R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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. Type 'q()' to quit R. > x <- array(list(4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,4 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + 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,1 + ,0 + ,2 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0) + ,dim=c(7 + ,154) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'T40enT20' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UseLimit','T40enT20','Used','CorrectAnalysis','Useful','Outcome'),1:154)) > 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' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) 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 CorrectAnalysis Weeks UseLimit T40enT20 Used Useful Outcome 1 0 4 1 1 0 1 0 2 0 4 0 0 0 0 0 3 0 4 0 0 0 0 0 4 0 4 0 0 0 0 0 5 0 4 0 0 0 0 0 6 1 4 1 0 0 1 0 7 0 4 0 0 0 0 0 8 0 4 0 1 0 0 0 9 0 4 0 0 0 1 0 10 0 4 1 0 0 0 0 11 0 4 1 1 0 0 0 12 0 4 0 0 0 0 0 13 1 4 0 0 1 0 0 14 0 4 1 1 0 0 0 15 1 4 0 0 1 1 0 16 1 4 0 1 1 1 0 17 1 4 1 1 1 0 1 18 0 4 1 1 0 0 0 19 0 4 0 0 0 1 0 20 1 4 0 1 1 1 1 21 1 4 1 0 0 0 0 22 1 4 1 0 1 1 0 23 1 4 0 0 0 1 0 24 1 4 1 0 0 1 0 25 0 4 0 1 1 1 0 26 1 4 0 0 1 0 0 27 0 4 1 0 0 1 0 28 0 4 0 0 1 0 0 29 0 4 0 0 0 1 0 30 1 4 0 0 0 0 0 31 0 4 0 0 0 0 0 32 0 4 1 0 0 0 0 33 1 4 1 0 0 0 0 34 0 4 0 1 0 1 0 35 0 4 0 0 0 0 0 36 0 4 0 0 0 0 0 37 1 4 1 1 1 0 0 38 0 4 0 0 1 1 0 39 1 4 0 0 0 1 0 40 1 4 0 1 0 0 0 41 1 4 0 0 1 1 1 42 0 4 0 0 1 1 0 43 1 4 1 0 0 1 0 44 0 4 1 1 0 0 0 45 1 4 0 0 0 0 0 46 1 4 0 0 0 1 0 47 0 4 0 0 0 0 0 48 0 4 0 0 0 1 0 49 1 4 0 0 0 1 0 50 0 4 0 0 0 0 0 51 0 4 0 1 1 0 0 52 1 4 1 1 1 0 1 53 0 4 0 0 0 1 0 54 0 4 0 0 1 0 1 55 0 4 0 0 0 0 0 56 0 4 0 1 1 1 0 57 1 4 0 0 1 1 0 58 0 4 0 0 0 1 0 59 0 4 0 0 0 1 0 60 1 4 1 1 1 1 1 61 0 4 1 1 0 1 0 62 1 4 0 0 1 0 0 63 0 4 0 0 0 0 0 64 0 4 1 1 0 1 0 65 0 4 0 0 0 0 0 66 0 4 0 0 0 0 0 67 1 4 0 1 1 0 1 68 0 4 1 0 0 0 0 69 0 4 0 0 0 1 0 70 0 4 0 0 1 0 0 71 0 4 0 0 0 0 0 72 0 4 0 0 0 1 0 73 0 4 0 0 1 1 0 74 0 4 1 0 1 0 0 75 0 4 0 0 0 1 0 76 1 4 0 1 0 1 0 77 0 4 0 0 0 1 0 78 1 4 0 0 1 1 0 79 0 4 0 1 1 1 1 80 1 4 0 1 0 0 0 81 0 4 0 0 0 0 0 82 0 4 1 0 1 1 0 83 0 4 0 0 0 0 0 84 0 4 0 0 1 0 1 85 1 4 0 0 0 1 0 86 0 4 1 0 0 0 0 87 0 2 1 0 0 1 0 88 0 2 1 1 1 1 0 89 0 2 0 0 0 0 0 90 0 2 0 0 0 1 0 91 1 2 0 0 0 0 0 92 0 2 1 1 0 0 0 93 1 2 1 0 0 0 0 94 0 2 0 0 0 0 0 95 0 2 0 1 0 0 0 96 0 2 0 0 0 1 0 97 0 2 1 1 0 0 0 98 0 2 0 0 0 0 0 99 0 2 1 0 0 0 0 100 0 2 0 0 0 1 0 101 0 2 1 0 0 1 0 102 0 2 0 0 0 0 0 103 0 2 0 0 0 0 0 104 0 2 0 0 0 0 0 105 0 2 0 1 1 0 0 106 0 2 0 0 0 0 0 107 0 2 0 0 0 0 0 108 0 2 1 1 1 0 0 109 0 2 0 0 0 0 0 110 0 2 1 0 0 0 0 111 1 2 1 1 1 0 0 112 0 2 0 1 0 0 0 113 0 2 0 0 1 0 0 114 0 2 1 1 1 0 0 115 0 2 1 0 0 0 0 116 0 2 0 0 0 0 0 117 0 2 1 0 0 1 0 118 0 2 1 0 0 0 0 119 0 2 0 0 0 0 0 120 0 2 0 0 0 1 0 121 0 2 1 0 0 0 0 122 0 2 0 0 0 0 0 123 0 2 1 1 1 0 0 124 1 2 0 0 1 1 0 125 0 2 0 0 0 1 0 126 0 2 0 1 0 0 0 127 1 2 0 0 0 0 0 128 0 2 0 0 0 1 0 129 0 2 0 0 0 0 0 130 0 2 0 0 0 1 0 131 0 2 1 0 0 0 0 132 0 2 1 0 0 1 0 133 0 2 1 0 1 0 0 134 0 2 0 0 0 0 0 135 0 2 0 0 0 0 0 136 0 2 0 0 0 0 0 137 1 2 1 0 1 1 0 138 1 2 1 1 1 1 0 139 0 2 0 1 0 0 0 140 0 2 0 0 0 0 0 141 0 2 0 0 1 1 1 142 0 2 0 1 1 1 0 143 0 2 1 0 0 0 0 144 1 2 0 0 0 1 0 145 1 2 0 0 0 0 0 146 0 2 0 1 0 1 0 147 0 2 0 1 1 0 0 148 0 2 0 1 0 0 0 149 0 2 1 0 0 0 0 150 1 2 0 0 0 1 0 151 0 2 0 0 0 1 0 152 0 2 1 0 1 0 1 153 1 2 1 0 1 0 1 154 0 2 1 0 1 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit T40enT20 Used Useful -0.09340 0.07549 0.05957 -0.03464 0.18304 0.11884 Outcome 0.17246 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.6483 -0.2675 -0.1764 0.3431 0.9424 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.09340 0.11921 -0.784 0.4346 Weeks 0.07549 0.03510 2.151 0.0331 * UseLimit 0.05957 0.07475 0.797 0.4268 T40enT20 -0.03464 0.08184 -0.423 0.6727 Used 0.18304 0.08668 2.112 0.0364 * Useful 0.11884 0.07093 1.675 0.0960 . Outcome 0.17246 0.14297 1.206 0.2297 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4218 on 147 degrees of freedom Multiple R-squared: 0.1308, Adjusted R-squared: 0.09532 F-statistic: 3.687 on 6 and 147 DF, p-value: 0.001926 > 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,] 0.560759916 0.878480167 0.439240084 [2,] 0.391490062 0.782980123 0.608509938 [3,] 0.254032645 0.508065290 0.745967355 [4,] 0.158105350 0.316210699 0.841894650 [5,] 0.091181061 0.182362121 0.908818939 [6,] 0.058798055 0.117596111 0.941201945 [7,] 0.034797564 0.069595128 0.965202436 [8,] 0.018213975 0.036427949 0.981786025 [9,] 0.009112559 0.018225119 0.990887441 [10,] 0.005916675 0.011833350 0.994083325 [11,] 0.002952677 0.005905354 0.997047323 [12,] 0.027357727 0.054715455 0.972642273 [13,] 0.035907700 0.071815400 0.964092300 [14,] 0.131563864 0.263127728 0.868436136 [15,] 0.137670169 0.275340337 0.862329831 [16,] 0.210526070 0.421052140 0.789473930 [17,] 0.193915713 0.387831425 0.806084287 [18,] 0.268306385 0.536612770 0.731693615 [19,] 0.389161269 0.778322538 0.610838731 [20,] 0.357794285 0.715588570 0.642205715 [21,] 0.575784311 0.848431378 0.424215689 [22,] 0.521491868 0.957016264 0.478508132 [23,] 0.505895142 0.988209717 0.494104858 [24,] 0.585117731 0.829764539 0.414882269 [25,] 0.534383185 0.931233629 0.465616815 [26,] 0.482524783 0.965049565 0.517475217 [27,] 0.430904580 0.861809161 0.569095420 [28,] 0.436026555 0.872053110 0.563973445 [29,] 0.559244532 0.881510935 0.440755468 [30,] 0.666382644 0.667234712 0.333617356 [31,] 0.854111913 0.291776173 0.145888087 [32,] 0.831235412 0.337529175 0.168764588 [33,] 0.861348855 0.277302289 0.138651145 [34,] 0.879828216 0.240343567 0.120171784 [35,] 0.860433683 0.279132633 0.139566317 [36,] 0.920596943 0.158806114 0.079403057 [37,] 0.946284794 0.107430413 0.053715206 [38,] 0.934551632 0.130896736 0.065448368 [39,] 0.926600442 0.146799117 0.073399558 [40,] 0.950991353 0.098017294 0.049008647 [41,] 0.939923939 0.120152121 0.060076061 [42,] 0.931847765 0.136304471 0.068152235 [43,] 0.929016167 0.141967666 0.070983833 [44,] 0.921504769 0.156990462 0.078495231 [45,] 0.946744128 0.106511744 0.053255872 [46,] 0.934128862 0.131742277 0.065871138 [47,] 0.934933862 0.130132277 0.065066138 [48,] 0.939034620 0.121930760 0.060965380 [49,] 0.931373232 0.137253536 0.068626768 [50,] 0.922479362 0.155041276 0.077520638 [51,] 0.919158375 0.161683249 0.080841625 [52,] 0.910573615 0.178852771 0.089426385 [53,] 0.929877472 0.140245057 0.070122528 [54,] 0.914736552 0.170526896 0.085263448 [55,] 0.904435425 0.191129149 0.095564575 [56,] 0.885677126 0.228645747 0.114322874 [57,] 0.864422860 0.271154281 0.135577140 [58,] 0.888345994 0.223308013 0.111654006 [59,] 0.877636463 0.244727074 0.122363537 [60,] 0.862827812 0.274344376 0.137172188 [61,] 0.861974334 0.276051333 0.138025666 [62,] 0.837981269 0.324037461 0.162018731 [63,] 0.821784503 0.356430993 0.178215497 [64,] 0.839515181 0.320969638 0.160484819 [65,] 0.855036974 0.289926051 0.144963026 [66,] 0.845147568 0.309704864 0.154852432 [67,] 0.901299287 0.197401427 0.098700713 [68,] 0.892638210 0.214723581 0.107361790 [69,] 0.898719153 0.202561693 0.101280847 [70,] 0.913556196 0.172887607 0.086443804 [71,] 0.965420822 0.069158356 0.034579178 [72,] 0.956139511 0.087720977 0.043860489 [73,] 0.963119269 0.073761462 0.036880731 [74,] 0.955223399 0.089553202 0.044776601 [75,] 0.961651602 0.076696796 0.038348398 [76,] 0.974545306 0.050909388 0.025454694 [77,] 0.967731322 0.064537357 0.032268678 [78,] 0.959721312 0.080557375 0.040278688 [79,] 0.953553599 0.092892802 0.046446401 [80,] 0.942643663 0.114712675 0.057356337 [81,] 0.930412484 0.139175031 0.069587516 [82,] 0.977816115 0.044367770 0.022183885 [83,] 0.970504513 0.058990973 0.029495487 [84,] 0.990598068 0.018803865 0.009401932 [85,] 0.987110800 0.025778401 0.012889200 [86,] 0.982351144 0.035297711 0.017648856 [87,] 0.978132376 0.043735248 0.021867624 [88,] 0.970948190 0.058103619 0.029051810 [89,] 0.961615119 0.076769762 0.038384881 [90,] 0.950372635 0.099254731 0.049627365 [91,] 0.940504288 0.118991424 0.059495712 [92,] 0.930534598 0.138930805 0.069465402 [93,] 0.911672420 0.176655160 0.088327580 [94,] 0.889151461 0.221697077 0.110848539 [95,] 0.862698018 0.274603965 0.137301982 [96,] 0.839353939 0.321292123 0.160646061 [97,] 0.805513824 0.388972352 0.194486176 [98,] 0.767571469 0.464857062 0.232428531 [99,] 0.738275006 0.523449987 0.261724994 [100,] 0.693531574 0.612936851 0.306468426 [101,] 0.646006500 0.707986999 0.353993500 [102,] 0.766798533 0.466402934 0.233201467 [103,] 0.722385606 0.555228788 0.277614394 [104,] 0.707940749 0.584118501 0.292059251 [105,] 0.666817817 0.666364366 0.333182183 [106,] 0.614475138 0.771049725 0.385524862 [107,] 0.562412128 0.875175743 0.437587872 [108,] 0.521714888 0.956570223 0.478285112 [109,] 0.465395615 0.930791230 0.534604385 [110,] 0.411369007 0.822738015 0.588630993 [111,] 0.381753067 0.763506135 0.618246933 [112,] 0.328378319 0.656756638 0.671621681 [113,] 0.280457278 0.560914555 0.719542722 [114,] 0.238751828 0.477503656 0.761248172 [115,] 0.255782043 0.511564085 0.744217957 [116,] 0.228960039 0.457920079 0.771039961 [117,] 0.182576332 0.365152665 0.817423668 [118,] 0.347669191 0.695338382 0.652330809 [119,] 0.317908827 0.635817654 0.682091173 [120,] 0.259966446 0.519932892 0.740033554 [121,] 0.243370887 0.486741773 0.756629113 [122,] 0.196680365 0.393360730 0.803319635 [123,] 0.233855550 0.467711100 0.766144450 [124,] 0.204873008 0.409746016 0.795126992 [125,] 0.156449087 0.312898174 0.843550913 [126,] 0.116273364 0.232546729 0.883726636 [127,] 0.084525759 0.169051517 0.915474241 [128,] 0.070239980 0.140479961 0.929760020 [129,] 0.168952320 0.337904640 0.831047680 [130,] 0.115337465 0.230674930 0.884662535 [131,] 0.146164695 0.292329390 0.853835305 [132,] 0.319978499 0.639956997 0.680021501 [133,] 0.224906780 0.449813560 0.775093220 [134,] 0.137843417 0.275686834 0.862156583 [135,] 0.107935526 0.215871052 0.892064474 > postscript(file="/var/wessaorg/rcomp/tmp/1fy6j1356101860.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/2bvzy1356101860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3221o1356101860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4lsyo1356101860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5vr2d1356101860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 154 Frequency = 1 1 2 3 4 5 6 -0.35232259 -0.20856202 -0.20856202 -0.20856202 -0.20856202 0.61303331 7 8 9 10 11 12 -0.20856202 -0.17391791 -0.32740046 -0.26812825 -0.23348415 -0.20856202 13 14 15 16 17 18 0.60839465 -0.23348415 0.48955621 0.52420031 0.41101250 -0.23348415 19 20 21 22 23 24 -0.32740046 0.35174028 0.73187175 0.42998998 0.67259954 0.61303331 25 26 27 28 29 30 -0.47579969 0.60839465 -0.38696669 -0.39160535 -0.32740046 0.79143798 31 32 33 34 35 36 -0.20856202 -0.26812825 0.73187175 -0.29275636 -0.20856202 -0.20856202 37 38 39 40 41 42 0.58347253 -0.51044379 0.67259954 0.82608209 0.31709618 -0.51044379 43 44 45 46 47 48 0.61303331 -0.23348415 0.79143798 0.67259954 -0.20856202 -0.32740046 49 50 51 52 53 54 0.67259954 -0.20856202 -0.35696124 0.41101250 -0.32740046 -0.56406537 55 56 57 58 59 60 -0.20856202 -0.47579969 0.48955621 -0.32740046 -0.32740046 0.29217405 61 62 63 64 65 66 -0.35232259 0.60839465 -0.20856202 -0.35232259 -0.20856202 -0.20856202 67 68 69 70 71 72 0.47057873 -0.26812825 -0.32740046 -0.39160535 -0.20856202 -0.32740046 73 74 75 76 77 78 -0.51044379 -0.45117158 -0.32740046 0.70724364 -0.32740046 0.48955621 79 80 81 82 83 84 -0.64825972 0.82608209 -0.20856202 -0.57001002 -0.20856202 -0.56406537 85 86 87 88 89 90 0.67259954 -0.26812825 -0.23598561 -0.38438484 -0.05758094 -0.17641938 91 92 93 94 95 96 0.94241906 -0.08250306 0.88285283 -0.05758094 -0.02293683 -0.17641938 97 98 99 100 101 102 -0.08250306 -0.05758094 -0.11714717 -0.17641938 -0.23598561 -0.05758094 103 104 105 106 107 108 -0.05758094 -0.05758094 -0.20598016 -0.05758094 -0.05758094 -0.26554639 109 110 111 112 113 114 -0.05758094 -0.11714717 0.73445361 -0.02293683 -0.24062427 -0.26554639 115 116 117 118 119 120 -0.11714717 -0.05758094 -0.23598561 -0.11714717 -0.05758094 -0.17641938 121 122 123 124 125 126 -0.11714717 -0.05758094 -0.26554639 0.64053729 -0.17641938 -0.02293683 127 128 129 130 131 132 0.94241906 -0.17641938 -0.05758094 -0.17641938 -0.11714717 -0.23598561 133 134 135 136 137 138 -0.30019050 -0.05758094 -0.05758094 -0.05758094 0.58097106 0.61561516 139 140 141 142 143 144 -0.02293683 -0.05758094 -0.53192274 -0.32481861 -0.11714717 0.82358062 145 146 147 148 149 150 0.94241906 -0.14177528 -0.20598016 -0.02293683 -0.11714717 0.82358062 151 152 153 154 -0.17641938 -0.47265052 0.52734948 -0.30019050 > postscript(file="/var/wessaorg/rcomp/tmp/6ed9a1356101860.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.35232259 NA 1 -0.20856202 -0.35232259 2 -0.20856202 -0.20856202 3 -0.20856202 -0.20856202 4 -0.20856202 -0.20856202 5 0.61303331 -0.20856202 6 -0.20856202 0.61303331 7 -0.17391791 -0.20856202 8 -0.32740046 -0.17391791 9 -0.26812825 -0.32740046 10 -0.23348415 -0.26812825 11 -0.20856202 -0.23348415 12 0.60839465 -0.20856202 13 -0.23348415 0.60839465 14 0.48955621 -0.23348415 15 0.52420031 0.48955621 16 0.41101250 0.52420031 17 -0.23348415 0.41101250 18 -0.32740046 -0.23348415 19 0.35174028 -0.32740046 20 0.73187175 0.35174028 21 0.42998998 0.73187175 22 0.67259954 0.42998998 23 0.61303331 0.67259954 24 -0.47579969 0.61303331 25 0.60839465 -0.47579969 26 -0.38696669 0.60839465 27 -0.39160535 -0.38696669 28 -0.32740046 -0.39160535 29 0.79143798 -0.32740046 30 -0.20856202 0.79143798 31 -0.26812825 -0.20856202 32 0.73187175 -0.26812825 33 -0.29275636 0.73187175 34 -0.20856202 -0.29275636 35 -0.20856202 -0.20856202 36 0.58347253 -0.20856202 37 -0.51044379 0.58347253 38 0.67259954 -0.51044379 39 0.82608209 0.67259954 40 0.31709618 0.82608209 41 -0.51044379 0.31709618 42 0.61303331 -0.51044379 43 -0.23348415 0.61303331 44 0.79143798 -0.23348415 45 0.67259954 0.79143798 46 -0.20856202 0.67259954 47 -0.32740046 -0.20856202 48 0.67259954 -0.32740046 49 -0.20856202 0.67259954 50 -0.35696124 -0.20856202 51 0.41101250 -0.35696124 52 -0.32740046 0.41101250 53 -0.56406537 -0.32740046 54 -0.20856202 -0.56406537 55 -0.47579969 -0.20856202 56 0.48955621 -0.47579969 57 -0.32740046 0.48955621 58 -0.32740046 -0.32740046 59 0.29217405 -0.32740046 60 -0.35232259 0.29217405 61 0.60839465 -0.35232259 62 -0.20856202 0.60839465 63 -0.35232259 -0.20856202 64 -0.20856202 -0.35232259 65 -0.20856202 -0.20856202 66 0.47057873 -0.20856202 67 -0.26812825 0.47057873 68 -0.32740046 -0.26812825 69 -0.39160535 -0.32740046 70 -0.20856202 -0.39160535 71 -0.32740046 -0.20856202 72 -0.51044379 -0.32740046 73 -0.45117158 -0.51044379 74 -0.32740046 -0.45117158 75 0.70724364 -0.32740046 76 -0.32740046 0.70724364 77 0.48955621 -0.32740046 78 -0.64825972 0.48955621 79 0.82608209 -0.64825972 80 -0.20856202 0.82608209 81 -0.57001002 -0.20856202 82 -0.20856202 -0.57001002 83 -0.56406537 -0.20856202 84 0.67259954 -0.56406537 85 -0.26812825 0.67259954 86 -0.23598561 -0.26812825 87 -0.38438484 -0.23598561 88 -0.05758094 -0.38438484 89 -0.17641938 -0.05758094 90 0.94241906 -0.17641938 91 -0.08250306 0.94241906 92 0.88285283 -0.08250306 93 -0.05758094 0.88285283 94 -0.02293683 -0.05758094 95 -0.17641938 -0.02293683 96 -0.08250306 -0.17641938 97 -0.05758094 -0.08250306 98 -0.11714717 -0.05758094 99 -0.17641938 -0.11714717 100 -0.23598561 -0.17641938 101 -0.05758094 -0.23598561 102 -0.05758094 -0.05758094 103 -0.05758094 -0.05758094 104 -0.20598016 -0.05758094 105 -0.05758094 -0.20598016 106 -0.05758094 -0.05758094 107 -0.26554639 -0.05758094 108 -0.05758094 -0.26554639 109 -0.11714717 -0.05758094 110 0.73445361 -0.11714717 111 -0.02293683 0.73445361 112 -0.24062427 -0.02293683 113 -0.26554639 -0.24062427 114 -0.11714717 -0.26554639 115 -0.05758094 -0.11714717 116 -0.23598561 -0.05758094 117 -0.11714717 -0.23598561 118 -0.05758094 -0.11714717 119 -0.17641938 -0.05758094 120 -0.11714717 -0.17641938 121 -0.05758094 -0.11714717 122 -0.26554639 -0.05758094 123 0.64053729 -0.26554639 124 -0.17641938 0.64053729 125 -0.02293683 -0.17641938 126 0.94241906 -0.02293683 127 -0.17641938 0.94241906 128 -0.05758094 -0.17641938 129 -0.17641938 -0.05758094 130 -0.11714717 -0.17641938 131 -0.23598561 -0.11714717 132 -0.30019050 -0.23598561 133 -0.05758094 -0.30019050 134 -0.05758094 -0.05758094 135 -0.05758094 -0.05758094 136 0.58097106 -0.05758094 137 0.61561516 0.58097106 138 -0.02293683 0.61561516 139 -0.05758094 -0.02293683 140 -0.53192274 -0.05758094 141 -0.32481861 -0.53192274 142 -0.11714717 -0.32481861 143 0.82358062 -0.11714717 144 0.94241906 0.82358062 145 -0.14177528 0.94241906 146 -0.20598016 -0.14177528 147 -0.02293683 -0.20598016 148 -0.11714717 -0.02293683 149 0.82358062 -0.11714717 150 -0.17641938 0.82358062 151 -0.47265052 -0.17641938 152 0.52734948 -0.47265052 153 -0.30019050 0.52734948 154 NA -0.30019050 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.20856202 -0.35232259 [2,] -0.20856202 -0.20856202 [3,] -0.20856202 -0.20856202 [4,] -0.20856202 -0.20856202 [5,] 0.61303331 -0.20856202 [6,] -0.20856202 0.61303331 [7,] -0.17391791 -0.20856202 [8,] -0.32740046 -0.17391791 [9,] -0.26812825 -0.32740046 [10,] -0.23348415 -0.26812825 [11,] -0.20856202 -0.23348415 [12,] 0.60839465 -0.20856202 [13,] -0.23348415 0.60839465 [14,] 0.48955621 -0.23348415 [15,] 0.52420031 0.48955621 [16,] 0.41101250 0.52420031 [17,] -0.23348415 0.41101250 [18,] -0.32740046 -0.23348415 [19,] 0.35174028 -0.32740046 [20,] 0.73187175 0.35174028 [21,] 0.42998998 0.73187175 [22,] 0.67259954 0.42998998 [23,] 0.61303331 0.67259954 [24,] -0.47579969 0.61303331 [25,] 0.60839465 -0.47579969 [26,] -0.38696669 0.60839465 [27,] -0.39160535 -0.38696669 [28,] -0.32740046 -0.39160535 [29,] 0.79143798 -0.32740046 [30,] -0.20856202 0.79143798 [31,] -0.26812825 -0.20856202 [32,] 0.73187175 -0.26812825 [33,] -0.29275636 0.73187175 [34,] -0.20856202 -0.29275636 [35,] -0.20856202 -0.20856202 [36,] 0.58347253 -0.20856202 [37,] -0.51044379 0.58347253 [38,] 0.67259954 -0.51044379 [39,] 0.82608209 0.67259954 [40,] 0.31709618 0.82608209 [41,] -0.51044379 0.31709618 [42,] 0.61303331 -0.51044379 [43,] -0.23348415 0.61303331 [44,] 0.79143798 -0.23348415 [45,] 0.67259954 0.79143798 [46,] -0.20856202 0.67259954 [47,] -0.32740046 -0.20856202 [48,] 0.67259954 -0.32740046 [49,] -0.20856202 0.67259954 [50,] -0.35696124 -0.20856202 [51,] 0.41101250 -0.35696124 [52,] -0.32740046 0.41101250 [53,] -0.56406537 -0.32740046 [54,] -0.20856202 -0.56406537 [55,] -0.47579969 -0.20856202 [56,] 0.48955621 -0.47579969 [57,] -0.32740046 0.48955621 [58,] -0.32740046 -0.32740046 [59,] 0.29217405 -0.32740046 [60,] -0.35232259 0.29217405 [61,] 0.60839465 -0.35232259 [62,] -0.20856202 0.60839465 [63,] -0.35232259 -0.20856202 [64,] -0.20856202 -0.35232259 [65,] -0.20856202 -0.20856202 [66,] 0.47057873 -0.20856202 [67,] -0.26812825 0.47057873 [68,] -0.32740046 -0.26812825 [69,] -0.39160535 -0.32740046 [70,] -0.20856202 -0.39160535 [71,] -0.32740046 -0.20856202 [72,] -0.51044379 -0.32740046 [73,] -0.45117158 -0.51044379 [74,] -0.32740046 -0.45117158 [75,] 0.70724364 -0.32740046 [76,] -0.32740046 0.70724364 [77,] 0.48955621 -0.32740046 [78,] -0.64825972 0.48955621 [79,] 0.82608209 -0.64825972 [80,] -0.20856202 0.82608209 [81,] -0.57001002 -0.20856202 [82,] -0.20856202 -0.57001002 [83,] -0.56406537 -0.20856202 [84,] 0.67259954 -0.56406537 [85,] -0.26812825 0.67259954 [86,] -0.23598561 -0.26812825 [87,] -0.38438484 -0.23598561 [88,] -0.05758094 -0.38438484 [89,] -0.17641938 -0.05758094 [90,] 0.94241906 -0.17641938 [91,] -0.08250306 0.94241906 [92,] 0.88285283 -0.08250306 [93,] -0.05758094 0.88285283 [94,] -0.02293683 -0.05758094 [95,] -0.17641938 -0.02293683 [96,] -0.08250306 -0.17641938 [97,] -0.05758094 -0.08250306 [98,] -0.11714717 -0.05758094 [99,] -0.17641938 -0.11714717 [100,] -0.23598561 -0.17641938 [101,] -0.05758094 -0.23598561 [102,] -0.05758094 -0.05758094 [103,] -0.05758094 -0.05758094 [104,] -0.20598016 -0.05758094 [105,] -0.05758094 -0.20598016 [106,] -0.05758094 -0.05758094 [107,] -0.26554639 -0.05758094 [108,] -0.05758094 -0.26554639 [109,] -0.11714717 -0.05758094 [110,] 0.73445361 -0.11714717 [111,] -0.02293683 0.73445361 [112,] -0.24062427 -0.02293683 [113,] -0.26554639 -0.24062427 [114,] -0.11714717 -0.26554639 [115,] -0.05758094 -0.11714717 [116,] -0.23598561 -0.05758094 [117,] -0.11714717 -0.23598561 [118,] -0.05758094 -0.11714717 [119,] -0.17641938 -0.05758094 [120,] -0.11714717 -0.17641938 [121,] -0.05758094 -0.11714717 [122,] -0.26554639 -0.05758094 [123,] 0.64053729 -0.26554639 [124,] -0.17641938 0.64053729 [125,] -0.02293683 -0.17641938 [126,] 0.94241906 -0.02293683 [127,] -0.17641938 0.94241906 [128,] -0.05758094 -0.17641938 [129,] -0.17641938 -0.05758094 [130,] -0.11714717 -0.17641938 [131,] -0.23598561 -0.11714717 [132,] -0.30019050 -0.23598561 [133,] -0.05758094 -0.30019050 [134,] -0.05758094 -0.05758094 [135,] -0.05758094 -0.05758094 [136,] 0.58097106 -0.05758094 [137,] 0.61561516 0.58097106 [138,] -0.02293683 0.61561516 [139,] -0.05758094 -0.02293683 [140,] -0.53192274 -0.05758094 [141,] -0.32481861 -0.53192274 [142,] -0.11714717 -0.32481861 [143,] 0.82358062 -0.11714717 [144,] 0.94241906 0.82358062 [145,] -0.14177528 0.94241906 [146,] -0.20598016 -0.14177528 [147,] -0.02293683 -0.20598016 [148,] -0.11714717 -0.02293683 [149,] 0.82358062 -0.11714717 [150,] -0.17641938 0.82358062 [151,] -0.47265052 -0.17641938 [152,] 0.52734948 -0.47265052 [153,] -0.30019050 0.52734948 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.20856202 -0.35232259 2 -0.20856202 -0.20856202 3 -0.20856202 -0.20856202 4 -0.20856202 -0.20856202 5 0.61303331 -0.20856202 6 -0.20856202 0.61303331 7 -0.17391791 -0.20856202 8 -0.32740046 -0.17391791 9 -0.26812825 -0.32740046 10 -0.23348415 -0.26812825 11 -0.20856202 -0.23348415 12 0.60839465 -0.20856202 13 -0.23348415 0.60839465 14 0.48955621 -0.23348415 15 0.52420031 0.48955621 16 0.41101250 0.52420031 17 -0.23348415 0.41101250 18 -0.32740046 -0.23348415 19 0.35174028 -0.32740046 20 0.73187175 0.35174028 21 0.42998998 0.73187175 22 0.67259954 0.42998998 23 0.61303331 0.67259954 24 -0.47579969 0.61303331 25 0.60839465 -0.47579969 26 -0.38696669 0.60839465 27 -0.39160535 -0.38696669 28 -0.32740046 -0.39160535 29 0.79143798 -0.32740046 30 -0.20856202 0.79143798 31 -0.26812825 -0.20856202 32 0.73187175 -0.26812825 33 -0.29275636 0.73187175 34 -0.20856202 -0.29275636 35 -0.20856202 -0.20856202 36 0.58347253 -0.20856202 37 -0.51044379 0.58347253 38 0.67259954 -0.51044379 39 0.82608209 0.67259954 40 0.31709618 0.82608209 41 -0.51044379 0.31709618 42 0.61303331 -0.51044379 43 -0.23348415 0.61303331 44 0.79143798 -0.23348415 45 0.67259954 0.79143798 46 -0.20856202 0.67259954 47 -0.32740046 -0.20856202 48 0.67259954 -0.32740046 49 -0.20856202 0.67259954 50 -0.35696124 -0.20856202 51 0.41101250 -0.35696124 52 -0.32740046 0.41101250 53 -0.56406537 -0.32740046 54 -0.20856202 -0.56406537 55 -0.47579969 -0.20856202 56 0.48955621 -0.47579969 57 -0.32740046 0.48955621 58 -0.32740046 -0.32740046 59 0.29217405 -0.32740046 60 -0.35232259 0.29217405 61 0.60839465 -0.35232259 62 -0.20856202 0.60839465 63 -0.35232259 -0.20856202 64 -0.20856202 -0.35232259 65 -0.20856202 -0.20856202 66 0.47057873 -0.20856202 67 -0.26812825 0.47057873 68 -0.32740046 -0.26812825 69 -0.39160535 -0.32740046 70 -0.20856202 -0.39160535 71 -0.32740046 -0.20856202 72 -0.51044379 -0.32740046 73 -0.45117158 -0.51044379 74 -0.32740046 -0.45117158 75 0.70724364 -0.32740046 76 -0.32740046 0.70724364 77 0.48955621 -0.32740046 78 -0.64825972 0.48955621 79 0.82608209 -0.64825972 80 -0.20856202 0.82608209 81 -0.57001002 -0.20856202 82 -0.20856202 -0.57001002 83 -0.56406537 -0.20856202 84 0.67259954 -0.56406537 85 -0.26812825 0.67259954 86 -0.23598561 -0.26812825 87 -0.38438484 -0.23598561 88 -0.05758094 -0.38438484 89 -0.17641938 -0.05758094 90 0.94241906 -0.17641938 91 -0.08250306 0.94241906 92 0.88285283 -0.08250306 93 -0.05758094 0.88285283 94 -0.02293683 -0.05758094 95 -0.17641938 -0.02293683 96 -0.08250306 -0.17641938 97 -0.05758094 -0.08250306 98 -0.11714717 -0.05758094 99 -0.17641938 -0.11714717 100 -0.23598561 -0.17641938 101 -0.05758094 -0.23598561 102 -0.05758094 -0.05758094 103 -0.05758094 -0.05758094 104 -0.20598016 -0.05758094 105 -0.05758094 -0.20598016 106 -0.05758094 -0.05758094 107 -0.26554639 -0.05758094 108 -0.05758094 -0.26554639 109 -0.11714717 -0.05758094 110 0.73445361 -0.11714717 111 -0.02293683 0.73445361 112 -0.24062427 -0.02293683 113 -0.26554639 -0.24062427 114 -0.11714717 -0.26554639 115 -0.05758094 -0.11714717 116 -0.23598561 -0.05758094 117 -0.11714717 -0.23598561 118 -0.05758094 -0.11714717 119 -0.17641938 -0.05758094 120 -0.11714717 -0.17641938 121 -0.05758094 -0.11714717 122 -0.26554639 -0.05758094 123 0.64053729 -0.26554639 124 -0.17641938 0.64053729 125 -0.02293683 -0.17641938 126 0.94241906 -0.02293683 127 -0.17641938 0.94241906 128 -0.05758094 -0.17641938 129 -0.17641938 -0.05758094 130 -0.11714717 -0.17641938 131 -0.23598561 -0.11714717 132 -0.30019050 -0.23598561 133 -0.05758094 -0.30019050 134 -0.05758094 -0.05758094 135 -0.05758094 -0.05758094 136 0.58097106 -0.05758094 137 0.61561516 0.58097106 138 -0.02293683 0.61561516 139 -0.05758094 -0.02293683 140 -0.53192274 -0.05758094 141 -0.32481861 -0.53192274 142 -0.11714717 -0.32481861 143 0.82358062 -0.11714717 144 0.94241906 0.82358062 145 -0.14177528 0.94241906 146 -0.20598016 -0.14177528 147 -0.02293683 -0.20598016 148 -0.11714717 -0.02293683 149 0.82358062 -0.11714717 150 -0.17641938 0.82358062 151 -0.47265052 -0.17641938 152 0.52734948 -0.47265052 153 -0.30019050 0.52734948 > 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/7mzdg1356101860.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/8ryrb1356101860.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/9v2bt1356101860.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/106xln1356101860.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11x6rf1356101860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12ncc51356101860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13z1hs1356101860.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14lm8a1356101860.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/15o80x1356101860.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/164olk1356101860.tab") + } > > try(system("convert tmp/1fy6j1356101860.ps tmp/1fy6j1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/2bvzy1356101860.ps tmp/2bvzy1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/3221o1356101860.ps tmp/3221o1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/4lsyo1356101860.ps tmp/4lsyo1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/5vr2d1356101860.ps tmp/5vr2d1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/6ed9a1356101860.ps tmp/6ed9a1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/7mzdg1356101860.ps tmp/7mzdg1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/8ryrb1356101860.ps tmp/8ryrb1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/9v2bt1356101860.ps tmp/9v2bt1356101860.png",intern=TRUE)) character(0) > try(system("convert tmp/106xln1356101860.ps tmp/106xln1356101860.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.792 0.930 8.718