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(58.58527778 + ,79 + ,30 + ,112285 + ,33.60611111 + ,58 + ,28 + ,84786 + ,49.03 + ,60 + ,38 + ,83123 + ,49.81138889 + ,108 + ,30 + ,101193 + ,34.21805556 + ,49 + ,22 + ,38361 + ,14.65166667 + ,0 + ,26 + ,68504 + ,107.0927778 + ,121 + ,25 + ,119182 + ,9.213888889 + ,1 + ,18 + ,22807 + ,28.23472222 + ,20 + ,11 + ,17140 + ,41.40583333 + ,43 + ,26 + ,116174 + ,45.95722222 + ,69 + ,25 + ,57635 + ,65.8925 + ,78 + ,38 + ,66198 + ,48.14611111 + ,86 + ,44 + ,71701 + ,36.98083333 + ,44 + ,30 + ,57793 + ,71.90916667 + ,104 + ,40 + ,80444 + ,50.02305556 + ,63 + ,34 + ,53855 + ,90.22194444 + ,158 + ,47 + ,97668 + ,64.15666667 + ,102 + ,30 + ,133824 + ,65.77361111 + ,77 + ,31 + ,101481 + ,37.63138889 + ,82 + ,23 + ,99645 + ,56.36805556 + ,115 + ,36 + ,114789 + ,59.76305556 + ,101 + ,36 + ,99052 + ,95.63805556 + ,80 + ,30 + ,67654 + ,42.75972222 + ,50 + ,25 + ,65553 + ,36.92861111 + ,83 + ,39 + ,97500 + ,48.53444444 + ,123 + ,34 + ,69112 + ,48.44861111 + ,73 + 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,28 + ,19 + ,25272 + ,24.75361111 + ,19 + ,14 + ,82206 + ,25.27916667 + ,29 + ,11 + ,32073 + ,11.18 + ,8 + ,4 + ,5444 + ,17.82972222 + ,10 + ,16 + ,20154 + ,14.12694444 + ,15 + ,20 + ,36944 + ,15.72583333 + ,15 + ,12 + ,8019 + ,17.44222222 + ,28 + ,15 + ,30884 + ,20.14861111 + ,17 + ,16 + ,19540) + ,dim=c(4 + ,289) + ,dimnames=list(c('UREN' + ,'blogged_computations' + ,'compendiums_reviewed' + ,'totsize ') + ,1:289)) > y <- array(NA,dim=c(4,289),dimnames=list(c('UREN','blogged_computations','compendiums_reviewed','totsize '),1:289)) > 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' > 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, 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 UREN blogged_computations compendiums_reviewed totsize\r 1 58.585278 79 30 112285 2 33.606111 58 28 84786 3 49.030000 60 38 83123 4 49.811389 108 30 101193 5 34.218056 49 22 38361 6 14.651667 0 26 68504 7 107.092778 121 25 119182 8 9.213889 1 18 22807 9 28.234722 20 11 17140 10 41.405833 43 26 116174 11 45.957222 69 25 57635 12 65.892500 78 38 66198 13 48.146111 86 44 71701 14 36.980833 44 30 57793 15 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13497 132 42.353889 106 32 65567 133 17.182500 23 11 25162 134 27.756389 44 25 32334 135 36.801944 71 36 40735 136 88.165000 116 31 91413 137 5.848333 4 0 855 138 58.233611 62 24 97068 139 6.291111 12 13 44339 140 8.726111 18 8 14116 141 12.971667 14 13 10288 142 36.582778 60 19 65622 143 25.481944 7 18 16563 144 67.985833 98 33 76643 145 51.252778 64 40 110681 146 22.184167 29 22 29011 147 35.673056 32 38 92696 148 27.177500 25 24 94785 149 10.615000 16 8 8773 150 41.972500 48 35 83209 151 75.682778 100 43 93815 152 47.915000 46 43 86687 153 30.011944 45 14 34553 154 91.140833 129 41 105547 155 69.605278 130 38 103487 156 97.518611 136 45 213688 157 43.893056 59 31 71220 158 27.462778 25 13 23517 159 23.733056 32 28 56926 160 63.678333 63 31 91721 161 97.671944 95 40 115168 162 23.390833 14 30 111194 163 33.456944 36 16 51009 164 90.166111 113 37 135777 165 36.408056 47 30 51513 166 56.741944 92 35 74163 167 45.984167 70 32 51633 168 39.367222 19 27 75345 169 32.235556 50 20 33416 170 69.457500 41 18 83305 171 83.270833 91 31 98952 172 54.399444 111 31 102372 173 48.127778 41 21 37238 174 70.691111 120 39 103772 175 28.996944 135 41 123969 176 37.801111 27 13 27142 177 55.410000 87 32 135400 178 25.694167 25 18 21399 179 62.313889 131 39 130115 180 37.716944 45 14 24874 181 20.668889 29 7 34988 182 22.566667 58 17 45549 183 4.080000 4 0 6023 184 50.453611 47 30 64466 185 75.515556 109 37 54990 186 1.999722 7 0 1644 187 12.961111 12 5 6179 188 4.874167 0 1 3926 189 37.046667 37 16 32755 190 26.451944 37 32 34777 191 42.389167 46 24 73224 192 27.262778 15 17 27114 193 22.116389 42 11 20760 194 16.442778 7 24 37636 195 38.872778 54 22 65461 196 32.947778 54 12 30080 197 20.244444 14 19 24094 198 18.187500 16 13 69008 199 27.678611 33 17 54968 200 19.990278 32 15 46090 201 21.464444 21 16 27507 202 13.691389 15 24 10672 203 37.536389 38 15 34029 204 30.123889 22 17 46300 205 24.929444 28 18 24760 206 12.304444 10 20 18779 207 21.568889 31 16 21280 208 50.424444 32 16 40662 209 37.227500 32 18 28987 210 34.462222 43 22 22827 211 25.730556 27 8 18513 212 33.846667 37 17 30594 213 14.698611 20 18 24006 214 22.742222 32 16 27913 215 16.383611 0 23 42744 216 14.865278 5 22 12934 217 16.892222 26 13 22574 218 15.659722 10 13 41385 219 18.191667 27 16 18653 220 22.485833 11 16 18472 221 21.195000 29 20 30976 222 28.891944 25 22 63339 223 27.251111 55 17 25568 224 18.885833 23 18 33747 225 8.608056 5 17 4154 226 37.627222 43 12 19474 227 20.417778 23 7 35130 228 17.534167 34 17 39067 229 17.015000 36 14 13310 230 20.809444 35 23 65892 231 8.826111 0 17 4143 232 22.621389 37 14 28579 233 24.218333 28 15 51776 234 13.913889 16 17 21152 235 18.262500 26 21 38084 236 15.736944 38 18 27717 237 43.999722 23 18 32928 238 12.904167 22 17 11342 239 20.451111 30 17 19499 240 10.665278 16 16 16380 241 25.527500 18 15 36874 242 38.757222 28 21 48259 243 14.490000 32 16 16734 244 14.324167 21 14 28207 245 19.597500 23 15 30143 246 23.571111 29 17 41369 247 28.482778 50 15 45833 248 24.077222 12 15 29156 249 23.808056 21 10 35944 250 9.628333 18 6 36278 251 41.827778 27 22 45588 252 27.669722 41 21 45097 253 5.374722 13 1 3895 254 27.603611 12 18 28394 255 23.952778 21 17 18632 256 8.565833 8 4 2325 257 8.807222 26 10 25139 258 24.946111 27 16 27975 259 17.246667 13 16 14483 260 11.153056 16 9 13127 261 7.676111 2 16 5839 262 21.386111 42 17 24069 263 10.405556 5 7 3738 264 15.043611 37 15 18625 265 13.850556 17 14 36341 266 23.426944 38 14 24548 267 17.826389 37 18 21792 268 16.495000 29 12 26263 269 33.141111 32 16 23686 270 21.306111 35 21 49303 271 28.729167 17 19 25659 272 19.540000 20 16 28904 273 12.058333 7 1 2781 274 29.121667 46 16 29236 275 17.281944 24 10 19546 276 19.251111 40 19 22818 277 14.754722 3 12 32689 278 5.490000 10 2 5752 279 24.077778 37 14 22197 280 23.362500 17 17 20055 281 21.651389 28 19 25272 282 24.753611 19 14 82206 283 25.279167 29 11 32073 284 11.180000 8 4 5444 285 17.829722 10 16 20154 286 14.126944 15 20 36944 287 15.725833 15 12 8019 288 17.442222 28 15 30884 289 20.148611 17 16 19540 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) blogged_computations compendiums_reviewed 4.083548 0.318850 0.492732 `totsize\\r` 0.000106 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -51.475 -6.234 -1.660 5.081 44.894 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.084e+00 1.692e+00 2.413 0.016437 * blogged_computations 3.189e-01 3.250e-02 9.811 < 2e-16 *** compendiums_reviewed 4.927e-01 1.083e-01 4.549 7.97e-06 *** `totsize\\r` 1.060e-04 2.829e-05 3.747 0.000217 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 11.72 on 285 degrees of freedom Multiple R-squared: 0.7402, Adjusted R-squared: 0.7375 F-statistic: 270.7 on 3 and 285 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,] 0.9633273 7.334541e-02 3.667270e-02 [2,] 0.9257279 1.485443e-01 7.427213e-02 [3,] 0.8768804 2.462392e-01 1.231196e-01 [4,] 0.8200441 3.599118e-01 1.799559e-01 [5,] 0.7383030 5.233941e-01 2.616970e-01 [6,] 0.8112905 3.774190e-01 1.887095e-01 [7,] 0.7594717 4.810566e-01 2.405283e-01 [8,] 0.6868835 6.262331e-01 3.131165e-01 [9,] 0.6200817 7.598367e-01 3.799183e-01 [10,] 0.5574093 8.851814e-01 4.425907e-01 [11,] 0.4975217 9.950434e-01 5.024783e-01 [12,] 0.4600968 9.201937e-01 5.399032e-01 [13,] 0.4376141 8.752281e-01 5.623859e-01 [14,] 0.6120534 7.758932e-01 3.879466e-01 [15,] 0.6763612 6.472777e-01 3.236388e-01 [16,] 0.6224309 7.551381e-01 3.775691e-01 [17,] 0.9700739 5.985224e-02 2.992612e-02 [18,] 0.9581349 8.373013e-02 4.186507e-02 [19,] 0.9710654 5.786914e-02 2.893457e-02 [20,] 0.9904115 1.917696e-02 9.588478e-03 [21,] 0.9861807 2.763861e-02 1.381930e-02 [22,] 0.9827731 3.445381e-02 1.722691e-02 [23,] 0.9761684 4.766321e-02 2.383160e-02 [24,] 0.9675975 6.480504e-02 3.240252e-02 [25,] 0.9570187 8.596255e-02 4.298127e-02 [26,] 0.9441225 1.117549e-01 5.587747e-02 [27,] 0.9387014 1.225971e-01 6.129856e-02 [28,] 0.9223384 1.553233e-01 7.766164e-02 [29,] 0.9193413 1.613174e-01 8.065870e-02 [30,] 0.9272756 1.454487e-01 7.272436e-02 [31,] 0.9087922 1.824156e-01 9.120778e-02 [32,] 0.9046413 1.907174e-01 9.535870e-02 [33,] 0.9805305 3.893892e-02 1.946946e-02 [34,] 0.9775209 4.495829e-02 2.247914e-02 [35,] 0.9707514 5.849715e-02 2.924858e-02 [36,] 0.9622262 7.554764e-02 3.777382e-02 [37,] 0.9535637 9.287251e-02 4.643625e-02 [38,] 0.9528895 9.422097e-02 4.711048e-02 [39,] 0.9493447 1.013107e-01 5.065533e-02 [40,] 0.9369971 1.260059e-01 6.300293e-02 [41,] 0.9299416 1.401169e-01 7.005844e-02 [42,] 0.9142877 1.714246e-01 8.571230e-02 [43,] 0.8971648 2.056705e-01 1.028352e-01 [44,] 0.8797056 2.405888e-01 1.202944e-01 [45,] 0.9940341 1.193187e-02 5.965935e-03 [46,] 0.9921493 1.570142e-02 7.850710e-03 [47,] 0.9916068 1.678635e-02 8.393177e-03 [48,] 0.9889503 2.209941e-02 1.104971e-02 [49,] 0.9902236 1.955285e-02 9.776423e-03 [50,] 0.9877638 2.447239e-02 1.223619e-02 [51,] 0.9849806 3.003879e-02 1.501939e-02 [52,] 0.9964427 7.114664e-03 3.557332e-03 [53,] 0.9954115 9.177044e-03 4.588522e-03 [54,] 0.9939692 1.206169e-02 6.030843e-03 [55,] 0.9928776 1.424489e-02 7.122443e-03 [56,] 0.9914190 1.716195e-02 8.580977e-03 [57,] 0.9984559 3.088292e-03 1.544146e-03 [58,] 0.9979859 4.028297e-03 2.014148e-03 [59,] 0.9974259 5.148169e-03 2.574085e-03 [60,] 0.9968412 6.317583e-03 3.158792e-03 [61,] 0.9960796 7.840718e-03 3.920359e-03 [62,] 0.9952936 9.412861e-03 4.706431e-03 [63,] 0.9949850 1.003003e-02 5.015017e-03 [64,] 0.9939767 1.204652e-02 6.023260e-03 [65,] 0.9935483 1.290335e-02 6.451673e-03 [66,] 0.9936621 1.267576e-02 6.337879e-03 [67,] 0.9918799 1.624014e-02 8.120070e-03 [68,] 0.9922839 1.543223e-02 7.716117e-03 [69,] 0.9900850 1.982993e-02 9.914966e-03 [70,] 0.9920044 1.599126e-02 7.995630e-03 [71,] 0.9983734 3.253165e-03 1.626582e-03 [72,] 0.9979053 4.189477e-03 2.094739e-03 [73,] 0.9972958 5.408388e-03 2.704194e-03 [74,] 0.9964460 7.107905e-03 3.553952e-03 [75,] 0.9954686 9.062800e-03 4.531400e-03 [76,] 0.9975266 4.946870e-03 2.473435e-03 [77,] 0.9971925 5.615033e-03 2.807516e-03 [78,] 0.9969937 6.012553e-03 3.006277e-03 [79,] 0.9982258 3.548397e-03 1.774198e-03 [80,] 0.9989451 2.109741e-03 1.054870e-03 [81,] 0.9986283 2.743328e-03 1.371664e-03 [82,] 0.9982163 3.567464e-03 1.783732e-03 [83,] 0.9992817 1.436545e-03 7.182723e-04 [84,] 0.9992102 1.579503e-03 7.897516e-04 [85,] 0.9990926 1.814731e-03 9.073656e-04 [86,] 0.9989594 2.081250e-03 1.040625e-03 [87,] 0.9988095 2.380928e-03 1.190464e-03 [88,] 0.9989193 2.161383e-03 1.080691e-03 [89,] 0.9985568 2.886433e-03 1.443216e-03 [90,] 0.9998211 3.577044e-04 1.788522e-04 [91,] 0.9998047 3.906925e-04 1.953463e-04 [92,] 0.9999918 1.648890e-05 8.244452e-06 [93,] 0.9999911 1.785267e-05 8.926334e-06 [94,] 0.9999956 8.884210e-06 4.442105e-06 [95,] 0.9999941 1.184290e-05 5.921452e-06 [96,] 0.9999917 1.652321e-05 8.261606e-06 [97,] 0.9999954 9.242968e-06 4.621484e-06 [98,] 0.9999941 1.175476e-05 5.877380e-06 [99,] 0.9999926 1.470804e-05 7.354018e-06 [100,] 0.9999934 1.328948e-05 6.644742e-06 [101,] 0.9999904 1.923762e-05 9.618808e-06 [102,] 0.9999871 2.579673e-05 1.289837e-05 [103,] 0.9999872 2.555532e-05 1.277766e-05 [104,] 0.9999817 3.652449e-05 1.826224e-05 [105,] 0.9999765 4.703084e-05 2.351542e-05 [106,] 0.9999677 6.456489e-05 3.228244e-05 [107,] 0.9999660 6.803971e-05 3.401985e-05 [108,] 0.9999517 9.654824e-05 4.827412e-05 [109,] 0.9999429 1.141246e-04 5.706231e-05 [110,] 0.9999281 1.438702e-04 7.193509e-05 [111,] 0.9999277 1.446899e-04 7.234493e-05 [112,] 0.9999843 3.132663e-05 1.566331e-05 [113,] 0.9999907 1.850725e-05 9.253624e-06 [114,] 0.9999871 2.586945e-05 1.293473e-05 [115,] 0.9999813 3.732450e-05 1.866225e-05 [116,] 0.9999742 5.162130e-05 2.581065e-05 [117,] 0.9999675 6.494151e-05 3.247075e-05 [118,] 0.9999981 3.764178e-06 1.882089e-06 [119,] 0.9999982 3.671061e-06 1.835531e-06 [120,] 0.9999973 5.403397e-06 2.701699e-06 [121,] 0.9999961 7.820873e-06 3.910437e-06 [122,] 0.9999964 7.256887e-06 3.628443e-06 [123,] 0.9999959 8.128161e-06 4.064080e-06 [124,] 0.9999942 1.160523e-05 5.802613e-06 [125,] 0.9999915 1.691928e-05 8.459638e-06 [126,] 0.9999953 9.371661e-06 4.685831e-06 [127,] 0.9999933 1.345890e-05 6.729449e-06 [128,] 0.9999911 1.781234e-05 8.906169e-06 [129,] 0.9999910 1.797880e-05 8.989398e-06 [130,] 0.9999967 6.635661e-06 3.317831e-06 [131,] 0.9999951 9.828142e-06 4.914071e-06 [132,] 0.9999951 9.811005e-06 4.905502e-06 [133,] 0.9999958 8.416433e-06 4.208217e-06 [134,] 0.9999946 1.079116e-05 5.395581e-06 [135,] 0.9999923 1.546216e-05 7.731081e-06 [136,] 0.9999891 2.186850e-05 1.093425e-05 [137,] 0.9999874 2.514843e-05 1.257421e-05 [138,] 0.9999852 2.962176e-05 1.481088e-05 [139,] 0.9999799 4.024987e-05 2.012494e-05 [140,] 0.9999728 5.443902e-05 2.721951e-05 [141,] 0.9999663 6.730285e-05 3.365143e-05 [142,] 0.9999603 7.940902e-05 3.970451e-05 [143,] 0.9999453 1.093132e-04 5.465661e-05 [144,] 0.9999256 1.488948e-04 7.444742e-05 [145,] 0.9999165 1.670369e-04 8.351843e-05 [146,] 0.9998829 2.341095e-04 1.170547e-04 [147,] 0.9998372 3.256524e-04 1.628262e-04 [148,] 0.9998808 2.383041e-04 1.191520e-04 [149,] 0.9998417 3.165333e-04 1.582666e-04 [150,] 0.9997895 4.210215e-04 2.105107e-04 [151,] 0.9997111 5.777521e-04 2.888760e-04 [152,] 0.9996440 7.119719e-04 3.559859e-04 [153,] 0.9996270 7.459820e-04 3.729910e-04 [154,] 0.9996886 6.227038e-04 3.113519e-04 [155,] 0.9999771 4.576605e-05 2.288302e-05 [156,] 0.9999818 3.640790e-05 1.820395e-05 [157,] 0.9999749 5.024912e-05 2.512456e-05 [158,] 0.9999891 2.189695e-05 1.094848e-05 [159,] 0.9999840 3.193549e-05 1.596774e-05 [160,] 0.9999776 4.479504e-05 2.239752e-05 [161,] 0.9999684 6.321353e-05 3.160677e-05 [162,] 0.9999612 7.769866e-05 3.884933e-05 [163,] 0.9999445 1.109972e-04 5.549862e-05 [164,] 0.9999987 2.506967e-06 1.253484e-06 [165,] 1.0000000 9.460124e-08 4.730062e-08 [166,] 0.9999999 1.331074e-07 6.655368e-08 [167,] 1.0000000 4.346300e-08 2.173150e-08 [168,] 1.0000000 4.908674e-08 2.454337e-08 [169,] 1.0000000 8.367739e-13 4.183870e-13 [170,] 1.0000000 2.237062e-13 1.118531e-13 [171,] 1.0000000 3.804879e-13 1.902439e-13 [172,] 1.0000000 6.934349e-13 3.467174e-13 [173,] 1.0000000 1.239825e-13 6.199126e-14 [174,] 1.0000000 1.141113e-13 5.705567e-14 [175,] 1.0000000 2.330708e-13 1.165354e-13 [176,] 1.0000000 8.937278e-14 4.468639e-14 [177,] 1.0000000 1.812949e-13 9.064746e-14 [178,] 1.0000000 1.750911e-13 8.754557e-14 [179,] 1.0000000 7.559650e-14 3.779825e-14 [180,] 1.0000000 1.258091e-13 6.290453e-14 [181,] 1.0000000 2.529540e-13 1.264770e-13 [182,] 1.0000000 5.195727e-13 2.597864e-13 [183,] 1.0000000 3.959381e-13 1.979690e-13 [184,] 1.0000000 6.174988e-13 3.087494e-13 [185,] 1.0000000 1.062105e-12 5.310523e-13 [186,] 1.0000000 1.201499e-12 6.007495e-13 [187,] 1.0000000 2.355531e-12 1.177766e-12 [188,] 1.0000000 4.160587e-12 2.080294e-12 [189,] 1.0000000 8.378632e-12 4.189316e-12 [190,] 1.0000000 1.508320e-11 7.541601e-12 [191,] 1.0000000 2.950234e-11 1.475117e-11 [192,] 1.0000000 4.255835e-11 2.127918e-11 [193,] 1.0000000 8.318434e-11 4.159217e-11 [194,] 1.0000000 1.160628e-10 5.803142e-11 [195,] 1.0000000 2.248085e-10 1.124043e-10 [196,] 1.0000000 3.532603e-10 1.766301e-10 [197,] 1.0000000 2.474879e-10 1.237440e-10 [198,] 1.0000000 3.657206e-10 1.828603e-10 [199,] 1.0000000 6.794805e-10 3.397403e-10 [200,] 1.0000000 1.051239e-09 5.256193e-10 [201,] 1.0000000 1.997308e-09 9.986542e-10 [202,] 1.0000000 1.834332e-11 9.171661e-12 [203,] 1.0000000 6.875563e-12 3.437781e-12 [204,] 1.0000000 8.130955e-12 4.065478e-12 [205,] 1.0000000 8.190545e-12 4.095272e-12 [206,] 1.0000000 6.823110e-12 3.411555e-12 [207,] 1.0000000 1.097848e-11 5.489242e-12 [208,] 1.0000000 2.335513e-11 1.167756e-11 [209,] 1.0000000 4.166897e-11 2.083448e-11 [210,] 1.0000000 8.416908e-11 4.208454e-11 [211,] 1.0000000 1.675959e-10 8.379795e-11 [212,] 1.0000000 3.183636e-10 1.591818e-10 [213,] 1.0000000 6.344778e-10 3.172389e-10 [214,] 1.0000000 9.473124e-10 4.736562e-10 [215,] 1.0000000 1.801723e-09 9.008616e-10 [216,] 1.0000000 3.666680e-09 1.833340e-09 [217,] 1.0000000 7.174948e-09 3.587474e-09 [218,] 1.0000000 1.286365e-08 6.431824e-09 [219,] 1.0000000 1.999768e-08 9.998842e-09 [220,] 1.0000000 2.686172e-09 1.343086e-09 [221,] 1.0000000 5.010112e-09 2.505056e-09 [222,] 1.0000000 6.400808e-09 3.200404e-09 [223,] 1.0000000 1.242744e-08 6.213718e-09 [224,] 1.0000000 5.774308e-09 2.887154e-09 [225,] 1.0000000 9.135014e-09 4.567507e-09 [226,] 1.0000000 1.864724e-08 9.323622e-09 [227,] 1.0000000 3.791390e-08 1.895695e-08 [228,] 1.0000000 5.610118e-08 2.805059e-08 [229,] 1.0000000 5.719475e-08 2.859738e-08 [230,] 1.0000000 5.508622e-08 2.754311e-08 [231,] 1.0000000 8.407517e-10 4.203759e-10 [232,] 1.0000000 1.225375e-09 6.126876e-10 [233,] 1.0000000 2.817013e-09 1.408507e-09 [234,] 1.0000000 2.782753e-09 1.391377e-09 [235,] 1.0000000 5.178761e-09 2.589380e-09 [236,] 1.0000000 2.294107e-09 1.147053e-09 [237,] 1.0000000 2.974851e-09 1.487426e-09 [238,] 1.0000000 4.743892e-09 2.371946e-09 [239,] 1.0000000 1.115982e-08 5.579910e-09 [240,] 1.0000000 2.623947e-08 1.311974e-08 [241,] 1.0000000 5.701166e-08 2.850583e-08 [242,] 1.0000000 9.470399e-08 4.735200e-08 [243,] 0.9999999 1.275306e-07 6.376529e-08 [244,] 0.9999999 2.144321e-07 1.072160e-07 [245,] 1.0000000 1.441051e-08 7.205254e-09 [246,] 1.0000000 3.671126e-08 1.835563e-08 [247,] 1.0000000 7.811165e-08 3.905582e-08 [248,] 1.0000000 5.583792e-08 2.791896e-08 [249,] 1.0000000 9.224648e-08 4.612324e-08 [250,] 0.9999999 2.340378e-07 1.170189e-07 [251,] 0.9999999 1.249486e-07 6.247432e-08 [252,] 0.9999999 2.321923e-07 1.160961e-07 [253,] 0.9999997 5.987794e-07 2.993897e-07 [254,] 0.9999994 1.245337e-06 6.226685e-07 [255,] 0.9999992 1.678587e-06 8.392934e-07 [256,] 0.9999980 3.949302e-06 1.974651e-06 [257,] 0.9999951 9.808251e-06 4.904125e-06 [258,] 0.9999948 1.038637e-05 5.193186e-06 [259,] 0.9999918 1.645555e-05 8.227776e-06 [260,] 0.9999791 4.173944e-05 2.086972e-05 [261,] 0.9999733 5.331013e-05 2.665507e-05 [262,] 0.9999506 9.883743e-05 4.941872e-05 [263,] 0.9999869 2.616793e-05 1.308396e-05 [264,] 0.9999812 3.769588e-05 1.884794e-05 [265,] 0.9999944 1.118467e-05 5.592336e-06 [266,] 0.9999810 3.790612e-05 1.895306e-05 [267,] 0.9999483 1.034654e-04 5.173269e-05 [268,] 0.9998816 2.368044e-04 1.184022e-04 [269,] 0.9996250 7.499315e-04 3.749657e-04 [270,] 0.9995306 9.387744e-04 4.693872e-04 [271,] 0.9985531 2.893857e-03 1.446928e-03 [272,] 0.9983878 3.224401e-03 1.612200e-03 [273,] 0.9946113 1.077735e-02 5.388674e-03 [274,] 0.9932989 1.340220e-02 6.701101e-03 [275,] 0.9769704 4.605921e-02 2.302960e-02 [276,] 0.9370958 1.258085e-01 6.290424e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1h0l21352144422.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2rqse1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3km4w1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4l73v1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5yspi1352144422.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 = 289 Frequency = 1 1 2 3 4 5 6 2.62765311 -11.75511741 -1.71995530 -14.21706720 -0.39577136 -9.50478305 7 8 9 10 11 12 39.47597190 -6.47537160 10.53716743 -1.51451587 1.44502280 11.19740308 13 14 15 16 17 18 -12.63953185 -2.04051637 6.42832882 3.39007910 2.24821284 -1.41779180 19 20 21 22 23 24 11.10625157 -14.49374138 -14.29000836 -4.76287764 44.09276470 3.46631683 25 26 27 28 29 30 -23.17168969 -18.84689142 -2.95805432 8.42231756 0.59522468 0.02632286 31 32 33 34 35 36 -0.49524640 -5.20814003 0.64319067 1.19812147 -13.93538941 16.56473599 37 38 39 40 41 42 5.08144100 11.49440125 33.30487567 -2.24106241 3.96801151 4.75913591 43 44 45 46 47 48 -1.46720259 14.55265285 14.80772961 -4.18643909 12.48793720 -2.69711542 49 50 51 52 53 54 -4.57732846 -9.39232941 44.89440597 -4.88429710 -10.22103297 2.50155126 55 56 57 58 59 60 -13.19412020 -2.88130462 -2.51712731 30.70226183 -5.03558362 4.17468477 61 62 63 64 65 66 9.66139211 -5.39349652 38.06380866 5.67894175 4.80057395 -0.45966194 67 68 69 70 71 72 7.72331353 8.90000803 0.78889488 5.10551351 10.61856988 13.05423594 73 74 75 76 77 78 3.17562262 -10.26754991 -1.42858769 -11.94814037 -28.31961345 -5.16158260 79 80 81 82 83 84 -4.39373834 1.47824626 -2.23293592 -21.70790087 9.33612773 11.48896971 85 86 87 88 89 90 -17.22846263 -19.33790512 -4.31275708 -3.07489230 24.56504548 9.93553585 91 92 93 94 95 96 -8.48247145 -7.16442891 8.84775479 -13.57133992 -0.15547190 33.51937188 97 98 99 100 101 102 9.66178973 -37.57092315 10.47810092 -20.20746797 6.57841386 4.24088301 103 104 105 106 107 108 18.79766411 -7.10121024 -7.67348853 -13.78049353 -3.36892040 -5.03017259 109 110 111 112 113 114 11.71593400 -3.06413210 -7.19921459 4.00590530 -10.20412633 1.42251840 115 116 117 118 119 120 -8.73690901 -7.12146512 -12.05720378 27.41092737 -17.12791245 -3.20965534 121 122 123 124 125 126 0.90513489 -2.96981423 7.28987333 -32.27679748 13.00760452 2.99489516 127 128 129 130 131 132 4.21975757 -12.00731774 -10.28591752 -2.15463201 1.59720689 -18.24573582 133 134 135 136 137 138 -2.32199114 -6.10247910 -11.97648954 22.12975563 0.39874835 12.26593368 139 140 141 142 143 144 -12.72438034 -6.53498435 -3.07189188 -2.95005543 8.54148810 8.27014917 145 146 147 148 149 150 -4.67937368 -5.06149264 -7.16389445 -6.75070463 -3.44200127 -3.48217417 151 152 153 154 155 156 8.58173105 -1.21251718 1.01904429 14.53492183 -5.62291561 5.24615342 157 158 159 160 161 162 -1.82713123 6.50950403 -10.38472018 14.50950259 31.37978300 -11.72587127 163 164 165 166 167 168 4.60378763 17.42814800 -2.90412298 -1.78320468 -1.65975169 7.93469436 169 170 171 172 173 174 -1.18745552 34.60103714 24.40766106 -11.20327583 16.67652996 -1.87152314 175 176 177 178 179 180 -51.47492364 15.82586264 -6.53423419 2.50175738 -16.54863213 9.75008293 181 182 183 184 185 186 0.18060072 -13.21513089 -1.91742750 9.76832806 12.61695502 -4.49005281 187 188 189 190 191 192 1.93268681 -0.11829533 9.80970552 -8.88306682 4.05071662 7.14577444 193 194 195 196 197 198 -2.97961576 -5.68794652 -0.20808142 2.54485625 -0.21903154 -4.71847124 199 200 201 202 203 204 -1.13040690 -6.57329960 -0.11458953 -8.13177340 10.33825651 5.74108961 205 206 207 208 209 210 0.42419593 -6.81293655 -2.53854408 23.94353977 10.99876023 3.40820460 211 212 213 214 215 216 7.13369640 6.34605441 -7.17590630 -2.38720331 -3.56391196 -3.02370596 217 218 219 220 221 222 -4.27993749 -2.40492498 -4.36188572 5.05307228 -5.27349936 -0.71731110 223 224 225 226 227 228 -5.45601623 -4.97784591 -5.88653186 11.85596036 1.82753822 -9.90808940 229 230 231 232 233 234 -6.85634796 -12.75168027 -4.07305894 -3.18742530 -1.67259700 -5.88995289 235 236 237 238 239 240 -8.49567559 -12.27026840 20.22286245 -7.77285114 -3.64140524 -8.13996859 241 242 243 244 245 246 4.40478378 10.28272816 -9.45437687 -6.34360890 -2.40593656 -2.52092104 247 248 249 250 251 252 -3.79286054 5.68576710 4.29103246 -6.99661907 13.46254628 -4.61463208 253 254 255 256 257 258 -3.75950693 7.81473846 2.82182154 -0.28590865 -11.15865000 1.40436446 259 260 261 262 263 264 -0.40093425 -3.85823032 -5.54781524 -7.01705886 0.88238230 -10.20274414 265 266 267 268 269 270 -6.40407730 -2.27340709 -9.23388412 -5.53203758 8.45977581 -9.51100573 271 272 273 274 275 276 7.14323946 -1.86827504 4.95529717 -0.61190931 -1.45333192 -9.36720707 277 278 279 280 281 282 0.33659244 -3.37726432 -1.05450179 3.35609729 -3.40086658 -1.00071858 283 284 285 286 287 288 3.12896182 1.99762318 0.53750820 -8.51029916 0.09668521 -6.23401658 289 0.68953338 > postscript(file="/var/wessaorg/rcomp/tmp/6e5bk1352144422.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 = 289 Frequency = 1 lag(myerror, k = 1) myerror 0 2.62765311 NA 1 -11.75511741 2.62765311 2 -1.71995530 -11.75511741 3 -14.21706720 -1.71995530 4 -0.39577136 -14.21706720 5 -9.50478305 -0.39577136 6 39.47597190 -9.50478305 7 -6.47537160 39.47597190 8 10.53716743 -6.47537160 9 -1.51451587 10.53716743 10 1.44502280 -1.51451587 11 11.19740308 1.44502280 12 -12.63953185 11.19740308 13 -2.04051637 -12.63953185 14 6.42832882 -2.04051637 15 3.39007910 6.42832882 16 2.24821284 3.39007910 17 -1.41779180 2.24821284 18 11.10625157 -1.41779180 19 -14.49374138 11.10625157 20 -14.29000836 -14.49374138 21 -4.76287764 -14.29000836 22 44.09276470 -4.76287764 23 3.46631683 44.09276470 24 -23.17168969 3.46631683 25 -18.84689142 -23.17168969 26 -2.95805432 -18.84689142 27 8.42231756 -2.95805432 28 0.59522468 8.42231756 29 0.02632286 0.59522468 30 -0.49524640 0.02632286 31 -5.20814003 -0.49524640 32 0.64319067 -5.20814003 33 1.19812147 0.64319067 34 -13.93538941 1.19812147 35 16.56473599 -13.93538941 36 5.08144100 16.56473599 37 11.49440125 5.08144100 38 33.30487567 11.49440125 39 -2.24106241 33.30487567 40 3.96801151 -2.24106241 41 4.75913591 3.96801151 42 -1.46720259 4.75913591 43 14.55265285 -1.46720259 44 14.80772961 14.55265285 45 -4.18643909 14.80772961 46 12.48793720 -4.18643909 47 -2.69711542 12.48793720 48 -4.57732846 -2.69711542 49 -9.39232941 -4.57732846 50 44.89440597 -9.39232941 51 -4.88429710 44.89440597 52 -10.22103297 -4.88429710 53 2.50155126 -10.22103297 54 -13.19412020 2.50155126 55 -2.88130462 -13.19412020 56 -2.51712731 -2.88130462 57 30.70226183 -2.51712731 58 -5.03558362 30.70226183 59 4.17468477 -5.03558362 60 9.66139211 4.17468477 61 -5.39349652 9.66139211 62 38.06380866 -5.39349652 63 5.67894175 38.06380866 64 4.80057395 5.67894175 65 -0.45966194 4.80057395 66 7.72331353 -0.45966194 67 8.90000803 7.72331353 68 0.78889488 8.90000803 69 5.10551351 0.78889488 70 10.61856988 5.10551351 71 13.05423594 10.61856988 72 3.17562262 13.05423594 73 -10.26754991 3.17562262 74 -1.42858769 -10.26754991 75 -11.94814037 -1.42858769 76 -28.31961345 -11.94814037 77 -5.16158260 -28.31961345 78 -4.39373834 -5.16158260 79 1.47824626 -4.39373834 80 -2.23293592 1.47824626 81 -21.70790087 -2.23293592 82 9.33612773 -21.70790087 83 11.48896971 9.33612773 84 -17.22846263 11.48896971 85 -19.33790512 -17.22846263 86 -4.31275708 -19.33790512 87 -3.07489230 -4.31275708 88 24.56504548 -3.07489230 89 9.93553585 24.56504548 90 -8.48247145 9.93553585 91 -7.16442891 -8.48247145 92 8.84775479 -7.16442891 93 -13.57133992 8.84775479 94 -0.15547190 -13.57133992 95 33.51937188 -0.15547190 96 9.66178973 33.51937188 97 -37.57092315 9.66178973 98 10.47810092 -37.57092315 99 -20.20746797 10.47810092 100 6.57841386 -20.20746797 101 4.24088301 6.57841386 102 18.79766411 4.24088301 103 -7.10121024 18.79766411 104 -7.67348853 -7.10121024 105 -13.78049353 -7.67348853 106 -3.36892040 -13.78049353 107 -5.03017259 -3.36892040 108 11.71593400 -5.03017259 109 -3.06413210 11.71593400 110 -7.19921459 -3.06413210 111 4.00590530 -7.19921459 112 -10.20412633 4.00590530 113 1.42251840 -10.20412633 114 -8.73690901 1.42251840 115 -7.12146512 -8.73690901 116 -12.05720378 -7.12146512 117 27.41092737 -12.05720378 118 -17.12791245 27.41092737 119 -3.20965534 -17.12791245 120 0.90513489 -3.20965534 121 -2.96981423 0.90513489 122 7.28987333 -2.96981423 123 -32.27679748 7.28987333 124 13.00760452 -32.27679748 125 2.99489516 13.00760452 126 4.21975757 2.99489516 127 -12.00731774 4.21975757 128 -10.28591752 -12.00731774 129 -2.15463201 -10.28591752 130 1.59720689 -2.15463201 131 -18.24573582 1.59720689 132 -2.32199114 -18.24573582 133 -6.10247910 -2.32199114 134 -11.97648954 -6.10247910 135 22.12975563 -11.97648954 136 0.39874835 22.12975563 137 12.26593368 0.39874835 138 -12.72438034 12.26593368 139 -6.53498435 -12.72438034 140 -3.07189188 -6.53498435 141 -2.95005543 -3.07189188 142 8.54148810 -2.95005543 143 8.27014917 8.54148810 144 -4.67937368 8.27014917 145 -5.06149264 -4.67937368 146 -7.16389445 -5.06149264 147 -6.75070463 -7.16389445 148 -3.44200127 -6.75070463 149 -3.48217417 -3.44200127 150 8.58173105 -3.48217417 151 -1.21251718 8.58173105 152 1.01904429 -1.21251718 153 14.53492183 1.01904429 154 -5.62291561 14.53492183 155 5.24615342 -5.62291561 156 -1.82713123 5.24615342 157 6.50950403 -1.82713123 158 -10.38472018 6.50950403 159 14.50950259 -10.38472018 160 31.37978300 14.50950259 161 -11.72587127 31.37978300 162 4.60378763 -11.72587127 163 17.42814800 4.60378763 164 -2.90412298 17.42814800 165 -1.78320468 -2.90412298 166 -1.65975169 -1.78320468 167 7.93469436 -1.65975169 168 -1.18745552 7.93469436 169 34.60103714 -1.18745552 170 24.40766106 34.60103714 171 -11.20327583 24.40766106 172 16.67652996 -11.20327583 173 -1.87152314 16.67652996 174 -51.47492364 -1.87152314 175 15.82586264 -51.47492364 176 -6.53423419 15.82586264 177 2.50175738 -6.53423419 178 -16.54863213 2.50175738 179 9.75008293 -16.54863213 180 0.18060072 9.75008293 181 -13.21513089 0.18060072 182 -1.91742750 -13.21513089 183 9.76832806 -1.91742750 184 12.61695502 9.76832806 185 -4.49005281 12.61695502 186 1.93268681 -4.49005281 187 -0.11829533 1.93268681 188 9.80970552 -0.11829533 189 -8.88306682 9.80970552 190 4.05071662 -8.88306682 191 7.14577444 4.05071662 192 -2.97961576 7.14577444 193 -5.68794652 -2.97961576 194 -0.20808142 -5.68794652 195 2.54485625 -0.20808142 196 -0.21903154 2.54485625 197 -4.71847124 -0.21903154 198 -1.13040690 -4.71847124 199 -6.57329960 -1.13040690 200 -0.11458953 -6.57329960 201 -8.13177340 -0.11458953 202 10.33825651 -8.13177340 203 5.74108961 10.33825651 204 0.42419593 5.74108961 205 -6.81293655 0.42419593 206 -2.53854408 -6.81293655 207 23.94353977 -2.53854408 208 10.99876023 23.94353977 209 3.40820460 10.99876023 210 7.13369640 3.40820460 211 6.34605441 7.13369640 212 -7.17590630 6.34605441 213 -2.38720331 -7.17590630 214 -3.56391196 -2.38720331 215 -3.02370596 -3.56391196 216 -4.27993749 -3.02370596 217 -2.40492498 -4.27993749 218 -4.36188572 -2.40492498 219 5.05307228 -4.36188572 220 -5.27349936 5.05307228 221 -0.71731110 -5.27349936 222 -5.45601623 -0.71731110 223 -4.97784591 -5.45601623 224 -5.88653186 -4.97784591 225 11.85596036 -5.88653186 226 1.82753822 11.85596036 227 -9.90808940 1.82753822 228 -6.85634796 -9.90808940 229 -12.75168027 -6.85634796 230 -4.07305894 -12.75168027 231 -3.18742530 -4.07305894 232 -1.67259700 -3.18742530 233 -5.88995289 -1.67259700 234 -8.49567559 -5.88995289 235 -12.27026840 -8.49567559 236 20.22286245 -12.27026840 237 -7.77285114 20.22286245 238 -3.64140524 -7.77285114 239 -8.13996859 -3.64140524 240 4.40478378 -8.13996859 241 10.28272816 4.40478378 242 -9.45437687 10.28272816 243 -6.34360890 -9.45437687 244 -2.40593656 -6.34360890 245 -2.52092104 -2.40593656 246 -3.79286054 -2.52092104 247 5.68576710 -3.79286054 248 4.29103246 5.68576710 249 -6.99661907 4.29103246 250 13.46254628 -6.99661907 251 -4.61463208 13.46254628 252 -3.75950693 -4.61463208 253 7.81473846 -3.75950693 254 2.82182154 7.81473846 255 -0.28590865 2.82182154 256 -11.15865000 -0.28590865 257 1.40436446 -11.15865000 258 -0.40093425 1.40436446 259 -3.85823032 -0.40093425 260 -5.54781524 -3.85823032 261 -7.01705886 -5.54781524 262 0.88238230 -7.01705886 263 -10.20274414 0.88238230 264 -6.40407730 -10.20274414 265 -2.27340709 -6.40407730 266 -9.23388412 -2.27340709 267 -5.53203758 -9.23388412 268 8.45977581 -5.53203758 269 -9.51100573 8.45977581 270 7.14323946 -9.51100573 271 -1.86827504 7.14323946 272 4.95529717 -1.86827504 273 -0.61190931 4.95529717 274 -1.45333192 -0.61190931 275 -9.36720707 -1.45333192 276 0.33659244 -9.36720707 277 -3.37726432 0.33659244 278 -1.05450179 -3.37726432 279 3.35609729 -1.05450179 280 -3.40086658 3.35609729 281 -1.00071858 -3.40086658 282 3.12896182 -1.00071858 283 1.99762318 3.12896182 284 0.53750820 1.99762318 285 -8.51029916 0.53750820 286 0.09668521 -8.51029916 287 -6.23401658 0.09668521 288 0.68953338 -6.23401658 289 NA 0.68953338 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -11.75511741 2.62765311 [2,] -1.71995530 -11.75511741 [3,] -14.21706720 -1.71995530 [4,] -0.39577136 -14.21706720 [5,] -9.50478305 -0.39577136 [6,] 39.47597190 -9.50478305 [7,] -6.47537160 39.47597190 [8,] 10.53716743 -6.47537160 [9,] -1.51451587 10.53716743 [10,] 1.44502280 -1.51451587 [11,] 11.19740308 1.44502280 [12,] -12.63953185 11.19740308 [13,] -2.04051637 -12.63953185 [14,] 6.42832882 -2.04051637 [15,] 3.39007910 6.42832882 [16,] 2.24821284 3.39007910 [17,] -1.41779180 2.24821284 [18,] 11.10625157 -1.41779180 [19,] -14.49374138 11.10625157 [20,] -14.29000836 -14.49374138 [21,] -4.76287764 -14.29000836 [22,] 44.09276470 -4.76287764 [23,] 3.46631683 44.09276470 [24,] -23.17168969 3.46631683 [25,] -18.84689142 -23.17168969 [26,] -2.95805432 -18.84689142 [27,] 8.42231756 -2.95805432 [28,] 0.59522468 8.42231756 [29,] 0.02632286 0.59522468 [30,] -0.49524640 0.02632286 [31,] -5.20814003 -0.49524640 [32,] 0.64319067 -5.20814003 [33,] 1.19812147 0.64319067 [34,] -13.93538941 1.19812147 [35,] 16.56473599 -13.93538941 [36,] 5.08144100 16.56473599 [37,] 11.49440125 5.08144100 [38,] 33.30487567 11.49440125 [39,] -2.24106241 33.30487567 [40,] 3.96801151 -2.24106241 [41,] 4.75913591 3.96801151 [42,] -1.46720259 4.75913591 [43,] 14.55265285 -1.46720259 [44,] 14.80772961 14.55265285 [45,] -4.18643909 14.80772961 [46,] 12.48793720 -4.18643909 [47,] -2.69711542 12.48793720 [48,] -4.57732846 -2.69711542 [49,] -9.39232941 -4.57732846 [50,] 44.89440597 -9.39232941 [51,] -4.88429710 44.89440597 [52,] -10.22103297 -4.88429710 [53,] 2.50155126 -10.22103297 [54,] -13.19412020 2.50155126 [55,] -2.88130462 -13.19412020 [56,] -2.51712731 -2.88130462 [57,] 30.70226183 -2.51712731 [58,] -5.03558362 30.70226183 [59,] 4.17468477 -5.03558362 [60,] 9.66139211 4.17468477 [61,] -5.39349652 9.66139211 [62,] 38.06380866 -5.39349652 [63,] 5.67894175 38.06380866 [64,] 4.80057395 5.67894175 [65,] -0.45966194 4.80057395 [66,] 7.72331353 -0.45966194 [67,] 8.90000803 7.72331353 [68,] 0.78889488 8.90000803 [69,] 5.10551351 0.78889488 [70,] 10.61856988 5.10551351 [71,] 13.05423594 10.61856988 [72,] 3.17562262 13.05423594 [73,] -10.26754991 3.17562262 [74,] -1.42858769 -10.26754991 [75,] -11.94814037 -1.42858769 [76,] -28.31961345 -11.94814037 [77,] -5.16158260 -28.31961345 [78,] -4.39373834 -5.16158260 [79,] 1.47824626 -4.39373834 [80,] -2.23293592 1.47824626 [81,] -21.70790087 -2.23293592 [82,] 9.33612773 -21.70790087 [83,] 11.48896971 9.33612773 [84,] -17.22846263 11.48896971 [85,] -19.33790512 -17.22846263 [86,] -4.31275708 -19.33790512 [87,] -3.07489230 -4.31275708 [88,] 24.56504548 -3.07489230 [89,] 9.93553585 24.56504548 [90,] -8.48247145 9.93553585 [91,] -7.16442891 -8.48247145 [92,] 8.84775479 -7.16442891 [93,] -13.57133992 8.84775479 [94,] -0.15547190 -13.57133992 [95,] 33.51937188 -0.15547190 [96,] 9.66178973 33.51937188 [97,] -37.57092315 9.66178973 [98,] 10.47810092 -37.57092315 [99,] -20.20746797 10.47810092 [100,] 6.57841386 -20.20746797 [101,] 4.24088301 6.57841386 [102,] 18.79766411 4.24088301 [103,] -7.10121024 18.79766411 [104,] -7.67348853 -7.10121024 [105,] -13.78049353 -7.67348853 [106,] -3.36892040 -13.78049353 [107,] -5.03017259 -3.36892040 [108,] 11.71593400 -5.03017259 [109,] -3.06413210 11.71593400 [110,] -7.19921459 -3.06413210 [111,] 4.00590530 -7.19921459 [112,] -10.20412633 4.00590530 [113,] 1.42251840 -10.20412633 [114,] -8.73690901 1.42251840 [115,] -7.12146512 -8.73690901 [116,] -12.05720378 -7.12146512 [117,] 27.41092737 -12.05720378 [118,] -17.12791245 27.41092737 [119,] -3.20965534 -17.12791245 [120,] 0.90513489 -3.20965534 [121,] -2.96981423 0.90513489 [122,] 7.28987333 -2.96981423 [123,] -32.27679748 7.28987333 [124,] 13.00760452 -32.27679748 [125,] 2.99489516 13.00760452 [126,] 4.21975757 2.99489516 [127,] -12.00731774 4.21975757 [128,] -10.28591752 -12.00731774 [129,] -2.15463201 -10.28591752 [130,] 1.59720689 -2.15463201 [131,] -18.24573582 1.59720689 [132,] -2.32199114 -18.24573582 [133,] -6.10247910 -2.32199114 [134,] -11.97648954 -6.10247910 [135,] 22.12975563 -11.97648954 [136,] 0.39874835 22.12975563 [137,] 12.26593368 0.39874835 [138,] -12.72438034 12.26593368 [139,] -6.53498435 -12.72438034 [140,] -3.07189188 -6.53498435 [141,] -2.95005543 -3.07189188 [142,] 8.54148810 -2.95005543 [143,] 8.27014917 8.54148810 [144,] -4.67937368 8.27014917 [145,] -5.06149264 -4.67937368 [146,] -7.16389445 -5.06149264 [147,] -6.75070463 -7.16389445 [148,] -3.44200127 -6.75070463 [149,] -3.48217417 -3.44200127 [150,] 8.58173105 -3.48217417 [151,] -1.21251718 8.58173105 [152,] 1.01904429 -1.21251718 [153,] 14.53492183 1.01904429 [154,] -5.62291561 14.53492183 [155,] 5.24615342 -5.62291561 [156,] -1.82713123 5.24615342 [157,] 6.50950403 -1.82713123 [158,] -10.38472018 6.50950403 [159,] 14.50950259 -10.38472018 [160,] 31.37978300 14.50950259 [161,] -11.72587127 31.37978300 [162,] 4.60378763 -11.72587127 [163,] 17.42814800 4.60378763 [164,] -2.90412298 17.42814800 [165,] -1.78320468 -2.90412298 [166,] -1.65975169 -1.78320468 [167,] 7.93469436 -1.65975169 [168,] -1.18745552 7.93469436 [169,] 34.60103714 -1.18745552 [170,] 24.40766106 34.60103714 [171,] -11.20327583 24.40766106 [172,] 16.67652996 -11.20327583 [173,] -1.87152314 16.67652996 [174,] -51.47492364 -1.87152314 [175,] 15.82586264 -51.47492364 [176,] -6.53423419 15.82586264 [177,] 2.50175738 -6.53423419 [178,] -16.54863213 2.50175738 [179,] 9.75008293 -16.54863213 [180,] 0.18060072 9.75008293 [181,] -13.21513089 0.18060072 [182,] -1.91742750 -13.21513089 [183,] 9.76832806 -1.91742750 [184,] 12.61695502 9.76832806 [185,] -4.49005281 12.61695502 [186,] 1.93268681 -4.49005281 [187,] -0.11829533 1.93268681 [188,] 9.80970552 -0.11829533 [189,] -8.88306682 9.80970552 [190,] 4.05071662 -8.88306682 [191,] 7.14577444 4.05071662 [192,] -2.97961576 7.14577444 [193,] -5.68794652 -2.97961576 [194,] -0.20808142 -5.68794652 [195,] 2.54485625 -0.20808142 [196,] -0.21903154 2.54485625 [197,] -4.71847124 -0.21903154 [198,] -1.13040690 -4.71847124 [199,] -6.57329960 -1.13040690 [200,] -0.11458953 -6.57329960 [201,] -8.13177340 -0.11458953 [202,] 10.33825651 -8.13177340 [203,] 5.74108961 10.33825651 [204,] 0.42419593 5.74108961 [205,] -6.81293655 0.42419593 [206,] -2.53854408 -6.81293655 [207,] 23.94353977 -2.53854408 [208,] 10.99876023 23.94353977 [209,] 3.40820460 10.99876023 [210,] 7.13369640 3.40820460 [211,] 6.34605441 7.13369640 [212,] -7.17590630 6.34605441 [213,] -2.38720331 -7.17590630 [214,] -3.56391196 -2.38720331 [215,] -3.02370596 -3.56391196 [216,] -4.27993749 -3.02370596 [217,] -2.40492498 -4.27993749 [218,] -4.36188572 -2.40492498 [219,] 5.05307228 -4.36188572 [220,] -5.27349936 5.05307228 [221,] -0.71731110 -5.27349936 [222,] -5.45601623 -0.71731110 [223,] -4.97784591 -5.45601623 [224,] -5.88653186 -4.97784591 [225,] 11.85596036 -5.88653186 [226,] 1.82753822 11.85596036 [227,] -9.90808940 1.82753822 [228,] -6.85634796 -9.90808940 [229,] -12.75168027 -6.85634796 [230,] -4.07305894 -12.75168027 [231,] -3.18742530 -4.07305894 [232,] -1.67259700 -3.18742530 [233,] -5.88995289 -1.67259700 [234,] -8.49567559 -5.88995289 [235,] -12.27026840 -8.49567559 [236,] 20.22286245 -12.27026840 [237,] -7.77285114 20.22286245 [238,] -3.64140524 -7.77285114 [239,] -8.13996859 -3.64140524 [240,] 4.40478378 -8.13996859 [241,] 10.28272816 4.40478378 [242,] -9.45437687 10.28272816 [243,] -6.34360890 -9.45437687 [244,] -2.40593656 -6.34360890 [245,] -2.52092104 -2.40593656 [246,] -3.79286054 -2.52092104 [247,] 5.68576710 -3.79286054 [248,] 4.29103246 5.68576710 [249,] -6.99661907 4.29103246 [250,] 13.46254628 -6.99661907 [251,] -4.61463208 13.46254628 [252,] -3.75950693 -4.61463208 [253,] 7.81473846 -3.75950693 [254,] 2.82182154 7.81473846 [255,] -0.28590865 2.82182154 [256,] -11.15865000 -0.28590865 [257,] 1.40436446 -11.15865000 [258,] -0.40093425 1.40436446 [259,] -3.85823032 -0.40093425 [260,] -5.54781524 -3.85823032 [261,] -7.01705886 -5.54781524 [262,] 0.88238230 -7.01705886 [263,] -10.20274414 0.88238230 [264,] -6.40407730 -10.20274414 [265,] -2.27340709 -6.40407730 [266,] -9.23388412 -2.27340709 [267,] -5.53203758 -9.23388412 [268,] 8.45977581 -5.53203758 [269,] -9.51100573 8.45977581 [270,] 7.14323946 -9.51100573 [271,] -1.86827504 7.14323946 [272,] 4.95529717 -1.86827504 [273,] -0.61190931 4.95529717 [274,] -1.45333192 -0.61190931 [275,] -9.36720707 -1.45333192 [276,] 0.33659244 -9.36720707 [277,] -3.37726432 0.33659244 [278,] -1.05450179 -3.37726432 [279,] 3.35609729 -1.05450179 [280,] -3.40086658 3.35609729 [281,] -1.00071858 -3.40086658 [282,] 3.12896182 -1.00071858 [283,] 1.99762318 3.12896182 [284,] 0.53750820 1.99762318 [285,] -8.51029916 0.53750820 [286,] 0.09668521 -8.51029916 [287,] -6.23401658 0.09668521 [288,] 0.68953338 -6.23401658 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -11.75511741 2.62765311 2 -1.71995530 -11.75511741 3 -14.21706720 -1.71995530 4 -0.39577136 -14.21706720 5 -9.50478305 -0.39577136 6 39.47597190 -9.50478305 7 -6.47537160 39.47597190 8 10.53716743 -6.47537160 9 -1.51451587 10.53716743 10 1.44502280 -1.51451587 11 11.19740308 1.44502280 12 -12.63953185 11.19740308 13 -2.04051637 -12.63953185 14 6.42832882 -2.04051637 15 3.39007910 6.42832882 16 2.24821284 3.39007910 17 -1.41779180 2.24821284 18 11.10625157 -1.41779180 19 -14.49374138 11.10625157 20 -14.29000836 -14.49374138 21 -4.76287764 -14.29000836 22 44.09276470 -4.76287764 23 3.46631683 44.09276470 24 -23.17168969 3.46631683 25 -18.84689142 -23.17168969 26 -2.95805432 -18.84689142 27 8.42231756 -2.95805432 28 0.59522468 8.42231756 29 0.02632286 0.59522468 30 -0.49524640 0.02632286 31 -5.20814003 -0.49524640 32 0.64319067 -5.20814003 33 1.19812147 0.64319067 34 -13.93538941 1.19812147 35 16.56473599 -13.93538941 36 5.08144100 16.56473599 37 11.49440125 5.08144100 38 33.30487567 11.49440125 39 -2.24106241 33.30487567 40 3.96801151 -2.24106241 41 4.75913591 3.96801151 42 -1.46720259 4.75913591 43 14.55265285 -1.46720259 44 14.80772961 14.55265285 45 -4.18643909 14.80772961 46 12.48793720 -4.18643909 47 -2.69711542 12.48793720 48 -4.57732846 -2.69711542 49 -9.39232941 -4.57732846 50 44.89440597 -9.39232941 51 -4.88429710 44.89440597 52 -10.22103297 -4.88429710 53 2.50155126 -10.22103297 54 -13.19412020 2.50155126 55 -2.88130462 -13.19412020 56 -2.51712731 -2.88130462 57 30.70226183 -2.51712731 58 -5.03558362 30.70226183 59 4.17468477 -5.03558362 60 9.66139211 4.17468477 61 -5.39349652 9.66139211 62 38.06380866 -5.39349652 63 5.67894175 38.06380866 64 4.80057395 5.67894175 65 -0.45966194 4.80057395 66 7.72331353 -0.45966194 67 8.90000803 7.72331353 68 0.78889488 8.90000803 69 5.10551351 0.78889488 70 10.61856988 5.10551351 71 13.05423594 10.61856988 72 3.17562262 13.05423594 73 -10.26754991 3.17562262 74 -1.42858769 -10.26754991 75 -11.94814037 -1.42858769 76 -28.31961345 -11.94814037 77 -5.16158260 -28.31961345 78 -4.39373834 -5.16158260 79 1.47824626 -4.39373834 80 -2.23293592 1.47824626 81 -21.70790087 -2.23293592 82 9.33612773 -21.70790087 83 11.48896971 9.33612773 84 -17.22846263 11.48896971 85 -19.33790512 -17.22846263 86 -4.31275708 -19.33790512 87 -3.07489230 -4.31275708 88 24.56504548 -3.07489230 89 9.93553585 24.56504548 90 -8.48247145 9.93553585 91 -7.16442891 -8.48247145 92 8.84775479 -7.16442891 93 -13.57133992 8.84775479 94 -0.15547190 -13.57133992 95 33.51937188 -0.15547190 96 9.66178973 33.51937188 97 -37.57092315 9.66178973 98 10.47810092 -37.57092315 99 -20.20746797 10.47810092 100 6.57841386 -20.20746797 101 4.24088301 6.57841386 102 18.79766411 4.24088301 103 -7.10121024 18.79766411 104 -7.67348853 -7.10121024 105 -13.78049353 -7.67348853 106 -3.36892040 -13.78049353 107 -5.03017259 -3.36892040 108 11.71593400 -5.03017259 109 -3.06413210 11.71593400 110 -7.19921459 -3.06413210 111 4.00590530 -7.19921459 112 -10.20412633 4.00590530 113 1.42251840 -10.20412633 114 -8.73690901 1.42251840 115 -7.12146512 -8.73690901 116 -12.05720378 -7.12146512 117 27.41092737 -12.05720378 118 -17.12791245 27.41092737 119 -3.20965534 -17.12791245 120 0.90513489 -3.20965534 121 -2.96981423 0.90513489 122 7.28987333 -2.96981423 123 -32.27679748 7.28987333 124 13.00760452 -32.27679748 125 2.99489516 13.00760452 126 4.21975757 2.99489516 127 -12.00731774 4.21975757 128 -10.28591752 -12.00731774 129 -2.15463201 -10.28591752 130 1.59720689 -2.15463201 131 -18.24573582 1.59720689 132 -2.32199114 -18.24573582 133 -6.10247910 -2.32199114 134 -11.97648954 -6.10247910 135 22.12975563 -11.97648954 136 0.39874835 22.12975563 137 12.26593368 0.39874835 138 -12.72438034 12.26593368 139 -6.53498435 -12.72438034 140 -3.07189188 -6.53498435 141 -2.95005543 -3.07189188 142 8.54148810 -2.95005543 143 8.27014917 8.54148810 144 -4.67937368 8.27014917 145 -5.06149264 -4.67937368 146 -7.16389445 -5.06149264 147 -6.75070463 -7.16389445 148 -3.44200127 -6.75070463 149 -3.48217417 -3.44200127 150 8.58173105 -3.48217417 151 -1.21251718 8.58173105 152 1.01904429 -1.21251718 153 14.53492183 1.01904429 154 -5.62291561 14.53492183 155 5.24615342 -5.62291561 156 -1.82713123 5.24615342 157 6.50950403 -1.82713123 158 -10.38472018 6.50950403 159 14.50950259 -10.38472018 160 31.37978300 14.50950259 161 -11.72587127 31.37978300 162 4.60378763 -11.72587127 163 17.42814800 4.60378763 164 -2.90412298 17.42814800 165 -1.78320468 -2.90412298 166 -1.65975169 -1.78320468 167 7.93469436 -1.65975169 168 -1.18745552 7.93469436 169 34.60103714 -1.18745552 170 24.40766106 34.60103714 171 -11.20327583 24.40766106 172 16.67652996 -11.20327583 173 -1.87152314 16.67652996 174 -51.47492364 -1.87152314 175 15.82586264 -51.47492364 176 -6.53423419 15.82586264 177 2.50175738 -6.53423419 178 -16.54863213 2.50175738 179 9.75008293 -16.54863213 180 0.18060072 9.75008293 181 -13.21513089 0.18060072 182 -1.91742750 -13.21513089 183 9.76832806 -1.91742750 184 12.61695502 9.76832806 185 -4.49005281 12.61695502 186 1.93268681 -4.49005281 187 -0.11829533 1.93268681 188 9.80970552 -0.11829533 189 -8.88306682 9.80970552 190 4.05071662 -8.88306682 191 7.14577444 4.05071662 192 -2.97961576 7.14577444 193 -5.68794652 -2.97961576 194 -0.20808142 -5.68794652 195 2.54485625 -0.20808142 196 -0.21903154 2.54485625 197 -4.71847124 -0.21903154 198 -1.13040690 -4.71847124 199 -6.57329960 -1.13040690 200 -0.11458953 -6.57329960 201 -8.13177340 -0.11458953 202 10.33825651 -8.13177340 203 5.74108961 10.33825651 204 0.42419593 5.74108961 205 -6.81293655 0.42419593 206 -2.53854408 -6.81293655 207 23.94353977 -2.53854408 208 10.99876023 23.94353977 209 3.40820460 10.99876023 210 7.13369640 3.40820460 211 6.34605441 7.13369640 212 -7.17590630 6.34605441 213 -2.38720331 -7.17590630 214 -3.56391196 -2.38720331 215 -3.02370596 -3.56391196 216 -4.27993749 -3.02370596 217 -2.40492498 -4.27993749 218 -4.36188572 -2.40492498 219 5.05307228 -4.36188572 220 -5.27349936 5.05307228 221 -0.71731110 -5.27349936 222 -5.45601623 -0.71731110 223 -4.97784591 -5.45601623 224 -5.88653186 -4.97784591 225 11.85596036 -5.88653186 226 1.82753822 11.85596036 227 -9.90808940 1.82753822 228 -6.85634796 -9.90808940 229 -12.75168027 -6.85634796 230 -4.07305894 -12.75168027 231 -3.18742530 -4.07305894 232 -1.67259700 -3.18742530 233 -5.88995289 -1.67259700 234 -8.49567559 -5.88995289 235 -12.27026840 -8.49567559 236 20.22286245 -12.27026840 237 -7.77285114 20.22286245 238 -3.64140524 -7.77285114 239 -8.13996859 -3.64140524 240 4.40478378 -8.13996859 241 10.28272816 4.40478378 242 -9.45437687 10.28272816 243 -6.34360890 -9.45437687 244 -2.40593656 -6.34360890 245 -2.52092104 -2.40593656 246 -3.79286054 -2.52092104 247 5.68576710 -3.79286054 248 4.29103246 5.68576710 249 -6.99661907 4.29103246 250 13.46254628 -6.99661907 251 -4.61463208 13.46254628 252 -3.75950693 -4.61463208 253 7.81473846 -3.75950693 254 2.82182154 7.81473846 255 -0.28590865 2.82182154 256 -11.15865000 -0.28590865 257 1.40436446 -11.15865000 258 -0.40093425 1.40436446 259 -3.85823032 -0.40093425 260 -5.54781524 -3.85823032 261 -7.01705886 -5.54781524 262 0.88238230 -7.01705886 263 -10.20274414 0.88238230 264 -6.40407730 -10.20274414 265 -2.27340709 -6.40407730 266 -9.23388412 -2.27340709 267 -5.53203758 -9.23388412 268 8.45977581 -5.53203758 269 -9.51100573 8.45977581 270 7.14323946 -9.51100573 271 -1.86827504 7.14323946 272 4.95529717 -1.86827504 273 -0.61190931 4.95529717 274 -1.45333192 -0.61190931 275 -9.36720707 -1.45333192 276 0.33659244 -9.36720707 277 -3.37726432 0.33659244 278 -1.05450179 -3.37726432 279 3.35609729 -1.05450179 280 -3.40086658 3.35609729 281 -1.00071858 -3.40086658 282 3.12896182 -1.00071858 283 1.99762318 3.12896182 284 0.53750820 1.99762318 285 -8.51029916 0.53750820 286 0.09668521 -8.51029916 287 -6.23401658 0.09668521 288 0.68953338 -6.23401658 > 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/7iq1j1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8rn691352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/907gi1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10etmy1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11x6hy1352144422.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/12x0771352144422.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/1333au1352144422.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/148snn1352144422.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/15lp5h1352144422.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/164t7m1352144422.tab") + } > > try(system("convert tmp/1h0l21352144422.ps tmp/1h0l21352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/2rqse1352144422.ps tmp/2rqse1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/3km4w1352144422.ps tmp/3km4w1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/4l73v1352144422.ps tmp/4l73v1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/5yspi1352144422.ps tmp/5yspi1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/6e5bk1352144422.ps tmp/6e5bk1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/7iq1j1352144422.ps tmp/7iq1j1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/8rn691352144422.ps tmp/8rn691352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/907gi1352144422.ps tmp/907gi1352144422.png",intern=TRUE)) character(0) > try(system("convert tmp/10etmy1352144422.ps tmp/10etmy1352144422.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.796 1.140 13.959