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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,72) + ,dim=c(7 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Sport1') + ,1:264)) > y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1'),1:264)) > 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 = '5' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '5' > #'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 Connected Separate Learning Software Depression Sport1 1 14 41 38 13 12 12.0 53 2 18 39 32 16 11 11.0 83 3 11 30 35 19 15 14.0 66 4 12 31 33 15 6 12.0 67 5 16 34 37 14 13 21.0 76 6 18 35 29 13 10 12.0 78 7 14 39 31 19 12 22.0 53 8 14 34 36 15 14 11.0 80 9 15 36 35 14 12 10.0 74 10 15 37 38 15 9 13.0 76 11 17 38 31 16 10 10.0 79 12 19 36 34 16 12 8.0 54 13 10 38 35 16 12 15.0 67 14 16 39 38 16 11 14.0 54 15 18 33 37 17 15 10.0 87 16 14 32 33 15 12 14.0 58 17 14 36 32 15 10 14.0 75 18 17 38 38 20 12 11.0 88 19 14 39 38 18 11 10.0 64 20 16 32 32 16 12 13.0 57 21 18 32 33 16 11 9.5 66 22 11 31 31 16 12 14.0 68 23 14 39 38 19 13 12.0 54 24 12 37 39 16 11 14.0 56 25 17 39 32 17 12 11.0 86 26 9 41 32 17 13 9.0 80 27 16 36 35 16 10 11.0 76 28 14 33 37 15 14 15.0 69 29 15 33 33 16 12 14.0 78 30 11 34 33 14 10 13.0 67 31 16 31 31 15 12 9.0 80 32 13 27 32 12 8 15.0 54 33 17 37 31 14 10 10.0 71 34 15 34 37 16 12 11.0 84 35 14 34 30 14 12 13.0 74 36 16 32 33 10 7 8.0 71 37 9 29 31 10 9 20.0 63 38 15 36 33 14 12 12.0 71 39 17 29 31 16 10 10.0 76 40 13 35 33 16 10 10.0 69 41 15 37 32 16 10 9.0 74 42 16 34 33 14 12 14.0 75 43 16 38 32 20 15 8.0 54 44 12 35 33 14 10 14.0 52 45 15 38 28 14 10 11.0 69 46 11 37 35 11 12 13.0 68 47 15 38 39 14 13 9.0 65 48 15 33 34 15 11 11.0 75 49 17 36 38 16 11 15.0 74 50 13 38 32 14 12 11.0 75 51 16 32 38 16 14 10.0 72 52 14 32 30 14 10 14.0 67 53 11 32 33 12 12 18.0 63 54 12 34 38 16 13 14.0 62 55 12 32 32 9 5 11.0 63 56 15 37 35 14 6 14.5 76 57 16 39 34 16 12 13.0 74 58 15 29 34 16 12 9.0 67 59 12 37 36 15 11 10.0 73 60 12 35 34 16 10 15.0 70 61 8 30 28 12 7 20.0 53 62 13 38 34 16 12 12.0 77 63 11 34 35 16 14 12.0 80 64 14 31 35 14 11 14.0 52 65 15 34 31 16 12 13.0 54 66 10 35 37 17 13 11.0 80 67 11 36 35 18 14 17.0 66 68 12 30 27 18 11 12.0 73 69 15 39 40 12 12 13.0 63 70 15 35 37 16 12 14.0 69 71 14 38 36 10 8 13.0 67 72 16 31 38 14 11 15.0 54 73 15 34 39 18 14 13.0 81 74 15 38 41 18 14 10.0 69 75 13 34 27 16 12 11.0 84 76 12 39 30 17 9 19.0 80 77 17 37 37 16 13 13.0 70 78 13 34 31 16 11 17.0 69 79 15 28 31 13 12 13.0 77 80 13 37 27 16 12 9.0 54 81 15 33 36 16 12 11.0 79 82 15 35 37 16 12 9.0 71 83 16 37 33 15 12 12.0 73 84 15 32 34 15 11 12.0 72 85 14 33 31 16 10 13.0 77 86 15 38 39 14 9 13.0 75 87 14 33 34 16 12 12.0 69 88 13 29 32 16 12 15.0 54 89 7 33 33 15 12 22.0 70 90 17 31 36 12 9 13.0 73 91 13 36 32 17 15 15.0 54 92 15 35 41 16 12 13.0 77 93 14 32 28 15 12 15.0 82 94 13 29 30 13 12 12.5 80 95 16 39 36 16 10 11.0 80 96 12 37 35 16 13 16.0 69 97 14 35 31 16 9 11.0 78 98 17 37 34 16 12 11.0 81 99 15 32 36 14 10 10.0 76 100 17 38 36 16 14 10.0 76 101 12 37 35 16 11 16.0 73 102 16 36 37 20 15 12.0 85 103 11 32 28 15 11 11.0 66 104 15 33 39 16 11 16.0 79 105 9 40 32 13 12 19.0 68 106 16 38 35 17 12 11.0 76 107 15 41 39 16 12 16.0 71 108 10 36 35 16 11 15.0 54 109 10 43 42 12 7 24.0 46 110 15 30 34 16 12 14.0 85 111 11 31 33 16 14 15.0 74 112 13 32 41 17 11 11.0 88 113 14 32 33 13 11 15.0 38 114 18 37 34 12 10 12.0 76 115 16 37 32 18 13 10.0 86 116 14 33 40 14 13 14.0 54 117 14 34 40 14 8 13.0 67 118 14 33 35 13 11 9.0 69 119 14 38 36 16 12 15.0 90 120 12 33 37 13 11 15.0 54 121 14 31 27 16 13 14.0 76 122 15 38 39 13 12 11.0 89 123 15 37 38 16 14 8.0 76 124 15 36 31 15 13 11.0 73 125 13 31 33 16 15 11.0 79 126 17 39 32 15 10 8.0 90 127 17 44 39 17 11 10.0 74 128 19 33 36 15 9 11.0 81 129 15 35 33 12 11 13.0 72 130 13 32 33 16 10 11.0 71 131 9 28 32 10 11 20.0 66 132 15 40 37 16 8 10.0 77 133 15 27 30 12 11 15.0 65 134 15 37 38 14 12 12.0 74 135 16 32 29 15 12 14.0 85 136 11 28 22 13 9 23.0 54 137 14 34 35 15 11 14.0 63 138 11 30 35 11 10 16.0 54 139 15 35 34 12 8 11.0 64 140 13 31 35 11 9 12.0 69 141 15 32 34 16 8 10.0 54 142 16 30 37 15 9 14.0 84 143 14 30 35 17 15 12.0 86 144 15 31 23 16 11 12.0 77 145 16 40 31 10 8 11.0 89 146 16 32 27 18 13 12.0 76 147 11 36 36 13 12 13.0 60 148 12 32 31 16 12 11.0 75 149 9 35 32 13 9 19.0 73 150 16 38 39 10 7 12.0 85 151 13 42 37 15 13 17.0 79 152 16 34 38 16 9 9.0 71 153 12 35 39 16 6 12.0 72 154 9 38 34 14 8 19.0 69 155 13 33 31 10 8 18.0 78 156 13 36 32 17 15 15.0 54 157 14 32 37 13 6 14.0 69 158 19 33 36 15 9 11.0 81 159 13 34 32 16 11 9.0 84 160 12 32 38 12 8 18.0 84 161 13 34 36 13 8 16.0 69 162 10 27 26 13 10 24.0 66 163 14 31 26 12 8 14.0 81 164 16 38 33 17 14 20.0 82 165 10 34 39 15 10 18.0 72 166 11 24 30 10 8 23.0 54 167 14 30 33 14 11 12.0 78 168 12 26 25 11 12 14.0 74 169 9 34 38 13 12 16.0 82 170 9 27 37 16 12 18.0 73 171 11 37 31 12 5 20.0 55 172 16 36 37 16 12 12.0 72 173 9 41 35 12 10 12.0 78 174 13 29 25 9 7 17.0 59 175 16 36 28 12 12 13.0 72 176 13 32 35 15 11 9.0 78 177 9 37 33 12 8 16.0 68 178 12 30 30 12 9 18.0 69 179 16 31 31 14 10 10.0 67 180 11 38 37 12 9 14.0 74 181 14 36 36 16 12 11.0 54 182 13 35 30 11 6 9.0 67 183 15 31 36 19 15 11.0 70 184 14 38 32 15 12 10.0 80 185 16 22 28 8 12 11.0 89 186 13 32 36 16 12 19.0 76 187 14 36 34 17 11 14.0 74 188 15 39 31 12 7 12.0 87 189 13 28 28 11 7 14.0 54 190 11 32 36 11 5 21.0 61 191 11 32 36 14 12 13.0 38 192 14 38 40 16 12 10.0 75 193 15 32 33 12 3 15.0 69 194 11 35 37 16 11 16.0 62 195 15 32 32 13 10 14.0 72 196 12 37 38 15 12 12.0 70 197 14 34 31 16 9 19.0 79 198 14 33 37 16 12 15.0 87 199 8 33 33 14 9 19.0 62 200 13 26 32 16 12 13.0 77 201 9 30 30 16 12 17.0 69 202 15 24 30 14 10 12.0 69 203 17 34 31 11 9 11.0 75 204 13 34 32 12 12 14.0 54 205 15 33 34 15 8 11.0 72 206 15 34 36 15 11 13.0 74 207 14 35 37 16 11 12.0 85 208 16 35 36 16 12 15.0 52 209 13 36 33 11 10 14.0 70 210 16 34 33 15 10 12.0 84 211 9 34 33 12 12 17.0 64 212 16 41 44 12 12 11.0 84 213 11 32 39 15 11 18.0 87 214 10 30 32 15 8 13.0 79 215 11 35 35 16 12 17.0 67 216 15 28 25 14 10 13.0 65 217 17 33 35 17 11 11.0 85 218 14 39 34 14 10 12.0 83 219 8 36 35 13 8 22.0 61 220 15 36 39 15 12 14.0 82 221 11 35 33 13 12 12.0 76 222 16 38 36 14 10 12.0 58 223 10 33 32 15 12 17.0 72 224 15 31 32 12 9 9.0 72 225 9 34 36 13 9 21.0 38 226 16 32 36 8 6 10.0 78 227 19 31 32 14 10 11.0 54 228 12 33 34 14 9 12.0 63 229 8 34 33 11 9 23.0 66 230 11 34 35 12 9 13.0 70 231 14 34 30 13 6 12.0 71 232 9 33 38 10 10 16.0 67 233 15 32 34 16 6 9.0 58 234 13 41 33 18 14 17.0 72 235 16 34 32 13 10 9.0 72 236 11 36 31 11 10 14.0 70 237 12 37 30 4 6 17.0 76 238 13 36 27 13 12 13.0 50 239 10 29 31 16 12 11.0 72 240 11 37 30 10 7 12.0 72 241 12 27 32 12 8 10.0 88 242 8 35 35 12 11 19.0 53 243 12 28 28 10 3 16.0 58 244 12 35 33 13 6 16.0 66 245 15 37 31 15 10 14.0 82 246 11 29 35 12 8 20.0 69 247 13 32 35 14 9 15.0 68 248 14 36 32 10 9 23.0 44 249 10 19 21 12 8 20.0 56 250 12 21 20 12 9 16.0 53 251 15 31 34 11 7 14.0 70 252 13 33 32 10 7 17.0 78 253 13 36 34 12 6 11.0 71 254 13 33 32 16 9 13.0 72 255 12 37 33 12 10 17.0 68 256 12 34 33 14 11 15.0 67 257 9 35 37 16 12 21.0 75 258 9 31 32 14 8 18.0 62 259 15 37 34 13 11 15.0 67 260 10 35 30 4 3 8.0 83 261 14 27 30 15 11 12.0 64 262 15 34 38 11 12 12.0 68 263 7 40 36 11 7 22.0 62 264 14 29 32 14 9 12.0 72 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Learning Software Depression 14.653495 0.014201 0.010280 0.112860 -0.007641 -0.376277 Sport1 0.023006 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.8380 -1.3748 0.2488 1.2801 5.1560 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 14.653495 1.831853 7.999 4.29e-14 *** Connected 0.014201 0.037218 0.382 0.7031 Separate 0.010280 0.038258 0.269 0.7884 Learning 0.112860 0.066521 1.697 0.0910 . Software -0.007641 0.068779 -0.111 0.9116 Depression -0.376277 0.038824 -9.692 < 2e-16 *** Sport1 0.023006 0.012750 1.804 0.0724 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.017 on 257 degrees of freedom Multiple R-squared: 0.363, Adjusted R-squared: 0.3481 F-statistic: 24.41 on 6 and 257 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.05059810 0.101196198 0.949401901 [2,] 0.01419615 0.028392305 0.985803848 [3,] 0.82769286 0.344614279 0.172307139 [4,] 0.96570703 0.068585939 0.034292970 [5,] 0.96753486 0.064930273 0.032465137 [6,] 0.97634527 0.047309460 0.023654730 [7,] 0.96089885 0.078202296 0.039101148 [8,] 0.94807015 0.103859700 0.051929850 [9,] 0.92927222 0.141455552 0.070727776 [10,] 0.90677095 0.186458099 0.093229050 [11,] 0.90111651 0.197766975 0.098883488 [12,] 0.91710950 0.165781003 0.082890501 [13,] 0.95086311 0.098273778 0.049136889 [14,] 0.93176933 0.136461342 0.068230671 [15,] 0.91520952 0.169580959 0.084790480 [16,] 0.89089232 0.218215351 0.109107675 [17,] 0.99832589 0.003348225 0.001674113 [18,] 0.99744868 0.005102642 0.002551321 [19,] 0.99608885 0.007822309 0.003911155 [20,] 0.99418376 0.011632476 0.005816238 [21,] 0.99682040 0.006359202 0.003179601 [22,] 0.99521991 0.009560189 0.004780094 [23,] 0.99306200 0.013876003 0.006938001 [24,] 0.99170013 0.016599730 0.008299865 [25,] 0.98819120 0.023617610 0.011808805 [26,] 0.98431053 0.031378947 0.015689473 [27,] 0.97845706 0.043085887 0.021542944 [28,] 0.98420422 0.031591555 0.015795777 [29,] 0.97861620 0.042767591 0.021383796 [30,] 0.97602425 0.047951491 0.023975746 [31,] 0.97632442 0.047351152 0.023675576 [32,] 0.96939837 0.061203259 0.030601630 [33,] 0.96716936 0.065661285 0.032830642 [34,] 0.95753940 0.084921191 0.042460596 [35,] 0.94974357 0.100512860 0.050256430 [36,] 0.93628599 0.127428025 0.063714012 [37,] 0.94676654 0.106466914 0.053233457 [38,] 0.93269691 0.134606178 0.067303089 [39,] 0.91610753 0.167784938 0.083892469 [40,] 0.93356011 0.132879777 0.066439889 [41,] 0.92947223 0.141055531 0.070527765 [42,] 0.91499250 0.170015002 0.085007501 [43,] 0.89676139 0.206477213 0.103238607 [44,] 0.88066334 0.238673320 0.119336660 [45,] 0.87257572 0.254848550 0.127424275 [46,] 0.86295547 0.274089068 0.137044534 [47,] 0.84354765 0.312904695 0.156452348 [48,] 0.83035902 0.339281962 0.169640981 [49,] 0.80140032 0.397199355 0.198599677 [50,] 0.83802479 0.323950410 0.161975205 [51,] 0.83305385 0.333892297 0.166946149 [52,] 0.85161983 0.296760346 0.148380173 [53,] 0.84775864 0.304482723 0.152241362 [54,] 0.90052711 0.198945772 0.099472886 [55,] 0.88813929 0.223721424 0.111860712 [56,] 0.87812110 0.243757797 0.121878898 [57,] 0.95478400 0.090432004 0.045216002 [58,] 0.95324437 0.093511257 0.046755629 [59,] 0.95806449 0.083871030 0.041935515 [60,] 0.95274450 0.094510997 0.047255499 [61,] 0.94608360 0.107832804 0.053916402 [62,] 0.93480399 0.130392016 0.065196008 [63,] 0.95023326 0.099533473 0.049766737 [64,] 0.93959841 0.120803181 0.060401590 [65,] 0.92743464 0.145130721 0.072565360 [66,] 0.92259802 0.154803959 0.077401980 [67,] 0.90827681 0.183446381 0.091723190 [68,] 0.92135268 0.157294636 0.078647318 [69,] 0.90683418 0.186331631 0.093165816 [70,] 0.89745915 0.205081703 0.102540851 [71,] 0.89012595 0.219748090 0.109874045 [72,] 0.87086372 0.258272557 0.129136278 [73,] 0.85060499 0.298790021 0.149395011 [74,] 0.84304945 0.313901105 0.156950552 [75,] 0.82184264 0.356314728 0.178157364 [76,] 0.79572833 0.408543331 0.204271665 [77,] 0.77147667 0.457046650 0.228523325 [78,] 0.74214416 0.515711681 0.257855841 [79,] 0.71100309 0.577993824 0.288996912 [80,] 0.78669010 0.426619794 0.213309897 [81,] 0.82135578 0.357288432 0.178644216 [82,] 0.79632211 0.407355777 0.203677888 [83,] 0.77215039 0.455699228 0.227849614 [84,] 0.74807373 0.503852532 0.251926266 [85,] 0.72285155 0.554296905 0.277148453 [86,] 0.69589428 0.608211435 0.304105717 [87,] 0.67004542 0.659909164 0.329954582 [88,] 0.64207451 0.715850987 0.357925494 [89,] 0.64067019 0.718659614 0.359329807 [90,] 0.60650403 0.786991946 0.393495973 [91,] 0.59701342 0.805973169 0.402986585 [92,] 0.57272732 0.854545361 0.427272680 [93,] 0.54313173 0.913736550 0.456868275 [94,] 0.59333641 0.813327170 0.406663585 [95,] 0.58206185 0.835876300 0.417938150 [96,] 0.59820291 0.803594176 0.401797088 [97,] 0.57055374 0.858892528 0.429446264 [98,] 0.56320293 0.873594138 0.436797069 [99,] 0.60034277 0.799314457 0.399657229 [100,] 0.57403577 0.851928450 0.425964225 [101,] 0.54799056 0.904018883 0.452009442 [102,] 0.55260346 0.894793083 0.447396542 [103,] 0.58366975 0.832660509 0.416330254 [104,] 0.58346627 0.833067457 0.416533728 [105,] 0.66876824 0.662463529 0.331231765 [106,] 0.63713973 0.725720534 0.362860267 [107,] 0.61402342 0.771953157 0.385976578 [108,] 0.58404540 0.831909200 0.415954600 [109,] 0.56042843 0.879143144 0.439571572 [110,] 0.52605198 0.947896046 0.473948023 [111,] 0.49495425 0.989908505 0.505045748 [112,] 0.46388099 0.927761975 0.536119013 [113,] 0.43288261 0.865765221 0.567117389 [114,] 0.40672448 0.813448951 0.593275525 [115,] 0.37406572 0.748131447 0.625934277 [116,] 0.36324334 0.726486681 0.636756659 [117,] 0.33375508 0.667510166 0.666244917 [118,] 0.32071743 0.641434859 0.679282571 [119,] 0.42436794 0.848735882 0.575632059 [120,] 0.40719393 0.814387854 0.592806073 [121,] 0.39550360 0.791007191 0.604496405 [122,] 0.38094434 0.761888681 0.619055659 [123,] 0.35290836 0.705816715 0.647091643 [124,] 0.37356253 0.747125063 0.626437468 [125,] 0.34759670 0.695193399 0.652403301 [126,] 0.35811186 0.716223720 0.641888140 [127,] 0.34724991 0.694499821 0.652750089 [128,] 0.31999703 0.639994050 0.680002975 [129,] 0.29611088 0.592221765 0.703889118 [130,] 0.27165290 0.543305808 0.728347096 [131,] 0.24850684 0.497013681 0.751493160 [132,] 0.22234287 0.444685732 0.777657134 [133,] 0.22827862 0.456557238 0.771721381 [134,] 0.20401885 0.408037699 0.795981151 [135,] 0.18345875 0.366917503 0.816541248 [136,] 0.17071072 0.341421432 0.829289284 [137,] 0.16300292 0.326005831 0.836997085 [138,] 0.17013356 0.340267129 0.829866436 [139,] 0.18597210 0.371944201 0.814027900 [140,] 0.20280648 0.405612965 0.797193518 [141,] 0.20410170 0.408203405 0.795898298 [142,] 0.18264977 0.365299539 0.817350230 [143,] 0.16351714 0.327034282 0.836482859 [144,] 0.17569120 0.351382401 0.824308800 [145,] 0.19093141 0.381862825 0.809068588 [146,] 0.17834900 0.356698009 0.821650995 [147,] 0.15608477 0.312169535 0.843915233 [148,] 0.13991024 0.279820473 0.860089764 [149,] 0.22711410 0.454228205 0.772885897 [150,] 0.24584076 0.491681521 0.754159240 [151,] 0.22459989 0.449199784 0.775400108 [152,] 0.20301175 0.406023502 0.796988249 [153,] 0.17988890 0.359777791 0.820111105 [154,] 0.15871814 0.317436282 0.841281859 [155,] 0.26151873 0.523037456 0.738481272 [156,] 0.25837033 0.516740668 0.741629666 [157,] 0.25593215 0.511864303 0.744067849 [158,] 0.22782402 0.455648039 0.772175981 [159,] 0.20658528 0.413170568 0.793414716 [160,] 0.26459632 0.529192638 0.735403681 [161,] 0.29339153 0.586783067 0.706608466 [162,] 0.26578497 0.531569932 0.734215034 [163,] 0.26142331 0.522846625 0.738576688 [164,] 0.43174889 0.863497789 0.568251106 [165,] 0.41736411 0.834728220 0.582635890 [166,] 0.43949633 0.878992665 0.560503667 [167,] 0.45274319 0.905486371 0.547256814 [168,] 0.50960713 0.980785750 0.490392875 [169,] 0.47572330 0.951446594 0.524276703 [170,] 0.45372858 0.907457166 0.546271417 [171,] 0.45687228 0.913744566 0.543127717 [172,] 0.41936786 0.838735726 0.580632137 [173,] 0.41216183 0.824323669 0.587838165 [174,] 0.37489428 0.749788555 0.625105722 [175,] 0.34825204 0.696504083 0.651747958 [176,] 0.36481377 0.729627540 0.635186230 [177,] 0.35553346 0.711066926 0.644466537 [178,] 0.32045820 0.640916409 0.679541795 [179,] 0.29133486 0.582669710 0.708665145 [180,] 0.25941273 0.518825460 0.740587270 [181,] 0.23787676 0.475753526 0.762123237 [182,] 0.24688332 0.493766642 0.753116679 [183,] 0.22806557 0.456131148 0.771934426 [184,] 0.24561305 0.491226099 0.754386950 [185,] 0.23435724 0.468714471 0.765642764 [186,] 0.23432042 0.468640845 0.765679578 [187,] 0.24656004 0.493120085 0.753439958 [188,] 0.29599579 0.591991579 0.704004210 [189,] 0.27708633 0.554172662 0.722913669 [190,] 0.31358541 0.627170824 0.686414588 [191,] 0.28035475 0.560709494 0.719645253 [192,] 0.31753299 0.635065983 0.682467008 [193,] 0.29450492 0.589009850 0.705495075 [194,] 0.34222291 0.684445824 0.657777088 [195,] 0.30462173 0.609243452 0.695378274 [196,] 0.27005619 0.540112390 0.729943805 [197,] 0.24836167 0.496723333 0.751638333 [198,] 0.21633353 0.432667059 0.783666470 [199,] 0.23990985 0.479819708 0.760090146 [200,] 0.20747290 0.414945808 0.792527096 [201,] 0.21570494 0.431409871 0.784295064 [202,] 0.24176578 0.483531563 0.758234219 [203,] 0.23061381 0.461227623 0.769386188 [204,] 0.20356022 0.407120430 0.796439785 [205,] 0.25094869 0.501897376 0.749051312 [206,] 0.22496248 0.449924950 0.775037525 [207,] 0.21330843 0.426616860 0.786691570 [208,] 0.24493602 0.489872041 0.755063979 [209,] 0.21347418 0.426948354 0.786525823 [210,] 0.20232572 0.404651433 0.797674283 [211,] 0.21685812 0.433716245 0.783141878 [212,] 0.23012202 0.460244034 0.769877983 [213,] 0.22192161 0.443843210 0.778078395 [214,] 0.20804703 0.416094052 0.791952974 [215,] 0.17534095 0.350681906 0.824659047 [216,] 0.17327734 0.346554676 0.826722662 [217,] 0.20631558 0.412631154 0.793684423 [218,] 0.41309999 0.826199985 0.586900007 [219,] 0.38442413 0.768848270 0.615575865 [220,] 0.35802500 0.716049991 0.641975004 [221,] 0.34212627 0.684252533 0.657873733 [222,] 0.29923637 0.598472734 0.700763633 [223,] 0.34257143 0.685142867 0.657428567 [224,] 0.29686119 0.593722374 0.703138813 [225,] 0.25261030 0.505220610 0.747389695 [226,] 0.25218057 0.504361136 0.747819432 [227,] 0.22796201 0.455924027 0.772037986 [228,] 0.19471389 0.389427785 0.805286107 [229,] 0.15510065 0.310201308 0.844899346 [230,] 0.28809508 0.576190166 0.711904917 [231,] 0.27750225 0.555004490 0.722497755 [232,] 0.24586253 0.491725062 0.754137469 [233,] 0.47215141 0.944302812 0.527848594 [234,] 0.42924765 0.858495290 0.570752355 [235,] 0.36594598 0.731891960 0.634054020 [236,] 0.49285762 0.985715237 0.507142381 [237,] 0.40757103 0.815142057 0.592428972 [238,] 0.32408191 0.648163822 0.675918089 [239,] 0.49086026 0.981720522 0.509139739 [240,] 0.39061565 0.781231299 0.609384350 [241,] 0.29418335 0.588366707 0.705816647 [242,] 0.44419938 0.888398768 0.555800616 [243,] 0.92160195 0.156796091 0.078398045 [244,] 0.84056285 0.318874297 0.159437149 [245,] 0.79058870 0.418822607 0.209411304 > postscript(file="/var/wessaorg/rcomp/tmp/1pq5d1383549487.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2hhtn1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3mlzb1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/43hb11383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5s3bu1383549487.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 = 264 Frequency = 1 1 2 3 4 5 6 0.29415296 2.97156405 -2.71955366 -2.10608395 5.15597970 3.88145267 7 8 9 10 11 12 3.48011849 -0.79374834 0.04746467 0.94945878 1.70415011 3.53958524 13 14 15 16 17 18 -3.16423507 2.70588100 2.45477720 0.88516657 0.43226211 1.36525834 19 20 21 22 23 24 -1.25500295 2.42931528 2.88737479 -2.42299009 -0.36997043 -1.32200788 25 26 27 28 29 30 1.79732787 -6.83795216 1.13672708 0.96834232 1.29799083 -2.62898598 31 32 33 34 35 36 0.53241853 0.74276756 2.12811742 -0.02419354 0.25609567 0.85452779 37 38 39 40 41 42 -1.36766194 0.88959456 1.90097779 -2.04374875 -0.55317650 2.57852721 43 44 45 46 47 48 0.10322519 -0.92182478 0.56704362 -2.36129183 -0.18364074 0.33311746 49 50 51 52 53 54 3.66464672 -1.59682746 0.90900340 0.80653229 -0.38617506 -1.39187656 55 56 57 58 59 60 -1.72473905 1.63464968 1.91825032 -0.28380433 -3.07451193 -1.19565129 61 62 63 64 65 66 -2.36196933 -1.51284226 -3.52005217 1.12206152 1.48020988 -5.05159086 67 68 69 70 71 72 -1.57071188 -2.46861314 1.56107534 1.43552085 0.71952845 3.42148734 73 74 75 76 77 78 0.56637919 -0.36374619 -1.92139575 -0.05678904 3.01547737 0.63258929 79 80 81 82 83 84 1.37486595 -2.02638088 0.11531600 -0.49187355 1.71652176 0.79261237 85 86 87 88 89 90 -0.05000283 1.06084375 -0.25779066 0.29348913 -3.39489049 3.46282310 91 92 93 94 95 96 0.10414427 0.83407939 0.76070481 -0.88621089 0.99182092 -0.81212758 97 98 99 100 101 102 -0.86160525 2.03305944 0.03269578 1.75233306 -0.91943280 0.87215773 103 104 105 106 107 108 -3.38395123 1.95821845 -2.34111684 1.01074689 2.03629602 -2.84439957 109 110 111 112 113 114 0.97564497 1.16927453 -2.19002502 -2.24943456 1.93963676 3.96052307 115 116 117 118 119 120 0.34423460 1.01153119 0.28377322 -1.06596080 0.29635362 -0.48377503 121 122 123 124 125 126 0.44172445 0.12199422 -1.00657851 0.38264707 -1.80251880 0.78691327 127 128 129 130 131 132 1.54651443 4.15924124 1.47514583 -1.67088009 -1.41747536 -0.35520181 133 134 135 136 137 138 2.53318773 0.75497734 2.30513128 1.73635789 0.71353491 -0.82625615 139 140 141 142 143 144 0.87343509 -0.69829064 0.31837841 2.25137759 -0.70650083 0.69200232 145 146 147 148 149 150 1.48384601 1.44924971 -2.39904508 -2.72706106 -2.40806310 1.89066841 151 152 153 154 155 156 0.35538662 0.48912432 -2.45245609 -2.49970467 1.47025324 0.10414427 157 158 159 160 161 162 0.77085790 4.15924124 -2.73298900 0.04874126 0.52057087 0.81728909 163 164 165 166 167 168 0.75021056 4.29504381 -2.03717048 2.04186375 -0.19387963 -0.86403890 169 170 171 172 173 174 -3.76849833 -3.03778631 0.44648937 1.59974934 -5.15257282 1.75478768 175 176 177 178 179 180 2.51998472 -2.48453149 -3.35532738 0.51210878 1.30534731 -2.29359399 181 182 183 184 185 186 -0.35214454 -1.80943828 0.03511274 -1.20099284 2.02659148 1.20874706 187 188 189 190 191 192 0.21662905 0.68697364 0.49862794 0.81719880 -1.94897479 -1.28106268 193 194 195 196 197 198 2.27819029 -1.65852711 1.78380433 -2.26585998 2.13980294 0.42609684 199 200 201 202 203 204 -3.24973784 -0.94559208 -3.29268508 1.12157702 2.78591406 0.29764748 205 206 207 208 209 210 0.37921109 1.07391563 -0.69276501 3.21317443 -0.01154829 1.49078002 211 212 213 214 215 216 -2.81385971 1.25588061 -1.34621275 -3.96611269 -1.36907838 1.58447071 217 218 219 220 221 222 1.86706010 -0.45463969 -2.05584638 1.21454597 -3.09837266 2.11414488 223 224 225 226 227 228 -2.31200510 0.02184157 -0.87722771 1.63328083 4.97041847 -1.91695948 229 230 231 232 233 234 -1.51227492 -2.50048347 0.01584939 -3.08591314 -0.16520341 0.24080752 235 236 237 238 239 240 0.87401904 -1.99098873 0.75534202 -0.07646988 -4.61544040 -2.70353807 241 242 243 244 245 246 -2.92080984 -2.85064552 0.24145375 -0.40905615 1.26730069 0.21982306 247 248 249 250 251 252 0.10076309 5.08858853 -0.19517400 0.35825540 2.02625426 1.07605570 253 254 255 256 257 258 -1.31708845 -0.95289519 0.03623156 -0.86879162 -2.06857732 -2.59497348 259 260 261 262 263 264 2.19118529 -4.78670852 0.08878308 1.27419575 -2.92785734 -0.04664671 > postscript(file="/var/wessaorg/rcomp/tmp/6t0e91383549487.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 = 264 Frequency = 1 lag(myerror, k = 1) myerror 0 0.29415296 NA 1 2.97156405 0.29415296 2 -2.71955366 2.97156405 3 -2.10608395 -2.71955366 4 5.15597970 -2.10608395 5 3.88145267 5.15597970 6 3.48011849 3.88145267 7 -0.79374834 3.48011849 8 0.04746467 -0.79374834 9 0.94945878 0.04746467 10 1.70415011 0.94945878 11 3.53958524 1.70415011 12 -3.16423507 3.53958524 13 2.70588100 -3.16423507 14 2.45477720 2.70588100 15 0.88516657 2.45477720 16 0.43226211 0.88516657 17 1.36525834 0.43226211 18 -1.25500295 1.36525834 19 2.42931528 -1.25500295 20 2.88737479 2.42931528 21 -2.42299009 2.88737479 22 -0.36997043 -2.42299009 23 -1.32200788 -0.36997043 24 1.79732787 -1.32200788 25 -6.83795216 1.79732787 26 1.13672708 -6.83795216 27 0.96834232 1.13672708 28 1.29799083 0.96834232 29 -2.62898598 1.29799083 30 0.53241853 -2.62898598 31 0.74276756 0.53241853 32 2.12811742 0.74276756 33 -0.02419354 2.12811742 34 0.25609567 -0.02419354 35 0.85452779 0.25609567 36 -1.36766194 0.85452779 37 0.88959456 -1.36766194 38 1.90097779 0.88959456 39 -2.04374875 1.90097779 40 -0.55317650 -2.04374875 41 2.57852721 -0.55317650 42 0.10322519 2.57852721 43 -0.92182478 0.10322519 44 0.56704362 -0.92182478 45 -2.36129183 0.56704362 46 -0.18364074 -2.36129183 47 0.33311746 -0.18364074 48 3.66464672 0.33311746 49 -1.59682746 3.66464672 50 0.90900340 -1.59682746 51 0.80653229 0.90900340 52 -0.38617506 0.80653229 53 -1.39187656 -0.38617506 54 -1.72473905 -1.39187656 55 1.63464968 -1.72473905 56 1.91825032 1.63464968 57 -0.28380433 1.91825032 58 -3.07451193 -0.28380433 59 -1.19565129 -3.07451193 60 -2.36196933 -1.19565129 61 -1.51284226 -2.36196933 62 -3.52005217 -1.51284226 63 1.12206152 -3.52005217 64 1.48020988 1.12206152 65 -5.05159086 1.48020988 66 -1.57071188 -5.05159086 67 -2.46861314 -1.57071188 68 1.56107534 -2.46861314 69 1.43552085 1.56107534 70 0.71952845 1.43552085 71 3.42148734 0.71952845 72 0.56637919 3.42148734 73 -0.36374619 0.56637919 74 -1.92139575 -0.36374619 75 -0.05678904 -1.92139575 76 3.01547737 -0.05678904 77 0.63258929 3.01547737 78 1.37486595 0.63258929 79 -2.02638088 1.37486595 80 0.11531600 -2.02638088 81 -0.49187355 0.11531600 82 1.71652176 -0.49187355 83 0.79261237 1.71652176 84 -0.05000283 0.79261237 85 1.06084375 -0.05000283 86 -0.25779066 1.06084375 87 0.29348913 -0.25779066 88 -3.39489049 0.29348913 89 3.46282310 -3.39489049 90 0.10414427 3.46282310 91 0.83407939 0.10414427 92 0.76070481 0.83407939 93 -0.88621089 0.76070481 94 0.99182092 -0.88621089 95 -0.81212758 0.99182092 96 -0.86160525 -0.81212758 97 2.03305944 -0.86160525 98 0.03269578 2.03305944 99 1.75233306 0.03269578 100 -0.91943280 1.75233306 101 0.87215773 -0.91943280 102 -3.38395123 0.87215773 103 1.95821845 -3.38395123 104 -2.34111684 1.95821845 105 1.01074689 -2.34111684 106 2.03629602 1.01074689 107 -2.84439957 2.03629602 108 0.97564497 -2.84439957 109 1.16927453 0.97564497 110 -2.19002502 1.16927453 111 -2.24943456 -2.19002502 112 1.93963676 -2.24943456 113 3.96052307 1.93963676 114 0.34423460 3.96052307 115 1.01153119 0.34423460 116 0.28377322 1.01153119 117 -1.06596080 0.28377322 118 0.29635362 -1.06596080 119 -0.48377503 0.29635362 120 0.44172445 -0.48377503 121 0.12199422 0.44172445 122 -1.00657851 0.12199422 123 0.38264707 -1.00657851 124 -1.80251880 0.38264707 125 0.78691327 -1.80251880 126 1.54651443 0.78691327 127 4.15924124 1.54651443 128 1.47514583 4.15924124 129 -1.67088009 1.47514583 130 -1.41747536 -1.67088009 131 -0.35520181 -1.41747536 132 2.53318773 -0.35520181 133 0.75497734 2.53318773 134 2.30513128 0.75497734 135 1.73635789 2.30513128 136 0.71353491 1.73635789 137 -0.82625615 0.71353491 138 0.87343509 -0.82625615 139 -0.69829064 0.87343509 140 0.31837841 -0.69829064 141 2.25137759 0.31837841 142 -0.70650083 2.25137759 143 0.69200232 -0.70650083 144 1.48384601 0.69200232 145 1.44924971 1.48384601 146 -2.39904508 1.44924971 147 -2.72706106 -2.39904508 148 -2.40806310 -2.72706106 149 1.89066841 -2.40806310 150 0.35538662 1.89066841 151 0.48912432 0.35538662 152 -2.45245609 0.48912432 153 -2.49970467 -2.45245609 154 1.47025324 -2.49970467 155 0.10414427 1.47025324 156 0.77085790 0.10414427 157 4.15924124 0.77085790 158 -2.73298900 4.15924124 159 0.04874126 -2.73298900 160 0.52057087 0.04874126 161 0.81728909 0.52057087 162 0.75021056 0.81728909 163 4.29504381 0.75021056 164 -2.03717048 4.29504381 165 2.04186375 -2.03717048 166 -0.19387963 2.04186375 167 -0.86403890 -0.19387963 168 -3.76849833 -0.86403890 169 -3.03778631 -3.76849833 170 0.44648937 -3.03778631 171 1.59974934 0.44648937 172 -5.15257282 1.59974934 173 1.75478768 -5.15257282 174 2.51998472 1.75478768 175 -2.48453149 2.51998472 176 -3.35532738 -2.48453149 177 0.51210878 -3.35532738 178 1.30534731 0.51210878 179 -2.29359399 1.30534731 180 -0.35214454 -2.29359399 181 -1.80943828 -0.35214454 182 0.03511274 -1.80943828 183 -1.20099284 0.03511274 184 2.02659148 -1.20099284 185 1.20874706 2.02659148 186 0.21662905 1.20874706 187 0.68697364 0.21662905 188 0.49862794 0.68697364 189 0.81719880 0.49862794 190 -1.94897479 0.81719880 191 -1.28106268 -1.94897479 192 2.27819029 -1.28106268 193 -1.65852711 2.27819029 194 1.78380433 -1.65852711 195 -2.26585998 1.78380433 196 2.13980294 -2.26585998 197 0.42609684 2.13980294 198 -3.24973784 0.42609684 199 -0.94559208 -3.24973784 200 -3.29268508 -0.94559208 201 1.12157702 -3.29268508 202 2.78591406 1.12157702 203 0.29764748 2.78591406 204 0.37921109 0.29764748 205 1.07391563 0.37921109 206 -0.69276501 1.07391563 207 3.21317443 -0.69276501 208 -0.01154829 3.21317443 209 1.49078002 -0.01154829 210 -2.81385971 1.49078002 211 1.25588061 -2.81385971 212 -1.34621275 1.25588061 213 -3.96611269 -1.34621275 214 -1.36907838 -3.96611269 215 1.58447071 -1.36907838 216 1.86706010 1.58447071 217 -0.45463969 1.86706010 218 -2.05584638 -0.45463969 219 1.21454597 -2.05584638 220 -3.09837266 1.21454597 221 2.11414488 -3.09837266 222 -2.31200510 2.11414488 223 0.02184157 -2.31200510 224 -0.87722771 0.02184157 225 1.63328083 -0.87722771 226 4.97041847 1.63328083 227 -1.91695948 4.97041847 228 -1.51227492 -1.91695948 229 -2.50048347 -1.51227492 230 0.01584939 -2.50048347 231 -3.08591314 0.01584939 232 -0.16520341 -3.08591314 233 0.24080752 -0.16520341 234 0.87401904 0.24080752 235 -1.99098873 0.87401904 236 0.75534202 -1.99098873 237 -0.07646988 0.75534202 238 -4.61544040 -0.07646988 239 -2.70353807 -4.61544040 240 -2.92080984 -2.70353807 241 -2.85064552 -2.92080984 242 0.24145375 -2.85064552 243 -0.40905615 0.24145375 244 1.26730069 -0.40905615 245 0.21982306 1.26730069 246 0.10076309 0.21982306 247 5.08858853 0.10076309 248 -0.19517400 5.08858853 249 0.35825540 -0.19517400 250 2.02625426 0.35825540 251 1.07605570 2.02625426 252 -1.31708845 1.07605570 253 -0.95289519 -1.31708845 254 0.03623156 -0.95289519 255 -0.86879162 0.03623156 256 -2.06857732 -0.86879162 257 -2.59497348 -2.06857732 258 2.19118529 -2.59497348 259 -4.78670852 2.19118529 260 0.08878308 -4.78670852 261 1.27419575 0.08878308 262 -2.92785734 1.27419575 263 -0.04664671 -2.92785734 264 NA -0.04664671 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.97156405 0.29415296 [2,] -2.71955366 2.97156405 [3,] -2.10608395 -2.71955366 [4,] 5.15597970 -2.10608395 [5,] 3.88145267 5.15597970 [6,] 3.48011849 3.88145267 [7,] -0.79374834 3.48011849 [8,] 0.04746467 -0.79374834 [9,] 0.94945878 0.04746467 [10,] 1.70415011 0.94945878 [11,] 3.53958524 1.70415011 [12,] -3.16423507 3.53958524 [13,] 2.70588100 -3.16423507 [14,] 2.45477720 2.70588100 [15,] 0.88516657 2.45477720 [16,] 0.43226211 0.88516657 [17,] 1.36525834 0.43226211 [18,] -1.25500295 1.36525834 [19,] 2.42931528 -1.25500295 [20,] 2.88737479 2.42931528 [21,] -2.42299009 2.88737479 [22,] -0.36997043 -2.42299009 [23,] -1.32200788 -0.36997043 [24,] 1.79732787 -1.32200788 [25,] -6.83795216 1.79732787 [26,] 1.13672708 -6.83795216 [27,] 0.96834232 1.13672708 [28,] 1.29799083 0.96834232 [29,] -2.62898598 1.29799083 [30,] 0.53241853 -2.62898598 [31,] 0.74276756 0.53241853 [32,] 2.12811742 0.74276756 [33,] -0.02419354 2.12811742 [34,] 0.25609567 -0.02419354 [35,] 0.85452779 0.25609567 [36,] -1.36766194 0.85452779 [37,] 0.88959456 -1.36766194 [38,] 1.90097779 0.88959456 [39,] -2.04374875 1.90097779 [40,] -0.55317650 -2.04374875 [41,] 2.57852721 -0.55317650 [42,] 0.10322519 2.57852721 [43,] -0.92182478 0.10322519 [44,] 0.56704362 -0.92182478 [45,] -2.36129183 0.56704362 [46,] -0.18364074 -2.36129183 [47,] 0.33311746 -0.18364074 [48,] 3.66464672 0.33311746 [49,] -1.59682746 3.66464672 [50,] 0.90900340 -1.59682746 [51,] 0.80653229 0.90900340 [52,] -0.38617506 0.80653229 [53,] -1.39187656 -0.38617506 [54,] -1.72473905 -1.39187656 [55,] 1.63464968 -1.72473905 [56,] 1.91825032 1.63464968 [57,] -0.28380433 1.91825032 [58,] -3.07451193 -0.28380433 [59,] -1.19565129 -3.07451193 [60,] -2.36196933 -1.19565129 [61,] -1.51284226 -2.36196933 [62,] -3.52005217 -1.51284226 [63,] 1.12206152 -3.52005217 [64,] 1.48020988 1.12206152 [65,] -5.05159086 1.48020988 [66,] -1.57071188 -5.05159086 [67,] -2.46861314 -1.57071188 [68,] 1.56107534 -2.46861314 [69,] 1.43552085 1.56107534 [70,] 0.71952845 1.43552085 [71,] 3.42148734 0.71952845 [72,] 0.56637919 3.42148734 [73,] -0.36374619 0.56637919 [74,] -1.92139575 -0.36374619 [75,] -0.05678904 -1.92139575 [76,] 3.01547737 -0.05678904 [77,] 0.63258929 3.01547737 [78,] 1.37486595 0.63258929 [79,] -2.02638088 1.37486595 [80,] 0.11531600 -2.02638088 [81,] -0.49187355 0.11531600 [82,] 1.71652176 -0.49187355 [83,] 0.79261237 1.71652176 [84,] -0.05000283 0.79261237 [85,] 1.06084375 -0.05000283 [86,] -0.25779066 1.06084375 [87,] 0.29348913 -0.25779066 [88,] -3.39489049 0.29348913 [89,] 3.46282310 -3.39489049 [90,] 0.10414427 3.46282310 [91,] 0.83407939 0.10414427 [92,] 0.76070481 0.83407939 [93,] -0.88621089 0.76070481 [94,] 0.99182092 -0.88621089 [95,] -0.81212758 0.99182092 [96,] -0.86160525 -0.81212758 [97,] 2.03305944 -0.86160525 [98,] 0.03269578 2.03305944 [99,] 1.75233306 0.03269578 [100,] -0.91943280 1.75233306 [101,] 0.87215773 -0.91943280 [102,] -3.38395123 0.87215773 [103,] 1.95821845 -3.38395123 [104,] -2.34111684 1.95821845 [105,] 1.01074689 -2.34111684 [106,] 2.03629602 1.01074689 [107,] -2.84439957 2.03629602 [108,] 0.97564497 -2.84439957 [109,] 1.16927453 0.97564497 [110,] -2.19002502 1.16927453 [111,] -2.24943456 -2.19002502 [112,] 1.93963676 -2.24943456 [113,] 3.96052307 1.93963676 [114,] 0.34423460 3.96052307 [115,] 1.01153119 0.34423460 [116,] 0.28377322 1.01153119 [117,] -1.06596080 0.28377322 [118,] 0.29635362 -1.06596080 [119,] -0.48377503 0.29635362 [120,] 0.44172445 -0.48377503 [121,] 0.12199422 0.44172445 [122,] -1.00657851 0.12199422 [123,] 0.38264707 -1.00657851 [124,] -1.80251880 0.38264707 [125,] 0.78691327 -1.80251880 [126,] 1.54651443 0.78691327 [127,] 4.15924124 1.54651443 [128,] 1.47514583 4.15924124 [129,] -1.67088009 1.47514583 [130,] -1.41747536 -1.67088009 [131,] -0.35520181 -1.41747536 [132,] 2.53318773 -0.35520181 [133,] 0.75497734 2.53318773 [134,] 2.30513128 0.75497734 [135,] 1.73635789 2.30513128 [136,] 0.71353491 1.73635789 [137,] -0.82625615 0.71353491 [138,] 0.87343509 -0.82625615 [139,] -0.69829064 0.87343509 [140,] 0.31837841 -0.69829064 [141,] 2.25137759 0.31837841 [142,] -0.70650083 2.25137759 [143,] 0.69200232 -0.70650083 [144,] 1.48384601 0.69200232 [145,] 1.44924971 1.48384601 [146,] -2.39904508 1.44924971 [147,] -2.72706106 -2.39904508 [148,] -2.40806310 -2.72706106 [149,] 1.89066841 -2.40806310 [150,] 0.35538662 1.89066841 [151,] 0.48912432 0.35538662 [152,] -2.45245609 0.48912432 [153,] -2.49970467 -2.45245609 [154,] 1.47025324 -2.49970467 [155,] 0.10414427 1.47025324 [156,] 0.77085790 0.10414427 [157,] 4.15924124 0.77085790 [158,] -2.73298900 4.15924124 [159,] 0.04874126 -2.73298900 [160,] 0.52057087 0.04874126 [161,] 0.81728909 0.52057087 [162,] 0.75021056 0.81728909 [163,] 4.29504381 0.75021056 [164,] -2.03717048 4.29504381 [165,] 2.04186375 -2.03717048 [166,] -0.19387963 2.04186375 [167,] -0.86403890 -0.19387963 [168,] -3.76849833 -0.86403890 [169,] -3.03778631 -3.76849833 [170,] 0.44648937 -3.03778631 [171,] 1.59974934 0.44648937 [172,] -5.15257282 1.59974934 [173,] 1.75478768 -5.15257282 [174,] 2.51998472 1.75478768 [175,] -2.48453149 2.51998472 [176,] -3.35532738 -2.48453149 [177,] 0.51210878 -3.35532738 [178,] 1.30534731 0.51210878 [179,] -2.29359399 1.30534731 [180,] -0.35214454 -2.29359399 [181,] -1.80943828 -0.35214454 [182,] 0.03511274 -1.80943828 [183,] -1.20099284 0.03511274 [184,] 2.02659148 -1.20099284 [185,] 1.20874706 2.02659148 [186,] 0.21662905 1.20874706 [187,] 0.68697364 0.21662905 [188,] 0.49862794 0.68697364 [189,] 0.81719880 0.49862794 [190,] -1.94897479 0.81719880 [191,] -1.28106268 -1.94897479 [192,] 2.27819029 -1.28106268 [193,] -1.65852711 2.27819029 [194,] 1.78380433 -1.65852711 [195,] -2.26585998 1.78380433 [196,] 2.13980294 -2.26585998 [197,] 0.42609684 2.13980294 [198,] -3.24973784 0.42609684 [199,] -0.94559208 -3.24973784 [200,] -3.29268508 -0.94559208 [201,] 1.12157702 -3.29268508 [202,] 2.78591406 1.12157702 [203,] 0.29764748 2.78591406 [204,] 0.37921109 0.29764748 [205,] 1.07391563 0.37921109 [206,] -0.69276501 1.07391563 [207,] 3.21317443 -0.69276501 [208,] -0.01154829 3.21317443 [209,] 1.49078002 -0.01154829 [210,] -2.81385971 1.49078002 [211,] 1.25588061 -2.81385971 [212,] -1.34621275 1.25588061 [213,] -3.96611269 -1.34621275 [214,] -1.36907838 -3.96611269 [215,] 1.58447071 -1.36907838 [216,] 1.86706010 1.58447071 [217,] -0.45463969 1.86706010 [218,] -2.05584638 -0.45463969 [219,] 1.21454597 -2.05584638 [220,] -3.09837266 1.21454597 [221,] 2.11414488 -3.09837266 [222,] -2.31200510 2.11414488 [223,] 0.02184157 -2.31200510 [224,] -0.87722771 0.02184157 [225,] 1.63328083 -0.87722771 [226,] 4.97041847 1.63328083 [227,] -1.91695948 4.97041847 [228,] -1.51227492 -1.91695948 [229,] -2.50048347 -1.51227492 [230,] 0.01584939 -2.50048347 [231,] -3.08591314 0.01584939 [232,] -0.16520341 -3.08591314 [233,] 0.24080752 -0.16520341 [234,] 0.87401904 0.24080752 [235,] -1.99098873 0.87401904 [236,] 0.75534202 -1.99098873 [237,] -0.07646988 0.75534202 [238,] -4.61544040 -0.07646988 [239,] -2.70353807 -4.61544040 [240,] -2.92080984 -2.70353807 [241,] -2.85064552 -2.92080984 [242,] 0.24145375 -2.85064552 [243,] -0.40905615 0.24145375 [244,] 1.26730069 -0.40905615 [245,] 0.21982306 1.26730069 [246,] 0.10076309 0.21982306 [247,] 5.08858853 0.10076309 [248,] -0.19517400 5.08858853 [249,] 0.35825540 -0.19517400 [250,] 2.02625426 0.35825540 [251,] 1.07605570 2.02625426 [252,] -1.31708845 1.07605570 [253,] -0.95289519 -1.31708845 [254,] 0.03623156 -0.95289519 [255,] -0.86879162 0.03623156 [256,] -2.06857732 -0.86879162 [257,] -2.59497348 -2.06857732 [258,] 2.19118529 -2.59497348 [259,] -4.78670852 2.19118529 [260,] 0.08878308 -4.78670852 [261,] 1.27419575 0.08878308 [262,] -2.92785734 1.27419575 [263,] -0.04664671 -2.92785734 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.97156405 0.29415296 2 -2.71955366 2.97156405 3 -2.10608395 -2.71955366 4 5.15597970 -2.10608395 5 3.88145267 5.15597970 6 3.48011849 3.88145267 7 -0.79374834 3.48011849 8 0.04746467 -0.79374834 9 0.94945878 0.04746467 10 1.70415011 0.94945878 11 3.53958524 1.70415011 12 -3.16423507 3.53958524 13 2.70588100 -3.16423507 14 2.45477720 2.70588100 15 0.88516657 2.45477720 16 0.43226211 0.88516657 17 1.36525834 0.43226211 18 -1.25500295 1.36525834 19 2.42931528 -1.25500295 20 2.88737479 2.42931528 21 -2.42299009 2.88737479 22 -0.36997043 -2.42299009 23 -1.32200788 -0.36997043 24 1.79732787 -1.32200788 25 -6.83795216 1.79732787 26 1.13672708 -6.83795216 27 0.96834232 1.13672708 28 1.29799083 0.96834232 29 -2.62898598 1.29799083 30 0.53241853 -2.62898598 31 0.74276756 0.53241853 32 2.12811742 0.74276756 33 -0.02419354 2.12811742 34 0.25609567 -0.02419354 35 0.85452779 0.25609567 36 -1.36766194 0.85452779 37 0.88959456 -1.36766194 38 1.90097779 0.88959456 39 -2.04374875 1.90097779 40 -0.55317650 -2.04374875 41 2.57852721 -0.55317650 42 0.10322519 2.57852721 43 -0.92182478 0.10322519 44 0.56704362 -0.92182478 45 -2.36129183 0.56704362 46 -0.18364074 -2.36129183 47 0.33311746 -0.18364074 48 3.66464672 0.33311746 49 -1.59682746 3.66464672 50 0.90900340 -1.59682746 51 0.80653229 0.90900340 52 -0.38617506 0.80653229 53 -1.39187656 -0.38617506 54 -1.72473905 -1.39187656 55 1.63464968 -1.72473905 56 1.91825032 1.63464968 57 -0.28380433 1.91825032 58 -3.07451193 -0.28380433 59 -1.19565129 -3.07451193 60 -2.36196933 -1.19565129 61 -1.51284226 -2.36196933 62 -3.52005217 -1.51284226 63 1.12206152 -3.52005217 64 1.48020988 1.12206152 65 -5.05159086 1.48020988 66 -1.57071188 -5.05159086 67 -2.46861314 -1.57071188 68 1.56107534 -2.46861314 69 1.43552085 1.56107534 70 0.71952845 1.43552085 71 3.42148734 0.71952845 72 0.56637919 3.42148734 73 -0.36374619 0.56637919 74 -1.92139575 -0.36374619 75 -0.05678904 -1.92139575 76 3.01547737 -0.05678904 77 0.63258929 3.01547737 78 1.37486595 0.63258929 79 -2.02638088 1.37486595 80 0.11531600 -2.02638088 81 -0.49187355 0.11531600 82 1.71652176 -0.49187355 83 0.79261237 1.71652176 84 -0.05000283 0.79261237 85 1.06084375 -0.05000283 86 -0.25779066 1.06084375 87 0.29348913 -0.25779066 88 -3.39489049 0.29348913 89 3.46282310 -3.39489049 90 0.10414427 3.46282310 91 0.83407939 0.10414427 92 0.76070481 0.83407939 93 -0.88621089 0.76070481 94 0.99182092 -0.88621089 95 -0.81212758 0.99182092 96 -0.86160525 -0.81212758 97 2.03305944 -0.86160525 98 0.03269578 2.03305944 99 1.75233306 0.03269578 100 -0.91943280 1.75233306 101 0.87215773 -0.91943280 102 -3.38395123 0.87215773 103 1.95821845 -3.38395123 104 -2.34111684 1.95821845 105 1.01074689 -2.34111684 106 2.03629602 1.01074689 107 -2.84439957 2.03629602 108 0.97564497 -2.84439957 109 1.16927453 0.97564497 110 -2.19002502 1.16927453 111 -2.24943456 -2.19002502 112 1.93963676 -2.24943456 113 3.96052307 1.93963676 114 0.34423460 3.96052307 115 1.01153119 0.34423460 116 0.28377322 1.01153119 117 -1.06596080 0.28377322 118 0.29635362 -1.06596080 119 -0.48377503 0.29635362 120 0.44172445 -0.48377503 121 0.12199422 0.44172445 122 -1.00657851 0.12199422 123 0.38264707 -1.00657851 124 -1.80251880 0.38264707 125 0.78691327 -1.80251880 126 1.54651443 0.78691327 127 4.15924124 1.54651443 128 1.47514583 4.15924124 129 -1.67088009 1.47514583 130 -1.41747536 -1.67088009 131 -0.35520181 -1.41747536 132 2.53318773 -0.35520181 133 0.75497734 2.53318773 134 2.30513128 0.75497734 135 1.73635789 2.30513128 136 0.71353491 1.73635789 137 -0.82625615 0.71353491 138 0.87343509 -0.82625615 139 -0.69829064 0.87343509 140 0.31837841 -0.69829064 141 2.25137759 0.31837841 142 -0.70650083 2.25137759 143 0.69200232 -0.70650083 144 1.48384601 0.69200232 145 1.44924971 1.48384601 146 -2.39904508 1.44924971 147 -2.72706106 -2.39904508 148 -2.40806310 -2.72706106 149 1.89066841 -2.40806310 150 0.35538662 1.89066841 151 0.48912432 0.35538662 152 -2.45245609 0.48912432 153 -2.49970467 -2.45245609 154 1.47025324 -2.49970467 155 0.10414427 1.47025324 156 0.77085790 0.10414427 157 4.15924124 0.77085790 158 -2.73298900 4.15924124 159 0.04874126 -2.73298900 160 0.52057087 0.04874126 161 0.81728909 0.52057087 162 0.75021056 0.81728909 163 4.29504381 0.75021056 164 -2.03717048 4.29504381 165 2.04186375 -2.03717048 166 -0.19387963 2.04186375 167 -0.86403890 -0.19387963 168 -3.76849833 -0.86403890 169 -3.03778631 -3.76849833 170 0.44648937 -3.03778631 171 1.59974934 0.44648937 172 -5.15257282 1.59974934 173 1.75478768 -5.15257282 174 2.51998472 1.75478768 175 -2.48453149 2.51998472 176 -3.35532738 -2.48453149 177 0.51210878 -3.35532738 178 1.30534731 0.51210878 179 -2.29359399 1.30534731 180 -0.35214454 -2.29359399 181 -1.80943828 -0.35214454 182 0.03511274 -1.80943828 183 -1.20099284 0.03511274 184 2.02659148 -1.20099284 185 1.20874706 2.02659148 186 0.21662905 1.20874706 187 0.68697364 0.21662905 188 0.49862794 0.68697364 189 0.81719880 0.49862794 190 -1.94897479 0.81719880 191 -1.28106268 -1.94897479 192 2.27819029 -1.28106268 193 -1.65852711 2.27819029 194 1.78380433 -1.65852711 195 -2.26585998 1.78380433 196 2.13980294 -2.26585998 197 0.42609684 2.13980294 198 -3.24973784 0.42609684 199 -0.94559208 -3.24973784 200 -3.29268508 -0.94559208 201 1.12157702 -3.29268508 202 2.78591406 1.12157702 203 0.29764748 2.78591406 204 0.37921109 0.29764748 205 1.07391563 0.37921109 206 -0.69276501 1.07391563 207 3.21317443 -0.69276501 208 -0.01154829 3.21317443 209 1.49078002 -0.01154829 210 -2.81385971 1.49078002 211 1.25588061 -2.81385971 212 -1.34621275 1.25588061 213 -3.96611269 -1.34621275 214 -1.36907838 -3.96611269 215 1.58447071 -1.36907838 216 1.86706010 1.58447071 217 -0.45463969 1.86706010 218 -2.05584638 -0.45463969 219 1.21454597 -2.05584638 220 -3.09837266 1.21454597 221 2.11414488 -3.09837266 222 -2.31200510 2.11414488 223 0.02184157 -2.31200510 224 -0.87722771 0.02184157 225 1.63328083 -0.87722771 226 4.97041847 1.63328083 227 -1.91695948 4.97041847 228 -1.51227492 -1.91695948 229 -2.50048347 -1.51227492 230 0.01584939 -2.50048347 231 -3.08591314 0.01584939 232 -0.16520341 -3.08591314 233 0.24080752 -0.16520341 234 0.87401904 0.24080752 235 -1.99098873 0.87401904 236 0.75534202 -1.99098873 237 -0.07646988 0.75534202 238 -4.61544040 -0.07646988 239 -2.70353807 -4.61544040 240 -2.92080984 -2.70353807 241 -2.85064552 -2.92080984 242 0.24145375 -2.85064552 243 -0.40905615 0.24145375 244 1.26730069 -0.40905615 245 0.21982306 1.26730069 246 0.10076309 0.21982306 247 5.08858853 0.10076309 248 -0.19517400 5.08858853 249 0.35825540 -0.19517400 250 2.02625426 0.35825540 251 1.07605570 2.02625426 252 -1.31708845 1.07605570 253 -0.95289519 -1.31708845 254 0.03623156 -0.95289519 255 -0.86879162 0.03623156 256 -2.06857732 -0.86879162 257 -2.59497348 -2.06857732 258 2.19118529 -2.59497348 259 -4.78670852 2.19118529 260 0.08878308 -4.78670852 261 1.27419575 0.08878308 262 -2.92785734 1.27419575 263 -0.04664671 -2.92785734 > 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/7rpxh1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/85urj1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9ybfg1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10g6cb1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11657b1383549487.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/12a4ba1383549487.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/1377i01383549488.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/145jba1383549488.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/15l8xj1383549488.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/16d8li1383549488.tab") + } > > try(system("convert tmp/1pq5d1383549487.ps tmp/1pq5d1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/2hhtn1383549487.ps tmp/2hhtn1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/3mlzb1383549487.ps tmp/3mlzb1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/43hb11383549487.ps tmp/43hb11383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/5s3bu1383549487.ps tmp/5s3bu1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/6t0e91383549487.ps tmp/6t0e91383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/7rpxh1383549487.ps tmp/7rpxh1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/85urj1383549487.ps tmp/85urj1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/9ybfg1383549487.ps tmp/9ybfg1383549487.png",intern=TRUE)) character(0) > try(system("convert tmp/10g6cb1383549487.ps tmp/10g6cb1383549487.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.250 2.579 17.810