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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,14 + ,11 + ,15 + ,32 + ,35 + ,0 + ,0 + ,15 + ,12 + ,14 + ,34 + ,33 + ,1 + ,0 + ,11 + ,9 + ,11 + ,36 + ,37 + ,0 + ,0 + ,15 + ,12 + ,16 + ,31 + ,38 + ,0 + ,0 + ,14 + ,10 + ,15 + ,35 + ,34 + ,1 + ,0 + ,13 + ,9 + ,12 + ,29 + ,27 + ,0 + ,0 + ,12 + ,6 + ,6 + ,22 + ,16 + ,1 + ,0 + ,16 + ,10 + ,16 + ,41 + ,40 + ,0 + ,0 + ,16 + ,9 + ,10 + ,36 + ,36 + ,0 + ,0 + ,9 + ,13 + ,15 + ,42 + ,42 + ,1 + ,0 + ,14 + ,12 + ,14 + ,33 + ,30 + ,1 + ,0) + ,dim=c(7 + ,288) + ,dimnames=list(c('Happiness' + ,'Software' + ,'Learning' + ,'Separate' + ,'Connected' + ,'Gender' + ,'Pop') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Happiness','Software','Learning','Separate','Connected','Gender','Pop'),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 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'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 Happiness Software Learning Separate Connected Gender Pop 1 14 12 13 38 41 1 1 2 18 11 16 32 39 1 1 3 11 15 19 35 30 1 1 4 12 6 15 33 31 0 1 5 16 13 14 37 34 1 1 6 18 10 13 29 35 1 1 7 14 12 19 31 39 1 1 8 14 14 15 36 34 1 1 9 15 12 14 35 36 1 1 10 15 9 15 38 37 1 1 11 17 10 16 31 38 0 1 12 19 12 16 34 36 1 1 13 10 12 16 35 38 0 1 14 16 11 16 38 39 1 1 15 18 15 17 37 33 1 1 16 14 12 15 33 32 0 1 17 14 10 15 32 36 0 1 18 17 12 20 38 38 1 1 19 14 11 18 38 39 0 1 20 16 12 16 32 32 1 1 21 18 11 16 33 32 0 1 22 11 12 16 31 31 1 1 23 14 13 19 38 39 1 1 24 12 11 16 39 37 1 1 25 17 12 17 32 39 0 1 26 9 13 17 32 41 1 1 27 16 10 16 35 36 0 1 28 14 14 15 37 33 1 1 29 15 12 16 33 33 1 1 30 11 10 14 33 34 0 1 31 16 12 15 31 31 1 1 32 13 8 12 32 27 0 1 33 17 10 14 31 37 1 1 34 15 12 16 37 34 1 1 35 14 12 14 30 34 0 1 36 16 7 10 33 32 0 1 37 9 9 10 31 29 0 1 38 15 12 14 33 36 0 1 39 17 10 16 31 29 1 1 40 13 10 16 33 35 0 1 41 15 10 16 32 37 0 1 42 16 12 14 33 34 1 1 43 16 15 20 32 38 0 1 44 12 10 14 33 35 0 1 45 15 10 14 28 38 1 1 46 11 12 11 35 37 1 1 47 15 13 14 39 38 1 1 48 15 11 15 34 33 1 1 49 17 11 16 38 36 1 1 50 13 12 14 32 38 0 1 51 16 14 16 38 32 1 1 52 14 10 14 30 32 0 1 53 11 12 12 33 32 0 1 54 12 13 16 38 34 1 1 55 12 5 9 32 32 0 1 56 15 6 14 35 37 1 1 57 16 12 16 34 39 1 1 58 15 12 16 34 29 1 1 59 12 11 15 36 37 0 1 60 12 10 16 34 35 1 1 61 8 7 12 28 30 0 1 62 13 12 16 34 38 0 1 63 11 14 16 35 34 1 1 64 14 11 14 35 31 1 1 65 15 12 16 31 34 1 1 66 10 13 17 37 35 0 1 67 11 14 18 35 36 1 1 68 12 11 18 27 30 0 1 69 15 12 12 40 39 1 1 70 15 12 16 37 35 0 1 71 14 8 10 36 38 0 1 72 16 11 14 38 31 1 1 73 15 14 18 39 34 1 1 74 15 14 18 41 38 0 1 75 13 12 16 27 34 0 1 76 12 9 17 30 39 1 1 77 17 13 16 37 37 1 1 78 13 11 16 31 34 1 1 79 15 12 13 31 28 0 1 80 13 12 16 27 37 0 1 81 15 12 16 36 33 0 1 82 15 12 16 37 35 1 1 83 16 12 15 33 37 0 1 84 15 11 15 34 32 1 1 85 14 10 16 31 33 1 1 86 15 9 14 39 38 0 1 87 14 12 16 34 33 1 1 88 13 12 16 32 29 1 1 89 7 12 15 33 33 1 1 90 17 9 12 36 31 1 1 91 13 15 17 32 36 1 1 92 15 12 16 41 35 1 1 93 14 12 15 28 32 1 1 94 13 12 13 30 29 1 1 95 16 10 16 36 39 1 1 96 12 13 16 35 37 1 1 97 14 9 16 31 35 1 1 98 17 12 16 34 37 0 1 99 15 10 14 36 32 0 1 100 17 14 16 36 38 1 1 101 12 11 16 35 37 0 1 102 16 15 20 37 36 1 1 103 11 11 15 28 32 0 1 104 15 11 16 39 33 1 1 105 9 12 13 32 40 0 1 106 16 12 17 35 38 1 1 107 15 12 16 39 41 0 1 108 10 11 16 35 36 0 1 109 10 7 12 42 43 1 1 110 15 12 16 34 30 1 1 111 11 14 16 33 31 1 1 112 13 11 17 41 32 1 1 113 18 10 12 34 37 1 1 114 16 13 18 32 37 0 1 115 14 13 14 40 33 1 1 116 14 8 14 40 34 1 1 117 14 11 13 35 33 1 1 118 14 12 16 36 38 1 1 119 12 11 13 37 33 0 1 120 14 13 16 27 31 1 1 121 15 12 13 39 38 1 1 122 15 14 16 38 37 1 1 123 15 13 15 31 36 1 1 124 13 15 16 33 31 1 1 125 17 10 15 32 39 0 1 126 17 11 17 39 44 1 1 127 19 9 15 36 33 1 1 128 15 11 12 33 35 1 1 129 13 10 16 33 32 0 1 130 9 11 10 32 28 0 1 131 15 8 16 37 40 1 1 132 15 11 12 30 27 0 1 133 15 12 14 38 37 0 1 134 16 12 15 29 32 1 1 135 11 9 13 22 28 0 1 136 14 11 15 35 34 0 1 137 11 10 11 35 30 1 1 138 15 8 12 34 35 1 1 139 13 9 11 35 31 0 1 140 15 8 16 34 32 1 1 141 16 9 15 37 30 0 1 142 14 15 17 35 30 1 1 143 15 11 16 23 31 0 1 144 16 8 10 31 40 1 1 145 16 13 18 27 32 1 1 146 11 12 13 36 36 0 1 147 12 12 16 31 32 0 1 148 9 9 13 32 35 0 1 149 16 7 10 39 38 1 1 150 13 13 15 37 42 1 1 151 16 9 16 38 34 0 1 152 12 6 16 39 35 1 1 153 9 8 14 34 38 1 1 154 13 8 10 31 33 1 1 155 14 6 13 37 32 1 1 156 19 9 15 36 33 1 1 157 13 11 16 32 34 1 1 158 12 8 12 38 32 1 1 159 10 10 13 26 27 0 0 160 14 8 12 26 31 0 0 161 16 14 17 33 38 0 0 162 10 10 15 39 34 1 0 163 11 8 10 30 24 0 0 164 14 11 14 33 30 0 0 165 12 12 11 25 26 1 0 166 9 12 13 38 34 1 0 167 9 12 16 37 27 0 0 168 11 5 12 31 37 0 0 169 16 12 16 37 36 1 0 170 9 10 12 35 41 0 0 171 13 7 9 25 29 1 0 172 16 12 12 28 36 1 0 173 13 11 15 35 32 0 0 174 9 8 12 33 37 1 0 175 12 9 12 30 30 0 0 176 16 10 14 31 31 1 0 177 11 9 12 37 38 1 0 178 14 12 16 36 36 1 0 179 13 6 11 30 35 0 0 180 15 15 19 36 31 0 0 181 14 12 15 32 38 0 0 182 16 12 8 28 22 1 0 183 13 12 16 36 32 1 0 184 14 11 17 34 36 0 0 185 15 7 12 31 39 1 0 186 13 7 11 28 28 0 0 187 11 5 11 36 32 0 0 188 11 12 14 36 32 1 0 189 14 12 16 40 38 1 0 190 15 3 12 33 32 1 0 191 11 11 16 37 35 1 0 192 15 10 13 32 32 1 0 193 12 12 15 38 37 0 0 194 14 9 16 31 34 1 0 195 14 12 16 37 33 1 0 196 8 9 14 33 33 0 0 197 9 12 16 30 30 0 0 198 15 10 14 30 24 0 0 199 17 9 11 31 34 0 0 200 13 12 12 32 34 0 0 201 15 8 15 34 33 1 0 202 15 11 15 36 34 1 0 203 14 11 16 37 35 1 0 204 16 12 16 36 35 0 0 205 13 10 11 33 36 0 0 206 16 10 15 33 34 0 0 207 9 12 12 33 34 1 0 208 16 12 12 44 41 0 0 209 11 11 15 39 32 0 0 210 10 8 15 32 30 0 0 211 11 12 16 35 35 1 0 212 15 10 14 25 28 0 0 213 17 11 17 35 33 1 0 214 14 10 14 34 39 1 0 215 8 8 13 35 36 0 0 216 15 12 15 39 36 1 0 217 11 12 13 33 35 0 0 218 16 10 14 36 38 0 0 219 10 12 15 32 33 1 0 220 15 9 12 32 31 0 0 221 16 6 8 36 32 1 0 222 19 10 14 32 31 0 0 223 12 9 14 34 33 0 0 224 8 9 11 33 34 0 0 225 11 9 12 35 34 0 0 226 14 6 13 30 34 1 0 227 9 10 10 38 33 0 0 228 15 6 16 34 32 0 0 229 13 14 18 33 41 1 0 230 16 10 13 32 34 1 0 231 11 10 11 31 36 0 0 232 12 6 4 30 37 0 0 233 13 12 13 27 36 0 0 234 10 12 16 31 29 1 0 235 11 7 10 30 37 0 0 236 12 8 12 32 27 0 0 237 8 11 12 35 35 0 0 238 12 3 10 28 28 0 0 239 12 6 13 33 35 0 0 240 11 8 12 35 29 0 0 241 13 9 14 35 32 0 0 242 14 9 10 32 36 1 0 243 10 8 12 21 19 1 0 244 12 9 12 20 21 1 0 245 15 7 11 34 31 0 0 246 13 7 10 32 33 0 0 247 13 6 12 34 36 1 0 248 13 9 16 32 33 1 0 249 12 10 12 33 37 0 0 250 12 11 14 33 34 0 0 251 9 12 16 37 35 0 0 252 9 8 14 32 31 1 0 253 15 11 13 34 37 1 0 254 10 3 4 30 35 1 0 255 14 11 15 30 27 1 0 256 15 12 11 38 34 0 0 257 7 7 11 36 40 0 0 258 14 9 14 32 29 0 0 259 8 12 15 34 38 0 0 260 10 8 14 33 34 1 0 261 13 11 13 27 21 0 0 262 13 8 11 32 36 0 0 263 13 10 15 34 38 1 0 264 8 8 11 29 30 0 0 265 12 7 13 35 35 0 0 266 13 8 13 27 30 1 0 267 12 10 16 33 36 1 0 268 10 8 13 38 34 0 0 269 13 12 16 36 35 1 0 270 12 14 16 33 34 0 0 271 9 7 12 39 32 0 0 272 15 6 7 29 33 1 0 273 13 11 16 32 33 0 0 274 13 4 5 34 26 1 0 275 13 9 16 38 35 0 0 276 15 5 4 17 21 0 0 277 15 9 12 35 38 0 0 278 14 11 15 32 35 0 0 279 15 12 14 34 33 1 0 280 11 9 11 36 37 0 0 281 15 12 16 31 38 0 0 282 14 10 15 35 34 1 0 283 13 9 12 29 27 0 0 284 12 6 6 22 16 1 0 285 16 10 16 41 40 0 0 286 16 9 10 36 36 0 0 287 9 13 15 42 42 1 0 288 14 12 14 33 30 1 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Software Learning Separate Connected Gender 10.56678 -0.01610 0.11895 -0.01599 0.02730 0.79035 Pop 0.99814 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.3195 -1.4882 0.4788 1.6463 6.5943 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.56678 1.42810 7.399 1.59e-12 *** Software -0.01610 0.07939 -0.203 0.83948 Learning 0.11895 0.07406 1.606 0.10937 Separate -0.01599 0.04295 -0.372 0.70991 Connected 0.02730 0.04130 0.661 0.50913 Gender 0.79035 0.28870 2.738 0.00658 ** Pop 0.99814 0.30798 3.241 0.00133 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.369 on 281 degrees of freedom Multiple R-squared: 0.1167, Adjusted R-squared: 0.09789 F-statistic: 6.19 on 6 and 281 DF, p-value: 4.068e-06 > 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,] 0.35893020 0.71786039 0.64106980 [2,] 0.44873969 0.89747938 0.55126031 [3,] 0.67636287 0.64727427 0.32363713 [4,] 0.69597349 0.60805303 0.30402651 [5,] 0.63418501 0.73162999 0.36581499 [6,] 0.83022306 0.33955387 0.16977694 [7,] 0.77380653 0.45238694 0.22619347 [8,] 0.69832945 0.60334109 0.30167055 [9,] 0.71915372 0.56169257 0.28084628 [10,] 0.66600268 0.66799464 0.33399732 [11,] 0.59042677 0.81914645 0.40957323 [12,] 0.75460142 0.49079716 0.24539858 [13,] 0.86523117 0.26953767 0.13476883 [14,] 0.83159936 0.33680128 0.16840064 [15,] 0.83785534 0.32428931 0.16214466 [16,] 0.81685972 0.36628056 0.18314028 [17,] 0.96855152 0.06289696 0.03144848 [18,] 0.96083366 0.07833268 0.03916634 [19,] 0.94672417 0.10655166 0.05327583 [20,] 0.92840887 0.14318227 0.07159113 [21,] 0.94382718 0.11234565 0.05617282 [22,] 0.92913330 0.14173339 0.07086670 [23,] 0.91249933 0.17500135 0.08750067 [24,] 0.89822208 0.20355583 0.10177792 [25,] 0.87201769 0.25596461 0.12798231 [26,] 0.84162201 0.31675597 0.15837799 [27,] 0.82158866 0.35682268 0.17841134 [28,] 0.90046270 0.19907459 0.09953730 [29,] 0.88184604 0.23630792 0.11815396 [30,] 0.87340022 0.25319956 0.12659978 [31,] 0.85047163 0.29905675 0.14952837 [32,] 0.82199782 0.35600436 0.17800218 [33,] 0.79574948 0.40850103 0.20425052 [34,] 0.77857048 0.44285904 0.22142952 [35,] 0.76354660 0.47290681 0.23645340 [36,] 0.73126937 0.53746125 0.26873063 [37,] 0.75371369 0.49257261 0.24628631 [38,] 0.72085763 0.55828475 0.27914237 [39,] 0.68027912 0.63944175 0.31972088 [40,] 0.67252545 0.65494911 0.32747455 [41,] 0.63181528 0.73636944 0.36818472 [42,] 0.60794036 0.78411927 0.39205964 [43,] 0.56359535 0.87280929 0.43640465 [44,] 0.54445659 0.91108682 0.45554341 [45,] 0.54953698 0.90092605 0.45046302 [46,] 0.51544999 0.96910002 0.48455001 [47,] 0.47404604 0.94809208 0.52595396 [48,] 0.43720267 0.87440534 0.56279733 [49,] 0.39491763 0.78983527 0.60508237 [50,] 0.36922185 0.73844370 0.63077815 [51,] 0.40447445 0.80894889 0.59552555 [52,] 0.57562777 0.84874447 0.42437223 [53,] 0.53818791 0.92362419 0.46181209 [54,] 0.58857338 0.82285323 0.41142662 [55,] 0.54707519 0.90584962 0.45292481 [56,] 0.50595789 0.98808422 0.49404211 [57,] 0.55151082 0.89697835 0.44848918 [58,] 0.61879691 0.76240619 0.38120309 [59,] 0.60373981 0.79252037 0.39626019 [60,] 0.56989081 0.86021837 0.43010919 [61,] 0.55065146 0.89869708 0.44934854 [62,] 0.51577338 0.96845324 0.48422662 [63,] 0.50104131 0.99791738 0.49895869 [64,] 0.46200342 0.92400684 0.53799658 [65,] 0.43393413 0.86786825 0.56606587 [66,] 0.39642595 0.79285189 0.60357405 [67,] 0.43141730 0.86283461 0.56858270 [68,] 0.42899442 0.85798884 0.57100558 [69,] 0.40666775 0.81333550 0.59333225 [70,] 0.40219736 0.80439472 0.59780264 [71,] 0.36708516 0.73417032 0.63291484 [72,] 0.34376482 0.68752964 0.65623518 [73,] 0.30983244 0.61966488 0.69016756 [74,] 0.31095675 0.62191350 0.68904325 [75,] 0.28019753 0.56039506 0.71980247 [76,] 0.25002625 0.50005249 0.74997375 [77,] 0.22779077 0.45558154 0.77220923 [78,] 0.20109811 0.40219622 0.79890189 [79,] 0.18162897 0.36325794 0.81837103 [80,] 0.45115236 0.90230472 0.54884764 [81,] 0.47078979 0.94157959 0.52921021 [82,] 0.44548482 0.89096965 0.55451518 [83,] 0.41056999 0.82113999 0.58943001 [84,] 0.37586681 0.75173363 0.62413319 [85,] 0.34416551 0.68833101 0.65583449 [86,] 0.31793672 0.63587344 0.68206328 [87,] 0.32272404 0.64544807 0.67727596 [88,] 0.29229364 0.58458728 0.70770636 [89,] 0.31862170 0.63724339 0.68137830 [90,] 0.29841894 0.59683788 0.70158106 [91,] 0.30057794 0.60115587 0.69942206 [92,] 0.29058223 0.58116446 0.70941777 [93,] 0.26659057 0.53318113 0.73340943 [94,] 0.26290719 0.52581438 0.73709281 [95,] 0.23575556 0.47151112 0.76424444 [96,] 0.30976115 0.61952230 0.69023885 [97,] 0.28671217 0.57342434 0.71328783 [98,] 0.26127912 0.52255824 0.73872088 [99,] 0.31106348 0.62212695 0.68893652 [100,] 0.41444151 0.82888303 0.58555849 [101,] 0.38245824 0.76491649 0.61754176 [102,] 0.41297338 0.82594676 0.58702662 [103,] 0.39976152 0.79952304 0.60023848 [104,] 0.45828337 0.91656674 0.54171663 [105,] 0.44921229 0.89842457 0.55078771 [106,] 0.41533093 0.83066186 0.58466907 [107,] 0.38327994 0.76655987 0.61672006 [108,] 0.35029163 0.70058326 0.64970837 [109,] 0.32055326 0.64110653 0.67944674 [110,] 0.29864242 0.59728485 0.70135758 [111,] 0.27033218 0.54066435 0.72966782 [112,] 0.24518512 0.49037024 0.75481488 [113,] 0.21968157 0.43936313 0.78031843 [114,] 0.19685891 0.39371782 0.80314109 [115,] 0.17963955 0.35927910 0.82036045 [116,] 0.19884750 0.39769501 0.80115250 [117,] 0.19136899 0.38273798 0.80863101 [118,] 0.26521015 0.53042030 0.73478985 [119,] 0.24367866 0.48735733 0.75632134 [120,] 0.21876308 0.43752617 0.78123692 [121,] 0.24912784 0.49825567 0.75087216 [122,] 0.22544308 0.45088615 0.77455692 [123,] 0.22501796 0.45003592 0.77498204 [124,] 0.21102826 0.42205652 0.78897174 [125,] 0.20283938 0.40567875 0.79716062 [126,] 0.19694632 0.39389263 0.80305368 [127,] 0.17513781 0.35027562 0.82486219 [128,] 0.17906556 0.35813113 0.82093444 [129,] 0.16137248 0.32274496 0.83862752 [130,] 0.14131748 0.28263497 0.85868252 [131,] 0.12446343 0.24892686 0.87553657 [132,] 0.12902666 0.25805332 0.87097334 [133,] 0.11186986 0.22373971 0.88813014 [134,] 0.10399254 0.20798509 0.89600746 [135,] 0.10252795 0.20505590 0.89747205 [136,] 0.09577651 0.19155303 0.90422349 [137,] 0.09157718 0.18315435 0.90842282 [138,] 0.08210684 0.16421368 0.91789316 [139,] 0.11526413 0.23052826 0.88473587 [140,] 0.11414730 0.22829460 0.88585270 [141,] 0.10418923 0.20837846 0.89581077 [142,] 0.10592215 0.21184430 0.89407785 [143,] 0.11051263 0.22102527 0.88948737 [144,] 0.18940462 0.37880923 0.81059538 [145,] 0.17085241 0.34170483 0.82914759 [146,] 0.15021813 0.30043626 0.84978187 [147,] 0.22425537 0.44851075 0.77574463 [148,] 0.20404718 0.40809435 0.79595282 [149,] 0.18932694 0.37865388 0.81067306 [150,] 0.18403388 0.36806775 0.81596612 [151,] 0.17822278 0.35644557 0.82177722 [152,] 0.18211945 0.36423891 0.81788055 [153,] 0.21079790 0.42159580 0.78920210 [154,] 0.19021589 0.38043179 0.80978411 [155,] 0.17734649 0.35469297 0.82265351 [156,] 0.16010012 0.32020024 0.83989988 [157,] 0.20034862 0.40069725 0.79965138 [158,] 0.22468681 0.44937362 0.77531319 [159,] 0.20365618 0.40731235 0.79634382 [160,] 0.21310166 0.42620331 0.78689834 [161,] 0.23232685 0.46465370 0.76767315 [162,] 0.21121224 0.42242448 0.78878776 [163,] 0.22788650 0.45577300 0.77211350 [164,] 0.20418823 0.40837646 0.79581177 [165,] 0.24590348 0.49180696 0.75409652 [166,] 0.22084194 0.44168388 0.77915806 [167,] 0.23528454 0.47056908 0.76471546 [168,] 0.22452653 0.44905307 0.77547347 [169,] 0.20134225 0.40268450 0.79865775 [170,] 0.18114538 0.36229076 0.81885462 [171,] 0.17689703 0.35379406 0.82310297 [172,] 0.16187094 0.32374188 0.83812906 [173,] 0.19423282 0.38846563 0.80576718 [174,] 0.17123250 0.34246499 0.82876750 [175,] 0.15471001 0.30942002 0.84528999 [176,] 0.14599585 0.29199169 0.85400415 [177,] 0.12896473 0.25792945 0.87103527 [178,] 0.11531721 0.23063441 0.88468279 [179,] 0.11084970 0.22169940 0.88915030 [180,] 0.09619140 0.19238281 0.90380860 [181,] 0.09171580 0.18343160 0.90828420 [182,] 0.09060778 0.18121557 0.90939222 [183,] 0.08548082 0.17096163 0.91451918 [184,] 0.07289973 0.14579945 0.92710027 [185,] 0.06226340 0.12452679 0.93773660 [186,] 0.05281755 0.10563510 0.94718245 [187,] 0.08002638 0.16005276 0.91997362 [188,] 0.09942237 0.19884473 0.90057763 [189,] 0.10144820 0.20289641 0.89855180 [190,] 0.15522521 0.31045042 0.84477479 [191,] 0.13575117 0.27150234 0.86424883 [192,] 0.12738305 0.25476610 0.87261695 [193,] 0.11972493 0.23944986 0.88027507 [194,] 0.10440235 0.20880471 0.89559765 [195,] 0.11976043 0.23952086 0.88023957 [196,] 0.10343615 0.20687230 0.89656385 [197,] 0.12091884 0.24183768 0.87908116 [198,] 0.15521911 0.31043821 0.84478089 [199,] 0.19079029 0.38158058 0.80920971 [200,] 0.17308083 0.34616165 0.82691917 [201,] 0.17479064 0.34958127 0.82520936 [202,] 0.17091085 0.34182171 0.82908915 [203,] 0.17063239 0.34126478 0.82936761 [204,] 0.20734218 0.41468437 0.79265782 [205,] 0.18555760 0.37111520 0.81444240 [206,] 0.24821828 0.49643657 0.75178172 [207,] 0.24347273 0.48694547 0.75652727 [208,] 0.22225572 0.44451145 0.77774428 [209,] 0.26079280 0.52158559 0.73920720 [210,] 0.27827227 0.55654454 0.72172773 [211,] 0.28801537 0.57603075 0.71198463 [212,] 0.34084632 0.68169264 0.65915368 [213,] 0.61450754 0.77098492 0.38549246 [214,] 0.57468001 0.85063997 0.42531999 [215,] 0.65609018 0.68781963 0.34390982 [216,] 0.62469025 0.75061951 0.37530975 [217,] 0.59399781 0.81200437 0.40600219 [218,] 0.61797354 0.76405292 0.38202646 [219,] 0.65346629 0.69306742 0.34653371 [220,] 0.61435804 0.77128391 0.38564196 [221,] 0.65385644 0.69228713 0.34614356 [222,] 0.62946209 0.74107583 0.37053791 [223,] 0.59087108 0.81825784 0.40912892 [224,] 0.54673585 0.90652831 0.45326415 [225,] 0.56668433 0.86663135 0.43331567 [226,] 0.53410492 0.93179015 0.46589508 [227,] 0.48801958 0.97603917 0.51198042 [228,] 0.62688899 0.74622203 0.37311101 [229,] 0.58628983 0.82742034 0.41371017 [230,] 0.54345895 0.91308210 0.45654105 [231,] 0.50480871 0.99038258 0.49519129 [232,] 0.46425005 0.92850010 0.53574995 [233,] 0.42412497 0.84824995 0.57587503 [234,] 0.44147601 0.88295202 0.55852399 [235,] 0.41267026 0.82534051 0.58732974 [236,] 0.44886921 0.89773843 0.55113079 [237,] 0.40774250 0.81548500 0.59225750 [238,] 0.37832752 0.75665503 0.62167248 [239,] 0.33908592 0.67817184 0.66091408 [240,] 0.29567187 0.59134373 0.70432813 [241,] 0.25562588 0.51125175 0.74437412 [242,] 0.31166156 0.62332312 0.68833844 [243,] 0.34584472 0.69168944 0.65415528 [244,] 0.32605361 0.65210722 0.67394639 [245,] 0.30292838 0.60585677 0.69707162 [246,] 0.25936681 0.51873362 0.74063319 [247,] 0.24980207 0.49960414 0.75019793 [248,] 0.44199418 0.88398837 0.55800582 [249,] 0.41671385 0.83342769 0.58328615 [250,] 0.66403695 0.67192611 0.33596305 [251,] 0.68062298 0.63875405 0.31937702 [252,] 0.62877737 0.74244527 0.37122263 [253,] 0.56783548 0.86432903 0.43216452 [254,] 0.50473408 0.99053184 0.49526592 [255,] 0.78477469 0.43045061 0.21522531 [256,] 0.73683520 0.52632960 0.26316480 [257,] 0.68931839 0.62136321 0.31068161 [258,] 0.70924024 0.58151951 0.29075976 [259,] 0.71104222 0.57791555 0.28895778 [260,] 0.63361166 0.73277668 0.36638834 [261,] 0.55549211 0.88901577 0.44450789 [262,] 0.66119615 0.67760771 0.33880385 [263,] 0.57344967 0.85310066 0.42655033 [264,] 0.50288675 0.99422651 0.49711325 [265,] 0.40712836 0.81425672 0.59287164 [266,] 0.35147399 0.70294798 0.64852601 [267,] 0.26409678 0.52819357 0.73590322 [268,] 0.18973939 0.37947878 0.81026061 [269,] 0.11203010 0.22406019 0.88796990 > postscript(file="/var/fisher/rcomp/tmp/1z58r1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2yb0o1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3x6fu1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4d51d1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/53bbl1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 288 Frequency = 1 1 2 3 4 5 6 -0.22010233 3.36560198 -3.63315023 -1.57115678 1.85217238 3.76757867 7 8 9 10 11 12 -0.99113900 -0.26667386 0.74948307 0.60292935 3.15116752 4.49559428 13 14 15 16 17 18 -3.75266585 1.46156637 3.55482365 0.49811135 0.34071494 2.02917508 19 20 21 22 23 24 0.01402344 1.57281812 4.36306880 -3.41587295 -0.86308536 -2.46783358 25 26 27 28 29 30 3.05310160 -5.77576100 2.26974977 -0.22337680 0.56150919 -2.46973764 31 32 33 34 35 36 1.70307441 -0.08890639 2.62601344 0.59818246 0.51447054 3.01237225 37 38 39 40 41 42 -3.90551653 1.50784674 2.60654266 -0.73493537 1.19446458 1.77210093 43 44 45 46 47 48 1.77184805 -1.49704064 0.55072825 -2.92097782 0.77494854 0.68035544 49 50 51 52 53 54 2.54347535 -0.56275331 1.70097289 0.53688615 -2.14504655 -2.36972829 55 56 57 58 59 60 -0.91686482 0.62560896 1.41368529 0.68671523 -1.60651661 -2.50929311 61 62 63 64 65 66 -5.25088682 -0.76865991 -3.40161530 -0.13009714 0.50221807 -3.74162091 67 68 69 70 71 72 -3.69411602 -1.91618434 0.98543915 1.36123127 0.91263167 1.91788505 73 74 75 76 77 78 0.42446623 1.13759419 -0.77140639 -2.81752390 2.53236867 -1.51387712 79 80 81 82 83 84 1.81322993 -0.85331538 1.39984319 0.57087947 2.36159638 0.70765843 85 86 87 88 89 90 -0.50266931 1.50091959 -0.42249674 -1.34527290 -7.31954344 3.09160128 91 92 93 94 95 96 -1.60705566 0.63485573 -0.37221078 -1.02041893 1.41348305 -2.49961947 97 98 99 100 101 102 -0.57337049 3.25864308 1.63285054 2.50516679 -1.74145804 1.11607257 103 104 105 106 107 108 -2.59795416 0.64137840 -4.49841193 1.33803499 1.22940144 -3.71415505 109 110 111 112 113 114 -4.17226063 0.65941224 -3.35169445 -1.41827784 3.91189037 2.00485541 115 116 117 118 119 120 -0.07254243 -0.18032136 -0.06575576 -0.52702358 -1.24341583 -0.46375403 121 122 123 124 125 126 0.87780072 0.56445792 0.58265463 -1.33559926 3.25880596 2.22209810 127 128 129 130 131 132 4.68015319 0.96659748 -0.65302639 -3.83002909 0.36998375 1.92739104 133 134 135 136 137 138 1.56051408 1.64378329 -2.37900222 0.45939831 -2.76204724 0.93430599 139 140 141 142 143 144 -0.01509362 0.54042550 2.56840804 -0.39525550 1.23043114 1.98770355 145 146 147 148 149 150 1.27104825 -2.32522369 -1.65282414 -4.41018252 2.15416688 -1.48519894 151 152 153 154 155 156 2.35624276 -2.49370353 -5.38549773 -0.82117549 -0.08694058 4.68015319 157 158 159 160 161 162 -1.49788306 -1.91980877 -2.27348350 1.70406152 3.12673331 -3.28492814 163 164 165 166 167 168 -0.80294653 1.65371380 -0.78244127 -4.03083710 -3.42220050 -1.42807168 169 170 171 172 173 174 2.54172075 -3.39283146 0.29306855 2.87356363 0.51214857 -4.13814979 175 176 177 178 179 180 -0.18856404 2.78797568 -2.08538133 0.52572668 0.74558279 2.14403692 181 182 183 184 185 186 1.31644360 3.73159501 -0.36506134 1.14904780 1.75916090 0.92081081 187 188 189 190 191 192 -1.09263902 -2.12716661 0.53509696 1.91788924 -2.44707144 1.89561412 193 194 195 196 197 198 -0.56028901 0.45207678 0.62362973 -4.46038556 -3.61606795 2.75345438 199 200 201 202 203 204 4.83716542 0.78249768 1.63021415 1.68318485 0.55292856 3.34338148 205 206 207 208 209 210 0.83064275 3.40945927 -3.99186006 3.78330551 -1.42387516 -2.52951320 211 212 213 214 215 216 -2.46296439 2.56427207 3.45659905 0.61753393 -4.40745423 1.69265625 217 218 219 220 221 222 -1.34775862 3.46717686 -3.33739323 2.81612110 3.48994647 6.59432155 223 224 225 226 227 228 -0.44439149 -4.13084645 -1.21780569 0.74463925 -2.88853058 2.29673120 229 230 231 232 233 234 -0.86447484 2.84100813 -1.20134538 0.52360837 0.52897400 -3.36312269 235 236 237 238 239 240 -1.17398064 -0.09076212 -4.21291831 -0.02462258 -0.44432974 -1.09738591 241 242 243 244 245 246 0.59890557 1.12714906 -2.83862469 -0.89312955 2.93486622 0.96721947 247 248 249 250 251 252 -0.12704311 -0.50462616 -0.31560761 -0.45549818 -3.64062446 -4.22822063 253 254 255 256 257 258 1.80718247 -2.26042300 0.77834141 2.99740944 -5.27887259 1.63283235 259 260 261 262 263 264 -4.65156827 -3.29413554 0.92242371 0.78245831 -0.47411044 -4.10170593 265 266 267 268 269 270 -0.39624642 -0.16194059 -1.55444589 -2.30486605 -0.44697032 -0.64510735 271 272 273 274 275 276 -3.13141381 2.46963238 0.31791602 0.94642802 0.32708405 3.73643823 277 278 279 280 281 282 2.67298234 1.38225740 1.81354227 -1.16477324 2.18150217 0.65109560 283 284 285 286 287 288 0.87735087 -0.05922782 3.25464646 3.98147712 -4.40708433 0.87945718 > postscript(file="/var/fisher/rcomp/tmp/6zvmt1386543953.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 -0.22010233 NA 1 3.36560198 -0.22010233 2 -3.63315023 3.36560198 3 -1.57115678 -3.63315023 4 1.85217238 -1.57115678 5 3.76757867 1.85217238 6 -0.99113900 3.76757867 7 -0.26667386 -0.99113900 8 0.74948307 -0.26667386 9 0.60292935 0.74948307 10 3.15116752 0.60292935 11 4.49559428 3.15116752 12 -3.75266585 4.49559428 13 1.46156637 -3.75266585 14 3.55482365 1.46156637 15 0.49811135 3.55482365 16 0.34071494 0.49811135 17 2.02917508 0.34071494 18 0.01402344 2.02917508 19 1.57281812 0.01402344 20 4.36306880 1.57281812 21 -3.41587295 4.36306880 22 -0.86308536 -3.41587295 23 -2.46783358 -0.86308536 24 3.05310160 -2.46783358 25 -5.77576100 3.05310160 26 2.26974977 -5.77576100 27 -0.22337680 2.26974977 28 0.56150919 -0.22337680 29 -2.46973764 0.56150919 30 1.70307441 -2.46973764 31 -0.08890639 1.70307441 32 2.62601344 -0.08890639 33 0.59818246 2.62601344 34 0.51447054 0.59818246 35 3.01237225 0.51447054 36 -3.90551653 3.01237225 37 1.50784674 -3.90551653 38 2.60654266 1.50784674 39 -0.73493537 2.60654266 40 1.19446458 -0.73493537 41 1.77210093 1.19446458 42 1.77184805 1.77210093 43 -1.49704064 1.77184805 44 0.55072825 -1.49704064 45 -2.92097782 0.55072825 46 0.77494854 -2.92097782 47 0.68035544 0.77494854 48 2.54347535 0.68035544 49 -0.56275331 2.54347535 50 1.70097289 -0.56275331 51 0.53688615 1.70097289 52 -2.14504655 0.53688615 53 -2.36972829 -2.14504655 54 -0.91686482 -2.36972829 55 0.62560896 -0.91686482 56 1.41368529 0.62560896 57 0.68671523 1.41368529 58 -1.60651661 0.68671523 59 -2.50929311 -1.60651661 60 -5.25088682 -2.50929311 61 -0.76865991 -5.25088682 62 -3.40161530 -0.76865991 63 -0.13009714 -3.40161530 64 0.50221807 -0.13009714 65 -3.74162091 0.50221807 66 -3.69411602 -3.74162091 67 -1.91618434 -3.69411602 68 0.98543915 -1.91618434 69 1.36123127 0.98543915 70 0.91263167 1.36123127 71 1.91788505 0.91263167 72 0.42446623 1.91788505 73 1.13759419 0.42446623 74 -0.77140639 1.13759419 75 -2.81752390 -0.77140639 76 2.53236867 -2.81752390 77 -1.51387712 2.53236867 78 1.81322993 -1.51387712 79 -0.85331538 1.81322993 80 1.39984319 -0.85331538 81 0.57087947 1.39984319 82 2.36159638 0.57087947 83 0.70765843 2.36159638 84 -0.50266931 0.70765843 85 1.50091959 -0.50266931 86 -0.42249674 1.50091959 87 -1.34527290 -0.42249674 88 -7.31954344 -1.34527290 89 3.09160128 -7.31954344 90 -1.60705566 3.09160128 91 0.63485573 -1.60705566 92 -0.37221078 0.63485573 93 -1.02041893 -0.37221078 94 1.41348305 -1.02041893 95 -2.49961947 1.41348305 96 -0.57337049 -2.49961947 97 3.25864308 -0.57337049 98 1.63285054 3.25864308 99 2.50516679 1.63285054 100 -1.74145804 2.50516679 101 1.11607257 -1.74145804 102 -2.59795416 1.11607257 103 0.64137840 -2.59795416 104 -4.49841193 0.64137840 105 1.33803499 -4.49841193 106 1.22940144 1.33803499 107 -3.71415505 1.22940144 108 -4.17226063 -3.71415505 109 0.65941224 -4.17226063 110 -3.35169445 0.65941224 111 -1.41827784 -3.35169445 112 3.91189037 -1.41827784 113 2.00485541 3.91189037 114 -0.07254243 2.00485541 115 -0.18032136 -0.07254243 116 -0.06575576 -0.18032136 117 -0.52702358 -0.06575576 118 -1.24341583 -0.52702358 119 -0.46375403 -1.24341583 120 0.87780072 -0.46375403 121 0.56445792 0.87780072 122 0.58265463 0.56445792 123 -1.33559926 0.58265463 124 3.25880596 -1.33559926 125 2.22209810 3.25880596 126 4.68015319 2.22209810 127 0.96659748 4.68015319 128 -0.65302639 0.96659748 129 -3.83002909 -0.65302639 130 0.36998375 -3.83002909 131 1.92739104 0.36998375 132 1.56051408 1.92739104 133 1.64378329 1.56051408 134 -2.37900222 1.64378329 135 0.45939831 -2.37900222 136 -2.76204724 0.45939831 137 0.93430599 -2.76204724 138 -0.01509362 0.93430599 139 0.54042550 -0.01509362 140 2.56840804 0.54042550 141 -0.39525550 2.56840804 142 1.23043114 -0.39525550 143 1.98770355 1.23043114 144 1.27104825 1.98770355 145 -2.32522369 1.27104825 146 -1.65282414 -2.32522369 147 -4.41018252 -1.65282414 148 2.15416688 -4.41018252 149 -1.48519894 2.15416688 150 2.35624276 -1.48519894 151 -2.49370353 2.35624276 152 -5.38549773 -2.49370353 153 -0.82117549 -5.38549773 154 -0.08694058 -0.82117549 155 4.68015319 -0.08694058 156 -1.49788306 4.68015319 157 -1.91980877 -1.49788306 158 -2.27348350 -1.91980877 159 1.70406152 -2.27348350 160 3.12673331 1.70406152 161 -3.28492814 3.12673331 162 -0.80294653 -3.28492814 163 1.65371380 -0.80294653 164 -0.78244127 1.65371380 165 -4.03083710 -0.78244127 166 -3.42220050 -4.03083710 167 -1.42807168 -3.42220050 168 2.54172075 -1.42807168 169 -3.39283146 2.54172075 170 0.29306855 -3.39283146 171 2.87356363 0.29306855 172 0.51214857 2.87356363 173 -4.13814979 0.51214857 174 -0.18856404 -4.13814979 175 2.78797568 -0.18856404 176 -2.08538133 2.78797568 177 0.52572668 -2.08538133 178 0.74558279 0.52572668 179 2.14403692 0.74558279 180 1.31644360 2.14403692 181 3.73159501 1.31644360 182 -0.36506134 3.73159501 183 1.14904780 -0.36506134 184 1.75916090 1.14904780 185 0.92081081 1.75916090 186 -1.09263902 0.92081081 187 -2.12716661 -1.09263902 188 0.53509696 -2.12716661 189 1.91788924 0.53509696 190 -2.44707144 1.91788924 191 1.89561412 -2.44707144 192 -0.56028901 1.89561412 193 0.45207678 -0.56028901 194 0.62362973 0.45207678 195 -4.46038556 0.62362973 196 -3.61606795 -4.46038556 197 2.75345438 -3.61606795 198 4.83716542 2.75345438 199 0.78249768 4.83716542 200 1.63021415 0.78249768 201 1.68318485 1.63021415 202 0.55292856 1.68318485 203 3.34338148 0.55292856 204 0.83064275 3.34338148 205 3.40945927 0.83064275 206 -3.99186006 3.40945927 207 3.78330551 -3.99186006 208 -1.42387516 3.78330551 209 -2.52951320 -1.42387516 210 -2.46296439 -2.52951320 211 2.56427207 -2.46296439 212 3.45659905 2.56427207 213 0.61753393 3.45659905 214 -4.40745423 0.61753393 215 1.69265625 -4.40745423 216 -1.34775862 1.69265625 217 3.46717686 -1.34775862 218 -3.33739323 3.46717686 219 2.81612110 -3.33739323 220 3.48994647 2.81612110 221 6.59432155 3.48994647 222 -0.44439149 6.59432155 223 -4.13084645 -0.44439149 224 -1.21780569 -4.13084645 225 0.74463925 -1.21780569 226 -2.88853058 0.74463925 227 2.29673120 -2.88853058 228 -0.86447484 2.29673120 229 2.84100813 -0.86447484 230 -1.20134538 2.84100813 231 0.52360837 -1.20134538 232 0.52897400 0.52360837 233 -3.36312269 0.52897400 234 -1.17398064 -3.36312269 235 -0.09076212 -1.17398064 236 -4.21291831 -0.09076212 237 -0.02462258 -4.21291831 238 -0.44432974 -0.02462258 239 -1.09738591 -0.44432974 240 0.59890557 -1.09738591 241 1.12714906 0.59890557 242 -2.83862469 1.12714906 243 -0.89312955 -2.83862469 244 2.93486622 -0.89312955 245 0.96721947 2.93486622 246 -0.12704311 0.96721947 247 -0.50462616 -0.12704311 248 -0.31560761 -0.50462616 249 -0.45549818 -0.31560761 250 -3.64062446 -0.45549818 251 -4.22822063 -3.64062446 252 1.80718247 -4.22822063 253 -2.26042300 1.80718247 254 0.77834141 -2.26042300 255 2.99740944 0.77834141 256 -5.27887259 2.99740944 257 1.63283235 -5.27887259 258 -4.65156827 1.63283235 259 -3.29413554 -4.65156827 260 0.92242371 -3.29413554 261 0.78245831 0.92242371 262 -0.47411044 0.78245831 263 -4.10170593 -0.47411044 264 -0.39624642 -4.10170593 265 -0.16194059 -0.39624642 266 -1.55444589 -0.16194059 267 -2.30486605 -1.55444589 268 -0.44697032 -2.30486605 269 -0.64510735 -0.44697032 270 -3.13141381 -0.64510735 271 2.46963238 -3.13141381 272 0.31791602 2.46963238 273 0.94642802 0.31791602 274 0.32708405 0.94642802 275 3.73643823 0.32708405 276 2.67298234 3.73643823 277 1.38225740 2.67298234 278 1.81354227 1.38225740 279 -1.16477324 1.81354227 280 2.18150217 -1.16477324 281 0.65109560 2.18150217 282 0.87735087 0.65109560 283 -0.05922782 0.87735087 284 3.25464646 -0.05922782 285 3.98147712 3.25464646 286 -4.40708433 3.98147712 287 0.87945718 -4.40708433 288 NA 0.87945718 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 3.36560198 -0.22010233 [2,] -3.63315023 3.36560198 [3,] -1.57115678 -3.63315023 [4,] 1.85217238 -1.57115678 [5,] 3.76757867 1.85217238 [6,] -0.99113900 3.76757867 [7,] -0.26667386 -0.99113900 [8,] 0.74948307 -0.26667386 [9,] 0.60292935 0.74948307 [10,] 3.15116752 0.60292935 [11,] 4.49559428 3.15116752 [12,] -3.75266585 4.49559428 [13,] 1.46156637 -3.75266585 [14,] 3.55482365 1.46156637 [15,] 0.49811135 3.55482365 [16,] 0.34071494 0.49811135 [17,] 2.02917508 0.34071494 [18,] 0.01402344 2.02917508 [19,] 1.57281812 0.01402344 [20,] 4.36306880 1.57281812 [21,] -3.41587295 4.36306880 [22,] -0.86308536 -3.41587295 [23,] -2.46783358 -0.86308536 [24,] 3.05310160 -2.46783358 [25,] -5.77576100 3.05310160 [26,] 2.26974977 -5.77576100 [27,] -0.22337680 2.26974977 [28,] 0.56150919 -0.22337680 [29,] -2.46973764 0.56150919 [30,] 1.70307441 -2.46973764 [31,] -0.08890639 1.70307441 [32,] 2.62601344 -0.08890639 [33,] 0.59818246 2.62601344 [34,] 0.51447054 0.59818246 [35,] 3.01237225 0.51447054 [36,] -3.90551653 3.01237225 [37,] 1.50784674 -3.90551653 [38,] 2.60654266 1.50784674 [39,] -0.73493537 2.60654266 [40,] 1.19446458 -0.73493537 [41,] 1.77210093 1.19446458 [42,] 1.77184805 1.77210093 [43,] -1.49704064 1.77184805 [44,] 0.55072825 -1.49704064 [45,] -2.92097782 0.55072825 [46,] 0.77494854 -2.92097782 [47,] 0.68035544 0.77494854 [48,] 2.54347535 0.68035544 [49,] -0.56275331 2.54347535 [50,] 1.70097289 -0.56275331 [51,] 0.53688615 1.70097289 [52,] -2.14504655 0.53688615 [53,] -2.36972829 -2.14504655 [54,] -0.91686482 -2.36972829 [55,] 0.62560896 -0.91686482 [56,] 1.41368529 0.62560896 [57,] 0.68671523 1.41368529 [58,] -1.60651661 0.68671523 [59,] -2.50929311 -1.60651661 [60,] -5.25088682 -2.50929311 [61,] -0.76865991 -5.25088682 [62,] -3.40161530 -0.76865991 [63,] -0.13009714 -3.40161530 [64,] 0.50221807 -0.13009714 [65,] -3.74162091 0.50221807 [66,] -3.69411602 -3.74162091 [67,] -1.91618434 -3.69411602 [68,] 0.98543915 -1.91618434 [69,] 1.36123127 0.98543915 [70,] 0.91263167 1.36123127 [71,] 1.91788505 0.91263167 [72,] 0.42446623 1.91788505 [73,] 1.13759419 0.42446623 [74,] -0.77140639 1.13759419 [75,] -2.81752390 -0.77140639 [76,] 2.53236867 -2.81752390 [77,] -1.51387712 2.53236867 [78,] 1.81322993 -1.51387712 [79,] -0.85331538 1.81322993 [80,] 1.39984319 -0.85331538 [81,] 0.57087947 1.39984319 [82,] 2.36159638 0.57087947 [83,] 0.70765843 2.36159638 [84,] -0.50266931 0.70765843 [85,] 1.50091959 -0.50266931 [86,] -0.42249674 1.50091959 [87,] -1.34527290 -0.42249674 [88,] -7.31954344 -1.34527290 [89,] 3.09160128 -7.31954344 [90,] -1.60705566 3.09160128 [91,] 0.63485573 -1.60705566 [92,] -0.37221078 0.63485573 [93,] -1.02041893 -0.37221078 [94,] 1.41348305 -1.02041893 [95,] -2.49961947 1.41348305 [96,] -0.57337049 -2.49961947 [97,] 3.25864308 -0.57337049 [98,] 1.63285054 3.25864308 [99,] 2.50516679 1.63285054 [100,] -1.74145804 2.50516679 [101,] 1.11607257 -1.74145804 [102,] -2.59795416 1.11607257 [103,] 0.64137840 -2.59795416 [104,] -4.49841193 0.64137840 [105,] 1.33803499 -4.49841193 [106,] 1.22940144 1.33803499 [107,] -3.71415505 1.22940144 [108,] -4.17226063 -3.71415505 [109,] 0.65941224 -4.17226063 [110,] -3.35169445 0.65941224 [111,] -1.41827784 -3.35169445 [112,] 3.91189037 -1.41827784 [113,] 2.00485541 3.91189037 [114,] -0.07254243 2.00485541 [115,] -0.18032136 -0.07254243 [116,] -0.06575576 -0.18032136 [117,] -0.52702358 -0.06575576 [118,] -1.24341583 -0.52702358 [119,] -0.46375403 -1.24341583 [120,] 0.87780072 -0.46375403 [121,] 0.56445792 0.87780072 [122,] 0.58265463 0.56445792 [123,] -1.33559926 0.58265463 [124,] 3.25880596 -1.33559926 [125,] 2.22209810 3.25880596 [126,] 4.68015319 2.22209810 [127,] 0.96659748 4.68015319 [128,] -0.65302639 0.96659748 [129,] -3.83002909 -0.65302639 [130,] 0.36998375 -3.83002909 [131,] 1.92739104 0.36998375 [132,] 1.56051408 1.92739104 [133,] 1.64378329 1.56051408 [134,] -2.37900222 1.64378329 [135,] 0.45939831 -2.37900222 [136,] -2.76204724 0.45939831 [137,] 0.93430599 -2.76204724 [138,] -0.01509362 0.93430599 [139,] 0.54042550 -0.01509362 [140,] 2.56840804 0.54042550 [141,] -0.39525550 2.56840804 [142,] 1.23043114 -0.39525550 [143,] 1.98770355 1.23043114 [144,] 1.27104825 1.98770355 [145,] -2.32522369 1.27104825 [146,] -1.65282414 -2.32522369 [147,] -4.41018252 -1.65282414 [148,] 2.15416688 -4.41018252 [149,] -1.48519894 2.15416688 [150,] 2.35624276 -1.48519894 [151,] -2.49370353 2.35624276 [152,] -5.38549773 -2.49370353 [153,] -0.82117549 -5.38549773 [154,] -0.08694058 -0.82117549 [155,] 4.68015319 -0.08694058 [156,] -1.49788306 4.68015319 [157,] -1.91980877 -1.49788306 [158,] -2.27348350 -1.91980877 [159,] 1.70406152 -2.27348350 [160,] 3.12673331 1.70406152 [161,] -3.28492814 3.12673331 [162,] -0.80294653 -3.28492814 [163,] 1.65371380 -0.80294653 [164,] -0.78244127 1.65371380 [165,] -4.03083710 -0.78244127 [166,] -3.42220050 -4.03083710 [167,] -1.42807168 -3.42220050 [168,] 2.54172075 -1.42807168 [169,] -3.39283146 2.54172075 [170,] 0.29306855 -3.39283146 [171,] 2.87356363 0.29306855 [172,] 0.51214857 2.87356363 [173,] -4.13814979 0.51214857 [174,] -0.18856404 -4.13814979 [175,] 2.78797568 -0.18856404 [176,] -2.08538133 2.78797568 [177,] 0.52572668 -2.08538133 [178,] 0.74558279 0.52572668 [179,] 2.14403692 0.74558279 [180,] 1.31644360 2.14403692 [181,] 3.73159501 1.31644360 [182,] -0.36506134 3.73159501 [183,] 1.14904780 -0.36506134 [184,] 1.75916090 1.14904780 [185,] 0.92081081 1.75916090 [186,] -1.09263902 0.92081081 [187,] -2.12716661 -1.09263902 [188,] 0.53509696 -2.12716661 [189,] 1.91788924 0.53509696 [190,] -2.44707144 1.91788924 [191,] 1.89561412 -2.44707144 [192,] -0.56028901 1.89561412 [193,] 0.45207678 -0.56028901 [194,] 0.62362973 0.45207678 [195,] -4.46038556 0.62362973 [196,] -3.61606795 -4.46038556 [197,] 2.75345438 -3.61606795 [198,] 4.83716542 2.75345438 [199,] 0.78249768 4.83716542 [200,] 1.63021415 0.78249768 [201,] 1.68318485 1.63021415 [202,] 0.55292856 1.68318485 [203,] 3.34338148 0.55292856 [204,] 0.83064275 3.34338148 [205,] 3.40945927 0.83064275 [206,] -3.99186006 3.40945927 [207,] 3.78330551 -3.99186006 [208,] -1.42387516 3.78330551 [209,] -2.52951320 -1.42387516 [210,] -2.46296439 -2.52951320 [211,] 2.56427207 -2.46296439 [212,] 3.45659905 2.56427207 [213,] 0.61753393 3.45659905 [214,] -4.40745423 0.61753393 [215,] 1.69265625 -4.40745423 [216,] -1.34775862 1.69265625 [217,] 3.46717686 -1.34775862 [218,] -3.33739323 3.46717686 [219,] 2.81612110 -3.33739323 [220,] 3.48994647 2.81612110 [221,] 6.59432155 3.48994647 [222,] -0.44439149 6.59432155 [223,] -4.13084645 -0.44439149 [224,] -1.21780569 -4.13084645 [225,] 0.74463925 -1.21780569 [226,] -2.88853058 0.74463925 [227,] 2.29673120 -2.88853058 [228,] -0.86447484 2.29673120 [229,] 2.84100813 -0.86447484 [230,] -1.20134538 2.84100813 [231,] 0.52360837 -1.20134538 [232,] 0.52897400 0.52360837 [233,] -3.36312269 0.52897400 [234,] -1.17398064 -3.36312269 [235,] -0.09076212 -1.17398064 [236,] -4.21291831 -0.09076212 [237,] -0.02462258 -4.21291831 [238,] -0.44432974 -0.02462258 [239,] -1.09738591 -0.44432974 [240,] 0.59890557 -1.09738591 [241,] 1.12714906 0.59890557 [242,] -2.83862469 1.12714906 [243,] -0.89312955 -2.83862469 [244,] 2.93486622 -0.89312955 [245,] 0.96721947 2.93486622 [246,] -0.12704311 0.96721947 [247,] -0.50462616 -0.12704311 [248,] -0.31560761 -0.50462616 [249,] -0.45549818 -0.31560761 [250,] -3.64062446 -0.45549818 [251,] -4.22822063 -3.64062446 [252,] 1.80718247 -4.22822063 [253,] -2.26042300 1.80718247 [254,] 0.77834141 -2.26042300 [255,] 2.99740944 0.77834141 [256,] -5.27887259 2.99740944 [257,] 1.63283235 -5.27887259 [258,] -4.65156827 1.63283235 [259,] -3.29413554 -4.65156827 [260,] 0.92242371 -3.29413554 [261,] 0.78245831 0.92242371 [262,] -0.47411044 0.78245831 [263,] -4.10170593 -0.47411044 [264,] -0.39624642 -4.10170593 [265,] -0.16194059 -0.39624642 [266,] -1.55444589 -0.16194059 [267,] -2.30486605 -1.55444589 [268,] -0.44697032 -2.30486605 [269,] -0.64510735 -0.44697032 [270,] -3.13141381 -0.64510735 [271,] 2.46963238 -3.13141381 [272,] 0.31791602 2.46963238 [273,] 0.94642802 0.31791602 [274,] 0.32708405 0.94642802 [275,] 3.73643823 0.32708405 [276,] 2.67298234 3.73643823 [277,] 1.38225740 2.67298234 [278,] 1.81354227 1.38225740 [279,] -1.16477324 1.81354227 [280,] 2.18150217 -1.16477324 [281,] 0.65109560 2.18150217 [282,] 0.87735087 0.65109560 [283,] -0.05922782 0.87735087 [284,] 3.25464646 -0.05922782 [285,] 3.98147712 3.25464646 [286,] -4.40708433 3.98147712 [287,] 0.87945718 -4.40708433 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 3.36560198 -0.22010233 2 -3.63315023 3.36560198 3 -1.57115678 -3.63315023 4 1.85217238 -1.57115678 5 3.76757867 1.85217238 6 -0.99113900 3.76757867 7 -0.26667386 -0.99113900 8 0.74948307 -0.26667386 9 0.60292935 0.74948307 10 3.15116752 0.60292935 11 4.49559428 3.15116752 12 -3.75266585 4.49559428 13 1.46156637 -3.75266585 14 3.55482365 1.46156637 15 0.49811135 3.55482365 16 0.34071494 0.49811135 17 2.02917508 0.34071494 18 0.01402344 2.02917508 19 1.57281812 0.01402344 20 4.36306880 1.57281812 21 -3.41587295 4.36306880 22 -0.86308536 -3.41587295 23 -2.46783358 -0.86308536 24 3.05310160 -2.46783358 25 -5.77576100 3.05310160 26 2.26974977 -5.77576100 27 -0.22337680 2.26974977 28 0.56150919 -0.22337680 29 -2.46973764 0.56150919 30 1.70307441 -2.46973764 31 -0.08890639 1.70307441 32 2.62601344 -0.08890639 33 0.59818246 2.62601344 34 0.51447054 0.59818246 35 3.01237225 0.51447054 36 -3.90551653 3.01237225 37 1.50784674 -3.90551653 38 2.60654266 1.50784674 39 -0.73493537 2.60654266 40 1.19446458 -0.73493537 41 1.77210093 1.19446458 42 1.77184805 1.77210093 43 -1.49704064 1.77184805 44 0.55072825 -1.49704064 45 -2.92097782 0.55072825 46 0.77494854 -2.92097782 47 0.68035544 0.77494854 48 2.54347535 0.68035544 49 -0.56275331 2.54347535 50 1.70097289 -0.56275331 51 0.53688615 1.70097289 52 -2.14504655 0.53688615 53 -2.36972829 -2.14504655 54 -0.91686482 -2.36972829 55 0.62560896 -0.91686482 56 1.41368529 0.62560896 57 0.68671523 1.41368529 58 -1.60651661 0.68671523 59 -2.50929311 -1.60651661 60 -5.25088682 -2.50929311 61 -0.76865991 -5.25088682 62 -3.40161530 -0.76865991 63 -0.13009714 -3.40161530 64 0.50221807 -0.13009714 65 -3.74162091 0.50221807 66 -3.69411602 -3.74162091 67 -1.91618434 -3.69411602 68 0.98543915 -1.91618434 69 1.36123127 0.98543915 70 0.91263167 1.36123127 71 1.91788505 0.91263167 72 0.42446623 1.91788505 73 1.13759419 0.42446623 74 -0.77140639 1.13759419 75 -2.81752390 -0.77140639 76 2.53236867 -2.81752390 77 -1.51387712 2.53236867 78 1.81322993 -1.51387712 79 -0.85331538 1.81322993 80 1.39984319 -0.85331538 81 0.57087947 1.39984319 82 2.36159638 0.57087947 83 0.70765843 2.36159638 84 -0.50266931 0.70765843 85 1.50091959 -0.50266931 86 -0.42249674 1.50091959 87 -1.34527290 -0.42249674 88 -7.31954344 -1.34527290 89 3.09160128 -7.31954344 90 -1.60705566 3.09160128 91 0.63485573 -1.60705566 92 -0.37221078 0.63485573 93 -1.02041893 -0.37221078 94 1.41348305 -1.02041893 95 -2.49961947 1.41348305 96 -0.57337049 -2.49961947 97 3.25864308 -0.57337049 98 1.63285054 3.25864308 99 2.50516679 1.63285054 100 -1.74145804 2.50516679 101 1.11607257 -1.74145804 102 -2.59795416 1.11607257 103 0.64137840 -2.59795416 104 -4.49841193 0.64137840 105 1.33803499 -4.49841193 106 1.22940144 1.33803499 107 -3.71415505 1.22940144 108 -4.17226063 -3.71415505 109 0.65941224 -4.17226063 110 -3.35169445 0.65941224 111 -1.41827784 -3.35169445 112 3.91189037 -1.41827784 113 2.00485541 3.91189037 114 -0.07254243 2.00485541 115 -0.18032136 -0.07254243 116 -0.06575576 -0.18032136 117 -0.52702358 -0.06575576 118 -1.24341583 -0.52702358 119 -0.46375403 -1.24341583 120 0.87780072 -0.46375403 121 0.56445792 0.87780072 122 0.58265463 0.56445792 123 -1.33559926 0.58265463 124 3.25880596 -1.33559926 125 2.22209810 3.25880596 126 4.68015319 2.22209810 127 0.96659748 4.68015319 128 -0.65302639 0.96659748 129 -3.83002909 -0.65302639 130 0.36998375 -3.83002909 131 1.92739104 0.36998375 132 1.56051408 1.92739104 133 1.64378329 1.56051408 134 -2.37900222 1.64378329 135 0.45939831 -2.37900222 136 -2.76204724 0.45939831 137 0.93430599 -2.76204724 138 -0.01509362 0.93430599 139 0.54042550 -0.01509362 140 2.56840804 0.54042550 141 -0.39525550 2.56840804 142 1.23043114 -0.39525550 143 1.98770355 1.23043114 144 1.27104825 1.98770355 145 -2.32522369 1.27104825 146 -1.65282414 -2.32522369 147 -4.41018252 -1.65282414 148 2.15416688 -4.41018252 149 -1.48519894 2.15416688 150 2.35624276 -1.48519894 151 -2.49370353 2.35624276 152 -5.38549773 -2.49370353 153 -0.82117549 -5.38549773 154 -0.08694058 -0.82117549 155 4.68015319 -0.08694058 156 -1.49788306 4.68015319 157 -1.91980877 -1.49788306 158 -2.27348350 -1.91980877 159 1.70406152 -2.27348350 160 3.12673331 1.70406152 161 -3.28492814 3.12673331 162 -0.80294653 -3.28492814 163 1.65371380 -0.80294653 164 -0.78244127 1.65371380 165 -4.03083710 -0.78244127 166 -3.42220050 -4.03083710 167 -1.42807168 -3.42220050 168 2.54172075 -1.42807168 169 -3.39283146 2.54172075 170 0.29306855 -3.39283146 171 2.87356363 0.29306855 172 0.51214857 2.87356363 173 -4.13814979 0.51214857 174 -0.18856404 -4.13814979 175 2.78797568 -0.18856404 176 -2.08538133 2.78797568 177 0.52572668 -2.08538133 178 0.74558279 0.52572668 179 2.14403692 0.74558279 180 1.31644360 2.14403692 181 3.73159501 1.31644360 182 -0.36506134 3.73159501 183 1.14904780 -0.36506134 184 1.75916090 1.14904780 185 0.92081081 1.75916090 186 -1.09263902 0.92081081 187 -2.12716661 -1.09263902 188 0.53509696 -2.12716661 189 1.91788924 0.53509696 190 -2.44707144 1.91788924 191 1.89561412 -2.44707144 192 -0.56028901 1.89561412 193 0.45207678 -0.56028901 194 0.62362973 0.45207678 195 -4.46038556 0.62362973 196 -3.61606795 -4.46038556 197 2.75345438 -3.61606795 198 4.83716542 2.75345438 199 0.78249768 4.83716542 200 1.63021415 0.78249768 201 1.68318485 1.63021415 202 0.55292856 1.68318485 203 3.34338148 0.55292856 204 0.83064275 3.34338148 205 3.40945927 0.83064275 206 -3.99186006 3.40945927 207 3.78330551 -3.99186006 208 -1.42387516 3.78330551 209 -2.52951320 -1.42387516 210 -2.46296439 -2.52951320 211 2.56427207 -2.46296439 212 3.45659905 2.56427207 213 0.61753393 3.45659905 214 -4.40745423 0.61753393 215 1.69265625 -4.40745423 216 -1.34775862 1.69265625 217 3.46717686 -1.34775862 218 -3.33739323 3.46717686 219 2.81612110 -3.33739323 220 3.48994647 2.81612110 221 6.59432155 3.48994647 222 -0.44439149 6.59432155 223 -4.13084645 -0.44439149 224 -1.21780569 -4.13084645 225 0.74463925 -1.21780569 226 -2.88853058 0.74463925 227 2.29673120 -2.88853058 228 -0.86447484 2.29673120 229 2.84100813 -0.86447484 230 -1.20134538 2.84100813 231 0.52360837 -1.20134538 232 0.52897400 0.52360837 233 -3.36312269 0.52897400 234 -1.17398064 -3.36312269 235 -0.09076212 -1.17398064 236 -4.21291831 -0.09076212 237 -0.02462258 -4.21291831 238 -0.44432974 -0.02462258 239 -1.09738591 -0.44432974 240 0.59890557 -1.09738591 241 1.12714906 0.59890557 242 -2.83862469 1.12714906 243 -0.89312955 -2.83862469 244 2.93486622 -0.89312955 245 0.96721947 2.93486622 246 -0.12704311 0.96721947 247 -0.50462616 -0.12704311 248 -0.31560761 -0.50462616 249 -0.45549818 -0.31560761 250 -3.64062446 -0.45549818 251 -4.22822063 -3.64062446 252 1.80718247 -4.22822063 253 -2.26042300 1.80718247 254 0.77834141 -2.26042300 255 2.99740944 0.77834141 256 -5.27887259 2.99740944 257 1.63283235 -5.27887259 258 -4.65156827 1.63283235 259 -3.29413554 -4.65156827 260 0.92242371 -3.29413554 261 0.78245831 0.92242371 262 -0.47411044 0.78245831 263 -4.10170593 -0.47411044 264 -0.39624642 -4.10170593 265 -0.16194059 -0.39624642 266 -1.55444589 -0.16194059 267 -2.30486605 -1.55444589 268 -0.44697032 -2.30486605 269 -0.64510735 -0.44697032 270 -3.13141381 -0.64510735 271 2.46963238 -3.13141381 272 0.31791602 2.46963238 273 0.94642802 0.31791602 274 0.32708405 0.94642802 275 3.73643823 0.32708405 276 2.67298234 3.73643823 277 1.38225740 2.67298234 278 1.81354227 1.38225740 279 -1.16477324 1.81354227 280 2.18150217 -1.16477324 281 0.65109560 2.18150217 282 0.87735087 0.65109560 283 -0.05922782 0.87735087 284 3.25464646 -0.05922782 285 3.98147712 3.25464646 286 -4.40708433 3.98147712 287 0.87945718 -4.40708433 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7o4b11386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8xlf61386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9bnfu1386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10l9341386543953.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, 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/fisher/rcomp/tmp/113lz41386543953.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/fisher/rcomp/tmp/12dqoc1386543953.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/fisher/rcomp/tmp/13p9an1386543954.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/fisher/rcomp/tmp/14okib1386543954.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/fisher/rcomp/tmp/1516bw1386543954.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/fisher/rcomp/tmp/16oeke1386543954.tab") + } > > try(system("convert tmp/1z58r1386543953.ps tmp/1z58r1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/2yb0o1386543953.ps tmp/2yb0o1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/3x6fu1386543953.ps tmp/3x6fu1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/4d51d1386543953.ps tmp/4d51d1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/53bbl1386543953.ps tmp/53bbl1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/6zvmt1386543953.ps tmp/6zvmt1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/7o4b11386543953.ps tmp/7o4b11386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/8xlf61386543953.ps tmp/8xlf61386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/9bnfu1386543953.ps tmp/9bnfu1386543953.png",intern=TRUE)) character(0) > try(system("convert tmp/10l9341386543953.ps tmp/10l9341386543953.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 22.407 3.387 25.815