R version 2.9.0 (2009-04-17) Copyright (C) 2009 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. 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,14 + ,14 + ,15 + ,15 + ,1 + ,13 + ,20 + ,20 + ,22 + ,22 + ,13 + ,13 + ,12 + ,12 + ,13 + ,13 + ,16 + ,16 + ,10 + ,10 + ,16 + ,16 + ,1 + ,13 + ,21 + ,21 + ,9 + ,9 + ,6 + ,6 + ,8 + ,8 + ,11 + ,11 + ,12 + ,12 + ,17 + ,17 + ,8 + ,8 + ,1 + ,19 + ,22 + ,22 + ,20 + ,20 + ,7 + ,7 + ,11 + ,11 + ,9 + ,9 + ,16 + ,16 + ,15 + ,15 + ,6 + ,6 + ,1 + ,13 + ,23 + ,23 + ,16 + ,16 + ,8 + ,8 + ,13 + ,13 + ,5 + ,5 + ,12 + ,12 + ,16 + ,16 + ,4 + ,4) + ,dim=c(18 + ,144) + ,dimnames=list(c('Pop' + ,'Happiness' + ,'Age' + ,'Age_p' + ,'Concern_over_mistakes' + ,'Concern_over_mistakes_p' + ,'Doubts_about_actions' + ,'Doubts_about_actions_p' + ,'Parental_expectations' + ,'Parental_expectations_p' + ,'Parental_criticism' + ,'Parental_criticism_p' + ,'Popularity' + ,'Popularity_p' + ,'Perceived_learning_competence' + ,'Perceived_learning_competence_p' + ,'Amotivation' + ,'Amotivation_p') + ,1:144)) > y <- array(NA,dim=c(18,144),dimnames=list(c('Pop','Happiness','Age','Age_p','Concern_over_mistakes','Concern_over_mistakes_p','Doubts_about_actions','Doubts_about_actions_p','Parental_expectations','Parental_expectations_p','Parental_criticism','Parental_criticism_p','Popularity','Popularity_p','Perceived_learning_competence','Perceived_learning_competence_p','Amotivation','Amotivation_p'),1:144)) > 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 Attaching package: 'zoo' The following object(s) are masked from package:base : 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 Happiness Pop Age Age_p Concern_over_mistakes Concern_over_mistakes_p 1 14 1 23 23 26 26 2 18 1 21 21 20 20 3 11 1 21 21 21 21 4 12 0 21 0 31 0 5 16 1 24 24 21 21 6 18 1 22 22 18 18 7 14 1 21 21 26 26 8 14 1 22 22 22 22 9 15 1 21 21 22 22 10 15 1 20 20 29 29 11 17 0 22 0 15 0 12 19 1 21 21 16 16 13 10 0 21 0 24 0 14 18 1 23 23 17 17 15 14 0 22 0 19 0 16 14 0 23 0 22 0 17 17 1 22 22 31 31 18 14 0 24 0 28 0 19 16 1 23 23 38 38 20 18 0 21 0 26 0 21 14 1 23 23 25 25 22 12 1 23 23 25 25 23 17 0 21 0 29 0 24 9 1 20 20 28 28 25 16 0 32 0 15 0 26 14 1 22 22 18 18 27 11 0 21 0 21 0 28 16 1 21 21 25 25 29 13 0 21 0 23 0 30 17 1 22 22 23 23 31 15 1 21 21 19 19 32 14 0 21 0 18 0 33 16 0 21 0 18 0 34 9 0 22 0 26 0 35 15 0 21 0 18 0 36 17 1 21 21 18 18 37 13 0 21 0 28 0 38 15 0 21 0 17 0 39 16 1 23 23 29 29 40 16 0 21 0 12 0 41 12 1 23 23 28 28 42 11 1 23 23 20 20 43 15 1 21 21 17 17 44 17 1 20 20 17 17 45 13 0 21 0 20 0 46 16 1 20 20 31 31 47 14 0 21 0 21 0 48 11 0 21 0 19 0 49 12 1 22 22 23 23 50 12 0 21 0 15 0 51 15 1 21 21 24 24 52 16 1 22 22 28 28 53 15 1 20 20 16 16 54 12 0 22 0 19 0 55 12 1 22 22 21 21 56 8 0 21 0 21 0 57 13 0 23 0 20 0 58 11 1 22 22 16 16 59 14 1 24 24 25 25 60 15 1 23 23 30 30 61 10 0 21 0 29 0 62 11 1 22 22 22 22 63 12 0 22 0 19 0 64 15 1 21 21 33 33 65 15 0 21 0 17 0 66 14 0 21 0 9 0 67 16 1 21 21 14 14 68 15 1 20 20 15 15 69 15 0 22 0 12 0 70 13 0 22 0 21 0 71 17 1 22 22 20 20 72 13 1 23 23 29 29 73 15 0 21 0 33 0 74 13 0 23 0 21 0 75 15 0 22 0 15 0 76 16 0 21 0 19 0 77 15 1 21 21 23 23 78 16 0 20 0 20 0 79 15 1 24 24 20 20 80 14 1 24 24 18 18 81 15 0 21 0 31 0 82 7 1 20 20 18 18 83 17 1 21 21 13 13 84 13 1 21 21 9 9 85 15 1 21 21 20 20 86 14 1 21 21 18 18 87 13 1 22 22 23 23 88 16 1 22 22 17 17 89 12 1 21 21 17 17 90 14 1 22 22 16 16 91 17 0 21 0 31 0 92 15 0 23 0 15 0 93 17 1 21 21 28 28 94 12 0 22 0 26 0 95 16 1 22 22 20 20 96 11 0 22 0 19 0 97 15 1 20 20 25 25 98 9 0 21 0 18 0 99 16 1 21 21 20 20 100 10 0 22 0 33 0 101 10 1 25 25 24 24 102 15 1 22 22 22 22 103 11 1 22 22 32 32 104 13 1 21 21 31 31 105 14 0 22 0 13 0 106 18 1 21 21 18 18 107 16 0 24 0 17 0 108 14 1 23 23 29 29 109 14 1 0 0 22 22 110 14 1 23 23 18 18 111 14 1 22 22 22 22 112 12 1 22 22 25 25 113 14 1 25 25 20 20 114 15 1 23 23 20 20 115 15 0 22 0 17 0 116 13 1 21 21 26 26 117 17 0 21 0 10 0 118 17 1 22 22 15 15 119 19 1 22 22 20 20 120 15 1 21 21 14 14 121 13 0 0 0 16 0 122 9 0 21 0 23 0 123 15 1 22 22 11 11 124 15 0 21 0 19 0 125 16 1 24 24 30 30 126 11 0 21 0 21 0 127 14 0 23 0 20 0 128 11 1 23 23 22 22 129 15 1 22 22 30 30 130 13 0 21 0 25 0 131 16 0 21 0 23 0 132 14 1 21 21 23 23 133 15 0 21 0 21 0 134 16 1 22 22 30 30 135 16 1 20 20 22 22 136 11 0 21 0 32 0 137 13 1 23 23 22 22 138 16 0 32 0 15 0 139 12 1 22 22 21 21 140 9 1 24 24 27 27 141 13 1 20 20 22 22 142 13 1 21 21 9 9 143 19 1 22 22 20 20 144 13 1 23 23 16 16 Doubts_about_actions Doubts_about_actions_p Parental_expectations 1 9 9 15 2 9 9 15 3 9 9 14 4 14 0 10 5 8 8 10 6 8 8 12 7 11 11 18 8 10 10 12 9 9 9 14 10 15 15 18 11 14 0 9 12 11 11 11 13 14 0 11 14 6 6 17 15 20 0 8 16 9 0 16 17 10 10 21 18 8 0 24 19 11 11 21 20 14 0 14 21 11 11 7 22 16 16 18 23 14 0 18 24 11 11 13 25 11 0 11 26 12 12 13 27 9 0 13 28 7 7 18 29 13 0 14 30 10 10 12 31 9 9 9 32 9 0 12 33 13 0 8 34 16 0 5 35 12 0 10 36 6 6 11 37 14 0 11 38 14 0 12 39 10 10 12 40 4 0 15 41 12 12 16 42 14 14 14 43 9 9 17 44 9 9 13 45 10 0 10 46 14 14 17 47 10 0 12 48 9 0 13 49 14 14 13 50 8 0 11 51 9 9 13 52 8 8 12 53 9 9 12 54 9 0 12 55 9 9 9 56 15 0 7 57 8 0 17 58 10 10 12 59 8 8 12 60 14 14 9 61 11 0 9 62 10 10 13 63 12 0 10 64 14 14 11 65 9 0 12 66 13 0 10 67 15 15 13 68 8 8 6 69 7 0 7 70 10 0 13 71 10 10 11 72 13 13 18 73 11 0 9 74 8 0 9 75 12 0 11 76 9 0 11 77 10 10 15 78 11 0 8 79 11 11 11 80 10 10 14 81 16 0 14 82 16 16 12 83 8 8 12 84 6 6 8 85 11 11 11 86 12 12 10 87 14 14 17 88 9 9 16 89 11 11 13 90 8 8 15 91 8 0 11 92 7 0 12 93 16 16 16 94 13 0 20 95 8 8 16 96 11 0 11 97 14 14 15 98 10 0 15 99 10 10 12 100 14 0 9 101 14 14 24 102 10 10 15 103 12 12 18 104 9 9 17 105 16 0 12 106 8 8 15 107 9 0 11 108 16 16 11 109 13 13 15 110 13 13 12 111 8 8 14 112 14 14 11 113 11 11 20 114 9 9 11 115 8 0 12 116 13 13 12 117 10 0 11 118 8 8 10 119 7 7 11 120 11 11 12 121 11 0 9 122 14 0 8 123 6 6 6 124 10 0 12 125 9 9 15 126 12 0 13 127 11 0 17 128 14 14 14 129 12 12 16 130 14 0 15 131 14 0 11 132 8 8 11 133 11 0 16 134 12 12 15 135 9 9 14 136 16 0 9 137 11 11 13 138 11 0 11 139 12 12 14 140 15 15 11 141 13 13 12 142 6 6 8 143 7 7 11 144 8 8 13 Parental_expectations_p Parental_criticism Parental_criticism_p Popularity 1 15 6 6 11 2 15 6 6 12 3 14 13 13 15 4 0 8 0 10 5 10 7 7 12 6 12 9 9 11 7 18 5 5 5 8 12 8 8 16 9 14 9 9 11 10 18 11 11 15 11 0 8 0 12 12 11 11 11 9 13 0 12 0 11 14 17 8 8 15 15 0 7 0 12 16 0 9 0 16 17 21 12 12 14 18 0 20 0 11 19 21 7 7 10 20 0 8 0 7 21 7 8 8 11 22 18 16 16 10 23 0 10 0 11 24 13 6 6 16 25 0 8 0 14 26 13 9 9 12 27 0 9 0 12 28 18 11 11 11 29 0 12 0 6 30 12 8 8 14 31 9 7 7 9 32 0 8 0 15 33 0 9 0 12 34 0 4 0 12 35 0 8 0 9 36 11 8 8 13 37 0 8 0 15 38 0 6 0 11 39 12 8 8 10 40 0 4 0 13 41 16 14 14 16 42 14 10 10 13 43 17 9 9 14 44 13 6 6 14 45 0 8 0 16 46 17 11 11 9 47 0 8 0 8 48 0 8 0 8 49 13 10 10 12 50 0 8 0 10 51 13 10 10 16 52 12 7 7 13 53 12 8 8 11 54 0 7 0 14 55 9 9 9 15 56 0 5 0 8 57 0 7 0 9 58 12 7 7 17 59 12 7 7 9 60 9 9 9 13 61 0 5 0 6 62 13 8 8 13 63 0 8 0 8 64 11 8 8 12 65 0 9 0 13 66 0 6 0 14 67 13 8 8 11 68 6 6 6 15 69 0 4 0 7 70 0 6 0 16 71 11 4 4 16 72 18 12 12 14 73 0 6 0 11 74 0 11 0 13 75 0 8 0 13 76 0 10 0 7 77 15 10 10 15 78 0 4 0 11 79 11 8 8 15 80 14 9 9 13 81 0 9 0 11 82 12 7 7 12 83 12 7 7 10 84 8 11 11 12 85 11 8 8 12 86 10 8 8 12 87 17 7 7 14 88 16 5 5 6 89 13 7 7 14 90 15 9 9 15 91 0 8 0 8 92 0 6 0 12 93 16 8 8 10 94 0 10 0 15 95 16 10 10 11 96 0 8 0 9 97 15 11 11 14 98 0 8 0 10 99 12 8 8 16 100 0 6 0 5 101 24 20 20 8 102 15 6 6 13 103 18 12 12 16 104 17 9 9 16 105 0 5 0 14 106 15 10 10 14 107 0 5 0 10 108 11 6 6 9 109 15 10 10 14 110 12 6 6 8 111 14 10 10 8 112 11 5 5 16 113 20 13 13 12 114 11 7 7 9 115 0 9 0 15 116 12 8 8 12 117 0 5 0 14 118 10 4 4 12 119 11 9 9 16 120 12 7 7 12 121 0 5 0 14 122 0 5 0 8 123 6 4 4 15 124 0 7 0 16 125 15 9 9 12 126 0 8 0 4 127 0 8 0 8 128 14 11 11 11 129 16 10 10 4 130 0 9 0 14 131 0 10 0 14 132 11 10 10 13 133 0 7 0 14 134 15 10 10 7 135 14 6 6 19 136 0 6 0 12 137 13 11 11 10 138 0 8 0 14 139 14 9 9 16 140 11 9 9 11 141 12 13 13 16 142 8 11 11 12 143 11 9 9 16 144 13 5 5 12 Popularity_p Perceived_learning_competence Perceived_learning_competence_p 1 11 13 13 2 12 16 16 3 15 19 19 4 0 15 0 5 12 14 14 6 11 13 13 7 5 19 19 8 16 15 15 9 11 14 14 10 15 15 15 11 0 16 0 12 9 16 16 13 0 16 0 14 15 17 17 15 0 15 0 16 0 15 0 17 14 20 20 18 0 18 0 19 10 16 16 20 0 16 0 21 11 19 19 22 10 16 16 23 0 17 0 24 16 17 17 25 0 16 0 26 12 15 15 27 0 14 0 28 11 15 15 29 0 12 0 30 14 14 14 31 9 16 16 32 0 14 0 33 0 7 0 34 0 10 0 35 0 14 0 36 13 16 16 37 0 16 0 38 0 16 0 39 10 14 14 40 0 20 0 41 16 14 14 42 13 11 11 43 14 15 15 44 14 16 16 45 0 14 0 46 9 16 16 47 0 14 0 48 0 12 0 49 12 16 16 50 0 9 0 51 16 14 14 52 13 16 16 53 11 16 16 54 0 15 0 55 15 16 16 56 0 12 0 57 0 16 0 58 17 16 16 59 9 14 14 60 13 16 16 61 0 17 0 62 13 18 18 63 0 18 0 64 12 12 12 65 0 16 0 66 0 10 0 67 11 14 14 68 15 18 18 69 0 18 0 70 0 16 0 71 16 16 16 72 14 16 16 73 0 13 0 74 0 16 0 75 0 16 0 76 0 20 0 77 15 16 16 78 0 15 0 79 15 15 15 80 13 16 16 81 0 14 0 82 12 15 15 83 10 12 12 84 12 17 17 85 12 16 16 86 12 15 15 87 14 13 13 88 6 16 16 89 14 16 16 90 15 16 16 91 0 16 0 92 0 14 0 93 10 16 16 94 0 16 0 95 11 20 20 96 0 15 0 97 14 16 16 98 0 13 0 99 16 17 17 100 0 16 0 101 8 12 12 102 13 16 16 103 16 16 16 104 16 17 17 105 0 13 0 106 14 12 12 107 0 18 0 108 9 14 14 109 14 14 14 110 8 13 13 111 8 16 16 112 16 13 13 113 12 16 16 114 9 13 13 115 0 16 0 116 12 16 16 117 0 15 0 118 12 17 17 119 16 15 15 120 12 12 12 121 0 16 0 122 0 10 0 123 15 16 16 124 0 14 0 125 12 15 15 126 0 13 0 127 0 15 0 128 11 11 11 129 4 12 12 130 0 8 0 131 0 15 0 132 13 17 17 133 0 16 0 134 7 10 10 135 19 18 18 136 0 13 0 137 10 15 15 138 0 16 0 139 16 16 16 140 11 14 14 141 16 10 10 142 12 17 17 143 16 15 15 144 12 16 16 Amotivation Amotivation_p 1 4 4 2 4 4 3 6 6 4 8 0 5 8 8 6 4 4 7 4 4 8 5 5 9 5 5 10 8 8 11 4 0 12 4 4 13 4 0 14 4 4 15 4 0 16 8 0 17 4 4 18 4 0 19 4 4 20 4 0 21 8 8 22 3 3 23 4 0 24 4 4 25 4 0 26 10 10 27 5 0 28 4 4 29 4 0 30 4 4 31 4 4 32 4 0 33 10 0 34 4 0 35 8 0 36 4 4 37 4 0 38 4 0 39 7 7 40 4 0 41 4 4 42 4 4 43 4 4 44 6 6 45 5 0 46 16 16 47 5 0 48 12 0 49 6 6 50 9 0 51 9 9 52 4 4 53 4 4 54 4 0 55 5 5 56 4 0 57 5 0 58 4 4 59 6 6 60 4 4 61 4 0 62 18 18 63 4 0 64 4 4 65 6 0 66 4 0 67 5 5 68 4 4 69 4 0 70 5 0 71 5 5 72 8 8 73 5 0 74 4 0 75 4 0 76 4 0 77 5 5 78 4 0 79 4 4 80 4 4 81 8 0 82 14 14 83 4 4 84 8 8 85 8 8 86 4 4 87 6 6 88 4 4 89 7 7 90 3 3 91 4 0 92 4 0 93 4 4 94 7 0 95 4 4 96 4 0 97 6 6 98 8 0 99 4 4 100 4 0 101 4 4 102 5 5 103 6 6 104 4 4 105 5 0 106 7 7 107 4 0 108 8 8 109 6 6 110 8 8 111 8 8 112 4 4 113 5 5 114 6 6 115 5 0 116 5 5 117 4 0 118 4 4 119 6 6 120 7 7 121 4 0 122 10 0 123 8 8 124 5 0 125 11 11 126 7 0 127 4 0 128 8 8 129 6 6 130 4 0 131 8 0 132 5 5 133 4 0 134 8 8 135 4 4 136 6 0 137 4 4 138 4 0 139 6 6 140 15 15 141 16 16 142 8 8 143 6 6 144 4 4 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop 7.803059 17.416371 Age Age_p 0.057917 -0.156072 Concern_over_mistakes Concern_over_mistakes_p -0.092195 0.149225 Doubts_about_actions Doubts_about_actions_p 0.011622 -0.445948 Parental_expectations Parental_expectations_p 0.044157 -0.009354 Parental_criticism Parental_criticism_p 0.047104 -0.194861 Popularity Popularity_p 0.151966 -0.265854 Perceived_learning_competence Perceived_learning_competence_p 0.275841 -0.419494 Amotivation Amotivation_p -0.059154 -0.092356 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.7711 -1.3616 -0.0353 1.1223 4.9794 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.803059 3.855283 2.024 0.045086 * Pop 17.416371 5.139877 3.388 0.000938 *** Age 0.057917 0.080771 0.717 0.474672 Age_p -0.156072 0.120191 -1.299 0.196475 Concern_over_mistakes -0.092195 0.057377 -1.607 0.110594 Concern_over_mistakes_p 0.149225 0.076320 1.955 0.052768 . Doubts_about_actions 0.011622 0.116138 0.100 0.920451 Doubts_about_actions_p -0.445948 0.156561 -2.848 0.005133 ** Parental_expectations 0.044157 0.113091 0.390 0.696857 Parental_expectations_p -0.009354 0.144955 -0.065 0.948648 Parental_criticism 0.047104 0.144518 0.326 0.745012 Parental_criticism_p -0.194861 0.176933 -1.101 0.272855 Popularity 0.151966 0.097226 1.563 0.120557 Popularity_p -0.265854 0.128616 -2.067 0.040779 * Perceived_learning_competence 0.275841 0.136637 2.019 0.045631 * Perceived_learning_competence_p -0.419494 0.182803 -2.295 0.023399 * Amotivation -0.059154 0.171926 -0.344 0.731369 Amotivation_p -0.092356 0.190163 -0.486 0.628048 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.119 on 126 degrees of freedom Multiple R-squared: 0.3038, Adjusted R-squared: 0.2098 F-statistic: 3.234 on 17 and 126 DF, p-value: 7.94e-05 > 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,] 0.9433012 0.11339751 0.05669876 [2,] 0.9842330 0.03153408 0.01576704 [3,] 0.9712400 0.05752000 0.02876000 [4,] 0.9869534 0.02609322 0.01304661 [5,] 0.9754345 0.04913092 0.02456546 [6,] 0.9718670 0.05626594 0.02813297 [7,] 0.9602286 0.07954284 0.03977142 [8,] 0.9443344 0.11133119 0.05566559 [9,] 0.9249286 0.15014287 0.07507144 [10,] 0.9278866 0.14422681 0.07211340 [11,] 0.8961190 0.20776210 0.10388105 [12,] 0.9055081 0.18898390 0.09449195 [13,] 0.9633721 0.07325590 0.03662795 [14,] 0.9509469 0.09810628 0.04905314 [15,] 0.9475609 0.10487822 0.05243911 [16,] 0.9288689 0.14226220 0.07113110 [17,] 0.9278163 0.14436747 0.07218374 [18,] 0.9258873 0.14822536 0.07411268 [19,] 0.9030133 0.19397330 0.09698665 [20,] 0.8729609 0.25407830 0.12703915 [21,] 0.8584022 0.28319552 0.14159776 [22,] 0.8780599 0.24388013 0.12194006 [23,] 0.8470172 0.30596560 0.15298280 [24,] 0.8335102 0.33297956 0.16648978 [25,] 0.8162200 0.36756003 0.18378002 [26,] 0.8354563 0.32908747 0.16454373 [27,] 0.8021920 0.39561605 0.19780803 [28,] 0.8745200 0.25096009 0.12548004 [29,] 0.8444576 0.31108480 0.15554240 [30,] 0.8143699 0.37126020 0.18563010 [31,] 0.7743159 0.45136814 0.22568407 [32,] 0.7373908 0.52521830 0.26260915 [33,] 0.6928037 0.61439251 0.30719625 [34,] 0.6961574 0.60768523 0.30384262 [35,] 0.7060370 0.58792597 0.29396298 [36,] 0.8259081 0.34818376 0.17409188 [37,] 0.8075401 0.38491988 0.19245994 [38,] 0.8383195 0.32336102 0.16168051 [39,] 0.8551077 0.28978457 0.14489228 [40,] 0.8848998 0.23020043 0.11510021 [41,] 0.8878283 0.22434349 0.11217174 [42,] 0.8952591 0.20948172 0.10474086 [43,] 0.8876644 0.22467117 0.11233559 [44,] 0.8625903 0.27481945 0.13740972 [45,] 0.8374196 0.32516079 0.16258039 [46,] 0.8100126 0.37997472 0.18998736 [47,] 0.8616520 0.27669599 0.13834800 [48,] 0.8315273 0.33694548 0.16847274 [49,] 0.8222682 0.35546369 0.17773184 [50,] 0.8241561 0.35168771 0.17584386 [51,] 0.8418505 0.31629902 0.15814951 [52,] 0.8095861 0.38082784 0.19041392 [53,] 0.8750901 0.24981976 0.12490988 [54,] 0.8812716 0.23745687 0.11872843 [55,] 0.8533247 0.29335067 0.14667534 [56,] 0.8335720 0.33285592 0.16642796 [57,] 0.7991605 0.40167902 0.20083951 [58,] 0.8099838 0.38003247 0.19001623 [59,] 0.7804944 0.43901115 0.21950557 [60,] 0.7377268 0.52454645 0.26227323 [61,] 0.7821473 0.43570542 0.21785271 [62,] 0.8588670 0.28226597 0.14113299 [63,] 0.8271060 0.34578805 0.17289402 [64,] 0.8126411 0.37471775 0.18735888 [65,] 0.7860139 0.42797212 0.21398606 [66,] 0.7423971 0.51520587 0.25760294 [67,] 0.6965791 0.60684175 0.30342087 [68,] 0.6475641 0.70487171 0.35243586 [69,] 0.6281053 0.74378932 0.37189466 [70,] 0.5923810 0.81523810 0.40761905 [71,] 0.7450309 0.50993820 0.25496910 [72,] 0.7027863 0.59442736 0.29721368 [73,] 0.8584589 0.28308224 0.14154112 [74,] 0.8605099 0.27898015 0.13949008 [75,] 0.8564402 0.28711962 0.14355981 [76,] 0.8773990 0.24520198 0.12260099 [77,] 0.9287118 0.14257649 0.07128825 [78,] 0.9677276 0.06454473 0.03227237 [79,] 0.9716578 0.05668444 0.02834222 [80,] 0.9626806 0.07463884 0.03731942 [81,] 0.9556781 0.08864376 0.04432188 [82,] 0.9371793 0.12564150 0.06282075 [83,] 0.9385867 0.12282665 0.06141333 [84,] 0.9693595 0.06128095 0.03064047 [85,] 0.9625880 0.07482390 0.03741195 [86,] 0.9527227 0.09455466 0.04727733 [87,] 0.9533829 0.09323415 0.04661707 [88,] 0.9809816 0.03803671 0.01901836 [89,] 0.9722425 0.05551509 0.02775755 [90,] 0.9839387 0.03212251 0.01606125 [91,] 0.9779439 0.04411218 0.02205609 [92,] 0.9735933 0.05281337 0.02640669 [93,] 0.9789401 0.04211978 0.02105989 [94,] 0.9689623 0.06207537 0.03103769 [95,] 0.9464984 0.10700323 0.05350161 [96,] 0.9133336 0.17333287 0.08666644 [97,] 0.9037759 0.19244825 0.09622412 [98,] 0.9557250 0.08855001 0.04427500 [99,] 0.9437897 0.11242069 0.05621034 [100,] 0.9567454 0.08650913 0.04325456 [101,] 0.9114055 0.17718890 0.08859445 [102,] 0.8257382 0.34852365 0.17426183 [103,] 0.6797981 0.64040389 0.32020195 > postscript(file="/var/www/html/rcomp/tmp/1muex1290549744.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/3fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5fmdi1290549744.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 = 144 Frequency = 1 1 2 3 4 5 6 -2.444903811 2.245810250 -2.666478389 -0.326414369 0.689427695 2.026532812 7 8 9 10 11 12 -1.846137955 -0.472437219 -0.639859864 2.678766249 2.368315427 4.878914985 13 14 15 16 17 18 -3.868776962 2.021456110 0.034468925 -0.437739942 2.631071515 -1.106577532 19 20 21 22 23 24 -0.004601356 4.979420693 0.522673292 0.191127752 3.101462787 -5.771122771 25 26 27 28 29 30 0.431767830 1.039287254 -2.575386624 -0.531157980 0.781372810 1.947594771 31 32 33 34 35 36 -0.682247612 -0.275762771 4.548989286 -2.620688634 1.926100353 0.605504979 37 38 39 40 41 42 -0.919445247 0.724323651 0.702550216 -0.065997848 -1.395643570 -2.364794092 43 44 45 46 47 48 -0.125313674 1.919145534 -1.107491221 3.837589568 1.112117339 -1.139046771 49 50 51 52 53 54 -0.691840103 -0.061761182 0.604118119 -0.180545743 -0.338190944 -2.318255619 55 56 57 58 59 60 -2.567802335 -4.091363105 -0.949999629 -3.171983867 -2.252980959 1.809429857 61 62 63 64 65 66 -2.470905774 -1.448315807 -2.227637101 0.536166618 0.455496023 0.285849468 67 68 69 70 71 72 3.309382772 -0.059325649 0.657959335 -1.663159844 2.229051799 0.282103175 73 74 75 76 77 78 3.253459838 -1.359979092 0.151277653 1.327062264 0.593246352 2.640368992 79 80 81 82 83 84 1.041665852 -0.319375576 2.550484117 -4.074390282 0.660472666 -1.697741673 85 86 87 88 89 90 1.155227991 -0.011275471 -0.477505730 -0.350830017 -1.814779742 -1.228817355 91 92 93 94 95 96 4.490631695 0.905168084 3.862789074 -2.464292566 0.926587251 -2.584615008 97 98 99 100 101 102 2.303714329 -4.147568394 1.679262575 -1.814203311 -2.692851697 -0.070370017 103 104 105 106 107 108 -2.496713007 -2.408651336 -0.247732325 2.624264341 1.300226633 1.085419166 109 110 111 112 113 114 -1.357700406 0.117426045 -1.428101093 -1.753504865 0.518877006 -0.740510815 115 116 117 118 119 120 0.046114795 -0.807631977 2.036649052 1.216944356 3.672713803 0.588726342 121 122 123 124 125 126 -0.393081380 -2.032901141 -0.480326139 0.759102758 1.330165215 -0.953269376 127 128 129 130 131 132 0.336684949 -1.952832876 -0.549823784 0.938932632 2.189795689 -1.220316093 133 134 135 136 137 138 0.448336818 0.842356455 1.152917104 -0.989654958 -1.366324228 0.431767830 139 140 141 142 143 144 -1.173441719 -2.405076558 1.321357979 -1.697741673 3.672713803 -2.842235195 > postscript(file="/var/www/html/rcomp/tmp/6qvck1290549744.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 = 144 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.444903811 NA 1 2.245810250 -2.444903811 2 -2.666478389 2.245810250 3 -0.326414369 -2.666478389 4 0.689427695 -0.326414369 5 2.026532812 0.689427695 6 -1.846137955 2.026532812 7 -0.472437219 -1.846137955 8 -0.639859864 -0.472437219 9 2.678766249 -0.639859864 10 2.368315427 2.678766249 11 4.878914985 2.368315427 12 -3.868776962 4.878914985 13 2.021456110 -3.868776962 14 0.034468925 2.021456110 15 -0.437739942 0.034468925 16 2.631071515 -0.437739942 17 -1.106577532 2.631071515 18 -0.004601356 -1.106577532 19 4.979420693 -0.004601356 20 0.522673292 4.979420693 21 0.191127752 0.522673292 22 3.101462787 0.191127752 23 -5.771122771 3.101462787 24 0.431767830 -5.771122771 25 1.039287254 0.431767830 26 -2.575386624 1.039287254 27 -0.531157980 -2.575386624 28 0.781372810 -0.531157980 29 1.947594771 0.781372810 30 -0.682247612 1.947594771 31 -0.275762771 -0.682247612 32 4.548989286 -0.275762771 33 -2.620688634 4.548989286 34 1.926100353 -2.620688634 35 0.605504979 1.926100353 36 -0.919445247 0.605504979 37 0.724323651 -0.919445247 38 0.702550216 0.724323651 39 -0.065997848 0.702550216 40 -1.395643570 -0.065997848 41 -2.364794092 -1.395643570 42 -0.125313674 -2.364794092 43 1.919145534 -0.125313674 44 -1.107491221 1.919145534 45 3.837589568 -1.107491221 46 1.112117339 3.837589568 47 -1.139046771 1.112117339 48 -0.691840103 -1.139046771 49 -0.061761182 -0.691840103 50 0.604118119 -0.061761182 51 -0.180545743 0.604118119 52 -0.338190944 -0.180545743 53 -2.318255619 -0.338190944 54 -2.567802335 -2.318255619 55 -4.091363105 -2.567802335 56 -0.949999629 -4.091363105 57 -3.171983867 -0.949999629 58 -2.252980959 -3.171983867 59 1.809429857 -2.252980959 60 -2.470905774 1.809429857 61 -1.448315807 -2.470905774 62 -2.227637101 -1.448315807 63 0.536166618 -2.227637101 64 0.455496023 0.536166618 65 0.285849468 0.455496023 66 3.309382772 0.285849468 67 -0.059325649 3.309382772 68 0.657959335 -0.059325649 69 -1.663159844 0.657959335 70 2.229051799 -1.663159844 71 0.282103175 2.229051799 72 3.253459838 0.282103175 73 -1.359979092 3.253459838 74 0.151277653 -1.359979092 75 1.327062264 0.151277653 76 0.593246352 1.327062264 77 2.640368992 0.593246352 78 1.041665852 2.640368992 79 -0.319375576 1.041665852 80 2.550484117 -0.319375576 81 -4.074390282 2.550484117 82 0.660472666 -4.074390282 83 -1.697741673 0.660472666 84 1.155227991 -1.697741673 85 -0.011275471 1.155227991 86 -0.477505730 -0.011275471 87 -0.350830017 -0.477505730 88 -1.814779742 -0.350830017 89 -1.228817355 -1.814779742 90 4.490631695 -1.228817355 91 0.905168084 4.490631695 92 3.862789074 0.905168084 93 -2.464292566 3.862789074 94 0.926587251 -2.464292566 95 -2.584615008 0.926587251 96 2.303714329 -2.584615008 97 -4.147568394 2.303714329 98 1.679262575 -4.147568394 99 -1.814203311 1.679262575 100 -2.692851697 -1.814203311 101 -0.070370017 -2.692851697 102 -2.496713007 -0.070370017 103 -2.408651336 -2.496713007 104 -0.247732325 -2.408651336 105 2.624264341 -0.247732325 106 1.300226633 2.624264341 107 1.085419166 1.300226633 108 -1.357700406 1.085419166 109 0.117426045 -1.357700406 110 -1.428101093 0.117426045 111 -1.753504865 -1.428101093 112 0.518877006 -1.753504865 113 -0.740510815 0.518877006 114 0.046114795 -0.740510815 115 -0.807631977 0.046114795 116 2.036649052 -0.807631977 117 1.216944356 2.036649052 118 3.672713803 1.216944356 119 0.588726342 3.672713803 120 -0.393081380 0.588726342 121 -2.032901141 -0.393081380 122 -0.480326139 -2.032901141 123 0.759102758 -0.480326139 124 1.330165215 0.759102758 125 -0.953269376 1.330165215 126 0.336684949 -0.953269376 127 -1.952832876 0.336684949 128 -0.549823784 -1.952832876 129 0.938932632 -0.549823784 130 2.189795689 0.938932632 131 -1.220316093 2.189795689 132 0.448336818 -1.220316093 133 0.842356455 0.448336818 134 1.152917104 0.842356455 135 -0.989654958 1.152917104 136 -1.366324228 -0.989654958 137 0.431767830 -1.366324228 138 -1.173441719 0.431767830 139 -2.405076558 -1.173441719 140 1.321357979 -2.405076558 141 -1.697741673 1.321357979 142 3.672713803 -1.697741673 143 -2.842235195 3.672713803 144 NA -2.842235195 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.245810250 -2.444903811 [2,] -2.666478389 2.245810250 [3,] -0.326414369 -2.666478389 [4,] 0.689427695 -0.326414369 [5,] 2.026532812 0.689427695 [6,] -1.846137955 2.026532812 [7,] -0.472437219 -1.846137955 [8,] -0.639859864 -0.472437219 [9,] 2.678766249 -0.639859864 [10,] 2.368315427 2.678766249 [11,] 4.878914985 2.368315427 [12,] -3.868776962 4.878914985 [13,] 2.021456110 -3.868776962 [14,] 0.034468925 2.021456110 [15,] -0.437739942 0.034468925 [16,] 2.631071515 -0.437739942 [17,] -1.106577532 2.631071515 [18,] -0.004601356 -1.106577532 [19,] 4.979420693 -0.004601356 [20,] 0.522673292 4.979420693 [21,] 0.191127752 0.522673292 [22,] 3.101462787 0.191127752 [23,] -5.771122771 3.101462787 [24,] 0.431767830 -5.771122771 [25,] 1.039287254 0.431767830 [26,] -2.575386624 1.039287254 [27,] -0.531157980 -2.575386624 [28,] 0.781372810 -0.531157980 [29,] 1.947594771 0.781372810 [30,] -0.682247612 1.947594771 [31,] -0.275762771 -0.682247612 [32,] 4.548989286 -0.275762771 [33,] -2.620688634 4.548989286 [34,] 1.926100353 -2.620688634 [35,] 0.605504979 1.926100353 [36,] -0.919445247 0.605504979 [37,] 0.724323651 -0.919445247 [38,] 0.702550216 0.724323651 [39,] -0.065997848 0.702550216 [40,] -1.395643570 -0.065997848 [41,] -2.364794092 -1.395643570 [42,] -0.125313674 -2.364794092 [43,] 1.919145534 -0.125313674 [44,] -1.107491221 1.919145534 [45,] 3.837589568 -1.107491221 [46,] 1.112117339 3.837589568 [47,] -1.139046771 1.112117339 [48,] -0.691840103 -1.139046771 [49,] -0.061761182 -0.691840103 [50,] 0.604118119 -0.061761182 [51,] -0.180545743 0.604118119 [52,] -0.338190944 -0.180545743 [53,] -2.318255619 -0.338190944 [54,] -2.567802335 -2.318255619 [55,] -4.091363105 -2.567802335 [56,] -0.949999629 -4.091363105 [57,] -3.171983867 -0.949999629 [58,] -2.252980959 -3.171983867 [59,] 1.809429857 -2.252980959 [60,] -2.470905774 1.809429857 [61,] -1.448315807 -2.470905774 [62,] -2.227637101 -1.448315807 [63,] 0.536166618 -2.227637101 [64,] 0.455496023 0.536166618 [65,] 0.285849468 0.455496023 [66,] 3.309382772 0.285849468 [67,] -0.059325649 3.309382772 [68,] 0.657959335 -0.059325649 [69,] -1.663159844 0.657959335 [70,] 2.229051799 -1.663159844 [71,] 0.282103175 2.229051799 [72,] 3.253459838 0.282103175 [73,] -1.359979092 3.253459838 [74,] 0.151277653 -1.359979092 [75,] 1.327062264 0.151277653 [76,] 0.593246352 1.327062264 [77,] 2.640368992 0.593246352 [78,] 1.041665852 2.640368992 [79,] -0.319375576 1.041665852 [80,] 2.550484117 -0.319375576 [81,] -4.074390282 2.550484117 [82,] 0.660472666 -4.074390282 [83,] -1.697741673 0.660472666 [84,] 1.155227991 -1.697741673 [85,] -0.011275471 1.155227991 [86,] -0.477505730 -0.011275471 [87,] -0.350830017 -0.477505730 [88,] -1.814779742 -0.350830017 [89,] -1.228817355 -1.814779742 [90,] 4.490631695 -1.228817355 [91,] 0.905168084 4.490631695 [92,] 3.862789074 0.905168084 [93,] -2.464292566 3.862789074 [94,] 0.926587251 -2.464292566 [95,] -2.584615008 0.926587251 [96,] 2.303714329 -2.584615008 [97,] -4.147568394 2.303714329 [98,] 1.679262575 -4.147568394 [99,] -1.814203311 1.679262575 [100,] -2.692851697 -1.814203311 [101,] -0.070370017 -2.692851697 [102,] -2.496713007 -0.070370017 [103,] -2.408651336 -2.496713007 [104,] -0.247732325 -2.408651336 [105,] 2.624264341 -0.247732325 [106,] 1.300226633 2.624264341 [107,] 1.085419166 1.300226633 [108,] -1.357700406 1.085419166 [109,] 0.117426045 -1.357700406 [110,] -1.428101093 0.117426045 [111,] -1.753504865 -1.428101093 [112,] 0.518877006 -1.753504865 [113,] -0.740510815 0.518877006 [114,] 0.046114795 -0.740510815 [115,] -0.807631977 0.046114795 [116,] 2.036649052 -0.807631977 [117,] 1.216944356 2.036649052 [118,] 3.672713803 1.216944356 [119,] 0.588726342 3.672713803 [120,] -0.393081380 0.588726342 [121,] -2.032901141 -0.393081380 [122,] -0.480326139 -2.032901141 [123,] 0.759102758 -0.480326139 [124,] 1.330165215 0.759102758 [125,] -0.953269376 1.330165215 [126,] 0.336684949 -0.953269376 [127,] -1.952832876 0.336684949 [128,] -0.549823784 -1.952832876 [129,] 0.938932632 -0.549823784 [130,] 2.189795689 0.938932632 [131,] -1.220316093 2.189795689 [132,] 0.448336818 -1.220316093 [133,] 0.842356455 0.448336818 [134,] 1.152917104 0.842356455 [135,] -0.989654958 1.152917104 [136,] -1.366324228 -0.989654958 [137,] 0.431767830 -1.366324228 [138,] -1.173441719 0.431767830 [139,] -2.405076558 -1.173441719 [140,] 1.321357979 -2.405076558 [141,] -1.697741673 1.321357979 [142,] 3.672713803 -1.697741673 [143,] -2.842235195 3.672713803 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.245810250 -2.444903811 2 -2.666478389 2.245810250 3 -0.326414369 -2.666478389 4 0.689427695 -0.326414369 5 2.026532812 0.689427695 6 -1.846137955 2.026532812 7 -0.472437219 -1.846137955 8 -0.639859864 -0.472437219 9 2.678766249 -0.639859864 10 2.368315427 2.678766249 11 4.878914985 2.368315427 12 -3.868776962 4.878914985 13 2.021456110 -3.868776962 14 0.034468925 2.021456110 15 -0.437739942 0.034468925 16 2.631071515 -0.437739942 17 -1.106577532 2.631071515 18 -0.004601356 -1.106577532 19 4.979420693 -0.004601356 20 0.522673292 4.979420693 21 0.191127752 0.522673292 22 3.101462787 0.191127752 23 -5.771122771 3.101462787 24 0.431767830 -5.771122771 25 1.039287254 0.431767830 26 -2.575386624 1.039287254 27 -0.531157980 -2.575386624 28 0.781372810 -0.531157980 29 1.947594771 0.781372810 30 -0.682247612 1.947594771 31 -0.275762771 -0.682247612 32 4.548989286 -0.275762771 33 -2.620688634 4.548989286 34 1.926100353 -2.620688634 35 0.605504979 1.926100353 36 -0.919445247 0.605504979 37 0.724323651 -0.919445247 38 0.702550216 0.724323651 39 -0.065997848 0.702550216 40 -1.395643570 -0.065997848 41 -2.364794092 -1.395643570 42 -0.125313674 -2.364794092 43 1.919145534 -0.125313674 44 -1.107491221 1.919145534 45 3.837589568 -1.107491221 46 1.112117339 3.837589568 47 -1.139046771 1.112117339 48 -0.691840103 -1.139046771 49 -0.061761182 -0.691840103 50 0.604118119 -0.061761182 51 -0.180545743 0.604118119 52 -0.338190944 -0.180545743 53 -2.318255619 -0.338190944 54 -2.567802335 -2.318255619 55 -4.091363105 -2.567802335 56 -0.949999629 -4.091363105 57 -3.171983867 -0.949999629 58 -2.252980959 -3.171983867 59 1.809429857 -2.252980959 60 -2.470905774 1.809429857 61 -1.448315807 -2.470905774 62 -2.227637101 -1.448315807 63 0.536166618 -2.227637101 64 0.455496023 0.536166618 65 0.285849468 0.455496023 66 3.309382772 0.285849468 67 -0.059325649 3.309382772 68 0.657959335 -0.059325649 69 -1.663159844 0.657959335 70 2.229051799 -1.663159844 71 0.282103175 2.229051799 72 3.253459838 0.282103175 73 -1.359979092 3.253459838 74 0.151277653 -1.359979092 75 1.327062264 0.151277653 76 0.593246352 1.327062264 77 2.640368992 0.593246352 78 1.041665852 2.640368992 79 -0.319375576 1.041665852 80 2.550484117 -0.319375576 81 -4.074390282 2.550484117 82 0.660472666 -4.074390282 83 -1.697741673 0.660472666 84 1.155227991 -1.697741673 85 -0.011275471 1.155227991 86 -0.477505730 -0.011275471 87 -0.350830017 -0.477505730 88 -1.814779742 -0.350830017 89 -1.228817355 -1.814779742 90 4.490631695 -1.228817355 91 0.905168084 4.490631695 92 3.862789074 0.905168084 93 -2.464292566 3.862789074 94 0.926587251 -2.464292566 95 -2.584615008 0.926587251 96 2.303714329 -2.584615008 97 -4.147568394 2.303714329 98 1.679262575 -4.147568394 99 -1.814203311 1.679262575 100 -2.692851697 -1.814203311 101 -0.070370017 -2.692851697 102 -2.496713007 -0.070370017 103 -2.408651336 -2.496713007 104 -0.247732325 -2.408651336 105 2.624264341 -0.247732325 106 1.300226633 2.624264341 107 1.085419166 1.300226633 108 -1.357700406 1.085419166 109 0.117426045 -1.357700406 110 -1.428101093 0.117426045 111 -1.753504865 -1.428101093 112 0.518877006 -1.753504865 113 -0.740510815 0.518877006 114 0.046114795 -0.740510815 115 -0.807631977 0.046114795 116 2.036649052 -0.807631977 117 1.216944356 2.036649052 118 3.672713803 1.216944356 119 0.588726342 3.672713803 120 -0.393081380 0.588726342 121 -2.032901141 -0.393081380 122 -0.480326139 -2.032901141 123 0.759102758 -0.480326139 124 1.330165215 0.759102758 125 -0.953269376 1.330165215 126 0.336684949 -0.953269376 127 -1.952832876 0.336684949 128 -0.549823784 -1.952832876 129 0.938932632 -0.549823784 130 2.189795689 0.938932632 131 -1.220316093 2.189795689 132 0.448336818 -1.220316093 133 0.842356455 0.448336818 134 1.152917104 0.842356455 135 -0.989654958 1.152917104 136 -1.366324228 -0.989654958 137 0.431767830 -1.366324228 138 -1.173441719 0.431767830 139 -2.405076558 -1.173441719 140 1.321357979 -2.405076558 141 -1.697741673 1.321357979 142 3.672713803 -1.697741673 143 -2.842235195 3.672713803 > 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/html/rcomp/tmp/7i4u51290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8i4u51290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9tet91290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10tet91290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/html/rcomp/tmp/11ew9e1290549744.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/html/rcomp/tmp/12ixqk1290549744.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/html/rcomp/tmp/13wo6t1290549744.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/html/rcomp/tmp/140pnz1290549744.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/html/rcomp/tmp/15l8l51290549744.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/html/rcomp/tmp/166q2t1290549744.tab") + } > > try(system("convert tmp/1muex1290549744.ps tmp/1muex1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/2fmdi1290549744.ps tmp/2fmdi1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/3fmdi1290549744.ps tmp/3fmdi1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/4fmdi1290549744.ps tmp/4fmdi1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/5fmdi1290549744.ps tmp/5fmdi1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/6qvck1290549744.ps tmp/6qvck1290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/7i4u51290549744.ps tmp/7i4u51290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/8i4u51290549744.ps tmp/8i4u51290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/9tet91290549744.ps tmp/9tet91290549744.png",intern=TRUE)) character(0) > try(system("convert tmp/10tet91290549744.ps tmp/10tet91290549744.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.838 1.713 10.948