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