R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > 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 = '5' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '5' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects 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 Learning Pop Gender Connected Separate Software Happiness 1 13 1 1 41 38 12 14 2 16 1 1 39 32 11 18 3 19 1 1 30 35 15 11 4 15 1 0 31 33 6 12 5 14 1 1 34 37 13 16 6 13 1 1 35 29 10 18 7 19 1 1 39 31 12 14 8 15 1 1 34 36 14 14 9 14 1 1 36 35 12 15 10 15 1 1 37 38 9 15 11 16 1 0 38 31 10 17 12 16 1 1 36 34 12 19 13 16 1 0 38 35 12 10 14 16 1 1 39 38 11 16 15 17 1 1 33 37 15 18 16 15 1 0 32 33 12 14 17 15 1 0 36 32 10 14 18 20 1 1 38 38 12 17 19 18 1 0 39 38 11 14 20 16 1 1 32 32 12 16 21 16 1 0 32 33 11 18 22 16 1 1 31 31 12 11 23 19 1 1 39 38 13 14 24 16 1 1 37 39 11 12 25 17 1 0 39 32 12 17 26 17 1 1 41 32 13 9 27 16 1 0 36 35 10 16 28 15 1 1 33 37 14 14 29 16 1 1 33 33 12 15 30 14 1 0 34 33 10 11 31 15 1 1 31 31 12 16 32 12 1 0 27 32 8 13 33 14 1 1 37 31 10 17 34 16 1 1 34 37 12 15 35 14 1 0 34 30 12 14 36 10 1 0 32 33 7 16 37 10 1 0 29 31 9 9 38 14 1 0 36 33 12 15 39 16 1 1 29 31 10 17 40 16 1 0 35 33 10 13 41 16 1 0 37 32 10 15 42 14 1 1 34 33 12 16 43 20 1 0 38 32 15 16 44 14 1 0 35 33 10 12 45 14 1 1 38 28 10 15 46 11 1 1 37 35 12 11 47 14 1 1 38 39 13 15 48 15 1 1 33 34 11 15 49 16 1 1 36 38 11 17 50 14 1 0 38 32 12 13 51 16 1 1 32 38 14 16 52 14 1 0 32 30 10 14 53 12 1 0 32 33 12 11 54 16 1 1 34 38 13 12 55 9 1 0 32 32 5 12 56 14 1 1 37 35 6 15 57 16 1 1 39 34 12 16 58 16 1 1 29 34 12 15 59 15 1 0 37 36 11 12 60 16 1 1 35 34 10 12 61 12 1 0 30 28 7 8 62 16 1 0 38 34 12 13 63 16 1 1 34 35 14 11 64 14 1 1 31 35 11 14 65 16 1 1 34 31 12 15 66 17 1 0 35 37 13 10 67 18 1 1 36 35 14 11 68 18 1 0 30 27 11 12 69 12 1 1 39 40 12 15 70 16 1 0 35 37 12 15 71 10 1 0 38 36 8 14 72 14 1 1 31 38 11 16 73 18 1 1 34 39 14 15 74 18 1 0 38 41 14 15 75 16 1 0 34 27 12 13 76 17 1 1 39 30 9 12 77 16 1 1 37 37 13 17 78 16 1 1 34 31 11 13 79 13 1 0 28 31 12 15 80 16 1 0 37 27 12 13 81 16 1 0 33 36 12 15 82 16 1 1 35 37 12 15 83 15 1 0 37 33 12 16 84 15 1 1 32 34 11 15 85 16 1 1 33 31 10 14 86 14 1 0 38 39 9 15 87 16 1 1 33 34 12 14 88 16 1 1 29 32 12 13 89 15 1 1 33 33 12 7 90 12 1 1 31 36 9 17 91 17 1 1 36 32 15 13 92 16 1 1 35 41 12 15 93 15 1 1 32 28 12 14 94 13 1 1 29 30 12 13 95 16 1 1 39 36 10 16 96 16 1 1 37 35 13 12 97 16 1 1 35 31 9 14 98 16 1 0 37 34 12 17 99 14 1 0 32 36 10 15 100 16 1 1 38 36 14 17 101 16 1 0 37 35 11 12 102 20 1 1 36 37 15 16 103 15 1 0 32 28 11 11 104 16 1 1 33 39 11 15 105 13 1 0 40 32 12 9 106 17 1 1 38 35 12 16 107 16 1 0 41 39 12 15 108 16 1 0 36 35 11 10 109 12 1 1 43 42 7 10 110 16 1 1 30 34 12 15 111 16 1 1 31 33 14 11 112 17 1 1 32 41 11 13 113 12 1 1 37 34 10 18 114 18 1 0 37 32 13 16 115 14 1 1 33 40 13 14 116 14 1 1 34 40 8 14 117 13 1 1 33 35 11 14 118 16 1 1 38 36 12 14 119 13 1 0 33 37 11 12 120 16 1 1 31 27 13 14 121 13 1 1 38 39 12 15 122 16 1 1 37 38 14 15 123 15 1 1 36 31 13 15 124 16 1 1 31 33 15 13 125 15 1 0 39 32 10 17 126 17 1 1 44 39 11 17 127 15 1 1 33 36 9 19 128 12 1 1 35 33 11 15 129 16 1 0 32 33 10 13 130 10 1 0 28 32 11 9 131 16 1 1 40 37 8 15 132 12 1 0 27 30 11 15 133 14 1 0 37 38 12 15 134 15 1 1 32 29 12 16 135 13 1 0 28 22 9 11 136 15 1 0 34 35 11 14 137 11 1 1 30 35 10 11 138 12 1 1 35 34 8 15 139 11 1 0 31 35 9 13 140 16 1 1 32 34 8 15 141 15 1 0 30 37 9 16 142 17 1 1 30 35 15 14 143 16 1 0 31 23 11 15 144 10 1 1 40 31 8 16 145 18 1 1 32 27 13 16 146 13 1 0 36 36 12 11 147 16 1 0 32 31 12 12 148 13 1 0 35 32 9 9 149 10 1 1 38 39 7 16 150 15 1 1 42 37 13 13 151 16 1 0 34 38 9 16 152 16 1 1 35 39 6 12 153 14 1 1 38 34 8 9 154 10 1 1 33 31 8 13 155 13 1 1 32 37 6 14 156 15 1 1 33 36 9 19 157 16 1 1 34 32 11 13 158 12 1 1 32 38 8 12 159 13 0 0 27 26 10 10 160 12 0 0 31 26 8 14 161 17 0 0 38 33 14 16 162 15 0 1 34 39 10 10 163 10 0 0 24 30 8 11 164 14 0 0 30 33 11 14 165 11 0 1 26 25 12 12 166 13 0 1 34 38 12 9 167 16 0 0 27 37 12 9 168 12 0 0 37 31 5 11 169 16 0 1 36 37 12 16 170 12 0 0 41 35 10 9 171 9 0 1 29 25 7 13 172 12 0 1 36 28 12 16 173 15 0 0 32 35 11 13 174 12 0 1 37 33 8 9 175 12 0 0 30 30 9 12 176 14 0 1 31 31 10 16 177 12 0 1 38 37 9 11 178 16 0 1 36 36 12 14 179 11 0 0 35 30 6 13 180 19 0 0 31 36 15 15 181 15 0 0 38 32 12 14 182 8 0 1 22 28 12 16 183 16 0 1 32 36 12 13 184 17 0 0 36 34 11 14 185 12 0 1 39 31 7 15 186 11 0 0 28 28 7 13 187 11 0 0 32 36 5 11 188 14 0 1 32 36 12 11 189 16 0 1 38 40 12 14 190 12 0 1 32 33 3 15 191 16 0 1 35 37 11 11 192 13 0 1 32 32 10 15 193 15 0 0 37 38 12 12 194 16 0 1 34 31 9 14 195 16 0 1 33 37 12 14 196 14 0 0 33 33 9 8 197 16 0 0 30 30 12 9 198 14 0 0 24 30 10 15 199 11 0 0 34 31 9 17 200 12 0 0 34 32 12 13 201 15 0 1 33 34 8 15 202 15 0 1 34 36 11 15 203 16 0 1 35 37 11 14 204 16 0 0 35 36 12 16 205 11 0 0 36 33 10 13 206 15 0 0 34 33 10 16 207 12 0 1 34 33 12 9 208 12 0 0 41 44 12 16 209 15 0 0 32 39 11 11 210 15 0 0 30 32 8 10 211 16 0 1 35 35 12 11 212 14 0 0 28 25 10 15 213 17 0 1 33 35 11 17 214 14 0 1 39 34 10 14 215 13 0 0 36 35 8 8 216 15 0 1 36 39 12 15 217 13 0 0 35 33 12 11 218 14 0 0 38 36 10 16 219 15 0 1 33 32 12 10 220 12 0 0 31 32 9 15 221 8 0 1 32 36 6 16 222 14 0 0 31 32 10 19 223 14 0 0 33 34 9 12 224 11 0 0 34 33 9 8 225 12 0 0 34 35 9 11 226 13 0 1 34 30 6 14 227 10 0 0 33 38 10 9 228 16 0 0 32 34 6 15 229 18 0 1 41 33 14 13 230 13 0 1 34 32 10 16 231 11 0 0 36 31 10 11 232 4 0 0 37 30 6 12 233 13 0 0 36 27 12 13 234 16 0 1 29 31 12 10 235 10 0 0 37 30 7 11 236 12 0 0 27 32 8 12 237 12 0 0 35 35 11 8 238 10 0 0 28 28 3 12 239 13 0 0 35 33 6 12 240 12 0 0 29 35 8 11 241 14 0 0 32 35 9 13 242 10 0 1 36 32 9 14 243 12 0 1 19 21 8 10 244 12 0 1 21 20 9 12 245 11 0 0 31 34 7 15 246 10 0 0 33 32 7 13 247 12 0 1 36 34 6 13 248 16 0 1 33 32 9 13 249 12 0 0 37 33 10 12 250 14 0 0 34 33 11 12 251 16 0 0 35 37 12 9 252 14 0 1 31 32 8 9 253 13 0 1 37 34 11 15 254 4 0 1 35 30 3 10 255 15 0 1 27 30 11 14 256 11 0 0 34 38 12 15 257 11 0 0 40 36 7 7 258 14 0 0 29 32 9 14 259 15 0 0 38 34 12 8 260 14 0 1 34 33 8 10 261 13 0 0 21 27 11 13 262 11 0 0 36 32 8 13 263 15 0 1 38 34 10 13 264 11 0 0 30 29 8 8 265 13 0 0 35 35 7 12 266 13 0 1 30 27 8 13 267 16 0 1 36 33 10 12 268 13 0 0 34 38 8 10 269 16 0 1 35 36 12 13 270 16 0 0 34 33 14 12 271 12 0 0 32 39 7 9 272 7 0 1 33 29 6 15 273 16 0 0 33 32 11 13 274 5 0 1 26 34 4 13 275 16 0 0 35 38 9 13 276 4 0 0 21 17 5 15 277 12 0 0 38 35 9 15 278 15 0 0 35 32 11 14 279 14 0 1 33 34 12 15 280 11 0 0 37 36 9 11 281 16 0 0 38 31 12 15 282 15 0 1 34 35 10 14 283 12 0 0 27 29 9 13 284 6 0 1 16 22 6 12 285 16 0 0 40 41 10 16 286 10 0 0 36 36 9 16 287 15 0 1 42 42 13 9 288 14 0 1 30 33 12 14 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop Gender Connected Separate Software 1.94933 0.65349 0.03052 0.07000 0.05639 0.62331 Happiness 0.07648 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.8888 -1.2435 0.2133 1.2756 5.0063 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.94933 1.24627 1.564 0.11891 Pop 0.65349 0.24848 2.630 0.00901 ** Gender 0.03052 0.23455 0.130 0.89657 Connected 0.07000 0.03288 2.129 0.03412 * Separate 0.05639 0.03429 1.645 0.10113 Software 0.62331 0.05167 12.063 < 2e-16 *** Happiness 0.07648 0.04762 1.606 0.10937 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.899 on 281 degrees of freedom Multiple R-squared: 0.486, Adjusted R-squared: 0.475 F-statistic: 44.28 on 6 and 281 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,] 0.814086261 0.371827477 0.1859137 [2,] 0.761229326 0.477541349 0.2387707 [3,] 0.784144666 0.431710668 0.2158553 [4,] 0.719161451 0.561677098 0.2808385 [5,] 0.712411879 0.575176242 0.2875881 [6,] 0.669044512 0.661910976 0.3309555 [7,] 0.612600387 0.774799226 0.3873996 [8,] 0.524822802 0.950354395 0.4751772 [9,] 0.855741155 0.288517690 0.1442588 [10,] 0.849998820 0.300002361 0.1500012 [11,] 0.802002810 0.395994379 0.1979972 [12,] 0.745897870 0.508204260 0.2541021 [13,] 0.684099119 0.631801762 0.3159009 [14,] 0.703792204 0.592415592 0.2962078 [15,] 0.640615638 0.718768725 0.3593844 [16,] 0.577482789 0.845034422 0.4225172 [17,] 0.510840200 0.978319600 0.4891598 [18,] 0.447186102 0.894372204 0.5528139 [19,] 0.430402656 0.860805312 0.5695973 [20,] 0.369874071 0.739748142 0.6301259 [21,] 0.356279887 0.712559774 0.6437201 [22,] 0.300856874 0.601713748 0.6991431 [23,] 0.285203192 0.570406384 0.7147968 [24,] 0.251839766 0.503679532 0.7481602 [25,] 0.207049744 0.414099488 0.7929503 [26,] 0.205931839 0.411863678 0.7940682 [27,] 0.294191590 0.588383180 0.7058084 [28,] 0.381722840 0.763445680 0.6182772 [29,] 0.383989809 0.767979618 0.6160102 [30,] 0.412444715 0.824889429 0.5875553 [31,] 0.391693744 0.783387488 0.6083063 [32,] 0.357991619 0.715983237 0.6420084 [33,] 0.344927714 0.689855428 0.6550723 [34,] 0.357907769 0.715815537 0.6420922 [35,] 0.317195446 0.634390891 0.6828046 [36,] 0.281112065 0.562224130 0.7188879 [37,] 0.516421228 0.967157543 0.4835788 [38,] 0.567873752 0.864252496 0.4321262 [39,] 0.521401394 0.957197212 0.4785986 [40,] 0.477155337 0.954310674 0.5228447 [41,] 0.480679222 0.961358445 0.5193208 [42,] 0.438573971 0.877147942 0.5614260 [43,] 0.393324924 0.786649849 0.6066751 [44,] 0.446961455 0.893922910 0.5530385 [45,] 0.404471451 0.808942903 0.5955285 [46,] 0.406474981 0.812949963 0.5935250 [47,] 0.390812561 0.781625122 0.6091874 [48,] 0.349516668 0.699033335 0.6504833 [49,] 0.326711949 0.653423899 0.6732881 [50,] 0.287653472 0.575306944 0.7123465 [51,] 0.289704324 0.579408649 0.7102957 [52,] 0.260293166 0.520586333 0.7397068 [53,] 0.226680753 0.453361507 0.7733192 [54,] 0.195968930 0.391937860 0.8040311 [55,] 0.169447927 0.338895854 0.8305521 [56,] 0.146350811 0.292701622 0.8536492 [57,] 0.134138662 0.268277324 0.8658613 [58,] 0.127503793 0.255007586 0.8724962 [59,] 0.218035433 0.436070866 0.7819646 [60,] 0.342188535 0.684377069 0.6578115 [61,] 0.306235927 0.612471855 0.6937641 [62,] 0.390194196 0.780388393 0.6098058 [63,] 0.356334238 0.712668476 0.6436658 [64,] 0.338884780 0.677769561 0.6611152 [65,] 0.311393238 0.622786475 0.6886068 [66,] 0.280709104 0.561418208 0.7192909 [67,] 0.335687512 0.671375023 0.6643125 [68,] 0.303569759 0.607139518 0.6964302 [69,] 0.281518156 0.563036311 0.7184818 [70,] 0.285126827 0.570253653 0.7148732 [71,] 0.257586402 0.515172803 0.7424136 [72,] 0.231165044 0.462330088 0.7688350 [73,] 0.203898145 0.407796290 0.7961019 [74,] 0.184079681 0.368159362 0.8159203 [75,] 0.159801219 0.319602438 0.8401988 [76,] 0.156752145 0.313504290 0.8432479 [77,] 0.134875696 0.269751391 0.8651243 [78,] 0.116908596 0.233817192 0.8830914 [79,] 0.103935724 0.207871447 0.8960643 [80,] 0.087982579 0.175965159 0.9120174 [81,] 0.083526027 0.167052053 0.9164740 [82,] 0.071594285 0.143188569 0.9284057 [83,] 0.060963792 0.121927585 0.9390362 [84,] 0.051702019 0.103404038 0.9482980 [85,] 0.053240891 0.106481782 0.9467591 [86,] 0.046734753 0.093469506 0.9532652 [87,] 0.038512707 0.077025414 0.9614873 [88,] 0.041736268 0.083472536 0.9582637 [89,] 0.034171955 0.068343910 0.9658280 [90,] 0.027750279 0.055500558 0.9722497 [91,] 0.024878295 0.049756590 0.9751217 [92,] 0.021493140 0.042986280 0.9785069 [93,] 0.025415873 0.050831745 0.9745841 [94,] 0.021323148 0.042646295 0.9786769 [95,] 0.018844085 0.037688171 0.9811559 [96,] 0.023909467 0.047818933 0.9760905 [97,] 0.020477894 0.040955788 0.9795221 [98,] 0.016393892 0.032787784 0.9836061 [99,] 0.014925212 0.029850425 0.9850748 [100,] 0.012739701 0.025479402 0.9872603 [101,] 0.010505172 0.021010344 0.9894948 [102,] 0.008264614 0.016529227 0.9917354 [103,] 0.009715276 0.019430552 0.9902847 [104,] 0.013634877 0.027269753 0.9863651 [105,] 0.013176572 0.026353144 0.9868234 [106,] 0.013994326 0.027988653 0.9860057 [107,] 0.011734841 0.023469683 0.9882652 [108,] 0.011625949 0.023251897 0.9883741 [109,] 0.009221128 0.018442257 0.9907789 [110,] 0.008611334 0.017222667 0.9913887 [111,] 0.006928218 0.013856437 0.9930718 [112,] 0.009996397 0.019992795 0.9900036 [113,] 0.008508087 0.017016174 0.9914919 [114,] 0.007583086 0.015166172 0.9924169 [115,] 0.006323853 0.012647706 0.9936761 [116,] 0.005013055 0.010026111 0.9949869 [117,] 0.004200403 0.008400805 0.9957996 [118,] 0.003470473 0.006940946 0.9965295 [119,] 0.005358243 0.010716485 0.9946418 [120,] 0.005686990 0.011373980 0.9943130 [121,] 0.012427026 0.024854053 0.9875730 [122,] 0.013781515 0.027563030 0.9862185 [123,] 0.014818087 0.029636174 0.9851819 [124,] 0.014641765 0.029283530 0.9853582 [125,] 0.011871640 0.023743280 0.9881284 [126,] 0.009855848 0.019711695 0.9901442 [127,] 0.007829475 0.015658949 0.9921705 [128,] 0.010436722 0.020873444 0.9895633 [129,] 0.009335755 0.018671511 0.9906642 [130,] 0.010730875 0.021461751 0.9892691 [131,] 0.014799578 0.029599155 0.9852004 [132,] 0.013737221 0.027474443 0.9862628 [133,] 0.011368732 0.022737464 0.9886313 [134,] 0.011390066 0.022780132 0.9886099 [135,] 0.021437033 0.042874066 0.9785630 [136,] 0.023037788 0.046075576 0.9769622 [137,] 0.026102764 0.052205527 0.9738972 [138,] 0.022577864 0.045155727 0.9774221 [139,] 0.018377801 0.036755602 0.9816222 [140,] 0.027090985 0.054181971 0.9729090 [141,] 0.027386042 0.054772084 0.9726140 [142,] 0.027495660 0.054991321 0.9725043 [143,] 0.050873200 0.101746401 0.9491268 [144,] 0.045233530 0.090467061 0.9547665 [145,] 0.056643534 0.113287067 0.9433565 [146,] 0.049761281 0.099522563 0.9502387 [147,] 0.042769043 0.085538085 0.9572310 [148,] 0.040657050 0.081314101 0.9593429 [149,] 0.034182477 0.068364954 0.9658175 [150,] 0.028872170 0.057744341 0.9711278 [151,] 0.024613635 0.049227270 0.9753864 [152,] 0.020641001 0.041282001 0.9793590 [153,] 0.018120523 0.036241046 0.9818795 [154,] 0.016738207 0.033476415 0.9832618 [155,] 0.013470075 0.026940149 0.9865299 [156,] 0.016783515 0.033567030 0.9832165 [157,] 0.016632753 0.033265506 0.9833672 [158,] 0.017050873 0.034101745 0.9829491 [159,] 0.017120433 0.034240867 0.9828796 [160,] 0.014027253 0.028054505 0.9859727 [161,] 0.013326680 0.026653361 0.9866733 [162,] 0.013158568 0.026317136 0.9868414 [163,] 0.015814713 0.031629426 0.9841853 [164,] 0.013620341 0.027240682 0.9863797 [165,] 0.010880816 0.021761633 0.9891192 [166,] 0.008641516 0.017283033 0.9913585 [167,] 0.007006757 0.014013514 0.9929932 [168,] 0.006047189 0.012094379 0.9939528 [169,] 0.005039130 0.010078261 0.9949609 [170,] 0.004017474 0.008034948 0.9959825 [171,] 0.004510273 0.009020546 0.9954897 [172,] 0.003508500 0.007016999 0.9964915 [173,] 0.027560078 0.055120156 0.9724399 [174,] 0.024492803 0.048985606 0.9755072 [175,] 0.030840616 0.061681231 0.9691594 [176,] 0.025660095 0.051320191 0.9743399 [177,] 0.020985043 0.041970086 0.9790150 [178,] 0.017700483 0.035400966 0.9822995 [179,] 0.015546434 0.031092868 0.9844536 [180,] 0.012604127 0.025208254 0.9873959 [181,] 0.017848677 0.035697353 0.9821513 [182,] 0.016361046 0.032722093 0.9836390 [183,] 0.013322581 0.026645163 0.9866774 [184,] 0.010581794 0.021163588 0.9894182 [185,] 0.016019144 0.032038288 0.9839809 [186,] 0.013208414 0.026416828 0.9867916 [187,] 0.012259546 0.024519092 0.9877405 [188,] 0.012019439 0.024038877 0.9879806 [189,] 0.010045137 0.020090273 0.9899549 [190,] 0.009926722 0.019853444 0.9900733 [191,] 0.012262614 0.024525227 0.9877374 [192,] 0.014708934 0.029417869 0.9852911 [193,] 0.011691882 0.023383763 0.9883081 [194,] 0.010158255 0.020316511 0.9898417 [195,] 0.008182881 0.016365762 0.9918171 [196,] 0.009555560 0.019111119 0.9904444 [197,] 0.008558420 0.017116840 0.9914416 [198,] 0.011226190 0.022452380 0.9887738 [199,] 0.025524797 0.051049594 0.9744752 [200,] 0.020842136 0.041684272 0.9791579 [201,] 0.032616972 0.065233945 0.9673830 [202,] 0.027450962 0.054901925 0.9725490 [203,] 0.025254050 0.050508100 0.9747460 [204,] 0.027089655 0.054179311 0.9729103 [205,] 0.021715753 0.043431506 0.9782842 [206,] 0.018580140 0.037160280 0.9814199 [207,] 0.015442639 0.030885279 0.9845574 [208,] 0.014541980 0.029083960 0.9854580 [209,] 0.011382272 0.022764544 0.9886177 [210,] 0.008832455 0.017664909 0.9911675 [211,] 0.006945925 0.013891849 0.9930541 [212,] 0.011079604 0.022159209 0.9889204 [213,] 0.008605177 0.017210354 0.9913948 [214,] 0.007446686 0.014893373 0.9925533 [215,] 0.006339805 0.012679610 0.9936602 [216,] 0.004940182 0.009880364 0.9950598 [217,] 0.006294580 0.012589161 0.9937054 [218,] 0.014254882 0.028509764 0.9857451 [219,] 0.078645740 0.157291481 0.9213543 [220,] 0.071046729 0.142093458 0.9289533 [221,] 0.058423443 0.116846886 0.9415766 [222,] 0.059090845 0.118181689 0.9409092 [223,] 0.242418527 0.484837055 0.7575815 [224,] 0.223150129 0.446300259 0.7768499 [225,] 0.203024330 0.406048660 0.7969757 [226,] 0.180125768 0.360251536 0.8198742 [227,] 0.153650275 0.307300550 0.8463497 [228,] 0.184216521 0.368433043 0.8157835 [229,] 0.236459002 0.472918004 0.7635410 [230,] 0.298129055 0.596258111 0.7018709 [231,] 0.258940353 0.517880706 0.7410596 [232,] 0.243732363 0.487464726 0.7562676 [233,] 0.278933514 0.557867028 0.7210665 [234,] 0.267274511 0.534549022 0.7327255 [235,] 0.242977297 0.485954593 0.7570227 [236,] 0.214090077 0.428180154 0.7859099 [237,] 0.183488832 0.366977664 0.8165112 [238,] 0.182159755 0.364319510 0.8178402 [239,] 0.278669369 0.557338737 0.7213306 [240,] 0.262232422 0.524464844 0.7377676 [241,] 0.222656633 0.445313266 0.7773434 [242,] 0.189286492 0.378572984 0.8107135 [243,] 0.212337552 0.424675105 0.7876624 [244,] 0.194095848 0.388191696 0.8059042 [245,] 0.258985607 0.517971214 0.7410144 [246,] 0.248278850 0.496557700 0.7517212 [247,] 0.500867585 0.998264831 0.4991324 [248,] 0.453196418 0.906392836 0.5468036 [249,] 0.461761174 0.923522349 0.5382388 [250,] 0.440693714 0.881387428 0.5593063 [251,] 0.463087072 0.926174144 0.5369129 [252,] 0.403509463 0.807018926 0.5964905 [253,] 0.354189024 0.708378049 0.6458110 [254,] 0.316609907 0.633219814 0.6833901 [255,] 0.259566944 0.519133888 0.7404331 [256,] 0.257080674 0.514161348 0.7429193 [257,] 0.368586065 0.737172130 0.6314139 [258,] 0.613750885 0.772498230 0.3862491 [259,] 0.552043750 0.895912499 0.4479562 [260,] 0.503051090 0.993897819 0.4969489 [261,] 0.513661354 0.972677291 0.4863386 [262,] 0.441941819 0.883883639 0.5580582 [263,] 0.418639515 0.837279030 0.5813605 [264,] 0.364864531 0.729729062 0.6351355 [265,] 0.288946915 0.577893830 0.7110531 [266,] 0.425595101 0.851190203 0.5744049 [267,] 0.376777276 0.753554551 0.6232227 [268,] 0.266605613 0.533211226 0.7333944 [269,] 0.171092306 0.342184612 0.8289077 > postscript(file="/var/wessaorg/rcomp/tmp/178pe1386364016.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/24t981386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3ob4w1386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4pdqg1386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5fr7r1386364016.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 = 288 Frequency = 1 1 2 3 4 5 6 -3.196720526 0.599048387 2.101982563 3.708589526 -2.426569443 -1.328454956 7 8 9 10 11 12 3.338031851 -1.840536023 -1.754005451 0.876741887 1.455745221 -0.003513068 13 14 15 16 17 18 0.518882294 0.413643599 -0.756135145 -0.254217635 0.768782887 3.783862016 19 20 21 22 23 24 2.597111350 0.618707087 1.063191374 1.127477205 2.319975450 0.803156287 25 26 27 28 29 30 1.082732019 0.900700321 1.446655427 -1.826925959 0.568787101 0.081820554 31 32 33 34 35 36 -0.254897905 -0.278101073 -0.504769950 0.273214676 -1.225045290 -2.290622195 37 38 39 40 41 42 -2.679122681 -1.610702801 2.055250338 1.858867973 1.622305329 -1.577690457 43 44 45 46 47 48 2.359282287 -0.064657005 -0.252645025 -4.518107899 -2.742889509 0.135703725 49 50 51 52 53 54 0.547176185 -1.541365356 -0.966265940 0.161577975 -3.024792568 -0.177061826 55 56 57 58 59 60 -1.681711441 1.915846593 0.015904391 0.792404772 0.002851410 1.848432817 61 62 63 64 65 66 0.743145415 0.345849700 -0.554718485 -0.704208653 0.611569509 0.992795861 67 68 69 70 71 72 1.305276444 4.000401411 -4.245975420 0.233729847 -3.350173880 -1.026336113 73 74 75 76 77 78 0.913811538 0.551534157 1.020607148 3.417301658 -0.713052073 1.387828650 79 80 81 82 83 84 -1.937897569 0.810599541 0.430127391 0.203212140 -0.757180359 0.205706261 85 86 87 88 89 90 2.004665261 -0.219135415 0.588869651 1.058139761 0.180587278 -1.743407998 91 92 93 94 95 96 -0.301805281 -0.022357748 -0.002772981 -1.829075295 1.149737640 -0.217892018 97 98 99 100 101 102 2.487969285 0.109952147 -0.253251879 -1.349971233 1.059243882 2.186807292 103 104 105 106 107 108 0.880478889 0.853741365 -2.375470339 1.029514454 -0.299070313 1.282196463 109 110 111 112 113 114 -1.139849913 0.722402236 -0.231925932 1.963909001 -2.750422388 1.675903017 115 116 117 118 119 120 -2.372794279 0.673748669 -1.844213725 0.126072027 -1.773530918 0.500312931 121 122 123 124 125 126 -3.119580412 -1.239803597 -1.151744660 -1.008185073 0.329350213 0.930763425 127 128 129 130 131 132 0.963636886 -2.947908874 2.068875581 -3.912130811 2.346435848 -2.188193464 133 134 135 136 137 138 -1.962667697 -0.212115497 0.745462059 0.116301446 -2.781471954 -1.134374056 139 140 141 142 143 144 -2.350597730 3.075633552 1.377194795 -0.127442504 1.926543697 -3.391684342 145 146 147 148 149 150 2.277360350 -2.473980128 1.011517354 -0.155530369 -3.079509950 -1.757164664 151 152 153 154 155 156 2.040792179 4.059706843 1.114468469 -2.672241524 1.229549351 0.963636886 157 158 159 160 161 162 1.331436178 -0.920511270 0.696553779 0.357261740 0.579692059 1.442916183 163 164 165 166 167 168 -1.148865330 0.162589681 -2.607137157 -1.670834516 1.906093414 1.754636521 169 170 171 172 173 174 0.710227729 -1.714538951 -1.777074303 -2.782240022 0.986274687 -0.105643376 175 176 177 178 179 180 -0.268664665 0.645213435 -1.177474942 0.919570245 0.174774924 2.353698319 181 182 183 184 185 186 0.035652769 -5.802204518 1.276055411 2.686181993 0.031595460 0.154268523 187 188 189 190 191 192 0.822686840 -0.570994544 0.553995285 2.902064655 1.785914473 -0.404706551 193 194 195 196 197 198 -0.079749484 3.211464968 1.073185381 1.658050400 2.090833111 1.298616388 199 200 201 202 203 204 -1.987442391 -2.607862065 2.659124163 0.606409392 1.556489406 0.867140444 205 206 207 208 209 210 -2.557641416 1.352938590 -2.388872155 -4.004014549 0.913654843 3.394809532 211 212 213 214 215 216 1.275390320 1.300568605 2.579854356 0.068965775 0.958566944 -0.326082193 217 218 219 220 221 222 -1.581307029 -0.096248970 0.661047830 -0.680877211 -3.213515074 0.389913603 223 224 225 226 227 228 1.295757839 -1.411952136 -0.754162147 2.137784731 -3.323696080 5.006262599 229 230 231 232 233 234 1.568591811 -0.621186645 -2.291906427 -6.888755126 -1.465904777 1.997450446 235 236 237 238 239 240 -1.435589201 0.451867096 -1.841357810 1.723979932 2.082072530 0.219159630 241 242 243 244 245 246 1.232892881 -2.984932576 1.754636914 0.894765173 -0.547043962 -1.421314045 247 248 249 250 251 252 0.848684793 3.301550054 -1.551168930 0.035529582 1.346073127 2.370764312 253 254 255 256 257 258 -1.490813272 -4.756390426 1.511256999 -4.099166942 -0.678051553 1.535602883 259 260 261 262 263 264 0.381717957 2.027889210 0.207442360 -1.254630750 1.215443334 -0.283063007 265 266 267 268 269 270 1.345978489 1.416829120 2.488315900 0.776444556 1.066047804 0.165602291 271 272 273 274 275 276 0.559841274 -3.812295283 2.085449567 -4.204671654 2.853707856 -4.641728383 277 278 279 280 281 282 -1.340072379 0.868969473 -0.834112225 -2.020562227 1.015570219 1.362585983 283 284 285 286 287 288 -0.078739607 -2.998079801 1.481783597 -3.332934802 -1.079733789 -0.491237122 > postscript(file="/var/wessaorg/rcomp/tmp/6a5zj1386364016.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 = 288 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.196720526 NA 1 0.599048387 -3.196720526 2 2.101982563 0.599048387 3 3.708589526 2.101982563 4 -2.426569443 3.708589526 5 -1.328454956 -2.426569443 6 3.338031851 -1.328454956 7 -1.840536023 3.338031851 8 -1.754005451 -1.840536023 9 0.876741887 -1.754005451 10 1.455745221 0.876741887 11 -0.003513068 1.455745221 12 0.518882294 -0.003513068 13 0.413643599 0.518882294 14 -0.756135145 0.413643599 15 -0.254217635 -0.756135145 16 0.768782887 -0.254217635 17 3.783862016 0.768782887 18 2.597111350 3.783862016 19 0.618707087 2.597111350 20 1.063191374 0.618707087 21 1.127477205 1.063191374 22 2.319975450 1.127477205 23 0.803156287 2.319975450 24 1.082732019 0.803156287 25 0.900700321 1.082732019 26 1.446655427 0.900700321 27 -1.826925959 1.446655427 28 0.568787101 -1.826925959 29 0.081820554 0.568787101 30 -0.254897905 0.081820554 31 -0.278101073 -0.254897905 32 -0.504769950 -0.278101073 33 0.273214676 -0.504769950 34 -1.225045290 0.273214676 35 -2.290622195 -1.225045290 36 -2.679122681 -2.290622195 37 -1.610702801 -2.679122681 38 2.055250338 -1.610702801 39 1.858867973 2.055250338 40 1.622305329 1.858867973 41 -1.577690457 1.622305329 42 2.359282287 -1.577690457 43 -0.064657005 2.359282287 44 -0.252645025 -0.064657005 45 -4.518107899 -0.252645025 46 -2.742889509 -4.518107899 47 0.135703725 -2.742889509 48 0.547176185 0.135703725 49 -1.541365356 0.547176185 50 -0.966265940 -1.541365356 51 0.161577975 -0.966265940 52 -3.024792568 0.161577975 53 -0.177061826 -3.024792568 54 -1.681711441 -0.177061826 55 1.915846593 -1.681711441 56 0.015904391 1.915846593 57 0.792404772 0.015904391 58 0.002851410 0.792404772 59 1.848432817 0.002851410 60 0.743145415 1.848432817 61 0.345849700 0.743145415 62 -0.554718485 0.345849700 63 -0.704208653 -0.554718485 64 0.611569509 -0.704208653 65 0.992795861 0.611569509 66 1.305276444 0.992795861 67 4.000401411 1.305276444 68 -4.245975420 4.000401411 69 0.233729847 -4.245975420 70 -3.350173880 0.233729847 71 -1.026336113 -3.350173880 72 0.913811538 -1.026336113 73 0.551534157 0.913811538 74 1.020607148 0.551534157 75 3.417301658 1.020607148 76 -0.713052073 3.417301658 77 1.387828650 -0.713052073 78 -1.937897569 1.387828650 79 0.810599541 -1.937897569 80 0.430127391 0.810599541 81 0.203212140 0.430127391 82 -0.757180359 0.203212140 83 0.205706261 -0.757180359 84 2.004665261 0.205706261 85 -0.219135415 2.004665261 86 0.588869651 -0.219135415 87 1.058139761 0.588869651 88 0.180587278 1.058139761 89 -1.743407998 0.180587278 90 -0.301805281 -1.743407998 91 -0.022357748 -0.301805281 92 -0.002772981 -0.022357748 93 -1.829075295 -0.002772981 94 1.149737640 -1.829075295 95 -0.217892018 1.149737640 96 2.487969285 -0.217892018 97 0.109952147 2.487969285 98 -0.253251879 0.109952147 99 -1.349971233 -0.253251879 100 1.059243882 -1.349971233 101 2.186807292 1.059243882 102 0.880478889 2.186807292 103 0.853741365 0.880478889 104 -2.375470339 0.853741365 105 1.029514454 -2.375470339 106 -0.299070313 1.029514454 107 1.282196463 -0.299070313 108 -1.139849913 1.282196463 109 0.722402236 -1.139849913 110 -0.231925932 0.722402236 111 1.963909001 -0.231925932 112 -2.750422388 1.963909001 113 1.675903017 -2.750422388 114 -2.372794279 1.675903017 115 0.673748669 -2.372794279 116 -1.844213725 0.673748669 117 0.126072027 -1.844213725 118 -1.773530918 0.126072027 119 0.500312931 -1.773530918 120 -3.119580412 0.500312931 121 -1.239803597 -3.119580412 122 -1.151744660 -1.239803597 123 -1.008185073 -1.151744660 124 0.329350213 -1.008185073 125 0.930763425 0.329350213 126 0.963636886 0.930763425 127 -2.947908874 0.963636886 128 2.068875581 -2.947908874 129 -3.912130811 2.068875581 130 2.346435848 -3.912130811 131 -2.188193464 2.346435848 132 -1.962667697 -2.188193464 133 -0.212115497 -1.962667697 134 0.745462059 -0.212115497 135 0.116301446 0.745462059 136 -2.781471954 0.116301446 137 -1.134374056 -2.781471954 138 -2.350597730 -1.134374056 139 3.075633552 -2.350597730 140 1.377194795 3.075633552 141 -0.127442504 1.377194795 142 1.926543697 -0.127442504 143 -3.391684342 1.926543697 144 2.277360350 -3.391684342 145 -2.473980128 2.277360350 146 1.011517354 -2.473980128 147 -0.155530369 1.011517354 148 -3.079509950 -0.155530369 149 -1.757164664 -3.079509950 150 2.040792179 -1.757164664 151 4.059706843 2.040792179 152 1.114468469 4.059706843 153 -2.672241524 1.114468469 154 1.229549351 -2.672241524 155 0.963636886 1.229549351 156 1.331436178 0.963636886 157 -0.920511270 1.331436178 158 0.696553779 -0.920511270 159 0.357261740 0.696553779 160 0.579692059 0.357261740 161 1.442916183 0.579692059 162 -1.148865330 1.442916183 163 0.162589681 -1.148865330 164 -2.607137157 0.162589681 165 -1.670834516 -2.607137157 166 1.906093414 -1.670834516 167 1.754636521 1.906093414 168 0.710227729 1.754636521 169 -1.714538951 0.710227729 170 -1.777074303 -1.714538951 171 -2.782240022 -1.777074303 172 0.986274687 -2.782240022 173 -0.105643376 0.986274687 174 -0.268664665 -0.105643376 175 0.645213435 -0.268664665 176 -1.177474942 0.645213435 177 0.919570245 -1.177474942 178 0.174774924 0.919570245 179 2.353698319 0.174774924 180 0.035652769 2.353698319 181 -5.802204518 0.035652769 182 1.276055411 -5.802204518 183 2.686181993 1.276055411 184 0.031595460 2.686181993 185 0.154268523 0.031595460 186 0.822686840 0.154268523 187 -0.570994544 0.822686840 188 0.553995285 -0.570994544 189 2.902064655 0.553995285 190 1.785914473 2.902064655 191 -0.404706551 1.785914473 192 -0.079749484 -0.404706551 193 3.211464968 -0.079749484 194 1.073185381 3.211464968 195 1.658050400 1.073185381 196 2.090833111 1.658050400 197 1.298616388 2.090833111 198 -1.987442391 1.298616388 199 -2.607862065 -1.987442391 200 2.659124163 -2.607862065 201 0.606409392 2.659124163 202 1.556489406 0.606409392 203 0.867140444 1.556489406 204 -2.557641416 0.867140444 205 1.352938590 -2.557641416 206 -2.388872155 1.352938590 207 -4.004014549 -2.388872155 208 0.913654843 -4.004014549 209 3.394809532 0.913654843 210 1.275390320 3.394809532 211 1.300568605 1.275390320 212 2.579854356 1.300568605 213 0.068965775 2.579854356 214 0.958566944 0.068965775 215 -0.326082193 0.958566944 216 -1.581307029 -0.326082193 217 -0.096248970 -1.581307029 218 0.661047830 -0.096248970 219 -0.680877211 0.661047830 220 -3.213515074 -0.680877211 221 0.389913603 -3.213515074 222 1.295757839 0.389913603 223 -1.411952136 1.295757839 224 -0.754162147 -1.411952136 225 2.137784731 -0.754162147 226 -3.323696080 2.137784731 227 5.006262599 -3.323696080 228 1.568591811 5.006262599 229 -0.621186645 1.568591811 230 -2.291906427 -0.621186645 231 -6.888755126 -2.291906427 232 -1.465904777 -6.888755126 233 1.997450446 -1.465904777 234 -1.435589201 1.997450446 235 0.451867096 -1.435589201 236 -1.841357810 0.451867096 237 1.723979932 -1.841357810 238 2.082072530 1.723979932 239 0.219159630 2.082072530 240 1.232892881 0.219159630 241 -2.984932576 1.232892881 242 1.754636914 -2.984932576 243 0.894765173 1.754636914 244 -0.547043962 0.894765173 245 -1.421314045 -0.547043962 246 0.848684793 -1.421314045 247 3.301550054 0.848684793 248 -1.551168930 3.301550054 249 0.035529582 -1.551168930 250 1.346073127 0.035529582 251 2.370764312 1.346073127 252 -1.490813272 2.370764312 253 -4.756390426 -1.490813272 254 1.511256999 -4.756390426 255 -4.099166942 1.511256999 256 -0.678051553 -4.099166942 257 1.535602883 -0.678051553 258 0.381717957 1.535602883 259 2.027889210 0.381717957 260 0.207442360 2.027889210 261 -1.254630750 0.207442360 262 1.215443334 -1.254630750 263 -0.283063007 1.215443334 264 1.345978489 -0.283063007 265 1.416829120 1.345978489 266 2.488315900 1.416829120 267 0.776444556 2.488315900 268 1.066047804 0.776444556 269 0.165602291 1.066047804 270 0.559841274 0.165602291 271 -3.812295283 0.559841274 272 2.085449567 -3.812295283 273 -4.204671654 2.085449567 274 2.853707856 -4.204671654 275 -4.641728383 2.853707856 276 -1.340072379 -4.641728383 277 0.868969473 -1.340072379 278 -0.834112225 0.868969473 279 -2.020562227 -0.834112225 280 1.015570219 -2.020562227 281 1.362585983 1.015570219 282 -0.078739607 1.362585983 283 -2.998079801 -0.078739607 284 1.481783597 -2.998079801 285 -3.332934802 1.481783597 286 -1.079733789 -3.332934802 287 -0.491237122 -1.079733789 288 NA -0.491237122 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.599048387 -3.196720526 [2,] 2.101982563 0.599048387 [3,] 3.708589526 2.101982563 [4,] -2.426569443 3.708589526 [5,] -1.328454956 -2.426569443 [6,] 3.338031851 -1.328454956 [7,] -1.840536023 3.338031851 [8,] -1.754005451 -1.840536023 [9,] 0.876741887 -1.754005451 [10,] 1.455745221 0.876741887 [11,] -0.003513068 1.455745221 [12,] 0.518882294 -0.003513068 [13,] 0.413643599 0.518882294 [14,] -0.756135145 0.413643599 [15,] -0.254217635 -0.756135145 [16,] 0.768782887 -0.254217635 [17,] 3.783862016 0.768782887 [18,] 2.597111350 3.783862016 [19,] 0.618707087 2.597111350 [20,] 1.063191374 0.618707087 [21,] 1.127477205 1.063191374 [22,] 2.319975450 1.127477205 [23,] 0.803156287 2.319975450 [24,] 1.082732019 0.803156287 [25,] 0.900700321 1.082732019 [26,] 1.446655427 0.900700321 [27,] -1.826925959 1.446655427 [28,] 0.568787101 -1.826925959 [29,] 0.081820554 0.568787101 [30,] -0.254897905 0.081820554 [31,] -0.278101073 -0.254897905 [32,] -0.504769950 -0.278101073 [33,] 0.273214676 -0.504769950 [34,] -1.225045290 0.273214676 [35,] -2.290622195 -1.225045290 [36,] -2.679122681 -2.290622195 [37,] -1.610702801 -2.679122681 [38,] 2.055250338 -1.610702801 [39,] 1.858867973 2.055250338 [40,] 1.622305329 1.858867973 [41,] -1.577690457 1.622305329 [42,] 2.359282287 -1.577690457 [43,] -0.064657005 2.359282287 [44,] -0.252645025 -0.064657005 [45,] -4.518107899 -0.252645025 [46,] -2.742889509 -4.518107899 [47,] 0.135703725 -2.742889509 [48,] 0.547176185 0.135703725 [49,] -1.541365356 0.547176185 [50,] -0.966265940 -1.541365356 [51,] 0.161577975 -0.966265940 [52,] -3.024792568 0.161577975 [53,] -0.177061826 -3.024792568 [54,] -1.681711441 -0.177061826 [55,] 1.915846593 -1.681711441 [56,] 0.015904391 1.915846593 [57,] 0.792404772 0.015904391 [58,] 0.002851410 0.792404772 [59,] 1.848432817 0.002851410 [60,] 0.743145415 1.848432817 [61,] 0.345849700 0.743145415 [62,] -0.554718485 0.345849700 [63,] -0.704208653 -0.554718485 [64,] 0.611569509 -0.704208653 [65,] 0.992795861 0.611569509 [66,] 1.305276444 0.992795861 [67,] 4.000401411 1.305276444 [68,] -4.245975420 4.000401411 [69,] 0.233729847 -4.245975420 [70,] -3.350173880 0.233729847 [71,] -1.026336113 -3.350173880 [72,] 0.913811538 -1.026336113 [73,] 0.551534157 0.913811538 [74,] 1.020607148 0.551534157 [75,] 3.417301658 1.020607148 [76,] -0.713052073 3.417301658 [77,] 1.387828650 -0.713052073 [78,] -1.937897569 1.387828650 [79,] 0.810599541 -1.937897569 [80,] 0.430127391 0.810599541 [81,] 0.203212140 0.430127391 [82,] -0.757180359 0.203212140 [83,] 0.205706261 -0.757180359 [84,] 2.004665261 0.205706261 [85,] -0.219135415 2.004665261 [86,] 0.588869651 -0.219135415 [87,] 1.058139761 0.588869651 [88,] 0.180587278 1.058139761 [89,] -1.743407998 0.180587278 [90,] -0.301805281 -1.743407998 [91,] -0.022357748 -0.301805281 [92,] -0.002772981 -0.022357748 [93,] -1.829075295 -0.002772981 [94,] 1.149737640 -1.829075295 [95,] -0.217892018 1.149737640 [96,] 2.487969285 -0.217892018 [97,] 0.109952147 2.487969285 [98,] -0.253251879 0.109952147 [99,] -1.349971233 -0.253251879 [100,] 1.059243882 -1.349971233 [101,] 2.186807292 1.059243882 [102,] 0.880478889 2.186807292 [103,] 0.853741365 0.880478889 [104,] -2.375470339 0.853741365 [105,] 1.029514454 -2.375470339 [106,] -0.299070313 1.029514454 [107,] 1.282196463 -0.299070313 [108,] -1.139849913 1.282196463 [109,] 0.722402236 -1.139849913 [110,] -0.231925932 0.722402236 [111,] 1.963909001 -0.231925932 [112,] -2.750422388 1.963909001 [113,] 1.675903017 -2.750422388 [114,] -2.372794279 1.675903017 [115,] 0.673748669 -2.372794279 [116,] -1.844213725 0.673748669 [117,] 0.126072027 -1.844213725 [118,] -1.773530918 0.126072027 [119,] 0.500312931 -1.773530918 [120,] -3.119580412 0.500312931 [121,] -1.239803597 -3.119580412 [122,] -1.151744660 -1.239803597 [123,] -1.008185073 -1.151744660 [124,] 0.329350213 -1.008185073 [125,] 0.930763425 0.329350213 [126,] 0.963636886 0.930763425 [127,] -2.947908874 0.963636886 [128,] 2.068875581 -2.947908874 [129,] -3.912130811 2.068875581 [130,] 2.346435848 -3.912130811 [131,] -2.188193464 2.346435848 [132,] -1.962667697 -2.188193464 [133,] -0.212115497 -1.962667697 [134,] 0.745462059 -0.212115497 [135,] 0.116301446 0.745462059 [136,] -2.781471954 0.116301446 [137,] -1.134374056 -2.781471954 [138,] -2.350597730 -1.134374056 [139,] 3.075633552 -2.350597730 [140,] 1.377194795 3.075633552 [141,] -0.127442504 1.377194795 [142,] 1.926543697 -0.127442504 [143,] -3.391684342 1.926543697 [144,] 2.277360350 -3.391684342 [145,] -2.473980128 2.277360350 [146,] 1.011517354 -2.473980128 [147,] -0.155530369 1.011517354 [148,] -3.079509950 -0.155530369 [149,] -1.757164664 -3.079509950 [150,] 2.040792179 -1.757164664 [151,] 4.059706843 2.040792179 [152,] 1.114468469 4.059706843 [153,] -2.672241524 1.114468469 [154,] 1.229549351 -2.672241524 [155,] 0.963636886 1.229549351 [156,] 1.331436178 0.963636886 [157,] -0.920511270 1.331436178 [158,] 0.696553779 -0.920511270 [159,] 0.357261740 0.696553779 [160,] 0.579692059 0.357261740 [161,] 1.442916183 0.579692059 [162,] -1.148865330 1.442916183 [163,] 0.162589681 -1.148865330 [164,] -2.607137157 0.162589681 [165,] -1.670834516 -2.607137157 [166,] 1.906093414 -1.670834516 [167,] 1.754636521 1.906093414 [168,] 0.710227729 1.754636521 [169,] -1.714538951 0.710227729 [170,] -1.777074303 -1.714538951 [171,] -2.782240022 -1.777074303 [172,] 0.986274687 -2.782240022 [173,] -0.105643376 0.986274687 [174,] -0.268664665 -0.105643376 [175,] 0.645213435 -0.268664665 [176,] -1.177474942 0.645213435 [177,] 0.919570245 -1.177474942 [178,] 0.174774924 0.919570245 [179,] 2.353698319 0.174774924 [180,] 0.035652769 2.353698319 [181,] -5.802204518 0.035652769 [182,] 1.276055411 -5.802204518 [183,] 2.686181993 1.276055411 [184,] 0.031595460 2.686181993 [185,] 0.154268523 0.031595460 [186,] 0.822686840 0.154268523 [187,] -0.570994544 0.822686840 [188,] 0.553995285 -0.570994544 [189,] 2.902064655 0.553995285 [190,] 1.785914473 2.902064655 [191,] -0.404706551 1.785914473 [192,] -0.079749484 -0.404706551 [193,] 3.211464968 -0.079749484 [194,] 1.073185381 3.211464968 [195,] 1.658050400 1.073185381 [196,] 2.090833111 1.658050400 [197,] 1.298616388 2.090833111 [198,] -1.987442391 1.298616388 [199,] -2.607862065 -1.987442391 [200,] 2.659124163 -2.607862065 [201,] 0.606409392 2.659124163 [202,] 1.556489406 0.606409392 [203,] 0.867140444 1.556489406 [204,] -2.557641416 0.867140444 [205,] 1.352938590 -2.557641416 [206,] -2.388872155 1.352938590 [207,] -4.004014549 -2.388872155 [208,] 0.913654843 -4.004014549 [209,] 3.394809532 0.913654843 [210,] 1.275390320 3.394809532 [211,] 1.300568605 1.275390320 [212,] 2.579854356 1.300568605 [213,] 0.068965775 2.579854356 [214,] 0.958566944 0.068965775 [215,] -0.326082193 0.958566944 [216,] -1.581307029 -0.326082193 [217,] -0.096248970 -1.581307029 [218,] 0.661047830 -0.096248970 [219,] -0.680877211 0.661047830 [220,] -3.213515074 -0.680877211 [221,] 0.389913603 -3.213515074 [222,] 1.295757839 0.389913603 [223,] -1.411952136 1.295757839 [224,] -0.754162147 -1.411952136 [225,] 2.137784731 -0.754162147 [226,] -3.323696080 2.137784731 [227,] 5.006262599 -3.323696080 [228,] 1.568591811 5.006262599 [229,] -0.621186645 1.568591811 [230,] -2.291906427 -0.621186645 [231,] -6.888755126 -2.291906427 [232,] -1.465904777 -6.888755126 [233,] 1.997450446 -1.465904777 [234,] -1.435589201 1.997450446 [235,] 0.451867096 -1.435589201 [236,] -1.841357810 0.451867096 [237,] 1.723979932 -1.841357810 [238,] 2.082072530 1.723979932 [239,] 0.219159630 2.082072530 [240,] 1.232892881 0.219159630 [241,] -2.984932576 1.232892881 [242,] 1.754636914 -2.984932576 [243,] 0.894765173 1.754636914 [244,] -0.547043962 0.894765173 [245,] -1.421314045 -0.547043962 [246,] 0.848684793 -1.421314045 [247,] 3.301550054 0.848684793 [248,] -1.551168930 3.301550054 [249,] 0.035529582 -1.551168930 [250,] 1.346073127 0.035529582 [251,] 2.370764312 1.346073127 [252,] -1.490813272 2.370764312 [253,] -4.756390426 -1.490813272 [254,] 1.511256999 -4.756390426 [255,] -4.099166942 1.511256999 [256,] -0.678051553 -4.099166942 [257,] 1.535602883 -0.678051553 [258,] 0.381717957 1.535602883 [259,] 2.027889210 0.381717957 [260,] 0.207442360 2.027889210 [261,] -1.254630750 0.207442360 [262,] 1.215443334 -1.254630750 [263,] -0.283063007 1.215443334 [264,] 1.345978489 -0.283063007 [265,] 1.416829120 1.345978489 [266,] 2.488315900 1.416829120 [267,] 0.776444556 2.488315900 [268,] 1.066047804 0.776444556 [269,] 0.165602291 1.066047804 [270,] 0.559841274 0.165602291 [271,] -3.812295283 0.559841274 [272,] 2.085449567 -3.812295283 [273,] -4.204671654 2.085449567 [274,] 2.853707856 -4.204671654 [275,] -4.641728383 2.853707856 [276,] -1.340072379 -4.641728383 [277,] 0.868969473 -1.340072379 [278,] -0.834112225 0.868969473 [279,] -2.020562227 -0.834112225 [280,] 1.015570219 -2.020562227 [281,] 1.362585983 1.015570219 [282,] -0.078739607 1.362585983 [283,] -2.998079801 -0.078739607 [284,] 1.481783597 -2.998079801 [285,] -3.332934802 1.481783597 [286,] -1.079733789 -3.332934802 [287,] -0.491237122 -1.079733789 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.599048387 -3.196720526 2 2.101982563 0.599048387 3 3.708589526 2.101982563 4 -2.426569443 3.708589526 5 -1.328454956 -2.426569443 6 3.338031851 -1.328454956 7 -1.840536023 3.338031851 8 -1.754005451 -1.840536023 9 0.876741887 -1.754005451 10 1.455745221 0.876741887 11 -0.003513068 1.455745221 12 0.518882294 -0.003513068 13 0.413643599 0.518882294 14 -0.756135145 0.413643599 15 -0.254217635 -0.756135145 16 0.768782887 -0.254217635 17 3.783862016 0.768782887 18 2.597111350 3.783862016 19 0.618707087 2.597111350 20 1.063191374 0.618707087 21 1.127477205 1.063191374 22 2.319975450 1.127477205 23 0.803156287 2.319975450 24 1.082732019 0.803156287 25 0.900700321 1.082732019 26 1.446655427 0.900700321 27 -1.826925959 1.446655427 28 0.568787101 -1.826925959 29 0.081820554 0.568787101 30 -0.254897905 0.081820554 31 -0.278101073 -0.254897905 32 -0.504769950 -0.278101073 33 0.273214676 -0.504769950 34 -1.225045290 0.273214676 35 -2.290622195 -1.225045290 36 -2.679122681 -2.290622195 37 -1.610702801 -2.679122681 38 2.055250338 -1.610702801 39 1.858867973 2.055250338 40 1.622305329 1.858867973 41 -1.577690457 1.622305329 42 2.359282287 -1.577690457 43 -0.064657005 2.359282287 44 -0.252645025 -0.064657005 45 -4.518107899 -0.252645025 46 -2.742889509 -4.518107899 47 0.135703725 -2.742889509 48 0.547176185 0.135703725 49 -1.541365356 0.547176185 50 -0.966265940 -1.541365356 51 0.161577975 -0.966265940 52 -3.024792568 0.161577975 53 -0.177061826 -3.024792568 54 -1.681711441 -0.177061826 55 1.915846593 -1.681711441 56 0.015904391 1.915846593 57 0.792404772 0.015904391 58 0.002851410 0.792404772 59 1.848432817 0.002851410 60 0.743145415 1.848432817 61 0.345849700 0.743145415 62 -0.554718485 0.345849700 63 -0.704208653 -0.554718485 64 0.611569509 -0.704208653 65 0.992795861 0.611569509 66 1.305276444 0.992795861 67 4.000401411 1.305276444 68 -4.245975420 4.000401411 69 0.233729847 -4.245975420 70 -3.350173880 0.233729847 71 -1.026336113 -3.350173880 72 0.913811538 -1.026336113 73 0.551534157 0.913811538 74 1.020607148 0.551534157 75 3.417301658 1.020607148 76 -0.713052073 3.417301658 77 1.387828650 -0.713052073 78 -1.937897569 1.387828650 79 0.810599541 -1.937897569 80 0.430127391 0.810599541 81 0.203212140 0.430127391 82 -0.757180359 0.203212140 83 0.205706261 -0.757180359 84 2.004665261 0.205706261 85 -0.219135415 2.004665261 86 0.588869651 -0.219135415 87 1.058139761 0.588869651 88 0.180587278 1.058139761 89 -1.743407998 0.180587278 90 -0.301805281 -1.743407998 91 -0.022357748 -0.301805281 92 -0.002772981 -0.022357748 93 -1.829075295 -0.002772981 94 1.149737640 -1.829075295 95 -0.217892018 1.149737640 96 2.487969285 -0.217892018 97 0.109952147 2.487969285 98 -0.253251879 0.109952147 99 -1.349971233 -0.253251879 100 1.059243882 -1.349971233 101 2.186807292 1.059243882 102 0.880478889 2.186807292 103 0.853741365 0.880478889 104 -2.375470339 0.853741365 105 1.029514454 -2.375470339 106 -0.299070313 1.029514454 107 1.282196463 -0.299070313 108 -1.139849913 1.282196463 109 0.722402236 -1.139849913 110 -0.231925932 0.722402236 111 1.963909001 -0.231925932 112 -2.750422388 1.963909001 113 1.675903017 -2.750422388 114 -2.372794279 1.675903017 115 0.673748669 -2.372794279 116 -1.844213725 0.673748669 117 0.126072027 -1.844213725 118 -1.773530918 0.126072027 119 0.500312931 -1.773530918 120 -3.119580412 0.500312931 121 -1.239803597 -3.119580412 122 -1.151744660 -1.239803597 123 -1.008185073 -1.151744660 124 0.329350213 -1.008185073 125 0.930763425 0.329350213 126 0.963636886 0.930763425 127 -2.947908874 0.963636886 128 2.068875581 -2.947908874 129 -3.912130811 2.068875581 130 2.346435848 -3.912130811 131 -2.188193464 2.346435848 132 -1.962667697 -2.188193464 133 -0.212115497 -1.962667697 134 0.745462059 -0.212115497 135 0.116301446 0.745462059 136 -2.781471954 0.116301446 137 -1.134374056 -2.781471954 138 -2.350597730 -1.134374056 139 3.075633552 -2.350597730 140 1.377194795 3.075633552 141 -0.127442504 1.377194795 142 1.926543697 -0.127442504 143 -3.391684342 1.926543697 144 2.277360350 -3.391684342 145 -2.473980128 2.277360350 146 1.011517354 -2.473980128 147 -0.155530369 1.011517354 148 -3.079509950 -0.155530369 149 -1.757164664 -3.079509950 150 2.040792179 -1.757164664 151 4.059706843 2.040792179 152 1.114468469 4.059706843 153 -2.672241524 1.114468469 154 1.229549351 -2.672241524 155 0.963636886 1.229549351 156 1.331436178 0.963636886 157 -0.920511270 1.331436178 158 0.696553779 -0.920511270 159 0.357261740 0.696553779 160 0.579692059 0.357261740 161 1.442916183 0.579692059 162 -1.148865330 1.442916183 163 0.162589681 -1.148865330 164 -2.607137157 0.162589681 165 -1.670834516 -2.607137157 166 1.906093414 -1.670834516 167 1.754636521 1.906093414 168 0.710227729 1.754636521 169 -1.714538951 0.710227729 170 -1.777074303 -1.714538951 171 -2.782240022 -1.777074303 172 0.986274687 -2.782240022 173 -0.105643376 0.986274687 174 -0.268664665 -0.105643376 175 0.645213435 -0.268664665 176 -1.177474942 0.645213435 177 0.919570245 -1.177474942 178 0.174774924 0.919570245 179 2.353698319 0.174774924 180 0.035652769 2.353698319 181 -5.802204518 0.035652769 182 1.276055411 -5.802204518 183 2.686181993 1.276055411 184 0.031595460 2.686181993 185 0.154268523 0.031595460 186 0.822686840 0.154268523 187 -0.570994544 0.822686840 188 0.553995285 -0.570994544 189 2.902064655 0.553995285 190 1.785914473 2.902064655 191 -0.404706551 1.785914473 192 -0.079749484 -0.404706551 193 3.211464968 -0.079749484 194 1.073185381 3.211464968 195 1.658050400 1.073185381 196 2.090833111 1.658050400 197 1.298616388 2.090833111 198 -1.987442391 1.298616388 199 -2.607862065 -1.987442391 200 2.659124163 -2.607862065 201 0.606409392 2.659124163 202 1.556489406 0.606409392 203 0.867140444 1.556489406 204 -2.557641416 0.867140444 205 1.352938590 -2.557641416 206 -2.388872155 1.352938590 207 -4.004014549 -2.388872155 208 0.913654843 -4.004014549 209 3.394809532 0.913654843 210 1.275390320 3.394809532 211 1.300568605 1.275390320 212 2.579854356 1.300568605 213 0.068965775 2.579854356 214 0.958566944 0.068965775 215 -0.326082193 0.958566944 216 -1.581307029 -0.326082193 217 -0.096248970 -1.581307029 218 0.661047830 -0.096248970 219 -0.680877211 0.661047830 220 -3.213515074 -0.680877211 221 0.389913603 -3.213515074 222 1.295757839 0.389913603 223 -1.411952136 1.295757839 224 -0.754162147 -1.411952136 225 2.137784731 -0.754162147 226 -3.323696080 2.137784731 227 5.006262599 -3.323696080 228 1.568591811 5.006262599 229 -0.621186645 1.568591811 230 -2.291906427 -0.621186645 231 -6.888755126 -2.291906427 232 -1.465904777 -6.888755126 233 1.997450446 -1.465904777 234 -1.435589201 1.997450446 235 0.451867096 -1.435589201 236 -1.841357810 0.451867096 237 1.723979932 -1.841357810 238 2.082072530 1.723979932 239 0.219159630 2.082072530 240 1.232892881 0.219159630 241 -2.984932576 1.232892881 242 1.754636914 -2.984932576 243 0.894765173 1.754636914 244 -0.547043962 0.894765173 245 -1.421314045 -0.547043962 246 0.848684793 -1.421314045 247 3.301550054 0.848684793 248 -1.551168930 3.301550054 249 0.035529582 -1.551168930 250 1.346073127 0.035529582 251 2.370764312 1.346073127 252 -1.490813272 2.370764312 253 -4.756390426 -1.490813272 254 1.511256999 -4.756390426 255 -4.099166942 1.511256999 256 -0.678051553 -4.099166942 257 1.535602883 -0.678051553 258 0.381717957 1.535602883 259 2.027889210 0.381717957 260 0.207442360 2.027889210 261 -1.254630750 0.207442360 262 1.215443334 -1.254630750 263 -0.283063007 1.215443334 264 1.345978489 -0.283063007 265 1.416829120 1.345978489 266 2.488315900 1.416829120 267 0.776444556 2.488315900 268 1.066047804 0.776444556 269 0.165602291 1.066047804 270 0.559841274 0.165602291 271 -3.812295283 0.559841274 272 2.085449567 -3.812295283 273 -4.204671654 2.085449567 274 2.853707856 -4.204671654 275 -4.641728383 2.853707856 276 -1.340072379 -4.641728383 277 0.868969473 -1.340072379 278 -0.834112225 0.868969473 279 -2.020562227 -0.834112225 280 1.015570219 -2.020562227 281 1.362585983 1.015570219 282 -0.078739607 1.362585983 283 -2.998079801 -0.078739607 284 1.481783597 -2.998079801 285 -3.332934802 1.481783597 286 -1.079733789 -3.332934802 287 -0.491237122 -1.079733789 > 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/7dav31386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/84mlp1386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/95r3n1386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10we0o1386364016.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$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/11vdiq1386364016.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$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12s5px1386364016.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, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1379u01386364016.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,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/142gs91386364016.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,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15woow1386364016.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,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + 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,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + 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,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + 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/161qol1386364016.tab") + } > > try(system("convert tmp/178pe1386364016.ps tmp/178pe1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/24t981386364016.ps tmp/24t981386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/3ob4w1386364016.ps tmp/3ob4w1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/4pdqg1386364016.ps tmp/4pdqg1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/5fr7r1386364016.ps tmp/5fr7r1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/6a5zj1386364016.ps tmp/6a5zj1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/7dav31386364016.ps tmp/7dav31386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/84mlp1386364016.ps tmp/84mlp1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/95r3n1386364016.ps tmp/95r3n1386364016.png",intern=TRUE)) character(0) > try(system("convert tmp/10we0o1386364016.ps tmp/10we0o1386364016.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 21.627 3.592 25.213