R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(15 + ,2 + ,0 + ,0 + ,9 + ,12 + ,1 + ,1 + ,2 + ,9 + ,9 + ,1 + ,2 + ,1 + ,9 + ,10 + ,0 + ,0 + ,0 + ,9 + ,13 + ,0 + ,0 + ,0 + ,9 + ,16 + ,1 + ,0 + ,0 + ,9 + ,14 + ,0 + ,0 + ,0 + ,9 + ,16 + ,1 + ,1 + ,0 + ,9 + ,10 + ,0 + ,0 + ,0 + ,9 + ,8 + ,2 + ,0 + ,1 + ,10 + ,12 + ,1 + ,0 + ,0 + ,10 + ,15 + ,0 + ,0 + ,0 + ,10 + ,14 + ,0 + ,1 + ,0 + ,10 + ,14 + ,1 + ,1 + ,2 + ,10 + ,12 + ,1 + ,2 + ,1 + ,10 + ,12 + ,0 + ,0 + ,0 + ,10 + ,10 + ,0 + ,0 + ,0 + ,10 + ,4 + ,0 + ,0 + ,0 + ,10 + ,14 + ,0 + ,1 + ,0 + ,10 + ,15 + ,0 + ,0 + ,0 + ,10 + ,16 + ,0 + ,0 + ,0 + ,10 + ,12 + ,0 + ,1 + ,0 + ,10 + ,12 + ,0 + ,0 + ,0 + ,10 + ,12 + ,0 + ,0 + ,1 + ,10 + ,12 + ,1 + ,0 + ,1 + ,9 + ,12 + ,0 + ,0 + ,0 + ,9 + ,11 + ,3 + ,2 + ,1 + ,9 + ,11 + ,1 + ,0 + ,0 + ,9 + ,11 + ,1 + ,1 + ,0 + ,9 + ,11 + ,1 + ,1 + ,0 + ,9 + ,11 + ,3 + ,1 + ,1 + ,9 + ,11 + ,0 + ,0 + ,0 + ,9 + ,15 + ,0 + ,0 + ,0 + ,9 + ,15 + ,0 + ,0 + ,0 + ,9 + ,9 + ,0 + ,0 + ,0 + ,9 + ,16 + ,0 + ,0 + ,0 + ,9 + ,13 + ,0 + ,2 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+ ,14 + ,1 + ,0 + ,0 + ,12 + ,12 + ,0 + ,0 + ,0 + ,12 + ,15 + ,0 + ,0 + ,0 + ,12 + ,13 + ,0 + ,0 + ,1 + ,12 + ,16 + ,1 + ,0 + ,0 + ,12 + ,14 + ,0 + ,0 + ,1 + ,12 + ,8 + ,0 + ,0 + ,0 + ,12 + ,16 + ,0 + ,0 + ,0 + ,12 + ,16 + ,1 + ,0 + ,1 + ,12 + ,12 + ,0 + ,1 + ,0 + ,12 + ,11 + ,0 + ,0 + ,0 + ,12 + ,16 + ,0 + ,0 + ,0 + ,12 + ,9 + ,0 + ,0 + ,0 + ,12) + ,dim=c(5 + ,312) + ,dimnames=list(c('Popularity' + ,'B' + ,'2B' + ,'3B' + ,'Month') + ,1:312)) > y <- array(NA,dim=c(5,312),dimnames=list(c('Popularity','B','2B','3B','Month'),1:312)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Popularity B 2B 3B Month 1 15 2 0 0 9 2 12 1 1 2 9 3 9 1 2 1 9 4 10 0 0 0 9 5 13 0 0 0 9 6 16 1 0 0 9 7 14 0 0 0 9 8 16 1 1 0 9 9 10 0 0 0 9 10 8 2 0 1 10 11 12 1 0 0 10 12 15 0 0 0 10 13 14 0 1 0 10 14 14 1 1 2 10 15 12 1 2 1 10 16 12 0 0 0 10 17 10 0 0 0 10 18 4 0 0 0 10 19 14 0 1 0 10 20 15 0 0 0 10 21 16 0 0 0 10 22 12 0 1 0 10 23 12 0 0 0 10 24 12 0 0 1 10 25 12 1 0 1 9 26 12 0 0 0 9 27 11 3 2 1 9 28 11 1 0 0 9 29 11 1 1 0 9 30 11 1 1 0 9 31 11 3 1 1 9 32 11 0 0 0 9 33 15 0 0 0 9 34 15 0 0 0 9 35 9 0 0 0 9 36 16 0 0 0 9 37 13 0 2 1 9 38 9 1 0 0 9 39 16 0 0 0 9 40 12 0 0 0 9 41 15 0 0 2 9 42 5 0 0 0 9 43 11 2 2 0 9 44 17 2 2 0 9 45 9 0 1 1 9 46 13 0 0 0 9 47 16 0 0 0 10 48 16 0 0 0 10 49 14 2 0 2 10 50 16 1 0 0 10 51 11 0 0 0 10 52 11 0 0 0 10 53 11 0 0 0 10 54 12 0 0 0 10 55 12 1 1 1 10 56 12 0 0 0 10 57 14 0 0 0 10 58 10 2 0 0 10 59 9 0 2 0 10 60 12 0 0 1 10 61 10 0 0 0 10 62 14 0 0 0 10 63 8 0 0 0 10 64 16 1 0 0 10 65 14 1 0 0 10 66 14 0 0 0 10 67 12 0 0 0 10 68 14 1 0 0 10 69 7 1 1 1 10 70 19 0 0 0 10 71 15 0 0 0 10 72 8 0 0 0 10 73 10 0 0 0 10 74 13 0 0 0 10 75 13 0 0 0 9 76 10 0 0 0 9 77 12 0 0 0 9 78 15 0 1 1 9 79 7 1 0 0 9 80 14 0 0 0 9 81 10 0 0 0 9 82 6 0 3 0 9 83 11 2 0 0 9 84 12 0 0 0 9 85 14 0 0 2 9 86 12 0 0 0 9 87 14 0 0 0 9 88 11 2 2 0 9 89 10 1 0 1 9 90 13 0 0 1 9 91 8 0 0 0 9 92 9 0 0 0 9 93 6 0 0 0 10 94 12 1 0 2 10 95 14 0 0 0 10 96 11 0 0 0 10 97 8 1 0 1 10 98 7 0 0 0 10 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1 0 11 163 14 1 2 0 11 164 16 2 0 1 11 165 10 1 1 1 11 166 8 0 0 0 11 167 12 2 1 0 11 168 15 1 0 2 11 169 14 1 0 0 11 170 14 1 0 0 11 171 12 1 0 1 11 172 12 0 2 1 11 173 10 0 3 1 11 174 4 0 2 0 11 175 14 0 2 1 11 176 15 0 0 0 11 177 16 1 0 0 11 178 12 2 0 0 11 179 12 0 0 0 11 180 12 0 1 0 11 181 12 0 2 0 11 182 12 1 0 0 11 183 11 1 1 0 11 184 11 3 0 0 11 185 11 0 1 3 11 186 11 0 1 2 11 187 11 1 0 0 11 188 11 2 0 1 11 189 15 1 0 0 11 190 15 1 0 1 11 191 9 0 2 2 11 192 16 0 2 1 11 193 13 0 0 1 11 194 9 2 2 0 11 195 16 1 2 0 11 196 12 1 0 0 11 197 15 2 1 0 11 198 5 0 3 0 11 199 11 1 2 0 11 200 17 2 0 0 11 201 9 0 2 1 11 202 13 2 0 0 11 203 16 0 1 1 11 204 16 0 1 0 11 205 14 1 1 0 11 206 16 0 1 1 11 207 11 1 0 0 11 208 11 0 1 2 11 209 11 1 2 1 11 210 12 1 0 0 11 211 12 1 1 1 11 212 12 1 1 0 11 213 14 1 1 1 11 214 10 1 0 2 11 215 9 0 1 0 11 216 12 0 1 0 11 217 10 1 0 0 11 218 14 2 2 0 11 219 8 1 0 0 11 220 16 0 2 1 11 221 14 0 1 3 11 222 14 0 0 1 11 223 12 0 1 0 11 224 14 0 1 0 11 225 7 0 1 0 11 226 19 2 1 0 11 227 15 0 0 2 11 228 8 0 0 0 11 229 10 1 0 0 11 230 13 1 1 0 11 231 13 1 0 3 11 232 10 1 0 1 11 233 12 1 0 0 11 234 15 0 0 1 11 235 7 0 0 1 11 236 14 0 0 1 11 237 10 1 1 1 11 238 6 2 0 0 11 239 11 1 1 0 11 240 12 2 2 0 11 241 14 3 1 0 11 242 12 1 2 0 11 243 14 0 1 2 11 244 11 2 1 1 11 245 10 1 0 0 11 246 13 0 1 2 11 247 8 0 0 1 11 248 9 2 0 4 11 249 6 1 1 0 11 250 12 1 0 0 11 251 14 0 0 0 11 252 11 2 2 0 11 253 8 0 0 1 11 254 7 1 0 0 11 255 9 0 2 0 11 256 14 3 1 0 11 257 13 0 0 0 11 258 15 0 1 2 11 259 5 1 1 2 11 260 15 0 2 2 11 261 13 0 1 0 11 262 12 0 0 1 11 263 6 1 0 0 11 264 7 1 0 0 11 265 13 3 0 0 11 266 16 2 0 0 11 267 10 0 1 0 11 268 16 1 0 0 11 269 15 1 0 0 11 270 8 1 0 1 11 271 11 1 0 0 11 272 13 1 1 2 11 273 16 0 2 1 11 274 11 0 1 3 11 275 14 0 1 1 11 276 9 0 0 2 11 277 8 0 1 0 12 278 8 2 0 0 12 279 11 1 3 0 12 280 12 0 2 0 12 281 11 2 1 0 12 282 14 1 0 0 12 283 11 1 0 0 12 284 14 1 1 0 12 285 13 0 0 1 12 286 12 1 1 0 12 287 4 1 0 0 12 288 15 0 0 0 12 289 10 0 0 1 12 290 13 0 0 0 12 291 15 0 0 2 12 292 12 0 0 0 12 293 13 1 0 0 12 294 8 0 0 0 12 295 10 0 0 1 12 296 15 1 0 0 12 297 16 0 1 0 12 298 16 1 0 0 12 299 14 0 0 0 12 300 14 1 0 0 12 301 12 0 0 0 12 302 15 0 0 0 12 303 13 0 0 1 12 304 16 1 0 0 12 305 14 0 0 1 12 306 8 0 0 0 12 307 16 0 0 0 12 308 16 1 0 1 12 309 12 0 1 0 12 310 11 0 0 0 12 311 16 0 0 0 12 312 9 0 0 0 12 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) B `2B` `3B` Month 11.3073 0.1029 -0.1205 0.1935 0.0625 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.16007 -1.87085 0.06778 2.06778 7.06778 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 11.3073 1.9499 5.799 1.66e-08 *** B 0.1029 0.2130 0.483 0.630 `2B` -0.1205 0.2230 -0.540 0.589 `3B` 0.1935 0.2247 0.861 0.390 Month 0.0625 0.1870 0.334 0.738 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.945 on 307 degrees of freedom Multiple R-squared: 0.00401, Adjusted R-squared: -0.008967 F-statistic: 0.309 on 4 and 307 DF, p-value: 0.8719 > 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,] 0.58523587 0.8295283 0.4147641 [2,] 0.53103836 0.9379233 0.4689616 [3,] 0.38838495 0.7767699 0.6116150 [4,] 0.42048571 0.8409714 0.5795143 [5,] 0.59080431 0.8183914 0.4091957 [6,] 0.51362240 0.9727552 0.4863776 [7,] 0.58630600 0.8273880 0.4136940 [8,] 0.49337995 0.9867599 0.5066200 [9,] 0.41038801 0.8207760 0.5896120 [10,] 0.38902717 0.7780543 0.6109728 [11,] 0.78915760 0.4216848 0.2108424 [12,] 0.75823970 0.4835206 0.2417603 [13,] 0.77335925 0.4532815 0.2266408 [14,] 0.80780939 0.3843812 0.1921906 [15,] 0.75862457 0.4827509 0.2413754 [16,] 0.70184143 0.5963171 0.2981586 [17,] 0.64602116 0.7079577 0.3539788 [18,] 0.58247587 0.8350483 0.4175241 [19,] 0.52073210 0.9585358 0.4792679 [20,] 0.48069177 0.9613835 0.5193082 [21,] 0.43621211 0.8724242 0.5637879 [22,] 0.39448958 0.7889792 0.6055104 [23,] 0.35064794 0.7012959 0.6493521 [24,] 0.29901809 0.5980362 0.7009819 [25,] 0.25979887 0.5195977 0.7402011 [26,] 0.25266757 0.5053351 0.7473324 [27,] 0.24015361 0.4803072 0.7598464 [28,] 0.26510332 0.5302066 0.7348967 [29,] 0.28755889 0.5751178 0.7124411 [30,] 0.24484442 0.4896888 0.7551556 [31,] 0.25709823 0.5141965 0.7429018 [32,] 0.27500561 0.5500112 0.7249944 [33,] 0.23569650 0.4713930 0.7643035 [34,] 0.22393564 0.4478713 0.7760644 [35,] 0.49160914 0.9832183 0.5083909 [36,] 0.44357563 0.8871513 0.5564244 [37,] 0.53802823 0.9239435 0.4619718 [38,] 0.55334538 0.8933092 0.4466546 [39,] 0.50887982 0.9822404 0.4911202 [40,] 0.53665120 0.9266976 0.4633488 [41,] 0.55608802 0.8878240 0.4439120 [42,] 0.53172022 0.9365596 0.4682798 [43,] 0.54403315 0.9119337 0.4559668 [44,] 0.51469432 0.9706114 0.4853057 [45,] 0.48380025 0.9676005 0.5161998 [46,] 0.45181731 0.9036346 0.5481827 [47,] 0.40958319 0.8191664 0.5904168 [48,] 0.36762480 0.7352496 0.6323752 [49,] 0.32816930 0.6563386 0.6718307 [50,] 0.30048254 0.6009651 0.6995175 [51,] 0.28878896 0.5775779 0.7112110 [52,] 0.29609968 0.5921994 0.7039003 [53,] 0.26053153 0.5210631 0.7394685 [54,] 0.24763428 0.4952686 0.7523657 [55,] 0.22683921 0.4536784 0.7731608 [56,] 0.26427971 0.5285594 0.7357203 [57,] 0.28707599 0.5741520 0.7129240 [58,] 0.26335079 0.5267016 0.7366492 [59,] 0.24208251 0.4841650 0.7579175 [60,] 0.21171162 0.4234232 0.7882884 [61,] 0.19140752 0.3828150 0.8085925 [62,] 0.25277959 0.5055592 0.7472204 [63,] 0.40583627 0.8116725 0.5941637 [64,] 0.39827540 0.7965508 0.6017246 [65,] 0.44647484 0.8929497 0.5535252 [66,] 0.43200194 0.8640039 0.5679981 [67,] 0.39677438 0.7935488 0.6032256 [68,] 0.36328353 0.7265671 0.6367165 [69,] 0.34817898 0.6963580 0.6518210 [70,] 0.31397440 0.6279488 0.6860256 [71,] 0.32179268 0.6435854 0.6782073 [72,] 0.39779470 0.7955894 0.6022053 [73,] 0.37778321 0.7555664 0.6222168 [74,] 0.36044316 0.7208863 0.6395568 [75,] 0.42722681 0.8544536 0.5727732 [76,] 0.39776080 0.7955216 0.6022392 [77,] 0.36261027 0.7252205 0.6373897 [78,] 0.33857024 0.6771405 0.6614298 [79,] 0.30581479 0.6116296 0.6941852 [80,] 0.28943804 0.5788761 0.7105620 [81,] 0.25924585 0.5184917 0.7407541 [82,] 0.24830742 0.4966148 0.7516926 [83,] 0.22279801 0.4455960 0.7772020 [84,] 0.24700708 0.4940142 0.7529929 [85,] 0.24693844 0.4938769 0.7530616 [86,] 0.35471105 0.7094221 0.6452890 [87,] 0.32376123 0.6475225 0.6762388 [88,] 0.30511930 0.6102386 0.6948807 [89,] 0.27906669 0.5581334 0.7209333 [90,] 0.31672007 0.6334401 0.6832799 [91,] 0.38020519 0.7604104 0.6197948 [92,] 0.36954185 0.7390837 0.6304581 [93,] 0.35343284 0.7068657 0.6465672 [94,] 0.32476436 0.6495287 0.6752356 [95,] 0.32527000 0.6505400 0.6747300 [96,] 0.48114240 0.9622848 0.5188576 [97,] 0.47636346 0.9527269 0.5236365 [98,] 0.44939097 0.8987819 0.5506090 [99,] 0.41530034 0.8306007 0.5846997 [100,] 0.53061887 0.9387623 0.4693811 [101,] 0.59077294 0.8184541 0.4092271 [102,] 0.56139180 0.8772164 0.4386082 [103,] 0.59288992 0.8142202 0.4071101 [104,] 0.57409933 0.8518013 0.4259007 [105,] 0.60158627 0.7968275 0.3984137 [106,] 0.60243028 0.7951394 0.3975697 [107,] 0.62780525 0.7443895 0.3721947 [108,] 0.59888219 0.8022356 0.4011178 [109,] 0.57687843 0.8462431 0.4231216 [110,] 0.59858452 0.8028310 0.4014155 [111,] 0.56928521 0.8614296 0.4307148 [112,] 0.55135003 0.8972999 0.4486500 [113,] 0.55441118 0.8911776 0.4455888 [114,] 0.58048617 0.8390277 0.4195138 [115,] 0.61353882 0.7729224 0.3864612 [116,] 0.58699643 0.8260071 0.4130036 [117,] 0.55430680 0.8913864 0.4456932 [118,] 0.52894955 0.9421009 0.4710505 [119,] 0.51013975 0.9797205 0.4898602 [120,] 0.48109918 0.9621984 0.5189008 [121,] 0.46284449 0.9256890 0.5371555 [122,] 0.43222497 0.8644499 0.5677750 [123,] 0.39990113 0.7998023 0.6000989 [124,] 0.60701662 0.7859668 0.3929834 [125,] 0.60293316 0.7941337 0.3970668 [126,] 0.58719099 0.8256180 0.4128090 [127,] 0.55866987 0.8826603 0.4413301 [128,] 0.55043871 0.8991226 0.4495613 [129,] 0.51784618 0.9643076 0.4821538 [130,] 0.48849125 0.9769825 0.5115087 [131,] 0.51969111 0.9606178 0.4803089 [132,] 0.50720828 0.9855834 0.4927917 [133,] 0.50490337 0.9901933 0.4950966 [134,] 0.52764607 0.9447079 0.4723539 [135,] 0.55111464 0.8977707 0.4488854 [136,] 0.53240743 0.9351851 0.4675926 [137,] 0.51145998 0.9770800 0.4885400 [138,] 0.47902368 0.9580474 0.5209763 [139,] 0.47205798 0.9441160 0.5279420 [140,] 0.44252007 0.8850401 0.5574799 [141,] 0.47257134 0.9451427 0.5274287 [142,] 0.45827862 0.9165572 0.5417214 [143,] 0.47909467 0.9581893 0.5209053 [144,] 0.51516913 0.9696617 0.4848309 [145,] 0.54174354 0.9165129 0.4582565 [146,] 0.51048610 0.9790278 0.4895139 [147,] 0.48065059 0.9613012 0.5193494 [148,] 0.51129217 0.9774157 0.4887078 [149,] 0.50270349 0.9945930 0.4972965 [150,] 0.48712013 0.9742403 0.5128799 [151,] 0.45474440 0.9094888 0.5452556 [152,] 0.48033033 0.9606607 0.5196697 [153,] 0.45923264 0.9184653 0.5407674 [154,] 0.42770843 0.8554169 0.5722916 [155,] 0.45656923 0.9131385 0.5434308 [156,] 0.44056312 0.8811262 0.5594369 [157,] 0.45442773 0.9088555 0.5455723 [158,] 0.44132997 0.8826599 0.5586700 [159,] 0.46591473 0.9318295 0.5340853 [160,] 0.43360998 0.8672200 0.5663900 [161,] 0.42452565 0.8490513 0.5754743 [162,] 0.40650888 0.8130178 0.5934911 [163,] 0.38862372 0.7772474 0.6113763 [164,] 0.35838465 0.7167693 0.6416154 [165,] 0.32864638 0.6572928 0.6713536 [166,] 0.31152725 0.6230545 0.6884728 [167,] 0.49182670 0.9836534 0.5081733 [168,] 0.47507597 0.9501519 0.5249240 [169,] 0.47896245 0.9579249 0.5210376 [170,] 0.50709054 0.9858189 0.4929095 [171,] 0.47483942 0.9496788 0.5251606 [172,] 0.44253892 0.8850778 0.5574611 [173,] 0.41037443 0.8207489 0.5896256 [174,] 0.37889613 0.7577923 0.6211039 [175,] 0.34831223 0.6966245 0.6516878 [176,] 0.32101092 0.6420218 0.6789891 [177,] 0.29684574 0.5936915 0.7031543 [178,] 0.27665122 0.5533024 0.7233488 [179,] 0.25435344 0.5087069 0.7456466 [180,] 0.23099763 0.4619953 0.7690024 [181,] 0.21114796 0.4222959 0.7888520 [182,] 0.21361536 0.4272307 0.7863846 [183,] 0.21248066 0.4249613 0.7875193 [184,] 0.21709137 0.4341827 0.7829086 [185,] 0.23750539 0.4750108 0.7624946 [186,] 0.21590042 0.4318008 0.7840996 [187,] 0.21682480 0.4336496 0.7831752 [188,] 0.24006346 0.4801269 0.7599365 [189,] 0.21523530 0.4304706 0.7847647 [190,] 0.21584018 0.4316804 0.7841598 [191,] 0.33824960 0.6764992 0.6617504 [192,] 0.31198115 0.6239623 0.6880188 [193,] 0.37969353 0.7593871 0.6203065 [194,] 0.38941797 0.7788359 0.6105820 [195,] 0.36652581 0.7330516 0.6334742 [196,] 0.39066959 0.7813392 0.6093304 [197,] 0.42513640 0.8502728 0.5748636 [198,] 0.41031906 0.8206381 0.5896809 [199,] 0.43870270 0.8774054 0.5612973 [200,] 0.40823647 0.8164729 0.5917635 [201,] 0.38147028 0.7629406 0.6185297 [202,] 0.35581413 0.7116283 0.6441859 [203,] 0.32707154 0.6541431 0.6729285 [204,] 0.29653367 0.5930673 0.7034663 [205,] 0.26750677 0.5350135 0.7324932 [206,] 0.25151102 0.5030220 0.7484890 [207,] 0.23923817 0.4784763 0.7607618 [208,] 0.23414706 0.4682941 0.7658529 [209,] 0.20831675 0.4166335 0.7916833 [210,] 0.19094388 0.3818878 0.8090561 [211,] 0.17729464 0.3545893 0.8227054 [212,] 0.18667384 0.3733477 0.8133262 [213,] 0.20274233 0.4054847 0.7972577 [214,] 0.18521142 0.3704228 0.8147886 [215,] 0.17674246 0.3534849 0.8232575 [216,] 0.15498515 0.3099703 0.8450149 [217,] 0.14853056 0.2970611 0.8514694 [218,] 0.17731922 0.3546384 0.8226808 [219,] 0.32285183 0.6457037 0.6771482 [220,] 0.32894893 0.6578979 0.6710511 [221,] 0.33989144 0.6797829 0.6601086 [222,] 0.31537726 0.6307545 0.6846227 [223,] 0.29103743 0.5820749 0.7089626 [224,] 0.26765217 0.5353043 0.7323478 [225,] 0.24714265 0.4942853 0.7528574 [226,] 0.22089641 0.4417928 0.7791036 [227,] 0.23266955 0.4653391 0.7673305 [228,] 0.27159425 0.5431885 0.7284057 [229,] 0.26340328 0.5268066 0.7365967 [230,] 0.24277542 0.4855508 0.7572246 [231,] 0.31473732 0.6294746 0.6852627 [232,] 0.28269197 0.5653839 0.7173080 [233,] 0.25094566 0.5018913 0.7490543 [234,] 0.24217401 0.4843480 0.7578260 [235,] 0.21308363 0.4261673 0.7869164 [236,] 0.20134116 0.4026823 0.7986588 [237,] 0.17641361 0.3528272 0.8235864 [238,] 0.15719430 0.3143886 0.8428057 [239,] 0.13951702 0.2790340 0.8604830 [240,] 0.14743785 0.2948757 0.8525621 [241,] 0.14638364 0.2927673 0.8536164 [242,] 0.20735146 0.4147029 0.7926485 [243,] 0.18004832 0.3600966 0.8199517 [244,] 0.17255900 0.3451180 0.8274410 [245,] 0.14915191 0.2983038 0.8508481 [246,] 0.15876314 0.3175263 0.8412369 [247,] 0.19516903 0.3903381 0.8048310 [248,] 0.19567113 0.3913423 0.8043289 [249,] 0.18216306 0.3643261 0.8178369 [250,] 0.15824875 0.3164975 0.8417513 [251,] 0.15770814 0.3154163 0.8422919 [252,] 0.30063302 0.6012660 0.6993670 [253,] 0.28934286 0.5786857 0.7106571 [254,] 0.25878555 0.5175711 0.7412144 [255,] 0.22401121 0.4480224 0.7759888 [256,] 0.32917546 0.6583509 0.6708245 [257,] 0.43082799 0.8616560 0.5691720 [258,] 0.38595815 0.7719163 0.6140419 [259,] 0.40370948 0.8074190 0.5962905 [260,] 0.39535262 0.7907052 0.6046474 [261,] 0.42013750 0.8402750 0.5798625 [262,] 0.43735543 0.8747109 0.5626446 [263,] 0.45944741 0.9188948 0.5405526 [264,] 0.41581375 0.8316275 0.5841862 [265,] 0.37035150 0.7407030 0.6296485 [266,] 0.41648018 0.8329604 0.5835198 [267,] 0.37021065 0.7404213 0.6297894 [268,] 0.40326072 0.8065214 0.5967393 [269,] 0.35886213 0.7177243 0.6411379 [270,] 0.41065415 0.8213083 0.5893459 [271,] 0.47090297 0.9418059 0.5290970 [272,] 0.42603676 0.8520735 0.5739632 [273,] 0.37557820 0.7511564 0.6244218 [274,] 0.36429114 0.7285823 0.6357089 [275,] 0.32009769 0.6401954 0.6799023 [276,] 0.28773183 0.5754637 0.7122682 [277,] 0.24130767 0.4826153 0.7586923 [278,] 0.19699457 0.3939891 0.8030054 [279,] 0.17821401 0.3564280 0.8217860 [280,] 0.77568150 0.4486370 0.2243185 [281,] 0.78191511 0.4361698 0.2180849 [282,] 0.78444269 0.4311146 0.2155573 [283,] 0.73450026 0.5309995 0.2654997 [284,] 0.68810130 0.6237974 0.3118987 [285,] 0.61694690 0.7661062 0.3830531 [286,] 0.58074666 0.8385067 0.4192533 [287,] 0.67949525 0.6410095 0.3205048 [288,] 0.68762990 0.6247402 0.3123701 [289,] 0.60841504 0.7831699 0.3915850 [290,] 0.61083266 0.7783347 0.3891673 [291,] 0.52753990 0.9449202 0.4724601 [292,] 0.45135626 0.9027125 0.5486437 [293,] 0.36525214 0.7305043 0.6347479 [294,] 0.26700959 0.5340192 0.7329904 [295,] 0.23519632 0.4703926 0.7648037 [296,] 0.14631345 0.2926269 0.8536866 [297,] 0.08435575 0.1687115 0.9156442 > postscript(file="/var/www/html/rcomp/tmp/12jjp1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/22jjp1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/32jjp1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4daia1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5daia1292680918.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 = 312 Frequency = 1 1 2 3 4 5 6 2.924545480 -0.239188189 -2.925178185 -1.869720370 1.130279630 4.027412555 7 8 9 10 11 12 2.130279630 4.147885643 -1.869720370 -4.331486618 -0.035082626 3.067784449 13 14 15 16 17 18 2.188257537 1.698316629 0.012326633 0.067784449 -1.932215551 -7.932215551 19 20 21 22 23 24 2.188257537 3.067784449 4.067784449 0.188257537 0.067784449 -0.125752468 25 26 27 28 29 30 -0.166124361 0.130279630 -1.130912335 -0.972587445 -0.852114357 -0.852114357 31 32 33 34 35 36 -1.251385423 -0.869720370 3.130279630 3.130279630 -2.869720370 4.130279630 37 38 39 40 41 42 1.177688890 -2.972587445 4.130279630 0.130279630 2.743205798 -6.869720370 43 44 45 46 47 48 -0.834508344 5.165491656 -2.942784198 1.130279630 4.067784449 4.067784449 49 50 51 52 53 54 1.474976466 3.964917374 -0.932215551 -0.932215551 -0.932215551 0.067784449 55 56 57 58 59 60 -0.108146455 0.067784449 2.067784449 -2.137949702 -2.691269375 -0.125752468 61 62 63 64 65 66 -1.932215551 2.067784449 -3.932215551 3.964917374 1.964917374 2.067784449 67 68 69 70 71 72 0.067784449 1.964917374 -5.108146455 7.067784449 3.067784449 -3.932215551 73 74 75 76 77 78 -1.932215551 1.067784449 1.130279630 -1.869720370 0.130279630 3.057215802 79 80 81 82 83 84 -4.972587445 2.130279630 -1.869720370 -5.508301106 -1.075454520 0.130279630 85 86 87 88 89 90 1.743205798 0.130279630 2.130279630 -0.834508344 -2.166124361 0.936742714 91 92 93 94 95 96 -3.869720370 -2.869720370 -5.932215551 -0.422156459 2.067784449 -0.932215551 97 98 99 100 101 102 -4.228619543 -4.932215551 -2.884806292 1.982523386 1.067784449 3.067784449 103 104 105 106 107 108 -6.932215551 2.879656311 1.188257537 0.067784449 -6.035082626 -4.932215551 109 110 111 112 113 114 1.067784449 4.085390462 -1.932215551 4.067784449 3.067784449 -3.932215551 115 116 117 118 119 120 -0.932215551 1.235666796 3.964917374 -0.932215551 2.067784449 -3.017476614 121 122 123 124 125 126 -3.932215551 -4.228619543 -1.137949702 0.067784449 -1.343683852 1.801183704 127 128 129 130 131 132 -1.017476614 2.067784449 0.595449554 0.067784449 -7.932215551 2.788986470 133 134 135 136 137 138 -1.932215551 1.012326633 2.698316629 0.012326633 1.067784449 -3.932215551 139 140 141 142 143 144 -2.137949702 3.067784449 4.067784449 4.067784449 2.067784449 1.891853545 145 146 147 148 149 150 -0.108146455 2.863678886 0.954348727 4.067784449 2.067784449 -3.932215551 151 152 153 154 155 156 4.188257537 3.891853545 0.067784449 -0.764333204 3.891853545 -2.932215551 157 158 159 160 161 162 2.442284531 -0.097577808 -3.678188557 -1.736158544 0.442284531 4.022895280 163 164 165 166 167 168 2.143368368 3.606018201 -2.170641636 -3.994710733 -0.079971795 2.515348360 169 170 171 172 173 174 1.902422192 1.902422192 -0.291114724 0.052698527 -1.826828385 -7.753764557 175 176 177 178 179 180 2.052698527 3.005289267 3.902422192 -0.200444883 0.005289267 0.125762355 181 182 183 184 185 186 0.246235443 -0.097577808 -0.977104720 -1.303311958 -1.454848394 -1.261311477 187 188 189 190 191 192 -1.097577808 -1.393981799 2.902422192 2.708885276 -3.140838389 4.052698527 193 194 195 196 197 198 0.811752351 -2.959498707 4.143368368 -0.097577808 2.920028205 -6.633291469 199 200 201 202 203 204 -0.856631632 4.799555117 -2.947301473 0.799555117 3.932225439 4.125762355 205 206 207 208 209 210 2.022895280 3.932225439 -1.097577808 -1.261311477 -1.050168548 -0.097577808 211 212 213 214 215 216 -0.170641636 0.022895280 1.829358364 -2.484651640 -2.874237645 0.125762355 217 218 219 220 221 222 -2.097577808 2.040501293 -4.097577808 4.052698527 1.545151606 1.811752351 223 224 225 226 227 228 0.125762355 2.125762355 -4.874237645 6.920028205 2.618215435 -3.994710733 229 230 231 232 233 234 -2.097577808 1.022895280 0.321811443 -2.291114724 -0.097577808 2.811752351 235 236 237 238 239 240 -5.188247649 1.811752351 -2.170641636 -6.200444883 -0.977104720 0.040501293 241 242 243 244 245 246 1.817161130 0.143368368 1.738688523 -1.273508711 -2.097577808 0.738688523 247 248 249 250 251 252 -4.188247649 -3.974592548 -5.977104720 -0.097577808 2.005289267 -0.959498707 253 254 255 256 257 258 -4.188247649 -5.097577808 -2.753764557 1.817161130 1.005289267 2.738688523 259 260 261 262 263 264 -7.364178552 2.859161611 1.125762355 -0.188247649 -6.097577808 -5.097577808 265 266 267 268 269 270 0.696688042 3.799555117 -1.874237645 3.902422192 2.902422192 -4.291114724 271 272 273 274 275 276 -1.097577808 0.635821448 4.052698527 -1.454848394 1.932225439 -3.381784565 277 278 279 280 281 282 -3.936732826 -4.262940065 -0.798653726 0.183740262 -1.142466977 1.839927010 283 284 285 286 287 288 -1.160072990 1.960400098 0.749257169 -0.039599902 -8.160072990 2.942794086 289 290 291 292 293 294 -2.250742831 0.942794086 2.555720253 -0.057205914 0.839927010 -4.057205914 295 296 297 298 299 300 -2.250742831 2.839927010 4.063267174 3.839927010 1.942794086 1.839927010 301 302 303 304 305 306 -0.057205914 2.942794086 0.749257169 3.839927010 1.749257169 -4.057205914 307 308 309 310 311 312 3.942794086 3.646390094 0.063267174 -1.057205914 3.942794086 -3.057205914 > postscript(file="/var/www/html/rcomp/tmp/6daia1292680918.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 = 312 Frequency = 1 lag(myerror, k = 1) myerror 0 2.924545480 NA 1 -0.239188189 2.924545480 2 -2.925178185 -0.239188189 3 -1.869720370 -2.925178185 4 1.130279630 -1.869720370 5 4.027412555 1.130279630 6 2.130279630 4.027412555 7 4.147885643 2.130279630 8 -1.869720370 4.147885643 9 -4.331486618 -1.869720370 10 -0.035082626 -4.331486618 11 3.067784449 -0.035082626 12 2.188257537 3.067784449 13 1.698316629 2.188257537 14 0.012326633 1.698316629 15 0.067784449 0.012326633 16 -1.932215551 0.067784449 17 -7.932215551 -1.932215551 18 2.188257537 -7.932215551 19 3.067784449 2.188257537 20 4.067784449 3.067784449 21 0.188257537 4.067784449 22 0.067784449 0.188257537 23 -0.125752468 0.067784449 24 -0.166124361 -0.125752468 25 0.130279630 -0.166124361 26 -1.130912335 0.130279630 27 -0.972587445 -1.130912335 28 -0.852114357 -0.972587445 29 -0.852114357 -0.852114357 30 -1.251385423 -0.852114357 31 -0.869720370 -1.251385423 32 3.130279630 -0.869720370 33 3.130279630 3.130279630 34 -2.869720370 3.130279630 35 4.130279630 -2.869720370 36 1.177688890 4.130279630 37 -2.972587445 1.177688890 38 4.130279630 -2.972587445 39 0.130279630 4.130279630 40 2.743205798 0.130279630 41 -6.869720370 2.743205798 42 -0.834508344 -6.869720370 43 5.165491656 -0.834508344 44 -2.942784198 5.165491656 45 1.130279630 -2.942784198 46 4.067784449 1.130279630 47 4.067784449 4.067784449 48 1.474976466 4.067784449 49 3.964917374 1.474976466 50 -0.932215551 3.964917374 51 -0.932215551 -0.932215551 52 -0.932215551 -0.932215551 53 0.067784449 -0.932215551 54 -0.108146455 0.067784449 55 0.067784449 -0.108146455 56 2.067784449 0.067784449 57 -2.137949702 2.067784449 58 -2.691269375 -2.137949702 59 -0.125752468 -2.691269375 60 -1.932215551 -0.125752468 61 2.067784449 -1.932215551 62 -3.932215551 2.067784449 63 3.964917374 -3.932215551 64 1.964917374 3.964917374 65 2.067784449 1.964917374 66 0.067784449 2.067784449 67 1.964917374 0.067784449 68 -5.108146455 1.964917374 69 7.067784449 -5.108146455 70 3.067784449 7.067784449 71 -3.932215551 3.067784449 72 -1.932215551 -3.932215551 73 1.067784449 -1.932215551 74 1.130279630 1.067784449 75 -1.869720370 1.130279630 76 0.130279630 -1.869720370 77 3.057215802 0.130279630 78 -4.972587445 3.057215802 79 2.130279630 -4.972587445 80 -1.869720370 2.130279630 81 -5.508301106 -1.869720370 82 -1.075454520 -5.508301106 83 0.130279630 -1.075454520 84 1.743205798 0.130279630 85 0.130279630 1.743205798 86 2.130279630 0.130279630 87 -0.834508344 2.130279630 88 -2.166124361 -0.834508344 89 0.936742714 -2.166124361 90 -3.869720370 0.936742714 91 -2.869720370 -3.869720370 92 -5.932215551 -2.869720370 93 -0.422156459 -5.932215551 94 2.067784449 -0.422156459 95 -0.932215551 2.067784449 96 -4.228619543 -0.932215551 97 -4.932215551 -4.228619543 98 -2.884806292 -4.932215551 99 1.982523386 -2.884806292 100 1.067784449 1.982523386 101 3.067784449 1.067784449 102 -6.932215551 3.067784449 103 2.879656311 -6.932215551 104 1.188257537 2.879656311 105 0.067784449 1.188257537 106 -6.035082626 0.067784449 107 -4.932215551 -6.035082626 108 1.067784449 -4.932215551 109 4.085390462 1.067784449 110 -1.932215551 4.085390462 111 4.067784449 -1.932215551 112 3.067784449 4.067784449 113 -3.932215551 3.067784449 114 -0.932215551 -3.932215551 115 1.235666796 -0.932215551 116 3.964917374 1.235666796 117 -0.932215551 3.964917374 118 2.067784449 -0.932215551 119 -3.017476614 2.067784449 120 -3.932215551 -3.017476614 121 -4.228619543 -3.932215551 122 -1.137949702 -4.228619543 123 0.067784449 -1.137949702 124 -1.343683852 0.067784449 125 1.801183704 -1.343683852 126 -1.017476614 1.801183704 127 2.067784449 -1.017476614 128 0.595449554 2.067784449 129 0.067784449 0.595449554 130 -7.932215551 0.067784449 131 2.788986470 -7.932215551 132 -1.932215551 2.788986470 133 1.012326633 -1.932215551 134 2.698316629 1.012326633 135 0.012326633 2.698316629 136 1.067784449 0.012326633 137 -3.932215551 1.067784449 138 -2.137949702 -3.932215551 139 3.067784449 -2.137949702 140 4.067784449 3.067784449 141 4.067784449 4.067784449 142 2.067784449 4.067784449 143 1.891853545 2.067784449 144 -0.108146455 1.891853545 145 2.863678886 -0.108146455 146 0.954348727 2.863678886 147 4.067784449 0.954348727 148 2.067784449 4.067784449 149 -3.932215551 2.067784449 150 4.188257537 -3.932215551 151 3.891853545 4.188257537 152 0.067784449 3.891853545 153 -0.764333204 0.067784449 154 3.891853545 -0.764333204 155 -2.932215551 3.891853545 156 2.442284531 -2.932215551 157 -0.097577808 2.442284531 158 -3.678188557 -0.097577808 159 -1.736158544 -3.678188557 160 0.442284531 -1.736158544 161 4.022895280 0.442284531 162 2.143368368 4.022895280 163 3.606018201 2.143368368 164 -2.170641636 3.606018201 165 -3.994710733 -2.170641636 166 -0.079971795 -3.994710733 167 2.515348360 -0.079971795 168 1.902422192 2.515348360 169 1.902422192 1.902422192 170 -0.291114724 1.902422192 171 0.052698527 -0.291114724 172 -1.826828385 0.052698527 173 -7.753764557 -1.826828385 174 2.052698527 -7.753764557 175 3.005289267 2.052698527 176 3.902422192 3.005289267 177 -0.200444883 3.902422192 178 0.005289267 -0.200444883 179 0.125762355 0.005289267 180 0.246235443 0.125762355 181 -0.097577808 0.246235443 182 -0.977104720 -0.097577808 183 -1.303311958 -0.977104720 184 -1.454848394 -1.303311958 185 -1.261311477 -1.454848394 186 -1.097577808 -1.261311477 187 -1.393981799 -1.097577808 188 2.902422192 -1.393981799 189 2.708885276 2.902422192 190 -3.140838389 2.708885276 191 4.052698527 -3.140838389 192 0.811752351 4.052698527 193 -2.959498707 0.811752351 194 4.143368368 -2.959498707 195 -0.097577808 4.143368368 196 2.920028205 -0.097577808 197 -6.633291469 2.920028205 198 -0.856631632 -6.633291469 199 4.799555117 -0.856631632 200 -2.947301473 4.799555117 201 0.799555117 -2.947301473 202 3.932225439 0.799555117 203 4.125762355 3.932225439 204 2.022895280 4.125762355 205 3.932225439 2.022895280 206 -1.097577808 3.932225439 207 -1.261311477 -1.097577808 208 -1.050168548 -1.261311477 209 -0.097577808 -1.050168548 210 -0.170641636 -0.097577808 211 0.022895280 -0.170641636 212 1.829358364 0.022895280 213 -2.484651640 1.829358364 214 -2.874237645 -2.484651640 215 0.125762355 -2.874237645 216 -2.097577808 0.125762355 217 2.040501293 -2.097577808 218 -4.097577808 2.040501293 219 4.052698527 -4.097577808 220 1.545151606 4.052698527 221 1.811752351 1.545151606 222 0.125762355 1.811752351 223 2.125762355 0.125762355 224 -4.874237645 2.125762355 225 6.920028205 -4.874237645 226 2.618215435 6.920028205 227 -3.994710733 2.618215435 228 -2.097577808 -3.994710733 229 1.022895280 -2.097577808 230 0.321811443 1.022895280 231 -2.291114724 0.321811443 232 -0.097577808 -2.291114724 233 2.811752351 -0.097577808 234 -5.188247649 2.811752351 235 1.811752351 -5.188247649 236 -2.170641636 1.811752351 237 -6.200444883 -2.170641636 238 -0.977104720 -6.200444883 239 0.040501293 -0.977104720 240 1.817161130 0.040501293 241 0.143368368 1.817161130 242 1.738688523 0.143368368 243 -1.273508711 1.738688523 244 -2.097577808 -1.273508711 245 0.738688523 -2.097577808 246 -4.188247649 0.738688523 247 -3.974592548 -4.188247649 248 -5.977104720 -3.974592548 249 -0.097577808 -5.977104720 250 2.005289267 -0.097577808 251 -0.959498707 2.005289267 252 -4.188247649 -0.959498707 253 -5.097577808 -4.188247649 254 -2.753764557 -5.097577808 255 1.817161130 -2.753764557 256 1.005289267 1.817161130 257 2.738688523 1.005289267 258 -7.364178552 2.738688523 259 2.859161611 -7.364178552 260 1.125762355 2.859161611 261 -0.188247649 1.125762355 262 -6.097577808 -0.188247649 263 -5.097577808 -6.097577808 264 0.696688042 -5.097577808 265 3.799555117 0.696688042 266 -1.874237645 3.799555117 267 3.902422192 -1.874237645 268 2.902422192 3.902422192 269 -4.291114724 2.902422192 270 -1.097577808 -4.291114724 271 0.635821448 -1.097577808 272 4.052698527 0.635821448 273 -1.454848394 4.052698527 274 1.932225439 -1.454848394 275 -3.381784565 1.932225439 276 -3.936732826 -3.381784565 277 -4.262940065 -3.936732826 278 -0.798653726 -4.262940065 279 0.183740262 -0.798653726 280 -1.142466977 0.183740262 281 1.839927010 -1.142466977 282 -1.160072990 1.839927010 283 1.960400098 -1.160072990 284 0.749257169 1.960400098 285 -0.039599902 0.749257169 286 -8.160072990 -0.039599902 287 2.942794086 -8.160072990 288 -2.250742831 2.942794086 289 0.942794086 -2.250742831 290 2.555720253 0.942794086 291 -0.057205914 2.555720253 292 0.839927010 -0.057205914 293 -4.057205914 0.839927010 294 -2.250742831 -4.057205914 295 2.839927010 -2.250742831 296 4.063267174 2.839927010 297 3.839927010 4.063267174 298 1.942794086 3.839927010 299 1.839927010 1.942794086 300 -0.057205914 1.839927010 301 2.942794086 -0.057205914 302 0.749257169 2.942794086 303 3.839927010 0.749257169 304 1.749257169 3.839927010 305 -4.057205914 1.749257169 306 3.942794086 -4.057205914 307 3.646390094 3.942794086 308 0.063267174 3.646390094 309 -1.057205914 0.063267174 310 3.942794086 -1.057205914 311 -3.057205914 3.942794086 312 NA -3.057205914 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.239188189 2.924545480 [2,] -2.925178185 -0.239188189 [3,] -1.869720370 -2.925178185 [4,] 1.130279630 -1.869720370 [5,] 4.027412555 1.130279630 [6,] 2.130279630 4.027412555 [7,] 4.147885643 2.130279630 [8,] -1.869720370 4.147885643 [9,] -4.331486618 -1.869720370 [10,] -0.035082626 -4.331486618 [11,] 3.067784449 -0.035082626 [12,] 2.188257537 3.067784449 [13,] 1.698316629 2.188257537 [14,] 0.012326633 1.698316629 [15,] 0.067784449 0.012326633 [16,] -1.932215551 0.067784449 [17,] -7.932215551 -1.932215551 [18,] 2.188257537 -7.932215551 [19,] 3.067784449 2.188257537 [20,] 4.067784449 3.067784449 [21,] 0.188257537 4.067784449 [22,] 0.067784449 0.188257537 [23,] -0.125752468 0.067784449 [24,] -0.166124361 -0.125752468 [25,] 0.130279630 -0.166124361 [26,] -1.130912335 0.130279630 [27,] -0.972587445 -1.130912335 [28,] -0.852114357 -0.972587445 [29,] -0.852114357 -0.852114357 [30,] -1.251385423 -0.852114357 [31,] -0.869720370 -1.251385423 [32,] 3.130279630 -0.869720370 [33,] 3.130279630 3.130279630 [34,] -2.869720370 3.130279630 [35,] 4.130279630 -2.869720370 [36,] 1.177688890 4.130279630 [37,] -2.972587445 1.177688890 [38,] 4.130279630 -2.972587445 [39,] 0.130279630 4.130279630 [40,] 2.743205798 0.130279630 [41,] -6.869720370 2.743205798 [42,] -0.834508344 -6.869720370 [43,] 5.165491656 -0.834508344 [44,] -2.942784198 5.165491656 [45,] 1.130279630 -2.942784198 [46,] 4.067784449 1.130279630 [47,] 4.067784449 4.067784449 [48,] 1.474976466 4.067784449 [49,] 3.964917374 1.474976466 [50,] -0.932215551 3.964917374 [51,] -0.932215551 -0.932215551 [52,] -0.932215551 -0.932215551 [53,] 0.067784449 -0.932215551 [54,] -0.108146455 0.067784449 [55,] 0.067784449 -0.108146455 [56,] 2.067784449 0.067784449 [57,] -2.137949702 2.067784449 [58,] -2.691269375 -2.137949702 [59,] -0.125752468 -2.691269375 [60,] -1.932215551 -0.125752468 [61,] 2.067784449 -1.932215551 [62,] -3.932215551 2.067784449 [63,] 3.964917374 -3.932215551 [64,] 1.964917374 3.964917374 [65,] 2.067784449 1.964917374 [66,] 0.067784449 2.067784449 [67,] 1.964917374 0.067784449 [68,] -5.108146455 1.964917374 [69,] 7.067784449 -5.108146455 [70,] 3.067784449 7.067784449 [71,] -3.932215551 3.067784449 [72,] -1.932215551 -3.932215551 [73,] 1.067784449 -1.932215551 [74,] 1.130279630 1.067784449 [75,] -1.869720370 1.130279630 [76,] 0.130279630 -1.869720370 [77,] 3.057215802 0.130279630 [78,] -4.972587445 3.057215802 [79,] 2.130279630 -4.972587445 [80,] -1.869720370 2.130279630 [81,] -5.508301106 -1.869720370 [82,] -1.075454520 -5.508301106 [83,] 0.130279630 -1.075454520 [84,] 1.743205798 0.130279630 [85,] 0.130279630 1.743205798 [86,] 2.130279630 0.130279630 [87,] -0.834508344 2.130279630 [88,] -2.166124361 -0.834508344 [89,] 0.936742714 -2.166124361 [90,] -3.869720370 0.936742714 [91,] -2.869720370 -3.869720370 [92,] -5.932215551 -2.869720370 [93,] -0.422156459 -5.932215551 [94,] 2.067784449 -0.422156459 [95,] -0.932215551 2.067784449 [96,] -4.228619543 -0.932215551 [97,] -4.932215551 -4.228619543 [98,] -2.884806292 -4.932215551 [99,] 1.982523386 -2.884806292 [100,] 1.067784449 1.982523386 [101,] 3.067784449 1.067784449 [102,] -6.932215551 3.067784449 [103,] 2.879656311 -6.932215551 [104,] 1.188257537 2.879656311 [105,] 0.067784449 1.188257537 [106,] -6.035082626 0.067784449 [107,] -4.932215551 -6.035082626 [108,] 1.067784449 -4.932215551 [109,] 4.085390462 1.067784449 [110,] -1.932215551 4.085390462 [111,] 4.067784449 -1.932215551 [112,] 3.067784449 4.067784449 [113,] -3.932215551 3.067784449 [114,] -0.932215551 -3.932215551 [115,] 1.235666796 -0.932215551 [116,] 3.964917374 1.235666796 [117,] -0.932215551 3.964917374 [118,] 2.067784449 -0.932215551 [119,] -3.017476614 2.067784449 [120,] -3.932215551 -3.017476614 [121,] -4.228619543 -3.932215551 [122,] -1.137949702 -4.228619543 [123,] 0.067784449 -1.137949702 [124,] -1.343683852 0.067784449 [125,] 1.801183704 -1.343683852 [126,] -1.017476614 1.801183704 [127,] 2.067784449 -1.017476614 [128,] 0.595449554 2.067784449 [129,] 0.067784449 0.595449554 [130,] -7.932215551 0.067784449 [131,] 2.788986470 -7.932215551 [132,] -1.932215551 2.788986470 [133,] 1.012326633 -1.932215551 [134,] 2.698316629 1.012326633 [135,] 0.012326633 2.698316629 [136,] 1.067784449 0.012326633 [137,] -3.932215551 1.067784449 [138,] -2.137949702 -3.932215551 [139,] 3.067784449 -2.137949702 [140,] 4.067784449 3.067784449 [141,] 4.067784449 4.067784449 [142,] 2.067784449 4.067784449 [143,] 1.891853545 2.067784449 [144,] -0.108146455 1.891853545 [145,] 2.863678886 -0.108146455 [146,] 0.954348727 2.863678886 [147,] 4.067784449 0.954348727 [148,] 2.067784449 4.067784449 [149,] -3.932215551 2.067784449 [150,] 4.188257537 -3.932215551 [151,] 3.891853545 4.188257537 [152,] 0.067784449 3.891853545 [153,] -0.764333204 0.067784449 [154,] 3.891853545 -0.764333204 [155,] -2.932215551 3.891853545 [156,] 2.442284531 -2.932215551 [157,] -0.097577808 2.442284531 [158,] -3.678188557 -0.097577808 [159,] -1.736158544 -3.678188557 [160,] 0.442284531 -1.736158544 [161,] 4.022895280 0.442284531 [162,] 2.143368368 4.022895280 [163,] 3.606018201 2.143368368 [164,] -2.170641636 3.606018201 [165,] -3.994710733 -2.170641636 [166,] -0.079971795 -3.994710733 [167,] 2.515348360 -0.079971795 [168,] 1.902422192 2.515348360 [169,] 1.902422192 1.902422192 [170,] -0.291114724 1.902422192 [171,] 0.052698527 -0.291114724 [172,] -1.826828385 0.052698527 [173,] -7.753764557 -1.826828385 [174,] 2.052698527 -7.753764557 [175,] 3.005289267 2.052698527 [176,] 3.902422192 3.005289267 [177,] -0.200444883 3.902422192 [178,] 0.005289267 -0.200444883 [179,] 0.125762355 0.005289267 [180,] 0.246235443 0.125762355 [181,] -0.097577808 0.246235443 [182,] -0.977104720 -0.097577808 [183,] -1.303311958 -0.977104720 [184,] -1.454848394 -1.303311958 [185,] -1.261311477 -1.454848394 [186,] -1.097577808 -1.261311477 [187,] -1.393981799 -1.097577808 [188,] 2.902422192 -1.393981799 [189,] 2.708885276 2.902422192 [190,] -3.140838389 2.708885276 [191,] 4.052698527 -3.140838389 [192,] 0.811752351 4.052698527 [193,] -2.959498707 0.811752351 [194,] 4.143368368 -2.959498707 [195,] -0.097577808 4.143368368 [196,] 2.920028205 -0.097577808 [197,] -6.633291469 2.920028205 [198,] -0.856631632 -6.633291469 [199,] 4.799555117 -0.856631632 [200,] -2.947301473 4.799555117 [201,] 0.799555117 -2.947301473 [202,] 3.932225439 0.799555117 [203,] 4.125762355 3.932225439 [204,] 2.022895280 4.125762355 [205,] 3.932225439 2.022895280 [206,] -1.097577808 3.932225439 [207,] -1.261311477 -1.097577808 [208,] -1.050168548 -1.261311477 [209,] -0.097577808 -1.050168548 [210,] -0.170641636 -0.097577808 [211,] 0.022895280 -0.170641636 [212,] 1.829358364 0.022895280 [213,] -2.484651640 1.829358364 [214,] -2.874237645 -2.484651640 [215,] 0.125762355 -2.874237645 [216,] -2.097577808 0.125762355 [217,] 2.040501293 -2.097577808 [218,] -4.097577808 2.040501293 [219,] 4.052698527 -4.097577808 [220,] 1.545151606 4.052698527 [221,] 1.811752351 1.545151606 [222,] 0.125762355 1.811752351 [223,] 2.125762355 0.125762355 [224,] -4.874237645 2.125762355 [225,] 6.920028205 -4.874237645 [226,] 2.618215435 6.920028205 [227,] -3.994710733 2.618215435 [228,] -2.097577808 -3.994710733 [229,] 1.022895280 -2.097577808 [230,] 0.321811443 1.022895280 [231,] -2.291114724 0.321811443 [232,] -0.097577808 -2.291114724 [233,] 2.811752351 -0.097577808 [234,] -5.188247649 2.811752351 [235,] 1.811752351 -5.188247649 [236,] -2.170641636 1.811752351 [237,] -6.200444883 -2.170641636 [238,] -0.977104720 -6.200444883 [239,] 0.040501293 -0.977104720 [240,] 1.817161130 0.040501293 [241,] 0.143368368 1.817161130 [242,] 1.738688523 0.143368368 [243,] -1.273508711 1.738688523 [244,] -2.097577808 -1.273508711 [245,] 0.738688523 -2.097577808 [246,] -4.188247649 0.738688523 [247,] -3.974592548 -4.188247649 [248,] -5.977104720 -3.974592548 [249,] -0.097577808 -5.977104720 [250,] 2.005289267 -0.097577808 [251,] -0.959498707 2.005289267 [252,] -4.188247649 -0.959498707 [253,] -5.097577808 -4.188247649 [254,] -2.753764557 -5.097577808 [255,] 1.817161130 -2.753764557 [256,] 1.005289267 1.817161130 [257,] 2.738688523 1.005289267 [258,] -7.364178552 2.738688523 [259,] 2.859161611 -7.364178552 [260,] 1.125762355 2.859161611 [261,] -0.188247649 1.125762355 [262,] -6.097577808 -0.188247649 [263,] -5.097577808 -6.097577808 [264,] 0.696688042 -5.097577808 [265,] 3.799555117 0.696688042 [266,] -1.874237645 3.799555117 [267,] 3.902422192 -1.874237645 [268,] 2.902422192 3.902422192 [269,] -4.291114724 2.902422192 [270,] -1.097577808 -4.291114724 [271,] 0.635821448 -1.097577808 [272,] 4.052698527 0.635821448 [273,] -1.454848394 4.052698527 [274,] 1.932225439 -1.454848394 [275,] -3.381784565 1.932225439 [276,] -3.936732826 -3.381784565 [277,] -4.262940065 -3.936732826 [278,] -0.798653726 -4.262940065 [279,] 0.183740262 -0.798653726 [280,] -1.142466977 0.183740262 [281,] 1.839927010 -1.142466977 [282,] -1.160072990 1.839927010 [283,] 1.960400098 -1.160072990 [284,] 0.749257169 1.960400098 [285,] -0.039599902 0.749257169 [286,] -8.160072990 -0.039599902 [287,] 2.942794086 -8.160072990 [288,] -2.250742831 2.942794086 [289,] 0.942794086 -2.250742831 [290,] 2.555720253 0.942794086 [291,] -0.057205914 2.555720253 [292,] 0.839927010 -0.057205914 [293,] -4.057205914 0.839927010 [294,] -2.250742831 -4.057205914 [295,] 2.839927010 -2.250742831 [296,] 4.063267174 2.839927010 [297,] 3.839927010 4.063267174 [298,] 1.942794086 3.839927010 [299,] 1.839927010 1.942794086 [300,] -0.057205914 1.839927010 [301,] 2.942794086 -0.057205914 [302,] 0.749257169 2.942794086 [303,] 3.839927010 0.749257169 [304,] 1.749257169 3.839927010 [305,] -4.057205914 1.749257169 [306,] 3.942794086 -4.057205914 [307,] 3.646390094 3.942794086 [308,] 0.063267174 3.646390094 [309,] -1.057205914 0.063267174 [310,] 3.942794086 -1.057205914 [311,] -3.057205914 3.942794086 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.239188189 2.924545480 2 -2.925178185 -0.239188189 3 -1.869720370 -2.925178185 4 1.130279630 -1.869720370 5 4.027412555 1.130279630 6 2.130279630 4.027412555 7 4.147885643 2.130279630 8 -1.869720370 4.147885643 9 -4.331486618 -1.869720370 10 -0.035082626 -4.331486618 11 3.067784449 -0.035082626 12 2.188257537 3.067784449 13 1.698316629 2.188257537 14 0.012326633 1.698316629 15 0.067784449 0.012326633 16 -1.932215551 0.067784449 17 -7.932215551 -1.932215551 18 2.188257537 -7.932215551 19 3.067784449 2.188257537 20 4.067784449 3.067784449 21 0.188257537 4.067784449 22 0.067784449 0.188257537 23 -0.125752468 0.067784449 24 -0.166124361 -0.125752468 25 0.130279630 -0.166124361 26 -1.130912335 0.130279630 27 -0.972587445 -1.130912335 28 -0.852114357 -0.972587445 29 -0.852114357 -0.852114357 30 -1.251385423 -0.852114357 31 -0.869720370 -1.251385423 32 3.130279630 -0.869720370 33 3.130279630 3.130279630 34 -2.869720370 3.130279630 35 4.130279630 -2.869720370 36 1.177688890 4.130279630 37 -2.972587445 1.177688890 38 4.130279630 -2.972587445 39 0.130279630 4.130279630 40 2.743205798 0.130279630 41 -6.869720370 2.743205798 42 -0.834508344 -6.869720370 43 5.165491656 -0.834508344 44 -2.942784198 5.165491656 45 1.130279630 -2.942784198 46 4.067784449 1.130279630 47 4.067784449 4.067784449 48 1.474976466 4.067784449 49 3.964917374 1.474976466 50 -0.932215551 3.964917374 51 -0.932215551 -0.932215551 52 -0.932215551 -0.932215551 53 0.067784449 -0.932215551 54 -0.108146455 0.067784449 55 0.067784449 -0.108146455 56 2.067784449 0.067784449 57 -2.137949702 2.067784449 58 -2.691269375 -2.137949702 59 -0.125752468 -2.691269375 60 -1.932215551 -0.125752468 61 2.067784449 -1.932215551 62 -3.932215551 2.067784449 63 3.964917374 -3.932215551 64 1.964917374 3.964917374 65 2.067784449 1.964917374 66 0.067784449 2.067784449 67 1.964917374 0.067784449 68 -5.108146455 1.964917374 69 7.067784449 -5.108146455 70 3.067784449 7.067784449 71 -3.932215551 3.067784449 72 -1.932215551 -3.932215551 73 1.067784449 -1.932215551 74 1.130279630 1.067784449 75 -1.869720370 1.130279630 76 0.130279630 -1.869720370 77 3.057215802 0.130279630 78 -4.972587445 3.057215802 79 2.130279630 -4.972587445 80 -1.869720370 2.130279630 81 -5.508301106 -1.869720370 82 -1.075454520 -5.508301106 83 0.130279630 -1.075454520 84 1.743205798 0.130279630 85 0.130279630 1.743205798 86 2.130279630 0.130279630 87 -0.834508344 2.130279630 88 -2.166124361 -0.834508344 89 0.936742714 -2.166124361 90 -3.869720370 0.936742714 91 -2.869720370 -3.869720370 92 -5.932215551 -2.869720370 93 -0.422156459 -5.932215551 94 2.067784449 -0.422156459 95 -0.932215551 2.067784449 96 -4.228619543 -0.932215551 97 -4.932215551 -4.228619543 98 -2.884806292 -4.932215551 99 1.982523386 -2.884806292 100 1.067784449 1.982523386 101 3.067784449 1.067784449 102 -6.932215551 3.067784449 103 2.879656311 -6.932215551 104 1.188257537 2.879656311 105 0.067784449 1.188257537 106 -6.035082626 0.067784449 107 -4.932215551 -6.035082626 108 1.067784449 -4.932215551 109 4.085390462 1.067784449 110 -1.932215551 4.085390462 111 4.067784449 -1.932215551 112 3.067784449 4.067784449 113 -3.932215551 3.067784449 114 -0.932215551 -3.932215551 115 1.235666796 -0.932215551 116 3.964917374 1.235666796 117 -0.932215551 3.964917374 118 2.067784449 -0.932215551 119 -3.017476614 2.067784449 120 -3.932215551 -3.017476614 121 -4.228619543 -3.932215551 122 -1.137949702 -4.228619543 123 0.067784449 -1.137949702 124 -1.343683852 0.067784449 125 1.801183704 -1.343683852 126 -1.017476614 1.801183704 127 2.067784449 -1.017476614 128 0.595449554 2.067784449 129 0.067784449 0.595449554 130 -7.932215551 0.067784449 131 2.788986470 -7.932215551 132 -1.932215551 2.788986470 133 1.012326633 -1.932215551 134 2.698316629 1.012326633 135 0.012326633 2.698316629 136 1.067784449 0.012326633 137 -3.932215551 1.067784449 138 -2.137949702 -3.932215551 139 3.067784449 -2.137949702 140 4.067784449 3.067784449 141 4.067784449 4.067784449 142 2.067784449 4.067784449 143 1.891853545 2.067784449 144 -0.108146455 1.891853545 145 2.863678886 -0.108146455 146 0.954348727 2.863678886 147 4.067784449 0.954348727 148 2.067784449 4.067784449 149 -3.932215551 2.067784449 150 4.188257537 -3.932215551 151 3.891853545 4.188257537 152 0.067784449 3.891853545 153 -0.764333204 0.067784449 154 3.891853545 -0.764333204 155 -2.932215551 3.891853545 156 2.442284531 -2.932215551 157 -0.097577808 2.442284531 158 -3.678188557 -0.097577808 159 -1.736158544 -3.678188557 160 0.442284531 -1.736158544 161 4.022895280 0.442284531 162 2.143368368 4.022895280 163 3.606018201 2.143368368 164 -2.170641636 3.606018201 165 -3.994710733 -2.170641636 166 -0.079971795 -3.994710733 167 2.515348360 -0.079971795 168 1.902422192 2.515348360 169 1.902422192 1.902422192 170 -0.291114724 1.902422192 171 0.052698527 -0.291114724 172 -1.826828385 0.052698527 173 -7.753764557 -1.826828385 174 2.052698527 -7.753764557 175 3.005289267 2.052698527 176 3.902422192 3.005289267 177 -0.200444883 3.902422192 178 0.005289267 -0.200444883 179 0.125762355 0.005289267 180 0.246235443 0.125762355 181 -0.097577808 0.246235443 182 -0.977104720 -0.097577808 183 -1.303311958 -0.977104720 184 -1.454848394 -1.303311958 185 -1.261311477 -1.454848394 186 -1.097577808 -1.261311477 187 -1.393981799 -1.097577808 188 2.902422192 -1.393981799 189 2.708885276 2.902422192 190 -3.140838389 2.708885276 191 4.052698527 -3.140838389 192 0.811752351 4.052698527 193 -2.959498707 0.811752351 194 4.143368368 -2.959498707 195 -0.097577808 4.143368368 196 2.920028205 -0.097577808 197 -6.633291469 2.920028205 198 -0.856631632 -6.633291469 199 4.799555117 -0.856631632 200 -2.947301473 4.799555117 201 0.799555117 -2.947301473 202 3.932225439 0.799555117 203 4.125762355 3.932225439 204 2.022895280 4.125762355 205 3.932225439 2.022895280 206 -1.097577808 3.932225439 207 -1.261311477 -1.097577808 208 -1.050168548 -1.261311477 209 -0.097577808 -1.050168548 210 -0.170641636 -0.097577808 211 0.022895280 -0.170641636 212 1.829358364 0.022895280 213 -2.484651640 1.829358364 214 -2.874237645 -2.484651640 215 0.125762355 -2.874237645 216 -2.097577808 0.125762355 217 2.040501293 -2.097577808 218 -4.097577808 2.040501293 219 4.052698527 -4.097577808 220 1.545151606 4.052698527 221 1.811752351 1.545151606 222 0.125762355 1.811752351 223 2.125762355 0.125762355 224 -4.874237645 2.125762355 225 6.920028205 -4.874237645 226 2.618215435 6.920028205 227 -3.994710733 2.618215435 228 -2.097577808 -3.994710733 229 1.022895280 -2.097577808 230 0.321811443 1.022895280 231 -2.291114724 0.321811443 232 -0.097577808 -2.291114724 233 2.811752351 -0.097577808 234 -5.188247649 2.811752351 235 1.811752351 -5.188247649 236 -2.170641636 1.811752351 237 -6.200444883 -2.170641636 238 -0.977104720 -6.200444883 239 0.040501293 -0.977104720 240 1.817161130 0.040501293 241 0.143368368 1.817161130 242 1.738688523 0.143368368 243 -1.273508711 1.738688523 244 -2.097577808 -1.273508711 245 0.738688523 -2.097577808 246 -4.188247649 0.738688523 247 -3.974592548 -4.188247649 248 -5.977104720 -3.974592548 249 -0.097577808 -5.977104720 250 2.005289267 -0.097577808 251 -0.959498707 2.005289267 252 -4.188247649 -0.959498707 253 -5.097577808 -4.188247649 254 -2.753764557 -5.097577808 255 1.817161130 -2.753764557 256 1.005289267 1.817161130 257 2.738688523 1.005289267 258 -7.364178552 2.738688523 259 2.859161611 -7.364178552 260 1.125762355 2.859161611 261 -0.188247649 1.125762355 262 -6.097577808 -0.188247649 263 -5.097577808 -6.097577808 264 0.696688042 -5.097577808 265 3.799555117 0.696688042 266 -1.874237645 3.799555117 267 3.902422192 -1.874237645 268 2.902422192 3.902422192 269 -4.291114724 2.902422192 270 -1.097577808 -4.291114724 271 0.635821448 -1.097577808 272 4.052698527 0.635821448 273 -1.454848394 4.052698527 274 1.932225439 -1.454848394 275 -3.381784565 1.932225439 276 -3.936732826 -3.381784565 277 -4.262940065 -3.936732826 278 -0.798653726 -4.262940065 279 0.183740262 -0.798653726 280 -1.142466977 0.183740262 281 1.839927010 -1.142466977 282 -1.160072990 1.839927010 283 1.960400098 -1.160072990 284 0.749257169 1.960400098 285 -0.039599902 0.749257169 286 -8.160072990 -0.039599902 287 2.942794086 -8.160072990 288 -2.250742831 2.942794086 289 0.942794086 -2.250742831 290 2.555720253 0.942794086 291 -0.057205914 2.555720253 292 0.839927010 -0.057205914 293 -4.057205914 0.839927010 294 -2.250742831 -4.057205914 295 2.839927010 -2.250742831 296 4.063267174 2.839927010 297 3.839927010 4.063267174 298 1.942794086 3.839927010 299 1.839927010 1.942794086 300 -0.057205914 1.839927010 301 2.942794086 -0.057205914 302 0.749257169 2.942794086 303 3.839927010 0.749257169 304 1.749257169 3.839927010 305 -4.057205914 1.749257169 306 3.942794086 -4.057205914 307 3.646390094 3.942794086 308 0.063267174 3.646390094 309 -1.057205914 0.063267174 310 3.942794086 -1.057205914 311 -3.057205914 3.942794086 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7n2zd1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8gtzy1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9gtzy1292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10rkg11292680918.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11ulw71292680918.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12y3vu1292680918.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13uva31292680918.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14n4so1292680918.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/158nqu1292680918.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16mx631292680918.tab") + } > > try(system("convert tmp/12jjp1292680918.ps tmp/12jjp1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/22jjp1292680918.ps tmp/22jjp1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/32jjp1292680918.ps tmp/32jjp1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/4daia1292680918.ps tmp/4daia1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/5daia1292680918.ps tmp/5daia1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/6daia1292680918.ps tmp/6daia1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/7n2zd1292680918.ps tmp/7n2zd1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/8gtzy1292680918.ps tmp/8gtzy1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/9gtzy1292680918.ps tmp/9gtzy1292680918.png",intern=TRUE)) character(0) > try(system("convert tmp/10rkg11292680918.ps tmp/10rkg11292680918.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.770 2.010 17.195