R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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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 = '6' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Software Pop Gender Connected Separate Learning Happiness 1 12 1 1 41 38 13 14 2 11 1 1 39 32 16 18 3 15 1 1 30 35 19 11 4 6 1 0 31 33 15 12 5 13 1 1 34 37 14 16 6 10 1 1 35 29 13 18 7 12 1 1 39 31 19 14 8 14 1 1 34 36 15 14 9 12 1 1 36 35 14 15 10 9 1 1 37 38 15 15 11 10 1 0 38 31 16 17 12 12 1 1 36 34 16 19 13 12 1 0 38 35 16 10 14 11 1 1 39 38 16 16 15 15 1 1 33 37 17 18 16 12 1 0 32 33 15 14 17 10 1 0 36 32 15 14 18 12 1 1 38 38 20 17 19 11 1 0 39 38 18 14 20 12 1 1 32 32 16 16 21 11 1 0 32 33 16 18 22 12 1 1 31 31 16 11 23 13 1 1 39 38 19 14 24 11 1 1 37 39 16 12 25 12 1 0 39 32 17 17 26 13 1 1 41 32 17 9 27 10 1 0 36 35 16 16 28 14 1 1 33 37 15 14 29 12 1 1 33 33 16 15 30 10 1 0 34 33 14 11 31 12 1 1 31 31 15 16 32 8 1 0 27 32 12 13 33 10 1 1 37 31 14 17 34 12 1 1 34 37 16 15 35 12 1 0 34 30 14 14 36 7 1 0 32 33 10 16 37 9 1 0 29 31 10 9 38 12 1 0 36 33 14 15 39 10 1 1 29 31 16 17 40 10 1 0 35 33 16 13 41 10 1 0 37 32 16 15 42 12 1 1 34 33 14 16 43 15 1 0 38 32 20 16 44 10 1 0 35 33 14 12 45 10 1 1 38 28 14 15 46 12 1 1 37 35 11 11 47 13 1 1 38 39 14 15 48 11 1 1 33 34 15 15 49 11 1 1 36 38 16 17 50 12 1 0 38 32 14 13 51 14 1 1 32 38 16 16 52 10 1 0 32 30 14 14 53 12 1 0 32 33 12 11 54 13 1 1 34 38 16 12 55 5 1 0 32 32 9 12 56 6 1 1 37 35 14 15 57 12 1 1 39 34 16 16 58 12 1 1 29 34 16 15 59 11 1 0 37 36 15 12 60 10 1 1 35 34 16 12 61 7 1 0 30 28 12 8 62 12 1 0 38 34 16 13 63 14 1 1 34 35 16 11 64 11 1 1 31 35 14 14 65 12 1 1 34 31 16 15 66 13 1 0 35 37 17 10 67 14 1 1 36 35 18 11 68 11 1 0 30 27 18 12 69 12 1 1 39 40 12 15 70 12 1 0 35 37 16 15 71 8 1 0 38 36 10 14 72 11 1 1 31 38 14 16 73 14 1 1 34 39 18 15 74 14 1 0 38 41 18 15 75 12 1 0 34 27 16 13 76 9 1 1 39 30 17 12 77 13 1 1 37 37 16 17 78 11 1 1 34 31 16 13 79 12 1 0 28 31 13 15 80 12 1 0 37 27 16 13 81 12 1 0 33 36 16 15 82 12 1 1 35 37 16 15 83 12 1 0 37 33 15 16 84 11 1 1 32 34 15 15 85 10 1 1 33 31 16 14 86 9 1 0 38 39 14 15 87 12 1 1 33 34 16 14 88 12 1 1 29 32 16 13 89 12 1 1 33 33 15 7 90 9 1 1 31 36 12 17 91 15 1 1 36 32 17 13 92 12 1 1 35 41 16 15 93 12 1 1 32 28 15 14 94 12 1 1 29 30 13 13 95 10 1 1 39 36 16 16 96 13 1 1 37 35 16 12 97 9 1 1 35 31 16 14 98 12 1 0 37 34 16 17 99 10 1 0 32 36 14 15 100 14 1 1 38 36 16 17 101 11 1 0 37 35 16 12 102 15 1 1 36 37 20 16 103 11 1 0 32 28 15 11 104 11 1 1 33 39 16 15 105 12 1 0 40 32 13 9 106 12 1 1 38 35 17 16 107 12 1 0 41 39 16 15 108 11 1 0 36 35 16 10 109 7 1 1 43 42 12 10 110 12 1 1 30 34 16 15 111 14 1 1 31 33 16 11 112 11 1 1 32 41 17 13 113 10 1 1 37 34 12 18 114 13 1 0 37 32 18 16 115 13 1 1 33 40 14 14 116 8 1 1 34 40 14 14 117 11 1 1 33 35 13 14 118 12 1 1 38 36 16 14 119 11 1 0 33 37 13 12 120 13 1 1 31 27 16 14 121 12 1 1 38 39 13 15 122 14 1 1 37 38 16 15 123 13 1 1 36 31 15 15 124 15 1 1 31 33 16 13 125 10 1 0 39 32 15 17 126 11 1 1 44 39 17 17 127 9 1 1 33 36 15 19 128 11 1 1 35 33 12 15 129 10 1 0 32 33 16 13 130 11 1 0 28 32 10 9 131 8 1 1 40 37 16 15 132 11 1 0 27 30 12 15 133 12 1 0 37 38 14 15 134 12 1 1 32 29 15 16 135 9 1 0 28 22 13 11 136 11 1 0 34 35 15 14 137 10 1 1 30 35 11 11 138 8 1 1 35 34 12 15 139 9 1 0 31 35 11 13 140 8 1 1 32 34 16 15 141 9 1 0 30 37 15 16 142 15 1 1 30 35 17 14 143 11 1 0 31 23 16 15 144 8 1 1 40 31 10 16 145 13 1 1 32 27 18 16 146 12 1 0 36 36 13 11 147 12 1 0 32 31 16 12 148 9 1 0 35 32 13 9 149 7 1 1 38 39 10 16 150 13 1 1 42 37 15 13 151 9 1 0 34 38 16 16 152 6 1 1 35 39 16 12 153 8 1 1 38 34 14 9 154 8 1 1 33 31 10 13 155 6 1 1 32 37 13 14 156 9 1 1 33 36 15 19 157 11 1 1 34 32 16 13 158 8 1 1 32 38 12 12 159 10 0 0 27 26 13 10 160 8 0 0 31 26 12 14 161 14 0 0 38 33 17 16 162 10 0 1 34 39 15 10 163 8 0 0 24 30 10 11 164 11 0 0 30 33 14 14 165 12 0 1 26 25 11 12 166 12 0 1 34 38 13 9 167 12 0 0 27 37 16 9 168 5 0 0 37 31 12 11 169 12 0 1 36 37 16 16 170 10 0 0 41 35 12 9 171 7 0 1 29 25 9 13 172 12 0 1 36 28 12 16 173 11 0 0 32 35 15 13 174 8 0 1 37 33 12 9 175 9 0 0 30 30 12 12 176 10 0 1 31 31 14 16 177 9 0 1 38 37 12 11 178 12 0 1 36 36 16 14 179 6 0 0 35 30 11 13 180 15 0 0 31 36 19 15 181 12 0 0 38 32 15 14 182 12 0 1 22 28 8 16 183 12 0 1 32 36 16 13 184 11 0 0 36 34 17 14 185 7 0 1 39 31 12 15 186 7 0 0 28 28 11 13 187 5 0 0 32 36 11 11 188 12 0 1 32 36 14 11 189 12 0 1 38 40 16 14 190 3 0 1 32 33 12 15 191 11 0 1 35 37 16 11 192 10 0 1 32 32 13 15 193 12 0 0 37 38 15 12 194 9 0 1 34 31 16 14 195 12 0 1 33 37 16 14 196 9 0 0 33 33 14 8 197 12 0 0 30 30 16 9 198 10 0 0 24 30 14 15 199 9 0 0 34 31 11 17 200 12 0 0 34 32 12 13 201 8 0 1 33 34 15 15 202 11 0 1 34 36 15 15 203 11 0 1 35 37 16 14 204 12 0 0 35 36 16 16 205 10 0 0 36 33 11 13 206 10 0 0 34 33 15 16 207 12 0 1 34 33 12 9 208 12 0 0 41 44 12 16 209 11 0 0 32 39 15 11 210 8 0 0 30 32 15 10 211 12 0 1 35 35 16 11 212 10 0 0 28 25 14 15 213 11 0 1 33 35 17 17 214 10 0 1 39 34 14 14 215 8 0 0 36 35 13 8 216 12 0 1 36 39 15 15 217 12 0 0 35 33 13 11 218 10 0 0 38 36 14 16 219 12 0 1 33 32 15 10 220 9 0 0 31 32 12 15 221 6 0 1 32 36 8 16 222 10 0 0 31 32 14 19 223 9 0 0 33 34 14 12 224 9 0 0 34 33 11 8 225 9 0 0 34 35 12 11 226 6 0 1 34 30 13 14 227 10 0 0 33 38 10 9 228 6 0 0 32 34 16 15 229 14 0 1 41 33 18 13 230 10 0 1 34 32 13 16 231 10 0 0 36 31 11 11 232 6 0 0 37 30 4 12 233 12 0 0 36 27 13 13 234 12 0 1 29 31 16 10 235 7 0 0 37 30 10 11 236 8 0 0 27 32 12 12 237 11 0 0 35 35 12 8 238 3 0 0 28 28 10 12 239 6 0 0 35 33 13 12 240 8 0 0 29 35 12 11 241 9 0 0 32 35 14 13 242 9 0 1 36 32 10 14 243 8 0 1 19 21 12 10 244 9 0 1 21 20 12 12 245 7 0 0 31 34 11 15 246 7 0 0 33 32 10 13 247 6 0 1 36 34 12 13 248 9 0 1 33 32 16 13 249 10 0 0 37 33 12 12 250 11 0 0 34 33 14 12 251 12 0 0 35 37 16 9 252 8 0 1 31 32 14 9 253 11 0 1 37 34 13 15 254 3 0 1 35 30 4 10 255 11 0 1 27 30 15 14 256 12 0 0 34 38 11 15 257 7 0 0 40 36 11 7 258 9 0 0 29 32 14 14 259 12 0 0 38 34 15 8 260 8 0 1 34 33 14 10 261 11 0 0 21 27 13 13 262 8 0 0 36 32 11 13 263 10 0 1 38 34 15 13 264 8 0 0 30 29 11 8 265 7 0 0 35 35 13 12 266 8 0 1 30 27 13 13 267 10 0 1 36 33 16 12 268 8 0 0 34 38 13 10 269 12 0 1 35 36 16 13 270 14 0 0 34 33 16 12 271 7 0 0 32 39 12 9 272 6 0 1 33 29 7 15 273 11 0 0 33 32 16 13 274 4 0 1 26 34 5 13 275 9 0 0 35 38 16 13 276 5 0 0 21 17 4 15 277 9 0 0 38 35 12 15 278 11 0 0 35 32 15 14 279 12 0 1 33 34 14 15 280 9 0 0 37 36 11 11 281 12 0 0 38 31 16 15 282 10 0 1 34 35 15 14 283 9 0 0 27 29 12 13 284 6 0 1 16 22 6 12 285 10 0 0 40 41 16 16 286 9 0 0 36 36 10 16 287 13 0 1 42 42 15 9 288 12 0 1 30 33 14 14 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop Gender Connected Separate Learning 2.062851 0.452576 0.239170 -0.013256 0.024356 0.547343 Happiness -0.009087 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.1134 -1.0103 0.1771 1.1442 5.0743 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.062851 1.166455 1.768 0.0781 . Pop 0.452576 0.234148 1.933 0.0543 . Gender 0.239170 0.219332 1.090 0.2765 Connected -0.013256 0.031048 -0.427 0.6698 Separate 0.024356 0.032249 0.755 0.4507 Learning 0.547343 0.045375 12.063 <2e-16 *** Happiness -0.009087 0.044820 -0.203 0.8395 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.78 on 281 degrees of freedom Multiple R-squared: 0.4415, Adjusted R-squared: 0.4296 F-statistic: 37.02 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,] 0.728426691 0.54314662 0.27157331 [2,] 0.932111292 0.13577742 0.06788871 [3,] 0.883927529 0.23214494 0.11607247 [4,] 0.907996117 0.18400777 0.09200388 [5,] 0.882918643 0.23416271 0.11708136 [6,] 0.893372417 0.21325517 0.10662758 [7,] 0.901444286 0.19711143 0.09855571 [8,] 0.860478231 0.27904354 0.13952177 [9,] 0.850882362 0.29823528 0.14911764 [10,] 0.800875674 0.39824865 0.19912433 [11,] 0.741526956 0.51694609 0.25847304 [12,] 0.680045375 0.63990925 0.31995463 [13,] 0.611980282 0.77603944 0.38801972 [14,] 0.538947838 0.92210432 0.46105216 [15,] 0.514659703 0.97068059 0.48534030 [16,] 0.513177509 0.97364498 0.48682249 [17,] 0.479991011 0.95998202 0.52000899 [18,] 0.421395820 0.84279164 0.57860418 [19,] 0.425690363 0.85138073 0.57430964 [20,] 0.365084396 0.73016879 0.63491560 [21,] 0.306802041 0.61360408 0.69319796 [22,] 0.254354710 0.50870942 0.74564529 [23,] 0.248959157 0.49791831 0.75104084 [24,] 0.217421179 0.43484236 0.78257882 [25,] 0.178369235 0.35673847 0.82163077 [26,] 0.217867818 0.43573564 0.78213218 [27,] 0.201215206 0.40243041 0.79878479 [28,] 0.165356409 0.33071282 0.83464359 [29,] 0.193459405 0.38691881 0.80654060 [30,] 0.206894026 0.41378805 0.79310597 [31,] 0.177929303 0.35585861 0.82207070 [32,] 0.149026372 0.29805274 0.85097363 [33,] 0.126655282 0.25331056 0.87334472 [34,] 0.183568355 0.36713671 0.81643165 [35,] 0.151176229 0.30235246 0.84882377 [36,] 0.128675511 0.25735102 0.87132449 [37,] 0.137559111 0.27511822 0.86244089 [38,] 0.130673344 0.26134669 0.86932666 [39,] 0.109771478 0.21954296 0.89022852 [40,] 0.098803901 0.19760780 0.90119610 [41,] 0.103123587 0.20624717 0.89687641 [42,] 0.106559722 0.21311944 0.89344028 [43,] 0.086225542 0.17245108 0.91377446 [44,] 0.104472982 0.20894596 0.89552702 [45,] 0.086456044 0.17291209 0.91354396 [46,] 0.147758140 0.29551628 0.85224186 [47,] 0.431409104 0.86281821 0.56859090 [48,] 0.388468979 0.77693796 0.61153102 [49,] 0.346869908 0.69373982 0.65313009 [50,] 0.306952257 0.61390451 0.69304774 [51,] 0.324717126 0.64943425 0.67528287 [52,] 0.340600684 0.68120137 0.65939932 [53,] 0.311522200 0.62304440 0.68847780 [54,] 0.315766119 0.63153224 0.68423388 [55,] 0.279619399 0.55923880 0.72038060 [56,] 0.246153018 0.49230604 0.75384698 [57,] 0.221143290 0.44228658 0.77885671 [58,] 0.197420643 0.39484129 0.80257936 [59,] 0.176192401 0.35238480 0.82380760 [60,] 0.171067476 0.34213495 0.82893252 [61,] 0.148045216 0.29609043 0.85195478 [62,] 0.127234060 0.25446812 0.87276594 [63,] 0.108818488 0.21763698 0.89118151 [64,] 0.093295548 0.18659110 0.90670445 [65,] 0.083399275 0.16679855 0.91660073 [66,] 0.080250178 0.16050036 0.91974982 [67,] 0.117197684 0.23439537 0.88280232 [68,] 0.103749000 0.20749800 0.89625100 [69,] 0.088686409 0.17737282 0.91131359 [70,] 0.102924772 0.20584954 0.89707523 [71,] 0.100437323 0.20087465 0.89956268 [72,] 0.084718555 0.16943711 0.91528145 [73,] 0.071107711 0.14221542 0.92889229 [74,] 0.065514231 0.13102846 0.93448577 [75,] 0.054810475 0.10962095 0.94518952 [76,] 0.053225704 0.10645141 0.94677430 [77,] 0.055466180 0.11093236 0.94453382 [78,] 0.045528133 0.09105627 0.95447187 [79,] 0.037109584 0.07421917 0.96289042 [80,] 0.030630681 0.06126136 0.96936932 [81,] 0.027078672 0.05415734 0.97292133 [82,] 0.038591033 0.07718207 0.96140897 [83,] 0.032498776 0.06499755 0.96750122 [84,] 0.028515413 0.05703083 0.97148459 [85,] 0.029159403 0.05831881 0.97084060 [86,] 0.030352778 0.06070556 0.96964722 [87,] 0.026536311 0.05307262 0.97346369 [88,] 0.035460158 0.07092032 0.96453984 [89,] 0.030176926 0.06035385 0.96982307 [90,] 0.024980877 0.04996175 0.97501912 [91,] 0.028417284 0.05683457 0.97158272 [92,] 0.023308761 0.04661752 0.97669124 [93,] 0.019998776 0.03999755 0.98000122 [94,] 0.016269827 0.03253965 0.98373017 [95,] 0.014915210 0.02983042 0.98508479 [96,] 0.017143104 0.03428621 0.98285690 [97,] 0.013758715 0.02751743 0.98624128 [98,] 0.011007060 0.02201412 0.98899294 [99,] 0.008928292 0.01785658 0.99107171 [100,] 0.016329717 0.03265943 0.98367028 [101,] 0.013087159 0.02617432 0.98691284 [102,] 0.014475063 0.02895013 0.98552494 [103,] 0.015059464 0.03011893 0.98494054 [104,] 0.012193924 0.02438785 0.98780608 [105,] 0.010047724 0.02009545 0.98995228 [106,] 0.010978684 0.02195737 0.98902132 [107,] 0.017447367 0.03489473 0.98255263 [108,] 0.014696337 0.02939267 0.98530366 [109,] 0.011809809 0.02361962 0.98819019 [110,] 0.010184320 0.02036864 0.98981568 [111,] 0.009407333 0.01881467 0.99059267 [112,] 0.009639000 0.01927800 0.99036100 [113,] 0.011297296 0.02259459 0.98870270 [114,] 0.012158688 0.02431738 0.98784131 [115,] 0.021655322 0.04331064 0.97834468 [116,] 0.017859678 0.03571936 0.98214032 [117,] 0.015770055 0.03154011 0.98422994 [118,] 0.017894760 0.03578952 0.98210524 [119,] 0.017349566 0.03469913 0.98265043 [120,] 0.015882242 0.03176448 0.98411776 [121,] 0.021531604 0.04306321 0.97846840 [122,] 0.040160940 0.08032188 0.95983906 [123,] 0.040631181 0.08126236 0.95936882 [124,] 0.041567335 0.08313467 0.95843266 [125,] 0.037682891 0.07536578 0.96231711 [126,] 0.032238578 0.06447716 0.96776142 [127,] 0.026982968 0.05396594 0.97301703 [128,] 0.025336796 0.05067359 0.97466320 [129,] 0.024257359 0.04851472 0.97574264 [130,] 0.020812578 0.04162516 0.97918742 [131,] 0.038789484 0.07757897 0.96121052 [132,] 0.039104915 0.07820983 0.96089509 [133,] 0.056593467 0.11318693 0.94340653 [134,] 0.047649781 0.09529956 0.95235022 [135,] 0.040295736 0.08059147 0.95970426 [136,] 0.035495727 0.07099145 0.96450427 [137,] 0.046036377 0.09207275 0.95396362 [138,] 0.044050552 0.08810110 0.95594945 [139,] 0.039529122 0.07905824 0.96047088 [140,] 0.036791511 0.07358302 0.96320849 [141,] 0.051325762 0.10265152 0.94867424 [142,] 0.052222827 0.10444565 0.94777717 [143,] 0.166016846 0.33203369 0.83398315 [144,] 0.176969114 0.35393823 0.82303089 [145,] 0.166829392 0.33365878 0.83317061 [146,] 0.237590108 0.47518022 0.76240989 [147,] 0.235014057 0.47002811 0.76498594 [148,] 0.213986860 0.42797372 0.78601314 [149,] 0.203038268 0.40607654 0.79696173 [150,] 0.182208974 0.36441795 0.81779103 [151,] 0.164331255 0.32866251 0.83566875 [152,] 0.178340170 0.35668034 0.82165983 [153,] 0.166535898 0.33307180 0.83346410 [154,] 0.146495944 0.29299189 0.85350406 [155,] 0.131699757 0.26339951 0.86830024 [156,] 0.180864718 0.36172944 0.81913528 [157,] 0.184754625 0.36950925 0.81524538 [158,] 0.166763965 0.33352793 0.83323603 [159,] 0.272531411 0.54506282 0.72746859 [160,] 0.246579674 0.49315935 0.75342033 [161,] 0.227264993 0.45452999 0.77273501 [162,] 0.206148438 0.41229688 0.79385156 [163,] 0.244480732 0.48896146 0.75551927 [164,] 0.218676287 0.43735257 0.78132371 [165,] 0.206846189 0.41369238 0.79315381 [166,] 0.183531097 0.36706219 0.81646890 [167,] 0.163016291 0.32603258 0.83698371 [168,] 0.143149062 0.28629812 0.85685094 [169,] 0.126345431 0.25269086 0.87365457 [170,] 0.140624580 0.28124916 0.85937542 [171,] 0.148518312 0.29703662 0.85148169 [172,] 0.140445517 0.28089103 0.85955448 [173,] 0.330391013 0.66078203 0.66960899 [174,] 0.306685119 0.61337024 0.69331488 [175,] 0.282606853 0.56521371 0.71739315 [176,] 0.299879637 0.59975927 0.70012036 [177,] 0.288743014 0.57748603 0.71125699 [178,] 0.378482369 0.75696474 0.62151763 [179,] 0.383746818 0.76749364 0.61625318 [180,] 0.355025911 0.71005182 0.64497409 [181,] 0.719695050 0.56060990 0.28030495 [182,] 0.689307330 0.62138534 0.31069267 [183,] 0.659233787 0.68153243 0.34076621 [184,] 0.644470940 0.71105812 0.35552906 [185,] 0.663296545 0.67340691 0.33670346 [186,] 0.637665068 0.72466986 0.36233493 [187,] 0.613621057 0.77275789 0.38637894 [188,] 0.590738125 0.81852375 0.40926188 [189,] 0.558668743 0.88266251 0.44133126 [190,] 0.525758067 0.94848387 0.47424193 [191,] 0.597638961 0.80472208 0.40236104 [192,] 0.642066927 0.71586615 0.35793307 [193,] 0.607307988 0.78538402 0.39269201 [194,] 0.571295748 0.85740850 0.42870425 [195,] 0.543423273 0.91315345 0.45657673 [196,] 0.534025902 0.93194820 0.46597410 [197,] 0.498789093 0.99757819 0.50121091 [198,] 0.567149306 0.86570139 0.43285069 [199,] 0.626169596 0.74766081 0.37383040 [200,] 0.595775077 0.80844985 0.40422492 [201,] 0.626797661 0.74640468 0.37320234 [202,] 0.597860726 0.80427855 0.40213927 [203,] 0.559449706 0.88110059 0.44055029 [204,] 0.525777027 0.94844595 0.47422297 [205,] 0.486822653 0.97364531 0.51317735 [206,] 0.475073244 0.95014649 0.52492676 [207,] 0.463329927 0.92665985 0.53667007 [208,] 0.501002315 0.99799537 0.49899768 [209,] 0.460515567 0.92103113 0.53948443 [210,] 0.448083061 0.89616612 0.55191694 [211,] 0.408872582 0.81774516 0.59112742 [212,] 0.375727179 0.75145436 0.62427282 [213,] 0.337751750 0.67550350 0.66224825 [214,] 0.309783300 0.61956660 0.69021670 [215,] 0.278692799 0.55738560 0.72130720 [216,] 0.245135587 0.49027117 0.75486441 [217,] 0.348811289 0.69762258 0.65118871 [218,] 0.394249563 0.78849913 0.60575044 [219,] 0.698373734 0.60325253 0.30162627 [220,] 0.680000357 0.63999929 0.31999964 [221,] 0.640145694 0.71970861 0.35985431 [222,] 0.634317602 0.73136480 0.36568240 [223,] 0.631447483 0.73710503 0.36855252 [224,] 0.672882503 0.65423499 0.32711750 [225,] 0.648688012 0.70262398 0.35131199 [226,] 0.608816825 0.78236635 0.39118317 [227,] 0.570738534 0.85852293 0.42926147 [228,] 0.637305774 0.72538845 0.36269423 [229,] 0.851833287 0.29633343 0.14816671 [230,] 0.922131016 0.15573797 0.07786898 [231,] 0.905562826 0.18887435 0.09443717 [232,] 0.893475460 0.21304908 0.10652454 [233,] 0.884291041 0.23141792 0.11570896 [234,] 0.860671442 0.27865712 0.13932856 [235,] 0.830883729 0.33823254 0.16911627 [236,] 0.832544721 0.33491056 0.16745528 [237,] 0.808498231 0.38300354 0.19150177 [238,] 0.862589238 0.27482152 0.13741076 [239,] 0.883531141 0.23293772 0.11646886 [240,] 0.866265834 0.26746833 0.13373417 [241,] 0.843303151 0.31339370 0.15669685 [242,] 0.827808695 0.34438261 0.17219131 [243,] 0.821910790 0.35617842 0.17808921 [244,] 0.801542567 0.39691487 0.19845743 [245,] 0.770086330 0.45982734 0.22991367 [246,] 0.722155187 0.55568963 0.27784481 [247,] 0.875956133 0.24808773 0.12404387 [248,] 0.849459041 0.30108192 0.15054096 [249,] 0.826345905 0.34730819 0.17365409 [250,] 0.831923224 0.33615355 0.16807678 [251,] 0.848636175 0.30272765 0.15136382 [252,] 0.845162515 0.30967497 0.15483749 [253,] 0.801396043 0.39720791 0.19860396 [254,] 0.778310490 0.44337902 0.22168951 [255,] 0.719715416 0.56056917 0.28028458 [256,] 0.772381155 0.45523769 0.22761885 [257,] 0.839922674 0.32015465 0.16007733 [258,] 0.926917810 0.14616438 0.07308219 [259,] 0.906671518 0.18665696 0.09332848 [260,] 0.865007780 0.26998444 0.13499222 [261,] 0.926659219 0.14668156 0.07334078 [262,] 0.904538175 0.19092365 0.09546182 [263,] 0.943421373 0.11315725 0.05657863 [264,] 0.902042734 0.19591453 0.09795727 [265,] 0.903071709 0.19385658 0.09692829 [266,] 0.889933918 0.22013216 0.11006608 [267,] 0.811762000 0.37647600 0.18823800 [268,] 0.719977651 0.56004470 0.28002235 [269,] 0.555516236 0.88896753 0.44448376 > postscript(file="/var/fisher/rcomp/tmp/1xi191355153594.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/fisher/rcomp/tmp/29llc1355153594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3bp3j1355153594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4q03n1355153594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5glie1355153594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 1.8750970296 -0.6109594616 1.4910331280 -5.0093684057 2.2774947977 6 7 8 9 10 0.0511172658 -1.2649813138 2.7363336933 1.3436313701 -2.2635251254 11 12 13 14 15 -1.3697759301 0.3096484152 0.4691907408 -0.7752705341 2.6403827848 16 17 18 19 20 1.0220610089 -0.9005605867 -1.9688129209 -1.6489614692 0.2780778194 21 22 23 24 25 -0.4889347055 0.2437438105 -0.4354747239 -0.8624855646 0.0717799772 26 27 28 29 30 0.7864258107 -1.5027987692 2.6987219411 0.2578902368 -0.4313452434 31 32 33 34 35 0.8365219121 -1.3869171493 -0.5275144095 0.1737209818 1.6689841567 36 37 38 39 40 -1.2230479173 0.7222893100 1.6315135858 -1.7282456995 -1.4946027128 41 42 43 44 45 -1.4255615461 1.3749196035 1.4074071685 -0.4090027607 -0.4593641183 46 47 48 49 50 2.9625695193 2.2727176659 -0.2191225226 -0.8059502544 1.6642070249 51 52 53 54 55 2.1319406108 -0.3575269448 2.6368305392 1.1221039846 -2.6876960015 56 57 58 59 60 -4.6431130791 0.3221542717 0.1805118324 -0.0029037056 -1.7672156589 61 62 63 64 65 -2.2951603509 0.5208077380 2.1860856570 0.2682666845 0.3198581904 66 67 68 69 70 0.8333682437 1.1179098744 -1.5185170738 2.3563038992 0.4261463452 71 72 73 74 75 -0.2347570810 0.2133719440 1.0303216949 1.2738013077 0.6382789451 76 77 78 79 80 -3.1641120922 1.2316614978 -0.6983156734 2.1215250247 0.6780455973 81 82 83 84 85 0.4239914452 0.1869765325 1.1065126264 -0.2323780734 -1.7024842923 86 87 88 89 90 -1.4881125214 0.2244471034 0.2110503714 0.7325382235 -0.6341418372 91 92 93 94 95 2.7564957846 0.0895517267 0.9046722033 1.9017931003 -1.7265581312 96 97 98 99 100 1.2349392411 -2.6759731908 0.5438999149 -0.4945772215 2.2692732500 101 102 103 104 105 -0.5258909461 1.0199452471 0.1165812203 -0.8882469719 2.2017138407 106 107 108 109 110 -0.2628009225 0.4569672468 -0.5573203607 -2.6848209603 0.1937673831 111 112 113 114 115 2.1950314076 -1.5157322313 0.5031908021 0.4888385018 2.1729967788 116 117 118 119 120 -2.8137476704 0.8421212280 0.2420124543 1.0144047740 1.3684294120 121 122 123 124 125 1.8200611079 2.1891314325 1.8937127339 3.2132052715 -0.8335331388 126 127 128 129 130 -1.2716054919 -2.2314871979 1.4737751063 -1.5343693651 2.6846775578 131 132 133 134 135 -3.7467457138 1.6799691174 1.5229881293 0.8984898657 -0.6956168900 136 137 138 139 140 -0.0001402925 0.8697806641 -1.5505810951 0.1403798914 -3.7797215154 141 142 143 144 145 -2.0837010345 2.6129808078 -0.2858890376 -0.3074609212 0.3051719426 146 147 148 149 150 2.0694406958 0.5052561059 -0.8645639130 -1.5288216342 1.8089349659 151 152 153 154 155 -2.6023784750 -5.8889966661 -2.6600229184 -0.4275105721 -4.2198467256 156 157 158 159 160 -2.2314871979 -0.7226718749 -1.7150333489 0.6371918584 -0.7260947690 161 162 163 164 165 2.4776573299 -0.9205066019 0.1511176584 0.9954693862 3.5219834442 166 167 168 169 170 2.1894495517 0.7181563846 -3.7956032675 0.6618950520 1.1418202659 171 172 173 174 175 -0.3344760876 3.0704746329 0.4168377109 -1.1016593469 0.1450510107 176 177 178 179 180 -0.1635586091 -0.1676547381 0.6680773896 -2.2322408617 2.2080260545 181 182 183 184 185 1.5785265515 5.0742706906 0.6059682547 -0.5913838368 -1.9719142511 186 187 188 189 190 -1.2763173140 -3.4363185864 1.6824812749 0.5971636853 -6.1134155091 191 192 193 194 195 -0.3967951584 0.3635972503 1.4009599283 -2.2366527047 0.6039545359 196 197 198 199 200 -1.0192855531 0.9284164469 -0.0019083820 0.7664951138 3.1584477427 201 202 203 204 205 -2.7665464859 0.1979966620 -0.3695343626 0.9121655154 1.7079460847 206 207 208 209 210 -0.4806779890 2.8585740009 2.9862229764 0.3012390413 -2.5638655820 211 212 213 214 215 0.6519172445 0.1728948281 -0.8674157075 -0.1487566713 -1.4808878617 216 217 218 219 220 1.1514391592 2.5818297862 0.0466190516 1.2367312575 0.1368549543 221 222 223 224 225 -0.9880234135 0.0785157979 -1.0072940269 0.6360003237 0.0672052745 226 227 228 229 230 -3.5702661773 2.0573941397 -5.0879756659 1.7036499317 0.3991952837 231 232 233 234 235 1.7384846238 1.6165874020 2.7593964094 0.6607218139 -0.6765601820 236 237 238 239 240 -0.9434280444 2.0532000295 -4.7380608039 -3.4090832819 -0.9990724792 241 242 243 244 245 -1.0358188471 1.0495628474 -1.0388979111 0.0301432557 -1.3645140066 246 247 248 249 250 -0.7601209240 -3.1029233714 -2.2833513888 1.1647712616 1.0303177253 251 252 253 254 255 0.8242007905 -2.2515233339 1.3811626011 -1.6672673761 0.2422580835 256 257 258 259 260 3.5778278399 -1.3666219081 -0.9934299631 1.4752925572 -2.2270259512 261 262 263 264 265 1.5605631482 -0.2676977138 -0.7184425960 -0.3195970735 -2.4577956848 266 267 268 269 270 -1.5593067078 -1.2770278699 -1.5622937037 0.6457349069 2.9356308413 271 272 273 274 275 -2.0749044965 -0.2660179426 -0.0441815761 -1.4040747848 -2.1638076832 276 277 278 279 280 0.7483900046 0.1565752052 0.5387598993 1.7807969562 0.6299591673 281 282 283 284 285 1.0646262429 -0.7867340685 0.1387274918 0.1992137512 -1.1433377381 286 287 288 1.2094817182 2.1033822678 1.7562995735 > postscript(file="/var/fisher/rcomp/tmp/63du71355153594.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 1.8750970296 NA 1 -0.6109594616 1.8750970296 2 1.4910331280 -0.6109594616 3 -5.0093684057 1.4910331280 4 2.2774947977 -5.0093684057 5 0.0511172658 2.2774947977 6 -1.2649813138 0.0511172658 7 2.7363336933 -1.2649813138 8 1.3436313701 2.7363336933 9 -2.2635251254 1.3436313701 10 -1.3697759301 -2.2635251254 11 0.3096484152 -1.3697759301 12 0.4691907408 0.3096484152 13 -0.7752705341 0.4691907408 14 2.6403827848 -0.7752705341 15 1.0220610089 2.6403827848 16 -0.9005605867 1.0220610089 17 -1.9688129209 -0.9005605867 18 -1.6489614692 -1.9688129209 19 0.2780778194 -1.6489614692 20 -0.4889347055 0.2780778194 21 0.2437438105 -0.4889347055 22 -0.4354747239 0.2437438105 23 -0.8624855646 -0.4354747239 24 0.0717799772 -0.8624855646 25 0.7864258107 0.0717799772 26 -1.5027987692 0.7864258107 27 2.6987219411 -1.5027987692 28 0.2578902368 2.6987219411 29 -0.4313452434 0.2578902368 30 0.8365219121 -0.4313452434 31 -1.3869171493 0.8365219121 32 -0.5275144095 -1.3869171493 33 0.1737209818 -0.5275144095 34 1.6689841567 0.1737209818 35 -1.2230479173 1.6689841567 36 0.7222893100 -1.2230479173 37 1.6315135858 0.7222893100 38 -1.7282456995 1.6315135858 39 -1.4946027128 -1.7282456995 40 -1.4255615461 -1.4946027128 41 1.3749196035 -1.4255615461 42 1.4074071685 1.3749196035 43 -0.4090027607 1.4074071685 44 -0.4593641183 -0.4090027607 45 2.9625695193 -0.4593641183 46 2.2727176659 2.9625695193 47 -0.2191225226 2.2727176659 48 -0.8059502544 -0.2191225226 49 1.6642070249 -0.8059502544 50 2.1319406108 1.6642070249 51 -0.3575269448 2.1319406108 52 2.6368305392 -0.3575269448 53 1.1221039846 2.6368305392 54 -2.6876960015 1.1221039846 55 -4.6431130791 -2.6876960015 56 0.3221542717 -4.6431130791 57 0.1805118324 0.3221542717 58 -0.0029037056 0.1805118324 59 -1.7672156589 -0.0029037056 60 -2.2951603509 -1.7672156589 61 0.5208077380 -2.2951603509 62 2.1860856570 0.5208077380 63 0.2682666845 2.1860856570 64 0.3198581904 0.2682666845 65 0.8333682437 0.3198581904 66 1.1179098744 0.8333682437 67 -1.5185170738 1.1179098744 68 2.3563038992 -1.5185170738 69 0.4261463452 2.3563038992 70 -0.2347570810 0.4261463452 71 0.2133719440 -0.2347570810 72 1.0303216949 0.2133719440 73 1.2738013077 1.0303216949 74 0.6382789451 1.2738013077 75 -3.1641120922 0.6382789451 76 1.2316614978 -3.1641120922 77 -0.6983156734 1.2316614978 78 2.1215250247 -0.6983156734 79 0.6780455973 2.1215250247 80 0.4239914452 0.6780455973 81 0.1869765325 0.4239914452 82 1.1065126264 0.1869765325 83 -0.2323780734 1.1065126264 84 -1.7024842923 -0.2323780734 85 -1.4881125214 -1.7024842923 86 0.2244471034 -1.4881125214 87 0.2110503714 0.2244471034 88 0.7325382235 0.2110503714 89 -0.6341418372 0.7325382235 90 2.7564957846 -0.6341418372 91 0.0895517267 2.7564957846 92 0.9046722033 0.0895517267 93 1.9017931003 0.9046722033 94 -1.7265581312 1.9017931003 95 1.2349392411 -1.7265581312 96 -2.6759731908 1.2349392411 97 0.5438999149 -2.6759731908 98 -0.4945772215 0.5438999149 99 2.2692732500 -0.4945772215 100 -0.5258909461 2.2692732500 101 1.0199452471 -0.5258909461 102 0.1165812203 1.0199452471 103 -0.8882469719 0.1165812203 104 2.2017138407 -0.8882469719 105 -0.2628009225 2.2017138407 106 0.4569672468 -0.2628009225 107 -0.5573203607 0.4569672468 108 -2.6848209603 -0.5573203607 109 0.1937673831 -2.6848209603 110 2.1950314076 0.1937673831 111 -1.5157322313 2.1950314076 112 0.5031908021 -1.5157322313 113 0.4888385018 0.5031908021 114 2.1729967788 0.4888385018 115 -2.8137476704 2.1729967788 116 0.8421212280 -2.8137476704 117 0.2420124543 0.8421212280 118 1.0144047740 0.2420124543 119 1.3684294120 1.0144047740 120 1.8200611079 1.3684294120 121 2.1891314325 1.8200611079 122 1.8937127339 2.1891314325 123 3.2132052715 1.8937127339 124 -0.8335331388 3.2132052715 125 -1.2716054919 -0.8335331388 126 -2.2314871979 -1.2716054919 127 1.4737751063 -2.2314871979 128 -1.5343693651 1.4737751063 129 2.6846775578 -1.5343693651 130 -3.7467457138 2.6846775578 131 1.6799691174 -3.7467457138 132 1.5229881293 1.6799691174 133 0.8984898657 1.5229881293 134 -0.6956168900 0.8984898657 135 -0.0001402925 -0.6956168900 136 0.8697806641 -0.0001402925 137 -1.5505810951 0.8697806641 138 0.1403798914 -1.5505810951 139 -3.7797215154 0.1403798914 140 -2.0837010345 -3.7797215154 141 2.6129808078 -2.0837010345 142 -0.2858890376 2.6129808078 143 -0.3074609212 -0.2858890376 144 0.3051719426 -0.3074609212 145 2.0694406958 0.3051719426 146 0.5052561059 2.0694406958 147 -0.8645639130 0.5052561059 148 -1.5288216342 -0.8645639130 149 1.8089349659 -1.5288216342 150 -2.6023784750 1.8089349659 151 -5.8889966661 -2.6023784750 152 -2.6600229184 -5.8889966661 153 -0.4275105721 -2.6600229184 154 -4.2198467256 -0.4275105721 155 -2.2314871979 -4.2198467256 156 -0.7226718749 -2.2314871979 157 -1.7150333489 -0.7226718749 158 0.6371918584 -1.7150333489 159 -0.7260947690 0.6371918584 160 2.4776573299 -0.7260947690 161 -0.9205066019 2.4776573299 162 0.1511176584 -0.9205066019 163 0.9954693862 0.1511176584 164 3.5219834442 0.9954693862 165 2.1894495517 3.5219834442 166 0.7181563846 2.1894495517 167 -3.7956032675 0.7181563846 168 0.6618950520 -3.7956032675 169 1.1418202659 0.6618950520 170 -0.3344760876 1.1418202659 171 3.0704746329 -0.3344760876 172 0.4168377109 3.0704746329 173 -1.1016593469 0.4168377109 174 0.1450510107 -1.1016593469 175 -0.1635586091 0.1450510107 176 -0.1676547381 -0.1635586091 177 0.6680773896 -0.1676547381 178 -2.2322408617 0.6680773896 179 2.2080260545 -2.2322408617 180 1.5785265515 2.2080260545 181 5.0742706906 1.5785265515 182 0.6059682547 5.0742706906 183 -0.5913838368 0.6059682547 184 -1.9719142511 -0.5913838368 185 -1.2763173140 -1.9719142511 186 -3.4363185864 -1.2763173140 187 1.6824812749 -3.4363185864 188 0.5971636853 1.6824812749 189 -6.1134155091 0.5971636853 190 -0.3967951584 -6.1134155091 191 0.3635972503 -0.3967951584 192 1.4009599283 0.3635972503 193 -2.2366527047 1.4009599283 194 0.6039545359 -2.2366527047 195 -1.0192855531 0.6039545359 196 0.9284164469 -1.0192855531 197 -0.0019083820 0.9284164469 198 0.7664951138 -0.0019083820 199 3.1584477427 0.7664951138 200 -2.7665464859 3.1584477427 201 0.1979966620 -2.7665464859 202 -0.3695343626 0.1979966620 203 0.9121655154 -0.3695343626 204 1.7079460847 0.9121655154 205 -0.4806779890 1.7079460847 206 2.8585740009 -0.4806779890 207 2.9862229764 2.8585740009 208 0.3012390413 2.9862229764 209 -2.5638655820 0.3012390413 210 0.6519172445 -2.5638655820 211 0.1728948281 0.6519172445 212 -0.8674157075 0.1728948281 213 -0.1487566713 -0.8674157075 214 -1.4808878617 -0.1487566713 215 1.1514391592 -1.4808878617 216 2.5818297862 1.1514391592 217 0.0466190516 2.5818297862 218 1.2367312575 0.0466190516 219 0.1368549543 1.2367312575 220 -0.9880234135 0.1368549543 221 0.0785157979 -0.9880234135 222 -1.0072940269 0.0785157979 223 0.6360003237 -1.0072940269 224 0.0672052745 0.6360003237 225 -3.5702661773 0.0672052745 226 2.0573941397 -3.5702661773 227 -5.0879756659 2.0573941397 228 1.7036499317 -5.0879756659 229 0.3991952837 1.7036499317 230 1.7384846238 0.3991952837 231 1.6165874020 1.7384846238 232 2.7593964094 1.6165874020 233 0.6607218139 2.7593964094 234 -0.6765601820 0.6607218139 235 -0.9434280444 -0.6765601820 236 2.0532000295 -0.9434280444 237 -4.7380608039 2.0532000295 238 -3.4090832819 -4.7380608039 239 -0.9990724792 -3.4090832819 240 -1.0358188471 -0.9990724792 241 1.0495628474 -1.0358188471 242 -1.0388979111 1.0495628474 243 0.0301432557 -1.0388979111 244 -1.3645140066 0.0301432557 245 -0.7601209240 -1.3645140066 246 -3.1029233714 -0.7601209240 247 -2.2833513888 -3.1029233714 248 1.1647712616 -2.2833513888 249 1.0303177253 1.1647712616 250 0.8242007905 1.0303177253 251 -2.2515233339 0.8242007905 252 1.3811626011 -2.2515233339 253 -1.6672673761 1.3811626011 254 0.2422580835 -1.6672673761 255 3.5778278399 0.2422580835 256 -1.3666219081 3.5778278399 257 -0.9934299631 -1.3666219081 258 1.4752925572 -0.9934299631 259 -2.2270259512 1.4752925572 260 1.5605631482 -2.2270259512 261 -0.2676977138 1.5605631482 262 -0.7184425960 -0.2676977138 263 -0.3195970735 -0.7184425960 264 -2.4577956848 -0.3195970735 265 -1.5593067078 -2.4577956848 266 -1.2770278699 -1.5593067078 267 -1.5622937037 -1.2770278699 268 0.6457349069 -1.5622937037 269 2.9356308413 0.6457349069 270 -2.0749044965 2.9356308413 271 -0.2660179426 -2.0749044965 272 -0.0441815761 -0.2660179426 273 -1.4040747848 -0.0441815761 274 -2.1638076832 -1.4040747848 275 0.7483900046 -2.1638076832 276 0.1565752052 0.7483900046 277 0.5387598993 0.1565752052 278 1.7807969562 0.5387598993 279 0.6299591673 1.7807969562 280 1.0646262429 0.6299591673 281 -0.7867340685 1.0646262429 282 0.1387274918 -0.7867340685 283 0.1992137512 0.1387274918 284 -1.1433377381 0.1992137512 285 1.2094817182 -1.1433377381 286 2.1033822678 1.2094817182 287 1.7562995735 2.1033822678 288 NA 1.7562995735 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.6109594616 1.8750970296 [2,] 1.4910331280 -0.6109594616 [3,] -5.0093684057 1.4910331280 [4,] 2.2774947977 -5.0093684057 [5,] 0.0511172658 2.2774947977 [6,] -1.2649813138 0.0511172658 [7,] 2.7363336933 -1.2649813138 [8,] 1.3436313701 2.7363336933 [9,] -2.2635251254 1.3436313701 [10,] -1.3697759301 -2.2635251254 [11,] 0.3096484152 -1.3697759301 [12,] 0.4691907408 0.3096484152 [13,] -0.7752705341 0.4691907408 [14,] 2.6403827848 -0.7752705341 [15,] 1.0220610089 2.6403827848 [16,] -0.9005605867 1.0220610089 [17,] -1.9688129209 -0.9005605867 [18,] -1.6489614692 -1.9688129209 [19,] 0.2780778194 -1.6489614692 [20,] -0.4889347055 0.2780778194 [21,] 0.2437438105 -0.4889347055 [22,] -0.4354747239 0.2437438105 [23,] -0.8624855646 -0.4354747239 [24,] 0.0717799772 -0.8624855646 [25,] 0.7864258107 0.0717799772 [26,] -1.5027987692 0.7864258107 [27,] 2.6987219411 -1.5027987692 [28,] 0.2578902368 2.6987219411 [29,] -0.4313452434 0.2578902368 [30,] 0.8365219121 -0.4313452434 [31,] -1.3869171493 0.8365219121 [32,] -0.5275144095 -1.3869171493 [33,] 0.1737209818 -0.5275144095 [34,] 1.6689841567 0.1737209818 [35,] -1.2230479173 1.6689841567 [36,] 0.7222893100 -1.2230479173 [37,] 1.6315135858 0.7222893100 [38,] -1.7282456995 1.6315135858 [39,] -1.4946027128 -1.7282456995 [40,] -1.4255615461 -1.4946027128 [41,] 1.3749196035 -1.4255615461 [42,] 1.4074071685 1.3749196035 [43,] -0.4090027607 1.4074071685 [44,] -0.4593641183 -0.4090027607 [45,] 2.9625695193 -0.4593641183 [46,] 2.2727176659 2.9625695193 [47,] -0.2191225226 2.2727176659 [48,] -0.8059502544 -0.2191225226 [49,] 1.6642070249 -0.8059502544 [50,] 2.1319406108 1.6642070249 [51,] -0.3575269448 2.1319406108 [52,] 2.6368305392 -0.3575269448 [53,] 1.1221039846 2.6368305392 [54,] -2.6876960015 1.1221039846 [55,] -4.6431130791 -2.6876960015 [56,] 0.3221542717 -4.6431130791 [57,] 0.1805118324 0.3221542717 [58,] -0.0029037056 0.1805118324 [59,] -1.7672156589 -0.0029037056 [60,] -2.2951603509 -1.7672156589 [61,] 0.5208077380 -2.2951603509 [62,] 2.1860856570 0.5208077380 [63,] 0.2682666845 2.1860856570 [64,] 0.3198581904 0.2682666845 [65,] 0.8333682437 0.3198581904 [66,] 1.1179098744 0.8333682437 [67,] -1.5185170738 1.1179098744 [68,] 2.3563038992 -1.5185170738 [69,] 0.4261463452 2.3563038992 [70,] -0.2347570810 0.4261463452 [71,] 0.2133719440 -0.2347570810 [72,] 1.0303216949 0.2133719440 [73,] 1.2738013077 1.0303216949 [74,] 0.6382789451 1.2738013077 [75,] -3.1641120922 0.6382789451 [76,] 1.2316614978 -3.1641120922 [77,] -0.6983156734 1.2316614978 [78,] 2.1215250247 -0.6983156734 [79,] 0.6780455973 2.1215250247 [80,] 0.4239914452 0.6780455973 [81,] 0.1869765325 0.4239914452 [82,] 1.1065126264 0.1869765325 [83,] -0.2323780734 1.1065126264 [84,] -1.7024842923 -0.2323780734 [85,] -1.4881125214 -1.7024842923 [86,] 0.2244471034 -1.4881125214 [87,] 0.2110503714 0.2244471034 [88,] 0.7325382235 0.2110503714 [89,] -0.6341418372 0.7325382235 [90,] 2.7564957846 -0.6341418372 [91,] 0.0895517267 2.7564957846 [92,] 0.9046722033 0.0895517267 [93,] 1.9017931003 0.9046722033 [94,] -1.7265581312 1.9017931003 [95,] 1.2349392411 -1.7265581312 [96,] -2.6759731908 1.2349392411 [97,] 0.5438999149 -2.6759731908 [98,] -0.4945772215 0.5438999149 [99,] 2.2692732500 -0.4945772215 [100,] -0.5258909461 2.2692732500 [101,] 1.0199452471 -0.5258909461 [102,] 0.1165812203 1.0199452471 [103,] -0.8882469719 0.1165812203 [104,] 2.2017138407 -0.8882469719 [105,] -0.2628009225 2.2017138407 [106,] 0.4569672468 -0.2628009225 [107,] -0.5573203607 0.4569672468 [108,] -2.6848209603 -0.5573203607 [109,] 0.1937673831 -2.6848209603 [110,] 2.1950314076 0.1937673831 [111,] -1.5157322313 2.1950314076 [112,] 0.5031908021 -1.5157322313 [113,] 0.4888385018 0.5031908021 [114,] 2.1729967788 0.4888385018 [115,] -2.8137476704 2.1729967788 [116,] 0.8421212280 -2.8137476704 [117,] 0.2420124543 0.8421212280 [118,] 1.0144047740 0.2420124543 [119,] 1.3684294120 1.0144047740 [120,] 1.8200611079 1.3684294120 [121,] 2.1891314325 1.8200611079 [122,] 1.8937127339 2.1891314325 [123,] 3.2132052715 1.8937127339 [124,] -0.8335331388 3.2132052715 [125,] -1.2716054919 -0.8335331388 [126,] -2.2314871979 -1.2716054919 [127,] 1.4737751063 -2.2314871979 [128,] -1.5343693651 1.4737751063 [129,] 2.6846775578 -1.5343693651 [130,] -3.7467457138 2.6846775578 [131,] 1.6799691174 -3.7467457138 [132,] 1.5229881293 1.6799691174 [133,] 0.8984898657 1.5229881293 [134,] -0.6956168900 0.8984898657 [135,] -0.0001402925 -0.6956168900 [136,] 0.8697806641 -0.0001402925 [137,] -1.5505810951 0.8697806641 [138,] 0.1403798914 -1.5505810951 [139,] -3.7797215154 0.1403798914 [140,] -2.0837010345 -3.7797215154 [141,] 2.6129808078 -2.0837010345 [142,] -0.2858890376 2.6129808078 [143,] -0.3074609212 -0.2858890376 [144,] 0.3051719426 -0.3074609212 [145,] 2.0694406958 0.3051719426 [146,] 0.5052561059 2.0694406958 [147,] -0.8645639130 0.5052561059 [148,] -1.5288216342 -0.8645639130 [149,] 1.8089349659 -1.5288216342 [150,] -2.6023784750 1.8089349659 [151,] -5.8889966661 -2.6023784750 [152,] -2.6600229184 -5.8889966661 [153,] -0.4275105721 -2.6600229184 [154,] -4.2198467256 -0.4275105721 [155,] -2.2314871979 -4.2198467256 [156,] -0.7226718749 -2.2314871979 [157,] -1.7150333489 -0.7226718749 [158,] 0.6371918584 -1.7150333489 [159,] -0.7260947690 0.6371918584 [160,] 2.4776573299 -0.7260947690 [161,] -0.9205066019 2.4776573299 [162,] 0.1511176584 -0.9205066019 [163,] 0.9954693862 0.1511176584 [164,] 3.5219834442 0.9954693862 [165,] 2.1894495517 3.5219834442 [166,] 0.7181563846 2.1894495517 [167,] -3.7956032675 0.7181563846 [168,] 0.6618950520 -3.7956032675 [169,] 1.1418202659 0.6618950520 [170,] -0.3344760876 1.1418202659 [171,] 3.0704746329 -0.3344760876 [172,] 0.4168377109 3.0704746329 [173,] -1.1016593469 0.4168377109 [174,] 0.1450510107 -1.1016593469 [175,] -0.1635586091 0.1450510107 [176,] -0.1676547381 -0.1635586091 [177,] 0.6680773896 -0.1676547381 [178,] -2.2322408617 0.6680773896 [179,] 2.2080260545 -2.2322408617 [180,] 1.5785265515 2.2080260545 [181,] 5.0742706906 1.5785265515 [182,] 0.6059682547 5.0742706906 [183,] -0.5913838368 0.6059682547 [184,] -1.9719142511 -0.5913838368 [185,] -1.2763173140 -1.9719142511 [186,] -3.4363185864 -1.2763173140 [187,] 1.6824812749 -3.4363185864 [188,] 0.5971636853 1.6824812749 [189,] -6.1134155091 0.5971636853 [190,] -0.3967951584 -6.1134155091 [191,] 0.3635972503 -0.3967951584 [192,] 1.4009599283 0.3635972503 [193,] -2.2366527047 1.4009599283 [194,] 0.6039545359 -2.2366527047 [195,] -1.0192855531 0.6039545359 [196,] 0.9284164469 -1.0192855531 [197,] -0.0019083820 0.9284164469 [198,] 0.7664951138 -0.0019083820 [199,] 3.1584477427 0.7664951138 [200,] -2.7665464859 3.1584477427 [201,] 0.1979966620 -2.7665464859 [202,] -0.3695343626 0.1979966620 [203,] 0.9121655154 -0.3695343626 [204,] 1.7079460847 0.9121655154 [205,] -0.4806779890 1.7079460847 [206,] 2.8585740009 -0.4806779890 [207,] 2.9862229764 2.8585740009 [208,] 0.3012390413 2.9862229764 [209,] -2.5638655820 0.3012390413 [210,] 0.6519172445 -2.5638655820 [211,] 0.1728948281 0.6519172445 [212,] -0.8674157075 0.1728948281 [213,] -0.1487566713 -0.8674157075 [214,] -1.4808878617 -0.1487566713 [215,] 1.1514391592 -1.4808878617 [216,] 2.5818297862 1.1514391592 [217,] 0.0466190516 2.5818297862 [218,] 1.2367312575 0.0466190516 [219,] 0.1368549543 1.2367312575 [220,] -0.9880234135 0.1368549543 [221,] 0.0785157979 -0.9880234135 [222,] -1.0072940269 0.0785157979 [223,] 0.6360003237 -1.0072940269 [224,] 0.0672052745 0.6360003237 [225,] -3.5702661773 0.0672052745 [226,] 2.0573941397 -3.5702661773 [227,] -5.0879756659 2.0573941397 [228,] 1.7036499317 -5.0879756659 [229,] 0.3991952837 1.7036499317 [230,] 1.7384846238 0.3991952837 [231,] 1.6165874020 1.7384846238 [232,] 2.7593964094 1.6165874020 [233,] 0.6607218139 2.7593964094 [234,] -0.6765601820 0.6607218139 [235,] -0.9434280444 -0.6765601820 [236,] 2.0532000295 -0.9434280444 [237,] -4.7380608039 2.0532000295 [238,] -3.4090832819 -4.7380608039 [239,] -0.9990724792 -3.4090832819 [240,] -1.0358188471 -0.9990724792 [241,] 1.0495628474 -1.0358188471 [242,] -1.0388979111 1.0495628474 [243,] 0.0301432557 -1.0388979111 [244,] -1.3645140066 0.0301432557 [245,] -0.7601209240 -1.3645140066 [246,] -3.1029233714 -0.7601209240 [247,] -2.2833513888 -3.1029233714 [248,] 1.1647712616 -2.2833513888 [249,] 1.0303177253 1.1647712616 [250,] 0.8242007905 1.0303177253 [251,] -2.2515233339 0.8242007905 [252,] 1.3811626011 -2.2515233339 [253,] -1.6672673761 1.3811626011 [254,] 0.2422580835 -1.6672673761 [255,] 3.5778278399 0.2422580835 [256,] -1.3666219081 3.5778278399 [257,] -0.9934299631 -1.3666219081 [258,] 1.4752925572 -0.9934299631 [259,] -2.2270259512 1.4752925572 [260,] 1.5605631482 -2.2270259512 [261,] -0.2676977138 1.5605631482 [262,] -0.7184425960 -0.2676977138 [263,] -0.3195970735 -0.7184425960 [264,] -2.4577956848 -0.3195970735 [265,] -1.5593067078 -2.4577956848 [266,] -1.2770278699 -1.5593067078 [267,] -1.5622937037 -1.2770278699 [268,] 0.6457349069 -1.5622937037 [269,] 2.9356308413 0.6457349069 [270,] -2.0749044965 2.9356308413 [271,] -0.2660179426 -2.0749044965 [272,] -0.0441815761 -0.2660179426 [273,] -1.4040747848 -0.0441815761 [274,] -2.1638076832 -1.4040747848 [275,] 0.7483900046 -2.1638076832 [276,] 0.1565752052 0.7483900046 [277,] 0.5387598993 0.1565752052 [278,] 1.7807969562 0.5387598993 [279,] 0.6299591673 1.7807969562 [280,] 1.0646262429 0.6299591673 [281,] -0.7867340685 1.0646262429 [282,] 0.1387274918 -0.7867340685 [283,] 0.1992137512 0.1387274918 [284,] -1.1433377381 0.1992137512 [285,] 1.2094817182 -1.1433377381 [286,] 2.1033822678 1.2094817182 [287,] 1.7562995735 2.1033822678 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.6109594616 1.8750970296 2 1.4910331280 -0.6109594616 3 -5.0093684057 1.4910331280 4 2.2774947977 -5.0093684057 5 0.0511172658 2.2774947977 6 -1.2649813138 0.0511172658 7 2.7363336933 -1.2649813138 8 1.3436313701 2.7363336933 9 -2.2635251254 1.3436313701 10 -1.3697759301 -2.2635251254 11 0.3096484152 -1.3697759301 12 0.4691907408 0.3096484152 13 -0.7752705341 0.4691907408 14 2.6403827848 -0.7752705341 15 1.0220610089 2.6403827848 16 -0.9005605867 1.0220610089 17 -1.9688129209 -0.9005605867 18 -1.6489614692 -1.9688129209 19 0.2780778194 -1.6489614692 20 -0.4889347055 0.2780778194 21 0.2437438105 -0.4889347055 22 -0.4354747239 0.2437438105 23 -0.8624855646 -0.4354747239 24 0.0717799772 -0.8624855646 25 0.7864258107 0.0717799772 26 -1.5027987692 0.7864258107 27 2.6987219411 -1.5027987692 28 0.2578902368 2.6987219411 29 -0.4313452434 0.2578902368 30 0.8365219121 -0.4313452434 31 -1.3869171493 0.8365219121 32 -0.5275144095 -1.3869171493 33 0.1737209818 -0.5275144095 34 1.6689841567 0.1737209818 35 -1.2230479173 1.6689841567 36 0.7222893100 -1.2230479173 37 1.6315135858 0.7222893100 38 -1.7282456995 1.6315135858 39 -1.4946027128 -1.7282456995 40 -1.4255615461 -1.4946027128 41 1.3749196035 -1.4255615461 42 1.4074071685 1.3749196035 43 -0.4090027607 1.4074071685 44 -0.4593641183 -0.4090027607 45 2.9625695193 -0.4593641183 46 2.2727176659 2.9625695193 47 -0.2191225226 2.2727176659 48 -0.8059502544 -0.2191225226 49 1.6642070249 -0.8059502544 50 2.1319406108 1.6642070249 51 -0.3575269448 2.1319406108 52 2.6368305392 -0.3575269448 53 1.1221039846 2.6368305392 54 -2.6876960015 1.1221039846 55 -4.6431130791 -2.6876960015 56 0.3221542717 -4.6431130791 57 0.1805118324 0.3221542717 58 -0.0029037056 0.1805118324 59 -1.7672156589 -0.0029037056 60 -2.2951603509 -1.7672156589 61 0.5208077380 -2.2951603509 62 2.1860856570 0.5208077380 63 0.2682666845 2.1860856570 64 0.3198581904 0.2682666845 65 0.8333682437 0.3198581904 66 1.1179098744 0.8333682437 67 -1.5185170738 1.1179098744 68 2.3563038992 -1.5185170738 69 0.4261463452 2.3563038992 70 -0.2347570810 0.4261463452 71 0.2133719440 -0.2347570810 72 1.0303216949 0.2133719440 73 1.2738013077 1.0303216949 74 0.6382789451 1.2738013077 75 -3.1641120922 0.6382789451 76 1.2316614978 -3.1641120922 77 -0.6983156734 1.2316614978 78 2.1215250247 -0.6983156734 79 0.6780455973 2.1215250247 80 0.4239914452 0.6780455973 81 0.1869765325 0.4239914452 82 1.1065126264 0.1869765325 83 -0.2323780734 1.1065126264 84 -1.7024842923 -0.2323780734 85 -1.4881125214 -1.7024842923 86 0.2244471034 -1.4881125214 87 0.2110503714 0.2244471034 88 0.7325382235 0.2110503714 89 -0.6341418372 0.7325382235 90 2.7564957846 -0.6341418372 91 0.0895517267 2.7564957846 92 0.9046722033 0.0895517267 93 1.9017931003 0.9046722033 94 -1.7265581312 1.9017931003 95 1.2349392411 -1.7265581312 96 -2.6759731908 1.2349392411 97 0.5438999149 -2.6759731908 98 -0.4945772215 0.5438999149 99 2.2692732500 -0.4945772215 100 -0.5258909461 2.2692732500 101 1.0199452471 -0.5258909461 102 0.1165812203 1.0199452471 103 -0.8882469719 0.1165812203 104 2.2017138407 -0.8882469719 105 -0.2628009225 2.2017138407 106 0.4569672468 -0.2628009225 107 -0.5573203607 0.4569672468 108 -2.6848209603 -0.5573203607 109 0.1937673831 -2.6848209603 110 2.1950314076 0.1937673831 111 -1.5157322313 2.1950314076 112 0.5031908021 -1.5157322313 113 0.4888385018 0.5031908021 114 2.1729967788 0.4888385018 115 -2.8137476704 2.1729967788 116 0.8421212280 -2.8137476704 117 0.2420124543 0.8421212280 118 1.0144047740 0.2420124543 119 1.3684294120 1.0144047740 120 1.8200611079 1.3684294120 121 2.1891314325 1.8200611079 122 1.8937127339 2.1891314325 123 3.2132052715 1.8937127339 124 -0.8335331388 3.2132052715 125 -1.2716054919 -0.8335331388 126 -2.2314871979 -1.2716054919 127 1.4737751063 -2.2314871979 128 -1.5343693651 1.4737751063 129 2.6846775578 -1.5343693651 130 -3.7467457138 2.6846775578 131 1.6799691174 -3.7467457138 132 1.5229881293 1.6799691174 133 0.8984898657 1.5229881293 134 -0.6956168900 0.8984898657 135 -0.0001402925 -0.6956168900 136 0.8697806641 -0.0001402925 137 -1.5505810951 0.8697806641 138 0.1403798914 -1.5505810951 139 -3.7797215154 0.1403798914 140 -2.0837010345 -3.7797215154 141 2.6129808078 -2.0837010345 142 -0.2858890376 2.6129808078 143 -0.3074609212 -0.2858890376 144 0.3051719426 -0.3074609212 145 2.0694406958 0.3051719426 146 0.5052561059 2.0694406958 147 -0.8645639130 0.5052561059 148 -1.5288216342 -0.8645639130 149 1.8089349659 -1.5288216342 150 -2.6023784750 1.8089349659 151 -5.8889966661 -2.6023784750 152 -2.6600229184 -5.8889966661 153 -0.4275105721 -2.6600229184 154 -4.2198467256 -0.4275105721 155 -2.2314871979 -4.2198467256 156 -0.7226718749 -2.2314871979 157 -1.7150333489 -0.7226718749 158 0.6371918584 -1.7150333489 159 -0.7260947690 0.6371918584 160 2.4776573299 -0.7260947690 161 -0.9205066019 2.4776573299 162 0.1511176584 -0.9205066019 163 0.9954693862 0.1511176584 164 3.5219834442 0.9954693862 165 2.1894495517 3.5219834442 166 0.7181563846 2.1894495517 167 -3.7956032675 0.7181563846 168 0.6618950520 -3.7956032675 169 1.1418202659 0.6618950520 170 -0.3344760876 1.1418202659 171 3.0704746329 -0.3344760876 172 0.4168377109 3.0704746329 173 -1.1016593469 0.4168377109 174 0.1450510107 -1.1016593469 175 -0.1635586091 0.1450510107 176 -0.1676547381 -0.1635586091 177 0.6680773896 -0.1676547381 178 -2.2322408617 0.6680773896 179 2.2080260545 -2.2322408617 180 1.5785265515 2.2080260545 181 5.0742706906 1.5785265515 182 0.6059682547 5.0742706906 183 -0.5913838368 0.6059682547 184 -1.9719142511 -0.5913838368 185 -1.2763173140 -1.9719142511 186 -3.4363185864 -1.2763173140 187 1.6824812749 -3.4363185864 188 0.5971636853 1.6824812749 189 -6.1134155091 0.5971636853 190 -0.3967951584 -6.1134155091 191 0.3635972503 -0.3967951584 192 1.4009599283 0.3635972503 193 -2.2366527047 1.4009599283 194 0.6039545359 -2.2366527047 195 -1.0192855531 0.6039545359 196 0.9284164469 -1.0192855531 197 -0.0019083820 0.9284164469 198 0.7664951138 -0.0019083820 199 3.1584477427 0.7664951138 200 -2.7665464859 3.1584477427 201 0.1979966620 -2.7665464859 202 -0.3695343626 0.1979966620 203 0.9121655154 -0.3695343626 204 1.7079460847 0.9121655154 205 -0.4806779890 1.7079460847 206 2.8585740009 -0.4806779890 207 2.9862229764 2.8585740009 208 0.3012390413 2.9862229764 209 -2.5638655820 0.3012390413 210 0.6519172445 -2.5638655820 211 0.1728948281 0.6519172445 212 -0.8674157075 0.1728948281 213 -0.1487566713 -0.8674157075 214 -1.4808878617 -0.1487566713 215 1.1514391592 -1.4808878617 216 2.5818297862 1.1514391592 217 0.0466190516 2.5818297862 218 1.2367312575 0.0466190516 219 0.1368549543 1.2367312575 220 -0.9880234135 0.1368549543 221 0.0785157979 -0.9880234135 222 -1.0072940269 0.0785157979 223 0.6360003237 -1.0072940269 224 0.0672052745 0.6360003237 225 -3.5702661773 0.0672052745 226 2.0573941397 -3.5702661773 227 -5.0879756659 2.0573941397 228 1.7036499317 -5.0879756659 229 0.3991952837 1.7036499317 230 1.7384846238 0.3991952837 231 1.6165874020 1.7384846238 232 2.7593964094 1.6165874020 233 0.6607218139 2.7593964094 234 -0.6765601820 0.6607218139 235 -0.9434280444 -0.6765601820 236 2.0532000295 -0.9434280444 237 -4.7380608039 2.0532000295 238 -3.4090832819 -4.7380608039 239 -0.9990724792 -3.4090832819 240 -1.0358188471 -0.9990724792 241 1.0495628474 -1.0358188471 242 -1.0388979111 1.0495628474 243 0.0301432557 -1.0388979111 244 -1.3645140066 0.0301432557 245 -0.7601209240 -1.3645140066 246 -3.1029233714 -0.7601209240 247 -2.2833513888 -3.1029233714 248 1.1647712616 -2.2833513888 249 1.0303177253 1.1647712616 250 0.8242007905 1.0303177253 251 -2.2515233339 0.8242007905 252 1.3811626011 -2.2515233339 253 -1.6672673761 1.3811626011 254 0.2422580835 -1.6672673761 255 3.5778278399 0.2422580835 256 -1.3666219081 3.5778278399 257 -0.9934299631 -1.3666219081 258 1.4752925572 -0.9934299631 259 -2.2270259512 1.4752925572 260 1.5605631482 -2.2270259512 261 -0.2676977138 1.5605631482 262 -0.7184425960 -0.2676977138 263 -0.3195970735 -0.7184425960 264 -2.4577956848 -0.3195970735 265 -1.5593067078 -2.4577956848 266 -1.2770278699 -1.5593067078 267 -1.5622937037 -1.2770278699 268 0.6457349069 -1.5622937037 269 2.9356308413 0.6457349069 270 -2.0749044965 2.9356308413 271 -0.2660179426 -2.0749044965 272 -0.0441815761 -0.2660179426 273 -1.4040747848 -0.0441815761 274 -2.1638076832 -1.4040747848 275 0.7483900046 -2.1638076832 276 0.1565752052 0.7483900046 277 0.5387598993 0.1565752052 278 1.7807969562 0.5387598993 279 0.6299591673 1.7807969562 280 1.0646262429 0.6299591673 281 -0.7867340685 1.0646262429 282 0.1387274918 -0.7867340685 283 0.1992137512 0.1387274918 284 -1.1433377381 0.1992137512 285 1.2094817182 -1.1433377381 286 2.1033822678 1.2094817182 287 1.7562995735 2.1033822678 > 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/fisher/rcomp/tmp/7pixr1355153594.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/fisher/rcomp/tmp/8vnj71355153594.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/fisher/rcomp/tmp/92ggr1355153594.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/fisher/rcomp/tmp/10z1841355153594.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/118ona1355153594.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12jszt1355153594.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13illo1355153594.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/143x9e1355153594.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/fisher/rcomp/tmp/15131s1355153594.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/fisher/rcomp/tmp/163ap71355153595.tab") + } > > try(system("convert tmp/1xi191355153594.ps tmp/1xi191355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/29llc1355153594.ps tmp/29llc1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/3bp3j1355153594.ps tmp/3bp3j1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/4q03n1355153594.ps tmp/4q03n1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/5glie1355153594.ps tmp/5glie1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/63du71355153594.ps tmp/63du71355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/7pixr1355153594.ps tmp/7pixr1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/8vnj71355153594.ps tmp/8vnj71355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/92ggr1355153594.ps tmp/92ggr1355153594.png",intern=TRUE)) character(0) > try(system("convert tmp/10z1841355153594.ps tmp/10z1841355153594.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.645 1.565 13.210