.1
+ ,1.2
+ ,2.8
+ ,7.1
+ ,20.8
+ ,12.6
+ ,-17.9
+ ,1.6
+ ,-8.0
+ ,3.5
+ ,25.6
+ ,17.4
+ ,-7.1
+ ,0.8
+ ,1.3
+ ,25.4
+ ,19.9
+ ,2.4
+ ,1.5
+ ,4.9
+ ,5.9
+ ,5.9
+ ,10.6
+ ,-8.0
+ ,-3.4
+ ,-2.2
+ ,6.8
+ ,-7.8
+ ,21.1
+ ,-11.0
+ ,-4.3
+ ,2.3
+ ,-14.9
+ ,1.9
+ ,20.0
+ ,5.9
+ ,-7.1
+ ,-3.6
+ ,-13.7
+ ,30.8
+ ,20.4
+ ,12.2
+ ,-13.4
+ ,-3.8
+ ,-15.7
+ ,7.8
+ ,11.3
+ ,4.2
+ ,-5.4
+ ,-3.9
+ ,-21.0
+ ,2.9
+ ,19.6
+ ,3.3
+ ,23.8
+ ,-3.3
+ ,-18.7
+ ,13.7
+ ,8.5
+ ,1.7
+ ,-2.4
+ ,-15.8
+ ,-29.9
+ ,8.7
+ ,-0.1
+ ,-5.3
+ ,1.7
+ ,-18.4
+ ,-32.5
+ ,-33.0
+ ,0.2
+ ,-8.1
+ ,-28.8
+ ,-19.5
+ ,-37.6
+ ,5.1
+ ,6.3
+ ,-11.6
+ ,-27.6
+ ,-21.6
+ ,-26.3
+ ,-1.4
+ ,1.5
+ ,-21.8
+ ,-5.9
+ ,-23.0
+ ,-39.7
+ ,-12.1
+ ,-1.9
+ ,-21.4
+ ,-22.7
+ ,-22.2
+ ,-41.9
+ ,-34.9
+ ,-7.3
+ ,-12.1
+ ,-45.5
+ ,-19.4
+ ,-39.5
+ ,-25.6
+ ,-2.2
+ ,-19.0
+ ,-8.9
+ ,-17.2
+ ,-38.9
+ ,-11.2
+ ,-6.2
+ ,-12.6
+ ,-16.8
+ ,-18.5
+ ,-3.6
+ ,-40.0
+ ,6.1
+ ,-17.7
+ ,6.5
+ ,-11.1
+ ,-35.7
+ ,-32.4
+ ,2.7
+ ,-13.2
+ ,4.2
+ ,-13.6
+ ,-21.7
+ ,-39.1
+ ,-4.5
+ ,-14.7
+ ,-5.6
+ ,-17.4
+ ,-30.4
+ ,-48.4
+ ,5.4
+ ,-13.2
+ ,7.9
+ ,-14.5
+ ,-37.1
+ ,-37.9
+ ,-1.4
+ ,-0.3
+ ,-18.3
+ ,-15.2
+ ,-45.2)
+ ,dim=c(6
+ ,145)
+ ,dimnames=list(c('Personenwagens'
+ ,'voedingsproducten'
+ ,'schoenen'
+ ,'meubelen'
+ ,'textielartikelen'
+ ,'elektrischetoestellen')
+ ,1:145))
> y <- array(NA,dim=c(6,145),dimnames=list(c('Personenwagens','voedingsproducten','schoenen','meubelen','textielartikelen','elektrischetoestellen'),1:145))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '3'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
schoenen Personenwagens voedingsproducten meubelen textielartikelen
1 -2.2 32.5 15.5 3.1 -6.6
2 -1.3 14.4 18.2 3.2 -6.2
3 -6.4 11.5 22.1 6.0 -4.0
4 3.0 17.4 15.8 9.7 -7.8
5 10.1 12.1 18.7 4.4 -6.1
6 12.8 5.0 20.5 1.3 -6.9
7 8.2 5.2 15.9 -2.6 -3.3
8 -5.4 -1.0 19.1 2.3 -6.9
9 -9.4 -14.1 12.9 -6.1 1.5
10 3.6 -13.9 13.2 -4.2 0.1
11 -0.4 -5.8 13.4 -14.3 -7.2
12 -1.8 -6.4 14.3 -5.7 -10.7
13 -0.1 -17.0 12.3 -7.5 -5.9
14 -0.9 -33.0 8.7 -12.2 -23.8
15 5.2 -7.8 15.0 -15.9 -15.3
16 -1.9 11.7 16.7 -11.2 -13.5
17 -1.9 6.1 16.6 -14.8 -8.3
18 2.7 -14.2 17.0 -12.6 -8.9
19 6.0 7.7 23.7 -12.0 1.4
20 3.0 -7.4 15.6 -8.8 -6.0
21 6.6 8.2 17.2 -2.8 -13.3
22 2.9 28.4 18.5 -3.5 -13.6
23 1.5 -1.7 18.9 3.0 -9.4
24 -6.5 -31.6 9.4 -2.3 -9.4
25 -7.8 -0.4 17.0 -18.1 -11.2
26 -5.4 -8.6 17.8 1.1 -2.3
27 4.1 -2.5 7.4 -0.3 -7.3
28 -6.1 -28.5 9.4 -4.2 -11.9
29 -19.9 -35.3 2.3 -10.1 -13.3
30 -12.9 -16.1 6.2 -15.0 -11.2
31 -10.6 -15.3 3.7 -8.0 -25.2
32 -5.1 -14.4 10.8 -2.0 -24.8
33 3.0 -0.6 1.6 -4.7 -28.6
34 -9.2 -16.0 8.0 -6.8 -25.3
35 -3.3 -20.4 5.9 -14.5 -24.0
36 9.2 -19.9 11.1 -6.3 -20.1
37 6.3 -5.6 12.2 -8.0 -25.4
38 5.8 1.9 13.2 -9.1 -22.8
39 -2.2 1.2 26.6 -9.3 -25.7
40 1.8 8.3 20.2 -3.8 -13.2
41 1.6 17.0 16.0 6.5 -20.3
42 4.0 24.0 12.5 9.6 -21.1
43 -5.3 8.1 21.3 3.8 0.1
44 -7.3 15.1 16.0 0.2 -4.1
45 -8.1 3.9 12.7 -5.7 -11.6
46 -12.8 12.9 9.5 3.2 -9.1
47 2.7 21.7 10.5 8.1 -9.3
48 5.8 24.3 16.0 0.4 -6.5
49 -0.4 16.3 9.3 -7.9 -6.3
50 7.3 24.3 12.3 0.2 -7.3
51 -1.2 12.8 7.7 1.6 -0.9
52 4.9 26.1 9.2 -3.4 -3.5
53 2.7 0.8 13.0 1.7 -4.6
54 1.3 0.7 6.0 -5.4 -8.7
55 6.7 5.2 10.2 -2.5 -7.1
56 -0.4 0.4 5.7 -2.8 -2.5
57 -8.5 0.4 9.9 -3.4 -2.5
58 -0.9 5.7 4.9 -5.4 -3.9
59 -5.3 1.8 3.2 1.8 -20.5
60 -4.7 -1.4 8.6 -0.4 -5.9
61 4.7 12.8 8.5 3.7 -0.8
62 3.2 14.4 8.3 1.0 -10.6
63 13.4 9.8 4.9 -7.1 -8.1
64 11.1 14.2 7.5 1.6 -4.1
65 17.4 22.0 8.6 -12.5 5.3
66 5.8 13.5 5.1 -0.6 0.9
67 -0.2 7.6 -7.2 3.9 -7.8
68 11.2 15.2 3.5 3.6 -3.7
69 17.3 21.4 14.6 8.0 -8.0
70 4.9 7.2 15.9 -11.3 2.2
71 7.9 21.3 15.7 -7.4 6.1
72 9.2 22.3 10.3 1.1 5.4
73 9.7 8.5 3.5 0.0 7.4
74 4.7 0.3 12.0 2.1 7.5
75 3.5 10.4 9.3 3.3 5.5
76 -3.6 -3.0 15.0 1.6 6.0
77 10.6 22.5 11.5 1.5 1.5
78 6.1 18.3 11.2 3.5 5.7
79 7.9 14.8 17.8 -1.2 16.7
80 11.5 15.9 11.7 0.7 11.1
81 12.7 13.1 5.6 2.4 6.7
82 17.8 10.0 -4.2 6.5 12.0
83 14.3 7.0 9.3 6.0 11.2
84 -2.9 2.8 11.1 2.0 11.3
85 2.3 12.7 6.7 0.9 15.7
86 -3.8 13.2 2.4 -1.5 8.8
87 -0.9 8.6 13.3 2.4 4.7
88 -3.5 30.4 8.1 -1.2 3.4
89 -9.2 -18.5 14.0 -0.1 -2.8
90 -4.5 0.9 18.1 -0.1 3.0
91 -8.0 9.8 17.0 7.7 -6.7
92 -10.7 -6.4 17.7 -1.3 -10.4
93 -10.2 -5.3 10.6 -3.1 -8.7
94 -14.1 27.0 13.3 -2.4 -14.7
95 -27.6 -22.0 5.3 -3.8 -21.8
96 -15.6 -5.7 4.4 -11.1 -26.0
97 -23.6 -36.0 14.0 -11.2 -28.4
98 -15.9 -38.1 7.6 8.3 -26.7
99 -18.8 -48.0 4.5 -3.1 -26.2
100 -25.4 -55.5 -0.3 -40.9 -32.7
101 -20.0 -65.5 1.9 -44.2 -32.8
102 -20.2 -44.6 3.3 -45.2 -37.1
103 -9.2 -40.4 -1.4 -53.0 -36.3
104 -9.6 -13.2 -5.0 -44.3 -32.2
105 -9.6 -28.6 3.4 -41.6 -36.0
106 -16.4 -30.6 8.3 -44.3 -30.6
107 -11.4 -34.2 7.9 -41.3 -23.6
108 -17.3 -37.4 -2.6 -43.9 -16.3
109 -12.0 -7.5 7.1 -33.3 -32.4
110 1.6 5.8 10.2 -3.6 -25.8
111 8.8 13.3 5.4 -4.7 -23.1
112 10.5 2.4 -1.4 -4.8 -17.4
113 2.2 0.9 -5.8 -1.1 -10.5
114 1.4 -5.3 6.0 7.7 -7.8
115 11.5 -1.7 6.2 6.1 1.7
116 2.8 -4.4 5.1 1.6 4.6
117 4.3 -5.2 -4.0 2.1 -6.4
118 12.0 2.9 -2.1 -7.8 -0.1
119 11.4 2.0 -1.0 25.1 -3.3
120 4.9 -18.2 11.2 1.7 1.9
121 10.5 -13.5 3.4 0.2 2.5
122 6.2 12.5 25.7 -9.1 1.2
123 12.6 7.1 20.8 -17.9 1.6
124 17.4 3.5 25.6 -7.1 0.8
125 2.4 25.4 19.9 1.5 4.9
126 -8.0 5.9 10.6 -3.4 -2.2
127 -11.0 -7.8 21.1 -4.3 2.3
128 5.9 1.9 20.0 -7.1 -3.6
129 12.2 30.8 20.4 -13.4 -3.8
130 4.2 7.8 11.3 -5.4 -3.9
131 3.3 2.9 19.6 23.8 -3.3
132 1.7 13.7 8.5 -2.4 -15.8
133 -5.3 8.7 -0.1 1.7 -18.4
134 -8.1 -33.0 0.2 -28.8 -19.5
135 -11.6 5.1 6.3 -27.6 -21.6
136 -21.8 -1.4 1.5 -5.9 -23.0
137 -21.4 -12.1 -1.9 -22.7 -22.2
138 -12.1 -34.9 -7.3 -45.5 -19.4
139 -19.0 -25.6 -2.2 -8.9 -17.2
140 -12.6 -11.2 -6.2 -16.8 -18.5
141 -17.7 -40.0 6.1 6.5 -11.1
142 -13.2 -32.4 2.7 4.2 -13.6
143 -14.7 -39.1 -4.5 -5.6 -17.4
144 -13.2 -48.4 5.4 7.9 -14.5
145 -0.3 -37.9 -1.4 -18.3 -15.2
elektrischetoestellen t
1 7.7 1
2 10.9 2
3 10.0 3
4 0.4 4
5 9.6 5
6 2.4 6
7 1.6 7
8 1.1 8
9 2.9 9
10 4.4 10
11 -3.8 11
12 -0.4 12
13 -23.6 13
14 -7.9 14
15 -4.6 15
16 4.4 16
17 -7.4 17
18 -4.2 18
19 -3.5 19
20 -7.4 20
21 -3.3 21
22 -1.8 22
23 -11.3 23
24 10.7 24
25 1.9 25
26 5.1 26
27 3.8 27
28 -8.3 28
29 -13.1 29
30 -16.5 30
31 -21.1 31
32 -12.9 32
33 -13.5 33
34 -28.0 34
35 -11.5 35
36 -22.2 36
37 -6.2 37
38 4.9 38
39 -28.0 39
40 -23.2 40
41 -3.7 41
42 -3.3 42
43 -2.4 43
44 -3.6 44
45 -11.1 45
46 -12.3 46
47 -9.4 47
48 -6.6 48
49 -8.8 49
50 -17.1 50
51 -7.3 51
52 -16.9 52
53 -16.3 53
54 -8.9 54
55 -17.3 55
56 -19.2 56
57 -35.5 57
58 -6.5 58
59 -10.7 59
60 -11.5 60
61 -11.8 61
62 -11.4 62
63 -2.2 63
64 -3.6 64
65 -3.7 65
66 -0.2 66
67 5.9 67
68 -8.3 68
69 5.1 69
70 -3.7 70
71 -10.3 71
72 2.0 72
73 1.3 73
74 -0.1 74
75 -11.4 75
76 33.7 76
77 22.4 77
78 -3.8 78
79 9.9 79
80 15.8 80
81 13.5 81
82 12.8 82
83 13.7 83
84 12.6 84
85 9.0 85
86 44.7 86
87 7.2 87
88 -7.7 88
89 -12.5 89
90 -23.6 90
91 -25.1 91
92 -11.8 92
93 -26.6 93
94 -18.5 94
95 -28.6 95
96 -45.5 96
97 -43.2 97
98 -42.3 98
99 -50.0 99
100 -39.6 100
101 -44.6 101
102 -40.9 102
103 -46.1 103
104 -46.3 104
105 -45.5 105
106 -34.4 106
107 -35.1 107
108 -41.1 108
109 -35.8 109
110 -35.4 110
111 -12.8 111
112 -23.5 112
113 -7.8 113
114 -18.4 114
115 -6.9 115
116 -16.6 116
117 0.5 117
118 4.7 118
119 -8.3 119
120 8.6 120
121 3.5 121
122 2.8 122
123 -8.0 123
124 1.3 124
125 5.9 125
126 6.8 126
127 -14.9 127
128 -13.7 128
129 -15.7 129
130 -21.0 130
131 -18.7 131
132 -29.9 132
133 -32.5 133
134 -37.6 134
135 -26.3 135
136 -39.7 136
137 -41.9 137
138 -39.5 138
139 -38.9 139
140 -3.6 140
141 -35.7 141
142 -21.7 142
143 -30.4 143
144 -37.1 144
145 -45.2 145
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Personenwagens voedingsproducten
2.5355340 0.2061851 -0.0440014
meubelen textielartikelen elektrischetoestellen
-0.0102547 0.1582519 0.1384410
t
-0.0009146
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.9090 -5.0511 0.6692 4.5787 17.4785
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.5355340 2.1054102 1.204 0.2305
Personenwagens 0.2061851 0.0408500 5.047 1.39e-06 ***
voedingsproducten -0.0440014 0.0971979 -0.453 0.6515
meubelen -0.0102547 0.0575432 -0.178 0.8588
textielartikelen 0.1582519 0.0839820 1.884 0.0616 .
elektrischetoestellen 0.1384410 0.0580025 2.387 0.0184 *
t -0.0009146 0.0183473 -0.050 0.9603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.215 on 138 degrees of freedom
Multiple R-squared: 0.499, Adjusted R-squared: 0.4772
F-statistic: 22.91 on 6 and 138 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.896680219 0.20663956 0.10331978
[2,] 0.872807727 0.25438455 0.12719227
[3,] 0.844938651 0.31012270 0.15506135
[4,] 0.763606923 0.47278615 0.23639308
[5,] 0.721215726 0.55756855 0.27878427
[6,] 0.632033501 0.73593300 0.36796650
[7,] 0.615570920 0.76885816 0.38442908
[8,] 0.537116039 0.92576792 0.46288396
[9,] 0.455171571 0.91034314 0.54482843
[10,] 0.378274225 0.75654845 0.62172578
[11,] 0.303723575 0.60744715 0.69627643
[12,] 0.241443156 0.48288631 0.75855684
[13,] 0.188032409 0.37606482 0.81196759
[14,] 0.151708026 0.30341605 0.84829197
[15,] 0.112542093 0.22508419 0.88745791
[16,] 0.127746805 0.25549361 0.87225319
[17,] 0.101036988 0.20207398 0.89896301
[18,] 0.113792894 0.22758579 0.88620711
[19,] 0.094792554 0.18958511 0.90520745
[20,] 0.202983307 0.40596661 0.79701669
[21,] 0.195609664 0.39121933 0.80439034
[22,] 0.160394551 0.32078910 0.83960545
[23,] 0.123776686 0.24755337 0.87622331
[24,] 0.161343196 0.32268639 0.83865680
[25,] 0.140519531 0.28103906 0.85948047
[26,] 0.115160836 0.23032167 0.88483916
[27,] 0.224330564 0.44866113 0.77566944
[28,] 0.232992395 0.46598479 0.76700761
[29,] 0.215050313 0.43010063 0.78494969
[30,] 0.249678344 0.49935669 0.75032166
[31,] 0.212559786 0.42511957 0.78744021
[32,] 0.177505006 0.35501001 0.82249499
[33,] 0.147125682 0.29425136 0.85287432
[34,] 0.133883896 0.26776779 0.86611610
[35,] 0.128429096 0.25685819 0.87157090
[36,] 0.112086726 0.22417345 0.88791327
[37,] 0.128553930 0.25710786 0.87144607
[38,] 0.128781123 0.25756225 0.87121888
[39,] 0.130173083 0.26034617 0.86982692
[40,] 0.113020772 0.22604154 0.88697923
[41,] 0.124252917 0.24850583 0.87574708
[42,] 0.106846548 0.21369310 0.89315345
[43,] 0.097170565 0.19434113 0.90282944
[44,] 0.088940753 0.17788151 0.91105925
[45,] 0.076739622 0.15347924 0.92326038
[46,] 0.085098713 0.17019743 0.91490129
[47,] 0.068546647 0.13709329 0.93145335
[48,] 0.063560480 0.12712096 0.93643952
[49,] 0.050203074 0.10040615 0.94979693
[50,] 0.039928352 0.07985670 0.96007165
[51,] 0.031219830 0.06243966 0.96878017
[52,] 0.027405939 0.05481188 0.97259406
[53,] 0.021278927 0.04255785 0.97872107
[54,] 0.041186468 0.08237294 0.95881353
[55,] 0.048291129 0.09658226 0.95170887
[56,] 0.069241481 0.13848296 0.93075852
[57,] 0.054673765 0.10934753 0.94532624
[58,] 0.043185372 0.08637074 0.95681463
[59,] 0.048589751 0.09717950 0.95141025
[60,] 0.078982230 0.15796446 0.92101777
[61,] 0.064219336 0.12843867 0.93578066
[62,] 0.050698580 0.10139716 0.94930142
[63,] 0.039859879 0.07971976 0.96014012
[64,] 0.034480412 0.06896082 0.96551959
[65,] 0.026554636 0.05310927 0.97344536
[66,] 0.019884312 0.03976862 0.98011569
[67,] 0.028381721 0.05676344 0.97161828
[68,] 0.021931474 0.04386295 0.97806853
[69,] 0.016338688 0.03267738 0.98366131
[70,] 0.011896969 0.02379394 0.98810303
[71,] 0.009224235 0.01844847 0.99077577
[72,] 0.008494371 0.01698874 0.99150563
[73,] 0.014539897 0.02907979 0.98546010
[74,] 0.017454532 0.03490906 0.98254547
[75,] 0.019092323 0.03818465 0.98090768
[76,] 0.016189425 0.03237885 0.98381057
[77,] 0.042072602 0.08414520 0.95792740
[78,] 0.039319846 0.07863969 0.96068015
[79,] 0.059537555 0.11907511 0.94046245
[80,] 0.055768379 0.11153676 0.94423162
[81,] 0.047846695 0.09569339 0.95215331
[82,] 0.047932271 0.09586454 0.95206773
[83,] 0.050647436 0.10129487 0.94935256
[84,] 0.051545589 0.10309118 0.94845441
[85,] 0.159185694 0.31837139 0.84081431
[86,] 0.409660668 0.81932134 0.59033933
[87,] 0.435101840 0.87020368 0.56489816
[88,] 0.458762968 0.91752594 0.54123703
[89,] 0.429444248 0.85888850 0.57055575
[90,] 0.412280427 0.82456085 0.58771957
[91,] 0.425039901 0.85007980 0.57496010
[92,] 0.382694126 0.76538825 0.61730587
[93,] 0.347866437 0.69573287 0.65213356
[94,] 0.345287820 0.69057564 0.65471218
[95,] 0.295397121 0.59079424 0.70460288
[96,] 0.270550646 0.54110129 0.72944935
[97,] 0.236245067 0.47249013 0.76375493
[98,] 0.197923732 0.39584746 0.80207627
[99,] 0.270223808 0.54044762 0.72977619
[100,] 0.295380229 0.59076046 0.70461977
[101,] 0.273090271 0.54618054 0.72690973
[102,] 0.270417303 0.54083461 0.72958270
[103,] 0.332010365 0.66402073 0.66798963
[104,] 0.278598493 0.55719699 0.72140151
[105,] 0.238661774 0.47732355 0.76133823
[106,] 0.224930688 0.44986138 0.77506931
[107,] 0.251168250 0.50233650 0.74883175
[108,] 0.203602096 0.40720419 0.79639790
[109,] 0.172588922 0.34517784 0.82741108
[110,] 0.187930891 0.37586178 0.81206911
[111,] 0.154496379 0.30899276 0.84550362
[112,] 0.250904492 0.50180898 0.74909551
[113,] 0.201745307 0.40349061 0.79825469
[114,] 0.195644031 0.39128806 0.80435597
[115,] 0.401330489 0.80266098 0.59866951
[116,] 0.363359529 0.72671906 0.63664047
[117,] 0.328154042 0.65630808 0.67184596
[118,] 0.690492636 0.61901473 0.30950736
[119,] 0.606747878 0.78650424 0.39325212
[120,] 0.518072896 0.96385421 0.48192710
[121,] 0.491822024 0.98364405 0.50817798
[122,] 0.434163316 0.86832663 0.56583668
[123,] 0.380923174 0.76184635 0.61907683
[124,] 0.764395502 0.47120900 0.23560450
[125,] 0.984794419 0.03041116 0.01520558
[126,] 0.949167138 0.10166572 0.05083286
> postscript(file="/var/wessaorg/rcomp/tmp/12rgf1353348882.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/wessaorg/rcomp/tmp/29dru1353348882.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3mgoo1353348882.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/450wl1353348882.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/53b4q1353348882.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 145
Frequency = 1
1 2 3 4 5 6
-10.74335629 -6.49697417 -11.02136142 -1.14581437 5.57844991 10.91406802
7 8 9 10 11 12
5.57239158 -5.91836610 -9.15388518 3.85236811 0.37886588 -0.68553393
13 14 15 16 17 18
5.54670264 8.49916263 7.84148262 -4.68603455 -2.76110718 5.71746584
19 20 21 22 23 24
3.07698564 4.57868311 5.68267211 -2.29151448 3.25035893 -2.10185695
25 26 27 28 29 30
-8.15839754 -5.68612618 3.05632120 0.66915207 -11.21471819 -7.91282986
31 32 33 34 35 36
-2.96272813 1.52804153 7.03552322 -0.24307509 3.90368596 17.47854008
37 38 39 40 41 42
10.28565605 6.32475389 4.07120430 3.74033127 0.09224340 0.99887240
43 44 45 46 47 48
-8.17367301 -11.05539121 -7.52571441 -14.35950345 -0.94859640 0.94854300
49 50 51 52 53 54
-3.70806559 3.86574792 -4.81979249 0.29407713 3.62199157 1.48707601
55 56 57 58 59 60
7.08440341 0.30900280 -5.35484175 -2.88046000 -3.26795766 -3.99192900
61 62 63 64 65 66
1.75324914 1.38327202 10.63068316 7.18881296 10.31156950 0.64484909
67 68 69 70 71 72
-4.10051315 7.51816215 11.69963980 1.69177884 2.11320483 1.46544462
73 74 75 76 77 78
4.28162866 1.54679999 0.03963398 -10.38601008 0.67867105 0.01536750
79 80 81 82 83 84
-0.85727066 2.33732497 4.87930515 9.48839788 7.19876385 -8.95970074
85 86 87 88 89 90
-6.20282581 -16.46922547 -6.25988144 -11.35102812 -5.05109553 -3.55093234
91 92 93 94 95 96
-7.11077439 -7.78688581 -6.06374508 -16.66848711 -17.90902832 -6.37908105
97 98 99 100 101 102
-7.64797992 0.01065691 -0.11363495 -6.17631678 2.05744121 -2.23131366
103 104 105 106 107 108
8.21012156 1.51246832 5.57653738 -3.01351398 1.73197587 -4.32058737
109 110 111 112 113 114
-2.83497430 7.36480979 9.24080321 13.46818283 2.05725001 4.18616447
115 116 117 118 119 120
10.44174101 3.08875434 3.73276235 8.16721920 10.44562187 5.24576882
121 122 123 124 125 126
10.13011837 1.65871869 10.29904597 15.00328536 -5.95953195 -11.79847743
127 128 129 130 131 132
-9.22800632 6.36335532 6.96704882 4.14140820 4.50391450 3.44962834
133 134 135 136 137 138
-2.08349753 4.29589213 -8.01018494 -14.78108420 -12.31790495 0.43725032
139 140 141 142 143 144
-8.21084442 -9.71725191 -4.82516307 -3.60698955 -2.33614755 2.12496518
145
13.52520079
> postscript(file="/var/wessaorg/rcomp/tmp/67c431353348882.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -10.74335629 NA
1 -6.49697417 -10.74335629
2 -11.02136142 -6.49697417
3 -1.14581437 -11.02136142
4 5.57844991 -1.14581437
5 10.91406802 5.57844991
6 5.57239158 10.91406802
7 -5.91836610 5.57239158
8 -9.15388518 -5.91836610
9 3.85236811 -9.15388518
10 0.37886588 3.85236811
11 -0.68553393 0.37886588
12 5.54670264 -0.68553393
13 8.49916263 5.54670264
14 7.84148262 8.49916263
15 -4.68603455 7.84148262
16 -2.76110718 -4.68603455
17 5.71746584 -2.76110718
18 3.07698564 5.71746584
19 4.57868311 3.07698564
20 5.68267211 4.57868311
21 -2.29151448 5.68267211
22 3.25035893 -2.29151448
23 -2.10185695 3.25035893
24 -8.15839754 -2.10185695
25 -5.68612618 -8.15839754
26 3.05632120 -5.68612618
27 0.66915207 3.05632120
28 -11.21471819 0.66915207
29 -7.91282986 -11.21471819
30 -2.96272813 -7.91282986
31 1.52804153 -2.96272813
32 7.03552322 1.52804153
33 -0.24307509 7.03552322
34 3.90368596 -0.24307509
35 17.47854008 3.90368596
36 10.28565605 17.47854008
37 6.32475389 10.28565605
38 4.07120430 6.32475389
39 3.74033127 4.07120430
40 0.09224340 3.74033127
41 0.99887240 0.09224340
42 -8.17367301 0.99887240
43 -11.05539121 -8.17367301
44 -7.52571441 -11.05539121
45 -14.35950345 -7.52571441
46 -0.94859640 -14.35950345
47 0.94854300 -0.94859640
48 -3.70806559 0.94854300
49 3.86574792 -3.70806559
50 -4.81979249 3.86574792
51 0.29407713 -4.81979249
52 3.62199157 0.29407713
53 1.48707601 3.62199157
54 7.08440341 1.48707601
55 0.30900280 7.08440341
56 -5.35484175 0.30900280
57 -2.88046000 -5.35484175
58 -3.26795766 -2.88046000
59 -3.99192900 -3.26795766
60 1.75324914 -3.99192900
61 1.38327202 1.75324914
62 10.63068316 1.38327202
63 7.18881296 10.63068316
64 10.31156950 7.18881296
65 0.64484909 10.31156950
66 -4.10051315 0.64484909
67 7.51816215 -4.10051315
68 11.69963980 7.51816215
69 1.69177884 11.69963980
70 2.11320483 1.69177884
71 1.46544462 2.11320483
72 4.28162866 1.46544462
73 1.54679999 4.28162866
74 0.03963398 1.54679999
75 -10.38601008 0.03963398
76 0.67867105 -10.38601008
77 0.01536750 0.67867105
78 -0.85727066 0.01536750
79 2.33732497 -0.85727066
80 4.87930515 2.33732497
81 9.48839788 4.87930515
82 7.19876385 9.48839788
83 -8.95970074 7.19876385
84 -6.20282581 -8.95970074
85 -16.46922547 -6.20282581
86 -6.25988144 -16.46922547
87 -11.35102812 -6.25988144
88 -5.05109553 -11.35102812
89 -3.55093234 -5.05109553
90 -7.11077439 -3.55093234
91 -7.78688581 -7.11077439
92 -6.06374508 -7.78688581
93 -16.66848711 -6.06374508
94 -17.90902832 -16.66848711
95 -6.37908105 -17.90902832
96 -7.64797992 -6.37908105
97 0.01065691 -7.64797992
98 -0.11363495 0.01065691
99 -6.17631678 -0.11363495
100 2.05744121 -6.17631678
101 -2.23131366 2.05744121
102 8.21012156 -2.23131366
103 1.51246832 8.21012156
104 5.57653738 1.51246832
105 -3.01351398 5.57653738
106 1.73197587 -3.01351398
107 -4.32058737 1.73197587
108 -2.83497430 -4.32058737
109 7.36480979 -2.83497430
110 9.24080321 7.36480979
111 13.46818283 9.24080321
112 2.05725001 13.46818283
113 4.18616447 2.05725001
114 10.44174101 4.18616447
115 3.08875434 10.44174101
116 3.73276235 3.08875434
117 8.16721920 3.73276235
118 10.44562187 8.16721920
119 5.24576882 10.44562187
120 10.13011837 5.24576882
121 1.65871869 10.13011837
122 10.29904597 1.65871869
123 15.00328536 10.29904597
124 -5.95953195 15.00328536
125 -11.79847743 -5.95953195
126 -9.22800632 -11.79847743
127 6.36335532 -9.22800632
128 6.96704882 6.36335532
129 4.14140820 6.96704882
130 4.50391450 4.14140820
131 3.44962834 4.50391450
132 -2.08349753 3.44962834
133 4.29589213 -2.08349753
134 -8.01018494 4.29589213
135 -14.78108420 -8.01018494
136 -12.31790495 -14.78108420
137 0.43725032 -12.31790495
138 -8.21084442 0.43725032
139 -9.71725191 -8.21084442
140 -4.82516307 -9.71725191
141 -3.60698955 -4.82516307
142 -2.33614755 -3.60698955
143 2.12496518 -2.33614755
144 13.52520079 2.12496518
145 NA 13.52520079
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.49697417 -10.74335629
[2,] -11.02136142 -6.49697417
[3,] -1.14581437 -11.02136142
[4,] 5.57844991 -1.14581437
[5,] 10.91406802 5.57844991
[6,] 5.57239158 10.91406802
[7,] -5.91836610 5.57239158
[8,] -9.15388518 -5.91836610
[9,] 3.85236811 -9.15388518
[10,] 0.37886588 3.85236811
[11,] -0.68553393 0.37886588
[12,] 5.54670264 -0.68553393
[13,] 8.49916263 5.54670264
[14,] 7.84148262 8.49916263
[15,] -4.68603455 7.84148262
[16,] -2.76110718 -4.68603455
[17,] 5.71746584 -2.76110718
[18,] 3.07698564 5.71746584
[19,] 4.57868311 3.07698564
[20,] 5.68267211 4.57868311
[21,] -2.29151448 5.68267211
[22,] 3.25035893 -2.29151448
[23,] -2.10185695 3.25035893
[24,] -8.15839754 -2.10185695
[25,] -5.68612618 -8.15839754
[26,] 3.05632120 -5.68612618
[27,] 0.66915207 3.05632120
[28,] -11.21471819 0.66915207
[29,] -7.91282986 -11.21471819
[30,] -2.96272813 -7.91282986
[31,] 1.52804153 -2.96272813
[32,] 7.03552322 1.52804153
[33,] -0.24307509 7.03552322
[34,] 3.90368596 -0.24307509
[35,] 17.47854008 3.90368596
[36,] 10.28565605 17.47854008
[37,] 6.32475389 10.28565605
[38,] 4.07120430 6.32475389
[39,] 3.74033127 4.07120430
[40,] 0.09224340 3.74033127
[41,] 0.99887240 0.09224340
[42,] -8.17367301 0.99887240
[43,] -11.05539121 -8.17367301
[44,] -7.52571441 -11.05539121
[45,] -14.35950345 -7.52571441
[46,] -0.94859640 -14.35950345
[47,] 0.94854300 -0.94859640
[48,] -3.70806559 0.94854300
[49,] 3.86574792 -3.70806559
[50,] -4.81979249 3.86574792
[51,] 0.29407713 -4.81979249
[52,] 3.62199157 0.29407713
[53,] 1.48707601 3.62199157
[54,] 7.08440341 1.48707601
[55,] 0.30900280 7.08440341
[56,] -5.35484175 0.30900280
[57,] -2.88046000 -5.35484175
[58,] -3.26795766 -2.88046000
[59,] -3.99192900 -3.26795766
[60,] 1.75324914 -3.99192900
[61,] 1.38327202 1.75324914
[62,] 10.63068316 1.38327202
[63,] 7.18881296 10.63068316
[64,] 10.31156950 7.18881296
[65,] 0.64484909 10.31156950
[66,] -4.10051315 0.64484909
[67,] 7.51816215 -4.10051315
[68,] 11.69963980 7.51816215
[69,] 1.69177884 11.69963980
[70,] 2.11320483 1.69177884
[71,] 1.46544462 2.11320483
[72,] 4.28162866 1.46544462
[73,] 1.54679999 4.28162866
[74,] 0.03963398 1.54679999
[75,] -10.38601008 0.03963398
[76,] 0.67867105 -10.38601008
[77,] 0.01536750 0.67867105
[78,] -0.85727066 0.01536750
[79,] 2.33732497 -0.85727066
[80,] 4.87930515 2.33732497
[81,] 9.48839788 4.87930515
[82,] 7.19876385 9.48839788
[83,] -8.95970074 7.19876385
[84,] -6.20282581 -8.95970074
[85,] -16.46922547 -6.20282581
[86,] -6.25988144 -16.46922547
[87,] -11.35102812 -6.25988144
[88,] -5.05109553 -11.35102812
[89,] -3.55093234 -5.05109553
[90,] -7.11077439 -3.55093234
[91,] -7.78688581 -7.11077439
[92,] -6.06374508 -7.78688581
[93,] -16.66848711 -6.06374508
[94,] -17.90902832 -16.66848711
[95,] -6.37908105 -17.90902832
[96,] -7.64797992 -6.37908105
[97,] 0.01065691 -7.64797992
[98,] -0.11363495 0.01065691
[99,] -6.17631678 -0.11363495
[100,] 2.05744121 -6.17631678
[101,] -2.23131366 2.05744121
[102,] 8.21012156 -2.23131366
[103,] 1.51246832 8.21012156
[104,] 5.57653738 1.51246832
[105,] -3.01351398 5.57653738
[106,] 1.73197587 -3.01351398
[107,] -4.32058737 1.73197587
[108,] -2.83497430 -4.32058737
[109,] 7.36480979 -2.83497430
[110,] 9.24080321 7.36480979
[111,] 13.46818283 9.24080321
[112,] 2.05725001 13.46818283
[113,] 4.18616447 2.05725001
[114,] 10.44174101 4.18616447
[115,] 3.08875434 10.44174101
[116,] 3.73276235 3.08875434
[117,] 8.16721920 3.73276235
[118,] 10.44562187 8.16721920
[119,] 5.24576882 10.44562187
[120,] 10.13011837 5.24576882
[121,] 1.65871869 10.13011837
[122,] 10.29904597 1.65871869
[123,] 15.00328536 10.29904597
[124,] -5.95953195 15.00328536
[125,] -11.79847743 -5.95953195
[126,] -9.22800632 -11.79847743
[127,] 6.36335532 -9.22800632
[128,] 6.96704882 6.36335532
[129,] 4.14140820 6.96704882
[130,] 4.50391450 4.14140820
[131,] 3.44962834 4.50391450
[132,] -2.08349753 3.44962834
[133,] 4.29589213 -2.08349753
[134,] -8.01018494 4.29589213
[135,] -14.78108420 -8.01018494
[136,] -12.31790495 -14.78108420
[137,] 0.43725032 -12.31790495
[138,] -8.21084442 0.43725032
[139,] -9.71725191 -8.21084442
[140,] -4.82516307 -9.71725191
[141,] -3.60698955 -4.82516307
[142,] -2.33614755 -3.60698955
[143,] 2.12496518 -2.33614755
[144,] 13.52520079 2.12496518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.49697417 -10.74335629
2 -11.02136142 -6.49697417
3 -1.14581437 -11.02136142
4 5.57844991 -1.14581437
5 10.91406802 5.57844991
6 5.57239158 10.91406802
7 -5.91836610 5.57239158
8 -9.15388518 -5.91836610
9 3.85236811 -9.15388518
10 0.37886588 3.85236811
11 -0.68553393 0.37886588
12 5.54670264 -0.68553393
13 8.49916263 5.54670264
14 7.84148262 8.49916263
15 -4.68603455 7.84148262
16 -2.76110718 -4.68603455
17 5.71746584 -2.76110718
18 3.07698564 5.71746584
19 4.57868311 3.07698564
20 5.68267211 4.57868311
21 -2.29151448 5.68267211
22 3.25035893 -2.29151448
23 -2.10185695 3.25035893
24 -8.15839754 -2.10185695
25 -5.68612618 -8.15839754
26 3.05632120 -5.68612618
27 0.66915207 3.05632120
28 -11.21471819 0.66915207
29 -7.91282986 -11.21471819
30 -2.96272813 -7.91282986
31 1.52804153 -2.96272813
32 7.03552322 1.52804153
33 -0.24307509 7.03552322
34 3.90368596 -0.24307509
35 17.47854008 3.90368596
36 10.28565605 17.47854008
37 6.32475389 10.28565605
38 4.07120430 6.32475389
39 3.74033127 4.07120430
40 0.09224340 3.74033127
41 0.99887240 0.09224340
42 -8.17367301 0.99887240
43 -11.05539121 -8.17367301
44 -7.52571441 -11.05539121
45 -14.35950345 -7.52571441
46 -0.94859640 -14.35950345
47 0.94854300 -0.94859640
48 -3.70806559 0.94854300
49 3.86574792 -3.70806559
50 -4.81979249 3.86574792
51 0.29407713 -4.81979249
52 3.62199157 0.29407713
53 1.48707601 3.62199157
54 7.08440341 1.48707601
55 0.30900280 7.08440341
56 -5.35484175 0.30900280
57 -2.88046000 -5.35484175
58 -3.26795766 -2.88046000
59 -3.99192900 -3.26795766
60 1.75324914 -3.99192900
61 1.38327202 1.75324914
62 10.63068316 1.38327202
63 7.18881296 10.63068316
64 10.31156950 7.18881296
65 0.64484909 10.31156950
66 -4.10051315 0.64484909
67 7.51816215 -4.10051315
68 11.69963980 7.51816215
69 1.69177884 11.69963980
70 2.11320483 1.69177884
71 1.46544462 2.11320483
72 4.28162866 1.46544462
73 1.54679999 4.28162866
74 0.03963398 1.54679999
75 -10.38601008 0.03963398
76 0.67867105 -10.38601008
77 0.01536750 0.67867105
78 -0.85727066 0.01536750
79 2.33732497 -0.85727066
80 4.87930515 2.33732497
81 9.48839788 4.87930515
82 7.19876385 9.48839788
83 -8.95970074 7.19876385
84 -6.20282581 -8.95970074
85 -16.46922547 -6.20282581
86 -6.25988144 -16.46922547
87 -11.35102812 -6.25988144
88 -5.05109553 -11.35102812
89 -3.55093234 -5.05109553
90 -7.11077439 -3.55093234
91 -7.78688581 -7.11077439
92 -6.06374508 -7.78688581
93 -16.66848711 -6.06374508
94 -17.90902832 -16.66848711
95 -6.37908105 -17.90902832
96 -7.64797992 -6.37908105
97 0.01065691 -7.64797992
98 -0.11363495 0.01065691
99 -6.17631678 -0.11363495
100 2.05744121 -6.17631678
101 -2.23131366 2.05744121
102 8.21012156 -2.23131366
103 1.51246832 8.21012156
104 5.57653738 1.51246832
105 -3.01351398 5.57653738
106 1.73197587 -3.01351398
107 -4.32058737 1.73197587
108 -2.83497430 -4.32058737
109 7.36480979 -2.83497430
110 9.24080321 7.36480979
111 13.46818283 9.24080321
112 2.05725001 13.46818283
113 4.18616447 2.05725001
114 10.44174101 4.18616447
115 3.08875434 10.44174101
116 3.73276235 3.08875434
117 8.16721920 3.73276235
118 10.44562187 8.16721920
119 5.24576882 10.44562187
120 10.13011837 5.24576882
121 1.65871869 10.13011837
122 10.29904597 1.65871869
123 15.00328536 10.29904597
124 -5.95953195 15.00328536
125 -11.79847743 -5.95953195
126 -9.22800632 -11.79847743
127 6.36335532 -9.22800632
128 6.96704882 6.36335532
129 4.14140820 6.96704882
130 4.50391450 4.14140820
131 3.44962834 4.50391450
132 -2.08349753 3.44962834
133 4.29589213 -2.08349753
134 -8.01018494 4.29589213
135 -14.78108420 -8.01018494
136 -12.31790495 -14.78108420
137 0.43725032 -12.31790495
138 -8.21084442 0.43725032
139 -9.71725191 -8.21084442
140 -4.82516307 -9.71725191
141 -3.60698955 -4.82516307
142 -2.33614755 -3.60698955
143 2.12496518 -2.33614755
144 13.52520079 2.12496518
> 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/wessaorg/rcomp/tmp/75ijv1353348882.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/wessaorg/rcomp/tmp/8ujeb1353348882.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/wessaorg/rcomp/tmp/9cuje1353348882.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/wessaorg/rcomp/tmp/10d6xx1353348882.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11i37a1353348882.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122ynl1353348882.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13st7v1353348882.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14vtkl1353348882.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/wessaorg/rcomp/tmp/1534bu1353348882.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/wessaorg/rcomp/tmp/16569q1353348882.tab")
+ }
>
> try(system("convert tmp/12rgf1353348882.ps tmp/12rgf1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/29dru1353348882.ps tmp/29dru1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mgoo1353348882.ps tmp/3mgoo1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/450wl1353348882.ps tmp/450wl1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/53b4q1353348882.ps tmp/53b4q1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/67c431353348882.ps tmp/67c431353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/75ijv1353348882.ps tmp/75ijv1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ujeb1353348882.ps tmp/8ujeb1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cuje1353348882.ps tmp/9cuje1353348882.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d6xx1353348882.ps tmp/10d6xx1353348882.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.292 0.808 8.108