R version 2.12.0 (2010-10-15) Copyright (C) 2010 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. 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,21 + ,0 + ,1 + ,28 + ,28 + ,14 + ,14 + ,11 + ,11 + ,8 + ,8 + ,24 + ,24 + ,24 + ,0 + ,22 + ,0 + ,13 + ,0 + ,15 + ,0 + ,10 + ,0 + ,22 + ,22 + ,0 + ,0 + ,31 + ,0 + ,16 + ,0 + ,19 + ,0 + ,16 + ,0 + ,17 + ,20 + ,0) + ,dim=c(12 + ,154) + ,dimnames=list(c('M' + ,'CM' + ,'CM_M' + ,'D' + ,'D_M' + ,'PE' + ,'PE_M' + ,'PC' + ,'PC_M' + ,'PS' + ,'O' + ,'O_M') + ,1:154)) > y <- array(NA,dim=c(12,154),dimnames=list(c('M','CM','CM_M','D','D_M','PE','PE_M','PC','PC_M','PS','O','O_M'),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 = '10' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > 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 PS M CM CM_M D D_M PE PE_M PC PC_M O O_M 1 24 1 24 24 14 14 11 11 12 12 26 26 2 25 0 25 0 11 0 7 0 8 0 23 0 3 30 0 17 0 6 0 17 0 8 0 25 0 4 19 1 18 18 12 12 10 10 8 8 23 23 5 22 0 18 0 8 0 12 0 9 0 19 0 6 22 0 16 0 10 0 12 0 7 0 29 0 7 25 0 20 0 10 0 11 0 4 0 25 0 8 23 0 16 0 11 0 11 0 11 0 21 0 9 17 0 18 0 16 0 12 0 7 0 22 0 10 21 0 17 0 11 0 13 0 7 0 25 0 11 19 1 23 23 13 13 14 14 12 12 24 24 12 19 0 30 0 12 0 16 0 10 0 18 0 13 15 0 23 0 8 0 11 0 10 0 22 0 14 16 0 18 0 12 0 10 0 8 0 15 0 15 23 1 15 15 11 11 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14 0 14 0 10 0 24 0 46 32 1 33 33 14 14 9 9 6 6 24 24 47 25 0 29 0 10 0 12 0 8 0 23 0 48 25 0 23 0 14 0 17 0 7 0 21 0 49 23 1 26 26 16 16 5 5 4 4 24 24 50 21 1 18 18 9 9 12 12 8 8 24 24 51 20 0 20 0 10 0 12 0 8 0 28 0 52 15 0 11 0 6 0 6 0 4 0 16 0 53 30 1 28 28 8 8 24 24 20 20 20 20 54 24 0 26 0 13 0 12 0 8 0 29 0 55 26 0 22 0 10 0 12 0 8 0 27 0 56 24 1 17 17 8 8 14 14 6 6 22 22 57 22 1 12 12 7 7 7 7 4 4 28 28 58 14 0 14 0 15 0 13 0 8 0 16 0 59 24 1 17 17 9 9 12 12 9 9 25 25 60 24 1 21 21 10 10 13 13 6 6 24 24 61 24 0 19 0 12 0 14 0 7 0 28 0 62 24 1 18 18 13 13 8 8 9 9 24 24 63 19 1 10 10 10 10 11 11 5 5 23 23 64 31 1 29 29 11 11 9 9 5 5 30 30 65 22 1 31 31 8 8 11 11 8 8 24 24 66 27 1 19 19 9 9 13 13 8 8 21 21 67 19 1 9 9 13 13 10 10 6 6 25 25 68 25 0 20 0 11 0 11 0 8 0 25 0 69 20 0 28 0 8 0 12 0 7 0 22 0 70 21 0 19 0 9 0 9 0 7 0 23 0 71 27 0 30 0 9 0 15 0 9 0 26 0 72 23 0 29 0 15 0 18 0 11 0 23 0 73 25 0 26 0 9 0 15 0 6 0 25 0 74 20 0 23 0 10 0 12 0 8 0 21 0 75 22 0 21 0 12 0 14 0 9 0 24 0 76 23 1 19 19 12 12 10 10 8 8 29 29 77 25 0 28 0 11 0 13 0 6 0 22 0 78 25 0 23 0 14 0 13 0 10 0 27 0 79 17 0 18 0 6 0 11 0 8 0 26 0 80 19 1 21 21 12 12 13 13 8 8 22 22 81 25 0 20 0 8 0 16 0 10 0 24 0 82 19 1 23 23 14 14 8 8 5 5 27 27 83 20 1 21 21 11 11 16 16 7 7 24 24 84 26 0 21 0 10 0 11 0 5 0 24 0 85 23 1 15 15 14 14 9 9 8 8 29 29 86 27 0 28 0 12 0 16 0 14 0 22 0 87 17 1 19 19 10 10 12 12 7 7 21 21 88 17 1 26 26 14 14 14 14 8 8 24 24 89 17 1 16 16 11 11 9 9 5 5 23 23 90 22 0 22 0 10 0 15 0 6 0 20 0 91 21 1 19 19 9 9 11 11 10 10 27 27 92 32 0 31 0 10 0 21 0 12 0 26 0 93 21 1 31 31 16 16 14 14 9 9 25 25 94 21 0 29 0 13 0 18 0 12 0 21 0 95 18 1 19 19 9 9 12 12 7 7 21 21 96 18 0 22 0 10 0 13 0 8 0 19 0 97 23 0 23 0 10 0 15 0 10 0 21 0 98 19 1 15 15 7 7 12 12 6 6 21 21 99 20 0 20 0 9 0 19 0 10 0 16 0 100 21 0 18 0 8 0 15 0 10 0 22 0 101 20 1 23 23 14 14 11 11 10 10 29 29 102 17 0 25 0 14 0 11 0 5 0 15 0 103 18 0 21 0 8 0 10 0 7 0 17 0 104 19 0 24 0 9 0 13 0 10 0 15 0 105 22 0 25 0 14 0 15 0 11 0 21 0 106 15 1 17 17 14 14 12 12 6 6 21 21 107 14 0 13 0 8 0 12 0 7 0 19 0 108 18 0 28 0 8 0 16 0 12 0 24 0 109 24 1 21 21 8 8 9 9 11 11 20 20 110 35 0 25 0 7 0 18 0 11 0 17 0 111 29 0 9 0 6 0 8 0 11 0 23 0 112 21 0 16 0 8 0 13 0 5 0 24 0 113 20 1 17 17 11 11 9 9 6 6 19 19 114 22 1 25 25 14 14 15 15 9 9 24 24 115 13 1 20 20 11 11 8 8 4 4 13 13 116 26 0 29 0 11 0 7 0 4 0 22 0 117 17 0 14 0 11 0 12 0 7 0 16 0 118 25 0 22 0 14 0 14 0 11 0 19 0 119 20 0 15 0 8 0 6 0 6 0 25 0 120 19 1 19 19 20 20 8 8 7 7 25 25 121 21 1 20 20 11 11 17 17 8 8 23 23 122 22 0 15 0 8 0 10 0 4 0 24 0 123 24 0 20 0 11 0 11 0 8 0 26 0 124 21 0 18 0 10 0 14 0 9 0 26 0 125 26 0 33 0 14 0 11 0 8 0 25 0 126 24 0 22 0 11 0 13 0 11 0 18 0 127 16 0 16 0 9 0 12 0 8 0 21 0 128 23 1 17 17 9 9 11 11 5 5 26 26 129 18 0 16 0 8 0 9 0 4 0 23 0 130 16 1 21 21 10 10 12 12 8 8 23 23 131 26 1 26 26 13 13 20 20 10 10 22 22 132 19 0 18 0 13 0 12 0 6 0 20 0 133 21 0 18 0 12 0 13 0 9 0 13 0 134 21 0 17 0 8 0 12 0 9 0 24 0 135 22 0 22 0 13 0 12 0 13 0 15 0 136 23 0 30 0 14 0 9 0 9 0 14 0 137 29 0 30 0 12 0 15 0 10 0 22 0 138 21 0 24 0 14 0 24 0 20 0 10 0 139 21 1 21 21 15 15 7 7 5 5 24 24 140 23 0 21 0 13 0 17 0 11 0 22 0 141 27 0 29 0 16 0 11 0 6 0 24 0 142 25 0 31 0 9 0 17 0 9 0 19 0 143 21 0 20 0 9 0 11 0 7 0 20 0 144 10 0 16 0 9 0 12 0 9 0 13 0 145 20 0 22 0 8 0 14 0 10 0 20 0 146 26 0 20 0 7 0 11 0 9 0 22 0 147 24 0 28 0 16 0 16 0 8 0 24 0 148 29 0 38 0 11 0 21 0 7 0 29 0 149 19 0 22 0 9 0 14 0 6 0 12 0 150 24 0 20 0 11 0 20 0 13 0 20 0 151 19 0 17 0 9 0 13 0 6 0 21 0 152 24 1 28 28 14 14 11 11 8 8 24 24 153 22 0 22 0 13 0 15 0 10 0 22 0 154 17 0 31 0 16 0 19 0 16 0 20 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) M CM CM_M D D_M 7.05980 -0.62509 0.29618 0.07064 -0.28347 -0.19329 PE PE_M PC PC_M O O_M 0.26080 -0.27411 -0.01770 0.11429 0.39223 0.13545 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.4591 -2.1628 -0.2324 2.1567 11.3523 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.05980 2.88598 2.446 0.015658 * M -0.62509 5.01780 -0.125 0.901037 CM 0.29618 0.07776 3.809 0.000207 *** CM_M 0.07064 0.11858 0.596 0.552312 D -0.28347 0.15572 -1.820 0.070804 . D_M -0.19329 0.23927 -0.808 0.420535 PE 0.26080 0.13640 1.912 0.057885 . PE_M -0.27411 0.22474 -1.220 0.224614 PC -0.01770 0.16192 -0.109 0.913109 PC_M 0.11429 0.28282 0.404 0.686726 O 0.39223 0.09401 4.172 5.22e-05 *** O_M 0.13545 0.16631 0.814 0.416763 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.456 on 142 degrees of freedom Multiple R-squared: 0.3902, Adjusted R-squared: 0.343 F-statistic: 8.261 on 11 and 142 DF, p-value: 4.335e-11 > 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,] 0.97114022 0.05771957 0.02885978 [2,] 0.94099178 0.11801643 0.05900822 [3,] 0.89645266 0.20709468 0.10354734 [4,] 0.83286085 0.33427830 0.16713915 [5,] 0.82146023 0.35707954 0.17853977 [6,] 0.80784521 0.38430959 0.19215479 [7,] 0.79035620 0.41928761 0.20964380 [8,] 0.74391249 0.51217502 0.25608751 [9,] 0.69225423 0.61549155 0.30774577 [10,] 0.71361946 0.57276108 0.28638054 [11,] 0.63821757 0.72356485 0.36178243 [12,] 0.56128262 0.87743476 0.43871738 [13,] 0.51091465 0.97817069 0.48908535 [14,] 0.48225410 0.96450820 0.51774590 [15,] 0.41457075 0.82914151 0.58542925 [16,] 0.42595428 0.85190857 0.57404572 [17,] 0.51770120 0.96459759 0.48229880 [18,] 0.65830291 0.68339419 0.34169709 [19,] 0.62077565 0.75844870 0.37922435 [20,] 0.69036259 0.61927482 0.30963741 [21,] 0.73098912 0.53802176 0.26901088 [22,] 0.69128590 0.61742820 0.30871410 [23,] 0.63958222 0.72083556 0.36041778 [24,] 0.73985112 0.52029776 0.26014888 [25,] 0.68812490 0.62375020 0.31187510 [26,] 0.63650072 0.72699856 0.36349928 [27,] 0.62921821 0.74156357 0.37078179 [28,] 0.59101454 0.81797092 0.40898546 [29,] 0.60371803 0.79256395 0.39628197 [30,] 0.54894192 0.90211615 0.45105808 [31,] 0.55857159 0.88285682 0.44142841 [32,] 0.64710222 0.70579556 0.35289778 [33,] 0.59714880 0.80570239 0.40285120 [34,] 0.56662667 0.86674666 0.43337333 [35,] 0.57325543 0.85348914 0.42674457 [36,] 0.52557762 0.94884477 0.47442238 [37,] 0.56338182 0.87323637 0.43661818 [38,] 0.51971853 0.96056294 0.48028147 [39,] 0.67460368 0.65079264 0.32539632 [40,] 0.63445608 0.73108784 0.36554392 [41,] 0.59355868 0.81288265 0.40644132 [42,] 0.57417260 0.85165481 0.42582740 [43,] 0.52342907 0.95314185 0.47657093 [44,] 0.48555764 0.97111527 0.51444236 [45,] 0.44598320 0.89196640 0.55401680 [46,] 0.40700760 0.81401521 0.59299240 [47,] 0.35919672 0.71839345 0.64080328 [48,] 0.36940334 0.73880667 0.63059666 [49,] 0.32613410 0.65226821 0.67386590 [50,] 0.36624343 0.73248685 0.63375657 [51,] 0.43550234 0.87100468 0.56449766 [52,] 0.54569938 0.90860124 0.45430062 [53,] 0.49749601 0.99499203 0.50250399 [54,] 0.47854570 0.95709140 0.52145430 [55,] 0.54817926 0.90364147 0.45182074 [56,] 0.49932848 0.99865697 0.50067152 [57,] 0.45334467 0.90668935 0.54665533 [58,] 0.41930442 0.83860885 0.58069558 [59,] 0.38326396 0.76652792 0.61673604 [60,] 0.35592247 0.71184494 0.64407753 [61,] 0.31238297 0.62476593 0.68761703 [62,] 0.27456907 0.54913814 0.72543093 [63,] 0.23746117 0.47492234 0.76253883 [64,] 0.20774205 0.41548411 0.79225795 [65,] 0.32193902 0.64387803 0.67806098 [66,] 0.29363788 0.58727576 0.70636212 [67,] 0.25513315 0.51026630 0.74486685 [68,] 0.25414186 0.50828372 0.74585814 [69,] 0.22942673 0.45885347 0.77057327 [70,] 0.22859855 0.45719709 0.77140145 [71,] 0.20305544 0.40611089 0.79694456 [72,] 0.18733675 0.37467351 0.81266325 [73,] 0.18031945 0.36063890 0.81968055 [74,] 0.22039296 0.44078592 0.77960704 [75,] 0.19823975 0.39647950 0.80176025 [76,] 0.16961741 0.33923482 0.83038259 [77,] 0.15683149 0.31366298 0.84316851 [78,] 0.15657467 0.31314934 0.84342533 [79,] 0.14523951 0.29047903 0.85476049 [80,] 0.14578108 0.29156216 0.85421892 [81,] 0.13055134 0.26110268 0.86944866 [82,] 0.12599001 0.25198001 0.87400999 [83,] 0.10123637 0.20247274 0.89876363 [84,] 0.08132906 0.16265812 0.91867094 [85,] 0.06566879 0.13133758 0.93433121 [86,] 0.05201297 0.10402595 0.94798703 [87,] 0.05984402 0.11968803 0.94015598 [88,] 0.04949821 0.09899642 0.95050179 [89,] 0.04271586 0.08543173 0.95728414 [90,] 0.03647168 0.07294336 0.96352832 [91,] 0.02720680 0.05441359 0.97279320 [92,] 0.02309519 0.04619039 0.97690481 [93,] 0.02922516 0.05845033 0.97077484 [94,] 0.12802811 0.25605623 0.87197189 [95,] 0.12992395 0.25984790 0.87007605 [96,] 0.59929145 0.80141711 0.40070855 [97,] 0.88324847 0.23350307 0.11675153 [98,] 0.85428711 0.29142579 0.14571289 [99,] 0.91336890 0.17326220 0.08663110 [100,] 0.88957229 0.22085542 0.11042771 [101,] 0.86524974 0.26950052 0.13475026 [102,] 0.84260528 0.31478944 0.15739472 [103,] 0.80034222 0.39931556 0.19965778 [104,] 0.81893736 0.36212528 0.18106264 [105,] 0.77415155 0.45169690 0.22584845 [106,] 0.72733747 0.54532506 0.27266253 [107,] 0.66704572 0.66590856 0.33295428 [108,] 0.62185902 0.75628197 0.37814098 [109,] 0.56551932 0.86896136 0.43448068 [110,] 0.50133220 0.99733560 0.49866780 [111,] 0.43820345 0.87640689 0.56179655 [112,] 0.41716359 0.83432718 0.58283641 [113,] 0.42172684 0.84345367 0.57827316 [114,] 0.34602443 0.69204887 0.65397557 [115,] 0.30999455 0.61998911 0.69000545 [116,] 0.26089044 0.52178088 0.73910956 [117,] 0.20448452 0.40896904 0.79551548 [118,] 0.15078045 0.30156091 0.84921955 [119,] 0.14108122 0.28216244 0.85891878 [120,] 0.09790674 0.19581349 0.90209326 [121,] 0.07187083 0.14374167 0.92812917 [122,] 0.05094699 0.10189398 0.94905301 [123,] 0.05630714 0.11261428 0.94369286 [124,] 0.09255202 0.18510403 0.90744798 [125,] 0.04601236 0.09202472 0.95398764 > postscript(file="/var/www/rcomp/tmp/13uhh1291573233.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/23uhh1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3v3gk1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4v3gk1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5v3gk1291573233.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 0.703639424 2.948479030 5.508094320 -1.092828824 1.453964228 -1.344489017 7 8 9 10 11 12 2.247433109 3.408461134 -2.490387995 -1.049059566 -3.311006778 -4.599573594 13 14 15 16 17 18 -7.925135274 -1.339319624 4.071867772 3.108501469 1.239061785 -1.081554583 19 20 21 22 23 24 -1.071453280 -1.356383899 1.248966973 -3.386372329 -5.907985183 -3.561531044 25 26 27 28 29 30 0.302747407 1.348398952 -4.207929638 0.992855748 0.150771469 2.246391317 31 32 33 34 35 36 5.691571119 6.991900432 2.945141785 4.275535886 2.635659003 1.492395866 37 38 39 40 41 42 1.752929150 6.462630135 -0.954873524 1.462177115 -3.813699289 2.350670500 43 44 45 46 47 48 4.220009428 -0.034429555 -3.902653371 7.010566674 0.176292607 2.550012223 49 50 51 52 53 54 1.671793466 -1.024179536 -4.119270055 -1.386716827 4.942153621 -1.438171986 55 56 57 58 59 60 1.680606660 3.141054199 -0.567675340 -2.478831227 1.718362550 1.558610398 61 62 63 64 65 66 0.204546568 3.733037770 1.191308846 2.978051972 -5.282921873 6.205363226 67 68 69 70 71 72 1.823137292 2.601703958 -4.719932161 -0.380685554 -0.344760437 -1.918061415 73 74 75 76 77 78 -0.820910463 -2.262163088 -0.783471203 -0.625764018 0.851974719 1.292904879 79 80 81 82 83 84 -6.615518563 -1.625685852 0.874931296 -3.820991767 -2.021302463 3.361189333 85 86 87 88 89 90 1.781733168 2.494646709 -3.234590626 -5.548336907 -2.559472683 -0.391550502 91 92 93 94 95 96 -3.180551626 3.130829808 -3.053202730 -3.682829536 -2.711350293 -3.442314403 97 98 99 100 101 102 -0.009162792 -1.100991342 -1.486120971 -1.487440566 -4.319409450 -2.159536362 103 104 105 106 107 108 -2.163903734 -1.713801336 -0.449943899 -2.497316088 -5.100541178 -8.459100560 109 110 111 112 113 114 2.179638535 11.352310984 10.062299708 -1.246453917 2.087855312 -0.264802180 115 116 117 118 119 120 -2.666614386 3.085195882 -0.369608112 4.483863326 -0.499208020 1.369035479 121 122 123 124 125 126 -0.210082071 0.814425296 1.209469068 -2.246342043 0.601783304 3.286490914 127 128 129 130 131 132 -4.472378637 0.563743735 -2.828718719 -6.120197826 3.916929700 -0.574026121 133 134 135 136 137 138 3.680450031 -1.211031134 3.326334933 3.344205143 4.092287028 0.972921134 139 140 141 142 143 144 0.959160331 0.537468035 3.710272023 -0.416895660 -0.021760337 -7.316799096 145 146 147 148 149 150 -2.626886954 3.662232405 -0.262148957 -2.924161423 -0.276340362 1.304178876 151 152 153 154 -2.064758751 0.678099750 -0.254811110 -7.222543831 > postscript(file="/var/www/rcomp/tmp/66ufn1291573233.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 0.703639424 NA 1 2.948479030 0.703639424 2 5.508094320 2.948479030 3 -1.092828824 5.508094320 4 1.453964228 -1.092828824 5 -1.344489017 1.453964228 6 2.247433109 -1.344489017 7 3.408461134 2.247433109 8 -2.490387995 3.408461134 9 -1.049059566 -2.490387995 10 -3.311006778 -1.049059566 11 -4.599573594 -3.311006778 12 -7.925135274 -4.599573594 13 -1.339319624 -7.925135274 14 4.071867772 -1.339319624 15 3.108501469 4.071867772 16 1.239061785 3.108501469 17 -1.081554583 1.239061785 18 -1.071453280 -1.081554583 19 -1.356383899 -1.071453280 20 1.248966973 -1.356383899 21 -3.386372329 1.248966973 22 -5.907985183 -3.386372329 23 -3.561531044 -5.907985183 24 0.302747407 -3.561531044 25 1.348398952 0.302747407 26 -4.207929638 1.348398952 27 0.992855748 -4.207929638 28 0.150771469 0.992855748 29 2.246391317 0.150771469 30 5.691571119 2.246391317 31 6.991900432 5.691571119 32 2.945141785 6.991900432 33 4.275535886 2.945141785 34 2.635659003 4.275535886 35 1.492395866 2.635659003 36 1.752929150 1.492395866 37 6.462630135 1.752929150 38 -0.954873524 6.462630135 39 1.462177115 -0.954873524 40 -3.813699289 1.462177115 41 2.350670500 -3.813699289 42 4.220009428 2.350670500 43 -0.034429555 4.220009428 44 -3.902653371 -0.034429555 45 7.010566674 -3.902653371 46 0.176292607 7.010566674 47 2.550012223 0.176292607 48 1.671793466 2.550012223 49 -1.024179536 1.671793466 50 -4.119270055 -1.024179536 51 -1.386716827 -4.119270055 52 4.942153621 -1.386716827 53 -1.438171986 4.942153621 54 1.680606660 -1.438171986 55 3.141054199 1.680606660 56 -0.567675340 3.141054199 57 -2.478831227 -0.567675340 58 1.718362550 -2.478831227 59 1.558610398 1.718362550 60 0.204546568 1.558610398 61 3.733037770 0.204546568 62 1.191308846 3.733037770 63 2.978051972 1.191308846 64 -5.282921873 2.978051972 65 6.205363226 -5.282921873 66 1.823137292 6.205363226 67 2.601703958 1.823137292 68 -4.719932161 2.601703958 69 -0.380685554 -4.719932161 70 -0.344760437 -0.380685554 71 -1.918061415 -0.344760437 72 -0.820910463 -1.918061415 73 -2.262163088 -0.820910463 74 -0.783471203 -2.262163088 75 -0.625764018 -0.783471203 76 0.851974719 -0.625764018 77 1.292904879 0.851974719 78 -6.615518563 1.292904879 79 -1.625685852 -6.615518563 80 0.874931296 -1.625685852 81 -3.820991767 0.874931296 82 -2.021302463 -3.820991767 83 3.361189333 -2.021302463 84 1.781733168 3.361189333 85 2.494646709 1.781733168 86 -3.234590626 2.494646709 87 -5.548336907 -3.234590626 88 -2.559472683 -5.548336907 89 -0.391550502 -2.559472683 90 -3.180551626 -0.391550502 91 3.130829808 -3.180551626 92 -3.053202730 3.130829808 93 -3.682829536 -3.053202730 94 -2.711350293 -3.682829536 95 -3.442314403 -2.711350293 96 -0.009162792 -3.442314403 97 -1.100991342 -0.009162792 98 -1.486120971 -1.100991342 99 -1.487440566 -1.486120971 100 -4.319409450 -1.487440566 101 -2.159536362 -4.319409450 102 -2.163903734 -2.159536362 103 -1.713801336 -2.163903734 104 -0.449943899 -1.713801336 105 -2.497316088 -0.449943899 106 -5.100541178 -2.497316088 107 -8.459100560 -5.100541178 108 2.179638535 -8.459100560 109 11.352310984 2.179638535 110 10.062299708 11.352310984 111 -1.246453917 10.062299708 112 2.087855312 -1.246453917 113 -0.264802180 2.087855312 114 -2.666614386 -0.264802180 115 3.085195882 -2.666614386 116 -0.369608112 3.085195882 117 4.483863326 -0.369608112 118 -0.499208020 4.483863326 119 1.369035479 -0.499208020 120 -0.210082071 1.369035479 121 0.814425296 -0.210082071 122 1.209469068 0.814425296 123 -2.246342043 1.209469068 124 0.601783304 -2.246342043 125 3.286490914 0.601783304 126 -4.472378637 3.286490914 127 0.563743735 -4.472378637 128 -2.828718719 0.563743735 129 -6.120197826 -2.828718719 130 3.916929700 -6.120197826 131 -0.574026121 3.916929700 132 3.680450031 -0.574026121 133 -1.211031134 3.680450031 134 3.326334933 -1.211031134 135 3.344205143 3.326334933 136 4.092287028 3.344205143 137 0.972921134 4.092287028 138 0.959160331 0.972921134 139 0.537468035 0.959160331 140 3.710272023 0.537468035 141 -0.416895660 3.710272023 142 -0.021760337 -0.416895660 143 -7.316799096 -0.021760337 144 -2.626886954 -7.316799096 145 3.662232405 -2.626886954 146 -0.262148957 3.662232405 147 -2.924161423 -0.262148957 148 -0.276340362 -2.924161423 149 1.304178876 -0.276340362 150 -2.064758751 1.304178876 151 0.678099750 -2.064758751 152 -0.254811110 0.678099750 153 -7.222543831 -0.254811110 154 NA -7.222543831 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.948479030 0.703639424 [2,] 5.508094320 2.948479030 [3,] -1.092828824 5.508094320 [4,] 1.453964228 -1.092828824 [5,] -1.344489017 1.453964228 [6,] 2.247433109 -1.344489017 [7,] 3.408461134 2.247433109 [8,] -2.490387995 3.408461134 [9,] -1.049059566 -2.490387995 [10,] -3.311006778 -1.049059566 [11,] -4.599573594 -3.311006778 [12,] -7.925135274 -4.599573594 [13,] -1.339319624 -7.925135274 [14,] 4.071867772 -1.339319624 [15,] 3.108501469 4.071867772 [16,] 1.239061785 3.108501469 [17,] -1.081554583 1.239061785 [18,] -1.071453280 -1.081554583 [19,] -1.356383899 -1.071453280 [20,] 1.248966973 -1.356383899 [21,] -3.386372329 1.248966973 [22,] -5.907985183 -3.386372329 [23,] -3.561531044 -5.907985183 [24,] 0.302747407 -3.561531044 [25,] 1.348398952 0.302747407 [26,] -4.207929638 1.348398952 [27,] 0.992855748 -4.207929638 [28,] 0.150771469 0.992855748 [29,] 2.246391317 0.150771469 [30,] 5.691571119 2.246391317 [31,] 6.991900432 5.691571119 [32,] 2.945141785 6.991900432 [33,] 4.275535886 2.945141785 [34,] 2.635659003 4.275535886 [35,] 1.492395866 2.635659003 [36,] 1.752929150 1.492395866 [37,] 6.462630135 1.752929150 [38,] -0.954873524 6.462630135 [39,] 1.462177115 -0.954873524 [40,] -3.813699289 1.462177115 [41,] 2.350670500 -3.813699289 [42,] 4.220009428 2.350670500 [43,] -0.034429555 4.220009428 [44,] -3.902653371 -0.034429555 [45,] 7.010566674 -3.902653371 [46,] 0.176292607 7.010566674 [47,] 2.550012223 0.176292607 [48,] 1.671793466 2.550012223 [49,] -1.024179536 1.671793466 [50,] -4.119270055 -1.024179536 [51,] -1.386716827 -4.119270055 [52,] 4.942153621 -1.386716827 [53,] -1.438171986 4.942153621 [54,] 1.680606660 -1.438171986 [55,] 3.141054199 1.680606660 [56,] -0.567675340 3.141054199 [57,] -2.478831227 -0.567675340 [58,] 1.718362550 -2.478831227 [59,] 1.558610398 1.718362550 [60,] 0.204546568 1.558610398 [61,] 3.733037770 0.204546568 [62,] 1.191308846 3.733037770 [63,] 2.978051972 1.191308846 [64,] -5.282921873 2.978051972 [65,] 6.205363226 -5.282921873 [66,] 1.823137292 6.205363226 [67,] 2.601703958 1.823137292 [68,] -4.719932161 2.601703958 [69,] -0.380685554 -4.719932161 [70,] -0.344760437 -0.380685554 [71,] -1.918061415 -0.344760437 [72,] -0.820910463 -1.918061415 [73,] -2.262163088 -0.820910463 [74,] -0.783471203 -2.262163088 [75,] -0.625764018 -0.783471203 [76,] 0.851974719 -0.625764018 [77,] 1.292904879 0.851974719 [78,] -6.615518563 1.292904879 [79,] -1.625685852 -6.615518563 [80,] 0.874931296 -1.625685852 [81,] -3.820991767 0.874931296 [82,] -2.021302463 -3.820991767 [83,] 3.361189333 -2.021302463 [84,] 1.781733168 3.361189333 [85,] 2.494646709 1.781733168 [86,] -3.234590626 2.494646709 [87,] -5.548336907 -3.234590626 [88,] -2.559472683 -5.548336907 [89,] -0.391550502 -2.559472683 [90,] -3.180551626 -0.391550502 [91,] 3.130829808 -3.180551626 [92,] -3.053202730 3.130829808 [93,] -3.682829536 -3.053202730 [94,] -2.711350293 -3.682829536 [95,] -3.442314403 -2.711350293 [96,] -0.009162792 -3.442314403 [97,] -1.100991342 -0.009162792 [98,] -1.486120971 -1.100991342 [99,] -1.487440566 -1.486120971 [100,] -4.319409450 -1.487440566 [101,] -2.159536362 -4.319409450 [102,] -2.163903734 -2.159536362 [103,] -1.713801336 -2.163903734 [104,] -0.449943899 -1.713801336 [105,] -2.497316088 -0.449943899 [106,] -5.100541178 -2.497316088 [107,] -8.459100560 -5.100541178 [108,] 2.179638535 -8.459100560 [109,] 11.352310984 2.179638535 [110,] 10.062299708 11.352310984 [111,] -1.246453917 10.062299708 [112,] 2.087855312 -1.246453917 [113,] -0.264802180 2.087855312 [114,] -2.666614386 -0.264802180 [115,] 3.085195882 -2.666614386 [116,] -0.369608112 3.085195882 [117,] 4.483863326 -0.369608112 [118,] -0.499208020 4.483863326 [119,] 1.369035479 -0.499208020 [120,] -0.210082071 1.369035479 [121,] 0.814425296 -0.210082071 [122,] 1.209469068 0.814425296 [123,] -2.246342043 1.209469068 [124,] 0.601783304 -2.246342043 [125,] 3.286490914 0.601783304 [126,] -4.472378637 3.286490914 [127,] 0.563743735 -4.472378637 [128,] -2.828718719 0.563743735 [129,] -6.120197826 -2.828718719 [130,] 3.916929700 -6.120197826 [131,] -0.574026121 3.916929700 [132,] 3.680450031 -0.574026121 [133,] -1.211031134 3.680450031 [134,] 3.326334933 -1.211031134 [135,] 3.344205143 3.326334933 [136,] 4.092287028 3.344205143 [137,] 0.972921134 4.092287028 [138,] 0.959160331 0.972921134 [139,] 0.537468035 0.959160331 [140,] 3.710272023 0.537468035 [141,] -0.416895660 3.710272023 [142,] -0.021760337 -0.416895660 [143,] -7.316799096 -0.021760337 [144,] -2.626886954 -7.316799096 [145,] 3.662232405 -2.626886954 [146,] -0.262148957 3.662232405 [147,] -2.924161423 -0.262148957 [148,] -0.276340362 -2.924161423 [149,] 1.304178876 -0.276340362 [150,] -2.064758751 1.304178876 [151,] 0.678099750 -2.064758751 [152,] -0.254811110 0.678099750 [153,] -7.222543831 -0.254811110 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.948479030 0.703639424 2 5.508094320 2.948479030 3 -1.092828824 5.508094320 4 1.453964228 -1.092828824 5 -1.344489017 1.453964228 6 2.247433109 -1.344489017 7 3.408461134 2.247433109 8 -2.490387995 3.408461134 9 -1.049059566 -2.490387995 10 -3.311006778 -1.049059566 11 -4.599573594 -3.311006778 12 -7.925135274 -4.599573594 13 -1.339319624 -7.925135274 14 4.071867772 -1.339319624 15 3.108501469 4.071867772 16 1.239061785 3.108501469 17 -1.081554583 1.239061785 18 -1.071453280 -1.081554583 19 -1.356383899 -1.071453280 20 1.248966973 -1.356383899 21 -3.386372329 1.248966973 22 -5.907985183 -3.386372329 23 -3.561531044 -5.907985183 24 0.302747407 -3.561531044 25 1.348398952 0.302747407 26 -4.207929638 1.348398952 27 0.992855748 -4.207929638 28 0.150771469 0.992855748 29 2.246391317 0.150771469 30 5.691571119 2.246391317 31 6.991900432 5.691571119 32 2.945141785 6.991900432 33 4.275535886 2.945141785 34 2.635659003 4.275535886 35 1.492395866 2.635659003 36 1.752929150 1.492395866 37 6.462630135 1.752929150 38 -0.954873524 6.462630135 39 1.462177115 -0.954873524 40 -3.813699289 1.462177115 41 2.350670500 -3.813699289 42 4.220009428 2.350670500 43 -0.034429555 4.220009428 44 -3.902653371 -0.034429555 45 7.010566674 -3.902653371 46 0.176292607 7.010566674 47 2.550012223 0.176292607 48 1.671793466 2.550012223 49 -1.024179536 1.671793466 50 -4.119270055 -1.024179536 51 -1.386716827 -4.119270055 52 4.942153621 -1.386716827 53 -1.438171986 4.942153621 54 1.680606660 -1.438171986 55 3.141054199 1.680606660 56 -0.567675340 3.141054199 57 -2.478831227 -0.567675340 58 1.718362550 -2.478831227 59 1.558610398 1.718362550 60 0.204546568 1.558610398 61 3.733037770 0.204546568 62 1.191308846 3.733037770 63 2.978051972 1.191308846 64 -5.282921873 2.978051972 65 6.205363226 -5.282921873 66 1.823137292 6.205363226 67 2.601703958 1.823137292 68 -4.719932161 2.601703958 69 -0.380685554 -4.719932161 70 -0.344760437 -0.380685554 71 -1.918061415 -0.344760437 72 -0.820910463 -1.918061415 73 -2.262163088 -0.820910463 74 -0.783471203 -2.262163088 75 -0.625764018 -0.783471203 76 0.851974719 -0.625764018 77 1.292904879 0.851974719 78 -6.615518563 1.292904879 79 -1.625685852 -6.615518563 80 0.874931296 -1.625685852 81 -3.820991767 0.874931296 82 -2.021302463 -3.820991767 83 3.361189333 -2.021302463 84 1.781733168 3.361189333 85 2.494646709 1.781733168 86 -3.234590626 2.494646709 87 -5.548336907 -3.234590626 88 -2.559472683 -5.548336907 89 -0.391550502 -2.559472683 90 -3.180551626 -0.391550502 91 3.130829808 -3.180551626 92 -3.053202730 3.130829808 93 -3.682829536 -3.053202730 94 -2.711350293 -3.682829536 95 -3.442314403 -2.711350293 96 -0.009162792 -3.442314403 97 -1.100991342 -0.009162792 98 -1.486120971 -1.100991342 99 -1.487440566 -1.486120971 100 -4.319409450 -1.487440566 101 -2.159536362 -4.319409450 102 -2.163903734 -2.159536362 103 -1.713801336 -2.163903734 104 -0.449943899 -1.713801336 105 -2.497316088 -0.449943899 106 -5.100541178 -2.497316088 107 -8.459100560 -5.100541178 108 2.179638535 -8.459100560 109 11.352310984 2.179638535 110 10.062299708 11.352310984 111 -1.246453917 10.062299708 112 2.087855312 -1.246453917 113 -0.264802180 2.087855312 114 -2.666614386 -0.264802180 115 3.085195882 -2.666614386 116 -0.369608112 3.085195882 117 4.483863326 -0.369608112 118 -0.499208020 4.483863326 119 1.369035479 -0.499208020 120 -0.210082071 1.369035479 121 0.814425296 -0.210082071 122 1.209469068 0.814425296 123 -2.246342043 1.209469068 124 0.601783304 -2.246342043 125 3.286490914 0.601783304 126 -4.472378637 3.286490914 127 0.563743735 -4.472378637 128 -2.828718719 0.563743735 129 -6.120197826 -2.828718719 130 3.916929700 -6.120197826 131 -0.574026121 3.916929700 132 3.680450031 -0.574026121 133 -1.211031134 3.680450031 134 3.326334933 -1.211031134 135 3.344205143 3.326334933 136 4.092287028 3.344205143 137 0.972921134 4.092287028 138 0.959160331 0.972921134 139 0.537468035 0.959160331 140 3.710272023 0.537468035 141 -0.416895660 3.710272023 142 -0.021760337 -0.416895660 143 -7.316799096 -0.021760337 144 -2.626886954 -7.316799096 145 3.662232405 -2.626886954 146 -0.262148957 3.662232405 147 -2.924161423 -0.262148957 148 -0.276340362 -2.924161423 149 1.304178876 -0.276340362 150 -2.064758751 1.304178876 151 0.678099750 -2.064758751 152 -0.254811110 0.678099750 153 -7.222543831 -0.254811110 > 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/www/rcomp/tmp/7z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/109dwb1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/rcomp/tmp/11vduz1291573233.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/www/rcomp/tmp/12gwt51291573233.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/www/rcomp/tmp/13cn9w1291573233.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/www/rcomp/tmp/14go711291573233.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/www/rcomp/tmp/15m7md1291573233.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/www/rcomp/tmp/16li321291573234.tab") + } > > try(system("convert tmp/13uhh1291573233.ps tmp/13uhh1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/23uhh1291573233.ps tmp/23uhh1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/3v3gk1291573233.ps tmp/3v3gk1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/4v3gk1291573233.ps tmp/4v3gk1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/5v3gk1291573233.ps tmp/5v3gk1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/66ufn1291573233.ps tmp/66ufn1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/7z3eq1291573233.ps tmp/7z3eq1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/8z3eq1291573233.ps tmp/8z3eq1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/9z3eq1291573233.ps tmp/9z3eq1291573233.png",intern=TRUE)) character(0) > try(system("convert tmp/109dwb1291573233.ps tmp/109dwb1291573233.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.190 1.840 7.043