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(1 + ,1 + ,41 + ,38 + ,13 + ,12 + ,14 + ,1 + ,1 + ,39 + ,32 + ,16 + ,11 + ,18 + ,1 + ,1 + ,30 + ,35 + ,19 + ,15 + ,11 + ,1 + ,0 + ,31 + ,33 + ,15 + ,6 + ,12 + ,1 + ,1 + ,34 + ,37 + ,14 + ,13 + ,16 + ,1 + ,1 + ,35 + ,29 + ,13 + ,10 + ,18 + ,1 + ,1 + ,39 + ,31 + ,19 + ,12 + ,14 + ,1 + ,1 + ,34 + ,36 + ,15 + ,14 + ,14 + ,1 + ,1 + ,36 + ,35 + ,14 + ,12 + ,15 + ,1 + ,1 + ,37 + ,38 + ,15 + ,9 + ,15 + ,1 + ,0 + ,38 + ,31 + ,16 + ,10 + ,17 + ,1 + ,1 + ,36 + ,34 + ,16 + ,12 + ,19 + ,1 + ,0 + ,38 + ,35 + ,16 + ,12 + ,10 + ,1 + ,1 + ,39 + ,38 + ,16 + ,11 + ,16 + ,1 + ,1 + ,33 + ,37 + ,17 + ,15 + ,18 + ,1 + ,0 + ,32 + ,33 + ,15 + ,12 + ,14 + ,1 + ,0 + ,36 + ,32 + ,15 + ,10 + ,14 + ,1 + ,1 + ,38 + ,38 + ,20 + ,12 + ,17 + ,1 + ,0 + ,39 + ,38 + ,18 + ,11 + ,14 + ,1 + ,1 + ,32 + ,32 + ,16 + ,12 + ,16 + ,1 + ,0 + ,32 + ,33 + ,16 + ,11 + ,18 + ,1 + ,1 + ,31 + ,31 + ,16 + ,12 + ,11 + ,1 + ,1 + ,39 + ,38 + ,19 + ,13 + ,14 + ,1 + ,1 + ,37 + ,39 + ,16 + ,11 + ,12 + ,1 + ,0 + ,39 + ,32 + ,17 + ,12 + ,17 + ,1 + ,1 + ,41 + ,32 + ,17 + ,13 + ,9 + ,1 + ,0 + ,36 + ,35 + ,16 + ,10 + ,16 + ,1 + ,1 + ,33 + ,37 + ,15 + ,14 + ,14 + ,1 + ,1 + ,33 + ,33 + ,16 + ,12 + ,15 + ,1 + ,0 + ,34 + ,33 + ,14 + ,10 + ,11 + ,1 + ,1 + ,31 + ,31 + ,15 + ,12 + ,16 + ,1 + ,0 + ,27 + ,32 + ,12 + ,8 + ,13 + ,1 + ,1 + ,37 + ,31 + ,14 + ,10 + ,17 + ,1 + ,1 + ,34 + ,37 + ,16 + ,12 + ,15 + ,1 + ,0 + ,34 + ,30 + ,14 + ,12 + ,14 + ,1 + ,0 + ,32 + ,33 + ,10 + ,7 + ,16 + ,1 + ,0 + ,29 + ,31 + ,10 + ,9 + ,9 + ,1 + ,0 + ,36 + ,33 + ,14 + ,12 + ,15 + ,1 + ,1 + ,29 + ,31 + ,16 + ,10 + ,17 + ,1 + ,0 + ,35 + ,33 + ,16 + ,10 + ,13 + ,1 + ,0 + ,37 + ,32 + ,16 + ,10 + ,15 + ,1 + ,1 + ,34 + ,33 + ,14 + ,12 + ,16 + ,1 + ,0 + ,38 + ,32 + ,20 + ,15 + ,16 + ,1 + ,0 + ,35 + ,33 + ,14 + ,10 + ,12 + ,1 + ,1 + ,38 + ,28 + ,14 + ,10 + ,15 + ,1 + ,1 + ,37 + ,35 + ,11 + ,12 + ,11 + ,1 + ,1 + ,38 + ,39 + ,14 + ,13 + ,15 + ,1 + ,1 + ,33 + ,34 + ,15 + ,11 + ,15 + ,1 + ,1 + ,36 + ,38 + ,16 + ,11 + ,17 + ,1 + ,0 + ,38 + ,32 + ,14 + ,12 + ,13 + ,1 + ,1 + ,32 + ,38 + ,16 + ,14 + ,16 + ,1 + ,0 + ,32 + ,30 + ,14 + ,10 + ,14 + ,1 + ,0 + ,32 + ,33 + ,12 + ,12 + ,11 + ,1 + ,1 + ,34 + ,38 + ,16 + ,13 + ,12 + ,1 + ,0 + ,32 + ,32 + ,9 + ,5 + ,12 + ,1 + ,1 + ,37 + ,35 + ,14 + ,6 + ,15 + ,1 + ,1 + ,39 + ,34 + ,16 + ,12 + ,16 + ,1 + ,1 + ,29 + ,34 + ,16 + ,12 + ,15 + ,1 + ,0 + ,37 + ,36 + ,15 + ,11 + ,12 + ,1 + ,1 + ,35 + ,34 + ,16 + ,10 + ,12 + ,1 + ,0 + ,30 + ,28 + ,12 + ,7 + ,8 + ,1 + ,0 + ,38 + ,34 + ,16 + ,12 + ,13 + ,1 + ,1 + ,34 + ,35 + ,16 + ,14 + ,11 + ,1 + ,1 + ,31 + ,35 + ,14 + ,11 + ,14 + ,1 + ,1 + ,34 + ,31 + ,16 + ,12 + ,15 + ,1 + ,0 + ,35 + ,37 + ,17 + ,13 + ,10 + ,1 + ,1 + ,36 + ,35 + ,18 + ,14 + ,11 + ,1 + ,0 + ,30 + ,27 + ,18 + ,11 + ,12 + ,1 + ,1 + ,39 + ,40 + ,12 + ,12 + ,15 + ,1 + ,0 + ,35 + ,37 + ,16 + ,12 + ,15 + ,1 + ,0 + ,38 + ,36 + ,10 + ,8 + ,14 + ,1 + ,1 + ,31 + ,38 + ,14 + ,11 + ,16 + ,1 + ,1 + ,34 + ,39 + ,18 + ,14 + ,15 + ,1 + ,0 + ,38 + ,41 + ,18 + ,14 + ,15 + ,1 + ,0 + ,34 + ,27 + ,16 + ,12 + ,13 + ,1 + ,1 + ,39 + ,30 + ,17 + ,9 + ,12 + ,1 + ,1 + ,37 + ,37 + ,16 + ,13 + ,17 + ,1 + ,1 + ,34 + ,31 + ,16 + ,11 + ,13 + ,1 + ,0 + ,28 + ,31 + ,13 + ,12 + ,15 + ,1 + ,0 + ,37 + ,27 + ,16 + ,12 + ,13 + ,1 + ,0 + ,33 + ,36 + ,16 + ,12 + ,15 + ,1 + ,1 + ,35 + ,37 + ,16 + ,12 + ,15 + ,1 + ,0 + ,37 + ,33 + ,15 + ,12 + ,16 + ,1 + ,1 + ,32 + ,34 + ,15 + ,11 + ,15 + ,1 + ,1 + ,33 + ,31 + ,16 + ,10 + ,14 + ,1 + ,0 + ,38 + ,39 + ,14 + ,9 + ,15 + ,1 + ,1 + ,33 + ,34 + ,16 + ,12 + ,14 + ,1 + ,1 + ,29 + ,32 + ,16 + ,12 + ,13 + ,1 + ,1 + ,33 + ,33 + ,15 + ,12 + ,7 + ,1 + ,1 + ,31 + ,36 + ,12 + ,9 + ,17 + ,1 + ,1 + ,36 + ,32 + ,17 + ,15 + ,13 + ,1 + ,1 + ,35 + ,41 + ,16 + ,12 + ,15 + ,1 + ,1 + ,32 + ,28 + ,15 + ,12 + ,14 + ,1 + ,1 + ,29 + ,30 + ,13 + ,12 + ,13 + ,1 + ,1 + ,39 + ,36 + ,16 + ,10 + ,16 + ,1 + ,1 + ,37 + ,35 + ,16 + ,13 + ,12 + ,1 + ,1 + ,35 + ,31 + ,16 + ,9 + ,14 + ,1 + ,0 + ,37 + ,34 + ,16 + ,12 + ,17 + ,1 + ,0 + ,32 + ,36 + ,14 + ,10 + ,15 + ,1 + ,1 + ,38 + ,36 + ,16 + ,14 + ,17 + ,1 + ,0 + ,37 + ,35 + ,16 + ,11 + ,12 + ,1 + ,1 + ,36 + ,37 + ,20 + ,15 + ,16 + ,1 + ,0 + ,32 + ,28 + ,15 + ,11 + ,11 + ,1 + ,1 + ,33 + ,39 + ,16 + ,11 + ,15 + ,1 + ,0 + ,40 + ,32 + ,13 + ,12 + ,9 + ,1 + ,1 + ,38 + ,35 + ,17 + ,12 + ,16 + ,1 + ,0 + ,41 + ,39 + ,16 + ,12 + ,15 + ,1 + ,0 + ,36 + ,35 + ,16 + ,11 + ,10 + ,1 + ,1 + ,43 + ,42 + ,12 + ,7 + ,10 + ,1 + ,1 + ,30 + ,34 + ,16 + ,12 + ,15 + ,1 + ,1 + ,31 + ,33 + ,16 + ,14 + ,11 + ,1 + ,1 + ,32 + ,41 + ,17 + ,11 + ,13 + ,1 + ,1 + ,37 + ,34 + ,12 + ,10 + ,18 + ,1 + ,0 + ,37 + ,32 + ,18 + ,13 + ,16 + ,1 + ,1 + ,33 + ,40 + ,14 + ,13 + ,14 + ,1 + ,1 + ,34 + ,40 + ,14 + ,8 + ,14 + ,1 + ,1 + ,33 + ,35 + ,13 + ,11 + ,14 + ,1 + ,1 + ,38 + ,36 + ,16 + ,12 + ,14 + ,1 + ,0 + ,33 + ,37 + ,13 + ,11 + ,12 + ,1 + ,1 + ,31 + ,27 + ,16 + ,13 + ,14 + ,1 + ,1 + ,38 + ,39 + ,13 + ,12 + ,15 + ,1 + ,1 + ,37 + ,38 + ,16 + ,14 + ,15 + ,1 + ,1 + ,36 + ,31 + ,15 + ,13 + ,15 + ,1 + ,1 + ,31 + ,33 + ,16 + ,15 + ,13 + ,1 + ,0 + ,39 + ,32 + ,15 + ,10 + ,17 + ,1 + ,1 + ,44 + ,39 + ,17 + ,11 + ,17 + ,1 + ,1 + ,33 + ,36 + ,15 + ,9 + ,19 + ,1 + ,1 + ,35 + ,33 + ,12 + ,11 + ,15 + ,1 + ,0 + ,32 + ,33 + ,16 + ,10 + ,13 + ,1 + ,0 + ,28 + ,32 + ,10 + ,11 + ,9 + ,1 + ,1 + ,40 + ,37 + ,16 + ,8 + ,15 + ,1 + ,0 + ,27 + ,30 + ,12 + ,11 + ,15 + ,1 + ,0 + ,37 + ,38 + ,14 + ,12 + ,15 + ,1 + ,1 + ,32 + ,29 + ,15 + ,12 + ,16 + ,1 + ,0 + ,28 + ,22 + ,13 + ,9 + ,11 + ,1 + ,0 + ,34 + ,35 + ,15 + ,11 + ,14 + ,1 + ,1 + ,30 + ,35 + ,11 + ,10 + ,11 + ,1 + ,1 + ,35 + ,34 + ,12 + ,8 + ,15 + ,1 + ,0 + ,31 + ,35 + ,11 + ,9 + ,13 + ,1 + ,1 + ,32 + ,34 + ,16 + ,8 + ,15 + ,1 + ,0 + ,30 + ,37 + ,15 + ,9 + ,16 + ,1 + ,1 + ,30 + ,35 + ,17 + ,15 + ,14 + ,1 + ,0 + ,31 + ,23 + ,16 + ,11 + ,15 + ,1 + ,1 + ,40 + ,31 + ,10 + ,8 + ,16 + ,1 + ,1 + ,32 + ,27 + ,18 + ,13 + ,16 + ,1 + ,0 + ,36 + ,36 + ,13 + ,12 + ,11 + ,1 + ,0 + ,32 + ,31 + ,16 + ,12 + ,12 + ,1 + ,0 + ,35 + ,32 + ,13 + ,9 + ,9 + ,1 + ,1 + ,38 + ,39 + ,10 + ,7 + ,16 + ,1 + ,1 + ,42 + ,37 + ,15 + ,13 + ,13 + ,1 + ,0 + ,34 + ,38 + ,16 + ,9 + ,16 + ,1 + ,1 + ,35 + ,39 + ,16 + ,6 + ,12 + ,1 + ,1 + ,38 + ,34 + ,14 + ,8 + ,9 + ,1 + ,1 + ,33 + ,31 + ,10 + ,8 + ,13 + ,1 + ,1 + ,32 + ,37 + ,13 + ,6 + ,14 + ,1 + ,1 + ,33 + ,36 + ,15 + ,9 + ,19 + ,1 + ,1 + ,34 + ,32 + ,16 + ,11 + ,13 + ,1 + ,1 + ,32 + ,38 + ,12 + ,8 + ,12 + ,0 + ,0 + ,27 + ,26 + ,13 + ,10 + ,10 + ,0 + ,0 + ,31 + ,26 + ,12 + ,8 + ,14 + ,0 + ,0 + ,38 + ,33 + ,17 + ,14 + ,16 + ,0 + ,1 + ,34 + ,39 + ,15 + ,10 + ,10 + ,0 + ,0 + ,24 + ,30 + ,10 + ,8 + ,11 + ,0 + ,0 + ,30 + ,33 + ,14 + ,11 + ,14 + ,0 + ,1 + ,26 + ,25 + ,11 + ,12 + ,12 + ,0 + ,1 + ,34 + ,38 + ,13 + ,12 + ,9 + ,0 + ,0 + ,27 + ,37 + ,16 + ,12 + ,9 + ,0 + ,0 + ,37 + ,31 + ,12 + ,5 + ,11 + ,0 + ,1 + ,36 + ,37 + ,16 + ,12 + ,16 + ,0 + ,0 + ,41 + ,35 + ,12 + ,10 + ,9 + ,0 + ,1 + ,29 + ,25 + ,9 + ,7 + ,13 + ,0 + ,1 + ,36 + ,28 + ,12 + ,12 + ,16 + ,0 + ,0 + ,32 + ,35 + ,15 + ,11 + ,13 + ,0 + ,1 + ,37 + ,33 + ,12 + ,8 + ,9 + ,0 + ,0 + ,30 + ,30 + ,12 + ,9 + ,12 + ,0 + ,1 + ,31 + ,31 + ,14 + ,10 + ,16 + ,0 + ,1 + ,38 + ,37 + ,12 + ,9 + ,11 + ,0 + ,1 + ,36 + ,36 + ,16 + ,12 + ,14 + ,0 + ,0 + ,35 + ,30 + ,11 + ,6 + ,13 + ,0 + ,0 + ,31 + ,36 + ,19 + ,15 + ,15 + ,0 + ,0 + ,38 + ,32 + ,15 + ,12 + ,14 + ,0 + ,1 + ,22 + ,28 + ,8 + ,12 + ,16 + ,0 + ,1 + ,32 + ,36 + ,16 + ,12 + ,13 + ,0 + ,0 + ,36 + ,34 + ,17 + ,11 + ,14 + ,0 + ,1 + ,39 + ,31 + ,12 + ,7 + ,15 + ,0 + ,0 + ,28 + ,28 + ,11 + ,7 + ,13 + ,0 + ,0 + ,32 + ,36 + ,11 + ,5 + ,11 + ,0 + ,1 + ,32 + ,36 + ,14 + ,12 + ,11 + ,0 + ,1 + ,38 + ,40 + ,16 + ,12 + ,14 + ,0 + ,1 + ,32 + ,33 + ,12 + ,3 + ,15 + ,0 + ,1 + ,35 + ,37 + ,16 + ,11 + ,11 + ,0 + ,1 + ,32 + ,32 + ,13 + ,10 + ,15 + ,0 + ,0 + ,37 + ,38 + ,15 + ,12 + ,12 + ,0 + ,1 + ,34 + ,31 + ,16 + ,9 + ,14 + ,0 + ,1 + ,33 + ,37 + ,16 + ,12 + ,14 + ,0 + ,0 + ,33 + ,33 + ,14 + ,9 + ,8 + ,0 + ,0 + ,30 + ,30 + ,16 + ,12 + ,9 + ,0 + ,0 + ,24 + ,30 + ,14 + ,10 + ,15 + ,0 + ,0 + ,34 + ,31 + ,11 + ,9 + ,17 + ,0 + ,0 + ,34 + ,32 + ,12 + ,12 + ,13 + ,0 + ,1 + ,33 + ,34 + ,15 + ,8 + ,15 + ,0 + ,1 + ,34 + ,36 + ,15 + ,11 + ,15 + ,0 + ,1 + ,35 + ,37 + ,16 + ,11 + ,14 + ,0 + ,0 + ,35 + ,36 + ,16 + ,12 + ,16 + ,0 + ,0 + ,36 + ,33 + ,11 + ,10 + ,13 + ,0 + ,0 + ,34 + ,33 + ,15 + ,10 + ,16 + ,0 + ,1 + ,34 + ,33 + ,12 + ,12 + ,9 + ,0 + ,0 + ,41 + ,44 + ,12 + ,12 + ,16 + ,0 + ,0 + ,32 + ,39 + ,15 + ,11 + ,11 + ,0 + ,0 + ,30 + ,32 + ,15 + ,8 + ,10 + ,0 + ,1 + ,35 + ,35 + ,16 + ,12 + ,11 + ,0 + ,0 + ,28 + ,25 + ,14 + ,10 + ,15 + ,0 + ,1 + ,33 + ,35 + ,17 + ,11 + ,17 + ,0 + ,1 + ,39 + ,34 + ,14 + ,10 + ,14 + ,0 + ,0 + ,36 + ,35 + ,13 + ,8 + ,8 + ,0 + ,1 + ,36 + ,39 + ,15 + ,12 + ,15 + ,0 + ,0 + ,35 + ,33 + ,13 + ,12 + ,11 + ,0 + ,0 + ,38 + ,36 + ,14 + ,10 + ,16 + ,0 + ,1 + ,33 + ,32 + ,15 + ,12 + ,10 + ,0 + ,0 + ,31 + ,32 + ,12 + ,9 + ,15 + ,0 + ,1 + ,32 + ,36 + ,8 + ,6 + ,16 + ,0 + ,0 + ,31 + ,32 + ,14 + ,10 + ,19 + ,0 + ,0 + ,33 + ,34 + ,14 + ,9 + ,12 + ,0 + ,0 + ,34 + ,33 + ,11 + ,9 + ,8 + ,0 + ,0 + ,34 + ,35 + ,12 + ,9 + ,11 + ,0 + ,1 + ,34 + ,30 + ,13 + ,6 + ,14 + ,0 + ,0 + ,33 + ,38 + ,10 + ,10 + ,9 + ,0 + ,0 + ,32 + ,34 + ,16 + ,6 + ,15 + ,0 + ,1 + ,41 + ,33 + ,18 + ,14 + ,13 + ,0 + ,1 + ,34 + ,32 + ,13 + ,10 + ,16 + ,0 + ,0 + ,36 + ,31 + ,11 + ,10 + ,11 + ,0 + ,0 + ,37 + ,30 + ,4 + ,6 + ,12 + ,0 + ,0 + ,36 + ,27 + ,13 + ,12 + ,13 + ,0 + ,1 + ,29 + ,31 + ,16 + ,12 + ,10 + ,0 + ,0 + ,37 + ,30 + ,10 + ,7 + ,11 + ,0 + ,0 + ,27 + ,32 + ,12 + ,8 + ,12 + ,0 + ,0 + ,35 + ,35 + ,12 + ,11 + ,8 + ,0 + ,0 + ,28 + ,28 + ,10 + ,3 + ,12 + ,0 + ,0 + ,35 + ,33 + ,13 + ,6 + ,12 + ,0 + ,0 + ,29 + ,35 + ,12 + ,8 + ,11 + ,0 + ,0 + ,32 + ,35 + ,14 + ,9 + ,13 + ,0 + ,1 + ,36 + ,32 + ,10 + ,9 + ,14 + ,0 + ,1 + ,19 + ,21 + ,12 + ,8 + ,10 + ,0 + ,1 + ,21 + ,20 + ,12 + ,9 + ,12 + ,0 + ,0 + ,31 + ,34 + ,11 + ,7 + ,15 + ,0 + ,0 + ,33 + ,32 + ,10 + ,7 + ,13 + ,0 + ,1 + ,36 + ,34 + ,12 + ,6 + ,13 + ,0 + ,1 + ,33 + ,32 + ,16 + ,9 + ,13 + ,0 + ,0 + ,37 + ,33 + ,12 + ,10 + ,12 + ,0 + ,0 + ,34 + ,33 + ,14 + ,11 + ,12 + ,0 + ,0 + ,35 + ,37 + ,16 + ,12 + ,9 + ,0 + ,1 + ,31 + ,32 + ,14 + ,8 + ,9 + ,0 + ,1 + ,37 + ,34 + ,13 + ,11 + ,15 + ,0 + ,1 + ,35 + ,30 + ,4 + ,3 + ,10 + ,0 + ,1 + ,27 + ,30 + ,15 + ,11 + ,14 + ,0 + ,0 + ,34 + ,38 + ,11 + ,12 + ,15 + ,0 + ,0 + ,40 + ,36 + ,11 + ,7 + ,7 + ,0 + ,0 + ,29 + ,32 + ,14 + ,9 + ,14 + ,0 + ,0 + ,38 + ,34 + ,15 + ,12 + ,8 + ,0 + ,1 + ,34 + ,33 + ,14 + ,8 + ,10 + ,0 + ,0 + ,21 + ,27 + ,13 + ,11 + ,13 + ,0 + ,0 + ,36 + ,32 + ,11 + ,8 + ,13 + ,0 + ,1 + ,38 + ,34 + ,15 + ,10 + ,13 + ,0 + ,0 + ,30 + ,29 + ,11 + ,8 + ,8 + ,0 + ,0 + ,35 + ,35 + ,13 + ,7 + ,12 + ,0 + ,1 + ,30 + ,27 + ,13 + ,8 + ,13 + ,0 + ,1 + ,36 + ,33 + ,16 + ,10 + ,12 + ,0 + ,0 + ,34 + ,38 + ,13 + ,8 + ,10 + ,0 + ,1 + ,35 + ,36 + ,16 + ,12 + ,13 + ,0 + ,0 + ,34 + ,33 + ,16 + ,14 + ,12 + ,0 + ,0 + ,32 + ,39 + ,12 + ,7 + ,9 + ,0 + ,1 + ,33 + ,29 + ,7 + ,6 + ,15 + ,0 + ,0 + ,33 + ,32 + ,16 + ,11 + ,13 + ,0 + ,1 + ,26 + ,34 + ,5 + ,4 + ,13 + ,0 + ,0 + ,35 + ,38 + ,16 + ,9 + ,13 + ,0 + ,0 + ,21 + ,17 + ,4 + ,5 + ,15 + ,0 + ,0 + ,38 + ,35 + ,12 + ,9 + ,15 + ,0 + ,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > 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 = 'no' > par3 = '3' > par2 = 'none' > par1 = '3' > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Connected" > x[,par1] [1] 41 39 30 31 34 35 39 34 36 37 38 36 38 39 33 32 36 38 39 32 32 31 39 37 39 [26] 41 36 33 33 34 31 27 37 34 34 32 29 36 29 35 37 34 38 35 38 37 38 33 36 38 [51] 32 32 32 34 32 37 39 29 37 35 30 38 34 31 34 35 36 30 39 35 38 31 34 38 34 [76] 39 37 34 28 37 33 35 37 32 33 38 33 29 33 31 36 35 32 29 39 37 35 37 32 38 [101] 37 36 32 33 40 38 41 36 43 30 31 32 37 37 33 34 33 38 33 31 38 37 36 31 39 [126] 44 33 35 32 28 40 27 37 32 28 34 30 35 31 32 30 30 31 40 32 36 32 35 38 42 [151] 34 35 38 33 32 33 34 32 27 31 38 34 24 30 26 34 27 37 36 41 29 36 32 37 30 [176] 31 38 36 35 31 38 22 32 36 39 28 32 32 38 32 35 32 37 34 33 33 30 24 34 34 [201] 33 34 35 35 36 34 34 41 32 30 35 28 33 39 36 36 35 38 33 31 32 31 33 34 34 [226] 34 33 32 41 34 36 37 36 29 37 27 35 28 35 29 32 36 19 21 31 33 36 33 37 34 [251] 35 31 37 35 27 34 40 29 38 34 21 36 38 30 35 30 36 34 35 34 32 33 33 26 35 [276] 21 38 35 33 37 38 34 27 16 40 36 42 30 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$centers,1:par3),ncol=2) + clm <- clm[sort.list(clm[,1]),] + for (i in 1:par3) { + cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') + } + cl$cluster <- as.factor(cl$cluster) + print(cl$cluster) + x[,par1] <- cl$cluster + } > if (par2 == 'quantiles') { + x[,par1] <- cut2(x[,par1],g=par3) + } > if (par2 == 'hclust') { + hc <- hclust(dist(x[,par1])^2, 'cen') + print(hc) + memb <- cutree(hc, k = par3) + dum <- c(mean(x[memb==1,par1])) + for (i in 2:par3) { + dum <- c(dum, mean(x[memb==i,par1])) + } + hcm <- matrix(cbind(dum,1:par3),ncol=2) + hcm <- hcm[sort.list(hcm[,1]),] + for (i in 1:par3) { + memb[memb==hcm[i,2]] <- paste('C',i,sep='') + } + memb <- as.factor(memb) + print(memb) + x[,par1] <- memb + } > if (par2=='equal') { + ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) + x[,par1] <- as.factor(ed) + } > table(x[,par1]) 16 19 21 22 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 1 1 3 1 2 2 7 6 9 14 18 31 27 34 27 26 25 26 13 5 6 2 1 1 > colnames(x) [1] "Pop" "Gender" "Connected" "Separate" "Learning" "Software" [7] "Happiness" > colnames(x)[par1] [1] "Connected" > x[,par1] [1] 41 39 30 31 34 35 39 34 36 37 38 36 38 39 33 32 36 38 39 32 32 31 39 37 39 [26] 41 36 33 33 34 31 27 37 34 34 32 29 36 29 35 37 34 38 35 38 37 38 33 36 38 [51] 32 32 32 34 32 37 39 29 37 35 30 38 34 31 34 35 36 30 39 35 38 31 34 38 34 [76] 39 37 34 28 37 33 35 37 32 33 38 33 29 33 31 36 35 32 29 39 37 35 37 32 38 [101] 37 36 32 33 40 38 41 36 43 30 31 32 37 37 33 34 33 38 33 31 38 37 36 31 39 [126] 44 33 35 32 28 40 27 37 32 28 34 30 35 31 32 30 30 31 40 32 36 32 35 38 42 [151] 34 35 38 33 32 33 34 32 27 31 38 34 24 30 26 34 27 37 36 41 29 36 32 37 30 [176] 31 38 36 35 31 38 22 32 36 39 28 32 32 38 32 35 32 37 34 33 33 30 24 34 34 [201] 33 34 35 35 36 34 34 41 32 30 35 28 33 39 36 36 35 38 33 31 32 31 33 34 34 [226] 34 33 32 41 34 36 37 36 29 37 27 35 28 35 29 32 36 19 21 31 33 36 33 37 34 [251] 35 31 37 35 27 34 40 29 38 34 21 36 38 30 35 30 36 34 35 34 32 33 33 26 35 [276] 21 38 35 33 37 38 34 27 16 40 36 42 30 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/1vkj41355235629.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Connected Inputs: Pop, Gender, Separate, Learning, Software, Happiness Number of observations: 288 1) Separate <= 30; criterion = 1, statistic = 78.931 2) Separate <= 25; criterion = 0.999, statistic = 13.943 3)* weights = 9 2) Separate > 25 4)* weights = 38 1) Separate > 30 5) Separate <= 37; criterion = 1, statistic = 17.342 6)* weights = 196 5) Separate > 37 7)* weights = 45 > postscript(file="/var/wessaorg/rcomp/tmp/29ftt1355235629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3i6gv1355235629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > if (par2 == 'none') { + forec <- predict(m) + result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) + colnames(result) <- c('Actuals','Forecasts','Residuals') + print(result) + } Actuals Forecasts Residuals 1 41 36.40000 4.6000000 2 39 34.33673 4.6632653 3 30 34.33673 -4.3367347 4 31 34.33673 -3.3367347 5 34 34.33673 -0.3367347 6 35 31.21053 3.7894737 7 39 34.33673 4.6632653 8 34 34.33673 -0.3367347 9 36 34.33673 1.6632653 10 37 36.40000 0.6000000 11 38 34.33673 3.6632653 12 36 34.33673 1.6632653 13 38 34.33673 3.6632653 14 39 36.40000 2.6000000 15 33 34.33673 -1.3367347 16 32 34.33673 -2.3367347 17 36 34.33673 1.6632653 18 38 36.40000 1.6000000 19 39 36.40000 2.6000000 20 32 34.33673 -2.3367347 21 32 34.33673 -2.3367347 22 31 34.33673 -3.3367347 23 39 36.40000 2.6000000 24 37 36.40000 0.6000000 25 39 34.33673 4.6632653 26 41 34.33673 6.6632653 27 36 34.33673 1.6632653 28 33 34.33673 -1.3367347 29 33 34.33673 -1.3367347 30 34 34.33673 -0.3367347 31 31 34.33673 -3.3367347 32 27 34.33673 -7.3367347 33 37 34.33673 2.6632653 34 34 34.33673 -0.3367347 35 34 31.21053 2.7894737 36 32 34.33673 -2.3367347 37 29 34.33673 -5.3367347 38 36 34.33673 1.6632653 39 29 34.33673 -5.3367347 40 35 34.33673 0.6632653 41 37 34.33673 2.6632653 42 34 34.33673 -0.3367347 43 38 34.33673 3.6632653 44 35 34.33673 0.6632653 45 38 31.21053 6.7894737 46 37 34.33673 2.6632653 47 38 36.40000 1.6000000 48 33 34.33673 -1.3367347 49 36 36.40000 -0.4000000 50 38 34.33673 3.6632653 51 32 36.40000 -4.4000000 52 32 31.21053 0.7894737 53 32 34.33673 -2.3367347 54 34 36.40000 -2.4000000 55 32 34.33673 -2.3367347 56 37 34.33673 2.6632653 57 39 34.33673 4.6632653 58 29 34.33673 -5.3367347 59 37 34.33673 2.6632653 60 35 34.33673 0.6632653 61 30 31.21053 -1.2105263 62 38 34.33673 3.6632653 63 34 34.33673 -0.3367347 64 31 34.33673 -3.3367347 65 34 34.33673 -0.3367347 66 35 34.33673 0.6632653 67 36 34.33673 1.6632653 68 30 31.21053 -1.2105263 69 39 36.40000 2.6000000 70 35 34.33673 0.6632653 71 38 34.33673 3.6632653 72 31 36.40000 -5.4000000 73 34 36.40000 -2.4000000 74 38 36.40000 1.6000000 75 34 31.21053 2.7894737 76 39 31.21053 7.7894737 77 37 34.33673 2.6632653 78 34 34.33673 -0.3367347 79 28 34.33673 -6.3367347 80 37 31.21053 5.7894737 81 33 34.33673 -1.3367347 82 35 34.33673 0.6632653 83 37 34.33673 2.6632653 84 32 34.33673 -2.3367347 85 33 34.33673 -1.3367347 86 38 36.40000 1.6000000 87 33 34.33673 -1.3367347 88 29 34.33673 -5.3367347 89 33 34.33673 -1.3367347 90 31 34.33673 -3.3367347 91 36 34.33673 1.6632653 92 35 36.40000 -1.4000000 93 32 31.21053 0.7894737 94 29 31.21053 -2.2105263 95 39 34.33673 4.6632653 96 37 34.33673 2.6632653 97 35 34.33673 0.6632653 98 37 34.33673 2.6632653 99 32 34.33673 -2.3367347 100 38 34.33673 3.6632653 101 37 34.33673 2.6632653 102 36 34.33673 1.6632653 103 32 31.21053 0.7894737 104 33 36.40000 -3.4000000 105 40 34.33673 5.6632653 106 38 34.33673 3.6632653 107 41 36.40000 4.6000000 108 36 34.33673 1.6632653 109 43 36.40000 6.6000000 110 30 34.33673 -4.3367347 111 31 34.33673 -3.3367347 112 32 36.40000 -4.4000000 113 37 34.33673 2.6632653 114 37 34.33673 2.6632653 115 33 36.40000 -3.4000000 116 34 36.40000 -2.4000000 117 33 34.33673 -1.3367347 118 38 34.33673 3.6632653 119 33 34.33673 -1.3367347 120 31 31.21053 -0.2105263 121 38 36.40000 1.6000000 122 37 36.40000 0.6000000 123 36 34.33673 1.6632653 124 31 34.33673 -3.3367347 125 39 34.33673 4.6632653 126 44 36.40000 7.6000000 127 33 34.33673 -1.3367347 128 35 34.33673 0.6632653 129 32 34.33673 -2.3367347 130 28 34.33673 -6.3367347 131 40 34.33673 5.6632653 132 27 31.21053 -4.2105263 133 37 36.40000 0.6000000 134 32 31.21053 0.7894737 135 28 24.33333 3.6666667 136 34 34.33673 -0.3367347 137 30 34.33673 -4.3367347 138 35 34.33673 0.6632653 139 31 34.33673 -3.3367347 140 32 34.33673 -2.3367347 141 30 34.33673 -4.3367347 142 30 34.33673 -4.3367347 143 31 24.33333 6.6666667 144 40 34.33673 5.6632653 145 32 31.21053 0.7894737 146 36 34.33673 1.6632653 147 32 34.33673 -2.3367347 148 35 34.33673 0.6632653 149 38 36.40000 1.6000000 150 42 34.33673 7.6632653 151 34 36.40000 -2.4000000 152 35 36.40000 -1.4000000 153 38 34.33673 3.6632653 154 33 34.33673 -1.3367347 155 32 34.33673 -2.3367347 156 33 34.33673 -1.3367347 157 34 34.33673 -0.3367347 158 32 36.40000 -4.4000000 159 27 31.21053 -4.2105263 160 31 31.21053 -0.2105263 161 38 34.33673 3.6632653 162 34 36.40000 -2.4000000 163 24 31.21053 -7.2105263 164 30 34.33673 -4.3367347 165 26 24.33333 1.6666667 166 34 36.40000 -2.4000000 167 27 34.33673 -7.3367347 168 37 34.33673 2.6632653 169 36 34.33673 1.6632653 170 41 34.33673 6.6632653 171 29 24.33333 4.6666667 172 36 31.21053 4.7894737 173 32 34.33673 -2.3367347 174 37 34.33673 2.6632653 175 30 31.21053 -1.2105263 176 31 34.33673 -3.3367347 177 38 34.33673 3.6632653 178 36 34.33673 1.6632653 179 35 31.21053 3.7894737 180 31 34.33673 -3.3367347 181 38 34.33673 3.6632653 182 22 31.21053 -9.2105263 183 32 34.33673 -2.3367347 184 36 34.33673 1.6632653 185 39 34.33673 4.6632653 186 28 31.21053 -3.2105263 187 32 34.33673 -2.3367347 188 32 34.33673 -2.3367347 189 38 36.40000 1.6000000 190 32 34.33673 -2.3367347 191 35 34.33673 0.6632653 192 32 34.33673 -2.3367347 193 37 36.40000 0.6000000 194 34 34.33673 -0.3367347 195 33 34.33673 -1.3367347 196 33 34.33673 -1.3367347 197 30 31.21053 -1.2105263 198 24 31.21053 -7.2105263 199 34 34.33673 -0.3367347 200 34 34.33673 -0.3367347 201 33 34.33673 -1.3367347 202 34 34.33673 -0.3367347 203 35 34.33673 0.6632653 204 35 34.33673 0.6632653 205 36 34.33673 1.6632653 206 34 34.33673 -0.3367347 207 34 34.33673 -0.3367347 208 41 36.40000 4.6000000 209 32 36.40000 -4.4000000 210 30 34.33673 -4.3367347 211 35 34.33673 0.6632653 212 28 24.33333 3.6666667 213 33 34.33673 -1.3367347 214 39 34.33673 4.6632653 215 36 34.33673 1.6632653 216 36 36.40000 -0.4000000 217 35 34.33673 0.6632653 218 38 34.33673 3.6632653 219 33 34.33673 -1.3367347 220 31 34.33673 -3.3367347 221 32 34.33673 -2.3367347 222 31 34.33673 -3.3367347 223 33 34.33673 -1.3367347 224 34 34.33673 -0.3367347 225 34 34.33673 -0.3367347 226 34 31.21053 2.7894737 227 33 36.40000 -3.4000000 228 32 34.33673 -2.3367347 229 41 34.33673 6.6632653 230 34 34.33673 -0.3367347 231 36 34.33673 1.6632653 232 37 31.21053 5.7894737 233 36 31.21053 4.7894737 234 29 34.33673 -5.3367347 235 37 31.21053 5.7894737 236 27 34.33673 -7.3367347 237 35 34.33673 0.6632653 238 28 31.21053 -3.2105263 239 35 34.33673 0.6632653 240 29 34.33673 -5.3367347 241 32 34.33673 -2.3367347 242 36 34.33673 1.6632653 243 19 24.33333 -5.3333333 244 21 24.33333 -3.3333333 245 31 34.33673 -3.3367347 246 33 34.33673 -1.3367347 247 36 34.33673 1.6632653 248 33 34.33673 -1.3367347 249 37 34.33673 2.6632653 250 34 34.33673 -0.3367347 251 35 34.33673 0.6632653 252 31 34.33673 -3.3367347 253 37 34.33673 2.6632653 254 35 31.21053 3.7894737 255 27 31.21053 -4.2105263 256 34 36.40000 -2.4000000 257 40 34.33673 5.6632653 258 29 34.33673 -5.3367347 259 38 34.33673 3.6632653 260 34 34.33673 -0.3367347 261 21 31.21053 -10.2105263 262 36 34.33673 1.6632653 263 38 34.33673 3.6632653 264 30 31.21053 -1.2105263 265 35 34.33673 0.6632653 266 30 31.21053 -1.2105263 267 36 34.33673 1.6632653 268 34 36.40000 -2.4000000 269 35 34.33673 0.6632653 270 34 34.33673 -0.3367347 271 32 36.40000 -4.4000000 272 33 31.21053 1.7894737 273 33 34.33673 -1.3367347 274 26 34.33673 -8.3367347 275 35 36.40000 -1.4000000 276 21 24.33333 -3.3333333 277 38 34.33673 3.6632653 278 35 34.33673 0.6632653 279 33 34.33673 -1.3367347 280 37 34.33673 2.6632653 281 38 34.33673 3.6632653 282 34 34.33673 -0.3367347 283 27 31.21053 -4.2105263 284 16 24.33333 -8.3333333 285 40 36.40000 3.6000000 286 36 34.33673 1.6632653 287 42 36.40000 5.6000000 288 30 34.33673 -4.3367347 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/4a9zy1355235629.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5r04e1355235629.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/6qcu61355235629.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/72a4p1355235629.tab") + } > > try(system("convert tmp/29ftt1355235629.ps tmp/29ftt1355235629.png",intern=TRUE)) character(0) > try(system("convert tmp/3i6gv1355235629.ps tmp/3i6gv1355235629.png",intern=TRUE)) character(0) > try(system("convert tmp/4a9zy1355235629.ps tmp/4a9zy1355235629.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.094 1.029 11.027