R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(98.8
+ ,99.2
+ ,101.7
+ ,91.4
+ ,98.5
+ ,94.7
+ ,114.2
+ ,100.5
+ ,103.0
+ ,85.1
+ ,99.9
+ ,93.5
+ ,104.8
+ ,124.9
+ ,95.3
+ ,101.3
+ ,86.9
+ ,100.5
+ ,93.6
+ ,102.8
+ ,85.4
+ ,96.7
+ ,90.2
+ ,99.4
+ ,99.0
+ ,97.3
+ ,94.2
+ ,98.6
+ ,94.7
+ ,105.6
+ ,132.0
+ ,97.2
+ ,100.9
+ ,99.7
+ ,110.4
+ ,104.2
+ ,109.2
+ ,110.4
+ ,113.1
+ ,104.3
+ ,98.0
+ ,104.1
+ ,109.5
+ ,107.3
+ ,107.2
+ ,112.9
+ ,118.3
+ ,151.4
+ ,108.6
+ ,104.1
+ ,109.1
+ ,96.4
+ ,95.3
+ ,88.5
+ ,90.1
+ ,100.0
+ ,96.0
+ ,90.8
+ ,98.6
+ ,97.0
+ ,98.3
+ ,95.7
+ ,99.2
+ ,89.9
+ ,108.9
+ ,89.6
+ ,96.2
+ ,94.6
+ ,101.9
+ ,102.7
+ ,93.9
+ ,103.4
+ ,104.7
+ ,96.6
+ ,111.4
+ ,101.4
+ ,102.6
+ ,111.0
+ ,93.7
+ ,105.6
+ ,90.2
+ ,121.3
+ ,99.8
+ ,87.0
+ ,111.2
+ ,106.2
+ ,103.1
+ ,97.4
+ ,118.7
+ ,108.5
+ ,103.5
+ ,104.3
+ ,102.1
+ ,106.1
+ ,114.8
+ ,106.7
+ ,113.0
+ ,107.0
+ ,123.4
+ ,106.3
+ ,88.7
+ ,112.8
+ ,81
+ ,100.0
+ ,86.7
+ ,76.4
+ ,90.5
+ ,79.9
+ ,99.6
+ ,93.0
+ ,88.5
+ ,75.9
+ ,86.7
+ ,83.1
+ ,64.5
+ ,90.3
+ ,71.6
+ ,87.0
+ ,53.5
+ ,94.7
+ ,107.2
+ ,80.1
+ ,92.7
+ ,88.6
+ ,88.5
+ ,108.0
+ ,96.9
+ ,85.4
+ ,99.5
+ ,95.3
+ ,81.1
+ ,92.6
+ ,79.3
+ ,75.8
+ ,87.7
+ ,107.5
+ ,101
+ ,107.0
+ ,104.1
+ ,105.2
+ ,105.4
+ ,103.3
+ ,113.1
+ ,91.2
+ ,100.2
+ ,102.9
+ ,99.3
+ ,96.9
+ ,95.8
+ ,117.2
+ ,101.4
+ ,91.3
+ ,105.2
+ ,109.4
+ ,119.0
+ ,108.9
+ ,91.5
+ ,119.9
+ ,113.5
+ ,112.7
+ ,96.9
+ ,106.1
+ ,117.1
+ ,101.8
+ ,104.3
+ ,94.3
+ ,116.9
+ ,109.1
+ ,103.3
+ ,122.8
+ ,102.3
+ ,110.4
+ ,94.1
+ ,75.3
+ ,107.2
+ ,100.8
+ ,114.3
+ ,94.0
+ ,95.3
+ ,101.0
+ ,96.0
+ ,97.7
+ ,91.2
+ ,120.8
+ ,101.0
+ ,110.6
+ ,103.4
+ ,90.7
+ ,101.7
+ ,72.4
+ ,60.5
+ ,84.1
+ ,95.3
+ ,108.6
+ ,90.4
+ ,73.8
+ ,73.2
+ ,91.7
+ ,102.6
+ ,86.3
+ ,96.1
+ ,91.0
+ ,111.2
+ ,76.9
+ ,96.2
+ ,102.4
+ ,94.7
+ ,80.4
+ ,101.4
+ ,92.6
+ ,116.2
+ ,105.2
+ ,94.1
+ ,84.0
+ ,95.3
+ ,89.9
+ ,77.6
+ ,100.8
+ ,86.4
+ ,105.8
+ ,89.6
+ ,96.1
+ ,98.8
+ ,95.3
+ ,84.5
+ ,105.1
+ ,89.0
+ ,101.9
+ ,103.4
+ ,90.1
+ ,90.0
+ ,96.6
+ ,96.0
+ ,82.5
+ ,105.3
+ ,89.7
+ ,98.3
+ ,92.8
+ ,106
+ ,105.6
+ ,96.4
+ ,93.9
+ ,118.7
+ ,98.7
+ ,107.3
+ ,111.7
+ ,99.4
+ ,105.2
+ ,107.2
+ ,112.7
+ ,97.7
+ ,116.1
+ ,97.9
+ ,97.6
+ ,107.6
+ ,103.1
+ ,104.4
+ ,91.7
+ ,78.0
+ ,113.8
+ ,94.5
+ ,111.4
+ ,114.2
+ ,100.1
+ ,111.2
+ ,108.0
+ ,107.1
+ ,83.3
+ ,112.8
+ ,91.5
+ ,88.4
+ ,104.6
+ ,102
+ ,106.3
+ ,84.0
+ ,92.3
+ ,113.8
+ ,96.9
+ ,121.9
+ ,111.4
+ ,96.2
+ ,109.7
+ ,98.4
+ ,106.2
+ ,84.2
+ ,114.5
+ ,93.4
+ ,93.0
+ ,103.0
+ ,104.7
+ ,107.2
+ ,86.2
+ ,90.0
+ ,118.9
+ ,98.1
+ ,123.1
+ ,106.3
+ ,102.0
+ ,115.7
+ ,103.1
+ ,121.0
+ ,92.8
+ ,117.2
+ ,97.2
+ ,93.5
+ ,106.9
+ ,86
+ ,108.5
+ ,88.8
+ ,72.1
+ ,98.5
+ ,82.1
+ ,108.1
+ ,111.8
+ ,88.0
+ ,85.3
+ ,81.1
+ ,101.2
+ ,77.4
+ ,77.1
+ ,67.1
+ ,94.5
+ ,56.3
+ ,92.1
+ ,106.9
+ ,74.9
+ ,76.9
+ ,91.0
+ ,86.5
+ ,114.5
+ ,101.5
+ ,78.2
+ ,92.1
+ ,96.6
+ ,83.2
+ ,72.5
+ ,80.1
+ ,59.4
+ ,98.1
+ ,93.4
+ ,106.9
+ ,114.2
+ ,111.3
+ ,76.0
+ ,120.7
+ ,104.1
+ ,119.7
+ ,103.0
+ ,99.3
+ ,116.7
+ ,103.7
+ ,105.1
+ ,88.8
+ ,120.3
+ ,101.6
+ ,98.3
+ ,109.1
+ ,112.6
+ ,125.9
+ ,103.6
+ ,88.7
+ ,127.9
+ ,110.9
+ ,117.0
+ ,105.2
+ ,107.2
+ ,119.4
+ ,106.6
+ ,113.3
+ ,93.4
+ ,133.4
+ ,99.7
+ ,114.8
+ ,113.8
+ ,101.7
+ ,110.6
+ ,83.4
+ ,55.4
+ ,112.4
+ ,114.5
+ ,114.8
+ ,101.1
+ ,93.4
+ ,99.9
+ ,97.6
+ ,99.1
+ ,92.6
+ ,109.4
+ ,89.1
+ ,107.8
+ ,97.4
+ ,92
+ ,110.5
+ ,69.0
+ ,46.6
+ ,93.1
+ ,112.2
+ ,115.8
+ ,100.7
+ ,74.0
+ ,70.8
+ ,87.6
+ ,100.3
+ ,90.7
+ ,93.2
+ ,78.7
+ ,107.9
+ ,72.5
+ ,97.4
+ ,106.7
+ ,96.7
+ ,90.9
+ ,107.5
+ ,96.4
+ ,117.9
+ ,116.7
+ ,96.0
+ ,82.0
+ ,99.4
+ ,93.5
+ ,81.6
+ ,91.2
+ ,73.7
+ ,112.7
+ ,82.7
+ ,97
+ ,104.7
+ ,94.7
+ ,84.9
+ ,107.3
+ ,92.0
+ ,97.5
+ ,109.0
+ ,99.2
+ ,89.7
+ ,98.5
+ ,98.8
+ ,84.1
+ ,99.2
+ ,75.3
+ ,100.2
+ ,88.9
+ ,105.4
+ ,107.4
+ ,90.1
+ ,89.0
+ ,114.8
+ ,102.0
+ ,121.2
+ ,119.5
+ ,103.4
+ ,109.0
+ ,105.2
+ ,106.2
+ ,88.1
+ ,108.2
+ ,84.5
+ ,96.9
+ ,105.9
+ ,102.7
+ ,109.8
+ ,87.9
+ ,90.2
+ ,120.8
+ ,99.7
+ ,118.2
+ ,115.1
+ ,102.2
+ ,112.3
+ ,104.6
+ ,98.3
+ ,85.3
+ ,101.5
+ ,76.0
+ ,96.6
+ ,100.8
+ ,98.1
+ ,103.4
+ ,76.3
+ ,72.3
+ ,112.2
+ ,102.0
+ ,120.2
+ ,107.1
+ ,96.3
+ ,103.1
+ ,97.5
+ ,102.1
+ ,82.9
+ ,106.9
+ ,71.6
+ ,95.5
+ ,94.0
+ ,104.5
+ ,114.8
+ ,82.6
+ ,83.0
+ ,123.3
+ ,98.9
+ ,110.7
+ ,109.7
+ ,106.4
+ ,116.2
+ ,108.9
+ ,117.1
+ ,84.8
+ ,104.4
+ ,74.0
+ ,96.9
+ ,105.0
+ ,87.4
+ ,114.3
+ ,81.8
+ ,71.6
+ ,100.6
+ ,87.4
+ ,117.4
+ ,110.4
+ ,95.3
+ ,89.4
+ ,86.8
+ ,101.5
+ ,71.2
+ ,77.9
+ ,56.0
+ ,99.7
+ ,58.5
+ ,89.9
+ ,109.6
+ ,66.2
+ ,75.4
+ ,86.7
+ ,94.4
+ ,110.0
+ ,105.0
+ ,82.3
+ ,91.4
+ ,88.9
+ ,80.5
+ ,68.9
+ ,60.0
+ ,52.3
+ ,105.1
+ ,87.6
+ ,109.8
+ ,118.3
+ ,97.8
+ ,85.1
+ ,123.6
+ ,109.3
+ ,106.6
+ ,115.8
+ ,108.5
+ ,121.1
+ ,110.3
+ ,105.9
+ ,94.3
+ ,99.5
+ ,82.1
+ ,106.1
+ ,113.1
+ ,111.7
+ ,127.3
+ ,94.7
+ ,81.2
+ ,125.3
+ ,116.4
+ ,114.7
+ ,116.4
+ ,113.9
+ ,122.8
+ ,114.8
+ ,109.5
+ ,97.6
+ ,95.0
+ ,80.2
+ ,106.2
+ ,112.5
+ ,98.6
+ ,112.3
+ ,81.2
+ ,68.7
+ ,111.1
+ ,101.0
+ ,105.6
+ ,111.1
+ ,94.9
+ ,98.4
+ ,94.6
+ ,97.2
+ ,85.6
+ ,105.6
+ ,71.7
+ ,103.9
+ ,89.6
+ ,96.9
+ ,114.9
+ ,70.6
+ ,68.4
+ ,98.4
+ ,105.5
+ ,115.2
+ ,119.5
+ ,84.5
+ ,81.3
+ ,92.0
+ ,114.5
+ ,91.9
+ ,102.5
+ ,71.9
+ ,109.2
+ ,74.5
+ ,95.1
+ ,108.2
+ ,87.6
+ ,93.7
+ ,102.3
+ ,97.8
+ ,112.3
+ ,110.9
+ ,97.5
+ ,84.0
+ ,93.8
+ ,93.5
+ ,75.8
+ ,93.3
+ ,61.0
+ ,110.5
+ ,82.7
+ ,97
+ ,105.4
+ ,89.3
+ ,96.6
+ ,105.0
+ ,95.5
+ ,105.9
+ ,115.1
+ ,100.0
+ ,92.4
+ ,93.8
+ ,100.9
+ ,79.8
+ ,97.3
+ ,65.8
+ ,98.0
+ ,90.1
+ ,112.7
+ ,122.1
+ ,99.6
+ ,101.8
+ ,128.2
+ ,113.7
+ ,106.4
+ ,125.2
+ ,118.5
+ ,116.4
+ ,107.6
+ ,121.1
+ ,99.0
+ ,127.0
+ ,83.7
+ ,94.1
+ ,109.4
+ ,102.9
+ ,113.5
+ ,83.9
+ ,93.6
+ ,124.7
+ ,103.7
+ ,113.8
+ ,116.0
+ ,108.9
+ ,112.3
+ ,101.0
+ ,116.5
+ ,88.5
+ ,111.7
+ ,68.1
+ ,90.2
+ ,96.0
+ ,97.4
+ ,110.0
+ ,74.7
+ ,88.9
+ ,116.1
+ ,100.8
+ ,108.9
+ ,112.9
+ ,98.7
+ ,105.8
+ ,95.4
+ ,109.3
+ ,86.7
+ ,96.4
+ ,65.2
+ ,89.5
+ ,89.2
+ ,111.4
+ ,125.3
+ ,91.2
+ ,114.1
+ ,131.2
+ ,113.8
+ ,100.4
+ ,121.7
+ ,119.2
+ ,130.7
+ ,96.5
+ ,118.1
+ ,97.9
+ ,133.0
+ ,81.8
+ ,91.2
+ ,109.1
+ ,87.4
+ ,114.3
+ ,80.8
+ ,82.3
+ ,97.7
+ ,84.6
+ ,105.3
+ ,123.2
+ ,94.2
+ ,82.9
+ ,89.2
+ ,108.3
+ ,94.3
+ ,72.2
+ ,49.5
+ ,98.2
+ ,49.1
+ ,96.8
+ ,115.6
+ ,72.3
+ ,96.4
+ ,88.8
+ ,95.3
+ ,99.6
+ ,116.6
+ ,88.3
+ ,98.0
+ ,87.1
+ ,105.4
+ ,72.9
+ ,95.8
+ ,52.4
+ ,103.7
+ ,92.9
+ ,114.1
+ ,127.1
+ ,99.7
+ ,104.0
+ ,132.8
+ ,110.0
+ ,104.4
+ ,136.2
+ ,115.6
+ ,119.6
+ ,110.5
+ ,116.2
+ ,91.8
+ ,124.1
+ ,78.2
+ ,103.9
+ ,107.7
+ ,110.3
+ ,123.0
+ ,90.1
+ ,88.2
+ ,113.9
+ ,107.5
+ ,111.5
+ ,120.9
+ ,109.3
+ ,120.7
+ ,110.8
+ ,111.2
+ ,93.2
+ ,127.6
+ ,72.7
+ ,106.5
+ ,103.5
+ ,103.9
+ ,122.2
+ ,83.1
+ ,85.2
+ ,112.6
+ ,107.6
+ ,97.0
+ ,119.6
+ ,102.8
+ ,106.8
+ ,104.2
+ ,105.8
+ ,86.5
+ ,110.7
+ ,70.7
+ ,107.2
+ ,91.1
+ ,101.6
+ ,126.4
+ ,71.9
+ ,87.1
+ ,104.3
+ ,116.0
+ ,107.3
+ ,125.9
+ ,92.9
+ ,89.3
+ ,88.9
+ ,122.7
+ ,98.9
+ ,104.6
+ ,67.5
+ ,111.0
+ ,79.8
+ ,94.6
+ ,112.7
+ ,78.6
+ ,85.5
+ ,107.5
+ ,96.9
+ ,104.1
+ ,116.1
+ ,99.7
+ ,80.6
+ ,89.8
+ ,99.5
+ ,77.2
+ ,112.7
+ ,61.2
+ ,111.8
+ ,71.9
+ ,95.9
+ ,105.8
+ ,87.2
+ ,89.1
+ ,106.0
+ ,97.0
+ ,97.8
+ ,107.5
+ ,101.8
+ ,89.9
+ ,90.0
+ ,107.9
+ ,79.4
+ ,115.3
+ ,65.4
+ ,101.5
+ ,82.9
+ ,104.7
+ ,120.9
+ ,90.6
+ ,105.2
+ ,117.3
+ ,108.1
+ ,99.9
+ ,116.7
+ ,113.1
+ ,102.9
+ ,93.9
+ ,124.6
+ ,90.4
+ ,139.4
+ ,77.4
+ ,95.3
+ ,90.1
+ ,102.8
+ ,116.3
+ ,80.0
+ ,82.9
+ ,123.1
+ ,101.9
+ ,96.8
+ ,112.5
+ ,111.5
+ ,116.9
+ ,91.3
+ ,115.0
+ ,81.4
+ ,119.0
+ ,68.9
+ ,92.7
+ ,100.7
+ ,98.1
+ ,115.7
+ ,73.1
+ ,86.8
+ ,114.3
+ ,107.2
+ ,81.3
+ ,113.0
+ ,103.8
+ ,110.3
+ ,87.8
+ ,110.3
+ ,85.8
+ ,97.4
+ ,62.0
+ ,93.5
+ ,90.7
+ ,113.9
+ ,127.9
+ ,85.6
+ ,112.0
+ ,132.0
+ ,110.2
+ ,72.7
+ ,126.4
+ ,118.1
+ ,129.8
+ ,99.7
+ ,132.7
+ ,103.6
+ ,154.0
+ ,71.0
+ ,96.2
+ ,108.8
+ ,80.9
+ ,108.3
+ ,73.8
+ ,97.4
+ ,92.3
+ ,78.7
+ ,89.1
+ ,114.1
+ ,94.4
+ ,78.6
+ ,73.5
+ ,99.7
+ ,73.6
+ ,81.5
+ ,44.4
+ ,102.1
+ ,44.1
+ ,95.7
+ ,121.1
+ ,70.6
+ ,88.9
+ ,93.7
+ ,96.5
+ ,96.0
+ ,112.5
+ ,95.4
+ ,100.5
+ ,79.2
+ ,96.5
+ ,75.7
+ ,88.8
+ ,50.0
+ ,102.3
+ ,93.6
+ ,113.2
+ ,128.6
+ ,91.8
+ ,109.4
+ ,121.3
+ ,115.2
+ ,97.4
+ ,112.4
+ ,121.2
+ ,123.1
+ ,96.9
+ ,118.7
+ ,99.2
+ ,127.7
+ ,79.3
+ ,127.9
+ ,107.4
+ ,105.9
+ ,123.1
+ ,81.3
+ ,87.8
+ ,113.6
+ ,104.7
+ ,98.0
+ ,113.1
+ ,113.6
+ ,110.8
+ ,95.2
+ ,112.9
+ ,88.7
+ ,105.1
+ ,64.7
+ ,130.8
+ ,96.5
+ ,108.8
+ ,127.7
+ ,85.2
+ ,90.5
+ ,116.3
+ ,109.1
+ ,89.0
+ ,116.3
+ ,113.8
+ ,109.0
+ ,95.6
+ ,130.5
+ ,94.6
+ ,114.9
+ ,69.6
+ ,134.9
+ ,93.6
+ ,102.3
+ ,126.6
+ ,69.6
+ ,79.3
+ ,98.3
+ ,108.4
+ ,97.9
+ ,111.7
+ ,99.3
+ ,88.7
+ ,89.7
+ ,137.9
+ ,98.7
+ ,106.4
+ ,64.1
+ ,141.9
+ ,76.5
+ ,99
+ ,118.4
+ ,83.3
+ ,114.9
+ ,111.9
+ ,95.5
+ ,90.7
+ ,118.8
+ ,112.3
+ ,87.1
+ ,92.8
+ ,115.0
+ ,84.2
+ ,104.5
+ ,59.8
+ ,124.6
+ ,76.7
+ ,100.7
+ ,110.0
+ ,89.8
+ ,118.8
+ ,109.3
+ ,97.8
+ ,85.8
+ ,116.5
+ ,111.2
+ ,94.9
+ ,88.0
+ ,116.8
+ ,87.7
+ ,121.6
+ ,64.2
+ ,118.0
+ ,84.0
+ ,115.5
+ ,129.6
+ ,99.5
+ ,125.0
+ ,133.2
+ ,115.1
+ ,97.1
+ ,125.1
+ ,125.7
+ ,118.7
+ ,101.1
+ ,140.9
+ ,103.3
+ ,141.4
+ ,76.2
+ ,115.1
+ ,103.3
+ ,100.7
+ ,115.8
+ ,78.9
+ ,96.1
+ ,118.0
+ ,96.2
+ ,102.2
+ ,113.1
+ ,111.5
+ ,110.3
+ ,92.7
+ ,120.7
+ ,88.2
+ ,99.0
+ ,58.8
+ ,111.2
+ ,88.5
+ ,109.9
+ ,125.9
+ ,83.8
+ ,116.7
+ ,131.6
+ ,112.0
+ ,80.0
+ ,119.6
+ ,124.1
+ ,123.6
+ ,95.8
+ ,134.2
+ ,93.4
+ ,126.7
+ ,69.0
+ ,113.5
+ ,99.0
+ ,114.6
+ ,128.4
+ ,92.0
+ ,119.5
+ ,134.1
+ ,111.8
+ ,70.7
+ ,114.4
+ ,126.6
+ ,132.5
+ ,103.8
+ ,147.3
+ ,106.3
+ ,134.1
+ ,73.0
+ ,115.2
+ ,105.9
+ ,85.4
+ ,114.0
+ ,80.9
+ ,104.1
+ ,96.7
+ ,82.5
+ ,90.8
+ ,114.0
+ ,100.5
+ ,84.3
+ ,81.8
+ ,112.4
+ ,73.1
+ ,81.3
+ ,47.5
+ ,119.4
+ ,44.7
+ ,100.5
+ ,125.6
+ ,74.6
+ ,121.0
+ ,99.8
+ ,100.8
+ ,102.2
+ ,117.8
+ ,101.8
+ ,105.2
+ ,87.1
+ ,107.1
+ ,78.6
+ ,88.6
+ ,49.4
+ ,116.0
+ ,94.0
+ ,114.8
+ ,128.5
+ ,97.9
+ ,127.3
+ ,128.3
+ ,116.0
+ ,91.0
+ ,117.0
+ ,125.9
+ ,127.5
+ ,105.9
+ ,128.4
+ ,101.6
+ ,132.7
+ ,71.8
+ ,115.7
+ ,107.1
+ ,116.5
+ ,136.6
+ ,88.3
+ ,117.7
+ ,134.9
+ ,116.3
+ ,93.8
+ ,120.9
+ ,129.1
+ ,127.0
+ ,108.1
+ ,137.7
+ ,101.4
+ ,132.9
+ ,74.3
+ ,121.1
+ ,104.8
+ ,112.9
+ ,133.1
+ ,88.1
+ ,108.0
+ ,130.7
+ ,116.6
+ ,53.2
+ ,115.0
+ ,122.0
+ ,119.6
+ ,102.6
+ ,135.0
+ ,98.5
+ ,134.4
+ ,70.4
+ ,120.8
+ ,102.5
+ ,102
+ ,124.6
+ ,66.4
+ ,89.4
+ ,107.3
+ ,112.9
+ ,76.6
+ ,117.3
+ ,99.9
+ ,93.4
+ ,93.7
+ ,151.0
+ ,99.0
+ ,103.7
+ ,62.2
+ ,125.2
+ ,77.7
+ ,106
+ ,123.5
+ ,92.3
+ ,137.4
+ ,121.6
+ ,100.9
+ ,69.6
+ ,119.4
+ ,121.7
+ ,98.2
+ ,103.5
+ ,137.4
+ ,89.5
+ ,119.7
+ ,64.4
+ ,124.0
+ ,85.2
+ ,105.3
+ ,117.2
+ ,95.6
+ ,142.0
+ ,120.6
+ ,104.1
+ ,64.7
+ ,114.9
+ ,119.7
+ ,106.3
+ ,100.6
+ ,132.4
+ ,83.5
+ ,115.0
+ ,63.8
+ ,119.1
+ ,91.3
+ ,118.8
+ ,135.5
+ ,99.7
+ ,137.3
+ ,140.5
+ ,117.4
+ ,88.7
+ ,125.8
+ ,137.1
+ ,122.4
+ ,113.3
+ ,161.3
+ ,97.4
+ ,132.9
+ ,69.6
+ ,119.2
+ ,106.5
+ ,106.1
+ ,124.8
+ ,78.9
+ ,122.8
+ ,124.8
+ ,103.3
+ ,70.2
+ ,117.6
+ ,124.1
+ ,116.4
+ ,102.4
+ ,139.8
+ ,87.8
+ ,108.5
+ ,60.9
+ ,113.9
+ ,92.4
+ ,109.3
+ ,127.8
+ ,79.4
+ ,126.1
+ ,129.9
+ ,111.6
+ ,78.1
+ ,117.6
+ ,129.8
+ ,121.5
+ ,102.1
+ ,146.0
+ ,90.4
+ ,113.9
+ ,65.1
+ ,113.3
+ ,97.5
+ ,117.2
+ ,133.1
+ ,87.8
+ ,147.6
+ ,159.4
+ ,115.0
+ ,78.3
+ ,114.9
+ ,137.2
+ ,133.5
+ ,106.9
+ ,166.5
+ ,101.6
+ ,142.0
+ ,71.7
+ ,116.8
+ ,107.0
+ ,92.5
+ ,125.7
+ ,80.5
+ ,115.7
+ ,111.0
+ ,91.0
+ ,73.0
+ ,121.9
+ ,113.9
+ ,88.3
+ ,87.3
+ ,143.3
+ ,80.0
+ ,97.7
+ ,53.0
+ ,114.8
+ ,51.1
+ ,104.2
+ ,128.4
+ ,71.8
+ ,139.2
+ ,110.1
+ ,104.8
+ ,82.5
+ ,117.0
+ ,109.3
+ ,110.1
+ ,93.1
+ ,121.0
+ ,81.7
+ ,92.2
+ ,56.5
+ ,119.2
+ ,98.6
+ ,112.5
+ ,131.9
+ ,89.2
+ ,151.2
+ ,132.7
+ ,114.1
+ ,73.9
+ ,106.4
+ ,126.1
+ ,121.9
+ ,109.1
+ ,152.6
+ ,96.4
+ ,128.8
+ ,68.7
+ ,117.8
+ ,102.2
+ ,122.4
+ ,146.3
+ ,96.4
+ ,123.8
+ ,135.0
+ ,124.2
+ ,101.9
+ ,110.5
+ ,140.8
+ ,134.8
+ ,120.3
+ ,154.4
+ ,110.2
+ ,134.9
+ ,77.7
+ ,122.5
+ ,114.3
+ ,113.3
+ ,140.6
+ ,83.5
+ ,109.0
+ ,118.6
+ ,117.8
+ ,53.9
+ ,113.6
+ ,129.9
+ ,116.4
+ ,104.9
+ ,154.6
+ ,101.1
+ ,128.2
+ ,68.7
+ ,125.1
+ ,99.4
+ ,100
+ ,129.5
+ ,64.3
+ ,112.1
+ ,94.0
+ ,109.8
+ ,78.5
+ ,114.2
+ ,98.1
+ ,85.0
+ ,92.6
+ ,158.0
+ ,89.3
+ ,114.8
+ ,58.0
+ ,125.0
+ ,72.5
+ ,110.7
+ ,132.4
+ ,85.9
+ ,136.4
+ ,117.9
+ ,105.1
+ ,85.7
+ ,125.4
+ ,129.9
+ ,106.2
+ ,109.8
+ ,142.6
+ ,90.0
+ ,117.9
+ ,63.2
+ ,125.1
+ ,92.3
+ ,112.8
+ ,125.9
+ ,89.2
+ ,135.5
+ ,114.7
+ ,103.3
+ ,87.1
+ ,124.6
+ ,129.8
+ ,114.5
+ ,111.4
+ ,153.4
+ ,95.4
+ ,119.1
+ ,66.7
+ ,121.2
+ ,99.4
+ ,109.8
+ ,126.9
+ ,81.8
+ ,138.7
+ ,113.6
+ ,105.7
+ ,78.7
+ ,120.2
+ ,124.9
+ ,110.5
+ ,117.9
+ ,163.4
+ ,100.3
+ ,120.7
+ ,67.3
+ ,118.9
+ ,85.9
+ ,117.3
+ ,135.8
+ ,79.5
+ ,137.5
+ ,130.6
+ ,107.4
+ ,64.2
+ ,120.8
+ ,135.9
+ ,132.5
+ ,121.6
+ ,167.3
+ ,99.5
+ ,129.1
+ ,70.2
+ ,109.8
+ ,109.4
+ ,109.1
+ ,129.5
+ ,68.7
+ ,141.5
+ ,117.1
+ ,101.5
+ ,70.3
+ ,111.4
+ ,123.6
+ ,124.3
+ ,117.8
+ ,154.8
+ ,93.9
+ ,117.6
+ ,61.3
+ ,109.2
+ ,97.6
+ ,115.9
+ ,130.2
+ ,76.4
+ ,143.6
+ ,123.2
+ ,103.4
+ ,75.4
+ ,124.1
+ ,132.8
+ ,131.3
+ ,124.2
+ ,165.7
+ ,100.6
+ ,129.2
+ ,68.3
+ ,109.0
+ ,104.7
+ ,96
+ ,133.8
+ ,73.6
+ ,146.5
+ ,106.1
+ ,83.0
+ ,71.7
+ ,120.2
+ ,121.1
+ ,97.1
+ ,106.8
+ ,144.7
+ ,84.7
+ ,100.0
+ ,50.3
+ ,110.9
+ ,56.9
+ ,99.8
+ ,123.3
+ ,57.7
+ ,200.7
+ ,88.7
+ ,89.2
+ ,82.7
+ ,125.5
+ ,102.1
+ ,96.8
+ ,102.7
+ ,120.9
+ ,81.6
+ ,87.0
+ ,45.9
+ ,114.5
+ ,86.7
+ ,116.8
+ ,140.7
+ ,78.3
+ ,196.1
+ ,115.5
+ ,110.5
+ ,73.9
+ ,116.0
+ ,131.3
+ ,131.0
+ ,116.8
+ ,152.8
+ ,109.0
+ ,128.0
+ ,71.6
+ ,114.0
+ ,108.5
+ ,115.7
+ ,145.9
+ ,75.5
+ ,169.5
+ ,116.0
+ ,114.1
+ ,82.4
+ ,117.0
+ ,129.6
+ ,126.7
+ ,113.6
+ ,160.2
+ ,99.0
+ ,127.7
+ ,67.0
+ ,118.7
+ ,103.4
+ ,99.4
+ ,128.5
+ ,62.4
+ ,176.9
+ ,96.7
+ ,99.3
+ ,77.8
+ ,105.7
+ ,106.1
+ ,100.9
+ ,96.1
+ ,128.3
+ ,81.1
+ ,93.4
+ ,54.3
+ ,123.0
+ ,86.2
+ ,94.3
+ ,135.9
+ ,55.6
+ ,189.8
+ ,82.0
+ ,105.0
+ ,77.4
+ ,102.0
+ ,92.6
+ ,88.2
+ ,85.0
+ ,150.5
+ ,81.8
+ ,84.1
+ ,47.7
+ ,126.5
+ ,71.0
+ ,91
+ ,120.2
+ ,62.9
+ ,138.1
+ ,92.0
+ ,89.5
+ ,77.2
+ ,106.4
+ ,99.5
+ ,76.3
+ ,83.2
+ ,117.0
+ ,66.5
+ ,71.7
+ ,46.9
+ ,126.9
+ ,75.9
+ ,93.2
+ ,119.2
+ ,66.7
+ ,132.5
+ ,89.7
+ ,92.2
+ ,71.8
+ ,96.9
+ ,99.4
+ ,87.0
+ ,84.9
+ ,116.0
+ ,66.4
+ ,83.2
+ ,54.5
+ ,117.8
+ ,87.1
+ ,103.1
+ ,132.5
+ ,66.8
+ ,131.2
+ ,99.1
+ ,102.0
+ ,75.7
+ ,107.6
+ ,111.9
+ ,109.9
+ ,83.0
+ ,133.3
+ ,86.3
+ ,89.1
+ ,61.4
+ ,116.6
+ ,102.0
+ ,94.1
+ ,130.5
+ ,59.9
+ ,110.3
+ ,98.9
+ ,93.5
+ ,75.2
+ ,98.8
+ ,108.7
+ ,101.8
+ ,79.6
+ ,116.4
+ ,73.6
+ ,79.6
+ ,52.0
+ ,109.4
+ ,88.5
+ ,91.8
+ ,124.8
+ ,52.0
+ ,123.7
+ ,88.6
+ ,89.8
+ ,68.9
+ ,101.1
+ ,101.6
+ ,98.8
+ ,83.2
+ ,104.0
+ ,71.5
+ ,62.8
+ ,50.6
+ ,113.8
+ ,87.8
+ ,102.7
+ ,136.7
+ ,61.2
+ ,149.9
+ ,105.5
+ ,96.1
+ ,66.8
+ ,105.7
+ ,116.1
+ ,117.1
+ ,83.8
+ ,126.6
+ ,87.2
+ ,95.1
+ ,57.1
+ ,116.6
+ ,100.8
+ ,82.6
+ ,129.2
+ ,60.0
+ ,162.9
+ ,84.4
+ ,76.0
+ ,68.2
+ ,104.6
+ ,102.5
+ ,83.4
+ ,82.8
+ ,92.9
+ ,65.3
+ ,63.6
+ ,42.5
+ ,119.9
+ ,50.6
+ ,89.1
+ ,127.9
+ ,49.9
+ ,138.1
+ ,77.9
+ ,85.1
+ ,73.0
+ ,103.2
+ ,90.5
+ ,90.1
+ ,71.4
+ ,83.6
+ ,69.7
+ ,61.4
+ ,42.2
+ ,120.0
+ ,85.9
+ ,104.5
+ ,133.6
+ ,66.3
+ ,133.3
+ ,100.8
+ ,103.2
+ ,69.3
+ ,101.6
+ ,118.7
+ ,116.0
+ ,87.2
+ ,112.8
+ ,95.5
+ ,98.2
+ ,63.1
+ ,121.0
+ ,103.8
+ ,105.1
+ ,137.1
+ ,67.9
+ ,122.9
+ ,100.2
+ ,102.9
+ ,75.5
+ ,106.7
+ ,117.3
+ ,116.6
+ ,86.3
+ ,113.2
+ ,86.3
+ ,95.3
+ ,61.1
+ ,127.7
+ ,102.9
+ ,95.1
+ ,128.9
+ ,59.1
+ ,106.5
+ ,96.1
+ ,113.6
+ ,67.1
+ ,99.5
+ ,106.1
+ ,96.1
+ ,77.7
+ ,118.5
+ ,81.0
+ ,81.5
+ ,56.6
+ ,127.2
+ ,79.7
+ ,88.7
+ ,129.4
+ ,54.0
+ ,122.3
+ ,80.3
+ ,101.3
+ ,69.3
+ ,101.0
+ ,91.8
+ ,72.9
+ ,62.5
+ ,125.5
+ ,88.7
+ ,85.5
+ ,51.0
+ ,128.6
+ ,63.7
+ ,86.3
+ ,118.4
+ ,59.6
+ ,140.5
+ ,87.2
+ ,87.1
+ ,67.6
+ ,104.9
+ ,101.5
+ ,65.7
+ ,69.2
+ ,91.3
+ ,71.9
+ ,71.1
+ ,48.8
+ ,128.9
+ ,69.5
+ ,91.8
+ ,120.9
+ ,66.0
+ ,136.2
+ ,89.6
+ ,88.6
+ ,63.8
+ ,118.4
+ ,104.3
+ ,72.9
+ ,73.4
+ ,105.4
+ ,78.6
+ ,78.1
+ ,52.6
+ ,121.3
+ ,75.0
+ ,111.5
+ ,142.4
+ ,72.3
+ ,139.9
+ ,112.6
+ ,105.9
+ ,68.5
+ ,129.0
+ ,123.9
+ ,113.9
+ ,97.4
+ ,121.3
+ ,96.0
+ ,103.0
+ ,61.2
+ ,119.7
+ ,104.7
+ ,99.7
+ ,132.2
+ ,63.7
+ ,108.7
+ ,104.5
+ ,93.4
+ ,66.7
+ ,123.7
+ ,118.0
+ ,106.9
+ ,80.8
+ ,106.9
+ ,81.1
+ ,86.0
+ ,54.3
+ ,113.7
+ ,90.2
+ ,97.5
+ ,124.9
+ ,57.5
+ ,108.6
+ ,100.3
+ ,89.5
+ ,70.8
+ ,127.6
+ ,108.9
+ ,102.0
+ ,84.9
+ ,109.4
+ ,77.5
+ ,86.2
+ ,50.1
+ ,117.5
+ ,82.1
+ ,111.7
+ ,140.2
+ ,71.5
+ ,128.0
+ ,116.3
+ ,99.4
+ ,57.8
+ ,129.4
+ ,128.5
+ ,124.8
+ ,94.3
+ ,132.6
+ ,97.3
+ ,105.7
+ ,58.5
+ ,123.5
+ ,103.0
+ ,86.2
+ ,127.9
+ ,61.2
+ ,161.8
+ ,88.8
+ ,77.0
+ ,56.6
+ ,128.3
+ ,105.1
+ ,80.2
+ ,87.6
+ ,96.8
+ ,78.6
+ ,57.2
+ ,40.4
+ ,122.2
+ ,47.1
+ ,95.4
+ ,127.9
+ ,50.7
+ ,147.0
+ ,87.2
+ ,87.2
+ ,68.7
+ ,124.8
+ ,99.8
+ ,95.0
+ ,79.5
+ ,100.3
+ ,79.0
+ ,73.7
+ ,45.7
+ ,124.6
+ ,85.6
+ ,113
+ ,139.2
+ ,71.8
+ ,147.9
+ ,110.8
+ ,104.2
+ ,75.8
+ ,125.2
+ ,125.3
+ ,119.5
+ ,101.2
+ ,119.2
+ ,93.4
+ ,120.5
+ ,62.7
+ ,126.0
+ ,104.6
+ ,111
+ ,137.4
+ ,68.0
+ ,102.7
+ ,110.0
+ ,99.9
+ ,73.9
+ ,129.6
+ ,116.4
+ ,116.8
+ ,95.4
+ ,120.7
+ ,99.4
+ ,107.7
+ ,59.5
+ ,124.1
+ ,103.8
+ ,104.5
+ ,140.0
+ ,62.3
+ ,102.3
+ ,103.5
+ ,97.4
+ ,76.5
+ ,124.8
+ ,111.9
+ ,98.7
+ ,96.1
+ ,111.6
+ ,87.3
+ ,105.3
+ ,55.9
+ ,123.7
+ ,89.2
+ ,97.3
+ ,137.5
+ ,55.4
+ ,100.3
+ ,85.3
+ ,102.6
+ ,81.1
+ ,121.9
+ ,97.0
+ ,63.9
+ ,88.5
+ ,134.9
+ ,98.8
+ ,109.8
+ ,55.9
+ ,128.6
+ ,60.6
+ ,97.1
+ ,130.2
+ ,60.0
+ ,116.8
+ ,95.5
+ ,87.4
+ ,77.1
+ ,129.2
+ ,106.9
+ ,81.9
+ ,87.2
+ ,105.9
+ ,84.4
+ ,94.0
+ ,49.3
+ ,129.8
+ ,73.3
+ ,104.1
+ ,129.9
+ ,65.4
+ ,139.0
+ ,99.1
+ ,96.6
+ ,64.3
+ ,124.1
+ ,113.7
+ ,97.1
+ ,93.3
+ ,118.8
+ ,85.4
+ ,108.2
+ ,59.9
+ ,123.1
+ ,90.4
+ ,119.3
+ ,148.3
+ ,73.1
+ ,122.1
+ ,116.9
+ ,104.1
+ ,66.5
+ ,143.7
+ ,130.1
+ ,120.6
+ ,105.8
+ ,145.9
+ ,105.2
+ ,131.9
+ ,64.0
+ ,119.6
+ ,106.4)
+ ,dim=c(17
+ ,123)
+ ,dimnames=list(c('Totale_industrie'
+ ,'Voedings-en_genotmiddelen'
+ ,'Textiel_en_kleding'
+ ,'Leer_en_schoeisel'
+ ,'Hout'
+ ,'Papier_en_karton'
+ ,'Cokes_raffinage_splijt-en_kweekstoffen'
+ ,'Chemische_nijverheid'
+ ,'Rubber-en_kunststof'
+ ,'Niet-metaalhoudende_minerale_producten'
+ ,'Metallurgie_en_producten_van_metaal'
+ ,'Machines'
+ ,'Elektronische_apparaten'
+ ,'Transportmiddelen'
+ ,'Overige_industrie'
+ ,'Elektriciteit_gas_en_water'
+ ,'Bouwnijverheid')
+ ,1:123))
> y <- array(NA,dim=c(17,123),dimnames=list(c('Totale_industrie','Voedings-en_genotmiddelen','Textiel_en_kleding','Leer_en_schoeisel','Hout','Papier_en_karton','Cokes_raffinage_splijt-en_kweekstoffen','Chemische_nijverheid','Rubber-en_kunststof','Niet-metaalhoudende_minerale_producten','Metallurgie_en_producten_van_metaal','Machines','Elektronische_apparaten','Transportmiddelen','Overige_industrie','Elektriciteit_gas_en_water','Bouwnijverheid'),1:123))
> 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
Totale_industrie Voedings-en_genotmiddelen Textiel_en_kleding
1 98.8 99.2 101.7
2 100.5 93.6 102.8
3 110.4 104.2 109.2
4 96.4 95.3 88.5
5 101.9 102.7 93.9
6 106.2 103.1 97.4
7 81.0 100.0 86.7
8 94.7 107.2 80.1
9 101.0 107.0 104.1
10 109.4 119.0 108.9
11 102.3 110.4 94.1
12 90.7 101.7 72.4
13 96.2 102.4 94.7
14 96.1 98.8 95.3
15 106.0 105.6 96.4
16 103.1 104.4 91.7
17 102.0 106.3 84.0
18 104.7 107.2 86.2
19 86.0 108.5 88.8
20 92.1 106.9 74.9
21 106.9 114.2 111.3
22 112.6 125.9 103.6
23 101.7 110.6 83.4
24 92.0 110.5 69.0
25 97.4 106.7 96.7
26 97.0 104.7 94.7
27 105.4 107.4 90.1
28 102.7 109.8 87.9
29 98.1 103.4 76.3
30 104.5 114.8 82.6
31 87.4 114.3 81.8
32 89.9 109.6 66.2
33 109.8 118.3 97.8
34 111.7 127.3 94.7
35 98.6 112.3 81.2
36 96.9 114.9 70.6
37 95.1 108.2 87.6
38 97.0 105.4 89.3
39 112.7 122.1 99.6
40 102.9 113.5 83.9
41 97.4 110.0 74.7
42 111.4 125.3 91.2
43 87.4 114.3 80.8
44 96.8 115.6 72.3
45 114.1 127.1 99.7
46 110.3 123.0 90.1
47 103.9 122.2 83.1
48 101.6 126.4 71.9
49 94.6 112.7 78.6
50 95.9 105.8 87.2
51 104.7 120.9 90.6
52 102.8 116.3 80.0
53 98.1 115.7 73.1
54 113.9 127.9 85.6
55 80.9 108.3 73.8
56 95.7 121.1 70.6
57 113.2 128.6 91.8
58 105.9 123.1 81.3
59 108.8 127.7 85.2
60 102.3 126.6 69.6
61 99.0 118.4 83.3
62 100.7 110.0 89.8
63 115.5 129.6 99.5
64 100.7 115.8 78.9
65 109.9 125.9 83.8
66 114.6 128.4 92.0
67 85.4 114.0 80.9
68 100.5 125.6 74.6
69 114.8 128.5 97.9
70 116.5 136.6 88.3
71 112.9 133.1 88.1
72 102.0 124.6 66.4
73 106.0 123.5 92.3
74 105.3 117.2 95.6
75 118.8 135.5 99.7
76 106.1 124.8 78.9
77 109.3 127.8 79.4
78 117.2 133.1 87.8
79 92.5 125.7 80.5
80 104.2 128.4 71.8
81 112.5 131.9 89.2
82 122.4 146.3 96.4
83 113.3 140.6 83.5
84 100.0 129.5 64.3
85 110.7 132.4 85.9
86 112.8 125.9 89.2
87 109.8 126.9 81.8
88 117.3 135.8 79.5
89 109.1 129.5 68.7
90 115.9 130.2 76.4
91 96.0 133.8 73.6
92 99.8 123.3 57.7
93 116.8 140.7 78.3
94 115.7 145.9 75.5
95 99.4 128.5 62.4
96 94.3 135.9 55.6
97 91.0 120.2 62.9
98 93.2 119.2 66.7
99 103.1 132.5 66.8
100 94.1 130.5 59.9
101 91.8 124.8 52.0
102 102.7 136.7 61.2
103 82.6 129.2 60.0
104 89.1 127.9 49.9
105 104.5 133.6 66.3
106 105.1 137.1 67.9
107 95.1 128.9 59.1
108 88.7 129.4 54.0
109 86.3 118.4 59.6
110 91.8 120.9 66.0
111 111.5 142.4 72.3
112 99.7 132.2 63.7
113 97.5 124.9 57.5
114 111.7 140.2 71.5
115 86.2 127.9 61.2
116 95.4 127.9 50.7
117 113.0 139.2 71.8
118 111.0 137.4 68.0
119 104.5 140.0 62.3
120 97.3 137.5 55.4
121 97.1 130.2 60.0
122 104.1 129.9 65.4
123 119.3 148.3 73.1
Leer_en_schoeisel Hout Papier_en_karton
1 91.4 98.5 94.7
2 85.4 96.7 90.2
3 110.4 113.1 104.3
4 90.1 100.0 96.0
5 103.4 104.7 96.6
6 118.7 108.5 103.5
7 76.4 90.5 79.9
8 92.7 88.6 88.5
9 105.2 105.4 103.3
10 91.5 119.9 113.5
11 75.3 107.2 100.8
12 60.5 84.1 95.3
13 80.4 101.4 92.6
14 84.5 105.1 89.0
15 93.9 118.7 98.7
16 78.0 113.8 94.5
17 92.3 113.8 96.9
18 90.0 118.9 98.1
19 72.1 98.5 82.1
20 76.9 91.0 86.5
21 76.0 120.7 104.1
22 88.7 127.9 110.9
23 55.4 112.4 114.5
24 46.6 93.1 112.2
25 90.9 107.5 96.4
26 84.9 107.3 92.0
27 89.0 114.8 102.0
28 90.2 120.8 99.7
29 72.3 112.2 102.0
30 83.0 123.3 98.9
31 71.6 100.6 87.4
32 75.4 86.7 94.4
33 85.1 123.6 109.3
34 81.2 125.3 116.4
35 68.7 111.1 101.0
36 68.4 98.4 105.5
37 93.7 102.3 97.8
38 96.6 105.0 95.5
39 101.8 128.2 113.7
40 93.6 124.7 103.7
41 88.9 116.1 100.8
42 114.1 131.2 113.8
43 82.3 97.7 84.6
44 96.4 88.8 95.3
45 104.0 132.8 110.0
46 88.2 113.9 107.5
47 85.2 112.6 107.6
48 87.1 104.3 116.0
49 85.5 107.5 96.9
50 89.1 106.0 97.0
51 105.2 117.3 108.1
52 82.9 123.1 101.9
53 86.8 114.3 107.2
54 112.0 132.0 110.2
55 97.4 92.3 78.7
56 88.9 93.7 96.5
57 109.4 121.3 115.2
58 87.8 113.6 104.7
59 90.5 116.3 109.1
60 79.3 98.3 108.4
61 114.9 111.9 95.5
62 118.8 109.3 97.8
63 125.0 133.2 115.1
64 96.1 118.0 96.2
65 116.7 131.6 112.0
66 119.5 134.1 111.8
67 104.1 96.7 82.5
68 121.0 99.8 100.8
69 127.3 128.3 116.0
70 117.7 134.9 116.3
71 108.0 130.7 116.6
72 89.4 107.3 112.9
73 137.4 121.6 100.9
74 142.0 120.6 104.1
75 137.3 140.5 117.4
76 122.8 124.8 103.3
77 126.1 129.9 111.6
78 147.6 159.4 115.0
79 115.7 111.0 91.0
80 139.2 110.1 104.8
81 151.2 132.7 114.1
82 123.8 135.0 124.2
83 109.0 118.6 117.8
84 112.1 94.0 109.8
85 136.4 117.9 105.1
86 135.5 114.7 103.3
87 138.7 113.6 105.7
88 137.5 130.6 107.4
89 141.5 117.1 101.5
90 143.6 123.2 103.4
91 146.5 106.1 83.0
92 200.7 88.7 89.2
93 196.1 115.5 110.5
94 169.5 116.0 114.1
95 176.9 96.7 99.3
96 189.8 82.0 105.0
97 138.1 92.0 89.5
98 132.5 89.7 92.2
99 131.2 99.1 102.0
100 110.3 98.9 93.5
101 123.7 88.6 89.8
102 149.9 105.5 96.1
103 162.9 84.4 76.0
104 138.1 77.9 85.1
105 133.3 100.8 103.2
106 122.9 100.2 102.9
107 106.5 96.1 113.6
108 122.3 80.3 101.3
109 140.5 87.2 87.1
110 136.2 89.6 88.6
111 139.9 112.6 105.9
112 108.7 104.5 93.4
113 108.6 100.3 89.5
114 128.0 116.3 99.4
115 161.8 88.8 77.0
116 147.0 87.2 87.2
117 147.9 110.8 104.2
118 102.7 110.0 99.9
119 102.3 103.5 97.4
120 100.3 85.3 102.6
121 116.8 95.5 87.4
122 139.0 99.1 96.6
123 122.1 116.9 104.1
Cokes_raffinage_splijt-en_kweekstoffen Chemische_nijverheid
1 114.2 100.5
2 99.4 99.0
3 98.0 104.1
4 90.8 98.6
5 111.4 101.4
6 104.3 102.1
7 99.6 93.0
8 108.0 96.9
9 113.1 91.2
10 112.7 96.9
11 114.3 94.0
12 108.6 90.4
13 116.2 105.2
14 101.9 103.4
15 107.3 111.7
16 111.4 114.2
17 121.9 111.4
18 123.1 106.3
19 108.1 111.8
20 114.5 101.5
21 119.7 103.0
22 117.0 105.2
23 114.8 101.1
24 115.8 100.7
25 117.9 116.7
26 97.5 109.0
27 121.2 119.5
28 118.2 115.1
29 120.2 107.1
30 110.7 109.7
31 117.4 110.4
32 110.0 105.0
33 106.6 115.8
34 114.7 116.4
35 105.6 111.1
36 115.2 119.5
37 112.3 110.9
38 105.9 115.1
39 106.4 125.2
40 113.8 116.0
41 108.9 112.9
42 100.4 121.7
43 105.3 123.2
44 99.6 116.6
45 104.4 136.2
46 111.5 120.9
47 97.0 119.6
48 107.3 125.9
49 104.1 116.1
50 97.8 107.5
51 99.9 116.7
52 96.8 112.5
53 81.3 113.0
54 72.7 126.4
55 89.1 114.1
56 96.0 112.5
57 97.4 112.4
58 98.0 113.1
59 89.0 116.3
60 97.9 111.7
61 90.7 118.8
62 85.8 116.5
63 97.1 125.1
64 102.2 113.1
65 80.0 119.6
66 70.7 114.4
67 90.8 114.0
68 102.2 117.8
69 91.0 117.0
70 93.8 120.9
71 53.2 115.0
72 76.6 117.3
73 69.6 119.4
74 64.7 114.9
75 88.7 125.8
76 70.2 117.6
77 78.1 117.6
78 78.3 114.9
79 73.0 121.9
80 82.5 117.0
81 73.9 106.4
82 101.9 110.5
83 53.9 113.6
84 78.5 114.2
85 85.7 125.4
86 87.1 124.6
87 78.7 120.2
88 64.2 120.8
89 70.3 111.4
90 75.4 124.1
91 71.7 120.2
92 82.7 125.5
93 73.9 116.0
94 82.4 117.0
95 77.8 105.7
96 77.4 102.0
97 77.2 106.4
98 71.8 96.9
99 75.7 107.6
100 75.2 98.8
101 68.9 101.1
102 66.8 105.7
103 68.2 104.6
104 73.0 103.2
105 69.3 101.6
106 75.5 106.7
107 67.1 99.5
108 69.3 101.0
109 67.6 104.9
110 63.8 118.4
111 68.5 129.0
112 66.7 123.7
113 70.8 127.6
114 57.8 129.4
115 56.6 128.3
116 68.7 124.8
117 75.8 125.2
118 73.9 129.6
119 76.5 124.8
120 81.1 121.9
121 77.1 129.2
122 64.3 124.1
123 66.5 143.7
Rubber-en_kunststof Niet-metaalhoudende_minerale_producten
1 103.0 85.1
2 97.3 94.2
3 109.5 107.3
4 97.0 98.3
5 102.6 111.0
6 106.1 114.8
7 88.5 75.9
8 85.4 99.5
9 100.2 102.9
10 106.1 117.1
11 95.3 101.0
12 73.8 73.2
13 94.1 84.0
14 90.1 90.0
15 99.4 105.2
16 100.1 111.2
17 96.2 109.7
18 102.0 115.7
19 88.0 85.3
20 78.2 92.1
21 99.3 116.7
22 107.2 119.4
23 93.4 99.9
24 74.0 70.8
25 96.0 82.0
26 99.2 89.7
27 103.4 109.0
28 102.2 112.3
29 96.3 103.1
30 106.4 116.2
31 95.3 89.4
32 82.3 91.4
33 108.5 121.1
34 113.9 122.8
35 94.9 98.4
36 84.5 81.3
37 97.5 84.0
38 100.0 92.4
39 118.5 116.4
40 108.9 112.3
41 98.7 105.8
42 119.2 130.7
43 94.2 82.9
44 88.3 98.0
45 115.6 119.6
46 109.3 120.7
47 102.8 106.8
48 92.9 89.3
49 99.7 80.6
50 101.8 89.9
51 113.1 102.9
52 111.5 116.9
53 103.8 110.3
54 118.1 129.8
55 94.4 78.6
56 95.4 100.5
57 121.2 123.1
58 113.6 110.8
59 113.8 109.0
60 99.3 88.7
61 112.3 87.1
62 111.2 94.9
63 125.7 118.7
64 111.5 110.3
65 124.1 123.6
66 126.6 132.5
67 100.5 84.3
68 101.8 105.2
69 125.9 127.5
70 129.1 127.0
71 122.0 119.6
72 99.9 93.4
73 121.7 98.2
74 119.7 106.3
75 137.1 122.4
76 124.1 116.4
77 129.8 121.5
78 137.2 133.5
79 113.9 88.3
80 109.3 110.1
81 126.1 121.9
82 140.8 134.8
83 129.9 116.4
84 98.1 85.0
85 129.9 106.2
86 129.8 114.5
87 124.9 110.5
88 135.9 132.5
89 123.6 124.3
90 132.8 131.3
91 121.1 97.1
92 102.1 96.8
93 131.3 131.0
94 129.6 126.7
95 106.1 100.9
96 92.6 88.2
97 99.5 76.3
98 99.4 87.0
99 111.9 109.9
100 108.7 101.8
101 101.6 98.8
102 116.1 117.1
103 102.5 83.4
104 90.5 90.1
105 118.7 116.0
106 117.3 116.6
107 106.1 96.1
108 91.8 72.9
109 101.5 65.7
110 104.3 72.9
111 123.9 113.9
112 118.0 106.9
113 108.9 102.0
114 128.5 124.8
115 105.1 80.2
116 99.8 95.0
117 125.3 119.5
118 116.4 116.8
119 111.9 98.7
120 97.0 63.9
121 106.9 81.9
122 113.7 97.1
123 130.1 120.6
Metallurgie_en_producten_van_metaal Machines Elektronische_apparaten
1 99.9 93.5 104.8
2 98.6 94.7 105.6
3 107.2 112.9 118.3
4 95.7 99.2 89.9
5 93.7 105.6 90.2
6 106.7 113.0 107.0
7 86.7 83.1 64.5
8 95.3 81.1 92.6
9 99.3 96.9 95.8
10 101.8 104.3 94.3
11 96.0 97.7 91.2
12 91.7 102.6 86.3
13 95.3 89.9 77.6
14 96.6 96.0 82.5
15 107.2 112.7 97.7
16 108.0 107.1 83.3
17 98.4 106.2 84.2
18 103.1 121.0 92.8
19 81.1 101.2 77.4
20 96.6 83.2 72.5
21 103.7 105.1 88.8
22 106.6 113.3 93.4
23 97.6 99.1 92.6
24 87.6 100.3 90.7
25 99.4 93.5 81.6
26 98.5 98.8 84.1
27 105.2 106.2 88.1
28 104.6 98.3 85.3
29 97.5 102.1 82.9
30 108.9 117.1 84.8
31 86.8 101.5 71.2
32 88.9 80.5 68.9
33 110.3 105.9 94.3
34 114.8 109.5 97.6
35 94.6 97.2 85.6
36 92.0 114.5 91.9
37 93.8 93.5 75.8
38 93.8 100.9 79.8
39 107.6 121.1 99.0
40 101.0 116.5 88.5
41 95.4 109.3 86.7
42 96.5 118.1 97.9
43 89.2 108.3 94.3
44 87.1 105.4 72.9
45 110.5 116.2 91.8
46 110.8 111.2 93.2
47 104.2 105.8 86.5
48 88.9 122.7 98.9
49 89.8 99.5 77.2
50 90.0 107.9 79.4
51 93.9 124.6 90.4
52 91.3 115.0 81.4
53 87.8 110.3 85.8
54 99.7 132.7 103.6
55 73.5 99.7 73.6
56 79.2 96.5 75.7
57 96.9 118.7 99.2
58 95.2 112.9 88.7
59 95.6 130.5 94.6
60 89.7 137.9 98.7
61 92.8 115.0 84.2
62 88.0 116.8 87.7
63 101.1 140.9 103.3
64 92.7 120.7 88.2
65 95.8 134.2 93.4
66 103.8 147.3 106.3
67 81.8 112.4 73.1
68 87.1 107.1 78.6
69 105.9 128.4 101.6
70 108.1 137.7 101.4
71 102.6 135.0 98.5
72 93.7 151.0 99.0
73 103.5 137.4 89.5
74 100.6 132.4 83.5
75 113.3 161.3 97.4
76 102.4 139.8 87.8
77 102.1 146.0 90.4
78 106.9 166.5 101.6
79 87.3 143.3 80.0
80 93.1 121.0 81.7
81 109.1 152.6 96.4
82 120.3 154.4 110.2
83 104.9 154.6 101.1
84 92.6 158.0 89.3
85 109.8 142.6 90.0
86 111.4 153.4 95.4
87 117.9 163.4 100.3
88 121.6 167.3 99.5
89 117.8 154.8 93.9
90 124.2 165.7 100.6
91 106.8 144.7 84.7
92 102.7 120.9 81.6
93 116.8 152.8 109.0
94 113.6 160.2 99.0
95 96.1 128.3 81.1
96 85.0 150.5 81.8
97 83.2 117.0 66.5
98 84.9 116.0 66.4
99 83.0 133.3 86.3
100 79.6 116.4 73.6
101 83.2 104.0 71.5
102 83.8 126.6 87.2
103 82.8 92.9 65.3
104 71.4 83.6 69.7
105 87.2 112.8 95.5
106 86.3 113.2 86.3
107 77.7 118.5 81.0
108 62.5 125.5 88.7
109 69.2 91.3 71.9
110 73.4 105.4 78.6
111 97.4 121.3 96.0
112 80.8 106.9 81.1
113 84.9 109.4 77.5
114 94.3 132.6 97.3
115 87.6 96.8 78.6
116 79.5 100.3 79.0
117 101.2 119.2 93.4
118 95.4 120.7 99.4
119 96.1 111.6 87.3
120 88.5 134.9 98.8
121 87.2 105.9 84.4
122 93.3 118.8 85.4
123 105.8 145.9 105.2
Transportmiddelen Overige_industrie Elektriciteit_gas_en_water
1 124.9 95.3 101.3
2 132.0 97.2 100.9
3 151.4 108.6 104.1
4 108.9 89.6 96.2
5 121.3 99.8 87.0
6 123.4 106.3 88.7
7 90.3 71.6 87.0
8 79.3 75.8 87.7
9 117.2 101.4 91.3
10 116.9 109.1 103.3
11 120.8 101.0 110.6
12 96.1 91.0 111.2
13 100.8 86.4 105.8
14 105.3 89.7 98.3
15 116.1 97.9 97.6
16 112.8 91.5 88.4
17 114.5 93.4 93.0
18 117.2 97.2 93.5
19 77.1 67.1 94.5
20 80.1 59.4 98.1
21 120.3 101.6 98.3
22 133.4 99.7 114.8
23 109.4 89.1 107.8
24 93.2 78.7 107.9
25 91.2 73.7 112.7
26 99.2 75.3 100.2
27 108.2 84.5 96.9
28 101.5 76.0 96.6
29 106.9 71.6 95.5
30 104.4 74.0 96.9
31 77.9 56.0 99.7
32 60.0 52.3 105.1
33 99.5 82.1 106.1
34 95.0 80.2 106.2
35 105.6 71.7 103.9
36 102.5 71.9 109.2
37 93.3 61.0 110.5
38 97.3 65.8 98.0
39 127.0 83.7 94.1
40 111.7 68.1 90.2
41 96.4 65.2 89.5
42 133.0 81.8 91.2
43 72.2 49.5 98.2
44 95.8 52.4 103.7
45 124.1 78.2 103.9
46 127.6 72.7 106.5
47 110.7 70.7 107.2
48 104.6 67.5 111.0
49 112.7 61.2 111.8
50 115.3 65.4 101.5
51 139.4 77.4 95.3
52 119.0 68.9 92.7
53 97.4 62.0 93.5
54 154.0 71.0 96.2
55 81.5 44.4 102.1
56 88.8 50.0 102.3
57 127.7 79.3 127.9
58 105.1 64.7 130.8
59 114.9 69.6 134.9
60 106.4 64.1 141.9
61 104.5 59.8 124.6
62 121.6 64.2 118.0
63 141.4 76.2 115.1
64 99.0 58.8 111.2
65 126.7 69.0 113.5
66 134.1 73.0 115.2
67 81.3 47.5 119.4
68 88.6 49.4 116.0
69 132.7 71.8 115.7
70 132.9 74.3 121.1
71 134.4 70.4 120.8
72 103.7 62.2 125.2
73 119.7 64.4 124.0
74 115.0 63.8 119.1
75 132.9 69.6 119.2
76 108.5 60.9 113.9
77 113.9 65.1 113.3
78 142.0 71.7 116.8
79 97.7 53.0 114.8
80 92.2 56.5 119.2
81 128.8 68.7 117.8
82 134.9 77.7 122.5
83 128.2 68.7 125.1
84 114.8 58.0 125.0
85 117.9 63.2 125.1
86 119.1 66.7 121.2
87 120.7 67.3 118.9
88 129.1 70.2 109.8
89 117.6 61.3 109.2
90 129.2 68.3 109.0
91 100.0 50.3 110.9
92 87.0 45.9 114.5
93 128.0 71.6 114.0
94 127.7 67.0 118.7
95 93.4 54.3 123.0
96 84.1 47.7 126.5
97 71.7 46.9 126.9
98 83.2 54.5 117.8
99 89.1 61.4 116.6
100 79.6 52.0 109.4
101 62.8 50.6 113.8
102 95.1 57.1 116.6
103 63.6 42.5 119.9
104 61.4 42.2 120.0
105 98.2 63.1 121.0
106 95.3 61.1 127.7
107 81.5 56.6 127.2
108 85.5 51.0 128.6
109 71.1 48.8 128.9
110 78.1 52.6 121.3
111 103.0 61.2 119.7
112 86.0 54.3 113.7
113 86.2 50.1 117.5
114 105.7 58.5 123.5
115 57.2 40.4 122.2
116 73.7 45.7 124.6
117 120.5 62.7 126.0
118 107.7 59.5 124.1
119 105.3 55.9 123.7
120 109.8 55.9 128.6
121 94.0 49.3 129.8
122 108.2 59.9 123.1
123 131.9 64.0 119.6
Bouwnijverheid
1 86.9
2 99.7
3 109.1
4 94.6
5 111.2
6 112.8
7 53.5
8 107.5
9 105.2
10 122.8
11 103.4
12 76.9
13 89.6
14 92.8
15 107.6
16 104.6
17 103.0
18 106.9
19 56.3
20 93.4
21 109.1
22 113.8
23 97.4
24 72.5
25 82.7
26 88.9
27 105.9
28 100.8
29 94.0
30 105.0
31 58.5
32 87.6
33 113.1
34 112.5
35 89.6
36 74.5
37 82.7
38 90.1
39 109.4
40 96.0
41 89.2
42 109.1
43 49.1
44 92.9
45 107.7
46 103.5
47 91.1
48 79.8
49 71.9
50 82.9
51 90.1
52 100.7
53 90.7
54 108.8
55 44.1
56 93.6
57 107.4
58 96.5
59 93.6
60 76.5
61 76.7
62 84.0
63 103.3
64 88.5
65 99.0
66 105.9
67 44.7
68 94.0
69 107.1
70 104.8
71 102.5
72 77.7
73 85.2
74 91.3
75 106.5
76 92.4
77 97.5
78 107.0
79 51.1
80 98.6
81 102.2
82 114.3
83 99.4
84 72.5
85 92.3
86 99.4
87 85.9
88 109.4
89 97.6
90 104.7
91 56.9
92 86.7
93 108.5
94 103.4
95 86.2
96 71.0
97 75.9
98 87.1
99 102.0
100 88.5
101 87.8
102 100.8
103 50.6
104 85.9
105 103.8
106 102.9
107 79.7
108 63.7
109 69.5
110 75.0
111 104.7
112 90.2
113 82.1
114 103.0
115 47.1
116 85.6
117 104.6
118 103.8
119 89.2
120 60.6
121 73.3
122 90.4
123 106.4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept)
-0.892024
`Voedings-en_genotmiddelen`
0.090993
Textiel_en_kleding
0.036018
Leer_en_schoeisel
-0.005033
Hout
0.006673
Papier_en_karton
0.063832
`Cokes_raffinage_splijt-en_kweekstoffen`
0.012936
Chemische_nijverheid
0.134625
`Rubber-en_kunststof`
0.042132
`Niet-metaalhoudende_minerale_producten`
0.040657
Metallurgie_en_producten_van_metaal
0.102544
Machines
0.035376
Elektronische_apparaten
0.057135
Transportmiddelen
0.068195
Overige_industrie
0.015718
Elektriciteit_gas_en_water
0.096063
Bouwnijverheid
0.212027
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.94090 -0.16332 -0.02286 0.15150 1.22735
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.892024 0.794558 -1.123 0.264115
`Voedings-en_genotmiddelen` 0.090993 0.007009 12.982 < 2e-16
Textiel_en_kleding 0.036018 0.005686 6.335 5.88e-09
Leer_en_schoeisel -0.005033 0.001878 -2.681 0.008524
Hout 0.006673 0.006706 0.995 0.321978
Papier_en_karton 0.063832 0.007403 8.623 6.95e-14
`Cokes_raffinage_splijt-en_kweekstoffen` 0.012936 0.003582 3.611 0.000467
Chemische_nijverheid 0.134625 0.005116 26.315 < 2e-16
`Rubber-en_kunststof` 0.042132 0.007886 5.343 5.28e-07
`Niet-metaalhoudende_minerale_producten` 0.040657 0.006551 6.206 1.07e-08
Metallurgie_en_producten_van_metaal 0.102544 0.005413 18.945 < 2e-16
Machines 0.035376 0.003611 9.796 < 2e-16
Elektronische_apparaten 0.057135 0.006210 9.201 3.53e-15
Transportmiddelen 0.068195 0.003777 18.055 < 2e-16
Overige_industrie 0.015718 0.006083 2.584 0.011128
Elektriciteit_gas_en_water 0.096063 0.004672 20.562 < 2e-16
Bouwnijverheid 0.212027 0.005128 41.347 < 2e-16
(Intercept)
`Voedings-en_genotmiddelen` ***
Textiel_en_kleding ***
Leer_en_schoeisel **
Hout
Papier_en_karton ***
`Cokes_raffinage_splijt-en_kweekstoffen` ***
Chemische_nijverheid ***
`Rubber-en_kunststof` ***
`Niet-metaalhoudende_minerale_producten` ***
Metallurgie_en_producten_van_metaal ***
Machines ***
Elektronische_apparaten ***
Transportmiddelen ***
Overige_industrie *
Elektriciteit_gas_en_water ***
Bouwnijverheid ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3375 on 106 degrees of freedom
Multiple R-squared: 0.9988, Adjusted R-squared: 0.9986
F-statistic: 5565 on 16 and 106 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,] 0.09843685 0.19687370 0.901563148
[2,] 0.38440312 0.76880624 0.615596880
[3,] 0.26469334 0.52938668 0.735306661
[4,] 0.17377975 0.34755950 0.826220248
[5,] 0.15043953 0.30087906 0.849560471
[6,] 0.18480396 0.36960792 0.815196039
[7,] 0.14680555 0.29361110 0.853194449
[8,] 0.17789766 0.35579532 0.822102341
[9,] 0.12768415 0.25536830 0.872315851
[10,] 0.12259537 0.24519073 0.877404633
[11,] 0.08560079 0.17120158 0.914399210
[12,] 0.30993130 0.61986259 0.690068704
[13,] 0.37053515 0.74107029 0.629464855
[14,] 0.44782285 0.89564570 0.552177151
[15,] 0.44975134 0.89950268 0.550248661
[16,] 0.42024952 0.84049903 0.579750484
[17,] 0.44108064 0.88216127 0.558919365
[18,] 0.37003658 0.74007315 0.629963424
[19,] 0.39909676 0.79819352 0.600903239
[20,] 0.68638478 0.62723044 0.313615218
[21,] 0.63139125 0.73721750 0.368608752
[22,] 0.56622835 0.86754330 0.433771650
[23,] 0.69498564 0.61002871 0.305014357
[24,] 0.87035092 0.25929816 0.129649079
[25,] 0.83726552 0.32546896 0.162734479
[26,] 0.86164372 0.27671256 0.138356282
[27,] 0.87098139 0.25803723 0.129018615
[28,] 0.90534478 0.18931045 0.094655223
[29,] 0.88350855 0.23298290 0.116491448
[30,] 0.92860626 0.14278748 0.071393742
[31,] 0.91095108 0.17809784 0.089048919
[32,] 0.88421359 0.23157282 0.115786412
[33,] 0.86412035 0.27175929 0.135879647
[34,] 0.83206934 0.33586132 0.167930662
[35,] 0.86518354 0.26963291 0.134816457
[36,] 0.93572366 0.12855268 0.064276338
[37,] 0.94614511 0.10770979 0.053854893
[38,] 0.99006134 0.01987732 0.009938661
[39,] 0.98831659 0.02336683 0.011683413
[40,] 0.98368392 0.03263217 0.016316084
[41,] 0.98157541 0.03684918 0.018424591
[42,] 0.97896710 0.04206580 0.021032899
[43,] 0.97165866 0.05668269 0.028341344
[44,] 0.97795641 0.04408718 0.022043588
[45,] 0.97383439 0.05233123 0.026165613
[46,] 0.96611566 0.06776869 0.033884344
[47,] 0.95897349 0.08205302 0.041026512
[48,] 0.98890910 0.02218180 0.011090900
[49,] 0.98867524 0.02264953 0.011324765
[50,] 0.98793832 0.02412335 0.012061676
[51,] 0.98463987 0.03072026 0.015360128
[52,] 0.98087831 0.03824338 0.019121689
[53,] 0.98297966 0.03404067 0.017020336
[54,] 0.97836517 0.04326967 0.021634835
[55,] 0.96983374 0.06033251 0.030166257
[56,] 0.95799040 0.08401919 0.042009596
[57,] 0.94130288 0.11739423 0.058697115
[58,] 0.92062539 0.15874922 0.079374612
[59,] 0.89470636 0.21058729 0.105293643
[60,] 0.93195247 0.13609505 0.068047527
[61,] 0.92883598 0.14232804 0.071164022
[62,] 0.90514596 0.18970808 0.094854039
[63,] 0.87860778 0.24278444 0.121392220
[64,] 0.97043894 0.05912213 0.029561065
[65,] 0.99186144 0.01627712 0.008138561
[66,] 0.98915231 0.02169538 0.010847691
[67,] 0.98602059 0.02795881 0.013979406
[68,] 0.97853035 0.04293929 0.021469647
[69,] 0.97879637 0.04240726 0.021203630
[70,] 0.96907518 0.06184965 0.030924824
[71,] 0.96542431 0.06915138 0.034575690
[72,] 0.96987611 0.06024778 0.030123892
[73,] 0.97219866 0.05560268 0.027801340
[74,] 0.96724574 0.06550852 0.032754258
[75,] 0.98973264 0.02053473 0.010267363
[76,] 0.98252250 0.03495499 0.017477497
[77,] 0.98263373 0.03473253 0.017366267
[78,] 0.97433217 0.05133566 0.025667828
[79,] 0.98277122 0.03445756 0.017228779
[80,] 0.96758818 0.06482364 0.032411821
[81,] 0.97355192 0.05289616 0.026448082
[82,] 0.93973456 0.12053087 0.060265436
[83,] 0.87132803 0.25734395 0.128671973
[84,] 0.86290691 0.27418618 0.137093088
> postscript(file="/var/wessaorg/rcomp/tmp/15bl61353151184.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/225jt1353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36nx71353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4unk21353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nu301353151184.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 = 123
Frequency = 1
1 2 3 4 5 6
0.242243220 -0.199601241 -0.147355256 -0.033981067 -0.480544444 -0.338811418
7 8 9 10 11 12
1.227345044 -0.656121265 -0.253762260 -0.940898351 -0.153305031 0.795386579
13 14 15 16 17 18
0.355385595 0.193163606 0.092761593 -0.186708830 -0.080669229 -0.096934611
19 20 21 22 23 24
1.014443910 0.026028151 0.179311016 -0.325351696 -0.020981412 0.709125748
25 26 27 28 29 30
0.170858151 0.206981295 -0.045723208 -0.022864228 -0.149341305 -0.163407420
31 32 33 34 35 36
0.290935376 -0.016259300 -0.125010201 -0.221662384 -0.152639760 0.162325012
37 38 39 40 41 42
-0.096299514 0.004396431 0.151121782 -0.022635467 0.050583858 0.106013734
43 44 45 46 47 48
0.144371372 -0.152957348 -0.163224052 -0.294927279 -0.184788381 -0.027830283
49 50 51 52 53 54
-0.291735877 0.091253182 0.039222890 -0.064355918 -0.096585171 -0.255775714
55 56 57 58 59 60
-0.239526489 -0.175358606 0.151874612 -0.155647593 0.125365942 0.064464155
61 62 63 64 65 66
-0.279024852 0.023099174 0.135554390 0.101265103 -0.038471769 0.287421479
67 68 69 70 71 72
-0.345414104 -0.065560131 0.229497406 0.123787167 0.029721982 -0.256424267
73 74 75 76 77 78
-0.064505898 0.263952780 0.117001224 -0.067239647 -0.050250317 0.092664981
79 80 81 82 83 84
-0.606983763 0.171443630 0.277918020 0.177225576 -0.485215283 -0.565469328
85 86 87 88 89 90
-0.207027406 0.131383221 -0.088237705 0.007113458 -0.070121252 -0.022450843
91 92 93 94 95 96
-0.599780668 0.409332482 0.380532803 -0.096602959 0.107322144 -0.096078192
97 98 99 100 101 102
0.060082202 0.409535785 0.343368826 -0.110348271 0.041086459 0.108563934
103 104 105 106 107 108
-0.453055261 0.133470715 0.257206185 0.099903066 -0.215792010 -0.247885361
109 110 111 112 113 114
-0.274987013 0.169176126 0.471783358 -0.143929890 -0.073780157 0.376292829
115 116 117 118 119 120
-0.296176558 -0.414812139 0.604429942 0.348611539 -0.147923769 -0.134518998
121 122 123
-0.575514520 0.449398597 0.593055107
> postscript(file="/var/wessaorg/rcomp/tmp/6k8821353151184.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 = 123
Frequency = 1
lag(myerror, k = 1) myerror
0 0.242243220 NA
1 -0.199601241 0.242243220
2 -0.147355256 -0.199601241
3 -0.033981067 -0.147355256
4 -0.480544444 -0.033981067
5 -0.338811418 -0.480544444
6 1.227345044 -0.338811418
7 -0.656121265 1.227345044
8 -0.253762260 -0.656121265
9 -0.940898351 -0.253762260
10 -0.153305031 -0.940898351
11 0.795386579 -0.153305031
12 0.355385595 0.795386579
13 0.193163606 0.355385595
14 0.092761593 0.193163606
15 -0.186708830 0.092761593
16 -0.080669229 -0.186708830
17 -0.096934611 -0.080669229
18 1.014443910 -0.096934611
19 0.026028151 1.014443910
20 0.179311016 0.026028151
21 -0.325351696 0.179311016
22 -0.020981412 -0.325351696
23 0.709125748 -0.020981412
24 0.170858151 0.709125748
25 0.206981295 0.170858151
26 -0.045723208 0.206981295
27 -0.022864228 -0.045723208
28 -0.149341305 -0.022864228
29 -0.163407420 -0.149341305
30 0.290935376 -0.163407420
31 -0.016259300 0.290935376
32 -0.125010201 -0.016259300
33 -0.221662384 -0.125010201
34 -0.152639760 -0.221662384
35 0.162325012 -0.152639760
36 -0.096299514 0.162325012
37 0.004396431 -0.096299514
38 0.151121782 0.004396431
39 -0.022635467 0.151121782
40 0.050583858 -0.022635467
41 0.106013734 0.050583858
42 0.144371372 0.106013734
43 -0.152957348 0.144371372
44 -0.163224052 -0.152957348
45 -0.294927279 -0.163224052
46 -0.184788381 -0.294927279
47 -0.027830283 -0.184788381
48 -0.291735877 -0.027830283
49 0.091253182 -0.291735877
50 0.039222890 0.091253182
51 -0.064355918 0.039222890
52 -0.096585171 -0.064355918
53 -0.255775714 -0.096585171
54 -0.239526489 -0.255775714
55 -0.175358606 -0.239526489
56 0.151874612 -0.175358606
57 -0.155647593 0.151874612
58 0.125365942 -0.155647593
59 0.064464155 0.125365942
60 -0.279024852 0.064464155
61 0.023099174 -0.279024852
62 0.135554390 0.023099174
63 0.101265103 0.135554390
64 -0.038471769 0.101265103
65 0.287421479 -0.038471769
66 -0.345414104 0.287421479
67 -0.065560131 -0.345414104
68 0.229497406 -0.065560131
69 0.123787167 0.229497406
70 0.029721982 0.123787167
71 -0.256424267 0.029721982
72 -0.064505898 -0.256424267
73 0.263952780 -0.064505898
74 0.117001224 0.263952780
75 -0.067239647 0.117001224
76 -0.050250317 -0.067239647
77 0.092664981 -0.050250317
78 -0.606983763 0.092664981
79 0.171443630 -0.606983763
80 0.277918020 0.171443630
81 0.177225576 0.277918020
82 -0.485215283 0.177225576
83 -0.565469328 -0.485215283
84 -0.207027406 -0.565469328
85 0.131383221 -0.207027406
86 -0.088237705 0.131383221
87 0.007113458 -0.088237705
88 -0.070121252 0.007113458
89 -0.022450843 -0.070121252
90 -0.599780668 -0.022450843
91 0.409332482 -0.599780668
92 0.380532803 0.409332482
93 -0.096602959 0.380532803
94 0.107322144 -0.096602959
95 -0.096078192 0.107322144
96 0.060082202 -0.096078192
97 0.409535785 0.060082202
98 0.343368826 0.409535785
99 -0.110348271 0.343368826
100 0.041086459 -0.110348271
101 0.108563934 0.041086459
102 -0.453055261 0.108563934
103 0.133470715 -0.453055261
104 0.257206185 0.133470715
105 0.099903066 0.257206185
106 -0.215792010 0.099903066
107 -0.247885361 -0.215792010
108 -0.274987013 -0.247885361
109 0.169176126 -0.274987013
110 0.471783358 0.169176126
111 -0.143929890 0.471783358
112 -0.073780157 -0.143929890
113 0.376292829 -0.073780157
114 -0.296176558 0.376292829
115 -0.414812139 -0.296176558
116 0.604429942 -0.414812139
117 0.348611539 0.604429942
118 -0.147923769 0.348611539
119 -0.134518998 -0.147923769
120 -0.575514520 -0.134518998
121 0.449398597 -0.575514520
122 0.593055107 0.449398597
123 NA 0.593055107
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.199601241 0.242243220
[2,] -0.147355256 -0.199601241
[3,] -0.033981067 -0.147355256
[4,] -0.480544444 -0.033981067
[5,] -0.338811418 -0.480544444
[6,] 1.227345044 -0.338811418
[7,] -0.656121265 1.227345044
[8,] -0.253762260 -0.656121265
[9,] -0.940898351 -0.253762260
[10,] -0.153305031 -0.940898351
[11,] 0.795386579 -0.153305031
[12,] 0.355385595 0.795386579
[13,] 0.193163606 0.355385595
[14,] 0.092761593 0.193163606
[15,] -0.186708830 0.092761593
[16,] -0.080669229 -0.186708830
[17,] -0.096934611 -0.080669229
[18,] 1.014443910 -0.096934611
[19,] 0.026028151 1.014443910
[20,] 0.179311016 0.026028151
[21,] -0.325351696 0.179311016
[22,] -0.020981412 -0.325351696
[23,] 0.709125748 -0.020981412
[24,] 0.170858151 0.709125748
[25,] 0.206981295 0.170858151
[26,] -0.045723208 0.206981295
[27,] -0.022864228 -0.045723208
[28,] -0.149341305 -0.022864228
[29,] -0.163407420 -0.149341305
[30,] 0.290935376 -0.163407420
[31,] -0.016259300 0.290935376
[32,] -0.125010201 -0.016259300
[33,] -0.221662384 -0.125010201
[34,] -0.152639760 -0.221662384
[35,] 0.162325012 -0.152639760
[36,] -0.096299514 0.162325012
[37,] 0.004396431 -0.096299514
[38,] 0.151121782 0.004396431
[39,] -0.022635467 0.151121782
[40,] 0.050583858 -0.022635467
[41,] 0.106013734 0.050583858
[42,] 0.144371372 0.106013734
[43,] -0.152957348 0.144371372
[44,] -0.163224052 -0.152957348
[45,] -0.294927279 -0.163224052
[46,] -0.184788381 -0.294927279
[47,] -0.027830283 -0.184788381
[48,] -0.291735877 -0.027830283
[49,] 0.091253182 -0.291735877
[50,] 0.039222890 0.091253182
[51,] -0.064355918 0.039222890
[52,] -0.096585171 -0.064355918
[53,] -0.255775714 -0.096585171
[54,] -0.239526489 -0.255775714
[55,] -0.175358606 -0.239526489
[56,] 0.151874612 -0.175358606
[57,] -0.155647593 0.151874612
[58,] 0.125365942 -0.155647593
[59,] 0.064464155 0.125365942
[60,] -0.279024852 0.064464155
[61,] 0.023099174 -0.279024852
[62,] 0.135554390 0.023099174
[63,] 0.101265103 0.135554390
[64,] -0.038471769 0.101265103
[65,] 0.287421479 -0.038471769
[66,] -0.345414104 0.287421479
[67,] -0.065560131 -0.345414104
[68,] 0.229497406 -0.065560131
[69,] 0.123787167 0.229497406
[70,] 0.029721982 0.123787167
[71,] -0.256424267 0.029721982
[72,] -0.064505898 -0.256424267
[73,] 0.263952780 -0.064505898
[74,] 0.117001224 0.263952780
[75,] -0.067239647 0.117001224
[76,] -0.050250317 -0.067239647
[77,] 0.092664981 -0.050250317
[78,] -0.606983763 0.092664981
[79,] 0.171443630 -0.606983763
[80,] 0.277918020 0.171443630
[81,] 0.177225576 0.277918020
[82,] -0.485215283 0.177225576
[83,] -0.565469328 -0.485215283
[84,] -0.207027406 -0.565469328
[85,] 0.131383221 -0.207027406
[86,] -0.088237705 0.131383221
[87,] 0.007113458 -0.088237705
[88,] -0.070121252 0.007113458
[89,] -0.022450843 -0.070121252
[90,] -0.599780668 -0.022450843
[91,] 0.409332482 -0.599780668
[92,] 0.380532803 0.409332482
[93,] -0.096602959 0.380532803
[94,] 0.107322144 -0.096602959
[95,] -0.096078192 0.107322144
[96,] 0.060082202 -0.096078192
[97,] 0.409535785 0.060082202
[98,] 0.343368826 0.409535785
[99,] -0.110348271 0.343368826
[100,] 0.041086459 -0.110348271
[101,] 0.108563934 0.041086459
[102,] -0.453055261 0.108563934
[103,] 0.133470715 -0.453055261
[104,] 0.257206185 0.133470715
[105,] 0.099903066 0.257206185
[106,] -0.215792010 0.099903066
[107,] -0.247885361 -0.215792010
[108,] -0.274987013 -0.247885361
[109,] 0.169176126 -0.274987013
[110,] 0.471783358 0.169176126
[111,] -0.143929890 0.471783358
[112,] -0.073780157 -0.143929890
[113,] 0.376292829 -0.073780157
[114,] -0.296176558 0.376292829
[115,] -0.414812139 -0.296176558
[116,] 0.604429942 -0.414812139
[117,] 0.348611539 0.604429942
[118,] -0.147923769 0.348611539
[119,] -0.134518998 -0.147923769
[120,] -0.575514520 -0.134518998
[121,] 0.449398597 -0.575514520
[122,] 0.593055107 0.449398597
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.199601241 0.242243220
2 -0.147355256 -0.199601241
3 -0.033981067 -0.147355256
4 -0.480544444 -0.033981067
5 -0.338811418 -0.480544444
6 1.227345044 -0.338811418
7 -0.656121265 1.227345044
8 -0.253762260 -0.656121265
9 -0.940898351 -0.253762260
10 -0.153305031 -0.940898351
11 0.795386579 -0.153305031
12 0.355385595 0.795386579
13 0.193163606 0.355385595
14 0.092761593 0.193163606
15 -0.186708830 0.092761593
16 -0.080669229 -0.186708830
17 -0.096934611 -0.080669229
18 1.014443910 -0.096934611
19 0.026028151 1.014443910
20 0.179311016 0.026028151
21 -0.325351696 0.179311016
22 -0.020981412 -0.325351696
23 0.709125748 -0.020981412
24 0.170858151 0.709125748
25 0.206981295 0.170858151
26 -0.045723208 0.206981295
27 -0.022864228 -0.045723208
28 -0.149341305 -0.022864228
29 -0.163407420 -0.149341305
30 0.290935376 -0.163407420
31 -0.016259300 0.290935376
32 -0.125010201 -0.016259300
33 -0.221662384 -0.125010201
34 -0.152639760 -0.221662384
35 0.162325012 -0.152639760
36 -0.096299514 0.162325012
37 0.004396431 -0.096299514
38 0.151121782 0.004396431
39 -0.022635467 0.151121782
40 0.050583858 -0.022635467
41 0.106013734 0.050583858
42 0.144371372 0.106013734
43 -0.152957348 0.144371372
44 -0.163224052 -0.152957348
45 -0.294927279 -0.163224052
46 -0.184788381 -0.294927279
47 -0.027830283 -0.184788381
48 -0.291735877 -0.027830283
49 0.091253182 -0.291735877
50 0.039222890 0.091253182
51 -0.064355918 0.039222890
52 -0.096585171 -0.064355918
53 -0.255775714 -0.096585171
54 -0.239526489 -0.255775714
55 -0.175358606 -0.239526489
56 0.151874612 -0.175358606
57 -0.155647593 0.151874612
58 0.125365942 -0.155647593
59 0.064464155 0.125365942
60 -0.279024852 0.064464155
61 0.023099174 -0.279024852
62 0.135554390 0.023099174
63 0.101265103 0.135554390
64 -0.038471769 0.101265103
65 0.287421479 -0.038471769
66 -0.345414104 0.287421479
67 -0.065560131 -0.345414104
68 0.229497406 -0.065560131
69 0.123787167 0.229497406
70 0.029721982 0.123787167
71 -0.256424267 0.029721982
72 -0.064505898 -0.256424267
73 0.263952780 -0.064505898
74 0.117001224 0.263952780
75 -0.067239647 0.117001224
76 -0.050250317 -0.067239647
77 0.092664981 -0.050250317
78 -0.606983763 0.092664981
79 0.171443630 -0.606983763
80 0.277918020 0.171443630
81 0.177225576 0.277918020
82 -0.485215283 0.177225576
83 -0.565469328 -0.485215283
84 -0.207027406 -0.565469328
85 0.131383221 -0.207027406
86 -0.088237705 0.131383221
87 0.007113458 -0.088237705
88 -0.070121252 0.007113458
89 -0.022450843 -0.070121252
90 -0.599780668 -0.022450843
91 0.409332482 -0.599780668
92 0.380532803 0.409332482
93 -0.096602959 0.380532803
94 0.107322144 -0.096602959
95 -0.096078192 0.107322144
96 0.060082202 -0.096078192
97 0.409535785 0.060082202
98 0.343368826 0.409535785
99 -0.110348271 0.343368826
100 0.041086459 -0.110348271
101 0.108563934 0.041086459
102 -0.453055261 0.108563934
103 0.133470715 -0.453055261
104 0.257206185 0.133470715
105 0.099903066 0.257206185
106 -0.215792010 0.099903066
107 -0.247885361 -0.215792010
108 -0.274987013 -0.247885361
109 0.169176126 -0.274987013
110 0.471783358 0.169176126
111 -0.143929890 0.471783358
112 -0.073780157 -0.143929890
113 0.376292829 -0.073780157
114 -0.296176558 0.376292829
115 -0.414812139 -0.296176558
116 0.604429942 -0.414812139
117 0.348611539 0.604429942
118 -0.147923769 0.348611539
119 -0.134518998 -0.147923769
120 -0.575514520 -0.134518998
121 0.449398597 -0.575514520
122 0.593055107 0.449398597
> 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/739m81353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8dkup1353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9lf8m1353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10n2bb1353151184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1112n21353151184.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/12b8i81353151184.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/13mt2i1353151184.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/14161w1353151184.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/156nsr1353151184.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/16adux1353151184.tab")
+ }
>
> try(system("convert tmp/15bl61353151184.ps tmp/15bl61353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/225jt1353151184.ps tmp/225jt1353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/36nx71353151184.ps tmp/36nx71353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/4unk21353151184.ps tmp/4unk21353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nu301353151184.ps tmp/5nu301353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/6k8821353151184.ps tmp/6k8821353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/739m81353151184.ps tmp/739m81353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dkup1353151184.ps tmp/8dkup1353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lf8m1353151184.ps tmp/9lf8m1353151184.png",intern=TRUE))
character(0)
> try(system("convert tmp/10n2bb1353151184.ps tmp/10n2bb1353151184.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.409 1.427 13.310