R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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. Type 'q()' to quit R. > x <- array(list(3 + ,3 + ,3 + ,4 + ,4 + ,5 + ,5 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,5 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,5 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,5 + ,5 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,3 + ,4 + ,3 + ,2 + ,2 + ,3 + ,3 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,5 + ,5 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,5 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,5 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,5 + ,5 + ,3 + ,4 + ,5 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,3 + ,4 + ,5 + ,5 + ,5 + ,4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,3 + ,5 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,3 + ,3 + ,4 + ,5 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,5 + ,5 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,1 + ,3 + ,3 + ,5 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,5 + ,3 + ,5 + ,5 + ,5 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,3 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,3 + ,3 + ,3 + ,4 + ,2 + ,4 + ,5 + ,5 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,5 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,5 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,3 + ,5 + ,4 + ,5 + ,5 + ,5 + ,1 + ,4 + ,4 + ,4 + ,3 + ,3 + ,5 + ,5 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,3 + ,3 + ,5 + ,5 + ,4 + ,4 + ,2 + ,3 + ,5 + ,3 + ,4 + ,5 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4) + ,dim=c(3 + ,162) + ,dimnames=list(c('A' + ,'B' + ,'C') + ,1:162)) > y <- array(NA,dim=c(3,162),dimnames=list(c('A','B','C'),1:162)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > library(lattice) > library(lmtest) Loading required package: zoo > 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 C A B t 1 3 3 3 1 2 5 4 4 2 3 3 5 4 3 4 3 4 4 4 5 4 3 4 5 6 5 3 3 6 7 4 4 5 7 8 4 4 4 8 9 4 3 3 9 10 4 4 3 10 11 4 4 4 11 12 5 4 4 12 13 2 4 4 13 14 4 4 4 14 15 5 4 4 15 16 4 4 4 16 17 4 4 4 17 18 4 5 5 18 19 4 5 4 19 20 5 4 4 20 21 4 4 4 21 22 3 4 4 22 23 3 5 5 23 24 3 4 4 24 25 5 4 4 25 26 2 4 4 26 27 4 4 4 27 28 4 4 4 28 29 4 4 4 29 30 3 3 4 30 31 4 3 4 31 32 3 3 3 32 33 4 4 4 33 34 4 4 4 34 35 4 3 3 35 36 4 2 3 36 37 2 3 2 37 38 4 3 3 38 39 5 4 4 39 40 3 4 4 40 41 4 4 4 41 42 4 4 4 42 43 5 5 5 43 44 3 3 4 44 45 4 3 4 45 46 3 3 4 46 47 4 4 3 47 48 4 4 4 48 49 5 4 4 49 50 4 3 3 50 51 3 4 4 51 52 3 3 3 52 53 4 3 3 53 54 3 4 5 54 55 4 3 2 55 56 4 4 4 56 57 4 4 4 57 58 3 4 4 58 59 3 5 3 59 60 3 4 4 60 61 3 3 3 61 62 4 4 4 62 63 4 4 4 63 64 4 3 4 64 65 4 4 4 65 66 3 3 4 66 67 3 5 5 67 68 3 4 5 68 69 4 3 3 69 70 4 4 4 70 71 4 2 3 71 72 5 3 4 72 73 4 5 5 73 74 4 4 5 74 75 3 4 4 75 76 4 5 4 76 77 5 4 4 77 78 3 4 4 78 79 5 3 4 79 80 4 4 4 80 81 4 3 4 81 82 4 5 5 82 83 3 4 4 83 84 4 4 4 84 85 4 4 4 85 86 4 4 4 86 87 4 3 4 87 88 4 4 4 88 89 4 4 4 89 90 1 3 4 90 91 5 3 3 91 92 4 5 4 92 93 4 4 4 93 94 4 4 4 94 95 4 3 4 95 96 4 4 4 96 97 2 4 4 97 98 4 4 4 98 99 4 4 4 99 100 4 4 4 100 101 4 4 4 101 102 3 3 5 102 103 5 5 5 103 104 3 3 4 104 105 4 4 4 105 106 2 3 3 106 107 4 4 4 107 108 5 4 4 108 109 2 3 4 109 110 4 3 2 110 111 4 4 4 111 112 4 4 4 112 113 3 5 4 113 114 4 3 3 114 115 5 2 4 115 116 3 5 4 116 117 4 3 3 117 118 4 4 4 118 119 4 3 4 119 120 4 4 4 120 121 2 3 4 121 122 3 4 4 122 123 4 3 4 123 124 4 4 4 124 125 4 3 4 125 126 3 4 4 126 127 4 4 4 127 128 4 4 4 128 129 5 3 4 129 130 4 3 3 130 131 3 4 4 131 132 3 3 3 132 133 4 4 4 133 134 4 3 3 134 135 4 4 3 135 136 5 4 3 136 137 3 3 4 137 138 4 4 4 138 139 3 3 4 139 140 4 3 4 140 141 2 3 3 141 142 3 4 4 142 143 5 5 4 143 144 1 5 5 144 145 4 4 4 145 146 5 3 3 146 147 4 5 4 147 148 3 3 3 148 149 3 4 4 149 150 2 3 4 150 151 4 2 3 151 152 4 4 4 152 153 5 4 4 153 154 3 4 4 154 155 2 3 4 155 156 5 3 3 156 157 4 5 4 157 158 5 2 3 158 159 5 3 4 159 160 4 4 4 160 161 4 3 3 161 162 4 4 4 162 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) A B t 3.9752414 0.0638968 -0.1063116 -0.0006228 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.6856 -0.7130 0.2266 0.2841 1.3938 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.9752414 0.4879939 8.146 1.07e-13 *** A 0.0638968 0.1132392 0.564 0.573 B -0.1063116 0.1372291 -0.775 0.440 t -0.0006228 0.0014101 -0.442 0.659 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.8326 on 158 degrees of freedom Multiple R-squared: 0.005328, Adjusted R-squared: -0.01356 F-statistic: 0.2821 on 3 and 158 DF, p-value: 0.8383 > 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,] 0.864079543 0.27184091 0.13592046 [2,] 0.769070855 0.46185829 0.23092914 [3,] 0.674053243 0.65189351 0.32594676 [4,] 0.552478658 0.89504268 0.44752134 [5,] 0.437013220 0.87402644 0.56298678 [6,] 0.410080864 0.82016173 0.58991914 [7,] 0.839651210 0.32069758 0.16034879 [8,] 0.778848269 0.44230346 0.22115173 [9,] 0.793736535 0.41252693 0.20626347 [10,] 0.730141444 0.53971711 0.26985856 [11,] 0.660011182 0.67997764 0.33998882 [12,] 0.584357903 0.83128419 0.41564210 [13,] 0.507881165 0.98423767 0.49211884 [14,] 0.489708486 0.97941697 0.51029151 [15,] 0.433500600 0.86700120 0.56649940 [16,] 0.518811415 0.96237717 0.48118858 [17,] 0.517671669 0.96465666 0.48232833 [18,] 0.533824294 0.93235141 0.46617571 [19,] 0.571776720 0.85644656 0.42822328 [20,] 0.784019867 0.43196027 0.21598013 [21,] 0.738653293 0.52269341 0.26134671 [22,] 0.688695229 0.62260954 0.31130477 [23,] 0.635042126 0.72991575 0.36495787 [24,] 0.634701320 0.73059736 0.36529868 [25,] 0.581753186 0.83649363 0.41824681 [26,] 0.565815770 0.86836846 0.43418423 [27,] 0.518874130 0.96225174 0.48112587 [28,] 0.470455434 0.94091087 0.52954457 [29,] 0.419351858 0.83870372 0.58064814 [30,] 0.367228973 0.73445795 0.63277103 [31,] 0.496242162 0.99248432 0.50375784 [32,] 0.459145951 0.91829190 0.54085405 [33,] 0.542492808 0.91501438 0.45750719 [34,] 0.525345801 0.94930840 0.47465420 [35,] 0.479600406 0.95920081 0.52039959 [36,] 0.433431948 0.86686390 0.56656805 [37,] 0.481999936 0.96399987 0.51800006 [38,] 0.482683513 0.96536703 0.51731649 [39,] 0.434888328 0.86977666 0.56511167 [40,] 0.425415772 0.85083154 0.57458423 [41,] 0.389733239 0.77946648 0.61026676 [42,] 0.345941497 0.69188299 0.65405850 [43,] 0.401385382 0.80277076 0.59861462 [44,] 0.360368109 0.72073622 0.63963189 [45,] 0.358069359 0.71613872 0.64193064 [46,] 0.341982132 0.68396426 0.65801787 [47,] 0.306455500 0.61291100 0.69354450 [48,] 0.304697407 0.60939481 0.69530259 [49,] 0.273377715 0.54675543 0.72662229 [50,] 0.237553733 0.47510747 0.76244627 [51,] 0.204412381 0.40882476 0.79558762 [52,] 0.198065951 0.39613190 0.80193405 [53,] 0.197729578 0.39545916 0.80227042 [54,] 0.188439144 0.37687829 0.81156086 [55,] 0.181662043 0.36332409 0.81833796 [56,] 0.156819160 0.31363832 0.84318084 [57,] 0.133937529 0.26787506 0.86606247 [58,] 0.113707393 0.22741479 0.88629261 [59,] 0.095125186 0.19025037 0.90487481 [60,] 0.088799449 0.17759890 0.91120055 [61,] 0.083364318 0.16672864 0.91663568 [62,] 0.075354383 0.15070877 0.92464562 [63,] 0.064440595 0.12888119 0.93555941 [64,] 0.053591370 0.10718274 0.94640863 [65,] 0.044565302 0.08913060 0.95543470 [66,] 0.065178000 0.13035600 0.93482200 [67,] 0.053825044 0.10765009 0.94617496 [68,] 0.044554089 0.08910818 0.95544591 [69,] 0.041823853 0.08364771 0.95817615 [70,] 0.033953179 0.06790636 0.96604682 [71,] 0.048261255 0.09652251 0.95173874 [72,] 0.045831671 0.09166334 0.95416833 [73,] 0.064480199 0.12896040 0.93551980 [74,] 0.052467659 0.10493532 0.94753234 [75,] 0.042764344 0.08552869 0.95723566 [76,] 0.035080977 0.07016195 0.96491902 [77,] 0.033035669 0.06607134 0.96696433 [78,] 0.026229940 0.05245988 0.97377006 [79,] 0.020637094 0.04127419 0.97936291 [80,] 0.016093139 0.03218628 0.98390686 [81,] 0.012679444 0.02535889 0.98732056 [82,] 0.009743941 0.01948788 0.99025606 [83,] 0.007430295 0.01486059 0.99256971 [84,] 0.084048412 0.16809682 0.91595159 [85,] 0.105382476 0.21076495 0.89461752 [86,] 0.087271617 0.17454323 0.91272838 [87,] 0.072327264 0.14465453 0.92767274 [88,] 0.059497116 0.11899423 0.94050288 [89,] 0.049420895 0.09884179 0.95057910 [90,] 0.040150907 0.08030181 0.95984909 [91,] 0.080164008 0.16032802 0.91983599 [92,] 0.066156399 0.13231280 0.93384360 [93,] 0.054178373 0.10835675 0.94582163 [94,] 0.044048867 0.08809773 0.95595113 [95,] 0.035574978 0.07114996 0.96442502 [96,] 0.030142695 0.06028539 0.96985730 [97,] 0.051817680 0.10363536 0.94818232 [98,] 0.045024637 0.09004927 0.95497536 [99,] 0.037147425 0.07429485 0.96285257 [100,] 0.085028615 0.17005723 0.91497139 [101,] 0.071221093 0.14244219 0.92877891 [102,] 0.105695966 0.21139193 0.89430403 [103,] 0.165301378 0.33060276 0.83469862 [104,] 0.147113525 0.29422705 0.85288648 [105,] 0.126083968 0.25216794 0.87391603 [106,] 0.107792626 0.21558525 0.89220737 [107,] 0.099122636 0.19824527 0.90087736 [108,] 0.080982234 0.16196447 0.91901777 [109,] 0.136855801 0.27371160 0.86314420 [110,] 0.128705606 0.25741121 0.87129439 [111,] 0.105501233 0.21100247 0.89449877 [112,] 0.088881732 0.17776346 0.91111827 [113,] 0.079796218 0.15959244 0.92020378 [114,] 0.067792322 0.13558464 0.93220768 [115,] 0.101245104 0.20249021 0.89875490 [116,] 0.089458353 0.17891671 0.91054165 [117,] 0.077984716 0.15596943 0.92201528 [118,] 0.064869794 0.12973959 0.93513021 [119,] 0.058981403 0.11796281 0.94101860 [120,] 0.049233524 0.09846705 0.95076648 [121,] 0.040846791 0.08169358 0.95915321 [122,] 0.034369177 0.06873835 0.96563082 [123,] 0.107206492 0.21441298 0.89279351 [124,] 0.085594948 0.17118990 0.91440505 [125,] 0.068413893 0.13682779 0.93158611 [126,] 0.065479838 0.13095968 0.93452016 [127,] 0.061336042 0.12267208 0.93866396 [128,] 0.046012109 0.09202422 0.95398789 [129,] 0.034982803 0.06996561 0.96501720 [130,] 0.034233795 0.06846759 0.96576620 [131,] 0.025861908 0.05172382 0.97413809 [132,] 0.024926621 0.04985324 0.97507338 [133,] 0.018987464 0.03797493 0.98101254 [134,] 0.039545004 0.07909001 0.96045500 [135,] 0.112408794 0.22481759 0.88759121 [136,] 0.084917711 0.16983542 0.91508229 [137,] 0.133621015 0.26724203 0.86637898 [138,] 0.213481381 0.42696276 0.78651862 [139,] 0.198916438 0.39783288 0.80108356 [140,] 0.250181873 0.50036375 0.74981813 [141,] 0.226417790 0.45283558 0.77358221 [142,] 0.207501439 0.41500288 0.79249856 [143,] 0.152780347 0.30556069 0.84721965 [144,] 0.202887693 0.40577539 0.79711231 [145,] 0.144897049 0.28979410 0.85510295 [146,] 0.094570771 0.18914154 0.90542923 [147,] 0.207504712 0.41500942 0.79249529 [148,] 0.130197507 0.26039501 0.86980249 [149,] 0.985657283 0.02868543 0.01434272 > postscript(file="/var/wessaorg/rcomp/tmp/11aw91322070936.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/2638j1322070936.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3tlhr1322070936.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4d7jy1322070936.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5eqe01322070936.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 162 Frequency = 1 1 2 3 4 5 6 -0.84737430 1.19566326 -0.86761076 -0.80309113 0.26142850 1.15573972 7 8 9 10 11 12 0.30508886 0.19940008 0.15760813 0.09433411 0.20126849 1.20189130 13 14 15 16 17 18 -1.79748590 0.20313690 1.20375971 0.20438251 0.20500531 0.24804287 19 20 21 22 23 24 0.14235409 1.20687372 0.20749653 -0.79188067 -0.74884311 -0.79063506 25 26 27 28 29 30 1.20998774 -1.78938945 0.21123335 0.21185615 0.21247896 -0.72300141 31 32 33 34 35 36 0.27762139 -0.82806738 0.21497017 0.21559297 0.17380103 0.23832066 37 38 39 40 41 42 -1.93126495 0.17566944 1.21870699 -0.78067020 0.21995260 0.22057540 43 44 45 46 47 48 1.26361296 -0.71428216 0.28634064 -0.71303656 0.11737784 0.22431222 49 50 51 52 53 54 1.22493503 0.18314308 -0.77381936 -0.81561131 0.18501149 -0.66563937 55 56 57 58 59 60 0.07994552 0.22929465 0.22991746 -0.76945974 -0.93904534 -0.76821413 61 62 63 64 65 66 -0.81000608 0.23303148 0.23365428 0.29817391 0.23489989 -0.70058048 67 68 69 70 71 72 -0.72143975 -0.65692012 0.19497635 0.23801390 0.26011878 1.30315634 73 74 75 76 77 78 0.28229707 0.34681670 -0.75887208 0.17785390 1.24237353 -0.75700367 79 80 81 82 83 84 1.30751596 0.24424194 0.30876157 0.28790230 -0.75388965 0.24673315 85 86 87 88 89 90 0.24735596 0.24797876 0.31249839 0.24922437 0.24984717 -2.68563320 91 92 93 94 95 96 1.20867803 0.18781876 0.25233839 0.25296119 0.31748082 0.25420680 97 98 99 100 101 102 -1.74517040 0.25545240 0.25607521 0.25669801 0.25732081 -0.57184798 103 104 105 106 107 108 1.30098117 -0.67691395 0.25981203 -1.78197992 0.26105764 1.26168044 109 110 111 112 113 114 -1.67379993 0.11419971 0.26354885 0.26417165 -0.79910237 0.22300251 115 116 117 118 119 120 1.39383372 -0.79723396 0.22487092 0.26790848 0.33242811 0.26915408 121 122 123 124 125 126 -1.66632629 -0.72960031 0.33491932 0.27164530 0.33616493 -0.72710910 127 128 129 130 131 132 0.27351371 0.27413651 1.33865614 0.23296737 -0.72399508 -0.76578703 133 134 135 136 137 138 0.27725053 0.23545858 0.17218456 1.17280736 -0.65636143 0.28036455 139 140 141 142 143 144 -0.65511582 0.34550698 -1.76018180 -0.71714424 1.21958174 -2.67348388 145 146 147 148 149 150 0.28472417 1.24293222 0.22207295 -0.75582217 -0.71278461 -1.64826498 151 152 153 154 155 156 0.30994307 0.28908380 1.28970660 -0.70967060 -1.64515097 1.24916026 157 158 159 160 161 162 0.22830099 1.31430269 1.35734025 0.29406623 0.25227428 0.29531183 > postscript(file="/var/wessaorg/rcomp/tmp/6qifg1322070936.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.84737430 NA 1 1.19566326 -0.84737430 2 -0.86761076 1.19566326 3 -0.80309113 -0.86761076 4 0.26142850 -0.80309113 5 1.15573972 0.26142850 6 0.30508886 1.15573972 7 0.19940008 0.30508886 8 0.15760813 0.19940008 9 0.09433411 0.15760813 10 0.20126849 0.09433411 11 1.20189130 0.20126849 12 -1.79748590 1.20189130 13 0.20313690 -1.79748590 14 1.20375971 0.20313690 15 0.20438251 1.20375971 16 0.20500531 0.20438251 17 0.24804287 0.20500531 18 0.14235409 0.24804287 19 1.20687372 0.14235409 20 0.20749653 1.20687372 21 -0.79188067 0.20749653 22 -0.74884311 -0.79188067 23 -0.79063506 -0.74884311 24 1.20998774 -0.79063506 25 -1.78938945 1.20998774 26 0.21123335 -1.78938945 27 0.21185615 0.21123335 28 0.21247896 0.21185615 29 -0.72300141 0.21247896 30 0.27762139 -0.72300141 31 -0.82806738 0.27762139 32 0.21497017 -0.82806738 33 0.21559297 0.21497017 34 0.17380103 0.21559297 35 0.23832066 0.17380103 36 -1.93126495 0.23832066 37 0.17566944 -1.93126495 38 1.21870699 0.17566944 39 -0.78067020 1.21870699 40 0.21995260 -0.78067020 41 0.22057540 0.21995260 42 1.26361296 0.22057540 43 -0.71428216 1.26361296 44 0.28634064 -0.71428216 45 -0.71303656 0.28634064 46 0.11737784 -0.71303656 47 0.22431222 0.11737784 48 1.22493503 0.22431222 49 0.18314308 1.22493503 50 -0.77381936 0.18314308 51 -0.81561131 -0.77381936 52 0.18501149 -0.81561131 53 -0.66563937 0.18501149 54 0.07994552 -0.66563937 55 0.22929465 0.07994552 56 0.22991746 0.22929465 57 -0.76945974 0.22991746 58 -0.93904534 -0.76945974 59 -0.76821413 -0.93904534 60 -0.81000608 -0.76821413 61 0.23303148 -0.81000608 62 0.23365428 0.23303148 63 0.29817391 0.23365428 64 0.23489989 0.29817391 65 -0.70058048 0.23489989 66 -0.72143975 -0.70058048 67 -0.65692012 -0.72143975 68 0.19497635 -0.65692012 69 0.23801390 0.19497635 70 0.26011878 0.23801390 71 1.30315634 0.26011878 72 0.28229707 1.30315634 73 0.34681670 0.28229707 74 -0.75887208 0.34681670 75 0.17785390 -0.75887208 76 1.24237353 0.17785390 77 -0.75700367 1.24237353 78 1.30751596 -0.75700367 79 0.24424194 1.30751596 80 0.30876157 0.24424194 81 0.28790230 0.30876157 82 -0.75388965 0.28790230 83 0.24673315 -0.75388965 84 0.24735596 0.24673315 85 0.24797876 0.24735596 86 0.31249839 0.24797876 87 0.24922437 0.31249839 88 0.24984717 0.24922437 89 -2.68563320 0.24984717 90 1.20867803 -2.68563320 91 0.18781876 1.20867803 92 0.25233839 0.18781876 93 0.25296119 0.25233839 94 0.31748082 0.25296119 95 0.25420680 0.31748082 96 -1.74517040 0.25420680 97 0.25545240 -1.74517040 98 0.25607521 0.25545240 99 0.25669801 0.25607521 100 0.25732081 0.25669801 101 -0.57184798 0.25732081 102 1.30098117 -0.57184798 103 -0.67691395 1.30098117 104 0.25981203 -0.67691395 105 -1.78197992 0.25981203 106 0.26105764 -1.78197992 107 1.26168044 0.26105764 108 -1.67379993 1.26168044 109 0.11419971 -1.67379993 110 0.26354885 0.11419971 111 0.26417165 0.26354885 112 -0.79910237 0.26417165 113 0.22300251 -0.79910237 114 1.39383372 0.22300251 115 -0.79723396 1.39383372 116 0.22487092 -0.79723396 117 0.26790848 0.22487092 118 0.33242811 0.26790848 119 0.26915408 0.33242811 120 -1.66632629 0.26915408 121 -0.72960031 -1.66632629 122 0.33491932 -0.72960031 123 0.27164530 0.33491932 124 0.33616493 0.27164530 125 -0.72710910 0.33616493 126 0.27351371 -0.72710910 127 0.27413651 0.27351371 128 1.33865614 0.27413651 129 0.23296737 1.33865614 130 -0.72399508 0.23296737 131 -0.76578703 -0.72399508 132 0.27725053 -0.76578703 133 0.23545858 0.27725053 134 0.17218456 0.23545858 135 1.17280736 0.17218456 136 -0.65636143 1.17280736 137 0.28036455 -0.65636143 138 -0.65511582 0.28036455 139 0.34550698 -0.65511582 140 -1.76018180 0.34550698 141 -0.71714424 -1.76018180 142 1.21958174 -0.71714424 143 -2.67348388 1.21958174 144 0.28472417 -2.67348388 145 1.24293222 0.28472417 146 0.22207295 1.24293222 147 -0.75582217 0.22207295 148 -0.71278461 -0.75582217 149 -1.64826498 -0.71278461 150 0.30994307 -1.64826498 151 0.28908380 0.30994307 152 1.28970660 0.28908380 153 -0.70967060 1.28970660 154 -1.64515097 -0.70967060 155 1.24916026 -1.64515097 156 0.22830099 1.24916026 157 1.31430269 0.22830099 158 1.35734025 1.31430269 159 0.29406623 1.35734025 160 0.25227428 0.29406623 161 0.29531183 0.25227428 162 NA 0.29531183 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.19566326 -0.84737430 [2,] -0.86761076 1.19566326 [3,] -0.80309113 -0.86761076 [4,] 0.26142850 -0.80309113 [5,] 1.15573972 0.26142850 [6,] 0.30508886 1.15573972 [7,] 0.19940008 0.30508886 [8,] 0.15760813 0.19940008 [9,] 0.09433411 0.15760813 [10,] 0.20126849 0.09433411 [11,] 1.20189130 0.20126849 [12,] -1.79748590 1.20189130 [13,] 0.20313690 -1.79748590 [14,] 1.20375971 0.20313690 [15,] 0.20438251 1.20375971 [16,] 0.20500531 0.20438251 [17,] 0.24804287 0.20500531 [18,] 0.14235409 0.24804287 [19,] 1.20687372 0.14235409 [20,] 0.20749653 1.20687372 [21,] -0.79188067 0.20749653 [22,] -0.74884311 -0.79188067 [23,] -0.79063506 -0.74884311 [24,] 1.20998774 -0.79063506 [25,] -1.78938945 1.20998774 [26,] 0.21123335 -1.78938945 [27,] 0.21185615 0.21123335 [28,] 0.21247896 0.21185615 [29,] -0.72300141 0.21247896 [30,] 0.27762139 -0.72300141 [31,] -0.82806738 0.27762139 [32,] 0.21497017 -0.82806738 [33,] 0.21559297 0.21497017 [34,] 0.17380103 0.21559297 [35,] 0.23832066 0.17380103 [36,] -1.93126495 0.23832066 [37,] 0.17566944 -1.93126495 [38,] 1.21870699 0.17566944 [39,] -0.78067020 1.21870699 [40,] 0.21995260 -0.78067020 [41,] 0.22057540 0.21995260 [42,] 1.26361296 0.22057540 [43,] -0.71428216 1.26361296 [44,] 0.28634064 -0.71428216 [45,] -0.71303656 0.28634064 [46,] 0.11737784 -0.71303656 [47,] 0.22431222 0.11737784 [48,] 1.22493503 0.22431222 [49,] 0.18314308 1.22493503 [50,] -0.77381936 0.18314308 [51,] -0.81561131 -0.77381936 [52,] 0.18501149 -0.81561131 [53,] -0.66563937 0.18501149 [54,] 0.07994552 -0.66563937 [55,] 0.22929465 0.07994552 [56,] 0.22991746 0.22929465 [57,] -0.76945974 0.22991746 [58,] -0.93904534 -0.76945974 [59,] -0.76821413 -0.93904534 [60,] -0.81000608 -0.76821413 [61,] 0.23303148 -0.81000608 [62,] 0.23365428 0.23303148 [63,] 0.29817391 0.23365428 [64,] 0.23489989 0.29817391 [65,] -0.70058048 0.23489989 [66,] -0.72143975 -0.70058048 [67,] -0.65692012 -0.72143975 [68,] 0.19497635 -0.65692012 [69,] 0.23801390 0.19497635 [70,] 0.26011878 0.23801390 [71,] 1.30315634 0.26011878 [72,] 0.28229707 1.30315634 [73,] 0.34681670 0.28229707 [74,] -0.75887208 0.34681670 [75,] 0.17785390 -0.75887208 [76,] 1.24237353 0.17785390 [77,] -0.75700367 1.24237353 [78,] 1.30751596 -0.75700367 [79,] 0.24424194 1.30751596 [80,] 0.30876157 0.24424194 [81,] 0.28790230 0.30876157 [82,] -0.75388965 0.28790230 [83,] 0.24673315 -0.75388965 [84,] 0.24735596 0.24673315 [85,] 0.24797876 0.24735596 [86,] 0.31249839 0.24797876 [87,] 0.24922437 0.31249839 [88,] 0.24984717 0.24922437 [89,] -2.68563320 0.24984717 [90,] 1.20867803 -2.68563320 [91,] 0.18781876 1.20867803 [92,] 0.25233839 0.18781876 [93,] 0.25296119 0.25233839 [94,] 0.31748082 0.25296119 [95,] 0.25420680 0.31748082 [96,] -1.74517040 0.25420680 [97,] 0.25545240 -1.74517040 [98,] 0.25607521 0.25545240 [99,] 0.25669801 0.25607521 [100,] 0.25732081 0.25669801 [101,] -0.57184798 0.25732081 [102,] 1.30098117 -0.57184798 [103,] -0.67691395 1.30098117 [104,] 0.25981203 -0.67691395 [105,] -1.78197992 0.25981203 [106,] 0.26105764 -1.78197992 [107,] 1.26168044 0.26105764 [108,] -1.67379993 1.26168044 [109,] 0.11419971 -1.67379993 [110,] 0.26354885 0.11419971 [111,] 0.26417165 0.26354885 [112,] -0.79910237 0.26417165 [113,] 0.22300251 -0.79910237 [114,] 1.39383372 0.22300251 [115,] -0.79723396 1.39383372 [116,] 0.22487092 -0.79723396 [117,] 0.26790848 0.22487092 [118,] 0.33242811 0.26790848 [119,] 0.26915408 0.33242811 [120,] -1.66632629 0.26915408 [121,] -0.72960031 -1.66632629 [122,] 0.33491932 -0.72960031 [123,] 0.27164530 0.33491932 [124,] 0.33616493 0.27164530 [125,] -0.72710910 0.33616493 [126,] 0.27351371 -0.72710910 [127,] 0.27413651 0.27351371 [128,] 1.33865614 0.27413651 [129,] 0.23296737 1.33865614 [130,] -0.72399508 0.23296737 [131,] -0.76578703 -0.72399508 [132,] 0.27725053 -0.76578703 [133,] 0.23545858 0.27725053 [134,] 0.17218456 0.23545858 [135,] 1.17280736 0.17218456 [136,] -0.65636143 1.17280736 [137,] 0.28036455 -0.65636143 [138,] -0.65511582 0.28036455 [139,] 0.34550698 -0.65511582 [140,] -1.76018180 0.34550698 [141,] -0.71714424 -1.76018180 [142,] 1.21958174 -0.71714424 [143,] -2.67348388 1.21958174 [144,] 0.28472417 -2.67348388 [145,] 1.24293222 0.28472417 [146,] 0.22207295 1.24293222 [147,] -0.75582217 0.22207295 [148,] -0.71278461 -0.75582217 [149,] -1.64826498 -0.71278461 [150,] 0.30994307 -1.64826498 [151,] 0.28908380 0.30994307 [152,] 1.28970660 0.28908380 [153,] -0.70967060 1.28970660 [154,] -1.64515097 -0.70967060 [155,] 1.24916026 -1.64515097 [156,] 0.22830099 1.24916026 [157,] 1.31430269 0.22830099 [158,] 1.35734025 1.31430269 [159,] 0.29406623 1.35734025 [160,] 0.25227428 0.29406623 [161,] 0.29531183 0.25227428 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.19566326 -0.84737430 2 -0.86761076 1.19566326 3 -0.80309113 -0.86761076 4 0.26142850 -0.80309113 5 1.15573972 0.26142850 6 0.30508886 1.15573972 7 0.19940008 0.30508886 8 0.15760813 0.19940008 9 0.09433411 0.15760813 10 0.20126849 0.09433411 11 1.20189130 0.20126849 12 -1.79748590 1.20189130 13 0.20313690 -1.79748590 14 1.20375971 0.20313690 15 0.20438251 1.20375971 16 0.20500531 0.20438251 17 0.24804287 0.20500531 18 0.14235409 0.24804287 19 1.20687372 0.14235409 20 0.20749653 1.20687372 21 -0.79188067 0.20749653 22 -0.74884311 -0.79188067 23 -0.79063506 -0.74884311 24 1.20998774 -0.79063506 25 -1.78938945 1.20998774 26 0.21123335 -1.78938945 27 0.21185615 0.21123335 28 0.21247896 0.21185615 29 -0.72300141 0.21247896 30 0.27762139 -0.72300141 31 -0.82806738 0.27762139 32 0.21497017 -0.82806738 33 0.21559297 0.21497017 34 0.17380103 0.21559297 35 0.23832066 0.17380103 36 -1.93126495 0.23832066 37 0.17566944 -1.93126495 38 1.21870699 0.17566944 39 -0.78067020 1.21870699 40 0.21995260 -0.78067020 41 0.22057540 0.21995260 42 1.26361296 0.22057540 43 -0.71428216 1.26361296 44 0.28634064 -0.71428216 45 -0.71303656 0.28634064 46 0.11737784 -0.71303656 47 0.22431222 0.11737784 48 1.22493503 0.22431222 49 0.18314308 1.22493503 50 -0.77381936 0.18314308 51 -0.81561131 -0.77381936 52 0.18501149 -0.81561131 53 -0.66563937 0.18501149 54 0.07994552 -0.66563937 55 0.22929465 0.07994552 56 0.22991746 0.22929465 57 -0.76945974 0.22991746 58 -0.93904534 -0.76945974 59 -0.76821413 -0.93904534 60 -0.81000608 -0.76821413 61 0.23303148 -0.81000608 62 0.23365428 0.23303148 63 0.29817391 0.23365428 64 0.23489989 0.29817391 65 -0.70058048 0.23489989 66 -0.72143975 -0.70058048 67 -0.65692012 -0.72143975 68 0.19497635 -0.65692012 69 0.23801390 0.19497635 70 0.26011878 0.23801390 71 1.30315634 0.26011878 72 0.28229707 1.30315634 73 0.34681670 0.28229707 74 -0.75887208 0.34681670 75 0.17785390 -0.75887208 76 1.24237353 0.17785390 77 -0.75700367 1.24237353 78 1.30751596 -0.75700367 79 0.24424194 1.30751596 80 0.30876157 0.24424194 81 0.28790230 0.30876157 82 -0.75388965 0.28790230 83 0.24673315 -0.75388965 84 0.24735596 0.24673315 85 0.24797876 0.24735596 86 0.31249839 0.24797876 87 0.24922437 0.31249839 88 0.24984717 0.24922437 89 -2.68563320 0.24984717 90 1.20867803 -2.68563320 91 0.18781876 1.20867803 92 0.25233839 0.18781876 93 0.25296119 0.25233839 94 0.31748082 0.25296119 95 0.25420680 0.31748082 96 -1.74517040 0.25420680 97 0.25545240 -1.74517040 98 0.25607521 0.25545240 99 0.25669801 0.25607521 100 0.25732081 0.25669801 101 -0.57184798 0.25732081 102 1.30098117 -0.57184798 103 -0.67691395 1.30098117 104 0.25981203 -0.67691395 105 -1.78197992 0.25981203 106 0.26105764 -1.78197992 107 1.26168044 0.26105764 108 -1.67379993 1.26168044 109 0.11419971 -1.67379993 110 0.26354885 0.11419971 111 0.26417165 0.26354885 112 -0.79910237 0.26417165 113 0.22300251 -0.79910237 114 1.39383372 0.22300251 115 -0.79723396 1.39383372 116 0.22487092 -0.79723396 117 0.26790848 0.22487092 118 0.33242811 0.26790848 119 0.26915408 0.33242811 120 -1.66632629 0.26915408 121 -0.72960031 -1.66632629 122 0.33491932 -0.72960031 123 0.27164530 0.33491932 124 0.33616493 0.27164530 125 -0.72710910 0.33616493 126 0.27351371 -0.72710910 127 0.27413651 0.27351371 128 1.33865614 0.27413651 129 0.23296737 1.33865614 130 -0.72399508 0.23296737 131 -0.76578703 -0.72399508 132 0.27725053 -0.76578703 133 0.23545858 0.27725053 134 0.17218456 0.23545858 135 1.17280736 0.17218456 136 -0.65636143 1.17280736 137 0.28036455 -0.65636143 138 -0.65511582 0.28036455 139 0.34550698 -0.65511582 140 -1.76018180 0.34550698 141 -0.71714424 -1.76018180 142 1.21958174 -0.71714424 143 -2.67348388 1.21958174 144 0.28472417 -2.67348388 145 1.24293222 0.28472417 146 0.22207295 1.24293222 147 -0.75582217 0.22207295 148 -0.71278461 -0.75582217 149 -1.64826498 -0.71278461 150 0.30994307 -1.64826498 151 0.28908380 0.30994307 152 1.28970660 0.28908380 153 -0.70967060 1.28970660 154 -1.64515097 -0.70967060 155 1.24916026 -1.64515097 156 0.22830099 1.24916026 157 1.31430269 0.22830099 158 1.35734025 1.31430269 159 0.29406623 1.35734025 160 0.25227428 0.29406623 161 0.29531183 0.25227428 > 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/7awh41322070936.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/8n87p1322070936.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/9j3yp1322070936.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/1067901322070936.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11sqs61322070936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12xb5c1322070936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/132jds1322070936.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/141s8c1322070936.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15oqhc1322070936.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16yo1h1322070936.tab") + } > > try(system("convert tmp/11aw91322070936.ps tmp/11aw91322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/2638j1322070936.ps tmp/2638j1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/3tlhr1322070936.ps tmp/3tlhr1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/4d7jy1322070936.ps tmp/4d7jy1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/5eqe01322070936.ps tmp/5eqe01322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/6qifg1322070936.ps tmp/6qifg1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/7awh41322070936.ps tmp/7awh41322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/8n87p1322070936.ps tmp/8n87p1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/9j3yp1322070936.ps tmp/9j3yp1322070936.png",intern=TRUE)) character(0) > try(system("convert tmp/1067901322070936.ps tmp/1067901322070936.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.631 0.553 5.202