R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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. 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,'Leveringssnelheid' + ,'Prijsflexibiliteit' + ,'Prijszetting' + ,'Productgamma' + ,'Productkwaliteit' + ,'Productontwikkeling' + ,'Facturatie') + ,1:200)) > y <- array(NA,dim=c(8,200),dimnames=list(c('Klantentevredenheid','Leveringssnelheid','Prijsflexibiliteit','Prijszetting','Productgamma','Productkwaliteit','Productontwikkeling','Facturatie'),1:200)) > 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 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Klantentevredenheid Leveringssnelheid Prijsflexibiliteit Prijszetting 1 8.2 3.7 5.1 6.8 2 5.7 4.9 4.3 5.3 3 8.9 4.5 4.0 4.5 4 4.8 3.0 4.1 8.8 5 7.1 3.5 3.5 6.8 6 4.7 3.3 4.7 8.5 7 5.7 2.0 4.2 8.9 8 6.3 3.7 6.3 6.9 9 7.0 4.6 6.1 9.3 10 5.5 4.4 5.8 8.4 11 7.4 4.0 3.7 6.8 12 6.0 3.2 4.9 8.2 13 8.4 4.4 4.5 7.6 14 7.6 4.2 2.6 7.1 15 8.0 5.2 6.2 8.8 16 6.6 4.5 3.9 4.9 17 6.4 4.5 6.2 6.2 18 7.4 4.8 5.8 8.4 19 6.8 4.5 6.0 9.1 20 7.6 4.4 6.1 8.4 21 5.4 3.3 4.9 8.4 22 9.9 4.3 3.0 4.5 23 7.0 4.0 3.4 3.7 24 8.6 4.5 4.4 6.2 25 4.8 4.0 5.3 8.0 26 6.6 3.9 6.6 7.1 27 6.3 4.4 3.8 4.8 28 5.4 3.7 5.2 9.0 29 6.3 4.4 3.8 4.8 30 5.4 3.5 5.5 7.7 31 6.1 3.3 2.7 5.2 32 6.4 3.0 3.5 6.6 33 5.4 3.4 4.5 9.2 34 7.3 4.2 6.6 8.7 35 6.3 3.5 4.3 8.4 36 5.4 2.5 2.9 5.6 37 7.1 3.5 3.5 6.8 38 8.7 4.9 4.6 7.7 39 7.6 4.5 6.9 9.0 40 6.0 3.2 4.9 8.2 41 7.0 3.9 5.8 9.1 42 7.6 4.1 4.5 8.5 43 8.9 4.3 4.6 7.4 44 7.6 4.5 6.3 5.9 45 5.5 4.7 4.2 5.2 46 7.4 4.8 5.8 8.4 47 7.1 3.5 4.0 3.8 48 7.6 5.2 7.3 8.2 49 8.7 3.9 3.4 6.8 50 8.6 4.3 4.2 4.7 51 5.4 2.8 3.6 7.2 52 5.7 4.9 4.3 5.3 53 8.7 4.6 4.6 6.3 54 6.1 3.3 2.7 5.2 55 7.3 4.2 6.6 8.7 56 7.7 3.4 3.2 7.4 57 9.0 5.5 6.5 9.6 58 8.2 4.0 3.9 4.4 59 7.1 3.5 4.0 3.8 60 7.9 4.0 4.9 5.4 61 6.6 4.5 3.9 4.9 62 8.0 3.6 5.0 6.7 63 6.3 2.9 3.7 5.8 64 6.0 2.6 3.1 6.2 65 5.4 2.8 3.6 7.2 66 7.6 5.2 7.3 8.2 67 6.4 4.5 6.2 6.2 68 6.1 4.3 5.9 6.0 69 5.2 3.4 5.4 7.6 70 6.6 3.9 6.6 7.1 71 7.6 4.4 6.1 8.4 72 5.8 3.1 2.6 5.0 73 7.9 4.6 5.6 8.7 74 8.6 3.9 3.4 6.8 75 8.2 3.7 5.1 6.8 76 7.1 3.8 4.3 4.9 77 6.4 3.9 5.8 7.4 78 7.6 4.1 4.5 8.5 79 8.9 4.6 4.1 4.6 80 5.7 2.7 3.1 7.8 81 7.1 3.8 4.3 4.9 82 7.4 4.0 3.7 6.8 83 6.6 3.0 3.0 6.3 84 5.0 1.6 3.7 8.4 85 8.2 4.3 3.9 5.9 86 5.2 3.4 5.4 7.6 87 5.2 3.1 4.8 8.2 88 8.2 4.3 3.9 5.9 89 7.3 3.9 4.3 8.3 90 8.2 4.9 6.7 6.3 91 7.4 3.3 3.0 7.3 92 4.8 2.4 4.0 9.9 93 7.6 4.2 2.6 7.1 94 8.9 4.6 4.1 4.6 95 7.7 3.4 3.2 7.4 96 7.3 3.6 3.6 6.7 97 6.3 3.7 5.6 7.2 98 5.4 2.5 2.9 5.6 99 6.4 3.9 4.9 7.9 100 6.4 3.5 5.4 9.7 101 5.4 3.5 5.5 7.7 102 8.7 4.2 4.6 7.3 103 6.1 3.7 4.7 7.7 104 8.4 4.4 4.5 7.6 105 7.9 4.6 5.6 8.7 106 7.0 3.9 5.8 9.1 107 8.7 4.9 4.6 7.7 108 7.9 5.4 7.5 8.4 109 7.1 4.2 3.5 3.8 110 5.8 3.1 2.6 5.0 111 8.4 4.1 3.4 6.7 112 7.1 3.9 2.3 6.7 113 7.6 4.5 6.9 9.0 114 7.3 4.2 5.9 8.2 115 8.0 3.6 5.0 6.7 116 6.1 3.7 4.7 7.7 117 8.7 4.2 4.6 7.3 118 5.8 2.9 3.3 8.0 119 6.4 3.1 3.9 6.0 120 6.4 3.0 3.5 6.6 121 9.0 5.5 6.5 9.6 122 6.4 3.5 5.4 9.7 123 6.0 2.6 3.1 6.2 124 8.7 4.6 4.6 6.3 125 5.0 2.5 4.1 10.0 126 7.4 3.1 2.9 5.3 127 8.6 4.3 4.2 4.7 128 5.8 2.9 3.3 8.0 129 9.8 4.3 3.0 4.5 130 4.8 2.1 2.5 5.2 131 7.0 4.0 3.4 3.7 132 5.5 4.7 4.2 5.2 133 5.0 1.6 3.7 8.4 134 6.0 3.3 4.1 8.2 135 8.0 4.2 3.8 5.8 136 7.9 4.4 3.7 7.6 137 4.8 2.1 2.5 5.2 138 6.4 3.9 4.9 7.9 139 4.8 2.4 4.0 9.9 140 6.4 3.9 5.8 7.4 141 6.8 4.5 6.0 9.1 142 7.9 4.0 4.9 5.4 143 8.9 4.5 4.0 4.5 144 7.4 4.2 3.4 7.3 145 7.0 3.5 4.0 3.8 146 7.0 3.5 4.0 3.8 147 6.0 3.3 4.1 8.2 148 7.4 3.3 3.0 7.3 149 7.6 4.5 6.3 5.9 150 4.8 4.0 5.3 8.0 151 7.3 4.2 5.9 8.2 152 6.3 3.7 6.3 6.9 153 5.0 2.5 4.1 10.0 154 7.1 3.9 2.3 6.7 155 6.3 3.4 5.1 8.4 156 6.8 3.6 4.1 4.8 157 5.2 3.1 4.8 8.2 158 6.3 3.7 5.6 7.2 159 6.1 4.3 5.9 6.0 160 7.3 3.9 4.3 8.3 161 5.4 3.4 4.5 9.2 162 8.0 5.2 6.2 8.8 163 7.4 3.1 2.9 5.3 164 7.3 3.0 2.8 5.2 165 7.3 3.0 2.8 5.2 166 6.4 3.1 3.9 6.0 167 5.7 2.7 3.1 7.8 168 5.7 2.0 4.2 8.9 169 6.6 3.0 3.0 6.3 170 6.3 3.5 4.3 8.4 171 5.4 3.7 5.2 9.0 172 7.4 3.8 4.7 5.2 173 8.6 3.9 3.4 6.8 174 7.3 3.6 3.6 6.7 175 6.3 3.4 5.1 8.4 176 8.7 3.9 3.4 6.8 177 8.6 4.5 4.4 6.2 178 8.4 4.1 3.4 6.7 179 7.4 3.8 4.7 5.2 180 9.9 4.3 3.0 4.5 181 8.0 4.2 3.8 5.8 182 7.9 4.4 3.7 7.6 183 9.8 4.3 3.0 4.5 184 8.9 4.3 4.6 7.4 185 6.8 3.6 4.1 4.8 186 7.4 4.2 3.4 7.3 187 4.7 3.3 4.7 8.5 188 5.4 2.8 3.6 7.2 189 7.0 4.6 6.1 9.3 190 7.1 4.2 3.5 3.8 191 6.3 2.9 3.7 5.8 192 5.5 4.4 5.8 8.4 193 5.4 2.8 3.6 7.2 194 5.4 3.3 4.9 8.4 195 4.8 3.0 4.1 8.8 196 8.2 4.0 3.9 4.4 197 7.9 5.4 7.5 8.4 198 8.6 4.2 3.5 6.8 199 8.2 4.9 6.7 6.3 200 8.6 4.2 3.5 6.8 Productgamma Productkwaliteit Productontwikkeling Facturatie 1 4.9 8.5 4.3 5.0 2 7.9 8.2 4.0 3.9 3 7.4 9.2 4.6 5.4 4 4.7 6.4 3.6 4.3 5 6.0 9.0 4.5 4.5 6 4.3 6.5 9.5 3.6 7 2.3 6.9 2.5 2.1 8 3.6 6.2 4.8 4.3 9 5.9 5.8 4.4 4.4 10 5.7 6.4 5.3 4.1 11 6.8 8.7 7.5 3.8 12 3.9 6.1 5.9 3.0 13 6.9 9.5 5.3 5.1 14 8.4 9.2 3.0 4.5 15 6.8 6.3 5.4 4.8 16 7.8 8.7 5.0 4.3 17 5.5 5.7 5.4 4.2 18 6.4 5.9 6.3 5.7 19 5.7 5.6 6.1 5.0 20 5.3 9.1 6.7 4.5 21 4.3 5.2 4.6 3.3 22 8.3 9.6 6.5 4.3 23 7.3 8.6 6.0 4.8 24 7.2 9.3 4.2 6.7 25 5.3 6.0 3.9 4.7 26 3.9 6.4 3.7 5.6 27 7.6 8.5 6.7 5.3 28 4.8 7.0 5.9 4.3 29 7.6 8.5 6.0 5.7 30 4.2 7.6 7.2 4.7 31 6.4 6.9 3.3 3.7 32 5.1 8.1 6.1 3.0 33 5.1 6.7 4.2 3.5 34 4.6 8.0 3.8 4.7 35 5.4 6.7 6.0 2.5 36 6.1 8.7 6.5 3.1 37 6.0 9.0 4.3 3.9 38 7.7 9.6 4.4 5.2 39 4.9 8.2 7.1 4.7 40 3.9 6.1 6.8 4.5 41 4.6 8.3 1.7 4.6 42 6.5 9.4 6.2 4.1 43 6.6 9.3 4.1 4.6 44 5.4 5.1 5.2 4.9 45 7.7 8.0 3.9 4.3 46 6.4 5.9 5.1 5.2 47 5.4 10.0 3.7 5.0 48 5.7 5.7 4.8 6.5 49 7.0 9.9 7.2 4.5 50 6.9 7.9 3.6 4.1 51 4.7 6.7 5.3 4.0 52 7.9 8.2 5.0 4.5 53 7.3 9.4 9.2 4.7 54 6.4 6.9 4.4 3.2 55 4.6 8.0 4.2 4.9 56 6.4 9.3 5.9 4.1 57 7.2 7.4 7.4 5.7 58 6.6 7.6 6.4 4.6 59 5.4 10.0 4.5 3.7 60 5.8 9.9 7.0 5.6 61 7.8 8.7 4.5 5.4 62 4.7 8.4 4.2 2.7 63 4.7 8.8 7.2 4.4 64 4.7 7.7 4.7 3.3 65 4.7 6.6 3.9 3.5 66 5.7 5.7 5.0 4.7 67 5.5 5.7 6.4 5.0 68 5.3 5.5 2.5 4.5 69 4.1 7.5 5.2 4.0 70 3.9 6.4 5.5 4.7 71 5.3 9.1 5.7 5.4 72 6.3 6.7 2.5 2.9 73 6.3 6.5 6.3 4.6 74 7.0 9.9 4.6 4.1 75 4.9 8.5 3.6 4.4 76 5.9 9.9 7.6 3.1 77 4.6 7.6 6.6 4.5 78 6.5 9.4 2.4 4.3 79 7.5 9.3 3.1 5.2 80 5.0 7.1 3.5 2.6 81 5.9 9.9 6.9 3.2 82 6.8 8.7 5.1 4.3 83 5.6 8.6 4.0 2.7 84 2.9 6.4 6.5 2.0 85 7.2 7.7 4.1 4.7 86 4.1 7.5 2.8 3.4 87 4.2 5.0 7.6 2.4 88 7.2 7.7 7.7 5.1 89 6.2 9.1 4.1 4.6 90 5.7 5.5 4.9 5.5 91 6.3 9.1 4.6 4.4 92 3.3 7.1 3.5 2.0 93 8.4 9.2 6.6 4.4 94 7.5 9.3 4.9 4.8 95 6.4 9.3 4.8 3.6 96 6.0 8.6 3.6 4.9 97 4.4 7.4 6.4 4.2 98 6.1 8.7 4.3 3.1 99 5.3 7.8 5.7 4.3 100 4.2 7.9 5.8 3.4 101 4.2 7.6 5.1 3.1 102 6.5 9.2 8.6 5.1 103 5.2 7.7 5.4 4.0 104 6.9 9.5 4.4 5.6 105 6.3 6.5 6.9 5.0 106 4.6 8.3 5.2 4.2 107 7.7 9.6 5.5 4.4 108 5.9 5.9 5.3 5.8 109 7.4 8.7 5.7 4.6 110 6.3 6.7 6.5 3.8 111 7.5 9.7 5.2 3.7 112 8.1 8.8 2.7 4.0 113 4.9 8.2 4.3 4.5 114 5.1 8.9 6.7 4.2 115 4.7 8.4 6.6 4.0 116 5.2 7.7 7.4 5.1 117 6.5 9.2 8.9 4.2 118 5.2 7.3 3.7 2.8 119 4.8 9.0 4.9 3.3 120 5.1 8.1 6.2 2.6 121 7.2 7.4 4.3 5.7 122 4.2 7.9 4.6 4.8 123 4.7 7.7 4.3 3.2 124 7.3 9.4 5.4 5.8 125 3.4 7.2 3.6 3.2 126 6.1 8.3 7.4 4.1 127 6.9 7.9 6.7 4.6 128 5.2 7.3 2.9 3.3 129 8.2 9.6 4.8 4.4 130 5.7 8.3 2.8 1.2 131 7.3 8.6 5.2 5.0 132 7.7 8.0 6.8 4.6 133 2.9 6.4 7.0 2.4 134 5.3 6.6 2.9 4.3 135 7.1 7.6 6.2 3.6 136 7.8 9.4 6.0 5.1 137 5.7 8.3 4.2 1.8 138 5.3 7.8 5.2 4.1 139 3.3 7.1 3.1 2.8 140 4.6 7.6 5.3 4.4 141 5.7 5.6 6.0 4.5 142 5.8 9.9 6.8 4.0 143 7.4 9.2 6.1 4.2 144 7.5 9.1 5.2 4.5 145 5.4 9.9 8.0 3.8 146 5.4 9.9 6.2 4.1 147 5.3 6.6 3.5 4.6 148 6.3 9.1 6.5 3.7 149 5.4 5.1 3.9 5.1 150 5.3 6.0 3.6 4.3 151 5.1 8.9 3.8 5.0 152 3.6 6.2 4.7 4.0 153 3.4 7.2 2.9 3.0 154 8.1 8.8 5.6 4.1 155 4.1 6.3 4.2 4.4 156 5.7 9.7 1.6 4.0 157 4.2 5.0 4.0 3.7 158 4.4 7.4 5.1 4.0 159 5.3 5.5 6.0 4.3 160 6.2 9.1 6.1 4.6 161 5.1 6.7 5.6 3.7 162 6.8 6.3 6.5 6.4 163 6.1 8.3 5.5 3.6 164 6.0 8.2 5.9 4.7 165 6.0 8.2 6.2 4.0 166 4.8 9.0 5.6 4.3 167 5.0 7.1 7.2 3.6 168 2.3 6.9 3.4 2.7 169 5.6 8.6 5.1 4.0 170 5.4 6.7 4.0 3.8 171 4.8 7.0 5.3 3.3 172 5.6 9.7 8.4 4.5 173 7.0 9.9 8.0 5.0 174 6.0 8.6 2.8 4.8 175 4.1 6.3 2.4 2.8 176 7.0 9.9 5.2 4.3 177 7.2 9.3 4.1 4.0 178 7.5 9.7 6.1 4.9 179 5.6 9.7 7.1 4.6 180 8.3 9.6 6.2 4.0 181 7.1 7.6 5.5 4.4 182 7.8 9.4 6.5 4.7 183 8.2 9.6 5.6 4.6 184 6.6 9.3 5.7 4.4 185 5.7 9.7 6.3 4.7 186 7.5 9.1 5.1 6.0 187 4.3 6.5 4.8 4.3 188 4.7 6.6 4.8 3.2 189 5.9 5.8 3.4 5.9 190 7.4 8.7 3.6 5.5 191 4.7 8.8 5.8 3.8 192 5.7 6.4 5.0 4.0 193 4.7 6.7 5.0 2.9 194 4.3 5.2 3.6 4.3 195 4.7 6.4 7.0 3.6 196 6.6 7.6 6.8 4.4 197 5.9 5.9 6.6 6.0 198 7.6 9.7 5.2 4.4 199 5.7 5.5 5.3 5.9 200 7.6 9.7 1.2 4.3 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Leveringssnelheid Prijsflexibiliteit 0.193996 0.985986 -0.124939 Prijszetting Productgamma Productkwaliteit -0.005293 -0.025678 0.376652 Productontwikkeling Facturatie 0.034198 0.138601 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.46147 -0.37288 0.03398 0.48591 1.69574 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.193996 0.963459 0.201 0.841 Leveringssnelheid 0.985986 0.481649 2.047 0.042 * Prijsflexibiliteit -0.124939 0.252278 -0.495 0.621 Prijszetting -0.005293 0.042685 -0.124 0.901 Productgamma -0.025678 0.242638 -0.106 0.916 Productkwaliteit 0.376652 0.050330 7.484 2.54e-12 *** Productontwikkeling 0.034198 0.037090 0.922 0.358 Facturatie 0.138601 0.094240 1.471 0.143 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.761 on 192 degrees of freedom Multiple R-squared: 0.6372, Adjusted R-squared: 0.624 F-statistic: 48.18 on 7 and 192 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.93424538 0.131509235 0.0657546177 [2,] 0.94783974 0.104320519 0.0521602597 [3,] 0.92509694 0.149806117 0.0749030584 [4,] 0.88898305 0.222033909 0.1110169544 [5,] 0.86155698 0.276886045 0.1384430224 [6,] 0.83758706 0.324825870 0.1624129350 [7,] 0.83925086 0.321498284 0.1607491422 [8,] 0.78478432 0.430431362 0.2152156812 [9,] 0.71780770 0.564384596 0.2821922982 [10,] 0.72017048 0.559659030 0.2798295151 [11,] 0.66195146 0.676097074 0.3380485370 [12,] 0.97149327 0.057013457 0.0285067283 [13,] 0.96669455 0.066610892 0.0333054461 [14,] 0.95466183 0.090676331 0.0453381655 [15,] 0.97774304 0.044513916 0.0222569578 [16,] 0.96858578 0.062828443 0.0314142215 [17,] 0.98321782 0.033564367 0.0167821836 [18,] 0.98698590 0.026028197 0.0130140985 [19,] 0.99256384 0.014872313 0.0074361565 [20,] 0.99523065 0.009538709 0.0047693546 [21,] 0.99348676 0.013026476 0.0065132379 [22,] 0.99079072 0.018418570 0.0092092849 [23,] 0.98852842 0.022943163 0.0114715817 [24,] 0.98393191 0.032136182 0.0160680912 [25,] 0.98036233 0.039275333 0.0196376664 [26,] 0.97456836 0.050863279 0.0254316395 [27,] 0.96529645 0.069407102 0.0347035512 [28,] 0.95352740 0.092945191 0.0464725954 [29,] 0.93884310 0.122313805 0.0611569024 [30,] 0.92800657 0.143986866 0.0719934330 [31,] 0.91495264 0.170094718 0.0850473589 [32,] 0.89316339 0.213673215 0.1068366076 [33,] 0.90266540 0.194669195 0.0973345973 [34,] 0.94471531 0.110569387 0.0552846936 [35,] 0.99048495 0.019030092 0.0095150460 [36,] 0.98888348 0.022233049 0.0111165246 [37,] 0.98638041 0.027239177 0.0136195885 [38,] 0.98189707 0.036205865 0.0181029327 [39,] 0.98372153 0.032556946 0.0162784731 [40,] 0.99178078 0.016438442 0.0082192212 [41,] 0.98880908 0.022381843 0.0111909213 [42,] 0.99900239 0.001995227 0.0009976135 [43,] 0.99881652 0.002366970 0.0011834848 [44,] 0.99846349 0.003073029 0.0015365145 [45,] 0.99783492 0.004330163 0.0021650815 [46,] 0.99740613 0.005187736 0.0025938681 [47,] 0.99723785 0.005524297 0.0027621485 [48,] 0.99834277 0.003314457 0.0016572286 [49,] 0.99774614 0.004507723 0.0022538613 [50,] 0.99687314 0.006253711 0.0031268557 [51,] 0.99850898 0.002982045 0.0014910224 [52,] 0.99922936 0.001541279 0.0007706395 [53,] 0.99893854 0.002122921 0.0010614607 [54,] 0.99854293 0.002914140 0.0014570700 [55,] 0.99793169 0.004136627 0.0020683137 [56,] 0.99735147 0.005297054 0.0026485272 [57,] 0.99657750 0.006845001 0.0034225004 [58,] 0.99547263 0.009054733 0.0045273666 [59,] 0.99696469 0.006070625 0.0030353124 [60,] 0.99591878 0.008162441 0.0040812204 [61,] 0.99488751 0.010224979 0.0051124897 [62,] 0.99342736 0.013145285 0.0065726423 [63,] 0.99369139 0.012617217 0.0063086086 [64,] 0.99379367 0.012412669 0.0062063346 [65,] 0.99602153 0.007956944 0.0039784718 [66,] 0.99531838 0.009363247 0.0046816234 [67,] 0.99433577 0.011328462 0.0056642310 [68,] 0.99248460 0.015030799 0.0075153994 [69,] 0.99175038 0.016499246 0.0082496232 [70,] 0.98925958 0.021480837 0.0107404184 [71,] 0.98755867 0.024882665 0.0124413324 [72,] 0.98391549 0.032169011 0.0160845054 [73,] 0.97960163 0.040796746 0.0203983729 [74,] 0.98189750 0.036204992 0.0181024961 [75,] 0.98267623 0.034647548 0.0173237738 [76,] 0.98580695 0.028386097 0.0141930483 [77,] 0.98191113 0.036177745 0.0180888724 [78,] 0.98112208 0.037755842 0.0188779212 [79,] 0.97619208 0.047615844 0.0238079220 [80,] 0.98320260 0.033594809 0.0167974047 [81,] 0.97942166 0.041156689 0.0205783444 [82,] 0.97493816 0.050123681 0.0250618406 [83,] 0.97172850 0.056543000 0.0282715001 [84,] 0.96886556 0.062268881 0.0311344407 [85,] 0.96596840 0.068063205 0.0340316026 [86,] 0.95760639 0.084787218 0.0423936091 [87,] 0.94867784 0.102644311 0.0513221555 [88,] 0.94268522 0.114629565 0.0573147827 [89,] 0.93763798 0.124724043 0.0623620213 [90,] 0.92428822 0.151423561 0.0757117807 [91,] 0.92889272 0.142214567 0.0711072837 [92,] 0.92678932 0.146421353 0.0732106765 [93,] 0.92214452 0.155710962 0.0778554811 [94,] 0.90636379 0.187272415 0.0936362073 [95,] 0.90254152 0.194916961 0.0974584806 [96,] 0.88357108 0.232857831 0.1164289156 [97,] 0.86242735 0.275145301 0.1375726505 [98,] 0.84116653 0.317666935 0.1588334676 [99,] 0.84536778 0.309264442 0.1546322212 [100,] 0.82609077 0.347818459 0.1739092297 [101,] 0.80852616 0.382947678 0.1914738388 [102,] 0.79306266 0.413874673 0.2069373364 [103,] 0.76625548 0.467489038 0.2337445191 [104,] 0.73533503 0.529329933 0.2646649667 [105,] 0.77764223 0.444715530 0.2223577652 [106,] 0.78184117 0.436317670 0.2181588350 [107,] 0.79042323 0.419153549 0.2095767744 [108,] 0.75832101 0.483357980 0.2416789898 [109,] 0.72616895 0.547662092 0.2738310459 [110,] 0.69231296 0.615374073 0.3076870364 [111,] 0.69461987 0.610760262 0.3053801311 [112,] 0.66147387 0.677052266 0.3385261332 [113,] 0.62476339 0.750473212 0.3752366062 [114,] 0.58864845 0.822703110 0.4113515549 [115,] 0.55180317 0.896393668 0.4481968341 [116,] 0.54365396 0.912692088 0.4563460442 [117,] 0.56544701 0.869105976 0.4345529881 [118,] 0.52214733 0.955705332 0.4778526662 [119,] 0.63255602 0.734887959 0.3674439793 [120,] 0.61074396 0.778512081 0.3892560406 [121,] 0.61901093 0.761978148 0.3809890740 [122,] 0.95104449 0.097911027 0.0489555135 [123,] 0.96563465 0.068730703 0.0343653514 [124,] 0.95592162 0.088156756 0.0440783781 [125,] 0.94959861 0.100802788 0.0504013940 [126,] 0.94043280 0.119134398 0.0595671989 [127,] 0.94508358 0.109832833 0.0549164165 [128,] 0.93513559 0.129728814 0.0648644071 [129,] 0.92091148 0.158177036 0.0790885180 [130,] 0.90478404 0.190431928 0.0952159640 [131,] 0.88405998 0.231880032 0.1159400161 [132,] 0.85898782 0.282024363 0.1410121815 [133,] 0.84425633 0.311487341 0.1557436704 [134,] 0.83359381 0.332812382 0.1664061909 [135,] 0.81931147 0.361377068 0.1806885338 [136,] 0.81296243 0.374075147 0.1870375737 [137,] 0.77794392 0.444112164 0.2220560819 [138,] 0.74751299 0.504974030 0.2524870148 [139,] 0.76781592 0.464368157 0.2321840783 [140,] 0.88045735 0.239085290 0.1195426451 [141,] 0.85391197 0.292176056 0.1460880281 [142,] 0.82984979 0.340300422 0.1701502112 [143,] 0.79703635 0.405927305 0.2029636523 [144,] 0.83424885 0.331502291 0.1657511454 [145,] 0.82635449 0.347291010 0.1736455051 [146,] 0.83717599 0.325648027 0.1628240134 [147,] 0.80220571 0.395588579 0.1977942894 [148,] 0.76248966 0.475020677 0.2375103387 [149,] 0.75555688 0.488886246 0.2444431232 [150,] 0.71023810 0.579523801 0.2897619003 [151,] 0.68733875 0.625322504 0.3126612521 [152,] 0.65432305 0.691353897 0.3456769483 [153,] 0.61951977 0.760960458 0.3804802290 [154,] 0.60016971 0.799660587 0.3998302937 [155,] 0.58536175 0.829276494 0.4146382468 [156,] 0.53258378 0.934832432 0.4674162159 [157,] 0.47819810 0.956396195 0.5218019024 [158,] 0.75516825 0.489663504 0.2448317521 [159,] 0.70735134 0.585297313 0.2926486563 [160,] 0.65641553 0.687168944 0.3435844720 [161,] 0.67548115 0.649037708 0.3245188539 [162,] 0.61727922 0.765441570 0.3827207848 [163,] 0.61732409 0.765351818 0.3826759089 [164,] 0.57064399 0.858712024 0.4293560119 [165,] 0.56004449 0.879911021 0.4399555103 [166,] 0.58180391 0.836392184 0.4181960920 [167,] 0.51250482 0.974990363 0.4874951816 [168,] 0.44106871 0.882137422 0.5589312890 [169,] 0.36835718 0.736714363 0.6316428183 [170,] 0.34562673 0.691253457 0.6543732714 [171,] 0.27609983 0.552199663 0.7239001687 [172,] 0.23893047 0.477860941 0.7610695293 [173,] 0.24391901 0.487838028 0.7560809858 [174,] 0.32495736 0.649914723 0.6750426386 [175,] 0.29412472 0.588249441 0.7058752793 [176,] 0.20873538 0.417470757 0.7912646217 [177,] 0.14779193 0.295583861 0.8522080695 [178,] 0.08751486 0.175029728 0.9124851358 [179,] 0.04380733 0.087614656 0.9561926722 > postscript(file="/var/wessaorg/rcomp/tmp/1y1n11322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ko8a1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3flp71322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4grk31322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5upp91322083116.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 = 200 Frequency = 1 1 2 3 4 5 1.1152616777 -2.3230629199 0.6117034934 -0.8021068308 -0.0850631717 6 7 8 9 10 -1.2772114806 1.1894890410 0.2785584303 0.2884269279 -1.2769466671 11 12 13 14 15 -0.1251038308 0.4914501765 0.1809883520 -0.3485167281 0.4518281688 16 17 18 19 20 -1.3612939886 -0.1959741949 0.1790001814 0.1023705545 -0.2700149145 21 22 23 24 25 0.1460456843 1.6438954755 -0.6157950029 0.1613752371 -1.5420327263 26 27 28 29 30 0.2097112449 -1.7022613748 -1.0558843884 -1.7337630925 -1.1693830184 31 32 33 34 35 -0.0431274478 0.1759324247 -0.5567707992 0.1590369500 0.3001558036 36 37 38 39 40 -0.6391845485 0.0049369829 0.0008139636 0.0218302608 0.2527705652 41 42 43 44 45 0.0296792490 -0.1832350782 0.9689873506 1.1481742185 -2.3207146993 46 47 48 49 50 0.2893383079 -0.4724751322 0.1687279688 0.7024046632 1.2261353936 51 52 53 54 55 -0.2054008541 -2.4404215244 0.2594078481 -0.0114448647 0.1176375443 56 57 58 59 60 0.4840683044 0.6005656136 1.0230986787 -0.3196524075 -0.1926311594 61 62 63 64 65 -1.4966559513 1.3555685572 -0.3103028696 0.2649205228 -0.0505578871 66 67 68 69 70 0.4113699826 -0.3410529801 -0.2095284305 -1.0832934908 0.2728955458 71 72 73 74 75 -0.3605576653 0.0515189272 0.7766994101 0.7467599867 1.2223608634 76 77 78 79 80 -0.5444929187 -0.4693734966 -0.0810026220 0.5700479972 0.2465445492 81 82 83 84 85 -0.5344143654 -0.1123289328 0.2497838852 0.8995677086 0.7777801266 86 87 88 89 90 -0.9180575990 0.2245995127 0.5992267407 -0.2042766052 1.1900142382 91 92 93 94 95 0.3327896267 -0.1945901316 -0.4577696622 0.5639318464 0.5909866248 96 97 98 99 100 0.1543016203 -0.2796080722 -0.5639488113 -0.5780293230 -0.0562271545 101 102 103 104 105 -0.8758056419 0.6789622750 -0.6199415452 0.1424661564 0.7007402113 106 107 108 109 110 -0.0345736079 0.0740768177 0.3073040292 -0.6970867265 -0.2100141354 111 112 113 114 115 0.4721243890 -0.3698026669 0.1453050161 -0.2870891026 1.0933120330 116 117 118 119 120 -0.8407986637 0.7934436694 0.0706392152 -0.2230996484 0.2279529799 121 122 123 124 125 0.7065796069 -0.2092307445 0.2924598381 0.2368994912 -0.2850037925 126 127 128 129 130 0.7489178433 1.0508209486 0.0286972133 1.5856043160 -0.3666189193 131 132 133 134 135 -0.6161567337 -2.4614693512 0.8270283161 0.0629362587 0.7790979599 136 137 138 139 140 -0.3821261772 -0.4976567487 -0.5332101111 -0.2917916294 -0.4110559252 141 142 143 144 145 0.1750908125 0.0359698986 0.7267274841 -0.5081874264 -0.5155404881 146 147 148 149 150 -0.4955642469 0.0008371502 0.3648339406 1.1649115189 -1.4763329462 151 152 153 154 155 -0.2987954445 0.3235585076 -0.2333449681 -0.4828371380 0.4142000768 156 157 158 159 160 -0.5221704681 0.1675313626 -0.2074304104 -0.3015014680 -0.2726727299 161 162 163 164 165 -0.6323682672 0.1924488546 0.8831946135 0.7377271652 0.8244883789 166 167 168 169 170 -0.3856391955 -0.0185891850 1.0755502428 0.0319848421 0.1883707538 171 172 173 174 175 -0.8967646475 -0.3467019119 0.5057457615 0.1955201606 0.6975180346 176 177 178 179 180 0.7985209686 0.5390174828 0.2750250487 -0.3161045212 1.6957351652 181 182 183 184 185 0.6921558808 -0.3437848470 1.5305256854 0.9419906316 -0.7799219936 186 187 188 189 190 -0.7126689754 -1.2135012200 -0.0397558721 0.1147236351 -0.7500116086 191 192 193 194 195 -0.1792650402 -1.2528271581 -0.0426804416 0.0416428431 -0.8213596103 196 197 198 199 200 1.0371396344 0.2351263674 0.4920962141 1.1208946518 0.6427485539 > postscript(file="/var/wessaorg/rcomp/tmp/6w51j1322083116.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 = 200 Frequency = 1 lag(myerror, k = 1) myerror 0 1.1152616777 NA 1 -2.3230629199 1.1152616777 2 0.6117034934 -2.3230629199 3 -0.8021068308 0.6117034934 4 -0.0850631717 -0.8021068308 5 -1.2772114806 -0.0850631717 6 1.1894890410 -1.2772114806 7 0.2785584303 1.1894890410 8 0.2884269279 0.2785584303 9 -1.2769466671 0.2884269279 10 -0.1251038308 -1.2769466671 11 0.4914501765 -0.1251038308 12 0.1809883520 0.4914501765 13 -0.3485167281 0.1809883520 14 0.4518281688 -0.3485167281 15 -1.3612939886 0.4518281688 16 -0.1959741949 -1.3612939886 17 0.1790001814 -0.1959741949 18 0.1023705545 0.1790001814 19 -0.2700149145 0.1023705545 20 0.1460456843 -0.2700149145 21 1.6438954755 0.1460456843 22 -0.6157950029 1.6438954755 23 0.1613752371 -0.6157950029 24 -1.5420327263 0.1613752371 25 0.2097112449 -1.5420327263 26 -1.7022613748 0.2097112449 27 -1.0558843884 -1.7022613748 28 -1.7337630925 -1.0558843884 29 -1.1693830184 -1.7337630925 30 -0.0431274478 -1.1693830184 31 0.1759324247 -0.0431274478 32 -0.5567707992 0.1759324247 33 0.1590369500 -0.5567707992 34 0.3001558036 0.1590369500 35 -0.6391845485 0.3001558036 36 0.0049369829 -0.6391845485 37 0.0008139636 0.0049369829 38 0.0218302608 0.0008139636 39 0.2527705652 0.0218302608 40 0.0296792490 0.2527705652 41 -0.1832350782 0.0296792490 42 0.9689873506 -0.1832350782 43 1.1481742185 0.9689873506 44 -2.3207146993 1.1481742185 45 0.2893383079 -2.3207146993 46 -0.4724751322 0.2893383079 47 0.1687279688 -0.4724751322 48 0.7024046632 0.1687279688 49 1.2261353936 0.7024046632 50 -0.2054008541 1.2261353936 51 -2.4404215244 -0.2054008541 52 0.2594078481 -2.4404215244 53 -0.0114448647 0.2594078481 54 0.1176375443 -0.0114448647 55 0.4840683044 0.1176375443 56 0.6005656136 0.4840683044 57 1.0230986787 0.6005656136 58 -0.3196524075 1.0230986787 59 -0.1926311594 -0.3196524075 60 -1.4966559513 -0.1926311594 61 1.3555685572 -1.4966559513 62 -0.3103028696 1.3555685572 63 0.2649205228 -0.3103028696 64 -0.0505578871 0.2649205228 65 0.4113699826 -0.0505578871 66 -0.3410529801 0.4113699826 67 -0.2095284305 -0.3410529801 68 -1.0832934908 -0.2095284305 69 0.2728955458 -1.0832934908 70 -0.3605576653 0.2728955458 71 0.0515189272 -0.3605576653 72 0.7766994101 0.0515189272 73 0.7467599867 0.7766994101 74 1.2223608634 0.7467599867 75 -0.5444929187 1.2223608634 76 -0.4693734966 -0.5444929187 77 -0.0810026220 -0.4693734966 78 0.5700479972 -0.0810026220 79 0.2465445492 0.5700479972 80 -0.5344143654 0.2465445492 81 -0.1123289328 -0.5344143654 82 0.2497838852 -0.1123289328 83 0.8995677086 0.2497838852 84 0.7777801266 0.8995677086 85 -0.9180575990 0.7777801266 86 0.2245995127 -0.9180575990 87 0.5992267407 0.2245995127 88 -0.2042766052 0.5992267407 89 1.1900142382 -0.2042766052 90 0.3327896267 1.1900142382 91 -0.1945901316 0.3327896267 92 -0.4577696622 -0.1945901316 93 0.5639318464 -0.4577696622 94 0.5909866248 0.5639318464 95 0.1543016203 0.5909866248 96 -0.2796080722 0.1543016203 97 -0.5639488113 -0.2796080722 98 -0.5780293230 -0.5639488113 99 -0.0562271545 -0.5780293230 100 -0.8758056419 -0.0562271545 101 0.6789622750 -0.8758056419 102 -0.6199415452 0.6789622750 103 0.1424661564 -0.6199415452 104 0.7007402113 0.1424661564 105 -0.0345736079 0.7007402113 106 0.0740768177 -0.0345736079 107 0.3073040292 0.0740768177 108 -0.6970867265 0.3073040292 109 -0.2100141354 -0.6970867265 110 0.4721243890 -0.2100141354 111 -0.3698026669 0.4721243890 112 0.1453050161 -0.3698026669 113 -0.2870891026 0.1453050161 114 1.0933120330 -0.2870891026 115 -0.8407986637 1.0933120330 116 0.7934436694 -0.8407986637 117 0.0706392152 0.7934436694 118 -0.2230996484 0.0706392152 119 0.2279529799 -0.2230996484 120 0.7065796069 0.2279529799 121 -0.2092307445 0.7065796069 122 0.2924598381 -0.2092307445 123 0.2368994912 0.2924598381 124 -0.2850037925 0.2368994912 125 0.7489178433 -0.2850037925 126 1.0508209486 0.7489178433 127 0.0286972133 1.0508209486 128 1.5856043160 0.0286972133 129 -0.3666189193 1.5856043160 130 -0.6161567337 -0.3666189193 131 -2.4614693512 -0.6161567337 132 0.8270283161 -2.4614693512 133 0.0629362587 0.8270283161 134 0.7790979599 0.0629362587 135 -0.3821261772 0.7790979599 136 -0.4976567487 -0.3821261772 137 -0.5332101111 -0.4976567487 138 -0.2917916294 -0.5332101111 139 -0.4110559252 -0.2917916294 140 0.1750908125 -0.4110559252 141 0.0359698986 0.1750908125 142 0.7267274841 0.0359698986 143 -0.5081874264 0.7267274841 144 -0.5155404881 -0.5081874264 145 -0.4955642469 -0.5155404881 146 0.0008371502 -0.4955642469 147 0.3648339406 0.0008371502 148 1.1649115189 0.3648339406 149 -1.4763329462 1.1649115189 150 -0.2987954445 -1.4763329462 151 0.3235585076 -0.2987954445 152 -0.2333449681 0.3235585076 153 -0.4828371380 -0.2333449681 154 0.4142000768 -0.4828371380 155 -0.5221704681 0.4142000768 156 0.1675313626 -0.5221704681 157 -0.2074304104 0.1675313626 158 -0.3015014680 -0.2074304104 159 -0.2726727299 -0.3015014680 160 -0.6323682672 -0.2726727299 161 0.1924488546 -0.6323682672 162 0.8831946135 0.1924488546 163 0.7377271652 0.8831946135 164 0.8244883789 0.7377271652 165 -0.3856391955 0.8244883789 166 -0.0185891850 -0.3856391955 167 1.0755502428 -0.0185891850 168 0.0319848421 1.0755502428 169 0.1883707538 0.0319848421 170 -0.8967646475 0.1883707538 171 -0.3467019119 -0.8967646475 172 0.5057457615 -0.3467019119 173 0.1955201606 0.5057457615 174 0.6975180346 0.1955201606 175 0.7985209686 0.6975180346 176 0.5390174828 0.7985209686 177 0.2750250487 0.5390174828 178 -0.3161045212 0.2750250487 179 1.6957351652 -0.3161045212 180 0.6921558808 1.6957351652 181 -0.3437848470 0.6921558808 182 1.5305256854 -0.3437848470 183 0.9419906316 1.5305256854 184 -0.7799219936 0.9419906316 185 -0.7126689754 -0.7799219936 186 -1.2135012200 -0.7126689754 187 -0.0397558721 -1.2135012200 188 0.1147236351 -0.0397558721 189 -0.7500116086 0.1147236351 190 -0.1792650402 -0.7500116086 191 -1.2528271581 -0.1792650402 192 -0.0426804416 -1.2528271581 193 0.0416428431 -0.0426804416 194 -0.8213596103 0.0416428431 195 1.0371396344 -0.8213596103 196 0.2351263674 1.0371396344 197 0.4920962141 0.2351263674 198 1.1208946518 0.4920962141 199 0.6427485539 1.1208946518 200 NA 0.6427485539 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.3230629199 1.1152616777 [2,] 0.6117034934 -2.3230629199 [3,] -0.8021068308 0.6117034934 [4,] -0.0850631717 -0.8021068308 [5,] -1.2772114806 -0.0850631717 [6,] 1.1894890410 -1.2772114806 [7,] 0.2785584303 1.1894890410 [8,] 0.2884269279 0.2785584303 [9,] -1.2769466671 0.2884269279 [10,] -0.1251038308 -1.2769466671 [11,] 0.4914501765 -0.1251038308 [12,] 0.1809883520 0.4914501765 [13,] -0.3485167281 0.1809883520 [14,] 0.4518281688 -0.3485167281 [15,] -1.3612939886 0.4518281688 [16,] -0.1959741949 -1.3612939886 [17,] 0.1790001814 -0.1959741949 [18,] 0.1023705545 0.1790001814 [19,] -0.2700149145 0.1023705545 [20,] 0.1460456843 -0.2700149145 [21,] 1.6438954755 0.1460456843 [22,] -0.6157950029 1.6438954755 [23,] 0.1613752371 -0.6157950029 [24,] -1.5420327263 0.1613752371 [25,] 0.2097112449 -1.5420327263 [26,] -1.7022613748 0.2097112449 [27,] -1.0558843884 -1.7022613748 [28,] -1.7337630925 -1.0558843884 [29,] -1.1693830184 -1.7337630925 [30,] -0.0431274478 -1.1693830184 [31,] 0.1759324247 -0.0431274478 [32,] -0.5567707992 0.1759324247 [33,] 0.1590369500 -0.5567707992 [34,] 0.3001558036 0.1590369500 [35,] -0.6391845485 0.3001558036 [36,] 0.0049369829 -0.6391845485 [37,] 0.0008139636 0.0049369829 [38,] 0.0218302608 0.0008139636 [39,] 0.2527705652 0.0218302608 [40,] 0.0296792490 0.2527705652 [41,] -0.1832350782 0.0296792490 [42,] 0.9689873506 -0.1832350782 [43,] 1.1481742185 0.9689873506 [44,] -2.3207146993 1.1481742185 [45,] 0.2893383079 -2.3207146993 [46,] -0.4724751322 0.2893383079 [47,] 0.1687279688 -0.4724751322 [48,] 0.7024046632 0.1687279688 [49,] 1.2261353936 0.7024046632 [50,] -0.2054008541 1.2261353936 [51,] -2.4404215244 -0.2054008541 [52,] 0.2594078481 -2.4404215244 [53,] -0.0114448647 0.2594078481 [54,] 0.1176375443 -0.0114448647 [55,] 0.4840683044 0.1176375443 [56,] 0.6005656136 0.4840683044 [57,] 1.0230986787 0.6005656136 [58,] -0.3196524075 1.0230986787 [59,] -0.1926311594 -0.3196524075 [60,] -1.4966559513 -0.1926311594 [61,] 1.3555685572 -1.4966559513 [62,] -0.3103028696 1.3555685572 [63,] 0.2649205228 -0.3103028696 [64,] -0.0505578871 0.2649205228 [65,] 0.4113699826 -0.0505578871 [66,] -0.3410529801 0.4113699826 [67,] -0.2095284305 -0.3410529801 [68,] -1.0832934908 -0.2095284305 [69,] 0.2728955458 -1.0832934908 [70,] -0.3605576653 0.2728955458 [71,] 0.0515189272 -0.3605576653 [72,] 0.7766994101 0.0515189272 [73,] 0.7467599867 0.7766994101 [74,] 1.2223608634 0.7467599867 [75,] -0.5444929187 1.2223608634 [76,] -0.4693734966 -0.5444929187 [77,] -0.0810026220 -0.4693734966 [78,] 0.5700479972 -0.0810026220 [79,] 0.2465445492 0.5700479972 [80,] -0.5344143654 0.2465445492 [81,] -0.1123289328 -0.5344143654 [82,] 0.2497838852 -0.1123289328 [83,] 0.8995677086 0.2497838852 [84,] 0.7777801266 0.8995677086 [85,] -0.9180575990 0.7777801266 [86,] 0.2245995127 -0.9180575990 [87,] 0.5992267407 0.2245995127 [88,] -0.2042766052 0.5992267407 [89,] 1.1900142382 -0.2042766052 [90,] 0.3327896267 1.1900142382 [91,] -0.1945901316 0.3327896267 [92,] -0.4577696622 -0.1945901316 [93,] 0.5639318464 -0.4577696622 [94,] 0.5909866248 0.5639318464 [95,] 0.1543016203 0.5909866248 [96,] -0.2796080722 0.1543016203 [97,] -0.5639488113 -0.2796080722 [98,] -0.5780293230 -0.5639488113 [99,] -0.0562271545 -0.5780293230 [100,] -0.8758056419 -0.0562271545 [101,] 0.6789622750 -0.8758056419 [102,] -0.6199415452 0.6789622750 [103,] 0.1424661564 -0.6199415452 [104,] 0.7007402113 0.1424661564 [105,] -0.0345736079 0.7007402113 [106,] 0.0740768177 -0.0345736079 [107,] 0.3073040292 0.0740768177 [108,] -0.6970867265 0.3073040292 [109,] -0.2100141354 -0.6970867265 [110,] 0.4721243890 -0.2100141354 [111,] -0.3698026669 0.4721243890 [112,] 0.1453050161 -0.3698026669 [113,] -0.2870891026 0.1453050161 [114,] 1.0933120330 -0.2870891026 [115,] -0.8407986637 1.0933120330 [116,] 0.7934436694 -0.8407986637 [117,] 0.0706392152 0.7934436694 [118,] -0.2230996484 0.0706392152 [119,] 0.2279529799 -0.2230996484 [120,] 0.7065796069 0.2279529799 [121,] -0.2092307445 0.7065796069 [122,] 0.2924598381 -0.2092307445 [123,] 0.2368994912 0.2924598381 [124,] -0.2850037925 0.2368994912 [125,] 0.7489178433 -0.2850037925 [126,] 1.0508209486 0.7489178433 [127,] 0.0286972133 1.0508209486 [128,] 1.5856043160 0.0286972133 [129,] -0.3666189193 1.5856043160 [130,] -0.6161567337 -0.3666189193 [131,] -2.4614693512 -0.6161567337 [132,] 0.8270283161 -2.4614693512 [133,] 0.0629362587 0.8270283161 [134,] 0.7790979599 0.0629362587 [135,] -0.3821261772 0.7790979599 [136,] -0.4976567487 -0.3821261772 [137,] -0.5332101111 -0.4976567487 [138,] -0.2917916294 -0.5332101111 [139,] -0.4110559252 -0.2917916294 [140,] 0.1750908125 -0.4110559252 [141,] 0.0359698986 0.1750908125 [142,] 0.7267274841 0.0359698986 [143,] -0.5081874264 0.7267274841 [144,] -0.5155404881 -0.5081874264 [145,] -0.4955642469 -0.5155404881 [146,] 0.0008371502 -0.4955642469 [147,] 0.3648339406 0.0008371502 [148,] 1.1649115189 0.3648339406 [149,] -1.4763329462 1.1649115189 [150,] -0.2987954445 -1.4763329462 [151,] 0.3235585076 -0.2987954445 [152,] -0.2333449681 0.3235585076 [153,] -0.4828371380 -0.2333449681 [154,] 0.4142000768 -0.4828371380 [155,] -0.5221704681 0.4142000768 [156,] 0.1675313626 -0.5221704681 [157,] -0.2074304104 0.1675313626 [158,] -0.3015014680 -0.2074304104 [159,] -0.2726727299 -0.3015014680 [160,] -0.6323682672 -0.2726727299 [161,] 0.1924488546 -0.6323682672 [162,] 0.8831946135 0.1924488546 [163,] 0.7377271652 0.8831946135 [164,] 0.8244883789 0.7377271652 [165,] -0.3856391955 0.8244883789 [166,] -0.0185891850 -0.3856391955 [167,] 1.0755502428 -0.0185891850 [168,] 0.0319848421 1.0755502428 [169,] 0.1883707538 0.0319848421 [170,] -0.8967646475 0.1883707538 [171,] -0.3467019119 -0.8967646475 [172,] 0.5057457615 -0.3467019119 [173,] 0.1955201606 0.5057457615 [174,] 0.6975180346 0.1955201606 [175,] 0.7985209686 0.6975180346 [176,] 0.5390174828 0.7985209686 [177,] 0.2750250487 0.5390174828 [178,] -0.3161045212 0.2750250487 [179,] 1.6957351652 -0.3161045212 [180,] 0.6921558808 1.6957351652 [181,] -0.3437848470 0.6921558808 [182,] 1.5305256854 -0.3437848470 [183,] 0.9419906316 1.5305256854 [184,] -0.7799219936 0.9419906316 [185,] -0.7126689754 -0.7799219936 [186,] -1.2135012200 -0.7126689754 [187,] -0.0397558721 -1.2135012200 [188,] 0.1147236351 -0.0397558721 [189,] -0.7500116086 0.1147236351 [190,] -0.1792650402 -0.7500116086 [191,] -1.2528271581 -0.1792650402 [192,] -0.0426804416 -1.2528271581 [193,] 0.0416428431 -0.0426804416 [194,] -0.8213596103 0.0416428431 [195,] 1.0371396344 -0.8213596103 [196,] 0.2351263674 1.0371396344 [197,] 0.4920962141 0.2351263674 [198,] 1.1208946518 0.4920962141 [199,] 0.6427485539 1.1208946518 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.3230629199 1.1152616777 2 0.6117034934 -2.3230629199 3 -0.8021068308 0.6117034934 4 -0.0850631717 -0.8021068308 5 -1.2772114806 -0.0850631717 6 1.1894890410 -1.2772114806 7 0.2785584303 1.1894890410 8 0.2884269279 0.2785584303 9 -1.2769466671 0.2884269279 10 -0.1251038308 -1.2769466671 11 0.4914501765 -0.1251038308 12 0.1809883520 0.4914501765 13 -0.3485167281 0.1809883520 14 0.4518281688 -0.3485167281 15 -1.3612939886 0.4518281688 16 -0.1959741949 -1.3612939886 17 0.1790001814 -0.1959741949 18 0.1023705545 0.1790001814 19 -0.2700149145 0.1023705545 20 0.1460456843 -0.2700149145 21 1.6438954755 0.1460456843 22 -0.6157950029 1.6438954755 23 0.1613752371 -0.6157950029 24 -1.5420327263 0.1613752371 25 0.2097112449 -1.5420327263 26 -1.7022613748 0.2097112449 27 -1.0558843884 -1.7022613748 28 -1.7337630925 -1.0558843884 29 -1.1693830184 -1.7337630925 30 -0.0431274478 -1.1693830184 31 0.1759324247 -0.0431274478 32 -0.5567707992 0.1759324247 33 0.1590369500 -0.5567707992 34 0.3001558036 0.1590369500 35 -0.6391845485 0.3001558036 36 0.0049369829 -0.6391845485 37 0.0008139636 0.0049369829 38 0.0218302608 0.0008139636 39 0.2527705652 0.0218302608 40 0.0296792490 0.2527705652 41 -0.1832350782 0.0296792490 42 0.9689873506 -0.1832350782 43 1.1481742185 0.9689873506 44 -2.3207146993 1.1481742185 45 0.2893383079 -2.3207146993 46 -0.4724751322 0.2893383079 47 0.1687279688 -0.4724751322 48 0.7024046632 0.1687279688 49 1.2261353936 0.7024046632 50 -0.2054008541 1.2261353936 51 -2.4404215244 -0.2054008541 52 0.2594078481 -2.4404215244 53 -0.0114448647 0.2594078481 54 0.1176375443 -0.0114448647 55 0.4840683044 0.1176375443 56 0.6005656136 0.4840683044 57 1.0230986787 0.6005656136 58 -0.3196524075 1.0230986787 59 -0.1926311594 -0.3196524075 60 -1.4966559513 -0.1926311594 61 1.3555685572 -1.4966559513 62 -0.3103028696 1.3555685572 63 0.2649205228 -0.3103028696 64 -0.0505578871 0.2649205228 65 0.4113699826 -0.0505578871 66 -0.3410529801 0.4113699826 67 -0.2095284305 -0.3410529801 68 -1.0832934908 -0.2095284305 69 0.2728955458 -1.0832934908 70 -0.3605576653 0.2728955458 71 0.0515189272 -0.3605576653 72 0.7766994101 0.0515189272 73 0.7467599867 0.7766994101 74 1.2223608634 0.7467599867 75 -0.5444929187 1.2223608634 76 -0.4693734966 -0.5444929187 77 -0.0810026220 -0.4693734966 78 0.5700479972 -0.0810026220 79 0.2465445492 0.5700479972 80 -0.5344143654 0.2465445492 81 -0.1123289328 -0.5344143654 82 0.2497838852 -0.1123289328 83 0.8995677086 0.2497838852 84 0.7777801266 0.8995677086 85 -0.9180575990 0.7777801266 86 0.2245995127 -0.9180575990 87 0.5992267407 0.2245995127 88 -0.2042766052 0.5992267407 89 1.1900142382 -0.2042766052 90 0.3327896267 1.1900142382 91 -0.1945901316 0.3327896267 92 -0.4577696622 -0.1945901316 93 0.5639318464 -0.4577696622 94 0.5909866248 0.5639318464 95 0.1543016203 0.5909866248 96 -0.2796080722 0.1543016203 97 -0.5639488113 -0.2796080722 98 -0.5780293230 -0.5639488113 99 -0.0562271545 -0.5780293230 100 -0.8758056419 -0.0562271545 101 0.6789622750 -0.8758056419 102 -0.6199415452 0.6789622750 103 0.1424661564 -0.6199415452 104 0.7007402113 0.1424661564 105 -0.0345736079 0.7007402113 106 0.0740768177 -0.0345736079 107 0.3073040292 0.0740768177 108 -0.6970867265 0.3073040292 109 -0.2100141354 -0.6970867265 110 0.4721243890 -0.2100141354 111 -0.3698026669 0.4721243890 112 0.1453050161 -0.3698026669 113 -0.2870891026 0.1453050161 114 1.0933120330 -0.2870891026 115 -0.8407986637 1.0933120330 116 0.7934436694 -0.8407986637 117 0.0706392152 0.7934436694 118 -0.2230996484 0.0706392152 119 0.2279529799 -0.2230996484 120 0.7065796069 0.2279529799 121 -0.2092307445 0.7065796069 122 0.2924598381 -0.2092307445 123 0.2368994912 0.2924598381 124 -0.2850037925 0.2368994912 125 0.7489178433 -0.2850037925 126 1.0508209486 0.7489178433 127 0.0286972133 1.0508209486 128 1.5856043160 0.0286972133 129 -0.3666189193 1.5856043160 130 -0.6161567337 -0.3666189193 131 -2.4614693512 -0.6161567337 132 0.8270283161 -2.4614693512 133 0.0629362587 0.8270283161 134 0.7790979599 0.0629362587 135 -0.3821261772 0.7790979599 136 -0.4976567487 -0.3821261772 137 -0.5332101111 -0.4976567487 138 -0.2917916294 -0.5332101111 139 -0.4110559252 -0.2917916294 140 0.1750908125 -0.4110559252 141 0.0359698986 0.1750908125 142 0.7267274841 0.0359698986 143 -0.5081874264 0.7267274841 144 -0.5155404881 -0.5081874264 145 -0.4955642469 -0.5155404881 146 0.0008371502 -0.4955642469 147 0.3648339406 0.0008371502 148 1.1649115189 0.3648339406 149 -1.4763329462 1.1649115189 150 -0.2987954445 -1.4763329462 151 0.3235585076 -0.2987954445 152 -0.2333449681 0.3235585076 153 -0.4828371380 -0.2333449681 154 0.4142000768 -0.4828371380 155 -0.5221704681 0.4142000768 156 0.1675313626 -0.5221704681 157 -0.2074304104 0.1675313626 158 -0.3015014680 -0.2074304104 159 -0.2726727299 -0.3015014680 160 -0.6323682672 -0.2726727299 161 0.1924488546 -0.6323682672 162 0.8831946135 0.1924488546 163 0.7377271652 0.8831946135 164 0.8244883789 0.7377271652 165 -0.3856391955 0.8244883789 166 -0.0185891850 -0.3856391955 167 1.0755502428 -0.0185891850 168 0.0319848421 1.0755502428 169 0.1883707538 0.0319848421 170 -0.8967646475 0.1883707538 171 -0.3467019119 -0.8967646475 172 0.5057457615 -0.3467019119 173 0.1955201606 0.5057457615 174 0.6975180346 0.1955201606 175 0.7985209686 0.6975180346 176 0.5390174828 0.7985209686 177 0.2750250487 0.5390174828 178 -0.3161045212 0.2750250487 179 1.6957351652 -0.3161045212 180 0.6921558808 1.6957351652 181 -0.3437848470 0.6921558808 182 1.5305256854 -0.3437848470 183 0.9419906316 1.5305256854 184 -0.7799219936 0.9419906316 185 -0.7126689754 -0.7799219936 186 -1.2135012200 -0.7126689754 187 -0.0397558721 -1.2135012200 188 0.1147236351 -0.0397558721 189 -0.7500116086 0.1147236351 190 -0.1792650402 -0.7500116086 191 -1.2528271581 -0.1792650402 192 -0.0426804416 -1.2528271581 193 0.0416428431 -0.0426804416 194 -0.8213596103 0.0416428431 195 1.0371396344 -0.8213596103 196 0.2351263674 1.0371396344 197 0.4920962141 0.2351263674 198 1.1208946518 0.4920962141 199 0.6427485539 1.1208946518 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7zxeb1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/871z61322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/93n7o1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10cgzr1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/111ozi1322083116.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12gjma1322083116.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/138bk41322083116.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14gmog1322083116.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15j2rr1322083116.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/164rx81322083116.tab") + } > > try(system("convert tmp/1y1n11322083116.ps tmp/1y1n11322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/2ko8a1322083116.ps tmp/2ko8a1322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/3flp71322083116.ps tmp/3flp71322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/4grk31322083116.ps tmp/4grk31322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/5upp91322083116.ps tmp/5upp91322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/6w51j1322083116.ps tmp/6w51j1322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/7zxeb1322083116.ps tmp/7zxeb1322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/871z61322083116.ps tmp/871z61322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/93n7o1322083116.ps tmp/93n7o1322083116.png",intern=TRUE)) character(0) > try(system("convert tmp/10cgzr1322083116.ps tmp/10cgzr1322083116.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.079 0.619 6.826