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(9 + ,41 + ,38 + ,13 + ,12 + ,14 + ,12 + ,53 + ,32 + ,9 + ,39 + ,32 + ,16 + ,11 + ,18 + ,11 + ,83 + ,51 + ,9 + ,30 + ,35 + ,19 + ,15 + ,11 + ,14 + ,66 + ,42 + ,9 + ,31 + ,33 + ,15 + ,6 + ,12 + ,12 + ,67 + ,41 + ,9 + ,34 + ,37 + ,14 + ,13 + ,16 + ,21 + ,76 + ,46 + ,9 + ,35 + ,29 + ,13 + ,10 + ,18 + ,12 + ,78 + ,47 + ,9 + ,39 + ,31 + ,19 + ,12 + ,14 + ,22 + ,53 + ,37 + ,9 + ,34 + ,36 + ,15 + ,14 + ,14 + ,11 + ,80 + ,49 + ,9 + ,36 + ,35 + ,14 + ,12 + ,15 + ,10 + ,74 + ,45 + ,9 + ,37 + ,38 + ,15 + ,9 + ,15 + ,13 + ,76 + ,47 + ,9 + ,38 + ,31 + ,16 + ,10 + ,17 + ,10 + ,79 + ,49 + ,9 + ,36 + ,34 + ,16 + ,12 + ,19 + ,8 + ,54 + ,33 + ,9 + ,38 + ,35 + ,16 + ,12 + ,10 + ,15 + ,67 + ,42 + ,9 + ,39 + ,38 + ,16 + ,11 + ,16 + ,14 + ,54 + ,33 + ,9 + ,33 + ,37 + ,17 + ,15 + ,18 + ,10 + ,87 + ,53 + ,9 + ,32 + ,33 + ,15 + ,12 + ,14 + ,14 + ,58 + ,36 + ,9 + ,36 + ,32 + ,15 + ,10 + ,14 + ,14 + ,75 + ,45 + ,9 + ,38 + ,38 + ,20 + ,12 + ,17 + ,11 + ,88 + ,54 + ,9 + ,39 + ,38 + ,18 + ,11 + ,14 + ,10 + ,64 + ,41 + ,9 + ,32 + ,32 + ,16 + ,12 + ,16 + ,13 + ,57 + ,36 + ,9 + ,32 + ,33 + ,16 + ,11 + ,18 + ,9.5 + ,66 + ,41 + ,9 + ,31 + ,31 + ,16 + ,12 + ,11 + ,14 + ,68 + ,44 + ,9 + ,39 + ,38 + ,19 + ,13 + ,14 + ,12 + ,54 + ,33 + ,9 + ,37 + ,39 + ,16 + ,11 + ,12 + ,14 + ,56 + ,37 + ,9 + ,39 + ,32 + ,17 + ,12 + ,17 + ,11 + ,86 + ,52 + ,9 + ,41 + ,32 + ,17 + ,13 + ,9 + ,9 + ,80 + ,47 + ,9 + ,36 + ,35 + ,16 + ,10 + ,16 + ,11 + ,76 + ,43 + ,9 + ,33 + ,37 + ,15 + ,14 + ,14 + ,15 + ,69 + ,44 + ,9 + ,33 + ,33 + ,16 + ,12 + ,15 + ,14 + ,78 + ,45 + ,9 + ,34 + ,33 + ,14 + ,10 + ,11 + ,13 + ,67 + ,44 + ,9 + ,31 + ,31 + ,15 + ,12 + ,16 + ,9 + ,80 + ,49 + ,9 + ,27 + ,32 + ,12 + ,8 + ,13 + ,15 + ,54 + ,33 + ,9 + ,37 + ,31 + ,14 + ,10 + ,17 + ,10 + ,71 + ,43 + ,9 + ,34 + ,37 + ,16 + ,12 + ,15 + ,11 + ,84 + ,54 + ,9 + ,34 + ,30 + ,14 + ,12 + ,14 + ,13 + ,74 + ,42 + ,9 + ,32 + ,33 + ,10 + ,7 + ,16 + ,8 + ,71 + ,44 + ,9 + ,29 + ,31 + ,10 + ,9 + ,9 + ,20 + ,63 + ,37 + ,9 + ,36 + ,33 + ,14 + ,12 + ,15 + ,12 + ,71 + ,43 + ,9 + ,29 + ,31 + ,16 + ,10 + ,17 + ,10 + ,76 + ,46 + ,9 + ,35 + ,33 + ,16 + ,10 + ,13 + ,10 + ,69 + ,42 + ,9 + ,37 + ,32 + ,16 + ,10 + ,15 + ,9 + ,74 + ,45 + ,9 + ,34 + ,33 + ,14 + ,12 + ,16 + ,14 + ,75 + ,44 + ,9 + ,38 + ,32 + ,20 + ,15 + ,16 + ,8 + ,54 + ,33 + ,9 + ,35 + ,33 + ,14 + ,10 + ,12 + ,14 + ,52 + ,31 + ,9 + ,38 + ,28 + ,14 + ,10 + ,15 + ,11 + ,69 + ,42 + ,9 + ,37 + ,35 + ,11 + ,12 + ,11 + ,13 + ,68 + ,40 + ,9 + ,38 + ,39 + ,14 + ,13 + ,15 + ,9 + ,65 + ,43 + ,9 + ,33 + ,34 + ,15 + ,11 + ,15 + ,11 + ,75 + ,46 + ,9 + ,36 + ,38 + ,16 + ,11 + ,17 + ,15 + ,74 + ,42 + ,9 + ,38 + ,32 + ,14 + ,12 + ,13 + ,11 + ,75 + ,45 + ,9 + ,32 + ,38 + ,16 + ,14 + ,16 + ,10 + ,72 + ,44 + ,9 + ,32 + ,30 + ,14 + ,10 + ,14 + ,14 + ,67 + ,40 + ,9 + ,32 + ,33 + ,12 + ,12 + ,11 + ,18 + ,63 + ,37 + ,9 + ,34 + ,38 + ,16 + ,13 + ,12 + ,14 + ,62 + ,46 + ,9 + ,32 + ,32 + ,9 + ,5 + ,12 + ,11 + ,63 + ,36 + ,9 + ,37 + ,35 + ,14 + ,6 + ,15 + ,14.5 + ,76 + ,47 + ,9 + ,39 + ,34 + ,16 + ,12 + ,16 + ,13 + ,74 + ,45 + ,9 + ,29 + ,34 + ,16 + ,12 + ,15 + ,9 + ,67 + ,42 + ,9 + ,37 + ,36 + ,15 + ,11 + ,12 + ,10 + ,73 + ,43 + ,9 + ,35 + ,34 + ,16 + ,10 + ,12 + ,15 + ,70 + ,43 + ,9 + ,30 + ,28 + ,12 + ,7 + ,8 + ,20 + ,53 + ,32 + ,9 + ,38 + ,34 + ,16 + ,12 + ,13 + ,12 + ,77 + ,45 + ,9 + ,34 + ,35 + ,16 + ,14 + ,11 + ,12 + ,80 + ,48 + ,9 + ,31 + ,35 + ,14 + ,11 + ,14 + ,14 + ,52 + ,31 + ,9 + ,34 + ,31 + ,16 + ,12 + ,15 + ,13 + ,54 + ,33 + ,10 + ,35 + ,37 + ,17 + ,13 + ,10 + ,11 + ,80 + ,49 + ,10 + ,36 + ,35 + ,18 + ,14 + ,11 + ,17 + ,66 + ,42 + ,10 + ,30 + ,27 + ,18 + ,11 + ,12 + ,12 + ,73 + ,41 + ,10 + ,39 + ,40 + ,12 + ,12 + ,15 + ,13 + ,63 + ,38 + ,10 + ,35 + ,37 + ,16 + ,12 + ,15 + ,14 + ,69 + ,42 + ,10 + ,38 + ,36 + ,10 + ,8 + ,14 + ,13 + ,67 + ,44 + ,10 + ,31 + ,38 + ,14 + ,11 + ,16 + ,15 + ,54 + ,33 + ,10 + ,34 + ,39 + ,18 + ,14 + ,15 + ,13 + ,81 + ,48 + ,10 + ,38 + ,41 + ,18 + ,14 + ,15 + ,10 + ,69 + ,40 + ,10 + ,34 + ,27 + ,16 + ,12 + ,13 + ,11 + ,84 + ,50 + ,10 + ,39 + ,30 + ,17 + ,9 + ,12 + ,19 + ,80 + ,49 + ,10 + ,37 + ,37 + ,16 + ,13 + ,17 + ,13 + ,70 + ,43 + ,10 + ,34 + ,31 + ,16 + ,11 + ,13 + ,17 + ,69 + ,44 + ,10 + ,28 + ,31 + ,13 + ,12 + ,15 + ,13 + ,77 + ,47 + ,10 + ,37 + ,27 + ,16 + ,12 + ,13 + ,9 + ,54 + ,33 + ,10 + ,33 + ,36 + ,16 + ,12 + ,15 + ,11 + ,79 + ,46 + ,10 + ,35 + ,37 + ,16 + ,12 + ,15 + ,9 + ,71 + ,45 + ,10 + ,37 + ,33 + ,15 + ,12 + ,16 + ,12 + ,73 + ,43 + ,10 + ,32 + ,34 + ,15 + ,11 + ,15 + ,12 + ,72 + ,44 + ,10 + ,33 + ,31 + ,16 + ,10 + ,14 + ,13 + ,77 + ,47 + ,10 + ,38 + ,39 + ,14 + ,9 + ,15 + ,13 + ,75 + ,45 + ,10 + ,33 + ,34 + ,16 + ,12 + ,14 + ,12 + ,69 + ,42 + ,10 + ,29 + ,32 + ,16 + ,12 + ,13 + ,15 + ,54 + ,33 + ,10 + ,33 + ,33 + ,15 + ,12 + ,7 + ,22 + ,70 + ,43 + ,10 + ,31 + ,36 + ,12 + ,9 + ,17 + ,13 + ,73 + ,46 + ,10 + ,36 + ,32 + ,17 + ,15 + ,13 + ,15 + ,54 + ,33 + ,10 + ,35 + ,41 + ,16 + ,12 + ,15 + ,13 + ,77 + ,46 + ,10 + ,32 + ,28 + ,15 + ,12 + ,14 + ,15 + ,82 + ,48 + ,10 + ,29 + ,30 + ,13 + ,12 + ,13 + ,12.5 + ,80 + ,47 + ,10 + ,39 + ,36 + ,16 + ,10 + ,16 + ,11 + ,80 + ,47 + ,10 + ,37 + ,35 + ,16 + ,13 + ,12 + ,16 + ,69 + ,43 + ,10 + ,35 + ,31 + ,16 + ,9 + ,14 + ,11 + ,78 + ,46 + ,10 + ,37 + ,34 + ,16 + ,12 + ,17 + ,11 + ,81 + ,48 + ,10 + ,32 + ,36 + ,14 + ,10 + ,15 + ,10 + ,76 + ,46 + ,10 + ,38 + ,36 + ,16 + ,14 + ,17 + ,10 + ,76 + ,45 + ,10 + ,37 + ,35 + ,16 + ,11 + ,12 + ,16 + ,73 + ,45 + ,10 + ,36 + ,37 + ,20 + ,15 + ,16 + ,12 + ,85 + ,52 + ,10 + ,32 + ,28 + ,15 + ,11 + ,11 + ,11 + ,66 + ,42 + ,10 + ,33 + ,39 + ,16 + ,11 + ,15 + ,16 + ,79 + ,47 + ,10 + ,40 + ,32 + ,13 + ,12 + ,9 + ,19 + ,68 + ,41 + ,10 + ,38 + ,35 + ,17 + ,12 + ,16 + ,11 + ,76 + ,47 + ,10 + ,41 + ,39 + ,16 + ,12 + ,15 + ,16 + ,71 + ,43 + ,10 + ,36 + ,35 + ,16 + ,11 + ,10 + ,15 + ,54 + ,33 + ,10 + ,43 + ,42 + ,12 + ,7 + ,10 + ,24 + ,46 + ,30 + ,10 + ,30 + ,34 + ,16 + ,12 + ,15 + ,14 + ,85 + ,52 + ,10 + ,31 + ,33 + ,16 + ,14 + ,11 + ,15 + ,74 + ,44 + ,10 + ,32 + ,41 + ,17 + ,11 + ,13 + ,11 + ,88 + ,55 + ,10 + ,32 + ,33 + ,13 + ,11 + ,14 + ,15 + ,38 + ,11 + ,10 + ,37 + ,34 + ,12 + ,10 + ,18 + ,12 + ,76 + ,47 + ,10 + ,37 + ,32 + ,18 + ,13 + ,16 + ,10 + ,86 + ,53 + ,10 + ,33 + ,40 + ,14 + ,13 + ,14 + ,14 + ,54 + ,33 + ,10 + ,34 + ,40 + ,14 + ,8 + ,14 + ,13 + ,67 + ,44 + ,10 + ,33 + ,35 + ,13 + ,11 + ,14 + ,9 + ,69 + ,42 + ,10 + ,38 + ,36 + ,16 + ,12 + ,14 + ,15 + ,90 + ,55 + ,10 + ,33 + ,37 + ,13 + ,11 + ,12 + ,15 + ,54 + ,33 + ,10 + ,31 + ,27 + ,16 + ,13 + ,14 + ,14 + ,76 + ,46 + ,10 + ,38 + ,39 + ,13 + ,12 + ,15 + ,11 + ,89 + ,54 + ,10 + ,37 + ,38 + ,16 + ,14 + ,15 + ,8 + ,76 + ,47 + ,10 + ,36 + ,31 + ,15 + ,13 + ,15 + ,11 + ,73 + ,45 + ,10 + ,31 + ,33 + ,16 + ,15 + ,13 + ,11 + ,79 + ,47 + ,10 + ,39 + ,32 + ,15 + ,10 + ,17 + ,8 + ,90 + ,55 + ,10 + ,44 + ,39 + ,17 + ,11 + ,17 + ,10 + ,74 + ,44 + ,10 + ,33 + ,36 + ,15 + ,9 + ,19 + ,11 + ,81 + ,53 + ,10 + ,35 + ,33 + ,12 + ,11 + ,15 + ,13 + ,72 + ,44 + ,10 + ,32 + ,33 + ,16 + ,10 + ,13 + ,11 + ,71 + ,42 + ,10 + ,28 + ,32 + ,10 + ,11 + ,9 + ,20 + ,66 + ,40 + ,10 + ,40 + ,37 + ,16 + ,8 + ,15 + ,10 + ,77 + ,46 + ,10 + ,27 + ,30 + ,12 + ,11 + ,15 + ,15 + ,65 + ,40 + ,10 + ,37 + ,38 + ,14 + ,12 + ,15 + ,12 + ,74 + ,46 + ,10 + ,32 + ,29 + ,15 + ,12 + ,16 + ,14 + ,85 + ,53 + ,10 + ,28 + ,22 + ,13 + ,9 + ,11 + ,23 + ,54 + ,33 + ,10 + ,34 + ,35 + ,15 + ,11 + ,14 + ,14 + ,63 + ,42 + ,10 + ,30 + ,35 + ,11 + ,10 + ,11 + ,16 + ,54 + ,35 + ,10 + ,35 + ,34 + ,12 + ,8 + ,15 + ,11 + ,64 + ,40 + ,10 + ,31 + ,35 + ,11 + ,9 + ,13 + ,12 + ,69 + ,41 + ,10 + ,32 + ,34 + ,16 + ,8 + ,15 + ,10 + ,54 + ,33 + ,10 + ,30 + ,37 + ,15 + ,9 + ,16 + ,14 + ,84 + ,51 + ,10 + ,30 + ,35 + ,17 + ,15 + ,14 + ,12 + ,86 + ,53 + ,10 + ,31 + ,23 + ,16 + ,11 + ,15 + ,12 + ,77 + ,46 + ,10 + ,40 + ,31 + ,10 + ,8 + ,16 + ,11 + ,89 + ,55 + ,10 + ,32 + ,27 + ,18 + ,13 + ,16 + ,12 + ,76 + ,47 + ,10 + ,36 + ,36 + ,13 + ,12 + ,11 + ,13 + ,60 + ,38 + ,10 + ,32 + ,31 + ,16 + ,12 + ,12 + ,11 + ,75 + ,46 + ,10 + ,35 + ,32 + ,13 + ,9 + ,9 + ,19 + ,73 + ,46 + ,10 + ,38 + ,39 + ,10 + ,7 + ,16 + ,12 + ,85 + ,53 + ,10 + ,42 + ,37 + ,15 + ,13 + ,13 + ,17 + ,79 + ,47 + ,10 + ,34 + ,38 + ,16 + ,9 + ,16 + ,9 + ,71 + ,41 + ,10 + ,35 + ,39 + ,16 + ,6 + ,12 + ,12 + ,72 + ,44 + ,9 + ,38 + ,34 + ,14 + ,8 + ,9 + ,19 + ,69 + ,43 + ,10 + ,33 + ,31 + ,10 + ,8 + ,13 + ,18 + ,78 + ,51 + ,10 + ,36 + ,32 + ,17 + ,15 + ,13 + ,15 + ,54 + ,33 + ,10 + ,32 + ,37 + ,13 + ,6 + ,14 + ,14 + ,69 + ,43 + ,10 + ,33 + ,36 + ,15 + ,9 + ,19 + ,11 + ,81 + ,53 + ,10 + ,34 + ,32 + ,16 + ,11 + ,13 + ,9 + ,84 + ,51 + ,10 + ,32 + ,38 + ,12 + ,8 + ,12 + ,18 + ,84 + ,50 + ,10 + ,34 + ,36 + ,13 + ,8 + ,13 + ,16 + ,69 + ,46 + ,11 + ,27 + ,26 + ,13 + ,10 + ,10 + ,24 + ,66 + ,43 + ,11 + ,31 + ,26 + ,12 + ,8 + ,14 + ,14 + ,81 + ,47 + ,11 + ,38 + ,33 + ,17 + ,14 + ,16 + ,20 + ,82 + ,50 + ,11 + ,34 + ,39 + ,15 + ,10 + ,10 + ,18 + ,72 + ,43 + ,11 + ,24 + ,30 + ,10 + ,8 + ,11 + ,23 + ,54 + ,33 + ,11 + ,30 + ,33 + ,14 + ,11 + ,14 + ,12 + ,78 + ,48 + ,11 + ,26 + ,25 + ,11 + ,12 + ,12 + ,14 + ,74 + ,44 + ,11 + ,34 + ,38 + ,13 + ,12 + ,9 + ,16 + ,82 + ,50 + ,11 + ,27 + ,37 + ,16 + ,12 + ,9 + ,18 + ,73 + ,41 + ,11 + ,37 + ,31 + ,12 + ,5 + ,11 + ,20 + ,55 + ,34 + ,11 + ,36 + ,37 + ,16 + ,12 + ,16 + ,12 + ,72 + ,44 + ,11 + ,41 + ,35 + ,12 + ,10 + ,9 + ,12 + ,78 + ,47 + ,11 + ,29 + ,25 + ,9 + ,7 + ,13 + ,17 + ,59 + ,35 + ,11 + ,36 + ,28 + ,12 + ,12 + ,16 + ,13 + ,72 + ,44 + ,11 + ,32 + ,35 + ,15 + ,11 + ,13 + ,9 + ,78 + ,44 + ,11 + ,37 + ,33 + ,12 + ,8 + ,9 + ,16 + ,68 + ,43 + ,11 + ,30 + ,30 + ,12 + ,9 + ,12 + ,18 + ,69 + ,41 + ,11 + ,31 + ,31 + ,14 + ,10 + ,16 + ,10 + ,67 + ,41 + ,11 + ,38 + ,37 + ,12 + ,9 + ,11 + ,14 + ,74 + ,42 + ,11 + ,36 + ,36 + ,16 + ,12 + ,14 + ,11 + ,54 + ,33 + ,11 + ,35 + ,30 + ,11 + ,6 + ,13 + ,9 + ,67 + ,41 + ,11 + ,31 + ,36 + ,19 + ,15 + ,15 + ,11 + ,70 + ,44 + ,11 + ,38 + ,32 + ,15 + ,12 + ,14 + ,10 + ,80 + ,48 + ,11 + ,22 + ,28 + ,8 + ,12 + ,16 + ,11 + ,89 + ,55 + ,11 + ,32 + ,36 + ,16 + ,12 + ,13 + ,19 + ,76 + ,44 + ,11 + ,36 + ,34 + ,17 + ,11 + ,14 + ,14 + ,74 + ,43 + ,11 + ,39 + ,31 + ,12 + ,7 + ,15 + ,12 + ,87 + ,52 + ,11 + ,28 + ,28 + ,11 + ,7 + ,13 + ,14 + ,54 + ,30 + ,11 + ,32 + ,36 + ,11 + ,5 + ,11 + ,21 + ,61 + ,39 + ,11 + ,32 + ,36 + ,14 + ,12 + ,11 + ,13 + ,38 + ,11 + ,11 + ,38 + ,40 + ,16 + ,12 + ,14 + ,10 + ,75 + ,44 + ,11 + ,32 + ,33 + ,12 + ,3 + ,15 + ,15 + ,69 + ,42 + ,11 + ,35 + ,37 + ,16 + ,11 + ,11 + ,16 + ,62 + ,41 + ,11 + ,32 + ,32 + ,13 + ,10 + ,15 + ,14 + ,72 + ,44 + ,11 + ,37 + ,38 + ,15 + ,12 + ,12 + ,12 + ,70 + ,44 + ,11 + ,34 + ,31 + ,16 + ,9 + ,14 + ,19 + ,79 + ,48 + ,11 + ,33 + ,37 + ,16 + ,12 + ,14 + ,15 + ,87 + ,53 + ,11 + ,33 + ,33 + ,14 + ,9 + ,8 + ,19 + ,62 + ,37 + ,11 + ,26 + ,32 + ,16 + ,12 + ,13 + ,13 + ,77 + ,44 + ,11 + ,30 + ,30 + ,16 + ,12 + ,9 + ,17 + ,69 + ,44 + ,11 + ,24 + ,30 + ,14 + ,10 + ,15 + ,12 + ,69 + ,40 + ,11 + ,34 + ,31 + ,11 + ,9 + ,17 + ,11 + ,75 + ,42 + ,11 + ,34 + ,32 + ,12 + ,12 + ,13 + ,14 + ,54 + ,35 + ,11 + ,33 + ,34 + ,15 + ,8 + ,15 + ,11 + ,72 + ,43 + ,11 + ,34 + ,36 + ,15 + ,11 + ,15 + ,13 + ,74 + ,45 + ,11 + ,35 + ,37 + ,16 + ,11 + ,14 + ,12 + ,85 + ,55 + ,11 + ,35 + ,36 + ,16 + ,12 + ,16 + ,15 + ,52 + ,31 + ,11 + ,36 + ,33 + ,11 + ,10 + ,13 + ,14 + ,70 + ,44 + ,11 + ,34 + ,33 + ,15 + ,10 + ,16 + ,12 + ,84 + ,50 + ,11 + ,34 + ,33 + ,12 + ,12 + ,9 + ,17 + ,64 + ,40 + ,11 + ,41 + ,44 + ,12 + ,12 + ,16 + ,11 + ,84 + ,53 + ,11 + ,32 + ,39 + ,15 + ,11 + ,11 + ,18 + ,87 + ,54 + ,11 + ,30 + ,32 + ,15 + ,8 + ,10 + ,13 + ,79 + ,49 + ,11 + ,35 + ,35 + ,16 + ,12 + ,11 + ,17 + ,67 + ,40 + ,11 + ,28 + ,25 + ,14 + ,10 + ,15 + ,13 + ,65 + ,41 + ,11 + ,33 + ,35 + ,17 + ,11 + ,17 + ,11 + ,85 + ,52 + ,11 + ,39 + ,34 + ,14 + ,10 + ,14 + ,12 + ,83 + ,52 + ,11 + ,36 + ,35 + ,13 + ,8 + ,8 + ,22 + ,61 + ,36 + ,11 + ,36 + ,39 + ,15 + ,12 + ,15 + ,14 + ,82 + ,52 + ,11 + ,35 + ,33 + ,13 + ,12 + ,11 + ,12 + ,76 + ,46 + ,11 + ,38 + ,36 + ,14 + ,10 + ,16 + ,12 + ,58 + ,31 + ,11 + ,33 + ,32 + ,15 + ,12 + ,10 + ,17 + ,72 + ,44 + ,11 + ,31 + ,32 + ,12 + ,9 + ,15 + ,9 + ,72 + ,44 + ,11 + ,34 + ,36 + ,13 + ,9 + ,9 + ,21 + ,38 + ,11 + ,11 + ,32 + ,36 + ,8 + ,6 + ,16 + ,10 + ,78 + ,46 + ,11 + ,31 + ,32 + ,14 + ,10 + ,19 + ,11 + ,54 + ,33 + ,11 + ,33 + ,34 + ,14 + ,9 + ,12 + ,12 + ,63 + ,34 + ,11 + ,34 + ,33 + ,11 + ,9 + ,8 + ,23 + ,66 + ,42 + ,11 + ,34 + ,35 + ,12 + ,9 + ,11 + ,13 + ,70 + ,43 + ,11 + ,34 + ,30 + ,13 + ,6 + ,14 + ,12 + ,71 + ,43 + ,11 + ,33 + ,38 + ,10 + ,10 + ,9 + ,16 + ,67 + ,44 + ,11 + ,32 + ,34 + ,16 + ,6 + ,15 + ,9 + ,58 + ,36 + ,11 + ,41 + ,33 + ,18 + ,14 + ,13 + ,17 + ,72 + ,46 + ,11 + ,34 + ,32 + ,13 + ,10 + ,16 + ,9 + ,72 + ,44 + ,11 + ,36 + ,31 + ,11 + ,10 + ,11 + ,14 + ,70 + ,43 + ,11 + ,37 + ,30 + ,4 + ,6 + ,12 + ,17 + ,76 + ,50 + ,11 + ,36 + ,27 + ,13 + ,12 + ,13 + ,13 + ,50 + ,33 + ,11 + ,29 + ,31 + ,16 + ,12 + ,10 + ,11 + ,72 + ,43 + ,11 + ,37 + ,30 + ,10 + ,7 + ,11 + ,12 + ,72 + ,44 + ,11 + ,27 + ,32 + ,12 + ,8 + ,12 + ,10 + ,88 + ,53 + ,11 + ,35 + ,35 + ,12 + ,11 + ,8 + ,19 + ,53 + ,34 + ,11 + ,28 + ,28 + ,10 + ,3 + ,12 + ,16 + ,58 + ,35 + ,11 + ,35 + ,33 + ,13 + ,6 + ,12 + ,16 + ,66 + ,40 + ,11 + ,37 + ,31 + ,15 + ,10 + ,15 + ,14 + ,82 + ,53 + ,11 + ,29 + ,35 + ,12 + ,8 + ,11 + ,20 + ,69 + ,42 + ,11 + ,32 + ,35 + ,14 + ,9 + ,13 + ,15 + ,68 + ,43 + ,11 + ,36 + ,32 + ,10 + ,9 + ,14 + ,23 + ,44 + ,29 + ,11 + ,19 + ,21 + ,12 + ,8 + ,10 + ,20 + ,56 + ,36 + ,11 + ,21 + ,20 + ,12 + ,9 + ,12 + ,16 + ,53 + ,30 + ,11 + ,31 + ,34 + ,11 + ,7 + ,15 + ,14 + ,70 + ,42 + ,11 + ,33 + ,32 + ,10 + ,7 + ,13 + ,17 + ,78 + ,47 + ,11 + ,36 + ,34 + ,12 + ,6 + ,13 + ,11 + ,71 + ,44 + ,11 + ,33 + ,32 + ,16 + ,9 + ,13 + ,13 + ,72 + ,45 + ,11 + ,37 + ,33 + ,12 + ,10 + ,12 + ,17 + ,68 + ,44 + ,11 + ,34 + ,33 + ,14 + ,11 + ,12 + ,15 + ,67 + ,43 + ,11 + ,35 + ,37 + ,16 + ,12 + ,9 + ,21 + ,75 + ,43 + ,11 + ,31 + ,32 + ,14 + ,8 + ,9 + ,18 + ,62 + ,40 + ,11 + ,37 + ,34 + ,13 + ,11 + ,15 + ,15 + ,67 + ,41 + ,11 + ,35 + ,30 + ,4 + ,3 + ,10 + ,8 + ,83 + ,52 + ,11 + ,27 + ,30 + ,15 + ,11 + ,14 + ,12 + ,64 + ,38 + ,11 + ,34 + ,38 + ,11 + ,12 + ,15 + ,12 + ,68 + ,41 + ,11 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,11 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(9 + ,264) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:264)) > y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > 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 Learning month Connected Separate Software Happiness Depression Belonging 1 13 9 41 38 12 14 12.0 53 2 16 9 39 32 11 18 11.0 83 3 19 9 30 35 15 11 14.0 66 4 15 9 31 33 6 12 12.0 67 5 14 9 34 37 13 16 21.0 76 6 13 9 35 29 10 18 12.0 78 7 19 9 39 31 12 14 22.0 53 8 15 9 34 36 14 14 11.0 80 9 14 9 36 35 12 15 10.0 74 10 15 9 37 38 9 15 13.0 76 11 16 9 38 31 10 17 10.0 79 12 16 9 36 34 12 19 8.0 54 13 16 9 38 35 12 10 15.0 67 14 16 9 39 38 11 16 14.0 54 15 17 9 33 37 15 18 10.0 87 16 15 9 32 33 12 14 14.0 58 17 15 9 36 32 10 14 14.0 75 18 20 9 38 38 12 17 11.0 88 19 18 9 39 38 11 14 10.0 64 20 16 9 32 32 12 16 13.0 57 21 16 9 32 33 11 18 9.5 66 22 16 9 31 31 12 11 14.0 68 23 19 9 39 38 13 14 12.0 54 24 16 9 37 39 11 12 14.0 56 25 17 9 39 32 12 17 11.0 86 26 17 9 41 32 13 9 9.0 80 27 16 9 36 35 10 16 11.0 76 28 15 9 33 37 14 14 15.0 69 29 16 9 33 33 12 15 14.0 78 30 14 9 34 33 10 11 13.0 67 31 15 9 31 31 12 16 9.0 80 32 12 9 27 32 8 13 15.0 54 33 14 9 37 31 10 17 10.0 71 34 16 9 34 37 12 15 11.0 84 35 14 9 34 30 12 14 13.0 74 36 10 9 32 33 7 16 8.0 71 37 10 9 29 31 9 9 20.0 63 38 14 9 36 33 12 15 12.0 71 39 16 9 29 31 10 17 10.0 76 40 16 9 35 33 10 13 10.0 69 41 16 9 37 32 10 15 9.0 74 42 14 9 34 33 12 16 14.0 75 43 20 9 38 32 15 16 8.0 54 44 14 9 35 33 10 12 14.0 52 45 14 9 38 28 10 15 11.0 69 46 11 9 37 35 12 11 13.0 68 47 14 9 38 39 13 15 9.0 65 48 15 9 33 34 11 15 11.0 75 49 16 9 36 38 11 17 15.0 74 50 14 9 38 32 12 13 11.0 75 51 16 9 32 38 14 16 10.0 72 52 14 9 32 30 10 14 14.0 67 53 12 9 32 33 12 11 18.0 63 54 16 9 34 38 13 12 14.0 62 55 9 9 32 32 5 12 11.0 63 56 14 9 37 35 6 15 14.5 76 57 16 9 39 34 12 16 13.0 74 58 16 9 29 34 12 15 9.0 67 59 15 9 37 36 11 12 10.0 73 60 16 9 35 34 10 12 15.0 70 61 12 9 30 28 7 8 20.0 53 62 16 9 38 34 12 13 12.0 77 63 16 9 34 35 14 11 12.0 80 64 14 9 31 35 11 14 14.0 52 65 16 9 34 31 12 15 13.0 54 66 17 10 35 37 13 10 11.0 80 67 18 10 36 35 14 11 17.0 66 68 18 10 30 27 11 12 12.0 73 69 12 10 39 40 12 15 13.0 63 70 16 10 35 37 12 15 14.0 69 71 10 10 38 36 8 14 13.0 67 72 14 10 31 38 11 16 15.0 54 73 18 10 34 39 14 15 13.0 81 74 18 10 38 41 14 15 10.0 69 75 16 10 34 27 12 13 11.0 84 76 17 10 39 30 9 12 19.0 80 77 16 10 37 37 13 17 13.0 70 78 16 10 34 31 11 13 17.0 69 79 13 10 28 31 12 15 13.0 77 80 16 10 37 27 12 13 9.0 54 81 16 10 33 36 12 15 11.0 79 82 16 10 35 37 12 15 9.0 71 83 15 10 37 33 12 16 12.0 73 84 15 10 32 34 11 15 12.0 72 85 16 10 33 31 10 14 13.0 77 86 14 10 38 39 9 15 13.0 75 87 16 10 33 34 12 14 12.0 69 88 16 10 29 32 12 13 15.0 54 89 15 10 33 33 12 7 22.0 70 90 12 10 31 36 9 17 13.0 73 91 17 10 36 32 15 13 15.0 54 92 16 10 35 41 12 15 13.0 77 93 15 10 32 28 12 14 15.0 82 94 13 10 29 30 12 13 12.5 80 95 16 10 39 36 10 16 11.0 80 96 16 10 37 35 13 12 16.0 69 97 16 10 35 31 9 14 11.0 78 98 16 10 37 34 12 17 11.0 81 99 14 10 32 36 10 15 10.0 76 100 16 10 38 36 14 17 10.0 76 101 16 10 37 35 11 12 16.0 73 102 20 10 36 37 15 16 12.0 85 103 15 10 32 28 11 11 11.0 66 104 16 10 33 39 11 15 16.0 79 105 13 10 40 32 12 9 19.0 68 106 17 10 38 35 12 16 11.0 76 107 16 10 41 39 12 15 16.0 71 108 16 10 36 35 11 10 15.0 54 109 12 10 43 42 7 10 24.0 46 110 16 10 30 34 12 15 14.0 85 111 16 10 31 33 14 11 15.0 74 112 17 10 32 41 11 13 11.0 88 113 13 10 32 33 11 14 15.0 38 114 12 10 37 34 10 18 12.0 76 115 18 10 37 32 13 16 10.0 86 116 14 10 33 40 13 14 14.0 54 117 14 10 34 40 8 14 13.0 67 118 13 10 33 35 11 14 9.0 69 119 16 10 38 36 12 14 15.0 90 120 13 10 33 37 11 12 15.0 54 121 16 10 31 27 13 14 14.0 76 122 13 10 38 39 12 15 11.0 89 123 16 10 37 38 14 15 8.0 76 124 15 10 36 31 13 15 11.0 73 125 16 10 31 33 15 13 11.0 79 126 15 10 39 32 10 17 8.0 90 127 17 10 44 39 11 17 10.0 74 128 15 10 33 36 9 19 11.0 81 129 12 10 35 33 11 15 13.0 72 130 16 10 32 33 10 13 11.0 71 131 10 10 28 32 11 9 20.0 66 132 16 10 40 37 8 15 10.0 77 133 12 10 27 30 11 15 15.0 65 134 14 10 37 38 12 15 12.0 74 135 15 10 32 29 12 16 14.0 85 136 13 10 28 22 9 11 23.0 54 137 15 10 34 35 11 14 14.0 63 138 11 10 30 35 10 11 16.0 54 139 12 10 35 34 8 15 11.0 64 140 11 10 31 35 9 13 12.0 69 141 16 10 32 34 8 15 10.0 54 142 15 10 30 37 9 16 14.0 84 143 17 10 30 35 15 14 12.0 86 144 16 10 31 23 11 15 12.0 77 145 10 10 40 31 8 16 11.0 89 146 18 10 32 27 13 16 12.0 76 147 13 10 36 36 12 11 13.0 60 148 16 10 32 31 12 12 11.0 75 149 13 10 35 32 9 9 19.0 73 150 10 10 38 39 7 16 12.0 85 151 15 10 42 37 13 13 17.0 79 152 16 10 34 38 9 16 9.0 71 153 16 10 35 39 6 12 12.0 72 154 14 9 38 34 8 9 19.0 69 155 10 10 33 31 8 13 18.0 78 156 17 10 36 32 15 13 15.0 54 157 13 10 32 37 6 14 14.0 69 158 15 10 33 36 9 19 11.0 81 159 16 10 34 32 11 13 9.0 84 160 12 10 32 38 8 12 18.0 84 161 13 10 34 36 8 13 16.0 69 162 13 11 27 26 10 10 24.0 66 163 12 11 31 26 8 14 14.0 81 164 17 11 38 33 14 16 20.0 82 165 15 11 34 39 10 10 18.0 72 166 10 11 24 30 8 11 23.0 54 167 14 11 30 33 11 14 12.0 78 168 11 11 26 25 12 12 14.0 74 169 13 11 34 38 12 9 16.0 82 170 16 11 27 37 12 9 18.0 73 171 12 11 37 31 5 11 20.0 55 172 16 11 36 37 12 16 12.0 72 173 12 11 41 35 10 9 12.0 78 174 9 11 29 25 7 13 17.0 59 175 12 11 36 28 12 16 13.0 72 176 15 11 32 35 11 13 9.0 78 177 12 11 37 33 8 9 16.0 68 178 12 11 30 30 9 12 18.0 69 179 14 11 31 31 10 16 10.0 67 180 12 11 38 37 9 11 14.0 74 181 16 11 36 36 12 14 11.0 54 182 11 11 35 30 6 13 9.0 67 183 19 11 31 36 15 15 11.0 70 184 15 11 38 32 12 14 10.0 80 185 8 11 22 28 12 16 11.0 89 186 16 11 32 36 12 13 19.0 76 187 17 11 36 34 11 14 14.0 74 188 12 11 39 31 7 15 12.0 87 189 11 11 28 28 7 13 14.0 54 190 11 11 32 36 5 11 21.0 61 191 14 11 32 36 12 11 13.0 38 192 16 11 38 40 12 14 10.0 75 193 12 11 32 33 3 15 15.0 69 194 16 11 35 37 11 11 16.0 62 195 13 11 32 32 10 15 14.0 72 196 15 11 37 38 12 12 12.0 70 197 16 11 34 31 9 14 19.0 79 198 16 11 33 37 12 14 15.0 87 199 14 11 33 33 9 8 19.0 62 200 16 11 26 32 12 13 13.0 77 201 16 11 30 30 12 9 17.0 69 202 14 11 24 30 10 15 12.0 69 203 11 11 34 31 9 17 11.0 75 204 12 11 34 32 12 13 14.0 54 205 15 11 33 34 8 15 11.0 72 206 15 11 34 36 11 15 13.0 74 207 16 11 35 37 11 14 12.0 85 208 16 11 35 36 12 16 15.0 52 209 11 11 36 33 10 13 14.0 70 210 15 11 34 33 10 16 12.0 84 211 12 11 34 33 12 9 17.0 64 212 12 11 41 44 12 16 11.0 84 213 15 11 32 39 11 11 18.0 87 214 15 11 30 32 8 10 13.0 79 215 16 11 35 35 12 11 17.0 67 216 14 11 28 25 10 15 13.0 65 217 17 11 33 35 11 17 11.0 85 218 14 11 39 34 10 14 12.0 83 219 13 11 36 35 8 8 22.0 61 220 15 11 36 39 12 15 14.0 82 221 13 11 35 33 12 11 12.0 76 222 14 11 38 36 10 16 12.0 58 223 15 11 33 32 12 10 17.0 72 224 12 11 31 32 9 15 9.0 72 225 13 11 34 36 9 9 21.0 38 226 8 11 32 36 6 16 10.0 78 227 14 11 31 32 10 19 11.0 54 228 14 11 33 34 9 12 12.0 63 229 11 11 34 33 9 8 23.0 66 230 12 11 34 35 9 11 13.0 70 231 13 11 34 30 6 14 12.0 71 232 10 11 33 38 10 9 16.0 67 233 16 11 32 34 6 15 9.0 58 234 18 11 41 33 14 13 17.0 72 235 13 11 34 32 10 16 9.0 72 236 11 11 36 31 10 11 14.0 70 237 4 11 37 30 6 12 17.0 76 238 13 11 36 27 12 13 13.0 50 239 16 11 29 31 12 10 11.0 72 240 10 11 37 30 7 11 12.0 72 241 12 11 27 32 8 12 10.0 88 242 12 11 35 35 11 8 19.0 53 243 10 11 28 28 3 12 16.0 58 244 13 11 35 33 6 12 16.0 66 245 15 11 37 31 10 15 14.0 82 246 12 11 29 35 8 11 20.0 69 247 14 11 32 35 9 13 15.0 68 248 10 11 36 32 9 14 23.0 44 249 12 11 19 21 8 10 20.0 56 250 12 11 21 20 9 12 16.0 53 251 11 11 31 34 7 15 14.0 70 252 10 11 33 32 7 13 17.0 78 253 12 11 36 34 6 13 11.0 71 254 16 11 33 32 9 13 13.0 72 255 12 11 37 33 10 12 17.0 68 256 14 11 34 33 11 12 15.0 67 257 16 11 35 37 12 9 21.0 75 258 14 11 31 32 8 9 18.0 62 259 13 11 37 34 11 15 15.0 67 260 4 11 35 30 3 10 8.0 83 261 15 11 27 30 11 14 12.0 64 262 11 11 34 38 12 15 12.0 68 263 11 11 40 36 7 7 22.0 62 264 14 11 29 32 9 14 12.0 72 Belonging_Final 1 32 2 51 3 42 4 41 5 46 6 47 7 37 8 49 9 45 10 47 11 49 12 33 13 42 14 33 15 53 16 36 17 45 18 54 19 41 20 36 21 41 22 44 23 33 24 37 25 52 26 47 27 43 28 44 29 45 30 44 31 49 32 33 33 43 34 54 35 42 36 44 37 37 38 43 39 46 40 42 41 45 42 44 43 33 44 31 45 42 46 40 47 43 48 46 49 42 50 45 51 44 52 40 53 37 54 46 55 36 56 47 57 45 58 42 59 43 60 43 61 32 62 45 63 48 64 31 65 33 66 49 67 42 68 41 69 38 70 42 71 44 72 33 73 48 74 40 75 50 76 49 77 43 78 44 79 47 80 33 81 46 82 45 83 43 84 44 85 47 86 45 87 42 88 33 89 43 90 46 91 33 92 46 93 48 94 47 95 47 96 43 97 46 98 48 99 46 100 45 101 45 102 52 103 42 104 47 105 41 106 47 107 43 108 33 109 30 110 52 111 44 112 55 113 11 114 47 115 53 116 33 117 44 118 42 119 55 120 33 121 46 122 54 123 47 124 45 125 47 126 55 127 44 128 53 129 44 130 42 131 40 132 46 133 40 134 46 135 53 136 33 137 42 138 35 139 40 140 41 141 33 142 51 143 53 144 46 145 55 146 47 147 38 148 46 149 46 150 53 151 47 152 41 153 44 154 43 155 51 156 33 157 43 158 53 159 51 160 50 161 46 162 43 163 47 164 50 165 43 166 33 167 48 168 44 169 50 170 41 171 34 172 44 173 47 174 35 175 44 176 44 177 43 178 41 179 41 180 42 181 33 182 41 183 44 184 48 185 55 186 44 187 43 188 52 189 30 190 39 191 11 192 44 193 42 194 41 195 44 196 44 197 48 198 53 199 37 200 44 201 44 202 40 203 42 204 35 205 43 206 45 207 55 208 31 209 44 210 50 211 40 212 53 213 54 214 49 215 40 216 41 217 52 218 52 219 36 220 52 221 46 222 31 223 44 224 44 225 11 226 46 227 33 228 34 229 42 230 43 231 43 232 44 233 36 234 46 235 44 236 43 237 50 238 33 239 43 240 44 241 53 242 34 243 35 244 40 245 53 246 42 247 43 248 29 249 36 250 30 251 42 252 47 253 44 254 45 255 44 256 43 257 43 258 40 259 41 260 52 261 38 262 41 263 39 264 43 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Connected Separate 8.35047 -0.40460 0.03478 0.04334 Software Happiness Depression Belonging 0.57606 0.07962 -0.02610 0.02829 Belonging_Final -0.03265 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.9727 -1.1695 0.2867 1.1592 4.6323 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.35047 2.61698 3.191 0.0016 ** month -0.40460 0.15952 -2.536 0.0118 * Connected 0.03478 0.03476 1.001 0.3179 Separate 0.04334 0.03528 1.228 0.2204 Software 0.57606 0.05277 10.916 <2e-16 *** Happiness 0.07962 0.05784 1.376 0.1699 Depression -0.02610 0.04244 -0.615 0.5390 Belonging 0.02829 0.03777 0.749 0.4545 Belonging_Final -0.03265 0.05610 -0.582 0.5611 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.865 on 255 degrees of freedom Multiple R-squared: 0.441, Adjusted R-squared: 0.4235 F-statistic: 25.15 on 8 and 255 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,] 0.249272273 0.498544547 0.7507277 [2,] 0.168954109 0.337908219 0.8310459 [3,] 0.188652474 0.377304949 0.8113475 [4,] 0.113822200 0.227644399 0.8861778 [5,] 0.077358179 0.154716357 0.9226418 [6,] 0.111089922 0.222179843 0.8889101 [7,] 0.350297244 0.700594488 0.6497028 [8,] 0.273076148 0.546152296 0.7269239 [9,] 0.200364780 0.400729560 0.7996352 [10,] 0.147329038 0.294658076 0.8526710 [11,] 0.142429822 0.284859644 0.8575702 [12,] 0.329613611 0.659227221 0.6703864 [13,] 0.396108111 0.792216222 0.6038919 [14,] 0.364643783 0.729287565 0.6353562 [15,] 0.343013785 0.686027571 0.6569862 [16,] 0.419522217 0.839044434 0.5804778 [17,] 0.431039270 0.862078539 0.5689607 [18,] 0.411178258 0.822356515 0.5888217 [19,] 0.444562864 0.889125729 0.5554371 [20,] 0.389182053 0.778364107 0.6108179 [21,] 0.346620753 0.693241507 0.6533792 [22,] 0.316677131 0.633354262 0.6833229 [23,] 0.275674898 0.551349797 0.7243251 [24,] 0.233139525 0.466279050 0.7668605 [25,] 0.363623900 0.727247799 0.6363761 [26,] 0.400480018 0.800960036 0.5995200 [27,] 0.392518457 0.785036914 0.6074815 [28,] 0.420646898 0.841293796 0.5793531 [29,] 0.394036669 0.788073338 0.6059633 [30,] 0.352608511 0.705217021 0.6473915 [31,] 0.317815561 0.635631122 0.6821844 [32,] 0.330602998 0.661205997 0.6693970 [33,] 0.284193040 0.568386080 0.7158070 [34,] 0.259307354 0.518614708 0.7406926 [35,] 0.457373137 0.914746273 0.5426269 [36,] 0.613015043 0.773969915 0.3869850 [37,] 0.564790965 0.870418070 0.4352090 [38,] 0.550115831 0.899768338 0.4498842 [39,] 0.538886872 0.922226256 0.4611131 [40,] 0.496029188 0.992058376 0.5039708 [41,] 0.448976552 0.897953103 0.5510234 [42,] 0.469409971 0.938819942 0.5305900 [43,] 0.449833836 0.899667672 0.5501662 [44,] 0.452066183 0.904132367 0.5479338 [45,] 0.420708590 0.841417180 0.5792914 [46,] 0.377122147 0.754244294 0.6228779 [47,] 0.343504288 0.687008576 0.6564957 [48,] 0.305381737 0.610763474 0.6946183 [49,] 0.304715665 0.609431330 0.6952843 [50,] 0.274885364 0.549770729 0.7251146 [51,] 0.243391513 0.486783027 0.7566085 [52,] 0.213205556 0.426411111 0.7867944 [53,] 0.184850255 0.369700511 0.8151497 [54,] 0.159612090 0.319224181 0.8403879 [55,] 0.135701683 0.271403365 0.8642983 [56,] 0.118078555 0.236157110 0.8819214 [57,] 0.147491712 0.294983424 0.8525083 [58,] 0.347255539 0.694511077 0.6527445 [59,] 0.309383521 0.618767042 0.6906165 [60,] 0.466018474 0.932036948 0.5339815 [61,] 0.427694883 0.855389767 0.5723051 [62,] 0.415756314 0.831512629 0.5842437 [63,] 0.394545849 0.789091698 0.6054542 [64,] 0.359444377 0.718888754 0.6405556 [65,] 0.407073803 0.814147605 0.5929262 [66,] 0.374300585 0.748601169 0.6256994 [67,] 0.346591345 0.693182689 0.6534087 [68,] 0.375124097 0.750248194 0.6248759 [69,] 0.344523837 0.689047674 0.6554762 [70,] 0.311892770 0.623785539 0.6881072 [71,] 0.277800972 0.555601945 0.7221990 [72,] 0.254136867 0.508273734 0.7458631 [73,] 0.223381796 0.446763591 0.7766182 [74,] 0.213624304 0.427248607 0.7863757 [75,] 0.186095184 0.372190367 0.8139048 [76,] 0.162784483 0.325568967 0.8372155 [77,] 0.147969314 0.295938628 0.8520307 [78,] 0.127536796 0.255073591 0.8724632 [79,] 0.129326333 0.258652666 0.8706737 [80,] 0.111535332 0.223070664 0.8884647 [81,] 0.096128398 0.192256796 0.9038716 [82,] 0.082550296 0.165100591 0.9174497 [83,] 0.087742539 0.175485078 0.9122575 [84,] 0.078087986 0.156175972 0.9219120 [85,] 0.065829406 0.131658811 0.9341706 [86,] 0.068906807 0.137813615 0.9310932 [87,] 0.057357952 0.114715903 0.9426420 [88,] 0.047590636 0.095181272 0.9524094 [89,] 0.042543858 0.085087717 0.9574561 [90,] 0.036985931 0.073971862 0.9630141 [91,] 0.042000243 0.084000487 0.9579998 [92,] 0.035665390 0.071330780 0.9643346 [93,] 0.031666520 0.063333040 0.9683335 [94,] 0.038809498 0.077618995 0.9611905 [95,] 0.033580103 0.067160206 0.9664199 [96,] 0.027290386 0.054580772 0.9727096 [97,] 0.026013415 0.052026830 0.9739866 [98,] 0.021222130 0.042444260 0.9787779 [99,] 0.017203818 0.034407637 0.9827962 [100,] 0.013632186 0.027264372 0.9863678 [101,] 0.013622757 0.027245513 0.9863772 [102,] 0.012487296 0.024974592 0.9875127 [103,] 0.019146176 0.038292352 0.9808538 [104,] 0.017761714 0.035523429 0.9822383 [105,] 0.017603646 0.035207292 0.9823964 [106,] 0.014365082 0.028730163 0.9856349 [107,] 0.014933316 0.029866632 0.9850667 [108,] 0.012069116 0.024138232 0.9879309 [109,] 0.011017215 0.022034430 0.9889828 [110,] 0.008849280 0.017698560 0.9911507 [111,] 0.014052017 0.028104034 0.9859480 [112,] 0.012043124 0.024086249 0.9879569 [113,] 0.010823044 0.021646088 0.9891770 [114,] 0.009037311 0.018074622 0.9909627 [115,] 0.007284510 0.014569021 0.9927155 [116,] 0.006399142 0.012798283 0.9936009 [117,] 0.005106591 0.010213182 0.9948934 [118,] 0.007833751 0.015667501 0.9921662 [119,] 0.008079630 0.016159261 0.9919204 [120,] 0.018094933 0.036189866 0.9819051 [121,] 0.020368964 0.040737929 0.9796310 [122,] 0.023261761 0.046523522 0.9767382 [123,] 0.023256224 0.046512448 0.9767438 [124,] 0.019036038 0.038072076 0.9809640 [125,] 0.015637249 0.031274498 0.9843628 [126,] 0.012443014 0.024886029 0.9875570 [127,] 0.016577269 0.033154538 0.9834227 [128,] 0.014924063 0.029848125 0.9850759 [129,] 0.019031155 0.038062311 0.9809688 [130,] 0.026112930 0.052225859 0.9738871 [131,] 0.023296615 0.046593229 0.9767034 [132,] 0.019013388 0.038026776 0.9809866 [133,] 0.018077456 0.036154912 0.9819225 [134,] 0.035502616 0.071005232 0.9644974 [135,] 0.038422197 0.076844393 0.9615778 [136,] 0.043198752 0.086397504 0.9568012 [137,] 0.036964585 0.073929170 0.9630354 [138,] 0.030326830 0.060653660 0.9696732 [139,] 0.045729337 0.091458674 0.9542707 [140,] 0.041381104 0.082762209 0.9586189 [141,] 0.041087092 0.082174183 0.9589129 [142,] 0.071708180 0.143416360 0.9282918 [143,] 0.063159712 0.126319425 0.9368403 [144,] 0.077112812 0.154225624 0.9228872 [145,] 0.064814840 0.129629680 0.9351852 [146,] 0.056421290 0.112842581 0.9435787 [147,] 0.047850295 0.095700591 0.9521497 [148,] 0.045457012 0.090914024 0.9545430 [149,] 0.039035534 0.078071068 0.9609645 [150,] 0.031868811 0.063737623 0.9681312 [151,] 0.026187933 0.052375865 0.9738121 [152,] 0.021581342 0.043162684 0.9784187 [153,] 0.018252422 0.036504845 0.9817476 [154,] 0.016138330 0.032276661 0.9838617 [155,] 0.016251666 0.032503332 0.9837483 [156,] 0.012956801 0.025913601 0.9870432 [157,] 0.018479417 0.036958835 0.9815206 [158,] 0.017894260 0.035788519 0.9821057 [159,] 0.017018918 0.034037836 0.9829811 [160,] 0.016561667 0.033123335 0.9834383 [161,] 0.013436048 0.026872095 0.9865640 [162,] 0.012958356 0.025916711 0.9870416 [163,] 0.013877447 0.027754894 0.9861226 [164,] 0.017240763 0.034481526 0.9827592 [165,] 0.013895174 0.027790348 0.9861048 [166,] 0.011051999 0.022103998 0.9889480 [167,] 0.008870743 0.017741487 0.9911293 [168,] 0.006883216 0.013766432 0.9931168 [169,] 0.005774436 0.011548871 0.9942256 [170,] 0.004853936 0.009707871 0.9951461 [171,] 0.003788799 0.007577598 0.9962112 [172,] 0.004241499 0.008482999 0.9957585 [173,] 0.003270547 0.006541094 0.9967295 [174,] 0.072840711 0.145681421 0.9271593 [175,] 0.063836939 0.127673878 0.9361631 [176,] 0.075551377 0.151102755 0.9244486 [177,] 0.064037139 0.128074277 0.9359629 [178,] 0.053654156 0.107308313 0.9463458 [179,] 0.044308331 0.088616662 0.9556917 [180,] 0.038733534 0.077467069 0.9612665 [181,] 0.032480446 0.064960891 0.9675196 [182,] 0.038777019 0.077554038 0.9612230 [183,] 0.042580372 0.085160744 0.9574196 [184,] 0.035541480 0.071082959 0.9644585 [185,] 0.029143015 0.058286030 0.9708570 [186,] 0.039343221 0.078686442 0.9606568 [187,] 0.032175175 0.064350349 0.9678248 [188,] 0.029969169 0.059938339 0.9700308 [189,] 0.025468536 0.050937072 0.9745315 [190,] 0.024209926 0.048419852 0.9757901 [191,] 0.020183007 0.040366014 0.9798170 [192,] 0.025527130 0.051054260 0.9744729 [193,] 0.028059869 0.056119738 0.9719401 [194,] 0.031222434 0.062444868 0.9687776 [195,] 0.024571621 0.049143242 0.9754284 [196,] 0.024269913 0.048539826 0.9757301 [197,] 0.019969288 0.039938576 0.9800307 [198,] 0.021672832 0.043345664 0.9783272 [199,] 0.017456347 0.034912694 0.9825437 [200,] 0.018929280 0.037858560 0.9810707 [201,] 0.029270319 0.058540638 0.9707297 [202,] 0.022869354 0.045738709 0.9771306 [203,] 0.033024021 0.066048042 0.9669760 [204,] 0.028456855 0.056913710 0.9715431 [205,] 0.022084984 0.044169968 0.9779150 [206,] 0.024359112 0.048718225 0.9756409 [207,] 0.020489589 0.040979178 0.9795104 [208,] 0.018320551 0.036641101 0.9816794 [209,] 0.013754798 0.027509597 0.9862452 [210,] 0.011927236 0.023854471 0.9880728 [211,] 0.008581221 0.017162442 0.9914188 [212,] 0.006346574 0.012693148 0.9936534 [213,] 0.004964717 0.009929434 0.9950353 [214,] 0.004294646 0.008589292 0.9957054 [215,] 0.009946292 0.019892583 0.9900537 [216,] 0.007534384 0.015068767 0.9924656 [217,] 0.005373386 0.010746773 0.9946266 [218,] 0.003807178 0.007614357 0.9961928 [219,] 0.002664712 0.005329424 0.9973353 [220,] 0.002671582 0.005343163 0.9973284 [221,] 0.004943364 0.009886728 0.9950566 [222,] 0.025946735 0.051893471 0.9740533 [223,] 0.038457941 0.076915881 0.9615421 [224,] 0.027718788 0.055437577 0.9722812 [225,] 0.023961814 0.047923628 0.9760382 [226,] 0.187771175 0.375542351 0.8122288 [227,] 0.147044861 0.294089722 0.8529551 [228,] 0.130216166 0.260432332 0.8697838 [229,] 0.098868434 0.197736868 0.9011316 [230,] 0.076600142 0.153200283 0.9233999 [231,] 0.061463418 0.122926836 0.9385366 [232,] 0.056209776 0.112419552 0.9437902 [233,] 0.103562676 0.207125352 0.8964373 [234,] 0.090783773 0.181567545 0.9092162 [235,] 0.069638293 0.139276586 0.9303617 [236,] 0.046529644 0.093059287 0.9534704 [237,] 0.037806000 0.075612000 0.9621940 [238,] 0.034074745 0.068149490 0.9659253 [239,] 0.041448869 0.082897738 0.9585511 [240,] 0.021822448 0.043644896 0.9781776 [241,] 0.054742676 0.109485353 0.9452573 > postscript(file="/var/wessaorg/rcomp/tmp/1gngz1352118285.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/2fww71352118285.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3d4u21352118285.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4l7o81352118285.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/50txm1352118285.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 264 Frequency = 1 1 2 3 4 5 6 -2.950927427 0.381817133 2.083335628 3.127025193 -2.358051988 -1.736016272 7 8 9 10 11 12 3.846299991 -2.007756051 -1.948445497 0.701963529 1.137382762 -0.101807701 13 14 15 16 17 18 0.610689034 0.592021392 -1.004395063 -0.379815924 0.489441182 3.616619183 19 20 21 22 23 24 2.625165134 0.506467834 0.697230535 0.958823175 2.546938147 1.010762105 25 26 27 28 29 30 0.833157851 0.778791226 1.028275393 -1.763968014 0.233833016 -0.077895767 31 32 33 34 35 36 -0.746027284 -0.737377122 -0.797418619 0.071499880 -1.602200306 -2.921926401 37 38 39 40 41 42 -3.014672990 -1.789991690 1.437350999 1.527886673 1.272815400 -1.828353335 43 44 45 46 47 48 2.425987887 -0.166310878 -0.492903544 -4.579946776 -2.604228986 -0.194229434 49 50 51 52 53 54 0.370906087 -1.730933528 -1.147151290 -0.221677409 -3.145340373 0.130447599 55 56 57 58 59 60 -2.364552178 1.599325081 -0.010771712 0.412355641 -0.248651715 1.699039550 61 62 63 64 65 66 0.431959429 0.151906615 -0.732096822 -0.849165795 0.546781501 1.213271705 67 68 69 70 71 72 1.933602545 3.776399158 -3.703958865 0.552161863 -3.029195508 -0.699009795 73 74 75 76 77 78 1.178461283 0.952614163 0.938146133 3.731342200 -0.274453484 1.725904951 79 80 81 82 83 84 -2.033478554 1.075230233 0.434465248 0.463024793 -0.556378965 0.290823812 85 86 87 88 89 90 2.024351240 -0.008573324 0.779169251 1.293406531 0.645248973 -1.705080737 91 92 93 94 95 96 0.321735886 0.256979711 -0.019563985 -1.963598029 1.302627336 0.316937043 97 98 99 100 101 102 2.417701080 0.231489430 -0.319858082 -1.024703834 1.421202664 2.531232510 103 104 105 106 107 108 0.947695374 1.043666722 -1.801167422 1.341788163 0.285054577 1.734830472 109 110 111 112 113 114 -0.144502540 0.729968270 -0.019022897 2.027100876 -1.623526260 -2.561106515 115 116 117 118 119 120 1.817433595 -1.874239203 0.936575639 -1.766415230 0.427246196 -1.406745685 121 122 123 124 125 126 0.560835235 -2.891167271 -0.904258187 -0.892153489 -0.902236171 0.296367937 127 128 129 130 131 132 1.388689572 1.016139203 -2.744083838 2.006357737 -3.757672592 2.482367886 133 134 135 136 137 138 -2.216163118 -1.623796371 -0.169866756 0.857844295 0.499053080 -2.468624925 139 140 141 142 143 144 -1.015724374 -2.419446534 3.116868495 1.244149429 -0.009779507 1.726205342 145 146 147 148 149 150 -3.356731563 2.347253794 -2.022884772 1.037980818 0.122740838 -2.983866911 151 152 153 154 155 156 -1.149480124 1.972426862 4.088948985 1.118379703 -2.511077298 0.321735886 157 158 159 160 161 162 1.225159115 1.016139203 1.277952243 -0.902448709 0.276588644 0.640555249 163 164 165 166 167 168 -0.219714058 0.844074307 1.507288989 -1.369085649 -0.151183747 -3.047381403 169 170 171 172 173 174 -1.628420757 1.671359890 1.789630485 0.770582649 -1.678958916 -2.142243663 175 176 177 178 179 180 -2.813249493 0.563279913 -0.044313330 -0.527125898 0.347961979 -1.242363629 181 182 183 184 185 186 1.097128568 -0.230816591 2.369755033 -0.070957371 -6.500257228 1.261477725 187 188 189 190 191 192 2.598884232 -0.276935001 -0.337592691 0.766460632 -0.738347363 0.593164124 193 194 195 196 197 198 2.445135861 2.068898527 -0.589629361 0.067529462 3.102911981 0.981990529 199 200 201 202 203 204 1.650521446 1.458638605 2.055406796 0.677381539 -2.427517795 -2.436710987 205 206 207 208 209 210 2.330073087 0.541347575 1.532062754 1.068353080 -2.556278024 1.022060198 211 212 213 214 215 216 -2.202902206 -3.778443865 0.855979821 2.969296732 1.431517041 0.926865366 217 218 219 220 221 222 2.325387625 0.057640177 1.109497163 -0.205965436 -1.671017171 -0.131920566 223 224 225 226 227 228 0.699881753 -1.109293774 0.288333593 -3.747213134 0.198419826 0.979732572 229 230 231 232 233 234 -1.229759183 -0.896838278 1.754791518 -3.213065762 4.632282599 2.052583830 235 236 237 238 239 240 -0.869328864 -2.343002739 -6.972696730 -1.267817848 1.693092913 -1.682392341 241 242 243 244 245 246 -0.287911980 -1.501192112 1.148577467 1.897137210 1.290751900 0.031494268 247 248 249 250 251 252 1.122268014 -2.535800023 1.237586914 0.260609056 -0.922062430 -1.730463173 253 254 255 256 257 258 0.598033801 3.117443737 -1.376552283 0.095170915 1.480120220 2.331720115 259 260 261 262 263 264 -1.356690157 -5.383345806 1.152740672 -4.108360954 -0.347629194 1.085550529 > postscript(file="/var/wessaorg/rcomp/tmp/6qofu1352118285.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 = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.950927427 NA 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 264 NA 1.085550529 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.381817133 -2.950927427 [2,] 2.083335628 0.381817133 [3,] 3.127025193 2.083335628 [4,] -2.358051988 3.127025193 [5,] -1.736016272 -2.358051988 [6,] 3.846299991 -1.736016272 [7,] -2.007756051 3.846299991 [8,] -1.948445497 -2.007756051 [9,] 0.701963529 -1.948445497 [10,] 1.137382762 0.701963529 [11,] -0.101807701 1.137382762 [12,] 0.610689034 -0.101807701 [13,] 0.592021392 0.610689034 [14,] -1.004395063 0.592021392 [15,] -0.379815924 -1.004395063 [16,] 0.489441182 -0.379815924 [17,] 3.616619183 0.489441182 [18,] 2.625165134 3.616619183 [19,] 0.506467834 2.625165134 [20,] 0.697230535 0.506467834 [21,] 0.958823175 0.697230535 [22,] 2.546938147 0.958823175 [23,] 1.010762105 2.546938147 [24,] 0.833157851 1.010762105 [25,] 0.778791226 0.833157851 [26,] 1.028275393 0.778791226 [27,] -1.763968014 1.028275393 [28,] 0.233833016 -1.763968014 [29,] -0.077895767 0.233833016 [30,] -0.746027284 -0.077895767 [31,] -0.737377122 -0.746027284 [32,] -0.797418619 -0.737377122 [33,] 0.071499880 -0.797418619 [34,] -1.602200306 0.071499880 [35,] -2.921926401 -1.602200306 [36,] -3.014672990 -2.921926401 [37,] -1.789991690 -3.014672990 [38,] 1.437350999 -1.789991690 [39,] 1.527886673 1.437350999 [40,] 1.272815400 1.527886673 [41,] -1.828353335 1.272815400 [42,] 2.425987887 -1.828353335 [43,] -0.166310878 2.425987887 [44,] -0.492903544 -0.166310878 [45,] -4.579946776 -0.492903544 [46,] -2.604228986 -4.579946776 [47,] -0.194229434 -2.604228986 [48,] 0.370906087 -0.194229434 [49,] -1.730933528 0.370906087 [50,] -1.147151290 -1.730933528 [51,] -0.221677409 -1.147151290 [52,] -3.145340373 -0.221677409 [53,] 0.130447599 -3.145340373 [54,] -2.364552178 0.130447599 [55,] 1.599325081 -2.364552178 [56,] -0.010771712 1.599325081 [57,] 0.412355641 -0.010771712 [58,] -0.248651715 0.412355641 [59,] 1.699039550 -0.248651715 [60,] 0.431959429 1.699039550 [61,] 0.151906615 0.431959429 [62,] -0.732096822 0.151906615 [63,] -0.849165795 -0.732096822 [64,] 0.546781501 -0.849165795 [65,] 1.213271705 0.546781501 [66,] 1.933602545 1.213271705 [67,] 3.776399158 1.933602545 [68,] -3.703958865 3.776399158 [69,] 0.552161863 -3.703958865 [70,] -3.029195508 0.552161863 [71,] -0.699009795 -3.029195508 [72,] 1.178461283 -0.699009795 [73,] 0.952614163 1.178461283 [74,] 0.938146133 0.952614163 [75,] 3.731342200 0.938146133 [76,] -0.274453484 3.731342200 [77,] 1.725904951 -0.274453484 [78,] -2.033478554 1.725904951 [79,] 1.075230233 -2.033478554 [80,] 0.434465248 1.075230233 [81,] 0.463024793 0.434465248 [82,] -0.556378965 0.463024793 [83,] 0.290823812 -0.556378965 [84,] 2.024351240 0.290823812 [85,] -0.008573324 2.024351240 [86,] 0.779169251 -0.008573324 [87,] 1.293406531 0.779169251 [88,] 0.645248973 1.293406531 [89,] -1.705080737 0.645248973 [90,] 0.321735886 -1.705080737 [91,] 0.256979711 0.321735886 [92,] -0.019563985 0.256979711 [93,] -1.963598029 -0.019563985 [94,] 1.302627336 -1.963598029 [95,] 0.316937043 1.302627336 [96,] 2.417701080 0.316937043 [97,] 0.231489430 2.417701080 [98,] -0.319858082 0.231489430 [99,] -1.024703834 -0.319858082 [100,] 1.421202664 -1.024703834 [101,] 2.531232510 1.421202664 [102,] 0.947695374 2.531232510 [103,] 1.043666722 0.947695374 [104,] -1.801167422 1.043666722 [105,] 1.341788163 -1.801167422 [106,] 0.285054577 1.341788163 [107,] 1.734830472 0.285054577 [108,] -0.144502540 1.734830472 [109,] 0.729968270 -0.144502540 [110,] -0.019022897 0.729968270 [111,] 2.027100876 -0.019022897 [112,] -1.623526260 2.027100876 [113,] -2.561106515 -1.623526260 [114,] 1.817433595 -2.561106515 [115,] -1.874239203 1.817433595 [116,] 0.936575639 -1.874239203 [117,] -1.766415230 0.936575639 [118,] 0.427246196 -1.766415230 [119,] -1.406745685 0.427246196 [120,] 0.560835235 -1.406745685 [121,] -2.891167271 0.560835235 [122,] -0.904258187 -2.891167271 [123,] -0.892153489 -0.904258187 [124,] -0.902236171 -0.892153489 [125,] 0.296367937 -0.902236171 [126,] 1.388689572 0.296367937 [127,] 1.016139203 1.388689572 [128,] -2.744083838 1.016139203 [129,] 2.006357737 -2.744083838 [130,] -3.757672592 2.006357737 [131,] 2.482367886 -3.757672592 [132,] -2.216163118 2.482367886 [133,] -1.623796371 -2.216163118 [134,] -0.169866756 -1.623796371 [135,] 0.857844295 -0.169866756 [136,] 0.499053080 0.857844295 [137,] -2.468624925 0.499053080 [138,] -1.015724374 -2.468624925 [139,] -2.419446534 -1.015724374 [140,] 3.116868495 -2.419446534 [141,] 1.244149429 3.116868495 [142,] -0.009779507 1.244149429 [143,] 1.726205342 -0.009779507 [144,] -3.356731563 1.726205342 [145,] 2.347253794 -3.356731563 [146,] -2.022884772 2.347253794 [147,] 1.037980818 -2.022884772 [148,] 0.122740838 1.037980818 [149,] -2.983866911 0.122740838 [150,] -1.149480124 -2.983866911 [151,] 1.972426862 -1.149480124 [152,] 4.088948985 1.972426862 [153,] 1.118379703 4.088948985 [154,] -2.511077298 1.118379703 [155,] 0.321735886 -2.511077298 [156,] 1.225159115 0.321735886 [157,] 1.016139203 1.225159115 [158,] 1.277952243 1.016139203 [159,] -0.902448709 1.277952243 [160,] 0.276588644 -0.902448709 [161,] 0.640555249 0.276588644 [162,] -0.219714058 0.640555249 [163,] 0.844074307 -0.219714058 [164,] 1.507288989 0.844074307 [165,] -1.369085649 1.507288989 [166,] -0.151183747 -1.369085649 [167,] -3.047381403 -0.151183747 [168,] -1.628420757 -3.047381403 [169,] 1.671359890 -1.628420757 [170,] 1.789630485 1.671359890 [171,] 0.770582649 1.789630485 [172,] -1.678958916 0.770582649 [173,] -2.142243663 -1.678958916 [174,] -2.813249493 -2.142243663 [175,] 0.563279913 -2.813249493 [176,] -0.044313330 0.563279913 [177,] -0.527125898 -0.044313330 [178,] 0.347961979 -0.527125898 [179,] -1.242363629 0.347961979 [180,] 1.097128568 -1.242363629 [181,] -0.230816591 1.097128568 [182,] 2.369755033 -0.230816591 [183,] -0.070957371 2.369755033 [184,] -6.500257228 -0.070957371 [185,] 1.261477725 -6.500257228 [186,] 2.598884232 1.261477725 [187,] -0.276935001 2.598884232 [188,] -0.337592691 -0.276935001 [189,] 0.766460632 -0.337592691 [190,] -0.738347363 0.766460632 [191,] 0.593164124 -0.738347363 [192,] 2.445135861 0.593164124 [193,] 2.068898527 2.445135861 [194,] -0.589629361 2.068898527 [195,] 0.067529462 -0.589629361 [196,] 3.102911981 0.067529462 [197,] 0.981990529 3.102911981 [198,] 1.650521446 0.981990529 [199,] 1.458638605 1.650521446 [200,] 2.055406796 1.458638605 [201,] 0.677381539 2.055406796 [202,] -2.427517795 0.677381539 [203,] -2.436710987 -2.427517795 [204,] 2.330073087 -2.436710987 [205,] 0.541347575 2.330073087 [206,] 1.532062754 0.541347575 [207,] 1.068353080 1.532062754 [208,] -2.556278024 1.068353080 [209,] 1.022060198 -2.556278024 [210,] -2.202902206 1.022060198 [211,] -3.778443865 -2.202902206 [212,] 0.855979821 -3.778443865 [213,] 2.969296732 0.855979821 [214,] 1.431517041 2.969296732 [215,] 0.926865366 1.431517041 [216,] 2.325387625 0.926865366 [217,] 0.057640177 2.325387625 [218,] 1.109497163 0.057640177 [219,] -0.205965436 1.109497163 [220,] -1.671017171 -0.205965436 [221,] -0.131920566 -1.671017171 [222,] 0.699881753 -0.131920566 [223,] -1.109293774 0.699881753 [224,] 0.288333593 -1.109293774 [225,] -3.747213134 0.288333593 [226,] 0.198419826 -3.747213134 [227,] 0.979732572 0.198419826 [228,] -1.229759183 0.979732572 [229,] -0.896838278 -1.229759183 [230,] 1.754791518 -0.896838278 [231,] -3.213065762 1.754791518 [232,] 4.632282599 -3.213065762 [233,] 2.052583830 4.632282599 [234,] -0.869328864 2.052583830 [235,] -2.343002739 -0.869328864 [236,] -6.972696730 -2.343002739 [237,] -1.267817848 -6.972696730 [238,] 1.693092913 -1.267817848 [239,] -1.682392341 1.693092913 [240,] -0.287911980 -1.682392341 [241,] -1.501192112 -0.287911980 [242,] 1.148577467 -1.501192112 [243,] 1.897137210 1.148577467 [244,] 1.290751900 1.897137210 [245,] 0.031494268 1.290751900 [246,] 1.122268014 0.031494268 [247,] -2.535800023 1.122268014 [248,] 1.237586914 -2.535800023 [249,] 0.260609056 1.237586914 [250,] -0.922062430 0.260609056 [251,] -1.730463173 -0.922062430 [252,] 0.598033801 -1.730463173 [253,] 3.117443737 0.598033801 [254,] -1.376552283 3.117443737 [255,] 0.095170915 -1.376552283 [256,] 1.480120220 0.095170915 [257,] 2.331720115 1.480120220 [258,] -1.356690157 2.331720115 [259,] -5.383345806 -1.356690157 [260,] 1.152740672 -5.383345806 [261,] -4.108360954 1.152740672 [262,] -0.347629194 -4.108360954 [263,] 1.085550529 -0.347629194 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.381817133 -2.950927427 2 2.083335628 0.381817133 3 3.127025193 2.083335628 4 -2.358051988 3.127025193 5 -1.736016272 -2.358051988 6 3.846299991 -1.736016272 7 -2.007756051 3.846299991 8 -1.948445497 -2.007756051 9 0.701963529 -1.948445497 10 1.137382762 0.701963529 11 -0.101807701 1.137382762 12 0.610689034 -0.101807701 13 0.592021392 0.610689034 14 -1.004395063 0.592021392 15 -0.379815924 -1.004395063 16 0.489441182 -0.379815924 17 3.616619183 0.489441182 18 2.625165134 3.616619183 19 0.506467834 2.625165134 20 0.697230535 0.506467834 21 0.958823175 0.697230535 22 2.546938147 0.958823175 23 1.010762105 2.546938147 24 0.833157851 1.010762105 25 0.778791226 0.833157851 26 1.028275393 0.778791226 27 -1.763968014 1.028275393 28 0.233833016 -1.763968014 29 -0.077895767 0.233833016 30 -0.746027284 -0.077895767 31 -0.737377122 -0.746027284 32 -0.797418619 -0.737377122 33 0.071499880 -0.797418619 34 -1.602200306 0.071499880 35 -2.921926401 -1.602200306 36 -3.014672990 -2.921926401 37 -1.789991690 -3.014672990 38 1.437350999 -1.789991690 39 1.527886673 1.437350999 40 1.272815400 1.527886673 41 -1.828353335 1.272815400 42 2.425987887 -1.828353335 43 -0.166310878 2.425987887 44 -0.492903544 -0.166310878 45 -4.579946776 -0.492903544 46 -2.604228986 -4.579946776 47 -0.194229434 -2.604228986 48 0.370906087 -0.194229434 49 -1.730933528 0.370906087 50 -1.147151290 -1.730933528 51 -0.221677409 -1.147151290 52 -3.145340373 -0.221677409 53 0.130447599 -3.145340373 54 -2.364552178 0.130447599 55 1.599325081 -2.364552178 56 -0.010771712 1.599325081 57 0.412355641 -0.010771712 58 -0.248651715 0.412355641 59 1.699039550 -0.248651715 60 0.431959429 1.699039550 61 0.151906615 0.431959429 62 -0.732096822 0.151906615 63 -0.849165795 -0.732096822 64 0.546781501 -0.849165795 65 1.213271705 0.546781501 66 1.933602545 1.213271705 67 3.776399158 1.933602545 68 -3.703958865 3.776399158 69 0.552161863 -3.703958865 70 -3.029195508 0.552161863 71 -0.699009795 -3.029195508 72 1.178461283 -0.699009795 73 0.952614163 1.178461283 74 0.938146133 0.952614163 75 3.731342200 0.938146133 76 -0.274453484 3.731342200 77 1.725904951 -0.274453484 78 -2.033478554 1.725904951 79 1.075230233 -2.033478554 80 0.434465248 1.075230233 81 0.463024793 0.434465248 82 -0.556378965 0.463024793 83 0.290823812 -0.556378965 84 2.024351240 0.290823812 85 -0.008573324 2.024351240 86 0.779169251 -0.008573324 87 1.293406531 0.779169251 88 0.645248973 1.293406531 89 -1.705080737 0.645248973 90 0.321735886 -1.705080737 91 0.256979711 0.321735886 92 -0.019563985 0.256979711 93 -1.963598029 -0.019563985 94 1.302627336 -1.963598029 95 0.316937043 1.302627336 96 2.417701080 0.316937043 97 0.231489430 2.417701080 98 -0.319858082 0.231489430 99 -1.024703834 -0.319858082 100 1.421202664 -1.024703834 101 2.531232510 1.421202664 102 0.947695374 2.531232510 103 1.043666722 0.947695374 104 -1.801167422 1.043666722 105 1.341788163 -1.801167422 106 0.285054577 1.341788163 107 1.734830472 0.285054577 108 -0.144502540 1.734830472 109 0.729968270 -0.144502540 110 -0.019022897 0.729968270 111 2.027100876 -0.019022897 112 -1.623526260 2.027100876 113 -2.561106515 -1.623526260 114 1.817433595 -2.561106515 115 -1.874239203 1.817433595 116 0.936575639 -1.874239203 117 -1.766415230 0.936575639 118 0.427246196 -1.766415230 119 -1.406745685 0.427246196 120 0.560835235 -1.406745685 121 -2.891167271 0.560835235 122 -0.904258187 -2.891167271 123 -0.892153489 -0.904258187 124 -0.902236171 -0.892153489 125 0.296367937 -0.902236171 126 1.388689572 0.296367937 127 1.016139203 1.388689572 128 -2.744083838 1.016139203 129 2.006357737 -2.744083838 130 -3.757672592 2.006357737 131 2.482367886 -3.757672592 132 -2.216163118 2.482367886 133 -1.623796371 -2.216163118 134 -0.169866756 -1.623796371 135 0.857844295 -0.169866756 136 0.499053080 0.857844295 137 -2.468624925 0.499053080 138 -1.015724374 -2.468624925 139 -2.419446534 -1.015724374 140 3.116868495 -2.419446534 141 1.244149429 3.116868495 142 -0.009779507 1.244149429 143 1.726205342 -0.009779507 144 -3.356731563 1.726205342 145 2.347253794 -3.356731563 146 -2.022884772 2.347253794 147 1.037980818 -2.022884772 148 0.122740838 1.037980818 149 -2.983866911 0.122740838 150 -1.149480124 -2.983866911 151 1.972426862 -1.149480124 152 4.088948985 1.972426862 153 1.118379703 4.088948985 154 -2.511077298 1.118379703 155 0.321735886 -2.511077298 156 1.225159115 0.321735886 157 1.016139203 1.225159115 158 1.277952243 1.016139203 159 -0.902448709 1.277952243 160 0.276588644 -0.902448709 161 0.640555249 0.276588644 162 -0.219714058 0.640555249 163 0.844074307 -0.219714058 164 1.507288989 0.844074307 165 -1.369085649 1.507288989 166 -0.151183747 -1.369085649 167 -3.047381403 -0.151183747 168 -1.628420757 -3.047381403 169 1.671359890 -1.628420757 170 1.789630485 1.671359890 171 0.770582649 1.789630485 172 -1.678958916 0.770582649 173 -2.142243663 -1.678958916 174 -2.813249493 -2.142243663 175 0.563279913 -2.813249493 176 -0.044313330 0.563279913 177 -0.527125898 -0.044313330 178 0.347961979 -0.527125898 179 -1.242363629 0.347961979 180 1.097128568 -1.242363629 181 -0.230816591 1.097128568 182 2.369755033 -0.230816591 183 -0.070957371 2.369755033 184 -6.500257228 -0.070957371 185 1.261477725 -6.500257228 186 2.598884232 1.261477725 187 -0.276935001 2.598884232 188 -0.337592691 -0.276935001 189 0.766460632 -0.337592691 190 -0.738347363 0.766460632 191 0.593164124 -0.738347363 192 2.445135861 0.593164124 193 2.068898527 2.445135861 194 -0.589629361 2.068898527 195 0.067529462 -0.589629361 196 3.102911981 0.067529462 197 0.981990529 3.102911981 198 1.650521446 0.981990529 199 1.458638605 1.650521446 200 2.055406796 1.458638605 201 0.677381539 2.055406796 202 -2.427517795 0.677381539 203 -2.436710987 -2.427517795 204 2.330073087 -2.436710987 205 0.541347575 2.330073087 206 1.532062754 0.541347575 207 1.068353080 1.532062754 208 -2.556278024 1.068353080 209 1.022060198 -2.556278024 210 -2.202902206 1.022060198 211 -3.778443865 -2.202902206 212 0.855979821 -3.778443865 213 2.969296732 0.855979821 214 1.431517041 2.969296732 215 0.926865366 1.431517041 216 2.325387625 0.926865366 217 0.057640177 2.325387625 218 1.109497163 0.057640177 219 -0.205965436 1.109497163 220 -1.671017171 -0.205965436 221 -0.131920566 -1.671017171 222 0.699881753 -0.131920566 223 -1.109293774 0.699881753 224 0.288333593 -1.109293774 225 -3.747213134 0.288333593 226 0.198419826 -3.747213134 227 0.979732572 0.198419826 228 -1.229759183 0.979732572 229 -0.896838278 -1.229759183 230 1.754791518 -0.896838278 231 -3.213065762 1.754791518 232 4.632282599 -3.213065762 233 2.052583830 4.632282599 234 -0.869328864 2.052583830 235 -2.343002739 -0.869328864 236 -6.972696730 -2.343002739 237 -1.267817848 -6.972696730 238 1.693092913 -1.267817848 239 -1.682392341 1.693092913 240 -0.287911980 -1.682392341 241 -1.501192112 -0.287911980 242 1.148577467 -1.501192112 243 1.897137210 1.148577467 244 1.290751900 1.897137210 245 0.031494268 1.290751900 246 1.122268014 0.031494268 247 -2.535800023 1.122268014 248 1.237586914 -2.535800023 249 0.260609056 1.237586914 250 -0.922062430 0.260609056 251 -1.730463173 -0.922062430 252 0.598033801 -1.730463173 253 3.117443737 0.598033801 254 -1.376552283 3.117443737 255 0.095170915 -1.376552283 256 1.480120220 0.095170915 257 2.331720115 1.480120220 258 -1.356690157 2.331720115 259 -5.383345806 -1.356690157 260 1.152740672 -5.383345806 261 -4.108360954 1.152740672 262 -0.347629194 -4.108360954 263 1.085550529 -0.347629194 > 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/7jmgi1352118285.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/8i7qj1352118285.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/9hrh21352118285.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/10dryr1352118285.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/119jwi1352118285.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12xxjy1352118285.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13hyva1352118285.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/143nkz1352118285.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/15ydjy1352118285.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/161nwh1352118285.tab") + } > > try(system("convert tmp/1gngz1352118285.ps tmp/1gngz1352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/2fww71352118285.ps tmp/2fww71352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/3d4u21352118285.ps tmp/3d4u21352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/4l7o81352118285.ps tmp/4l7o81352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/50txm1352118285.ps tmp/50txm1352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/6qofu1352118285.ps tmp/6qofu1352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/7jmgi1352118285.ps tmp/7jmgi1352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/8i7qj1352118285.ps tmp/8i7qj1352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/9hrh21352118285.ps tmp/9hrh21352118285.png",intern=TRUE)) character(0) > try(system("convert tmp/10dryr1352118285.ps tmp/10dryr1352118285.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.585 0.911 12.496