R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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(2000
+ ,75.5
+ ,78.4
+ ,67.3
+ ,75.3
+ ,106.1
+ ,125.7
+ ,101.6
+ ,2000
+ ,83.2
+ ,79.3
+ ,75.2
+ ,83.6
+ ,112.7
+ ,153.8
+ ,113.4
+ ,2000
+ ,94.5
+ ,84.3
+ ,91.1
+ ,91.2
+ ,123.2
+ ,134.9
+ ,122.2
+ ,2000
+ ,83.3
+ ,81.2
+ ,83.7
+ ,85.2
+ ,101.7
+ ,95.3
+ ,102.2
+ ,2000
+ ,92.7
+ ,88.4
+ ,105.0
+ ,100.0
+ ,118.7
+ ,96.6
+ ,113.2
+ ,2000
+ ,89.8
+ ,83.1
+ ,106.2
+ ,89.8
+ ,107.1
+ ,100.5
+ ,115.3
+ ,2000
+ ,74.8
+ ,76.6
+ ,88.5
+ ,88.9
+ ,93.6
+ ,106.2
+ ,87.4
+ ,2000
+ ,81.5
+ ,82.6
+ ,100.1
+ ,85.6
+ ,77.5
+ ,153.4
+ ,98.7
+ ,2000
+ ,92.8
+ ,84.4
+ ,90.3
+ ,83.2
+ ,117.2
+ ,132.1
+ ,117.3
+ ,2000
+ ,92.8
+ ,94.6
+ ,85.3
+ ,97.1
+ ,124.5
+ ,110.9
+ ,121.2
+ ,2000
+ ,91.7
+ ,91.8
+ ,81.9
+ ,85.8
+ ,120.8
+ ,94.3
+ ,118.7
+ ,2000
+ ,83.5
+ ,89.3
+ ,77.2
+ ,80.9
+ ,97.0
+ ,91.7
+ ,112.1
+ ,2001
+ ,92.8
+ ,87.7
+ ,78.6
+ ,81.3
+ ,115.1
+ ,138.6
+ ,102.9
+ ,2001
+ ,91.3
+ ,83.1
+ ,75.1
+ ,83.2
+ ,112.9
+ ,154.3
+ ,108.8
+ ,2001
+ ,99.5
+ ,93.6
+ ,90.3
+ ,90.7
+ ,122.7
+ ,149.8
+ ,118.6
+ ,2001
+ ,87.6
+ ,85.1
+ ,88.5
+ ,88.4
+ ,106.9
+ ,99.2
+ ,99.2
+ ,2001
+ ,95.3
+ ,90.8
+ ,112.5
+ ,94.1
+ ,115.0
+ ,97.7
+ ,102.2
+ ,2001
+ ,98.5
+ ,90.5
+ ,101.1
+ ,92.0
+ ,114.9
+ ,107.7
+ ,108.8
+ ,2001
+ ,80.1
+ ,86.1
+ ,114.0
+ ,92.0
+ ,103.1
+ ,120.1
+ ,94.0
+ ,2001
+ ,84.2
+ ,93.3
+ ,107.7
+ ,89.3
+ ,80.8
+ ,164.5
+ ,96.2
+ ,2001
+ ,92.4
+ ,94.9
+ ,77.8
+ ,87.0
+ ,118.2
+ ,136.1
+ ,118.4
+ ,2001
+ ,98.0
+ ,102.6
+ ,101.4
+ ,97.7
+ ,129.6
+ ,117.5
+ ,120.0
+ ,2001
+ ,92.2
+ ,98.3
+ ,87.2
+ ,82.5
+ ,118.7
+ ,98.2
+ ,117.5
+ ,2001
+ ,80.0
+ ,93.4
+ ,75.9
+ ,96.5
+ ,88.4
+ ,91.9
+ ,102.6
+ ,2002
+ ,88.7
+ ,92.8
+ ,78.8
+ ,86.2
+ ,113.1
+ ,141.8
+ ,92.8
+ ,2002
+ ,87.4
+ ,86.5
+ ,82.3
+ ,84.9
+ ,109.8
+ ,154.2
+ ,100.3
+ ,2002
+ ,96.1
+ ,93.8
+ ,89.1
+ ,100.0
+ ,116.1
+ ,138.6
+ ,106.3
+ ,2002
+ ,94.1
+ ,90.4
+ ,100.1
+ ,92.7
+ ,113.6
+ ,97.9
+ ,103.9
+ ,2002
+ ,91.9
+ ,91.0
+ ,101.8
+ ,96.7
+ ,107.9
+ ,90.3
+ ,102.4
+ ,2002
+ ,93.6
+ ,89.1
+ ,98.5
+ ,105.8
+ ,107.4
+ ,90.9
+ ,114.5
+ ,2002
+ ,83.5
+ ,89.6
+ ,106.6
+ ,88.5
+ ,102.7
+ ,127.0
+ ,89.0
+ ,2002
+ ,80.8
+ ,89.3
+ ,101.8
+ ,78.7
+ ,78.3
+ ,156.8
+ ,94.3
+ ,2002
+ ,96.3
+ ,95.3
+ ,92.4
+ ,99.9
+ ,121.0
+ ,127.2
+ ,115.7
+ ,2002
+ ,101.5
+ ,104.1
+ ,94.4
+ ,107.8
+ ,132.2
+ ,111.3
+ ,120.2
+ ,2002
+ ,91.6
+ ,94.7
+ ,81.0
+ ,102.4
+ ,113.2
+ ,93.0
+ ,109.5
+ ,2002
+ ,84.0
+ ,97.6
+ ,94.6
+ ,106.0
+ ,89.2
+ ,89.5
+ ,99.4
+ ,2003
+ ,91.8
+ ,96.8
+ ,83.8
+ ,87.3
+ ,113.2
+ ,141.8
+ ,86.4
+ ,2003
+ ,90.4
+ ,92.8
+ ,79.4
+ ,93.3
+ ,107.6
+ ,152.0
+ ,95.1
+ ,2003
+ ,98.0
+ ,94.7
+ ,95.6
+ ,98.2
+ ,107.3
+ ,120.2
+ ,101.5
+ ,2003
+ ,95.5
+ ,95.8
+ ,106.0
+ ,102.0
+ ,110.9
+ ,88.8
+ ,92.9
+ ,2003
+ ,90.5
+ ,88.9
+ ,106.2
+ ,93.9
+ ,96.4
+ ,82.8
+ ,90.8
+ ,2003
+ ,97.1
+ ,91.2
+ ,115.0
+ ,106.6
+ ,101.2
+ ,82.8
+ ,100.4
+ ,2003
+ ,87.9
+ ,91.6
+ ,122.4
+ ,92.9
+ ,94.0
+ ,121.7
+ ,82.2
+ ,2003
+ ,79.8
+ ,87.3
+ ,113.7
+ ,78.0
+ ,70.5
+ ,147.1
+ ,75.3
+ ,2003
+ ,102.0
+ ,97.8
+ ,98.0
+ ,104.2
+ ,116.4
+ ,132.5
+ ,110.3
+ ,2003
+ ,104.3
+ ,105.1
+ ,105.8
+ ,115.9
+ ,121.9
+ ,107.5
+ ,113.5
+ ,2003
+ ,92.1
+ ,93.8
+ ,88.3
+ ,99.9
+ ,109.5
+ ,77.9
+ ,94.9
+ ,2003
+ ,95.9
+ ,99.0
+ ,95.7
+ ,103.9
+ ,91.1
+ ,85.5
+ ,95.7
+ ,2004
+ ,89.1
+ ,91.4
+ ,85.8
+ ,93.5
+ ,104.0
+ ,126.5
+ ,85.3
+ ,2004
+ ,92.2
+ ,89.0
+ ,83.9
+ ,101.7
+ ,101.2
+ ,135.4
+ ,92.5
+ ,2004
+ ,107.5
+ ,101.4
+ ,114.1
+ ,124.6
+ ,118.4
+ ,122.5
+ ,107.7
+ ,2004
+ ,99.7
+ ,95.4
+ ,102.0
+ ,124.2
+ ,106.9
+ ,79.2
+ ,97.9
+ ,2004
+ ,92.2
+ ,90.5
+ ,108.1
+ ,103.3
+ ,95.6
+ ,66.1
+ ,93.9
+ ,2004
+ ,108.9
+ ,98.7
+ ,125.4
+ ,120.5
+ ,114.2
+ ,77.9
+ ,111.5
+ ,2004
+ ,89.8
+ ,91.2
+ ,108.1
+ ,98.0
+ ,92.4
+ ,109.6
+ ,88.6
+ ,2004
+ ,89.4
+ ,91.7
+ ,110.4
+ ,100.4
+ ,75.3
+ ,142.9
+ ,82.5
+ ,2004
+ ,107.6
+ ,102.9
+ ,102.4
+ ,126.8
+ ,120.4
+ ,120.5
+ ,108.6
+ ,2004
+ ,105.6
+ ,105.5
+ ,89.6
+ ,120.2
+ ,115.9
+ ,96.3
+ ,113.8
+ ,2004
+ ,100.9
+ ,102.6
+ ,95.0
+ ,114.0
+ ,109.8
+ ,82.6
+ ,103.4
+ ,2004
+ ,102.9
+ ,107.2
+ ,93.7
+ ,109.1
+ ,94.9
+ ,78.4
+ ,99.0
+ ,2005
+ ,96.2
+ ,96.9
+ ,77.7
+ ,94.2
+ ,97.5
+ ,104.5
+ ,89.9
+ ,2005
+ ,94.7
+ ,88.9
+ ,80.1
+ ,86.0
+ ,101.3
+ ,137.9
+ ,97.9
+ ,2005
+ ,107.3
+ ,99.6
+ ,103.6
+ ,112.9
+ ,108.7
+ ,125.8
+ ,107.8
+ ,2005
+ ,103.0
+ ,96.7
+ ,103.1
+ ,99.7
+ ,105.1
+ ,78.0
+ ,103.7
+ ,2005
+ ,96.1
+ ,93.8
+ ,112.4
+ ,104.5
+ ,94.9
+ ,67.7
+ ,98.2
+ ,2005
+ ,109.8
+ ,101.9
+ ,119.2
+ ,111.6
+ ,108.9
+ ,78.4
+ ,111.7
+ ,2005
+ ,85.4
+ ,87.6
+ ,105.3
+ ,99.2
+ ,87.5
+ ,101.7
+ ,82.6
+ ,2005
+ ,89.9
+ ,100.0
+ ,107.2
+ ,90.9
+ ,73.0
+ ,154.1
+ ,86.1
+ ,2005
+ ,109.3
+ ,105.8
+ ,108.7
+ ,111.4
+ ,115.2
+ ,107.3
+ ,111.2
+ ,2005
+ ,101.2
+ ,105.5
+ ,93.7
+ ,98.2
+ ,107.5
+ ,86.5
+ ,105.3
+ ,2005
+ ,104.7
+ ,111.3
+ ,96.1
+ ,101.7
+ ,109.8
+ ,82.1
+ ,106.3
+ ,2005
+ ,102.4
+ ,112.1
+ ,92.9
+ ,89.7
+ ,90.7
+ ,76.1
+ ,99.4
+ ,2006
+ ,97.7
+ ,102.0
+ ,81.1
+ ,89.5
+ ,97.6
+ ,115.5
+ ,91.9
+ ,2006
+ ,98.9
+ ,93.2
+ ,83.2
+ ,85.1
+ ,98.7
+ ,129.6
+ ,96.2
+ ,2006
+ ,115.0
+ ,108.4
+ ,99.7
+ ,95.9
+ ,113.9
+ ,121.6
+ ,105.4
+ ,2006
+ ,97.5
+ ,97.9
+ ,96.8
+ ,88.9
+ ,96.6
+ ,64.0
+ ,95.0
+ ,2006
+ ,107.3
+ ,106.4
+ ,108.7
+ ,98.1
+ ,104.4
+ ,58.1
+ ,100.5
+ ,2006
+ ,112.3
+ ,102.8
+ ,120.9
+ ,109.7
+ ,115.1
+ ,79.7
+ ,111.6
+ ,2006
+ ,88.5
+ ,96.3
+ ,114.8
+ ,92.0
+ ,91.4
+ ,108.9
+ ,88.5
+ ,2006
+ ,92.9
+ ,105.7
+ ,108.7
+ ,74.3
+ ,76.2
+ ,138.5
+ ,83.7
+ ,2006
+ ,108.8
+ ,108.4
+ ,97.4
+ ,96.9
+ ,117.4
+ ,117.9
+ ,113.9
+ ,2006
+ ,112.3
+ ,115.8
+ ,98.6
+ ,100.3
+ ,122.0
+ ,96.7
+ ,115.2
+ ,2006
+ ,107.3
+ ,113.8
+ ,91.7
+ ,97.1
+ ,120.2
+ ,78.6
+ ,111.0
+ ,2006
+ ,101.8
+ ,106.4
+ ,91.2
+ ,86.0
+ ,93.6
+ ,64.1
+ ,96.9
+ ,2007
+ ,105.0
+ ,107.9
+ ,83.5
+ ,97.3
+ ,106.6
+ ,112.0
+ ,102.1
+ ,2007
+ ,103.4
+ ,98.2
+ ,82.4
+ ,86.4
+ ,108.4
+ ,139.4
+ ,101.5
+ ,2007
+ ,116.7
+ ,111.1
+ ,103.1
+ ,97.7
+ ,121.4
+ ,116.2
+ ,115.0
+ ,2007
+ ,103.6
+ ,99.8
+ ,110.3
+ ,90.6
+ ,104.8
+ ,63.4
+ ,105.0
+ ,2007
+ ,108.8
+ ,103.5
+ ,115.8
+ ,99.2
+ ,104.2
+ ,61.1
+ ,105.4
+ ,2007
+ ,117.0
+ ,105.4
+ ,120.1
+ ,107.4
+ ,115.0
+ ,65.5
+ ,119.7
+ ,2007
+ ,100.9
+ ,102.6
+ ,105.1
+ ,107.1
+ ,99.0
+ ,90.9
+ ,91.8
+ ,2007
+ ,100.8
+ ,107.4
+ ,108.6
+ ,78.9
+ ,82.8
+ ,115.3
+ ,89.1
+ ,2007
+ ,109.7
+ ,108.2
+ ,95.7
+ ,92.8
+ ,112.5
+ ,85.2
+ ,106.2
+ ,2007
+ ,121.0
+ ,121.7
+ ,103.2
+ ,106.2
+ ,127.9
+ ,87.0
+ ,119.9
+ ,2007
+ ,114.1
+ ,118.0
+ ,96.9
+ ,97.2
+ ,114.4
+ ,62.6
+ ,111.6
+ ,2007
+ ,105.5
+ ,109.6
+ ,95.7
+ ,80.0
+ ,83.7
+ ,62.7
+ ,95.1
+ ,2008
+ ,112.5
+ ,116.7
+ ,92.7
+ ,109.3
+ ,108.5
+ ,91.6
+ ,101.3
+ ,2008
+ ,113.8
+ ,110.6
+ ,81.3
+ ,111.3
+ ,109.7
+ ,104.3
+ ,118.3
+ ,2008
+ ,115.3
+ ,109.6
+ ,94.5
+ ,119.5
+ ,104.7
+ ,88.1
+ ,126.2
+ ,2008
+ ,120.4
+ ,117.4
+ ,105.6
+ ,119.8
+ ,112.2
+ ,62.3
+ ,113.2
+ ,2008
+ ,111.1
+ ,109.2
+ ,112.9
+ ,112.5
+ ,96.9
+ ,50.3
+ ,103.6
+ ,2008
+ ,120.1
+ ,110.8
+ ,102.6
+ ,125.6
+ ,103.8
+ ,64.1
+ ,116.2
+ ,2008
+ ,106.1
+ ,112.8
+ ,116.2
+ ,105.1
+ ,95.1
+ ,75.7
+ ,98.3
+ ,2008
+ ,95.9
+ ,106.5
+ ,104.9
+ ,91.9
+ ,66.7
+ ,85.5
+ ,84.2
+ ,2008
+ ,119.4
+ ,119.6
+ ,100.4
+ ,128.2
+ ,103.4
+ ,71.9
+ ,118.3
+ ,2008
+ ,117.4
+ ,127.2
+ ,97.1
+ ,122.6
+ ,105.4
+ ,66.9
+ ,117.4
+ ,2008
+ ,98.6
+ ,113.9
+ ,90.2
+ ,109.6
+ ,89.2
+ ,50.5
+ ,94.5
+ ,2008
+ ,99.7
+ ,120.0
+ ,100.5
+ ,120.4
+ ,72.5
+ ,57.9
+ ,93.3
+ ,2009
+ ,87.4
+ ,107.6
+ ,81.1
+ ,103.8
+ ,78.0
+ ,84.1
+ ,90.2
+ ,2009
+ ,90.8
+ ,105.2
+ ,87.2
+ ,96.6
+ ,77.3
+ ,87.0
+ ,88.5
+ ,2009
+ ,101.3
+ ,115.3
+ ,102.0
+ ,110.7
+ ,85.1
+ ,71.9
+ ,101.0
+ ,2009
+ ,93.2
+ ,113.9
+ ,107.0
+ ,111.7
+ ,80.9
+ ,45.0
+ ,87.0
+ ,2009
+ ,95.1
+ ,106.1
+ ,107.6
+ ,111.9
+ ,72.5
+ ,39.5
+ ,81.2
+ ,2009
+ ,101.9
+ ,114.3
+ ,123.5
+ ,131.5
+ ,82.1
+ ,53.8
+ ,98.1
+ ,2009
+ ,87.0
+ ,112.0
+ ,116.6
+ ,122.8
+ ,78.3
+ ,59.5
+ ,75.5
+ ,2009
+ ,86.2
+ ,109.0
+ ,103.2
+ ,98.3
+ ,57.8
+ ,68.4
+ ,70.7
+ ,2009
+ ,105.0
+ ,119.1
+ ,103.9
+ ,133.7
+ ,89.3
+ ,56.9
+ ,103.7
+ ,2009
+ ,104.1
+ ,124.4
+ ,95.4
+ ,120.0
+ ,91.4
+ ,61.9
+ ,100.4
+ ,2009
+ ,99.2
+ ,116.6
+ ,93.6
+ ,119.6
+ ,84.2
+ ,40.4
+ ,91.3
+ ,2009
+ ,95.2
+ ,118.5
+ ,102.1
+ ,108.7
+ ,72.5
+ ,49.4
+ ,97.2
+ ,2010
+ ,92.7
+ ,108.9
+ ,69.0
+ ,112.5
+ ,74.6
+ ,65.2
+ ,85.4
+ ,2010
+ ,99.3
+ ,107.5
+ ,88.9
+ ,102.7
+ ,80.3
+ ,82.1
+ ,86.5
+ ,2010
+ ,113.5
+ ,125.9
+ ,106.2
+ ,123.4
+ ,92.6
+ ,69.0
+ ,105.3
+ ,2010
+ ,104.7
+ ,117.7
+ ,103.0
+ ,116.5
+ ,86.3
+ ,45.9
+ ,97.7
+ ,2010
+ ,100.5
+ ,109.2
+ ,103.5
+ ,102.3
+ ,80.3
+ ,39.1
+ ,84.3
+ ,2010
+ ,116.2
+ ,118.8
+ ,124.5
+ ,148.4
+ ,93.6
+ ,56.9
+ ,109.8
+ ,2010
+ ,94.1
+ ,108.1
+ ,117.9
+ ,126.6
+ ,79.5
+ ,51.6
+ ,79.1
+ ,2010
+ ,94.8
+ ,112.1
+ ,104.2
+ ,106.6
+ ,61.8
+ ,62.9
+ ,83.4
+ ,2010
+ ,115.1
+ ,117.8
+ ,99.9
+ ,144.4
+ ,94.8
+ ,58.3
+ ,101.9
+ ,2010
+ ,110.0
+ ,121.8
+ ,89.4
+ ,132.4
+ ,91.6
+ ,56.9
+ ,113.0
+ ,2010
+ ,108.4
+ ,121.0
+ ,93.5
+ ,136.2
+ ,89.2
+ ,41.3
+ ,98.6
+ ,2010
+ ,103.9
+ ,121.7
+ ,89.6
+ ,121.6
+ ,74.1
+ ,46.9
+ ,94.7
+ ,2011
+ ,102.9
+ ,114.2
+ ,85.0
+ ,135.1
+ ,78.6
+ ,61.9
+ ,94.5
+ ,2011
+ ,107.7
+ ,109.8
+ ,90.0
+ ,124.7
+ ,78.2
+ ,74.8
+ ,90.7
+ ,2011
+ ,126.7
+ ,124.1
+ ,113.7
+ ,148.8
+ ,95.1
+ ,67.0
+ ,113.0
+ ,2011
+ ,108.8
+ ,112.9
+ ,112.1
+ ,145.6
+ ,78.7
+ ,53.3
+ ,89.9
+ ,2011
+ ,117.1
+ ,118.7
+ ,129.8
+ ,140.3
+ ,85.9
+ ,51.4
+ ,98.7
+ ,2011
+ ,112.2
+ ,113.3
+ ,119.1
+ ,138.5
+ ,81.2
+ ,50.3
+ ,102.2
+ ,2011
+ ,94.7
+ ,106.8
+ ,103.5
+ ,127.3
+ ,73.1
+ ,52.7
+ ,74.3
+ ,2011
+ ,102.7
+ ,119.3
+ ,105.5
+ ,117.9
+ ,58.7
+ ,70.3
+ ,84.5
+ ,2011
+ ,119.1
+ ,126.4
+ ,111.7
+ ,145.3
+ ,85.7
+ ,59.7
+ ,110.1
+ ,2011
+ ,110.6
+ ,126.6
+ ,98.6
+ ,120.7
+ ,81.8
+ ,52.0
+ ,100.4
+ ,2011
+ ,109.1
+ ,127.2
+ ,102.8
+ ,134.7
+ ,79.6
+ ,36.1
+ ,92.8
+ ,2011
+ ,105.3
+ ,123.8
+ ,101.1
+ ,124.4
+ ,70.7
+ ,39.7
+ ,92.2
+ ,2012
+ ,103.4
+ ,116.8
+ ,94.2
+ ,128.3
+ ,74.5
+ ,67.6
+ ,94.0
+ ,2012
+ ,103.7
+ ,113.8
+ ,92.6
+ ,128.4
+ ,84.8
+ ,72.8
+ ,100.7
+ ,2012
+ ,117.0
+ ,130.4
+ ,112.0
+ ,134.1
+ ,80.7
+ ,53.8
+ ,111.9
+ ,2012
+ ,101.2
+ ,112.8
+ ,108.6
+ ,133.3
+ ,69.9
+ ,39.6
+ ,95.9
+ ,2012
+ ,105.4
+ ,119.4
+ ,125.8
+ ,130.6
+ ,74.1
+ ,39.4
+ ,88.8
+ ,2012
+ ,110.3
+ ,117.5
+ ,138.7
+ ,165.7
+ ,76.1
+ ,41.2
+ ,102.0
+ ,2012
+ ,97.7
+ ,117.5
+ ,115.2
+ ,146.8
+ ,71.3
+ ,49.6
+ ,81.6)
+ ,dim=c(8
+ ,151)
+ ,dimnames=list(c('Jaar'
+ ,'Totaal'
+ ,'Voeding'
+ ,'Dranken'
+ ,'Tabaksproducten'
+ ,'Textiel'
+ ,'Kleding'
+ ,'Apparatuur
')
+ ,1:151))
> y <- array(NA,dim=c(8,151),dimnames=list(c('Jaar','Totaal','Voeding','Dranken','Tabaksproducten','Textiel','Kleding','Apparatuur
'),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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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 Jaar Voeding Dranken Tabaksproducten Textiel Kleding Apparatuur\r\r
1 75.5 2000 78.4 67.3 75.3 106.1 125.7 101.6
2 83.2 2000 79.3 75.2 83.6 112.7 153.8 113.4
3 94.5 2000 84.3 91.1 91.2 123.2 134.9 122.2
4 83.3 2000 81.2 83.7 85.2 101.7 95.3 102.2
5 92.7 2000 88.4 105.0 100.0 118.7 96.6 113.2
6 89.8 2000 83.1 106.2 89.8 107.1 100.5 115.3
7 74.8 2000 76.6 88.5 88.9 93.6 106.2 87.4
8 81.5 2000 82.6 100.1 85.6 77.5 153.4 98.7
9 92.8 2000 84.4 90.3 83.2 117.2 132.1 117.3
10 92.8 2000 94.6 85.3 97.1 124.5 110.9 121.2
11 91.7 2000 91.8 81.9 85.8 120.8 94.3 118.7
12 83.5 2000 89.3 77.2 80.9 97.0 91.7 112.1
13 92.8 2001 87.7 78.6 81.3 115.1 138.6 102.9
14 91.3 2001 83.1 75.1 83.2 112.9 154.3 108.8
15 99.5 2001 93.6 90.3 90.7 122.7 149.8 118.6
16 87.6 2001 85.1 88.5 88.4 106.9 99.2 99.2
17 95.3 2001 90.8 112.5 94.1 115.0 97.7 102.2
18 98.5 2001 90.5 101.1 92.0 114.9 107.7 108.8
19 80.1 2001 86.1 114.0 92.0 103.1 120.1 94.0
20 84.2 2001 93.3 107.7 89.3 80.8 164.5 96.2
21 92.4 2001 94.9 77.8 87.0 118.2 136.1 118.4
22 98.0 2001 102.6 101.4 97.7 129.6 117.5 120.0
23 92.2 2001 98.3 87.2 82.5 118.7 98.2 117.5
24 80.0 2001 93.4 75.9 96.5 88.4 91.9 102.6
25 88.7 2002 92.8 78.8 86.2 113.1 141.8 92.8
26 87.4 2002 86.5 82.3 84.9 109.8 154.2 100.3
27 96.1 2002 93.8 89.1 100.0 116.1 138.6 106.3
28 94.1 2002 90.4 100.1 92.7 113.6 97.9 103.9
29 91.9 2002 91.0 101.8 96.7 107.9 90.3 102.4
30 93.6 2002 89.1 98.5 105.8 107.4 90.9 114.5
31 83.5 2002 89.6 106.6 88.5 102.7 127.0 89.0
32 80.8 2002 89.3 101.8 78.7 78.3 156.8 94.3
33 96.3 2002 95.3 92.4 99.9 121.0 127.2 115.7
34 101.5 2002 104.1 94.4 107.8 132.2 111.3 120.2
35 91.6 2002 94.7 81.0 102.4 113.2 93.0 109.5
36 84.0 2002 97.6 94.6 106.0 89.2 89.5 99.4
37 91.8 2003 96.8 83.8 87.3 113.2 141.8 86.4
38 90.4 2003 92.8 79.4 93.3 107.6 152.0 95.1
39 98.0 2003 94.7 95.6 98.2 107.3 120.2 101.5
40 95.5 2003 95.8 106.0 102.0 110.9 88.8 92.9
41 90.5 2003 88.9 106.2 93.9 96.4 82.8 90.8
42 97.1 2003 91.2 115.0 106.6 101.2 82.8 100.4
43 87.9 2003 91.6 122.4 92.9 94.0 121.7 82.2
44 79.8 2003 87.3 113.7 78.0 70.5 147.1 75.3
45 102.0 2003 97.8 98.0 104.2 116.4 132.5 110.3
46 104.3 2003 105.1 105.8 115.9 121.9 107.5 113.5
47 92.1 2003 93.8 88.3 99.9 109.5 77.9 94.9
48 95.9 2003 99.0 95.7 103.9 91.1 85.5 95.7
49 89.1 2004 91.4 85.8 93.5 104.0 126.5 85.3
50 92.2 2004 89.0 83.9 101.7 101.2 135.4 92.5
51 107.5 2004 101.4 114.1 124.6 118.4 122.5 107.7
52 99.7 2004 95.4 102.0 124.2 106.9 79.2 97.9
53 92.2 2004 90.5 108.1 103.3 95.6 66.1 93.9
54 108.9 2004 98.7 125.4 120.5 114.2 77.9 111.5
55 89.8 2004 91.2 108.1 98.0 92.4 109.6 88.6
56 89.4 2004 91.7 110.4 100.4 75.3 142.9 82.5
57 107.6 2004 102.9 102.4 126.8 120.4 120.5 108.6
58 105.6 2004 105.5 89.6 120.2 115.9 96.3 113.8
59 100.9 2004 102.6 95.0 114.0 109.8 82.6 103.4
60 102.9 2004 107.2 93.7 109.1 94.9 78.4 99.0
61 96.2 2005 96.9 77.7 94.2 97.5 104.5 89.9
62 94.7 2005 88.9 80.1 86.0 101.3 137.9 97.9
63 107.3 2005 99.6 103.6 112.9 108.7 125.8 107.8
64 103.0 2005 96.7 103.1 99.7 105.1 78.0 103.7
65 96.1 2005 93.8 112.4 104.5 94.9 67.7 98.2
66 109.8 2005 101.9 119.2 111.6 108.9 78.4 111.7
67 85.4 2005 87.6 105.3 99.2 87.5 101.7 82.6
68 89.9 2005 100.0 107.2 90.9 73.0 154.1 86.1
69 109.3 2005 105.8 108.7 111.4 115.2 107.3 111.2
70 101.2 2005 105.5 93.7 98.2 107.5 86.5 105.3
71 104.7 2005 111.3 96.1 101.7 109.8 82.1 106.3
72 102.4 2005 112.1 92.9 89.7 90.7 76.1 99.4
73 97.7 2006 102.0 81.1 89.5 97.6 115.5 91.9
74 98.9 2006 93.2 83.2 85.1 98.7 129.6 96.2
75 115.0 2006 108.4 99.7 95.9 113.9 121.6 105.4
76 97.5 2006 97.9 96.8 88.9 96.6 64.0 95.0
77 107.3 2006 106.4 108.7 98.1 104.4 58.1 100.5
78 112.3 2006 102.8 120.9 109.7 115.1 79.7 111.6
79 88.5 2006 96.3 114.8 92.0 91.4 108.9 88.5
80 92.9 2006 105.7 108.7 74.3 76.2 138.5 83.7
81 108.8 2006 108.4 97.4 96.9 117.4 117.9 113.9
82 112.3 2006 115.8 98.6 100.3 122.0 96.7 115.2
83 107.3 2006 113.8 91.7 97.1 120.2 78.6 111.0
84 101.8 2006 106.4 91.2 86.0 93.6 64.1 96.9
85 105.0 2007 107.9 83.5 97.3 106.6 112.0 102.1
86 103.4 2007 98.2 82.4 86.4 108.4 139.4 101.5
87 116.7 2007 111.1 103.1 97.7 121.4 116.2 115.0
88 103.6 2007 99.8 110.3 90.6 104.8 63.4 105.0
89 108.8 2007 103.5 115.8 99.2 104.2 61.1 105.4
90 117.0 2007 105.4 120.1 107.4 115.0 65.5 119.7
91 100.9 2007 102.6 105.1 107.1 99.0 90.9 91.8
92 100.8 2007 107.4 108.6 78.9 82.8 115.3 89.1
93 109.7 2007 108.2 95.7 92.8 112.5 85.2 106.2
94 121.0 2007 121.7 103.2 106.2 127.9 87.0 119.9
95 114.1 2007 118.0 96.9 97.2 114.4 62.6 111.6
96 105.5 2007 109.6 95.7 80.0 83.7 62.7 95.1
97 112.5 2008 116.7 92.7 109.3 108.5 91.6 101.3
98 113.8 2008 110.6 81.3 111.3 109.7 104.3 118.3
99 115.3 2008 109.6 94.5 119.5 104.7 88.1 126.2
100 120.4 2008 117.4 105.6 119.8 112.2 62.3 113.2
101 111.1 2008 109.2 112.9 112.5 96.9 50.3 103.6
102 120.1 2008 110.8 102.6 125.6 103.8 64.1 116.2
103 106.1 2008 112.8 116.2 105.1 95.1 75.7 98.3
104 95.9 2008 106.5 104.9 91.9 66.7 85.5 84.2
105 119.4 2008 119.6 100.4 128.2 103.4 71.9 118.3
106 117.4 2008 127.2 97.1 122.6 105.4 66.9 117.4
107 98.6 2008 113.9 90.2 109.6 89.2 50.5 94.5
108 99.7 2008 120.0 100.5 120.4 72.5 57.9 93.3
109 87.4 2009 107.6 81.1 103.8 78.0 84.1 90.2
110 90.8 2009 105.2 87.2 96.6 77.3 87.0 88.5
111 101.3 2009 115.3 102.0 110.7 85.1 71.9 101.0
112 93.2 2009 113.9 107.0 111.7 80.9 45.0 87.0
113 95.1 2009 106.1 107.6 111.9 72.5 39.5 81.2
114 101.9 2009 114.3 123.5 131.5 82.1 53.8 98.1
115 87.0 2009 112.0 116.6 122.8 78.3 59.5 75.5
116 86.2 2009 109.0 103.2 98.3 57.8 68.4 70.7
117 105.0 2009 119.1 103.9 133.7 89.3 56.9 103.7
118 104.1 2009 124.4 95.4 120.0 91.4 61.9 100.4
119 99.2 2009 116.6 93.6 119.6 84.2 40.4 91.3
120 95.2 2009 118.5 102.1 108.7 72.5 49.4 97.2
121 92.7 2010 108.9 69.0 112.5 74.6 65.2 85.4
122 99.3 2010 107.5 88.9 102.7 80.3 82.1 86.5
123 113.5 2010 125.9 106.2 123.4 92.6 69.0 105.3
124 104.7 2010 117.7 103.0 116.5 86.3 45.9 97.7
125 100.5 2010 109.2 103.5 102.3 80.3 39.1 84.3
126 116.2 2010 118.8 124.5 148.4 93.6 56.9 109.8
127 94.1 2010 108.1 117.9 126.6 79.5 51.6 79.1
128 94.8 2010 112.1 104.2 106.6 61.8 62.9 83.4
129 115.1 2010 117.8 99.9 144.4 94.8 58.3 101.9
130 110.0 2010 121.8 89.4 132.4 91.6 56.9 113.0
131 108.4 2010 121.0 93.5 136.2 89.2 41.3 98.6
132 103.9 2010 121.7 89.6 121.6 74.1 46.9 94.7
133 102.9 2011 114.2 85.0 135.1 78.6 61.9 94.5
134 107.7 2011 109.8 90.0 124.7 78.2 74.8 90.7
135 126.7 2011 124.1 113.7 148.8 95.1 67.0 113.0
136 108.8 2011 112.9 112.1 145.6 78.7 53.3 89.9
137 117.1 2011 118.7 129.8 140.3 85.9 51.4 98.7
138 112.2 2011 113.3 119.1 138.5 81.2 50.3 102.2
139 94.7 2011 106.8 103.5 127.3 73.1 52.7 74.3
140 102.7 2011 119.3 105.5 117.9 58.7 70.3 84.5
141 119.1 2011 126.4 111.7 145.3 85.7 59.7 110.1
142 110.6 2011 126.6 98.6 120.7 81.8 52.0 100.4
143 109.1 2011 127.2 102.8 134.7 79.6 36.1 92.8
144 105.3 2011 123.8 101.1 124.4 70.7 39.7 92.2
145 103.4 2012 116.8 94.2 128.3 74.5 67.6 94.0
146 103.7 2012 113.8 92.6 128.4 84.8 72.8 100.7
147 117.0 2012 130.4 112.0 134.1 80.7 53.8 111.9
148 101.2 2012 112.8 108.6 133.3 69.9 39.6 95.9
149 105.4 2012 119.4 125.8 130.6 74.1 39.4 88.8
150 110.3 2012 117.5 138.7 165.7 76.1 41.2 102.0
151 97.7 2012 117.5 115.2 146.8 71.3 49.6 81.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Jaar Voeding Dranken
-4.314e+03 2.150e+00 2.511e-01 1.555e-01
Tabaksproducten Textiel Kleding `Apparatuur\\r\\r`
-1.779e-02 2.872e-01 2.618e-02 3.064e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.0648 -2.1228 0.2562 2.0515 8.6470
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.314e+03 4.700e+02 -9.178 4.48e-16 ***
Jaar 2.150e+00 2.358e-01 9.119 6.34e-16 ***
Voeding 2.511e-01 5.870e-02 4.278 3.44e-05 ***
Dranken 1.555e-01 2.591e-02 6.003 1.52e-08 ***
Tabaksproducten -1.779e-02 2.783e-02 -0.639 0.5235
Textiel 2.872e-01 3.385e-02 8.486 2.45e-14 ***
Kleding 2.618e-02 1.418e-02 1.847 0.0669 .
`Apparatuur\\r\\r` 3.064e-01 4.574e-02 6.697 4.50e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.623 on 143 degrees of freedom
Multiple R-squared: 0.8875, Adjusted R-squared: 0.882
F-statistic: 161.2 on 7 and 143 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,] 0.320785548 0.641571095 0.679214452
[2,] 0.175407188 0.350814376 0.824592812
[3,] 0.088241528 0.176483055 0.911758472
[4,] 0.040941467 0.081882934 0.959058533
[5,] 0.034304520 0.068609039 0.965695480
[6,] 0.021104524 0.042209049 0.978895476
[7,] 0.018509404 0.037018808 0.981490596
[8,] 0.012125008 0.024250015 0.987874992
[9,] 0.450471400 0.900942800 0.549528600
[10,] 0.384812855 0.769625711 0.615187145
[11,] 0.445992633 0.891985267 0.554007367
[12,] 0.473919375 0.947838750 0.526080625
[13,] 0.502933997 0.994132006 0.497066003
[14,] 0.492472462 0.984944924 0.507527538
[15,] 0.419292753 0.838585507 0.580707247
[16,] 0.426817808 0.853635615 0.573182192
[17,] 0.365301359 0.730602719 0.634698641
[18,] 0.304531739 0.609063477 0.695468261
[19,] 0.253478466 0.506956932 0.746521534
[20,] 0.224404750 0.448809500 0.775595250
[21,] 0.271996924 0.543993848 0.728003076
[22,] 0.256696003 0.513392007 0.743303997
[23,] 0.261744932 0.523489864 0.738255068
[24,] 0.275930055 0.551860110 0.724069945
[25,] 0.256496755 0.512993509 0.743503245
[26,] 0.260073348 0.520146696 0.739926652
[27,] 0.238405304 0.476810608 0.761594696
[28,] 0.201160825 0.402321650 0.798839175
[29,] 0.222896932 0.445793864 0.777103068
[30,] 0.193414858 0.386829716 0.806585142
[31,] 0.160062239 0.320124479 0.839937761
[32,] 0.138285956 0.276571911 0.861714044
[33,] 0.115581668 0.231163336 0.884418332
[34,] 0.090552326 0.181104652 0.909447674
[35,] 0.077408479 0.154816958 0.922591521
[36,] 0.069915235 0.139830469 0.930084765
[37,] 0.054329516 0.108659032 0.945670484
[38,] 0.108145735 0.216291470 0.891854265
[39,] 0.091190958 0.182381915 0.908809042
[40,] 0.070791848 0.141583695 0.929208152
[41,] 0.055856858 0.111713716 0.944143142
[42,] 0.044013630 0.088027261 0.955986370
[43,] 0.036922533 0.073845066 0.963077467
[44,] 0.028348917 0.056697835 0.971651083
[45,] 0.024196360 0.048392719 0.975803640
[46,] 0.020874447 0.041748893 0.979125553
[47,] 0.015588960 0.031177921 0.984411040
[48,] 0.011980497 0.023960993 0.988019503
[49,] 0.008716317 0.017432633 0.991283683
[50,] 0.023320460 0.046640919 0.976679540
[51,] 0.022883704 0.045767407 0.977116296
[52,] 0.017838065 0.035676131 0.982161935
[53,] 0.013536397 0.027072793 0.986463603
[54,] 0.009974417 0.019948833 0.990025583
[55,] 0.009762047 0.019524094 0.990237953
[56,] 0.007145319 0.014290638 0.992854681
[57,] 0.011542019 0.023084039 0.988457981
[58,] 0.008828724 0.017657449 0.991171276
[59,] 0.006313922 0.012627843 0.993686078
[60,] 0.005257425 0.010514851 0.994742575
[61,] 0.003816733 0.007633467 0.996183267
[62,] 0.004329151 0.008658302 0.995670849
[63,] 0.003140994 0.006281988 0.996859006
[64,] 0.002425042 0.004850084 0.997574958
[65,] 0.005840662 0.011681324 0.994159338
[66,] 0.004545231 0.009090461 0.995454769
[67,] 0.003819255 0.007638510 0.996180745
[68,] 0.002710036 0.005420072 0.997289964
[69,] 0.021687630 0.043375261 0.978312370
[70,] 0.016510027 0.033020053 0.983489973
[71,] 0.017015580 0.034031160 0.982984420
[72,] 0.015929455 0.031858911 0.984070545
[73,] 0.019797183 0.039594365 0.980202817
[74,] 0.019516210 0.039032420 0.980483790
[75,] 0.015238539 0.030477078 0.984761461
[76,] 0.011254206 0.022508411 0.988745794
[77,] 0.008432503 0.016865007 0.991567497
[78,] 0.008229378 0.016458757 0.991770622
[79,] 0.005841443 0.011682887 0.994158557
[80,] 0.004262016 0.008524032 0.995737984
[81,] 0.003410251 0.006820501 0.996589749
[82,] 0.002908069 0.005816138 0.997091931
[83,] 0.001978536 0.003957072 0.998021464
[84,] 0.001501672 0.003003345 0.998498328
[85,] 0.001012683 0.002025367 0.998987317
[86,] 0.003907872 0.007815743 0.996092128
[87,] 0.002982572 0.005965144 0.997017428
[88,] 0.002196393 0.004392785 0.997803607
[89,] 0.002041655 0.004083309 0.997958345
[90,] 0.001784445 0.003568891 0.998215555
[91,] 0.001605298 0.003210595 0.998394702
[92,] 0.003463383 0.006926765 0.996536617
[93,] 0.003308917 0.006617833 0.996691083
[94,] 0.005653642 0.011307284 0.994346358
[95,] 0.006314193 0.012628386 0.993685807
[96,] 0.005277917 0.010555833 0.994722083
[97,] 0.007258181 0.014516362 0.992741819
[98,] 0.008151819 0.016303638 0.991848181
[99,] 0.066192445 0.132384890 0.933807555
[100,] 0.089082000 0.178164001 0.910918000
[101,] 0.108963712 0.217927425 0.891036288
[102,] 0.176073319 0.352146638 0.823926681
[103,] 0.184380298 0.368760596 0.815619702
[104,] 0.195775109 0.391550218 0.804224891
[105,] 0.491032367 0.982064734 0.508967633
[106,] 0.437808281 0.875616562 0.562191719
[107,] 0.453745652 0.907491305 0.546254348
[108,] 0.556427631 0.887144738 0.443572369
[109,] 0.506520823 0.986958355 0.493479177
[110,] 0.670782285 0.658435430 0.329217715
[111,] 0.622070506 0.755858988 0.377929494
[112,] 0.556595322 0.886809356 0.443404678
[113,] 0.575000984 0.849998032 0.424999016
[114,] 0.554546703 0.890906595 0.445453297
[115,] 0.549528889 0.900942223 0.450471111
[116,] 0.546645162 0.906709676 0.453354838
[117,] 0.782997266 0.434005469 0.217002734
[118,] 0.868608451 0.262783098 0.131391549
[119,] 0.825671878 0.348656244 0.174328122
[120,] 0.956624340 0.086751320 0.043375660
[121,] 0.954347886 0.091304229 0.045652114
[122,] 0.974133710 0.051732579 0.025866290
[123,] 0.961275777 0.077448446 0.038724223
[124,] 0.977324290 0.045351420 0.022675710
[125,] 0.985110431 0.029779138 0.014889569
[126,] 0.994844529 0.010310943 0.005155471
[127,] 0.991874451 0.016251098 0.008125549
[128,] 0.979666630 0.040666740 0.020333370
[129,] 0.951614589 0.096770821 0.048385411
[130,] 0.874007850 0.251984299 0.125992150
> postscript(file="/var/wessaorg/rcomp/tmp/1wbm01351612710.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2y0p61351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mb0n1351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4cwyt1351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5szm41351612710.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
-4.66824825 -4.52169075 -2.03214184 1.92977266 -1.81465431 -1.16566133
7 8 9 10 11 12
0.47931921 3.73680458 -0.47731885 -4.74986887 -2.55587440 -0.55815964
13 14 15 16 17 18
3.17466645 1.82123689 -0.54529076 1.73414525 1.16497198 3.92110894
19 20 21 22 23 24
-7.78181961 0.01130760 -4.38077425 -7.47223631 -5.85239344 -1.38253222
25 26 27 28 29 30
-0.71494933 -2.67513732 0.16364488 -0.30481383 -0.55299672 -1.27976562
31 32 33 34 35 36
-4.85590945 -2.30369781 -4.51684073 -5.87622410 -2.21338688 -2.51343661
37 38 39 40 41 42
0.40443644 -0.52393352 3.12430446 1.22070903 2.74317984 3.30313396
43 44 45 46 47 48
-0.76660380 1.50004906 0.44766555 -1.99593640 1.11337578 7.36895280
49 50 51 52 53 54
0.08940016 2.59891427 1.23623941 4.25665553 1.48029077 2.69326255
55 56 57 58 59 60
0.21434972 5.28251210 2.02081174 1.57450947 1.94925417 8.64699861
61 62 63 64 65 66
5.96436808 1.53707228 3.43219292 3.24459874 0.59594382 2.89365422
67 68 69 70 71 72
-1.52263248 1.14168121 0.63120028 -0.73149315 0.14942179 5.68984567
73 74 75 76 77 78
2.49185735 3.49399018 6.42829919 1.55420775 3.76184146 0.93508094
79 80 81 82 83 84
-7.47913870 0.25619135 -2.90866573 -2.55735801 -3.76134597 4.81649310
85 86 87 88 89 90
0.30752256 0.06964153 -0.15049975 -2.44576852 1.23278749 0.83458446
91 92 93 94 95 96
0.24366754 2.73388081 0.70529529 -0.98000236 0.92791260 8.18788484
97 98 99 100 101 102
2.46380214 1.21892187 0.50271954 4.42692936 3.57021775 7.80042969
103 104 105 106 107 108
-1.50282321 3.62224706 4.54676779 0.88437775 -1.63626158 1.49293010
109 110 111 112 113 114
-8.43758603 -4.86598522 -4.62764525 -6.93647934 1.16576502 -4.52669866
115 116 117 118 119 120
-10.06477746 -1.33741363 -3.40888018 -4.28433224 -1.53430750 -6.20995084
121 122 123 124 125 126
-0.63534778 0.62981647 -1.06216584 -2.68577642 0.92454296 -0.33022412
127 128 129 130 131 132
-5.51107165 -0.56945733 4.61498784 -2.51443802 1.02561634 2.08209669
133 134 135 136 137 138
0.14683539 6.03015695 6.70033936 3.95047689 3.23248076 1.62712092
139 140 141 142 143 144
-1.20249242 3.73123162 2.55119003 -0.10567520 1.21598201 0.99672686
145 146 147 148 149 150
-2.52649566 -6.36983576 -1.91005621 -4.40090202 -3.60740856 -4.27783133
151
-6.15028942
> postscript(file="/var/wessaorg/rcomp/tmp/6ay4e1351612710.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 -4.66824825 NA
1 -4.52169075 -4.66824825
2 -2.03214184 -4.52169075
3 1.92977266 -2.03214184
4 -1.81465431 1.92977266
5 -1.16566133 -1.81465431
6 0.47931921 -1.16566133
7 3.73680458 0.47931921
8 -0.47731885 3.73680458
9 -4.74986887 -0.47731885
10 -2.55587440 -4.74986887
11 -0.55815964 -2.55587440
12 3.17466645 -0.55815964
13 1.82123689 3.17466645
14 -0.54529076 1.82123689
15 1.73414525 -0.54529076
16 1.16497198 1.73414525
17 3.92110894 1.16497198
18 -7.78181961 3.92110894
19 0.01130760 -7.78181961
20 -4.38077425 0.01130760
21 -7.47223631 -4.38077425
22 -5.85239344 -7.47223631
23 -1.38253222 -5.85239344
24 -0.71494933 -1.38253222
25 -2.67513732 -0.71494933
26 0.16364488 -2.67513732
27 -0.30481383 0.16364488
28 -0.55299672 -0.30481383
29 -1.27976562 -0.55299672
30 -4.85590945 -1.27976562
31 -2.30369781 -4.85590945
32 -4.51684073 -2.30369781
33 -5.87622410 -4.51684073
34 -2.21338688 -5.87622410
35 -2.51343661 -2.21338688
36 0.40443644 -2.51343661
37 -0.52393352 0.40443644
38 3.12430446 -0.52393352
39 1.22070903 3.12430446
40 2.74317984 1.22070903
41 3.30313396 2.74317984
42 -0.76660380 3.30313396
43 1.50004906 -0.76660380
44 0.44766555 1.50004906
45 -1.99593640 0.44766555
46 1.11337578 -1.99593640
47 7.36895280 1.11337578
48 0.08940016 7.36895280
49 2.59891427 0.08940016
50 1.23623941 2.59891427
51 4.25665553 1.23623941
52 1.48029077 4.25665553
53 2.69326255 1.48029077
54 0.21434972 2.69326255
55 5.28251210 0.21434972
56 2.02081174 5.28251210
57 1.57450947 2.02081174
58 1.94925417 1.57450947
59 8.64699861 1.94925417
60 5.96436808 8.64699861
61 1.53707228 5.96436808
62 3.43219292 1.53707228
63 3.24459874 3.43219292
64 0.59594382 3.24459874
65 2.89365422 0.59594382
66 -1.52263248 2.89365422
67 1.14168121 -1.52263248
68 0.63120028 1.14168121
69 -0.73149315 0.63120028
70 0.14942179 -0.73149315
71 5.68984567 0.14942179
72 2.49185735 5.68984567
73 3.49399018 2.49185735
74 6.42829919 3.49399018
75 1.55420775 6.42829919
76 3.76184146 1.55420775
77 0.93508094 3.76184146
78 -7.47913870 0.93508094
79 0.25619135 -7.47913870
80 -2.90866573 0.25619135
81 -2.55735801 -2.90866573
82 -3.76134597 -2.55735801
83 4.81649310 -3.76134597
84 0.30752256 4.81649310
85 0.06964153 0.30752256
86 -0.15049975 0.06964153
87 -2.44576852 -0.15049975
88 1.23278749 -2.44576852
89 0.83458446 1.23278749
90 0.24366754 0.83458446
91 2.73388081 0.24366754
92 0.70529529 2.73388081
93 -0.98000236 0.70529529
94 0.92791260 -0.98000236
95 8.18788484 0.92791260
96 2.46380214 8.18788484
97 1.21892187 2.46380214
98 0.50271954 1.21892187
99 4.42692936 0.50271954
100 3.57021775 4.42692936
101 7.80042969 3.57021775
102 -1.50282321 7.80042969
103 3.62224706 -1.50282321
104 4.54676779 3.62224706
105 0.88437775 4.54676779
106 -1.63626158 0.88437775
107 1.49293010 -1.63626158
108 -8.43758603 1.49293010
109 -4.86598522 -8.43758603
110 -4.62764525 -4.86598522
111 -6.93647934 -4.62764525
112 1.16576502 -6.93647934
113 -4.52669866 1.16576502
114 -10.06477746 -4.52669866
115 -1.33741363 -10.06477746
116 -3.40888018 -1.33741363
117 -4.28433224 -3.40888018
118 -1.53430750 -4.28433224
119 -6.20995084 -1.53430750
120 -0.63534778 -6.20995084
121 0.62981647 -0.63534778
122 -1.06216584 0.62981647
123 -2.68577642 -1.06216584
124 0.92454296 -2.68577642
125 -0.33022412 0.92454296
126 -5.51107165 -0.33022412
127 -0.56945733 -5.51107165
128 4.61498784 -0.56945733
129 -2.51443802 4.61498784
130 1.02561634 -2.51443802
131 2.08209669 1.02561634
132 0.14683539 2.08209669
133 6.03015695 0.14683539
134 6.70033936 6.03015695
135 3.95047689 6.70033936
136 3.23248076 3.95047689
137 1.62712092 3.23248076
138 -1.20249242 1.62712092
139 3.73123162 -1.20249242
140 2.55119003 3.73123162
141 -0.10567520 2.55119003
142 1.21598201 -0.10567520
143 0.99672686 1.21598201
144 -2.52649566 0.99672686
145 -6.36983576 -2.52649566
146 -1.91005621 -6.36983576
147 -4.40090202 -1.91005621
148 -3.60740856 -4.40090202
149 -4.27783133 -3.60740856
150 -6.15028942 -4.27783133
151 NA -6.15028942
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.52169075 -4.66824825
[2,] -2.03214184 -4.52169075
[3,] 1.92977266 -2.03214184
[4,] -1.81465431 1.92977266
[5,] -1.16566133 -1.81465431
[6,] 0.47931921 -1.16566133
[7,] 3.73680458 0.47931921
[8,] -0.47731885 3.73680458
[9,] -4.74986887 -0.47731885
[10,] -2.55587440 -4.74986887
[11,] -0.55815964 -2.55587440
[12,] 3.17466645 -0.55815964
[13,] 1.82123689 3.17466645
[14,] -0.54529076 1.82123689
[15,] 1.73414525 -0.54529076
[16,] 1.16497198 1.73414525
[17,] 3.92110894 1.16497198
[18,] -7.78181961 3.92110894
[19,] 0.01130760 -7.78181961
[20,] -4.38077425 0.01130760
[21,] -7.47223631 -4.38077425
[22,] -5.85239344 -7.47223631
[23,] -1.38253222 -5.85239344
[24,] -0.71494933 -1.38253222
[25,] -2.67513732 -0.71494933
[26,] 0.16364488 -2.67513732
[27,] -0.30481383 0.16364488
[28,] -0.55299672 -0.30481383
[29,] -1.27976562 -0.55299672
[30,] -4.85590945 -1.27976562
[31,] -2.30369781 -4.85590945
[32,] -4.51684073 -2.30369781
[33,] -5.87622410 -4.51684073
[34,] -2.21338688 -5.87622410
[35,] -2.51343661 -2.21338688
[36,] 0.40443644 -2.51343661
[37,] -0.52393352 0.40443644
[38,] 3.12430446 -0.52393352
[39,] 1.22070903 3.12430446
[40,] 2.74317984 1.22070903
[41,] 3.30313396 2.74317984
[42,] -0.76660380 3.30313396
[43,] 1.50004906 -0.76660380
[44,] 0.44766555 1.50004906
[45,] -1.99593640 0.44766555
[46,] 1.11337578 -1.99593640
[47,] 7.36895280 1.11337578
[48,] 0.08940016 7.36895280
[49,] 2.59891427 0.08940016
[50,] 1.23623941 2.59891427
[51,] 4.25665553 1.23623941
[52,] 1.48029077 4.25665553
[53,] 2.69326255 1.48029077
[54,] 0.21434972 2.69326255
[55,] 5.28251210 0.21434972
[56,] 2.02081174 5.28251210
[57,] 1.57450947 2.02081174
[58,] 1.94925417 1.57450947
[59,] 8.64699861 1.94925417
[60,] 5.96436808 8.64699861
[61,] 1.53707228 5.96436808
[62,] 3.43219292 1.53707228
[63,] 3.24459874 3.43219292
[64,] 0.59594382 3.24459874
[65,] 2.89365422 0.59594382
[66,] -1.52263248 2.89365422
[67,] 1.14168121 -1.52263248
[68,] 0.63120028 1.14168121
[69,] -0.73149315 0.63120028
[70,] 0.14942179 -0.73149315
[71,] 5.68984567 0.14942179
[72,] 2.49185735 5.68984567
[73,] 3.49399018 2.49185735
[74,] 6.42829919 3.49399018
[75,] 1.55420775 6.42829919
[76,] 3.76184146 1.55420775
[77,] 0.93508094 3.76184146
[78,] -7.47913870 0.93508094
[79,] 0.25619135 -7.47913870
[80,] -2.90866573 0.25619135
[81,] -2.55735801 -2.90866573
[82,] -3.76134597 -2.55735801
[83,] 4.81649310 -3.76134597
[84,] 0.30752256 4.81649310
[85,] 0.06964153 0.30752256
[86,] -0.15049975 0.06964153
[87,] -2.44576852 -0.15049975
[88,] 1.23278749 -2.44576852
[89,] 0.83458446 1.23278749
[90,] 0.24366754 0.83458446
[91,] 2.73388081 0.24366754
[92,] 0.70529529 2.73388081
[93,] -0.98000236 0.70529529
[94,] 0.92791260 -0.98000236
[95,] 8.18788484 0.92791260
[96,] 2.46380214 8.18788484
[97,] 1.21892187 2.46380214
[98,] 0.50271954 1.21892187
[99,] 4.42692936 0.50271954
[100,] 3.57021775 4.42692936
[101,] 7.80042969 3.57021775
[102,] -1.50282321 7.80042969
[103,] 3.62224706 -1.50282321
[104,] 4.54676779 3.62224706
[105,] 0.88437775 4.54676779
[106,] -1.63626158 0.88437775
[107,] 1.49293010 -1.63626158
[108,] -8.43758603 1.49293010
[109,] -4.86598522 -8.43758603
[110,] -4.62764525 -4.86598522
[111,] -6.93647934 -4.62764525
[112,] 1.16576502 -6.93647934
[113,] -4.52669866 1.16576502
[114,] -10.06477746 -4.52669866
[115,] -1.33741363 -10.06477746
[116,] -3.40888018 -1.33741363
[117,] -4.28433224 -3.40888018
[118,] -1.53430750 -4.28433224
[119,] -6.20995084 -1.53430750
[120,] -0.63534778 -6.20995084
[121,] 0.62981647 -0.63534778
[122,] -1.06216584 0.62981647
[123,] -2.68577642 -1.06216584
[124,] 0.92454296 -2.68577642
[125,] -0.33022412 0.92454296
[126,] -5.51107165 -0.33022412
[127,] -0.56945733 -5.51107165
[128,] 4.61498784 -0.56945733
[129,] -2.51443802 4.61498784
[130,] 1.02561634 -2.51443802
[131,] 2.08209669 1.02561634
[132,] 0.14683539 2.08209669
[133,] 6.03015695 0.14683539
[134,] 6.70033936 6.03015695
[135,] 3.95047689 6.70033936
[136,] 3.23248076 3.95047689
[137,] 1.62712092 3.23248076
[138,] -1.20249242 1.62712092
[139,] 3.73123162 -1.20249242
[140,] 2.55119003 3.73123162
[141,] -0.10567520 2.55119003
[142,] 1.21598201 -0.10567520
[143,] 0.99672686 1.21598201
[144,] -2.52649566 0.99672686
[145,] -6.36983576 -2.52649566
[146,] -1.91005621 -6.36983576
[147,] -4.40090202 -1.91005621
[148,] -3.60740856 -4.40090202
[149,] -4.27783133 -3.60740856
[150,] -6.15028942 -4.27783133
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.52169075 -4.66824825
2 -2.03214184 -4.52169075
3 1.92977266 -2.03214184
4 -1.81465431 1.92977266
5 -1.16566133 -1.81465431
6 0.47931921 -1.16566133
7 3.73680458 0.47931921
8 -0.47731885 3.73680458
9 -4.74986887 -0.47731885
10 -2.55587440 -4.74986887
11 -0.55815964 -2.55587440
12 3.17466645 -0.55815964
13 1.82123689 3.17466645
14 -0.54529076 1.82123689
15 1.73414525 -0.54529076
16 1.16497198 1.73414525
17 3.92110894 1.16497198
18 -7.78181961 3.92110894
19 0.01130760 -7.78181961
20 -4.38077425 0.01130760
21 -7.47223631 -4.38077425
22 -5.85239344 -7.47223631
23 -1.38253222 -5.85239344
24 -0.71494933 -1.38253222
25 -2.67513732 -0.71494933
26 0.16364488 -2.67513732
27 -0.30481383 0.16364488
28 -0.55299672 -0.30481383
29 -1.27976562 -0.55299672
30 -4.85590945 -1.27976562
31 -2.30369781 -4.85590945
32 -4.51684073 -2.30369781
33 -5.87622410 -4.51684073
34 -2.21338688 -5.87622410
35 -2.51343661 -2.21338688
36 0.40443644 -2.51343661
37 -0.52393352 0.40443644
38 3.12430446 -0.52393352
39 1.22070903 3.12430446
40 2.74317984 1.22070903
41 3.30313396 2.74317984
42 -0.76660380 3.30313396
43 1.50004906 -0.76660380
44 0.44766555 1.50004906
45 -1.99593640 0.44766555
46 1.11337578 -1.99593640
47 7.36895280 1.11337578
48 0.08940016 7.36895280
49 2.59891427 0.08940016
50 1.23623941 2.59891427
51 4.25665553 1.23623941
52 1.48029077 4.25665553
53 2.69326255 1.48029077
54 0.21434972 2.69326255
55 5.28251210 0.21434972
56 2.02081174 5.28251210
57 1.57450947 2.02081174
58 1.94925417 1.57450947
59 8.64699861 1.94925417
60 5.96436808 8.64699861
61 1.53707228 5.96436808
62 3.43219292 1.53707228
63 3.24459874 3.43219292
64 0.59594382 3.24459874
65 2.89365422 0.59594382
66 -1.52263248 2.89365422
67 1.14168121 -1.52263248
68 0.63120028 1.14168121
69 -0.73149315 0.63120028
70 0.14942179 -0.73149315
71 5.68984567 0.14942179
72 2.49185735 5.68984567
73 3.49399018 2.49185735
74 6.42829919 3.49399018
75 1.55420775 6.42829919
76 3.76184146 1.55420775
77 0.93508094 3.76184146
78 -7.47913870 0.93508094
79 0.25619135 -7.47913870
80 -2.90866573 0.25619135
81 -2.55735801 -2.90866573
82 -3.76134597 -2.55735801
83 4.81649310 -3.76134597
84 0.30752256 4.81649310
85 0.06964153 0.30752256
86 -0.15049975 0.06964153
87 -2.44576852 -0.15049975
88 1.23278749 -2.44576852
89 0.83458446 1.23278749
90 0.24366754 0.83458446
91 2.73388081 0.24366754
92 0.70529529 2.73388081
93 -0.98000236 0.70529529
94 0.92791260 -0.98000236
95 8.18788484 0.92791260
96 2.46380214 8.18788484
97 1.21892187 2.46380214
98 0.50271954 1.21892187
99 4.42692936 0.50271954
100 3.57021775 4.42692936
101 7.80042969 3.57021775
102 -1.50282321 7.80042969
103 3.62224706 -1.50282321
104 4.54676779 3.62224706
105 0.88437775 4.54676779
106 -1.63626158 0.88437775
107 1.49293010 -1.63626158
108 -8.43758603 1.49293010
109 -4.86598522 -8.43758603
110 -4.62764525 -4.86598522
111 -6.93647934 -4.62764525
112 1.16576502 -6.93647934
113 -4.52669866 1.16576502
114 -10.06477746 -4.52669866
115 -1.33741363 -10.06477746
116 -3.40888018 -1.33741363
117 -4.28433224 -3.40888018
118 -1.53430750 -4.28433224
119 -6.20995084 -1.53430750
120 -0.63534778 -6.20995084
121 0.62981647 -0.63534778
122 -1.06216584 0.62981647
123 -2.68577642 -1.06216584
124 0.92454296 -2.68577642
125 -0.33022412 0.92454296
126 -5.51107165 -0.33022412
127 -0.56945733 -5.51107165
128 4.61498784 -0.56945733
129 -2.51443802 4.61498784
130 1.02561634 -2.51443802
131 2.08209669 1.02561634
132 0.14683539 2.08209669
133 6.03015695 0.14683539
134 6.70033936 6.03015695
135 3.95047689 6.70033936
136 3.23248076 3.95047689
137 1.62712092 3.23248076
138 -1.20249242 1.62712092
139 3.73123162 -1.20249242
140 2.55119003 3.73123162
141 -0.10567520 2.55119003
142 1.21598201 -0.10567520
143 0.99672686 1.21598201
144 -2.52649566 0.99672686
145 -6.36983576 -2.52649566
146 -1.91005621 -6.36983576
147 -4.40090202 -1.91005621
148 -3.60740856 -4.40090202
149 -4.27783133 -3.60740856
150 -6.15028942 -4.27783133
> 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/7cfv01351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8eobv1351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9pxdz1351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10oz7i1351612710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o2vm1351612710.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/12ft161351612710.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/13r09o1351612710.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/14s7my1351612710.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/15rhya1351612710.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/16q17w1351612710.tab")
+ }
>
> try(system("convert tmp/1wbm01351612710.ps tmp/1wbm01351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y0p61351612710.ps tmp/2y0p61351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mb0n1351612710.ps tmp/3mb0n1351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cwyt1351612710.ps tmp/4cwyt1351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/5szm41351612710.ps tmp/5szm41351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ay4e1351612710.ps tmp/6ay4e1351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cfv01351612710.ps tmp/7cfv01351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/8eobv1351612710.ps tmp/8eobv1351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pxdz1351612710.ps tmp/9pxdz1351612710.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oz7i1351612710.ps tmp/10oz7i1351612710.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.507 1.050 8.664