R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,88.8 + ,150 + ,1 + ,150 + ,110.30 + ,117.50 + ,117.50 + ,138.70 + ,138.70 + ,165.70 + ,165.70 + ,76.10 + ,76.10 + ,41.20 + ,41.20 + ,102.00 + ,102 + ,151 + ,1 + ,151 + ,97.70 + ,117.50 + ,117.50 + ,115.20 + ,115.20 + ,146.80 + ,146.80 + ,71.30 + ,71.30 + ,49.60 + ,49.60 + ,81.60 + ,81.6) + ,dim=c(16 + ,151) + ,dimnames=list(c('s_t' + ,'s' + ,'t' + ,'Totaal' + ,'voeding' + ,'voeding_s' + ,'dranken' + ,'dranken_s' + ,'tabak' + ,'tabak_s' + ,'textiel' + ,'textiel_s' + ,'kleding' + ,'kleding_s' + ,'apparatuur' + ,'app_s ') + ,1:151)) > y <- array(NA,dim=c(16,151),dimnames=list(c('s_t','s','t','Totaal','voeding','voeding_s','dranken','dranken_s','tabak','tabak_s','textiel','textiel_s','kleding','kleding_s','apparatuur','app_s '),1:151)) > 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 = '4' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '4' > #'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, 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 Totaal s_t s t voeding voeding_s dranken dranken_s tabak tabak_s textiel 1 75.5 0 0 1 78.4 0.0 67.3 0.0 75.3 0.0 106.1 2 83.2 0 0 2 79.3 0.0 75.2 0.0 83.6 0.0 112.7 3 94.5 0 0 3 84.3 0.0 91.1 0.0 91.2 0.0 123.2 4 83.3 4 1 4 81.2 81.2 83.7 83.7 85.2 85.2 101.7 5 92.7 5 1 5 88.4 88.4 105.0 105.0 100.0 100.0 118.7 6 89.8 6 1 6 83.1 83.1 106.2 106.2 89.8 89.8 107.1 7 74.8 7 1 7 76.6 76.6 88.5 88.5 88.9 88.9 93.6 8 81.5 8 1 8 82.6 82.6 100.1 100.1 85.6 85.6 77.5 9 92.8 9 1 9 84.4 84.4 90.3 90.3 83.2 83.2 117.2 10 92.8 0 0 10 94.6 0.0 85.3 0.0 97.1 0.0 124.5 11 91.7 0 0 11 91.8 0.0 81.9 0.0 85.8 0.0 120.8 12 83.5 0 0 12 89.3 0.0 77.2 0.0 80.9 0.0 97.0 13 92.8 0 0 13 87.7 0.0 78.6 0.0 81.3 0.0 115.1 14 91.3 0 0 14 83.1 0.0 75.1 0.0 83.2 0.0 112.9 15 99.5 0 0 15 93.6 0.0 90.3 0.0 90.7 0.0 122.7 16 87.6 16 1 16 85.1 85.1 88.5 88.5 88.4 88.4 106.9 17 95.3 17 1 17 90.8 90.8 112.5 112.5 94.1 94.1 115.0 18 98.5 18 1 18 90.5 90.5 101.1 101.1 92.0 92.0 114.9 19 80.1 19 1 19 86.1 86.1 114.0 114.0 92.0 92.0 103.1 20 84.2 20 1 20 93.3 93.3 107.7 107.7 89.3 89.3 80.8 21 92.4 21 1 21 94.9 94.9 77.8 77.8 87.0 87.0 118.2 22 98.0 0 0 22 102.6 0.0 101.4 0.0 97.7 0.0 129.6 23 92.2 0 0 23 98.3 0.0 87.2 0.0 82.5 0.0 118.7 24 80.0 0 0 24 93.4 0.0 75.9 0.0 96.5 0.0 88.4 25 88.7 0 0 25 92.8 0.0 78.8 0.0 86.2 0.0 113.1 26 87.4 0 0 26 86.5 0.0 82.3 0.0 84.9 0.0 109.8 27 96.1 0 0 27 93.8 0.0 89.1 0.0 100.0 0.0 116.1 28 94.1 28 1 28 90.4 90.4 100.1 100.1 92.7 92.7 113.6 29 91.9 29 1 29 91.0 91.0 101.8 101.8 96.7 96.7 107.9 30 93.6 30 1 30 89.1 89.1 98.5 98.5 105.8 105.8 107.4 31 83.5 31 1 31 89.6 89.6 106.6 106.6 88.5 88.5 102.7 32 80.8 32 1 32 89.3 89.3 101.8 101.8 78.7 78.7 78.3 33 96.3 33 1 33 95.3 95.3 92.4 92.4 99.9 99.9 121.0 34 101.5 0 0 34 104.1 0.0 94.4 0.0 107.8 0.0 132.2 35 91.6 0 0 35 94.7 0.0 81.0 0.0 102.4 0.0 113.2 36 84.0 0 0 36 97.6 0.0 94.6 0.0 106.0 0.0 89.2 37 91.8 0 0 37 96.8 0.0 83.8 0.0 87.3 0.0 113.2 38 90.4 0 0 38 92.8 0.0 79.4 0.0 93.3 0.0 107.6 39 98.0 0 0 39 94.7 0.0 95.6 0.0 98.2 0.0 107.3 40 95.5 40 1 40 95.8 95.8 106.0 106.0 102.0 102.0 110.9 41 90.5 41 1 41 88.9 88.9 106.2 106.2 93.9 93.9 96.4 42 97.1 42 1 42 91.2 91.2 115.0 115.0 106.6 106.6 101.2 43 87.9 43 1 43 91.6 91.6 122.4 122.4 92.9 92.9 94.0 44 79.8 44 1 44 87.3 87.3 113.7 113.7 78.0 78.0 70.5 45 102.0 45 1 45 97.8 97.8 98.0 98.0 104.2 104.2 116.4 46 104.3 0 0 46 105.1 0.0 105.8 0.0 115.9 0.0 121.9 47 92.1 0 0 47 93.8 0.0 88.3 0.0 99.9 0.0 109.5 48 95.9 0 0 48 99.0 0.0 95.7 0.0 103.9 0.0 91.1 49 89.1 0 0 49 91.4 0.0 85.8 0.0 93.5 0.0 104.0 50 92.2 0 0 50 89.0 0.0 83.9 0.0 101.7 0.0 101.2 51 107.5 0 0 51 101.4 0.0 114.1 0.0 124.6 0.0 118.4 52 99.7 52 1 52 95.4 95.4 102.0 102.0 124.2 124.2 106.9 53 92.2 53 1 53 90.5 90.5 108.1 108.1 103.3 103.3 95.6 54 108.9 54 1 54 98.7 98.7 125.4 125.4 120.5 120.5 114.2 55 89.8 55 1 55 91.2 91.2 108.1 108.1 98.0 98.0 92.4 56 89.4 56 1 56 91.7 91.7 110.4 110.4 100.4 100.4 75.3 57 107.6 57 1 57 102.9 102.9 102.4 102.4 126.8 126.8 120.4 58 105.6 0 0 58 105.5 0.0 89.6 0.0 120.2 0.0 115.9 59 100.9 0 0 59 102.6 0.0 95.0 0.0 114.0 0.0 109.8 60 102.9 0 0 60 107.2 0.0 93.7 0.0 109.1 0.0 94.9 61 96.2 0 0 61 96.9 0.0 77.7 0.0 94.2 0.0 97.5 62 94.7 0 0 62 88.9 0.0 80.1 0.0 86.0 0.0 101.3 63 107.3 0 0 63 99.6 0.0 103.6 0.0 112.9 0.0 108.7 64 103.0 64 1 64 96.7 96.7 103.1 103.1 99.7 99.7 105.1 65 96.1 65 1 65 93.8 93.8 112.4 112.4 104.5 104.5 94.9 66 109.8 66 1 66 101.9 101.9 119.2 119.2 111.6 111.6 108.9 67 85.4 67 1 67 87.6 87.6 105.3 105.3 99.2 99.2 87.5 68 89.9 68 1 68 100.0 100.0 107.2 107.2 90.9 90.9 73.0 69 109.3 69 1 69 105.8 105.8 108.7 108.7 111.4 111.4 115.2 70 101.2 0 0 70 105.5 0.0 93.7 0.0 98.2 0.0 107.5 71 104.7 0 0 71 111.3 0.0 96.1 0.0 101.7 0.0 109.8 72 102.4 0 0 72 112.1 0.0 92.9 0.0 89.7 0.0 90.7 73 97.7 0 0 73 102.0 0.0 81.1 0.0 89.5 0.0 97.6 74 98.9 0 0 74 93.2 0.0 83.2 0.0 85.1 0.0 98.7 75 115.0 0 0 75 108.4 0.0 99.7 0.0 95.9 0.0 113.9 76 97.5 76 1 76 97.9 97.9 96.8 96.8 88.9 88.9 96.6 77 107.3 77 1 77 106.4 106.4 108.7 108.7 98.1 98.1 104.4 78 112.3 78 1 78 102.8 102.8 120.9 120.9 109.7 109.7 115.1 79 88.5 79 1 79 96.3 96.3 114.8 114.8 92.0 92.0 91.4 80 92.9 80 1 80 105.7 105.7 108.7 108.7 74.3 74.3 76.2 81 108.8 81 1 81 108.4 108.4 97.4 97.4 96.9 96.9 117.4 82 112.3 0 0 82 115.8 0.0 98.6 0.0 100.3 0.0 122.0 83 107.3 0 0 83 113.8 0.0 91.7 0.0 97.1 0.0 120.2 84 101.8 0 0 84 106.4 0.0 91.2 0.0 86.0 0.0 93.6 85 105.0 0 0 85 107.9 0.0 83.5 0.0 97.3 0.0 106.6 86 103.4 0 0 86 98.2 0.0 82.4 0.0 86.4 0.0 108.4 87 116.7 0 0 87 111.1 0.0 103.1 0.0 97.7 0.0 121.4 88 103.6 88 1 88 99.8 99.8 110.3 110.3 90.6 90.6 104.8 89 108.8 89 1 89 103.5 103.5 115.8 115.8 99.2 99.2 104.2 90 117.0 90 1 90 105.4 105.4 120.1 120.1 107.4 107.4 115.0 91 100.9 91 1 91 102.6 102.6 105.1 105.1 107.1 107.1 99.0 92 100.8 92 1 92 107.4 107.4 108.6 108.6 78.9 78.9 82.8 93 109.7 93 1 93 108.2 108.2 95.7 95.7 92.8 92.8 112.5 94 121.0 0 0 94 121.7 0.0 103.2 0.0 106.2 0.0 127.9 95 114.1 0 0 95 118.0 0.0 96.9 0.0 97.2 0.0 114.4 96 105.5 0 0 96 109.6 0.0 95.7 0.0 80.0 0.0 83.7 97 112.5 0 0 97 116.7 0.0 92.7 0.0 109.3 0.0 108.5 98 113.8 0 0 98 110.6 0.0 81.3 0.0 111.3 0.0 109.7 99 115.3 0 0 99 109.6 0.0 94.5 0.0 119.5 0.0 104.7 100 120.4 100 1 100 117.4 117.4 105.6 105.6 119.8 119.8 112.2 101 111.1 101 1 101 109.2 109.2 112.9 112.9 112.5 112.5 96.9 102 120.1 102 1 102 110.8 110.8 102.6 102.6 125.6 125.6 103.8 103 106.1 103 1 103 112.8 112.8 116.2 116.2 105.1 105.1 95.1 104 95.9 104 1 104 106.5 106.5 104.9 104.9 91.9 91.9 66.7 105 119.4 105 1 105 119.6 119.6 100.4 100.4 128.2 128.2 103.4 106 117.4 0 0 106 127.2 0.0 97.1 0.0 122.6 0.0 105.4 107 98.6 0 0 107 113.9 0.0 90.2 0.0 109.6 0.0 89.2 108 99.7 0 0 108 120.0 0.0 100.5 0.0 120.4 0.0 72.5 109 87.4 0 0 109 107.6 0.0 81.1 0.0 103.8 0.0 78.0 110 90.8 0 0 110 105.2 0.0 87.2 0.0 96.6 0.0 77.3 111 101.3 0 0 111 115.3 0.0 102.0 0.0 110.7 0.0 85.1 112 93.2 112 1 112 113.9 113.9 107.0 107.0 111.7 111.7 80.9 113 95.1 113 1 113 106.1 106.1 107.6 107.6 111.9 111.9 72.5 114 101.9 114 1 114 114.3 114.3 123.5 123.5 131.5 131.5 82.1 115 87.0 115 1 115 112.0 112.0 116.6 116.6 122.8 122.8 78.3 116 86.2 116 1 116 109.0 109.0 103.2 103.2 98.3 98.3 57.8 117 105.0 117 1 117 119.1 119.1 103.9 103.9 133.7 133.7 89.3 118 104.1 0 0 118 124.4 0.0 95.4 0.0 120.0 0.0 91.4 119 99.2 0 0 119 116.6 0.0 93.6 0.0 119.6 0.0 84.2 120 95.2 0 0 120 118.5 0.0 102.1 0.0 108.7 0.0 72.5 121 92.7 0 0 121 108.9 0.0 69.0 0.0 112.5 0.0 74.6 122 99.3 0 0 122 107.5 0.0 88.9 0.0 102.7 0.0 80.3 123 113.5 0 0 123 125.9 0.0 106.2 0.0 123.4 0.0 92.6 124 104.7 124 1 124 117.7 117.7 103.0 103.0 116.5 116.5 86.3 125 100.5 125 1 125 109.2 109.2 103.5 103.5 102.3 102.3 80.3 126 116.2 126 1 126 118.8 118.8 124.5 124.5 148.4 148.4 93.6 127 94.1 127 1 127 108.1 108.1 117.9 117.9 126.6 126.6 79.5 128 94.8 128 1 128 112.1 112.1 104.2 104.2 106.6 106.6 61.8 129 115.1 129 1 129 117.8 117.8 99.9 99.9 144.4 144.4 94.8 130 110.0 0 0 130 121.8 0.0 89.4 0.0 132.4 0.0 91.6 131 108.4 0 0 131 121.0 0.0 93.5 0.0 136.2 0.0 89.2 132 103.9 0 0 132 121.7 0.0 89.6 0.0 121.6 0.0 74.1 133 102.9 0 0 133 114.2 0.0 85.0 0.0 135.1 0.0 78.6 134 107.7 0 0 134 109.8 0.0 90.0 0.0 124.7 0.0 78.2 135 126.7 0 0 135 124.1 0.0 113.7 0.0 148.8 0.0 95.1 136 108.8 136 1 136 112.9 112.9 112.1 112.1 145.6 145.6 78.7 137 117.1 137 1 137 118.7 118.7 129.8 129.8 140.3 140.3 85.9 138 112.2 138 1 138 113.3 113.3 119.1 119.1 138.5 138.5 81.2 139 94.7 139 1 139 106.8 106.8 103.5 103.5 127.3 127.3 73.1 140 102.7 140 1 140 119.3 119.3 105.5 105.5 117.9 117.9 58.7 141 119.1 141 1 141 126.4 126.4 111.7 111.7 145.3 145.3 85.7 142 110.6 0 0 142 126.6 0.0 98.6 0.0 120.7 0.0 81.8 143 109.1 0 0 143 127.2 0.0 102.8 0.0 134.7 0.0 79.6 144 105.3 0 0 144 123.8 0.0 101.1 0.0 124.4 0.0 70.7 145 103.4 0 0 145 116.8 0.0 94.2 0.0 128.3 0.0 74.5 146 103.7 0 0 146 113.8 0.0 92.6 0.0 128.4 0.0 84.8 147 117.0 0 0 147 130.4 0.0 112.0 0.0 134.1 0.0 80.7 148 101.2 148 1 148 112.8 112.8 108.6 108.6 133.3 133.3 69.9 149 105.4 149 1 149 119.4 119.4 125.8 125.8 130.6 130.6 74.1 150 110.3 150 1 150 117.5 117.5 138.7 138.7 165.7 165.7 76.1 151 97.7 151 1 151 117.5 117.5 115.2 115.2 146.8 146.8 71.3 textiel_s kleding kleding_s apparatuur app_s\r 1 0.0 125.7 0.0 101.6 0.0 2 0.0 153.8 0.0 113.4 0.0 3 0.0 134.9 0.0 122.2 0.0 4 101.7 95.3 95.3 102.2 102.2 5 118.7 96.6 96.6 113.2 113.2 6 107.1 100.5 100.5 115.3 115.3 7 93.6 106.2 106.2 87.4 87.4 8 77.5 153.4 153.4 98.7 98.7 9 117.2 132.1 132.1 117.3 117.3 10 0.0 110.9 0.0 121.2 0.0 11 0.0 94.3 0.0 118.7 0.0 12 0.0 91.7 0.0 112.1 0.0 13 0.0 138.6 0.0 102.9 0.0 14 0.0 154.3 0.0 108.8 0.0 15 0.0 149.8 0.0 118.6 0.0 16 106.9 99.2 99.2 99.2 99.2 17 115.0 97.7 97.7 102.2 102.2 18 114.9 107.7 107.7 108.8 108.8 19 103.1 120.1 120.1 94.0 94.0 20 80.8 164.5 164.5 96.2 96.2 21 118.2 136.1 136.1 118.4 118.4 22 0.0 117.5 0.0 120.0 0.0 23 0.0 98.2 0.0 117.5 0.0 24 0.0 91.9 0.0 102.6 0.0 25 0.0 141.8 0.0 92.8 0.0 26 0.0 154.2 0.0 100.3 0.0 27 0.0 138.6 0.0 106.3 0.0 28 113.6 97.9 97.9 103.9 103.9 29 107.9 90.3 90.3 102.4 102.4 30 107.4 90.9 90.9 114.5 114.5 31 102.7 127.0 127.0 89.0 89.0 32 78.3 156.8 156.8 94.3 94.3 33 121.0 127.2 127.2 115.7 115.7 34 0.0 111.3 0.0 120.2 0.0 35 0.0 93.0 0.0 109.5 0.0 36 0.0 89.5 0.0 99.4 0.0 37 0.0 141.8 0.0 86.4 0.0 38 0.0 152.0 0.0 95.1 0.0 39 0.0 120.2 0.0 101.5 0.0 40 110.9 88.8 88.8 92.9 92.9 41 96.4 82.8 82.8 90.8 90.8 42 101.2 82.8 82.8 100.4 100.4 43 94.0 121.7 121.7 82.2 82.2 44 70.5 147.1 147.1 75.3 75.3 45 116.4 132.5 132.5 110.3 110.3 46 0.0 107.5 0.0 113.5 0.0 47 0.0 77.9 0.0 94.9 0.0 48 0.0 85.5 0.0 95.7 0.0 49 0.0 126.5 0.0 85.3 0.0 50 0.0 135.4 0.0 92.5 0.0 51 0.0 122.5 0.0 107.7 0.0 52 106.9 79.2 79.2 97.9 97.9 53 95.6 66.1 66.1 93.9 93.9 54 114.2 77.9 77.9 111.5 111.5 55 92.4 109.6 109.6 88.6 88.6 56 75.3 142.9 142.9 82.5 82.5 57 120.4 120.5 120.5 108.6 108.6 58 0.0 96.3 0.0 113.8 0.0 59 0.0 82.6 0.0 103.4 0.0 60 0.0 78.4 0.0 99.0 0.0 61 0.0 104.5 0.0 89.9 0.0 62 0.0 137.9 0.0 97.9 0.0 63 0.0 125.8 0.0 107.8 0.0 64 105.1 78.0 78.0 103.7 103.7 65 94.9 67.7 67.7 98.2 98.2 66 108.9 78.4 78.4 111.7 111.7 67 87.5 101.7 101.7 82.6 82.6 68 73.0 154.1 154.1 86.1 86.1 69 115.2 107.3 107.3 111.2 111.2 70 0.0 86.5 0.0 105.3 0.0 71 0.0 82.1 0.0 106.3 0.0 72 0.0 76.1 0.0 99.4 0.0 73 0.0 115.5 0.0 91.9 0.0 74 0.0 129.6 0.0 96.2 0.0 75 0.0 121.6 0.0 105.4 0.0 76 96.6 64.0 64.0 95.0 95.0 77 104.4 58.1 58.1 100.5 100.5 78 115.1 79.7 79.7 111.6 111.6 79 91.4 108.9 108.9 88.5 88.5 80 76.2 138.5 138.5 83.7 83.7 81 117.4 117.9 117.9 113.9 113.9 82 0.0 96.7 0.0 115.2 0.0 83 0.0 78.6 0.0 111.0 0.0 84 0.0 64.1 0.0 96.9 0.0 85 0.0 112.0 0.0 102.1 0.0 86 0.0 139.4 0.0 101.5 0.0 87 0.0 116.2 0.0 115.0 0.0 88 104.8 63.4 63.4 105.0 105.0 89 104.2 61.1 61.1 105.4 105.4 90 115.0 65.5 65.5 119.7 119.7 91 99.0 90.9 90.9 91.8 91.8 92 82.8 115.3 115.3 89.1 89.1 93 112.5 85.2 85.2 106.2 106.2 94 0.0 87.0 0.0 119.9 0.0 95 0.0 62.6 0.0 111.6 0.0 96 0.0 62.7 0.0 95.1 0.0 97 0.0 91.6 0.0 101.3 0.0 98 0.0 104.3 0.0 118.3 0.0 99 0.0 88.1 0.0 126.2 0.0 100 112.2 62.3 62.3 113.2 113.2 101 96.9 50.3 50.3 103.6 103.6 102 103.8 64.1 64.1 116.2 116.2 103 95.1 75.7 75.7 98.3 98.3 104 66.7 85.5 85.5 84.2 84.2 105 103.4 71.9 71.9 118.3 118.3 106 0.0 66.9 0.0 117.4 0.0 107 0.0 50.5 0.0 94.5 0.0 108 0.0 57.9 0.0 93.3 0.0 109 0.0 84.1 0.0 90.2 0.0 110 0.0 87.0 0.0 88.5 0.0 111 0.0 71.9 0.0 101.0 0.0 112 80.9 45.0 45.0 87.0 87.0 113 72.5 39.5 39.5 81.2 81.2 114 82.1 53.8 53.8 98.1 98.1 115 78.3 59.5 59.5 75.5 75.5 116 57.8 68.4 68.4 70.7 70.7 117 89.3 56.9 56.9 103.7 103.7 118 0.0 61.9 0.0 100.4 0.0 119 0.0 40.4 0.0 91.3 0.0 120 0.0 49.4 0.0 97.2 0.0 121 0.0 65.2 0.0 85.4 0.0 122 0.0 82.1 0.0 86.5 0.0 123 0.0 69.0 0.0 105.3 0.0 124 86.3 45.9 45.9 97.7 97.7 125 80.3 39.1 39.1 84.3 84.3 126 93.6 56.9 56.9 109.8 109.8 127 79.5 51.6 51.6 79.1 79.1 128 61.8 62.9 62.9 83.4 83.4 129 94.8 58.3 58.3 101.9 101.9 130 0.0 56.9 0.0 113.0 0.0 131 0.0 41.3 0.0 98.6 0.0 132 0.0 46.9 0.0 94.7 0.0 133 0.0 61.9 0.0 94.5 0.0 134 0.0 74.8 0.0 90.7 0.0 135 0.0 67.0 0.0 113.0 0.0 136 78.7 53.3 53.3 89.9 89.9 137 85.9 51.4 51.4 98.7 98.7 138 81.2 50.3 50.3 102.2 102.2 139 73.1 52.7 52.7 74.3 74.3 140 58.7 70.3 70.3 84.5 84.5 141 85.7 59.7 59.7 110.1 110.1 142 0.0 52.0 0.0 100.4 0.0 143 0.0 36.1 0.0 92.8 0.0 144 0.0 39.7 0.0 92.2 0.0 145 0.0 67.6 0.0 94.0 0.0 146 0.0 72.8 0.0 100.7 0.0 147 0.0 53.8 0.0 111.9 0.0 148 69.9 39.6 39.6 95.9 95.9 149 74.1 39.4 39.4 88.8 88.8 150 76.1 41.2 41.2 102.0 102.0 151 71.3 49.6 49.6 81.6 81.6 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) s_t s t voeding voeding_s -10.60594 -0.04174 -3.17744 0.20308 0.19700 0.06122 dranken dranken_s tabak tabak_s textiel textiel_s 0.24955 -0.11794 -0.01638 -0.01106 0.30294 -0.07081 kleding kleding_s apparatuur `app_s\\r` 0.06063 -0.05482 0.17342 0.23855 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.2916 -2.5015 0.1169 2.3033 7.9374 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -10.60594 9.21529 -1.151 0.251805 s_t -0.04174 0.05335 -0.782 0.435437 s -3.17744 13.13635 -0.242 0.809239 t 0.20308 0.03177 6.392 2.46e-09 *** voeding 0.19700 0.11967 1.646 0.102048 voeding_s 0.06122 0.17166 0.357 0.721905 dranken 0.24955 0.06469 3.858 0.000176 *** dranken_s -0.11794 0.08016 -1.471 0.143536 tabak -0.01638 0.04277 -0.383 0.702251 tabak_s -0.01106 0.05627 -0.197 0.844405 textiel 0.30294 0.05987 5.060 1.35e-06 *** textiel_s -0.07081 0.07893 -0.897 0.371258 kleding 0.06063 0.02893 2.095 0.038013 * kleding_s -0.05482 0.03621 -1.514 0.132328 apparatuur 0.17342 0.06427 2.698 0.007857 ** `app_s\\r` 0.23855 0.09170 2.602 0.010315 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.561 on 135 degrees of freedom Multiple R-squared: 0.8974, Adjusted R-squared: 0.886 F-statistic: 78.71 on 15 and 135 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,] 0.490485625 0.9809712496 0.5095143752 [2,] 0.520679386 0.9586412288 0.4793206144 [3,] 0.707558400 0.5848831990 0.2924415995 [4,] 0.890293567 0.2194128659 0.1097064329 [5,] 0.886540575 0.2269188497 0.1134594248 [6,] 0.839308987 0.3213820263 0.1606910132 [7,] 0.776307579 0.4473848430 0.2236924215 [8,] 0.749459317 0.5010813665 0.2505406832 [9,] 0.705016011 0.5899679786 0.2949839893 [10,] 0.640512958 0.7189740845 0.3594870422 [11,] 0.563297241 0.8734055186 0.4367027593 [12,] 0.510631963 0.9787360733 0.4893680366 [13,] 0.454183951 0.9083679015 0.5458160492 [14,] 0.431618354 0.8632367072 0.5683816464 [15,] 0.438090909 0.8761818183 0.5619090909 [16,] 0.409385226 0.8187704512 0.5906147744 [17,] 0.363064022 0.7261280449 0.6369359776 [18,] 0.339307977 0.6786159548 0.6606920226 [19,] 0.281038337 0.5620766744 0.7189616628 [20,] 0.242527090 0.4850541800 0.7574729100 [21,] 0.281141960 0.5622839207 0.7188580397 [22,] 0.261312270 0.5226245396 0.7386877302 [23,] 0.220494593 0.4409891856 0.7795054072 [24,] 0.205666950 0.4113339001 0.7943330500 [25,] 0.174418559 0.3488371181 0.8255814410 [26,] 0.142407044 0.2848140880 0.8575929560 [27,] 0.179025196 0.3580503912 0.8209748044 [28,] 0.165493654 0.3309873079 0.8345063461 [29,] 0.133198170 0.2663963392 0.8668018304 [30,] 0.185415738 0.3708314761 0.8145842619 [31,] 0.182330124 0.3646602487 0.8176698756 [32,] 0.147613900 0.2952278006 0.8523860997 [33,] 0.131050471 0.2621009414 0.8689495293 [34,] 0.112679187 0.2253583747 0.8873208127 [35,] 0.098685990 0.1973719801 0.9013140100 [36,] 0.080551339 0.1611026785 0.9194486607 [37,] 0.061314228 0.1226284551 0.9386857724 [38,] 0.071701349 0.1434026989 0.9282986506 [39,] 0.056138918 0.1122778351 0.9438610825 [40,] 0.056578555 0.1131571102 0.9434214449 [41,] 0.048067938 0.0961358755 0.9519320622 [42,] 0.082002271 0.1640045413 0.9179977294 [43,] 0.075372765 0.1507455299 0.9246272351 [44,] 0.080825918 0.1616518366 0.9191740817 [45,] 0.064942488 0.1298849755 0.9350575122 [46,] 0.052398259 0.1047965186 0.9476017407 [47,] 0.047126959 0.0942539188 0.9528730406 [48,] 0.036749369 0.0734987374 0.9632506313 [49,] 0.030249704 0.0604994074 0.9697502963 [50,] 0.022719149 0.0454382990 0.9772808505 [51,] 0.016453649 0.0329072979 0.9835463511 [52,] 0.017107838 0.0342156761 0.9828921620 [53,] 0.014362373 0.0287247459 0.9856376270 [54,] 0.012982546 0.0259650920 0.9870174540 [55,] 0.009578306 0.0191566128 0.9904216936 [56,] 0.006840248 0.0136804969 0.9931597516 [57,] 0.009798724 0.0195974480 0.9902012760 [58,] 0.006885933 0.0137718665 0.9931140668 [59,] 0.006178882 0.0123577646 0.9938211177 [60,] 0.004621038 0.0092420755 0.9953789623 [61,] 0.010673659 0.0213473179 0.9893263410 [62,] 0.007518737 0.0150374746 0.9924812627 [63,] 0.012402421 0.0248048412 0.9875975794 [64,] 0.013049791 0.0260995826 0.9869502087 [65,] 0.016813624 0.0336272470 0.9831863765 [66,] 0.014394046 0.0287880927 0.9856059536 [67,] 0.010678057 0.0213561134 0.9893219433 [68,] 0.008571545 0.0171430894 0.9914284553 [69,] 0.006161988 0.0123239766 0.9938380117 [70,] 0.005703240 0.0114064799 0.9942967601 [71,] 0.003946954 0.0078939085 0.9960530457 [72,] 0.003271197 0.0065423939 0.9967288031 [73,] 0.002293250 0.0045864992 0.9977067504 [74,] 0.001700509 0.0034010173 0.9982994914 [75,] 0.002919256 0.0058385123 0.9970807439 [76,] 0.002591129 0.0051822581 0.9974088709 [77,] 0.001822670 0.0036453409 0.9981773295 [78,] 0.024713455 0.0494269109 0.9752865445 [79,] 0.019512208 0.0390244156 0.9804877922 [80,] 0.017765903 0.0355318057 0.9822340972 [81,] 0.018990896 0.0379817921 0.9810091039 [82,] 0.015489728 0.0309794566 0.9845102717 [83,] 0.014506024 0.0290120483 0.9854939758 [84,] 0.012280682 0.0245613634 0.9877193183 [85,] 0.018699318 0.0373986356 0.9813006822 [86,] 0.014181268 0.0283625360 0.9858187320 [87,] 0.011919577 0.0238391531 0.9880804235 [88,] 0.012677459 0.0253549186 0.9873225407 [89,] 0.018832420 0.0376648401 0.9811675799 [90,] 0.021602329 0.0432046572 0.9783976714 [91,] 0.112540247 0.2250804937 0.8874597532 [92,] 0.145923474 0.2918469484 0.8540765258 [93,] 0.151373749 0.3027474970 0.8486262515 [94,] 0.251708575 0.5034171495 0.7482914253 [95,] 0.508517505 0.9829649909 0.4914824955 [96,] 0.528721592 0.9425568155 0.4712784078 [97,] 0.636815635 0.7263687300 0.3631843650 [98,] 0.577077632 0.8458447357 0.4229223679 [99,] 0.558935583 0.8821288333 0.4410644166 [100,] 0.649390427 0.7012191455 0.3506095728 [101,] 0.583139385 0.8337212295 0.4168606147 [102,] 0.747836364 0.5043272718 0.2521636359 [103,] 0.679978088 0.6400438247 0.3200219123 [104,] 0.611080605 0.7778387909 0.3889193955 [105,] 0.817731792 0.3645364157 0.1822682079 [106,] 0.784286785 0.4314264298 0.2157132149 [107,] 0.807277135 0.3854457296 0.1927228648 [108,] 0.812649451 0.3747010976 0.1873505488 [109,] 0.922281478 0.1554370431 0.0777185216 [110,] 0.998201408 0.0035971833 0.0017985917 [111,] 0.999647064 0.0007058723 0.0003529362 [112,] 0.998611701 0.0027765988 0.0013882994 [113,] 0.993607641 0.0127847175 0.0063923587 [114,] 0.997519434 0.0049611326 0.0024805663 > postscript(file="/var/wessaorg/rcomp/tmp/1wiio1352125226.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2t6do1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3m26t1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/46ul61352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/52v801352125226.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 = 151 Frequency = 1 1 2 3 4 5 6 -2.484534640 -2.749675260 -0.042128660 0.529129777 -2.973809527 -3.299593386 7 8 9 10 11 12 0.116896063 2.296850674 -2.559833115 -2.414107096 0.058548590 1.752667697 13 14 15 16 17 18 4.090879858 2.890011532 0.752716159 1.348287348 1.305522689 3.110456001 19 20 21 22 23 24 -7.248160429 -0.401444920 -6.566413381 -6.971945612 -3.927733552 -0.171349773 25 26 27 28 29 30 -1.256988552 -3.466388420 0.139663021 -0.348984323 -0.993987486 -3.152902140 31 32 33 34 35 36 -3.697579169 -2.811350223 -5.759132382 -4.738505947 -1.013461262 -3.488481455 37 38 39 40 41 42 -1.532057039 -1.581495970 2.387662092 2.410571725 3.048133122 3.014190718 43 44 45 46 47 48 1.142935834 2.878037649 0.001888224 -2.772027941 -0.067515702 5.698445232 49 50 51 52 53 54 -2.097329494 0.940923985 -0.630501335 4.837937776 1.412397219 2.392149182 55 56 57 58 59 60 1.037137324 6.398886323 2.231641330 2.569973111 1.271111372 7.937400263 61 62 63 64 65 66 6.020183980 0.596415158 2.236801654 3.084239716 0.372899396 2.246373489 67 68 69 70 71 72 -0.312794943 1.965660264 0.207433392 -1.037691095 -0.028361232 5.259353537 73 74 75 76 77 78 2.109055829 2.309681628 5.556519452 1.509476465 3.597461253 0.896448748 79 80 81 82 83 84 -6.221083123 1.241138492 -3.495451030 -2.319937270 -3.088583449 3.991475296 85 86 87 88 89 90 1.055585576 -0.843071375 -0.140754102 -2.566961893 1.016329458 -0.200187501 91 92 93 94 95 96 1.287924230 3.283514578 0.130772846 -0.284750426 1.774078988 6.798980336 97 98 99 100 101 102 3.085750798 4.180393211 3.641471722 4.082732113 3.153865600 6.421841672 103 104 105 106 107 108 -1.282062184 3.452535083 2.508764532 2.853985305 -2.146956417 -0.026601825 109 110 111 112 113 114 -8.234600985 -5.874032410 -4.644286572 -6.396486524 1.654080385 -4.653040020 115 116 117 118 119 120 -8.291627196 -0.702876862 -4.633005669 -4.457344121 -2.518457850 -7.420016359 121 122 123 124 125 126 0.542665643 -0.853262144 -0.651363254 -2.822468774 1.508189150 -0.626404450 127 128 129 130 131 132 -3.903219766 -0.871872146 4.143437259 -0.724154682 0.839531911 1.643758695 133 134 135 136 137 138 1.049362627 5.093038642 7.039446850 5.116605053 3.997006027 1.344341409 139 140 141 142 143 144 0.467414525 3.595345352 1.184395307 -0.543296781 -0.234890702 -0.730753352 145 146 147 148 149 150 -2.823841670 -6.332421034 -0.801882936 -4.620236548 -2.672378619 -4.090119256 151 -4.807807787 > postscript(file="/var/wessaorg/rcomp/tmp/6829t1352125226.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 = 151 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.484534640 NA 1 -2.749675260 -2.484534640 2 -0.042128660 -2.749675260 3 0.529129777 -0.042128660 4 -2.973809527 0.529129777 5 -3.299593386 -2.973809527 6 0.116896063 -3.299593386 7 2.296850674 0.116896063 8 -2.559833115 2.296850674 9 -2.414107096 -2.559833115 10 0.058548590 -2.414107096 11 1.752667697 0.058548590 12 4.090879858 1.752667697 13 2.890011532 4.090879858 14 0.752716159 2.890011532 15 1.348287348 0.752716159 16 1.305522689 1.348287348 17 3.110456001 1.305522689 18 -7.248160429 3.110456001 19 -0.401444920 -7.248160429 20 -6.566413381 -0.401444920 21 -6.971945612 -6.566413381 22 -3.927733552 -6.971945612 23 -0.171349773 -3.927733552 24 -1.256988552 -0.171349773 25 -3.466388420 -1.256988552 26 0.139663021 -3.466388420 27 -0.348984323 0.139663021 28 -0.993987486 -0.348984323 29 -3.152902140 -0.993987486 30 -3.697579169 -3.152902140 31 -2.811350223 -3.697579169 32 -5.759132382 -2.811350223 33 -4.738505947 -5.759132382 34 -1.013461262 -4.738505947 35 -3.488481455 -1.013461262 36 -1.532057039 -3.488481455 37 -1.581495970 -1.532057039 38 2.387662092 -1.581495970 39 2.410571725 2.387662092 40 3.048133122 2.410571725 41 3.014190718 3.048133122 42 1.142935834 3.014190718 43 2.878037649 1.142935834 44 0.001888224 2.878037649 45 -2.772027941 0.001888224 46 -0.067515702 -2.772027941 47 5.698445232 -0.067515702 48 -2.097329494 5.698445232 49 0.940923985 -2.097329494 50 -0.630501335 0.940923985 51 4.837937776 -0.630501335 52 1.412397219 4.837937776 53 2.392149182 1.412397219 54 1.037137324 2.392149182 55 6.398886323 1.037137324 56 2.231641330 6.398886323 57 2.569973111 2.231641330 58 1.271111372 2.569973111 59 7.937400263 1.271111372 60 6.020183980 7.937400263 61 0.596415158 6.020183980 62 2.236801654 0.596415158 63 3.084239716 2.236801654 64 0.372899396 3.084239716 65 2.246373489 0.372899396 66 -0.312794943 2.246373489 67 1.965660264 -0.312794943 68 0.207433392 1.965660264 69 -1.037691095 0.207433392 70 -0.028361232 -1.037691095 71 5.259353537 -0.028361232 72 2.109055829 5.259353537 73 2.309681628 2.109055829 74 5.556519452 2.309681628 75 1.509476465 5.556519452 76 3.597461253 1.509476465 77 0.896448748 3.597461253 78 -6.221083123 0.896448748 79 1.241138492 -6.221083123 80 -3.495451030 1.241138492 81 -2.319937270 -3.495451030 82 -3.088583449 -2.319937270 83 3.991475296 -3.088583449 84 1.055585576 3.991475296 85 -0.843071375 1.055585576 86 -0.140754102 -0.843071375 87 -2.566961893 -0.140754102 88 1.016329458 -2.566961893 89 -0.200187501 1.016329458 90 1.287924230 -0.200187501 91 3.283514578 1.287924230 92 0.130772846 3.283514578 93 -0.284750426 0.130772846 94 1.774078988 -0.284750426 95 6.798980336 1.774078988 96 3.085750798 6.798980336 97 4.180393211 3.085750798 98 3.641471722 4.180393211 99 4.082732113 3.641471722 100 3.153865600 4.082732113 101 6.421841672 3.153865600 102 -1.282062184 6.421841672 103 3.452535083 -1.282062184 104 2.508764532 3.452535083 105 2.853985305 2.508764532 106 -2.146956417 2.853985305 107 -0.026601825 -2.146956417 108 -8.234600985 -0.026601825 109 -5.874032410 -8.234600985 110 -4.644286572 -5.874032410 111 -6.396486524 -4.644286572 112 1.654080385 -6.396486524 113 -4.653040020 1.654080385 114 -8.291627196 -4.653040020 115 -0.702876862 -8.291627196 116 -4.633005669 -0.702876862 117 -4.457344121 -4.633005669 118 -2.518457850 -4.457344121 119 -7.420016359 -2.518457850 120 0.542665643 -7.420016359 121 -0.853262144 0.542665643 122 -0.651363254 -0.853262144 123 -2.822468774 -0.651363254 124 1.508189150 -2.822468774 125 -0.626404450 1.508189150 126 -3.903219766 -0.626404450 127 -0.871872146 -3.903219766 128 4.143437259 -0.871872146 129 -0.724154682 4.143437259 130 0.839531911 -0.724154682 131 1.643758695 0.839531911 132 1.049362627 1.643758695 133 5.093038642 1.049362627 134 7.039446850 5.093038642 135 5.116605053 7.039446850 136 3.997006027 5.116605053 137 1.344341409 3.997006027 138 0.467414525 1.344341409 139 3.595345352 0.467414525 140 1.184395307 3.595345352 141 -0.543296781 1.184395307 142 -0.234890702 -0.543296781 143 -0.730753352 -0.234890702 144 -2.823841670 -0.730753352 145 -6.332421034 -2.823841670 146 -0.801882936 -6.332421034 147 -4.620236548 -0.801882936 148 -2.672378619 -4.620236548 149 -4.090119256 -2.672378619 150 -4.807807787 -4.090119256 151 NA -4.807807787 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.749675260 -2.484534640 [2,] -0.042128660 -2.749675260 [3,] 0.529129777 -0.042128660 [4,] -2.973809527 0.529129777 [5,] -3.299593386 -2.973809527 [6,] 0.116896063 -3.299593386 [7,] 2.296850674 0.116896063 [8,] -2.559833115 2.296850674 [9,] -2.414107096 -2.559833115 [10,] 0.058548590 -2.414107096 [11,] 1.752667697 0.058548590 [12,] 4.090879858 1.752667697 [13,] 2.890011532 4.090879858 [14,] 0.752716159 2.890011532 [15,] 1.348287348 0.752716159 [16,] 1.305522689 1.348287348 [17,] 3.110456001 1.305522689 [18,] -7.248160429 3.110456001 [19,] -0.401444920 -7.248160429 [20,] -6.566413381 -0.401444920 [21,] -6.971945612 -6.566413381 [22,] -3.927733552 -6.971945612 [23,] -0.171349773 -3.927733552 [24,] -1.256988552 -0.171349773 [25,] -3.466388420 -1.256988552 [26,] 0.139663021 -3.466388420 [27,] -0.348984323 0.139663021 [28,] -0.993987486 -0.348984323 [29,] -3.152902140 -0.993987486 [30,] -3.697579169 -3.152902140 [31,] -2.811350223 -3.697579169 [32,] -5.759132382 -2.811350223 [33,] -4.738505947 -5.759132382 [34,] -1.013461262 -4.738505947 [35,] -3.488481455 -1.013461262 [36,] -1.532057039 -3.488481455 [37,] -1.581495970 -1.532057039 [38,] 2.387662092 -1.581495970 [39,] 2.410571725 2.387662092 [40,] 3.048133122 2.410571725 [41,] 3.014190718 3.048133122 [42,] 1.142935834 3.014190718 [43,] 2.878037649 1.142935834 [44,] 0.001888224 2.878037649 [45,] -2.772027941 0.001888224 [46,] -0.067515702 -2.772027941 [47,] 5.698445232 -0.067515702 [48,] -2.097329494 5.698445232 [49,] 0.940923985 -2.097329494 [50,] -0.630501335 0.940923985 [51,] 4.837937776 -0.630501335 [52,] 1.412397219 4.837937776 [53,] 2.392149182 1.412397219 [54,] 1.037137324 2.392149182 [55,] 6.398886323 1.037137324 [56,] 2.231641330 6.398886323 [57,] 2.569973111 2.231641330 [58,] 1.271111372 2.569973111 [59,] 7.937400263 1.271111372 [60,] 6.020183980 7.937400263 [61,] 0.596415158 6.020183980 [62,] 2.236801654 0.596415158 [63,] 3.084239716 2.236801654 [64,] 0.372899396 3.084239716 [65,] 2.246373489 0.372899396 [66,] -0.312794943 2.246373489 [67,] 1.965660264 -0.312794943 [68,] 0.207433392 1.965660264 [69,] -1.037691095 0.207433392 [70,] -0.028361232 -1.037691095 [71,] 5.259353537 -0.028361232 [72,] 2.109055829 5.259353537 [73,] 2.309681628 2.109055829 [74,] 5.556519452 2.309681628 [75,] 1.509476465 5.556519452 [76,] 3.597461253 1.509476465 [77,] 0.896448748 3.597461253 [78,] -6.221083123 0.896448748 [79,] 1.241138492 -6.221083123 [80,] -3.495451030 1.241138492 [81,] -2.319937270 -3.495451030 [82,] -3.088583449 -2.319937270 [83,] 3.991475296 -3.088583449 [84,] 1.055585576 3.991475296 [85,] -0.843071375 1.055585576 [86,] -0.140754102 -0.843071375 [87,] -2.566961893 -0.140754102 [88,] 1.016329458 -2.566961893 [89,] -0.200187501 1.016329458 [90,] 1.287924230 -0.200187501 [91,] 3.283514578 1.287924230 [92,] 0.130772846 3.283514578 [93,] -0.284750426 0.130772846 [94,] 1.774078988 -0.284750426 [95,] 6.798980336 1.774078988 [96,] 3.085750798 6.798980336 [97,] 4.180393211 3.085750798 [98,] 3.641471722 4.180393211 [99,] 4.082732113 3.641471722 [100,] 3.153865600 4.082732113 [101,] 6.421841672 3.153865600 [102,] -1.282062184 6.421841672 [103,] 3.452535083 -1.282062184 [104,] 2.508764532 3.452535083 [105,] 2.853985305 2.508764532 [106,] -2.146956417 2.853985305 [107,] -0.026601825 -2.146956417 [108,] -8.234600985 -0.026601825 [109,] -5.874032410 -8.234600985 [110,] -4.644286572 -5.874032410 [111,] -6.396486524 -4.644286572 [112,] 1.654080385 -6.396486524 [113,] -4.653040020 1.654080385 [114,] -8.291627196 -4.653040020 [115,] -0.702876862 -8.291627196 [116,] -4.633005669 -0.702876862 [117,] -4.457344121 -4.633005669 [118,] -2.518457850 -4.457344121 [119,] -7.420016359 -2.518457850 [120,] 0.542665643 -7.420016359 [121,] -0.853262144 0.542665643 [122,] -0.651363254 -0.853262144 [123,] -2.822468774 -0.651363254 [124,] 1.508189150 -2.822468774 [125,] -0.626404450 1.508189150 [126,] -3.903219766 -0.626404450 [127,] -0.871872146 -3.903219766 [128,] 4.143437259 -0.871872146 [129,] -0.724154682 4.143437259 [130,] 0.839531911 -0.724154682 [131,] 1.643758695 0.839531911 [132,] 1.049362627 1.643758695 [133,] 5.093038642 1.049362627 [134,] 7.039446850 5.093038642 [135,] 5.116605053 7.039446850 [136,] 3.997006027 5.116605053 [137,] 1.344341409 3.997006027 [138,] 0.467414525 1.344341409 [139,] 3.595345352 0.467414525 [140,] 1.184395307 3.595345352 [141,] -0.543296781 1.184395307 [142,] -0.234890702 -0.543296781 [143,] -0.730753352 -0.234890702 [144,] -2.823841670 -0.730753352 [145,] -6.332421034 -2.823841670 [146,] -0.801882936 -6.332421034 [147,] -4.620236548 -0.801882936 [148,] -2.672378619 -4.620236548 [149,] -4.090119256 -2.672378619 [150,] -4.807807787 -4.090119256 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.749675260 -2.484534640 2 -0.042128660 -2.749675260 3 0.529129777 -0.042128660 4 -2.973809527 0.529129777 5 -3.299593386 -2.973809527 6 0.116896063 -3.299593386 7 2.296850674 0.116896063 8 -2.559833115 2.296850674 9 -2.414107096 -2.559833115 10 0.058548590 -2.414107096 11 1.752667697 0.058548590 12 4.090879858 1.752667697 13 2.890011532 4.090879858 14 0.752716159 2.890011532 15 1.348287348 0.752716159 16 1.305522689 1.348287348 17 3.110456001 1.305522689 18 -7.248160429 3.110456001 19 -0.401444920 -7.248160429 20 -6.566413381 -0.401444920 21 -6.971945612 -6.566413381 22 -3.927733552 -6.971945612 23 -0.171349773 -3.927733552 24 -1.256988552 -0.171349773 25 -3.466388420 -1.256988552 26 0.139663021 -3.466388420 27 -0.348984323 0.139663021 28 -0.993987486 -0.348984323 29 -3.152902140 -0.993987486 30 -3.697579169 -3.152902140 31 -2.811350223 -3.697579169 32 -5.759132382 -2.811350223 33 -4.738505947 -5.759132382 34 -1.013461262 -4.738505947 35 -3.488481455 -1.013461262 36 -1.532057039 -3.488481455 37 -1.581495970 -1.532057039 38 2.387662092 -1.581495970 39 2.410571725 2.387662092 40 3.048133122 2.410571725 41 3.014190718 3.048133122 42 1.142935834 3.014190718 43 2.878037649 1.142935834 44 0.001888224 2.878037649 45 -2.772027941 0.001888224 46 -0.067515702 -2.772027941 47 5.698445232 -0.067515702 48 -2.097329494 5.698445232 49 0.940923985 -2.097329494 50 -0.630501335 0.940923985 51 4.837937776 -0.630501335 52 1.412397219 4.837937776 53 2.392149182 1.412397219 54 1.037137324 2.392149182 55 6.398886323 1.037137324 56 2.231641330 6.398886323 57 2.569973111 2.231641330 58 1.271111372 2.569973111 59 7.937400263 1.271111372 60 6.020183980 7.937400263 61 0.596415158 6.020183980 62 2.236801654 0.596415158 63 3.084239716 2.236801654 64 0.372899396 3.084239716 65 2.246373489 0.372899396 66 -0.312794943 2.246373489 67 1.965660264 -0.312794943 68 0.207433392 1.965660264 69 -1.037691095 0.207433392 70 -0.028361232 -1.037691095 71 5.259353537 -0.028361232 72 2.109055829 5.259353537 73 2.309681628 2.109055829 74 5.556519452 2.309681628 75 1.509476465 5.556519452 76 3.597461253 1.509476465 77 0.896448748 3.597461253 78 -6.221083123 0.896448748 79 1.241138492 -6.221083123 80 -3.495451030 1.241138492 81 -2.319937270 -3.495451030 82 -3.088583449 -2.319937270 83 3.991475296 -3.088583449 84 1.055585576 3.991475296 85 -0.843071375 1.055585576 86 -0.140754102 -0.843071375 87 -2.566961893 -0.140754102 88 1.016329458 -2.566961893 89 -0.200187501 1.016329458 90 1.287924230 -0.200187501 91 3.283514578 1.287924230 92 0.130772846 3.283514578 93 -0.284750426 0.130772846 94 1.774078988 -0.284750426 95 6.798980336 1.774078988 96 3.085750798 6.798980336 97 4.180393211 3.085750798 98 3.641471722 4.180393211 99 4.082732113 3.641471722 100 3.153865600 4.082732113 101 6.421841672 3.153865600 102 -1.282062184 6.421841672 103 3.452535083 -1.282062184 104 2.508764532 3.452535083 105 2.853985305 2.508764532 106 -2.146956417 2.853985305 107 -0.026601825 -2.146956417 108 -8.234600985 -0.026601825 109 -5.874032410 -8.234600985 110 -4.644286572 -5.874032410 111 -6.396486524 -4.644286572 112 1.654080385 -6.396486524 113 -4.653040020 1.654080385 114 -8.291627196 -4.653040020 115 -0.702876862 -8.291627196 116 -4.633005669 -0.702876862 117 -4.457344121 -4.633005669 118 -2.518457850 -4.457344121 119 -7.420016359 -2.518457850 120 0.542665643 -7.420016359 121 -0.853262144 0.542665643 122 -0.651363254 -0.853262144 123 -2.822468774 -0.651363254 124 1.508189150 -2.822468774 125 -0.626404450 1.508189150 126 -3.903219766 -0.626404450 127 -0.871872146 -3.903219766 128 4.143437259 -0.871872146 129 -0.724154682 4.143437259 130 0.839531911 -0.724154682 131 1.643758695 0.839531911 132 1.049362627 1.643758695 133 5.093038642 1.049362627 134 7.039446850 5.093038642 135 5.116605053 7.039446850 136 3.997006027 5.116605053 137 1.344341409 3.997006027 138 0.467414525 1.344341409 139 3.595345352 0.467414525 140 1.184395307 3.595345352 141 -0.543296781 1.184395307 142 -0.234890702 -0.543296781 143 -0.730753352 -0.234890702 144 -2.823841670 -0.730753352 145 -6.332421034 -2.823841670 146 -0.801882936 -6.332421034 147 -4.620236548 -0.801882936 148 -2.672378619 -4.620236548 149 -4.090119256 -2.672378619 150 -4.807807787 -4.090119256 > 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/7usbk1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8dsif1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/91l8g1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10mihq1352125226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11lv2u1352125226.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/126n1e1352125226.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/13iaii1352125226.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/14zfaj1352125226.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/15vfby1352125226.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/16jtpo1352125227.tab") + } > > try(system("convert tmp/1wiio1352125226.ps tmp/1wiio1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/2t6do1352125226.ps tmp/2t6do1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/3m26t1352125226.ps tmp/3m26t1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/46ul61352125226.ps tmp/46ul61352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/52v801352125226.ps tmp/52v801352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/6829t1352125226.ps tmp/6829t1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/7usbk1352125226.ps tmp/7usbk1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/8dsif1352125226.ps tmp/8dsif1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/91l8g1352125226.ps tmp/91l8g1352125226.png",intern=TRUE)) character(0) > try(system("convert tmp/10mihq1352125226.ps tmp/10mihq1352125226.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.652 1.739 14.440