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(1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,1 + ,3 + ,1 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,1 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,1 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,1 + ,1 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,1 + ,0 + ,3 + ,1 + ,0 + ,3 + ,1 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,0 + ,3 + ,0 + ,1 + ,3 + ,0 + ,1 + ,3 + ,0 + ,0) + ,dim=c(3 + ,154) + ,dimnames=list(c('T40' + ,'T20' + ,'CorrectAnalysis') + ,1:154)) > y <- array(NA,dim=c(3,154),dimnames=list(c('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 T40 T20 1 0 1 3 2 0 0 3 3 0 0 3 4 0 0 3 5 0 0 3 6 0 0 3 7 0 0 3 8 0 1 3 9 0 0 3 10 0 0 3 11 0 1 3 12 0 0 3 13 0 0 3 14 0 1 3 15 0 0 3 16 0 1 3 17 1 1 3 18 0 1 3 19 0 0 3 20 1 1 3 21 0 0 3 22 0 0 3 23 0 0 3 24 0 0 3 25 0 1 3 26 0 0 3 27 0 0 3 28 0 0 3 29 0 0 3 30 0 0 3 31 0 0 3 32 0 0 3 33 0 0 3 34 0 1 3 35 0 0 3 36 0 0 3 37 0 1 3 38 0 0 3 39 0 0 3 40 0 1 3 41 1 0 3 42 0 0 3 43 0 0 3 44 0 1 3 45 0 0 3 46 0 0 3 47 0 0 3 48 0 0 3 49 0 0 3 50 0 0 3 51 0 1 3 52 1 1 3 53 0 0 3 54 1 0 3 55 0 0 3 56 0 1 3 57 0 0 3 58 0 0 3 59 0 0 3 60 1 1 3 61 0 1 3 62 0 0 3 63 0 0 3 64 0 1 3 65 0 0 3 66 0 0 3 67 1 1 3 68 0 0 3 69 0 0 3 70 0 0 3 71 0 0 3 72 0 0 3 73 0 0 3 74 0 0 3 75 0 0 3 76 0 1 3 77 0 0 3 78 0 0 3 79 1 1 3 80 0 1 3 81 0 0 3 82 0 0 3 83 0 0 3 84 1 0 3 85 0 0 3 86 0 0 3 87 0 3 0 88 0 3 1 89 0 3 0 90 0 3 0 91 0 3 0 92 0 3 1 93 0 3 0 94 0 3 0 95 0 3 1 96 0 3 0 97 0 3 1 98 0 3 0 99 0 3 0 100 0 3 0 101 0 3 0 102 0 3 0 103 0 3 0 104 0 3 0 105 0 3 1 106 0 3 0 107 0 3 0 108 0 3 1 109 0 3 0 110 0 3 0 111 0 3 1 112 0 3 1 113 0 3 0 114 0 3 1 115 0 3 0 116 0 3 0 117 0 3 0 118 0 3 0 119 0 3 0 120 0 3 0 121 0 3 0 122 0 3 0 123 0 3 1 124 0 3 0 125 0 3 0 126 0 3 1 127 0 3 0 128 0 3 0 129 0 3 0 130 0 3 0 131 0 3 0 132 0 3 0 133 0 3 0 134 0 3 0 135 0 3 0 136 0 3 0 137 0 3 0 138 0 3 1 139 0 3 1 140 0 3 0 141 1 3 0 142 0 3 1 143 0 3 0 144 0 3 0 145 0 3 0 146 0 3 1 147 0 3 1 148 0 3 1 149 0 3 0 150 0 3 0 151 0 3 0 152 1 3 0 153 1 3 0 154 0 3 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) T40 T20 -0.24205 0.09039 0.10457 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.16206 -0.07167 -0.07167 -0.02912 0.97088 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.24205 0.16112 -1.502 0.1351 T40 0.09039 0.04957 1.823 0.0702 . T20 0.10457 0.04961 2.108 0.0367 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2665 on 151 degrees of freedom Multiple R-squared: 0.03069, Adjusted R-squared: 0.01786 F-statistic: 2.391 on 2 and 151 DF, p-value: 0.09502 > 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,] 0.3448685787 0.6897371573 0.6551314213 [13,] 0.2978228866 0.5956457732 0.7021771134 [14,] 0.2328112780 0.4656225560 0.7671887220 [15,] 0.7804406625 0.4391186749 0.2195593375 [16,] 0.7250139340 0.5499721320 0.2749860660 [17,] 0.6645608730 0.6708782540 0.3354391270 [18,] 0.6006008763 0.7987982473 0.3993991237 [19,] 0.5348640761 0.9302718477 0.4651359239 [20,] 0.5147586679 0.9704826643 0.4852413321 [21,] 0.4503149871 0.9006299742 0.5496850129 [22,] 0.3879984721 0.7759969441 0.6120015279 [23,] 0.3291979437 0.6583958874 0.6708020563 [24,] 0.2750093235 0.5500186470 0.7249906765 [25,] 0.2261948221 0.4523896443 0.7738051779 [26,] 0.1831774491 0.3663548982 0.8168225509 [27,] 0.1460656498 0.2921312997 0.8539343502 [28,] 0.1147000665 0.2294001330 0.8852999335 [29,] 0.1050010548 0.2100021095 0.8949989452 [30,] 0.0809945275 0.1619890550 0.9190054725 [31,] 0.0615678986 0.1231357971 0.9384321014 [32,] 0.0542812935 0.1085625869 0.9457187065 [33,] 0.0405071515 0.0810143030 0.9594928485 [34,] 0.0298120428 0.0596240856 0.9701879572 [35,] 0.0253631529 0.0507263059 0.9746368471 [36,] 0.4408638385 0.8817276769 0.5591361615 [37,] 0.3910313922 0.7820627844 0.6089686078 [38,] 0.3432917750 0.6865835499 0.6567082250 [39,] 0.3137654042 0.6275308085 0.6862345958 [40,] 0.2709379779 0.5418759557 0.7290620221 [41,] 0.2315464431 0.4630928862 0.7684535569 [42,] 0.1958487342 0.3916974684 0.8041512658 [43,] 0.1639642150 0.3279284299 0.8360357850 [44,] 0.1358860586 0.2717721172 0.8641139414 [45,] 0.1114991815 0.2229983631 0.8885008185 [46,] 0.0973475998 0.1946951997 0.9026524002 [47,] 0.4292879520 0.8585759040 0.5707120480 [48,] 0.3846619444 0.7693238888 0.6153380556 [49,] 0.8546684738 0.2906630524 0.1453315262 [50,] 0.8272928029 0.3454143942 0.1727071971 [51,] 0.8088920654 0.3822158692 0.1911079346 [52,] 0.7770143945 0.4459712110 0.2229856055 [53,] 0.7425666861 0.5148666277 0.2574333139 [54,] 0.7058450354 0.5883099293 0.2941549646 [55,] 0.9355261034 0.1289477933 0.0644738966 [56,] 0.9270988210 0.1458023580 0.0729011790 [57,] 0.9107467643 0.1785064714 0.0892532357 [58,] 0.8919782819 0.2160434361 0.1080217181 [59,] 0.8792864940 0.2414270120 0.1207135060 [60,] 0.8565652927 0.2868694146 0.1434347073 [61,] 0.8314357790 0.3371284421 0.1685642210 [62,] 0.9764690357 0.0470619286 0.0235309643 [63,] 0.9698462248 0.0603075504 0.0301537752 [64,] 0.9618465545 0.0763068910 0.0381534455 [65,] 0.9523379511 0.0953240977 0.0476620489 [66,] 0.9412262605 0.1175474789 0.0587737395 [67,] 0.9284774283 0.1430451435 0.0715225717 [68,] 0.9141465423 0.1717069153 0.0858534577 [69,] 0.8984171568 0.2031656865 0.1015828432 [70,] 0.8816576577 0.2366846847 0.1183423423 [71,] 0.8699851513 0.2600296974 0.1300148487 [72,] 0.8524974045 0.2950051910 0.1475025955 [73,] 0.8362975219 0.3274049562 0.1637024781 [74,] 0.9808976815 0.0382046370 0.0191023185 [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,] 0.9965284686 0.0069430629 0.0034715314 [84,] 0.9951569992 0.0096860017 0.0048430008 [85,] 0.9932837607 0.0134324786 0.0067162393 [86,] 0.9907795865 0.0184408270 0.0092204135 [87,] 0.9878686794 0.0242626412 0.0121313206 [88,] 0.9837334429 0.0325331143 0.0162665571 [89,] 0.9784185212 0.0431629576 0.0215814788 [90,] 0.9722710529 0.0554578942 0.0277289471 [91,] 0.9640312442 0.0719375115 0.0359687558 [92,] 0.9544625920 0.0910748161 0.0455374080 [93,] 0.9422232823 0.1155534355 0.0577767177 [94,] 0.9274739263 0.1450521474 0.0725260737 [95,] 0.9099423514 0.1801152972 0.0900576486 [96,] 0.8893882808 0.2212234383 0.1106117192 [97,] 0.8656206186 0.2687587628 0.1343793814 [98,] 0.8385148132 0.3229703736 0.1614851868 [99,] 0.8080290251 0.3839419498 0.1919709749 [100,] 0.7755605046 0.4488789907 0.2244394954 [101,] 0.7385955614 0.5228088772 0.2614044386 [102,] 0.6986805908 0.6026388185 0.3013194092 [103,] 0.6569140504 0.6861718993 0.3430859496 [104,] 0.6121768474 0.7756463052 0.3878231526 [105,] 0.5658485625 0.8683028751 0.4341514375 [106,] 0.5183585853 0.9632828295 0.4816414147 [107,] 0.4698355069 0.9396710138 0.5301644931 [108,] 0.4221915977 0.8443831955 0.5778084023 [109,] 0.3742832611 0.7485665222 0.6257167389 [110,] 0.3292276713 0.6584553427 0.6707723287 [111,] 0.2864773651 0.5729547302 0.7135226349 [112,] 0.2465293241 0.4930586483 0.7534706759 [113,] 0.2097682000 0.4195364000 0.7902318000 [114,] 0.1764564654 0.3529129308 0.8235435346 [115,] 0.1467324486 0.2934648973 0.8532675514 [116,] 0.1206158486 0.2412316971 0.8793841514 [117,] 0.0980196920 0.1960393839 0.9019803080 [118,] 0.0767782121 0.1535564242 0.9232217879 [119,] 0.0607006772 0.1214013544 0.9392993228 [120,] 0.0474449106 0.0948898212 0.9525550894 [121,] 0.0352005213 0.0704010425 0.9647994787 [122,] 0.0267204134 0.0534408268 0.9732795866 [123,] 0.0200666204 0.0401332408 0.9799333796 [124,] 0.0149245368 0.0298490736 0.9850754632 [125,] 0.0110091229 0.0220182459 0.9889908771 [126,] 0.0080702419 0.0161404838 0.9919297581 [127,] 0.0058947401 0.0117894802 0.9941052599 [128,] 0.0043059295 0.0086118590 0.9956940705 [129,] 0.0031612117 0.0063224235 0.9968387883 [130,] 0.0023485912 0.0046971824 0.9976514088 [131,] 0.0017828003 0.0035656006 0.9982171997 [132,] 0.0014017947 0.0028035895 0.9985982053 [133,] 0.0007897797 0.0015795594 0.9992102203 [134,] 0.0004259537 0.0008519074 0.9995740463 [135,] 0.0003345926 0.0006691852 0.9996654074 [136,] 0.0095668496 0.0191336991 0.9904331504 [137,] 0.0054024889 0.0108049779 0.9945975111 [138,] 0.0037909091 0.0075818183 0.9962090909 [139,] 0.0027920569 0.0055841137 0.9972079431 [140,] 0.0022718923 0.0045437846 0.9977281077 [141,] 0.0009989709 0.0019979417 0.9990010291 [142,] 0.0003879657 0.0007759314 0.9996120343 [143,] 0.0001278203 0.0002556406 0.9998721797 > postscript(file="/var/fisher/rcomp/tmp/1tkyt1356092924.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2teuj1356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3jlig1356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/49bd51356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/508gh1356092924.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 -0.16205685 -0.07166870 -0.07166870 -0.07166870 -0.07166870 -0.07166870 7 8 9 10 11 12 -0.07166870 -0.16205685 -0.07166870 -0.07166870 -0.16205685 -0.07166870 13 14 15 16 17 18 -0.07166870 -0.16205685 -0.07166870 -0.16205685 0.83794315 -0.16205685 19 20 21 22 23 24 -0.07166870 0.83794315 -0.07166870 -0.07166870 -0.07166870 -0.07166870 25 26 27 28 29 30 -0.16205685 -0.07166870 -0.07166870 -0.07166870 -0.07166870 -0.07166870 31 32 33 34 35 36 -0.07166870 -0.07166870 -0.07166870 -0.16205685 -0.07166870 -0.07166870 37 38 39 40 41 42 -0.16205685 -0.07166870 -0.07166870 -0.16205685 0.92833130 -0.07166870 43 44 45 46 47 48 -0.07166870 -0.16205685 -0.07166870 -0.07166870 -0.07166870 -0.07166870 49 50 51 52 53 54 -0.07166870 -0.07166870 -0.16205685 0.83794315 -0.07166870 0.92833130 55 56 57 58 59 60 -0.07166870 -0.16205685 -0.07166870 -0.07166870 -0.07166870 0.83794315 61 62 63 64 65 66 -0.16205685 -0.07166870 -0.07166870 -0.16205685 -0.07166870 -0.07166870 67 68 69 70 71 72 0.83794315 -0.07166870 -0.07166870 -0.07166870 -0.07166870 -0.07166870 73 74 75 76 77 78 -0.07166870 -0.07166870 -0.07166870 -0.16205685 -0.07166870 -0.07166870 79 80 81 82 83 84 0.83794315 -0.16205685 -0.07166870 -0.07166870 -0.07166870 0.92833130 85 86 87 88 89 90 -0.07166870 -0.07166870 -0.02911513 -0.13368780 -0.02911513 -0.02911513 91 92 93 94 95 96 -0.02911513 -0.13368780 -0.02911513 -0.02911513 -0.13368780 -0.02911513 97 98 99 100 101 102 -0.13368780 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 103 104 105 106 107 108 -0.02911513 -0.02911513 -0.13368780 -0.02911513 -0.02911513 -0.13368780 109 110 111 112 113 114 -0.02911513 -0.02911513 -0.13368780 -0.13368780 -0.02911513 -0.13368780 115 116 117 118 119 120 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 121 122 123 124 125 126 -0.02911513 -0.02911513 -0.13368780 -0.02911513 -0.02911513 -0.13368780 127 128 129 130 131 132 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 133 134 135 136 137 138 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.02911513 -0.13368780 139 140 141 142 143 144 -0.13368780 -0.02911513 0.97088487 -0.13368780 -0.02911513 -0.02911513 145 146 147 148 149 150 -0.02911513 -0.13368780 -0.13368780 -0.13368780 -0.02911513 -0.02911513 151 152 153 154 -0.02911513 0.97088487 0.97088487 -0.02911513 > postscript(file="/var/fisher/rcomp/tmp/6nddk1356092924.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 -0.16205685 NA 1 -0.07166870 -0.16205685 2 -0.07166870 -0.07166870 3 -0.07166870 -0.07166870 4 -0.07166870 -0.07166870 5 -0.07166870 -0.07166870 6 -0.07166870 -0.07166870 7 -0.16205685 -0.07166870 8 -0.07166870 -0.16205685 9 -0.07166870 -0.07166870 10 -0.16205685 -0.07166870 11 -0.07166870 -0.16205685 12 -0.07166870 -0.07166870 13 -0.16205685 -0.07166870 14 -0.07166870 -0.16205685 15 -0.16205685 -0.07166870 16 0.83794315 -0.16205685 17 -0.16205685 0.83794315 18 -0.07166870 -0.16205685 19 0.83794315 -0.07166870 20 -0.07166870 0.83794315 21 -0.07166870 -0.07166870 22 -0.07166870 -0.07166870 23 -0.07166870 -0.07166870 24 -0.16205685 -0.07166870 25 -0.07166870 -0.16205685 26 -0.07166870 -0.07166870 27 -0.07166870 -0.07166870 28 -0.07166870 -0.07166870 29 -0.07166870 -0.07166870 30 -0.07166870 -0.07166870 31 -0.07166870 -0.07166870 32 -0.07166870 -0.07166870 33 -0.16205685 -0.07166870 34 -0.07166870 -0.16205685 35 -0.07166870 -0.07166870 36 -0.16205685 -0.07166870 37 -0.07166870 -0.16205685 38 -0.07166870 -0.07166870 39 -0.16205685 -0.07166870 40 0.92833130 -0.16205685 41 -0.07166870 0.92833130 42 -0.07166870 -0.07166870 43 -0.16205685 -0.07166870 44 -0.07166870 -0.16205685 45 -0.07166870 -0.07166870 46 -0.07166870 -0.07166870 47 -0.07166870 -0.07166870 48 -0.07166870 -0.07166870 49 -0.07166870 -0.07166870 50 -0.16205685 -0.07166870 51 0.83794315 -0.16205685 52 -0.07166870 0.83794315 53 0.92833130 -0.07166870 54 -0.07166870 0.92833130 55 -0.16205685 -0.07166870 56 -0.07166870 -0.16205685 57 -0.07166870 -0.07166870 58 -0.07166870 -0.07166870 59 0.83794315 -0.07166870 60 -0.16205685 0.83794315 61 -0.07166870 -0.16205685 62 -0.07166870 -0.07166870 63 -0.16205685 -0.07166870 64 -0.07166870 -0.16205685 65 -0.07166870 -0.07166870 66 0.83794315 -0.07166870 67 -0.07166870 0.83794315 68 -0.07166870 -0.07166870 69 -0.07166870 -0.07166870 70 -0.07166870 -0.07166870 71 -0.07166870 -0.07166870 72 -0.07166870 -0.07166870 73 -0.07166870 -0.07166870 74 -0.07166870 -0.07166870 75 -0.16205685 -0.07166870 76 -0.07166870 -0.16205685 77 -0.07166870 -0.07166870 78 0.83794315 -0.07166870 79 -0.16205685 0.83794315 80 -0.07166870 -0.16205685 81 -0.07166870 -0.07166870 82 -0.07166870 -0.07166870 83 0.92833130 -0.07166870 84 -0.07166870 0.92833130 85 -0.07166870 -0.07166870 86 -0.02911513 -0.07166870 87 -0.13368780 -0.02911513 88 -0.02911513 -0.13368780 89 -0.02911513 -0.02911513 90 -0.02911513 -0.02911513 91 -0.13368780 -0.02911513 92 -0.02911513 -0.13368780 93 -0.02911513 -0.02911513 94 -0.13368780 -0.02911513 95 -0.02911513 -0.13368780 96 -0.13368780 -0.02911513 97 -0.02911513 -0.13368780 98 -0.02911513 -0.02911513 99 -0.02911513 -0.02911513 100 -0.02911513 -0.02911513 101 -0.02911513 -0.02911513 102 -0.02911513 -0.02911513 103 -0.02911513 -0.02911513 104 -0.13368780 -0.02911513 105 -0.02911513 -0.13368780 106 -0.02911513 -0.02911513 107 -0.13368780 -0.02911513 108 -0.02911513 -0.13368780 109 -0.02911513 -0.02911513 110 -0.13368780 -0.02911513 111 -0.13368780 -0.13368780 112 -0.02911513 -0.13368780 113 -0.13368780 -0.02911513 114 -0.02911513 -0.13368780 115 -0.02911513 -0.02911513 116 -0.02911513 -0.02911513 117 -0.02911513 -0.02911513 118 -0.02911513 -0.02911513 119 -0.02911513 -0.02911513 120 -0.02911513 -0.02911513 121 -0.02911513 -0.02911513 122 -0.13368780 -0.02911513 123 -0.02911513 -0.13368780 124 -0.02911513 -0.02911513 125 -0.13368780 -0.02911513 126 -0.02911513 -0.13368780 127 -0.02911513 -0.02911513 128 -0.02911513 -0.02911513 129 -0.02911513 -0.02911513 130 -0.02911513 -0.02911513 131 -0.02911513 -0.02911513 132 -0.02911513 -0.02911513 133 -0.02911513 -0.02911513 134 -0.02911513 -0.02911513 135 -0.02911513 -0.02911513 136 -0.02911513 -0.02911513 137 -0.13368780 -0.02911513 138 -0.13368780 -0.13368780 139 -0.02911513 -0.13368780 140 0.97088487 -0.02911513 141 -0.13368780 0.97088487 142 -0.02911513 -0.13368780 143 -0.02911513 -0.02911513 144 -0.02911513 -0.02911513 145 -0.13368780 -0.02911513 146 -0.13368780 -0.13368780 147 -0.13368780 -0.13368780 148 -0.02911513 -0.13368780 149 -0.02911513 -0.02911513 150 -0.02911513 -0.02911513 151 0.97088487 -0.02911513 152 0.97088487 0.97088487 153 -0.02911513 0.97088487 154 NA -0.02911513 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.07166870 -0.16205685 [2,] -0.07166870 -0.07166870 [3,] -0.07166870 -0.07166870 [4,] -0.07166870 -0.07166870 [5,] -0.07166870 -0.07166870 [6,] -0.07166870 -0.07166870 [7,] -0.16205685 -0.07166870 [8,] -0.07166870 -0.16205685 [9,] -0.07166870 -0.07166870 [10,] -0.16205685 -0.07166870 [11,] -0.07166870 -0.16205685 [12,] -0.07166870 -0.07166870 [13,] -0.16205685 -0.07166870 [14,] -0.07166870 -0.16205685 [15,] -0.16205685 -0.07166870 [16,] 0.83794315 -0.16205685 [17,] -0.16205685 0.83794315 [18,] -0.07166870 -0.16205685 [19,] 0.83794315 -0.07166870 [20,] -0.07166870 0.83794315 [21,] -0.07166870 -0.07166870 [22,] -0.07166870 -0.07166870 [23,] -0.07166870 -0.07166870 [24,] -0.16205685 -0.07166870 [25,] -0.07166870 -0.16205685 [26,] -0.07166870 -0.07166870 [27,] -0.07166870 -0.07166870 [28,] -0.07166870 -0.07166870 [29,] -0.07166870 -0.07166870 [30,] -0.07166870 -0.07166870 [31,] -0.07166870 -0.07166870 [32,] -0.07166870 -0.07166870 [33,] -0.16205685 -0.07166870 [34,] -0.07166870 -0.16205685 [35,] -0.07166870 -0.07166870 [36,] -0.16205685 -0.07166870 [37,] -0.07166870 -0.16205685 [38,] -0.07166870 -0.07166870 [39,] -0.16205685 -0.07166870 [40,] 0.92833130 -0.16205685 [41,] -0.07166870 0.92833130 [42,] -0.07166870 -0.07166870 [43,] -0.16205685 -0.07166870 [44,] -0.07166870 -0.16205685 [45,] -0.07166870 -0.07166870 [46,] -0.07166870 -0.07166870 [47,] -0.07166870 -0.07166870 [48,] -0.07166870 -0.07166870 [49,] -0.07166870 -0.07166870 [50,] -0.16205685 -0.07166870 [51,] 0.83794315 -0.16205685 [52,] -0.07166870 0.83794315 [53,] 0.92833130 -0.07166870 [54,] -0.07166870 0.92833130 [55,] -0.16205685 -0.07166870 [56,] -0.07166870 -0.16205685 [57,] -0.07166870 -0.07166870 [58,] -0.07166870 -0.07166870 [59,] 0.83794315 -0.07166870 [60,] -0.16205685 0.83794315 [61,] -0.07166870 -0.16205685 [62,] -0.07166870 -0.07166870 [63,] -0.16205685 -0.07166870 [64,] -0.07166870 -0.16205685 [65,] -0.07166870 -0.07166870 [66,] 0.83794315 -0.07166870 [67,] -0.07166870 0.83794315 [68,] -0.07166870 -0.07166870 [69,] -0.07166870 -0.07166870 [70,] -0.07166870 -0.07166870 [71,] -0.07166870 -0.07166870 [72,] -0.07166870 -0.07166870 [73,] -0.07166870 -0.07166870 [74,] -0.07166870 -0.07166870 [75,] -0.16205685 -0.07166870 [76,] -0.07166870 -0.16205685 [77,] -0.07166870 -0.07166870 [78,] 0.83794315 -0.07166870 [79,] -0.16205685 0.83794315 [80,] -0.07166870 -0.16205685 [81,] -0.07166870 -0.07166870 [82,] -0.07166870 -0.07166870 [83,] 0.92833130 -0.07166870 [84,] -0.07166870 0.92833130 [85,] -0.07166870 -0.07166870 [86,] -0.02911513 -0.07166870 [87,] -0.13368780 -0.02911513 [88,] -0.02911513 -0.13368780 [89,] -0.02911513 -0.02911513 [90,] -0.02911513 -0.02911513 [91,] -0.13368780 -0.02911513 [92,] -0.02911513 -0.13368780 [93,] -0.02911513 -0.02911513 [94,] -0.13368780 -0.02911513 [95,] -0.02911513 -0.13368780 [96,] -0.13368780 -0.02911513 [97,] -0.02911513 -0.13368780 [98,] -0.02911513 -0.02911513 [99,] -0.02911513 -0.02911513 [100,] -0.02911513 -0.02911513 [101,] -0.02911513 -0.02911513 [102,] -0.02911513 -0.02911513 [103,] -0.02911513 -0.02911513 [104,] -0.13368780 -0.02911513 [105,] -0.02911513 -0.13368780 [106,] -0.02911513 -0.02911513 [107,] -0.13368780 -0.02911513 [108,] -0.02911513 -0.13368780 [109,] -0.02911513 -0.02911513 [110,] -0.13368780 -0.02911513 [111,] -0.13368780 -0.13368780 [112,] -0.02911513 -0.13368780 [113,] -0.13368780 -0.02911513 [114,] -0.02911513 -0.13368780 [115,] -0.02911513 -0.02911513 [116,] -0.02911513 -0.02911513 [117,] -0.02911513 -0.02911513 [118,] -0.02911513 -0.02911513 [119,] -0.02911513 -0.02911513 [120,] -0.02911513 -0.02911513 [121,] -0.02911513 -0.02911513 [122,] -0.13368780 -0.02911513 [123,] -0.02911513 -0.13368780 [124,] -0.02911513 -0.02911513 [125,] -0.13368780 -0.02911513 [126,] -0.02911513 -0.13368780 [127,] -0.02911513 -0.02911513 [128,] -0.02911513 -0.02911513 [129,] -0.02911513 -0.02911513 [130,] -0.02911513 -0.02911513 [131,] -0.02911513 -0.02911513 [132,] -0.02911513 -0.02911513 [133,] -0.02911513 -0.02911513 [134,] -0.02911513 -0.02911513 [135,] -0.02911513 -0.02911513 [136,] -0.02911513 -0.02911513 [137,] -0.13368780 -0.02911513 [138,] -0.13368780 -0.13368780 [139,] -0.02911513 -0.13368780 [140,] 0.97088487 -0.02911513 [141,] -0.13368780 0.97088487 [142,] -0.02911513 -0.13368780 [143,] -0.02911513 -0.02911513 [144,] -0.02911513 -0.02911513 [145,] -0.13368780 -0.02911513 [146,] -0.13368780 -0.13368780 [147,] -0.13368780 -0.13368780 [148,] -0.02911513 -0.13368780 [149,] -0.02911513 -0.02911513 [150,] -0.02911513 -0.02911513 [151,] 0.97088487 -0.02911513 [152,] 0.97088487 0.97088487 [153,] -0.02911513 0.97088487 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.07166870 -0.16205685 2 -0.07166870 -0.07166870 3 -0.07166870 -0.07166870 4 -0.07166870 -0.07166870 5 -0.07166870 -0.07166870 6 -0.07166870 -0.07166870 7 -0.16205685 -0.07166870 8 -0.07166870 -0.16205685 9 -0.07166870 -0.07166870 10 -0.16205685 -0.07166870 11 -0.07166870 -0.16205685 12 -0.07166870 -0.07166870 13 -0.16205685 -0.07166870 14 -0.07166870 -0.16205685 15 -0.16205685 -0.07166870 16 0.83794315 -0.16205685 17 -0.16205685 0.83794315 18 -0.07166870 -0.16205685 19 0.83794315 -0.07166870 20 -0.07166870 0.83794315 21 -0.07166870 -0.07166870 22 -0.07166870 -0.07166870 23 -0.07166870 -0.07166870 24 -0.16205685 -0.07166870 25 -0.07166870 -0.16205685 26 -0.07166870 -0.07166870 27 -0.07166870 -0.07166870 28 -0.07166870 -0.07166870 29 -0.07166870 -0.07166870 30 -0.07166870 -0.07166870 31 -0.07166870 -0.07166870 32 -0.07166870 -0.07166870 33 -0.16205685 -0.07166870 34 -0.07166870 -0.16205685 35 -0.07166870 -0.07166870 36 -0.16205685 -0.07166870 37 -0.07166870 -0.16205685 38 -0.07166870 -0.07166870 39 -0.16205685 -0.07166870 40 0.92833130 -0.16205685 41 -0.07166870 0.92833130 42 -0.07166870 -0.07166870 43 -0.16205685 -0.07166870 44 -0.07166870 -0.16205685 45 -0.07166870 -0.07166870 46 -0.07166870 -0.07166870 47 -0.07166870 -0.07166870 48 -0.07166870 -0.07166870 49 -0.07166870 -0.07166870 50 -0.16205685 -0.07166870 51 0.83794315 -0.16205685 52 -0.07166870 0.83794315 53 0.92833130 -0.07166870 54 -0.07166870 0.92833130 55 -0.16205685 -0.07166870 56 -0.07166870 -0.16205685 57 -0.07166870 -0.07166870 58 -0.07166870 -0.07166870 59 0.83794315 -0.07166870 60 -0.16205685 0.83794315 61 -0.07166870 -0.16205685 62 -0.07166870 -0.07166870 63 -0.16205685 -0.07166870 64 -0.07166870 -0.16205685 65 -0.07166870 -0.07166870 66 0.83794315 -0.07166870 67 -0.07166870 0.83794315 68 -0.07166870 -0.07166870 69 -0.07166870 -0.07166870 70 -0.07166870 -0.07166870 71 -0.07166870 -0.07166870 72 -0.07166870 -0.07166870 73 -0.07166870 -0.07166870 74 -0.07166870 -0.07166870 75 -0.16205685 -0.07166870 76 -0.07166870 -0.16205685 77 -0.07166870 -0.07166870 78 0.83794315 -0.07166870 79 -0.16205685 0.83794315 80 -0.07166870 -0.16205685 81 -0.07166870 -0.07166870 82 -0.07166870 -0.07166870 83 0.92833130 -0.07166870 84 -0.07166870 0.92833130 85 -0.07166870 -0.07166870 86 -0.02911513 -0.07166870 87 -0.13368780 -0.02911513 88 -0.02911513 -0.13368780 89 -0.02911513 -0.02911513 90 -0.02911513 -0.02911513 91 -0.13368780 -0.02911513 92 -0.02911513 -0.13368780 93 -0.02911513 -0.02911513 94 -0.13368780 -0.02911513 95 -0.02911513 -0.13368780 96 -0.13368780 -0.02911513 97 -0.02911513 -0.13368780 98 -0.02911513 -0.02911513 99 -0.02911513 -0.02911513 100 -0.02911513 -0.02911513 101 -0.02911513 -0.02911513 102 -0.02911513 -0.02911513 103 -0.02911513 -0.02911513 104 -0.13368780 -0.02911513 105 -0.02911513 -0.13368780 106 -0.02911513 -0.02911513 107 -0.13368780 -0.02911513 108 -0.02911513 -0.13368780 109 -0.02911513 -0.02911513 110 -0.13368780 -0.02911513 111 -0.13368780 -0.13368780 112 -0.02911513 -0.13368780 113 -0.13368780 -0.02911513 114 -0.02911513 -0.13368780 115 -0.02911513 -0.02911513 116 -0.02911513 -0.02911513 117 -0.02911513 -0.02911513 118 -0.02911513 -0.02911513 119 -0.02911513 -0.02911513 120 -0.02911513 -0.02911513 121 -0.02911513 -0.02911513 122 -0.13368780 -0.02911513 123 -0.02911513 -0.13368780 124 -0.02911513 -0.02911513 125 -0.13368780 -0.02911513 126 -0.02911513 -0.13368780 127 -0.02911513 -0.02911513 128 -0.02911513 -0.02911513 129 -0.02911513 -0.02911513 130 -0.02911513 -0.02911513 131 -0.02911513 -0.02911513 132 -0.02911513 -0.02911513 133 -0.02911513 -0.02911513 134 -0.02911513 -0.02911513 135 -0.02911513 -0.02911513 136 -0.02911513 -0.02911513 137 -0.13368780 -0.02911513 138 -0.13368780 -0.13368780 139 -0.02911513 -0.13368780 140 0.97088487 -0.02911513 141 -0.13368780 0.97088487 142 -0.02911513 -0.13368780 143 -0.02911513 -0.02911513 144 -0.02911513 -0.02911513 145 -0.13368780 -0.02911513 146 -0.13368780 -0.13368780 147 -0.13368780 -0.13368780 148 -0.02911513 -0.13368780 149 -0.02911513 -0.02911513 150 -0.02911513 -0.02911513 151 0.97088487 -0.02911513 152 0.97088487 0.97088487 153 -0.02911513 0.97088487 > 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/7v03x1356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/81chs1356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9iqv81356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10lcn41356092924.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/119wxs1356092924.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/12gq0x1356092924.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/13oqu91356092924.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/1450f61356092924.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/15omng1356092924.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/16st8f1356092924.tab") + } > > try(system("convert tmp/1tkyt1356092924.ps tmp/1tkyt1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/2teuj1356092924.ps tmp/2teuj1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/3jlig1356092924.ps tmp/3jlig1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/49bd51356092924.ps tmp/49bd51356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/508gh1356092924.ps tmp/508gh1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/6nddk1356092924.ps tmp/6nddk1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/7v03x1356092924.ps tmp/7v03x1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/81chs1356092924.ps tmp/81chs1356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/9iqv81356092924.ps tmp/9iqv81356092924.png",intern=TRUE)) character(0) > try(system("convert tmp/10lcn41356092924.ps tmp/10lcn41356092924.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.456 1.752 9.253