R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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+ ,4 + ,90 + ,48 + ,2085 + ,21.7 + ,80 + ,0 + ,1 + ,31.9 + ,4 + ,89 + ,71 + ,1925 + ,14 + ,79 + ,0 + ,1) + ,dim=c(9 + ,392) + ,dimnames=list(c('MPG' + ,'Cylin' + ,'Displ' + ,'HP' + ,'Weight' + ,'Accel' + ,'Year' + ,'OriginJapan' + ,'OriginEurope') + ,1:392)) > y <- array(NA,dim=c(9,392),dimnames=list(c('MPG','Cylin','Displ','HP','Weight','Accel','Year','OriginJapan','OriginEurope'),1:392)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x MPG Cylin Displ HP Weight Accel Year OriginJapan OriginEurope 1 13.0 8 360.0 175 3821 11.0 73 0 0 2 15.0 8 390.0 190 3850 8.5 70 0 0 3 17.0 8 304.0 150 3672 11.5 72 0 0 4 19.4 6 232.0 90 3210 17.2 78 0 0 5 24.3 4 151.0 90 3003 20.1 80 0 0 6 18.1 6 258.0 120 3410 15.1 78 0 0 7 20.2 6 232.0 90 3265 18.2 79 0 0 8 18.0 6 232.0 100 2789 15.0 73 0 0 9 19.0 6 232.0 100 2634 13.0 71 0 0 10 20.0 6 232.0 100 2914 16.0 75 0 0 11 21.0 6 199.0 90 2648 15.0 70 0 0 12 18.0 6 199.0 97 2774 15.5 70 0 0 13 18.0 6 232.0 100 2945 16.0 73 0 0 14 19.0 6 232.0 100 2901 16.0 74 0 0 15 22.5 6 232.0 90 3085 17.6 76 0 0 16 18.0 6 258.0 110 2962 13.5 71 0 0 17 14.0 8 304.0 150 3672 11.5 73 0 0 18 15.0 6 258.0 110 3730 19.0 75 0 0 19 15.5 8 304.0 120 3962 13.9 76 0 0 20 16.0 6 258.0 110 3632 18.0 74 0 0 21 18.0 6 232.0 100 3288 15.5 71 0 0 22 14.0 8 304.0 150 4257 15.5 74 0 0 23 15.0 8 304.0 150 3892 12.5 72 0 0 24 19.0 6 232.0 90 3211 17.0 75 0 0 25 17.5 6 258.0 95 3193 17.8 76 0 0 26 16.0 8 304.0 150 3433 12.0 70 0 0 27 27.4 4 121.0 80 2670 15.0 79 0 0 28 24.0 4 107.0 90 2430 14.5 70 0 1 29 20.0 4 114.0 91 2582 14.0 73 0 1 30 23.0 4 115.0 95 2694 15.0 75 0 1 31 34.3 4 97.0 78 2188 15.8 80 0 1 32 20.3 5 131.0 103 2830 15.9 78 0 1 33 36.4 5 121.0 67 2950 19.9 80 0 1 34 29.0 4 98.0 83 2219 16.5 74 0 1 35 26.0 4 121.0 113 2234 12.5 70 0 1 36 21.5 4 121.0 110 2600 12.8 77 0 1 37 17.0 6 231.0 110 3907 21.0 75 0 0 38 22.4 6 231.0 110 3415 15.8 81 0 0 39 13.0 8 350.0 175 4100 13.0 73 0 0 40 25.0 6 181.0 110 2945 16.4 82 0 0 41 13.0 8 350.0 150 4699 14.5 74 0 0 42 20.6 6 231.0 105 3380 15.8 78 0 0 43 12.0 8 455.0 225 4951 11.0 73 0 0 44 14.0 8 455.0 225 3086 10.0 70 0 0 45 16.9 8 350.0 155 4360 14.9 79 0 0 46 13.0 8 350.0 155 4502 13.5 72 0 0 47 30.0 4 111.0 80 2155 14.8 77 0 0 48 17.7 6 231.0 165 3445 13.4 78 0 0 49 21.0 6 231.0 110 3039 15.0 75 0 0 50 20.5 6 231.0 105 3425 16.9 77 0 0 51 26.6 4 151.0 84 2635 16.4 81 0 0 52 15.0 8 350.0 165 3693 11.5 70 0 0 53 28.4 4 151.0 90 2670 16.0 79 0 0 54 23.0 8 350.0 125 3900 17.4 79 0 0 55 16.5 8 350.0 180 4380 12.1 76 0 0 56 25.0 4 140.0 92 2572 14.9 76 0 0 57 16.0 6 250.0 105 3897 18.5 75 0 0 58 15.0 8 350.0 145 4440 14.0 75 0 0 59 27.0 4 151.0 90 2950 17.3 82 0 0 60 13.0 8 400.0 150 4464 12.0 73 0 0 61 17.0 8 305.0 130 3840 15.4 79 0 0 62 17.5 8 305.0 145 3880 12.5 77 0 0 63 28.0 4 112.0 88 2605 19.6 82 0 0 64 34.0 4 112.0 88 2395 18.0 82 0 0 65 27.0 4 112.0 88 2640 18.6 82 0 0 66 13.0 8 307.0 130 4098 14.0 72 0 0 67 17.0 6 250.0 100 3329 15.5 71 0 0 68 18.0 8 307.0 130 3504 12.0 70 0 0 69 16.0 6 250.0 100 3781 17.0 74 0 0 70 17.5 8 305.0 140 4215 13.0 76 0 0 71 29.0 4 85.0 52 2035 22.2 76 0 0 72 30.0 4 98.0 68 2155 16.5 78 0 0 73 30.5 4 98.0 63 2051 17.0 77 0 0 74 32.1 4 98.0 70 2120 15.5 80 0 0 75 23.5 6 173.0 110 2725 12.6 81 0 0 76 28.0 4 151.0 90 2678 16.5 80 0 0 77 28.8 6 173.0 115 2595 11.3 79 0 0 78 17.5 6 250.0 110 3520 16.4 77 0 0 79 11.0 8 400.0 150 4997 14.0 73 0 0 80 13.0 8 350.0 165 4274 12.0 72 0 0 81 14.0 8 350.0 165 4209 12.0 71 0 0 82 14.0 8 454.0 220 4354 9.0 70 0 0 83 13.0 8 350.0 145 3988 13.0 73 0 0 84 20.5 6 200.0 95 3155 18.2 78 0 0 85 19.2 8 267.0 125 3605 15.0 79 0 0 86 15.0 8 400.0 150 3761 9.5 70 0 0 87 15.5 8 350.0 170 4165 11.4 77 0 0 88 19.2 8 305.0 145 3425 13.2 78 0 0 89 15.0 8 350.0 145 4082 13.0 73 0 0 90 20.0 8 262.0 110 3221 13.5 75 0 0 91 15.0 6 250.0 100 3336 17.0 74 0 0 92 18.0 6 250.0 105 3459 16.0 75 0 0 93 22.0 6 250.0 105 3353 14.5 76 0 0 94 16.0 6 250.0 100 3278 18.0 73 0 0 95 20.0 4 140.0 90 2408 19.5 72 0 0 96 21.0 4 140.0 72 2401 19.5 73 0 0 97 25.0 4 140.0 75 2542 17.0 74 0 0 98 22.0 4 140.0 72 2408 19.0 71 0 0 99 28.0 4 140.0 90 2264 15.5 71 0 0 100 24.5 4 98.0 60 2164 22.1 76 0 0 101 13.0 8 350.0 145 4055 12.0 76 0 0 102 10.0 8 307.0 200 4376 15.0 70 0 0 103 31.0 4 119.0 82 2720 19.4 82 0 0 104 15.5 8 400.0 190 4325 12.2 77 0 0 105 26.0 4 156.0 92 2585 14.5 82 0 0 106 17.6 6 225.0 85 3465 16.6 81 0 0 107 18.5 8 360.0 150 3940 13.0 79 0 0 108 13.0 8 440.0 215 4735 11.0 73 0 0 109 13.0 8 400.0 190 4422 12.5 72 0 0 110 35.0 4 72.0 69 1613 18.0 71 1 0 111 23.9 4 119.0 97 2405 14.9 78 1 0 112 32.9 4 119.0 100 2615 14.8 81 1 0 113 31.8 4 85.0 65 2020 19.2 79 1 0 114 40.8 4 85.0 65 2110 19.2 80 1 0 115 37.0 4 85.0 65 1975 19.4 81 1 0 116 32.7 6 168.0 132 2910 11.4 80 1 0 117 37.2 4 86.0 65 2019 16.4 80 1 0 118 38.0 4 91.0 67 1995 16.2 82 1 0 119 27.2 4 119.0 97 2300 14.7 78 1 0 120 28.0 4 97.0 92 2288 17.0 72 1 0 121 37.0 4 119.0 92 2434 15.0 80 1 0 122 22.0 4 108.0 94 2379 16.5 73 1 0 123 24.0 4 119.0 97 2545 17.0 75 1 0 124 32.0 4 83.0 61 2003 19.0 74 1 0 125 22.0 6 146.0 97 2815 14.5 77 1 0 126 24.2 6 146.0 120 2930 13.8 81 1 0 127 32.0 4 85.0 70 1990 17.0 76 1 0 128 31.0 4 79.0 67 1950 19.0 74 1 0 129 39.4 4 85.0 70 2070 18.6 78 1 0 130 33.5 4 85.0 70 1945 16.8 77 1 0 131 27.0 4 97.0 88 2130 14.5 70 1 0 132 27.0 4 97.0 88 2130 14.5 71 1 0 133 29.0 4 135.0 84 2525 16.0 82 0 0 134 25.8 4 156.0 92 2620 14.4 81 0 0 135 18.6 6 225.0 110 3620 18.7 78 0 0 136 19.1 6 225.0 90 3381 18.7 80 0 0 137 20.6 6 225.0 110 3360 16.6 79 0 0 138 20.0 6 225.0 100 3651 17.7 76 0 0 139 15.0 8 383.0 170 3563 10.0 70 0 0 140 36.0 4 135.0 84 2370 13.0 82 0 0 141 26.0 4 98.0 79 2255 17.7 76 0 0 142 27.9 4 156.0 105 2800 14.4 80 0 0 143 28.0 4 90.0 75 2125 14.5 74 0 0 144 28.0 4 98.0 80 2164 15.0 72 0 0 145 25.0 4 97.5 80 2126 17.0 72 0 0 146 35.7 4 98.0 80 1915 14.4 79 0 0 147 33.5 4 98.0 83 2075 15.9 77 0 0 148 16.0 8 318.0 150 4190 13.0 76 0 0 149 15.0 8 318.0 150 3777 12.5 73 0 0 150 14.0 8 318.0 150 4457 13.5 74 0 0 151 13.0 8 318.0 150 3755 14.0 76 0 0 152 11.0 8 318.0 210 4382 13.5 70 0 0 153 15.0 8 318.0 150 3399 11.0 73 0 0 154 19.4 8 318.0 140 3735 13.2 78 0 0 155 17.5 8 318.0 140 4080 13.7 78 0 0 156 12.0 8 383.0 180 4955 11.5 71 0 0 157 15.5 8 318.0 145 4140 13.7 77 0 0 158 30.9 4 105.0 75 2230 14.5 78 0 0 159 32.0 4 135.0 84 2295 11.6 82 0 0 160 18.2 8 318.0 135 3830 15.2 79 0 0 161 26.0 4 98.0 90 2265 15.5 73 0 1 162 26.0 4 116.0 75 2246 14.0 74 0 1 163 30.0 4 88.0 76 2065 14.5 71 0 1 164 24.0 4 90.0 75 2108 15.5 74 0 1 165 29.0 4 68.0 49 1867 19.5 73 0 1 166 28.0 4 107.0 86 2464 15.5 76 0 1 167 37.3 4 91.0 69 2130 14.7 79 0 1 168 31.0 4 79.0 67 2000 16.0 74 0 1 169 12.0 8 400.0 167 4906 12.5 73 0 0 170 13.0 8 400.0 170 4746 12.0 71 0 0 171 15.5 8 351.0 142 4054 14.3 79 0 0 172 29.9 4 98.0 65 2380 20.7 81 0 0 173 34.4 4 98.0 65 2045 16.2 81 0 0 174 13.0 8 302.0 130 3870 15.0 76 0 0 175 10.0 8 360.0 215 4615 14.0 70 0 0 176 26.4 4 140.0 88 2870 18.1 80 0 0 177 20.2 6 200.0 85 2965 15.8 78 0 0 178 25.1 4 140.0 88 2720 15.4 78 0 0 179 22.3 4 140.0 88 2890 17.3 79 0 0 180 24.0 4 140.0 92 2865 16.4 82 0 0 181 36.1 4 98.0 66 1800 14.4 78 0 0 182 18.1 8 302.0 139 3205 11.2 78 0 0 183 14.0 8 351.0 153 4129 13.0 72 0 0 184 14.0 8 351.0 153 4154 13.5 71 0 0 185 15.0 8 429.0 198 4341 10.0 70 0 0 186 14.0 8 302.0 137 4042 14.5 73 0 0 187 14.5 8 351.0 152 4215 12.8 76 0 0 188 16.0 8 302.0 140 4141 14.0 74 0 0 189 13.0 8 302.0 140 4294 16.0 72 0 0 190 14.0 8 302.0 140 4638 16.0 74 0 0 191 18.5 6 250.0 98 3525 19.0 77 0 0 192 18.0 6 250.0 78 3574 21.0 76 0 0 193 20.2 6 200.0 88 3060 17.1 81 0 0 194 22.0 6 232.0 112 2835 14.7 82 0 0 195 13.0 8 351.0 158 4363 13.0 73 0 0 196 14.0 8 351.0 148 4657 13.5 75 0 0 197 17.6 8 302.0 129 3725 13.4 79 0 0 198 15.0 6 250.0 72 3158 19.5 75 0 0 199 18.0 6 250.0 88 3021 16.5 73 0 0 200 21.0 6 200.0 85 2587 16.0 70 0 0 201 24.0 6 200.0 81 3012 17.6 76 0 0 202 18.0 6 250.0 88 3139 14.5 71 0 0 203 27.0 4 140.0 86 2790 15.6 82 0 0 204 13.0 8 302.0 129 3169 12.0 75 0 0 205 25.5 4 140.0 89 2755 15.8 77 0 0 206 18.0 6 171.0 97 2984 14.5 75 0 0 207 19.0 4 122.0 85 2310 18.5 73 0 0 208 23.0 4 140.0 83 2639 17.0 75 0 0 209 26.0 4 122.0 80 2451 16.5 74 0 0 210 26.5 4 140.0 72 2565 13.6 76 0 0 211 22.0 4 122.0 86 2395 16.0 72 0 0 212 21.0 4 122.0 86 2226 16.5 72 0 0 213 28.0 4 120.0 79 2625 18.6 82 0 0 214 16.0 8 351.0 149 4335 14.5 77 0 0 215 17.0 8 302.0 140 3449 10.5 70 0 0 216 19.0 6 250.0 88 3302 15.5 71 0 0 217 9.0 8 304.0 193 4732 18.5 70 0 0 218 32.4 4 107.0 72 2290 17.0 80 1 0 219 36.0 4 107.0 75 2205 14.5 82 1 0 220 31.5 4 98.0 68 2045 18.5 77 1 0 221 29.5 4 98.0 68 2135 16.6 78 1 0 222 24.0 4 120.0 97 2489 15.0 74 1 0 223 33.0 4 91.0 53 1795 17.4 76 1 0 224 38.0 4 91.0 67 1965 15.0 82 1 0 225 32.0 4 91.0 67 1965 15.7 82 1 0 226 35.1 4 81.0 60 1760 16.1 81 1 0 227 44.6 4 91.0 67 1850 13.8 80 1 0 228 33.0 4 91.0 53 1795 17.5 75 1 0 229 36.1 4 91.0 60 1800 16.4 78 1 0 230 33.7 4 107.0 75 2210 14.4 81 1 0 231 34.1 4 86.0 65 1975 15.2 79 1 0 232 18.0 3 70.0 90 2124 13.5 73 1 0 233 31.3 4 120.0 75 2542 17.5 80 1 0 234 31.6 4 120.0 74 2635 18.3 81 1 0 235 46.6 4 86.0 65 2110 17.9 80 1 0 236 34.1 4 91.0 68 1985 16.0 81 1 0 237 31.0 4 91.0 68 1970 17.6 82 1 0 238 37.0 4 91.0 68 2025 18.2 82 1 0 239 32.8 4 78.0 52 1985 19.4 78 1 0 240 21.5 3 80.0 110 2720 13.5 77 1 0 241 23.7 3 70.0 100 2420 12.5 80 1 0 242 19.0 3 70.0 97 2330 13.5 72 1 0 243 25.4 5 183.0 77 3530 20.1 79 0 1 244 30.0 4 146.0 67 3250 21.8 80 0 1 245 16.5 6 168.0 120 3820 16.7 76 0 1 246 23.0 4 122.0 86 2220 14.0 71 0 0 247 21.0 6 155.0 107 2472 14.0 73 0 0 248 15.0 8 302.0 130 4295 14.9 77 0 0 249 16.5 8 351.0 138 3955 13.2 79 0 0 250 36.0 4 98.0 70 2125 17.3 82 0 0 251 11.0 8 429.0 208 4633 11.0 72 0 0 252 12.0 8 429.0 198 4952 11.5 73 0 0 253 15.0 6 250.0 72 3432 21.0 75 0 0 254 20.2 8 302.0 139 3570 12.8 78 0 0 255 20.8 6 200.0 85 3070 16.7 78 0 0 256 19.8 6 200.0 85 2990 18.2 79 0 0 257 36.0 4 120.0 88 2160 14.5 82 1 0 258 38.0 6 262.0 85 3015 17.0 82 0 0 259 26.6 8 350.0 105 3725 19.0 81 0 0 260 19.9 8 260.0 110 3365 15.5 78 0 0 261 23.9 8 260.0 90 3420 22.2 79 0 0 262 17.0 8 260.0 110 4060 19.0 77 0 0 263 12.0 8 350.0 160 4456 13.5 72 0 0 264 11.0 8 350.0 180 3664 11.0 73 0 0 265 26.8 6 173.0 115 2700 12.9 79 0 0 266 23.8 4 151.0 85 2855 17.6 78 0 0 267 12.0 8 350.0 180 4499 12.5 73 0 0 268 25.0 4 116.0 81 2220 16.9 76 0 1 269 28.0 4 116.0 90 2123 14.0 71 0 1 270 24.0 4 116.0 75 2158 15.5 73 0 1 271 26.0 4 97.0 78 2300 14.5 74 0 1 272 30.0 4 79.0 70 2074 19.5 71 0 1 273 19.0 4 120.0 88 3270 21.9 76 0 1 274 23.0 4 120.0 88 2957 17.0 75 0 1 275 25.0 4 110.0 87 2672 17.5 70 0 1 276 27.2 4 141.0 71 3190 24.8 79 0 1 277 21.0 4 120.0 87 2979 19.5 72 0 1 278 28.1 4 141.0 80 3230 20.4 81 0 1 279 16.2 6 163.0 133 3410 15.8 78 0 1 280 14.0 8 340.0 160 3609 8.0 70 0 0 281 25.5 4 122.0 96 2300 15.5 77 0 0 282 39.0 4 86.0 64 1875 16.4 81 0 0 283 26.0 4 91.0 70 1955 20.5 71 0 0 284 13.0 8 360.0 170 4654 13.0 73 0 0 285 20.0 6 198.0 95 3102 16.5 74 0 0 286 22.0 6 198.0 95 2833 15.5 70 0 0 287 23.0 6 198.0 95 2904 16.0 73 0 0 288 18.0 6 225.0 95 3785 19.0 75 0 0 289 14.0 8 318.0 150 4237 14.5 73 0 0 290 14.0 8 318.0 150 4096 13.0 71 0 0 291 14.0 8 440.0 215 4312 8.5 70 0 0 292 15.0 8 318.0 150 4135 13.5 72 0 0 293 16.0 8 318.0 150 4498 14.5 75 0 0 294 34.2 4 105.0 70 2200 13.2 79 0 0 295 34.7 4 105.0 63 2215 14.9 81 0 0 296 38.0 4 105.0 63 2125 14.7 82 0 0 297 34.5 4 105.0 70 2150 14.9 79 0 0 298 27.2 4 135.0 84 2490 15.7 81 0 0 299 30.0 4 135.0 84 2385 12.9 81 0 0 300 23.2 4 156.0 105 2745 16.7 78 0 0 301 18.0 8 318.0 150 3436 11.0 70 0 0 302 16.0 6 225.0 105 3439 15.5 71 0 0 303 14.0 8 318.0 150 4077 14.0 72 0 0 304 18.0 6 225.0 105 3613 16.5 74 0 0 305 18.0 6 225.0 105 3121 16.5 73 0 0 306 22.0 6 225.0 100 3233 15.4 76 0 0 307 19.0 6 225.0 95 3264 16.0 75 0 0 308 20.5 6 225.0 100 3430 17.2 78 0 0 309 19.0 6 225.0 100 3630 17.7 77 0 0 310 13.0 8 318.0 150 3940 13.2 76 0 0 311 23.0 4 140.0 78 2592 18.5 75 0 0 312 14.0 8 400.0 175 4385 12.0 72 0 0 313 14.0 8 455.0 225 4425 10.0 70 0 0 314 16.0 8 400.0 170 4668 11.5 75 0 0 315 14.0 8 400.0 175 4464 11.5 71 0 0 316 19.0 6 250.0 100 3282 15.0 71 0 0 317 16.0 8 400.0 230 4278 9.5 73 0 0 318 16.0 8 400.0 180 4220 11.1 77 0 0 319 31.0 4 112.0 85 2575 16.2 82 0 0 320 21.5 6 231.0 115 3245 15.4 79 0 0 321 27.0 4 151.0 90 2735 18.0 82 0 0 322 33.5 4 151.0 90 2556 13.2 79 0 0 323 19.2 6 231.0 105 3535 19.2 78 0 0 324 13.0 8 400.0 175 5140 12.0 71 0 0 325 24.5 4 151.0 88 2740 16.0 77 0 0 326 18.5 6 250.0 110 3645 16.2 76 0 0 327 26.0 4 96.0 69 2189 18.0 72 0 1 328 27.0 4 101.0 83 2202 15.3 76 0 1 329 36.0 4 79.0 58 1825 18.6 77 0 1 330 25.0 4 104.0 95 2375 17.5 70 0 1 331 21.6 4 121.0 115 2795 15.7 78 0 1 332 24.0 4 121.0 110 2660 14.0 73 0 1 333 25.0 4 121.0 115 2671 13.5 75 0 1 334 26.0 4 108.0 93 2391 15.5 74 1 0 335 32.3 4 97.0 67 2065 17.8 81 1 0 336 30.0 4 97.0 67 1985 16.4 77 1 0 337 33.8 4 97.0 67 2145 18.0 80 1 0 338 20.0 4 97.0 88 2279 19.0 73 1 0 339 32.0 4 144.0 96 2665 13.9 82 1 0 340 21.1 4 134.0 95 2515 14.8 78 1 0 341 28.0 4 97.0 75 2155 16.4 76 1 0 342 29.0 4 97.0 75 2171 16.0 75 1 0 343 32.2 4 108.0 75 2265 15.2 80 1 0 344 32.4 4 108.0 75 2350 16.8 81 1 0 345 34.0 4 108.0 70 2245 16.9 82 1 0 346 31.0 4 71.0 65 1773 19.0 71 1 0 347 32.0 4 71.0 65 1836 21.0 74 1 0 348 27.0 4 97.0 88 2100 16.5 72 1 0 349 26.0 4 97.0 75 2265 18.2 77 1 0 350 38.1 4 89.0 60 1968 18.8 80 1 0 351 24.0 4 134.0 96 2702 13.5 75 1 0 352 25.0 4 113.0 95 2228 14.0 71 1 0 353 27.5 4 134.0 95 2560 14.2 78 1 0 354 31.0 4 76.0 52 1649 16.5 74 1 0 355 24.0 4 113.0 95 2278 15.5 72 1 0 356 29.8 4 134.0 90 2711 15.5 80 1 0 357 24.0 4 113.0 95 2372 15.0 70 1 0 358 25.4 6 168.0 116 2900 12.6 81 1 0 359 19.0 6 156.0 108 2930 15.5 76 1 0 360 20.0 6 156.0 122 2807 13.5 73 1 0 361 39.1 4 79.0 58 1755 16.9 81 1 0 362 37.7 4 89.0 62 2050 17.3 81 1 0 363 23.0 4 120.0 97 2506 14.5 72 1 0 364 35.0 4 122.0 88 2500 15.1 80 0 1 365 29.8 4 89.0 62 1845 15.3 80 0 1 366 26.0 4 97.0 46 1835 20.5 70 0 1 367 22.0 4 121.0 76 2511 18.0 72 0 1 368 25.0 4 90.0 71 2223 16.5 75 0 1 369 26.0 4 79.0 67 1963 15.5 74 0 1 370 30.5 4 97.0 78 2190 14.1 77 0 1 371 33.0 4 105.0 74 2190 14.2 81 0 1 372 27.0 4 97.0 60 1834 19.0 71 0 1 373 29.0 4 90.0 70 1937 14.0 75 0 1 374 29.5 4 97.0 71 1825 12.2 76 0 1 375 29.0 4 97.0 78 1940 14.5 77 0 1 376 43.1 4 90.0 48 1985 21.5 78 0 1 377 36.0 4 105.0 74 1980 15.3 82 0 1 378 31.5 4 89.0 71 1990 14.9 78 0 1 379 26.0 4 97.0 46 1950 21.0 73 0 1 380 23.0 4 97.0 54 2254 23.5 72 0 1 381 19.0 4 121.0 112 2868 15.5 73 0 1 382 18.0 4 121.0 112 2933 14.5 72 0 1 383 22.0 4 121.0 98 2945 14.5 75 0 1 384 20.0 4 130.0 102 3150 15.7 76 0 1 385 17.0 6 163.0 125 3140 13.6 78 0 1 386 30.7 6 145.0 76 3160 19.6 81 0 1 387 43.4 4 90.0 48 2335 23.7 80 0 1 388 44.0 4 97.0 52 2130 24.6 82 0 1 389 29.0 4 90.0 70 1937 14.2 76 0 1 390 41.5 4 98.0 76 2144 14.7 80 0 1 391 44.3 4 90.0 48 2085 21.7 80 0 1 392 31.9 4 89.0 71 1925 14.0 79 0 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Cylin Displ HP Weight -17.95460 -0.48971 0.02398 -0.01818 -0.00671 Accel Year OriginJapan OriginEurope 0.07910 0.77703 2.85323 2.63000 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -9.00949 -2.07848 -0.09824 1.98558 13.36078 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.795e+01 4.677e+00 -3.839 0.000145 *** Cylin -4.897e-01 3.212e-01 -1.524 0.128215 Displ 2.398e-02 7.653e-03 3.133 0.001863 ** HP -1.818e-02 1.371e-02 -1.326 0.185488 Weight -6.710e-03 6.551e-04 -10.243 < 2e-16 *** Accel 7.910e-02 9.822e-02 0.805 0.421101 Year 7.770e-01 5.178e-02 15.005 < 2e-16 *** OriginJapan 2.853e+00 5.527e-01 5.162 3.93e-07 *** OriginEurope 2.630e+00 5.664e-01 4.643 4.72e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.307 on 383 degrees of freedom Multiple R-squared: 0.8242, Adjusted R-squared: 0.8205 F-statistic: 224.5 on 8 and 383 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,] 2.392043e-01 4.784086e-01 0.7607957 [2,] 1.274502e-01 2.549003e-01 0.8725498 [3,] 5.986351e-02 1.197270e-01 0.9401365 [4,] 8.135980e-02 1.627196e-01 0.9186402 [5,] 4.589479e-02 9.178959e-02 0.9541052 [6,] 2.983529e-02 5.967058e-02 0.9701647 [7,] 3.810732e-02 7.621464e-02 0.9618927 [8,] 2.010918e-02 4.021835e-02 0.9798908 [9,] 1.023405e-02 2.046811e-02 0.9897659 [10,] 5.260917e-03 1.052183e-02 0.9947391 [11,] 2.542790e-03 5.085581e-03 0.9974572 [12,] 1.178557e-03 2.357115e-03 0.9988214 [13,] 5.294318e-04 1.058864e-03 0.9994706 [14,] 2.542635e-04 5.085271e-04 0.9997457 [15,] 1.230748e-04 2.461497e-04 0.9998769 [16,] 5.652021e-05 1.130404e-04 0.9999435 [17,] 2.283488e-05 4.566977e-05 0.9999772 [18,] 8.792428e-05 1.758486e-04 0.9999121 [19,] 4.010277e-05 8.020555e-05 0.9999599 [20,] 1.346142e-03 2.692284e-03 0.9986539 [21,] 5.479964e-03 1.095993e-02 0.9945200 [22,] 2.413182e-01 4.826364e-01 0.7586818 [23,] 2.111456e-01 4.222912e-01 0.7888544 [24,] 1.875641e-01 3.751281e-01 0.8124359 [25,] 2.371661e-01 4.743321e-01 0.7628339 [26,] 1.951037e-01 3.902073e-01 0.8048963 [27,] 1.614877e-01 3.229755e-01 0.8385123 [28,] 1.291616e-01 2.583232e-01 0.8708384 [29,] 1.058407e-01 2.116814e-01 0.8941593 [30,] 9.405960e-02 1.881192e-01 0.9059404 [31,] 7.372293e-02 1.474459e-01 0.9262771 [32,] 9.533063e-02 1.906613e-01 0.9046694 [33,] 8.199565e-02 1.639913e-01 0.9180043 [34,] 6.403574e-02 1.280715e-01 0.9359643 [35,] 5.178934e-02 1.035787e-01 0.9482107 [36,] 7.211002e-02 1.442200e-01 0.9278900 [37,] 6.591986e-02 1.318397e-01 0.9340801 [38,] 5.245489e-02 1.049098e-01 0.9475451 [39,] 4.079256e-02 8.158512e-02 0.9592074 [40,] 3.224471e-02 6.448943e-02 0.9677553 [41,] 2.607280e-02 5.214561e-02 0.9739272 [42,] 2.980846e-02 5.961693e-02 0.9701915 [43,] 3.735406e-02 7.470813e-02 0.9626459 [44,] 3.272459e-02 6.544917e-02 0.9672754 [45,] 2.717578e-02 5.435157e-02 0.9728242 [46,] 2.114026e-02 4.228052e-02 0.9788597 [47,] 1.635694e-02 3.271387e-02 0.9836431 [48,] 1.272746e-02 2.545491e-02 0.9872725 [49,] 9.446616e-03 1.889323e-02 0.9905534 [50,] 8.663629e-03 1.732726e-02 0.9913364 [51,] 6.348638e-03 1.269728e-02 0.9936514 [52,] 4.709310e-03 9.418620e-03 0.9952907 [53,] 9.157453e-03 1.831491e-02 0.9908425 [54,] 7.038270e-03 1.407654e-02 0.9929617 [55,] 5.217783e-03 1.043557e-02 0.9947822 [56,] 3.815724e-03 7.631449e-03 0.9961843 [57,] 4.033667e-03 8.067334e-03 0.9959663 [58,] 2.986223e-03 5.972446e-03 0.9970138 [59,] 2.415052e-03 4.830103e-03 0.9975849 [60,] 2.037627e-03 4.075254e-03 0.9979624 [61,] 1.760526e-03 3.521052e-03 0.9982395 [62,] 1.658785e-03 3.317570e-03 0.9983412 [63,] 1.474523e-03 2.949047e-03 0.9985255 [64,] 1.679456e-03 3.358911e-03 0.9983205 [65,] 1.312250e-03 2.624499e-03 0.9986878 [66,] 1.229558e-03 2.459115e-03 0.9987704 [67,] 1.120811e-03 2.241621e-03 0.9988792 [68,] 8.973292e-04 1.794658e-03 0.9991027 [69,] 6.482139e-04 1.296428e-03 0.9993518 [70,] 5.391097e-04 1.078219e-03 0.9994609 [71,] 7.222239e-04 1.444448e-03 0.9992778 [72,] 5.659691e-04 1.131938e-03 0.9994340 [73,] 4.743084e-04 9.486168e-04 0.9995257 [74,] 3.550672e-04 7.101344e-04 0.9996449 [75,] 2.667450e-04 5.334899e-04 0.9997333 [76,] 1.910531e-04 3.821063e-04 0.9998089 [77,] 1.425358e-04 2.850717e-04 0.9998575 [78,] 1.009160e-04 2.018321e-04 0.9998991 [79,] 6.911097e-05 1.382219e-04 0.9999309 [80,] 9.695227e-05 1.939045e-04 0.9999030 [81,] 6.994426e-05 1.398885e-04 0.9999301 [82,] 5.378705e-05 1.075741e-04 0.9999462 [83,] 4.782088e-05 9.564176e-05 0.9999522 [84,] 4.074594e-05 8.149188e-05 0.9999593 [85,] 3.365507e-05 6.731014e-05 0.9999663 [86,] 2.595776e-05 5.191553e-05 0.9999740 [87,] 1.794679e-05 3.589357e-05 0.9999821 [88,] 4.182767e-05 8.365534e-05 0.9999582 [89,] 3.293827e-05 6.587653e-05 0.9999671 [90,] 3.789273e-05 7.578546e-05 0.9999621 [91,] 2.655082e-05 5.310164e-05 0.9999734 [92,] 2.933649e-05 5.867299e-05 0.9999707 [93,] 2.034469e-05 4.068937e-05 0.9999797 [94,] 2.129796e-05 4.259592e-05 0.9999787 [95,] 5.307717e-05 1.061543e-04 0.9999469 [96,] 3.838946e-05 7.677892e-05 0.9999616 [97,] 3.228657e-05 6.457314e-05 0.9999677 [98,] 2.377336e-05 4.754673e-05 0.9999762 [99,] 2.031686e-05 4.063371e-05 0.9999797 [100,] 2.029561e-04 4.059123e-04 0.9997970 [101,] 1.700227e-04 3.400454e-04 0.9998300 [102,] 1.253381e-04 2.506762e-04 0.9998747 [103,] 6.127349e-04 1.225470e-03 0.9993873 [104,] 4.868324e-04 9.736648e-04 0.9995132 [105,] 5.130028e-04 1.026006e-03 0.9994870 [106,] 4.441699e-04 8.883398e-04 0.9995558 [107,] 3.511274e-04 7.022548e-04 0.9996489 [108,] 6.589220e-04 1.317844e-03 0.9993411 [109,] 5.317026e-04 1.063405e-03 0.9994683 [110,] 7.231117e-04 1.446223e-03 0.9992769 [111,] 1.845807e-03 3.691613e-03 0.9981542 [112,] 2.389557e-03 4.779113e-03 0.9976104 [113,] 2.012350e-03 4.024701e-03 0.9979876 [114,] 3.704832e-03 7.409664e-03 0.9962952 [115,] 5.405426e-03 1.081085e-02 0.9945946 [116,] 4.341200e-03 8.682400e-03 0.9956588 [117,] 3.495037e-03 6.990073e-03 0.9965050 [118,] 8.913401e-03 1.782680e-02 0.9910866 [119,] 7.381690e-03 1.476338e-02 0.9926183 [120,] 6.192243e-03 1.238449e-02 0.9938078 [121,] 5.139382e-03 1.027876e-02 0.9948606 [122,] 4.130023e-03 8.260046e-03 0.9958700 [123,] 3.751231e-03 7.502462e-03 0.9962488 [124,] 3.124931e-03 6.249862e-03 0.9968751 [125,] 3.645860e-03 7.291721e-03 0.9963541 [126,] 3.082558e-03 6.165117e-03 0.9969174 [127,] 2.557967e-03 5.115934e-03 0.9974420 [128,] 2.021923e-03 4.043846e-03 0.9979781 [129,] 3.731485e-03 7.462970e-03 0.9962685 [130,] 2.952461e-03 5.904923e-03 0.9970475 [131,] 2.433295e-03 4.866591e-03 0.9975667 [132,] 2.166551e-03 4.333102e-03 0.9978334 [133,] 2.425595e-03 4.851190e-03 0.9975744 [134,] 1.917099e-03 3.834197e-03 0.9980829 [135,] 2.782427e-03 5.564853e-03 0.9972176 [136,] 4.101462e-03 8.202924e-03 0.9958985 [137,] 3.311322e-03 6.622643e-03 0.9966887 [138,] 2.628480e-03 5.256961e-03 0.9973715 [139,] 2.255503e-03 4.511005e-03 0.9977445 [140,] 2.969926e-03 5.939852e-03 0.9970301 [141,] 2.663098e-03 5.326196e-03 0.9973369 [142,] 2.505043e-03 5.010085e-03 0.9974950 [143,] 1.998978e-03 3.997956e-03 0.9980010 [144,] 1.573789e-03 3.147579e-03 0.9984262 [145,] 1.920176e-03 3.840353e-03 0.9980798 [146,] 1.518110e-03 3.036219e-03 0.9984819 [147,] 1.376514e-03 2.753028e-03 0.9986235 [148,] 1.078551e-03 2.157102e-03 0.9989214 [149,] 8.885094e-04 1.777019e-03 0.9991115 [150,] 6.843986e-04 1.368797e-03 0.9993156 [151,] 5.470638e-04 1.094128e-03 0.9994529 [152,] 6.754289e-04 1.350858e-03 0.9993246 [153,] 7.454913e-04 1.490983e-03 0.9992545 [154,] 6.043937e-04 1.208787e-03 0.9993956 [155,] 4.727251e-04 9.454503e-04 0.9995273 [156,] 9.514392e-04 1.902878e-03 0.9990486 [157,] 8.657326e-04 1.731465e-03 0.9991343 [158,] 7.613599e-04 1.522720e-03 0.9992386 [159,] 8.329645e-04 1.665929e-03 0.9991670 [160,] 8.519334e-04 1.703867e-03 0.9991481 [161,] 6.770352e-04 1.354070e-03 0.9993230 [162,] 6.465629e-04 1.293126e-03 0.9993534 [163,] 7.504774e-04 1.500955e-03 0.9992495 [164,] 6.487089e-04 1.297418e-03 0.9993513 [165,] 4.988230e-04 9.976459e-04 0.9995012 [166,] 5.696302e-04 1.139260e-03 0.9994304 [167,] 4.491814e-04 8.983628e-04 0.9995508 [168,] 4.571880e-04 9.143759e-04 0.9995428 [169,] 5.155951e-04 1.031190e-03 0.9994844 [170,] 7.903040e-04 1.580608e-03 0.9992097 [171,] 9.992436e-04 1.998487e-03 0.9990008 [172,] 7.942577e-04 1.588515e-03 0.9992057 [173,] 6.681089e-04 1.336218e-03 0.9993319 [174,] 7.562510e-04 1.512502e-03 0.9992437 [175,] 5.858286e-04 1.171657e-03 0.9994142 [176,] 4.666830e-04 9.333659e-04 0.9995333 [177,] 4.043464e-04 8.086927e-04 0.9995957 [178,] 3.323140e-04 6.646281e-04 0.9996677 [179,] 3.389175e-04 6.778350e-04 0.9996611 [180,] 2.989113e-04 5.978226e-04 0.9997011 [181,] 2.680624e-04 5.361247e-04 0.9997319 [182,] 4.592195e-04 9.184390e-04 0.9995408 [183,] 9.614206e-04 1.922841e-03 0.9990386 [184,] 7.537926e-04 1.507585e-03 0.9992462 [185,] 6.313416e-04 1.262683e-03 0.9993687 [186,] 5.946497e-04 1.189299e-03 0.9994054 [187,] 1.687846e-03 3.375693e-03 0.9983122 [188,] 1.807272e-03 3.614545e-03 0.9981927 [189,] 1.438914e-03 2.877829e-03 0.9985611 [190,] 1.245395e-03 2.490791e-03 0.9987546 [191,] 1.022838e-03 2.045676e-03 0.9989772 [192,] 8.481868e-04 1.696374e-03 0.9991518 [193,] 3.202779e-03 6.405559e-03 0.9967972 [194,] 2.588076e-03 5.176152e-03 0.9974119 [195,] 2.729619e-03 5.459238e-03 0.9972704 [196,] 4.330893e-03 8.661785e-03 0.9956691 [197,] 3.636776e-03 7.273552e-03 0.9963632 [198,] 3.089779e-03 6.179557e-03 0.9969102 [199,] 2.498580e-03 4.997161e-03 0.9975014 [200,] 2.099080e-03 4.198159e-03 0.9979009 [201,] 2.150272e-03 4.300545e-03 0.9978497 [202,] 1.811065e-03 3.622130e-03 0.9981889 [203,] 1.486885e-03 2.973770e-03 0.9985131 [204,] 1.254809e-03 2.509618e-03 0.9987452 [205,] 1.014255e-03 2.028510e-03 0.9989857 [206,] 1.127570e-03 2.255139e-03 0.9988724 [207,] 8.824700e-04 1.764940e-03 0.9991175 [208,] 7.194056e-04 1.438811e-03 0.9992806 [209,] 5.573295e-04 1.114659e-03 0.9994427 [210,] 5.137112e-04 1.027422e-03 0.9994863 [211,] 5.544832e-04 1.108966e-03 0.9994455 [212,] 4.305309e-04 8.610618e-04 0.9995695 [213,] 3.758493e-04 7.516985e-04 0.9996242 [214,] 4.330842e-04 8.661683e-04 0.9995669 [215,] 3.476087e-04 6.952175e-04 0.9996524 [216,] 2.749319e-03 5.498638e-03 0.9972507 [217,] 2.242128e-03 4.484255e-03 0.9977579 [218,] 1.965175e-03 3.930351e-03 0.9980348 [219,] 1.574188e-03 3.148376e-03 0.9984258 [220,] 1.272248e-03 2.544496e-03 0.9987278 [221,] 1.044789e-02 2.089578e-02 0.9895521 [222,] 8.579549e-03 1.715910e-02 0.9914205 [223,] 7.014414e-03 1.402883e-02 0.9929856 [224,] 1.192581e-01 2.385162e-01 0.8807419 [225,] 1.058743e-01 2.117485e-01 0.8941257 [226,] 1.235451e-01 2.470901e-01 0.8764549 [227,] 1.128589e-01 2.257177e-01 0.8871411 [228,] 9.927792e-02 1.985558e-01 0.9007221 [229,] 1.284392e-01 2.568784e-01 0.8715608 [230,] 1.870488e-01 3.740976e-01 0.8129512 [231,] 2.361098e-01 4.722197e-01 0.7638902 [232,] 2.163535e-01 4.327069e-01 0.7836465 [233,] 2.111613e-01 4.223227e-01 0.7888387 [234,] 1.996252e-01 3.992504e-01 0.8003748 [235,] 1.792460e-01 3.584920e-01 0.8207540 [236,] 1.623197e-01 3.246394e-01 0.8376803 [237,] 1.444764e-01 2.889528e-01 0.8555236 [238,] 1.631080e-01 3.262159e-01 0.8368920 [239,] 1.685586e-01 3.371172e-01 0.8314414 [240,] 1.513615e-01 3.027229e-01 0.8486385 [241,] 1.416081e-01 2.832162e-01 0.8583919 [242,] 2.308402e-01 4.616804e-01 0.7691598 [243,] 2.110354e-01 4.220709e-01 0.7889646 [244,] 2.205206e-01 4.410412e-01 0.7794794 [245,] 2.995127e-01 5.990254e-01 0.7004873 [246,] 2.780740e-01 5.561480e-01 0.7219260 [247,] 4.129374e-01 8.258747e-01 0.5870626 [248,] 4.209559e-01 8.419119e-01 0.5790441 [249,] 4.164332e-01 8.328663e-01 0.5835668 [250,] 4.103692e-01 8.207384e-01 0.5896308 [251,] 3.829546e-01 7.659091e-01 0.6170454 [252,] 3.549732e-01 7.099464e-01 0.6450268 [253,] 4.383568e-01 8.767136e-01 0.5616432 [254,] 4.107747e-01 8.215494e-01 0.5892253 [255,] 3.932616e-01 7.865232e-01 0.6067384 [256,] 3.666428e-01 7.332856e-01 0.6333572 [257,] 4.057435e-01 8.114871e-01 0.5942565 [258,] 3.945594e-01 7.891188e-01 0.6054406 [259,] 4.065649e-01 8.131298e-01 0.5934351 [260,] 3.768985e-01 7.537971e-01 0.6231015 [261,] 3.980922e-01 7.961844e-01 0.6019078 [262,] 3.982529e-01 7.965057e-01 0.6017471 [263,] 3.688035e-01 7.376070e-01 0.6311965 [264,] 4.025714e-01 8.051428e-01 0.5974286 [265,] 3.781661e-01 7.563322e-01 0.6218339 [266,] 3.502213e-01 7.004425e-01 0.6497787 [267,] 3.220038e-01 6.440076e-01 0.6779962 [268,] 3.565345e-01 7.130691e-01 0.6434655 [269,] 3.287400e-01 6.574801e-01 0.6712600 [270,] 3.086122e-01 6.172245e-01 0.6913878 [271,] 3.594680e-01 7.189359e-01 0.6405320 [272,] 3.322745e-01 6.645491e-01 0.6677255 [273,] 3.169315e-01 6.338629e-01 0.6830685 [274,] 2.904574e-01 5.809149e-01 0.7095426 [275,] 2.836785e-01 5.673570e-01 0.7163215 [276,] 2.640354e-01 5.280709e-01 0.7359646 [277,] 2.383402e-01 4.766803e-01 0.7616598 [278,] 2.156819e-01 4.313638e-01 0.7843181 [279,] 2.017867e-01 4.035734e-01 0.7982133 [280,] 1.881145e-01 3.762290e-01 0.8118855 [281,] 1.795693e-01 3.591386e-01 0.8204307 [282,] 1.872996e-01 3.745993e-01 0.8127004 [283,] 2.255865e-01 4.511730e-01 0.7744135 [284,] 2.320753e-01 4.641507e-01 0.7679247 [285,] 2.887055e-01 5.774110e-01 0.7112945 [286,] 3.592426e-01 7.184851e-01 0.6407574 [287,] 3.397448e-01 6.794896e-01 0.6602552 [288,] 3.109313e-01 6.218626e-01 0.6890687 [289,] 2.955779e-01 5.911558e-01 0.7044221 [290,] 3.009814e-01 6.019629e-01 0.6990186 [291,] 2.762586e-01 5.525173e-01 0.7237414 [292,] 2.599950e-01 5.199901e-01 0.7400050 [293,] 2.387838e-01 4.775677e-01 0.7612162 [294,] 2.176126e-01 4.352252e-01 0.7823874 [295,] 1.937663e-01 3.875325e-01 0.8062337 [296,] 1.743532e-01 3.487065e-01 0.8256468 [297,] 1.593806e-01 3.187612e-01 0.8406194 [298,] 1.409543e-01 2.819086e-01 0.8590457 [299,] 1.489419e-01 2.978838e-01 0.8510581 [300,] 1.366191e-01 2.732382e-01 0.8633809 [301,] 1.198114e-01 2.396227e-01 0.8801886 [302,] 1.080241e-01 2.160483e-01 0.8919759 [303,] 9.683708e-02 1.936742e-01 0.9031629 [304,] 8.844603e-02 1.768921e-01 0.9115540 [305,] 7.552677e-02 1.510535e-01 0.9244732 [306,] 9.633333e-02 1.926667e-01 0.9036667 [307,] 8.243136e-02 1.648627e-01 0.9175686 [308,] 7.423502e-02 1.484700e-01 0.9257650 [309,] 6.990855e-02 1.398171e-01 0.9300914 [310,] 8.049296e-02 1.609859e-01 0.9195070 [311,] 1.164329e-01 2.328658e-01 0.8835671 [312,] 1.392543e-01 2.785086e-01 0.8607457 [313,] 3.236591e-01 6.473183e-01 0.6763409 [314,] 3.086774e-01 6.173548e-01 0.6913226 [315,] 2.743108e-01 5.486216e-01 0.7256892 [316,] 2.416595e-01 4.833190e-01 0.7583405 [317,] 2.183910e-01 4.367819e-01 0.7816090 [318,] 2.021894e-01 4.043789e-01 0.7978106 [319,] 2.035391e-01 4.070782e-01 0.7964609 [320,] 2.539462e-01 5.078925e-01 0.7460538 [321,] 2.455927e-01 4.911853e-01 0.7544073 [322,] 2.286557e-01 4.573115e-01 0.7713443 [323,] 2.051719e-01 4.103438e-01 0.7948281 [324,] 2.300163e-01 4.600326e-01 0.7699837 [325,] 2.086090e-01 4.172180e-01 0.7913910 [326,] 1.871213e-01 3.742427e-01 0.8128787 [327,] 3.138846e-01 6.277691e-01 0.6861154 [328,] 2.810324e-01 5.620648e-01 0.7189676 [329,] 4.560650e-01 9.121300e-01 0.5439350 [330,] 4.289467e-01 8.578934e-01 0.5710533 [331,] 3.855392e-01 7.710785e-01 0.6144608 [332,] 3.456648e-01 6.913296e-01 0.6543352 [333,] 3.374236e-01 6.748471e-01 0.6625764 [334,] 3.519045e-01 7.038090e-01 0.6480955 [335,] 3.399272e-01 6.798545e-01 0.6600728 [336,] 3.051791e-01 6.103582e-01 0.6948209 [337,] 2.702638e-01 5.405275e-01 0.7297362 [338,] 4.234381e-01 8.468762e-01 0.5765619 [339,] 3.964373e-01 7.928745e-01 0.6035627 [340,] 3.526032e-01 7.052063e-01 0.6473968 [341,] 3.575554e-01 7.151109e-01 0.6424446 [342,] 3.173696e-01 6.347393e-01 0.6826304 [343,] 2.720289e-01 5.440578e-01 0.7279711 [344,] 2.308650e-01 4.617300e-01 0.7691350 [345,] 2.171340e-01 4.342680e-01 0.7828660 [346,] 2.411506e-01 4.823013e-01 0.7588494 [347,] 2.066290e-01 4.132581e-01 0.7933710 [348,] 2.025758e-01 4.051515e-01 0.7974242 [349,] 2.534773e-01 5.069547e-01 0.7465227 [350,] 2.216549e-01 4.433097e-01 0.7783451 [351,] 2.414294e-01 4.828589e-01 0.7585706 [352,] 1.961670e-01 3.923340e-01 0.8038330 [353,] 1.925199e-01 3.850398e-01 0.8074801 [354,] 4.096759e-01 8.193517e-01 0.5903241 [355,] 3.637539e-01 7.275078e-01 0.6362461 [356,] 3.162463e-01 6.324927e-01 0.6837537 [357,] 3.327870e-01 6.655740e-01 0.6672130 [358,] 3.461390e-01 6.922781e-01 0.6538610 [359,] 2.774664e-01 5.549328e-01 0.7225336 [360,] 2.460634e-01 4.921269e-01 0.7539366 [361,] 3.163266e-01 6.326532e-01 0.6836734 [362,] 2.471893e-01 4.943786e-01 0.7528107 [363,] 2.340493e-01 4.680986e-01 0.7659507 [364,] 1.694304e-01 3.388609e-01 0.8305696 [365,] 3.362248e-01 6.724495e-01 0.6637752 [366,] 2.685896e-01 5.371792e-01 0.7314104 [367,] 2.376108e-01 4.752215e-01 0.7623892 [368,] 1.915197e-01 3.830395e-01 0.8084803 [369,] 1.069686e-01 2.139371e-01 0.8930314 > postscript(file="/var/www/html/rcomp/tmp/1fzar1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2fzar1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/38qrc1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/48qrc1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/58qrc1293670458.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 = 392 Frequency = 1 1 2 3 4 5 6 -2.53065038 1.74618180 2.09519527 -4.06201546 -1.37166634 -3.93176308 7 8 9 10 11 12 -3.74907431 -4.04609116 -2.37394075 -2.84045006 0.94828611 -1.11847276 13 14 15 16 17 18 -3.07837427 -3.15065812 -0.27840081 -1.65409638 -1.68183167 -3.04369583 19 20 21 22 23 24 -1.30225230 -1.84518349 0.81689288 1.15030396 1.49237674 -2.10840365 25 26 27 28 29 30 -5.10202736 1.00591582 1.21175285 1.12158793 -4.29963003 -2.13246871 31 32 33 34 35 36 1.94615650 -6.07061119 8.54935062 1.52790659 2.04707737 -5.51439192 37 38 39 40 41 42 0.63325948 -1.51907536 -0.57687284 -1.69851245 2.09237915 -1.31377530 43 44 45 46 47 48 2.68328570 -5.42139684 -0.10829965 2.49450772 2.16556595 -2.49674521 49 50 51 52 53 54 -0.71673572 -0.42179442 -2.13453419 0.95990155 1.59512513 2.16166214 55 56 57 58 59 60 2.63306395 0.25573366 -0.78259833 1.52599692 -0.35988207 0.29129121 61 62 63 64 65 66 -2.91279854 -0.08817853 -0.95810139 3.75928280 -1.64413491 0.32045610 67 68 69 70 71 72 -0.33959697 3.04674788 -0.75623871 2.80635825 0.66629208 1.34758465 73 74 75 76 77 78 1.79626281 1.77413730 -3.40534933 0.43222975 2.77010587 -3.10943332 79 80 81 82 83 84 1.70971988 1.26502933 2.60588130 3.09953459 -1.87393979 -2.55195569 85 86 87 88 89 90 -1.43782644 0.10272958 -0.71315809 -2.27380237 0.75683632 -0.14071038 91 92 93 94 95 96 -4.74235965 -1.52398898 1.10633791 -3.43363802 -3.13690248 -3.28820446 97 98 99 100 101 102 1.13324075 -0.64762637 4.99024129 -3.12641272 -3.67632183 1.93373622 103 104 105 106 107 108 2.55246210 -0.53804198 -3.67121007 -6.35755332 -1.50706898 2.41168775 109 110 111 112 113 114 1.47426906 5.81949046 -5.77771375 2.36284679 -1.34497859 7.48192904 115 116 117 118 119 120 1.98317964 5.57472351 3.46879395 2.48598516 -3.16648348 2.46982941 121 122 123 124 125 126 5.86400589 -3.88439921 -2.57329553 2.61712362 -3.78579265 -3.44861443 127 128 129 130 131 132 1.24973470 1.46648862 7.50594670 1.68656109 2.08866633 1.31163939 133 134 135 136 137 138 -0.83440386 -2.85140939 -1.69789273 -4.71939770 -2.05350317 1.36145145 139 140 141 142 143 144 -0.49417177 5.36279571 -0.32222859 1.46987173 2.73170036 4.40699587 145 146 147 148 149 150 1.00578452 5.04438347 5.40799464 1.00871092 -0.39204539 2.31488584 151 152 153 154 155 156 -4.98940921 3.01072263 -2.80991604 -0.39622299 -0.02069198 5.13283608 157 158 159 160 161 162 -0.75012467 2.86850328 0.97026115 -1.98488682 -0.18000151 -1.67023875 163 164 165 166 167 168 4.09629652 -4.09148157 -0.19330924 0.53574246 5.40121519 2.26254280 169 170 171 172 173 174 2.52684837 4.10134270 -3.77457905 0.03955712 2.64754210 -4.27682905 175 176 177 178 179 180 2.61850488 0.22145680 -4.11891603 -0.31746874 -2.90402615 -3.55893998 181 182 183 184 185 186 5.19514774 -4.72904566 0.97074038 1.87597541 4.13262645 0.37527360 187 188 189 190 191 192 -1.06263720 2.35667660 1.77921318 3.53353144 -2.49975087 -2.41579046 193 194 195 196 197 198 -5.86079391 -6.48872347 0.85486065 2.05227355 -2.87253417 -7.42072955 199 200 201 202 203 204 -3.25775376 0.34495367 1.33540658 -0.75366848 -1.10803715 -7.98465574 205 206 207 208 209 210 1.08096389 -2.84392172 -5.15174575 -0.84741122 2.08468022 1.44792564 211 212 213 214 215 216 -0.58839511 -2.76200154 -1.10027100 0.27655641 2.09805917 1.26102109 217 218 219 220 221 222 2.99042402 0.06357895 1.79145040 -0.12496496 -2.14776156 -2.03782268 223 224 225 226 227 228 0.45657783 2.37959728 -3.67577484 -1.09351567 9.85688063 1.22569447 229 230 231 232 233 234 2.24246316 0.30993956 0.94548763 -9.00949286 0.35787225 0.42344514 235 236 237 238 239 240 13.36078435 -0.67008767 -4.77433523 1.54727408 0.11282978 -4.49432884 241 242 243 244 245 246 -6.50147006 -5.72284254 0.89773846 2.92299457 -1.82494097 0.17252068 247 248 249 250 251 252 -1.12054006 -0.19403243 -3.42462760 4.01124987 0.64073634 2.78293578 253 254 255 256 257 258 -5.70073885 -0.30632031 -2.88551843 -5.31803065 1.41414577 9.32689585 259 260 261 262 263 264 2.54305690 -1.71574466 0.98263990 0.54813862 1.27674737 -5.25347693 265 266 267 268 269 270 1.34813135 -1.20390904 1.23103926 -4.51906063 2.10821678 -3.60238017 271 272 273 274 275 276 -0.83728489 3.86788181 -3.83730281 -0.77302122 3.38170545 0.45271643 277 278 279 280 281 282 -0.51025301 0.57878245 -6.00278135 -0.17804097 -1.38963014 6.36051646 283 284 285 286 287 288 1.33250169 2.80997620 -0.11706585 3.26505162 2.37085655 0.84391859 289 290 291 292 293 294 1.53652524 2.26306951 3.10203367 2.70819603 3.73388161 5.20208144 295 296 297 298 299 300 3.98692391 5.92178301 5.03208707 -2.06850946 0.24838871 -2.22708250 301 302 303 304 305 306 2.76944899 0.08892871 1.27944223 0.84635169 -1.67813036 0.73844787 307 308 309 310 311 312 -1.41488243 -1.13604580 -0.55649356 -3.68470572 -1.37237115 1.99278441 313 314 315 316 317 318 3.56380750 3.50937650 3.33948321 1.38456650 3.69559448 -0.83745362 319 320 321 322 323 324 2.05498701 -1.88322824 -1.85798679 6.15162984 -1.94261609 6.83615136 325 326 327 328 329 330 -0.31746103 -0.47778776 -0.44461681 -2.11723610 3.38782569 1.67806094 331 332 333 334 335 336 -5.02137544 0.40141524 0.05164443 -0.51998197 -2.23769773 -1.85567645 337 338 339 340 341 342 0.56033933 -6.59853091 0.42033184 -8.22770778 -1.79241650 0.12361780 343 344 345 346 347 348 -0.13122344 -0.26443259 -0.24487749 2.76529367 1.69876098 0.17509486 349 350 351 352 353 354 -3.97368665 3.67386382 -1.62076772 -0.24756550 -1.47827868 -0.55639544 355 356 357 358 359 360 -1.80772779 0.08718418 0.41665372 -2.95526634 -5.35594291 -2.43746477 361 362 363 364 365 366 2.82124034 3.20210986 -1.33014075 4.37753701 -4.91506001 -1.90599482 367 368 369 370 371 372 -2.75605498 -3.24865122 -2.94618989 0.62513325 -0.25544782 -1.31650910 373 374 375 376 377 378 -0.98824696 -2.02411850 -2.58410400 10.10946168 0.47133123 -0.49270804 379 380 381 382 383 384 -3.50493298 -3.74023915 -3.28511249 -2.99280754 -1.49793225 -3.13732802 385 386 387 388 389 390 -6.98602610 3.58310840 11.03001557 8.53402356 -1.78109450 8.87756737 391 392 10.41062561 -1.23471722 > postscript(file="/var/www/html/rcomp/tmp/6izrx1293670458.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 = 392 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.53065038 NA 1 1.74618180 -2.53065038 2 2.09519527 1.74618180 3 -4.06201546 2.09519527 4 -1.37166634 -4.06201546 5 -3.93176308 -1.37166634 6 -3.74907431 -3.93176308 7 -4.04609116 -3.74907431 8 -2.37394075 -4.04609116 9 -2.84045006 -2.37394075 10 0.94828611 -2.84045006 11 -1.11847276 0.94828611 12 -3.07837427 -1.11847276 13 -3.15065812 -3.07837427 14 -0.27840081 -3.15065812 15 -1.65409638 -0.27840081 16 -1.68183167 -1.65409638 17 -3.04369583 -1.68183167 18 -1.30225230 -3.04369583 19 -1.84518349 -1.30225230 20 0.81689288 -1.84518349 21 1.15030396 0.81689288 22 1.49237674 1.15030396 23 -2.10840365 1.49237674 24 -5.10202736 -2.10840365 25 1.00591582 -5.10202736 26 1.21175285 1.00591582 27 1.12158793 1.21175285 28 -4.29963003 1.12158793 29 -2.13246871 -4.29963003 30 1.94615650 -2.13246871 31 -6.07061119 1.94615650 32 8.54935062 -6.07061119 33 1.52790659 8.54935062 34 2.04707737 1.52790659 35 -5.51439192 2.04707737 36 0.63325948 -5.51439192 37 -1.51907536 0.63325948 38 -0.57687284 -1.51907536 39 -1.69851245 -0.57687284 40 2.09237915 -1.69851245 41 -1.31377530 2.09237915 42 2.68328570 -1.31377530 43 -5.42139684 2.68328570 44 -0.10829965 -5.42139684 45 2.49450772 -0.10829965 46 2.16556595 2.49450772 47 -2.49674521 2.16556595 48 -0.71673572 -2.49674521 49 -0.42179442 -0.71673572 50 -2.13453419 -0.42179442 51 0.95990155 -2.13453419 52 1.59512513 0.95990155 53 2.16166214 1.59512513 54 2.63306395 2.16166214 55 0.25573366 2.63306395 56 -0.78259833 0.25573366 57 1.52599692 -0.78259833 58 -0.35988207 1.52599692 59 0.29129121 -0.35988207 60 -2.91279854 0.29129121 61 -0.08817853 -2.91279854 62 -0.95810139 -0.08817853 63 3.75928280 -0.95810139 64 -1.64413491 3.75928280 65 0.32045610 -1.64413491 66 -0.33959697 0.32045610 67 3.04674788 -0.33959697 68 -0.75623871 3.04674788 69 2.80635825 -0.75623871 70 0.66629208 2.80635825 71 1.34758465 0.66629208 72 1.79626281 1.34758465 73 1.77413730 1.79626281 74 -3.40534933 1.77413730 75 0.43222975 -3.40534933 76 2.77010587 0.43222975 77 -3.10943332 2.77010587 78 1.70971988 -3.10943332 79 1.26502933 1.70971988 80 2.60588130 1.26502933 81 3.09953459 2.60588130 82 -1.87393979 3.09953459 83 -2.55195569 -1.87393979 84 -1.43782644 -2.55195569 85 0.10272958 -1.43782644 86 -0.71315809 0.10272958 87 -2.27380237 -0.71315809 88 0.75683632 -2.27380237 89 -0.14071038 0.75683632 90 -4.74235965 -0.14071038 91 -1.52398898 -4.74235965 92 1.10633791 -1.52398898 93 -3.43363802 1.10633791 94 -3.13690248 -3.43363802 95 -3.28820446 -3.13690248 96 1.13324075 -3.28820446 97 -0.64762637 1.13324075 98 4.99024129 -0.64762637 99 -3.12641272 4.99024129 100 -3.67632183 -3.12641272 101 1.93373622 -3.67632183 102 2.55246210 1.93373622 103 -0.53804198 2.55246210 104 -3.67121007 -0.53804198 105 -6.35755332 -3.67121007 106 -1.50706898 -6.35755332 107 2.41168775 -1.50706898 108 1.47426906 2.41168775 109 5.81949046 1.47426906 110 -5.77771375 5.81949046 111 2.36284679 -5.77771375 112 -1.34497859 2.36284679 113 7.48192904 -1.34497859 114 1.98317964 7.48192904 115 5.57472351 1.98317964 116 3.46879395 5.57472351 117 2.48598516 3.46879395 118 -3.16648348 2.48598516 119 2.46982941 -3.16648348 120 5.86400589 2.46982941 121 -3.88439921 5.86400589 122 -2.57329553 -3.88439921 123 2.61712362 -2.57329553 124 -3.78579265 2.61712362 125 -3.44861443 -3.78579265 126 1.24973470 -3.44861443 127 1.46648862 1.24973470 128 7.50594670 1.46648862 129 1.68656109 7.50594670 130 2.08866633 1.68656109 131 1.31163939 2.08866633 132 -0.83440386 1.31163939 133 -2.85140939 -0.83440386 134 -1.69789273 -2.85140939 135 -4.71939770 -1.69789273 136 -2.05350317 -4.71939770 137 1.36145145 -2.05350317 138 -0.49417177 1.36145145 139 5.36279571 -0.49417177 140 -0.32222859 5.36279571 141 1.46987173 -0.32222859 142 2.73170036 1.46987173 143 4.40699587 2.73170036 144 1.00578452 4.40699587 145 5.04438347 1.00578452 146 5.40799464 5.04438347 147 1.00871092 5.40799464 148 -0.39204539 1.00871092 149 2.31488584 -0.39204539 150 -4.98940921 2.31488584 151 3.01072263 -4.98940921 152 -2.80991604 3.01072263 153 -0.39622299 -2.80991604 154 -0.02069198 -0.39622299 155 5.13283608 -0.02069198 156 -0.75012467 5.13283608 157 2.86850328 -0.75012467 158 0.97026115 2.86850328 159 -1.98488682 0.97026115 160 -0.18000151 -1.98488682 161 -1.67023875 -0.18000151 162 4.09629652 -1.67023875 163 -4.09148157 4.09629652 164 -0.19330924 -4.09148157 165 0.53574246 -0.19330924 166 5.40121519 0.53574246 167 2.26254280 5.40121519 168 2.52684837 2.26254280 169 4.10134270 2.52684837 170 -3.77457905 4.10134270 171 0.03955712 -3.77457905 172 2.64754210 0.03955712 173 -4.27682905 2.64754210 174 2.61850488 -4.27682905 175 0.22145680 2.61850488 176 -4.11891603 0.22145680 177 -0.31746874 -4.11891603 178 -2.90402615 -0.31746874 179 -3.55893998 -2.90402615 180 5.19514774 -3.55893998 181 -4.72904566 5.19514774 182 0.97074038 -4.72904566 183 1.87597541 0.97074038 184 4.13262645 1.87597541 185 0.37527360 4.13262645 186 -1.06263720 0.37527360 187 2.35667660 -1.06263720 188 1.77921318 2.35667660 189 3.53353144 1.77921318 190 -2.49975087 3.53353144 191 -2.41579046 -2.49975087 192 -5.86079391 -2.41579046 193 -6.48872347 -5.86079391 194 0.85486065 -6.48872347 195 2.05227355 0.85486065 196 -2.87253417 2.05227355 197 -7.42072955 -2.87253417 198 -3.25775376 -7.42072955 199 0.34495367 -3.25775376 200 1.33540658 0.34495367 201 -0.75366848 1.33540658 202 -1.10803715 -0.75366848 203 -7.98465574 -1.10803715 204 1.08096389 -7.98465574 205 -2.84392172 1.08096389 206 -5.15174575 -2.84392172 207 -0.84741122 -5.15174575 208 2.08468022 -0.84741122 209 1.44792564 2.08468022 210 -0.58839511 1.44792564 211 -2.76200154 -0.58839511 212 -1.10027100 -2.76200154 213 0.27655641 -1.10027100 214 2.09805917 0.27655641 215 1.26102109 2.09805917 216 2.99042402 1.26102109 217 0.06357895 2.99042402 218 1.79145040 0.06357895 219 -0.12496496 1.79145040 220 -2.14776156 -0.12496496 221 -2.03782268 -2.14776156 222 0.45657783 -2.03782268 223 2.37959728 0.45657783 224 -3.67577484 2.37959728 225 -1.09351567 -3.67577484 226 9.85688063 -1.09351567 227 1.22569447 9.85688063 228 2.24246316 1.22569447 229 0.30993956 2.24246316 230 0.94548763 0.30993956 231 -9.00949286 0.94548763 232 0.35787225 -9.00949286 233 0.42344514 0.35787225 234 13.36078435 0.42344514 235 -0.67008767 13.36078435 236 -4.77433523 -0.67008767 237 1.54727408 -4.77433523 238 0.11282978 1.54727408 239 -4.49432884 0.11282978 240 -6.50147006 -4.49432884 241 -5.72284254 -6.50147006 242 0.89773846 -5.72284254 243 2.92299457 0.89773846 244 -1.82494097 2.92299457 245 0.17252068 -1.82494097 246 -1.12054006 0.17252068 247 -0.19403243 -1.12054006 248 -3.42462760 -0.19403243 249 4.01124987 -3.42462760 250 0.64073634 4.01124987 251 2.78293578 0.64073634 252 -5.70073885 2.78293578 253 -0.30632031 -5.70073885 254 -2.88551843 -0.30632031 255 -5.31803065 -2.88551843 256 1.41414577 -5.31803065 257 9.32689585 1.41414577 258 2.54305690 9.32689585 259 -1.71574466 2.54305690 260 0.98263990 -1.71574466 261 0.54813862 0.98263990 262 1.27674737 0.54813862 263 -5.25347693 1.27674737 264 1.34813135 -5.25347693 265 -1.20390904 1.34813135 266 1.23103926 -1.20390904 267 -4.51906063 1.23103926 268 2.10821678 -4.51906063 269 -3.60238017 2.10821678 270 -0.83728489 -3.60238017 271 3.86788181 -0.83728489 272 -3.83730281 3.86788181 273 -0.77302122 -3.83730281 274 3.38170545 -0.77302122 275 0.45271643 3.38170545 276 -0.51025301 0.45271643 277 0.57878245 -0.51025301 278 -6.00278135 0.57878245 279 -0.17804097 -6.00278135 280 -1.38963014 -0.17804097 281 6.36051646 -1.38963014 282 1.33250169 6.36051646 283 2.80997620 1.33250169 284 -0.11706585 2.80997620 285 3.26505162 -0.11706585 286 2.37085655 3.26505162 287 0.84391859 2.37085655 288 1.53652524 0.84391859 289 2.26306951 1.53652524 290 3.10203367 2.26306951 291 2.70819603 3.10203367 292 3.73388161 2.70819603 293 5.20208144 3.73388161 294 3.98692391 5.20208144 295 5.92178301 3.98692391 296 5.03208707 5.92178301 297 -2.06850946 5.03208707 298 0.24838871 -2.06850946 299 -2.22708250 0.24838871 300 2.76944899 -2.22708250 301 0.08892871 2.76944899 302 1.27944223 0.08892871 303 0.84635169 1.27944223 304 -1.67813036 0.84635169 305 0.73844787 -1.67813036 306 -1.41488243 0.73844787 307 -1.13604580 -1.41488243 308 -0.55649356 -1.13604580 309 -3.68470572 -0.55649356 310 -1.37237115 -3.68470572 311 1.99278441 -1.37237115 312 3.56380750 1.99278441 313 3.50937650 3.56380750 314 3.33948321 3.50937650 315 1.38456650 3.33948321 316 3.69559448 1.38456650 317 -0.83745362 3.69559448 318 2.05498701 -0.83745362 319 -1.88322824 2.05498701 320 -1.85798679 -1.88322824 321 6.15162984 -1.85798679 322 -1.94261609 6.15162984 323 6.83615136 -1.94261609 324 -0.31746103 6.83615136 325 -0.47778776 -0.31746103 326 -0.44461681 -0.47778776 327 -2.11723610 -0.44461681 328 3.38782569 -2.11723610 329 1.67806094 3.38782569 330 -5.02137544 1.67806094 331 0.40141524 -5.02137544 332 0.05164443 0.40141524 333 -0.51998197 0.05164443 334 -2.23769773 -0.51998197 335 -1.85567645 -2.23769773 336 0.56033933 -1.85567645 337 -6.59853091 0.56033933 338 0.42033184 -6.59853091 339 -8.22770778 0.42033184 340 -1.79241650 -8.22770778 341 0.12361780 -1.79241650 342 -0.13122344 0.12361780 343 -0.26443259 -0.13122344 344 -0.24487749 -0.26443259 345 2.76529367 -0.24487749 346 1.69876098 2.76529367 347 0.17509486 1.69876098 348 -3.97368665 0.17509486 349 3.67386382 -3.97368665 350 -1.62076772 3.67386382 351 -0.24756550 -1.62076772 352 -1.47827868 -0.24756550 353 -0.55639544 -1.47827868 354 -1.80772779 -0.55639544 355 0.08718418 -1.80772779 356 0.41665372 0.08718418 357 -2.95526634 0.41665372 358 -5.35594291 -2.95526634 359 -2.43746477 -5.35594291 360 2.82124034 -2.43746477 361 3.20210986 2.82124034 362 -1.33014075 3.20210986 363 4.37753701 -1.33014075 364 -4.91506001 4.37753701 365 -1.90599482 -4.91506001 366 -2.75605498 -1.90599482 367 -3.24865122 -2.75605498 368 -2.94618989 -3.24865122 369 0.62513325 -2.94618989 370 -0.25544782 0.62513325 371 -1.31650910 -0.25544782 372 -0.98824696 -1.31650910 373 -2.02411850 -0.98824696 374 -2.58410400 -2.02411850 375 10.10946168 -2.58410400 376 0.47133123 10.10946168 377 -0.49270804 0.47133123 378 -3.50493298 -0.49270804 379 -3.74023915 -3.50493298 380 -3.28511249 -3.74023915 381 -2.99280754 -3.28511249 382 -1.49793225 -2.99280754 383 -3.13732802 -1.49793225 384 -6.98602610 -3.13732802 385 3.58310840 -6.98602610 386 11.03001557 3.58310840 387 8.53402356 11.03001557 388 -1.78109450 8.53402356 389 8.87756737 -1.78109450 390 10.41062561 8.87756737 391 -1.23471722 10.41062561 392 NA -1.23471722 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.74618180 -2.53065038 [2,] 2.09519527 1.74618180 [3,] -4.06201546 2.09519527 [4,] -1.37166634 -4.06201546 [5,] -3.93176308 -1.37166634 [6,] -3.74907431 -3.93176308 [7,] -4.04609116 -3.74907431 [8,] -2.37394075 -4.04609116 [9,] -2.84045006 -2.37394075 [10,] 0.94828611 -2.84045006 [11,] -1.11847276 0.94828611 [12,] -3.07837427 -1.11847276 [13,] -3.15065812 -3.07837427 [14,] -0.27840081 -3.15065812 [15,] -1.65409638 -0.27840081 [16,] -1.68183167 -1.65409638 [17,] -3.04369583 -1.68183167 [18,] -1.30225230 -3.04369583 [19,] -1.84518349 -1.30225230 [20,] 0.81689288 -1.84518349 [21,] 1.15030396 0.81689288 [22,] 1.49237674 1.15030396 [23,] -2.10840365 1.49237674 [24,] -5.10202736 -2.10840365 [25,] 1.00591582 -5.10202736 [26,] 1.21175285 1.00591582 [27,] 1.12158793 1.21175285 [28,] -4.29963003 1.12158793 [29,] -2.13246871 -4.29963003 [30,] 1.94615650 -2.13246871 [31,] -6.07061119 1.94615650 [32,] 8.54935062 -6.07061119 [33,] 1.52790659 8.54935062 [34,] 2.04707737 1.52790659 [35,] -5.51439192 2.04707737 [36,] 0.63325948 -5.51439192 [37,] -1.51907536 0.63325948 [38,] -0.57687284 -1.51907536 [39,] -1.69851245 -0.57687284 [40,] 2.09237915 -1.69851245 [41,] -1.31377530 2.09237915 [42,] 2.68328570 -1.31377530 [43,] -5.42139684 2.68328570 [44,] -0.10829965 -5.42139684 [45,] 2.49450772 -0.10829965 [46,] 2.16556595 2.49450772 [47,] -2.49674521 2.16556595 [48,] -0.71673572 -2.49674521 [49,] -0.42179442 -0.71673572 [50,] -2.13453419 -0.42179442 [51,] 0.95990155 -2.13453419 [52,] 1.59512513 0.95990155 [53,] 2.16166214 1.59512513 [54,] 2.63306395 2.16166214 [55,] 0.25573366 2.63306395 [56,] -0.78259833 0.25573366 [57,] 1.52599692 -0.78259833 [58,] -0.35988207 1.52599692 [59,] 0.29129121 -0.35988207 [60,] -2.91279854 0.29129121 [61,] -0.08817853 -2.91279854 [62,] -0.95810139 -0.08817853 [63,] 3.75928280 -0.95810139 [64,] -1.64413491 3.75928280 [65,] 0.32045610 -1.64413491 [66,] -0.33959697 0.32045610 [67,] 3.04674788 -0.33959697 [68,] -0.75623871 3.04674788 [69,] 2.80635825 -0.75623871 [70,] 0.66629208 2.80635825 [71,] 1.34758465 0.66629208 [72,] 1.79626281 1.34758465 [73,] 1.77413730 1.79626281 [74,] -3.40534933 1.77413730 [75,] 0.43222975 -3.40534933 [76,] 2.77010587 0.43222975 [77,] -3.10943332 2.77010587 [78,] 1.70971988 -3.10943332 [79,] 1.26502933 1.70971988 [80,] 2.60588130 1.26502933 [81,] 3.09953459 2.60588130 [82,] -1.87393979 3.09953459 [83,] -2.55195569 -1.87393979 [84,] -1.43782644 -2.55195569 [85,] 0.10272958 -1.43782644 [86,] -0.71315809 0.10272958 [87,] -2.27380237 -0.71315809 [88,] 0.75683632 -2.27380237 [89,] -0.14071038 0.75683632 [90,] -4.74235965 -0.14071038 [91,] -1.52398898 -4.74235965 [92,] 1.10633791 -1.52398898 [93,] -3.43363802 1.10633791 [94,] -3.13690248 -3.43363802 [95,] -3.28820446 -3.13690248 [96,] 1.13324075 -3.28820446 [97,] -0.64762637 1.13324075 [98,] 4.99024129 -0.64762637 [99,] -3.12641272 4.99024129 [100,] -3.67632183 -3.12641272 [101,] 1.93373622 -3.67632183 [102,] 2.55246210 1.93373622 [103,] -0.53804198 2.55246210 [104,] -3.67121007 -0.53804198 [105,] -6.35755332 -3.67121007 [106,] -1.50706898 -6.35755332 [107,] 2.41168775 -1.50706898 [108,] 1.47426906 2.41168775 [109,] 5.81949046 1.47426906 [110,] -5.77771375 5.81949046 [111,] 2.36284679 -5.77771375 [112,] -1.34497859 2.36284679 [113,] 7.48192904 -1.34497859 [114,] 1.98317964 7.48192904 [115,] 5.57472351 1.98317964 [116,] 3.46879395 5.57472351 [117,] 2.48598516 3.46879395 [118,] -3.16648348 2.48598516 [119,] 2.46982941 -3.16648348 [120,] 5.86400589 2.46982941 [121,] -3.88439921 5.86400589 [122,] -2.57329553 -3.88439921 [123,] 2.61712362 -2.57329553 [124,] -3.78579265 2.61712362 [125,] -3.44861443 -3.78579265 [126,] 1.24973470 -3.44861443 [127,] 1.46648862 1.24973470 [128,] 7.50594670 1.46648862 [129,] 1.68656109 7.50594670 [130,] 2.08866633 1.68656109 [131,] 1.31163939 2.08866633 [132,] -0.83440386 1.31163939 [133,] -2.85140939 -0.83440386 [134,] -1.69789273 -2.85140939 [135,] -4.71939770 -1.69789273 [136,] -2.05350317 -4.71939770 [137,] 1.36145145 -2.05350317 [138,] -0.49417177 1.36145145 [139,] 5.36279571 -0.49417177 [140,] -0.32222859 5.36279571 [141,] 1.46987173 -0.32222859 [142,] 2.73170036 1.46987173 [143,] 4.40699587 2.73170036 [144,] 1.00578452 4.40699587 [145,] 5.04438347 1.00578452 [146,] 5.40799464 5.04438347 [147,] 1.00871092 5.40799464 [148,] -0.39204539 1.00871092 [149,] 2.31488584 -0.39204539 [150,] -4.98940921 2.31488584 [151,] 3.01072263 -4.98940921 [152,] -2.80991604 3.01072263 [153,] -0.39622299 -2.80991604 [154,] -0.02069198 -0.39622299 [155,] 5.13283608 -0.02069198 [156,] -0.75012467 5.13283608 [157,] 2.86850328 -0.75012467 [158,] 0.97026115 2.86850328 [159,] -1.98488682 0.97026115 [160,] -0.18000151 -1.98488682 [161,] -1.67023875 -0.18000151 [162,] 4.09629652 -1.67023875 [163,] -4.09148157 4.09629652 [164,] -0.19330924 -4.09148157 [165,] 0.53574246 -0.19330924 [166,] 5.40121519 0.53574246 [167,] 2.26254280 5.40121519 [168,] 2.52684837 2.26254280 [169,] 4.10134270 2.52684837 [170,] -3.77457905 4.10134270 [171,] 0.03955712 -3.77457905 [172,] 2.64754210 0.03955712 [173,] -4.27682905 2.64754210 [174,] 2.61850488 -4.27682905 [175,] 0.22145680 2.61850488 [176,] -4.11891603 0.22145680 [177,] -0.31746874 -4.11891603 [178,] -2.90402615 -0.31746874 [179,] -3.55893998 -2.90402615 [180,] 5.19514774 -3.55893998 [181,] -4.72904566 5.19514774 [182,] 0.97074038 -4.72904566 [183,] 1.87597541 0.97074038 [184,] 4.13262645 1.87597541 [185,] 0.37527360 4.13262645 [186,] -1.06263720 0.37527360 [187,] 2.35667660 -1.06263720 [188,] 1.77921318 2.35667660 [189,] 3.53353144 1.77921318 [190,] -2.49975087 3.53353144 [191,] -2.41579046 -2.49975087 [192,] -5.86079391 -2.41579046 [193,] -6.48872347 -5.86079391 [194,] 0.85486065 -6.48872347 [195,] 2.05227355 0.85486065 [196,] -2.87253417 2.05227355 [197,] -7.42072955 -2.87253417 [198,] -3.25775376 -7.42072955 [199,] 0.34495367 -3.25775376 [200,] 1.33540658 0.34495367 [201,] -0.75366848 1.33540658 [202,] -1.10803715 -0.75366848 [203,] -7.98465574 -1.10803715 [204,] 1.08096389 -7.98465574 [205,] -2.84392172 1.08096389 [206,] -5.15174575 -2.84392172 [207,] -0.84741122 -5.15174575 [208,] 2.08468022 -0.84741122 [209,] 1.44792564 2.08468022 [210,] -0.58839511 1.44792564 [211,] -2.76200154 -0.58839511 [212,] -1.10027100 -2.76200154 [213,] 0.27655641 -1.10027100 [214,] 2.09805917 0.27655641 [215,] 1.26102109 2.09805917 [216,] 2.99042402 1.26102109 [217,] 0.06357895 2.99042402 [218,] 1.79145040 0.06357895 [219,] -0.12496496 1.79145040 [220,] -2.14776156 -0.12496496 [221,] -2.03782268 -2.14776156 [222,] 0.45657783 -2.03782268 [223,] 2.37959728 0.45657783 [224,] -3.67577484 2.37959728 [225,] -1.09351567 -3.67577484 [226,] 9.85688063 -1.09351567 [227,] 1.22569447 9.85688063 [228,] 2.24246316 1.22569447 [229,] 0.30993956 2.24246316 [230,] 0.94548763 0.30993956 [231,] -9.00949286 0.94548763 [232,] 0.35787225 -9.00949286 [233,] 0.42344514 0.35787225 [234,] 13.36078435 0.42344514 [235,] -0.67008767 13.36078435 [236,] -4.77433523 -0.67008767 [237,] 1.54727408 -4.77433523 [238,] 0.11282978 1.54727408 [239,] -4.49432884 0.11282978 [240,] -6.50147006 -4.49432884 [241,] -5.72284254 -6.50147006 [242,] 0.89773846 -5.72284254 [243,] 2.92299457 0.89773846 [244,] -1.82494097 2.92299457 [245,] 0.17252068 -1.82494097 [246,] -1.12054006 0.17252068 [247,] -0.19403243 -1.12054006 [248,] -3.42462760 -0.19403243 [249,] 4.01124987 -3.42462760 [250,] 0.64073634 4.01124987 [251,] 2.78293578 0.64073634 [252,] -5.70073885 2.78293578 [253,] -0.30632031 -5.70073885 [254,] -2.88551843 -0.30632031 [255,] -5.31803065 -2.88551843 [256,] 1.41414577 -5.31803065 [257,] 9.32689585 1.41414577 [258,] 2.54305690 9.32689585 [259,] -1.71574466 2.54305690 [260,] 0.98263990 -1.71574466 [261,] 0.54813862 0.98263990 [262,] 1.27674737 0.54813862 [263,] -5.25347693 1.27674737 [264,] 1.34813135 -5.25347693 [265,] -1.20390904 1.34813135 [266,] 1.23103926 -1.20390904 [267,] -4.51906063 1.23103926 [268,] 2.10821678 -4.51906063 [269,] -3.60238017 2.10821678 [270,] -0.83728489 -3.60238017 [271,] 3.86788181 -0.83728489 [272,] -3.83730281 3.86788181 [273,] -0.77302122 -3.83730281 [274,] 3.38170545 -0.77302122 [275,] 0.45271643 3.38170545 [276,] -0.51025301 0.45271643 [277,] 0.57878245 -0.51025301 [278,] -6.00278135 0.57878245 [279,] -0.17804097 -6.00278135 [280,] -1.38963014 -0.17804097 [281,] 6.36051646 -1.38963014 [282,] 1.33250169 6.36051646 [283,] 2.80997620 1.33250169 [284,] -0.11706585 2.80997620 [285,] 3.26505162 -0.11706585 [286,] 2.37085655 3.26505162 [287,] 0.84391859 2.37085655 [288,] 1.53652524 0.84391859 [289,] 2.26306951 1.53652524 [290,] 3.10203367 2.26306951 [291,] 2.70819603 3.10203367 [292,] 3.73388161 2.70819603 [293,] 5.20208144 3.73388161 [294,] 3.98692391 5.20208144 [295,] 5.92178301 3.98692391 [296,] 5.03208707 5.92178301 [297,] -2.06850946 5.03208707 [298,] 0.24838871 -2.06850946 [299,] -2.22708250 0.24838871 [300,] 2.76944899 -2.22708250 [301,] 0.08892871 2.76944899 [302,] 1.27944223 0.08892871 [303,] 0.84635169 1.27944223 [304,] -1.67813036 0.84635169 [305,] 0.73844787 -1.67813036 [306,] -1.41488243 0.73844787 [307,] -1.13604580 -1.41488243 [308,] -0.55649356 -1.13604580 [309,] -3.68470572 -0.55649356 [310,] -1.37237115 -3.68470572 [311,] 1.99278441 -1.37237115 [312,] 3.56380750 1.99278441 [313,] 3.50937650 3.56380750 [314,] 3.33948321 3.50937650 [315,] 1.38456650 3.33948321 [316,] 3.69559448 1.38456650 [317,] -0.83745362 3.69559448 [318,] 2.05498701 -0.83745362 [319,] -1.88322824 2.05498701 [320,] -1.85798679 -1.88322824 [321,] 6.15162984 -1.85798679 [322,] -1.94261609 6.15162984 [323,] 6.83615136 -1.94261609 [324,] -0.31746103 6.83615136 [325,] -0.47778776 -0.31746103 [326,] -0.44461681 -0.47778776 [327,] -2.11723610 -0.44461681 [328,] 3.38782569 -2.11723610 [329,] 1.67806094 3.38782569 [330,] -5.02137544 1.67806094 [331,] 0.40141524 -5.02137544 [332,] 0.05164443 0.40141524 [333,] -0.51998197 0.05164443 [334,] -2.23769773 -0.51998197 [335,] -1.85567645 -2.23769773 [336,] 0.56033933 -1.85567645 [337,] -6.59853091 0.56033933 [338,] 0.42033184 -6.59853091 [339,] -8.22770778 0.42033184 [340,] -1.79241650 -8.22770778 [341,] 0.12361780 -1.79241650 [342,] -0.13122344 0.12361780 [343,] -0.26443259 -0.13122344 [344,] -0.24487749 -0.26443259 [345,] 2.76529367 -0.24487749 [346,] 1.69876098 2.76529367 [347,] 0.17509486 1.69876098 [348,] -3.97368665 0.17509486 [349,] 3.67386382 -3.97368665 [350,] -1.62076772 3.67386382 [351,] -0.24756550 -1.62076772 [352,] -1.47827868 -0.24756550 [353,] -0.55639544 -1.47827868 [354,] -1.80772779 -0.55639544 [355,] 0.08718418 -1.80772779 [356,] 0.41665372 0.08718418 [357,] -2.95526634 0.41665372 [358,] -5.35594291 -2.95526634 [359,] -2.43746477 -5.35594291 [360,] 2.82124034 -2.43746477 [361,] 3.20210986 2.82124034 [362,] -1.33014075 3.20210986 [363,] 4.37753701 -1.33014075 [364,] -4.91506001 4.37753701 [365,] -1.90599482 -4.91506001 [366,] -2.75605498 -1.90599482 [367,] -3.24865122 -2.75605498 [368,] -2.94618989 -3.24865122 [369,] 0.62513325 -2.94618989 [370,] -0.25544782 0.62513325 [371,] -1.31650910 -0.25544782 [372,] -0.98824696 -1.31650910 [373,] -2.02411850 -0.98824696 [374,] -2.58410400 -2.02411850 [375,] 10.10946168 -2.58410400 [376,] 0.47133123 10.10946168 [377,] -0.49270804 0.47133123 [378,] -3.50493298 -0.49270804 [379,] -3.74023915 -3.50493298 [380,] -3.28511249 -3.74023915 [381,] -2.99280754 -3.28511249 [382,] -1.49793225 -2.99280754 [383,] -3.13732802 -1.49793225 [384,] -6.98602610 -3.13732802 [385,] 3.58310840 -6.98602610 [386,] 11.03001557 3.58310840 [387,] 8.53402356 11.03001557 [388,] -1.78109450 8.53402356 [389,] 8.87756737 -1.78109450 [390,] 10.41062561 8.87756737 [391,] -1.23471722 10.41062561 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.74618180 -2.53065038 2 2.09519527 1.74618180 3 -4.06201546 2.09519527 4 -1.37166634 -4.06201546 5 -3.93176308 -1.37166634 6 -3.74907431 -3.93176308 7 -4.04609116 -3.74907431 8 -2.37394075 -4.04609116 9 -2.84045006 -2.37394075 10 0.94828611 -2.84045006 11 -1.11847276 0.94828611 12 -3.07837427 -1.11847276 13 -3.15065812 -3.07837427 14 -0.27840081 -3.15065812 15 -1.65409638 -0.27840081 16 -1.68183167 -1.65409638 17 -3.04369583 -1.68183167 18 -1.30225230 -3.04369583 19 -1.84518349 -1.30225230 20 0.81689288 -1.84518349 21 1.15030396 0.81689288 22 1.49237674 1.15030396 23 -2.10840365 1.49237674 24 -5.10202736 -2.10840365 25 1.00591582 -5.10202736 26 1.21175285 1.00591582 27 1.12158793 1.21175285 28 -4.29963003 1.12158793 29 -2.13246871 -4.29963003 30 1.94615650 -2.13246871 31 -6.07061119 1.94615650 32 8.54935062 -6.07061119 33 1.52790659 8.54935062 34 2.04707737 1.52790659 35 -5.51439192 2.04707737 36 0.63325948 -5.51439192 37 -1.51907536 0.63325948 38 -0.57687284 -1.51907536 39 -1.69851245 -0.57687284 40 2.09237915 -1.69851245 41 -1.31377530 2.09237915 42 2.68328570 -1.31377530 43 -5.42139684 2.68328570 44 -0.10829965 -5.42139684 45 2.49450772 -0.10829965 46 2.16556595 2.49450772 47 -2.49674521 2.16556595 48 -0.71673572 -2.49674521 49 -0.42179442 -0.71673572 50 -2.13453419 -0.42179442 51 0.95990155 -2.13453419 52 1.59512513 0.95990155 53 2.16166214 1.59512513 54 2.63306395 2.16166214 55 0.25573366 2.63306395 56 -0.78259833 0.25573366 57 1.52599692 -0.78259833 58 -0.35988207 1.52599692 59 0.29129121 -0.35988207 60 -2.91279854 0.29129121 61 -0.08817853 -2.91279854 62 -0.95810139 -0.08817853 63 3.75928280 -0.95810139 64 -1.64413491 3.75928280 65 0.32045610 -1.64413491 66 -0.33959697 0.32045610 67 3.04674788 -0.33959697 68 -0.75623871 3.04674788 69 2.80635825 -0.75623871 70 0.66629208 2.80635825 71 1.34758465 0.66629208 72 1.79626281 1.34758465 73 1.77413730 1.79626281 74 -3.40534933 1.77413730 75 0.43222975 -3.40534933 76 2.77010587 0.43222975 77 -3.10943332 2.77010587 78 1.70971988 -3.10943332 79 1.26502933 1.70971988 80 2.60588130 1.26502933 81 3.09953459 2.60588130 82 -1.87393979 3.09953459 83 -2.55195569 -1.87393979 84 -1.43782644 -2.55195569 85 0.10272958 -1.43782644 86 -0.71315809 0.10272958 87 -2.27380237 -0.71315809 88 0.75683632 -2.27380237 89 -0.14071038 0.75683632 90 -4.74235965 -0.14071038 91 -1.52398898 -4.74235965 92 1.10633791 -1.52398898 93 -3.43363802 1.10633791 94 -3.13690248 -3.43363802 95 -3.28820446 -3.13690248 96 1.13324075 -3.28820446 97 -0.64762637 1.13324075 98 4.99024129 -0.64762637 99 -3.12641272 4.99024129 100 -3.67632183 -3.12641272 101 1.93373622 -3.67632183 102 2.55246210 1.93373622 103 -0.53804198 2.55246210 104 -3.67121007 -0.53804198 105 -6.35755332 -3.67121007 106 -1.50706898 -6.35755332 107 2.41168775 -1.50706898 108 1.47426906 2.41168775 109 5.81949046 1.47426906 110 -5.77771375 5.81949046 111 2.36284679 -5.77771375 112 -1.34497859 2.36284679 113 7.48192904 -1.34497859 114 1.98317964 7.48192904 115 5.57472351 1.98317964 116 3.46879395 5.57472351 117 2.48598516 3.46879395 118 -3.16648348 2.48598516 119 2.46982941 -3.16648348 120 5.86400589 2.46982941 121 -3.88439921 5.86400589 122 -2.57329553 -3.88439921 123 2.61712362 -2.57329553 124 -3.78579265 2.61712362 125 -3.44861443 -3.78579265 126 1.24973470 -3.44861443 127 1.46648862 1.24973470 128 7.50594670 1.46648862 129 1.68656109 7.50594670 130 2.08866633 1.68656109 131 1.31163939 2.08866633 132 -0.83440386 1.31163939 133 -2.85140939 -0.83440386 134 -1.69789273 -2.85140939 135 -4.71939770 -1.69789273 136 -2.05350317 -4.71939770 137 1.36145145 -2.05350317 138 -0.49417177 1.36145145 139 5.36279571 -0.49417177 140 -0.32222859 5.36279571 141 1.46987173 -0.32222859 142 2.73170036 1.46987173 143 4.40699587 2.73170036 144 1.00578452 4.40699587 145 5.04438347 1.00578452 146 5.40799464 5.04438347 147 1.00871092 5.40799464 148 -0.39204539 1.00871092 149 2.31488584 -0.39204539 150 -4.98940921 2.31488584 151 3.01072263 -4.98940921 152 -2.80991604 3.01072263 153 -0.39622299 -2.80991604 154 -0.02069198 -0.39622299 155 5.13283608 -0.02069198 156 -0.75012467 5.13283608 157 2.86850328 -0.75012467 158 0.97026115 2.86850328 159 -1.98488682 0.97026115 160 -0.18000151 -1.98488682 161 -1.67023875 -0.18000151 162 4.09629652 -1.67023875 163 -4.09148157 4.09629652 164 -0.19330924 -4.09148157 165 0.53574246 -0.19330924 166 5.40121519 0.53574246 167 2.26254280 5.40121519 168 2.52684837 2.26254280 169 4.10134270 2.52684837 170 -3.77457905 4.10134270 171 0.03955712 -3.77457905 172 2.64754210 0.03955712 173 -4.27682905 2.64754210 174 2.61850488 -4.27682905 175 0.22145680 2.61850488 176 -4.11891603 0.22145680 177 -0.31746874 -4.11891603 178 -2.90402615 -0.31746874 179 -3.55893998 -2.90402615 180 5.19514774 -3.55893998 181 -4.72904566 5.19514774 182 0.97074038 -4.72904566 183 1.87597541 0.97074038 184 4.13262645 1.87597541 185 0.37527360 4.13262645 186 -1.06263720 0.37527360 187 2.35667660 -1.06263720 188 1.77921318 2.35667660 189 3.53353144 1.77921318 190 -2.49975087 3.53353144 191 -2.41579046 -2.49975087 192 -5.86079391 -2.41579046 193 -6.48872347 -5.86079391 194 0.85486065 -6.48872347 195 2.05227355 0.85486065 196 -2.87253417 2.05227355 197 -7.42072955 -2.87253417 198 -3.25775376 -7.42072955 199 0.34495367 -3.25775376 200 1.33540658 0.34495367 201 -0.75366848 1.33540658 202 -1.10803715 -0.75366848 203 -7.98465574 -1.10803715 204 1.08096389 -7.98465574 205 -2.84392172 1.08096389 206 -5.15174575 -2.84392172 207 -0.84741122 -5.15174575 208 2.08468022 -0.84741122 209 1.44792564 2.08468022 210 -0.58839511 1.44792564 211 -2.76200154 -0.58839511 212 -1.10027100 -2.76200154 213 0.27655641 -1.10027100 214 2.09805917 0.27655641 215 1.26102109 2.09805917 216 2.99042402 1.26102109 217 0.06357895 2.99042402 218 1.79145040 0.06357895 219 -0.12496496 1.79145040 220 -2.14776156 -0.12496496 221 -2.03782268 -2.14776156 222 0.45657783 -2.03782268 223 2.37959728 0.45657783 224 -3.67577484 2.37959728 225 -1.09351567 -3.67577484 226 9.85688063 -1.09351567 227 1.22569447 9.85688063 228 2.24246316 1.22569447 229 0.30993956 2.24246316 230 0.94548763 0.30993956 231 -9.00949286 0.94548763 232 0.35787225 -9.00949286 233 0.42344514 0.35787225 234 13.36078435 0.42344514 235 -0.67008767 13.36078435 236 -4.77433523 -0.67008767 237 1.54727408 -4.77433523 238 0.11282978 1.54727408 239 -4.49432884 0.11282978 240 -6.50147006 -4.49432884 241 -5.72284254 -6.50147006 242 0.89773846 -5.72284254 243 2.92299457 0.89773846 244 -1.82494097 2.92299457 245 0.17252068 -1.82494097 246 -1.12054006 0.17252068 247 -0.19403243 -1.12054006 248 -3.42462760 -0.19403243 249 4.01124987 -3.42462760 250 0.64073634 4.01124987 251 2.78293578 0.64073634 252 -5.70073885 2.78293578 253 -0.30632031 -5.70073885 254 -2.88551843 -0.30632031 255 -5.31803065 -2.88551843 256 1.41414577 -5.31803065 257 9.32689585 1.41414577 258 2.54305690 9.32689585 259 -1.71574466 2.54305690 260 0.98263990 -1.71574466 261 0.54813862 0.98263990 262 1.27674737 0.54813862 263 -5.25347693 1.27674737 264 1.34813135 -5.25347693 265 -1.20390904 1.34813135 266 1.23103926 -1.20390904 267 -4.51906063 1.23103926 268 2.10821678 -4.51906063 269 -3.60238017 2.10821678 270 -0.83728489 -3.60238017 271 3.86788181 -0.83728489 272 -3.83730281 3.86788181 273 -0.77302122 -3.83730281 274 3.38170545 -0.77302122 275 0.45271643 3.38170545 276 -0.51025301 0.45271643 277 0.57878245 -0.51025301 278 -6.00278135 0.57878245 279 -0.17804097 -6.00278135 280 -1.38963014 -0.17804097 281 6.36051646 -1.38963014 282 1.33250169 6.36051646 283 2.80997620 1.33250169 284 -0.11706585 2.80997620 285 3.26505162 -0.11706585 286 2.37085655 3.26505162 287 0.84391859 2.37085655 288 1.53652524 0.84391859 289 2.26306951 1.53652524 290 3.10203367 2.26306951 291 2.70819603 3.10203367 292 3.73388161 2.70819603 293 5.20208144 3.73388161 294 3.98692391 5.20208144 295 5.92178301 3.98692391 296 5.03208707 5.92178301 297 -2.06850946 5.03208707 298 0.24838871 -2.06850946 299 -2.22708250 0.24838871 300 2.76944899 -2.22708250 301 0.08892871 2.76944899 302 1.27944223 0.08892871 303 0.84635169 1.27944223 304 -1.67813036 0.84635169 305 0.73844787 -1.67813036 306 -1.41488243 0.73844787 307 -1.13604580 -1.41488243 308 -0.55649356 -1.13604580 309 -3.68470572 -0.55649356 310 -1.37237115 -3.68470572 311 1.99278441 -1.37237115 312 3.56380750 1.99278441 313 3.50937650 3.56380750 314 3.33948321 3.50937650 315 1.38456650 3.33948321 316 3.69559448 1.38456650 317 -0.83745362 3.69559448 318 2.05498701 -0.83745362 319 -1.88322824 2.05498701 320 -1.85798679 -1.88322824 321 6.15162984 -1.85798679 322 -1.94261609 6.15162984 323 6.83615136 -1.94261609 324 -0.31746103 6.83615136 325 -0.47778776 -0.31746103 326 -0.44461681 -0.47778776 327 -2.11723610 -0.44461681 328 3.38782569 -2.11723610 329 1.67806094 3.38782569 330 -5.02137544 1.67806094 331 0.40141524 -5.02137544 332 0.05164443 0.40141524 333 -0.51998197 0.05164443 334 -2.23769773 -0.51998197 335 -1.85567645 -2.23769773 336 0.56033933 -1.85567645 337 -6.59853091 0.56033933 338 0.42033184 -6.59853091 339 -8.22770778 0.42033184 340 -1.79241650 -8.22770778 341 0.12361780 -1.79241650 342 -0.13122344 0.12361780 343 -0.26443259 -0.13122344 344 -0.24487749 -0.26443259 345 2.76529367 -0.24487749 346 1.69876098 2.76529367 347 0.17509486 1.69876098 348 -3.97368665 0.17509486 349 3.67386382 -3.97368665 350 -1.62076772 3.67386382 351 -0.24756550 -1.62076772 352 -1.47827868 -0.24756550 353 -0.55639544 -1.47827868 354 -1.80772779 -0.55639544 355 0.08718418 -1.80772779 356 0.41665372 0.08718418 357 -2.95526634 0.41665372 358 -5.35594291 -2.95526634 359 -2.43746477 -5.35594291 360 2.82124034 -2.43746477 361 3.20210986 2.82124034 362 -1.33014075 3.20210986 363 4.37753701 -1.33014075 364 -4.91506001 4.37753701 365 -1.90599482 -4.91506001 366 -2.75605498 -1.90599482 367 -3.24865122 -2.75605498 368 -2.94618989 -3.24865122 369 0.62513325 -2.94618989 370 -0.25544782 0.62513325 371 -1.31650910 -0.25544782 372 -0.98824696 -1.31650910 373 -2.02411850 -0.98824696 374 -2.58410400 -2.02411850 375 10.10946168 -2.58410400 376 0.47133123 10.10946168 377 -0.49270804 0.47133123 378 -3.50493298 -0.49270804 379 -3.74023915 -3.50493298 380 -3.28511249 -3.74023915 381 -2.99280754 -3.28511249 382 -1.49793225 -2.99280754 383 -3.13732802 -1.49793225 384 -6.98602610 -3.13732802 385 3.58310840 -6.98602610 386 11.03001557 3.58310840 387 8.53402356 11.03001557 388 -1.78109450 8.53402356 389 8.87756737 -1.78109450 390 10.41062561 8.87756737 391 -1.23471722 10.41062561 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7izrx1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8br8i1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9br8i1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10br8i1293670458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11p0o91293670458.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/120a5u1293670458.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13pb2o1293670458.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14z21q1293670458.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/153liw1293670458.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16zux51293670458.tab") + } > > try(system("convert tmp/1fzar1293670458.ps tmp/1fzar1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/2fzar1293670458.ps tmp/2fzar1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/38qrc1293670458.ps tmp/38qrc1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/48qrc1293670458.ps tmp/48qrc1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/58qrc1293670458.ps tmp/58qrc1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/6izrx1293670458.ps tmp/6izrx1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/7izrx1293670458.ps tmp/7izrx1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/8br8i1293670458.ps tmp/8br8i1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/9br8i1293670458.ps tmp/9br8i1293670458.png",intern=TRUE)) character(0) > try(system("convert tmp/10br8i1293670458.ps tmp/10br8i1293670458.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.584 2.163 28.556