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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,147 + ,147 + ,3 + ,3 + ,3 + ,3 + ,2 + ,2 + ,4 + ,4 + ,2 + ,2 + ,5 + ,5 + ,3 + ,0 + ,148 + ,0 + ,3 + ,0 + ,2 + ,0 + ,2 + ,0 + ,4 + ,0 + ,2 + ,0 + ,3 + ,0 + ,4 + ,1 + ,149 + ,149 + ,3 + ,3 + ,2 + ,2 + ,1 + ,1 + ,1 + ,1 + ,3 + ,3 + ,2 + ,2 + ,3 + ,0 + ,150 + ,0 + ,4 + ,0 + ,4 + ,0 + ,4 + ,0 + ,4 + ,0 + ,2 + ,0 + ,4 + ,0 + ,4 + ,0 + ,151 + ,0 + ,4 + ,0 + ,3 + ,0 + ,2 + ,0 + ,4 + ,0 + ,1 + ,0 + ,3 + ,0 + ,4 + ,1 + ,152 + ,152 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,3 + ,3 + ,2 + ,2 + ,4 + ,4 + ,4 + ,0 + ,153 + ,0 + ,4 + ,0 + ,4 + ,0 + ,3 + ,0 + ,1 + ,0 + ,1 + ,0 + ,5 + ,0 + ,5 + ,1 + ,154 + ,154 + ,4 + ,4 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,2 + ,2 + ,3 + ,3 + ,2 + ,1 + ,155 + ,155 + ,5 + ,5 + ,5 + ,5 + ,4 + ,4 + ,2 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,0 + ,156 + ,0 + ,3 + ,0 + ,4 + ,0 + ,2 + ,0 + ,2 + ,0 + ,2 + ,0 + ,3 + ,0 + ,3 + ,1 + ,157 + ,157 + ,3 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,3 + ,2 + ,2 + ,5 + ,5 + ,4 + ,0 + ,158 + ,0 + ,4 + ,0 + ,4 + ,0 + ,4 + ,0 + ,3 + ,0 + ,2 + ,0 + ,4 + ,0 + ,4 + ,0 + ,159 + ,0 + ,4 + ,0 + ,3 + ,0 + ,4 + ,0 + ,3 + ,0 + ,4 + ,0 + ,2 + ,0 + ,3) + ,dim=c(16 + ,159) + ,dimnames=list(c('pop' + ,'t' + ,'pop_t' + ,'standards' + ,'standards_t' + ,'organization' + ,'organization_t' + ,'punished' + ,'punished_t' + ,'secondrate' + ,'secondrate_t' + ,'mistakes' + ,'mistakes_t' + ,'competent' + ,'competent_t' + ,'neat') + ,1:159)) > y <- array(NA,dim=c(16,159),dimnames=list(c('pop','t','pop_t','standards','standards_t','organization','organization_t','punished','punished_t','secondrate','secondrate_t','mistakes','mistakes_t','competent','competent_t','neat'),1:159)) > 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 = '16' > #'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 neat pop t pop_t standards standards_t organization organization_t 1 4 0 1 0 2 0 5 0 2 4 0 2 0 2 0 4 0 3 4 1 3 3 4 4 4 4 4 4 0 4 0 2 0 4 0 5 4 1 5 5 3 3 2 2 6 5 1 6 6 4 4 5 5 7 4 0 7 0 3 0 5 0 8 3 1 8 8 3 3 4 4 9 4 0 9 0 3 0 3 0 10 4 0 10 0 2 0 4 0 11 4 1 11 11 4 4 4 4 12 4 0 12 0 4 0 2 0 13 4 1 13 13 3 3 3 3 14 2 1 14 14 3 3 3 3 15 3 0 15 0 4 0 4 0 16 4 1 16 16 4 4 5 5 17 3 0 17 0 3 0 4 0 18 2 0 18 0 3 0 2 0 19 4 1 19 19 3 3 4 4 20 3 0 20 0 4 0 4 0 21 3 1 21 21 2 2 4 4 22 4 1 22 22 5 5 4 4 23 3 0 23 0 4 0 4 0 24 3 1 24 24 2 2 4 4 25 4 0 25 0 3 0 5 0 26 4 0 26 0 4 0 4 0 27 4 1 27 27 4 4 4 4 28 4 0 28 0 3 0 4 0 29 4 1 29 29 4 4 4 4 30 4 1 30 30 4 4 4 4 31 5 0 31 0 1 0 4 0 32 4 1 32 32 4 4 4 4 33 4 0 33 0 5 0 2 0 34 4 0 34 0 2 0 4 0 35 3 1 35 35 4 4 4 4 36 4 0 36 0 3 0 5 0 37 3 1 37 37 2 2 5 5 38 4 1 38 38 4 4 4 4 39 4 0 39 0 5 0 3 0 40 3 1 40 40 4 4 4 4 41 5 0 41 0 4 0 5 0 42 4 0 42 0 4 0 4 0 43 3 1 43 43 3 3 4 4 44 3 0 44 0 4 0 5 0 45 3 1 45 45 2 2 4 4 46 4 1 46 46 2 2 5 5 47 4 0 47 0 4 0 4 0 48 4 1 48 48 2 2 4 4 49 4 0 49 0 4 0 4 0 50 4 0 50 0 4 0 3 0 51 4 1 51 51 1 1 4 4 52 4 0 52 0 4 0 4 0 53 5 1 53 53 2 2 4 4 54 3 1 54 54 1 1 2 2 55 3 0 55 0 4 0 3 0 56 5 1 56 56 3 3 5 5 57 5 0 57 0 2 0 4 0 58 4 0 58 0 4 0 4 0 59 4 1 59 59 3 3 5 5 60 3 0 60 0 2 0 3 0 61 4 1 61 61 2 2 5 5 62 4 1 62 62 3 3 4 4 63 5 0 63 0 2 0 5 0 64 4 1 64 64 1 1 4 4 65 4 0 65 0 3 0 4 0 66 5 0 66 0 2 0 5 0 67 4 1 67 67 3 3 4 4 68 3 0 68 0 3 0 4 0 69 4 1 69 69 3 3 5 5 70 4 1 70 70 2 2 4 4 71 4 0 71 0 3 0 3 0 72 4 1 72 72 2 2 4 4 73 4 0 73 0 4 0 5 0 74 4 0 74 0 4 0 5 0 75 4 1 75 75 4 4 5 5 76 3 0 76 0 2 0 4 0 77 4 1 77 77 3 3 4 4 78 3 1 78 78 4 4 5 5 79 5 0 79 0 3 0 5 0 80 4 1 80 80 4 4 4 4 81 5 0 81 0 2 0 5 0 82 5 0 82 0 3 0 3 0 83 4 1 83 83 3 3 4 4 84 4 0 84 0 4 0 4 0 85 4 1 85 85 2 2 4 4 86 4 1 86 86 4 4 4 4 87 4 0 87 0 2 0 4 0 88 5 1 88 88 2 2 5 5 89 4 0 89 0 4 0 4 0 90 3 0 90 0 3 0 4 0 91 4 1 91 91 4 4 4 4 92 3 0 92 0 2 0 5 0 93 4 1 93 93 2 2 3 3 94 4 1 94 94 3 3 3 3 95 4 0 95 0 3 0 5 0 96 4 1 96 96 5 5 5 5 97 4 0 97 0 2 0 4 0 98 3 0 98 0 3 0 4 0 99 3 1 99 99 4 4 4 4 100 3 0 100 0 3 0 4 0 101 3 1 101 101 4 4 4 4 102 3 1 102 102 3 3 4 4 103 2 0 103 0 3 0 4 0 104 3 1 104 104 2 2 4 4 105 5 0 105 0 3 0 5 0 106 2 0 106 0 2 0 2 0 107 2 1 107 107 3 3 4 4 108 3 0 108 0 2 0 2 0 109 3 1 109 109 4 4 4 4 110 3 1 110 110 2 2 5 5 111 4 0 111 0 4 0 3 0 112 4 1 112 112 4 4 4 4 113 3 0 113 0 1 0 3 0 114 2 0 114 0 5 0 4 0 115 3 1 115 115 2 2 4 4 116 4 0 116 0 3 0 4 0 117 2 1 117 117 4 4 2 2 118 4 1 118 118 1 1 1 1 119 4 0 119 0 5 0 4 0 120 2 1 120 120 3 3 3 3 121 3 0 121 0 3 0 4 0 122 3 1 122 122 3 3 3 3 123 3 1 123 123 3 3 3 3 124 4 0 124 0 2 0 5 0 125 4 1 125 125 2 2 4 4 126 4 0 126 0 4 0 3 0 127 3 0 127 0 4 0 4 0 128 4 1 128 128 3 3 4 4 129 4 0 129 0 3 0 4 0 130 4 1 130 130 3 3 4 4 131 2 1 131 131 4 4 3 3 132 4 0 132 0 3 0 4 0 133 5 1 133 133 4 4 4 4 134 4 0 134 0 4 0 4 0 135 4 0 135 0 2 0 4 0 136 4 1 136 136 4 4 4 4 137 3 0 137 0 2 0 3 0 138 1 1 138 138 4 4 4 4 139 4 1 139 139 3 3 4 4 140 3 0 140 0 3 0 2 0 141 3 1 141 141 2 2 2 2 142 3 0 142 0 2 0 4 0 143 1 0 143 0 5 0 2 0 144 4 1 144 144 2 2 4 4 145 5 0 145 0 4 0 3 0 146 4 1 146 146 3 3 4 4 147 3 1 147 147 3 3 3 3 148 4 0 148 0 3 0 2 0 149 3 1 149 149 3 3 2 2 150 4 0 150 0 4 0 4 0 151 4 0 151 0 4 0 3 0 152 4 1 152 152 4 4 4 4 153 5 0 153 0 4 0 4 0 154 2 1 154 154 4 4 2 2 155 3 1 155 155 5 5 5 5 156 3 0 156 0 3 0 4 0 157 4 1 157 157 3 3 4 4 158 4 0 158 0 4 0 4 0 159 3 0 159 0 4 0 3 0 punished punished_t secondrate secondrate_t mistakes mistakes_t competent 1 2 0 3 0 3 0 4 2 2 0 4 0 3 0 4 3 2 2 4 4 2 2 5 4 2 0 2 0 2 0 2 5 2 2 2 2 3 3 2 6 1 1 3 3 2 2 4 7 1 0 2 0 1 0 4 8 3 3 3 3 3 3 4 9 2 0 3 0 2 0 4 10 1 0 3 0 2 0 2 11 4 4 3 3 3 3 3 12 2 0 4 0 2 0 4 13 3 3 2 2 2 2 3 14 2 2 2 2 2 2 4 15 1 0 1 0 3 0 4 16 1 1 1 1 1 1 4 17 2 0 3 0 3 0 4 18 2 0 2 0 2 0 2 19 2 2 2 2 3 3 4 20 2 0 3 0 4 0 4 21 1 1 4 4 2 2 4 22 2 2 4 4 3 3 3 23 4 0 3 0 5 0 2 24 2 2 2 2 2 2 4 25 2 0 3 0 2 0 2 26 2 0 4 0 3 0 3 27 2 2 3 3 2 2 4 28 2 0 2 0 2 0 3 29 3 3 1 1 2 2 4 30 2 2 3 3 2 2 4 31 1 0 2 0 3 0 4 32 4 4 4 4 4 4 4 33 1 0 4 0 1 0 4 34 2 0 5 0 3 0 4 35 2 2 2 2 3 3 4 36 2 0 4 0 2 0 5 37 2 2 4 4 1 1 4 38 2 2 2 2 1 1 2 39 2 0 4 0 2 0 4 40 2 2 4 4 2 2 4 41 2 0 2 0 2 0 5 42 2 0 3 0 1 0 4 43 2 2 2 2 2 2 2 44 2 0 4 0 1 0 4 45 2 2 3 3 2 2 4 46 1 1 1 1 2 2 4 47 2 0 2 0 4 0 2 48 1 1 5 5 2 2 5 49 2 0 2 0 2 0 4 50 1 0 4 0 2 0 4 51 1 1 4 4 1 1 4 52 2 0 2 0 2 0 4 53 2 2 2 2 2 2 4 54 1 1 2 2 1 1 3 55 5 0 4 0 5 0 5 56 2 2 3 3 2 2 4 57 2 0 4 0 2 0 4 58 1 0 2 0 2 0 4 59 1 1 3 3 1 1 4 60 2 0 2 0 3 0 2 61 2 2 2 2 1 1 4 62 1 1 3 3 1 1 4 63 1 0 2 0 2 0 4 64 2 2 3 3 3 3 4 65 1 0 2 0 2 0 3 66 1 0 4 0 2 0 4 67 2 2 2 2 2 2 2 68 1 0 5 0 4 0 4 69 1 1 1 1 1 1 4 70 2 2 3 3 2 2 4 71 1 0 2 0 2 0 4 72 1 1 2 2 2 2 4 73 3 0 3 0 2 0 4 74 3 0 4 0 2 0 3 75 2 2 4 4 1 1 4 76 2 0 2 0 2 0 4 77 1 1 3 3 2 2 4 78 3 3 4 4 2 2 4 79 2 0 2 0 2 0 4 80 2 2 2 2 1 1 4 81 2 0 4 0 4 0 4 82 2 0 2 0 2 0 2 83 1 1 4 4 3 3 3 84 4 0 2 0 2 0 5 85 1 1 3 3 1 1 3 86 1 1 4 4 2 2 3 87 1 0 3 0 2 0 4 88 1 1 1 1 1 1 4 89 4 0 3 0 2 0 4 90 2 0 2 0 1 0 4 91 2 2 2 2 2 2 4 92 1 0 1 0 1 0 3 93 1 1 3 3 2 2 4 94 1 1 2 2 2 2 4 95 3 0 3 0 3 0 4 96 4 4 5 5 4 4 5 97 4 0 3 0 1 0 4 98 3 0 4 0 3 0 4 99 2 2 2 2 1 1 2 100 2 0 2 0 1 0 3 101 3 3 3 3 2 2 3 102 1 1 2 2 1 1 3 103 3 0 2 0 3 0 4 104 2 2 2 2 2 2 4 105 2 0 3 0 2 0 2 106 2 0 5 0 1 0 3 107 2 2 2 2 2 2 3 108 4 0 3 0 2 0 4 109 3 3 3 3 1 1 4 110 1 1 1 1 2 2 2 111 1 0 1 0 2 0 3 112 2 2 3 3 4 4 4 113 1 0 4 0 3 0 4 114 3 0 5 0 2 0 5 115 2 2 3 3 5 5 3 116 2 0 3 0 1 0 3 117 2 2 3 3 2 2 4 118 1 1 2 2 1 1 3 119 3 0 3 0 2 0 3 120 1 1 2 2 1 1 2 121 1 0 3 0 1 0 4 122 2 2 2 2 2 2 3 123 3 3 4 4 2 2 4 124 2 0 2 0 2 0 5 125 1 1 2 2 3 3 4 126 2 0 4 0 2 0 3 127 1 0 4 0 1 0 3 128 2 2 3 3 2 2 3 129 1 0 3 0 2 0 3 130 2 2 3 3 3 3 4 131 3 3 4 4 2 2 4 132 2 0 2 0 2 0 3 133 1 1 1 1 2 2 2 134 1 0 3 0 1 0 3 135 2 0 2 0 2 0 2 136 2 2 3 3 2 2 4 137 1 0 2 0 2 0 4 138 2 2 2 2 3 3 4 139 3 3 3 3 1 1 4 140 4 0 2 0 3 0 4 141 2 2 4 4 4 4 4 142 4 0 4 0 2 0 5 143 5 0 2 0 5 0 3 144 1 1 2 2 1 1 4 145 3 0 3 0 2 0 4 146 2 2 4 4 3 3 4 147 2 2 4 4 2 2 5 148 2 0 4 0 2 0 3 149 1 1 1 1 3 3 2 150 4 0 4 0 2 0 4 151 2 0 4 0 1 0 3 152 2 2 3 3 2 2 4 153 3 0 1 0 1 0 5 154 1 1 2 2 2 2 3 155 4 4 2 2 3 3 3 156 2 0 2 0 2 0 3 157 2 2 3 3 2 2 5 158 4 0 3 0 2 0 4 159 4 0 3 0 4 0 2 competent_t 1 0 2 0 3 5 4 0 5 2 6 4 7 0 8 4 9 0 10 0 11 3 12 0 13 3 14 4 15 0 16 4 17 0 18 0 19 4 20 0 21 4 22 3 23 0 24 4 25 0 26 0 27 4 28 0 29 4 30 4 31 0 32 4 33 0 34 0 35 4 36 0 37 4 38 2 39 0 40 4 41 0 42 0 43 2 44 0 45 4 46 4 47 0 48 5 49 0 50 0 51 4 52 0 53 4 54 3 55 0 56 4 57 0 58 0 59 4 60 0 61 4 62 4 63 0 64 4 65 0 66 0 67 2 68 0 69 4 70 4 71 0 72 4 73 0 74 0 75 4 76 0 77 4 78 4 79 0 80 4 81 0 82 0 83 3 84 0 85 3 86 3 87 0 88 4 89 0 90 0 91 4 92 0 93 4 94 4 95 0 96 5 97 0 98 0 99 2 100 0 101 3 102 3 103 0 104 4 105 0 106 0 107 3 108 0 109 4 110 2 111 0 112 4 113 0 114 0 115 3 116 0 117 4 118 3 119 0 120 2 121 0 122 3 123 4 124 0 125 4 126 0 127 0 128 3 129 0 130 4 131 4 132 0 133 2 134 0 135 0 136 4 137 0 138 4 139 4 140 0 141 4 142 0 143 0 144 4 145 0 146 4 147 5 148 0 149 2 150 0 151 0 152 4 153 0 154 3 155 3 156 0 157 5 158 0 159 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) pop t pop_t standards 3.114962 -0.356878 -0.001194 -0.001795 -0.002195 standards_t organization organization_t punished punished_t -0.034934 0.304511 -0.002635 -0.097553 -0.091054 secondrate secondrate_t mistakes mistakes_t competent -0.004497 0.001896 -0.145120 0.186359 0.026159 competent_t 0.052098 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.4588 -0.5218 0.1222 0.4456 1.6452 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.114962 0.707599 4.402 2.08e-05 *** pop -0.356878 0.943670 -0.378 0.70586 t -0.001194 0.002018 -0.592 0.55503 pop_t -0.001795 0.002780 -0.646 0.51943 standards -0.002195 0.091643 -0.024 0.98093 standards_t -0.034934 0.136461 -0.256 0.79832 organization 0.304511 0.102162 2.981 0.00338 ** organization_t -0.002635 0.150280 -0.018 0.98603 punished -0.097553 0.100025 -0.975 0.33107 punished_t -0.091054 0.163360 -0.557 0.57814 secondrate -0.004497 0.085287 -0.053 0.95802 secondrate_t 0.001896 0.131187 0.014 0.98849 mistakes -0.145120 0.101501 -1.430 0.15497 mistakes_t 0.186359 0.147917 1.260 0.20976 competent 0.026159 0.103439 0.253 0.80072 competent_t 0.052098 0.163981 0.318 0.75117 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7435 on 143 degrees of freedom Multiple R-squared: 0.2094, Adjusted R-squared: 0.1265 F-statistic: 2.525 on 15 and 143 DF, p-value: 0.002393 > 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,] 0.688810286 0.622379429 0.3111897 [2,] 0.536650010 0.926699980 0.4633500 [3,] 0.571779371 0.856441258 0.4282206 [4,] 0.500041805 0.999916389 0.4999582 [5,] 0.383825778 0.767651555 0.6161742 [6,] 0.296712463 0.593424927 0.7032875 [7,] 0.237055919 0.474111839 0.7629441 [8,] 0.165618909 0.331237818 0.8343811 [9,] 0.179344099 0.358688198 0.8206559 [10,] 0.259775621 0.519551241 0.7402244 [11,] 0.228592403 0.457184806 0.7714076 [12,] 0.182264790 0.364529580 0.8177352 [13,] 0.236091967 0.472183934 0.7639080 [14,] 0.197939014 0.395878028 0.8020610 [15,] 0.149816161 0.299632322 0.8501838 [16,] 0.181396251 0.362792502 0.8186037 [17,] 0.174341143 0.348682287 0.8256589 [18,] 0.157011463 0.314022926 0.8429885 [19,] 0.133684754 0.267369508 0.8663152 [20,] 0.105739637 0.211479274 0.8942604 [21,] 0.085151200 0.170302401 0.9148488 [22,] 0.067799576 0.135599153 0.9322004 [23,] 0.077237893 0.154475786 0.9227621 [24,] 0.060774836 0.121549671 0.9392252 [25,] 0.051477502 0.102955003 0.9485225 [26,] 0.124441615 0.248883230 0.8755584 [27,] 0.116352989 0.232705978 0.8836470 [28,] 0.120116285 0.240232571 0.8798837 [29,] 0.104803317 0.209606635 0.8951967 [30,] 0.157347993 0.314695985 0.8426520 [31,] 0.124097620 0.248195240 0.8759024 [32,] 0.099065553 0.198131105 0.9009344 [33,] 0.116669988 0.233339976 0.8833300 [34,] 0.090633749 0.181267499 0.9093663 [35,] 0.197712734 0.395425468 0.8022873 [36,] 0.162817448 0.325634897 0.8371826 [37,] 0.135192717 0.270385435 0.8648073 [38,] 0.157052979 0.314105959 0.8429470 [39,] 0.156980386 0.313960771 0.8430196 [40,] 0.129932650 0.259865300 0.8700673 [41,] 0.105951895 0.211903789 0.8940481 [42,] 0.111516944 0.223033887 0.8884831 [43,] 0.088556288 0.177112575 0.9114437 [44,] 0.069544302 0.139088604 0.9304557 [45,] 0.059928430 0.119856860 0.9400716 [46,] 0.046578371 0.093156743 0.9534216 [47,] 0.035846023 0.071692046 0.9641540 [48,] 0.031716518 0.063433035 0.9682835 [49,] 0.026271236 0.052542473 0.9737288 [50,] 0.034846716 0.069693433 0.9651533 [51,] 0.026798361 0.053596722 0.9732016 [52,] 0.020090045 0.040180090 0.9799100 [53,] 0.016089153 0.032178306 0.9839108 [54,] 0.011576071 0.023152143 0.9884239 [55,] 0.008281484 0.016562968 0.9917185 [56,] 0.005794567 0.011589134 0.9942054 [57,] 0.004079423 0.008158845 0.9959206 [58,] 0.006809311 0.013618622 0.9931907 [59,] 0.004741880 0.009483759 0.9952581 [60,] 0.006325570 0.012651140 0.9936744 [61,] 0.006898992 0.013797983 0.9931010 [62,] 0.005167478 0.010334957 0.9948325 [63,] 0.010378450 0.020756900 0.9896215 [64,] 0.024712722 0.049425444 0.9752873 [65,] 0.018401456 0.036802912 0.9815985 [66,] 0.015474762 0.030949525 0.9845252 [67,] 0.011331328 0.022662655 0.9886687 [68,] 0.008166187 0.016332374 0.9918338 [69,] 0.008768757 0.017537514 0.9912312 [70,] 0.008981157 0.017962314 0.9910188 [71,] 0.007985369 0.015970738 0.9920146 [72,] 0.011714530 0.023429060 0.9882855 [73,] 0.010128865 0.020257730 0.9898711 [74,] 0.021100559 0.042201118 0.9788994 [75,] 0.016623107 0.033246214 0.9833769 [76,] 0.016707947 0.033415895 0.9832921 [77,] 0.015977538 0.031955075 0.9840225 [78,] 0.016323524 0.032647048 0.9836765 [79,] 0.012417344 0.024834688 0.9875827 [80,] 0.013431745 0.026863489 0.9865683 [81,] 0.012548839 0.025097679 0.9874512 [82,] 0.015121862 0.030243724 0.9848781 [83,] 0.013062322 0.026124644 0.9869377 [84,] 0.011808384 0.023616767 0.9881916 [85,] 0.021767877 0.043535754 0.9782321 [86,] 0.018577444 0.037154888 0.9814226 [87,] 0.028572854 0.057145707 0.9714271 [88,] 0.051257382 0.102514764 0.9487426 [89,] 0.088184792 0.176369585 0.9118152 [90,] 0.073709017 0.147418034 0.9262910 [91,] 0.056648434 0.113296868 0.9433516 [92,] 0.089398168 0.178796335 0.9106018 [93,] 0.076667345 0.153334690 0.9233327 [94,] 0.107501190 0.215002381 0.8924988 [95,] 0.108218762 0.216437524 0.8917812 [96,] 0.138453906 0.276907812 0.8615461 [97,] 0.127463761 0.254927521 0.8725362 [98,] 0.102651353 0.205302707 0.8973486 [99,] 0.101181979 0.202363958 0.8988180 [100,] 0.150694569 0.301389138 0.8493054 [101,] 0.125332739 0.250665478 0.8746673 [102,] 0.337302602 0.674605204 0.6626974 [103,] 0.384563302 0.769126604 0.6154367 [104,] 0.324315287 0.648630574 0.6756847 [105,] 0.270045356 0.540090712 0.7299546 [106,] 0.250653858 0.501307715 0.7493461 [107,] 0.207281454 0.414562908 0.7927185 [108,] 0.194903674 0.389807348 0.8050963 [109,] 0.256317113 0.512634227 0.7436829 [110,] 0.270960357 0.541920715 0.7290396 [111,] 0.260662439 0.521324877 0.7393376 [112,] 0.242091743 0.484183485 0.7579083 [113,] 0.233570852 0.467141705 0.7664291 [114,] 0.194969189 0.389938377 0.8050308 [115,] 0.262106705 0.524213409 0.7378933 [116,] 0.199013306 0.398026613 0.8009867 [117,] 0.230475258 0.460950517 0.7695247 [118,] 0.434114316 0.868228631 0.5658857 [119,] 0.326741429 0.653482858 0.6732586 [120,] 0.278394161 0.556788322 0.7216058 [121,] 0.308092156 0.616184313 0.6919078 [122,] 0.313951992 0.627903983 0.6860480 > postscript(file="/var/www/html/rcomp/tmp/1lhon1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2lhon1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3v9581291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4v9581291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5v9581291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 159 Frequency = 1 1 2 3 4 5 6 -0.092610142 0.217592081 0.105752077 0.118183589 0.866683127 0.699894083 7 8 9 10 11 12 -0.475540818 -0.693405290 0.383040024 0.032292900 0.619555202 0.697825379 13 14 15 16 17 18 0.740313484 -1.523559801 -0.873537408 -0.234171921 -0.766796917 -1.253880537 19 20 21 22 23 24 0.148273330 -0.615899152 -1.025042427 0.314957212 -0.219772923 -0.832668234 25 26 27 28 29 30 -0.154557341 0.276801325 0.253158700 0.122880781 0.442543271 0.262127568 31 32 33 34 35 36 1.143482855 0.565442562 0.482425275 0.260303894 -0.766763991 -0.215400073 37 38 39 40 41 42 -1.049238775 0.481195579 0.427752812 -0.705375570 0.783771850 -0.024987669 43 44 45 46 47 48 -0.582224061 -1.322613391 -0.767285522 -0.259979709 0.464165081 -0.019978292 49 50 51 52 53 54 0.123995326 0.341141568 0.068756577 0.127577972 1.254030825 -0.245467623 55 56 57 58 59 60 0.146526851 1.000853099 1.138153498 0.037190355 -0.137545590 -0.365308864 61 62 63 64 65 66 0.017310900 0.173299223 0.734260947 0.211149556 0.069513834 0.746837448 67 68 69 70 71 72 0.489526885 -0.649330579 -0.112850633 0.307455046 0.355031529 0.122227061 73 74 75 76 77 78 -0.049804753 -0.017954935 0.138624319 -0.848150269 0.176904526 -0.705039256 79 80 81 82 83 84 0.853116000 0.450247107 1.152544417 1.518038155 0.234460172 0.334739998 85 86 87 88 89 90 0.283188143 0.321796794 0.071930118 0.906823483 0.371366677 -0.974356971 91 92 93 94 95 96 0.441893919 -1.354565483 0.489485718 0.527003421 0.119393695 0.416375509 97 98 99 100 101 102 0.231410577 -0.568015652 -0.336437435 -0.936256144 -0.258746310 -0.631460190 103 104 105 106 107 108 -1.571038432 -0.593498417 0.940979873 -1.308772613 -1.469144520 -0.001310481 109 110 111 112 113 114 -0.271846604 -0.912131229 0.426656586 0.424798555 -0.445086566 -1.711300969 115 116 117 118 119 120 -0.603472733 0.087348227 -0.874023366 1.247744176 0.337993602 -1.197514723 121 122 123 124 125 126 -1.030392281 -0.122424234 -0.003883060 -0.121497695 0.239438027 0.555613505 127 128 129 130 131 132 -0.990376684 0.596238192 0.150440533 0.482722087 -0.942837362 0.247079159 133 134 135 136 137 138 1.532763470 0.013485894 0.274625777 0.579027577 -0.568344971 -2.458832851 139 140 141 142 143 144 0.780713362 0.179722597 0.043592707 -0.591391071 -1.398352936 0.378718935 145 146 147 148 149 150 1.645200898 0.533156685 -0.198995023 0.884202616 0.105981569 0.448710730 151 152 153 154 155 156 0.440348467 0.626861540 1.169970593 -0.876358242 -0.217286991 -0.724259677 157 158 159 0.526424625 0.453767524 0.102031003 > postscript(file="/var/www/html/rcomp/tmp/6o0mb1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 159 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.092610142 NA 1 0.217592081 -0.092610142 2 0.105752077 0.217592081 3 0.118183589 0.105752077 4 0.866683127 0.118183589 5 0.699894083 0.866683127 6 -0.475540818 0.699894083 7 -0.693405290 -0.475540818 8 0.383040024 -0.693405290 9 0.032292900 0.383040024 10 0.619555202 0.032292900 11 0.697825379 0.619555202 12 0.740313484 0.697825379 13 -1.523559801 0.740313484 14 -0.873537408 -1.523559801 15 -0.234171921 -0.873537408 16 -0.766796917 -0.234171921 17 -1.253880537 -0.766796917 18 0.148273330 -1.253880537 19 -0.615899152 0.148273330 20 -1.025042427 -0.615899152 21 0.314957212 -1.025042427 22 -0.219772923 0.314957212 23 -0.832668234 -0.219772923 24 -0.154557341 -0.832668234 25 0.276801325 -0.154557341 26 0.253158700 0.276801325 27 0.122880781 0.253158700 28 0.442543271 0.122880781 29 0.262127568 0.442543271 30 1.143482855 0.262127568 31 0.565442562 1.143482855 32 0.482425275 0.565442562 33 0.260303894 0.482425275 34 -0.766763991 0.260303894 35 -0.215400073 -0.766763991 36 -1.049238775 -0.215400073 37 0.481195579 -1.049238775 38 0.427752812 0.481195579 39 -0.705375570 0.427752812 40 0.783771850 -0.705375570 41 -0.024987669 0.783771850 42 -0.582224061 -0.024987669 43 -1.322613391 -0.582224061 44 -0.767285522 -1.322613391 45 -0.259979709 -0.767285522 46 0.464165081 -0.259979709 47 -0.019978292 0.464165081 48 0.123995326 -0.019978292 49 0.341141568 0.123995326 50 0.068756577 0.341141568 51 0.127577972 0.068756577 52 1.254030825 0.127577972 53 -0.245467623 1.254030825 54 0.146526851 -0.245467623 55 1.000853099 0.146526851 56 1.138153498 1.000853099 57 0.037190355 1.138153498 58 -0.137545590 0.037190355 59 -0.365308864 -0.137545590 60 0.017310900 -0.365308864 61 0.173299223 0.017310900 62 0.734260947 0.173299223 63 0.211149556 0.734260947 64 0.069513834 0.211149556 65 0.746837448 0.069513834 66 0.489526885 0.746837448 67 -0.649330579 0.489526885 68 -0.112850633 -0.649330579 69 0.307455046 -0.112850633 70 0.355031529 0.307455046 71 0.122227061 0.355031529 72 -0.049804753 0.122227061 73 -0.017954935 -0.049804753 74 0.138624319 -0.017954935 75 -0.848150269 0.138624319 76 0.176904526 -0.848150269 77 -0.705039256 0.176904526 78 0.853116000 -0.705039256 79 0.450247107 0.853116000 80 1.152544417 0.450247107 81 1.518038155 1.152544417 82 0.234460172 1.518038155 83 0.334739998 0.234460172 84 0.283188143 0.334739998 85 0.321796794 0.283188143 86 0.071930118 0.321796794 87 0.906823483 0.071930118 88 0.371366677 0.906823483 89 -0.974356971 0.371366677 90 0.441893919 -0.974356971 91 -1.354565483 0.441893919 92 0.489485718 -1.354565483 93 0.527003421 0.489485718 94 0.119393695 0.527003421 95 0.416375509 0.119393695 96 0.231410577 0.416375509 97 -0.568015652 0.231410577 98 -0.336437435 -0.568015652 99 -0.936256144 -0.336437435 100 -0.258746310 -0.936256144 101 -0.631460190 -0.258746310 102 -1.571038432 -0.631460190 103 -0.593498417 -1.571038432 104 0.940979873 -0.593498417 105 -1.308772613 0.940979873 106 -1.469144520 -1.308772613 107 -0.001310481 -1.469144520 108 -0.271846604 -0.001310481 109 -0.912131229 -0.271846604 110 0.426656586 -0.912131229 111 0.424798555 0.426656586 112 -0.445086566 0.424798555 113 -1.711300969 -0.445086566 114 -0.603472733 -1.711300969 115 0.087348227 -0.603472733 116 -0.874023366 0.087348227 117 1.247744176 -0.874023366 118 0.337993602 1.247744176 119 -1.197514723 0.337993602 120 -1.030392281 -1.197514723 121 -0.122424234 -1.030392281 122 -0.003883060 -0.122424234 123 -0.121497695 -0.003883060 124 0.239438027 -0.121497695 125 0.555613505 0.239438027 126 -0.990376684 0.555613505 127 0.596238192 -0.990376684 128 0.150440533 0.596238192 129 0.482722087 0.150440533 130 -0.942837362 0.482722087 131 0.247079159 -0.942837362 132 1.532763470 0.247079159 133 0.013485894 1.532763470 134 0.274625777 0.013485894 135 0.579027577 0.274625777 136 -0.568344971 0.579027577 137 -2.458832851 -0.568344971 138 0.780713362 -2.458832851 139 0.179722597 0.780713362 140 0.043592707 0.179722597 141 -0.591391071 0.043592707 142 -1.398352936 -0.591391071 143 0.378718935 -1.398352936 144 1.645200898 0.378718935 145 0.533156685 1.645200898 146 -0.198995023 0.533156685 147 0.884202616 -0.198995023 148 0.105981569 0.884202616 149 0.448710730 0.105981569 150 0.440348467 0.448710730 151 0.626861540 0.440348467 152 1.169970593 0.626861540 153 -0.876358242 1.169970593 154 -0.217286991 -0.876358242 155 -0.724259677 -0.217286991 156 0.526424625 -0.724259677 157 0.453767524 0.526424625 158 0.102031003 0.453767524 159 NA 0.102031003 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.217592081 -0.092610142 [2,] 0.105752077 0.217592081 [3,] 0.118183589 0.105752077 [4,] 0.866683127 0.118183589 [5,] 0.699894083 0.866683127 [6,] -0.475540818 0.699894083 [7,] -0.693405290 -0.475540818 [8,] 0.383040024 -0.693405290 [9,] 0.032292900 0.383040024 [10,] 0.619555202 0.032292900 [11,] 0.697825379 0.619555202 [12,] 0.740313484 0.697825379 [13,] -1.523559801 0.740313484 [14,] -0.873537408 -1.523559801 [15,] -0.234171921 -0.873537408 [16,] -0.766796917 -0.234171921 [17,] -1.253880537 -0.766796917 [18,] 0.148273330 -1.253880537 [19,] -0.615899152 0.148273330 [20,] -1.025042427 -0.615899152 [21,] 0.314957212 -1.025042427 [22,] -0.219772923 0.314957212 [23,] -0.832668234 -0.219772923 [24,] -0.154557341 -0.832668234 [25,] 0.276801325 -0.154557341 [26,] 0.253158700 0.276801325 [27,] 0.122880781 0.253158700 [28,] 0.442543271 0.122880781 [29,] 0.262127568 0.442543271 [30,] 1.143482855 0.262127568 [31,] 0.565442562 1.143482855 [32,] 0.482425275 0.565442562 [33,] 0.260303894 0.482425275 [34,] -0.766763991 0.260303894 [35,] -0.215400073 -0.766763991 [36,] -1.049238775 -0.215400073 [37,] 0.481195579 -1.049238775 [38,] 0.427752812 0.481195579 [39,] -0.705375570 0.427752812 [40,] 0.783771850 -0.705375570 [41,] -0.024987669 0.783771850 [42,] -0.582224061 -0.024987669 [43,] -1.322613391 -0.582224061 [44,] -0.767285522 -1.322613391 [45,] -0.259979709 -0.767285522 [46,] 0.464165081 -0.259979709 [47,] -0.019978292 0.464165081 [48,] 0.123995326 -0.019978292 [49,] 0.341141568 0.123995326 [50,] 0.068756577 0.341141568 [51,] 0.127577972 0.068756577 [52,] 1.254030825 0.127577972 [53,] -0.245467623 1.254030825 [54,] 0.146526851 -0.245467623 [55,] 1.000853099 0.146526851 [56,] 1.138153498 1.000853099 [57,] 0.037190355 1.138153498 [58,] -0.137545590 0.037190355 [59,] -0.365308864 -0.137545590 [60,] 0.017310900 -0.365308864 [61,] 0.173299223 0.017310900 [62,] 0.734260947 0.173299223 [63,] 0.211149556 0.734260947 [64,] 0.069513834 0.211149556 [65,] 0.746837448 0.069513834 [66,] 0.489526885 0.746837448 [67,] -0.649330579 0.489526885 [68,] -0.112850633 -0.649330579 [69,] 0.307455046 -0.112850633 [70,] 0.355031529 0.307455046 [71,] 0.122227061 0.355031529 [72,] -0.049804753 0.122227061 [73,] -0.017954935 -0.049804753 [74,] 0.138624319 -0.017954935 [75,] -0.848150269 0.138624319 [76,] 0.176904526 -0.848150269 [77,] -0.705039256 0.176904526 [78,] 0.853116000 -0.705039256 [79,] 0.450247107 0.853116000 [80,] 1.152544417 0.450247107 [81,] 1.518038155 1.152544417 [82,] 0.234460172 1.518038155 [83,] 0.334739998 0.234460172 [84,] 0.283188143 0.334739998 [85,] 0.321796794 0.283188143 [86,] 0.071930118 0.321796794 [87,] 0.906823483 0.071930118 [88,] 0.371366677 0.906823483 [89,] -0.974356971 0.371366677 [90,] 0.441893919 -0.974356971 [91,] -1.354565483 0.441893919 [92,] 0.489485718 -1.354565483 [93,] 0.527003421 0.489485718 [94,] 0.119393695 0.527003421 [95,] 0.416375509 0.119393695 [96,] 0.231410577 0.416375509 [97,] -0.568015652 0.231410577 [98,] -0.336437435 -0.568015652 [99,] -0.936256144 -0.336437435 [100,] -0.258746310 -0.936256144 [101,] -0.631460190 -0.258746310 [102,] -1.571038432 -0.631460190 [103,] -0.593498417 -1.571038432 [104,] 0.940979873 -0.593498417 [105,] -1.308772613 0.940979873 [106,] -1.469144520 -1.308772613 [107,] -0.001310481 -1.469144520 [108,] -0.271846604 -0.001310481 [109,] -0.912131229 -0.271846604 [110,] 0.426656586 -0.912131229 [111,] 0.424798555 0.426656586 [112,] -0.445086566 0.424798555 [113,] -1.711300969 -0.445086566 [114,] -0.603472733 -1.711300969 [115,] 0.087348227 -0.603472733 [116,] -0.874023366 0.087348227 [117,] 1.247744176 -0.874023366 [118,] 0.337993602 1.247744176 [119,] -1.197514723 0.337993602 [120,] -1.030392281 -1.197514723 [121,] -0.122424234 -1.030392281 [122,] -0.003883060 -0.122424234 [123,] -0.121497695 -0.003883060 [124,] 0.239438027 -0.121497695 [125,] 0.555613505 0.239438027 [126,] -0.990376684 0.555613505 [127,] 0.596238192 -0.990376684 [128,] 0.150440533 0.596238192 [129,] 0.482722087 0.150440533 [130,] -0.942837362 0.482722087 [131,] 0.247079159 -0.942837362 [132,] 1.532763470 0.247079159 [133,] 0.013485894 1.532763470 [134,] 0.274625777 0.013485894 [135,] 0.579027577 0.274625777 [136,] -0.568344971 0.579027577 [137,] -2.458832851 -0.568344971 [138,] 0.780713362 -2.458832851 [139,] 0.179722597 0.780713362 [140,] 0.043592707 0.179722597 [141,] -0.591391071 0.043592707 [142,] -1.398352936 -0.591391071 [143,] 0.378718935 -1.398352936 [144,] 1.645200898 0.378718935 [145,] 0.533156685 1.645200898 [146,] -0.198995023 0.533156685 [147,] 0.884202616 -0.198995023 [148,] 0.105981569 0.884202616 [149,] 0.448710730 0.105981569 [150,] 0.440348467 0.448710730 [151,] 0.626861540 0.440348467 [152,] 1.169970593 0.626861540 [153,] -0.876358242 1.169970593 [154,] -0.217286991 -0.876358242 [155,] -0.724259677 -0.217286991 [156,] 0.526424625 -0.724259677 [157,] 0.453767524 0.526424625 [158,] 0.102031003 0.453767524 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.217592081 -0.092610142 2 0.105752077 0.217592081 3 0.118183589 0.105752077 4 0.866683127 0.118183589 5 0.699894083 0.866683127 6 -0.475540818 0.699894083 7 -0.693405290 -0.475540818 8 0.383040024 -0.693405290 9 0.032292900 0.383040024 10 0.619555202 0.032292900 11 0.697825379 0.619555202 12 0.740313484 0.697825379 13 -1.523559801 0.740313484 14 -0.873537408 -1.523559801 15 -0.234171921 -0.873537408 16 -0.766796917 -0.234171921 17 -1.253880537 -0.766796917 18 0.148273330 -1.253880537 19 -0.615899152 0.148273330 20 -1.025042427 -0.615899152 21 0.314957212 -1.025042427 22 -0.219772923 0.314957212 23 -0.832668234 -0.219772923 24 -0.154557341 -0.832668234 25 0.276801325 -0.154557341 26 0.253158700 0.276801325 27 0.122880781 0.253158700 28 0.442543271 0.122880781 29 0.262127568 0.442543271 30 1.143482855 0.262127568 31 0.565442562 1.143482855 32 0.482425275 0.565442562 33 0.260303894 0.482425275 34 -0.766763991 0.260303894 35 -0.215400073 -0.766763991 36 -1.049238775 -0.215400073 37 0.481195579 -1.049238775 38 0.427752812 0.481195579 39 -0.705375570 0.427752812 40 0.783771850 -0.705375570 41 -0.024987669 0.783771850 42 -0.582224061 -0.024987669 43 -1.322613391 -0.582224061 44 -0.767285522 -1.322613391 45 -0.259979709 -0.767285522 46 0.464165081 -0.259979709 47 -0.019978292 0.464165081 48 0.123995326 -0.019978292 49 0.341141568 0.123995326 50 0.068756577 0.341141568 51 0.127577972 0.068756577 52 1.254030825 0.127577972 53 -0.245467623 1.254030825 54 0.146526851 -0.245467623 55 1.000853099 0.146526851 56 1.138153498 1.000853099 57 0.037190355 1.138153498 58 -0.137545590 0.037190355 59 -0.365308864 -0.137545590 60 0.017310900 -0.365308864 61 0.173299223 0.017310900 62 0.734260947 0.173299223 63 0.211149556 0.734260947 64 0.069513834 0.211149556 65 0.746837448 0.069513834 66 0.489526885 0.746837448 67 -0.649330579 0.489526885 68 -0.112850633 -0.649330579 69 0.307455046 -0.112850633 70 0.355031529 0.307455046 71 0.122227061 0.355031529 72 -0.049804753 0.122227061 73 -0.017954935 -0.049804753 74 0.138624319 -0.017954935 75 -0.848150269 0.138624319 76 0.176904526 -0.848150269 77 -0.705039256 0.176904526 78 0.853116000 -0.705039256 79 0.450247107 0.853116000 80 1.152544417 0.450247107 81 1.518038155 1.152544417 82 0.234460172 1.518038155 83 0.334739998 0.234460172 84 0.283188143 0.334739998 85 0.321796794 0.283188143 86 0.071930118 0.321796794 87 0.906823483 0.071930118 88 0.371366677 0.906823483 89 -0.974356971 0.371366677 90 0.441893919 -0.974356971 91 -1.354565483 0.441893919 92 0.489485718 -1.354565483 93 0.527003421 0.489485718 94 0.119393695 0.527003421 95 0.416375509 0.119393695 96 0.231410577 0.416375509 97 -0.568015652 0.231410577 98 -0.336437435 -0.568015652 99 -0.936256144 -0.336437435 100 -0.258746310 -0.936256144 101 -0.631460190 -0.258746310 102 -1.571038432 -0.631460190 103 -0.593498417 -1.571038432 104 0.940979873 -0.593498417 105 -1.308772613 0.940979873 106 -1.469144520 -1.308772613 107 -0.001310481 -1.469144520 108 -0.271846604 -0.001310481 109 -0.912131229 -0.271846604 110 0.426656586 -0.912131229 111 0.424798555 0.426656586 112 -0.445086566 0.424798555 113 -1.711300969 -0.445086566 114 -0.603472733 -1.711300969 115 0.087348227 -0.603472733 116 -0.874023366 0.087348227 117 1.247744176 -0.874023366 118 0.337993602 1.247744176 119 -1.197514723 0.337993602 120 -1.030392281 -1.197514723 121 -0.122424234 -1.030392281 122 -0.003883060 -0.122424234 123 -0.121497695 -0.003883060 124 0.239438027 -0.121497695 125 0.555613505 0.239438027 126 -0.990376684 0.555613505 127 0.596238192 -0.990376684 128 0.150440533 0.596238192 129 0.482722087 0.150440533 130 -0.942837362 0.482722087 131 0.247079159 -0.942837362 132 1.532763470 0.247079159 133 0.013485894 1.532763470 134 0.274625777 0.013485894 135 0.579027577 0.274625777 136 -0.568344971 0.579027577 137 -2.458832851 -0.568344971 138 0.780713362 -2.458832851 139 0.179722597 0.780713362 140 0.043592707 0.179722597 141 -0.591391071 0.043592707 142 -1.398352936 -0.591391071 143 0.378718935 -1.398352936 144 1.645200898 0.378718935 145 0.533156685 1.645200898 146 -0.198995023 0.533156685 147 0.884202616 -0.198995023 148 0.105981569 0.884202616 149 0.448710730 0.105981569 150 0.440348467 0.448710730 151 0.626861540 0.440348467 152 1.169970593 0.626861540 153 -0.876358242 1.169970593 154 -0.217286991 -0.876358242 155 -0.724259677 -0.217286991 156 0.526424625 -0.724259677 157 0.453767524 0.526424625 158 0.102031003 0.453767524 > 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/7zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10silh1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11djj41291229903.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/12y1is1291229903.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/13cbf11291229903.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/14ycep1291229903.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/151cdv1291229903.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/16nvbj1291229903.tab") + } > > try(system("convert tmp/1lhon1291229903.ps tmp/1lhon1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/2lhon1291229903.ps tmp/2lhon1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/3v9581291229903.ps tmp/3v9581291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/4v9581291229903.ps tmp/4v9581291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/5v9581291229903.ps tmp/5v9581291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/6o0mb1291229903.ps tmp/6o0mb1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/7zr3e1291229903.ps tmp/7zr3e1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/8zr3e1291229903.ps tmp/8zr3e1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/9zr3e1291229903.ps tmp/9zr3e1291229903.png",intern=TRUE)) character(0) > try(system("convert tmp/10silh1291229903.ps tmp/10silh1291229903.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.245 1.759 13.387