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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0
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+ ,77
+ ,1
+ ,77
+ ,107.30
+ ,106.40
+ ,106.40
+ ,108.70
+ ,108.70
+ ,98.10
+ ,98.10
+ ,104.40
+ ,104.40
+ ,58.10
+ ,58.10
+ ,100.50
+ ,100.5
+ ,78
+ ,1
+ ,78
+ ,112.30
+ ,102.80
+ ,102.80
+ ,120.90
+ ,120.90
+ ,109.70
+ ,109.70
+ ,115.10
+ ,115.10
+ ,79.70
+ ,79.70
+ ,111.60
+ ,111.6
+ ,79
+ ,1
+ ,79
+ ,88.50
+ ,96.30
+ ,96.30
+ ,114.80
+ ,114.80
+ ,92.00
+ ,92.00
+ ,91.40
+ ,91.40
+ ,108.90
+ ,108.90
+ ,88.50
+ ,88.5
+ ,80
+ ,1
+ ,80
+ ,92.90
+ ,105.70
+ ,105.70
+ ,108.70
+ ,108.70
+ ,74.30
+ ,74.30
+ ,76.20
+ ,76.20
+ ,138.50
+ ,138.50
+ ,83.70
+ ,83.7
+ ,81
+ ,1
+ ,81
+ ,108.80
+ ,108.40
+ ,108.40
+ ,97.40
+ ,97.40
+ ,96.90
+ ,96.90
+ ,117.40
+ ,117.40
+ ,117.90
+ ,117.90
+ ,113.90
+ ,113.9
+ ,0
+ ,0
+ ,82
+ ,112.30
+ ,115.80
+ ,0.00
+ ,98.60
+ ,0.00
+ ,100.30
+ ,0.00
+ ,122.00
+ ,0.00
+ ,96.70
+ ,0.00
+ ,115.20
+ ,0
+ ,0
+ ,0
+ ,83
+ ,107.30
+ ,113.80
+ ,0.00
+ ,91.70
+ ,0.00
+ ,97.10
+ ,0.00
+ ,120.20
+ ,0.00
+ ,78.60
+ ,0.00
+ ,111.00
+ ,0
+ ,0
+ ,0
+ ,84
+ ,101.80
+ ,106.40
+ ,0.00
+ ,91.20
+ ,0.00
+ ,86.00
+ ,0.00
+ ,93.60
+ ,0.00
+ ,64.10
+ ,0.00
+ ,96.90
+ ,0
+ ,0
+ ,0
+ ,85
+ ,105.00
+ ,107.90
+ ,0.00
+ ,83.50
+ ,0.00
+ ,97.30
+ ,0.00
+ ,106.60
+ ,0.00
+ ,112.00
+ ,0.00
+ ,102.10
+ ,0
+ ,0
+ ,0
+ ,86
+ ,103.40
+ ,98.20
+ ,0.00
+ ,82.40
+ ,0.00
+ ,86.40
+ ,0.00
+ ,108.40
+ ,0.00
+ ,139.40
+ ,0.00
+ ,101.50
+ ,0
+ ,0
+ ,0
+ ,87
+ ,116.70
+ ,111.10
+ ,0.00
+ ,103.10
+ ,0.00
+ ,97.70
+ ,0.00
+ ,121.40
+ ,0.00
+ ,116.20
+ ,0.00
+ ,115.00
+ ,0
+ ,88
+ ,1
+ ,88
+ ,103.60
+ ,99.80
+ ,99.80
+ ,110.30
+ ,110.30
+ ,90.60
+ ,90.60
+ ,104.80
+ ,104.80
+ ,63.40
+ ,63.40
+ ,105.00
+ ,105
+ ,89
+ ,1
+ ,89
+ ,108.80
+ ,103.50
+ ,103.50
+ ,115.80
+ ,115.80
+ ,99.20
+ ,99.20
+ ,104.20
+ ,104.20
+ ,61.10
+ ,61.10
+ ,105.40
+ ,105.4
+ ,90
+ ,1
+ ,90
+ ,117.00
+ ,105.40
+ ,105.40
+ ,120.10
+ ,120.10
+ ,107.40
+ ,107.40
+ ,115.00
+ ,115.00
+ ,65.50
+ ,65.50
+ ,119.70
+ ,119.7
+ ,91
+ ,1
+ ,91
+ ,100.90
+ ,102.60
+ ,102.60
+ ,105.10
+ ,105.10
+ ,107.10
+ ,107.10
+ ,99.00
+ ,99.00
+ ,90.90
+ ,90.90
+ ,91.80
+ ,91.8
+ ,92
+ ,1
+ ,92
+ ,100.80
+ ,107.40
+ ,107.40
+ ,108.60
+ ,108.60
+ ,78.90
+ ,78.90
+ ,82.80
+ ,82.80
+ ,115.30
+ ,115.30
+ ,89.10
+ ,89.1
+ ,93
+ ,1
+ ,93
+ ,109.70
+ ,108.20
+ ,108.20
+ ,95.70
+ ,95.70
+ ,92.80
+ ,92.80
+ ,112.50
+ ,112.50
+ ,85.20
+ ,85.20
+ ,106.20
+ ,106.2
+ ,0
+ ,0
+ ,94
+ ,121.00
+ ,121.70
+ ,0.00
+ ,103.20
+ ,0.00
+ ,106.20
+ ,0.00
+ ,127.90
+ ,0.00
+ ,87.00
+ ,0.00
+ ,119.90
+ ,0
+ ,0
+ ,0
+ ,95
+ ,114.10
+ ,118.00
+ ,0.00
+ ,96.90
+ ,0.00
+ ,97.20
+ ,0.00
+ ,114.40
+ ,0.00
+ ,62.60
+ ,0.00
+ ,111.60
+ ,0
+ ,0
+ ,0
+ ,96
+ ,105.50
+ ,109.60
+ ,0.00
+ ,95.70
+ ,0.00
+ ,80.00
+ ,0.00
+ ,83.70
+ ,0.00
+ ,62.70
+ ,0.00
+ ,95.10
+ ,0
+ ,0
+ ,0
+ ,97
+ ,112.50
+ ,116.70
+ ,0.00
+ ,92.70
+ ,0.00
+ ,109.30
+ ,0.00
+ ,108.50
+ ,0.00
+ ,91.60
+ ,0.00
+ ,101.30
+ ,0
+ ,0
+ ,0
+ ,98
+ ,113.80
+ ,110.60
+ ,0.00
+ ,81.30
+ ,0.00
+ ,111.30
+ ,0.00
+ ,109.70
+ ,0.00
+ ,104.30
+ ,0.00
+ ,118.30
+ ,0
+ ,0
+ ,0
+ ,99
+ ,115.30
+ ,109.60
+ ,0.00
+ ,94.50
+ ,0.00
+ ,119.50
+ ,0.00
+ ,104.70
+ ,0.00
+ ,88.10
+ ,0.00
+ ,126.20
+ ,0
+ ,100
+ ,1
+ ,100
+ ,120.40
+ ,117.40
+ ,117.40
+ ,105.60
+ ,105.60
+ ,119.80
+ ,119.80
+ ,112.20
+ ,112.20
+ ,62.30
+ ,62.30
+ ,113.20
+ ,113.2
+ ,101
+ ,1
+ ,101
+ ,111.10
+ ,109.20
+ ,109.20
+ ,112.90
+ ,112.90
+ ,112.50
+ ,112.50
+ ,96.90
+ ,96.90
+ ,50.30
+ ,50.30
+ ,103.60
+ ,103.6
+ ,102
+ ,1
+ ,102
+ ,120.10
+ ,110.80
+ ,110.80
+ ,102.60
+ ,102.60
+ ,125.60
+ ,125.60
+ ,103.80
+ ,103.80
+ ,64.10
+ ,64.10
+ ,116.20
+ ,116.2
+ ,103
+ ,1
+ ,103
+ ,106.10
+ ,112.80
+ ,112.80
+ ,116.20
+ ,116.20
+ ,105.10
+ ,105.10
+ ,95.10
+ ,95.10
+ ,75.70
+ ,75.70
+ ,98.30
+ ,98.3
+ ,104
+ ,1
+ ,104
+ ,95.90
+ ,106.50
+ ,106.50
+ ,104.90
+ ,104.90
+ ,91.90
+ ,91.90
+ ,66.70
+ ,66.70
+ ,85.50
+ ,85.50
+ ,84.20
+ ,84.2
+ ,105
+ ,1
+ ,105
+ ,119.40
+ ,119.60
+ ,119.60
+ ,100.40
+ ,100.40
+ ,128.20
+ ,128.20
+ ,103.40
+ ,103.40
+ ,71.90
+ ,71.90
+ ,118.30
+ ,118.3
+ ,0
+ ,0
+ ,106
+ ,117.40
+ ,127.20
+ ,0.00
+ ,97.10
+ ,0.00
+ ,122.60
+ ,0.00
+ ,105.40
+ ,0.00
+ ,66.90
+ ,0.00
+ ,117.40
+ ,0
+ ,0
+ ,0
+ ,107
+ ,98.60
+ ,113.90
+ ,0.00
+ ,90.20
+ ,0.00
+ ,109.60
+ ,0.00
+ ,89.20
+ ,0.00
+ ,50.50
+ ,0.00
+ ,94.50
+ ,0
+ ,0
+ ,0
+ ,108
+ ,99.70
+ ,120.00
+ ,0.00
+ ,100.50
+ ,0.00
+ ,120.40
+ ,0.00
+ ,72.50
+ ,0.00
+ ,57.90
+ ,0.00
+ ,93.30
+ ,0
+ ,0
+ ,0
+ ,109
+ ,87.40
+ ,107.60
+ ,0.00
+ ,81.10
+ ,0.00
+ ,103.80
+ ,0.00
+ ,78.00
+ ,0.00
+ ,84.10
+ ,0.00
+ ,90.20
+ ,0
+ ,0
+ ,0
+ ,110
+ ,90.80
+ ,105.20
+ ,0.00
+ ,87.20
+ ,0.00
+ ,96.60
+ ,0.00
+ ,77.30
+ ,0.00
+ ,87.00
+ ,0.00
+ ,88.50
+ ,0
+ ,0
+ ,0
+ ,111
+ ,101.30
+ ,115.30
+ ,0.00
+ ,102.00
+ ,0.00
+ ,110.70
+ ,0.00
+ ,85.10
+ ,0.00
+ ,71.90
+ ,0.00
+ ,101.00
+ ,0
+ ,112
+ ,1
+ ,112
+ ,93.20
+ ,113.90
+ ,113.90
+ ,107.00
+ ,107.00
+ ,111.70
+ ,111.70
+ ,80.90
+ ,80.90
+ ,45.00
+ ,45.00
+ ,87.00
+ ,87
+ ,113
+ ,1
+ ,113
+ ,95.10
+ ,106.10
+ ,106.10
+ ,107.60
+ ,107.60
+ ,111.90
+ ,111.90
+ ,72.50
+ ,72.50
+ ,39.50
+ ,39.50
+ ,81.20
+ ,81.2
+ ,114
+ ,1
+ ,114
+ ,101.90
+ ,114.30
+ ,114.30
+ ,123.50
+ ,123.50
+ ,131.50
+ ,131.50
+ ,82.10
+ ,82.10
+ ,53.80
+ ,53.80
+ ,98.10
+ ,98.1
+ ,115
+ ,1
+ ,115
+ ,87.00
+ ,112.00
+ ,112.00
+ ,116.60
+ ,116.60
+ ,122.80
+ ,122.80
+ ,78.30
+ ,78.30
+ ,59.50
+ ,59.50
+ ,75.50
+ ,75.5
+ ,116
+ ,1
+ ,116
+ ,86.20
+ ,109.00
+ ,109.00
+ ,103.20
+ ,103.20
+ ,98.30
+ ,98.30
+ ,57.80
+ ,57.80
+ ,68.40
+ ,68.40
+ ,70.70
+ ,70.7
+ ,117
+ ,1
+ ,117
+ ,105.00
+ ,119.10
+ ,119.10
+ ,103.90
+ ,103.90
+ ,133.70
+ ,133.70
+ ,89.30
+ ,89.30
+ ,56.90
+ ,56.90
+ ,103.70
+ ,103.7
+ ,0
+ ,0
+ ,118
+ ,104.10
+ ,124.40
+ ,0.00
+ ,95.40
+ ,0.00
+ ,120.00
+ ,0.00
+ ,91.40
+ ,0.00
+ ,61.90
+ ,0.00
+ ,100.40
+ ,0
+ ,0
+ ,0
+ ,119
+ ,99.20
+ ,116.60
+ ,0.00
+ ,93.60
+ ,0.00
+ ,119.60
+ ,0.00
+ ,84.20
+ ,0.00
+ ,40.40
+ ,0.00
+ ,91.30
+ ,0
+ ,0
+ ,0
+ ,120
+ ,95.20
+ ,118.50
+ ,0.00
+ ,102.10
+ ,0.00
+ ,108.70
+ ,0.00
+ ,72.50
+ ,0.00
+ ,49.40
+ ,0.00
+ ,97.20
+ ,0
+ ,0
+ ,0
+ ,121
+ ,92.70
+ ,108.90
+ ,0.00
+ ,69.00
+ ,0.00
+ ,112.50
+ ,0.00
+ ,74.60
+ ,0.00
+ ,65.20
+ ,0.00
+ ,85.40
+ ,0
+ ,0
+ ,0
+ ,122
+ ,99.30
+ ,107.50
+ ,0.00
+ ,88.90
+ ,0.00
+ ,102.70
+ ,0.00
+ ,80.30
+ ,0.00
+ ,82.10
+ ,0.00
+ ,86.50
+ ,0
+ ,0
+ ,0
+ ,123
+ ,113.50
+ ,125.90
+ ,0.00
+ ,106.20
+ ,0.00
+ ,123.40
+ ,0.00
+ ,92.60
+ ,0.00
+ ,69.00
+ ,0.00
+ ,105.30
+ ,0
+ ,124
+ ,1
+ ,124
+ ,104.70
+ ,117.70
+ ,117.70
+ ,103.00
+ ,103.00
+ ,116.50
+ ,116.50
+ ,86.30
+ ,86.30
+ ,45.90
+ ,45.90
+ ,97.70
+ ,97.7
+ ,125
+ ,1
+ ,125
+ ,100.50
+ ,109.20
+ ,109.20
+ ,103.50
+ ,103.50
+ ,102.30
+ ,102.30
+ ,80.30
+ ,80.30
+ ,39.10
+ ,39.10
+ ,84.30
+ ,84.3
+ ,126
+ ,1
+ ,126
+ ,116.20
+ ,118.80
+ ,118.80
+ ,124.50
+ ,124.50
+ ,148.40
+ ,148.40
+ ,93.60
+ ,93.60
+ ,56.90
+ ,56.90
+ ,109.80
+ ,109.8
+ ,127
+ ,1
+ ,127
+ ,94.10
+ ,108.10
+ ,108.10
+ ,117.90
+ ,117.90
+ ,126.60
+ ,126.60
+ ,79.50
+ ,79.50
+ ,51.60
+ ,51.60
+ ,79.10
+ ,79.1
+ ,128
+ ,1
+ ,128
+ ,94.80
+ ,112.10
+ ,112.10
+ ,104.20
+ ,104.20
+ ,106.60
+ ,106.60
+ ,61.80
+ ,61.80
+ ,62.90
+ ,62.90
+ ,83.40
+ ,83.4
+ ,129
+ ,1
+ ,129
+ ,115.10
+ ,117.80
+ ,117.80
+ ,99.90
+ ,99.90
+ ,144.40
+ ,144.40
+ ,94.80
+ ,94.80
+ ,58.30
+ ,58.30
+ ,101.90
+ ,101.9
+ ,0
+ ,0
+ ,130
+ ,110.00
+ ,121.80
+ ,0.00
+ ,89.40
+ ,0.00
+ ,132.40
+ ,0.00
+ ,91.60
+ ,0.00
+ ,56.90
+ ,0.00
+ ,113.00
+ ,0
+ ,0
+ ,0
+ ,131
+ ,108.40
+ ,121.00
+ ,0.00
+ ,93.50
+ ,0.00
+ ,136.20
+ ,0.00
+ ,89.20
+ ,0.00
+ ,41.30
+ ,0.00
+ ,98.60
+ ,0
+ ,0
+ ,0
+ ,132
+ ,103.90
+ ,121.70
+ ,0.00
+ ,89.60
+ ,0.00
+ ,121.60
+ ,0.00
+ ,74.10
+ ,0.00
+ ,46.90
+ ,0.00
+ ,94.70
+ ,0
+ ,0
+ ,0
+ ,133
+ ,102.90
+ ,114.20
+ ,0.00
+ ,85.00
+ ,0.00
+ ,135.10
+ ,0.00
+ ,78.60
+ ,0.00
+ ,61.90
+ ,0.00
+ ,94.50
+ ,0
+ ,0
+ ,0
+ ,134
+ ,107.70
+ ,109.80
+ ,0.00
+ ,90.00
+ ,0.00
+ ,124.70
+ ,0.00
+ ,78.20
+ ,0.00
+ ,74.80
+ ,0.00
+ ,90.70
+ ,0
+ ,0
+ ,0
+ ,135
+ ,126.70
+ ,124.10
+ ,0.00
+ ,113.70
+ ,0.00
+ ,148.80
+ ,0.00
+ ,95.10
+ ,0.00
+ ,67.00
+ ,0.00
+ ,113.00
+ ,0
+ ,136
+ ,1
+ ,136
+ ,108.80
+ ,112.90
+ ,112.90
+ ,112.10
+ ,112.10
+ ,145.60
+ ,145.60
+ ,78.70
+ ,78.70
+ ,53.30
+ ,53.30
+ ,89.90
+ ,89.9
+ ,137
+ ,1
+ ,137
+ ,117.10
+ ,118.70
+ ,118.70
+ ,129.80
+ ,129.80
+ ,140.30
+ ,140.30
+ ,85.90
+ ,85.90
+ ,51.40
+ ,51.40
+ ,98.70
+ ,98.7
+ ,138
+ ,1
+ ,138
+ ,112.20
+ ,113.30
+ ,113.30
+ ,119.10
+ ,119.10
+ ,138.50
+ ,138.50
+ ,81.20
+ ,81.20
+ ,50.30
+ ,50.30
+ ,102.20
+ ,102.2
+ ,139
+ ,1
+ ,139
+ ,94.70
+ ,106.80
+ ,106.80
+ ,103.50
+ ,103.50
+ ,127.30
+ ,127.30
+ ,73.10
+ ,73.10
+ ,52.70
+ ,52.70
+ ,74.30
+ ,74.3
+ ,140
+ ,1
+ ,140
+ ,102.70
+ ,119.30
+ ,119.30
+ ,105.50
+ ,105.50
+ ,117.90
+ ,117.90
+ ,58.70
+ ,58.70
+ ,70.30
+ ,70.30
+ ,84.50
+ ,84.5
+ ,141
+ ,1
+ ,141
+ ,119.10
+ ,126.40
+ ,126.40
+ ,111.70
+ ,111.70
+ ,145.30
+ ,145.30
+ ,85.70
+ ,85.70
+ ,59.70
+ ,59.70
+ ,110.10
+ ,110.1
+ ,0
+ ,0
+ ,142
+ ,110.60
+ ,126.60
+ ,0.00
+ ,98.60
+ ,0.00
+ ,120.70
+ ,0.00
+ ,81.80
+ ,0.00
+ ,52.00
+ ,0.00
+ ,100.40
+ ,0
+ ,0
+ ,0
+ ,143
+ ,109.10
+ ,127.20
+ ,0.00
+ ,102.80
+ ,0.00
+ ,134.70
+ ,0.00
+ ,79.60
+ ,0.00
+ ,36.10
+ ,0.00
+ ,92.80
+ ,0
+ ,0
+ ,0
+ ,144
+ ,105.30
+ ,123.80
+ ,0.00
+ ,101.10
+ ,0.00
+ ,124.40
+ ,0.00
+ ,70.70
+ ,0.00
+ ,39.70
+ ,0.00
+ ,92.20
+ ,0
+ ,0
+ ,0
+ ,145
+ ,103.40
+ ,116.80
+ ,0.00
+ ,94.20
+ ,0.00
+ ,128.30
+ ,0.00
+ ,74.50
+ ,0.00
+ ,67.60
+ ,0.00
+ ,94.00
+ ,0
+ ,0
+ ,0
+ ,146
+ ,103.70
+ ,113.80
+ ,0.00
+ ,92.60
+ ,0.00
+ ,128.40
+ ,0.00
+ ,84.80
+ ,0.00
+ ,72.80
+ ,0.00
+ ,100.70
+ ,0
+ ,0
+ ,0
+ ,147
+ ,117.00
+ ,130.40
+ ,0.00
+ ,112.00
+ ,0.00
+ ,134.10
+ ,0.00
+ ,80.70
+ ,0.00
+ ,53.80
+ ,0.00
+ ,111.90
+ ,0
+ ,148
+ ,1
+ ,148
+ ,101.20
+ ,112.80
+ ,112.80
+ ,108.60
+ ,108.60
+ ,133.30
+ ,133.30
+ ,69.90
+ ,69.90
+ ,39.60
+ ,39.60
+ ,95.90
+ ,95.9
+ ,149
+ ,1
+ ,149
+ ,105.40
+ ,119.40
+ ,119.40
+ ,125.80
+ ,125.80
+ ,130.60
+ ,130.60
+ ,74.10
+ ,74.10
+ ,39.40
+ ,39.40
+ ,88.80
+ ,88.8
+ ,150
+ ,1
+ ,150
+ ,110.30
+ ,117.50
+ ,117.50
+ ,138.70
+ ,138.70
+ ,165.70
+ ,165.70
+ ,76.10
+ ,76.10
+ ,41.20
+ ,41.20
+ ,102.00
+ ,102
+ ,151
+ ,1
+ ,151
+ ,97.70
+ ,117.50
+ ,117.50
+ ,115.20
+ ,115.20
+ ,146.80
+ ,146.80
+ ,71.30
+ ,71.30
+ ,49.60
+ ,49.60
+ ,81.60
+ ,81.6)
+ ,dim=c(16
+ ,151)
+ ,dimnames=list(c('s_t'
+ ,'s'
+ ,'t'
+ ,'Totaal'
+ ,'voeding'
+ ,'voeding_s'
+ ,'dranken'
+ ,'dranken_s'
+ ,'tabak'
+ ,'tabak_s'
+ ,'textiel'
+ ,'textiel_s'
+ ,'kleding'
+ ,'kleding_s'
+ ,'apparatuur'
+ ,'app_s
')
+ ,1:151))
> y <- array(NA,dim=c(16,151),dimnames=list(c('s_t','s','t','Totaal','voeding','voeding_s','dranken','dranken_s','tabak','tabak_s','textiel','textiel_s','kleding','kleding_s','apparatuur','app_s
'),1:151))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Totaal s_t s t voeding voeding_s dranken dranken_s tabak tabak_s textiel
1 75.5 0 0 1 78.4 0.0 67.3 0.0 75.3 0.0 106.1
2 83.2 0 0 2 79.3 0.0 75.2 0.0 83.6 0.0 112.7
3 94.5 0 0 3 84.3 0.0 91.1 0.0 91.2 0.0 123.2
4 83.3 4 1 4 81.2 81.2 83.7 83.7 85.2 85.2 101.7
5 92.7 5 1 5 88.4 88.4 105.0 105.0 100.0 100.0 118.7
6 89.8 6 1 6 83.1 83.1 106.2 106.2 89.8 89.8 107.1
7 74.8 7 1 7 76.6 76.6 88.5 88.5 88.9 88.9 93.6
8 81.5 8 1 8 82.6 82.6 100.1 100.1 85.6 85.6 77.5
9 92.8 9 1 9 84.4 84.4 90.3 90.3 83.2 83.2 117.2
10 92.8 0 0 10 94.6 0.0 85.3 0.0 97.1 0.0 124.5
11 91.7 0 0 11 91.8 0.0 81.9 0.0 85.8 0.0 120.8
12 83.5 0 0 12 89.3 0.0 77.2 0.0 80.9 0.0 97.0
13 92.8 0 0 13 87.7 0.0 78.6 0.0 81.3 0.0 115.1
14 91.3 0 0 14 83.1 0.0 75.1 0.0 83.2 0.0 112.9
15 99.5 0 0 15 93.6 0.0 90.3 0.0 90.7 0.0 122.7
16 87.6 16 1 16 85.1 85.1 88.5 88.5 88.4 88.4 106.9
17 95.3 17 1 17 90.8 90.8 112.5 112.5 94.1 94.1 115.0
18 98.5 18 1 18 90.5 90.5 101.1 101.1 92.0 92.0 114.9
19 80.1 19 1 19 86.1 86.1 114.0 114.0 92.0 92.0 103.1
20 84.2 20 1 20 93.3 93.3 107.7 107.7 89.3 89.3 80.8
21 92.4 21 1 21 94.9 94.9 77.8 77.8 87.0 87.0 118.2
22 98.0 0 0 22 102.6 0.0 101.4 0.0 97.7 0.0 129.6
23 92.2 0 0 23 98.3 0.0 87.2 0.0 82.5 0.0 118.7
24 80.0 0 0 24 93.4 0.0 75.9 0.0 96.5 0.0 88.4
25 88.7 0 0 25 92.8 0.0 78.8 0.0 86.2 0.0 113.1
26 87.4 0 0 26 86.5 0.0 82.3 0.0 84.9 0.0 109.8
27 96.1 0 0 27 93.8 0.0 89.1 0.0 100.0 0.0 116.1
28 94.1 28 1 28 90.4 90.4 100.1 100.1 92.7 92.7 113.6
29 91.9 29 1 29 91.0 91.0 101.8 101.8 96.7 96.7 107.9
30 93.6 30 1 30 89.1 89.1 98.5 98.5 105.8 105.8 107.4
31 83.5 31 1 31 89.6 89.6 106.6 106.6 88.5 88.5 102.7
32 80.8 32 1 32 89.3 89.3 101.8 101.8 78.7 78.7 78.3
33 96.3 33 1 33 95.3 95.3 92.4 92.4 99.9 99.9 121.0
34 101.5 0 0 34 104.1 0.0 94.4 0.0 107.8 0.0 132.2
35 91.6 0 0 35 94.7 0.0 81.0 0.0 102.4 0.0 113.2
36 84.0 0 0 36 97.6 0.0 94.6 0.0 106.0 0.0 89.2
37 91.8 0 0 37 96.8 0.0 83.8 0.0 87.3 0.0 113.2
38 90.4 0 0 38 92.8 0.0 79.4 0.0 93.3 0.0 107.6
39 98.0 0 0 39 94.7 0.0 95.6 0.0 98.2 0.0 107.3
40 95.5 40 1 40 95.8 95.8 106.0 106.0 102.0 102.0 110.9
41 90.5 41 1 41 88.9 88.9 106.2 106.2 93.9 93.9 96.4
42 97.1 42 1 42 91.2 91.2 115.0 115.0 106.6 106.6 101.2
43 87.9 43 1 43 91.6 91.6 122.4 122.4 92.9 92.9 94.0
44 79.8 44 1 44 87.3 87.3 113.7 113.7 78.0 78.0 70.5
45 102.0 45 1 45 97.8 97.8 98.0 98.0 104.2 104.2 116.4
46 104.3 0 0 46 105.1 0.0 105.8 0.0 115.9 0.0 121.9
47 92.1 0 0 47 93.8 0.0 88.3 0.0 99.9 0.0 109.5
48 95.9 0 0 48 99.0 0.0 95.7 0.0 103.9 0.0 91.1
49 89.1 0 0 49 91.4 0.0 85.8 0.0 93.5 0.0 104.0
50 92.2 0 0 50 89.0 0.0 83.9 0.0 101.7 0.0 101.2
51 107.5 0 0 51 101.4 0.0 114.1 0.0 124.6 0.0 118.4
52 99.7 52 1 52 95.4 95.4 102.0 102.0 124.2 124.2 106.9
53 92.2 53 1 53 90.5 90.5 108.1 108.1 103.3 103.3 95.6
54 108.9 54 1 54 98.7 98.7 125.4 125.4 120.5 120.5 114.2
55 89.8 55 1 55 91.2 91.2 108.1 108.1 98.0 98.0 92.4
56 89.4 56 1 56 91.7 91.7 110.4 110.4 100.4 100.4 75.3
57 107.6 57 1 57 102.9 102.9 102.4 102.4 126.8 126.8 120.4
58 105.6 0 0 58 105.5 0.0 89.6 0.0 120.2 0.0 115.9
59 100.9 0 0 59 102.6 0.0 95.0 0.0 114.0 0.0 109.8
60 102.9 0 0 60 107.2 0.0 93.7 0.0 109.1 0.0 94.9
61 96.2 0 0 61 96.9 0.0 77.7 0.0 94.2 0.0 97.5
62 94.7 0 0 62 88.9 0.0 80.1 0.0 86.0 0.0 101.3
63 107.3 0 0 63 99.6 0.0 103.6 0.0 112.9 0.0 108.7
64 103.0 64 1 64 96.7 96.7 103.1 103.1 99.7 99.7 105.1
65 96.1 65 1 65 93.8 93.8 112.4 112.4 104.5 104.5 94.9
66 109.8 66 1 66 101.9 101.9 119.2 119.2 111.6 111.6 108.9
67 85.4 67 1 67 87.6 87.6 105.3 105.3 99.2 99.2 87.5
68 89.9 68 1 68 100.0 100.0 107.2 107.2 90.9 90.9 73.0
69 109.3 69 1 69 105.8 105.8 108.7 108.7 111.4 111.4 115.2
70 101.2 0 0 70 105.5 0.0 93.7 0.0 98.2 0.0 107.5
71 104.7 0 0 71 111.3 0.0 96.1 0.0 101.7 0.0 109.8
72 102.4 0 0 72 112.1 0.0 92.9 0.0 89.7 0.0 90.7
73 97.7 0 0 73 102.0 0.0 81.1 0.0 89.5 0.0 97.6
74 98.9 0 0 74 93.2 0.0 83.2 0.0 85.1 0.0 98.7
75 115.0 0 0 75 108.4 0.0 99.7 0.0 95.9 0.0 113.9
76 97.5 76 1 76 97.9 97.9 96.8 96.8 88.9 88.9 96.6
77 107.3 77 1 77 106.4 106.4 108.7 108.7 98.1 98.1 104.4
78 112.3 78 1 78 102.8 102.8 120.9 120.9 109.7 109.7 115.1
79 88.5 79 1 79 96.3 96.3 114.8 114.8 92.0 92.0 91.4
80 92.9 80 1 80 105.7 105.7 108.7 108.7 74.3 74.3 76.2
81 108.8 81 1 81 108.4 108.4 97.4 97.4 96.9 96.9 117.4
82 112.3 0 0 82 115.8 0.0 98.6 0.0 100.3 0.0 122.0
83 107.3 0 0 83 113.8 0.0 91.7 0.0 97.1 0.0 120.2
84 101.8 0 0 84 106.4 0.0 91.2 0.0 86.0 0.0 93.6
85 105.0 0 0 85 107.9 0.0 83.5 0.0 97.3 0.0 106.6
86 103.4 0 0 86 98.2 0.0 82.4 0.0 86.4 0.0 108.4
87 116.7 0 0 87 111.1 0.0 103.1 0.0 97.7 0.0 121.4
88 103.6 88 1 88 99.8 99.8 110.3 110.3 90.6 90.6 104.8
89 108.8 89 1 89 103.5 103.5 115.8 115.8 99.2 99.2 104.2
90 117.0 90 1 90 105.4 105.4 120.1 120.1 107.4 107.4 115.0
91 100.9 91 1 91 102.6 102.6 105.1 105.1 107.1 107.1 99.0
92 100.8 92 1 92 107.4 107.4 108.6 108.6 78.9 78.9 82.8
93 109.7 93 1 93 108.2 108.2 95.7 95.7 92.8 92.8 112.5
94 121.0 0 0 94 121.7 0.0 103.2 0.0 106.2 0.0 127.9
95 114.1 0 0 95 118.0 0.0 96.9 0.0 97.2 0.0 114.4
96 105.5 0 0 96 109.6 0.0 95.7 0.0 80.0 0.0 83.7
97 112.5 0 0 97 116.7 0.0 92.7 0.0 109.3 0.0 108.5
98 113.8 0 0 98 110.6 0.0 81.3 0.0 111.3 0.0 109.7
99 115.3 0 0 99 109.6 0.0 94.5 0.0 119.5 0.0 104.7
100 120.4 100 1 100 117.4 117.4 105.6 105.6 119.8 119.8 112.2
101 111.1 101 1 101 109.2 109.2 112.9 112.9 112.5 112.5 96.9
102 120.1 102 1 102 110.8 110.8 102.6 102.6 125.6 125.6 103.8
103 106.1 103 1 103 112.8 112.8 116.2 116.2 105.1 105.1 95.1
104 95.9 104 1 104 106.5 106.5 104.9 104.9 91.9 91.9 66.7
105 119.4 105 1 105 119.6 119.6 100.4 100.4 128.2 128.2 103.4
106 117.4 0 0 106 127.2 0.0 97.1 0.0 122.6 0.0 105.4
107 98.6 0 0 107 113.9 0.0 90.2 0.0 109.6 0.0 89.2
108 99.7 0 0 108 120.0 0.0 100.5 0.0 120.4 0.0 72.5
109 87.4 0 0 109 107.6 0.0 81.1 0.0 103.8 0.0 78.0
110 90.8 0 0 110 105.2 0.0 87.2 0.0 96.6 0.0 77.3
111 101.3 0 0 111 115.3 0.0 102.0 0.0 110.7 0.0 85.1
112 93.2 112 1 112 113.9 113.9 107.0 107.0 111.7 111.7 80.9
113 95.1 113 1 113 106.1 106.1 107.6 107.6 111.9 111.9 72.5
114 101.9 114 1 114 114.3 114.3 123.5 123.5 131.5 131.5 82.1
115 87.0 115 1 115 112.0 112.0 116.6 116.6 122.8 122.8 78.3
116 86.2 116 1 116 109.0 109.0 103.2 103.2 98.3 98.3 57.8
117 105.0 117 1 117 119.1 119.1 103.9 103.9 133.7 133.7 89.3
118 104.1 0 0 118 124.4 0.0 95.4 0.0 120.0 0.0 91.4
119 99.2 0 0 119 116.6 0.0 93.6 0.0 119.6 0.0 84.2
120 95.2 0 0 120 118.5 0.0 102.1 0.0 108.7 0.0 72.5
121 92.7 0 0 121 108.9 0.0 69.0 0.0 112.5 0.0 74.6
122 99.3 0 0 122 107.5 0.0 88.9 0.0 102.7 0.0 80.3
123 113.5 0 0 123 125.9 0.0 106.2 0.0 123.4 0.0 92.6
124 104.7 124 1 124 117.7 117.7 103.0 103.0 116.5 116.5 86.3
125 100.5 125 1 125 109.2 109.2 103.5 103.5 102.3 102.3 80.3
126 116.2 126 1 126 118.8 118.8 124.5 124.5 148.4 148.4 93.6
127 94.1 127 1 127 108.1 108.1 117.9 117.9 126.6 126.6 79.5
128 94.8 128 1 128 112.1 112.1 104.2 104.2 106.6 106.6 61.8
129 115.1 129 1 129 117.8 117.8 99.9 99.9 144.4 144.4 94.8
130 110.0 0 0 130 121.8 0.0 89.4 0.0 132.4 0.0 91.6
131 108.4 0 0 131 121.0 0.0 93.5 0.0 136.2 0.0 89.2
132 103.9 0 0 132 121.7 0.0 89.6 0.0 121.6 0.0 74.1
133 102.9 0 0 133 114.2 0.0 85.0 0.0 135.1 0.0 78.6
134 107.7 0 0 134 109.8 0.0 90.0 0.0 124.7 0.0 78.2
135 126.7 0 0 135 124.1 0.0 113.7 0.0 148.8 0.0 95.1
136 108.8 136 1 136 112.9 112.9 112.1 112.1 145.6 145.6 78.7
137 117.1 137 1 137 118.7 118.7 129.8 129.8 140.3 140.3 85.9
138 112.2 138 1 138 113.3 113.3 119.1 119.1 138.5 138.5 81.2
139 94.7 139 1 139 106.8 106.8 103.5 103.5 127.3 127.3 73.1
140 102.7 140 1 140 119.3 119.3 105.5 105.5 117.9 117.9 58.7
141 119.1 141 1 141 126.4 126.4 111.7 111.7 145.3 145.3 85.7
142 110.6 0 0 142 126.6 0.0 98.6 0.0 120.7 0.0 81.8
143 109.1 0 0 143 127.2 0.0 102.8 0.0 134.7 0.0 79.6
144 105.3 0 0 144 123.8 0.0 101.1 0.0 124.4 0.0 70.7
145 103.4 0 0 145 116.8 0.0 94.2 0.0 128.3 0.0 74.5
146 103.7 0 0 146 113.8 0.0 92.6 0.0 128.4 0.0 84.8
147 117.0 0 0 147 130.4 0.0 112.0 0.0 134.1 0.0 80.7
148 101.2 148 1 148 112.8 112.8 108.6 108.6 133.3 133.3 69.9
149 105.4 149 1 149 119.4 119.4 125.8 125.8 130.6 130.6 74.1
150 110.3 150 1 150 117.5 117.5 138.7 138.7 165.7 165.7 76.1
151 97.7 151 1 151 117.5 117.5 115.2 115.2 146.8 146.8 71.3
textiel_s kleding kleding_s apparatuur app_s\r
1 0.0 125.7 0.0 101.6 0.0
2 0.0 153.8 0.0 113.4 0.0
3 0.0 134.9 0.0 122.2 0.0
4 101.7 95.3 95.3 102.2 102.2
5 118.7 96.6 96.6 113.2 113.2
6 107.1 100.5 100.5 115.3 115.3
7 93.6 106.2 106.2 87.4 87.4
8 77.5 153.4 153.4 98.7 98.7
9 117.2 132.1 132.1 117.3 117.3
10 0.0 110.9 0.0 121.2 0.0
11 0.0 94.3 0.0 118.7 0.0
12 0.0 91.7 0.0 112.1 0.0
13 0.0 138.6 0.0 102.9 0.0
14 0.0 154.3 0.0 108.8 0.0
15 0.0 149.8 0.0 118.6 0.0
16 106.9 99.2 99.2 99.2 99.2
17 115.0 97.7 97.7 102.2 102.2
18 114.9 107.7 107.7 108.8 108.8
19 103.1 120.1 120.1 94.0 94.0
20 80.8 164.5 164.5 96.2 96.2
21 118.2 136.1 136.1 118.4 118.4
22 0.0 117.5 0.0 120.0 0.0
23 0.0 98.2 0.0 117.5 0.0
24 0.0 91.9 0.0 102.6 0.0
25 0.0 141.8 0.0 92.8 0.0
26 0.0 154.2 0.0 100.3 0.0
27 0.0 138.6 0.0 106.3 0.0
28 113.6 97.9 97.9 103.9 103.9
29 107.9 90.3 90.3 102.4 102.4
30 107.4 90.9 90.9 114.5 114.5
31 102.7 127.0 127.0 89.0 89.0
32 78.3 156.8 156.8 94.3 94.3
33 121.0 127.2 127.2 115.7 115.7
34 0.0 111.3 0.0 120.2 0.0
35 0.0 93.0 0.0 109.5 0.0
36 0.0 89.5 0.0 99.4 0.0
37 0.0 141.8 0.0 86.4 0.0
38 0.0 152.0 0.0 95.1 0.0
39 0.0 120.2 0.0 101.5 0.0
40 110.9 88.8 88.8 92.9 92.9
41 96.4 82.8 82.8 90.8 90.8
42 101.2 82.8 82.8 100.4 100.4
43 94.0 121.7 121.7 82.2 82.2
44 70.5 147.1 147.1 75.3 75.3
45 116.4 132.5 132.5 110.3 110.3
46 0.0 107.5 0.0 113.5 0.0
47 0.0 77.9 0.0 94.9 0.0
48 0.0 85.5 0.0 95.7 0.0
49 0.0 126.5 0.0 85.3 0.0
50 0.0 135.4 0.0 92.5 0.0
51 0.0 122.5 0.0 107.7 0.0
52 106.9 79.2 79.2 97.9 97.9
53 95.6 66.1 66.1 93.9 93.9
54 114.2 77.9 77.9 111.5 111.5
55 92.4 109.6 109.6 88.6 88.6
56 75.3 142.9 142.9 82.5 82.5
57 120.4 120.5 120.5 108.6 108.6
58 0.0 96.3 0.0 113.8 0.0
59 0.0 82.6 0.0 103.4 0.0
60 0.0 78.4 0.0 99.0 0.0
61 0.0 104.5 0.0 89.9 0.0
62 0.0 137.9 0.0 97.9 0.0
63 0.0 125.8 0.0 107.8 0.0
64 105.1 78.0 78.0 103.7 103.7
65 94.9 67.7 67.7 98.2 98.2
66 108.9 78.4 78.4 111.7 111.7
67 87.5 101.7 101.7 82.6 82.6
68 73.0 154.1 154.1 86.1 86.1
69 115.2 107.3 107.3 111.2 111.2
70 0.0 86.5 0.0 105.3 0.0
71 0.0 82.1 0.0 106.3 0.0
72 0.0 76.1 0.0 99.4 0.0
73 0.0 115.5 0.0 91.9 0.0
74 0.0 129.6 0.0 96.2 0.0
75 0.0 121.6 0.0 105.4 0.0
76 96.6 64.0 64.0 95.0 95.0
77 104.4 58.1 58.1 100.5 100.5
78 115.1 79.7 79.7 111.6 111.6
79 91.4 108.9 108.9 88.5 88.5
80 76.2 138.5 138.5 83.7 83.7
81 117.4 117.9 117.9 113.9 113.9
82 0.0 96.7 0.0 115.2 0.0
83 0.0 78.6 0.0 111.0 0.0
84 0.0 64.1 0.0 96.9 0.0
85 0.0 112.0 0.0 102.1 0.0
86 0.0 139.4 0.0 101.5 0.0
87 0.0 116.2 0.0 115.0 0.0
88 104.8 63.4 63.4 105.0 105.0
89 104.2 61.1 61.1 105.4 105.4
90 115.0 65.5 65.5 119.7 119.7
91 99.0 90.9 90.9 91.8 91.8
92 82.8 115.3 115.3 89.1 89.1
93 112.5 85.2 85.2 106.2 106.2
94 0.0 87.0 0.0 119.9 0.0
95 0.0 62.6 0.0 111.6 0.0
96 0.0 62.7 0.0 95.1 0.0
97 0.0 91.6 0.0 101.3 0.0
98 0.0 104.3 0.0 118.3 0.0
99 0.0 88.1 0.0 126.2 0.0
100 112.2 62.3 62.3 113.2 113.2
101 96.9 50.3 50.3 103.6 103.6
102 103.8 64.1 64.1 116.2 116.2
103 95.1 75.7 75.7 98.3 98.3
104 66.7 85.5 85.5 84.2 84.2
105 103.4 71.9 71.9 118.3 118.3
106 0.0 66.9 0.0 117.4 0.0
107 0.0 50.5 0.0 94.5 0.0
108 0.0 57.9 0.0 93.3 0.0
109 0.0 84.1 0.0 90.2 0.0
110 0.0 87.0 0.0 88.5 0.0
111 0.0 71.9 0.0 101.0 0.0
112 80.9 45.0 45.0 87.0 87.0
113 72.5 39.5 39.5 81.2 81.2
114 82.1 53.8 53.8 98.1 98.1
115 78.3 59.5 59.5 75.5 75.5
116 57.8 68.4 68.4 70.7 70.7
117 89.3 56.9 56.9 103.7 103.7
118 0.0 61.9 0.0 100.4 0.0
119 0.0 40.4 0.0 91.3 0.0
120 0.0 49.4 0.0 97.2 0.0
121 0.0 65.2 0.0 85.4 0.0
122 0.0 82.1 0.0 86.5 0.0
123 0.0 69.0 0.0 105.3 0.0
124 86.3 45.9 45.9 97.7 97.7
125 80.3 39.1 39.1 84.3 84.3
126 93.6 56.9 56.9 109.8 109.8
127 79.5 51.6 51.6 79.1 79.1
128 61.8 62.9 62.9 83.4 83.4
129 94.8 58.3 58.3 101.9 101.9
130 0.0 56.9 0.0 113.0 0.0
131 0.0 41.3 0.0 98.6 0.0
132 0.0 46.9 0.0 94.7 0.0
133 0.0 61.9 0.0 94.5 0.0
134 0.0 74.8 0.0 90.7 0.0
135 0.0 67.0 0.0 113.0 0.0
136 78.7 53.3 53.3 89.9 89.9
137 85.9 51.4 51.4 98.7 98.7
138 81.2 50.3 50.3 102.2 102.2
139 73.1 52.7 52.7 74.3 74.3
140 58.7 70.3 70.3 84.5 84.5
141 85.7 59.7 59.7 110.1 110.1
142 0.0 52.0 0.0 100.4 0.0
143 0.0 36.1 0.0 92.8 0.0
144 0.0 39.7 0.0 92.2 0.0
145 0.0 67.6 0.0 94.0 0.0
146 0.0 72.8 0.0 100.7 0.0
147 0.0 53.8 0.0 111.9 0.0
148 69.9 39.6 39.6 95.9 95.9
149 74.1 39.4 39.4 88.8 88.8
150 76.1 41.2 41.2 102.0 102.0
151 71.3 49.6 49.6 81.6 81.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) s_t s t voeding voeding_s
-10.60594 -0.04174 -3.17744 0.20308 0.19700 0.06122
dranken dranken_s tabak tabak_s textiel textiel_s
0.24955 -0.11794 -0.01638 -0.01106 0.30294 -0.07081
kleding kleding_s apparatuur `app_s\\r`
0.06063 -0.05482 0.17342 0.23855
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2916 -2.5015 0.1169 2.3033 7.9374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -10.60594 9.21529 -1.151 0.251805
s_t -0.04174 0.05335 -0.782 0.435437
s -3.17744 13.13635 -0.242 0.809239
t 0.20308 0.03177 6.392 2.46e-09 ***
voeding 0.19700 0.11967 1.646 0.102048
voeding_s 0.06122 0.17166 0.357 0.721905
dranken 0.24955 0.06469 3.858 0.000176 ***
dranken_s -0.11794 0.08016 -1.471 0.143536
tabak -0.01638 0.04277 -0.383 0.702251
tabak_s -0.01106 0.05627 -0.197 0.844405
textiel 0.30294 0.05987 5.060 1.35e-06 ***
textiel_s -0.07081 0.07893 -0.897 0.371258
kleding 0.06063 0.02893 2.095 0.038013 *
kleding_s -0.05482 0.03621 -1.514 0.132328
apparatuur 0.17342 0.06427 2.698 0.007857 **
`app_s\\r` 0.23855 0.09170 2.602 0.010315 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.561 on 135 degrees of freedom
Multiple R-squared: 0.8974, Adjusted R-squared: 0.886
F-statistic: 78.71 on 15 and 135 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,] 0.490485625 0.9809712496 0.5095143752
[2,] 0.520679386 0.9586412288 0.4793206144
[3,] 0.707558400 0.5848831990 0.2924415995
[4,] 0.890293567 0.2194128659 0.1097064329
[5,] 0.886540575 0.2269188497 0.1134594248
[6,] 0.839308987 0.3213820263 0.1606910132
[7,] 0.776307579 0.4473848430 0.2236924215
[8,] 0.749459317 0.5010813665 0.2505406832
[9,] 0.705016011 0.5899679786 0.2949839893
[10,] 0.640512958 0.7189740845 0.3594870422
[11,] 0.563297241 0.8734055186 0.4367027593
[12,] 0.510631963 0.9787360733 0.4893680366
[13,] 0.454183951 0.9083679015 0.5458160492
[14,] 0.431618354 0.8632367072 0.5683816464
[15,] 0.438090909 0.8761818183 0.5619090909
[16,] 0.409385226 0.8187704512 0.5906147744
[17,] 0.363064022 0.7261280449 0.6369359776
[18,] 0.339307977 0.6786159548 0.6606920226
[19,] 0.281038337 0.5620766744 0.7189616628
[20,] 0.242527090 0.4850541800 0.7574729100
[21,] 0.281141960 0.5622839207 0.7188580397
[22,] 0.261312270 0.5226245396 0.7386877302
[23,] 0.220494593 0.4409891856 0.7795054072
[24,] 0.205666950 0.4113339001 0.7943330500
[25,] 0.174418559 0.3488371181 0.8255814410
[26,] 0.142407044 0.2848140880 0.8575929560
[27,] 0.179025196 0.3580503912 0.8209748044
[28,] 0.165493654 0.3309873079 0.8345063461
[29,] 0.133198170 0.2663963392 0.8668018304
[30,] 0.185415738 0.3708314761 0.8145842619
[31,] 0.182330124 0.3646602487 0.8176698756
[32,] 0.147613900 0.2952278006 0.8523860997
[33,] 0.131050471 0.2621009414 0.8689495293
[34,] 0.112679187 0.2253583747 0.8873208127
[35,] 0.098685990 0.1973719801 0.9013140100
[36,] 0.080551339 0.1611026785 0.9194486607
[37,] 0.061314228 0.1226284551 0.9386857724
[38,] 0.071701349 0.1434026989 0.9282986506
[39,] 0.056138918 0.1122778351 0.9438610825
[40,] 0.056578555 0.1131571102 0.9434214449
[41,] 0.048067938 0.0961358755 0.9519320622
[42,] 0.082002271 0.1640045413 0.9179977294
[43,] 0.075372765 0.1507455299 0.9246272351
[44,] 0.080825918 0.1616518366 0.9191740817
[45,] 0.064942488 0.1298849755 0.9350575122
[46,] 0.052398259 0.1047965186 0.9476017407
[47,] 0.047126959 0.0942539188 0.9528730406
[48,] 0.036749369 0.0734987374 0.9632506313
[49,] 0.030249704 0.0604994074 0.9697502963
[50,] 0.022719149 0.0454382990 0.9772808505
[51,] 0.016453649 0.0329072979 0.9835463511
[52,] 0.017107838 0.0342156761 0.9828921620
[53,] 0.014362373 0.0287247459 0.9856376270
[54,] 0.012982546 0.0259650920 0.9870174540
[55,] 0.009578306 0.0191566128 0.9904216936
[56,] 0.006840248 0.0136804969 0.9931597516
[57,] 0.009798724 0.0195974480 0.9902012760
[58,] 0.006885933 0.0137718665 0.9931140668
[59,] 0.006178882 0.0123577646 0.9938211177
[60,] 0.004621038 0.0092420755 0.9953789623
[61,] 0.010673659 0.0213473179 0.9893263410
[62,] 0.007518737 0.0150374746 0.9924812627
[63,] 0.012402421 0.0248048412 0.9875975794
[64,] 0.013049791 0.0260995826 0.9869502087
[65,] 0.016813624 0.0336272470 0.9831863765
[66,] 0.014394046 0.0287880927 0.9856059536
[67,] 0.010678057 0.0213561134 0.9893219433
[68,] 0.008571545 0.0171430894 0.9914284553
[69,] 0.006161988 0.0123239766 0.9938380117
[70,] 0.005703240 0.0114064799 0.9942967601
[71,] 0.003946954 0.0078939085 0.9960530457
[72,] 0.003271197 0.0065423939 0.9967288031
[73,] 0.002293250 0.0045864992 0.9977067504
[74,] 0.001700509 0.0034010173 0.9982994914
[75,] 0.002919256 0.0058385123 0.9970807439
[76,] 0.002591129 0.0051822581 0.9974088709
[77,] 0.001822670 0.0036453409 0.9981773295
[78,] 0.024713455 0.0494269109 0.9752865445
[79,] 0.019512208 0.0390244156 0.9804877922
[80,] 0.017765903 0.0355318057 0.9822340972
[81,] 0.018990896 0.0379817921 0.9810091039
[82,] 0.015489728 0.0309794566 0.9845102717
[83,] 0.014506024 0.0290120483 0.9854939758
[84,] 0.012280682 0.0245613634 0.9877193183
[85,] 0.018699318 0.0373986356 0.9813006822
[86,] 0.014181268 0.0283625360 0.9858187320
[87,] 0.011919577 0.0238391531 0.9880804235
[88,] 0.012677459 0.0253549186 0.9873225407
[89,] 0.018832420 0.0376648401 0.9811675799
[90,] 0.021602329 0.0432046572 0.9783976714
[91,] 0.112540247 0.2250804937 0.8874597532
[92,] 0.145923474 0.2918469484 0.8540765258
[93,] 0.151373749 0.3027474970 0.8486262515
[94,] 0.251708575 0.5034171495 0.7482914253
[95,] 0.508517505 0.9829649909 0.4914824955
[96,] 0.528721592 0.9425568155 0.4712784078
[97,] 0.636815635 0.7263687300 0.3631843650
[98,] 0.577077632 0.8458447357 0.4229223679
[99,] 0.558935583 0.8821288333 0.4410644166
[100,] 0.649390427 0.7012191455 0.3506095728
[101,] 0.583139385 0.8337212295 0.4168606147
[102,] 0.747836364 0.5043272718 0.2521636359
[103,] 0.679978088 0.6400438247 0.3200219123
[104,] 0.611080605 0.7778387909 0.3889193955
[105,] 0.817731792 0.3645364157 0.1822682079
[106,] 0.784286785 0.4314264298 0.2157132149
[107,] 0.807277135 0.3854457296 0.1927228648
[108,] 0.812649451 0.3747010976 0.1873505488
[109,] 0.922281478 0.1554370431 0.0777185216
[110,] 0.998201408 0.0035971833 0.0017985917
[111,] 0.999647064 0.0007058723 0.0003529362
[112,] 0.998611701 0.0027765988 0.0013882994
[113,] 0.993607641 0.0127847175 0.0063923587
[114,] 0.997519434 0.0049611326 0.0024805663
> postscript(file="/var/wessaorg/rcomp/tmp/1wiio1352125226.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2t6do1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3m26t1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/46ul61352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52v801352125226.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 = 151
Frequency = 1
1 2 3 4 5 6
-2.484534640 -2.749675260 -0.042128660 0.529129777 -2.973809527 -3.299593386
7 8 9 10 11 12
0.116896063 2.296850674 -2.559833115 -2.414107096 0.058548590 1.752667697
13 14 15 16 17 18
4.090879858 2.890011532 0.752716159 1.348287348 1.305522689 3.110456001
19 20 21 22 23 24
-7.248160429 -0.401444920 -6.566413381 -6.971945612 -3.927733552 -0.171349773
25 26 27 28 29 30
-1.256988552 -3.466388420 0.139663021 -0.348984323 -0.993987486 -3.152902140
31 32 33 34 35 36
-3.697579169 -2.811350223 -5.759132382 -4.738505947 -1.013461262 -3.488481455
37 38 39 40 41 42
-1.532057039 -1.581495970 2.387662092 2.410571725 3.048133122 3.014190718
43 44 45 46 47 48
1.142935834 2.878037649 0.001888224 -2.772027941 -0.067515702 5.698445232
49 50 51 52 53 54
-2.097329494 0.940923985 -0.630501335 4.837937776 1.412397219 2.392149182
55 56 57 58 59 60
1.037137324 6.398886323 2.231641330 2.569973111 1.271111372 7.937400263
61 62 63 64 65 66
6.020183980 0.596415158 2.236801654 3.084239716 0.372899396 2.246373489
67 68 69 70 71 72
-0.312794943 1.965660264 0.207433392 -1.037691095 -0.028361232 5.259353537
73 74 75 76 77 78
2.109055829 2.309681628 5.556519452 1.509476465 3.597461253 0.896448748
79 80 81 82 83 84
-6.221083123 1.241138492 -3.495451030 -2.319937270 -3.088583449 3.991475296
85 86 87 88 89 90
1.055585576 -0.843071375 -0.140754102 -2.566961893 1.016329458 -0.200187501
91 92 93 94 95 96
1.287924230 3.283514578 0.130772846 -0.284750426 1.774078988 6.798980336
97 98 99 100 101 102
3.085750798 4.180393211 3.641471722 4.082732113 3.153865600 6.421841672
103 104 105 106 107 108
-1.282062184 3.452535083 2.508764532 2.853985305 -2.146956417 -0.026601825
109 110 111 112 113 114
-8.234600985 -5.874032410 -4.644286572 -6.396486524 1.654080385 -4.653040020
115 116 117 118 119 120
-8.291627196 -0.702876862 -4.633005669 -4.457344121 -2.518457850 -7.420016359
121 122 123 124 125 126
0.542665643 -0.853262144 -0.651363254 -2.822468774 1.508189150 -0.626404450
127 128 129 130 131 132
-3.903219766 -0.871872146 4.143437259 -0.724154682 0.839531911 1.643758695
133 134 135 136 137 138
1.049362627 5.093038642 7.039446850 5.116605053 3.997006027 1.344341409
139 140 141 142 143 144
0.467414525 3.595345352 1.184395307 -0.543296781 -0.234890702 -0.730753352
145 146 147 148 149 150
-2.823841670 -6.332421034 -0.801882936 -4.620236548 -2.672378619 -4.090119256
151
-4.807807787
> postscript(file="/var/wessaorg/rcomp/tmp/6829t1352125226.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 = 151
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.484534640 NA
1 -2.749675260 -2.484534640
2 -0.042128660 -2.749675260
3 0.529129777 -0.042128660
4 -2.973809527 0.529129777
5 -3.299593386 -2.973809527
6 0.116896063 -3.299593386
7 2.296850674 0.116896063
8 -2.559833115 2.296850674
9 -2.414107096 -2.559833115
10 0.058548590 -2.414107096
11 1.752667697 0.058548590
12 4.090879858 1.752667697
13 2.890011532 4.090879858
14 0.752716159 2.890011532
15 1.348287348 0.752716159
16 1.305522689 1.348287348
17 3.110456001 1.305522689
18 -7.248160429 3.110456001
19 -0.401444920 -7.248160429
20 -6.566413381 -0.401444920
21 -6.971945612 -6.566413381
22 -3.927733552 -6.971945612
23 -0.171349773 -3.927733552
24 -1.256988552 -0.171349773
25 -3.466388420 -1.256988552
26 0.139663021 -3.466388420
27 -0.348984323 0.139663021
28 -0.993987486 -0.348984323
29 -3.152902140 -0.993987486
30 -3.697579169 -3.152902140
31 -2.811350223 -3.697579169
32 -5.759132382 -2.811350223
33 -4.738505947 -5.759132382
34 -1.013461262 -4.738505947
35 -3.488481455 -1.013461262
36 -1.532057039 -3.488481455
37 -1.581495970 -1.532057039
38 2.387662092 -1.581495970
39 2.410571725 2.387662092
40 3.048133122 2.410571725
41 3.014190718 3.048133122
42 1.142935834 3.014190718
43 2.878037649 1.142935834
44 0.001888224 2.878037649
45 -2.772027941 0.001888224
46 -0.067515702 -2.772027941
47 5.698445232 -0.067515702
48 -2.097329494 5.698445232
49 0.940923985 -2.097329494
50 -0.630501335 0.940923985
51 4.837937776 -0.630501335
52 1.412397219 4.837937776
53 2.392149182 1.412397219
54 1.037137324 2.392149182
55 6.398886323 1.037137324
56 2.231641330 6.398886323
57 2.569973111 2.231641330
58 1.271111372 2.569973111
59 7.937400263 1.271111372
60 6.020183980 7.937400263
61 0.596415158 6.020183980
62 2.236801654 0.596415158
63 3.084239716 2.236801654
64 0.372899396 3.084239716
65 2.246373489 0.372899396
66 -0.312794943 2.246373489
67 1.965660264 -0.312794943
68 0.207433392 1.965660264
69 -1.037691095 0.207433392
70 -0.028361232 -1.037691095
71 5.259353537 -0.028361232
72 2.109055829 5.259353537
73 2.309681628 2.109055829
74 5.556519452 2.309681628
75 1.509476465 5.556519452
76 3.597461253 1.509476465
77 0.896448748 3.597461253
78 -6.221083123 0.896448748
79 1.241138492 -6.221083123
80 -3.495451030 1.241138492
81 -2.319937270 -3.495451030
82 -3.088583449 -2.319937270
83 3.991475296 -3.088583449
84 1.055585576 3.991475296
85 -0.843071375 1.055585576
86 -0.140754102 -0.843071375
87 -2.566961893 -0.140754102
88 1.016329458 -2.566961893
89 -0.200187501 1.016329458
90 1.287924230 -0.200187501
91 3.283514578 1.287924230
92 0.130772846 3.283514578
93 -0.284750426 0.130772846
94 1.774078988 -0.284750426
95 6.798980336 1.774078988
96 3.085750798 6.798980336
97 4.180393211 3.085750798
98 3.641471722 4.180393211
99 4.082732113 3.641471722
100 3.153865600 4.082732113
101 6.421841672 3.153865600
102 -1.282062184 6.421841672
103 3.452535083 -1.282062184
104 2.508764532 3.452535083
105 2.853985305 2.508764532
106 -2.146956417 2.853985305
107 -0.026601825 -2.146956417
108 -8.234600985 -0.026601825
109 -5.874032410 -8.234600985
110 -4.644286572 -5.874032410
111 -6.396486524 -4.644286572
112 1.654080385 -6.396486524
113 -4.653040020 1.654080385
114 -8.291627196 -4.653040020
115 -0.702876862 -8.291627196
116 -4.633005669 -0.702876862
117 -4.457344121 -4.633005669
118 -2.518457850 -4.457344121
119 -7.420016359 -2.518457850
120 0.542665643 -7.420016359
121 -0.853262144 0.542665643
122 -0.651363254 -0.853262144
123 -2.822468774 -0.651363254
124 1.508189150 -2.822468774
125 -0.626404450 1.508189150
126 -3.903219766 -0.626404450
127 -0.871872146 -3.903219766
128 4.143437259 -0.871872146
129 -0.724154682 4.143437259
130 0.839531911 -0.724154682
131 1.643758695 0.839531911
132 1.049362627 1.643758695
133 5.093038642 1.049362627
134 7.039446850 5.093038642
135 5.116605053 7.039446850
136 3.997006027 5.116605053
137 1.344341409 3.997006027
138 0.467414525 1.344341409
139 3.595345352 0.467414525
140 1.184395307 3.595345352
141 -0.543296781 1.184395307
142 -0.234890702 -0.543296781
143 -0.730753352 -0.234890702
144 -2.823841670 -0.730753352
145 -6.332421034 -2.823841670
146 -0.801882936 -6.332421034
147 -4.620236548 -0.801882936
148 -2.672378619 -4.620236548
149 -4.090119256 -2.672378619
150 -4.807807787 -4.090119256
151 NA -4.807807787
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.749675260 -2.484534640
[2,] -0.042128660 -2.749675260
[3,] 0.529129777 -0.042128660
[4,] -2.973809527 0.529129777
[5,] -3.299593386 -2.973809527
[6,] 0.116896063 -3.299593386
[7,] 2.296850674 0.116896063
[8,] -2.559833115 2.296850674
[9,] -2.414107096 -2.559833115
[10,] 0.058548590 -2.414107096
[11,] 1.752667697 0.058548590
[12,] 4.090879858 1.752667697
[13,] 2.890011532 4.090879858
[14,] 0.752716159 2.890011532
[15,] 1.348287348 0.752716159
[16,] 1.305522689 1.348287348
[17,] 3.110456001 1.305522689
[18,] -7.248160429 3.110456001
[19,] -0.401444920 -7.248160429
[20,] -6.566413381 -0.401444920
[21,] -6.971945612 -6.566413381
[22,] -3.927733552 -6.971945612
[23,] -0.171349773 -3.927733552
[24,] -1.256988552 -0.171349773
[25,] -3.466388420 -1.256988552
[26,] 0.139663021 -3.466388420
[27,] -0.348984323 0.139663021
[28,] -0.993987486 -0.348984323
[29,] -3.152902140 -0.993987486
[30,] -3.697579169 -3.152902140
[31,] -2.811350223 -3.697579169
[32,] -5.759132382 -2.811350223
[33,] -4.738505947 -5.759132382
[34,] -1.013461262 -4.738505947
[35,] -3.488481455 -1.013461262
[36,] -1.532057039 -3.488481455
[37,] -1.581495970 -1.532057039
[38,] 2.387662092 -1.581495970
[39,] 2.410571725 2.387662092
[40,] 3.048133122 2.410571725
[41,] 3.014190718 3.048133122
[42,] 1.142935834 3.014190718
[43,] 2.878037649 1.142935834
[44,] 0.001888224 2.878037649
[45,] -2.772027941 0.001888224
[46,] -0.067515702 -2.772027941
[47,] 5.698445232 -0.067515702
[48,] -2.097329494 5.698445232
[49,] 0.940923985 -2.097329494
[50,] -0.630501335 0.940923985
[51,] 4.837937776 -0.630501335
[52,] 1.412397219 4.837937776
[53,] 2.392149182 1.412397219
[54,] 1.037137324 2.392149182
[55,] 6.398886323 1.037137324
[56,] 2.231641330 6.398886323
[57,] 2.569973111 2.231641330
[58,] 1.271111372 2.569973111
[59,] 7.937400263 1.271111372
[60,] 6.020183980 7.937400263
[61,] 0.596415158 6.020183980
[62,] 2.236801654 0.596415158
[63,] 3.084239716 2.236801654
[64,] 0.372899396 3.084239716
[65,] 2.246373489 0.372899396
[66,] -0.312794943 2.246373489
[67,] 1.965660264 -0.312794943
[68,] 0.207433392 1.965660264
[69,] -1.037691095 0.207433392
[70,] -0.028361232 -1.037691095
[71,] 5.259353537 -0.028361232
[72,] 2.109055829 5.259353537
[73,] 2.309681628 2.109055829
[74,] 5.556519452 2.309681628
[75,] 1.509476465 5.556519452
[76,] 3.597461253 1.509476465
[77,] 0.896448748 3.597461253
[78,] -6.221083123 0.896448748
[79,] 1.241138492 -6.221083123
[80,] -3.495451030 1.241138492
[81,] -2.319937270 -3.495451030
[82,] -3.088583449 -2.319937270
[83,] 3.991475296 -3.088583449
[84,] 1.055585576 3.991475296
[85,] -0.843071375 1.055585576
[86,] -0.140754102 -0.843071375
[87,] -2.566961893 -0.140754102
[88,] 1.016329458 -2.566961893
[89,] -0.200187501 1.016329458
[90,] 1.287924230 -0.200187501
[91,] 3.283514578 1.287924230
[92,] 0.130772846 3.283514578
[93,] -0.284750426 0.130772846
[94,] 1.774078988 -0.284750426
[95,] 6.798980336 1.774078988
[96,] 3.085750798 6.798980336
[97,] 4.180393211 3.085750798
[98,] 3.641471722 4.180393211
[99,] 4.082732113 3.641471722
[100,] 3.153865600 4.082732113
[101,] 6.421841672 3.153865600
[102,] -1.282062184 6.421841672
[103,] 3.452535083 -1.282062184
[104,] 2.508764532 3.452535083
[105,] 2.853985305 2.508764532
[106,] -2.146956417 2.853985305
[107,] -0.026601825 -2.146956417
[108,] -8.234600985 -0.026601825
[109,] -5.874032410 -8.234600985
[110,] -4.644286572 -5.874032410
[111,] -6.396486524 -4.644286572
[112,] 1.654080385 -6.396486524
[113,] -4.653040020 1.654080385
[114,] -8.291627196 -4.653040020
[115,] -0.702876862 -8.291627196
[116,] -4.633005669 -0.702876862
[117,] -4.457344121 -4.633005669
[118,] -2.518457850 -4.457344121
[119,] -7.420016359 -2.518457850
[120,] 0.542665643 -7.420016359
[121,] -0.853262144 0.542665643
[122,] -0.651363254 -0.853262144
[123,] -2.822468774 -0.651363254
[124,] 1.508189150 -2.822468774
[125,] -0.626404450 1.508189150
[126,] -3.903219766 -0.626404450
[127,] -0.871872146 -3.903219766
[128,] 4.143437259 -0.871872146
[129,] -0.724154682 4.143437259
[130,] 0.839531911 -0.724154682
[131,] 1.643758695 0.839531911
[132,] 1.049362627 1.643758695
[133,] 5.093038642 1.049362627
[134,] 7.039446850 5.093038642
[135,] 5.116605053 7.039446850
[136,] 3.997006027 5.116605053
[137,] 1.344341409 3.997006027
[138,] 0.467414525 1.344341409
[139,] 3.595345352 0.467414525
[140,] 1.184395307 3.595345352
[141,] -0.543296781 1.184395307
[142,] -0.234890702 -0.543296781
[143,] -0.730753352 -0.234890702
[144,] -2.823841670 -0.730753352
[145,] -6.332421034 -2.823841670
[146,] -0.801882936 -6.332421034
[147,] -4.620236548 -0.801882936
[148,] -2.672378619 -4.620236548
[149,] -4.090119256 -2.672378619
[150,] -4.807807787 -4.090119256
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.749675260 -2.484534640
2 -0.042128660 -2.749675260
3 0.529129777 -0.042128660
4 -2.973809527 0.529129777
5 -3.299593386 -2.973809527
6 0.116896063 -3.299593386
7 2.296850674 0.116896063
8 -2.559833115 2.296850674
9 -2.414107096 -2.559833115
10 0.058548590 -2.414107096
11 1.752667697 0.058548590
12 4.090879858 1.752667697
13 2.890011532 4.090879858
14 0.752716159 2.890011532
15 1.348287348 0.752716159
16 1.305522689 1.348287348
17 3.110456001 1.305522689
18 -7.248160429 3.110456001
19 -0.401444920 -7.248160429
20 -6.566413381 -0.401444920
21 -6.971945612 -6.566413381
22 -3.927733552 -6.971945612
23 -0.171349773 -3.927733552
24 -1.256988552 -0.171349773
25 -3.466388420 -1.256988552
26 0.139663021 -3.466388420
27 -0.348984323 0.139663021
28 -0.993987486 -0.348984323
29 -3.152902140 -0.993987486
30 -3.697579169 -3.152902140
31 -2.811350223 -3.697579169
32 -5.759132382 -2.811350223
33 -4.738505947 -5.759132382
34 -1.013461262 -4.738505947
35 -3.488481455 -1.013461262
36 -1.532057039 -3.488481455
37 -1.581495970 -1.532057039
38 2.387662092 -1.581495970
39 2.410571725 2.387662092
40 3.048133122 2.410571725
41 3.014190718 3.048133122
42 1.142935834 3.014190718
43 2.878037649 1.142935834
44 0.001888224 2.878037649
45 -2.772027941 0.001888224
46 -0.067515702 -2.772027941
47 5.698445232 -0.067515702
48 -2.097329494 5.698445232
49 0.940923985 -2.097329494
50 -0.630501335 0.940923985
51 4.837937776 -0.630501335
52 1.412397219 4.837937776
53 2.392149182 1.412397219
54 1.037137324 2.392149182
55 6.398886323 1.037137324
56 2.231641330 6.398886323
57 2.569973111 2.231641330
58 1.271111372 2.569973111
59 7.937400263 1.271111372
60 6.020183980 7.937400263
61 0.596415158 6.020183980
62 2.236801654 0.596415158
63 3.084239716 2.236801654
64 0.372899396 3.084239716
65 2.246373489 0.372899396
66 -0.312794943 2.246373489
67 1.965660264 -0.312794943
68 0.207433392 1.965660264
69 -1.037691095 0.207433392
70 -0.028361232 -1.037691095
71 5.259353537 -0.028361232
72 2.109055829 5.259353537
73 2.309681628 2.109055829
74 5.556519452 2.309681628
75 1.509476465 5.556519452
76 3.597461253 1.509476465
77 0.896448748 3.597461253
78 -6.221083123 0.896448748
79 1.241138492 -6.221083123
80 -3.495451030 1.241138492
81 -2.319937270 -3.495451030
82 -3.088583449 -2.319937270
83 3.991475296 -3.088583449
84 1.055585576 3.991475296
85 -0.843071375 1.055585576
86 -0.140754102 -0.843071375
87 -2.566961893 -0.140754102
88 1.016329458 -2.566961893
89 -0.200187501 1.016329458
90 1.287924230 -0.200187501
91 3.283514578 1.287924230
92 0.130772846 3.283514578
93 -0.284750426 0.130772846
94 1.774078988 -0.284750426
95 6.798980336 1.774078988
96 3.085750798 6.798980336
97 4.180393211 3.085750798
98 3.641471722 4.180393211
99 4.082732113 3.641471722
100 3.153865600 4.082732113
101 6.421841672 3.153865600
102 -1.282062184 6.421841672
103 3.452535083 -1.282062184
104 2.508764532 3.452535083
105 2.853985305 2.508764532
106 -2.146956417 2.853985305
107 -0.026601825 -2.146956417
108 -8.234600985 -0.026601825
109 -5.874032410 -8.234600985
110 -4.644286572 -5.874032410
111 -6.396486524 -4.644286572
112 1.654080385 -6.396486524
113 -4.653040020 1.654080385
114 -8.291627196 -4.653040020
115 -0.702876862 -8.291627196
116 -4.633005669 -0.702876862
117 -4.457344121 -4.633005669
118 -2.518457850 -4.457344121
119 -7.420016359 -2.518457850
120 0.542665643 -7.420016359
121 -0.853262144 0.542665643
122 -0.651363254 -0.853262144
123 -2.822468774 -0.651363254
124 1.508189150 -2.822468774
125 -0.626404450 1.508189150
126 -3.903219766 -0.626404450
127 -0.871872146 -3.903219766
128 4.143437259 -0.871872146
129 -0.724154682 4.143437259
130 0.839531911 -0.724154682
131 1.643758695 0.839531911
132 1.049362627 1.643758695
133 5.093038642 1.049362627
134 7.039446850 5.093038642
135 5.116605053 7.039446850
136 3.997006027 5.116605053
137 1.344341409 3.997006027
138 0.467414525 1.344341409
139 3.595345352 0.467414525
140 1.184395307 3.595345352
141 -0.543296781 1.184395307
142 -0.234890702 -0.543296781
143 -0.730753352 -0.234890702
144 -2.823841670 -0.730753352
145 -6.332421034 -2.823841670
146 -0.801882936 -6.332421034
147 -4.620236548 -0.801882936
148 -2.672378619 -4.620236548
149 -4.090119256 -2.672378619
150 -4.807807787 -4.090119256
> 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/7usbk1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8dsif1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91l8g1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10mihq1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lv2u1352125226.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/126n1e1352125226.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/13iaii1352125226.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/14zfaj1352125226.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/15vfby1352125226.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/16jtpo1352125227.tab")
+ }
>
> try(system("convert tmp/1wiio1352125226.ps tmp/1wiio1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t6do1352125226.ps tmp/2t6do1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m26t1352125226.ps tmp/3m26t1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/46ul61352125226.ps tmp/46ul61352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/52v801352125226.ps tmp/52v801352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/6829t1352125226.ps tmp/6829t1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/7usbk1352125226.ps tmp/7usbk1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dsif1352125226.ps tmp/8dsif1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/91l8g1352125226.ps tmp/91l8g1352125226.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mihq1352125226.ps tmp/10mihq1352125226.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.652 1.739 14.440