R version 2.11.1 (2010-05-31) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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(0 + ,69 + ,26 + ,0 + ,9 + ,0 + ,15 + ,0 + ,6 + ,0 + ,25 + ,0 + ,25 + ,0 + ,1 + ,53 + ,20 + ,20 + ,9 + ,9 + ,15 + ,15 + ,6 + ,6 + ,25 + ,25 + ,24 + ,24 + ,1 + ,43 + ,21 + ,21 + ,9 + ,9 + ,14 + ,14 + ,13 + ,13 + ,19 + ,19 + ,21 + ,21 + ,0 + ,60 + ,31 + ,0 + ,14 + ,0 + ,10 + ,0 + ,8 + ,0 + ,18 + ,0 + ,23 + ,0 + ,1 + ,49 + ,21 + ,21 + ,8 + ,8 + ,10 + ,10 + ,7 + ,7 + ,18 + ,18 + ,17 + ,17 + ,1 + ,62 + ,18 + ,18 + ,8 + ,8 + ,12 + ,12 + ,9 + ,9 + ,22 + ,22 + ,19 + ,19 + ,1 + ,45 + ,26 + ,26 + ,11 + ,11 + ,18 + ,18 + ,5 + ,5 + ,29 + ,29 + ,18 + ,18 + ,1 + ,50 + ,22 + ,22 + ,10 + ,10 + ,12 + ,12 + ,8 + ,8 + ,26 + ,26 + ,27 + ,27 + ,1 + ,75 + ,22 + ,22 + ,9 + ,9 + ,14 + ,14 + ,9 + ,9 + ,25 + ,25 + ,23 + ,23 + ,1 + ,82 + ,29 + ,29 + ,15 + ,15 + ,18 + ,18 + ,11 + ,11 + ,23 + ,23 + ,23 + ,23 + ,0 + ,60 + ,15 + ,0 + ,14 + ,0 + ,9 + ,0 + ,8 + ,0 + ,23 + ,0 + ,29 + ,0 + ,1 + ,59 + ,16 + ,16 + ,11 + ,11 + ,11 + ,11 + ,11 + ,11 + ,23 + ,23 + ,21 + ,21 + ,1 + ,21 + ,24 + ,24 + ,14 + ,14 + ,11 + ,11 + ,12 + ,12 + ,24 + ,24 + ,26 + ,26 + ,1 + ,62 + ,17 + ,17 + ,6 + ,6 + ,17 + ,17 + ,8 + ,8 + ,30 + ,30 + ,25 + ,25 + ,0 + ,54 + ,19 + ,0 + ,20 + ,0 + ,8 + ,0 + ,7 + ,0 + ,19 + ,0 + ,25 + ,0 + ,1 + ,47 + ,22 + ,22 + ,9 + ,9 + ,16 + ,16 + ,9 + ,9 + ,24 + ,24 + ,23 + ,23 + ,1 + ,59 + ,31 + ,31 + ,10 + ,10 + ,21 + ,21 + ,12 + ,12 + ,32 + ,32 + ,26 + ,26 + ,0 + ,37 + ,28 + ,0 + ,8 + ,0 + ,24 + ,0 + ,20 + ,0 + ,30 + ,0 + ,20 + ,0 + ,0 + ,43 + ,38 + ,0 + ,11 + ,0 + ,21 + ,0 + ,7 + ,0 + ,29 + ,0 + ,29 + ,0 + ,1 + ,48 + ,26 + ,26 + ,14 + ,14 + ,14 + ,14 + ,8 + ,8 + ,17 + ,17 + ,24 + ,24 + ,0 + ,79 + ,25 + ,0 + ,11 + ,0 + ,7 + ,0 + ,8 + ,0 + ,25 + ,0 + ,23 + ,0 + ,0 + ,62 + ,25 + ,0 + ,16 + ,0 + ,18 + ,0 + ,16 + ,0 + ,26 + ,0 + ,24 + ,0 + ,1 + ,16 + ,29 + ,29 + ,14 + ,14 + ,18 + ,18 + ,10 + ,10 + ,26 + ,26 + ,30 + ,30 + ,0 + ,38 + ,28 + ,0 + ,11 + ,0 + ,13 + ,0 + ,6 + ,0 + ,25 + ,0 + ,22 + ,0 + ,1 + ,58 + ,15 + ,15 + ,11 + ,11 + ,11 + ,11 + ,8 + ,8 + ,23 + ,23 + ,22 + ,22 + ,0 + ,60 + ,18 + ,0 + ,12 + ,0 + ,13 + ,0 + ,9 + ,0 + ,21 + ,0 + ,13 + ,0 + ,0 + ,67 + ,21 + ,0 + ,9 + ,0 + ,13 + ,0 + ,9 + ,0 + ,19 + ,0 + ,24 + ,0 + ,0 + ,55 + ,25 + ,0 + ,7 + ,0 + ,18 + ,0 + ,11 + ,0 + ,35 + ,0 + ,17 + ,0 + ,1 + ,47 + ,23 + ,23 + ,13 + ,13 + ,14 + ,14 + ,12 + ,12 + ,19 + ,19 + ,24 + ,24 + ,0 + ,59 + ,23 + ,0 + ,10 + ,0 + ,12 + ,0 + ,8 + ,0 + ,20 + ,0 + ,21 + ,0 + ,1 + ,49 + ,19 + ,19 + ,9 + ,9 + ,9 + ,9 + ,7 + ,7 + ,21 + ,21 + ,23 + ,23 + ,0 + ,47 + ,18 + ,0 + ,9 + ,0 + ,12 + ,0 + ,8 + ,0 + ,21 + ,0 + ,24 + ,0 + ,1 + ,57 + ,18 + ,18 + ,13 + ,13 + ,8 + ,8 + ,9 + ,9 + ,24 + ,24 + ,24 + ,24 + ,0 + ,39 + ,26 + ,0 + ,16 + ,0 + ,5 + ,0 + ,4 + ,0 + ,23 + ,0 + ,24 + ,0 + ,1 + ,49 + ,18 + ,18 + ,12 + ,12 + ,10 + ,10 + ,8 + ,8 + ,19 + ,19 + ,23 + ,23 + ,1 + ,26 + ,18 + ,18 + ,6 + ,6 + ,11 + ,11 + ,8 + ,8 + ,17 + ,17 + ,26 + ,26 + ,0 + ,53 + ,28 + ,0 + ,14 + ,0 + ,11 + ,0 + ,8 + ,0 + ,24 + ,0 + ,24 + ,0 + ,0 + ,75 + ,17 + ,0 + ,14 + ,0 + ,12 + ,0 + ,6 + ,0 + ,15 + ,0 + ,21 + ,0 + ,1 + ,65 + ,29 + ,29 + ,10 + ,10 + ,12 + ,12 + ,8 + ,8 + ,25 + ,25 + ,23 + ,23 + ,1 + ,49 + ,12 + ,12 + ,4 + ,4 + ,15 + ,15 + ,4 + ,4 + ,27 + ,27 + ,28 + ,28 + ,0 + ,48 + ,25 + ,0 + ,12 + ,0 + ,12 + ,0 + ,7 + ,0 + ,29 + ,0 + ,23 + ,0 + ,0 + ,45 + ,28 + ,0 + ,12 + ,0 + ,16 + ,0 + ,14 + ,0 + ,27 + ,0 + ,22 + ,0 + ,0 + ,31 + ,20 + ,0 + ,14 + ,0 + ,14 + ,0 + ,10 + ,0 + ,18 + ,0 + ,24 + ,0 + ,1 + ,61 + ,17 + ,17 + ,9 + ,9 + ,17 + ,17 + ,9 + ,9 + ,25 + ,25 + ,21 + ,21 + ,1 + ,49 + ,17 + ,17 + ,9 + ,9 + ,13 + ,13 + ,6 + ,6 + ,22 + ,22 + ,23 + ,23 + ,1 + ,69 + ,20 + ,20 + ,10 + ,10 + ,10 + ,10 + ,8 + ,8 + ,26 + ,26 + ,23 + ,23 + ,0 + ,54 + ,31 + ,0 + ,14 + ,0 + ,17 + ,0 + ,11 + ,0 + ,23 + ,0 + ,20 + ,0 + ,0 + ,80 + ,21 + ,0 + ,10 + ,0 + ,12 + ,0 + ,8 + ,0 + ,16 + ,0 + ,23 + ,0 + ,0 + ,57 + ,19 + ,0 + ,9 + ,0 + ,13 + ,0 + ,8 + ,0 + ,27 + ,0 + ,21 + ,0 + ,0 + ,34 + ,23 + ,0 + ,14 + ,0 + ,13 + ,0 + ,10 + ,0 + ,25 + ,0 + ,27 + ,0 + ,0 + ,69 + ,15 + ,0 + ,8 + ,0 + ,11 + ,0 + ,8 + ,0 + ,14 + ,0 + ,12 + ,0 + ,1 + ,44 + ,24 + ,24 + ,9 + ,9 + ,13 + ,13 + ,10 + ,10 + ,19 + ,19 + ,15 + ,15 + ,0 + ,70 + ,28 + ,0 + ,8 + ,0 + ,12 + ,0 + ,7 + ,0 + ,20 + ,0 + ,22 + ,0 + ,0 + ,51 + ,16 + ,0 + ,9 + ,0 + ,12 + ,0 + ,8 + ,0 + ,16 + ,0 + ,21 + ,0 + ,1 + ,66 + ,19 + ,19 + ,9 + ,9 + ,12 + ,12 + ,7 + ,7 + ,18 + ,18 + ,21 + ,21 + ,1 + ,18 + ,21 + ,21 + ,9 + ,9 + ,9 + ,9 + ,9 + ,9 + ,22 + ,22 + ,20 + ,20 + ,1 + ,74 + ,21 + ,21 + ,15 + ,15 + ,7 + ,7 + ,5 + ,5 + ,21 + ,21 + ,24 + ,24 + ,1 + ,59 + ,20 + ,20 + ,8 + ,8 + ,17 + ,17 + ,7 + ,7 + ,22 + ,22 + ,24 + ,24 + ,0 + ,48 + ,16 + ,0 + ,10 + ,0 + ,12 + ,0 + ,7 + ,0 + ,22 + ,0 + ,29 + ,0 + ,1 + ,55 + ,25 + ,25 + ,8 + ,8 + ,12 + ,12 + ,7 + ,7 + ,32 + ,32 + ,25 + ,25 + ,0 + ,44 + ,30 + ,0 + ,14 + ,0 + ,9 + ,0 + ,9 + ,0 + ,23 + ,0 + ,14 + ,0 + ,0 + ,56 + ,29 + ,0 + ,11 + ,0 + ,9 + ,0 + ,5 + ,0 + ,31 + ,0 + ,30 + ,0 + ,0 + ,65 + ,22 + ,0 + ,10 + ,0 + ,13 + ,0 + ,8 + ,0 + ,18 + ,0 + ,19 + ,0 + ,0 + ,77 + ,19 + ,0 + ,12 + ,0 + ,10 + ,0 + ,8 + ,0 + ,23 + ,0 + ,29 + ,0 + ,1 + ,46 + ,33 + ,33 + ,14 + ,14 + ,11 + ,11 + ,8 + ,8 + ,26 + ,26 + ,25 + ,25 + ,0 + ,70 + ,17 + ,0 + ,9 + ,0 + ,12 + ,0 + ,9 + ,0 + ,24 + ,0 + ,25 + ,0 + ,1 + ,39 + ,9 + ,9 + ,13 + ,13 + ,10 + ,10 + ,6 + ,6 + ,19 + ,19 + ,25 + ,25 + ,0 + ,55 + ,14 + ,0 + ,15 + ,0 + ,13 + ,0 + ,8 + ,0 + ,14 + ,0 + ,16 + ,0 + ,0 + ,44 + ,15 + ,0 + ,8 + ,0 + ,6 + ,0 + ,6 + ,0 + ,20 + ,0 + ,25 + ,0 + ,0 + ,45 + ,12 + ,0 + ,7 + ,0 + ,7 + ,0 + ,4 + ,0 + ,22 + ,0 + ,28 + ,0 + ,1 + ,45 + ,21 + ,21 + ,10 + ,10 + ,13 + ,13 + ,6 + ,6 + ,24 + ,24 + ,24 + ,24 + ,0 + ,49 + ,20 + ,0 + ,10 + ,0 + ,11 + ,0 + ,4 + ,0 + ,25 + ,0 + ,25 + ,0 + ,1 + ,65 + ,29 + ,29 + ,13 + ,13 + ,18 + ,18 + ,12 + ,12 + ,21 + ,21 + ,21 + ,21 + ,0 + ,45 + ,33 + ,0 + ,11 + ,0 + ,9 + ,0 + ,6 + ,0 + ,28 + ,0 + ,22 + ,0 + ,0 + ,71 + ,21 + ,0 + ,8 + ,0 + ,9 + ,0 + ,11 + ,0 + ,24 + ,0 + ,20 + ,0 + ,1 + ,48 + ,15 + ,15 + ,12 + ,12 + ,11 + ,11 + ,8 + ,8 + ,20 + ,20 + ,25 + ,25 + ,1 + ,41 + ,19 + ,19 + ,9 + ,9 + ,11 + ,11 + ,10 + ,10 + ,21 + ,21 + ,27 + ,27 + ,0 + ,40 + ,23 + ,0 + ,10 + ,0 + ,15 + ,0 + ,10 + ,0 + ,23 + ,0 + ,21 + ,0 + ,1 + ,64 + ,20 + ,20 + ,11 + ,11 + ,8 + ,8 + ,4 + ,4 + ,13 + ,13 + ,13 + ,13 + ,0 + ,56 + ,20 + ,0 + ,11 + ,0 + ,11 + ,0 + ,8 + ,0 + ,24 + ,0 + ,26 + ,0 + ,0 + ,52 + ,18 + ,0 + ,10 + ,0 + ,14 + ,0 + ,9 + ,0 + ,21 + ,0 + ,26 + ,0 + ,1 + ,41 + ,31 + ,31 + ,16 + ,16 + ,14 + ,14 + ,9 + ,9 + ,21 + ,21 + ,25 + ,25 + ,1 + ,42 + ,18 + ,18 + ,16 + ,16 + ,12 + ,12 + ,7 + ,7 + ,17 + ,17 + ,22 + ,22 + ,0 + ,54 + ,13 + ,0 + ,8 + ,0 + ,12 + ,0 + ,7 + ,0 + ,14 + ,0 + ,19 + ,0 + ,1 + ,40 + ,9 + ,9 + ,6 + ,6 + ,8 + ,8 + ,11 + ,11 + ,29 + ,29 + ,23 + ,23 + ,1 + ,40 + ,20 + ,20 + ,11 + ,11 + ,11 + ,11 + ,8 + ,8 + ,25 + ,25 + ,25 + ,25 + ,0 + ,51 + ,18 + ,0 + ,12 + ,0 + ,10 + ,0 + ,8 + ,0 + ,16 + ,0 + ,15 + ,0 + ,1 + ,48 + ,23 + ,23 + ,14 + ,14 + ,17 + ,17 + ,7 + ,7 + ,25 + ,25 + ,21 + ,21 + ,0 + ,80 + ,17 + ,0 + ,9 + ,0 + ,16 + ,0 + ,5 + ,0 + ,25 + ,0 + ,23 + ,0 + ,0 + ,38 + ,17 + ,0 + ,11 + ,0 + ,13 + ,0 + ,7 + ,0 + ,21 + ,0 + ,25 + ,0 + ,0 + ,57 + ,16 + ,0 + ,8 + ,0 + ,15 + ,0 + ,9 + ,0 + ,23 + ,0 + ,24 + ,0 + ,1 + ,28 + ,31 + ,31 + ,8 + ,8 + ,11 + ,11 + ,8 + ,8 + ,22 + ,22 + ,24 + ,24 + ,1 + ,51 + ,15 + ,15 + ,7 + ,7 + ,12 + ,12 + ,6 + ,6 + ,19 + ,19 + ,21 + ,21 + ,1 + ,46 + ,28 + ,28 + ,16 + ,16 + ,16 + ,16 + ,8 + ,8 + ,24 + ,24 + ,24 + ,24 + ,1 + ,58 + ,26 + ,26 + ,13 + ,13 + ,20 + ,20 + ,10 + ,10 + ,26 + ,26 + ,22 + ,22 + ,1 + ,67 + ,20 + ,20 + ,8 + ,8 + ,16 + ,16 + ,10 + ,10 + ,25 + ,25 + ,24 + ,24 + ,1 + ,72 + ,19 + ,19 + ,11 + ,11 + ,11 + ,11 + ,8 + ,8 + ,20 + ,20 + ,28 + ,28 + ,1 + ,26 + ,25 + ,25 + ,14 + ,14 + ,15 + ,15 + ,11 + ,11 + ,22 + ,22 + ,21 + ,21 + ,1 + ,54 + ,18 + ,18 + ,10 + ,10 + ,15 + ,15 + ,8 + ,8 + ,14 + ,14 + ,17 + ,17 + ,0 + ,53 + ,20 + ,0 + ,10 + ,0 + ,12 + ,0 + ,8 + ,0 + ,20 + ,0 + ,28 + ,0 + ,1 + ,64 + ,33 + ,33 + ,14 + ,14 + ,9 + ,9 + ,6 + ,6 + ,32 + ,32 + ,24 + ,24 + ,1 + ,47 + ,24 + ,24 + ,14 + ,14 + ,24 + ,24 + ,20 + ,20 + ,21 + ,21 + ,10 + ,10 + ,1 + ,43 + ,22 + ,22 + ,10 + ,10 + ,15 + ,15 + ,6 + ,6 + ,22 + ,22 + ,20 + ,20 + ,1 + ,66 + ,32 + ,32 + ,12 + ,12 + ,18 + ,18 + ,12 + ,12 + ,28 + ,28 + ,22 + ,22 + ,1 + ,54 + ,31 + ,31 + ,9 + ,9 + ,17 + ,17 + ,9 + ,9 + ,25 + ,25 + ,19 + ,19 + ,1 + ,62 + ,13 + ,13 + ,16 + ,16 + ,12 + ,12 + ,5 + ,5 + ,17 + ,17 + ,22 + ,22 + ,1 + ,52 + ,18 + ,18 + ,8 + ,8 + ,15 + ,15 + ,10 + ,10 + ,21 + ,21 + ,22 + ,22 + ,1 + ,64 + ,17 + ,17 + ,9 + ,9 + ,11 + ,11 + ,5 + ,5 + ,23 + ,23 + ,26 + ,26 + ,1 + ,55 + ,29 + ,29 + ,16 + ,16 + ,11 + ,11 + ,6 + ,6 + ,27 + ,27 + ,24 + ,24 + ,0 + ,57 + ,22 + ,0 + ,13 + ,0 + ,15 + ,0 + ,10 + ,0 + ,22 + ,0 + ,22 + ,0 + ,1 + ,74 + ,18 + ,18 + ,13 + ,13 + ,12 + ,12 + ,6 + ,6 + ,19 + ,19 + ,20 + ,20 + ,1 + ,32 + ,22 + ,22 + ,8 + ,8 + ,14 + ,14 + ,10 + ,10 + ,20 + ,20 + ,20 + ,20 + ,1 + ,38 + ,25 + ,25 + ,14 + ,14 + ,11 + ,11 + ,5 + ,5 + ,17 + ,17 + ,15 + ,15 + ,1 + ,66 + ,20 + ,20 + ,11 + ,11 + ,20 + ,20 + ,13 + ,13 + ,24 + ,24 + ,20 + ,20 + ,0 + ,37 + ,20 + ,0 + ,9 + ,0 + ,11 + ,0 + ,7 + ,0 + ,21 + ,0 + ,20 + ,0 + ,1 + ,26 + ,17 + ,17 + ,8 + ,8 + ,12 + ,12 + ,9 + ,9 + ,21 + ,21 + ,24 + ,24 + ,1 + ,64 + ,21 + ,21 + ,13 + ,13 + ,17 + ,17 + ,11 + ,11 + ,23 + ,23 + ,22 + ,22 + ,1 + ,28 + ,26 + ,26 + ,13 + ,13 + ,12 + ,12 + ,8 + ,8 + ,24 + ,24 + ,29 + ,29 + ,0 + ,66 + ,10 + ,0 + ,10 + ,0 + ,11 + ,0 + ,5 + ,0 + ,19 + ,0 + ,23 + ,0 + ,1 + ,65 + ,15 + ,15 + ,8 + ,8 + ,10 + ,10 + ,4 + ,4 + ,22 + ,22 + ,24 + ,24 + ,1 + ,48 + ,20 + ,20 + ,7 + ,7 + ,11 + ,11 + ,9 + ,9 + ,26 + ,26 + ,22 + ,22 + ,1 + ,44 + ,14 + ,14 + ,11 + ,11 + ,12 + ,12 + ,7 + ,7 + ,17 + ,17 + ,16 + ,16 + ,0 + ,64 + ,16 + ,0 + ,11 + ,0 + ,9 + ,0 + ,5 + ,0 + ,17 + ,0 + ,23 + ,0 + ,1 + ,39 + ,23 + ,23 + ,14 + ,14 + ,8 + ,8 + ,5 + ,5 + ,19 + ,19 + ,27 + ,27 + ,1 + ,50 + ,11 + ,11 + ,6 + ,6 + ,6 + ,6 + ,4 + ,4 + ,15 + ,15 + ,16 + ,16 + ,1 + ,66 + ,19 + ,19 + ,10 + ,10 + ,12 + ,12 + ,7 + ,7 + ,17 + ,17 + ,21 + ,21 + ,0 + ,48 + ,30 + ,0 + ,9 + ,0 + ,15 + ,0 + ,9 + ,0 + ,27 + ,0 + ,26 + ,0 + ,0 + ,70 + ,21 + ,0 + ,12 + ,0 + ,13 + ,0 + ,8 + ,0 + ,19 + ,0 + ,22 + ,0 + ,0 + ,66 + ,20 + ,0 + ,11 + ,0 + ,17 + ,0 + ,8 + ,0 + ,21 + ,0 + ,23 + ,0 + ,1 + ,61 + ,22 + ,22 + ,14 + ,14 + ,14 + ,14 + ,11 + ,11 + ,25 + ,25 + ,19 + ,19 + ,1 + ,31 + ,30 + ,30 + ,12 + ,12 + ,16 + ,16 + ,10 + ,10 + ,19 + ,19 + ,18 + ,18 + ,0 + ,61 + ,25 + ,0 + ,14 + ,0 + ,15 + ,0 + ,9 + ,0 + ,22 + ,0 + ,24 + ,0 + ,1 + ,54 + ,28 + ,28 + ,8 + ,8 + ,16 + ,16 + ,12 + ,12 + ,18 + ,18 + ,24 + ,24 + ,1 + ,34 + ,23 + ,23 + ,14 + ,14 + ,11 + ,11 + ,10 + ,10 + ,20 + ,20 + ,29 + ,29 + ,0 + ,62 + ,23 + ,0 + ,8 + ,0 + ,11 + ,0 + ,10 + ,0 + ,15 + ,0 + ,22 + ,0 + ,1 + ,47 + ,21 + ,21 + ,11 + ,11 + ,16 + ,16 + ,7 + ,7 + ,20 + ,20 + ,24 + ,24 + ,1 + ,52 + ,30 + ,30 + ,12 + ,12 + ,15 + ,15 + ,10 + ,10 + ,29 + ,29 + ,22 + ,22 + ,0 + ,37 + ,22 + ,0 + ,9 + ,0 + ,14 + ,0 + ,6 + ,0 + ,19 + ,0 + ,12 + ,0 + ,1 + ,46 + ,32 + ,32 + ,16 + ,16 + ,9 + ,9 + ,6 + ,6 + ,29 + ,29 + ,26 + ,26 + ,0 + ,38 + ,22 + ,0 + ,11 + ,0 + ,13 + ,0 + ,11 + ,0 + ,24 + ,0 + ,18 + ,0 + ,1 + ,63 + ,15 + ,15 + ,11 + ,11 + ,11 + ,11 + ,8 + ,8 + ,23 + ,23 + ,22 + ,22 + ,0 + ,34 + ,21 + ,0 + ,12 + ,0 + ,14 + ,0 + ,9 + ,0 + ,22 + ,0 + ,24 + ,0 + ,1 + ,46 + ,27 + ,27 + ,15 + ,15 + ,11 + ,11 + ,9 + ,9 + ,23 + ,23 + ,21 + ,21 + ,1 + ,40 + ,22 + ,22 + ,13 + ,13 + ,12 + ,12 + ,13 + ,13 + ,22 + ,22 + ,15 + ,15 + ,1 + ,30 + ,9 + ,9 + ,6 + ,6 + ,8 + ,8 + ,11 + ,11 + ,29 + ,29 + ,23 + ,23 + ,1 + ,35 + ,29 + ,29 + ,11 + ,11 + ,7 + ,7 + ,4 + ,4 + ,26 + ,26 + ,22 + ,22 + ,1 + ,51 + ,20 + ,20 + ,7 + ,7 + ,11 + ,11 + ,9 + ,9 + ,26 + ,26 + ,22 + ,22 + ,1 + ,56 + ,16 + ,16 + ,8 + ,8 + ,13 + ,13 + ,5 + ,5 + ,21 + ,21 + ,24 + ,24 + ,1 + ,68 + ,16 + ,16 + ,8 + ,8 + ,9 + ,9 + ,4 + ,4 + ,18 + ,18 + ,23 + ,23 + ,1 + ,39 + ,16 + ,16 + ,9 + ,9 + ,12 + ,12 + ,9 + ,9 + ,10 + ,10 + ,13 + ,13 + ,0 + ,44 + ,18 + ,0 + ,12 + ,0 + ,10 + ,0 + ,8 + ,0 + ,19 + ,0 + ,23 + ,0 + ,1 + ,58 + ,16 + ,16 + ,9 + ,9 + ,12 + ,12 + ,9 + ,9 + ,10 + ,10 + ,13 + ,13) + ,dim=c(14 + ,152) + ,dimnames=list(c('Gender' + ,'Anxiety' + ,'Concern' + ,'Concern_G' + ,'Doubts' + ,'Doubts_G' + ,'Expectations' + ,'Expectations_G' + ,'Criticism' + ,'Criticism_G' + ,'Perstandards' + ,'Perstandards_G' + ,'Organization' + ,'Organization_G') + ,1:152)) > y <- array(NA,dim=c(14,152),dimnames=list(c('Gender','Anxiety','Concern','Concern_G','Doubts','Doubts_G','Expectations','Expectations_G','Criticism','Criticism_G','Perstandards','Perstandards_G','Organization','Organization_G'),1:152)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > #'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 > 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 Anxiety Gender Concern Concern_G Doubts Doubts_G Expectations 1 69 0 26 0 9 0 15 2 53 1 20 20 9 9 15 3 43 1 21 21 9 9 14 4 60 0 31 0 14 0 10 5 49 1 21 21 8 8 10 6 62 1 18 18 8 8 12 7 45 1 26 26 11 11 18 8 50 1 22 22 10 10 12 9 75 1 22 22 9 9 14 10 82 1 29 29 15 15 18 11 60 0 15 0 14 0 9 12 59 1 16 16 11 11 11 13 21 1 24 24 14 14 11 14 62 1 17 17 6 6 17 15 54 0 19 0 20 0 8 16 47 1 22 22 9 9 16 17 59 1 31 31 10 10 21 18 37 0 28 0 8 0 24 19 43 0 38 0 11 0 21 20 48 1 26 26 14 14 14 21 79 0 25 0 11 0 7 22 62 0 25 0 16 0 18 23 16 1 29 29 14 14 18 24 38 0 28 0 11 0 13 25 58 1 15 15 11 11 11 26 60 0 18 0 12 0 13 27 67 0 21 0 9 0 13 28 55 0 25 0 7 0 18 29 47 1 23 23 13 13 14 30 59 0 23 0 10 0 12 31 49 1 19 19 9 9 9 32 47 0 18 0 9 0 12 33 57 1 18 18 13 13 8 34 39 0 26 0 16 0 5 35 49 1 18 18 12 12 10 36 26 1 18 18 6 6 11 37 53 0 28 0 14 0 11 38 75 0 17 0 14 0 12 39 65 1 29 29 10 10 12 40 49 1 12 12 4 4 15 41 48 0 25 0 12 0 12 42 45 0 28 0 12 0 16 43 31 0 20 0 14 0 14 44 61 1 17 17 9 9 17 45 49 1 17 17 9 9 13 46 69 1 20 20 10 10 10 47 54 0 31 0 14 0 17 48 80 0 21 0 10 0 12 49 57 0 19 0 9 0 13 50 34 0 23 0 14 0 13 51 69 0 15 0 8 0 11 52 44 1 24 24 9 9 13 53 70 0 28 0 8 0 12 54 51 0 16 0 9 0 12 55 66 1 19 19 9 9 12 56 18 1 21 21 9 9 9 57 74 1 21 21 15 15 7 58 59 1 20 20 8 8 17 59 48 0 16 0 10 0 12 60 55 1 25 25 8 8 12 61 44 0 30 0 14 0 9 62 56 0 29 0 11 0 9 63 65 0 22 0 10 0 13 64 77 0 19 0 12 0 10 65 46 1 33 33 14 14 11 66 70 0 17 0 9 0 12 67 39 1 9 9 13 13 10 68 55 0 14 0 15 0 13 69 44 0 15 0 8 0 6 70 45 0 12 0 7 0 7 71 45 1 21 21 10 10 13 72 49 0 20 0 10 0 11 73 65 1 29 29 13 13 18 74 45 0 33 0 11 0 9 75 71 0 21 0 8 0 9 76 48 1 15 15 12 12 11 77 41 1 19 19 9 9 11 78 40 0 23 0 10 0 15 79 64 1 20 20 11 11 8 80 56 0 20 0 11 0 11 81 52 0 18 0 10 0 14 82 41 1 31 31 16 16 14 83 42 1 18 18 16 16 12 84 54 0 13 0 8 0 12 85 40 1 9 9 6 6 8 86 40 1 20 20 11 11 11 87 51 0 18 0 12 0 10 88 48 1 23 23 14 14 17 89 80 0 17 0 9 0 16 90 38 0 17 0 11 0 13 91 57 0 16 0 8 0 15 92 28 1 31 31 8 8 11 93 51 1 15 15 7 7 12 94 46 1 28 28 16 16 16 95 58 1 26 26 13 13 20 96 67 1 20 20 8 8 16 97 72 1 19 19 11 11 11 98 26 1 25 25 14 14 15 99 54 1 18 18 10 10 15 100 53 0 20 0 10 0 12 101 64 1 33 33 14 14 9 102 47 1 24 24 14 14 24 103 43 1 22 22 10 10 15 104 66 1 32 32 12 12 18 105 54 1 31 31 9 9 17 106 62 1 13 13 16 16 12 107 52 1 18 18 8 8 15 108 64 1 17 17 9 9 11 109 55 1 29 29 16 16 11 110 57 0 22 0 13 0 15 111 74 1 18 18 13 13 12 112 32 1 22 22 8 8 14 113 38 1 25 25 14 14 11 114 66 1 20 20 11 11 20 115 37 0 20 0 9 0 11 116 26 1 17 17 8 8 12 117 64 1 21 21 13 13 17 118 28 1 26 26 13 13 12 119 66 0 10 0 10 0 11 120 65 1 15 15 8 8 10 121 48 1 20 20 7 7 11 122 44 1 14 14 11 11 12 123 64 0 16 0 11 0 9 124 39 1 23 23 14 14 8 125 50 1 11 11 6 6 6 126 66 1 19 19 10 10 12 127 48 0 30 0 9 0 15 128 70 0 21 0 12 0 13 129 66 0 20 0 11 0 17 130 61 1 22 22 14 14 14 131 31 1 30 30 12 12 16 132 61 0 25 0 14 0 15 133 54 1 28 28 8 8 16 134 34 1 23 23 14 14 11 135 62 0 23 0 8 0 11 136 47 1 21 21 11 11 16 137 52 1 30 30 12 12 15 138 37 0 22 0 9 0 14 139 46 1 32 32 16 16 9 140 38 0 22 0 11 0 13 141 63 1 15 15 11 11 11 142 34 0 21 0 12 0 14 143 46 1 27 27 15 15 11 144 40 1 22 22 13 13 12 145 30 1 9 9 6 6 8 146 35 1 29 29 11 11 7 147 51 1 20 20 7 7 11 148 56 1 16 16 8 8 13 149 68 1 16 16 8 8 9 150 39 1 16 16 9 9 12 151 44 0 18 0 12 0 10 152 58 1 16 16 9 9 12 Expectations_G Criticism Criticism_G Perstandards Perstandards_G 1 0 6 0 25 0 2 15 6 6 25 25 3 14 13 13 19 19 4 0 8 0 18 0 5 10 7 7 18 18 6 12 9 9 22 22 7 18 5 5 29 29 8 12 8 8 26 26 9 14 9 9 25 25 10 18 11 11 23 23 11 0 8 0 23 0 12 11 11 11 23 23 13 11 12 12 24 24 14 17 8 8 30 30 15 0 7 0 19 0 16 16 9 9 24 24 17 21 12 12 32 32 18 0 20 0 30 0 19 0 7 0 29 0 20 14 8 8 17 17 21 0 8 0 25 0 22 0 16 0 26 0 23 18 10 10 26 26 24 0 6 0 25 0 25 11 8 8 23 23 26 0 9 0 21 0 27 0 9 0 19 0 28 0 11 0 35 0 29 14 12 12 19 19 30 0 8 0 20 0 31 9 7 7 21 21 32 0 8 0 21 0 33 8 9 9 24 24 34 0 4 0 23 0 35 10 8 8 19 19 36 11 8 8 17 17 37 0 8 0 24 0 38 0 6 0 15 0 39 12 8 8 25 25 40 15 4 4 27 27 41 0 7 0 29 0 42 0 14 0 27 0 43 0 10 0 18 0 44 17 9 9 25 25 45 13 6 6 22 22 46 10 8 8 26 26 47 0 11 0 23 0 48 0 8 0 16 0 49 0 8 0 27 0 50 0 10 0 25 0 51 0 8 0 14 0 52 13 10 10 19 19 53 0 7 0 20 0 54 0 8 0 16 0 55 12 7 7 18 18 56 9 9 9 22 22 57 7 5 5 21 21 58 17 7 7 22 22 59 0 7 0 22 0 60 12 7 7 32 32 61 0 9 0 23 0 62 0 5 0 31 0 63 0 8 0 18 0 64 0 8 0 23 0 65 11 8 8 26 26 66 0 9 0 24 0 67 10 6 6 19 19 68 0 8 0 14 0 69 0 6 0 20 0 70 0 4 0 22 0 71 13 6 6 24 24 72 0 4 0 25 0 73 18 12 12 21 21 74 0 6 0 28 0 75 0 11 0 24 0 76 11 8 8 20 20 77 11 10 10 21 21 78 0 10 0 23 0 79 8 4 4 13 13 80 0 8 0 24 0 81 0 9 0 21 0 82 14 9 9 21 21 83 12 7 7 17 17 84 0 7 0 14 0 85 8 11 11 29 29 86 11 8 8 25 25 87 0 8 0 16 0 88 17 7 7 25 25 89 0 5 0 25 0 90 0 7 0 21 0 91 0 9 0 23 0 92 11 8 8 22 22 93 12 6 6 19 19 94 16 8 8 24 24 95 20 10 10 26 26 96 16 10 10 25 25 97 11 8 8 20 20 98 15 11 11 22 22 99 15 8 8 14 14 100 0 8 0 20 0 101 9 6 6 32 32 102 24 20 20 21 21 103 15 6 6 22 22 104 18 12 12 28 28 105 17 9 9 25 25 106 12 5 5 17 17 107 15 10 10 21 21 108 11 5 5 23 23 109 11 6 6 27 27 110 0 10 0 22 0 111 12 6 6 19 19 112 14 10 10 20 20 113 11 5 5 17 17 114 20 13 13 24 24 115 0 7 0 21 0 116 12 9 9 21 21 117 17 11 11 23 23 118 12 8 8 24 24 119 0 5 0 19 0 120 10 4 4 22 22 121 11 9 9 26 26 122 12 7 7 17 17 123 0 5 0 17 0 124 8 5 5 19 19 125 6 4 4 15 15 126 12 7 7 17 17 127 0 9 0 27 0 128 0 8 0 19 0 129 0 8 0 21 0 130 14 11 11 25 25 131 16 10 10 19 19 132 0 9 0 22 0 133 16 12 12 18 18 134 11 10 10 20 20 135 0 10 0 15 0 136 16 7 7 20 20 137 15 10 10 29 29 138 0 6 0 19 0 139 9 6 6 29 29 140 0 11 0 24 0 141 11 8 8 23 23 142 0 9 0 22 0 143 11 9 9 23 23 144 12 13 13 22 22 145 8 11 11 29 29 146 7 4 4 26 26 147 11 9 9 26 26 148 13 5 5 21 21 149 9 4 4 18 18 150 12 9 9 10 10 151 0 8 0 19 0 152 12 9 9 10 10 Organization Organization_G 1 25 0 2 24 24 3 21 21 4 23 0 5 17 17 6 19 19 7 18 18 8 27 27 9 23 23 10 23 23 11 29 0 12 21 21 13 26 26 14 25 25 15 25 0 16 23 23 17 26 26 18 20 0 19 29 0 20 24 24 21 23 0 22 24 0 23 30 30 24 22 0 25 22 22 26 13 0 27 24 0 28 17 0 29 24 24 30 21 0 31 23 23 32 24 0 33 24 24 34 24 0 35 23 23 36 26 26 37 24 0 38 21 0 39 23 23 40 28 28 41 23 0 42 22 0 43 24 0 44 21 21 45 23 23 46 23 23 47 20 0 48 23 0 49 21 0 50 27 0 51 12 0 52 15 15 53 22 0 54 21 0 55 21 21 56 20 20 57 24 24 58 24 24 59 29 0 60 25 25 61 14 0 62 30 0 63 19 0 64 29 0 65 25 25 66 25 0 67 25 25 68 16 0 69 25 0 70 28 0 71 24 24 72 25 0 73 21 21 74 22 0 75 20 0 76 25 25 77 27 27 78 21 0 79 13 13 80 26 0 81 26 0 82 25 25 83 22 22 84 19 0 85 23 23 86 25 25 87 15 0 88 21 21 89 23 0 90 25 0 91 24 0 92 24 24 93 21 21 94 24 24 95 22 22 96 24 24 97 28 28 98 21 21 99 17 17 100 28 0 101 24 24 102 10 10 103 20 20 104 22 22 105 19 19 106 22 22 107 22 22 108 26 26 109 24 24 110 22 0 111 20 20 112 20 20 113 15 15 114 20 20 115 20 0 116 24 24 117 22 22 118 29 29 119 23 0 120 24 24 121 22 22 122 16 16 123 23 0 124 27 27 125 16 16 126 21 21 127 26 0 128 22 0 129 23 0 130 19 19 131 18 18 132 24 0 133 24 24 134 29 29 135 22 0 136 24 24 137 22 22 138 12 0 139 26 26 140 18 0 141 22 22 142 24 0 143 21 21 144 15 15 145 23 23 146 22 22 147 22 22 148 24 24 149 23 23 150 13 13 151 23 0 152 13 13 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender Concern Concern_G Doubts 72.67601 -18.28435 -0.08862 -0.39749 -0.60321 Doubts_G Expectations Expectations_G Criticism Criticism_G 1.07713 -0.03912 1.21867 -0.13902 -1.39669 Perstandards Perstandards_G Organization Organization_G -0.61531 1.19280 0.24899 -0.87790 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -32.9513 -8.9276 0.7461 9.1139 31.4405 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 72.67601 14.77301 4.920 2.43e-06 *** Gender -18.28435 18.84001 -0.971 0.3335 Concern -0.08862 0.39118 -0.227 0.8211 Concern_G -0.39749 0.50060 -0.794 0.4285 Doubts -0.60321 0.72433 -0.833 0.4064 Doubts_G 1.07713 0.91912 1.172 0.2433 Expectations -0.03912 0.68080 -0.057 0.9543 Expectations_G 1.21867 0.84632 1.440 0.1521 Criticism -0.13902 0.84518 -0.164 0.8696 Criticism_G -1.39669 1.05450 -1.325 0.1875 Perstandards -0.61531 0.52102 -1.181 0.2396 Perstandards_G 1.19280 0.65111 1.832 0.0691 . Organization 0.24899 0.46437 0.536 0.5927 Organization_G -0.87790 0.63550 -1.381 0.1694 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.17 on 138 degrees of freedom Multiple R-squared: 0.1205, Adjusted R-squared: 0.03766 F-statistic: 1.455 on 13 and 138 DF, p-value: 0.1424 > 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,] 0.9930112 0.01397762 0.00698881 [2,] 0.9840925 0.03181500 0.01590750 [3,] 0.9676119 0.06477614 0.03238807 [4,] 0.9465042 0.10699151 0.05349575 [5,] 0.9248632 0.15027359 0.07513679 [6,] 0.9474767 0.10504669 0.05252335 [7,] 0.9867853 0.02642950 0.01321475 [8,] 0.9889676 0.02206486 0.01103243 [9,] 0.9820041 0.03599188 0.01799594 [10,] 0.9780169 0.04396629 0.02198315 [11,] 0.9667085 0.06658296 0.03329148 [12,] 0.9512274 0.09754523 0.04877262 [13,] 0.9301488 0.13970234 0.06985117 [14,] 0.9034151 0.19316976 0.09658488 [15,] 0.8755026 0.24899479 0.12449739 [16,] 0.8740627 0.25187461 0.12593731 [17,] 0.8646896 0.27062090 0.13531045 [18,] 0.8996695 0.20066099 0.10033049 [19,] 0.8675456 0.26490884 0.13245442 [20,] 0.8582051 0.28358976 0.14179488 [21,] 0.8194157 0.36116853 0.18058426 [22,] 0.8295876 0.34082475 0.17041238 [23,] 0.8578277 0.28434461 0.14217231 [24,] 0.8373346 0.32533083 0.16266542 [25,] 0.7985302 0.40293951 0.20146976 [26,] 0.7603362 0.47932750 0.23966375 [27,] 0.8588422 0.28231568 0.14115784 [28,] 0.8234368 0.35312643 0.17656322 [29,] 0.7874867 0.42502661 0.21251331 [30,] 0.8011540 0.39769201 0.19884600 [31,] 0.7646318 0.47073635 0.23536817 [32,] 0.7926980 0.41460401 0.20730201 [33,] 0.7520619 0.49587616 0.24793808 [34,] 0.7724853 0.45502946 0.22751473 [35,] 0.7588442 0.48231158 0.24115579 [36,] 0.7615609 0.47687822 0.23843911 [37,] 0.7480024 0.50399510 0.25199755 [38,] 0.7423374 0.51532512 0.25766256 [39,] 0.7690873 0.46182535 0.23091268 [40,] 0.9002877 0.19942455 0.09971228 [41,] 0.9404168 0.11916635 0.05958317 [42,] 0.9271340 0.14573204 0.07286602 [43,] 0.9174078 0.16518441 0.08259220 [44,] 0.8962015 0.20759707 0.10379854 [45,] 0.8869029 0.22619415 0.11309707 [46,] 0.8637104 0.27257914 0.13628957 [47,] 0.8501496 0.29970086 0.14985043 [48,] 0.8830755 0.23384891 0.11692446 [49,] 0.8570329 0.28593427 0.14296713 [50,] 0.8527283 0.29454330 0.14727165 [51,] 0.8661588 0.26768237 0.13384119 [52,] 0.8364994 0.32700116 0.16350058 [53,] 0.8609061 0.27818775 0.13909387 [54,] 0.8710285 0.25794305 0.12897152 [55,] 0.8572980 0.28540406 0.14270203 [56,] 0.8340137 0.33197263 0.16598631 [57,] 0.8451429 0.30971412 0.15485706 [58,] 0.8181215 0.36375708 0.18187854 [59,] 0.8879868 0.22402632 0.11201316 [60,] 0.8618306 0.27633887 0.13816943 [61,] 0.8318775 0.33624496 0.16812248 [62,] 0.8233792 0.35324168 0.17662084 [63,] 0.8230339 0.35393221 0.17696611 [64,] 0.7983419 0.40331621 0.20165811 [65,] 0.7665436 0.46691289 0.23345644 [66,] 0.7321809 0.53563820 0.26781910 [67,] 0.7174159 0.56516814 0.28258407 [68,] 0.6957500 0.60849994 0.30424997 [69,] 0.6711732 0.65765364 0.32882682 [70,] 0.6500603 0.69987947 0.34993974 [71,] 0.6193860 0.76122790 0.38061395 [72,] 0.6353104 0.72937924 0.36468962 [73,] 0.7442963 0.51140741 0.25570371 [74,] 0.7905779 0.41884422 0.20942211 [75,] 0.7522547 0.49549063 0.24774531 [76,] 0.7445472 0.51090563 0.25545282 [77,] 0.7047995 0.59040096 0.29520048 [78,] 0.6827415 0.63451704 0.31725852 [79,] 0.6403398 0.71932037 0.35966018 [80,] 0.6369822 0.72603559 0.36301780 [81,] 0.7936920 0.41261600 0.20630800 [82,] 0.8667569 0.26648619 0.13324310 [83,] 0.8342999 0.33140030 0.16570015 [84,] 0.8149952 0.37000963 0.18500481 [85,] 0.8353575 0.32928493 0.16464246 [86,] 0.8098771 0.38024582 0.19012291 [87,] 0.8487400 0.30252001 0.15126000 [88,] 0.8618876 0.27622479 0.13811239 [89,] 0.8268520 0.34629608 0.17314804 [90,] 0.7948670 0.41026599 0.20513299 [91,] 0.7502467 0.49950656 0.24975328 [92,] 0.7222894 0.55542124 0.27771062 [93,] 0.6761551 0.64768989 0.32384494 [94,] 0.6257933 0.74841345 0.37420673 [95,] 0.6670744 0.66585113 0.33292556 [96,] 0.6897631 0.62047372 0.31023686 [97,] 0.7086219 0.58275618 0.29137809 [98,] 0.6679385 0.66412292 0.33206146 [99,] 0.6468869 0.70622623 0.35311312 [100,] 0.7472527 0.50549467 0.25274733 [101,] 0.7221393 0.55572134 0.27786067 [102,] 0.7641640 0.47167200 0.23583600 [103,] 0.7308267 0.53834658 0.26917329 [104,] 0.6931717 0.61365662 0.30682831 [105,] 0.6241982 0.75160362 0.37580181 [106,] 0.6141925 0.77161508 0.38580754 [107,] 0.5944303 0.81113950 0.40556975 [108,] 0.5374787 0.92504256 0.46252128 [109,] 0.4558318 0.91166362 0.54416819 [110,] 0.4558806 0.91176120 0.54411940 [111,] 0.6314883 0.73702332 0.36851166 [112,] 0.5458983 0.90820349 0.45410175 [113,] 0.4509367 0.90187337 0.54906331 [114,] 0.4513796 0.90275917 0.54862042 [115,] 0.5370936 0.92581280 0.46290640 [116,] 0.4196056 0.83921118 0.58039441 [117,] 0.3871099 0.77421986 0.61289007 [118,] 0.2685994 0.53719885 0.73140057 [119,] 0.1571519 0.31430387 0.84284806 > postscript(file="/var/www/rcomp/tmp/1zflz1290513336.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5ap221290513336.ps",horizontal=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 = 152 Frequency = 1 1 2 3 4 5 6 14.6361815 -3.7568885 0.2368686 5.3685842 0.2765607 11.4784427 7 8 9 10 11 12 -20.9458584 1.6607135 24.3730718 31.4405121 5.4941358 11.0157647 13 14 15 16 17 18 -20.4143305 3.6603524 1.8244719 -5.4085313 6.4687733 -11.1699938 19 20 21 22 23 24 -7.2548639 0.6610049 26.2170153 14.1418726 -32.9513194 -14.3114319 25 26 27 28 29 30 5.5514576 7.6022190 9.0889885 8.2985634 3.6644469 3.0535780 31 32 33 34 35 36 2.0510051 -10.1243963 10.8166267 -12.7922041 1.6542683 -16.6400335 37 38 39 40 41 42 1.5847169 17.5801088 18.1252950 -10.9868725 -1.6619347 -4.2481068 43 44 45 46 47 48 -24.4207387 3.1460636 -5.7525816 19.5319220 3.8829919 20.9171176 49 50 51 52 53 54 4.4421696 -17.6337692 9.6480432 -4.5058719 12.9022653 -8.6312422 55 56 57 58 59 60 15.9869980 -27.3696024 25.0962952 3.6261063 -9.4670588 2.8084251 61 62 63 64 65 66 -5.3027241 5.1816744 8.2714247 21.6813221 1.0339375 14.5229439 67 68 69 70 71 72 -15.0082060 -1.1357795 -15.3705363 -14.9948629 -8.8081248 -5.7268610 73 74 75 76 77 78 16.8211984 -5.1788692 16.6798147 -1.3032561 -1.1853057 -13.7050933 79 80 81 82 83 84 11.4924333 1.5681192 -4.8018939 -5.0016179 -10.6101510 -7.3719930 85 86 87 88 89 90 -7.6858442 -9.2862248 -5.2286863 -12.3782975 25.2366490 -18.3554706 91 92 93 94 95 96 0.5821455 -13.4137279 -2.1227861 -7.7161123 0.6738586 15.6803068 97 98 99 100 101 102 27.0018460 -23.1717129 0.8183212 -4.9551873 14.2277835 -5.7929689 103 104 105 106 107 108 -14.0417970 16.3399562 1.6937499 3.8878970 1.9397477 11.3800645 109 110 111 112 113 114 2.8638509 4.1516294 18.8631008 -15.6166195 -16.5538280 11.2093039 115 116 117 118 119 120 -19.1293466 -21.2855996 11.0501140 -17.4038094 7.3320271 10.8452621 121 122 123 124 125 126 -0.3190726 -11.9584786 5.1581152 -6.5953917 -1.4220760 16.0905639 127 128 129 130 131 132 -4.6106422 14.2575851 10.7038709 8.5592156 -17.6628685 8.3837271 133 134 135 136 137 138 13.6829881 -6.7751556 1.7605197 -6.9750292 1.2574773 -18.2124921 139 140 141 142 143 144 -2.2158750 -13.7674637 10.5514576 -20.2163151 -1.6041338 -7.3195699 145 146 147 148 149 150 -17.6858442 -13.8001238 2.6809274 0.9059277 17.1919444 -9.8113552 151 152 -12.3746343 9.1886448 > postscript(file="/var/www/rcomp/tmp/6lg151290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 152 Frequency = 1 lag(myerror, k = 1) myerror 0 14.6361815 NA 1 -3.7568885 14.6361815 2 0.2368686 -3.7568885 3 5.3685842 0.2368686 4 0.2765607 5.3685842 5 11.4784427 0.2765607 6 -20.9458584 11.4784427 7 1.6607135 -20.9458584 8 24.3730718 1.6607135 9 31.4405121 24.3730718 10 5.4941358 31.4405121 11 11.0157647 5.4941358 12 -20.4143305 11.0157647 13 3.6603524 -20.4143305 14 1.8244719 3.6603524 15 -5.4085313 1.8244719 16 6.4687733 -5.4085313 17 -11.1699938 6.4687733 18 -7.2548639 -11.1699938 19 0.6610049 -7.2548639 20 26.2170153 0.6610049 21 14.1418726 26.2170153 22 -32.9513194 14.1418726 23 -14.3114319 -32.9513194 24 5.5514576 -14.3114319 25 7.6022190 5.5514576 26 9.0889885 7.6022190 27 8.2985634 9.0889885 28 3.6644469 8.2985634 29 3.0535780 3.6644469 30 2.0510051 3.0535780 31 -10.1243963 2.0510051 32 10.8166267 -10.1243963 33 -12.7922041 10.8166267 34 1.6542683 -12.7922041 35 -16.6400335 1.6542683 36 1.5847169 -16.6400335 37 17.5801088 1.5847169 38 18.1252950 17.5801088 39 -10.9868725 18.1252950 40 -1.6619347 -10.9868725 41 -4.2481068 -1.6619347 42 -24.4207387 -4.2481068 43 3.1460636 -24.4207387 44 -5.7525816 3.1460636 45 19.5319220 -5.7525816 46 3.8829919 19.5319220 47 20.9171176 3.8829919 48 4.4421696 20.9171176 49 -17.6337692 4.4421696 50 9.6480432 -17.6337692 51 -4.5058719 9.6480432 52 12.9022653 -4.5058719 53 -8.6312422 12.9022653 54 15.9869980 -8.6312422 55 -27.3696024 15.9869980 56 25.0962952 -27.3696024 57 3.6261063 25.0962952 58 -9.4670588 3.6261063 59 2.8084251 -9.4670588 60 -5.3027241 2.8084251 61 5.1816744 -5.3027241 62 8.2714247 5.1816744 63 21.6813221 8.2714247 64 1.0339375 21.6813221 65 14.5229439 1.0339375 66 -15.0082060 14.5229439 67 -1.1357795 -15.0082060 68 -15.3705363 -1.1357795 69 -14.9948629 -15.3705363 70 -8.8081248 -14.9948629 71 -5.7268610 -8.8081248 72 16.8211984 -5.7268610 73 -5.1788692 16.8211984 74 16.6798147 -5.1788692 75 -1.3032561 16.6798147 76 -1.1853057 -1.3032561 77 -13.7050933 -1.1853057 78 11.4924333 -13.7050933 79 1.5681192 11.4924333 80 -4.8018939 1.5681192 81 -5.0016179 -4.8018939 82 -10.6101510 -5.0016179 83 -7.3719930 -10.6101510 84 -7.6858442 -7.3719930 85 -9.2862248 -7.6858442 86 -5.2286863 -9.2862248 87 -12.3782975 -5.2286863 88 25.2366490 -12.3782975 89 -18.3554706 25.2366490 90 0.5821455 -18.3554706 91 -13.4137279 0.5821455 92 -2.1227861 -13.4137279 93 -7.7161123 -2.1227861 94 0.6738586 -7.7161123 95 15.6803068 0.6738586 96 27.0018460 15.6803068 97 -23.1717129 27.0018460 98 0.8183212 -23.1717129 99 -4.9551873 0.8183212 100 14.2277835 -4.9551873 101 -5.7929689 14.2277835 102 -14.0417970 -5.7929689 103 16.3399562 -14.0417970 104 1.6937499 16.3399562 105 3.8878970 1.6937499 106 1.9397477 3.8878970 107 11.3800645 1.9397477 108 2.8638509 11.3800645 109 4.1516294 2.8638509 110 18.8631008 4.1516294 111 -15.6166195 18.8631008 112 -16.5538280 -15.6166195 113 11.2093039 -16.5538280 114 -19.1293466 11.2093039 115 -21.2855996 -19.1293466 116 11.0501140 -21.2855996 117 -17.4038094 11.0501140 118 7.3320271 -17.4038094 119 10.8452621 7.3320271 120 -0.3190726 10.8452621 121 -11.9584786 -0.3190726 122 5.1581152 -11.9584786 123 -6.5953917 5.1581152 124 -1.4220760 -6.5953917 125 16.0905639 -1.4220760 126 -4.6106422 16.0905639 127 14.2575851 -4.6106422 128 10.7038709 14.2575851 129 8.5592156 10.7038709 130 -17.6628685 8.5592156 131 8.3837271 -17.6628685 132 13.6829881 8.3837271 133 -6.7751556 13.6829881 134 1.7605197 -6.7751556 135 -6.9750292 1.7605197 136 1.2574773 -6.9750292 137 -18.2124921 1.2574773 138 -2.2158750 -18.2124921 139 -13.7674637 -2.2158750 140 10.5514576 -13.7674637 141 -20.2163151 10.5514576 142 -1.6041338 -20.2163151 143 -7.3195699 -1.6041338 144 -17.6858442 -7.3195699 145 -13.8001238 -17.6858442 146 2.6809274 -13.8001238 147 0.9059277 2.6809274 148 17.1919444 0.9059277 149 -9.8113552 17.1919444 150 -12.3746343 -9.8113552 151 9.1886448 -12.3746343 152 NA 9.1886448 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -3.7568885 14.6361815 [2,] 0.2368686 -3.7568885 [3,] 5.3685842 0.2368686 [4,] 0.2765607 5.3685842 [5,] 11.4784427 0.2765607 [6,] -20.9458584 11.4784427 [7,] 1.6607135 -20.9458584 [8,] 24.3730718 1.6607135 [9,] 31.4405121 24.3730718 [10,] 5.4941358 31.4405121 [11,] 11.0157647 5.4941358 [12,] -20.4143305 11.0157647 [13,] 3.6603524 -20.4143305 [14,] 1.8244719 3.6603524 [15,] -5.4085313 1.8244719 [16,] 6.4687733 -5.4085313 [17,] -11.1699938 6.4687733 [18,] -7.2548639 -11.1699938 [19,] 0.6610049 -7.2548639 [20,] 26.2170153 0.6610049 [21,] 14.1418726 26.2170153 [22,] -32.9513194 14.1418726 [23,] -14.3114319 -32.9513194 [24,] 5.5514576 -14.3114319 [25,] 7.6022190 5.5514576 [26,] 9.0889885 7.6022190 [27,] 8.2985634 9.0889885 [28,] 3.6644469 8.2985634 [29,] 3.0535780 3.6644469 [30,] 2.0510051 3.0535780 [31,] -10.1243963 2.0510051 [32,] 10.8166267 -10.1243963 [33,] -12.7922041 10.8166267 [34,] 1.6542683 -12.7922041 [35,] -16.6400335 1.6542683 [36,] 1.5847169 -16.6400335 [37,] 17.5801088 1.5847169 [38,] 18.1252950 17.5801088 [39,] -10.9868725 18.1252950 [40,] -1.6619347 -10.9868725 [41,] -4.2481068 -1.6619347 [42,] -24.4207387 -4.2481068 [43,] 3.1460636 -24.4207387 [44,] -5.7525816 3.1460636 [45,] 19.5319220 -5.7525816 [46,] 3.8829919 19.5319220 [47,] 20.9171176 3.8829919 [48,] 4.4421696 20.9171176 [49,] -17.6337692 4.4421696 [50,] 9.6480432 -17.6337692 [51,] -4.5058719 9.6480432 [52,] 12.9022653 -4.5058719 [53,] -8.6312422 12.9022653 [54,] 15.9869980 -8.6312422 [55,] -27.3696024 15.9869980 [56,] 25.0962952 -27.3696024 [57,] 3.6261063 25.0962952 [58,] -9.4670588 3.6261063 [59,] 2.8084251 -9.4670588 [60,] -5.3027241 2.8084251 [61,] 5.1816744 -5.3027241 [62,] 8.2714247 5.1816744 [63,] 21.6813221 8.2714247 [64,] 1.0339375 21.6813221 [65,] 14.5229439 1.0339375 [66,] -15.0082060 14.5229439 [67,] -1.1357795 -15.0082060 [68,] -15.3705363 -1.1357795 [69,] -14.9948629 -15.3705363 [70,] -8.8081248 -14.9948629 [71,] -5.7268610 -8.8081248 [72,] 16.8211984 -5.7268610 [73,] -5.1788692 16.8211984 [74,] 16.6798147 -5.1788692 [75,] -1.3032561 16.6798147 [76,] -1.1853057 -1.3032561 [77,] -13.7050933 -1.1853057 [78,] 11.4924333 -13.7050933 [79,] 1.5681192 11.4924333 [80,] -4.8018939 1.5681192 [81,] -5.0016179 -4.8018939 [82,] -10.6101510 -5.0016179 [83,] -7.3719930 -10.6101510 [84,] -7.6858442 -7.3719930 [85,] -9.2862248 -7.6858442 [86,] -5.2286863 -9.2862248 [87,] -12.3782975 -5.2286863 [88,] 25.2366490 -12.3782975 [89,] -18.3554706 25.2366490 [90,] 0.5821455 -18.3554706 [91,] -13.4137279 0.5821455 [92,] -2.1227861 -13.4137279 [93,] -7.7161123 -2.1227861 [94,] 0.6738586 -7.7161123 [95,] 15.6803068 0.6738586 [96,] 27.0018460 15.6803068 [97,] -23.1717129 27.0018460 [98,] 0.8183212 -23.1717129 [99,] -4.9551873 0.8183212 [100,] 14.2277835 -4.9551873 [101,] -5.7929689 14.2277835 [102,] -14.0417970 -5.7929689 [103,] 16.3399562 -14.0417970 [104,] 1.6937499 16.3399562 [105,] 3.8878970 1.6937499 [106,] 1.9397477 3.8878970 [107,] 11.3800645 1.9397477 [108,] 2.8638509 11.3800645 [109,] 4.1516294 2.8638509 [110,] 18.8631008 4.1516294 [111,] -15.6166195 18.8631008 [112,] -16.5538280 -15.6166195 [113,] 11.2093039 -16.5538280 [114,] -19.1293466 11.2093039 [115,] -21.2855996 -19.1293466 [116,] 11.0501140 -21.2855996 [117,] -17.4038094 11.0501140 [118,] 7.3320271 -17.4038094 [119,] 10.8452621 7.3320271 [120,] -0.3190726 10.8452621 [121,] -11.9584786 -0.3190726 [122,] 5.1581152 -11.9584786 [123,] -6.5953917 5.1581152 [124,] -1.4220760 -6.5953917 [125,] 16.0905639 -1.4220760 [126,] -4.6106422 16.0905639 [127,] 14.2575851 -4.6106422 [128,] 10.7038709 14.2575851 [129,] 8.5592156 10.7038709 [130,] -17.6628685 8.5592156 [131,] 8.3837271 -17.6628685 [132,] 13.6829881 8.3837271 [133,] -6.7751556 13.6829881 [134,] 1.7605197 -6.7751556 [135,] -6.9750292 1.7605197 [136,] 1.2574773 -6.9750292 [137,] -18.2124921 1.2574773 [138,] -2.2158750 -18.2124921 [139,] -13.7674637 -2.2158750 [140,] 10.5514576 -13.7674637 [141,] -20.2163151 10.5514576 [142,] -1.6041338 -20.2163151 [143,] -7.3195699 -1.6041338 [144,] -17.6858442 -7.3195699 [145,] -13.8001238 -17.6858442 [146,] 2.6809274 -13.8001238 [147,] 0.9059277 2.6809274 [148,] 17.1919444 0.9059277 [149,] -9.8113552 17.1919444 [150,] -12.3746343 -9.8113552 [151,] 9.1886448 -12.3746343 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -3.7568885 14.6361815 2 0.2368686 -3.7568885 3 5.3685842 0.2368686 4 0.2765607 5.3685842 5 11.4784427 0.2765607 6 -20.9458584 11.4784427 7 1.6607135 -20.9458584 8 24.3730718 1.6607135 9 31.4405121 24.3730718 10 5.4941358 31.4405121 11 11.0157647 5.4941358 12 -20.4143305 11.0157647 13 3.6603524 -20.4143305 14 1.8244719 3.6603524 15 -5.4085313 1.8244719 16 6.4687733 -5.4085313 17 -11.1699938 6.4687733 18 -7.2548639 -11.1699938 19 0.6610049 -7.2548639 20 26.2170153 0.6610049 21 14.1418726 26.2170153 22 -32.9513194 14.1418726 23 -14.3114319 -32.9513194 24 5.5514576 -14.3114319 25 7.6022190 5.5514576 26 9.0889885 7.6022190 27 8.2985634 9.0889885 28 3.6644469 8.2985634 29 3.0535780 3.6644469 30 2.0510051 3.0535780 31 -10.1243963 2.0510051 32 10.8166267 -10.1243963 33 -12.7922041 10.8166267 34 1.6542683 -12.7922041 35 -16.6400335 1.6542683 36 1.5847169 -16.6400335 37 17.5801088 1.5847169 38 18.1252950 17.5801088 39 -10.9868725 18.1252950 40 -1.6619347 -10.9868725 41 -4.2481068 -1.6619347 42 -24.4207387 -4.2481068 43 3.1460636 -24.4207387 44 -5.7525816 3.1460636 45 19.5319220 -5.7525816 46 3.8829919 19.5319220 47 20.9171176 3.8829919 48 4.4421696 20.9171176 49 -17.6337692 4.4421696 50 9.6480432 -17.6337692 51 -4.5058719 9.6480432 52 12.9022653 -4.5058719 53 -8.6312422 12.9022653 54 15.9869980 -8.6312422 55 -27.3696024 15.9869980 56 25.0962952 -27.3696024 57 3.6261063 25.0962952 58 -9.4670588 3.6261063 59 2.8084251 -9.4670588 60 -5.3027241 2.8084251 61 5.1816744 -5.3027241 62 8.2714247 5.1816744 63 21.6813221 8.2714247 64 1.0339375 21.6813221 65 14.5229439 1.0339375 66 -15.0082060 14.5229439 67 -1.1357795 -15.0082060 68 -15.3705363 -1.1357795 69 -14.9948629 -15.3705363 70 -8.8081248 -14.9948629 71 -5.7268610 -8.8081248 72 16.8211984 -5.7268610 73 -5.1788692 16.8211984 74 16.6798147 -5.1788692 75 -1.3032561 16.6798147 76 -1.1853057 -1.3032561 77 -13.7050933 -1.1853057 78 11.4924333 -13.7050933 79 1.5681192 11.4924333 80 -4.8018939 1.5681192 81 -5.0016179 -4.8018939 82 -10.6101510 -5.0016179 83 -7.3719930 -10.6101510 84 -7.6858442 -7.3719930 85 -9.2862248 -7.6858442 86 -5.2286863 -9.2862248 87 -12.3782975 -5.2286863 88 25.2366490 -12.3782975 89 -18.3554706 25.2366490 90 0.5821455 -18.3554706 91 -13.4137279 0.5821455 92 -2.1227861 -13.4137279 93 -7.7161123 -2.1227861 94 0.6738586 -7.7161123 95 15.6803068 0.6738586 96 27.0018460 15.6803068 97 -23.1717129 27.0018460 98 0.8183212 -23.1717129 99 -4.9551873 0.8183212 100 14.2277835 -4.9551873 101 -5.7929689 14.2277835 102 -14.0417970 -5.7929689 103 16.3399562 -14.0417970 104 1.6937499 16.3399562 105 3.8878970 1.6937499 106 1.9397477 3.8878970 107 11.3800645 1.9397477 108 2.8638509 11.3800645 109 4.1516294 2.8638509 110 18.8631008 4.1516294 111 -15.6166195 18.8631008 112 -16.5538280 -15.6166195 113 11.2093039 -16.5538280 114 -19.1293466 11.2093039 115 -21.2855996 -19.1293466 116 11.0501140 -21.2855996 117 -17.4038094 11.0501140 118 7.3320271 -17.4038094 119 10.8452621 7.3320271 120 -0.3190726 10.8452621 121 -11.9584786 -0.3190726 122 5.1581152 -11.9584786 123 -6.5953917 5.1581152 124 -1.4220760 -6.5953917 125 16.0905639 -1.4220760 126 -4.6106422 16.0905639 127 14.2575851 -4.6106422 128 10.7038709 14.2575851 129 8.5592156 10.7038709 130 -17.6628685 8.5592156 131 8.3837271 -17.6628685 132 13.6829881 8.3837271 133 -6.7751556 13.6829881 134 1.7605197 -6.7751556 135 -6.9750292 1.7605197 136 1.2574773 -6.9750292 137 -18.2124921 1.2574773 138 -2.2158750 -18.2124921 139 -13.7674637 -2.2158750 140 10.5514576 -13.7674637 141 -20.2163151 10.5514576 142 -1.6041338 -20.2163151 143 -7.3195699 -1.6041338 144 -17.6858442 -7.3195699 145 -13.8001238 -17.6858442 146 2.6809274 -13.8001238 147 0.9059277 2.6809274 148 17.1919444 0.9059277 149 -9.8113552 17.1919444 150 -12.3746343 -9.8113552 151 9.1886448 -12.3746343 > 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/www/rcomp/tmp/7vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/106g0t1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/rcomp/tmp/119hyz1290513336.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/www/rcomp/tmp/12dzf51290513336.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/www/rcomp/tmp/1310cy1290513336.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/www/rcomp/tmp/14cat11290513336.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/www/rcomp/tmp/15gs971290513336.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/www/rcomp/tmp/16jtqd1290513336.tab") + } > > try(system("convert tmp/1zflz1290513336.ps tmp/1zflz1290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/2ap221290513336.ps tmp/2ap221290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/3ap221290513336.ps tmp/3ap221290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/4ap221290513336.ps tmp/4ap221290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/5ap221290513336.ps tmp/5ap221290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/6lg151290513336.ps tmp/6lg151290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/7vpiq1290513336.ps tmp/7vpiq1290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/8vpiq1290513336.ps tmp/8vpiq1290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/9vpiq1290513336.ps tmp/9vpiq1290513336.png",intern=TRUE)) character(0) > try(system("convert tmp/106g0t1290513336.ps tmp/106g0t1290513336.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.410 2.250 8.738