R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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/freestat/rcomp/tmp/10xct1293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/20xct1293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3aotw1293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4aotw1293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5aotw1293204287.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/freestat/rcomp/tmp/63fsh1293204287.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/freestat/rcomp/tmp/7eoa21293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8eoa21293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9eoa21293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/106yr51293204287.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/11ay8t1293204287.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/12dzoh1293204287.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/139qm71293204287.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/14d92d1293204287.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/15yrj11293204287.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/161azp1293204287.tab") + } > > try(system("convert tmp/10xct1293204287.ps tmp/10xct1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/20xct1293204287.ps tmp/20xct1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/3aotw1293204287.ps tmp/3aotw1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/4aotw1293204287.ps tmp/4aotw1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/5aotw1293204287.ps tmp/5aotw1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/63fsh1293204287.ps tmp/63fsh1293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/7eoa21293204287.ps tmp/7eoa21293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/8eoa21293204287.ps tmp/8eoa21293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/9eoa21293204287.ps tmp/9eoa21293204287.png",intern=TRUE)) character(0) > try(system("convert tmp/106yr51293204287.ps tmp/106yr51293204287.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.690 3.049 12.443