R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale 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 + ,14 + ,35 + ,32 + ,15 + ,11 + ,0 + ,1 + ,15 + ,33 + ,34 + ,14 + ,12 + ,0 + ,0 + ,11 + ,37 + ,36 + ,11 + ,9 + ,0 + ,0 + ,15 + ,38 + ,31 + ,16 + ,12 + ,0 + ,1 + ,14 + ,34 + ,35 + ,15 + ,10 + ,0 + ,0 + ,13 + ,27 + ,29 + ,12 + ,9 + ,0 + ,1 + ,12 + ,16 + ,22 + ,6 + ,6 + ,0 + ,0 + ,16 + ,40 + ,41 + ,16 + ,10 + ,0 + ,0 + ,16 + ,36 + ,36 + ,10 + ,9 + ,0 + ,1 + ,9 + ,42 + ,42 + ,15 + ,13 + ,0 + ,1 + ,14 + ,30 + ,33 + ,14 + ,12) + ,dim=c(7 + ,288) + ,dimnames=list(c('Populatie' + ,'Geslacht' + ,'Happiness' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Populatie','Geslacht','Happiness','Connected','Separate','Learning','Software'),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' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Separate Populatie Geslacht Happiness Connected Learning Software 1 38 1 1 14 41 13 12 2 32 1 1 18 39 16 11 3 35 1 1 11 30 19 15 4 33 1 0 12 31 15 6 5 37 1 1 16 34 14 13 6 29 1 1 18 35 13 10 7 31 1 1 14 39 19 12 8 36 1 1 14 34 15 14 9 35 1 1 15 36 14 12 10 38 1 1 15 37 15 9 11 31 1 0 17 38 16 10 12 34 1 1 19 36 16 12 13 35 1 0 10 38 16 12 14 38 1 1 16 39 16 11 15 37 1 1 18 33 17 15 16 33 1 0 14 32 15 12 17 32 1 0 14 36 15 10 18 38 1 1 17 38 20 12 19 38 1 0 14 39 18 11 20 32 1 1 16 32 16 12 21 33 1 0 18 32 16 11 22 31 1 1 11 31 16 12 23 38 1 1 14 39 19 13 24 39 1 1 12 37 16 11 25 32 1 0 17 39 17 12 26 32 1 1 9 41 17 13 27 35 1 0 16 36 16 10 28 37 1 1 14 33 15 14 29 33 1 1 15 33 16 12 30 33 1 0 11 34 14 10 31 31 1 1 16 31 15 12 32 32 1 0 13 27 12 8 33 31 1 1 17 37 14 10 34 37 1 1 15 34 16 12 35 30 1 0 14 34 14 12 36 33 1 0 16 32 10 7 37 31 1 0 9 29 10 9 38 33 1 0 15 36 14 12 39 31 1 1 17 29 16 10 40 33 1 0 13 35 16 10 41 32 1 0 15 37 16 10 42 33 1 1 16 34 14 12 43 32 1 0 16 38 20 15 44 33 1 0 12 35 14 10 45 28 1 1 15 38 14 10 46 35 1 1 11 37 11 12 47 39 1 1 15 38 14 13 48 34 1 1 15 33 15 11 49 38 1 1 17 36 16 11 50 32 1 0 13 38 14 12 51 38 1 1 16 32 16 14 52 30 1 0 14 32 14 10 53 33 1 0 11 32 12 12 54 38 1 1 12 34 16 13 55 32 1 0 12 32 9 5 56 35 1 1 15 37 14 6 57 34 1 1 16 39 16 12 58 34 1 1 15 29 16 12 59 36 1 0 12 37 15 11 60 34 1 1 12 35 16 10 61 28 1 0 8 30 12 7 62 34 1 0 13 38 16 12 63 35 1 1 11 34 16 14 64 35 1 1 14 31 14 11 65 31 1 1 15 34 16 12 66 37 1 0 10 35 17 13 67 35 1 1 11 36 18 14 68 27 1 0 12 30 18 11 69 40 1 1 15 39 12 12 70 37 1 0 15 35 16 12 71 36 1 0 14 38 10 8 72 38 1 1 16 31 14 11 73 39 1 1 15 34 18 14 74 41 1 0 15 38 18 14 75 27 1 0 13 34 16 12 76 30 1 1 12 39 17 9 77 37 1 1 17 37 16 13 78 31 1 1 13 34 16 11 79 31 1 0 15 28 13 12 80 27 1 0 13 37 16 12 81 36 1 0 15 33 16 12 82 37 1 1 15 35 16 12 83 33 1 0 16 37 15 12 84 34 1 1 15 32 15 11 85 31 1 1 14 33 16 10 86 39 1 0 15 38 14 9 87 34 1 1 14 33 16 12 88 32 1 1 13 29 16 12 89 33 1 1 7 33 15 12 90 36 1 1 17 31 12 9 91 32 1 1 13 36 17 15 92 41 1 1 15 35 16 12 93 28 1 1 14 32 15 12 94 30 1 1 13 29 13 12 95 36 1 1 16 39 16 10 96 35 1 1 12 37 16 13 97 31 1 1 14 35 16 9 98 34 1 0 17 37 16 12 99 36 1 0 15 32 14 10 100 36 1 1 17 38 16 14 101 35 1 0 12 37 16 11 102 37 1 1 16 36 20 15 103 28 1 0 11 32 15 11 104 39 1 1 15 33 16 11 105 32 1 0 9 40 13 12 106 35 1 1 16 38 17 12 107 39 1 0 15 41 16 12 108 35 1 0 10 36 16 11 109 42 1 1 10 43 12 7 110 34 1 1 15 30 16 12 111 33 1 1 11 31 16 14 112 41 1 1 13 32 17 11 113 34 1 1 18 37 12 10 114 32 1 0 16 37 18 13 115 40 1 1 14 33 14 13 116 40 1 1 14 34 14 8 117 35 1 1 14 33 13 11 118 36 1 1 14 38 16 12 119 37 1 0 12 33 13 11 120 27 1 1 14 31 16 13 121 39 1 1 15 38 13 12 122 38 1 1 15 37 16 14 123 31 1 1 15 36 15 13 124 33 1 1 13 31 16 15 125 32 1 0 17 39 15 10 126 39 1 1 17 44 17 11 127 36 1 1 19 33 15 9 128 33 1 1 15 35 12 11 129 33 1 0 13 32 16 10 130 32 1 0 9 28 10 11 131 37 1 1 15 40 16 8 132 30 1 0 15 27 12 11 133 38 1 0 15 37 14 12 134 29 1 1 16 32 15 12 135 22 1 0 11 28 13 9 136 35 1 0 14 34 15 11 137 35 1 1 11 30 11 10 138 34 1 1 15 35 12 8 139 35 1 0 13 31 11 9 140 34 1 1 15 32 16 8 141 37 1 0 16 30 15 9 142 35 1 1 14 30 17 15 143 23 1 0 15 31 16 11 144 31 1 1 16 40 10 8 145 27 1 1 16 32 18 13 146 36 1 0 11 36 13 12 147 31 1 0 12 32 16 12 148 32 1 0 9 35 13 9 149 39 1 1 16 38 10 7 150 37 1 1 13 42 15 13 151 38 1 0 16 34 16 9 152 39 1 1 12 35 16 6 153 34 1 1 9 38 14 8 154 31 1 1 13 33 10 8 155 37 1 1 14 32 13 6 156 36 1 1 19 33 15 9 157 32 1 1 13 34 16 11 158 38 1 1 12 32 12 8 159 26 0 0 10 27 13 10 160 26 0 0 14 31 12 8 161 33 0 0 16 38 17 14 162 39 0 1 10 34 15 10 163 30 0 0 11 24 10 8 164 33 0 0 14 30 14 11 165 25 0 1 12 26 11 12 166 38 0 1 9 34 13 12 167 37 0 0 9 27 16 12 168 31 0 0 11 37 12 5 169 37 0 1 16 36 16 12 170 35 0 0 9 41 12 10 171 25 0 1 13 29 9 7 172 28 0 1 16 36 12 12 173 35 0 0 13 32 15 11 174 33 0 1 9 37 12 8 175 30 0 0 12 30 12 9 176 31 0 1 16 31 14 10 177 37 0 1 11 38 12 9 178 36 0 1 14 36 16 12 179 30 0 0 13 35 11 6 180 36 0 0 15 31 19 15 181 32 0 0 14 38 15 12 182 28 0 1 16 22 8 12 183 36 0 1 13 32 16 12 184 34 0 0 14 36 17 11 185 31 0 1 15 39 12 7 186 28 0 0 13 28 11 7 187 36 0 0 11 32 11 5 188 36 0 1 11 32 14 12 189 40 0 1 14 38 16 12 190 33 0 1 15 32 12 3 191 37 0 1 11 35 16 11 192 32 0 1 15 32 13 10 193 38 0 0 12 37 15 12 194 31 0 1 14 34 16 9 195 37 0 1 14 33 16 12 196 33 0 0 8 33 14 9 197 30 0 0 9 30 16 12 198 30 0 0 15 24 14 10 199 31 0 0 17 34 11 9 200 32 0 0 13 34 12 12 201 34 0 1 15 33 15 8 202 36 0 1 15 34 15 11 203 37 0 1 14 35 16 11 204 36 0 0 16 35 16 12 205 33 0 0 13 36 11 10 206 33 0 0 16 34 15 10 207 33 0 1 9 34 12 12 208 44 0 0 16 41 12 12 209 39 0 0 11 32 15 11 210 32 0 0 10 30 15 8 211 35 0 1 11 35 16 12 212 25 0 0 15 28 14 10 213 35 0 1 17 33 17 11 214 34 0 1 14 39 14 10 215 35 0 0 8 36 13 8 216 39 0 1 15 36 15 12 217 33 0 0 11 35 13 12 218 36 0 0 16 38 14 10 219 32 0 1 10 33 15 12 220 32 0 0 15 31 12 9 221 36 0 1 16 32 8 6 222 32 0 0 19 31 14 10 223 34 0 0 12 33 14 9 224 33 0 0 8 34 11 9 225 35 0 0 11 34 12 9 226 30 0 1 14 34 13 6 227 38 0 0 9 33 10 10 228 34 0 0 15 32 16 6 229 33 0 1 13 41 18 14 230 32 0 1 16 34 13 10 231 31 0 0 11 36 11 10 232 30 0 0 12 37 4 6 233 27 0 0 13 36 13 12 234 31 0 1 10 29 16 12 235 30 0 0 11 37 10 7 236 32 0 0 12 27 12 8 237 35 0 0 8 35 12 11 238 28 0 0 12 28 10 3 239 33 0 0 12 35 13 6 240 35 0 0 11 29 12 8 241 35 0 0 13 32 14 9 242 32 0 1 14 36 10 9 243 21 0 1 10 19 12 8 244 20 0 1 12 21 12 9 245 34 0 0 15 31 11 7 246 32 0 0 13 33 10 7 247 34 0 1 13 36 12 6 248 32 0 1 13 33 16 9 249 33 0 0 12 37 12 10 250 33 0 0 12 34 14 11 251 37 0 0 9 35 16 12 252 32 0 1 9 31 14 8 253 34 0 1 15 37 13 11 254 30 0 1 10 35 4 3 255 30 0 1 14 27 15 11 256 38 0 0 15 34 11 12 257 36 0 0 7 40 11 7 258 32 0 0 14 29 14 9 259 34 0 0 8 38 15 12 260 33 0 1 10 34 14 8 261 27 0 0 13 21 13 11 262 32 0 0 13 36 11 8 263 34 0 1 13 38 15 10 264 29 0 0 8 30 11 8 265 35 0 0 12 35 13 7 266 27 0 1 13 30 13 8 267 33 0 1 12 36 16 10 268 38 0 0 10 34 13 8 269 36 0 1 13 35 16 12 270 33 0 0 12 34 16 14 271 39 0 0 9 32 12 7 272 29 0 1 15 33 7 6 273 32 0 0 13 33 16 11 274 34 0 1 13 26 5 4 275 38 0 0 13 35 16 9 276 17 0 0 15 21 4 5 277 35 0 0 15 38 12 9 278 32 0 0 14 35 15 11 279 34 0 1 15 33 14 12 280 36 0 0 11 37 11 9 281 31 0 0 15 38 16 12 282 35 0 1 14 34 15 10 283 29 0 0 13 27 12 9 284 22 0 1 12 16 6 6 285 41 0 0 16 40 16 10 286 36 0 0 16 36 10 9 287 42 0 1 9 42 15 13 288 33 0 1 14 30 14 12 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Populatie Geslacht Happiness Connected Learning 14.77986 -0.04862 0.70053 -0.03084 0.46064 0.16910 Software 0.08317 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -9.1692 -1.9114 0.1335 2.1617 7.7998 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 14.77986 1.98005 7.464 1.05e-12 *** Populatie -0.04862 0.43554 -0.112 0.911 Geslacht 0.70053 0.40401 1.734 0.084 . Happiness -0.03084 0.08281 -0.372 0.710 Connected 0.46064 0.05039 9.142 < 2e-16 *** Learning 0.16910 0.10281 1.645 0.101 Software 0.08317 0.11013 0.755 0.451 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.289 on 281 degrees of freedom Multiple R-squared: 0.306, Adjusted R-squared: 0.2912 F-statistic: 20.65 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,] 0.671718758 0.656562485 0.32828124 [2,] 0.563349762 0.873300475 0.43665024 [3,] 0.547351250 0.905297500 0.45264875 [4,] 0.425183590 0.850367180 0.57481641 [5,] 0.473818924 0.947637847 0.52618108 [6,] 0.504815941 0.990368119 0.49518406 [7,] 0.404479091 0.808958183 0.59552091 [8,] 0.319736708 0.639473416 0.68026329 [9,] 0.387007917 0.774015835 0.61299208 [10,] 0.432235833 0.864471666 0.56776417 [11,] 0.380673117 0.761346234 0.61932688 [12,] 0.315209025 0.630418051 0.68479097 [13,] 0.325407007 0.650814015 0.67459299 [14,] 0.269055764 0.538111528 0.73094424 [15,] 0.275123267 0.550246534 0.72487673 [16,] 0.257133801 0.514267602 0.74286620 [17,] 0.391916916 0.783833832 0.60808308 [18,] 0.342979523 0.685959047 0.65702048 [19,] 0.311731858 0.623463717 0.68826814 [20,] 0.266465970 0.532931940 0.73353403 [21,] 0.216010016 0.432020032 0.78398998 [22,] 0.202634050 0.405268100 0.79736595 [23,] 0.166879054 0.333758109 0.83312095 [24,] 0.167201168 0.334402335 0.83279883 [25,] 0.156848465 0.313696930 0.84315153 [26,] 0.161593682 0.323187363 0.83840632 [27,] 0.140262999 0.280525998 0.85973700 [28,] 0.111984545 0.223969091 0.88801545 [29,] 0.087490215 0.174980430 0.91250979 [30,] 0.074231637 0.148463273 0.92576836 [31,] 0.056767019 0.113534038 0.94323298 [32,] 0.046581371 0.093162741 0.95341863 [33,] 0.035715030 0.071430059 0.96428497 [34,] 0.034469823 0.068939646 0.96553018 [35,] 0.025424894 0.050849789 0.97457511 [36,] 0.071990311 0.143980621 0.92800969 [37,] 0.056299304 0.112598608 0.94370070 [38,] 0.068076938 0.136153875 0.93192306 [39,] 0.052993087 0.105986174 0.94700691 [40,] 0.059976829 0.119953657 0.94002317 [41,] 0.052108941 0.104217882 0.94789106 [42,] 0.057251092 0.114502183 0.94274891 [43,] 0.050836431 0.101672861 0.94916357 [44,] 0.039547258 0.079094516 0.96045274 [45,] 0.038881749 0.077763497 0.96111825 [46,] 0.031403867 0.062807735 0.96859613 [47,] 0.025254230 0.050508461 0.97474577 [48,] 0.020578838 0.041157676 0.97942116 [49,] 0.015685320 0.031370640 0.98431468 [50,] 0.014048969 0.028097939 0.98595103 [51,] 0.010631875 0.021263751 0.98936812 [52,] 0.012033826 0.024067652 0.98796617 [53,] 0.009080413 0.018160826 0.99091959 [54,] 0.006691321 0.013382643 0.99330868 [55,] 0.005250491 0.010500982 0.99474951 [56,] 0.006038453 0.012076906 0.99396155 [57,] 0.005916213 0.011832426 0.99408379 [58,] 0.004451446 0.008902891 0.99554855 [59,] 0.008590532 0.017181063 0.99140947 [60,] 0.011001355 0.022002711 0.98899864 [61,] 0.012427048 0.024854095 0.98757295 [62,] 0.011264703 0.022529405 0.98873530 [63,] 0.015909241 0.031818483 0.98409076 [64,] 0.019002754 0.038005508 0.98099725 [65,] 0.035594886 0.071189771 0.96440511 [66,] 0.071978426 0.143956852 0.92802157 [67,] 0.095405547 0.190811095 0.90459445 [68,] 0.081276863 0.162553726 0.91872314 [69,] 0.080651325 0.161302650 0.91934867 [70,] 0.069452589 0.138905178 0.93054741 [71,] 0.149023297 0.298046594 0.85097670 [72,] 0.145103111 0.290206222 0.85489689 [73,] 0.133457718 0.266915435 0.86654228 [74,] 0.118134441 0.236268882 0.88186556 [75,] 0.100212202 0.200424404 0.89978780 [76,] 0.094227878 0.188455757 0.90577212 [77,] 0.127732038 0.255464076 0.87226796 [78,] 0.108773881 0.217547761 0.89122612 [79,] 0.093187425 0.186374849 0.90681258 [80,] 0.079177808 0.158355615 0.92082219 [81,] 0.078383220 0.156766439 0.92161678 [82,] 0.085676889 0.171353779 0.91432311 [83,] 0.134857888 0.269715776 0.86514211 [84,] 0.189727951 0.379455902 0.81027205 [85,] 0.185718436 0.371436873 0.81428156 [86,] 0.163634526 0.327269052 0.83636547 [87,] 0.142915442 0.285830884 0.85708456 [88,] 0.141177112 0.282354224 0.85882289 [89,] 0.123247910 0.246495821 0.87675209 [90,] 0.127482421 0.254964842 0.87251758 [91,] 0.109895929 0.219791859 0.89010407 [92,] 0.097044344 0.194088687 0.90295566 [93,] 0.084296048 0.168592097 0.91570395 [94,] 0.098902541 0.197805082 0.90109746 [95,] 0.125800586 0.251601172 0.87419941 [96,] 0.129536902 0.259073803 0.87046310 [97,] 0.114001830 0.228003661 0.88599817 [98,] 0.113203728 0.226407455 0.88679627 [99,] 0.103421448 0.206842896 0.89657855 [100,] 0.142950804 0.285901607 0.85704920 [101,] 0.125642333 0.251284667 0.87435767 [102,] 0.108630927 0.217261854 0.89136907 [103,] 0.199474676 0.398949352 0.80052532 [104,] 0.180819903 0.361639805 0.81918010 [105,] 0.180610023 0.361220047 0.81938998 [106,] 0.235242161 0.470484322 0.76475784 [107,] 0.309072720 0.618145439 0.69092728 [108,] 0.283703959 0.567407919 0.71629604 [109,] 0.255009695 0.510019391 0.74499030 [110,] 0.273141545 0.546283090 0.72685846 [111,] 0.367069827 0.734139654 0.63293017 [112,] 0.362386028 0.724772057 0.63761397 [113,] 0.342977245 0.685954491 0.65702275 [114,] 0.370494454 0.740988908 0.62950555 [115,] 0.340160363 0.680320726 0.65983964 [116,] 0.345279805 0.690559610 0.65472019 [117,] 0.317750175 0.635500349 0.68224983 [118,] 0.301401053 0.602802106 0.69859895 [119,] 0.281411531 0.562823063 0.71858847 [120,] 0.255185308 0.510370616 0.74481469 [121,] 0.234055542 0.468111084 0.76594446 [122,] 0.210514352 0.421028704 0.78948565 [123,] 0.188623107 0.377246213 0.81137689 [124,] 0.190112808 0.380225617 0.80988719 [125,] 0.215620402 0.431240804 0.78437960 [126,] 0.393153924 0.786307848 0.60684608 [127,] 0.368450429 0.736900858 0.63154957 [128,] 0.361931724 0.723863449 0.63806828 [129,] 0.331144042 0.662288083 0.66885596 [130,] 0.332603470 0.665206940 0.66739653 [131,] 0.303830599 0.607661199 0.69616940 [132,] 0.359726672 0.719453344 0.64027333 [133,] 0.342659419 0.685318838 0.65734058 [134,] 0.582489781 0.835020439 0.41751022 [135,] 0.635999457 0.728001086 0.36400054 [136,] 0.759301245 0.481397511 0.24069876 [137,] 0.737428047 0.525143907 0.26257195 [138,] 0.733830907 0.532338185 0.26616909 [139,] 0.730702114 0.538595771 0.26929789 [140,] 0.729903119 0.540193762 0.27009688 [141,] 0.715182992 0.569634017 0.28481701 [142,] 0.727738862 0.544522276 0.27226114 [143,] 0.740530163 0.518939674 0.25946984 [144,] 0.737166278 0.525667445 0.26283372 [145,] 0.736623746 0.526752508 0.26337625 [146,] 0.732027539 0.535944922 0.26797246 [147,] 0.708452143 0.583095714 0.29154786 [148,] 0.742894830 0.514210341 0.25710517 [149,] 0.736399122 0.527201755 0.26360088 [150,] 0.737822928 0.524354144 0.26217707 [151,] 0.757228753 0.485542493 0.24277125 [152,] 0.764389245 0.471221510 0.23561076 [153,] 0.826763071 0.346473859 0.17323693 [154,] 0.816235266 0.367529468 0.18376473 [155,] 0.799700761 0.400598477 0.20029924 [156,] 0.824400397 0.351199206 0.17559960 [157,] 0.844648573 0.310702855 0.15535143 [158,] 0.896436437 0.207127125 0.10356356 [159,] 0.896287244 0.207425512 0.10371276 [160,] 0.885492804 0.229014391 0.11450720 [161,] 0.873156796 0.253686408 0.12684320 [162,] 0.902241174 0.195517653 0.09775883 [163,] 0.936723152 0.126553696 0.06327685 [164,] 0.933030955 0.133938090 0.06696905 [165,] 0.923981608 0.152036784 0.07601839 [166,] 0.911711873 0.176576254 0.08828813 [167,] 0.898287468 0.203425063 0.10171253 [168,] 0.887983847 0.224032307 0.11201615 [169,] 0.872087826 0.255824348 0.12791217 [170,] 0.869023643 0.261952715 0.13097636 [171,] 0.866924787 0.266150426 0.13307521 [172,] 0.874706822 0.250586356 0.12529318 [173,] 0.865486789 0.269026421 0.13451321 [174,] 0.861677703 0.276644594 0.13832230 [175,] 0.846917090 0.306165819 0.15308291 [176,] 0.868265120 0.263469760 0.13173488 [177,] 0.853154371 0.293691258 0.14684563 [178,] 0.870490888 0.259018225 0.12950911 [179,] 0.872110143 0.255779715 0.12788986 [180,] 0.880530545 0.238938909 0.11946945 [181,] 0.863412993 0.273174014 0.13658701 [182,] 0.854062519 0.291874963 0.14593748 [183,] 0.832595623 0.334808753 0.16740438 [184,] 0.826758326 0.346483347 0.17324167 [185,] 0.824806484 0.350387032 0.17519352 [186,] 0.828585859 0.342828282 0.17141414 [187,] 0.804280074 0.391439852 0.19571993 [188,] 0.789203054 0.421593891 0.21079695 [189,] 0.769053481 0.461893039 0.23094652 [190,] 0.748991347 0.502017307 0.25100865 [191,] 0.722354749 0.555290502 0.27764525 [192,] 0.691875399 0.616249201 0.30812460 [193,] 0.675155268 0.649689464 0.32484473 [194,] 0.662472627 0.675054745 0.33752737 [195,] 0.635988040 0.728023920 0.36401196 [196,] 0.604262588 0.791474825 0.39573741 [197,] 0.570202840 0.859594319 0.42979716 [198,] 0.533237321 0.933525357 0.46676268 [199,] 0.687781755 0.624436490 0.31221825 [200,] 0.772160338 0.455679325 0.22783966 [201,] 0.741400930 0.517198140 0.25859907 [202,] 0.709979355 0.580041290 0.29002065 [203,] 0.768782083 0.462435835 0.23121792 [204,] 0.745322693 0.509354615 0.25467731 [205,] 0.723631337 0.552737325 0.27636866 [206,] 0.690376526 0.619246947 0.30962347 [207,] 0.728627543 0.542744914 0.27137246 [208,] 0.696174678 0.607650643 0.30382532 [209,] 0.661040943 0.677918114 0.33895906 [210,] 0.627920278 0.744159445 0.37207972 [211,] 0.589124250 0.821751500 0.41087575 [212,] 0.670223274 0.659553452 0.32977673 [213,] 0.632815643 0.734368714 0.36718436 [214,] 0.594727240 0.810545521 0.40527276 [215,] 0.554446760 0.891106480 0.44555324 [216,] 0.523128653 0.953742693 0.47687135 [217,] 0.516556674 0.966886652 0.48344333 [218,] 0.614447013 0.771105974 0.38555299 [219,] 0.575146750 0.849706499 0.42485325 [220,] 0.626414119 0.747171763 0.37358588 [221,] 0.588382956 0.823234089 0.41161704 [222,] 0.575796388 0.848407223 0.42420361 [223,] 0.555814558 0.888370884 0.44418544 [224,] 0.742182072 0.515635856 0.25781793 [225,] 0.705402534 0.589194933 0.29459747 [226,] 0.755666289 0.488667421 0.24433371 [227,] 0.742384691 0.515230618 0.25761531 [228,] 0.704920417 0.590159167 0.29507958 [229,] 0.673784794 0.652430412 0.32621521 [230,] 0.643372248 0.713255504 0.35662775 [231,] 0.678713146 0.642573709 0.32128685 [232,] 0.657047532 0.685904936 0.34295247 [233,] 0.623786729 0.752426541 0.37621327 [234,] 0.631806140 0.736387720 0.36819386 [235,] 0.733249304 0.533501392 0.26675070 [236,] 0.724789175 0.550421649 0.27521082 [237,] 0.679639623 0.640720754 0.32036038 [238,] 0.631270624 0.737458753 0.36872938 [239,] 0.593736568 0.812526864 0.40626343 [240,] 0.561858607 0.876282785 0.43814139 [241,] 0.513129183 0.973741634 0.48687082 [242,] 0.472733605 0.945467210 0.52726639 [243,] 0.419503569 0.839007137 0.58049643 [244,] 0.370542878 0.741085755 0.62945712 [245,] 0.350450215 0.700900430 0.64954979 [246,] 0.299726196 0.599452392 0.70027380 [247,] 0.409256787 0.818513574 0.59074321 [248,] 0.378175377 0.756350755 0.62182462 [249,] 0.331803575 0.663607150 0.66819642 [250,] 0.335398463 0.670796926 0.66460154 [251,] 0.319577633 0.639155266 0.68042237 [252,] 0.304496482 0.608992964 0.69550352 [253,] 0.285677036 0.571354072 0.71432296 [254,] 0.297596489 0.595192978 0.70240351 [255,] 0.345705453 0.691410906 0.65429455 [256,] 0.296437824 0.592875647 0.70356218 [257,] 0.438765984 0.877531968 0.56123402 [258,] 0.679157351 0.641685299 0.32084265 [259,] 0.611103371 0.777793257 0.38889663 [260,] 0.534277393 0.931445215 0.46572261 [261,] 0.466167053 0.932334106 0.53383295 [262,] 0.427434531 0.854869061 0.57256547 [263,] 0.703457459 0.593085081 0.29654254 [264,] 0.606001740 0.787996520 0.39399826 [265,] 0.530337366 0.939325267 0.46966263 [266,] 0.445618159 0.891236318 0.55438184 [267,] 0.629150952 0.741698096 0.37084905 [268,] 0.547323093 0.905353814 0.45267691 [269,] 0.392519143 0.785038285 0.60748086 > postscript(file="/var/www/html/freestat/rcomp/tmp/1r6z51292940095.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5nyin1292940096.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 0.91711635 -4.46238299 1.62758263 1.32325652 2.95102693 -5.02933078 7 8 9 10 11 12 -6.17619759 1.63708391 0.08207433 2.70184503 -4.24886820 -1.13278574 13 14 15 16 17 18 -0.63105890 1.47594650 2.79969653 0.42525058 -2.25098411 1.20785228 19 20 21 22 23 24 1.77660834 -1.38271288 0.46266301 -2.07624449 0.74063044 3.27389480 25 26 27 28 29 30 -4.04495733 -5.99663405 0.64158586 3.09772857 -0.87419279 -0.25310002 31 32 33 34 35 36 -1.75296769 2.53762808 -4.15055588 2.66516256 -3.32693819 1.74828362 37 38 39 40 41 42 0.74802688 -1.21739225 -0.80359973 -0.99027525 -2.84989405 -0.96580111 43 44 45 46 47 48 -4.37196545 -0.68290942 -7.67287104 0.00539029 3.07761306 0.37807972 49 50 51 52 53 54 2.88871572 -3.20035206 4.45094318 -2.23930495 0.84004644 3.48948483 55 56 57 58 59 60 0.96038709 0.12046148 -2.60722546 1.96838583 1.14352877 -0.72164393 61 62 63 64 65 66 -2.91531018 -1.53855314 0.37547761 2.43763431 -3.33483744 2.49860255 67 68 69 70 71 72 -0.88401278 -5.13926028 4.03834145 2.90505133 1.83957323 5.49930482 73 74 75 76 77 78 4.16061754 5.01857235 -6.69597453 -6.65015112 1.26172713 -3.31333598 79 80 81 82 83 84 0.63686553 -8.07790849 2.82634063 2.20451790 -1.81630219 0.83872437 85 86 87 88 89 90 -2.73868411 4.11083435 0.09497196 -0.09328468 -0.95177427 4.03468509 91 92 93 94 95 96 -3.73641370 6.20451790 -5.27528285 -1.58598305 -0.44088153 -0.89244913 97 98 99 100 101 102 -3.57680145 -0.95456748 3.79153030 -0.28208949 -0.02557178 0.84879043 103 104 105 106 107 108 -4.58408322 5.20897918 -4.07588184 -1.31568135 2.14118340 0.37340237 109 110 111 112 113 114 4.45744640 1.50774117 -0.24258843 7.43885278 -0.78151955 -3.40677578 115 116 117 118 119 120 6.35000107 6.30521625 1.68544555 -0.20825131 4.32430847 -6.06691070 121 122 123 124 125 126 3.32988556 2.11688466 -4.17019818 -0.26408989 -3.54041231 0.03445795 127 128 129 130 131 132 2.66776466 -1.03590796 0.39165871 2.04232760 0.23398250 0.34978269 133 134 135 136 137 138 3.32196310 -4.21361234 -8.23695958 1.58713323 3.39624680 0.21360793 139 140 141 142 143 144 3.78097804 0.91913973 5.65772629 2.05828948 -9.16919809 -4.72057900 145 146 147 148 149 150 -6.80408593 1.82836728 -1.80552047 -1.52314267 4.28388228 -0.99573661 151 152 153 154 155 156 4.64604713 4.61104394 -1.69153863 -1.58857218 4.56195003 2.66776466 157 158 159 160 161 162 -2.31333598 5.50303614 -3.93893834 -5.32273147 -2.83010805 4.79781457 163 164 165 166 167 168 2.14747643 1.55019620 -4.94529946 3.93883647 6.35658084 -2.92958926 169 170 171 172 173 174 1.72609231 -1.24969821 -5.54233725 -6.59750553 2.42897109 -1.94130909 175 176 177 178 179 180 -1.00692929 -1.46613941 1.57654480 0.66442180 -2.86070087 2.94219622 181 182 183 184 185 186 -3.38723354 0.52792180 2.47616516 -0.72097335 -4.59441491 -1.71936026 187 188 189 190 191 192 4.54273455 2.75269574 3.74313249 0.96278553 2.11573266 -0.78851877 193 194 195 196 197 198 3.01174060 -3.16477299 3.04635576 0.14959465 -2.02535312 1.42807134 199 200 201 202 203 204 -1.52623110 -1.06818855 0.57897942 1.86881887 2.20823842 1.88727038 205 206 207 208 209 210 -0.65403339 -0.31664049 -0.89206299 7.79980463 6.36730059 0.50727054 211 212 213 214 215 216 0.03256069 -5.41450727 1.05293295 -2.21296715 1.01993320 3.86435759 217 218 219 220 221 222 -0.75960426 1.00988144 -1.90788471 0.62493181 4.42050706 0.32689978 223 224 225 226 227 228 1.27293566 0.19625162 2.11965684 -3.40795547 5.77366010 1.73740089 229 230 231 232 233 234 -5.17418174 -1.67897283 -2.71570389 -2.62912164 -7.15857840 -1.23440663 235 236 237 238 239 240 -3.75773211 2.45817664 1.40016249 -1.24840710 -0.22973721 4.50605208 241 242 243 244 245 246 2.76441557 -2.07145905 -5.61887006 -7.56166083 2.96037629 0.14651702 247 248 249 250 251 252 -0.19097949 -1.73496359 -1.31461384 -0.35405293 2.67142361 -0.51564225 253 254 255 256 257 258 -1.17491401 -2.22052036 -0.93750381 5.16258249 0.56789238 1.17718478 259 260 261 262 263 264 -1.57224506 -0.86674096 -0.16573663 -1.48768946 -1.95225829 -1.87799780 265 266 267 268 269 270 1.68709083 -4.76255604 -2.23090477 5.00289301 1.09423120 -0.94176991 271 272 273 274 275 276 7.14561957 -2.90187231 -1.20077410 5.76551478 4.04428052 -8.08312944 277 278 279 280 281 282 0.40041923 -1.92212761 0.41539210 1.90682342 -4.52549883 0.92115558 283 284 285 286 287 288 -0.59416008 -1.99431841 4.75039105 2.69074488 3.83230618 0.76649081 > postscript(file="/var/www/html/freestat/rcomp/tmp/6nyin1292940096.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 0.91711635 NA 1 -4.46238299 0.91711635 2 1.62758263 -4.46238299 3 1.32325652 1.62758263 4 2.95102693 1.32325652 5 -5.02933078 2.95102693 6 -6.17619759 -5.02933078 7 1.63708391 -6.17619759 8 0.08207433 1.63708391 9 2.70184503 0.08207433 10 -4.24886820 2.70184503 11 -1.13278574 -4.24886820 12 -0.63105890 -1.13278574 13 1.47594650 -0.63105890 14 2.79969653 1.47594650 15 0.42525058 2.79969653 16 -2.25098411 0.42525058 17 1.20785228 -2.25098411 18 1.77660834 1.20785228 19 -1.38271288 1.77660834 20 0.46266301 -1.38271288 21 -2.07624449 0.46266301 22 0.74063044 -2.07624449 23 3.27389480 0.74063044 24 -4.04495733 3.27389480 25 -5.99663405 -4.04495733 26 0.64158586 -5.99663405 27 3.09772857 0.64158586 28 -0.87419279 3.09772857 29 -0.25310002 -0.87419279 30 -1.75296769 -0.25310002 31 2.53762808 -1.75296769 32 -4.15055588 2.53762808 33 2.66516256 -4.15055588 34 -3.32693819 2.66516256 35 1.74828362 -3.32693819 36 0.74802688 1.74828362 37 -1.21739225 0.74802688 38 -0.80359973 -1.21739225 39 -0.99027525 -0.80359973 40 -2.84989405 -0.99027525 41 -0.96580111 -2.84989405 42 -4.37196545 -0.96580111 43 -0.68290942 -4.37196545 44 -7.67287104 -0.68290942 45 0.00539029 -7.67287104 46 3.07761306 0.00539029 47 0.37807972 3.07761306 48 2.88871572 0.37807972 49 -3.20035206 2.88871572 50 4.45094318 -3.20035206 51 -2.23930495 4.45094318 52 0.84004644 -2.23930495 53 3.48948483 0.84004644 54 0.96038709 3.48948483 55 0.12046148 0.96038709 56 -2.60722546 0.12046148 57 1.96838583 -2.60722546 58 1.14352877 1.96838583 59 -0.72164393 1.14352877 60 -2.91531018 -0.72164393 61 -1.53855314 -2.91531018 62 0.37547761 -1.53855314 63 2.43763431 0.37547761 64 -3.33483744 2.43763431 65 2.49860255 -3.33483744 66 -0.88401278 2.49860255 67 -5.13926028 -0.88401278 68 4.03834145 -5.13926028 69 2.90505133 4.03834145 70 1.83957323 2.90505133 71 5.49930482 1.83957323 72 4.16061754 5.49930482 73 5.01857235 4.16061754 74 -6.69597453 5.01857235 75 -6.65015112 -6.69597453 76 1.26172713 -6.65015112 77 -3.31333598 1.26172713 78 0.63686553 -3.31333598 79 -8.07790849 0.63686553 80 2.82634063 -8.07790849 81 2.20451790 2.82634063 82 -1.81630219 2.20451790 83 0.83872437 -1.81630219 84 -2.73868411 0.83872437 85 4.11083435 -2.73868411 86 0.09497196 4.11083435 87 -0.09328468 0.09497196 88 -0.95177427 -0.09328468 89 4.03468509 -0.95177427 90 -3.73641370 4.03468509 91 6.20451790 -3.73641370 92 -5.27528285 6.20451790 93 -1.58598305 -5.27528285 94 -0.44088153 -1.58598305 95 -0.89244913 -0.44088153 96 -3.57680145 -0.89244913 97 -0.95456748 -3.57680145 98 3.79153030 -0.95456748 99 -0.28208949 3.79153030 100 -0.02557178 -0.28208949 101 0.84879043 -0.02557178 102 -4.58408322 0.84879043 103 5.20897918 -4.58408322 104 -4.07588184 5.20897918 105 -1.31568135 -4.07588184 106 2.14118340 -1.31568135 107 0.37340237 2.14118340 108 4.45744640 0.37340237 109 1.50774117 4.45744640 110 -0.24258843 1.50774117 111 7.43885278 -0.24258843 112 -0.78151955 7.43885278 113 -3.40677578 -0.78151955 114 6.35000107 -3.40677578 115 6.30521625 6.35000107 116 1.68544555 6.30521625 117 -0.20825131 1.68544555 118 4.32430847 -0.20825131 119 -6.06691070 4.32430847 120 3.32988556 -6.06691070 121 2.11688466 3.32988556 122 -4.17019818 2.11688466 123 -0.26408989 -4.17019818 124 -3.54041231 -0.26408989 125 0.03445795 -3.54041231 126 2.66776466 0.03445795 127 -1.03590796 2.66776466 128 0.39165871 -1.03590796 129 2.04232760 0.39165871 130 0.23398250 2.04232760 131 0.34978269 0.23398250 132 3.32196310 0.34978269 133 -4.21361234 3.32196310 134 -8.23695958 -4.21361234 135 1.58713323 -8.23695958 136 3.39624680 1.58713323 137 0.21360793 3.39624680 138 3.78097804 0.21360793 139 0.91913973 3.78097804 140 5.65772629 0.91913973 141 2.05828948 5.65772629 142 -9.16919809 2.05828948 143 -4.72057900 -9.16919809 144 -6.80408593 -4.72057900 145 1.82836728 -6.80408593 146 -1.80552047 1.82836728 147 -1.52314267 -1.80552047 148 4.28388228 -1.52314267 149 -0.99573661 4.28388228 150 4.64604713 -0.99573661 151 4.61104394 4.64604713 152 -1.69153863 4.61104394 153 -1.58857218 -1.69153863 154 4.56195003 -1.58857218 155 2.66776466 4.56195003 156 -2.31333598 2.66776466 157 5.50303614 -2.31333598 158 -3.93893834 5.50303614 159 -5.32273147 -3.93893834 160 -2.83010805 -5.32273147 161 4.79781457 -2.83010805 162 2.14747643 4.79781457 163 1.55019620 2.14747643 164 -4.94529946 1.55019620 165 3.93883647 -4.94529946 166 6.35658084 3.93883647 167 -2.92958926 6.35658084 168 1.72609231 -2.92958926 169 -1.24969821 1.72609231 170 -5.54233725 -1.24969821 171 -6.59750553 -5.54233725 172 2.42897109 -6.59750553 173 -1.94130909 2.42897109 174 -1.00692929 -1.94130909 175 -1.46613941 -1.00692929 176 1.57654480 -1.46613941 177 0.66442180 1.57654480 178 -2.86070087 0.66442180 179 2.94219622 -2.86070087 180 -3.38723354 2.94219622 181 0.52792180 -3.38723354 182 2.47616516 0.52792180 183 -0.72097335 2.47616516 184 -4.59441491 -0.72097335 185 -1.71936026 -4.59441491 186 4.54273455 -1.71936026 187 2.75269574 4.54273455 188 3.74313249 2.75269574 189 0.96278553 3.74313249 190 2.11573266 0.96278553 191 -0.78851877 2.11573266 192 3.01174060 -0.78851877 193 -3.16477299 3.01174060 194 3.04635576 -3.16477299 195 0.14959465 3.04635576 196 -2.02535312 0.14959465 197 1.42807134 -2.02535312 198 -1.52623110 1.42807134 199 -1.06818855 -1.52623110 200 0.57897942 -1.06818855 201 1.86881887 0.57897942 202 2.20823842 1.86881887 203 1.88727038 2.20823842 204 -0.65403339 1.88727038 205 -0.31664049 -0.65403339 206 -0.89206299 -0.31664049 207 7.79980463 -0.89206299 208 6.36730059 7.79980463 209 0.50727054 6.36730059 210 0.03256069 0.50727054 211 -5.41450727 0.03256069 212 1.05293295 -5.41450727 213 -2.21296715 1.05293295 214 1.01993320 -2.21296715 215 3.86435759 1.01993320 216 -0.75960426 3.86435759 217 1.00988144 -0.75960426 218 -1.90788471 1.00988144 219 0.62493181 -1.90788471 220 4.42050706 0.62493181 221 0.32689978 4.42050706 222 1.27293566 0.32689978 223 0.19625162 1.27293566 224 2.11965684 0.19625162 225 -3.40795547 2.11965684 226 5.77366010 -3.40795547 227 1.73740089 5.77366010 228 -5.17418174 1.73740089 229 -1.67897283 -5.17418174 230 -2.71570389 -1.67897283 231 -2.62912164 -2.71570389 232 -7.15857840 -2.62912164 233 -1.23440663 -7.15857840 234 -3.75773211 -1.23440663 235 2.45817664 -3.75773211 236 1.40016249 2.45817664 237 -1.24840710 1.40016249 238 -0.22973721 -1.24840710 239 4.50605208 -0.22973721 240 2.76441557 4.50605208 241 -2.07145905 2.76441557 242 -5.61887006 -2.07145905 243 -7.56166083 -5.61887006 244 2.96037629 -7.56166083 245 0.14651702 2.96037629 246 -0.19097949 0.14651702 247 -1.73496359 -0.19097949 248 -1.31461384 -1.73496359 249 -0.35405293 -1.31461384 250 2.67142361 -0.35405293 251 -0.51564225 2.67142361 252 -1.17491401 -0.51564225 253 -2.22052036 -1.17491401 254 -0.93750381 -2.22052036 255 5.16258249 -0.93750381 256 0.56789238 5.16258249 257 1.17718478 0.56789238 258 -1.57224506 1.17718478 259 -0.86674096 -1.57224506 260 -0.16573663 -0.86674096 261 -1.48768946 -0.16573663 262 -1.95225829 -1.48768946 263 -1.87799780 -1.95225829 264 1.68709083 -1.87799780 265 -4.76255604 1.68709083 266 -2.23090477 -4.76255604 267 5.00289301 -2.23090477 268 1.09423120 5.00289301 269 -0.94176991 1.09423120 270 7.14561957 -0.94176991 271 -2.90187231 7.14561957 272 -1.20077410 -2.90187231 273 5.76551478 -1.20077410 274 4.04428052 5.76551478 275 -8.08312944 4.04428052 276 0.40041923 -8.08312944 277 -1.92212761 0.40041923 278 0.41539210 -1.92212761 279 1.90682342 0.41539210 280 -4.52549883 1.90682342 281 0.92115558 -4.52549883 282 -0.59416008 0.92115558 283 -1.99431841 -0.59416008 284 4.75039105 -1.99431841 285 2.69074488 4.75039105 286 3.83230618 2.69074488 287 0.76649081 3.83230618 288 NA 0.76649081 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -4.46238299 0.91711635 [2,] 1.62758263 -4.46238299 [3,] 1.32325652 1.62758263 [4,] 2.95102693 1.32325652 [5,] -5.02933078 2.95102693 [6,] -6.17619759 -5.02933078 [7,] 1.63708391 -6.17619759 [8,] 0.08207433 1.63708391 [9,] 2.70184503 0.08207433 [10,] -4.24886820 2.70184503 [11,] -1.13278574 -4.24886820 [12,] -0.63105890 -1.13278574 [13,] 1.47594650 -0.63105890 [14,] 2.79969653 1.47594650 [15,] 0.42525058 2.79969653 [16,] -2.25098411 0.42525058 [17,] 1.20785228 -2.25098411 [18,] 1.77660834 1.20785228 [19,] -1.38271288 1.77660834 [20,] 0.46266301 -1.38271288 [21,] -2.07624449 0.46266301 [22,] 0.74063044 -2.07624449 [23,] 3.27389480 0.74063044 [24,] -4.04495733 3.27389480 [25,] -5.99663405 -4.04495733 [26,] 0.64158586 -5.99663405 [27,] 3.09772857 0.64158586 [28,] -0.87419279 3.09772857 [29,] -0.25310002 -0.87419279 [30,] -1.75296769 -0.25310002 [31,] 2.53762808 -1.75296769 [32,] -4.15055588 2.53762808 [33,] 2.66516256 -4.15055588 [34,] -3.32693819 2.66516256 [35,] 1.74828362 -3.32693819 [36,] 0.74802688 1.74828362 [37,] -1.21739225 0.74802688 [38,] -0.80359973 -1.21739225 [39,] -0.99027525 -0.80359973 [40,] -2.84989405 -0.99027525 [41,] -0.96580111 -2.84989405 [42,] -4.37196545 -0.96580111 [43,] -0.68290942 -4.37196545 [44,] -7.67287104 -0.68290942 [45,] 0.00539029 -7.67287104 [46,] 3.07761306 0.00539029 [47,] 0.37807972 3.07761306 [48,] 2.88871572 0.37807972 [49,] -3.20035206 2.88871572 [50,] 4.45094318 -3.20035206 [51,] -2.23930495 4.45094318 [52,] 0.84004644 -2.23930495 [53,] 3.48948483 0.84004644 [54,] 0.96038709 3.48948483 [55,] 0.12046148 0.96038709 [56,] -2.60722546 0.12046148 [57,] 1.96838583 -2.60722546 [58,] 1.14352877 1.96838583 [59,] -0.72164393 1.14352877 [60,] -2.91531018 -0.72164393 [61,] -1.53855314 -2.91531018 [62,] 0.37547761 -1.53855314 [63,] 2.43763431 0.37547761 [64,] -3.33483744 2.43763431 [65,] 2.49860255 -3.33483744 [66,] -0.88401278 2.49860255 [67,] -5.13926028 -0.88401278 [68,] 4.03834145 -5.13926028 [69,] 2.90505133 4.03834145 [70,] 1.83957323 2.90505133 [71,] 5.49930482 1.83957323 [72,] 4.16061754 5.49930482 [73,] 5.01857235 4.16061754 [74,] -6.69597453 5.01857235 [75,] -6.65015112 -6.69597453 [76,] 1.26172713 -6.65015112 [77,] -3.31333598 1.26172713 [78,] 0.63686553 -3.31333598 [79,] -8.07790849 0.63686553 [80,] 2.82634063 -8.07790849 [81,] 2.20451790 2.82634063 [82,] -1.81630219 2.20451790 [83,] 0.83872437 -1.81630219 [84,] -2.73868411 0.83872437 [85,] 4.11083435 -2.73868411 [86,] 0.09497196 4.11083435 [87,] -0.09328468 0.09497196 [88,] -0.95177427 -0.09328468 [89,] 4.03468509 -0.95177427 [90,] -3.73641370 4.03468509 [91,] 6.20451790 -3.73641370 [92,] -5.27528285 6.20451790 [93,] -1.58598305 -5.27528285 [94,] -0.44088153 -1.58598305 [95,] -0.89244913 -0.44088153 [96,] -3.57680145 -0.89244913 [97,] -0.95456748 -3.57680145 [98,] 3.79153030 -0.95456748 [99,] -0.28208949 3.79153030 [100,] -0.02557178 -0.28208949 [101,] 0.84879043 -0.02557178 [102,] -4.58408322 0.84879043 [103,] 5.20897918 -4.58408322 [104,] -4.07588184 5.20897918 [105,] -1.31568135 -4.07588184 [106,] 2.14118340 -1.31568135 [107,] 0.37340237 2.14118340 [108,] 4.45744640 0.37340237 [109,] 1.50774117 4.45744640 [110,] -0.24258843 1.50774117 [111,] 7.43885278 -0.24258843 [112,] -0.78151955 7.43885278 [113,] -3.40677578 -0.78151955 [114,] 6.35000107 -3.40677578 [115,] 6.30521625 6.35000107 [116,] 1.68544555 6.30521625 [117,] -0.20825131 1.68544555 [118,] 4.32430847 -0.20825131 [119,] -6.06691070 4.32430847 [120,] 3.32988556 -6.06691070 [121,] 2.11688466 3.32988556 [122,] -4.17019818 2.11688466 [123,] -0.26408989 -4.17019818 [124,] -3.54041231 -0.26408989 [125,] 0.03445795 -3.54041231 [126,] 2.66776466 0.03445795 [127,] -1.03590796 2.66776466 [128,] 0.39165871 -1.03590796 [129,] 2.04232760 0.39165871 [130,] 0.23398250 2.04232760 [131,] 0.34978269 0.23398250 [132,] 3.32196310 0.34978269 [133,] -4.21361234 3.32196310 [134,] -8.23695958 -4.21361234 [135,] 1.58713323 -8.23695958 [136,] 3.39624680 1.58713323 [137,] 0.21360793 3.39624680 [138,] 3.78097804 0.21360793 [139,] 0.91913973 3.78097804 [140,] 5.65772629 0.91913973 [141,] 2.05828948 5.65772629 [142,] -9.16919809 2.05828948 [143,] -4.72057900 -9.16919809 [144,] -6.80408593 -4.72057900 [145,] 1.82836728 -6.80408593 [146,] -1.80552047 1.82836728 [147,] -1.52314267 -1.80552047 [148,] 4.28388228 -1.52314267 [149,] -0.99573661 4.28388228 [150,] 4.64604713 -0.99573661 [151,] 4.61104394 4.64604713 [152,] -1.69153863 4.61104394 [153,] -1.58857218 -1.69153863 [154,] 4.56195003 -1.58857218 [155,] 2.66776466 4.56195003 [156,] -2.31333598 2.66776466 [157,] 5.50303614 -2.31333598 [158,] -3.93893834 5.50303614 [159,] -5.32273147 -3.93893834 [160,] -2.83010805 -5.32273147 [161,] 4.79781457 -2.83010805 [162,] 2.14747643 4.79781457 [163,] 1.55019620 2.14747643 [164,] -4.94529946 1.55019620 [165,] 3.93883647 -4.94529946 [166,] 6.35658084 3.93883647 [167,] -2.92958926 6.35658084 [168,] 1.72609231 -2.92958926 [169,] -1.24969821 1.72609231 [170,] -5.54233725 -1.24969821 [171,] -6.59750553 -5.54233725 [172,] 2.42897109 -6.59750553 [173,] -1.94130909 2.42897109 [174,] -1.00692929 -1.94130909 [175,] -1.46613941 -1.00692929 [176,] 1.57654480 -1.46613941 [177,] 0.66442180 1.57654480 [178,] -2.86070087 0.66442180 [179,] 2.94219622 -2.86070087 [180,] -3.38723354 2.94219622 [181,] 0.52792180 -3.38723354 [182,] 2.47616516 0.52792180 [183,] -0.72097335 2.47616516 [184,] -4.59441491 -0.72097335 [185,] -1.71936026 -4.59441491 [186,] 4.54273455 -1.71936026 [187,] 2.75269574 4.54273455 [188,] 3.74313249 2.75269574 [189,] 0.96278553 3.74313249 [190,] 2.11573266 0.96278553 [191,] -0.78851877 2.11573266 [192,] 3.01174060 -0.78851877 [193,] -3.16477299 3.01174060 [194,] 3.04635576 -3.16477299 [195,] 0.14959465 3.04635576 [196,] -2.02535312 0.14959465 [197,] 1.42807134 -2.02535312 [198,] -1.52623110 1.42807134 [199,] -1.06818855 -1.52623110 [200,] 0.57897942 -1.06818855 [201,] 1.86881887 0.57897942 [202,] 2.20823842 1.86881887 [203,] 1.88727038 2.20823842 [204,] -0.65403339 1.88727038 [205,] -0.31664049 -0.65403339 [206,] -0.89206299 -0.31664049 [207,] 7.79980463 -0.89206299 [208,] 6.36730059 7.79980463 [209,] 0.50727054 6.36730059 [210,] 0.03256069 0.50727054 [211,] -5.41450727 0.03256069 [212,] 1.05293295 -5.41450727 [213,] -2.21296715 1.05293295 [214,] 1.01993320 -2.21296715 [215,] 3.86435759 1.01993320 [216,] -0.75960426 3.86435759 [217,] 1.00988144 -0.75960426 [218,] -1.90788471 1.00988144 [219,] 0.62493181 -1.90788471 [220,] 4.42050706 0.62493181 [221,] 0.32689978 4.42050706 [222,] 1.27293566 0.32689978 [223,] 0.19625162 1.27293566 [224,] 2.11965684 0.19625162 [225,] -3.40795547 2.11965684 [226,] 5.77366010 -3.40795547 [227,] 1.73740089 5.77366010 [228,] -5.17418174 1.73740089 [229,] -1.67897283 -5.17418174 [230,] -2.71570389 -1.67897283 [231,] -2.62912164 -2.71570389 [232,] -7.15857840 -2.62912164 [233,] -1.23440663 -7.15857840 [234,] -3.75773211 -1.23440663 [235,] 2.45817664 -3.75773211 [236,] 1.40016249 2.45817664 [237,] -1.24840710 1.40016249 [238,] -0.22973721 -1.24840710 [239,] 4.50605208 -0.22973721 [240,] 2.76441557 4.50605208 [241,] -2.07145905 2.76441557 [242,] -5.61887006 -2.07145905 [243,] -7.56166083 -5.61887006 [244,] 2.96037629 -7.56166083 [245,] 0.14651702 2.96037629 [246,] -0.19097949 0.14651702 [247,] -1.73496359 -0.19097949 [248,] -1.31461384 -1.73496359 [249,] -0.35405293 -1.31461384 [250,] 2.67142361 -0.35405293 [251,] -0.51564225 2.67142361 [252,] -1.17491401 -0.51564225 [253,] -2.22052036 -1.17491401 [254,] -0.93750381 -2.22052036 [255,] 5.16258249 -0.93750381 [256,] 0.56789238 5.16258249 [257,] 1.17718478 0.56789238 [258,] -1.57224506 1.17718478 [259,] -0.86674096 -1.57224506 [260,] -0.16573663 -0.86674096 [261,] -1.48768946 -0.16573663 [262,] -1.95225829 -1.48768946 [263,] -1.87799780 -1.95225829 [264,] 1.68709083 -1.87799780 [265,] -4.76255604 1.68709083 [266,] -2.23090477 -4.76255604 [267,] 5.00289301 -2.23090477 [268,] 1.09423120 5.00289301 [269,] -0.94176991 1.09423120 [270,] 7.14561957 -0.94176991 [271,] -2.90187231 7.14561957 [272,] -1.20077410 -2.90187231 [273,] 5.76551478 -1.20077410 [274,] 4.04428052 5.76551478 [275,] -8.08312944 4.04428052 [276,] 0.40041923 -8.08312944 [277,] -1.92212761 0.40041923 [278,] 0.41539210 -1.92212761 [279,] 1.90682342 0.41539210 [280,] -4.52549883 1.90682342 [281,] 0.92115558 -4.52549883 [282,] -0.59416008 0.92115558 [283,] -1.99431841 -0.59416008 [284,] 4.75039105 -1.99431841 [285,] 2.69074488 4.75039105 [286,] 3.83230618 2.69074488 [287,] 0.76649081 3.83230618 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -4.46238299 0.91711635 2 1.62758263 -4.46238299 3 1.32325652 1.62758263 4 2.95102693 1.32325652 5 -5.02933078 2.95102693 6 -6.17619759 -5.02933078 7 1.63708391 -6.17619759 8 0.08207433 1.63708391 9 2.70184503 0.08207433 10 -4.24886820 2.70184503 11 -1.13278574 -4.24886820 12 -0.63105890 -1.13278574 13 1.47594650 -0.63105890 14 2.79969653 1.47594650 15 0.42525058 2.79969653 16 -2.25098411 0.42525058 17 1.20785228 -2.25098411 18 1.77660834 1.20785228 19 -1.38271288 1.77660834 20 0.46266301 -1.38271288 21 -2.07624449 0.46266301 22 0.74063044 -2.07624449 23 3.27389480 0.74063044 24 -4.04495733 3.27389480 25 -5.99663405 -4.04495733 26 0.64158586 -5.99663405 27 3.09772857 0.64158586 28 -0.87419279 3.09772857 29 -0.25310002 -0.87419279 30 -1.75296769 -0.25310002 31 2.53762808 -1.75296769 32 -4.15055588 2.53762808 33 2.66516256 -4.15055588 34 -3.32693819 2.66516256 35 1.74828362 -3.32693819 36 0.74802688 1.74828362 37 -1.21739225 0.74802688 38 -0.80359973 -1.21739225 39 -0.99027525 -0.80359973 40 -2.84989405 -0.99027525 41 -0.96580111 -2.84989405 42 -4.37196545 -0.96580111 43 -0.68290942 -4.37196545 44 -7.67287104 -0.68290942 45 0.00539029 -7.67287104 46 3.07761306 0.00539029 47 0.37807972 3.07761306 48 2.88871572 0.37807972 49 -3.20035206 2.88871572 50 4.45094318 -3.20035206 51 -2.23930495 4.45094318 52 0.84004644 -2.23930495 53 3.48948483 0.84004644 54 0.96038709 3.48948483 55 0.12046148 0.96038709 56 -2.60722546 0.12046148 57 1.96838583 -2.60722546 58 1.14352877 1.96838583 59 -0.72164393 1.14352877 60 -2.91531018 -0.72164393 61 -1.53855314 -2.91531018 62 0.37547761 -1.53855314 63 2.43763431 0.37547761 64 -3.33483744 2.43763431 65 2.49860255 -3.33483744 66 -0.88401278 2.49860255 67 -5.13926028 -0.88401278 68 4.03834145 -5.13926028 69 2.90505133 4.03834145 70 1.83957323 2.90505133 71 5.49930482 1.83957323 72 4.16061754 5.49930482 73 5.01857235 4.16061754 74 -6.69597453 5.01857235 75 -6.65015112 -6.69597453 76 1.26172713 -6.65015112 77 -3.31333598 1.26172713 78 0.63686553 -3.31333598 79 -8.07790849 0.63686553 80 2.82634063 -8.07790849 81 2.20451790 2.82634063 82 -1.81630219 2.20451790 83 0.83872437 -1.81630219 84 -2.73868411 0.83872437 85 4.11083435 -2.73868411 86 0.09497196 4.11083435 87 -0.09328468 0.09497196 88 -0.95177427 -0.09328468 89 4.03468509 -0.95177427 90 -3.73641370 4.03468509 91 6.20451790 -3.73641370 92 -5.27528285 6.20451790 93 -1.58598305 -5.27528285 94 -0.44088153 -1.58598305 95 -0.89244913 -0.44088153 96 -3.57680145 -0.89244913 97 -0.95456748 -3.57680145 98 3.79153030 -0.95456748 99 -0.28208949 3.79153030 100 -0.02557178 -0.28208949 101 0.84879043 -0.02557178 102 -4.58408322 0.84879043 103 5.20897918 -4.58408322 104 -4.07588184 5.20897918 105 -1.31568135 -4.07588184 106 2.14118340 -1.31568135 107 0.37340237 2.14118340 108 4.45744640 0.37340237 109 1.50774117 4.45744640 110 -0.24258843 1.50774117 111 7.43885278 -0.24258843 112 -0.78151955 7.43885278 113 -3.40677578 -0.78151955 114 6.35000107 -3.40677578 115 6.30521625 6.35000107 116 1.68544555 6.30521625 117 -0.20825131 1.68544555 118 4.32430847 -0.20825131 119 -6.06691070 4.32430847 120 3.32988556 -6.06691070 121 2.11688466 3.32988556 122 -4.17019818 2.11688466 123 -0.26408989 -4.17019818 124 -3.54041231 -0.26408989 125 0.03445795 -3.54041231 126 2.66776466 0.03445795 127 -1.03590796 2.66776466 128 0.39165871 -1.03590796 129 2.04232760 0.39165871 130 0.23398250 2.04232760 131 0.34978269 0.23398250 132 3.32196310 0.34978269 133 -4.21361234 3.32196310 134 -8.23695958 -4.21361234 135 1.58713323 -8.23695958 136 3.39624680 1.58713323 137 0.21360793 3.39624680 138 3.78097804 0.21360793 139 0.91913973 3.78097804 140 5.65772629 0.91913973 141 2.05828948 5.65772629 142 -9.16919809 2.05828948 143 -4.72057900 -9.16919809 144 -6.80408593 -4.72057900 145 1.82836728 -6.80408593 146 -1.80552047 1.82836728 147 -1.52314267 -1.80552047 148 4.28388228 -1.52314267 149 -0.99573661 4.28388228 150 4.64604713 -0.99573661 151 4.61104394 4.64604713 152 -1.69153863 4.61104394 153 -1.58857218 -1.69153863 154 4.56195003 -1.58857218 155 2.66776466 4.56195003 156 -2.31333598 2.66776466 157 5.50303614 -2.31333598 158 -3.93893834 5.50303614 159 -5.32273147 -3.93893834 160 -2.83010805 -5.32273147 161 4.79781457 -2.83010805 162 2.14747643 4.79781457 163 1.55019620 2.14747643 164 -4.94529946 1.55019620 165 3.93883647 -4.94529946 166 6.35658084 3.93883647 167 -2.92958926 6.35658084 168 1.72609231 -2.92958926 169 -1.24969821 1.72609231 170 -5.54233725 -1.24969821 171 -6.59750553 -5.54233725 172 2.42897109 -6.59750553 173 -1.94130909 2.42897109 174 -1.00692929 -1.94130909 175 -1.46613941 -1.00692929 176 1.57654480 -1.46613941 177 0.66442180 1.57654480 178 -2.86070087 0.66442180 179 2.94219622 -2.86070087 180 -3.38723354 2.94219622 181 0.52792180 -3.38723354 182 2.47616516 0.52792180 183 -0.72097335 2.47616516 184 -4.59441491 -0.72097335 185 -1.71936026 -4.59441491 186 4.54273455 -1.71936026 187 2.75269574 4.54273455 188 3.74313249 2.75269574 189 0.96278553 3.74313249 190 2.11573266 0.96278553 191 -0.78851877 2.11573266 192 3.01174060 -0.78851877 193 -3.16477299 3.01174060 194 3.04635576 -3.16477299 195 0.14959465 3.04635576 196 -2.02535312 0.14959465 197 1.42807134 -2.02535312 198 -1.52623110 1.42807134 199 -1.06818855 -1.52623110 200 0.57897942 -1.06818855 201 1.86881887 0.57897942 202 2.20823842 1.86881887 203 1.88727038 2.20823842 204 -0.65403339 1.88727038 205 -0.31664049 -0.65403339 206 -0.89206299 -0.31664049 207 7.79980463 -0.89206299 208 6.36730059 7.79980463 209 0.50727054 6.36730059 210 0.03256069 0.50727054 211 -5.41450727 0.03256069 212 1.05293295 -5.41450727 213 -2.21296715 1.05293295 214 1.01993320 -2.21296715 215 3.86435759 1.01993320 216 -0.75960426 3.86435759 217 1.00988144 -0.75960426 218 -1.90788471 1.00988144 219 0.62493181 -1.90788471 220 4.42050706 0.62493181 221 0.32689978 4.42050706 222 1.27293566 0.32689978 223 0.19625162 1.27293566 224 2.11965684 0.19625162 225 -3.40795547 2.11965684 226 5.77366010 -3.40795547 227 1.73740089 5.77366010 228 -5.17418174 1.73740089 229 -1.67897283 -5.17418174 230 -2.71570389 -1.67897283 231 -2.62912164 -2.71570389 232 -7.15857840 -2.62912164 233 -1.23440663 -7.15857840 234 -3.75773211 -1.23440663 235 2.45817664 -3.75773211 236 1.40016249 2.45817664 237 -1.24840710 1.40016249 238 -0.22973721 -1.24840710 239 4.50605208 -0.22973721 240 2.76441557 4.50605208 241 -2.07145905 2.76441557 242 -5.61887006 -2.07145905 243 -7.56166083 -5.61887006 244 2.96037629 -7.56166083 245 0.14651702 2.96037629 246 -0.19097949 0.14651702 247 -1.73496359 -0.19097949 248 -1.31461384 -1.73496359 249 -0.35405293 -1.31461384 250 2.67142361 -0.35405293 251 -0.51564225 2.67142361 252 -1.17491401 -0.51564225 253 -2.22052036 -1.17491401 254 -0.93750381 -2.22052036 255 5.16258249 -0.93750381 256 0.56789238 5.16258249 257 1.17718478 0.56789238 258 -1.57224506 1.17718478 259 -0.86674096 -1.57224506 260 -0.16573663 -0.86674096 261 -1.48768946 -0.16573663 262 -1.95225829 -1.48768946 263 -1.87799780 -1.95225829 264 1.68709083 -1.87799780 265 -4.76255604 1.68709083 266 -2.23090477 -4.76255604 267 5.00289301 -2.23090477 268 1.09423120 5.00289301 269 -0.94176991 1.09423120 270 7.14561957 -0.94176991 271 -2.90187231 7.14561957 272 -1.20077410 -2.90187231 273 5.76551478 -1.20077410 274 4.04428052 5.76551478 275 -8.08312944 4.04428052 276 0.40041923 -8.08312944 277 -1.92212761 0.40041923 278 0.41539210 -1.92212761 279 1.90682342 0.41539210 280 -4.52549883 1.90682342 281 0.92115558 -4.52549883 282 -0.59416008 0.92115558 283 -1.99431841 -0.59416008 284 4.75039105 -1.99431841 285 2.69074488 4.75039105 286 3.83230618 2.69074488 287 0.76649081 3.83230618 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/7yqz91292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8yqz91292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9qhgb1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10qhgb1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/11cixh1292940096.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/12fiw51292940096.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/134jth1292940096.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/14fask1292940096.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/15it9q1292940096.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/16wl6y1292940096.tab") + } > > try(system("convert tmp/1r6z51292940095.ps tmp/1r6z51292940095.png",intern=TRUE)) character(0) > try(system("convert tmp/2cpjl1292940096.ps tmp/2cpjl1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/3cpjl1292940096.ps tmp/3cpjl1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/4cpjl1292940096.ps tmp/4cpjl1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/5nyin1292940096.ps tmp/5nyin1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/6nyin1292940096.ps tmp/6nyin1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/7yqz91292940096.ps tmp/7yqz91292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/8yqz91292940096.ps tmp/8yqz91292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/9qhgb1292940096.ps tmp/9qhgb1292940096.png",intern=TRUE)) character(0) > try(system("convert tmp/10qhgb1292940096.ps tmp/10qhgb1292940096.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.288 3.041 11.852