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 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,1 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,1 + ,2 + ,0 + ,1 + ,2 + ,0 + ,0) + ,dim=c(3 + ,154) + ,dimnames=list(c('Weeks' + ,'T40:t20' + ,'CorrectAnalysis') + ,1:154)) > y <- array(NA,dim=c(3,154),dimnames=list(c('Weeks','T40:t20','CorrectAnalysis'),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 = '3' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'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 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 T40:t20 1 0 4 1 2 0 4 0 3 0 4 0 4 0 4 0 5 0 4 0 6 0 4 0 7 0 4 0 8 0 4 1 9 0 4 0 10 0 4 0 11 0 4 1 12 0 4 0 13 0 4 0 14 0 4 1 15 0 4 0 16 0 4 1 17 1 4 1 18 0 4 1 19 0 4 0 20 1 4 1 21 0 4 0 22 0 4 0 23 0 4 0 24 0 4 0 25 0 4 1 26 0 4 0 27 0 4 0 28 0 4 0 29 0 4 0 30 0 4 0 31 0 4 0 32 0 4 0 33 0 4 0 34 0 4 1 35 0 4 0 36 0 4 0 37 0 4 1 38 0 4 0 39 0 4 0 40 0 4 1 41 1 4 0 42 0 4 0 43 0 4 0 44 0 4 1 45 0 4 0 46 0 4 0 47 0 4 0 48 0 4 0 49 0 4 0 50 0 4 0 51 0 4 1 52 1 4 1 53 0 4 0 54 1 4 0 55 0 4 0 56 0 4 1 57 0 4 0 58 0 4 0 59 0 4 0 60 1 4 1 61 0 4 1 62 0 4 0 63 0 4 0 64 0 4 1 65 0 4 0 66 0 4 0 67 1 4 1 68 0 4 0 69 0 4 0 70 0 4 0 71 0 4 0 72 0 4 0 73 0 4 0 74 0 4 0 75 0 4 0 76 0 4 1 77 0 4 0 78 0 4 0 79 1 4 1 80 0 4 1 81 0 4 0 82 0 4 0 83 0 4 0 84 1 4 0 85 0 4 0 86 0 4 0 87 0 2 0 88 0 2 1 89 0 2 0 90 0 2 0 91 0 2 0 92 0 2 1 93 0 2 0 94 0 2 0 95 0 2 1 96 0 2 0 97 0 2 1 98 0 2 0 99 0 2 0 100 0 2 0 101 0 2 0 102 0 2 0 103 0 2 0 104 0 2 0 105 0 2 1 106 0 2 0 107 0 2 0 108 0 2 1 109 0 2 0 110 0 2 0 111 0 2 1 112 0 2 1 113 0 2 0 114 0 2 1 115 0 2 0 116 0 2 0 117 0 2 0 118 0 2 0 119 0 2 0 120 0 2 0 121 0 2 0 122 0 2 0 123 0 2 1 124 0 2 0 125 0 2 0 126 0 2 1 127 0 2 0 128 0 2 0 129 0 2 0 130 0 2 0 131 0 2 0 132 0 2 0 133 0 2 0 134 0 2 0 135 0 2 0 136 0 2 0 137 0 2 0 138 0 2 1 139 0 2 1 140 0 2 0 141 1 2 0 142 0 2 1 143 0 2 0 144 0 2 0 145 0 2 0 146 0 2 1 147 0 2 1 148 0 2 1 149 0 2 0 150 0 2 0 151 0 2 0 152 1 2 0 153 1 2 0 154 0 2 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks `T40:t20` -0.03875 0.02943 0.09605 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.17501 -0.07896 -0.07896 -0.02010 0.97990 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.03875 0.07141 -0.543 0.588 Weeks 0.02943 0.02156 1.365 0.174 `T40:t20` 0.09605 0.04882 1.967 0.051 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2656 on 151 degrees of freedom Multiple R-squared: 0.03726, Adjusted R-squared: 0.0245 F-statistic: 2.922 on 2 and 151 DF, p-value: 0.0569 > 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,] 0.0000000000 0.0000000000 1.0000000000 [2,] 0.0000000000 0.0000000000 1.0000000000 [3,] 0.0000000000 0.0000000000 1.0000000000 [4,] 0.0000000000 0.0000000000 1.0000000000 [5,] 0.0000000000 0.0000000000 1.0000000000 [6,] 0.0000000000 0.0000000000 1.0000000000 [7,] 0.0000000000 0.0000000000 1.0000000000 [8,] 0.0000000000 0.0000000000 1.0000000000 [9,] 0.0000000000 0.0000000000 1.0000000000 [10,] 0.0000000000 0.0000000000 1.0000000000 [11,] 0.0000000000 0.0000000000 1.0000000000 [12,] 0.3527001975 0.7054003950 0.6472998025 [13,] 0.3059357022 0.6118714044 0.6940642978 [14,] 0.2401960772 0.4803921544 0.7598039228 [15,] 0.7866362961 0.4267274077 0.2133637039 [16,] 0.7322162336 0.5355675327 0.2677837664 [17,] 0.6726666947 0.6546666106 0.3273333053 [18,] 0.6094475240 0.7811049521 0.3905524760 [19,] 0.5442409555 0.9115180889 0.4557590445 [20,] 0.5251046823 0.9497906353 0.4748953177 [21,] 0.4608926961 0.9217853923 0.5391073039 [22,] 0.3985398751 0.7970797503 0.6014601249 [23,] 0.3394482293 0.6788964585 0.6605517707 [24,] 0.2847438060 0.5694876120 0.7152561940 [25,] 0.2352314912 0.4704629824 0.7647685088 [26,] 0.1913842933 0.3827685866 0.8086157067 [27,] 0.1533627076 0.3067254151 0.8466372924 [28,] 0.1210568195 0.2421136390 0.8789431805 [29,] 0.1115761347 0.2231522694 0.8884238653 [30,] 0.0865849748 0.1731699496 0.9134150252 [31,] 0.0662340065 0.1324680131 0.9337659935 [32,] 0.0589572054 0.1179144108 0.9410427946 [33,] 0.0443200520 0.0886401041 0.9556799480 [34,] 0.0328695004 0.0657390007 0.9671304996 [35,] 0.0283356832 0.0566713665 0.9716643168 [36,] 0.4603617256 0.9207234512 0.5396382744 [37,] 0.4106400406 0.8212800812 0.5893599594 [38,] 0.3627078098 0.7254156197 0.6372921902 [39,] 0.3346155749 0.6692311499 0.6653844251 [40,] 0.2910906456 0.5821812913 0.7089093544 [41,] 0.2507482347 0.5014964694 0.7492517653 [42,] 0.2138918633 0.4277837267 0.7861081367 [43,] 0.1806913441 0.3613826881 0.8193086559 [44,] 0.1511917993 0.3023835987 0.8488082007 [45,] 0.1253285301 0.2506570602 0.8746714699 [46,] 0.1114723591 0.2229447183 0.8885276409 [47,] 0.4519067790 0.9038135580 0.5480932210 [48,] 0.4075524218 0.8151048436 0.5924475782 [49,] 0.8663841269 0.2672317463 0.1336158731 [50,] 0.8410124332 0.3179751335 0.1589875668 [51,] 0.8254417361 0.3491165278 0.1745582639 [52,] 0.7960013652 0.4079972696 0.2039986348 [53,] 0.7640890033 0.4718219934 0.2359109967 [54,] 0.7299557168 0.5400885664 0.2700442832 [55,] 0.9404177078 0.1191645843 0.0595822922 [56,] 0.9332721986 0.1334556027 0.0667278014 [57,] 0.9183695623 0.1632608755 0.0816304377 [58,] 0.9012765111 0.1974469778 0.0987234889 [59,] 0.8909851387 0.2180297226 0.1090148613 [60,] 0.8705824172 0.2588351657 0.1294175828 [61,] 0.8480454871 0.3039090257 0.1519545129 [62,] 0.9777747850 0.0444504300 0.0222252150 [63,] 0.9715988758 0.0568022483 0.0284011242 [64,] 0.9641660836 0.0716678328 0.0358339164 [65,] 0.9553648722 0.0892702557 0.0446351278 [66,] 0.9451209083 0.1097581835 0.0548790917 [67,] 0.9334168740 0.1331662520 0.0665831260 [68,] 0.9203175760 0.1593648479 0.0796824240 [69,] 0.9060024787 0.1879950426 0.0939975213 [70,] 0.8908097010 0.2183805981 0.1091902990 [71,] 0.8827773952 0.2344452096 0.1172226048 [72,] 0.8679275019 0.2641449963 0.1320724981 [73,] 0.8545504792 0.2908990416 0.1454495208 [74,] 0.9806389651 0.0387220697 0.0193610349 [75,] 0.9776380887 0.0447238227 0.0223619113 [76,] 0.9731020761 0.0537958478 0.0268979239 [77,] 0.9692100332 0.0615799337 0.0307899668 [78,] 0.9678669143 0.0642661714 0.0321330857 [79,] 0.9991981452 0.0016037097 0.0008018548 [80,] 0.9988023373 0.0023953255 0.0011976627 [81,] 0.9982330558 0.0035338885 0.0017669442 [82,] 0.9974551725 0.0050896549 0.0025448275 [83,] 0.9964533217 0.0070933565 0.0035466783 [84,] 0.9950634953 0.0098730094 0.0049365047 [85,] 0.9931688685 0.0136622629 0.0068311315 [86,] 0.9906374520 0.0187250959 0.0093625480 [87,] 0.9874684905 0.0250630190 0.0125315095 [88,] 0.9832409139 0.0335181723 0.0167590861 [89,] 0.9778167097 0.0443665807 0.0221832903 [90,] 0.9711147498 0.0577705005 0.0288852502 [91,] 0.9626419634 0.0747160731 0.0373580366 [92,] 0.9522007811 0.0955984377 0.0477992189 [93,] 0.9395594915 0.1208810170 0.0604405085 [94,] 0.9243731151 0.1512537697 0.0756268849 [95,] 0.9063749179 0.1872501642 0.0936250821 [96,] 0.8853325891 0.2293348219 0.1146674109 [97,] 0.8610654070 0.2778691861 0.1389345930 [98,] 0.8334613288 0.3330773424 0.1665386712 [99,] 0.8024927066 0.3950145868 0.1975072934 [100,] 0.7679637881 0.4640724238 0.2320362119 [101,] 0.7304846028 0.5390307945 0.2695153972 [102,] 0.6901336565 0.6197326869 0.3098663435 [103,] 0.6461804492 0.7076391017 0.3538195508 [104,] 0.6011662673 0.7976674653 0.3988337327 [105,] 0.5547065565 0.8905868869 0.4452934435 [106,] 0.5053852038 0.9892295924 0.4946147962 [107,] 0.4553166277 0.9106332554 0.5446833723 [108,] 0.4081132755 0.8162265510 0.5918867245 [109,] 0.3592847701 0.7185695401 0.6407152299 [110,] 0.3151077407 0.6302154814 0.6848922593 [111,] 0.2733771018 0.5467542036 0.7266228982 [112,] 0.2345515832 0.4691031663 0.7654484168 [113,] 0.1989758739 0.3979517478 0.8010241261 [114,] 0.1668731758 0.3337463516 0.8331268242 [115,] 0.1383454149 0.2766908299 0.8616545851 [116,] 0.1133805366 0.2267610733 0.8866194634 [117,] 0.0918657270 0.1837314540 0.9081342730 [118,] 0.0712229447 0.1424458894 0.9287770553 [119,] 0.0561257426 0.1122514852 0.9438742574 [120,] 0.0437290060 0.0874580119 0.9562709940 [121,] 0.0320784358 0.0641568715 0.9679215642 [122,] 0.0242676552 0.0485353105 0.9757323448 [123,] 0.0181644763 0.0363289526 0.9818355237 [124,] 0.0134668585 0.0269337171 0.9865331415 [125,] 0.0099037330 0.0198074660 0.9900962670 [126,] 0.0072392094 0.0144784188 0.9927607906 [127,] 0.0052737824 0.0105475647 0.9947262176 [128,] 0.0038432041 0.0076864082 0.9961567959 [129,] 0.0028157423 0.0056314846 0.9971842577 [130,] 0.0020885262 0.0041770524 0.9979114738 [131,] 0.0015836467 0.0031672934 0.9984163533 [132,] 0.0012446916 0.0024893833 0.9987553084 [133,] 0.0006917069 0.0013834138 0.9993082931 [134,] 0.0003678640 0.0007357280 0.9996321360 [135,] 0.0002891306 0.0005782613 0.9997108694 [136,] 0.0088994955 0.0177989910 0.9911005045 [137,] 0.0049804142 0.0099608283 0.9950195858 [138,] 0.0034909245 0.0069818489 0.9965090755 [139,] 0.0025709197 0.0051418394 0.9974290803 [140,] 0.0020950901 0.0041901801 0.9979049099 [141,] 0.0009126178 0.0018252357 0.9990873822 [142,] 0.0003509948 0.0007019895 0.9996490052 [143,] 0.0001144820 0.0002289640 0.9998855180 > postscript(file="/var/fisher/rcomp/tmp/1883d1356130241.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2yt3i1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3228q1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4adjb1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5yfog1356130241.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 -0.17501473 -0.07896288 -0.07896288 -0.07896288 -0.07896288 -0.07896288 7 8 9 10 11 12 -0.07896288 -0.17501473 -0.07896288 -0.07896288 -0.17501473 -0.07896288 13 14 15 16 17 18 -0.07896288 -0.17501473 -0.07896288 -0.17501473 0.82498527 -0.17501473 19 20 21 22 23 24 -0.07896288 0.82498527 -0.07896288 -0.07896288 -0.07896288 -0.07896288 25 26 27 28 29 30 -0.17501473 -0.07896288 -0.07896288 -0.07896288 -0.07896288 -0.07896288 31 32 33 34 35 36 -0.07896288 -0.07896288 -0.07896288 -0.17501473 -0.07896288 -0.07896288 37 38 39 40 41 42 -0.17501473 -0.07896288 -0.07896288 -0.17501473 0.92103712 -0.07896288 43 44 45 46 47 48 -0.07896288 -0.17501473 -0.07896288 -0.07896288 -0.07896288 -0.07896288 49 50 51 52 53 54 -0.07896288 -0.07896288 -0.17501473 0.82498527 -0.07896288 0.92103712 55 56 57 58 59 60 -0.07896288 -0.17501473 -0.07896288 -0.07896288 -0.07896288 0.82498527 61 62 63 64 65 66 -0.17501473 -0.07896288 -0.07896288 -0.17501473 -0.07896288 -0.07896288 67 68 69 70 71 72 0.82498527 -0.07896288 -0.07896288 -0.07896288 -0.07896288 -0.07896288 73 74 75 76 77 78 -0.07896288 -0.07896288 -0.07896288 -0.17501473 -0.07896288 -0.07896288 79 80 81 82 83 84 0.82498527 -0.17501473 -0.07896288 -0.07896288 -0.07896288 0.92103712 85 86 87 88 89 90 -0.07896288 -0.07896288 -0.02010468 -0.11615654 -0.02010468 -0.02010468 91 92 93 94 95 96 -0.02010468 -0.11615654 -0.02010468 -0.02010468 -0.11615654 -0.02010468 97 98 99 100 101 102 -0.11615654 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 103 104 105 106 107 108 -0.02010468 -0.02010468 -0.11615654 -0.02010468 -0.02010468 -0.11615654 109 110 111 112 113 114 -0.02010468 -0.02010468 -0.11615654 -0.11615654 -0.02010468 -0.11615654 115 116 117 118 119 120 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 121 122 123 124 125 126 -0.02010468 -0.02010468 -0.11615654 -0.02010468 -0.02010468 -0.11615654 127 128 129 130 131 132 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 133 134 135 136 137 138 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.02010468 -0.11615654 139 140 141 142 143 144 -0.11615654 -0.02010468 0.97989532 -0.11615654 -0.02010468 -0.02010468 145 146 147 148 149 150 -0.02010468 -0.11615654 -0.11615654 -0.11615654 -0.02010468 -0.02010468 151 152 153 154 -0.02010468 0.97989532 0.97989532 -0.02010468 > postscript(file="/var/fisher/rcomp/tmp/6hhzb1356130241.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 -0.17501473 NA 1 -0.07896288 -0.17501473 2 -0.07896288 -0.07896288 3 -0.07896288 -0.07896288 4 -0.07896288 -0.07896288 5 -0.07896288 -0.07896288 6 -0.07896288 -0.07896288 7 -0.17501473 -0.07896288 8 -0.07896288 -0.17501473 9 -0.07896288 -0.07896288 10 -0.17501473 -0.07896288 11 -0.07896288 -0.17501473 12 -0.07896288 -0.07896288 13 -0.17501473 -0.07896288 14 -0.07896288 -0.17501473 15 -0.17501473 -0.07896288 16 0.82498527 -0.17501473 17 -0.17501473 0.82498527 18 -0.07896288 -0.17501473 19 0.82498527 -0.07896288 20 -0.07896288 0.82498527 21 -0.07896288 -0.07896288 22 -0.07896288 -0.07896288 23 -0.07896288 -0.07896288 24 -0.17501473 -0.07896288 25 -0.07896288 -0.17501473 26 -0.07896288 -0.07896288 27 -0.07896288 -0.07896288 28 -0.07896288 -0.07896288 29 -0.07896288 -0.07896288 30 -0.07896288 -0.07896288 31 -0.07896288 -0.07896288 32 -0.07896288 -0.07896288 33 -0.17501473 -0.07896288 34 -0.07896288 -0.17501473 35 -0.07896288 -0.07896288 36 -0.17501473 -0.07896288 37 -0.07896288 -0.17501473 38 -0.07896288 -0.07896288 39 -0.17501473 -0.07896288 40 0.92103712 -0.17501473 41 -0.07896288 0.92103712 42 -0.07896288 -0.07896288 43 -0.17501473 -0.07896288 44 -0.07896288 -0.17501473 45 -0.07896288 -0.07896288 46 -0.07896288 -0.07896288 47 -0.07896288 -0.07896288 48 -0.07896288 -0.07896288 49 -0.07896288 -0.07896288 50 -0.17501473 -0.07896288 51 0.82498527 -0.17501473 52 -0.07896288 0.82498527 53 0.92103712 -0.07896288 54 -0.07896288 0.92103712 55 -0.17501473 -0.07896288 56 -0.07896288 -0.17501473 57 -0.07896288 -0.07896288 58 -0.07896288 -0.07896288 59 0.82498527 -0.07896288 60 -0.17501473 0.82498527 61 -0.07896288 -0.17501473 62 -0.07896288 -0.07896288 63 -0.17501473 -0.07896288 64 -0.07896288 -0.17501473 65 -0.07896288 -0.07896288 66 0.82498527 -0.07896288 67 -0.07896288 0.82498527 68 -0.07896288 -0.07896288 69 -0.07896288 -0.07896288 70 -0.07896288 -0.07896288 71 -0.07896288 -0.07896288 72 -0.07896288 -0.07896288 73 -0.07896288 -0.07896288 74 -0.07896288 -0.07896288 75 -0.17501473 -0.07896288 76 -0.07896288 -0.17501473 77 -0.07896288 -0.07896288 78 0.82498527 -0.07896288 79 -0.17501473 0.82498527 80 -0.07896288 -0.17501473 81 -0.07896288 -0.07896288 82 -0.07896288 -0.07896288 83 0.92103712 -0.07896288 84 -0.07896288 0.92103712 85 -0.07896288 -0.07896288 86 -0.02010468 -0.07896288 87 -0.11615654 -0.02010468 88 -0.02010468 -0.11615654 89 -0.02010468 -0.02010468 90 -0.02010468 -0.02010468 91 -0.11615654 -0.02010468 92 -0.02010468 -0.11615654 93 -0.02010468 -0.02010468 94 -0.11615654 -0.02010468 95 -0.02010468 -0.11615654 96 -0.11615654 -0.02010468 97 -0.02010468 -0.11615654 98 -0.02010468 -0.02010468 99 -0.02010468 -0.02010468 100 -0.02010468 -0.02010468 101 -0.02010468 -0.02010468 102 -0.02010468 -0.02010468 103 -0.02010468 -0.02010468 104 -0.11615654 -0.02010468 105 -0.02010468 -0.11615654 106 -0.02010468 -0.02010468 107 -0.11615654 -0.02010468 108 -0.02010468 -0.11615654 109 -0.02010468 -0.02010468 110 -0.11615654 -0.02010468 111 -0.11615654 -0.11615654 112 -0.02010468 -0.11615654 113 -0.11615654 -0.02010468 114 -0.02010468 -0.11615654 115 -0.02010468 -0.02010468 116 -0.02010468 -0.02010468 117 -0.02010468 -0.02010468 118 -0.02010468 -0.02010468 119 -0.02010468 -0.02010468 120 -0.02010468 -0.02010468 121 -0.02010468 -0.02010468 122 -0.11615654 -0.02010468 123 -0.02010468 -0.11615654 124 -0.02010468 -0.02010468 125 -0.11615654 -0.02010468 126 -0.02010468 -0.11615654 127 -0.02010468 -0.02010468 128 -0.02010468 -0.02010468 129 -0.02010468 -0.02010468 130 -0.02010468 -0.02010468 131 -0.02010468 -0.02010468 132 -0.02010468 -0.02010468 133 -0.02010468 -0.02010468 134 -0.02010468 -0.02010468 135 -0.02010468 -0.02010468 136 -0.02010468 -0.02010468 137 -0.11615654 -0.02010468 138 -0.11615654 -0.11615654 139 -0.02010468 -0.11615654 140 0.97989532 -0.02010468 141 -0.11615654 0.97989532 142 -0.02010468 -0.11615654 143 -0.02010468 -0.02010468 144 -0.02010468 -0.02010468 145 -0.11615654 -0.02010468 146 -0.11615654 -0.11615654 147 -0.11615654 -0.11615654 148 -0.02010468 -0.11615654 149 -0.02010468 -0.02010468 150 -0.02010468 -0.02010468 151 0.97989532 -0.02010468 152 0.97989532 0.97989532 153 -0.02010468 0.97989532 154 NA -0.02010468 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.07896288 -0.17501473 [2,] -0.07896288 -0.07896288 [3,] -0.07896288 -0.07896288 [4,] -0.07896288 -0.07896288 [5,] -0.07896288 -0.07896288 [6,] -0.07896288 -0.07896288 [7,] -0.17501473 -0.07896288 [8,] -0.07896288 -0.17501473 [9,] -0.07896288 -0.07896288 [10,] -0.17501473 -0.07896288 [11,] -0.07896288 -0.17501473 [12,] -0.07896288 -0.07896288 [13,] -0.17501473 -0.07896288 [14,] -0.07896288 -0.17501473 [15,] -0.17501473 -0.07896288 [16,] 0.82498527 -0.17501473 [17,] -0.17501473 0.82498527 [18,] -0.07896288 -0.17501473 [19,] 0.82498527 -0.07896288 [20,] -0.07896288 0.82498527 [21,] -0.07896288 -0.07896288 [22,] -0.07896288 -0.07896288 [23,] -0.07896288 -0.07896288 [24,] -0.17501473 -0.07896288 [25,] -0.07896288 -0.17501473 [26,] -0.07896288 -0.07896288 [27,] -0.07896288 -0.07896288 [28,] -0.07896288 -0.07896288 [29,] -0.07896288 -0.07896288 [30,] -0.07896288 -0.07896288 [31,] -0.07896288 -0.07896288 [32,] -0.07896288 -0.07896288 [33,] -0.17501473 -0.07896288 [34,] -0.07896288 -0.17501473 [35,] -0.07896288 -0.07896288 [36,] -0.17501473 -0.07896288 [37,] -0.07896288 -0.17501473 [38,] -0.07896288 -0.07896288 [39,] -0.17501473 -0.07896288 [40,] 0.92103712 -0.17501473 [41,] -0.07896288 0.92103712 [42,] -0.07896288 -0.07896288 [43,] -0.17501473 -0.07896288 [44,] -0.07896288 -0.17501473 [45,] -0.07896288 -0.07896288 [46,] -0.07896288 -0.07896288 [47,] -0.07896288 -0.07896288 [48,] -0.07896288 -0.07896288 [49,] -0.07896288 -0.07896288 [50,] -0.17501473 -0.07896288 [51,] 0.82498527 -0.17501473 [52,] -0.07896288 0.82498527 [53,] 0.92103712 -0.07896288 [54,] -0.07896288 0.92103712 [55,] -0.17501473 -0.07896288 [56,] -0.07896288 -0.17501473 [57,] -0.07896288 -0.07896288 [58,] -0.07896288 -0.07896288 [59,] 0.82498527 -0.07896288 [60,] -0.17501473 0.82498527 [61,] -0.07896288 -0.17501473 [62,] -0.07896288 -0.07896288 [63,] -0.17501473 -0.07896288 [64,] -0.07896288 -0.17501473 [65,] -0.07896288 -0.07896288 [66,] 0.82498527 -0.07896288 [67,] -0.07896288 0.82498527 [68,] -0.07896288 -0.07896288 [69,] -0.07896288 -0.07896288 [70,] -0.07896288 -0.07896288 [71,] -0.07896288 -0.07896288 [72,] -0.07896288 -0.07896288 [73,] -0.07896288 -0.07896288 [74,] -0.07896288 -0.07896288 [75,] -0.17501473 -0.07896288 [76,] -0.07896288 -0.17501473 [77,] -0.07896288 -0.07896288 [78,] 0.82498527 -0.07896288 [79,] -0.17501473 0.82498527 [80,] -0.07896288 -0.17501473 [81,] -0.07896288 -0.07896288 [82,] -0.07896288 -0.07896288 [83,] 0.92103712 -0.07896288 [84,] -0.07896288 0.92103712 [85,] -0.07896288 -0.07896288 [86,] -0.02010468 -0.07896288 [87,] -0.11615654 -0.02010468 [88,] -0.02010468 -0.11615654 [89,] -0.02010468 -0.02010468 [90,] -0.02010468 -0.02010468 [91,] -0.11615654 -0.02010468 [92,] -0.02010468 -0.11615654 [93,] -0.02010468 -0.02010468 [94,] -0.11615654 -0.02010468 [95,] -0.02010468 -0.11615654 [96,] -0.11615654 -0.02010468 [97,] -0.02010468 -0.11615654 [98,] -0.02010468 -0.02010468 [99,] -0.02010468 -0.02010468 [100,] -0.02010468 -0.02010468 [101,] -0.02010468 -0.02010468 [102,] -0.02010468 -0.02010468 [103,] -0.02010468 -0.02010468 [104,] -0.11615654 -0.02010468 [105,] -0.02010468 -0.11615654 [106,] -0.02010468 -0.02010468 [107,] -0.11615654 -0.02010468 [108,] -0.02010468 -0.11615654 [109,] -0.02010468 -0.02010468 [110,] -0.11615654 -0.02010468 [111,] -0.11615654 -0.11615654 [112,] -0.02010468 -0.11615654 [113,] -0.11615654 -0.02010468 [114,] -0.02010468 -0.11615654 [115,] -0.02010468 -0.02010468 [116,] -0.02010468 -0.02010468 [117,] -0.02010468 -0.02010468 [118,] -0.02010468 -0.02010468 [119,] -0.02010468 -0.02010468 [120,] -0.02010468 -0.02010468 [121,] -0.02010468 -0.02010468 [122,] -0.11615654 -0.02010468 [123,] -0.02010468 -0.11615654 [124,] -0.02010468 -0.02010468 [125,] -0.11615654 -0.02010468 [126,] -0.02010468 -0.11615654 [127,] -0.02010468 -0.02010468 [128,] -0.02010468 -0.02010468 [129,] -0.02010468 -0.02010468 [130,] -0.02010468 -0.02010468 [131,] -0.02010468 -0.02010468 [132,] -0.02010468 -0.02010468 [133,] -0.02010468 -0.02010468 [134,] -0.02010468 -0.02010468 [135,] -0.02010468 -0.02010468 [136,] -0.02010468 -0.02010468 [137,] -0.11615654 -0.02010468 [138,] -0.11615654 -0.11615654 [139,] -0.02010468 -0.11615654 [140,] 0.97989532 -0.02010468 [141,] -0.11615654 0.97989532 [142,] -0.02010468 -0.11615654 [143,] -0.02010468 -0.02010468 [144,] -0.02010468 -0.02010468 [145,] -0.11615654 -0.02010468 [146,] -0.11615654 -0.11615654 [147,] -0.11615654 -0.11615654 [148,] -0.02010468 -0.11615654 [149,] -0.02010468 -0.02010468 [150,] -0.02010468 -0.02010468 [151,] 0.97989532 -0.02010468 [152,] 0.97989532 0.97989532 [153,] -0.02010468 0.97989532 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.07896288 -0.17501473 2 -0.07896288 -0.07896288 3 -0.07896288 -0.07896288 4 -0.07896288 -0.07896288 5 -0.07896288 -0.07896288 6 -0.07896288 -0.07896288 7 -0.17501473 -0.07896288 8 -0.07896288 -0.17501473 9 -0.07896288 -0.07896288 10 -0.17501473 -0.07896288 11 -0.07896288 -0.17501473 12 -0.07896288 -0.07896288 13 -0.17501473 -0.07896288 14 -0.07896288 -0.17501473 15 -0.17501473 -0.07896288 16 0.82498527 -0.17501473 17 -0.17501473 0.82498527 18 -0.07896288 -0.17501473 19 0.82498527 -0.07896288 20 -0.07896288 0.82498527 21 -0.07896288 -0.07896288 22 -0.07896288 -0.07896288 23 -0.07896288 -0.07896288 24 -0.17501473 -0.07896288 25 -0.07896288 -0.17501473 26 -0.07896288 -0.07896288 27 -0.07896288 -0.07896288 28 -0.07896288 -0.07896288 29 -0.07896288 -0.07896288 30 -0.07896288 -0.07896288 31 -0.07896288 -0.07896288 32 -0.07896288 -0.07896288 33 -0.17501473 -0.07896288 34 -0.07896288 -0.17501473 35 -0.07896288 -0.07896288 36 -0.17501473 -0.07896288 37 -0.07896288 -0.17501473 38 -0.07896288 -0.07896288 39 -0.17501473 -0.07896288 40 0.92103712 -0.17501473 41 -0.07896288 0.92103712 42 -0.07896288 -0.07896288 43 -0.17501473 -0.07896288 44 -0.07896288 -0.17501473 45 -0.07896288 -0.07896288 46 -0.07896288 -0.07896288 47 -0.07896288 -0.07896288 48 -0.07896288 -0.07896288 49 -0.07896288 -0.07896288 50 -0.17501473 -0.07896288 51 0.82498527 -0.17501473 52 -0.07896288 0.82498527 53 0.92103712 -0.07896288 54 -0.07896288 0.92103712 55 -0.17501473 -0.07896288 56 -0.07896288 -0.17501473 57 -0.07896288 -0.07896288 58 -0.07896288 -0.07896288 59 0.82498527 -0.07896288 60 -0.17501473 0.82498527 61 -0.07896288 -0.17501473 62 -0.07896288 -0.07896288 63 -0.17501473 -0.07896288 64 -0.07896288 -0.17501473 65 -0.07896288 -0.07896288 66 0.82498527 -0.07896288 67 -0.07896288 0.82498527 68 -0.07896288 -0.07896288 69 -0.07896288 -0.07896288 70 -0.07896288 -0.07896288 71 -0.07896288 -0.07896288 72 -0.07896288 -0.07896288 73 -0.07896288 -0.07896288 74 -0.07896288 -0.07896288 75 -0.17501473 -0.07896288 76 -0.07896288 -0.17501473 77 -0.07896288 -0.07896288 78 0.82498527 -0.07896288 79 -0.17501473 0.82498527 80 -0.07896288 -0.17501473 81 -0.07896288 -0.07896288 82 -0.07896288 -0.07896288 83 0.92103712 -0.07896288 84 -0.07896288 0.92103712 85 -0.07896288 -0.07896288 86 -0.02010468 -0.07896288 87 -0.11615654 -0.02010468 88 -0.02010468 -0.11615654 89 -0.02010468 -0.02010468 90 -0.02010468 -0.02010468 91 -0.11615654 -0.02010468 92 -0.02010468 -0.11615654 93 -0.02010468 -0.02010468 94 -0.11615654 -0.02010468 95 -0.02010468 -0.11615654 96 -0.11615654 -0.02010468 97 -0.02010468 -0.11615654 98 -0.02010468 -0.02010468 99 -0.02010468 -0.02010468 100 -0.02010468 -0.02010468 101 -0.02010468 -0.02010468 102 -0.02010468 -0.02010468 103 -0.02010468 -0.02010468 104 -0.11615654 -0.02010468 105 -0.02010468 -0.11615654 106 -0.02010468 -0.02010468 107 -0.11615654 -0.02010468 108 -0.02010468 -0.11615654 109 -0.02010468 -0.02010468 110 -0.11615654 -0.02010468 111 -0.11615654 -0.11615654 112 -0.02010468 -0.11615654 113 -0.11615654 -0.02010468 114 -0.02010468 -0.11615654 115 -0.02010468 -0.02010468 116 -0.02010468 -0.02010468 117 -0.02010468 -0.02010468 118 -0.02010468 -0.02010468 119 -0.02010468 -0.02010468 120 -0.02010468 -0.02010468 121 -0.02010468 -0.02010468 122 -0.11615654 -0.02010468 123 -0.02010468 -0.11615654 124 -0.02010468 -0.02010468 125 -0.11615654 -0.02010468 126 -0.02010468 -0.11615654 127 -0.02010468 -0.02010468 128 -0.02010468 -0.02010468 129 -0.02010468 -0.02010468 130 -0.02010468 -0.02010468 131 -0.02010468 -0.02010468 132 -0.02010468 -0.02010468 133 -0.02010468 -0.02010468 134 -0.02010468 -0.02010468 135 -0.02010468 -0.02010468 136 -0.02010468 -0.02010468 137 -0.11615654 -0.02010468 138 -0.11615654 -0.11615654 139 -0.02010468 -0.11615654 140 0.97989532 -0.02010468 141 -0.11615654 0.97989532 142 -0.02010468 -0.11615654 143 -0.02010468 -0.02010468 144 -0.02010468 -0.02010468 145 -0.11615654 -0.02010468 146 -0.11615654 -0.11615654 147 -0.11615654 -0.11615654 148 -0.02010468 -0.11615654 149 -0.02010468 -0.02010468 150 -0.02010468 -0.02010468 151 0.97989532 -0.02010468 152 0.97989532 0.97989532 153 -0.02010468 0.97989532 > 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/fisher/rcomp/tmp/7y6mx1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8b9tv1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9wqkr1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10nias1356130241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/11c2rh1356130241.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/fisher/rcomp/tmp/126gwr1356130241.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/fisher/rcomp/tmp/13gocx1356130241.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/fisher/rcomp/tmp/14qm7r1356130241.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/fisher/rcomp/tmp/15h2d81356130241.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/fisher/rcomp/tmp/16wm5n1356130241.tab") + } > > try(system("convert tmp/1883d1356130241.ps tmp/1883d1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/2yt3i1356130241.ps tmp/2yt3i1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/3228q1356130241.ps tmp/3228q1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/4adjb1356130241.ps tmp/4adjb1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/5yfog1356130241.ps tmp/5yfog1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/6hhzb1356130241.ps tmp/6hhzb1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/7y6mx1356130241.ps tmp/7y6mx1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/8b9tv1356130241.ps tmp/8b9tv1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/9wqkr1356130241.ps tmp/9wqkr1356130241.png",intern=TRUE)) character(0) > try(system("convert tmp/10nias1356130241.ps tmp/10nias1356130241.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.500 1.706 9.222