R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '2' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Gender Pop Connected Separate Learning Software Happiness 1 1 1 41 38 13 12 14 2 1 1 39 32 16 11 18 3 1 1 30 35 19 15 11 4 0 1 31 33 15 6 12 5 1 1 34 37 14 13 16 6 1 1 35 29 13 10 18 7 1 1 39 31 19 12 14 8 1 1 34 36 15 14 14 9 1 1 36 35 14 12 15 10 1 1 37 38 15 9 15 11 0 1 38 31 16 10 17 12 1 1 36 34 16 12 19 13 0 1 38 35 16 12 10 14 1 1 39 38 16 11 16 15 1 1 33 37 17 15 18 16 0 1 32 33 15 12 14 17 0 1 36 32 15 10 14 18 1 1 38 38 20 12 17 19 0 1 39 38 18 11 14 20 1 1 32 32 16 12 16 21 0 1 32 33 16 11 18 22 1 1 31 31 16 12 11 23 1 1 39 38 19 13 14 24 1 1 37 39 16 11 12 25 0 1 39 32 17 12 17 26 1 1 41 32 17 13 9 27 0 1 36 35 16 10 16 28 1 1 33 37 15 14 14 29 1 1 33 33 16 12 15 30 0 1 34 33 14 10 11 31 1 1 31 31 15 12 16 32 0 1 27 32 12 8 13 33 1 1 37 31 14 10 17 34 1 1 34 37 16 12 15 35 0 1 34 30 14 12 14 36 0 1 32 33 10 7 16 37 0 1 29 31 10 9 9 38 0 1 36 33 14 12 15 39 1 1 29 31 16 10 17 40 0 1 35 33 16 10 13 41 0 1 37 32 16 10 15 42 1 1 34 33 14 12 16 43 0 1 38 32 20 15 16 44 0 1 35 33 14 10 12 45 1 1 38 28 14 10 15 46 1 1 37 35 11 12 11 47 1 1 38 39 14 13 15 48 1 1 33 34 15 11 15 49 1 1 36 38 16 11 17 50 0 1 38 32 14 12 13 51 1 1 32 38 16 14 16 52 0 1 32 30 14 10 14 53 0 1 32 33 12 12 11 54 1 1 34 38 16 13 12 55 0 1 32 32 9 5 12 56 1 1 37 35 14 6 15 57 1 1 39 34 16 12 16 58 1 1 29 34 16 12 15 59 0 1 37 36 15 11 12 60 1 1 35 34 16 10 12 61 0 1 30 28 12 7 8 62 0 1 38 34 16 12 13 63 1 1 34 35 16 14 11 64 1 1 31 35 14 11 14 65 1 1 34 31 16 12 15 66 0 1 35 37 17 13 10 67 1 1 36 35 18 14 11 68 0 1 30 27 18 11 12 69 1 1 39 40 12 12 15 70 0 1 35 37 16 12 15 71 0 1 38 36 10 8 14 72 1 1 31 38 14 11 16 73 1 1 34 39 18 14 15 74 0 1 38 41 18 14 15 75 0 1 34 27 16 12 13 76 1 1 39 30 17 9 12 77 1 1 37 37 16 13 17 78 1 1 34 31 16 11 13 79 0 1 28 31 13 12 15 80 0 1 37 27 16 12 13 81 0 1 33 36 16 12 15 82 1 1 35 37 16 12 15 83 0 1 37 33 15 12 16 84 1 1 32 34 15 11 15 85 1 1 33 31 16 10 14 86 0 1 38 39 14 9 15 87 1 1 33 34 16 12 14 88 1 1 29 32 16 12 13 89 1 1 33 33 15 12 7 90 1 1 31 36 12 9 17 91 1 1 36 32 17 15 13 92 1 1 35 41 16 12 15 93 1 1 32 28 15 12 14 94 1 1 29 30 13 12 13 95 1 1 39 36 16 10 16 96 1 1 37 35 16 13 12 97 1 1 35 31 16 9 14 98 0 1 37 34 16 12 17 99 0 1 32 36 14 10 15 100 1 1 38 36 16 14 17 101 0 1 37 35 16 11 12 102 1 1 36 37 20 15 16 103 0 1 32 28 15 11 11 104 1 1 33 39 16 11 15 105 0 1 40 32 13 12 9 106 1 1 38 35 17 12 16 107 0 1 41 39 16 12 15 108 0 1 36 35 16 11 10 109 1 1 43 42 12 7 10 110 1 1 30 34 16 12 15 111 1 1 31 33 16 14 11 112 1 1 32 41 17 11 13 113 1 1 37 34 12 10 18 114 0 1 37 32 18 13 16 115 1 1 33 40 14 13 14 116 1 1 34 40 14 8 14 117 1 1 33 35 13 11 14 118 1 1 38 36 16 12 14 119 0 1 33 37 13 11 12 120 1 1 31 27 16 13 14 121 1 1 38 39 13 12 15 122 1 1 37 38 16 14 15 123 1 1 36 31 15 13 15 124 1 1 31 33 16 15 13 125 0 1 39 32 15 10 17 126 1 1 44 39 17 11 17 127 1 1 33 36 15 9 19 128 1 1 35 33 12 11 15 129 0 1 32 33 16 10 13 130 0 1 28 32 10 11 9 131 1 1 40 37 16 8 15 132 0 1 27 30 12 11 15 133 0 1 37 38 14 12 15 134 1 1 32 29 15 12 16 135 0 1 28 22 13 9 11 136 0 1 34 35 15 11 14 137 1 1 30 35 11 10 11 138 1 1 35 34 12 8 15 139 0 1 31 35 11 9 13 140 1 1 32 34 16 8 15 141 0 1 30 37 15 9 16 142 1 1 30 35 17 15 14 143 0 1 31 23 16 11 15 144 1 1 40 31 10 8 16 145 1 1 32 27 18 13 16 146 0 1 36 36 13 12 11 147 0 1 32 31 16 12 12 148 0 1 35 32 13 9 9 149 1 1 38 39 10 7 16 150 1 1 42 37 15 13 13 151 0 1 34 38 16 9 16 152 1 1 35 39 16 6 12 153 1 1 38 34 14 8 9 154 1 1 33 31 10 8 13 155 1 1 32 37 13 6 14 156 1 1 33 36 15 9 19 157 1 1 34 32 16 11 13 158 1 1 32 38 12 8 12 159 0 0 27 26 13 10 10 160 0 0 31 26 12 8 14 161 0 0 38 33 17 14 16 162 1 0 34 39 15 10 10 163 0 0 24 30 10 8 11 164 0 0 30 33 14 11 14 165 1 0 26 25 11 12 12 166 1 0 34 38 13 12 9 167 0 0 27 37 16 12 9 168 0 0 37 31 12 5 11 169 1 0 36 37 16 12 16 170 0 0 41 35 12 10 9 171 1 0 29 25 9 7 13 172 1 0 36 28 12 12 16 173 0 0 32 35 15 11 13 174 1 0 37 33 12 8 9 175 0 0 30 30 12 9 12 176 1 0 31 31 14 10 16 177 1 0 38 37 12 9 11 178 1 0 36 36 16 12 14 179 0 0 35 30 11 6 13 180 0 0 31 36 19 15 15 181 0 0 38 32 15 12 14 182 1 0 22 28 8 12 16 183 1 0 32 36 16 12 13 184 0 0 36 34 17 11 14 185 1 0 39 31 12 7 15 186 0 0 28 28 11 7 13 187 0 0 32 36 11 5 11 188 1 0 32 36 14 12 11 189 1 0 38 40 16 12 14 190 1 0 32 33 12 3 15 191 1 0 35 37 16 11 11 192 1 0 32 32 13 10 15 193 0 0 37 38 15 12 12 194 1 0 34 31 16 9 14 195 1 0 33 37 16 12 14 196 0 0 33 33 14 9 8 197 0 0 30 30 16 12 9 198 0 0 24 30 14 10 15 199 0 0 34 31 11 9 17 200 0 0 34 32 12 12 13 201 1 0 33 34 15 8 15 202 1 0 34 36 15 11 15 203 1 0 35 37 16 11 14 204 0 0 35 36 16 12 16 205 0 0 36 33 11 10 13 206 0 0 34 33 15 10 16 207 1 0 34 33 12 12 9 208 0 0 41 44 12 12 16 209 0 0 32 39 15 11 11 210 0 0 30 32 15 8 10 211 1 0 35 35 16 12 11 212 0 0 28 25 14 10 15 213 1 0 33 35 17 11 17 214 1 0 39 34 14 10 14 215 0 0 36 35 13 8 8 216 1 0 36 39 15 12 15 217 0 0 35 33 13 12 11 218 0 0 38 36 14 10 16 219 1 0 33 32 15 12 10 220 0 0 31 32 12 9 15 221 1 0 32 36 8 6 16 222 0 0 31 32 14 10 19 223 0 0 33 34 14 9 12 224 0 0 34 33 11 9 8 225 0 0 34 35 12 9 11 226 1 0 34 30 13 6 14 227 0 0 33 38 10 10 9 228 0 0 32 34 16 6 15 229 1 0 41 33 18 14 13 230 1 0 34 32 13 10 16 231 0 0 36 31 11 10 11 232 0 0 37 30 4 6 12 233 0 0 36 27 13 12 13 234 1 0 29 31 16 12 10 235 0 0 37 30 10 7 11 236 0 0 27 32 12 8 12 237 0 0 35 35 12 11 8 238 0 0 28 28 10 3 12 239 0 0 35 33 13 6 12 240 0 0 29 35 12 8 11 241 0 0 32 35 14 9 13 242 1 0 36 32 10 9 14 243 1 0 19 21 12 8 10 244 1 0 21 20 12 9 12 245 0 0 31 34 11 7 15 246 0 0 33 32 10 7 13 247 1 0 36 34 12 6 13 248 1 0 33 32 16 9 13 249 0 0 37 33 12 10 12 250 0 0 34 33 14 11 12 251 0 0 35 37 16 12 9 252 1 0 31 32 14 8 9 253 1 0 37 34 13 11 15 254 1 0 35 30 4 3 10 255 1 0 27 30 15 11 14 256 0 0 34 38 11 12 15 257 0 0 40 36 11 7 7 258 0 0 29 32 14 9 14 259 0 0 38 34 15 12 8 260 1 0 34 33 14 8 10 261 0 0 21 27 13 11 13 262 0 0 36 32 11 8 13 263 1 0 38 34 15 10 13 264 0 0 30 29 11 8 8 265 0 0 35 35 13 7 12 266 1 0 30 27 13 8 13 267 1 0 36 33 16 10 12 268 0 0 34 38 13 8 10 269 1 0 35 36 16 12 13 270 0 0 34 33 16 14 12 271 0 0 32 39 12 7 9 272 1 0 33 29 7 6 15 273 0 0 33 32 16 11 13 274 1 0 26 34 5 4 13 275 0 0 35 38 16 9 13 276 0 0 21 17 4 5 15 277 0 0 38 35 12 9 15 278 0 0 35 32 15 11 14 279 1 0 33 34 14 12 15 280 0 0 37 36 11 9 11 281 0 0 38 31 16 12 15 282 1 0 34 35 15 10 14 283 0 0 27 29 12 9 13 284 1 0 16 22 6 6 12 285 0 0 40 41 16 10 16 286 0 0 36 36 10 9 16 287 1 0 42 42 15 13 9 288 1 0 30 33 14 12 14 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop Connected Separate Learning Software -0.430055 0.114625 -0.007721 0.015112 0.001974 0.017618 Happiness 0.032870 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.7860 -0.4793 0.2263 0.4228 0.8575 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.430055 0.317311 -1.355 0.17641 Pop 0.114625 0.063605 1.802 0.07260 . Connected -0.007721 0.008417 -0.917 0.35976 Separate 0.015112 0.008715 1.734 0.08402 . Learning 0.001974 0.015172 0.130 0.89657 Software 0.017618 0.016157 1.090 0.27645 Happiness 0.032870 0.012007 2.738 0.00658 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.483 on 281 degrees of freedom Multiple R-squared: 0.08587, Adjusted R-squared: 0.06635 F-statistic: 4.399 on 6 and 281 DF, p-value: 0.0002869 > 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,] 0.08223569 0.1644714 0.9177643 [2,] 0.48757763 0.9751553 0.5124224 [3,] 0.34237231 0.6847446 0.6576277 [4,] 0.40282211 0.8056442 0.5971779 [5,] 0.29017000 0.5803400 0.7098300 [6,] 0.28038919 0.5607784 0.7196108 [7,] 0.36327146 0.7265429 0.6367285 [8,] 0.34399937 0.6879987 0.6560006 [9,] 0.27070042 0.5414008 0.7292996 [10,] 0.37768941 0.7553788 0.6223106 [11,] 0.32462998 0.6492600 0.6753700 [12,] 0.45094487 0.9018897 0.5490551 [13,] 0.49733358 0.9946672 0.5026664 [14,] 0.43367582 0.8673516 0.5663242 [15,] 0.40636394 0.8127279 0.5936361 [16,] 0.48813766 0.9762753 0.5118623 [17,] 0.46350787 0.9270157 0.5364921 [18,] 0.47685710 0.9537142 0.5231429 [19,] 0.41332131 0.8266426 0.5866787 [20,] 0.38210314 0.7642063 0.6178969 [21,] 0.37078459 0.7415692 0.6292154 [22,] 0.34188368 0.6837674 0.6581163 [23,] 0.29741854 0.5948371 0.7025815 [24,] 0.29498859 0.5899772 0.7050114 [25,] 0.25818718 0.5163744 0.7418128 [26,] 0.30347416 0.6069483 0.6965258 [27,] 0.26833645 0.5366729 0.7316635 [28,] 0.22795746 0.4559149 0.7720425 [29,] 0.28288771 0.5657754 0.7171123 [30,] 0.29561652 0.5912330 0.7043835 [31,] 0.28366091 0.5673218 0.7163391 [32,] 0.27629004 0.5525801 0.7237100 [33,] 0.24969815 0.4993963 0.7503019 [34,] 0.37349778 0.7469956 0.6265022 [35,] 0.35160997 0.7032199 0.6483900 [36,] 0.41207845 0.8241569 0.5879216 [37,] 0.38875291 0.7775058 0.6112471 [38,] 0.34472640 0.6894528 0.6552736 [39,] 0.33163982 0.6632796 0.6683602 [40,] 0.29735188 0.5947038 0.7026481 [41,] 0.31654172 0.6330834 0.6834583 [42,] 0.27782075 0.5556415 0.7221792 [43,] 0.26302586 0.5260517 0.7369741 [44,] 0.27222056 0.5444411 0.7277794 [45,] 0.24469930 0.4893986 0.7553007 [46,] 0.21743527 0.4348705 0.7825647 [47,] 0.26116670 0.5223334 0.7388333 [48,] 0.23784703 0.4756941 0.7621530 [49,] 0.22038038 0.4407608 0.7796196 [50,] 0.23404679 0.4680936 0.7659532 [51,] 0.25398678 0.5079736 0.7460132 [52,] 0.23050036 0.4610007 0.7694996 [53,] 0.24691444 0.4938289 0.7530856 [54,] 0.23145273 0.4629055 0.7685473 [55,] 0.21621488 0.4324298 0.7837851 [56,] 0.21079883 0.4215977 0.7892012 [57,] 0.23366106 0.4673221 0.7663389 [58,] 0.22199898 0.4439980 0.7780010 [59,] 0.20416582 0.4083316 0.7958342 [60,] 0.17873661 0.3574732 0.8212634 [61,] 0.23194802 0.4638960 0.7680520 [62,] 0.23240190 0.4648038 0.7675981 [63,] 0.20625238 0.4125048 0.7937476 [64,] 0.18008675 0.3601735 0.8199132 [65,] 0.27520423 0.5504085 0.7247958 [66,] 0.26671739 0.5334348 0.7332826 [67,] 0.32314236 0.6462847 0.6768576 [68,] 0.29159124 0.5831825 0.7084088 [69,] 0.29970310 0.5994062 0.7002969 [70,] 0.32185755 0.6437151 0.6781425 [71,] 0.31312463 0.6262493 0.6868754 [72,] 0.35818891 0.7163778 0.6418111 [73,] 0.33308164 0.6661633 0.6669184 [74,] 0.36582247 0.7316449 0.6341775 [75,] 0.35218639 0.7043728 0.6478136 [76,] 0.35924647 0.7184929 0.6407535 [77,] 0.38856390 0.7771278 0.6114361 [78,] 0.37250932 0.7450186 0.6274907 [79,] 0.36219865 0.7243973 0.6378014 [80,] 0.38500548 0.7700110 0.6149945 [81,] 0.36804801 0.7360960 0.6319520 [82,] 0.34864545 0.6972909 0.6513546 [83,] 0.31911938 0.6382388 0.6808806 [84,] 0.31812124 0.6362425 0.6818788 [85,] 0.31133060 0.6226612 0.6886694 [86,] 0.29959165 0.5991833 0.7004083 [87,] 0.28739154 0.5747831 0.7126085 [88,] 0.29627202 0.5925440 0.7037280 [89,] 0.33661054 0.6732211 0.6633895 [90,] 0.36422438 0.7284488 0.6357756 [91,] 0.33520732 0.6704146 0.6647927 [92,] 0.34465627 0.6893125 0.6553437 [93,] 0.31460826 0.6292165 0.6853917 [94,] 0.30734467 0.6146893 0.6926553 [95,] 0.28429867 0.5685973 0.7157013 [96,] 0.27194752 0.5438950 0.7280525 [97,] 0.25316549 0.5063310 0.7468345 [98,] 0.28827490 0.5765498 0.7117251 [99,] 0.28894483 0.5778897 0.7110552 [100,] 0.31487996 0.6297599 0.6851200 [101,] 0.29368878 0.5873776 0.7063112 [102,] 0.28058412 0.5611682 0.7194159 [103,] 0.25846822 0.5169364 0.7415318 [104,] 0.24631386 0.4926277 0.7536861 [105,] 0.27748235 0.5549647 0.7225176 [106,] 0.25435253 0.5087051 0.7456475 [107,] 0.24131782 0.4826356 0.7586822 [108,] 0.22990447 0.4598089 0.7700955 [109,] 0.21724246 0.4344849 0.7827575 [110,] 0.23410304 0.4682061 0.7658970 [111,] 0.22956866 0.4591373 0.7704313 [112,] 0.21246753 0.4249351 0.7875325 [113,] 0.19345571 0.3869114 0.8065443 [114,] 0.18459821 0.3691964 0.8154018 [115,] 0.17168801 0.3433760 0.8283120 [116,] 0.18304363 0.3660873 0.8169564 [117,] 0.16842353 0.3368471 0.8315765 [118,] 0.15292248 0.3058450 0.8470775 [119,] 0.14797907 0.2959581 0.8520209 [120,] 0.15360934 0.3072187 0.8463907 [121,] 0.14896944 0.2979389 0.8510306 [122,] 0.14574958 0.2914992 0.8542504 [123,] 0.15551323 0.3110265 0.8444868 [124,] 0.18364559 0.3672912 0.8163544 [125,] 0.17496021 0.3499204 0.8250398 [126,] 0.16579981 0.3315996 0.8342002 [127,] 0.18426690 0.3685338 0.8157331 [128,] 0.18470963 0.3694193 0.8152904 [129,] 0.18303754 0.3660751 0.8169625 [130,] 0.19121832 0.3824366 0.8087817 [131,] 0.18347717 0.3669543 0.8165228 [132,] 0.21598814 0.4319763 0.7840119 [133,] 0.19570487 0.3914097 0.8042951 [134,] 0.20624180 0.4124836 0.7937582 [135,] 0.20877874 0.4175575 0.7912213 [136,] 0.19522627 0.3904525 0.8047737 [137,] 0.20931240 0.4186248 0.7906876 [138,] 0.23154540 0.4630908 0.7684546 [139,] 0.24172727 0.4834545 0.7582727 [140,] 0.22657873 0.4531575 0.7734213 [141,] 0.20768862 0.4153772 0.7923114 [142,] 0.26603306 0.5320661 0.7339669 [143,] 0.25967303 0.5193461 0.7403270 [144,] 0.26552291 0.5310458 0.7344771 [145,] 0.26346092 0.5269218 0.7365391 [146,] 0.24969219 0.4993844 0.7503078 [147,] 0.22638122 0.4527624 0.7736188 [148,] 0.21353046 0.4270609 0.7864695 [149,] 0.19870581 0.3974116 0.8012942 [150,] 0.18443462 0.3688692 0.8155654 [151,] 0.17356975 0.3471395 0.8264302 [152,] 0.17466398 0.3493280 0.8253360 [153,] 0.19696758 0.3939352 0.8030324 [154,] 0.18179994 0.3635999 0.8182001 [155,] 0.17706744 0.3541349 0.8229326 [156,] 0.20409401 0.4081880 0.7959060 [157,] 0.22113534 0.4422707 0.7788647 [158,] 0.21492662 0.4298532 0.7850734 [159,] 0.19808734 0.3961747 0.8019127 [160,] 0.19250329 0.3850066 0.8074967 [161,] 0.17744222 0.3548844 0.8225578 [162,] 0.20590068 0.4118014 0.7940993 [163,] 0.20657949 0.4131590 0.7934205 [164,] 0.20756004 0.4151201 0.7924400 [165,] 0.23819392 0.4763878 0.7618061 [166,] 0.22853127 0.4570625 0.7714687 [167,] 0.22566784 0.4513357 0.7743322 [168,] 0.24176361 0.4835272 0.7582364 [169,] 0.23856951 0.4771390 0.7614305 [170,] 0.22705157 0.4541031 0.7729484 [171,] 0.25101092 0.5020218 0.7489891 [172,] 0.25183029 0.5036606 0.7481697 [173,] 0.25284756 0.5056951 0.7471524 [174,] 0.25309483 0.5061897 0.7469052 [175,] 0.25880030 0.5176006 0.7411997 [176,] 0.26768773 0.5353755 0.7323123 [177,] 0.25754969 0.5150994 0.7424503 [178,] 0.24311656 0.4862331 0.7568834 [179,] 0.25532200 0.5106440 0.7446780 [180,] 0.25685872 0.5137174 0.7431413 [181,] 0.26560570 0.5312114 0.7343943 [182,] 0.27635524 0.5527105 0.7236448 [183,] 0.27899632 0.5579926 0.7210037 [184,] 0.27836434 0.5567287 0.7216357 [185,] 0.28102961 0.5620592 0.7189704 [186,] 0.28346158 0.5669232 0.7165384 [187,] 0.26664829 0.5332966 0.7333517 [188,] 0.25826295 0.5165259 0.7417371 [189,] 0.26266446 0.5253289 0.7373355 [190,] 0.26440063 0.5288013 0.7355994 [191,] 0.25953134 0.5190627 0.7404687 [192,] 0.26405927 0.5281185 0.7359407 [193,] 0.26777732 0.5355546 0.7322227 [194,] 0.27589377 0.5517875 0.7241062 [195,] 0.28399181 0.5679836 0.7160082 [196,] 0.27479446 0.5495889 0.7252055 [197,] 0.27806629 0.5561326 0.7219337 [198,] 0.30528989 0.6105798 0.6947101 [199,] 0.31194198 0.6238840 0.6880580 [200,] 0.30092771 0.6018554 0.6990723 [201,] 0.28724988 0.5744998 0.7127501 [202,] 0.30177876 0.6035575 0.6982212 [203,] 0.31299628 0.6259926 0.6870037 [204,] 0.30484371 0.6096874 0.6951563 [205,] 0.31865013 0.6373003 0.6813499 [206,] 0.29432539 0.5886508 0.7056746 [207,] 0.31367893 0.6273579 0.6863211 [208,] 0.29884918 0.5976984 0.7011508 [209,] 0.29282341 0.5856468 0.7071766 [210,] 0.31274822 0.6254964 0.6872518 [211,] 0.30658788 0.6131758 0.6934121 [212,] 0.34074207 0.6814841 0.6592579 [213,] 0.35275585 0.7055117 0.6472442 [214,] 0.33863124 0.6772625 0.6613688 [215,] 0.31087967 0.6217593 0.6891203 [216,] 0.28980736 0.5796147 0.7101926 [217,] 0.30106752 0.6021350 0.6989325 [218,] 0.27480367 0.5496073 0.7251963 [219,] 0.27268355 0.5453671 0.7273165 [220,] 0.28026475 0.5605295 0.7197352 [221,] 0.28942011 0.5788402 0.7105799 [222,] 0.26785040 0.5357008 0.7321496 [223,] 0.24080241 0.4816048 0.7591976 [224,] 0.23737958 0.4747592 0.7626204 [225,] 0.24870916 0.4974183 0.7512908 [226,] 0.23651662 0.4730332 0.7634834 [227,] 0.22248800 0.4449760 0.7775120 [228,] 0.20267355 0.4053471 0.7973264 [229,] 0.20137157 0.4027431 0.7986284 [230,] 0.19805617 0.3961123 0.8019438 [231,] 0.18413318 0.3682664 0.8158668 [232,] 0.17685909 0.3537182 0.8231409 [233,] 0.19609750 0.3921950 0.8039025 [234,] 0.19216111 0.3843222 0.8078389 [235,] 0.18953543 0.3790709 0.8104646 [236,] 0.18962753 0.3792551 0.8103725 [237,] 0.18412077 0.3682415 0.8158792 [238,] 0.18134888 0.3626978 0.8186511 [239,] 0.18320032 0.3664006 0.8167997 [240,] 0.16638285 0.3327657 0.8336172 [241,] 0.15286965 0.3057393 0.8471303 [242,] 0.13735743 0.2747149 0.8626426 [243,] 0.14995687 0.2999137 0.8500431 [244,] 0.16045199 0.3209040 0.8395480 [245,] 0.19294265 0.3858853 0.8070574 [246,] 0.19303474 0.3860695 0.8069653 [247,] 0.18819134 0.3763827 0.8118087 [248,] 0.15877999 0.3175600 0.8412200 [249,] 0.14790400 0.2958080 0.8520960 [250,] 0.12911107 0.2582221 0.8708889 [251,] 0.15656832 0.3131366 0.8434317 [252,] 0.17912417 0.3582483 0.8208758 [253,] 0.15003742 0.3000748 0.8499626 [254,] 0.19840652 0.3968130 0.8015935 [255,] 0.17018580 0.3403716 0.8298142 [256,] 0.13679499 0.2735900 0.8632050 [257,] 0.19234900 0.3846980 0.8076510 [258,] 0.41164935 0.8232987 0.5883507 [259,] 0.34976154 0.6995231 0.6502385 [260,] 0.37929913 0.7585983 0.6207009 [261,] 0.42028397 0.8405679 0.5797160 [262,] 0.39005474 0.7801095 0.6099453 [263,] 0.75570618 0.4885876 0.2442938 [264,] 0.69386087 0.6122783 0.3061391 [265,] 0.70386077 0.5922785 0.2961392 [266,] 0.61059568 0.7788086 0.3894043 [267,] 0.53728974 0.9254205 0.4627103 [268,] 0.43864428 0.8772886 0.5613557 [269,] 0.31460629 0.6292126 0.6853937 > postscript(file="/var/wessaorg/rcomp/tmp/1lfrt1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2fpdq1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/30owg1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/41ekb1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5aobn1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 288 Frequency = 1 1 2 3 4 5 6 7 0.3604840 0.3159296 0.3547983 -0.4736663 0.2362164 0.3539213 0.4389804 8 9 10 11 12 13 14 0.2974757 0.3323703 0.3456365 -0.6261915 0.2120546 -0.4917864 0.2909982 15 16 17 18 19 20 21 0.1215969 -0.6373944 -0.5561619 0.2248927 -0.6472102 0.3100036 -0.7532297 22 23 24 25 26 27 28 0.4817437 0.3155792 0.3919237 -0.6707929 0.5899899 -0.6692112 0.2746428 29 30 31 32 33 34 35 0.3354827 -0.4861322 0.3193685 -0.5516228 0.3700356 0.2827564 -0.5746427 36 37 38 39 40 41 42 -0.6051726 -0.4032595 -0.6374060 0.3043189 -0.5480990 -0.5832847 0.3142820 43 44 45 46 47 48 49 -0.7044210 -0.5112810 0.4888319 0.4774931 0.2697468 0.3399632 0.2349652 50 51 52 53 54 55 56 -0.5411123 0.1840960 -0.5548483 -0.5328626 0.3486358 -0.4213707 0.4458008 57 58 59 60 61 62 63 0.3338273 0.2894866 -0.5607667 0.4696590 -0.2860447 -0.5752841 0.4092230 64 65 66 67 68 69 70 0.3442531 0.3734274 -0.5647656 0.4207170 -0.4847297 0.2839224 -0.7095226 71 72 73 74 75 76 77 -0.5560603 0.2331779 0.2133480 -0.7859914 -0.5003854 0.5766348 0.2225616 78 79 80 81 82 83 84 0.4567854 -0.6669767 -0.4772223 -0.7098529 0.2904774 -0.6645288 0.3322421 85 86 87 88 89 90 91 0.4338127 -0.6597802 0.3532407 0.3854500 0.6004156 0.2697163 0.3846686 92 93 94 95 96 97 98 0.2300300 0.4381648 0.4215959 0.3388401 0.4171346 0.4668731 -0.7144846 99 100 101 102 103 104 105 -0.6783893 0.2277763 -0.5476290 0.2045776 -0.4456074 0.2624298 -0.3922166 106 107 108 109 110 111 112 0.3090204 -0.6934199 -0.4896103 0.5370235 0.2972077 0.4162835 0.2882507 113 114 115 116 117 118 119 0.2957783 -0.6729575 0.2488995 0.3447118 0.3616693 0.3616223 -0.6028147 120 121 122 123 124 125 126 0.4259633 0.2893391 0.2555712 0.3732254 0.3329256 -0.6316083 0.2796477 127 128 129 130 131 132 133 0.2134965 0.3764394 -0.5712622 -0.4613289 0.3995557 -0.6399936 -0.7052442 134 135 136 137 138 139 140 0.3573132 -0.3466359 -0.6345578 0.4586821 0.4141822 -0.5817183 0.3831227 141 142 143 144 145 146 147 -0.7261690 0.2601369 -0.5112227 0.4692014 0.3639965 -0.5492880 -0.5434051 148 149 150 151 152 153 154 -0.3779672 0.3504827 0.3946204 -0.7123706 0.4645727 0.6306164 0.5137635 155 156 157 158 159 160 161 0.4118158 0.2134965 0.4416736 0.4291812 -0.2849282 -0.3483128 -0.5813678 162 163 164 165 166 167 168 0.5687171 -0.3602499 -0.5186197 0.6254346 0.5854105 -0.4594473 -0.2260814 169 170 171 172 173 174 175 0.3799532 -0.2779960 0.7077673 0.5238562 -0.5025055 0.7565799 -0.3683598 176 177 178 179 180 181 182 0.4712036 0.6204956 0.4608048 -0.3077955 -0.6694474 -0.4613316 0.4236576 183 184 185 186 187 188 189 0.4627904 -0.4933274 0.6226448 -0.3492375 -0.3382718 0.5324783 0.4157995 190 191 192 193 194 195 196 0.6088467 0.5541997 0.4986568 -0.4939841 0.5737766 0.4225297 -0.2630008 197 198 199 200 201 202 203 -0.3305012 -0.5348621 -0.5149626 -0.4534238 0.5074425 0.4320851 0.4555901 204 205 206 207 208 209 210 -0.6126560 -0.4158830 -0.5378310 0.6629438 -0.6793281 -0.4972131 -0.3211477 211 212 213 214 215 216 217 0.5668051 -0.4284186 0.3697880 0.5533763 -0.2504690 0.3845734 -0.3970489 218 219 220 221 222 223 224 -0.5503082 0.6315425 -0.4894720 0.4856829 -0.6425178 -0.4095921 -0.2493575 225 226 227 228 229 230 231 -0.3801649 0.6476654 -0.3811519 -0.4670162 0.5384308 0.4812290 -0.3199195 232 233 234 235 236 237 238 -0.2456650 -0.3643965 0.6137960 -0.2422578 -0.4041284 -0.3090707 -0.2439206 239 240 241 242 243 244 245 -0.3242094 -0.4011520 -0.4652949 0.5859514 0.7660732 0.7132692 -0.4824851 246 247 248 249 250 251 252 -0.3691055 0.6375041 0.5838136 -0.3772661 -0.4219957 -0.3976789 0.7214173 253 254 255 256 257 258 259 0.4894201 0.8574873 0.5015786 -0.6078605 -0.1802604 -0.4759924 -0.2943362 260 261 262 263 264 265 266 0.6965987 -0.4625942 -0.3655346 0.5765510 -0.2021762 -0.3720513 0.6597501 267 268 269 270 271 272 273 0.6071165 -0.3769864 0.4859536 -0.4787986 -0.3550782 0.6340308 -0.4514229 274 275 276 277 278 279 280 0.6093485 -0.4914154 -0.2537393 -0.4807601 -0.4668766 0.4389436 -0.3701395 281 282 283 284 285 286 287 -0.4810637 0.4976850 -0.4092810 0.7091394 -0.6143735 -0.5402358 0.5651652 288 0.4637621 > postscript(file="/var/wessaorg/rcomp/tmp/69pl31386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 288 Frequency = 1 lag(myerror, k = 1) myerror 0 0.3604840 NA 1 0.3159296 0.3604840 2 0.3547983 0.3159296 3 -0.4736663 0.3547983 4 0.2362164 -0.4736663 5 0.3539213 0.2362164 6 0.4389804 0.3539213 7 0.2974757 0.4389804 8 0.3323703 0.2974757 9 0.3456365 0.3323703 10 -0.6261915 0.3456365 11 0.2120546 -0.6261915 12 -0.4917864 0.2120546 13 0.2909982 -0.4917864 14 0.1215969 0.2909982 15 -0.6373944 0.1215969 16 -0.5561619 -0.6373944 17 0.2248927 -0.5561619 18 -0.6472102 0.2248927 19 0.3100036 -0.6472102 20 -0.7532297 0.3100036 21 0.4817437 -0.7532297 22 0.3155792 0.4817437 23 0.3919237 0.3155792 24 -0.6707929 0.3919237 25 0.5899899 -0.6707929 26 -0.6692112 0.5899899 27 0.2746428 -0.6692112 28 0.3354827 0.2746428 29 -0.4861322 0.3354827 30 0.3193685 -0.4861322 31 -0.5516228 0.3193685 32 0.3700356 -0.5516228 33 0.2827564 0.3700356 34 -0.5746427 0.2827564 35 -0.6051726 -0.5746427 36 -0.4032595 -0.6051726 37 -0.6374060 -0.4032595 38 0.3043189 -0.6374060 39 -0.5480990 0.3043189 40 -0.5832847 -0.5480990 41 0.3142820 -0.5832847 42 -0.7044210 0.3142820 43 -0.5112810 -0.7044210 44 0.4888319 -0.5112810 45 0.4774931 0.4888319 46 0.2697468 0.4774931 47 0.3399632 0.2697468 48 0.2349652 0.3399632 49 -0.5411123 0.2349652 50 0.1840960 -0.5411123 51 -0.5548483 0.1840960 52 -0.5328626 -0.5548483 53 0.3486358 -0.5328626 54 -0.4213707 0.3486358 55 0.4458008 -0.4213707 56 0.3338273 0.4458008 57 0.2894866 0.3338273 58 -0.5607667 0.2894866 59 0.4696590 -0.5607667 60 -0.2860447 0.4696590 61 -0.5752841 -0.2860447 62 0.4092230 -0.5752841 63 0.3442531 0.4092230 64 0.3734274 0.3442531 65 -0.5647656 0.3734274 66 0.4207170 -0.5647656 67 -0.4847297 0.4207170 68 0.2839224 -0.4847297 69 -0.7095226 0.2839224 70 -0.5560603 -0.7095226 71 0.2331779 -0.5560603 72 0.2133480 0.2331779 73 -0.7859914 0.2133480 74 -0.5003854 -0.7859914 75 0.5766348 -0.5003854 76 0.2225616 0.5766348 77 0.4567854 0.2225616 78 -0.6669767 0.4567854 79 -0.4772223 -0.6669767 80 -0.7098529 -0.4772223 81 0.2904774 -0.7098529 82 -0.6645288 0.2904774 83 0.3322421 -0.6645288 84 0.4338127 0.3322421 85 -0.6597802 0.4338127 86 0.3532407 -0.6597802 87 0.3854500 0.3532407 88 0.6004156 0.3854500 89 0.2697163 0.6004156 90 0.3846686 0.2697163 91 0.2300300 0.3846686 92 0.4381648 0.2300300 93 0.4215959 0.4381648 94 0.3388401 0.4215959 95 0.4171346 0.3388401 96 0.4668731 0.4171346 97 -0.7144846 0.4668731 98 -0.6783893 -0.7144846 99 0.2277763 -0.6783893 100 -0.5476290 0.2277763 101 0.2045776 -0.5476290 102 -0.4456074 0.2045776 103 0.2624298 -0.4456074 104 -0.3922166 0.2624298 105 0.3090204 -0.3922166 106 -0.6934199 0.3090204 107 -0.4896103 -0.6934199 108 0.5370235 -0.4896103 109 0.2972077 0.5370235 110 0.4162835 0.2972077 111 0.2882507 0.4162835 112 0.2957783 0.2882507 113 -0.6729575 0.2957783 114 0.2488995 -0.6729575 115 0.3447118 0.2488995 116 0.3616693 0.3447118 117 0.3616223 0.3616693 118 -0.6028147 0.3616223 119 0.4259633 -0.6028147 120 0.2893391 0.4259633 121 0.2555712 0.2893391 122 0.3732254 0.2555712 123 0.3329256 0.3732254 124 -0.6316083 0.3329256 125 0.2796477 -0.6316083 126 0.2134965 0.2796477 127 0.3764394 0.2134965 128 -0.5712622 0.3764394 129 -0.4613289 -0.5712622 130 0.3995557 -0.4613289 131 -0.6399936 0.3995557 132 -0.7052442 -0.6399936 133 0.3573132 -0.7052442 134 -0.3466359 0.3573132 135 -0.6345578 -0.3466359 136 0.4586821 -0.6345578 137 0.4141822 0.4586821 138 -0.5817183 0.4141822 139 0.3831227 -0.5817183 140 -0.7261690 0.3831227 141 0.2601369 -0.7261690 142 -0.5112227 0.2601369 143 0.4692014 -0.5112227 144 0.3639965 0.4692014 145 -0.5492880 0.3639965 146 -0.5434051 -0.5492880 147 -0.3779672 -0.5434051 148 0.3504827 -0.3779672 149 0.3946204 0.3504827 150 -0.7123706 0.3946204 151 0.4645727 -0.7123706 152 0.6306164 0.4645727 153 0.5137635 0.6306164 154 0.4118158 0.5137635 155 0.2134965 0.4118158 156 0.4416736 0.2134965 157 0.4291812 0.4416736 158 -0.2849282 0.4291812 159 -0.3483128 -0.2849282 160 -0.5813678 -0.3483128 161 0.5687171 -0.5813678 162 -0.3602499 0.5687171 163 -0.5186197 -0.3602499 164 0.6254346 -0.5186197 165 0.5854105 0.6254346 166 -0.4594473 0.5854105 167 -0.2260814 -0.4594473 168 0.3799532 -0.2260814 169 -0.2779960 0.3799532 170 0.7077673 -0.2779960 171 0.5238562 0.7077673 172 -0.5025055 0.5238562 173 0.7565799 -0.5025055 174 -0.3683598 0.7565799 175 0.4712036 -0.3683598 176 0.6204956 0.4712036 177 0.4608048 0.6204956 178 -0.3077955 0.4608048 179 -0.6694474 -0.3077955 180 -0.4613316 -0.6694474 181 0.4236576 -0.4613316 182 0.4627904 0.4236576 183 -0.4933274 0.4627904 184 0.6226448 -0.4933274 185 -0.3492375 0.6226448 186 -0.3382718 -0.3492375 187 0.5324783 -0.3382718 188 0.4157995 0.5324783 189 0.6088467 0.4157995 190 0.5541997 0.6088467 191 0.4986568 0.5541997 192 -0.4939841 0.4986568 193 0.5737766 -0.4939841 194 0.4225297 0.5737766 195 -0.2630008 0.4225297 196 -0.3305012 -0.2630008 197 -0.5348621 -0.3305012 198 -0.5149626 -0.5348621 199 -0.4534238 -0.5149626 200 0.5074425 -0.4534238 201 0.4320851 0.5074425 202 0.4555901 0.4320851 203 -0.6126560 0.4555901 204 -0.4158830 -0.6126560 205 -0.5378310 -0.4158830 206 0.6629438 -0.5378310 207 -0.6793281 0.6629438 208 -0.4972131 -0.6793281 209 -0.3211477 -0.4972131 210 0.5668051 -0.3211477 211 -0.4284186 0.5668051 212 0.3697880 -0.4284186 213 0.5533763 0.3697880 214 -0.2504690 0.5533763 215 0.3845734 -0.2504690 216 -0.3970489 0.3845734 217 -0.5503082 -0.3970489 218 0.6315425 -0.5503082 219 -0.4894720 0.6315425 220 0.4856829 -0.4894720 221 -0.6425178 0.4856829 222 -0.4095921 -0.6425178 223 -0.2493575 -0.4095921 224 -0.3801649 -0.2493575 225 0.6476654 -0.3801649 226 -0.3811519 0.6476654 227 -0.4670162 -0.3811519 228 0.5384308 -0.4670162 229 0.4812290 0.5384308 230 -0.3199195 0.4812290 231 -0.2456650 -0.3199195 232 -0.3643965 -0.2456650 233 0.6137960 -0.3643965 234 -0.2422578 0.6137960 235 -0.4041284 -0.2422578 236 -0.3090707 -0.4041284 237 -0.2439206 -0.3090707 238 -0.3242094 -0.2439206 239 -0.4011520 -0.3242094 240 -0.4652949 -0.4011520 241 0.5859514 -0.4652949 242 0.7660732 0.5859514 243 0.7132692 0.7660732 244 -0.4824851 0.7132692 245 -0.3691055 -0.4824851 246 0.6375041 -0.3691055 247 0.5838136 0.6375041 248 -0.3772661 0.5838136 249 -0.4219957 -0.3772661 250 -0.3976789 -0.4219957 251 0.7214173 -0.3976789 252 0.4894201 0.7214173 253 0.8574873 0.4894201 254 0.5015786 0.8574873 255 -0.6078605 0.5015786 256 -0.1802604 -0.6078605 257 -0.4759924 -0.1802604 258 -0.2943362 -0.4759924 259 0.6965987 -0.2943362 260 -0.4625942 0.6965987 261 -0.3655346 -0.4625942 262 0.5765510 -0.3655346 263 -0.2021762 0.5765510 264 -0.3720513 -0.2021762 265 0.6597501 -0.3720513 266 0.6071165 0.6597501 267 -0.3769864 0.6071165 268 0.4859536 -0.3769864 269 -0.4787986 0.4859536 270 -0.3550782 -0.4787986 271 0.6340308 -0.3550782 272 -0.4514229 0.6340308 273 0.6093485 -0.4514229 274 -0.4914154 0.6093485 275 -0.2537393 -0.4914154 276 -0.4807601 -0.2537393 277 -0.4668766 -0.4807601 278 0.4389436 -0.4668766 279 -0.3701395 0.4389436 280 -0.4810637 -0.3701395 281 0.4976850 -0.4810637 282 -0.4092810 0.4976850 283 0.7091394 -0.4092810 284 -0.6143735 0.7091394 285 -0.5402358 -0.6143735 286 0.5651652 -0.5402358 287 0.4637621 0.5651652 288 NA 0.4637621 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.3159296 0.3604840 [2,] 0.3547983 0.3159296 [3,] -0.4736663 0.3547983 [4,] 0.2362164 -0.4736663 [5,] 0.3539213 0.2362164 [6,] 0.4389804 0.3539213 [7,] 0.2974757 0.4389804 [8,] 0.3323703 0.2974757 [9,] 0.3456365 0.3323703 [10,] -0.6261915 0.3456365 [11,] 0.2120546 -0.6261915 [12,] -0.4917864 0.2120546 [13,] 0.2909982 -0.4917864 [14,] 0.1215969 0.2909982 [15,] -0.6373944 0.1215969 [16,] -0.5561619 -0.6373944 [17,] 0.2248927 -0.5561619 [18,] -0.6472102 0.2248927 [19,] 0.3100036 -0.6472102 [20,] -0.7532297 0.3100036 [21,] 0.4817437 -0.7532297 [22,] 0.3155792 0.4817437 [23,] 0.3919237 0.3155792 [24,] -0.6707929 0.3919237 [25,] 0.5899899 -0.6707929 [26,] -0.6692112 0.5899899 [27,] 0.2746428 -0.6692112 [28,] 0.3354827 0.2746428 [29,] -0.4861322 0.3354827 [30,] 0.3193685 -0.4861322 [31,] -0.5516228 0.3193685 [32,] 0.3700356 -0.5516228 [33,] 0.2827564 0.3700356 [34,] -0.5746427 0.2827564 [35,] -0.6051726 -0.5746427 [36,] -0.4032595 -0.6051726 [37,] -0.6374060 -0.4032595 [38,] 0.3043189 -0.6374060 [39,] -0.5480990 0.3043189 [40,] -0.5832847 -0.5480990 [41,] 0.3142820 -0.5832847 [42,] -0.7044210 0.3142820 [43,] -0.5112810 -0.7044210 [44,] 0.4888319 -0.5112810 [45,] 0.4774931 0.4888319 [46,] 0.2697468 0.4774931 [47,] 0.3399632 0.2697468 [48,] 0.2349652 0.3399632 [49,] -0.5411123 0.2349652 [50,] 0.1840960 -0.5411123 [51,] -0.5548483 0.1840960 [52,] -0.5328626 -0.5548483 [53,] 0.3486358 -0.5328626 [54,] -0.4213707 0.3486358 [55,] 0.4458008 -0.4213707 [56,] 0.3338273 0.4458008 [57,] 0.2894866 0.3338273 [58,] -0.5607667 0.2894866 [59,] 0.4696590 -0.5607667 [60,] -0.2860447 0.4696590 [61,] -0.5752841 -0.2860447 [62,] 0.4092230 -0.5752841 [63,] 0.3442531 0.4092230 [64,] 0.3734274 0.3442531 [65,] -0.5647656 0.3734274 [66,] 0.4207170 -0.5647656 [67,] -0.4847297 0.4207170 [68,] 0.2839224 -0.4847297 [69,] -0.7095226 0.2839224 [70,] -0.5560603 -0.7095226 [71,] 0.2331779 -0.5560603 [72,] 0.2133480 0.2331779 [73,] -0.7859914 0.2133480 [74,] -0.5003854 -0.7859914 [75,] 0.5766348 -0.5003854 [76,] 0.2225616 0.5766348 [77,] 0.4567854 0.2225616 [78,] -0.6669767 0.4567854 [79,] -0.4772223 -0.6669767 [80,] -0.7098529 -0.4772223 [81,] 0.2904774 -0.7098529 [82,] -0.6645288 0.2904774 [83,] 0.3322421 -0.6645288 [84,] 0.4338127 0.3322421 [85,] -0.6597802 0.4338127 [86,] 0.3532407 -0.6597802 [87,] 0.3854500 0.3532407 [88,] 0.6004156 0.3854500 [89,] 0.2697163 0.6004156 [90,] 0.3846686 0.2697163 [91,] 0.2300300 0.3846686 [92,] 0.4381648 0.2300300 [93,] 0.4215959 0.4381648 [94,] 0.3388401 0.4215959 [95,] 0.4171346 0.3388401 [96,] 0.4668731 0.4171346 [97,] -0.7144846 0.4668731 [98,] -0.6783893 -0.7144846 [99,] 0.2277763 -0.6783893 [100,] -0.5476290 0.2277763 [101,] 0.2045776 -0.5476290 [102,] -0.4456074 0.2045776 [103,] 0.2624298 -0.4456074 [104,] -0.3922166 0.2624298 [105,] 0.3090204 -0.3922166 [106,] -0.6934199 0.3090204 [107,] -0.4896103 -0.6934199 [108,] 0.5370235 -0.4896103 [109,] 0.2972077 0.5370235 [110,] 0.4162835 0.2972077 [111,] 0.2882507 0.4162835 [112,] 0.2957783 0.2882507 [113,] -0.6729575 0.2957783 [114,] 0.2488995 -0.6729575 [115,] 0.3447118 0.2488995 [116,] 0.3616693 0.3447118 [117,] 0.3616223 0.3616693 [118,] -0.6028147 0.3616223 [119,] 0.4259633 -0.6028147 [120,] 0.2893391 0.4259633 [121,] 0.2555712 0.2893391 [122,] 0.3732254 0.2555712 [123,] 0.3329256 0.3732254 [124,] -0.6316083 0.3329256 [125,] 0.2796477 -0.6316083 [126,] 0.2134965 0.2796477 [127,] 0.3764394 0.2134965 [128,] -0.5712622 0.3764394 [129,] -0.4613289 -0.5712622 [130,] 0.3995557 -0.4613289 [131,] -0.6399936 0.3995557 [132,] -0.7052442 -0.6399936 [133,] 0.3573132 -0.7052442 [134,] -0.3466359 0.3573132 [135,] -0.6345578 -0.3466359 [136,] 0.4586821 -0.6345578 [137,] 0.4141822 0.4586821 [138,] -0.5817183 0.4141822 [139,] 0.3831227 -0.5817183 [140,] -0.7261690 0.3831227 [141,] 0.2601369 -0.7261690 [142,] -0.5112227 0.2601369 [143,] 0.4692014 -0.5112227 [144,] 0.3639965 0.4692014 [145,] -0.5492880 0.3639965 [146,] -0.5434051 -0.5492880 [147,] -0.3779672 -0.5434051 [148,] 0.3504827 -0.3779672 [149,] 0.3946204 0.3504827 [150,] -0.7123706 0.3946204 [151,] 0.4645727 -0.7123706 [152,] 0.6306164 0.4645727 [153,] 0.5137635 0.6306164 [154,] 0.4118158 0.5137635 [155,] 0.2134965 0.4118158 [156,] 0.4416736 0.2134965 [157,] 0.4291812 0.4416736 [158,] -0.2849282 0.4291812 [159,] -0.3483128 -0.2849282 [160,] -0.5813678 -0.3483128 [161,] 0.5687171 -0.5813678 [162,] -0.3602499 0.5687171 [163,] -0.5186197 -0.3602499 [164,] 0.6254346 -0.5186197 [165,] 0.5854105 0.6254346 [166,] -0.4594473 0.5854105 [167,] -0.2260814 -0.4594473 [168,] 0.3799532 -0.2260814 [169,] -0.2779960 0.3799532 [170,] 0.7077673 -0.2779960 [171,] 0.5238562 0.7077673 [172,] -0.5025055 0.5238562 [173,] 0.7565799 -0.5025055 [174,] -0.3683598 0.7565799 [175,] 0.4712036 -0.3683598 [176,] 0.6204956 0.4712036 [177,] 0.4608048 0.6204956 [178,] -0.3077955 0.4608048 [179,] -0.6694474 -0.3077955 [180,] -0.4613316 -0.6694474 [181,] 0.4236576 -0.4613316 [182,] 0.4627904 0.4236576 [183,] -0.4933274 0.4627904 [184,] 0.6226448 -0.4933274 [185,] -0.3492375 0.6226448 [186,] -0.3382718 -0.3492375 [187,] 0.5324783 -0.3382718 [188,] 0.4157995 0.5324783 [189,] 0.6088467 0.4157995 [190,] 0.5541997 0.6088467 [191,] 0.4986568 0.5541997 [192,] -0.4939841 0.4986568 [193,] 0.5737766 -0.4939841 [194,] 0.4225297 0.5737766 [195,] -0.2630008 0.4225297 [196,] -0.3305012 -0.2630008 [197,] -0.5348621 -0.3305012 [198,] -0.5149626 -0.5348621 [199,] -0.4534238 -0.5149626 [200,] 0.5074425 -0.4534238 [201,] 0.4320851 0.5074425 [202,] 0.4555901 0.4320851 [203,] -0.6126560 0.4555901 [204,] -0.4158830 -0.6126560 [205,] -0.5378310 -0.4158830 [206,] 0.6629438 -0.5378310 [207,] -0.6793281 0.6629438 [208,] -0.4972131 -0.6793281 [209,] -0.3211477 -0.4972131 [210,] 0.5668051 -0.3211477 [211,] -0.4284186 0.5668051 [212,] 0.3697880 -0.4284186 [213,] 0.5533763 0.3697880 [214,] -0.2504690 0.5533763 [215,] 0.3845734 -0.2504690 [216,] -0.3970489 0.3845734 [217,] -0.5503082 -0.3970489 [218,] 0.6315425 -0.5503082 [219,] -0.4894720 0.6315425 [220,] 0.4856829 -0.4894720 [221,] -0.6425178 0.4856829 [222,] -0.4095921 -0.6425178 [223,] -0.2493575 -0.4095921 [224,] -0.3801649 -0.2493575 [225,] 0.6476654 -0.3801649 [226,] -0.3811519 0.6476654 [227,] -0.4670162 -0.3811519 [228,] 0.5384308 -0.4670162 [229,] 0.4812290 0.5384308 [230,] -0.3199195 0.4812290 [231,] -0.2456650 -0.3199195 [232,] -0.3643965 -0.2456650 [233,] 0.6137960 -0.3643965 [234,] -0.2422578 0.6137960 [235,] -0.4041284 -0.2422578 [236,] -0.3090707 -0.4041284 [237,] -0.2439206 -0.3090707 [238,] -0.3242094 -0.2439206 [239,] -0.4011520 -0.3242094 [240,] -0.4652949 -0.4011520 [241,] 0.5859514 -0.4652949 [242,] 0.7660732 0.5859514 [243,] 0.7132692 0.7660732 [244,] -0.4824851 0.7132692 [245,] -0.3691055 -0.4824851 [246,] 0.6375041 -0.3691055 [247,] 0.5838136 0.6375041 [248,] -0.3772661 0.5838136 [249,] -0.4219957 -0.3772661 [250,] -0.3976789 -0.4219957 [251,] 0.7214173 -0.3976789 [252,] 0.4894201 0.7214173 [253,] 0.8574873 0.4894201 [254,] 0.5015786 0.8574873 [255,] -0.6078605 0.5015786 [256,] -0.1802604 -0.6078605 [257,] -0.4759924 -0.1802604 [258,] -0.2943362 -0.4759924 [259,] 0.6965987 -0.2943362 [260,] -0.4625942 0.6965987 [261,] -0.3655346 -0.4625942 [262,] 0.5765510 -0.3655346 [263,] -0.2021762 0.5765510 [264,] -0.3720513 -0.2021762 [265,] 0.6597501 -0.3720513 [266,] 0.6071165 0.6597501 [267,] -0.3769864 0.6071165 [268,] 0.4859536 -0.3769864 [269,] -0.4787986 0.4859536 [270,] -0.3550782 -0.4787986 [271,] 0.6340308 -0.3550782 [272,] -0.4514229 0.6340308 [273,] 0.6093485 -0.4514229 [274,] -0.4914154 0.6093485 [275,] -0.2537393 -0.4914154 [276,] -0.4807601 -0.2537393 [277,] -0.4668766 -0.4807601 [278,] 0.4389436 -0.4668766 [279,] -0.3701395 0.4389436 [280,] -0.4810637 -0.3701395 [281,] 0.4976850 -0.4810637 [282,] -0.4092810 0.4976850 [283,] 0.7091394 -0.4092810 [284,] -0.6143735 0.7091394 [285,] -0.5402358 -0.6143735 [286,] 0.5651652 -0.5402358 [287,] 0.4637621 0.5651652 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.3159296 0.3604840 2 0.3547983 0.3159296 3 -0.4736663 0.3547983 4 0.2362164 -0.4736663 5 0.3539213 0.2362164 6 0.4389804 0.3539213 7 0.2974757 0.4389804 8 0.3323703 0.2974757 9 0.3456365 0.3323703 10 -0.6261915 0.3456365 11 0.2120546 -0.6261915 12 -0.4917864 0.2120546 13 0.2909982 -0.4917864 14 0.1215969 0.2909982 15 -0.6373944 0.1215969 16 -0.5561619 -0.6373944 17 0.2248927 -0.5561619 18 -0.6472102 0.2248927 19 0.3100036 -0.6472102 20 -0.7532297 0.3100036 21 0.4817437 -0.7532297 22 0.3155792 0.4817437 23 0.3919237 0.3155792 24 -0.6707929 0.3919237 25 0.5899899 -0.6707929 26 -0.6692112 0.5899899 27 0.2746428 -0.6692112 28 0.3354827 0.2746428 29 -0.4861322 0.3354827 30 0.3193685 -0.4861322 31 -0.5516228 0.3193685 32 0.3700356 -0.5516228 33 0.2827564 0.3700356 34 -0.5746427 0.2827564 35 -0.6051726 -0.5746427 36 -0.4032595 -0.6051726 37 -0.6374060 -0.4032595 38 0.3043189 -0.6374060 39 -0.5480990 0.3043189 40 -0.5832847 -0.5480990 41 0.3142820 -0.5832847 42 -0.7044210 0.3142820 43 -0.5112810 -0.7044210 44 0.4888319 -0.5112810 45 0.4774931 0.4888319 46 0.2697468 0.4774931 47 0.3399632 0.2697468 48 0.2349652 0.3399632 49 -0.5411123 0.2349652 50 0.1840960 -0.5411123 51 -0.5548483 0.1840960 52 -0.5328626 -0.5548483 53 0.3486358 -0.5328626 54 -0.4213707 0.3486358 55 0.4458008 -0.4213707 56 0.3338273 0.4458008 57 0.2894866 0.3338273 58 -0.5607667 0.2894866 59 0.4696590 -0.5607667 60 -0.2860447 0.4696590 61 -0.5752841 -0.2860447 62 0.4092230 -0.5752841 63 0.3442531 0.4092230 64 0.3734274 0.3442531 65 -0.5647656 0.3734274 66 0.4207170 -0.5647656 67 -0.4847297 0.4207170 68 0.2839224 -0.4847297 69 -0.7095226 0.2839224 70 -0.5560603 -0.7095226 71 0.2331779 -0.5560603 72 0.2133480 0.2331779 73 -0.7859914 0.2133480 74 -0.5003854 -0.7859914 75 0.5766348 -0.5003854 76 0.2225616 0.5766348 77 0.4567854 0.2225616 78 -0.6669767 0.4567854 79 -0.4772223 -0.6669767 80 -0.7098529 -0.4772223 81 0.2904774 -0.7098529 82 -0.6645288 0.2904774 83 0.3322421 -0.6645288 84 0.4338127 0.3322421 85 -0.6597802 0.4338127 86 0.3532407 -0.6597802 87 0.3854500 0.3532407 88 0.6004156 0.3854500 89 0.2697163 0.6004156 90 0.3846686 0.2697163 91 0.2300300 0.3846686 92 0.4381648 0.2300300 93 0.4215959 0.4381648 94 0.3388401 0.4215959 95 0.4171346 0.3388401 96 0.4668731 0.4171346 97 -0.7144846 0.4668731 98 -0.6783893 -0.7144846 99 0.2277763 -0.6783893 100 -0.5476290 0.2277763 101 0.2045776 -0.5476290 102 -0.4456074 0.2045776 103 0.2624298 -0.4456074 104 -0.3922166 0.2624298 105 0.3090204 -0.3922166 106 -0.6934199 0.3090204 107 -0.4896103 -0.6934199 108 0.5370235 -0.4896103 109 0.2972077 0.5370235 110 0.4162835 0.2972077 111 0.2882507 0.4162835 112 0.2957783 0.2882507 113 -0.6729575 0.2957783 114 0.2488995 -0.6729575 115 0.3447118 0.2488995 116 0.3616693 0.3447118 117 0.3616223 0.3616693 118 -0.6028147 0.3616223 119 0.4259633 -0.6028147 120 0.2893391 0.4259633 121 0.2555712 0.2893391 122 0.3732254 0.2555712 123 0.3329256 0.3732254 124 -0.6316083 0.3329256 125 0.2796477 -0.6316083 126 0.2134965 0.2796477 127 0.3764394 0.2134965 128 -0.5712622 0.3764394 129 -0.4613289 -0.5712622 130 0.3995557 -0.4613289 131 -0.6399936 0.3995557 132 -0.7052442 -0.6399936 133 0.3573132 -0.7052442 134 -0.3466359 0.3573132 135 -0.6345578 -0.3466359 136 0.4586821 -0.6345578 137 0.4141822 0.4586821 138 -0.5817183 0.4141822 139 0.3831227 -0.5817183 140 -0.7261690 0.3831227 141 0.2601369 -0.7261690 142 -0.5112227 0.2601369 143 0.4692014 -0.5112227 144 0.3639965 0.4692014 145 -0.5492880 0.3639965 146 -0.5434051 -0.5492880 147 -0.3779672 -0.5434051 148 0.3504827 -0.3779672 149 0.3946204 0.3504827 150 -0.7123706 0.3946204 151 0.4645727 -0.7123706 152 0.6306164 0.4645727 153 0.5137635 0.6306164 154 0.4118158 0.5137635 155 0.2134965 0.4118158 156 0.4416736 0.2134965 157 0.4291812 0.4416736 158 -0.2849282 0.4291812 159 -0.3483128 -0.2849282 160 -0.5813678 -0.3483128 161 0.5687171 -0.5813678 162 -0.3602499 0.5687171 163 -0.5186197 -0.3602499 164 0.6254346 -0.5186197 165 0.5854105 0.6254346 166 -0.4594473 0.5854105 167 -0.2260814 -0.4594473 168 0.3799532 -0.2260814 169 -0.2779960 0.3799532 170 0.7077673 -0.2779960 171 0.5238562 0.7077673 172 -0.5025055 0.5238562 173 0.7565799 -0.5025055 174 -0.3683598 0.7565799 175 0.4712036 -0.3683598 176 0.6204956 0.4712036 177 0.4608048 0.6204956 178 -0.3077955 0.4608048 179 -0.6694474 -0.3077955 180 -0.4613316 -0.6694474 181 0.4236576 -0.4613316 182 0.4627904 0.4236576 183 -0.4933274 0.4627904 184 0.6226448 -0.4933274 185 -0.3492375 0.6226448 186 -0.3382718 -0.3492375 187 0.5324783 -0.3382718 188 0.4157995 0.5324783 189 0.6088467 0.4157995 190 0.5541997 0.6088467 191 0.4986568 0.5541997 192 -0.4939841 0.4986568 193 0.5737766 -0.4939841 194 0.4225297 0.5737766 195 -0.2630008 0.4225297 196 -0.3305012 -0.2630008 197 -0.5348621 -0.3305012 198 -0.5149626 -0.5348621 199 -0.4534238 -0.5149626 200 0.5074425 -0.4534238 201 0.4320851 0.5074425 202 0.4555901 0.4320851 203 -0.6126560 0.4555901 204 -0.4158830 -0.6126560 205 -0.5378310 -0.4158830 206 0.6629438 -0.5378310 207 -0.6793281 0.6629438 208 -0.4972131 -0.6793281 209 -0.3211477 -0.4972131 210 0.5668051 -0.3211477 211 -0.4284186 0.5668051 212 0.3697880 -0.4284186 213 0.5533763 0.3697880 214 -0.2504690 0.5533763 215 0.3845734 -0.2504690 216 -0.3970489 0.3845734 217 -0.5503082 -0.3970489 218 0.6315425 -0.5503082 219 -0.4894720 0.6315425 220 0.4856829 -0.4894720 221 -0.6425178 0.4856829 222 -0.4095921 -0.6425178 223 -0.2493575 -0.4095921 224 -0.3801649 -0.2493575 225 0.6476654 -0.3801649 226 -0.3811519 0.6476654 227 -0.4670162 -0.3811519 228 0.5384308 -0.4670162 229 0.4812290 0.5384308 230 -0.3199195 0.4812290 231 -0.2456650 -0.3199195 232 -0.3643965 -0.2456650 233 0.6137960 -0.3643965 234 -0.2422578 0.6137960 235 -0.4041284 -0.2422578 236 -0.3090707 -0.4041284 237 -0.2439206 -0.3090707 238 -0.3242094 -0.2439206 239 -0.4011520 -0.3242094 240 -0.4652949 -0.4011520 241 0.5859514 -0.4652949 242 0.7660732 0.5859514 243 0.7132692 0.7660732 244 -0.4824851 0.7132692 245 -0.3691055 -0.4824851 246 0.6375041 -0.3691055 247 0.5838136 0.6375041 248 -0.3772661 0.5838136 249 -0.4219957 -0.3772661 250 -0.3976789 -0.4219957 251 0.7214173 -0.3976789 252 0.4894201 0.7214173 253 0.8574873 0.4894201 254 0.5015786 0.8574873 255 -0.6078605 0.5015786 256 -0.1802604 -0.6078605 257 -0.4759924 -0.1802604 258 -0.2943362 -0.4759924 259 0.6965987 -0.2943362 260 -0.4625942 0.6965987 261 -0.3655346 -0.4625942 262 0.5765510 -0.3655346 263 -0.2021762 0.5765510 264 -0.3720513 -0.2021762 265 0.6597501 -0.3720513 266 0.6071165 0.6597501 267 -0.3769864 0.6071165 268 0.4859536 -0.3769864 269 -0.4787986 0.4859536 270 -0.3550782 -0.4787986 271 0.6340308 -0.3550782 272 -0.4514229 0.6340308 273 0.6093485 -0.4514229 274 -0.4914154 0.6093485 275 -0.2537393 -0.4914154 276 -0.4807601 -0.2537393 277 -0.4668766 -0.4807601 278 0.4389436 -0.4668766 279 -0.3701395 0.4389436 280 -0.4810637 -0.3701395 281 0.4976850 -0.4810637 282 -0.4092810 0.4976850 283 0.7091394 -0.4092810 284 -0.6143735 0.7091394 285 -0.5402358 -0.6143735 286 0.5651652 -0.5402358 287 0.4637621 0.5651652 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/78cva1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8cy8s1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9hglk1386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10gc751386667247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11xizc1386667247.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/124pc31386667247.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ga8p1386667248.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14kcja1386667248.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15sh891386667248.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16kfqv1386667248.tab") + } > > try(system("convert tmp/1lfrt1386667247.ps tmp/1lfrt1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/2fpdq1386667247.ps tmp/2fpdq1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/30owg1386667247.ps tmp/30owg1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/41ekb1386667247.ps tmp/41ekb1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/5aobn1386667247.ps tmp/5aobn1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/69pl31386667247.ps tmp/69pl31386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/78cva1386667247.ps tmp/78cva1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/8cy8s1386667247.ps tmp/8cy8s1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/9hglk1386667247.ps tmp/9hglk1386667247.png",intern=TRUE)) character(0) > try(system("convert tmp/10gc751386667247.ps tmp/10gc751386667247.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 22.058 3.840 25.931