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 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + 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as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > 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_Treatment UseLimit Used Useful Outcome\r t 1 0 4 1 0 0 1 1 2 0 0 0 0 0 0 2 3 0 0 0 0 0 0 3 4 0 0 0 0 0 0 4 5 0 0 0 0 0 0 5 6 0 0 1 0 1 1 6 7 0 0 0 0 0 0 7 8 0 4 0 0 0 0 8 9 0 0 0 0 0 1 9 10 0 0 1 0 0 0 10 11 0 4 1 0 0 0 11 12 0 0 0 0 0 0 12 13 0 0 0 1 1 0 13 14 0 4 1 0 0 0 14 15 0 0 0 1 1 1 15 16 0 4 0 1 1 1 16 17 1 4 1 1 1 0 17 18 0 4 1 0 0 0 18 19 0 0 0 0 0 1 19 20 1 4 0 1 1 1 20 21 0 0 1 0 1 0 21 22 0 0 1 1 1 1 22 23 0 0 0 0 1 1 23 24 0 0 1 0 1 1 24 25 0 4 0 1 0 1 25 26 0 0 0 1 1 0 26 27 0 0 1 0 0 1 27 28 0 0 0 1 0 0 28 29 0 0 0 0 0 1 29 30 0 0 0 0 1 0 30 31 0 0 0 0 0 0 31 32 0 0 1 0 0 0 32 33 0 0 1 0 1 0 33 34 0 4 0 0 0 1 34 35 0 0 0 0 0 0 35 36 0 0 0 0 0 0 36 37 0 4 1 1 1 0 37 38 0 0 0 1 0 1 38 39 0 0 0 0 1 1 39 40 0 4 0 0 1 0 40 41 1 0 0 1 1 1 41 42 0 0 0 1 0 1 42 43 0 0 1 0 1 1 43 44 0 4 1 0 0 0 44 45 0 0 0 0 1 0 45 46 0 0 0 0 1 1 46 47 0 0 0 0 0 0 47 48 0 0 0 0 0 1 48 49 0 0 0 0 1 1 49 50 0 0 0 0 0 0 50 51 0 4 0 1 0 0 51 52 1 4 1 1 1 0 52 53 0 0 0 0 0 1 53 54 1 0 0 1 0 0 54 55 0 0 0 0 0 0 55 56 0 4 0 1 0 1 56 57 0 0 0 1 1 1 57 58 0 0 0 0 0 1 58 59 0 0 0 0 0 1 59 60 1 4 1 1 1 1 60 61 0 4 1 0 0 1 61 62 0 0 0 1 1 0 62 63 0 0 0 0 0 0 63 64 0 4 1 0 0 1 64 65 0 0 0 0 0 0 65 66 0 0 0 0 0 0 66 67 1 4 0 1 1 0 67 68 0 0 1 0 0 0 68 69 0 0 0 0 0 1 69 70 0 0 0 1 0 0 70 71 0 0 0 0 0 0 71 72 0 0 0 0 0 1 72 73 0 0 0 1 0 1 73 74 0 0 1 1 0 0 74 75 0 0 0 0 0 1 75 76 0 4 0 0 1 1 76 77 0 0 0 0 0 1 77 78 0 0 0 1 1 1 78 79 1 4 0 1 0 1 79 80 0 4 0 0 1 0 80 81 0 0 0 0 0 0 81 82 0 0 1 1 0 1 82 83 0 0 0 0 0 0 83 84 1 0 0 1 0 0 84 85 0 0 0 0 1 1 85 86 0 0 1 0 0 0 86 87 0 0 1 0 0 1 87 88 0 2 1 1 0 1 88 89 0 0 0 0 0 0 89 90 0 0 0 0 0 1 90 91 0 0 0 0 1 0 91 92 0 2 1 0 0 0 92 93 0 0 1 0 1 0 93 94 0 0 0 0 0 0 94 95 0 2 0 0 0 0 95 96 0 0 0 0 0 1 96 97 0 2 1 0 0 0 97 98 0 0 0 0 0 0 98 99 0 0 1 0 0 0 99 100 0 0 0 0 0 1 100 101 0 0 1 0 0 1 101 102 0 0 0 0 0 0 102 103 0 0 0 0 0 0 103 104 0 0 0 0 0 0 104 105 0 2 0 1 0 0 105 106 0 0 0 0 0 0 106 107 0 0 0 0 0 0 107 108 0 2 1 1 0 0 108 109 0 0 0 0 0 0 109 110 0 0 1 0 0 0 110 111 0 2 1 1 1 0 111 112 0 2 0 0 0 0 112 113 0 0 0 1 0 0 113 114 0 2 1 1 0 0 114 115 0 0 1 0 0 0 115 116 0 0 0 0 0 0 116 117 0 0 1 0 0 1 117 118 0 0 1 0 0 0 118 119 0 0 0 0 0 0 119 120 0 0 0 0 0 1 120 121 0 0 1 0 0 0 121 122 0 0 0 0 0 0 122 123 0 2 1 1 0 0 123 124 0 0 0 1 1 1 124 125 0 0 0 0 0 1 125 126 0 2 0 0 0 0 126 127 0 0 0 0 1 0 127 128 0 0 0 0 0 1 128 129 0 0 0 0 0 0 129 130 0 0 0 0 0 1 130 131 0 0 1 0 0 0 131 132 0 0 1 0 0 1 132 133 0 0 1 1 0 0 133 134 0 0 0 0 0 0 134 135 0 0 0 0 0 0 135 136 0 0 0 0 0 0 136 137 0 0 1 1 1 1 137 138 0 2 1 1 1 1 138 139 0 2 0 0 0 0 139 140 0 0 0 0 0 0 140 141 1 0 0 1 0 1 141 142 0 2 0 1 0 1 142 143 0 0 1 0 0 0 143 144 0 0 0 0 1 1 144 145 0 0 0 0 1 0 145 146 0 2 0 0 0 1 146 147 0 2 0 1 0 0 147 148 0 2 0 0 0 0 148 149 0 0 1 0 0 0 149 150 0 0 0 0 1 1 150 151 0 0 0 0 0 1 151 152 1 0 1 1 0 0 152 153 1 0 1 1 1 0 153 154 0 0 1 1 0 0 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks_Treatment UseLimit Used -0.0301764 0.0248250 -0.0109502 0.2342283 Useful `Outcome\\r` t 0.0642435 -0.0242136 0.0001985 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.36399 -0.06258 0.00625 0.02913 0.79217 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0301764 0.0496553 -0.608 0.5443 Weeks_Treatment 0.0248250 0.0141025 1.760 0.0804 . UseLimit -0.0109502 0.0424871 -0.258 0.7970 Used 0.2342283 0.0456825 5.127 9.12e-07 *** Useful 0.0642435 0.0467406 1.374 0.1714 `Outcome\\r` -0.0242136 0.0405244 -0.598 0.5511 t 0.0001985 0.0004555 0.436 0.6636 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2405 on 147 degrees of freedom Multiple R-squared: 0.2319, Adjusted R-squared: 0.2005 F-statistic: 7.397 on 6 and 147 DF, p-value: 6.261e-07 > 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,] 0.000000000 0.000000000 1.000000000 [2,] 0.000000000 0.000000000 1.000000000 [3,] 0.000000000 0.000000000 1.000000000 [4,] 0.000000000 0.000000000 1.000000000 [5,] 0.000000000 0.000000000 1.000000000 [6,] 0.000000000 0.000000000 1.000000000 [7,] 0.000000000 0.000000000 1.000000000 [8,] 0.395078920 0.790157841 0.604921080 [9,] 0.321701721 0.643403441 0.678298279 [10,] 0.318057974 0.636115948 0.681942026 [11,] 0.811623107 0.376753786 0.188376893 [12,] 0.760169624 0.479660752 0.239830376 [13,] 0.750058960 0.499882080 0.249941040 [14,] 0.686738467 0.626523066 0.313261533 [15,] 0.618821709 0.762356582 0.381178291 [16,] 0.609563457 0.780873086 0.390436543 [17,] 0.597706854 0.804586291 0.402293146 [18,] 0.567011442 0.865977116 0.432988558 [19,] 0.509631543 0.980736915 0.490368457 [20,] 0.458881096 0.917762193 0.541118904 [21,] 0.399571581 0.799143163 0.600428419 [22,] 0.343347829 0.686695658 0.656652171 [23,] 0.289756812 0.579513623 0.710243188 [24,] 0.241235652 0.482471303 0.758764348 [25,] 0.199875794 0.399751588 0.800124206 [26,] 0.162170396 0.324340792 0.837829604 [27,] 0.128908369 0.257816738 0.871091631 [28,] 0.167562054 0.335124108 0.832437946 [29,] 0.138978378 0.277956756 0.861021622 [30,] 0.108873886 0.217747772 0.891126114 [31,] 0.095394318 0.190788636 0.904605682 [32,] 0.561139928 0.877720145 0.438860072 [33,] 0.527757126 0.944485748 0.472242874 [34,] 0.473747475 0.947494950 0.526252525 [35,] 0.421656082 0.843312164 0.578343918 [36,] 0.371059749 0.742119498 0.628940251 [37,] 0.322417464 0.644834929 0.677582536 [38,] 0.278858793 0.557717585 0.721141207 [39,] 0.237306424 0.474612848 0.762693576 [40,] 0.199263716 0.398527433 0.800736284 [41,] 0.165962990 0.331925980 0.834037010 [42,] 0.172127731 0.344255461 0.827872269 [43,] 0.439846390 0.879692779 0.560153610 [44,] 0.391681421 0.783362842 0.608318579 [45,] 0.795245710 0.409508579 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0.476638124 0.238319062 [70,] 0.977747319 0.044505363 0.022252681 [71,] 0.980896519 0.038206962 0.019103481 [72,] 0.974500763 0.050998475 0.025499237 [73,] 0.974205615 0.051588770 0.025794385 [74,] 0.966058931 0.067882138 0.033941069 [75,] 0.998861922 0.002276155 0.001138078 [76,] 0.998370737 0.003258526 0.001629263 [77,] 0.997604281 0.004791438 0.002395719 [78,] 0.996558664 0.006882673 0.003441336 [79,] 0.995924293 0.008151414 0.004075707 [80,] 0.994200228 0.011599544 0.005799772 [81,] 0.991964625 0.016070751 0.008035375 [82,] 0.989222504 0.021554992 0.010777496 [83,] 0.987287126 0.025425749 0.012712874 [84,] 0.983211779 0.033576441 0.016788221 [85,] 0.977398118 0.045203764 0.022601882 [86,] 0.975062299 0.049875403 0.024937701 [87,] 0.967622728 0.064754544 0.032377272 [88,] 0.966248901 0.067502197 0.033751099 [89,] 0.956142930 0.087714141 0.043857070 [90,] 0.943991075 0.112017851 0.056008925 [91,] 0.930287631 0.139424737 0.069712369 [92,] 0.915981558 0.168036883 0.084018442 [93,] 0.895716692 0.208566616 0.104283308 [94,] 0.872123160 0.255753679 0.127876840 [95,] 0.845083544 0.309832912 0.154916456 [96,] 0.831180706 0.337638589 0.168819294 [97,] 0.798246638 0.403506724 0.201753362 [98,] 0.761824751 0.476350497 0.238175249 [99,] 0.736937516 0.526124968 0.263062484 [100,] 0.694430838 0.611138324 0.305569162 [101,] 0.649806802 0.700386396 0.350193198 [102,] 0.628275520 0.743448960 0.371724480 [103,] 0.629817204 0.740365592 0.370182796 [104,] 0.618193258 0.763613484 0.381806742 [105,] 0.575843988 0.848312023 0.424156012 [106,] 0.525182645 0.949634709 0.474817355 [107,] 0.469738692 0.939477384 0.530261308 [108,] 0.425915184 0.851830368 0.574084816 [109,] 0.377855540 0.755711080 0.622144460 [110,] 0.324828046 0.649656092 0.675171954 [111,] 0.279105858 0.558211716 0.720894142 [112,] 0.239439943 0.478879885 0.760560057 [113,] 0.196440316 0.392880633 0.803559684 [114,] 0.163593191 0.327186382 0.836406809 [115,] 0.156351222 0.312702445 0.843648778 [116,] 0.122691276 0.245382552 0.877308724 [117,] 0.123206222 0.246412444 0.876793778 [118,] 0.096551571 0.193103143 0.903448429 [119,] 0.072339640 0.144679281 0.927660360 [120,] 0.052425187 0.104850374 0.947574813 [121,] 0.037174690 0.074349381 0.962825310 [122,] 0.027606823 0.055213646 0.972393177 [123,] 0.023048828 0.046097655 0.976951172 [124,] 0.021971406 0.043942813 0.978028594 [125,] 0.013855133 0.027710266 0.986144867 [126,] 0.008402515 0.016805030 0.991597485 [127,] 0.004936148 0.009872296 0.995063852 [128,] 0.006757388 0.013514776 0.993242612 [129,] 0.007072705 0.014145410 0.992927295 [130,] 0.005035892 0.010071784 0.994964108 [131,] 0.002536126 0.005072253 0.997463874 [132,] 0.050229044 0.100458087 0.949770956 [133,] 0.030091049 0.060182099 0.969908951 [134,] 0.014727767 0.029455534 0.985272233 [135,] 0.006552333 0.013104666 0.993447667 > postscript(file="/var/wessaorg/rcomp/tmp/1q9w11356141851.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/2hvo51356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/36gp51356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4m51x1356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5sjzv1356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -3.415824e-02 2.977930e-02 2.958077e-02 2.938223e-02 2.918370e-02 6 7 8 9 10 -9.450509e-05 2.878663e-02 -7.071185e-02 5.260321e-02 3.914127e-02 11 12 13 14 15 -6.035722e-02 2.779397e-02 -2.708764e-01 -6.095282e-02 -2.470598e-01 16 17 18 19 20 -3.465583e-01 6.399798e-01 -6.174696e-02 5.061787e-02 6.526476e-01 21 22 23 24 25 -2.728615e-02 -2.374993e-01 -1.441981e-02 -3.668111e-03 -2.841015e-01 26 27 28 29 30 -2.734573e-01 5.997984e-02 -2.096108e-01 4.863254e-02 -4.002319e-02 31 32 33 34 35 2.402183e-02 3.477352e-02 -2.966856e-02 -5.166009e-02 2.322769e-02 36 37 38 39 40 2.302916e-02 -3.639909e-01 -1.873825e-01 -1.759635e-02 -1.413085e-01 41 42 43 44 45 7.477783e-01 -1.881767e-01 -7.440250e-03 -6.690883e-02 -4.300119e-02 46 47 48 49 50 -1.898608e-02 2.084529e-02 4.486040e-02 -1.958168e-02 2.024969e-02 51 52 53 54 55 -3.134771e-01 6.330311e-01 4.386773e-02 7.852273e-01 1.925702e-02 56 57 58 59 60 -2.902561e-01 -2.553982e-01 4.287506e-02 4.267653e-02 6.556565e-01 61 62 63 64 65 -4.607026e-02 -2.806045e-01 1.766875e-02 -4.666586e-02 1.727168e-02 66 67 68 69 70 1.707315e-02 6.191029e-01 2.762631e-02 4.069119e-02 -2.179492e-01 71 72 73 74 75 1.608048e-02 4.009559e-02 -1.943312e-01 -2.077931e-01 3.949999e-02 76 77 78 79 80 -1.242420e-01 3.910292e-02 -2.595674e-01 7.051776e-01 -1.492498e-01 81 82 83 84 85 1.409514e-02 -1.851678e-01 1.369808e-02 7.792713e-01 -2.672890e-02 86 87 88 89 90 2.405271e-02 4.806782e-02 -2.360090e-01 1.250687e-02 3.652198e-02 91 92 93 94 95 -5.213374e-02 -2.678847e-02 -4.158057e-02 1.151421e-02 -3.833430e-02 96 97 98 99 100 3.533078e-02 -2.778114e-02 1.072007e-02 2.147177e-02 3.453665e-02 101 102 103 104 105 4.528834e-02 9.925936e-03 9.727403e-03 9.528869e-03 -2.745479e-01 106 107 108 109 110 9.131802e-03 8.933268e-03 -2.641933e-01 8.536201e-03 1.928790e-02 111 112 113 114 115 -3.290324e-01 -4.170938e-02 -2.264862e-01 -2.653845e-01 1.829523e-02 116 117 118 119 120 7.146465e-03 4.211181e-02 1.769963e-02 6.550864e-03 3.056597e-02 121 122 123 124 125 1.710403e-02 5.955263e-03 -2.671713e-01 -2.687000e-01 2.957330e-02 126 127 128 129 130 -4.448885e-02 -5.928095e-02 2.897770e-02 4.565528e-03 2.858064e-02 131 132 133 134 135 1.511869e-02 3.913380e-02 -2.195066e-01 3.572860e-03 3.374326e-03 136 137 138 139 140 3.175792e-03 -2.603307e-01 -3.101792e-01 -4.706979e-02 2.381658e-03 141 142 143 144 145 7.921685e-01 -2.576800e-01 1.273629e-02 -3.844238e-02 -6.285456e-02 146 147 148 149 150 -2.424588e-02 -2.828863e-01 -4.885659e-02 1.154509e-02 -3.963358e-02 151 152 153 154 2.441143e-02 7.767212e-01 7.122791e-01 -2.236758e-01 > postscript(file="/var/wessaorg/rcomp/tmp/6po6m1356141851.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 -3.415824e-02 NA 1 2.977930e-02 -3.415824e-02 2 2.958077e-02 2.977930e-02 3 2.938223e-02 2.958077e-02 4 2.918370e-02 2.938223e-02 5 -9.450509e-05 2.918370e-02 6 2.878663e-02 -9.450509e-05 7 -7.071185e-02 2.878663e-02 8 5.260321e-02 -7.071185e-02 9 3.914127e-02 5.260321e-02 10 -6.035722e-02 3.914127e-02 11 2.779397e-02 -6.035722e-02 12 -2.708764e-01 2.779397e-02 13 -6.095282e-02 -2.708764e-01 14 -2.470598e-01 -6.095282e-02 15 -3.465583e-01 -2.470598e-01 16 6.399798e-01 -3.465583e-01 17 -6.174696e-02 6.399798e-01 18 5.061787e-02 -6.174696e-02 19 6.526476e-01 5.061787e-02 20 -2.728615e-02 6.526476e-01 21 -2.374993e-01 -2.728615e-02 22 -1.441981e-02 -2.374993e-01 23 -3.668111e-03 -1.441981e-02 24 -2.841015e-01 -3.668111e-03 25 -2.734573e-01 -2.841015e-01 26 5.997984e-02 -2.734573e-01 27 -2.096108e-01 5.997984e-02 28 4.863254e-02 -2.096108e-01 29 -4.002319e-02 4.863254e-02 30 2.402183e-02 -4.002319e-02 31 3.477352e-02 2.402183e-02 32 -2.966856e-02 3.477352e-02 33 -5.166009e-02 -2.966856e-02 34 2.322769e-02 -5.166009e-02 35 2.302916e-02 2.322769e-02 36 -3.639909e-01 2.302916e-02 37 -1.873825e-01 -3.639909e-01 38 -1.759635e-02 -1.873825e-01 39 -1.413085e-01 -1.759635e-02 40 7.477783e-01 -1.413085e-01 41 -1.881767e-01 7.477783e-01 42 -7.440250e-03 -1.881767e-01 43 -6.690883e-02 -7.440250e-03 44 -4.300119e-02 -6.690883e-02 45 -1.898608e-02 -4.300119e-02 46 2.084529e-02 -1.898608e-02 47 4.486040e-02 2.084529e-02 48 -1.958168e-02 4.486040e-02 49 2.024969e-02 -1.958168e-02 50 -3.134771e-01 2.024969e-02 51 6.330311e-01 -3.134771e-01 52 4.386773e-02 6.330311e-01 53 7.852273e-01 4.386773e-02 54 1.925702e-02 7.852273e-01 55 -2.902561e-01 1.925702e-02 56 -2.553982e-01 -2.902561e-01 57 4.287506e-02 -2.553982e-01 58 4.267653e-02 4.287506e-02 59 6.556565e-01 4.267653e-02 60 -4.607026e-02 6.556565e-01 61 -2.806045e-01 -4.607026e-02 62 1.766875e-02 -2.806045e-01 63 -4.666586e-02 1.766875e-02 64 1.727168e-02 -4.666586e-02 65 1.707315e-02 1.727168e-02 66 6.191029e-01 1.707315e-02 67 2.762631e-02 6.191029e-01 68 4.069119e-02 2.762631e-02 69 -2.179492e-01 4.069119e-02 70 1.608048e-02 -2.179492e-01 71 4.009559e-02 1.608048e-02 72 -1.943312e-01 4.009559e-02 73 -2.077931e-01 -1.943312e-01 74 3.949999e-02 -2.077931e-01 75 -1.242420e-01 3.949999e-02 76 3.910292e-02 -1.242420e-01 77 -2.595674e-01 3.910292e-02 78 7.051776e-01 -2.595674e-01 79 -1.492498e-01 7.051776e-01 80 1.409514e-02 -1.492498e-01 81 -1.851678e-01 1.409514e-02 82 1.369808e-02 -1.851678e-01 83 7.792713e-01 1.369808e-02 84 -2.672890e-02 7.792713e-01 85 2.405271e-02 -2.672890e-02 86 4.806782e-02 2.405271e-02 87 -2.360090e-01 4.806782e-02 88 1.250687e-02 -2.360090e-01 89 3.652198e-02 1.250687e-02 90 -5.213374e-02 3.652198e-02 91 -2.678847e-02 -5.213374e-02 92 -4.158057e-02 -2.678847e-02 93 1.151421e-02 -4.158057e-02 94 -3.833430e-02 1.151421e-02 95 3.533078e-02 -3.833430e-02 96 -2.778114e-02 3.533078e-02 97 1.072007e-02 -2.778114e-02 98 2.147177e-02 1.072007e-02 99 3.453665e-02 2.147177e-02 100 4.528834e-02 3.453665e-02 101 9.925936e-03 4.528834e-02 102 9.727403e-03 9.925936e-03 103 9.528869e-03 9.727403e-03 104 -2.745479e-01 9.528869e-03 105 9.131802e-03 -2.745479e-01 106 8.933268e-03 9.131802e-03 107 -2.641933e-01 8.933268e-03 108 8.536201e-03 -2.641933e-01 109 1.928790e-02 8.536201e-03 110 -3.290324e-01 1.928790e-02 111 -4.170938e-02 -3.290324e-01 112 -2.264862e-01 -4.170938e-02 113 -2.653845e-01 -2.264862e-01 114 1.829523e-02 -2.653845e-01 115 7.146465e-03 1.829523e-02 116 4.211181e-02 7.146465e-03 117 1.769963e-02 4.211181e-02 118 6.550864e-03 1.769963e-02 119 3.056597e-02 6.550864e-03 120 1.710403e-02 3.056597e-02 121 5.955263e-03 1.710403e-02 122 -2.671713e-01 5.955263e-03 123 -2.687000e-01 -2.671713e-01 124 2.957330e-02 -2.687000e-01 125 -4.448885e-02 2.957330e-02 126 -5.928095e-02 -4.448885e-02 127 2.897770e-02 -5.928095e-02 128 4.565528e-03 2.897770e-02 129 2.858064e-02 4.565528e-03 130 1.511869e-02 2.858064e-02 131 3.913380e-02 1.511869e-02 132 -2.195066e-01 3.913380e-02 133 3.572860e-03 -2.195066e-01 134 3.374326e-03 3.572860e-03 135 3.175792e-03 3.374326e-03 136 -2.603307e-01 3.175792e-03 137 -3.101792e-01 -2.603307e-01 138 -4.706979e-02 -3.101792e-01 139 2.381658e-03 -4.706979e-02 140 7.921685e-01 2.381658e-03 141 -2.576800e-01 7.921685e-01 142 1.273629e-02 -2.576800e-01 143 -3.844238e-02 1.273629e-02 144 -6.285456e-02 -3.844238e-02 145 -2.424588e-02 -6.285456e-02 146 -2.828863e-01 -2.424588e-02 147 -4.885659e-02 -2.828863e-01 148 1.154509e-02 -4.885659e-02 149 -3.963358e-02 1.154509e-02 150 2.441143e-02 -3.963358e-02 151 7.767212e-01 2.441143e-02 152 7.122791e-01 7.767212e-01 153 -2.236758e-01 7.122791e-01 154 NA -2.236758e-01 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.977930e-02 -3.415824e-02 [2,] 2.958077e-02 2.977930e-02 [3,] 2.938223e-02 2.958077e-02 [4,] 2.918370e-02 2.938223e-02 [5,] -9.450509e-05 2.918370e-02 [6,] 2.878663e-02 -9.450509e-05 [7,] -7.071185e-02 2.878663e-02 [8,] 5.260321e-02 -7.071185e-02 [9,] 3.914127e-02 5.260321e-02 [10,] -6.035722e-02 3.914127e-02 [11,] 2.779397e-02 -6.035722e-02 [12,] -2.708764e-01 2.779397e-02 [13,] -6.095282e-02 -2.708764e-01 [14,] -2.470598e-01 -6.095282e-02 [15,] -3.465583e-01 -2.470598e-01 [16,] 6.399798e-01 -3.465583e-01 [17,] -6.174696e-02 6.399798e-01 [18,] 5.061787e-02 -6.174696e-02 [19,] 6.526476e-01 5.061787e-02 [20,] -2.728615e-02 6.526476e-01 [21,] -2.374993e-01 -2.728615e-02 [22,] -1.441981e-02 -2.374993e-01 [23,] -3.668111e-03 -1.441981e-02 [24,] -2.841015e-01 -3.668111e-03 [25,] -2.734573e-01 -2.841015e-01 [26,] 5.997984e-02 -2.734573e-01 [27,] -2.096108e-01 5.997984e-02 [28,] 4.863254e-02 -2.096108e-01 [29,] -4.002319e-02 4.863254e-02 [30,] 2.402183e-02 -4.002319e-02 [31,] 3.477352e-02 2.402183e-02 [32,] -2.966856e-02 3.477352e-02 [33,] -5.166009e-02 -2.966856e-02 [34,] 2.322769e-02 -5.166009e-02 [35,] 2.302916e-02 2.322769e-02 [36,] -3.639909e-01 2.302916e-02 [37,] -1.873825e-01 -3.639909e-01 [38,] -1.759635e-02 -1.873825e-01 [39,] -1.413085e-01 -1.759635e-02 [40,] 7.477783e-01 -1.413085e-01 [41,] -1.881767e-01 7.477783e-01 [42,] -7.440250e-03 -1.881767e-01 [43,] -6.690883e-02 -7.440250e-03 [44,] -4.300119e-02 -6.690883e-02 [45,] -1.898608e-02 -4.300119e-02 [46,] 2.084529e-02 -1.898608e-02 [47,] 4.486040e-02 2.084529e-02 [48,] -1.958168e-02 4.486040e-02 [49,] 2.024969e-02 -1.958168e-02 [50,] -3.134771e-01 2.024969e-02 [51,] 6.330311e-01 -3.134771e-01 [52,] 4.386773e-02 6.330311e-01 [53,] 7.852273e-01 4.386773e-02 [54,] 1.925702e-02 7.852273e-01 [55,] -2.902561e-01 1.925702e-02 [56,] -2.553982e-01 -2.902561e-01 [57,] 4.287506e-02 -2.553982e-01 [58,] 4.267653e-02 4.287506e-02 [59,] 6.556565e-01 4.267653e-02 [60,] -4.607026e-02 6.556565e-01 [61,] -2.806045e-01 -4.607026e-02 [62,] 1.766875e-02 -2.806045e-01 [63,] -4.666586e-02 1.766875e-02 [64,] 1.727168e-02 -4.666586e-02 [65,] 1.707315e-02 1.727168e-02 [66,] 6.191029e-01 1.707315e-02 [67,] 2.762631e-02 6.191029e-01 [68,] 4.069119e-02 2.762631e-02 [69,] -2.179492e-01 4.069119e-02 [70,] 1.608048e-02 -2.179492e-01 [71,] 4.009559e-02 1.608048e-02 [72,] -1.943312e-01 4.009559e-02 [73,] -2.077931e-01 -1.943312e-01 [74,] 3.949999e-02 -2.077931e-01 [75,] -1.242420e-01 3.949999e-02 [76,] 3.910292e-02 -1.242420e-01 [77,] -2.595674e-01 3.910292e-02 [78,] 7.051776e-01 -2.595674e-01 [79,] -1.492498e-01 7.051776e-01 [80,] 1.409514e-02 -1.492498e-01 [81,] -1.851678e-01 1.409514e-02 [82,] 1.369808e-02 -1.851678e-01 [83,] 7.792713e-01 1.369808e-02 [84,] -2.672890e-02 7.792713e-01 [85,] 2.405271e-02 -2.672890e-02 [86,] 4.806782e-02 2.405271e-02 [87,] -2.360090e-01 4.806782e-02 [88,] 1.250687e-02 -2.360090e-01 [89,] 3.652198e-02 1.250687e-02 [90,] -5.213374e-02 3.652198e-02 [91,] -2.678847e-02 -5.213374e-02 [92,] -4.158057e-02 -2.678847e-02 [93,] 1.151421e-02 -4.158057e-02 [94,] -3.833430e-02 1.151421e-02 [95,] 3.533078e-02 -3.833430e-02 [96,] -2.778114e-02 3.533078e-02 [97,] 1.072007e-02 -2.778114e-02 [98,] 2.147177e-02 1.072007e-02 [99,] 3.453665e-02 2.147177e-02 [100,] 4.528834e-02 3.453665e-02 [101,] 9.925936e-03 4.528834e-02 [102,] 9.727403e-03 9.925936e-03 [103,] 9.528869e-03 9.727403e-03 [104,] -2.745479e-01 9.528869e-03 [105,] 9.131802e-03 -2.745479e-01 [106,] 8.933268e-03 9.131802e-03 [107,] -2.641933e-01 8.933268e-03 [108,] 8.536201e-03 -2.641933e-01 [109,] 1.928790e-02 8.536201e-03 [110,] -3.290324e-01 1.928790e-02 [111,] -4.170938e-02 -3.290324e-01 [112,] -2.264862e-01 -4.170938e-02 [113,] -2.653845e-01 -2.264862e-01 [114,] 1.829523e-02 -2.653845e-01 [115,] 7.146465e-03 1.829523e-02 [116,] 4.211181e-02 7.146465e-03 [117,] 1.769963e-02 4.211181e-02 [118,] 6.550864e-03 1.769963e-02 [119,] 3.056597e-02 6.550864e-03 [120,] 1.710403e-02 3.056597e-02 [121,] 5.955263e-03 1.710403e-02 [122,] -2.671713e-01 5.955263e-03 [123,] -2.687000e-01 -2.671713e-01 [124,] 2.957330e-02 -2.687000e-01 [125,] -4.448885e-02 2.957330e-02 [126,] -5.928095e-02 -4.448885e-02 [127,] 2.897770e-02 -5.928095e-02 [128,] 4.565528e-03 2.897770e-02 [129,] 2.858064e-02 4.565528e-03 [130,] 1.511869e-02 2.858064e-02 [131,] 3.913380e-02 1.511869e-02 [132,] -2.195066e-01 3.913380e-02 [133,] 3.572860e-03 -2.195066e-01 [134,] 3.374326e-03 3.572860e-03 [135,] 3.175792e-03 3.374326e-03 [136,] -2.603307e-01 3.175792e-03 [137,] -3.101792e-01 -2.603307e-01 [138,] -4.706979e-02 -3.101792e-01 [139,] 2.381658e-03 -4.706979e-02 [140,] 7.921685e-01 2.381658e-03 [141,] -2.576800e-01 7.921685e-01 [142,] 1.273629e-02 -2.576800e-01 [143,] -3.844238e-02 1.273629e-02 [144,] -6.285456e-02 -3.844238e-02 [145,] -2.424588e-02 -6.285456e-02 [146,] -2.828863e-01 -2.424588e-02 [147,] -4.885659e-02 -2.828863e-01 [148,] 1.154509e-02 -4.885659e-02 [149,] -3.963358e-02 1.154509e-02 [150,] 2.441143e-02 -3.963358e-02 [151,] 7.767212e-01 2.441143e-02 [152,] 7.122791e-01 7.767212e-01 [153,] -2.236758e-01 7.122791e-01 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.977930e-02 -3.415824e-02 2 2.958077e-02 2.977930e-02 3 2.938223e-02 2.958077e-02 4 2.918370e-02 2.938223e-02 5 -9.450509e-05 2.918370e-02 6 2.878663e-02 -9.450509e-05 7 -7.071185e-02 2.878663e-02 8 5.260321e-02 -7.071185e-02 9 3.914127e-02 5.260321e-02 10 -6.035722e-02 3.914127e-02 11 2.779397e-02 -6.035722e-02 12 -2.708764e-01 2.779397e-02 13 -6.095282e-02 -2.708764e-01 14 -2.470598e-01 -6.095282e-02 15 -3.465583e-01 -2.470598e-01 16 6.399798e-01 -3.465583e-01 17 -6.174696e-02 6.399798e-01 18 5.061787e-02 -6.174696e-02 19 6.526476e-01 5.061787e-02 20 -2.728615e-02 6.526476e-01 21 -2.374993e-01 -2.728615e-02 22 -1.441981e-02 -2.374993e-01 23 -3.668111e-03 -1.441981e-02 24 -2.841015e-01 -3.668111e-03 25 -2.734573e-01 -2.841015e-01 26 5.997984e-02 -2.734573e-01 27 -2.096108e-01 5.997984e-02 28 4.863254e-02 -2.096108e-01 29 -4.002319e-02 4.863254e-02 30 2.402183e-02 -4.002319e-02 31 3.477352e-02 2.402183e-02 32 -2.966856e-02 3.477352e-02 33 -5.166009e-02 -2.966856e-02 34 2.322769e-02 -5.166009e-02 35 2.302916e-02 2.322769e-02 36 -3.639909e-01 2.302916e-02 37 -1.873825e-01 -3.639909e-01 38 -1.759635e-02 -1.873825e-01 39 -1.413085e-01 -1.759635e-02 40 7.477783e-01 -1.413085e-01 41 -1.881767e-01 7.477783e-01 42 -7.440250e-03 -1.881767e-01 43 -6.690883e-02 -7.440250e-03 44 -4.300119e-02 -6.690883e-02 45 -1.898608e-02 -4.300119e-02 46 2.084529e-02 -1.898608e-02 47 4.486040e-02 2.084529e-02 48 -1.958168e-02 4.486040e-02 49 2.024969e-02 -1.958168e-02 50 -3.134771e-01 2.024969e-02 51 6.330311e-01 -3.134771e-01 52 4.386773e-02 6.330311e-01 53 7.852273e-01 4.386773e-02 54 1.925702e-02 7.852273e-01 55 -2.902561e-01 1.925702e-02 56 -2.553982e-01 -2.902561e-01 57 4.287506e-02 -2.553982e-01 58 4.267653e-02 4.287506e-02 59 6.556565e-01 4.267653e-02 60 -4.607026e-02 6.556565e-01 61 -2.806045e-01 -4.607026e-02 62 1.766875e-02 -2.806045e-01 63 -4.666586e-02 1.766875e-02 64 1.727168e-02 -4.666586e-02 65 1.707315e-02 1.727168e-02 66 6.191029e-01 1.707315e-02 67 2.762631e-02 6.191029e-01 68 4.069119e-02 2.762631e-02 69 -2.179492e-01 4.069119e-02 70 1.608048e-02 -2.179492e-01 71 4.009559e-02 1.608048e-02 72 -1.943312e-01 4.009559e-02 73 -2.077931e-01 -1.943312e-01 74 3.949999e-02 -2.077931e-01 75 -1.242420e-01 3.949999e-02 76 3.910292e-02 -1.242420e-01 77 -2.595674e-01 3.910292e-02 78 7.051776e-01 -2.595674e-01 79 -1.492498e-01 7.051776e-01 80 1.409514e-02 -1.492498e-01 81 -1.851678e-01 1.409514e-02 82 1.369808e-02 -1.851678e-01 83 7.792713e-01 1.369808e-02 84 -2.672890e-02 7.792713e-01 85 2.405271e-02 -2.672890e-02 86 4.806782e-02 2.405271e-02 87 -2.360090e-01 4.806782e-02 88 1.250687e-02 -2.360090e-01 89 3.652198e-02 1.250687e-02 90 -5.213374e-02 3.652198e-02 91 -2.678847e-02 -5.213374e-02 92 -4.158057e-02 -2.678847e-02 93 1.151421e-02 -4.158057e-02 94 -3.833430e-02 1.151421e-02 95 3.533078e-02 -3.833430e-02 96 -2.778114e-02 3.533078e-02 97 1.072007e-02 -2.778114e-02 98 2.147177e-02 1.072007e-02 99 3.453665e-02 2.147177e-02 100 4.528834e-02 3.453665e-02 101 9.925936e-03 4.528834e-02 102 9.727403e-03 9.925936e-03 103 9.528869e-03 9.727403e-03 104 -2.745479e-01 9.528869e-03 105 9.131802e-03 -2.745479e-01 106 8.933268e-03 9.131802e-03 107 -2.641933e-01 8.933268e-03 108 8.536201e-03 -2.641933e-01 109 1.928790e-02 8.536201e-03 110 -3.290324e-01 1.928790e-02 111 -4.170938e-02 -3.290324e-01 112 -2.264862e-01 -4.170938e-02 113 -2.653845e-01 -2.264862e-01 114 1.829523e-02 -2.653845e-01 115 7.146465e-03 1.829523e-02 116 4.211181e-02 7.146465e-03 117 1.769963e-02 4.211181e-02 118 6.550864e-03 1.769963e-02 119 3.056597e-02 6.550864e-03 120 1.710403e-02 3.056597e-02 121 5.955263e-03 1.710403e-02 122 -2.671713e-01 5.955263e-03 123 -2.687000e-01 -2.671713e-01 124 2.957330e-02 -2.687000e-01 125 -4.448885e-02 2.957330e-02 126 -5.928095e-02 -4.448885e-02 127 2.897770e-02 -5.928095e-02 128 4.565528e-03 2.897770e-02 129 2.858064e-02 4.565528e-03 130 1.511869e-02 2.858064e-02 131 3.913380e-02 1.511869e-02 132 -2.195066e-01 3.913380e-02 133 3.572860e-03 -2.195066e-01 134 3.374326e-03 3.572860e-03 135 3.175792e-03 3.374326e-03 136 -2.603307e-01 3.175792e-03 137 -3.101792e-01 -2.603307e-01 138 -4.706979e-02 -3.101792e-01 139 2.381658e-03 -4.706979e-02 140 7.921685e-01 2.381658e-03 141 -2.576800e-01 7.921685e-01 142 1.273629e-02 -2.576800e-01 143 -3.844238e-02 1.273629e-02 144 -6.285456e-02 -3.844238e-02 145 -2.424588e-02 -6.285456e-02 146 -2.828863e-01 -2.424588e-02 147 -4.885659e-02 -2.828863e-01 148 1.154509e-02 -4.885659e-02 149 -3.963358e-02 1.154509e-02 150 2.441143e-02 -3.963358e-02 151 7.767212e-01 2.441143e-02 152 7.122791e-01 7.767212e-01 153 -2.236758e-01 7.122791e-01 > 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/70f7d1356141851.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/8de621356141851.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/9cjq11356141851.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/10a3f21356141851.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1168zh1356141851.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12oy0r1356141851.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ewgz1356141851.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14dxzz1356141851.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/15h5y31356141851.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/16jq301356141851.tab") + } > > try(system("convert tmp/1q9w11356141851.ps tmp/1q9w11356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/2hvo51356141851.ps tmp/2hvo51356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/36gp51356141851.ps tmp/36gp51356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/4m51x1356141851.ps tmp/4m51x1356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/5sjzv1356141851.ps tmp/5sjzv1356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/6po6m1356141851.ps tmp/6po6m1356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/70f7d1356141851.ps tmp/70f7d1356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/8de621356141851.ps tmp/8de621356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/9cjq11356141851.ps tmp/9cjq11356141851.png",intern=TRUE)) character(0) > try(system("convert tmp/10a3f21356141851.ps tmp/10a3f21356141851.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.838 1.712 12.536