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)
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> x <- array(list(75.5
+ ,78.4
+ ,67.3
+ ,75.3
+ ,106.1
+ ,125.7
+ ,101.6
+ ,83.2
+ ,79.3
+ ,75.2
+ ,83.6
+ ,112.7
+ ,153.8
+ ,113.4
+ ,94.5
+ ,84.3
+ ,91.1
+ ,91.2
+ ,123.2
+ ,134.9
+ ,122.2
+ ,83.3
+ ,81.2
+ ,83.7
+ ,85.2
+ ,101.7
+ ,95.3
+ ,102.2
+ ,92.7
+ ,88.4
+ ,105.0
+ ,100.0
+ ,118.7
+ ,96.6
+ ,113.2
+ ,89.8
+ ,83.1
+ ,106.2
+ ,89.8
+ ,107.1
+ ,100.5
+ ,115.3
+ ,74.8
+ ,76.6
+ ,88.5
+ ,88.9
+ ,93.6
+ ,106.2
+ ,87.4
+ ,81.5
+ ,82.6
+ ,100.1
+ ,85.6
+ ,77.5
+ ,153.4
+ ,98.7
+ ,92.8
+ ,84.4
+ ,90.3
+ ,83.2
+ ,117.2
+ ,132.1
+ ,117.3
+ ,92.8
+ ,94.6
+ ,85.3
+ ,97.1
+ ,124.5
+ ,110.9
+ ,121.2
+ ,91.7
+ ,91.8
+ ,81.9
+ ,85.8
+ ,120.8
+ ,94.3
+ ,118.7
+ ,83.5
+ ,89.3
+ ,77.2
+ ,80.9
+ ,97.0
+ ,91.7
+ ,112.1
+ ,92.8
+ ,87.7
+ ,78.6
+ ,81.3
+ ,115.1
+ ,138.6
+ ,102.9
+ ,91.3
+ ,83.1
+ ,75.1
+ ,83.2
+ ,112.9
+ ,154.3
+ ,108.8
+ ,99.5
+ ,93.6
+ ,90.3
+ ,90.7
+ ,122.7
+ ,149.8
+ ,118.6
+ ,87.6
+ ,85.1
+ ,88.5
+ ,88.4
+ ,106.9
+ ,99.2
+ ,99.2
+ ,95.3
+ ,90.8
+ ,112.5
+ ,94.1
+ ,115.0
+ ,97.7
+ ,102.2
+ ,98.5
+ ,90.5
+ ,101.1
+ ,92.0
+ ,114.9
+ ,107.7
+ ,108.8
+ ,80.1
+ ,86.1
+ ,114.0
+ ,92.0
+ ,103.1
+ ,120.1
+ ,94.0
+ ,84.2
+ ,93.3
+ ,107.7
+ ,89.3
+ ,80.8
+ ,164.5
+ ,96.2
+ ,92.4
+ ,94.9
+ ,77.8
+ ,87.0
+ ,118.2
+ ,136.1
+ ,118.4
+ ,98.0
+ ,102.6
+ ,101.4
+ ,97.7
+ ,129.6
+ ,117.5
+ ,120.0
+ ,92.2
+ ,98.3
+ ,87.2
+ ,82.5
+ ,118.7
+ ,98.2
+ ,117.5
+ ,80.0
+ ,93.4
+ ,75.9
+ ,96.5
+ ,88.4
+ ,91.9
+ ,102.6
+ ,88.7
+ ,92.8
+ ,78.8
+ ,86.2
+ ,113.1
+ ,141.8
+ ,92.8
+ ,87.4
+ ,86.5
+ ,82.3
+ ,84.9
+ ,109.8
+ ,154.2
+ ,100.3
+ ,96.1
+ ,93.8
+ ,89.1
+ ,100.0
+ ,116.1
+ ,138.6
+ ,106.3
+ ,94.1
+ ,90.4
+ ,100.1
+ ,92.7
+ ,113.6
+ ,97.9
+ ,103.9
+ ,91.9
+ ,91.0
+ ,101.8
+ ,96.7
+ ,107.9
+ ,90.3
+ ,102.4
+ ,93.6
+ ,89.1
+ ,98.5
+ ,105.8
+ ,107.4
+ ,90.9
+ ,114.5
+ ,83.5
+ ,89.6
+ ,106.6
+ ,88.5
+ ,102.7
+ ,127.0
+ ,89.0
+ ,80.8
+ ,89.3
+ ,101.8
+ ,78.7
+ ,78.3
+ ,156.8
+ ,94.3
+ ,96.3
+ ,95.3
+ ,92.4
+ ,99.9
+ ,121.0
+ ,127.2
+ ,115.7
+ ,101.5
+ ,104.1
+ ,94.4
+ ,107.8
+ ,132.2
+ ,111.3
+ ,120.2
+ ,91.6
+ ,94.7
+ ,81.0
+ ,102.4
+ ,113.2
+ ,93.0
+ ,109.5
+ ,84.0
+ ,97.6
+ ,94.6
+ ,106.0
+ ,89.2
+ ,89.5
+ ,99.4
+ ,91.8
+ ,96.8
+ ,83.8
+ ,87.3
+ ,113.2
+ ,141.8
+ ,86.4
+ ,90.4
+ ,92.8
+ ,79.4
+ ,93.3
+ ,107.6
+ ,152.0
+ ,95.1
+ ,98.0
+ ,94.7
+ ,95.6
+ ,98.2
+ ,107.3
+ ,120.2
+ ,101.5
+ ,95.5
+ ,95.8
+ ,106.0
+ ,102.0
+ ,110.9
+ ,88.8
+ ,92.9
+ ,90.5
+ ,88.9
+ ,106.2
+ ,93.9
+ ,96.4
+ ,82.8
+ ,90.8
+ ,97.1
+ ,91.2
+ ,115.0
+ ,106.6
+ ,101.2
+ ,82.8
+ ,100.4
+ ,87.9
+ ,91.6
+ ,122.4
+ ,92.9
+ ,94.0
+ ,121.7
+ ,82.2
+ ,79.8
+ ,87.3
+ ,113.7
+ ,78.0
+ ,70.5
+ ,147.1
+ ,75.3
+ ,102.0
+ ,97.8
+ ,98.0
+ ,104.2
+ ,116.4
+ ,132.5
+ ,110.3
+ ,104.3
+ ,105.1
+ ,105.8
+ ,115.9
+ ,121.9
+ ,107.5
+ ,113.5
+ ,92.1
+ ,93.8
+ ,88.3
+ ,99.9
+ ,109.5
+ ,77.9
+ ,94.9
+ ,95.9
+ ,99.0
+ ,95.7
+ ,103.9
+ ,91.1
+ ,85.5
+ ,95.7
+ ,89.1
+ ,91.4
+ ,85.8
+ ,93.5
+ ,104.0
+ ,126.5
+ ,85.3
+ ,92.2
+ ,89.0
+ ,83.9
+ ,101.7
+ ,101.2
+ ,135.4
+ ,92.5
+ ,107.5
+ ,101.4
+ ,114.1
+ ,124.6
+ ,118.4
+ ,122.5
+ ,107.7
+ ,99.7
+ ,95.4
+ ,102.0
+ ,124.2
+ ,106.9
+ ,79.2
+ ,97.9
+ ,92.2
+ ,90.5
+ ,108.1
+ ,103.3
+ ,95.6
+ ,66.1
+ ,93.9
+ ,108.9
+ ,98.7
+ ,125.4
+ ,120.5
+ ,114.2
+ ,77.9
+ ,111.5
+ ,89.8
+ ,91.2
+ ,108.1
+ ,98.0
+ ,92.4
+ ,109.6
+ ,88.6
+ ,89.4
+ ,91.7
+ ,110.4
+ ,100.4
+ ,75.3
+ ,142.9
+ ,82.5
+ ,107.6
+ ,102.9
+ ,102.4
+ ,126.8
+ ,120.4
+ ,120.5
+ ,108.6
+ ,105.6
+ ,105.5
+ ,89.6
+ ,120.2
+ ,115.9
+ ,96.3
+ ,113.8
+ ,100.9
+ ,102.6
+ ,95.0
+ ,114.0
+ ,109.8
+ ,82.6
+ ,103.4
+ ,102.9
+ ,107.2
+ ,93.7
+ ,109.1
+ ,94.9
+ ,78.4
+ ,99.0
+ ,96.2
+ ,96.9
+ ,77.7
+ ,94.2
+ ,97.5
+ ,104.5
+ ,89.9
+ ,94.7
+ ,88.9
+ ,80.1
+ ,86.0
+ ,101.3
+ ,137.9
+ ,97.9
+ ,107.3
+ ,99.6
+ ,103.6
+ ,112.9
+ ,108.7
+ ,125.8
+ ,107.8
+ ,103.0
+ ,96.7
+ ,103.1
+ ,99.7
+ ,105.1
+ ,78.0
+ ,103.7
+ ,96.1
+ ,93.8
+ ,112.4
+ ,104.5
+ ,94.9
+ ,67.7
+ ,98.2
+ ,109.8
+ ,101.9
+ ,119.2
+ ,111.6
+ ,108.9
+ ,78.4
+ ,111.7
+ ,85.4
+ ,87.6
+ ,105.3
+ ,99.2
+ ,87.5
+ ,101.7
+ ,82.6
+ ,89.9
+ ,100.0
+ ,107.2
+ ,90.9
+ ,73.0
+ ,154.1
+ ,86.1
+ ,109.3
+ ,105.8
+ ,108.7
+ ,111.4
+ ,115.2
+ ,107.3
+ ,111.2
+ ,101.2
+ ,105.5
+ ,93.7
+ ,98.2
+ ,107.5
+ ,86.5
+ ,105.3
+ ,104.7
+ ,111.3
+ ,96.1
+ ,101.7
+ ,109.8
+ ,82.1
+ ,106.3
+ ,102.4
+ ,112.1
+ ,92.9
+ ,89.7
+ ,90.7
+ ,76.1
+ ,99.4
+ ,97.7
+ ,102.0
+ ,81.1
+ ,89.5
+ ,97.6
+ ,115.5
+ ,91.9
+ ,98.9
+ ,93.2
+ ,83.2
+ ,85.1
+ ,98.7
+ ,129.6
+ ,96.2
+ ,115.0
+ ,108.4
+ ,99.7
+ ,95.9
+ ,113.9
+ ,121.6
+ ,105.4
+ ,97.5
+ ,97.9
+ ,96.8
+ ,88.9
+ ,96.6
+ ,64.0
+ ,95.0
+ ,107.3
+ ,106.4
+ ,108.7
+ ,98.1
+ ,104.4
+ ,58.1
+ ,100.5
+ ,112.3
+ ,102.8
+ ,120.9
+ ,109.7
+ ,115.1
+ ,79.7
+ ,111.6
+ ,88.5
+ ,96.3
+ ,114.8
+ ,92.0
+ ,91.4
+ ,108.9
+ ,88.5
+ ,92.9
+ ,105.7
+ ,108.7
+ ,74.3
+ ,76.2
+ ,138.5
+ ,83.7
+ ,108.8
+ ,108.4
+ ,97.4
+ ,96.9
+ ,117.4
+ ,117.9
+ ,113.9
+ ,112.3
+ ,115.8
+ ,98.6
+ ,100.3
+ ,122.0
+ ,96.7
+ ,115.2
+ ,107.3
+ ,113.8
+ ,91.7
+ ,97.1
+ ,120.2
+ ,78.6
+ ,111.0
+ ,101.8
+ ,106.4
+ ,91.2
+ ,86.0
+ ,93.6
+ ,64.1
+ ,96.9
+ ,105.0
+ ,107.9
+ ,83.5
+ ,97.3
+ ,106.6
+ ,112.0
+ ,102.1
+ ,103.4
+ ,98.2
+ ,82.4
+ ,86.4
+ ,108.4
+ ,139.4
+ ,101.5
+ ,116.7
+ ,111.1
+ ,103.1
+ ,97.7
+ ,121.4
+ ,116.2
+ ,115.0
+ ,103.6
+ ,99.8
+ ,110.3
+ ,90.6
+ ,104.8
+ ,63.4
+ ,105.0
+ ,108.8
+ ,103.5
+ ,115.8
+ ,99.2
+ ,104.2
+ ,61.1
+ ,105.4
+ ,117.0
+ ,105.4
+ ,120.1
+ ,107.4
+ ,115.0
+ ,65.5
+ ,119.7
+ ,100.9
+ ,102.6
+ ,105.1
+ ,107.1
+ ,99.0
+ ,90.9
+ ,91.8
+ ,100.8
+ ,107.4
+ ,108.6
+ ,78.9
+ ,82.8
+ ,115.3
+ ,89.1
+ ,109.7
+ ,108.2
+ ,95.7
+ ,92.8
+ ,112.5
+ ,85.2
+ ,106.2
+ ,121.0
+ ,121.7
+ ,103.2
+ ,106.2
+ ,127.9
+ ,87.0
+ ,119.9
+ ,114.1
+ ,118.0
+ ,96.9
+ ,97.2
+ ,114.4
+ ,62.6
+ ,111.6
+ ,105.5
+ ,109.6
+ ,95.7
+ ,80.0
+ ,83.7
+ ,62.7
+ ,95.1
+ ,112.5
+ ,116.7
+ ,92.7
+ ,109.3
+ ,108.5
+ ,91.6
+ ,101.3
+ ,113.8
+ ,110.6
+ ,81.3
+ ,111.3
+ ,109.7
+ ,104.3
+ ,118.3
+ ,115.3
+ ,109.6
+ ,94.5
+ ,119.5
+ ,104.7
+ ,88.1
+ ,126.2
+ ,120.4
+ ,117.4
+ ,105.6
+ ,119.8
+ ,112.2
+ ,62.3
+ ,113.2
+ ,111.1
+ ,109.2
+ ,112.9
+ ,112.5
+ ,96.9
+ ,50.3
+ ,103.6
+ ,120.1
+ ,110.8
+ ,102.6
+ ,125.6
+ ,103.8
+ ,64.1
+ ,116.2
+ ,106.1
+ ,112.8
+ ,116.2
+ ,105.1
+ ,95.1
+ ,75.7
+ ,98.3
+ ,95.9
+ ,106.5
+ ,104.9
+ ,91.9
+ ,66.7
+ ,85.5
+ ,84.2
+ ,119.4
+ ,119.6
+ ,100.4
+ ,128.2
+ ,103.4
+ ,71.9
+ ,118.3
+ ,117.4
+ ,127.2
+ ,97.1
+ ,122.6
+ ,105.4
+ ,66.9
+ ,117.4
+ ,98.6
+ ,113.9
+ ,90.2
+ ,109.6
+ ,89.2
+ ,50.5
+ ,94.5
+ ,99.7
+ ,120.0
+ ,100.5
+ ,120.4
+ ,72.5
+ ,57.9
+ ,93.3
+ ,87.4
+ ,107.6
+ ,81.1
+ ,103.8
+ ,78.0
+ ,84.1
+ ,90.2
+ ,90.8
+ ,105.2
+ ,87.2
+ ,96.6
+ ,77.3
+ ,87.0
+ ,88.5
+ ,101.3
+ ,115.3
+ ,102.0
+ ,110.7
+ ,85.1
+ ,71.9
+ ,101.0
+ ,93.2
+ ,113.9
+ ,107.0
+ ,111.7
+ ,80.9
+ ,45.0
+ ,87.0
+ ,95.1
+ ,106.1
+ ,107.6
+ ,111.9
+ ,72.5
+ ,39.5
+ ,81.2
+ ,101.9
+ ,114.3
+ ,123.5
+ ,131.5
+ ,82.1
+ ,53.8
+ ,98.1
+ ,87.0
+ ,112.0
+ ,116.6
+ ,122.8
+ ,78.3
+ ,59.5
+ ,75.5
+ ,86.2
+ ,109.0
+ ,103.2
+ ,98.3
+ ,57.8
+ ,68.4
+ ,70.7
+ ,105.0
+ ,119.1
+ ,103.9
+ ,133.7
+ ,89.3
+ ,56.9
+ ,103.7
+ ,104.1
+ ,124.4
+ ,95.4
+ ,120.0
+ ,91.4
+ ,61.9
+ ,100.4
+ ,99.2
+ ,116.6
+ ,93.6
+ ,119.6
+ ,84.2
+ ,40.4
+ ,91.3
+ ,95.2
+ ,118.5
+ ,102.1
+ ,108.7
+ ,72.5
+ ,49.4
+ ,97.2
+ ,92.7
+ ,108.9
+ ,69.0
+ ,112.5
+ ,74.6
+ ,65.2
+ ,85.4
+ ,99.3
+ ,107.5
+ ,88.9
+ ,102.7
+ ,80.3
+ ,82.1
+ ,86.5
+ ,113.5
+ ,125.9
+ ,106.2
+ ,123.4
+ ,92.6
+ ,69.0
+ ,105.3
+ ,104.7
+ ,117.7
+ ,103.0
+ ,116.5
+ ,86.3
+ ,45.9
+ ,97.7
+ ,100.5
+ ,109.2
+ ,103.5
+ ,102.3
+ ,80.3
+ ,39.1
+ ,84.3
+ ,116.2
+ ,118.8
+ ,124.5
+ ,148.4
+ ,93.6
+ ,56.9
+ ,109.8
+ ,94.1
+ ,108.1
+ ,117.9
+ ,126.6
+ ,79.5
+ ,51.6
+ ,79.1
+ ,94.8
+ ,112.1
+ ,104.2
+ ,106.6
+ ,61.8
+ ,62.9
+ ,83.4
+ ,115.1
+ ,117.8
+ ,99.9
+ ,144.4
+ ,94.8
+ ,58.3
+ ,101.9
+ ,110.0
+ ,121.8
+ ,89.4
+ ,132.4
+ ,91.6
+ ,56.9
+ ,113.0
+ ,108.4
+ ,121.0
+ ,93.5
+ ,136.2
+ ,89.2
+ ,41.3
+ ,98.6
+ ,103.9
+ ,121.7
+ ,89.6
+ ,121.6
+ ,74.1
+ ,46.9
+ ,94.7
+ ,102.9
+ ,114.2
+ ,85.0
+ ,135.1
+ ,78.6
+ ,61.9
+ ,94.5
+ ,107.7
+ ,109.8
+ ,90.0
+ ,124.7
+ ,78.2
+ ,74.8
+ ,90.7
+ ,126.7
+ ,124.1
+ ,113.7
+ ,148.8
+ ,95.1
+ ,67.0
+ ,113.0
+ ,108.8
+ ,112.9
+ ,112.1
+ ,145.6
+ ,78.7
+ ,53.3
+ ,89.9
+ ,117.1
+ ,118.7
+ ,129.8
+ ,140.3
+ ,85.9
+ ,51.4
+ ,98.7
+ ,112.2
+ ,113.3
+ ,119.1
+ ,138.5
+ ,81.2
+ ,50.3
+ ,102.2
+ ,94.7
+ ,106.8
+ ,103.5
+ ,127.3
+ ,73.1
+ ,52.7
+ ,74.3
+ ,102.7
+ ,119.3
+ ,105.5
+ ,117.9
+ ,58.7
+ ,70.3
+ ,84.5
+ ,119.1
+ ,126.4
+ ,111.7
+ ,145.3
+ ,85.7
+ ,59.7
+ ,110.1
+ ,110.6
+ ,126.6
+ ,98.6
+ ,120.7
+ ,81.8
+ ,52.0
+ ,100.4
+ ,109.1
+ ,127.2
+ ,102.8
+ ,134.7
+ ,79.6
+ ,36.1
+ ,92.8
+ ,105.3
+ ,123.8
+ ,101.1
+ ,124.4
+ ,70.7
+ ,39.7
+ ,92.2
+ ,103.4
+ ,116.8
+ ,94.2
+ ,128.3
+ ,74.5
+ ,67.6
+ ,94.0
+ ,103.7
+ ,113.8
+ ,92.6
+ ,128.4
+ ,84.8
+ ,72.8
+ ,100.7
+ ,117.0
+ ,130.4
+ ,112.0
+ ,134.1
+ ,80.7
+ ,53.8
+ ,111.9
+ ,101.2
+ ,112.8
+ ,108.6
+ ,133.3
+ ,69.9
+ ,39.6
+ ,95.9
+ ,105.4
+ ,119.4
+ ,125.8
+ ,130.6
+ ,74.1
+ ,39.4
+ ,88.8
+ ,110.3
+ ,117.5
+ ,138.7
+ ,165.7
+ ,76.1
+ ,41.2
+ ,102.0
+ ,97.7
+ ,117.5
+ ,115.2
+ ,146.8
+ ,71.3
+ ,49.6
+ ,81.6)
+ ,dim=c(7
+ ,151)
+ ,dimnames=list(c('Totaal'
+ ,'Voeding'
+ ,'Dranken'
+ ,'Tabaksproducten'
+ ,'Textiel'
+ ,'Kleding'
+ ,'Apparatuur
')
+ ,1:151))
> y <- array(NA,dim=c(7,151),dimnames=list(c('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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
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 Voeding Dranken Tabaksproducten Textiel Kleding Apparatuur\r
1 75.5 78.4 67.3 75.3 106.1 125.7 101.6
2 83.2 79.3 75.2 83.6 112.7 153.8 113.4
3 94.5 84.3 91.1 91.2 123.2 134.9 122.2
4 83.3 81.2 83.7 85.2 101.7 95.3 102.2
5 92.7 88.4 105.0 100.0 118.7 96.6 113.2
6 89.8 83.1 106.2 89.8 107.1 100.5 115.3
7 74.8 76.6 88.5 88.9 93.6 106.2 87.4
8 81.5 82.6 100.1 85.6 77.5 153.4 98.7
9 92.8 84.4 90.3 83.2 117.2 132.1 117.3
10 92.8 94.6 85.3 97.1 124.5 110.9 121.2
11 91.7 91.8 81.9 85.8 120.8 94.3 118.7
12 83.5 89.3 77.2 80.9 97.0 91.7 112.1
13 92.8 87.7 78.6 81.3 115.1 138.6 102.9
14 91.3 83.1 75.1 83.2 112.9 154.3 108.8
15 99.5 93.6 90.3 90.7 122.7 149.8 118.6
16 87.6 85.1 88.5 88.4 106.9 99.2 99.2
17 95.3 90.8 112.5 94.1 115.0 97.7 102.2
18 98.5 90.5 101.1 92.0 114.9 107.7 108.8
19 80.1 86.1 114.0 92.0 103.1 120.1 94.0
20 84.2 93.3 107.7 89.3 80.8 164.5 96.2
21 92.4 94.9 77.8 87.0 118.2 136.1 118.4
22 98.0 102.6 101.4 97.7 129.6 117.5 120.0
23 92.2 98.3 87.2 82.5 118.7 98.2 117.5
24 80.0 93.4 75.9 96.5 88.4 91.9 102.6
25 88.7 92.8 78.8 86.2 113.1 141.8 92.8
26 87.4 86.5 82.3 84.9 109.8 154.2 100.3
27 96.1 93.8 89.1 100.0 116.1 138.6 106.3
28 94.1 90.4 100.1 92.7 113.6 97.9 103.9
29 91.9 91.0 101.8 96.7 107.9 90.3 102.4
30 93.6 89.1 98.5 105.8 107.4 90.9 114.5
31 83.5 89.6 106.6 88.5 102.7 127.0 89.0
32 80.8 89.3 101.8 78.7 78.3 156.8 94.3
33 96.3 95.3 92.4 99.9 121.0 127.2 115.7
34 101.5 104.1 94.4 107.8 132.2 111.3 120.2
35 91.6 94.7 81.0 102.4 113.2 93.0 109.5
36 84.0 97.6 94.6 106.0 89.2 89.5 99.4
37 91.8 96.8 83.8 87.3 113.2 141.8 86.4
38 90.4 92.8 79.4 93.3 107.6 152.0 95.1
39 98.0 94.7 95.6 98.2 107.3 120.2 101.5
40 95.5 95.8 106.0 102.0 110.9 88.8 92.9
41 90.5 88.9 106.2 93.9 96.4 82.8 90.8
42 97.1 91.2 115.0 106.6 101.2 82.8 100.4
43 87.9 91.6 122.4 92.9 94.0 121.7 82.2
44 79.8 87.3 113.7 78.0 70.5 147.1 75.3
45 102.0 97.8 98.0 104.2 116.4 132.5 110.3
46 104.3 105.1 105.8 115.9 121.9 107.5 113.5
47 92.1 93.8 88.3 99.9 109.5 77.9 94.9
48 95.9 99.0 95.7 103.9 91.1 85.5 95.7
49 89.1 91.4 85.8 93.5 104.0 126.5 85.3
50 92.2 89.0 83.9 101.7 101.2 135.4 92.5
51 107.5 101.4 114.1 124.6 118.4 122.5 107.7
52 99.7 95.4 102.0 124.2 106.9 79.2 97.9
53 92.2 90.5 108.1 103.3 95.6 66.1 93.9
54 108.9 98.7 125.4 120.5 114.2 77.9 111.5
55 89.8 91.2 108.1 98.0 92.4 109.6 88.6
56 89.4 91.7 110.4 100.4 75.3 142.9 82.5
57 107.6 102.9 102.4 126.8 120.4 120.5 108.6
58 105.6 105.5 89.6 120.2 115.9 96.3 113.8
59 100.9 102.6 95.0 114.0 109.8 82.6 103.4
60 102.9 107.2 93.7 109.1 94.9 78.4 99.0
61 96.2 96.9 77.7 94.2 97.5 104.5 89.9
62 94.7 88.9 80.1 86.0 101.3 137.9 97.9
63 107.3 99.6 103.6 112.9 108.7 125.8 107.8
64 103.0 96.7 103.1 99.7 105.1 78.0 103.7
65 96.1 93.8 112.4 104.5 94.9 67.7 98.2
66 109.8 101.9 119.2 111.6 108.9 78.4 111.7
67 85.4 87.6 105.3 99.2 87.5 101.7 82.6
68 89.9 100.0 107.2 90.9 73.0 154.1 86.1
69 109.3 105.8 108.7 111.4 115.2 107.3 111.2
70 101.2 105.5 93.7 98.2 107.5 86.5 105.3
71 104.7 111.3 96.1 101.7 109.8 82.1 106.3
72 102.4 112.1 92.9 89.7 90.7 76.1 99.4
73 97.7 102.0 81.1 89.5 97.6 115.5 91.9
74 98.9 93.2 83.2 85.1 98.7 129.6 96.2
75 115.0 108.4 99.7 95.9 113.9 121.6 105.4
76 97.5 97.9 96.8 88.9 96.6 64.0 95.0
77 107.3 106.4 108.7 98.1 104.4 58.1 100.5
78 112.3 102.8 120.9 109.7 115.1 79.7 111.6
79 88.5 96.3 114.8 92.0 91.4 108.9 88.5
80 92.9 105.7 108.7 74.3 76.2 138.5 83.7
81 108.8 108.4 97.4 96.9 117.4 117.9 113.9
82 112.3 115.8 98.6 100.3 122.0 96.7 115.2
83 107.3 113.8 91.7 97.1 120.2 78.6 111.0
84 101.8 106.4 91.2 86.0 93.6 64.1 96.9
85 105.0 107.9 83.5 97.3 106.6 112.0 102.1
86 103.4 98.2 82.4 86.4 108.4 139.4 101.5
87 116.7 111.1 103.1 97.7 121.4 116.2 115.0
88 103.6 99.8 110.3 90.6 104.8 63.4 105.0
89 108.8 103.5 115.8 99.2 104.2 61.1 105.4
90 117.0 105.4 120.1 107.4 115.0 65.5 119.7
91 100.9 102.6 105.1 107.1 99.0 90.9 91.8
92 100.8 107.4 108.6 78.9 82.8 115.3 89.1
93 109.7 108.2 95.7 92.8 112.5 85.2 106.2
94 121.0 121.7 103.2 106.2 127.9 87.0 119.9
95 114.1 118.0 96.9 97.2 114.4 62.6 111.6
96 105.5 109.6 95.7 80.0 83.7 62.7 95.1
97 112.5 116.7 92.7 109.3 108.5 91.6 101.3
98 113.8 110.6 81.3 111.3 109.7 104.3 118.3
99 115.3 109.6 94.5 119.5 104.7 88.1 126.2
100 120.4 117.4 105.6 119.8 112.2 62.3 113.2
101 111.1 109.2 112.9 112.5 96.9 50.3 103.6
102 120.1 110.8 102.6 125.6 103.8 64.1 116.2
103 106.1 112.8 116.2 105.1 95.1 75.7 98.3
104 95.9 106.5 104.9 91.9 66.7 85.5 84.2
105 119.4 119.6 100.4 128.2 103.4 71.9 118.3
106 117.4 127.2 97.1 122.6 105.4 66.9 117.4
107 98.6 113.9 90.2 109.6 89.2 50.5 94.5
108 99.7 120.0 100.5 120.4 72.5 57.9 93.3
109 87.4 107.6 81.1 103.8 78.0 84.1 90.2
110 90.8 105.2 87.2 96.6 77.3 87.0 88.5
111 101.3 115.3 102.0 110.7 85.1 71.9 101.0
112 93.2 113.9 107.0 111.7 80.9 45.0 87.0
113 95.1 106.1 107.6 111.9 72.5 39.5 81.2
114 101.9 114.3 123.5 131.5 82.1 53.8 98.1
115 87.0 112.0 116.6 122.8 78.3 59.5 75.5
116 86.2 109.0 103.2 98.3 57.8 68.4 70.7
117 105.0 119.1 103.9 133.7 89.3 56.9 103.7
118 104.1 124.4 95.4 120.0 91.4 61.9 100.4
119 99.2 116.6 93.6 119.6 84.2 40.4 91.3
120 95.2 118.5 102.1 108.7 72.5 49.4 97.2
121 92.7 108.9 69.0 112.5 74.6 65.2 85.4
122 99.3 107.5 88.9 102.7 80.3 82.1 86.5
123 113.5 125.9 106.2 123.4 92.6 69.0 105.3
124 104.7 117.7 103.0 116.5 86.3 45.9 97.7
125 100.5 109.2 103.5 102.3 80.3 39.1 84.3
126 116.2 118.8 124.5 148.4 93.6 56.9 109.8
127 94.1 108.1 117.9 126.6 79.5 51.6 79.1
128 94.8 112.1 104.2 106.6 61.8 62.9 83.4
129 115.1 117.8 99.9 144.4 94.8 58.3 101.9
130 110.0 121.8 89.4 132.4 91.6 56.9 113.0
131 108.4 121.0 93.5 136.2 89.2 41.3 98.6
132 103.9 121.7 89.6 121.6 74.1 46.9 94.7
133 102.9 114.2 85.0 135.1 78.6 61.9 94.5
134 107.7 109.8 90.0 124.7 78.2 74.8 90.7
135 126.7 124.1 113.7 148.8 95.1 67.0 113.0
136 108.8 112.9 112.1 145.6 78.7 53.3 89.9
137 117.1 118.7 129.8 140.3 85.9 51.4 98.7
138 112.2 113.3 119.1 138.5 81.2 50.3 102.2
139 94.7 106.8 103.5 127.3 73.1 52.7 74.3
140 102.7 119.3 105.5 117.9 58.7 70.3 84.5
141 119.1 126.4 111.7 145.3 85.7 59.7 110.1
142 110.6 126.6 98.6 120.7 81.8 52.0 100.4
143 109.1 127.2 102.8 134.7 79.6 36.1 92.8
144 105.3 123.8 101.1 124.4 70.7 39.7 92.2
145 103.4 116.8 94.2 128.3 74.5 67.6 94.0
146 103.7 113.8 92.6 128.4 84.8 72.8 100.7
147 117.0 130.4 112.0 134.1 80.7 53.8 111.9
148 101.2 112.8 108.6 133.3 69.9 39.6 95.9
149 105.4 119.4 125.8 130.6 74.1 39.4 88.8
150 110.3 117.5 138.7 165.7 76.1 41.2 102.0
151 97.7 117.5 115.2 146.8 71.3 49.6 81.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Voeding Dranken Tabaksproducten
-28.281883 0.648098 0.143334 0.062337
Textiel Kleding `Apparatuur\\r`
0.211282 0.009738 0.183562
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.6545 -2.6855 -0.1293 3.4389 10.2475
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -28.281883 6.955720 -4.066 7.84e-05 ***
Voeding 0.648098 0.049335 13.137 < 2e-16 ***
Dranken 0.143334 0.032429 4.420 1.93e-05 ***
Tabaksproducten 0.062337 0.033087 1.884 0.06158 .
Textiel 0.211282 0.041112 5.139 8.83e-07 ***
Kleding 0.009738 0.017620 0.553 0.58134
`Apparatuur\\r` 0.183562 0.054785 3.351 0.00103 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.54 on 144 degrees of freedom
Multiple R-squared: 0.8221, Adjusted R-squared: 0.8147
F-statistic: 110.9 on 6 and 144 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,] 0.18634530 0.37269060 0.81365470
[2,] 0.08459675 0.16919350 0.91540325
[3,] 0.03530377 0.07060754 0.96469623
[4,] 0.04824401 0.09648802 0.95175599
[5,] 0.08229503 0.16459006 0.91770497
[6,] 0.04504096 0.09008192 0.95495904
[7,] 0.02899389 0.05798779 0.97100611
[8,] 0.02152776 0.04305551 0.97847224
[9,] 0.02095862 0.04191723 0.97904138
[10,] 0.30460680 0.60921361 0.69539320
[11,] 0.26743530 0.53487061 0.73256470
[12,] 0.23685280 0.47370560 0.76314720
[13,] 0.30556517 0.61113033 0.69443483
[14,] 0.31440206 0.62880412 0.68559794
[15,] 0.29519675 0.59039350 0.70480325
[16,] 0.23886563 0.47773125 0.76113437
[17,] 0.18796766 0.37593532 0.81203234
[18,] 0.17461639 0.34923279 0.82538361
[19,] 0.16334184 0.32668369 0.83665816
[20,] 0.14330815 0.28661629 0.85669185
[21,] 0.13406762 0.26813524 0.86593238
[22,] 0.14007822 0.28015644 0.85992178
[23,] 0.12571621 0.25143242 0.87428379
[24,] 0.11769675 0.23539350 0.88230325
[25,] 0.13776207 0.27552413 0.86223793
[26,] 0.13902805 0.27805610 0.86097195
[27,] 0.19787799 0.39575598 0.80212201
[28,] 0.17996671 0.35993342 0.82003329
[29,] 0.15902857 0.31805715 0.84097143
[30,] 0.25157275 0.50314549 0.74842725
[31,] 0.23797971 0.47595941 0.76202029
[32,] 0.27099738 0.54199477 0.72900262
[33,] 0.31266790 0.62533579 0.68733210
[34,] 0.27272729 0.54545458 0.72727271
[35,] 0.24255935 0.48511870 0.75744065
[36,] 0.25538704 0.51077408 0.74461296
[37,] 0.26581285 0.53162570 0.73418715
[38,] 0.24562570 0.49125141 0.75437430
[39,] 0.32775655 0.65551311 0.67224345
[40,] 0.28594335 0.57188670 0.71405665
[41,] 0.27139308 0.54278616 0.72860692
[42,] 0.23866882 0.47733764 0.76133118
[43,] 0.20468777 0.40937555 0.79531223
[44,] 0.19151756 0.38303512 0.80848244
[45,] 0.19834352 0.39668703 0.80165648
[46,] 0.17412284 0.34824568 0.82587716
[47,] 0.15753927 0.31507853 0.84246073
[48,] 0.13514917 0.27029833 0.86485083
[49,] 0.13868631 0.27737261 0.86131369
[50,] 0.13064299 0.26128599 0.86935701
[51,] 0.17248185 0.34496371 0.82751815
[52,] 0.24082264 0.48164528 0.75917736
[53,] 0.33522243 0.67044486 0.66477757
[54,] 0.36439360 0.72878720 0.63560640
[55,] 0.45349674 0.90699348 0.54650326
[56,] 0.43628804 0.87257607 0.56371196
[57,] 0.47217222 0.94434444 0.52782778
[58,] 0.45470612 0.90941223 0.54529388
[59,] 0.42756132 0.85512264 0.57243868
[60,] 0.41821025 0.83642050 0.58178975
[61,] 0.41409021 0.82818042 0.58590979
[62,] 0.39750169 0.79500337 0.60249831
[63,] 0.38825340 0.77650679 0.61174660
[64,] 0.37920563 0.75841126 0.62079437
[65,] 0.51099696 0.97800607 0.48900304
[66,] 0.66980719 0.66038561 0.33019281
[67,] 0.65301853 0.69396294 0.34698147
[68,] 0.63810484 0.72379031 0.36189516
[69,] 0.63337919 0.73324161 0.36662081
[70,] 0.71618367 0.56763266 0.28381633
[71,] 0.67551766 0.64896468 0.32448234
[72,] 0.66866752 0.66266497 0.33133248
[73,] 0.65433509 0.69132982 0.34566491
[74,] 0.66584464 0.66831072 0.33415536
[75,] 0.64956497 0.70087005 0.35043503
[76,] 0.61333909 0.77332183 0.38666091
[77,] 0.64794761 0.70410479 0.35205239
[78,] 0.63976315 0.72047370 0.36023685
[79,] 0.62157091 0.75685817 0.37842909
[80,] 0.60505613 0.78988773 0.39494387
[81,] 0.61285073 0.77429853 0.38714927
[82,] 0.56706860 0.86586280 0.43293140
[83,] 0.53684624 0.92630752 0.46315376
[84,] 0.50169142 0.99661717 0.49830858
[85,] 0.45723421 0.91446842 0.54276579
[86,] 0.40787494 0.81574987 0.59212506
[87,] 0.51567927 0.96864146 0.48432073
[88,] 0.48371866 0.96743733 0.51628134
[89,] 0.46347357 0.92694713 0.53652643
[90,] 0.45788701 0.91577401 0.54211299
[91,] 0.44534715 0.89069429 0.55465285
[92,] 0.43417171 0.86834342 0.56582829
[93,] 0.50460225 0.99079550 0.49539775
[94,] 0.47218661 0.94437322 0.52781339
[95,] 0.47176291 0.94352582 0.52823709
[96,] 0.43491051 0.86982103 0.56508949
[97,] 0.42179642 0.84359284 0.57820358
[98,] 0.42067853 0.84135706 0.57932147
[99,] 0.45545733 0.91091467 0.54454267
[100,] 0.58398000 0.83204000 0.41602000
[101,] 0.55717068 0.88565865 0.44282932
[102,] 0.56244485 0.87511030 0.43755515
[103,] 0.62731140 0.74537720 0.37268860
[104,] 0.59036315 0.81927370 0.40963685
[105,] 0.63720375 0.72559249 0.36279625
[106,] 0.88628974 0.22742052 0.11371026
[107,] 0.87278588 0.25442824 0.12721412
[108,] 0.90567152 0.18865696 0.09432848
[109,] 0.95054354 0.09891292 0.04945646
[110,] 0.93928919 0.12142163 0.06071081
[111,] 0.97638535 0.04722930 0.02361465
[112,] 0.96781324 0.06437352 0.03218676
[113,] 0.95607775 0.08784451 0.04392225
[114,] 0.95732326 0.08535348 0.04267674
[115,] 0.94884033 0.10231933 0.05115967
[116,] 0.94497203 0.11005594 0.05502797
[117,] 0.93570957 0.12858086 0.06429043
[118,] 0.97181258 0.05637483 0.02818742
[119,] 0.96616293 0.06767413 0.03383707
[120,] 0.95236182 0.09527636 0.04763818
[121,] 0.96422656 0.07154688 0.03577344
[122,] 0.94295418 0.11409165 0.05704582
[123,] 0.91136717 0.17726566 0.08863283
[124,] 0.86746980 0.26506040 0.13253020
[125,] 0.91025299 0.17949402 0.08974701
[126,] 0.92608600 0.14782801 0.07391400
[127,] 0.96914823 0.06170353 0.03085177
[128,] 0.95975369 0.08049262 0.04024631
[129,] 0.96815485 0.06369030 0.03184515
[130,] 0.97094809 0.05810382 0.02905191
[131,] 0.95421909 0.09156181 0.04578091
[132,] 0.95277591 0.09444818 0.04722409
> postscript(file="/var/wessaorg/rcomp/tmp/120831351609529.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/23su61351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nsed1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4l5z11351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5q1y91351609529.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
-3.66034277 -2.02750089 -0.37051382 0.47271239 -4.39282471 -1.36666139
7 8 9 10 11 12
-1.64273240 0.57945394 0.67244960 -8.13976329 -4.83104407 -4.16634732
13 14 15 16 17 18
3.35286026 4.44625417 -0.63062678 0.77169104 -1.26526478 2.60631599
19 20 21 22 23 24
-9.70197263 -5.32160021 -5.42994003 -11.39116223 -8.47165410 -7.55075830
25 26 27 28 29 30
-2.14118054 -0.57903290 -0.80663909 -0.35962132 -1.88783728 -1.17201792
31 32 33 34 35 36
-6.35633627 -3.67080919 -4.69530263 -9.01524223 -4.40915057 -9.10357217
37 38 39 40 41 42
-1.26514484 -0.32924704 2.61016693 -1.20649484 2.24913769 2.52914608
43 44 45 46 47 48
-2.65350789 0.19348519 0.22525063 -5.55920572 -0.60756271 2.17902804
49 50 51 52 53 54
1.15613204 4.75601086 -0.03517319 2.46300325 1.81648970 2.37477019
55 56 57 58 59 60
0.51857460 3.72360985 0.06425329 -1.14279444 -1.01954739 2.48765491
61 62 63 64 65 66
6.55214642 7.80747066 5.16469150 5.61739547 2.22962404 3.72253603
67 68 69 70 71 72
0.99184318 -0.38867977 0.69170008 -1.32857399 -2.77638615 0.97233280
73 74 75 76 77 78
4.05712078 9.77463089 8.16294271 4.44513898 3.85699750 4.20977081
79 80 81 82 83 84
-4.43658803 -0.34668362 -0.03345791 -2.71740706 -3.90519871 4.50385434
85 86 87 88 89 90
2.96332791 7.95002416 4.21931396 3.71048389 5.26382789 6.15531536
91 92 93 94 95 96
2.29323814 4.01936576 4.26255816 -0.88315331 0.69232548 8.29469139
97 98 99 100 101 102
2.63742177 5.90238599 5.41133911 4.89940571 5.43419525 10.15185198
103 104 105 106 107 108
-0.80483193 4.01389601 3.52492048 -2.78720335 -3.38207237 -4.70843656
109 110 111 112 113 114
-6.00469331 -1.04305716 -3.88461842 -8.13707810 1.61260579 -5.67237270
115 116 117 118 119 120
-12.65454562 -2.93662060 -5.59040272 -7.73959599 -3.90048000 -8.36939889
121 122 123 124 125 126
1.42829650 5.12339055 -2.29382939 -1.93953189 3.97646170 -0.09324575
127 128 129 130 131 132
-4.28760656 -0.12926026 4.41318544 -2.37396213 -0.97774732 -0.61058452
133 134 135 136 137 138
2.00781227 10.24750426 7.49222896 5.11845717 4.33479045 4.94166917
139 140 141 142 143 144
1.39788277 2.59468279 1.49591598 -1.04300538 -2.39185522 -1.14709742
145 146 147 148 149 150
0.83050295 -0.15880649 -1.75785203 -0.25700107 -2.21362845 -2.98238792
151
-6.35885408
> postscript(file="/var/wessaorg/rcomp/tmp/62bw01351609529.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 -3.66034277 NA
1 -2.02750089 -3.66034277
2 -0.37051382 -2.02750089
3 0.47271239 -0.37051382
4 -4.39282471 0.47271239
5 -1.36666139 -4.39282471
6 -1.64273240 -1.36666139
7 0.57945394 -1.64273240
8 0.67244960 0.57945394
9 -8.13976329 0.67244960
10 -4.83104407 -8.13976329
11 -4.16634732 -4.83104407
12 3.35286026 -4.16634732
13 4.44625417 3.35286026
14 -0.63062678 4.44625417
15 0.77169104 -0.63062678
16 -1.26526478 0.77169104
17 2.60631599 -1.26526478
18 -9.70197263 2.60631599
19 -5.32160021 -9.70197263
20 -5.42994003 -5.32160021
21 -11.39116223 -5.42994003
22 -8.47165410 -11.39116223
23 -7.55075830 -8.47165410
24 -2.14118054 -7.55075830
25 -0.57903290 -2.14118054
26 -0.80663909 -0.57903290
27 -0.35962132 -0.80663909
28 -1.88783728 -0.35962132
29 -1.17201792 -1.88783728
30 -6.35633627 -1.17201792
31 -3.67080919 -6.35633627
32 -4.69530263 -3.67080919
33 -9.01524223 -4.69530263
34 -4.40915057 -9.01524223
35 -9.10357217 -4.40915057
36 -1.26514484 -9.10357217
37 -0.32924704 -1.26514484
38 2.61016693 -0.32924704
39 -1.20649484 2.61016693
40 2.24913769 -1.20649484
41 2.52914608 2.24913769
42 -2.65350789 2.52914608
43 0.19348519 -2.65350789
44 0.22525063 0.19348519
45 -5.55920572 0.22525063
46 -0.60756271 -5.55920572
47 2.17902804 -0.60756271
48 1.15613204 2.17902804
49 4.75601086 1.15613204
50 -0.03517319 4.75601086
51 2.46300325 -0.03517319
52 1.81648970 2.46300325
53 2.37477019 1.81648970
54 0.51857460 2.37477019
55 3.72360985 0.51857460
56 0.06425329 3.72360985
57 -1.14279444 0.06425329
58 -1.01954739 -1.14279444
59 2.48765491 -1.01954739
60 6.55214642 2.48765491
61 7.80747066 6.55214642
62 5.16469150 7.80747066
63 5.61739547 5.16469150
64 2.22962404 5.61739547
65 3.72253603 2.22962404
66 0.99184318 3.72253603
67 -0.38867977 0.99184318
68 0.69170008 -0.38867977
69 -1.32857399 0.69170008
70 -2.77638615 -1.32857399
71 0.97233280 -2.77638615
72 4.05712078 0.97233280
73 9.77463089 4.05712078
74 8.16294271 9.77463089
75 4.44513898 8.16294271
76 3.85699750 4.44513898
77 4.20977081 3.85699750
78 -4.43658803 4.20977081
79 -0.34668362 -4.43658803
80 -0.03345791 -0.34668362
81 -2.71740706 -0.03345791
82 -3.90519871 -2.71740706
83 4.50385434 -3.90519871
84 2.96332791 4.50385434
85 7.95002416 2.96332791
86 4.21931396 7.95002416
87 3.71048389 4.21931396
88 5.26382789 3.71048389
89 6.15531536 5.26382789
90 2.29323814 6.15531536
91 4.01936576 2.29323814
92 4.26255816 4.01936576
93 -0.88315331 4.26255816
94 0.69232548 -0.88315331
95 8.29469139 0.69232548
96 2.63742177 8.29469139
97 5.90238599 2.63742177
98 5.41133911 5.90238599
99 4.89940571 5.41133911
100 5.43419525 4.89940571
101 10.15185198 5.43419525
102 -0.80483193 10.15185198
103 4.01389601 -0.80483193
104 3.52492048 4.01389601
105 -2.78720335 3.52492048
106 -3.38207237 -2.78720335
107 -4.70843656 -3.38207237
108 -6.00469331 -4.70843656
109 -1.04305716 -6.00469331
110 -3.88461842 -1.04305716
111 -8.13707810 -3.88461842
112 1.61260579 -8.13707810
113 -5.67237270 1.61260579
114 -12.65454562 -5.67237270
115 -2.93662060 -12.65454562
116 -5.59040272 -2.93662060
117 -7.73959599 -5.59040272
118 -3.90048000 -7.73959599
119 -8.36939889 -3.90048000
120 1.42829650 -8.36939889
121 5.12339055 1.42829650
122 -2.29382939 5.12339055
123 -1.93953189 -2.29382939
124 3.97646170 -1.93953189
125 -0.09324575 3.97646170
126 -4.28760656 -0.09324575
127 -0.12926026 -4.28760656
128 4.41318544 -0.12926026
129 -2.37396213 4.41318544
130 -0.97774732 -2.37396213
131 -0.61058452 -0.97774732
132 2.00781227 -0.61058452
133 10.24750426 2.00781227
134 7.49222896 10.24750426
135 5.11845717 7.49222896
136 4.33479045 5.11845717
137 4.94166917 4.33479045
138 1.39788277 4.94166917
139 2.59468279 1.39788277
140 1.49591598 2.59468279
141 -1.04300538 1.49591598
142 -2.39185522 -1.04300538
143 -1.14709742 -2.39185522
144 0.83050295 -1.14709742
145 -0.15880649 0.83050295
146 -1.75785203 -0.15880649
147 -0.25700107 -1.75785203
148 -2.21362845 -0.25700107
149 -2.98238792 -2.21362845
150 -6.35885408 -2.98238792
151 NA -6.35885408
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.02750089 -3.66034277
[2,] -0.37051382 -2.02750089
[3,] 0.47271239 -0.37051382
[4,] -4.39282471 0.47271239
[5,] -1.36666139 -4.39282471
[6,] -1.64273240 -1.36666139
[7,] 0.57945394 -1.64273240
[8,] 0.67244960 0.57945394
[9,] -8.13976329 0.67244960
[10,] -4.83104407 -8.13976329
[11,] -4.16634732 -4.83104407
[12,] 3.35286026 -4.16634732
[13,] 4.44625417 3.35286026
[14,] -0.63062678 4.44625417
[15,] 0.77169104 -0.63062678
[16,] -1.26526478 0.77169104
[17,] 2.60631599 -1.26526478
[18,] -9.70197263 2.60631599
[19,] -5.32160021 -9.70197263
[20,] -5.42994003 -5.32160021
[21,] -11.39116223 -5.42994003
[22,] -8.47165410 -11.39116223
[23,] -7.55075830 -8.47165410
[24,] -2.14118054 -7.55075830
[25,] -0.57903290 -2.14118054
[26,] -0.80663909 -0.57903290
[27,] -0.35962132 -0.80663909
[28,] -1.88783728 -0.35962132
[29,] -1.17201792 -1.88783728
[30,] -6.35633627 -1.17201792
[31,] -3.67080919 -6.35633627
[32,] -4.69530263 -3.67080919
[33,] -9.01524223 -4.69530263
[34,] -4.40915057 -9.01524223
[35,] -9.10357217 -4.40915057
[36,] -1.26514484 -9.10357217
[37,] -0.32924704 -1.26514484
[38,] 2.61016693 -0.32924704
[39,] -1.20649484 2.61016693
[40,] 2.24913769 -1.20649484
[41,] 2.52914608 2.24913769
[42,] -2.65350789 2.52914608
[43,] 0.19348519 -2.65350789
[44,] 0.22525063 0.19348519
[45,] -5.55920572 0.22525063
[46,] -0.60756271 -5.55920572
[47,] 2.17902804 -0.60756271
[48,] 1.15613204 2.17902804
[49,] 4.75601086 1.15613204
[50,] -0.03517319 4.75601086
[51,] 2.46300325 -0.03517319
[52,] 1.81648970 2.46300325
[53,] 2.37477019 1.81648970
[54,] 0.51857460 2.37477019
[55,] 3.72360985 0.51857460
[56,] 0.06425329 3.72360985
[57,] -1.14279444 0.06425329
[58,] -1.01954739 -1.14279444
[59,] 2.48765491 -1.01954739
[60,] 6.55214642 2.48765491
[61,] 7.80747066 6.55214642
[62,] 5.16469150 7.80747066
[63,] 5.61739547 5.16469150
[64,] 2.22962404 5.61739547
[65,] 3.72253603 2.22962404
[66,] 0.99184318 3.72253603
[67,] -0.38867977 0.99184318
[68,] 0.69170008 -0.38867977
[69,] -1.32857399 0.69170008
[70,] -2.77638615 -1.32857399
[71,] 0.97233280 -2.77638615
[72,] 4.05712078 0.97233280
[73,] 9.77463089 4.05712078
[74,] 8.16294271 9.77463089
[75,] 4.44513898 8.16294271
[76,] 3.85699750 4.44513898
[77,] 4.20977081 3.85699750
[78,] -4.43658803 4.20977081
[79,] -0.34668362 -4.43658803
[80,] -0.03345791 -0.34668362
[81,] -2.71740706 -0.03345791
[82,] -3.90519871 -2.71740706
[83,] 4.50385434 -3.90519871
[84,] 2.96332791 4.50385434
[85,] 7.95002416 2.96332791
[86,] 4.21931396 7.95002416
[87,] 3.71048389 4.21931396
[88,] 5.26382789 3.71048389
[89,] 6.15531536 5.26382789
[90,] 2.29323814 6.15531536
[91,] 4.01936576 2.29323814
[92,] 4.26255816 4.01936576
[93,] -0.88315331 4.26255816
[94,] 0.69232548 -0.88315331
[95,] 8.29469139 0.69232548
[96,] 2.63742177 8.29469139
[97,] 5.90238599 2.63742177
[98,] 5.41133911 5.90238599
[99,] 4.89940571 5.41133911
[100,] 5.43419525 4.89940571
[101,] 10.15185198 5.43419525
[102,] -0.80483193 10.15185198
[103,] 4.01389601 -0.80483193
[104,] 3.52492048 4.01389601
[105,] -2.78720335 3.52492048
[106,] -3.38207237 -2.78720335
[107,] -4.70843656 -3.38207237
[108,] -6.00469331 -4.70843656
[109,] -1.04305716 -6.00469331
[110,] -3.88461842 -1.04305716
[111,] -8.13707810 -3.88461842
[112,] 1.61260579 -8.13707810
[113,] -5.67237270 1.61260579
[114,] -12.65454562 -5.67237270
[115,] -2.93662060 -12.65454562
[116,] -5.59040272 -2.93662060
[117,] -7.73959599 -5.59040272
[118,] -3.90048000 -7.73959599
[119,] -8.36939889 -3.90048000
[120,] 1.42829650 -8.36939889
[121,] 5.12339055 1.42829650
[122,] -2.29382939 5.12339055
[123,] -1.93953189 -2.29382939
[124,] 3.97646170 -1.93953189
[125,] -0.09324575 3.97646170
[126,] -4.28760656 -0.09324575
[127,] -0.12926026 -4.28760656
[128,] 4.41318544 -0.12926026
[129,] -2.37396213 4.41318544
[130,] -0.97774732 -2.37396213
[131,] -0.61058452 -0.97774732
[132,] 2.00781227 -0.61058452
[133,] 10.24750426 2.00781227
[134,] 7.49222896 10.24750426
[135,] 5.11845717 7.49222896
[136,] 4.33479045 5.11845717
[137,] 4.94166917 4.33479045
[138,] 1.39788277 4.94166917
[139,] 2.59468279 1.39788277
[140,] 1.49591598 2.59468279
[141,] -1.04300538 1.49591598
[142,] -2.39185522 -1.04300538
[143,] -1.14709742 -2.39185522
[144,] 0.83050295 -1.14709742
[145,] -0.15880649 0.83050295
[146,] -1.75785203 -0.15880649
[147,] -0.25700107 -1.75785203
[148,] -2.21362845 -0.25700107
[149,] -2.98238792 -2.21362845
[150,] -6.35885408 -2.98238792
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.02750089 -3.66034277
2 -0.37051382 -2.02750089
3 0.47271239 -0.37051382
4 -4.39282471 0.47271239
5 -1.36666139 -4.39282471
6 -1.64273240 -1.36666139
7 0.57945394 -1.64273240
8 0.67244960 0.57945394
9 -8.13976329 0.67244960
10 -4.83104407 -8.13976329
11 -4.16634732 -4.83104407
12 3.35286026 -4.16634732
13 4.44625417 3.35286026
14 -0.63062678 4.44625417
15 0.77169104 -0.63062678
16 -1.26526478 0.77169104
17 2.60631599 -1.26526478
18 -9.70197263 2.60631599
19 -5.32160021 -9.70197263
20 -5.42994003 -5.32160021
21 -11.39116223 -5.42994003
22 -8.47165410 -11.39116223
23 -7.55075830 -8.47165410
24 -2.14118054 -7.55075830
25 -0.57903290 -2.14118054
26 -0.80663909 -0.57903290
27 -0.35962132 -0.80663909
28 -1.88783728 -0.35962132
29 -1.17201792 -1.88783728
30 -6.35633627 -1.17201792
31 -3.67080919 -6.35633627
32 -4.69530263 -3.67080919
33 -9.01524223 -4.69530263
34 -4.40915057 -9.01524223
35 -9.10357217 -4.40915057
36 -1.26514484 -9.10357217
37 -0.32924704 -1.26514484
38 2.61016693 -0.32924704
39 -1.20649484 2.61016693
40 2.24913769 -1.20649484
41 2.52914608 2.24913769
42 -2.65350789 2.52914608
43 0.19348519 -2.65350789
44 0.22525063 0.19348519
45 -5.55920572 0.22525063
46 -0.60756271 -5.55920572
47 2.17902804 -0.60756271
48 1.15613204 2.17902804
49 4.75601086 1.15613204
50 -0.03517319 4.75601086
51 2.46300325 -0.03517319
52 1.81648970 2.46300325
53 2.37477019 1.81648970
54 0.51857460 2.37477019
55 3.72360985 0.51857460
56 0.06425329 3.72360985
57 -1.14279444 0.06425329
58 -1.01954739 -1.14279444
59 2.48765491 -1.01954739
60 6.55214642 2.48765491
61 7.80747066 6.55214642
62 5.16469150 7.80747066
63 5.61739547 5.16469150
64 2.22962404 5.61739547
65 3.72253603 2.22962404
66 0.99184318 3.72253603
67 -0.38867977 0.99184318
68 0.69170008 -0.38867977
69 -1.32857399 0.69170008
70 -2.77638615 -1.32857399
71 0.97233280 -2.77638615
72 4.05712078 0.97233280
73 9.77463089 4.05712078
74 8.16294271 9.77463089
75 4.44513898 8.16294271
76 3.85699750 4.44513898
77 4.20977081 3.85699750
78 -4.43658803 4.20977081
79 -0.34668362 -4.43658803
80 -0.03345791 -0.34668362
81 -2.71740706 -0.03345791
82 -3.90519871 -2.71740706
83 4.50385434 -3.90519871
84 2.96332791 4.50385434
85 7.95002416 2.96332791
86 4.21931396 7.95002416
87 3.71048389 4.21931396
88 5.26382789 3.71048389
89 6.15531536 5.26382789
90 2.29323814 6.15531536
91 4.01936576 2.29323814
92 4.26255816 4.01936576
93 -0.88315331 4.26255816
94 0.69232548 -0.88315331
95 8.29469139 0.69232548
96 2.63742177 8.29469139
97 5.90238599 2.63742177
98 5.41133911 5.90238599
99 4.89940571 5.41133911
100 5.43419525 4.89940571
101 10.15185198 5.43419525
102 -0.80483193 10.15185198
103 4.01389601 -0.80483193
104 3.52492048 4.01389601
105 -2.78720335 3.52492048
106 -3.38207237 -2.78720335
107 -4.70843656 -3.38207237
108 -6.00469331 -4.70843656
109 -1.04305716 -6.00469331
110 -3.88461842 -1.04305716
111 -8.13707810 -3.88461842
112 1.61260579 -8.13707810
113 -5.67237270 1.61260579
114 -12.65454562 -5.67237270
115 -2.93662060 -12.65454562
116 -5.59040272 -2.93662060
117 -7.73959599 -5.59040272
118 -3.90048000 -7.73959599
119 -8.36939889 -3.90048000
120 1.42829650 -8.36939889
121 5.12339055 1.42829650
122 -2.29382939 5.12339055
123 -1.93953189 -2.29382939
124 3.97646170 -1.93953189
125 -0.09324575 3.97646170
126 -4.28760656 -0.09324575
127 -0.12926026 -4.28760656
128 4.41318544 -0.12926026
129 -2.37396213 4.41318544
130 -0.97774732 -2.37396213
131 -0.61058452 -0.97774732
132 2.00781227 -0.61058452
133 10.24750426 2.00781227
134 7.49222896 10.24750426
135 5.11845717 7.49222896
136 4.33479045 5.11845717
137 4.94166917 4.33479045
138 1.39788277 4.94166917
139 2.59468279 1.39788277
140 1.49591598 2.59468279
141 -1.04300538 1.49591598
142 -2.39185522 -1.04300538
143 -1.14709742 -2.39185522
144 0.83050295 -1.14709742
145 -0.15880649 0.83050295
146 -1.75785203 -0.15880649
147 -0.25700107 -1.75785203
148 -2.21362845 -0.25700107
149 -2.98238792 -2.21362845
150 -6.35885408 -2.98238792
> 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/7n8fv1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/877341351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9s0ih1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/101hj41351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h25c1351609529.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/12ioj81351609529.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/13eeib1351609530.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/14gfpx1351609530.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/15dki11351609530.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/164tin1351609530.tab")
+ }
>
> try(system("convert tmp/120831351609529.ps tmp/120831351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/23su61351609529.ps tmp/23su61351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nsed1351609529.ps tmp/3nsed1351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l5z11351609529.ps tmp/4l5z11351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q1y91351609529.ps tmp/5q1y91351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/62bw01351609529.ps tmp/62bw01351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n8fv1351609529.ps tmp/7n8fv1351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/877341351609529.ps tmp/877341351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/9s0ih1351609529.ps tmp/9s0ih1351609529.png",intern=TRUE))
character(0)
> try(system("convert tmp/101hj41351609529.ps tmp/101hj41351609529.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.285 1.512 14.548