R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(12.3 + ,23 + ,154.25 + ,67.75 + ,36.2 + ,93.1 + ,85.2 + ,94.5 + ,59.0 + ,37.3 + ,21.9 + ,32.0 + ,27.4 + ,17.1 + ,6.1 + ,22 + ,173.25 + ,72.25 + ,38.5 + ,93.6 + ,83.0 + ,98.7 + ,58.7 + ,37.3 + ,23.4 + ,30.5 + ,28.9 + ,18.2 + ,25.3 + ,22 + ,154.00 + ,66.25 + ,34.0 + ,95.8 + ,87.9 + ,99.2 + ,59.6 + ,38.9 + ,24.0 + ,28.8 + ,25.2 + ,16.6 + ,10.4 + ,26 + ,184.75 + ,72.25 + ,37.4 + ,101.8 + ,86.4 + ,101.2 + ,60.1 + ,37.3 + ,22.8 + ,32.4 + ,29.4 + ,18.2 + ,28.7 + ,24 + ,184.25 + ,71.25 + ,34.4 + ,97.3 + ,100.0 + ,101.9 + ,63.2 + ,42.2 + ,24.0 + ,32.2 + ,27.7 + ,17.7 + ,20.9 + ,24 + ,210.25 + ,74.75 + ,39.0 + ,104.5 + ,94.4 + ,107.8 + ,66.0 + ,42.0 + ,25.6 + ,35.7 + ,30.6 + ,18.8 + ,19.2 + ,26 + ,181.00 + ,69.75 + ,36.4 + ,105.1 + ,90.7 + ,100.3 + ,58.4 + ,38.3 + ,22.9 + ,31.9 + ,27.8 + ,17.7 + ,12.4 + ,25 + ,176.00 + ,72.50 + ,37.8 + ,99.6 + ,88.5 + ,97.1 + ,60.0 + ,39.4 + ,23.2 + ,30.5 + ,29.0 + ,18.8 + ,4.1 + ,25 + ,191.00 + ,74.00 + ,38.1 + ,100.9 + 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array(NA,dim=c(14,252),dimnames=list(c('Fat','Age','Weight','Height','Neck','Chest','Abdomen','Hip','Thigh','Knee','Ankle','Biceps','Forearm','Wrist'),1:252)) > 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' > 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 Fat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle Biceps 1 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 32.0 2 6.1 22 173.25 72.25 38.5 93.6 83.0 98.7 58.7 37.3 23.4 30.5 3 25.3 22 154.00 66.25 34.0 95.8 87.9 99.2 59.6 38.9 24.0 28.8 4 10.4 26 184.75 72.25 37.4 101.8 86.4 101.2 60.1 37.3 22.8 32.4 5 28.7 24 184.25 71.25 34.4 97.3 100.0 101.9 63.2 42.2 24.0 32.2 6 20.9 24 210.25 74.75 39.0 104.5 94.4 107.8 66.0 42.0 25.6 35.7 7 19.2 26 181.00 69.75 36.4 105.1 90.7 100.3 58.4 38.3 22.9 31.9 8 12.4 25 176.00 72.50 37.8 99.6 88.5 97.1 60.0 39.4 23.2 30.5 9 4.1 25 191.00 74.00 38.1 100.9 82.5 99.9 62.9 38.3 23.8 35.9 10 11.7 23 198.25 73.50 42.1 99.6 88.6 104.1 63.1 41.7 25.0 35.6 11 7.1 26 186.25 74.50 38.5 101.5 83.6 98.2 59.7 39.7 25.2 32.8 12 7.8 27 216.00 76.00 39.4 103.6 90.9 107.7 66.2 39.2 25.9 37.2 13 20.8 32 180.50 69.50 38.4 102.0 91.6 103.9 63.4 38.3 21.5 32.5 14 21.2 30 205.25 71.25 39.4 104.1 101.8 108.6 66.0 41.5 23.7 36.9 15 22.1 35 187.75 69.50 40.5 101.3 96.4 100.1 69.0 39.0 23.1 36.1 16 20.9 35 162.75 66.00 36.4 99.1 92.8 99.2 63.1 38.7 21.7 31.1 17 29.0 34 195.75 71.00 38.9 101.9 96.4 105.2 64.8 40.8 23.1 36.2 18 22.9 32 209.25 71.00 42.1 107.6 97.5 107.0 66.9 40.0 24.4 38.2 19 16.0 28 183.75 67.75 38.0 106.8 89.6 102.4 64.2 38.7 22.9 37.2 20 16.5 33 211.75 73.50 40.0 106.2 100.5 109.0 65.8 40.6 24.0 37.1 21 19.1 28 179.00 68.00 39.1 103.3 95.9 104.9 63.5 38.0 22.1 32.5 22 15.2 28 200.50 69.75 41.3 111.4 98.8 104.8 63.4 40.6 24.6 33.0 23 15.6 31 140.25 68.25 33.9 86.0 76.4 94.6 57.4 35.3 22.2 27.9 24 17.7 32 148.75 70.00 35.5 86.7 80.0 93.4 54.9 36.2 22.1 29.8 25 14.0 28 151.25 67.75 34.5 90.2 76.3 95.8 58.4 35.5 22.9 31.1 26 3.7 27 159.25 71.50 35.7 89.6 79.7 96.5 55.0 36.7 22.5 29.9 27 7.9 34 131.50 67.50 36.2 88.6 74.6 85.3 51.7 34.7 21.4 28.7 28 22.9 31 148.00 67.50 38.8 97.4 88.7 94.7 57.5 36.0 21.0 29.2 29 3.7 27 133.25 64.75 36.4 93.5 73.9 88.5 50.1 34.5 21.3 30.5 30 8.8 29 160.75 69.00 36.7 97.4 83.5 98.7 58.9 35.3 22.6 30.1 31 11.9 32 182.00 73.75 38.7 100.5 88.7 99.8 57.5 38.7 33.9 32.5 32 5.7 29 160.25 71.25 37.3 93.5 84.5 100.6 58.5 38.8 21.5 30.1 33 11.8 27 168.00 71.25 38.1 93.0 79.1 94.5 57.3 36.2 24.5 29.0 34 21.3 41 218.50 71.00 39.8 111.7 100.5 108.3 67.1 44.2 25.2 37.5 35 32.3 41 247.25 73.50 42.1 117.0 115.6 116.1 71.2 43.3 26.3 37.3 36 40.1 49 191.75 65.00 38.4 118.5 113.1 113.8 61.9 38.3 21.9 32.0 37 24.2 40 202.25 70.00 38.5 106.5 100.9 106.2 63.5 39.9 22.6 35.1 38 28.4 50 196.75 68.25 42.1 105.6 98.8 104.8 66.0 41.5 24.7 33.2 39 35.2 46 363.15 72.25 51.2 136.2 148.1 147.7 87.3 49.1 29.6 45.0 40 32.6 50 203.00 67.00 40.2 114.8 108.1 102.5 61.3 41.1 24.7 34.1 41 34.5 45 262.75 68.75 43.2 128.3 126.2 125.6 72.5 39.6 26.6 36.4 42 32.9 44 205.00 29.50 36.6 106.0 104.3 115.5 70.6 42.5 23.7 33.6 43 31.6 48 217.00 70.00 37.3 113.3 111.2 114.1 67.7 40.9 25.0 36.7 44 32.0 41 212.00 71.50 41.5 106.6 104.3 106.0 65.0 40.2 23.0 35.8 45 7.7 39 125.25 68.00 31.5 85.1 76.0 88.2 50.0 34.7 21.0 26.1 46 13.9 43 164.25 73.25 35.7 96.6 81.5 97.2 58.4 38.2 23.4 29.7 47 10.8 40 133.50 67.50 33.6 88.2 73.7 88.5 53.3 34.5 22.5 27.9 48 5.6 39 148.50 71.25 34.6 89.8 79.5 92.7 52.7 37.5 21.9 28.8 49 13.6 45 135.75 68.50 32.8 92.3 83.4 90.4 52.0 35.8 20.6 28.8 50 4.0 47 127.50 66.75 34.0 83.4 70.4 87.2 50.6 34.4 21.9 26.8 51 10.2 47 158.25 72.25 34.9 90.2 86.7 98.3 52.6 37.2 22.4 26.0 52 6.6 40 139.25 69.00 34.3 89.2 77.9 91.0 51.4 34.9 21.0 26.7 53 8.0 51 137.25 67.75 36.5 89.7 82.0 89.1 49.3 33.7 21.4 29.6 54 6.3 49 152.75 73.50 35.1 93.3 79.6 91.6 52.6 37.6 22.6 38.5 55 3.9 42 136.25 67.50 37.8 87.6 77.6 88.6 51.9 34.9 22.5 27.7 56 22.6 54 198.00 72.00 39.9 107.6 100.0 99.6 57.2 38.0 22.0 35.9 57 20.4 58 181.50 68.00 39.1 100.0 99.8 102.5 62.1 39.6 22.5 33.1 58 28.0 62 201.25 69.50 40.5 111.5 104.2 105.8 61.8 39.8 22.7 37.7 59 31.5 54 202.50 70.75 40.5 115.4 105.3 97.0 59.1 38.0 22.5 31.6 60 24.6 61 179.75 65.75 38.4 104.8 98.3 99.6 60.6 37.7 22.9 34.5 61 26.1 62 216.00 73.25 41.4 112.3 104.8 103.1 61.6 40.9 23.1 36.2 62 29.8 56 178.75 68.50 35.6 102.9 94.7 100.8 60.9 38.0 22.1 32.5 63 30.7 54 193.25 70.25 38.0 107.6 102.4 99.4 61.0 39.4 23.6 32.7 64 25.8 61 178.00 67.00 37.4 105.3 99.7 99.7 60.8 40.1 22.7 33.6 65 32.3 57 205.50 70.00 40.1 105.3 105.5 108.3 65.0 41.2 24.7 35.3 66 30.0 55 183.50 67.50 40.9 103.0 100.3 104.2 64.8 40.2 22.7 34.8 67 21.5 54 151.50 70.75 35.6 90.0 83.9 93.9 55.0 36.1 21.7 29.6 68 13.8 55 154.75 71.50 36.9 95.4 86.6 91.8 54.3 35.4 21.5 32.8 69 6.3 54 155.25 69.25 37.5 89.3 78.4 96.1 56.0 37.4 22.4 32.6 70 12.9 55 156.75 71.50 36.3 94.4 84.6 94.3 51.2 37.4 21.6 27.3 71 24.3 62 167.50 71.50 35.5 97.6 91.5 98.5 56.6 38.6 22.4 31.5 72 8.8 55 146.75 68.75 38.7 88.5 82.8 95.5 58.9 37.6 21.6 30.3 73 8.5 56 160.75 73.75 36.4 93.6 82.9 96.3 52.9 37.5 23.1 29.7 74 13.5 55 125.00 64.00 33.2 87.7 76.0 88.6 50.9 35.4 19.1 29.3 75 11.8 61 143.00 65.75 36.5 93.4 83.3 93.0 55.5 35.2 20.9 29.4 76 18.5 61 148.25 67.50 36.0 91.6 81.8 94.8 54.5 37.0 21.4 29.3 77 8.8 57 162.50 69.50 38.7 91.6 78.8 94.3 56.7 39.7 24.2 30.2 78 22.2 69 177.75 68.50 38.7 102.0 95.0 98.3 55.0 38.3 21.8 30.8 79 21.5 81 161.25 70.25 37.8 96.4 95.4 99.3 53.5 37.5 21.5 31.4 80 18.8 66 171.25 69.25 37.4 102.7 98.6 100.2 56.5 39.3 22.7 30.3 81 31.4 67 163.75 67.75 38.4 97.7 95.8 97.1 54.8 38.2 23.7 29.4 82 26.8 64 150.25 67.25 38.1 97.1 89.0 96.9 54.8 38.0 22.0 29.9 83 18.4 64 190.25 72.75 39.3 103.1 97.8 99.6 58.9 39.0 23.0 34.3 84 27.0 70 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31.3 251 26.0 72 190.75 70.50 38.9 108.3 101.3 97.8 56.0 41.6 22.7 30.5 252 31.9 74 207.50 70.00 40.8 112.4 108.5 107.1 59.3 42.2 24.6 33.7 Forearm Wrist 1 27.4 17.1 2 28.9 18.2 3 25.2 16.6 4 29.4 18.2 5 27.7 17.7 6 30.6 18.8 7 27.8 17.7 8 29.0 18.8 9 31.1 18.2 10 30.0 19.2 11 29.4 18.5 12 30.2 19.0 13 28.6 17.7 14 31.6 18.8 15 30.5 18.2 16 26.4 16.9 17 30.8 17.3 18 31.6 19.3 19 30.5 18.5 20 30.1 18.2 21 30.3 18.4 22 32.8 19.9 23 25.9 16.7 24 26.7 17.1 25 28.0 17.6 26 28.2 17.7 27 27.0 16.5 28 26.6 17.0 29 27.9 17.2 30 26.7 17.6 31 27.7 18.4 32 26.4 17.9 33 30.0 18.8 34 31.5 18.7 35 31.7 19.7 36 29.8 17.0 37 30.6 19.0 38 30.5 19.4 39 29.0 21.4 40 31.0 18.3 41 32.7 21.4 42 28.7 17.4 43 29.8 18.4 44 31.5 18.8 45 23.1 16.1 46 27.4 18.3 47 26.2 17.3 48 26.8 17.9 49 25.5 16.3 50 25.8 16.8 51 25.8 17.3 52 26.1 17.2 53 26.0 16.9 54 27.4 18.5 55 27.5 18.5 56 30.2 18.9 57 28.3 18.5 58 30.9 19.2 59 28.8 18.2 60 29.6 18.5 61 31.8 20.2 62 29.8 18.3 63 29.9 19.1 64 29.0 18.8 65 31.1 18.4 66 30.1 18.7 67 27.4 17.4 68 27.4 18.7 69 28.1 18.1 70 27.1 17.3 71 27.3 18.6 72 27.3 18.3 73 27.3 18.2 74 25.7 16.9 75 27.0 16.8 76 27.0 18.3 77 29.2 18.1 78 25.7 18.8 79 26.8 18.3 80 28.7 19.0 81 27.2 19.0 82 25.2 17.7 83 29.6 19.0 84 27.3 19.2 85 26.3 18.0 86 27.7 18.2 87 28.0 18.8 88 28.4 18.1 89 29.3 18.8 90 29.6 18.7 91 27.7 17.7 92 28.4 18.8 93 28.2 18.4 94 29.6 19.1 95 27.9 17.8 96 30.8 20.4 97 30.1 18.5 98 27.8 18.2 99 27.5 18.2 100 30.9 18.3 101 31.0 18.6 102 26.2 17.0 103 29.2 18.4 104 30.3 19.7 105 27.2 17.7 106 29.4 18.8 107 28.5 18.1 108 29.6 19.1 109 30.7 19.2 110 26.0 17.3 111 28.2 18.1 112 27.8 17.4 113 31.1 19.8 114 27.6 17.4 115 27.3 17.5 116 26.3 17.3 117 28.0 18.1 118 29.8 19.5 119 29.5 18.5 120 28.6 18.0 121 31.2 19.5 122 29.6 18.4 123 28.0 17.6 124 27.5 17.6 125 29.2 18.0 126 30.2 17.6 127 30.3 17.2 128 28.2 17.4 129 29.6 18.1 130 27.8 17.7 131 26.5 17.6 132 28.4 17.1 133 28.6 17.9 134 29.3 17.3 135 29.8 18.1 136 28.8 17.6 137 28.3 17.1 138 28.4 17.7 139 27.4 17.6 140 29.8 18.7 141 27.9 16.6 142 28.5 17.8 143 27.5 17.9 144 27.2 18.2 145 29.7 18.3 146 28.3 17.3 147 30.3 18.7 148 29.7 18.4 149 25.2 16.9 150 30.4 18.4 151 29.0 17.8 152 33.8 19.6 153 26.3 17.4 154 28.3 17.9 155 29.2 19.4 156 27.7 17.7 157 30.7 19.1 158 28.0 18.6 159 34.9 16.9 160 28.2 18.2 161 26.3 16.5 162 29.2 19.1 163 30.6 19.7 164 26.2 16.5 165 31.2 18.5 166 33.7 19.4 167 28.7 17.7 168 33.1 19.8 169 31.8 18.8 170 29.8 17.4 171 27.4 17.7 172 25.9 16.9 173 28.7 17.7 174 28.4 17.8 175 21.0 20.1 176 26.9 16.9 177 26.6 16.7 178 29.5 18.4 179 30.3 18.1 180 32.5 19.9 181 29.0 19.0 182 24.6 16.5 183 28.3 17.2 184 26.4 17.4 185 28.9 17.7 186 29.3 18.2 187 31.8 20.0 188 30.4 19.1 189 32.4 18.8 190 29.2 18.5 191 27.3 17.9 192 31.2 19.9 193 30.1 18.7 194 29.9 18.5 195 27.1 17.1 196 29.8 18.8 197 27.3 17.4 198 27.8 16.9 199 30.0 18.5 200 28.8 18.8 201 28.4 18.6 202 26.6 17.4 203 28.9 18.7 204 29.2 18.4 205 30.7 17.4 206 23.1 19.4 207 29.5 17.3 208 31.6 18.4 209 27.5 17.7 210 26.2 17.6 211 28.8 17.4 212 31.0 18.9 213 27.9 18.6 214 30.0 19.0 215 26.7 18.0 216 29.1 18.4 217 26.3 17.8 218 25.9 18.6 219 30.4 19.2 220 25.7 17.1 221 29.9 18.9 222 32.0 19.6 223 27.0 17.8 224 26.8 17.0 225 31.1 19.2 226 22.0 15.8 227 29.3 18.8 228 30.0 18.4 229 30.1 18.8 230 29.7 19.0 231 27.3 16.9 232 29.1 19.0 233 29.6 18.0 234 26.1 17.6 235 28.7 18.3 236 26.3 18.3 237 28.5 19.0 238 29.6 18.5 239 28.9 18.3 240 30.5 19.1 241 24.8 16.5 242 30.1 19.4 243 30.7 19.5 244 29.8 19.5 245 29.3 18.5 246 27.3 18.5 247 32.6 18.8 248 25.7 18.5 249 28.6 20.1 250 27.2 18.0 251 29.4 19.8 252 30.0 20.9 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Age Weight Height Neck Chest -18.18849 0.06208 -0.08844 -0.06959 -0.47060 -0.02386 Abdomen Hip Thigh Knee Ankle Biceps 0.95477 -0.20754 0.23610 0.01528 0.17400 0.18160 Forearm Wrist 0.45202 -1.62064 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -11.1687 -2.8639 -0.1014 3.2085 10.0068 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -18.18849 17.34857 -1.048 0.29551 Age 0.06208 0.03235 1.919 0.05618 . Weight -0.08844 0.05353 -1.652 0.09978 . Height -0.06959 0.09601 -0.725 0.46925 Neck -0.47060 0.23247 -2.024 0.04405 * Chest -0.02386 0.09915 -0.241 0.81000 Abdomen 0.95477 0.08645 11.044 < 2e-16 *** Hip -0.20754 0.14591 -1.422 0.15622 Thigh 0.23610 0.14436 1.636 0.10326 Knee 0.01528 0.24198 0.063 0.94970 Ankle 0.17400 0.22147 0.786 0.43285 Biceps 0.18160 0.17113 1.061 0.28966 Forearm 0.45202 0.19913 2.270 0.02410 * Wrist -1.62064 0.53495 -3.030 0.00272 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.305 on 238 degrees of freedom Multiple R-squared: 0.749, Adjusted R-squared: 0.7353 F-statistic: 54.65 on 13 and 238 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,] 0.08756083 0.17512165 0.9124392 [2,] 0.28210694 0.56421387 0.7178931 [3,] 0.22358148 0.44716297 0.7764185 [4,] 0.25422015 0.50844030 0.7457799 [5,] 0.19043848 0.38087697 0.8095615 [6,] 0.53266256 0.93467488 0.4673374 [7,] 0.56250711 0.87498577 0.4374929 [8,] 0.56289252 0.87421497 0.4371075 [9,] 0.47842865 0.95685730 0.5215713 [10,] 0.59125048 0.81749905 0.4087495 [11,] 0.51703178 0.96593643 0.4829682 [12,] 0.61337276 0.77325448 0.3866272 [13,] 0.56576854 0.86846292 0.4342315 [14,] 0.54844891 0.90310219 0.4515511 [15,] 0.49365603 0.98731206 0.5063440 [16,] 0.58227158 0.83545683 0.4177284 [17,] 0.56759574 0.86480852 0.4324043 [18,] 0.56909279 0.86181443 0.4309072 [19,] 0.50508025 0.98983950 0.4949197 [20,] 0.46496065 0.92992130 0.5350394 [21,] 0.41716257 0.83432514 0.5828374 [22,] 0.38495108 0.76990215 0.6150489 [23,] 0.51219788 0.97560424 0.4878021 [24,] 0.45250061 0.90500121 0.5474994 [25,] 0.40524913 0.81049826 0.5947509 [26,] 0.43815689 0.87631379 0.5618431 [27,] 0.40850110 0.81700220 0.5914989 [28,] 0.45659178 0.91318356 0.5434082 [29,] 0.44662512 0.89325024 0.5533749 [30,] 0.40965445 0.81930890 0.5903455 [31,] 0.36565140 0.73130280 0.6343486 [32,] 0.35813374 0.71626748 0.6418663 [33,] 0.34708068 0.69416136 0.6529193 [34,] 0.32729842 0.65459683 0.6727016 [35,] 0.34843722 0.69687443 0.6515628 [36,] 0.31364352 0.62728704 0.6863565 [37,] 0.29580835 0.59161671 0.7041916 [38,] 0.30190072 0.60380144 0.6980993 [39,] 0.28356178 0.56712356 0.7164382 [40,] 0.28511542 0.57023084 0.7148846 [41,] 0.30474275 0.60948551 0.6952572 [42,] 0.28302689 0.56605379 0.7169731 [43,] 0.27008461 0.54016922 0.7299154 [44,] 0.23577726 0.47155452 0.7642227 [45,] 0.21209491 0.42418983 0.7879051 [46,] 0.23017557 0.46035115 0.7698244 [47,] 0.21015786 0.42031572 0.7898421 [48,] 0.18444344 0.36888688 0.8155566 [49,] 0.15706336 0.31412672 0.8429366 [50,] 0.14397291 0.28794581 0.8560271 [51,] 0.16256912 0.32513823 0.8374309 [52,] 0.13808775 0.27617550 0.8619123 [53,] 0.12171231 0.24342461 0.8782877 [54,] 0.10162754 0.20325507 0.8983725 [55,] 0.11428192 0.22856383 0.8857181 [56,] 0.12447620 0.24895240 0.8755238 [57,] 0.10835181 0.21670362 0.8916482 [58,] 0.09610977 0.19221954 0.9038902 [59,] 0.12386381 0.24772762 0.8761362 [60,] 0.15722639 0.31445277 0.8427736 [61,] 0.13707723 0.27415445 0.8629228 [62,] 0.14215557 0.28431114 0.8578444 [63,] 0.12216637 0.24433273 0.8778336 [64,] 0.15535462 0.31070924 0.8446454 [65,] 0.27957005 0.55914009 0.7204300 [66,] 0.40607214 0.81214428 0.5939279 [67,] 0.41094992 0.82189984 0.5890501 [68,] 0.42568329 0.85136657 0.5743167 [69,] 0.39141170 0.78282340 0.6085883 [70,] 0.39628133 0.79256266 0.6037187 [71,] 0.38626806 0.77253612 0.6137319 [72,] 0.38472857 0.76945714 0.6152714 [73,] 0.40936334 0.81872668 0.5906367 [74,] 0.38052150 0.76104299 0.6194785 [75,] 0.34594884 0.69189768 0.6540512 [76,] 0.31090072 0.62180144 0.6890993 [77,] 0.28886349 0.57772699 0.7111365 [78,] 0.27841616 0.55683233 0.7215838 [79,] 0.29935127 0.59870255 0.7006487 [80,] 0.27635079 0.55270157 0.7236492 [81,] 0.33766602 0.67533205 0.6623340 [82,] 0.37627424 0.75254848 0.6237258 [83,] 0.33982499 0.67964999 0.6601750 [84,] 0.32861239 0.65722478 0.6713876 [85,] 0.31093501 0.62187002 0.6890650 [86,] 0.27796183 0.55592367 0.7220382 [87,] 0.25876347 0.51752694 0.7412365 [88,] 0.27306125 0.54612250 0.7269387 [89,] 0.24568205 0.49136410 0.7543180 [90,] 0.21693428 0.43386856 0.7830657 [91,] 0.25771746 0.51543492 0.7422825 [92,] 0.25317441 0.50634882 0.7468256 [93,] 0.26407974 0.52815947 0.7359203 [94,] 0.23669460 0.47338921 0.7633054 [95,] 0.20862756 0.41725512 0.7913724 [96,] 0.21164419 0.42328838 0.7883558 [97,] 0.18840309 0.37680617 0.8115969 [98,] 0.16658242 0.33316483 0.8334176 [99,] 0.20512411 0.41024822 0.7948759 [100,] 0.17951285 0.35902571 0.8204871 [101,] 0.16895795 0.33791591 0.8310420 [102,] 0.14628001 0.29256002 0.8537200 [103,] 0.20099742 0.40199484 0.7990026 [104,] 0.20571558 0.41143116 0.7942844 [105,] 0.26726999 0.53453997 0.7327300 [106,] 0.24959717 0.49919433 0.7504028 [107,] 0.22145842 0.44291684 0.7785416 [108,] 0.19662668 0.39325337 0.8033733 [109,] 0.17540387 0.35080774 0.8245961 [110,] 0.16813672 0.33627345 0.8318633 [111,] 0.18361957 0.36723914 0.8163804 [112,] 0.26020858 0.52041717 0.7397914 [113,] 0.23576732 0.47153463 0.7642327 [114,] 0.20765120 0.41530241 0.7923488 [115,] 0.18289744 0.36579488 0.8171026 [116,] 0.16723014 0.33446028 0.8327699 [117,] 0.14759962 0.29519923 0.8524004 [118,] 0.15446719 0.30893438 0.8455328 [119,] 0.22064286 0.44128572 0.7793571 [120,] 0.19359746 0.38719492 0.8064025 [121,] 0.20651933 0.41303867 0.7934807 [122,] 0.20223827 0.40447654 0.7977617 [123,] 0.20915476 0.41830953 0.7908452 [124,] 0.29128922 0.58257844 0.7087108 [125,] 0.27687917 0.55375833 0.7231208 [126,] 0.25953938 0.51907877 0.7404606 [127,] 0.26605059 0.53210119 0.7339494 [128,] 0.26760501 0.53521001 0.7323950 [129,] 0.23855493 0.47710987 0.7614451 [130,] 0.21534840 0.43069681 0.7846516 [131,] 0.20139782 0.40279565 0.7986022 [132,] 0.25045583 0.50091166 0.7495442 [133,] 0.22159086 0.44318172 0.7784091 [134,] 0.19839095 0.39678189 0.8016091 [135,] 0.17485351 0.34970701 0.8251465 [136,] 0.16195363 0.32390725 0.8380464 [137,] 0.20664504 0.41329009 0.7933550 [138,] 0.18044087 0.36088175 0.8195591 [139,] 0.15885384 0.31770768 0.8411462 [140,] 0.18114347 0.36228694 0.8188565 [141,] 0.17097816 0.34195632 0.8290218 [142,] 0.18021164 0.36042327 0.8197884 [143,] 0.17791245 0.35582490 0.8220876 [144,] 0.18671744 0.37343488 0.8132826 [145,] 0.16742875 0.33485750 0.8325712 [146,] 0.15283166 0.30566333 0.8471683 [147,] 0.13825699 0.27651399 0.8617430 [148,] 0.11786045 0.23572090 0.8821395 [149,] 0.09938415 0.19876831 0.9006158 [150,] 0.08835373 0.17670745 0.9116463 [151,] 0.09156255 0.18312511 0.9084374 [152,] 0.07697411 0.15394823 0.9230259 [153,] 0.06932292 0.13864584 0.9306771 [154,] 0.06315990 0.12631981 0.9368401 [155,] 0.09945224 0.19890449 0.9005478 [156,] 0.12191625 0.24383249 0.8780838 [157,] 0.11831842 0.23663684 0.8816816 [158,] 0.09938411 0.19876823 0.9006159 [159,] 0.10443613 0.20887227 0.8955639 [160,] 0.09016277 0.18032554 0.9098372 [161,] 0.08107420 0.16214840 0.9189258 [162,] 0.06709199 0.13418399 0.9329080 [163,] 0.06159375 0.12318750 0.9384062 [164,] 0.08598534 0.17197068 0.9140147 [165,] 0.07215475 0.14430950 0.9278453 [166,] 0.07003964 0.14007928 0.9299604 [167,] 0.06544951 0.13089902 0.9345505 [168,] 0.07056120 0.14112240 0.9294388 [169,] 0.05745098 0.11490195 0.9425490 [170,] 0.06369311 0.12738623 0.9363069 [171,] 0.06106150 0.12212300 0.9389385 [172,] 0.05265150 0.10530299 0.9473485 [173,] 0.04306049 0.08612097 0.9569395 [174,] 0.04402745 0.08805491 0.9559725 [175,] 0.03464796 0.06929593 0.9653520 [176,] 0.04115012 0.08230024 0.9588499 [177,] 0.03401975 0.06803949 0.9659803 [178,] 0.02938198 0.05876396 0.9706180 [179,] 0.03795305 0.07590610 0.9620470 [180,] 0.03134892 0.06269784 0.9686511 [181,] 0.05276163 0.10552325 0.9472384 [182,] 0.04412229 0.08824459 0.9558777 [183,] 0.03489294 0.06978587 0.9651071 [184,] 0.09360168 0.18720336 0.9063983 [185,] 0.07833736 0.15667473 0.9216626 [186,] 0.10065021 0.20130042 0.8993498 [187,] 0.11311260 0.22622519 0.8868874 [188,] 0.19809660 0.39619321 0.8019034 [189,] 0.16559585 0.33119170 0.8344042 [190,] 0.19108657 0.38217314 0.8089134 [191,] 0.50868765 0.98262469 0.4913123 [192,] 0.61218296 0.77563407 0.3878170 [193,] 0.56777008 0.86445984 0.4322299 [194,] 0.51764256 0.96471488 0.4823574 [195,] 0.47643158 0.95286316 0.5235684 [196,] 0.42817995 0.85635990 0.5718201 [197,] 0.42835075 0.85670149 0.5716493 [198,] 0.38586481 0.77172963 0.6141352 [199,] 0.44803691 0.89607383 0.5519631 [200,] 0.70557839 0.58884323 0.2944216 [201,] 0.65363198 0.69273605 0.3463680 [202,] 0.59183847 0.81632307 0.4081615 [203,] 0.56159921 0.87680158 0.4384008 [204,] 0.53100073 0.93799854 0.4689993 [205,] 0.49296330 0.98592660 0.5070367 [206,] 0.43771278 0.87542556 0.5622872 [207,] 0.42111156 0.84222312 0.5788884 [208,] 0.61830460 0.76339080 0.3816954 [209,] 0.71231784 0.57536432 0.2876822 [210,] 0.64080634 0.71838732 0.3591937 [211,] 0.56161916 0.87676169 0.4383808 [212,] 0.70795001 0.58409998 0.2920500 [213,] 0.66442447 0.67115106 0.3355755 [214,] 0.56870193 0.86259614 0.4312981 [215,] 0.47605697 0.95211393 0.5239430 [216,] 0.36721609 0.73443219 0.6327839 [217,] 0.27093761 0.54187522 0.7290624 [218,] 0.63173581 0.73652837 0.3682642 [219,] 0.60302885 0.79394229 0.3969711 > postscript(file="/var/wessaorg/rcomp/tmp/1mxpn1353327359.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/29to31353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3r7rr1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4bs1s1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/51qjx1353327359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 252 Frequency = 1 1 2 3 4 5 6 -3.85278087 -2.74370386 6.74323194 -1.52145524 1.40892172 3.95966210 7 8 9 10 11 12 2.46115045 -1.51591458 -5.57746578 1.51642818 -1.91723978 -5.06741067 13 14 15 16 17 18 2.97464464 -3.85091201 -1.98392619 -2.07452297 5.46168396 3.45178803 19 20 21 22 23 24 -0.74128899 -6.67087455 -2.00905260 -4.68770078 6.04857998 6.57898264 25 26 27 28 29 30 5.86727055 -4.80909674 -1.20335612 4.94504408 -2.66291763 -2.82651365 31 32 33 34 35 36 -2.70301698 -5.33116321 5.66487710 -2.51851712 0.02974837 2.52041509 37 38 39 40 41 42 0.08004236 6.49658883 -8.83488922 0.07467651 -2.17594557 0.36516227 43 44 45 46 47 48 -2.63488024 5.62472921 -3.20411583 3.94345389 3.02869809 -3.94087988 49 50 51 52 53 54 -4.20872402 -1.52042426 -4.53767109 -2.89173918 -6.22321615 -4.49423589 55 56 57 58 59 60 -3.77501483 -0.95682775 -5.45712319 0.31967653 2.86646987 -1.66758680 61 62 63 64 65 66 0.74163862 6.28140133 3.21806956 -1.43822057 2.20623005 3.78563402 67 68 69 70 71 72 6.06824492 -1.89080508 -2.42373308 -0.94226659 4.77880706 -3.99740207 73 74 75 76 77 78 -2.55938876 1.89475945 -4.99563587 6.38867553 -0.16221332 2.83098085 79 80 81 82 83 84 -1.68099936 -6.01058474 9.22654663 8.88470318 -4.56691829 5.70358573 85 86 87 88 89 90 -0.17629968 5.69086006 -2.66853862 1.27555018 -4.37681867 -0.07262775 91 92 93 94 95 96 -1.20482625 0.56047139 -2.54098411 3.20537655 -5.76198195 1.11853064 97 98 99 100 101 102 -7.22865948 -4.78864894 -0.03201518 3.06764261 2.70529180 0.48184404 103 104 105 106 107 108 2.80781511 5.12847319 1.51676978 0.20089741 -6.55550552 -3.94869164 109 110 111 112 113 114 4.84132018 0.35867218 0.46251366 -4.44792138 1.23522466 1.43454189 115 116 117 118 119 120 6.57177812 0.27032672 3.22990397 -0.48701604 7.62411008 4.63985912 121 122 123 124 125 126 7.54000755 2.76459355 0.61212986 -1.10932506 -1.66865976 -3.46492832 127 128 129 130 131 132 5.71852045 8.40431499 2.14455897 -0.09578241 -0.22495989 2.53161092 133 134 135 136 137 138 -1.18122590 5.03423515 8.35292724 0.26717143 5.15361246 3.35056925 139 140 141 142 143 144 4.49761508 -8.36935132 3.70989345 -2.98748325 4.40308875 4.01202028 145 146 147 148 149 150 1.00649828 1.67421640 -3.29006824 6.62875623 -0.77669239 -1.48530885 151 152 153 154 155 156 0.90061109 -2.25679297 6.01604670 -0.10709487 -0.53737885 6.21895547 157 158 159 160 161 162 2.61108726 -5.58282920 0.75238084 3.63444546 -2.41356126 -3.10933225 163 164 165 166 167 168 -2.47870477 -0.20295292 0.28494023 1.26647465 4.05540390 1.27754676 169 170 171 172 173 174 -3.16475546 -3.67495163 -8.66149228 -7.51931356 3.83959023 0.32257550 175 176 177 178 179 180 3.93806198 -1.25804279 -2.85467583 1.88092469 1.82498386 -6.69334583 181 182 183 184 185 186 1.55062518 -4.48358712 -4.78576956 -4.79868221 -0.42626135 -2.57263289 187 188 189 190 191 192 -3.88821800 -1.55668052 -2.15467481 -2.12497867 1.03246127 7.87549228 193 194 195 196 197 198 -2.04125942 -1.94764349 5.99799262 2.75815486 4.90169496 -2.13416993 199 200 201 202 203 204 0.81044609 5.61117211 -3.36883109 6.08990344 1.24076212 -9.08840687 205 206 207 208 209 210 -0.93658262 1.54090918 10.00677252 6.00368701 -4.20414525 -3.82188965 211 212 213 214 215 216 -5.71466023 1.90189046 4.10242879 -2.73419337 5.25784578 6.36391329 217 218 219 220 221 222 2.09645922 -1.57253141 0.70212350 -2.89762943 -8.45703269 -4.62496378 223 224 225 226 227 228 -5.81422316 -11.16865524 -9.40671198 -0.41753962 -4.30494468 3.42863303 229 230 231 232 233 234 -2.33677970 -3.61982793 -9.42021978 -5.25026020 -1.06033120 3.30807495 235 236 237 238 239 240 4.65436673 -3.83875295 3.28730568 -7.17810546 -1.88213706 3.96680349 241 242 243 244 245 246 2.01668075 -1.76036244 2.42426118 -0.33786486 -0.41701823 1.26423963 247 248 249 250 251 252 -0.54713168 -3.72502837 7.74658114 -7.61926679 1.41883129 4.50054460 > postscript(file="/var/wessaorg/rcomp/tmp/631tx1353327359.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 = 252 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.85278087 NA 1 -2.74370386 -3.85278087 2 6.74323194 -2.74370386 3 -1.52145524 6.74323194 4 1.40892172 -1.52145524 5 3.95966210 1.40892172 6 2.46115045 3.95966210 7 -1.51591458 2.46115045 8 -5.57746578 -1.51591458 9 1.51642818 -5.57746578 10 -1.91723978 1.51642818 11 -5.06741067 -1.91723978 12 2.97464464 -5.06741067 13 -3.85091201 2.97464464 14 -1.98392619 -3.85091201 15 -2.07452297 -1.98392619 16 5.46168396 -2.07452297 17 3.45178803 5.46168396 18 -0.74128899 3.45178803 19 -6.67087455 -0.74128899 20 -2.00905260 -6.67087455 21 -4.68770078 -2.00905260 22 6.04857998 -4.68770078 23 6.57898264 6.04857998 24 5.86727055 6.57898264 25 -4.80909674 5.86727055 26 -1.20335612 -4.80909674 27 4.94504408 -1.20335612 28 -2.66291763 4.94504408 29 -2.82651365 -2.66291763 30 -2.70301698 -2.82651365 31 -5.33116321 -2.70301698 32 5.66487710 -5.33116321 33 -2.51851712 5.66487710 34 0.02974837 -2.51851712 35 2.52041509 0.02974837 36 0.08004236 2.52041509 37 6.49658883 0.08004236 38 -8.83488922 6.49658883 39 0.07467651 -8.83488922 40 -2.17594557 0.07467651 41 0.36516227 -2.17594557 42 -2.63488024 0.36516227 43 5.62472921 -2.63488024 44 -3.20411583 5.62472921 45 3.94345389 -3.20411583 46 3.02869809 3.94345389 47 -3.94087988 3.02869809 48 -4.20872402 -3.94087988 49 -1.52042426 -4.20872402 50 -4.53767109 -1.52042426 51 -2.89173918 -4.53767109 52 -6.22321615 -2.89173918 53 -4.49423589 -6.22321615 54 -3.77501483 -4.49423589 55 -0.95682775 -3.77501483 56 -5.45712319 -0.95682775 57 0.31967653 -5.45712319 58 2.86646987 0.31967653 59 -1.66758680 2.86646987 60 0.74163862 -1.66758680 61 6.28140133 0.74163862 62 3.21806956 6.28140133 63 -1.43822057 3.21806956 64 2.20623005 -1.43822057 65 3.78563402 2.20623005 66 6.06824492 3.78563402 67 -1.89080508 6.06824492 68 -2.42373308 -1.89080508 69 -0.94226659 -2.42373308 70 4.77880706 -0.94226659 71 -3.99740207 4.77880706 72 -2.55938876 -3.99740207 73 1.89475945 -2.55938876 74 -4.99563587 1.89475945 75 6.38867553 -4.99563587 76 -0.16221332 6.38867553 77 2.83098085 -0.16221332 78 -1.68099936 2.83098085 79 -6.01058474 -1.68099936 80 9.22654663 -6.01058474 81 8.88470318 9.22654663 82 -4.56691829 8.88470318 83 5.70358573 -4.56691829 84 -0.17629968 5.70358573 85 5.69086006 -0.17629968 86 -2.66853862 5.69086006 87 1.27555018 -2.66853862 88 -4.37681867 1.27555018 89 -0.07262775 -4.37681867 90 -1.20482625 -0.07262775 91 0.56047139 -1.20482625 92 -2.54098411 0.56047139 93 3.20537655 -2.54098411 94 -5.76198195 3.20537655 95 1.11853064 -5.76198195 96 -7.22865948 1.11853064 97 -4.78864894 -7.22865948 98 -0.03201518 -4.78864894 99 3.06764261 -0.03201518 100 2.70529180 3.06764261 101 0.48184404 2.70529180 102 2.80781511 0.48184404 103 5.12847319 2.80781511 104 1.51676978 5.12847319 105 0.20089741 1.51676978 106 -6.55550552 0.20089741 107 -3.94869164 -6.55550552 108 4.84132018 -3.94869164 109 0.35867218 4.84132018 110 0.46251366 0.35867218 111 -4.44792138 0.46251366 112 1.23522466 -4.44792138 113 1.43454189 1.23522466 114 6.57177812 1.43454189 115 0.27032672 6.57177812 116 3.22990397 0.27032672 117 -0.48701604 3.22990397 118 7.62411008 -0.48701604 119 4.63985912 7.62411008 120 7.54000755 4.63985912 121 2.76459355 7.54000755 122 0.61212986 2.76459355 123 -1.10932506 0.61212986 124 -1.66865976 -1.10932506 125 -3.46492832 -1.66865976 126 5.71852045 -3.46492832 127 8.40431499 5.71852045 128 2.14455897 8.40431499 129 -0.09578241 2.14455897 130 -0.22495989 -0.09578241 131 2.53161092 -0.22495989 132 -1.18122590 2.53161092 133 5.03423515 -1.18122590 134 8.35292724 5.03423515 135 0.26717143 8.35292724 136 5.15361246 0.26717143 137 3.35056925 5.15361246 138 4.49761508 3.35056925 139 -8.36935132 4.49761508 140 3.70989345 -8.36935132 141 -2.98748325 3.70989345 142 4.40308875 -2.98748325 143 4.01202028 4.40308875 144 1.00649828 4.01202028 145 1.67421640 1.00649828 146 -3.29006824 1.67421640 147 6.62875623 -3.29006824 148 -0.77669239 6.62875623 149 -1.48530885 -0.77669239 150 0.90061109 -1.48530885 151 -2.25679297 0.90061109 152 6.01604670 -2.25679297 153 -0.10709487 6.01604670 154 -0.53737885 -0.10709487 155 6.21895547 -0.53737885 156 2.61108726 6.21895547 157 -5.58282920 2.61108726 158 0.75238084 -5.58282920 159 3.63444546 0.75238084 160 -2.41356126 3.63444546 161 -3.10933225 -2.41356126 162 -2.47870477 -3.10933225 163 -0.20295292 -2.47870477 164 0.28494023 -0.20295292 165 1.26647465 0.28494023 166 4.05540390 1.26647465 167 1.27754676 4.05540390 168 -3.16475546 1.27754676 169 -3.67495163 -3.16475546 170 -8.66149228 -3.67495163 171 -7.51931356 -8.66149228 172 3.83959023 -7.51931356 173 0.32257550 3.83959023 174 3.93806198 0.32257550 175 -1.25804279 3.93806198 176 -2.85467583 -1.25804279 177 1.88092469 -2.85467583 178 1.82498386 1.88092469 179 -6.69334583 1.82498386 180 1.55062518 -6.69334583 181 -4.48358712 1.55062518 182 -4.78576956 -4.48358712 183 -4.79868221 -4.78576956 184 -0.42626135 -4.79868221 185 -2.57263289 -0.42626135 186 -3.88821800 -2.57263289 187 -1.55668052 -3.88821800 188 -2.15467481 -1.55668052 189 -2.12497867 -2.15467481 190 1.03246127 -2.12497867 191 7.87549228 1.03246127 192 -2.04125942 7.87549228 193 -1.94764349 -2.04125942 194 5.99799262 -1.94764349 195 2.75815486 5.99799262 196 4.90169496 2.75815486 197 -2.13416993 4.90169496 198 0.81044609 -2.13416993 199 5.61117211 0.81044609 200 -3.36883109 5.61117211 201 6.08990344 -3.36883109 202 1.24076212 6.08990344 203 -9.08840687 1.24076212 204 -0.93658262 -9.08840687 205 1.54090918 -0.93658262 206 10.00677252 1.54090918 207 6.00368701 10.00677252 208 -4.20414525 6.00368701 209 -3.82188965 -4.20414525 210 -5.71466023 -3.82188965 211 1.90189046 -5.71466023 212 4.10242879 1.90189046 213 -2.73419337 4.10242879 214 5.25784578 -2.73419337 215 6.36391329 5.25784578 216 2.09645922 6.36391329 217 -1.57253141 2.09645922 218 0.70212350 -1.57253141 219 -2.89762943 0.70212350 220 -8.45703269 -2.89762943 221 -4.62496378 -8.45703269 222 -5.81422316 -4.62496378 223 -11.16865524 -5.81422316 224 -9.40671198 -11.16865524 225 -0.41753962 -9.40671198 226 -4.30494468 -0.41753962 227 3.42863303 -4.30494468 228 -2.33677970 3.42863303 229 -3.61982793 -2.33677970 230 -9.42021978 -3.61982793 231 -5.25026020 -9.42021978 232 -1.06033120 -5.25026020 233 3.30807495 -1.06033120 234 4.65436673 3.30807495 235 -3.83875295 4.65436673 236 3.28730568 -3.83875295 237 -7.17810546 3.28730568 238 -1.88213706 -7.17810546 239 3.96680349 -1.88213706 240 2.01668075 3.96680349 241 -1.76036244 2.01668075 242 2.42426118 -1.76036244 243 -0.33786486 2.42426118 244 -0.41701823 -0.33786486 245 1.26423963 -0.41701823 246 -0.54713168 1.26423963 247 -3.72502837 -0.54713168 248 7.74658114 -3.72502837 249 -7.61926679 7.74658114 250 1.41883129 -7.61926679 251 4.50054460 1.41883129 252 NA 4.50054460 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.74370386 -3.85278087 [2,] 6.74323194 -2.74370386 [3,] -1.52145524 6.74323194 [4,] 1.40892172 -1.52145524 [5,] 3.95966210 1.40892172 [6,] 2.46115045 3.95966210 [7,] -1.51591458 2.46115045 [8,] -5.57746578 -1.51591458 [9,] 1.51642818 -5.57746578 [10,] -1.91723978 1.51642818 [11,] -5.06741067 -1.91723978 [12,] 2.97464464 -5.06741067 [13,] -3.85091201 2.97464464 [14,] -1.98392619 -3.85091201 [15,] -2.07452297 -1.98392619 [16,] 5.46168396 -2.07452297 [17,] 3.45178803 5.46168396 [18,] -0.74128899 3.45178803 [19,] -6.67087455 -0.74128899 [20,] -2.00905260 -6.67087455 [21,] -4.68770078 -2.00905260 [22,] 6.04857998 -4.68770078 [23,] 6.57898264 6.04857998 [24,] 5.86727055 6.57898264 [25,] -4.80909674 5.86727055 [26,] -1.20335612 -4.80909674 [27,] 4.94504408 -1.20335612 [28,] -2.66291763 4.94504408 [29,] -2.82651365 -2.66291763 [30,] -2.70301698 -2.82651365 [31,] -5.33116321 -2.70301698 [32,] 5.66487710 -5.33116321 [33,] -2.51851712 5.66487710 [34,] 0.02974837 -2.51851712 [35,] 2.52041509 0.02974837 [36,] 0.08004236 2.52041509 [37,] 6.49658883 0.08004236 [38,] -8.83488922 6.49658883 [39,] 0.07467651 -8.83488922 [40,] -2.17594557 0.07467651 [41,] 0.36516227 -2.17594557 [42,] -2.63488024 0.36516227 [43,] 5.62472921 -2.63488024 [44,] -3.20411583 5.62472921 [45,] 3.94345389 -3.20411583 [46,] 3.02869809 3.94345389 [47,] -3.94087988 3.02869809 [48,] -4.20872402 -3.94087988 [49,] -1.52042426 -4.20872402 [50,] -4.53767109 -1.52042426 [51,] -2.89173918 -4.53767109 [52,] -6.22321615 -2.89173918 [53,] -4.49423589 -6.22321615 [54,] -3.77501483 -4.49423589 [55,] -0.95682775 -3.77501483 [56,] -5.45712319 -0.95682775 [57,] 0.31967653 -5.45712319 [58,] 2.86646987 0.31967653 [59,] -1.66758680 2.86646987 [60,] 0.74163862 -1.66758680 [61,] 6.28140133 0.74163862 [62,] 3.21806956 6.28140133 [63,] -1.43822057 3.21806956 [64,] 2.20623005 -1.43822057 [65,] 3.78563402 2.20623005 [66,] 6.06824492 3.78563402 [67,] -1.89080508 6.06824492 [68,] -2.42373308 -1.89080508 [69,] -0.94226659 -2.42373308 [70,] 4.77880706 -0.94226659 [71,] -3.99740207 4.77880706 [72,] -2.55938876 -3.99740207 [73,] 1.89475945 -2.55938876 [74,] -4.99563587 1.89475945 [75,] 6.38867553 -4.99563587 [76,] -0.16221332 6.38867553 [77,] 2.83098085 -0.16221332 [78,] -1.68099936 2.83098085 [79,] -6.01058474 -1.68099936 [80,] 9.22654663 -6.01058474 [81,] 8.88470318 9.22654663 [82,] -4.56691829 8.88470318 [83,] 5.70358573 -4.56691829 [84,] -0.17629968 5.70358573 [85,] 5.69086006 -0.17629968 [86,] -2.66853862 5.69086006 [87,] 1.27555018 -2.66853862 [88,] -4.37681867 1.27555018 [89,] -0.07262775 -4.37681867 [90,] -1.20482625 -0.07262775 [91,] 0.56047139 -1.20482625 [92,] -2.54098411 0.56047139 [93,] 3.20537655 -2.54098411 [94,] -5.76198195 3.20537655 [95,] 1.11853064 -5.76198195 [96,] -7.22865948 1.11853064 [97,] -4.78864894 -7.22865948 [98,] -0.03201518 -4.78864894 [99,] 3.06764261 -0.03201518 [100,] 2.70529180 3.06764261 [101,] 0.48184404 2.70529180 [102,] 2.80781511 0.48184404 [103,] 5.12847319 2.80781511 [104,] 1.51676978 5.12847319 [105,] 0.20089741 1.51676978 [106,] -6.55550552 0.20089741 [107,] -3.94869164 -6.55550552 [108,] 4.84132018 -3.94869164 [109,] 0.35867218 4.84132018 [110,] 0.46251366 0.35867218 [111,] -4.44792138 0.46251366 [112,] 1.23522466 -4.44792138 [113,] 1.43454189 1.23522466 [114,] 6.57177812 1.43454189 [115,] 0.27032672 6.57177812 [116,] 3.22990397 0.27032672 [117,] -0.48701604 3.22990397 [118,] 7.62411008 -0.48701604 [119,] 4.63985912 7.62411008 [120,] 7.54000755 4.63985912 [121,] 2.76459355 7.54000755 [122,] 0.61212986 2.76459355 [123,] -1.10932506 0.61212986 [124,] -1.66865976 -1.10932506 [125,] -3.46492832 -1.66865976 [126,] 5.71852045 -3.46492832 [127,] 8.40431499 5.71852045 [128,] 2.14455897 8.40431499 [129,] -0.09578241 2.14455897 [130,] -0.22495989 -0.09578241 [131,] 2.53161092 -0.22495989 [132,] -1.18122590 2.53161092 [133,] 5.03423515 -1.18122590 [134,] 8.35292724 5.03423515 [135,] 0.26717143 8.35292724 [136,] 5.15361246 0.26717143 [137,] 3.35056925 5.15361246 [138,] 4.49761508 3.35056925 [139,] -8.36935132 4.49761508 [140,] 3.70989345 -8.36935132 [141,] -2.98748325 3.70989345 [142,] 4.40308875 -2.98748325 [143,] 4.01202028 4.40308875 [144,] 1.00649828 4.01202028 [145,] 1.67421640 1.00649828 [146,] -3.29006824 1.67421640 [147,] 6.62875623 -3.29006824 [148,] -0.77669239 6.62875623 [149,] -1.48530885 -0.77669239 [150,] 0.90061109 -1.48530885 [151,] -2.25679297 0.90061109 [152,] 6.01604670 -2.25679297 [153,] -0.10709487 6.01604670 [154,] -0.53737885 -0.10709487 [155,] 6.21895547 -0.53737885 [156,] 2.61108726 6.21895547 [157,] -5.58282920 2.61108726 [158,] 0.75238084 -5.58282920 [159,] 3.63444546 0.75238084 [160,] -2.41356126 3.63444546 [161,] -3.10933225 -2.41356126 [162,] -2.47870477 -3.10933225 [163,] -0.20295292 -2.47870477 [164,] 0.28494023 -0.20295292 [165,] 1.26647465 0.28494023 [166,] 4.05540390 1.26647465 [167,] 1.27754676 4.05540390 [168,] -3.16475546 1.27754676 [169,] -3.67495163 -3.16475546 [170,] -8.66149228 -3.67495163 [171,] -7.51931356 -8.66149228 [172,] 3.83959023 -7.51931356 [173,] 0.32257550 3.83959023 [174,] 3.93806198 0.32257550 [175,] -1.25804279 3.93806198 [176,] -2.85467583 -1.25804279 [177,] 1.88092469 -2.85467583 [178,] 1.82498386 1.88092469 [179,] -6.69334583 1.82498386 [180,] 1.55062518 -6.69334583 [181,] -4.48358712 1.55062518 [182,] -4.78576956 -4.48358712 [183,] -4.79868221 -4.78576956 [184,] -0.42626135 -4.79868221 [185,] -2.57263289 -0.42626135 [186,] -3.88821800 -2.57263289 [187,] -1.55668052 -3.88821800 [188,] -2.15467481 -1.55668052 [189,] -2.12497867 -2.15467481 [190,] 1.03246127 -2.12497867 [191,] 7.87549228 1.03246127 [192,] -2.04125942 7.87549228 [193,] -1.94764349 -2.04125942 [194,] 5.99799262 -1.94764349 [195,] 2.75815486 5.99799262 [196,] 4.90169496 2.75815486 [197,] -2.13416993 4.90169496 [198,] 0.81044609 -2.13416993 [199,] 5.61117211 0.81044609 [200,] -3.36883109 5.61117211 [201,] 6.08990344 -3.36883109 [202,] 1.24076212 6.08990344 [203,] -9.08840687 1.24076212 [204,] -0.93658262 -9.08840687 [205,] 1.54090918 -0.93658262 [206,] 10.00677252 1.54090918 [207,] 6.00368701 10.00677252 [208,] -4.20414525 6.00368701 [209,] -3.82188965 -4.20414525 [210,] -5.71466023 -3.82188965 [211,] 1.90189046 -5.71466023 [212,] 4.10242879 1.90189046 [213,] -2.73419337 4.10242879 [214,] 5.25784578 -2.73419337 [215,] 6.36391329 5.25784578 [216,] 2.09645922 6.36391329 [217,] -1.57253141 2.09645922 [218,] 0.70212350 -1.57253141 [219,] -2.89762943 0.70212350 [220,] -8.45703269 -2.89762943 [221,] -4.62496378 -8.45703269 [222,] -5.81422316 -4.62496378 [223,] -11.16865524 -5.81422316 [224,] -9.40671198 -11.16865524 [225,] -0.41753962 -9.40671198 [226,] -4.30494468 -0.41753962 [227,] 3.42863303 -4.30494468 [228,] -2.33677970 3.42863303 [229,] -3.61982793 -2.33677970 [230,] -9.42021978 -3.61982793 [231,] -5.25026020 -9.42021978 [232,] -1.06033120 -5.25026020 [233,] 3.30807495 -1.06033120 [234,] 4.65436673 3.30807495 [235,] -3.83875295 4.65436673 [236,] 3.28730568 -3.83875295 [237,] -7.17810546 3.28730568 [238,] -1.88213706 -7.17810546 [239,] 3.96680349 -1.88213706 [240,] 2.01668075 3.96680349 [241,] -1.76036244 2.01668075 [242,] 2.42426118 -1.76036244 [243,] -0.33786486 2.42426118 [244,] -0.41701823 -0.33786486 [245,] 1.26423963 -0.41701823 [246,] -0.54713168 1.26423963 [247,] -3.72502837 -0.54713168 [248,] 7.74658114 -3.72502837 [249,] -7.61926679 7.74658114 [250,] 1.41883129 -7.61926679 [251,] 4.50054460 1.41883129 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.74370386 -3.85278087 2 6.74323194 -2.74370386 3 -1.52145524 6.74323194 4 1.40892172 -1.52145524 5 3.95966210 1.40892172 6 2.46115045 3.95966210 7 -1.51591458 2.46115045 8 -5.57746578 -1.51591458 9 1.51642818 -5.57746578 10 -1.91723978 1.51642818 11 -5.06741067 -1.91723978 12 2.97464464 -5.06741067 13 -3.85091201 2.97464464 14 -1.98392619 -3.85091201 15 -2.07452297 -1.98392619 16 5.46168396 -2.07452297 17 3.45178803 5.46168396 18 -0.74128899 3.45178803 19 -6.67087455 -0.74128899 20 -2.00905260 -6.67087455 21 -4.68770078 -2.00905260 22 6.04857998 -4.68770078 23 6.57898264 6.04857998 24 5.86727055 6.57898264 25 -4.80909674 5.86727055 26 -1.20335612 -4.80909674 27 4.94504408 -1.20335612 28 -2.66291763 4.94504408 29 -2.82651365 -2.66291763 30 -2.70301698 -2.82651365 31 -5.33116321 -2.70301698 32 5.66487710 -5.33116321 33 -2.51851712 5.66487710 34 0.02974837 -2.51851712 35 2.52041509 0.02974837 36 0.08004236 2.52041509 37 6.49658883 0.08004236 38 -8.83488922 6.49658883 39 0.07467651 -8.83488922 40 -2.17594557 0.07467651 41 0.36516227 -2.17594557 42 -2.63488024 0.36516227 43 5.62472921 -2.63488024 44 -3.20411583 5.62472921 45 3.94345389 -3.20411583 46 3.02869809 3.94345389 47 -3.94087988 3.02869809 48 -4.20872402 -3.94087988 49 -1.52042426 -4.20872402 50 -4.53767109 -1.52042426 51 -2.89173918 -4.53767109 52 -6.22321615 -2.89173918 53 -4.49423589 -6.22321615 54 -3.77501483 -4.49423589 55 -0.95682775 -3.77501483 56 -5.45712319 -0.95682775 57 0.31967653 -5.45712319 58 2.86646987 0.31967653 59 -1.66758680 2.86646987 60 0.74163862 -1.66758680 61 6.28140133 0.74163862 62 3.21806956 6.28140133 63 -1.43822057 3.21806956 64 2.20623005 -1.43822057 65 3.78563402 2.20623005 66 6.06824492 3.78563402 67 -1.89080508 6.06824492 68 -2.42373308 -1.89080508 69 -0.94226659 -2.42373308 70 4.77880706 -0.94226659 71 -3.99740207 4.77880706 72 -2.55938876 -3.99740207 73 1.89475945 -2.55938876 74 -4.99563587 1.89475945 75 6.38867553 -4.99563587 76 -0.16221332 6.38867553 77 2.83098085 -0.16221332 78 -1.68099936 2.83098085 79 -6.01058474 -1.68099936 80 9.22654663 -6.01058474 81 8.88470318 9.22654663 82 -4.56691829 8.88470318 83 5.70358573 -4.56691829 84 -0.17629968 5.70358573 85 5.69086006 -0.17629968 86 -2.66853862 5.69086006 87 1.27555018 -2.66853862 88 -4.37681867 1.27555018 89 -0.07262775 -4.37681867 90 -1.20482625 -0.07262775 91 0.56047139 -1.20482625 92 -2.54098411 0.56047139 93 3.20537655 -2.54098411 94 -5.76198195 3.20537655 95 1.11853064 -5.76198195 96 -7.22865948 1.11853064 97 -4.78864894 -7.22865948 98 -0.03201518 -4.78864894 99 3.06764261 -0.03201518 100 2.70529180 3.06764261 101 0.48184404 2.70529180 102 2.80781511 0.48184404 103 5.12847319 2.80781511 104 1.51676978 5.12847319 105 0.20089741 1.51676978 106 -6.55550552 0.20089741 107 -3.94869164 -6.55550552 108 4.84132018 -3.94869164 109 0.35867218 4.84132018 110 0.46251366 0.35867218 111 -4.44792138 0.46251366 112 1.23522466 -4.44792138 113 1.43454189 1.23522466 114 6.57177812 1.43454189 115 0.27032672 6.57177812 116 3.22990397 0.27032672 117 -0.48701604 3.22990397 118 7.62411008 -0.48701604 119 4.63985912 7.62411008 120 7.54000755 4.63985912 121 2.76459355 7.54000755 122 0.61212986 2.76459355 123 -1.10932506 0.61212986 124 -1.66865976 -1.10932506 125 -3.46492832 -1.66865976 126 5.71852045 -3.46492832 127 8.40431499 5.71852045 128 2.14455897 8.40431499 129 -0.09578241 2.14455897 130 -0.22495989 -0.09578241 131 2.53161092 -0.22495989 132 -1.18122590 2.53161092 133 5.03423515 -1.18122590 134 8.35292724 5.03423515 135 0.26717143 8.35292724 136 5.15361246 0.26717143 137 3.35056925 5.15361246 138 4.49761508 3.35056925 139 -8.36935132 4.49761508 140 3.70989345 -8.36935132 141 -2.98748325 3.70989345 142 4.40308875 -2.98748325 143 4.01202028 4.40308875 144 1.00649828 4.01202028 145 1.67421640 1.00649828 146 -3.29006824 1.67421640 147 6.62875623 -3.29006824 148 -0.77669239 6.62875623 149 -1.48530885 -0.77669239 150 0.90061109 -1.48530885 151 -2.25679297 0.90061109 152 6.01604670 -2.25679297 153 -0.10709487 6.01604670 154 -0.53737885 -0.10709487 155 6.21895547 -0.53737885 156 2.61108726 6.21895547 157 -5.58282920 2.61108726 158 0.75238084 -5.58282920 159 3.63444546 0.75238084 160 -2.41356126 3.63444546 161 -3.10933225 -2.41356126 162 -2.47870477 -3.10933225 163 -0.20295292 -2.47870477 164 0.28494023 -0.20295292 165 1.26647465 0.28494023 166 4.05540390 1.26647465 167 1.27754676 4.05540390 168 -3.16475546 1.27754676 169 -3.67495163 -3.16475546 170 -8.66149228 -3.67495163 171 -7.51931356 -8.66149228 172 3.83959023 -7.51931356 173 0.32257550 3.83959023 174 3.93806198 0.32257550 175 -1.25804279 3.93806198 176 -2.85467583 -1.25804279 177 1.88092469 -2.85467583 178 1.82498386 1.88092469 179 -6.69334583 1.82498386 180 1.55062518 -6.69334583 181 -4.48358712 1.55062518 182 -4.78576956 -4.48358712 183 -4.79868221 -4.78576956 184 -0.42626135 -4.79868221 185 -2.57263289 -0.42626135 186 -3.88821800 -2.57263289 187 -1.55668052 -3.88821800 188 -2.15467481 -1.55668052 189 -2.12497867 -2.15467481 190 1.03246127 -2.12497867 191 7.87549228 1.03246127 192 -2.04125942 7.87549228 193 -1.94764349 -2.04125942 194 5.99799262 -1.94764349 195 2.75815486 5.99799262 196 4.90169496 2.75815486 197 -2.13416993 4.90169496 198 0.81044609 -2.13416993 199 5.61117211 0.81044609 200 -3.36883109 5.61117211 201 6.08990344 -3.36883109 202 1.24076212 6.08990344 203 -9.08840687 1.24076212 204 -0.93658262 -9.08840687 205 1.54090918 -0.93658262 206 10.00677252 1.54090918 207 6.00368701 10.00677252 208 -4.20414525 6.00368701 209 -3.82188965 -4.20414525 210 -5.71466023 -3.82188965 211 1.90189046 -5.71466023 212 4.10242879 1.90189046 213 -2.73419337 4.10242879 214 5.25784578 -2.73419337 215 6.36391329 5.25784578 216 2.09645922 6.36391329 217 -1.57253141 2.09645922 218 0.70212350 -1.57253141 219 -2.89762943 0.70212350 220 -8.45703269 -2.89762943 221 -4.62496378 -8.45703269 222 -5.81422316 -4.62496378 223 -11.16865524 -5.81422316 224 -9.40671198 -11.16865524 225 -0.41753962 -9.40671198 226 -4.30494468 -0.41753962 227 3.42863303 -4.30494468 228 -2.33677970 3.42863303 229 -3.61982793 -2.33677970 230 -9.42021978 -3.61982793 231 -5.25026020 -9.42021978 232 -1.06033120 -5.25026020 233 3.30807495 -1.06033120 234 4.65436673 3.30807495 235 -3.83875295 4.65436673 236 3.28730568 -3.83875295 237 -7.17810546 3.28730568 238 -1.88213706 -7.17810546 239 3.96680349 -1.88213706 240 2.01668075 3.96680349 241 -1.76036244 2.01668075 242 2.42426118 -1.76036244 243 -0.33786486 2.42426118 244 -0.41701823 -0.33786486 245 1.26423963 -0.41701823 246 -0.54713168 1.26423963 247 -3.72502837 -0.54713168 248 7.74658114 -3.72502837 249 -7.61926679 7.74658114 250 1.41883129 -7.61926679 251 4.50054460 1.41883129 > 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/7kit21353327359.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/8pete1353327359.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/9xu1a1353327359.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/10m8a11353327359.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/112d8h1353327359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12gr3o1353327359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13esrd1353327359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14gwtv1353327359.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/1544du1353327359.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/165zob1353327359.tab") + } > > try(system("convert tmp/1mxpn1353327359.ps tmp/1mxpn1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/29to31353327359.ps tmp/29to31353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/3r7rr1353327359.ps tmp/3r7rr1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/4bs1s1353327359.ps tmp/4bs1s1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/51qjx1353327359.ps tmp/51qjx1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/631tx1353327359.ps tmp/631tx1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/7kit21353327359.ps tmp/7kit21353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/8pete1353327359.ps tmp/8pete1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/9xu1a1353327359.ps tmp/9xu1a1353327359.png",intern=TRUE)) character(0) > try(system("convert tmp/10m8a11353327359.ps tmp/10m8a11353327359.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 23.746 2.056 25.954