R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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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(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 = '9' > #'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.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 PS CM CM_G D D_G PE PE_G PC PC_G PS_G O O_G H H_G 1 24 24 1 14 1 11 1 12 1 1 26 1 10 1 2 25 25 0 11 0 7 0 8 0 0 23 0 14 0 3 30 17 0 6 0 17 0 8 0 0 25 0 18 0 4 19 18 1 12 1 10 1 8 1 1 23 1 15 1 5 22 18 0 8 0 12 0 9 0 0 19 0 18 0 6 22 16 0 10 0 12 0 7 0 0 29 0 11 0 7 25 20 0 10 0 11 0 4 0 0 25 0 17 0 8 23 16 0 11 0 11 0 11 0 0 21 0 19 0 9 17 18 0 16 0 12 0 7 0 0 22 0 7 0 10 21 17 0 11 0 13 0 7 0 0 25 0 12 0 11 19 23 1 13 1 14 1 12 1 1 24 1 13 1 12 19 30 0 12 0 16 0 10 0 0 18 0 15 0 13 15 23 0 8 0 11 0 10 0 0 22 0 14 0 14 16 18 0 12 0 10 0 8 0 0 15 0 14 0 15 23 15 1 11 1 11 1 8 1 1 22 1 16 1 16 27 12 1 4 1 15 1 4 1 1 28 1 16 1 17 22 21 0 9 0 9 0 9 0 0 20 0 12 0 18 14 15 1 8 1 11 1 8 1 1 12 1 12 1 19 22 20 1 8 1 17 1 7 1 1 24 1 13 1 20 23 31 0 14 0 17 0 11 0 0 20 0 16 0 21 23 27 0 15 0 11 0 9 0 0 21 0 9 0 22 19 21 0 9 0 14 0 13 0 0 21 0 11 0 23 18 31 1 14 1 10 1 8 1 1 23 1 12 1 24 20 19 1 11 1 11 1 8 1 1 28 1 11 1 25 23 16 0 8 0 15 0 9 0 0 24 0 14 0 26 25 20 0 9 0 15 0 6 0 0 24 0 18 0 27 19 21 1 9 1 13 1 9 1 1 24 1 11 1 28 24 22 1 9 1 16 1 9 1 1 23 1 14 1 29 22 17 0 9 0 13 0 6 0 0 23 0 17 0 30 26 25 0 16 0 18 0 16 0 0 24 0 12 0 31 29 26 0 11 0 18 0 5 0 0 18 0 14 0 32 32 25 0 8 0 12 0 7 0 0 25 0 14 0 33 25 17 0 9 0 17 0 9 0 0 21 0 15 0 34 29 32 1 16 1 9 1 6 1 1 26 1 11 1 35 28 33 1 11 1 9 1 6 1 1 22 1 15 1 36 17 13 1 16 1 12 1 5 1 1 22 1 14 1 37 28 32 0 12 0 18 0 12 0 0 22 0 11 0 38 29 25 1 12 1 12 1 7 1 1 23 1 12 1 39 26 29 1 14 1 18 1 10 1 1 30 1 17 1 40 25 22 0 9 0 14 0 9 0 0 23 0 15 0 41 14 18 1 10 1 15 1 8 1 1 17 1 9 1 42 25 17 0 9 0 16 0 5 0 0 23 0 16 0 43 26 20 1 10 1 10 1 8 1 1 23 1 13 1 44 20 15 1 12 1 11 1 8 1 1 25 1 15 1 45 18 20 0 14 0 14 0 10 0 0 24 0 11 0 46 32 33 1 14 1 9 1 6 1 1 24 1 10 1 47 25 29 0 10 0 12 0 8 0 0 23 0 16 0 48 25 23 0 14 0 17 0 7 0 0 21 0 13 0 49 23 26 1 16 1 5 1 4 1 1 24 1 9 1 50 21 18 1 9 1 12 1 8 1 1 24 1 14 1 51 20 20 0 10 0 12 0 8 0 0 28 0 16 0 52 15 11 0 6 0 6 0 4 0 0 16 0 15 0 53 30 28 1 8 1 24 1 20 1 1 20 1 14 1 54 24 26 0 13 0 12 0 8 0 0 29 0 13 0 55 26 22 0 10 0 12 0 8 0 0 27 0 14 0 56 24 17 1 8 1 14 1 6 1 1 22 1 16 1 57 22 12 1 7 1 7 1 4 1 1 28 1 15 1 58 14 14 0 15 0 13 0 8 0 0 16 0 16 0 59 24 17 1 9 1 12 1 9 1 1 25 1 15 1 60 24 21 1 10 1 13 1 6 1 1 24 1 13 1 61 24 19 0 12 0 14 0 7 0 0 28 0 11 0 62 24 18 1 13 1 8 1 9 1 1 24 1 16 1 63 19 10 1 10 1 11 1 5 1 1 23 1 17 1 64 31 29 1 11 1 9 1 5 1 1 30 1 10 1 65 22 31 1 8 1 11 1 8 1 1 24 1 17 1 66 27 19 1 9 1 13 1 8 1 1 21 1 11 1 67 19 9 1 13 1 10 1 6 1 1 25 1 14 1 68 25 20 0 11 0 11 0 8 0 0 25 0 15 0 69 20 28 0 8 0 12 0 7 0 0 22 0 16 0 70 21 19 0 9 0 9 0 7 0 0 23 0 15 0 71 27 30 0 9 0 15 0 9 0 0 26 0 16 0 72 23 29 0 15 0 18 0 11 0 0 23 0 15 0 73 25 26 0 9 0 15 0 6 0 0 25 0 14 0 74 20 23 0 10 0 12 0 8 0 0 21 0 17 0 75 22 21 0 12 0 14 0 9 0 0 24 0 12 0 76 23 19 1 12 1 10 1 8 1 1 29 1 12 1 77 25 28 0 11 0 13 0 6 0 0 22 0 9 0 78 25 23 0 14 0 13 0 10 0 0 27 0 12 0 79 17 18 0 6 0 11 0 8 0 0 26 0 17 0 80 19 21 1 12 1 13 1 8 1 1 22 1 11 1 81 25 20 0 8 0 16 0 10 0 0 24 0 16 0 82 19 23 1 14 1 8 1 5 1 1 27 1 9 1 83 20 21 1 11 1 16 1 7 1 1 24 1 15 1 84 26 21 0 10 0 11 0 5 0 0 24 0 17 0 85 23 15 1 14 1 9 1 8 1 1 29 1 17 1 86 27 28 0 12 0 16 0 14 0 0 22 0 12 0 87 17 19 1 10 1 12 1 7 1 1 21 1 15 1 88 17 26 1 14 1 14 1 8 1 1 24 1 18 1 89 17 16 1 11 1 9 1 5 1 1 23 1 13 1 90 22 22 0 10 0 15 0 6 0 0 20 0 15 0 91 21 19 1 9 1 11 1 10 1 1 27 1 16 1 92 32 31 0 10 0 21 0 12 0 0 26 0 17 0 93 21 31 1 16 1 14 1 9 1 1 25 1 15 1 94 21 29 0 13 0 18 0 12 0 0 21 0 13 0 95 18 19 1 9 1 12 1 7 1 1 21 1 12 1 96 18 22 0 10 0 13 0 8 0 0 19 0 11 0 97 23 23 0 10 0 15 0 10 0 0 21 0 15 0 98 19 15 1 7 1 12 1 6 1 1 21 1 15 1 99 20 20 0 9 0 19 0 10 0 0 16 0 15 0 100 21 18 0 8 0 15 0 10 0 0 22 0 18 0 101 20 23 1 14 1 11 1 10 1 1 29 1 16 1 102 17 25 0 14 0 11 0 5 0 0 15 0 12 0 103 18 21 0 8 0 10 0 7 0 0 17 0 16 0 104 19 24 0 9 0 13 0 10 0 0 15 0 15 0 105 22 25 0 14 0 15 0 11 0 0 21 0 15 0 106 15 17 1 14 1 12 1 6 1 1 21 1 15 1 107 14 13 0 8 0 12 0 7 0 0 19 0 17 0 108 18 28 0 8 0 16 0 12 0 0 24 0 15 0 109 24 21 1 8 1 9 1 11 1 1 20 1 13 1 110 35 25 0 7 0 18 0 11 0 0 17 0 16 0 111 29 9 0 6 0 8 0 11 0 0 23 0 13 0 112 21 16 0 8 0 13 0 5 0 0 24 0 13 0 113 20 17 1 11 1 9 1 6 1 1 19 1 15 1 114 22 25 1 14 1 15 1 9 1 1 24 1 13 1 115 13 20 1 11 1 8 1 4 1 1 13 1 16 1 116 26 29 0 11 0 7 0 4 0 0 22 0 14 0 117 17 14 0 11 0 12 0 7 0 0 16 0 15 0 118 25 22 0 14 0 14 0 11 0 0 19 0 11 0 119 20 15 0 8 0 6 0 6 0 0 25 0 15 0 120 19 19 1 20 1 8 1 7 1 1 25 1 14 1 121 21 20 1 11 1 17 1 8 1 1 23 1 14 1 122 22 15 0 8 0 10 0 4 0 0 24 0 17 0 123 24 20 0 11 0 11 0 8 0 0 26 0 15 0 124 21 18 0 10 0 14 0 9 0 0 26 0 14 0 125 26 33 0 14 0 11 0 8 0 0 25 0 15 0 126 24 22 0 11 0 13 0 11 0 0 18 0 13 0 127 16 16 0 9 0 12 0 8 0 0 21 0 15 0 128 23 17 1 9 1 11 1 5 1 1 26 1 16 1 129 18 16 0 8 0 9 0 4 0 0 23 0 12 0 130 16 21 1 10 1 12 1 8 1 1 23 1 14 1 131 26 26 1 13 1 20 1 10 1 1 22 1 12 1 132 19 18 0 13 0 12 0 6 0 0 20 0 14 0 133 21 18 0 12 0 13 0 9 0 0 13 0 14 0 134 21 17 0 8 0 12 0 9 0 0 24 0 15 0 135 22 22 0 13 0 12 0 13 0 0 15 0 13 0 136 23 30 0 14 0 9 0 9 0 0 14 0 15 0 137 29 30 0 12 0 15 0 10 0 0 22 0 16 0 138 21 24 0 14 0 24 0 20 0 0 10 0 10 0 139 21 21 1 15 1 7 1 5 1 1 24 1 8 1 140 23 21 0 13 0 17 0 11 0 0 22 0 15 0 141 27 29 0 16 0 11 0 6 0 0 24 0 14 0 142 25 31 0 9 0 17 0 9 0 0 19 0 13 0 143 21 20 0 9 0 11 0 7 0 0 20 0 15 0 144 10 16 0 9 0 12 0 9 0 0 13 0 13 0 145 20 22 0 8 0 14 0 10 0 0 20 0 14 0 146 26 20 0 7 0 11 0 9 0 0 22 0 19 0 147 24 28 0 16 0 16 0 8 0 0 24 0 17 0 148 29 38 0 11 0 21 0 7 0 0 29 0 16 0 149 19 22 0 9 0 14 0 6 0 0 12 0 16 0 150 24 20 0 11 0 20 0 13 0 0 20 0 14 0 151 19 17 0 9 0 13 0 6 0 0 21 0 12 0 152 24 28 1 14 1 11 1 8 1 1 24 1 13 1 153 22 22 0 13 0 15 0 10 0 0 22 0 14 0 154 17 31 0 16 0 19 0 16 0 0 20 0 15 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) CM CM_G D D_G PE 7.28551 0.32987 -0.65833 -0.33487 NA 0.15976 PE_G PC PC_G PS_G O O_G NA 0.03320 NA NA 0.43212 NA H H_G -0.02515 NA > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.79116 -2.20346 0.04065 2.19529 11.62723 Coefficients: (6 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 7.28551 3.10120 2.349 0.02015 * CM 0.32987 0.05801 5.686 6.84e-08 *** CM_G -0.65833 0.62291 -1.057 0.29232 D -0.33487 0.11797 -2.839 0.00518 ** D_G NA NA NA NA PE 0.15976 0.10565 1.512 0.13266 PE_G NA NA NA NA PC 0.03320 0.13125 0.253 0.80064 PC_G NA NA NA NA PS_G NA NA NA NA O 0.43212 0.07645 5.652 8.04e-08 *** O_G NA NA NA NA H -0.02515 0.12946 -0.194 0.84626 H_G NA NA NA NA --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.439 on 146 degrees of freedom Multiple R-squared: 0.379, Adjusted R-squared: 0.3493 F-statistic: 12.73 on 7 and 146 DF, p-value: 1.064e-12 > 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,] 0.96434849 0.07130303 0.03565151 [2,] 0.92743336 0.14513328 0.07256664 [3,] 0.87171124 0.25657751 0.12828876 [4,] 0.81084098 0.37831803 0.18915902 [5,] 0.82646307 0.34707385 0.17353693 [6,] 0.75940551 0.48118898 0.24059449 [7,] 0.74576447 0.50847107 0.25423553 [8,] 0.69824023 0.60351955 0.30175977 [9,] 0.61400898 0.77198204 0.38599102 [10,] 0.53300271 0.93399457 0.46699729 [11,] 0.46813006 0.93626012 0.53186994 [12,] 0.42190025 0.84380050 0.57809975 [13,] 0.36734223 0.73468445 0.63265777 [14,] 0.34292556 0.68585111 0.65707444 [15,] 0.53460296 0.93079407 0.46539704 [16,] 0.74182197 0.51635607 0.25817803 [17,] 0.70530729 0.58938543 0.29469271 [18,] 0.76453674 0.47092652 0.23546326 [19,] 0.74738989 0.50522023 0.25261011 [20,] 0.70643701 0.58712599 0.29356299 [21,] 0.66952905 0.66094190 0.33047095 [22,] 0.76972463 0.46055074 0.23027537 [23,] 0.76545223 0.46909553 0.23454777 [24,] 0.71830484 0.56339032 0.28169516 [25,] 0.72242527 0.55514945 0.27757473 [26,] 0.69073002 0.61853995 0.30926998 [27,] 0.73305919 0.53388162 0.26694081 [28,] 0.68313165 0.63373670 0.31686835 [29,] 0.68244226 0.63511547 0.31755774 [30,] 0.78395144 0.43209713 0.21604856 [31,] 0.75930648 0.48138705 0.24069352 [32,] 0.73768028 0.52463944 0.26231972 [33,] 0.69405694 0.61188612 0.30594306 [34,] 0.64614379 0.70771242 0.35385621 [35,] 0.70930332 0.58139336 0.29069668 [36,] 0.67527710 0.64944579 0.32472290 [37,] 0.73198392 0.53603215 0.26801608 [38,] 0.70533249 0.58933502 0.29466751 [39,] 0.66214032 0.67571935 0.33785968 [40,] 0.64069012 0.71861976 0.35930988 [41,] 0.59268374 0.81463253 0.40731626 [42,] 0.55146476 0.89707047 0.44853524 [43,] 0.51781171 0.96437658 0.48218829 [44,] 0.47628625 0.95257251 0.52371375 [45,] 0.42470905 0.84941810 0.57529095 [46,] 0.44343232 0.88686463 0.55656768 [47,] 0.40315720 0.80631440 0.59684280 [48,] 0.41181180 0.82362360 0.58818820 [49,] 0.49101570 0.98203139 0.50898430 [50,] 0.60299859 0.79400282 0.39700141 [51,] 0.56475337 0.87049326 0.43524663 [52,] 0.53432567 0.93134867 0.46567433 [53,] 0.60321891 0.79356219 0.39678109 [54,] 0.55528093 0.88943814 0.44471907 [55,] 0.51073556 0.97852888 0.48926444 [56,] 0.47655128 0.95310255 0.52344872 [57,] 0.43556757 0.87113513 0.56443243 [58,] 0.40885786 0.81771573 0.59114214 [59,] 0.36228472 0.72456943 0.63771528 [60,] 0.31890324 0.63780647 0.68109676 [61,] 0.27759057 0.55518114 0.72240943 [62,] 0.24079564 0.48159128 0.75920436 [63,] 0.35561538 0.71123077 0.64438462 [64,] 0.32715721 0.65431441 0.67284279 [65,] 0.28734384 0.57468769 0.71265616 [66,] 0.28299832 0.56599664 0.71700168 [67,] 0.27459183 0.54918366 0.72540817 [68,] 0.26899913 0.53799825 0.73100087 [69,] 0.24974470 0.49948940 0.75025530 [70,] 0.22848770 0.45697539 0.77151230 [71,] 0.22072052 0.44144105 0.77927948 [72,] 0.28748978 0.57497956 0.71251022 [73,] 0.25930674 0.51861347 0.74069326 [74,] 0.22142621 0.44285241 0.77857379 [75,] 0.19503129 0.39006257 0.80496871 [76,] 0.19565403 0.39130807 0.80434597 [77,] 0.18904049 0.37808098 0.81095951 [78,] 0.18482614 0.36965228 0.81517386 [79,] 0.16597519 0.33195038 0.83402481 [80,] 0.16275685 0.32551370 0.83724315 [81,] 0.13251670 0.26503340 0.86748330 [82,] 0.10755938 0.21511876 0.89244062 [83,] 0.08518580 0.17037159 0.91481420 [84,] 0.06723647 0.13447293 0.93276353 [85,] 0.06741508 0.13483016 0.93258492 [86,] 0.05520439 0.11040878 0.94479561 [87,] 0.04631520 0.09263040 0.95368480 [88,] 0.03804975 0.07609949 0.96195025 [89,] 0.02830998 0.05661995 0.97169002 [90,] 0.02494343 0.04988687 0.97505657 [91,] 0.02931385 0.05862770 0.97068615 [92,] 0.12140084 0.24280167 0.87859916 [93,] 0.10633456 0.21266912 0.89366544 [94,] 0.52570515 0.94858971 0.47429485 [95,] 0.81614710 0.36770581 0.18385290 [96,] 0.77412439 0.45175122 0.22587561 [97,] 0.75022570 0.49954861 0.24977430 [98,] 0.69817342 0.60365317 0.30182658 [99,] 0.69590442 0.60819116 0.30409558 [100,] 0.65235974 0.69528052 0.34764026 [101,] 0.59062975 0.81874049 0.40937025 [102,] 0.62240986 0.75518028 0.37759014 [103,] 0.55724305 0.88551389 0.44275695 [104,] 0.50330788 0.99338425 0.49669212 [105,] 0.43544532 0.87089063 0.56455468 [106,] 0.37613392 0.75226784 0.62386608 [107,] 0.32147606 0.64295211 0.67852394 [108,] 0.26287814 0.52575627 0.73712186 [109,] 0.20648547 0.41297094 0.79351453 [110,] 0.19771632 0.39543264 0.80228368 [111,] 0.19186668 0.38373336 0.80813332 [112,] 0.14945564 0.29891128 0.85054436 [113,] 0.11449297 0.22898593 0.88550703 [114,] 0.17835002 0.35670003 0.82164998 [115,] 0.13827741 0.27655482 0.86172259 [116,] 0.09413506 0.18827012 0.90586494 [117,] 0.08045972 0.16091943 0.91954028 [118,] 0.04950035 0.09900070 0.95049965 [119,] 0.03294465 0.06588929 0.96705535 [120,] 0.01983011 0.03966022 0.98016989 [121,] 0.01816745 0.03633490 0.98183255 > postscript(file="/var/www/html/rcomp/tmp/1suvn1291969915.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/223cq1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/323cq1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/423cq1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/523cq1291969915.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 1.004705251 2.180666005 5.784089268 -0.971146301 1.482225482 -1.619044795 7 8 9 10 11 12 2.200161236 3.400846743 -2.345346969 -1.020199788 -3.539895549 -4.452332212 13 14 15 16 17 18 -8.437517511 -1.197696571 3.981108643 3.527739290 0.723757978 -1.802932079 19 20 21 22 23 24 -1.537873054 -1.144511812 0.926671564 -3.665103663 -5.665201824 -3.056803247 25 26 27 28 29 30 0.401546773 1.617115535 -4.010548954 0.687857622 0.333220479 2.349651838 31 32 33 34 35 36 6.353656334 6.546246015 3.408519963 4.579339787 3.404156220 0.204773184 37 38 39 40 41 42 1.672956656 6.357991910 -1.248996302 1.374190795 -4.997851314 2.862009396 43 44 45 46 47 48 4.649075223 -0.005511595 -3.857610987 7.418810512 -0.222192954 3.119745967 49 50 51 52 53 54 2.077958470 -0.752530574 -4.413903507 -1.532925747 4.026787064 -1.896086897 55 56 57 58 59 60 1.308173403 2.903888961 0.785258605 -1.734685538 2.137169043 1.474224827 61 62 63 64 65 66 0.173675495 4.243063126 0.988251133 4.174209498 -4.140568543 5.978748353 67 68 69 70 71 72 1.509622073 2.351923382 -5.096739957 -0.770995048 -0.695756827 -1.631141082 73 74 75 76 77 78 -0.894823554 -2.353574445 -0.798875561 0.030853822 0.605301672 1.041324211 79 80 81 82 83 84 -7.044504337 -2.108503947 0.939380601 -3.410980117 -2.653089247 3.269198040 85 86 87 88 89 90 2.305572419 2.270702034 -3.392839477 -5.936103314 -2.437189221 -0.054737421 91 92 93 94 95 96 -2.235111233 3.276230963 -3.456487401 -3.520146107 -2.803145281 -3.470098498 97 98 99 100 101 102 0.050456067 -1.044750118 -0.773244221 -1.326594367 -3.744485995 -1.947507272 103 104 105 106 107 108 -2.307534103 -1.702084074 -0.303015406 -3.360406709 -4.827141629 -8.791164302 109 110 111 112 113 114 3.005949788 11.627228656 9.499781825 -1.171267342 1.978483196 -0.924916675 115 116 117 118 119 120 -3.167137254 2.426103207 0.093649753 4.610012203 -1.138125223 0.841283701 121 122 123 124 125 126 -1.109206689 0.771662040 0.919808320 -2.292933995 0.068173664 3.247563963 127 128 129 130 131 132 -4.429618001 1.022773538 -3.092066075 -5.975166936 2.417436037 -0.276506436 133 134 135 136 137 138 4.154058890 -1.423911608 3.306997434 3.097370045 4.004109510 1.917747696 139 140 141 142 143 144 1.014594496 0.929981307 3.530788077 -0.395774408 -0.124037234 -7.056192330 145 146 147 148 149 150 -2.722683791 3.376164380 0.070905010 -2.853484323 0.252214883 1.883520694 151 152 153 154 -1.928275467 0.757693158 -0.072319861 -6.985452198 > postscript(file="/var/www/html/rcomp/tmp/6vdub1291969915.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 1.004705251 NA 1 2.180666005 1.004705251 2 5.784089268 2.180666005 3 -0.971146301 5.784089268 4 1.482225482 -0.971146301 5 -1.619044795 1.482225482 6 2.200161236 -1.619044795 7 3.400846743 2.200161236 8 -2.345346969 3.400846743 9 -1.020199788 -2.345346969 10 -3.539895549 -1.020199788 11 -4.452332212 -3.539895549 12 -8.437517511 -4.452332212 13 -1.197696571 -8.437517511 14 3.981108643 -1.197696571 15 3.527739290 3.981108643 16 0.723757978 3.527739290 17 -1.802932079 0.723757978 18 -1.537873054 -1.802932079 19 -1.144511812 -1.537873054 20 0.926671564 -1.144511812 21 -3.665103663 0.926671564 22 -5.665201824 -3.665103663 23 -3.056803247 -5.665201824 24 0.401546773 -3.056803247 25 1.617115535 0.401546773 26 -4.010548954 1.617115535 27 0.687857622 -4.010548954 28 0.333220479 0.687857622 29 2.349651838 0.333220479 30 6.353656334 2.349651838 31 6.546246015 6.353656334 32 3.408519963 6.546246015 33 4.579339787 3.408519963 34 3.404156220 4.579339787 35 0.204773184 3.404156220 36 1.672956656 0.204773184 37 6.357991910 1.672956656 38 -1.248996302 6.357991910 39 1.374190795 -1.248996302 40 -4.997851314 1.374190795 41 2.862009396 -4.997851314 42 4.649075223 2.862009396 43 -0.005511595 4.649075223 44 -3.857610987 -0.005511595 45 7.418810512 -3.857610987 46 -0.222192954 7.418810512 47 3.119745967 -0.222192954 48 2.077958470 3.119745967 49 -0.752530574 2.077958470 50 -4.413903507 -0.752530574 51 -1.532925747 -4.413903507 52 4.026787064 -1.532925747 53 -1.896086897 4.026787064 54 1.308173403 -1.896086897 55 2.903888961 1.308173403 56 0.785258605 2.903888961 57 -1.734685538 0.785258605 58 2.137169043 -1.734685538 59 1.474224827 2.137169043 60 0.173675495 1.474224827 61 4.243063126 0.173675495 62 0.988251133 4.243063126 63 4.174209498 0.988251133 64 -4.140568543 4.174209498 65 5.978748353 -4.140568543 66 1.509622073 5.978748353 67 2.351923382 1.509622073 68 -5.096739957 2.351923382 69 -0.770995048 -5.096739957 70 -0.695756827 -0.770995048 71 -1.631141082 -0.695756827 72 -0.894823554 -1.631141082 73 -2.353574445 -0.894823554 74 -0.798875561 -2.353574445 75 0.030853822 -0.798875561 76 0.605301672 0.030853822 77 1.041324211 0.605301672 78 -7.044504337 1.041324211 79 -2.108503947 -7.044504337 80 0.939380601 -2.108503947 81 -3.410980117 0.939380601 82 -2.653089247 -3.410980117 83 3.269198040 -2.653089247 84 2.305572419 3.269198040 85 2.270702034 2.305572419 86 -3.392839477 2.270702034 87 -5.936103314 -3.392839477 88 -2.437189221 -5.936103314 89 -0.054737421 -2.437189221 90 -2.235111233 -0.054737421 91 3.276230963 -2.235111233 92 -3.456487401 3.276230963 93 -3.520146107 -3.456487401 94 -2.803145281 -3.520146107 95 -3.470098498 -2.803145281 96 0.050456067 -3.470098498 97 -1.044750118 0.050456067 98 -0.773244221 -1.044750118 99 -1.326594367 -0.773244221 100 -3.744485995 -1.326594367 101 -1.947507272 -3.744485995 102 -2.307534103 -1.947507272 103 -1.702084074 -2.307534103 104 -0.303015406 -1.702084074 105 -3.360406709 -0.303015406 106 -4.827141629 -3.360406709 107 -8.791164302 -4.827141629 108 3.005949788 -8.791164302 109 11.627228656 3.005949788 110 9.499781825 11.627228656 111 -1.171267342 9.499781825 112 1.978483196 -1.171267342 113 -0.924916675 1.978483196 114 -3.167137254 -0.924916675 115 2.426103207 -3.167137254 116 0.093649753 2.426103207 117 4.610012203 0.093649753 118 -1.138125223 4.610012203 119 0.841283701 -1.138125223 120 -1.109206689 0.841283701 121 0.771662040 -1.109206689 122 0.919808320 0.771662040 123 -2.292933995 0.919808320 124 0.068173664 -2.292933995 125 3.247563963 0.068173664 126 -4.429618001 3.247563963 127 1.022773538 -4.429618001 128 -3.092066075 1.022773538 129 -5.975166936 -3.092066075 130 2.417436037 -5.975166936 131 -0.276506436 2.417436037 132 4.154058890 -0.276506436 133 -1.423911608 4.154058890 134 3.306997434 -1.423911608 135 3.097370045 3.306997434 136 4.004109510 3.097370045 137 1.917747696 4.004109510 138 1.014594496 1.917747696 139 0.929981307 1.014594496 140 3.530788077 0.929981307 141 -0.395774408 3.530788077 142 -0.124037234 -0.395774408 143 -7.056192330 -0.124037234 144 -2.722683791 -7.056192330 145 3.376164380 -2.722683791 146 0.070905010 3.376164380 147 -2.853484323 0.070905010 148 0.252214883 -2.853484323 149 1.883520694 0.252214883 150 -1.928275467 1.883520694 151 0.757693158 -1.928275467 152 -0.072319861 0.757693158 153 -6.985452198 -0.072319861 154 NA -6.985452198 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.180666005 1.004705251 [2,] 5.784089268 2.180666005 [3,] -0.971146301 5.784089268 [4,] 1.482225482 -0.971146301 [5,] -1.619044795 1.482225482 [6,] 2.200161236 -1.619044795 [7,] 3.400846743 2.200161236 [8,] -2.345346969 3.400846743 [9,] -1.020199788 -2.345346969 [10,] -3.539895549 -1.020199788 [11,] -4.452332212 -3.539895549 [12,] -8.437517511 -4.452332212 [13,] -1.197696571 -8.437517511 [14,] 3.981108643 -1.197696571 [15,] 3.527739290 3.981108643 [16,] 0.723757978 3.527739290 [17,] -1.802932079 0.723757978 [18,] -1.537873054 -1.802932079 [19,] -1.144511812 -1.537873054 [20,] 0.926671564 -1.144511812 [21,] -3.665103663 0.926671564 [22,] -5.665201824 -3.665103663 [23,] -3.056803247 -5.665201824 [24,] 0.401546773 -3.056803247 [25,] 1.617115535 0.401546773 [26,] -4.010548954 1.617115535 [27,] 0.687857622 -4.010548954 [28,] 0.333220479 0.687857622 [29,] 2.349651838 0.333220479 [30,] 6.353656334 2.349651838 [31,] 6.546246015 6.353656334 [32,] 3.408519963 6.546246015 [33,] 4.579339787 3.408519963 [34,] 3.404156220 4.579339787 [35,] 0.204773184 3.404156220 [36,] 1.672956656 0.204773184 [37,] 6.357991910 1.672956656 [38,] -1.248996302 6.357991910 [39,] 1.374190795 -1.248996302 [40,] -4.997851314 1.374190795 [41,] 2.862009396 -4.997851314 [42,] 4.649075223 2.862009396 [43,] -0.005511595 4.649075223 [44,] -3.857610987 -0.005511595 [45,] 7.418810512 -3.857610987 [46,] -0.222192954 7.418810512 [47,] 3.119745967 -0.222192954 [48,] 2.077958470 3.119745967 [49,] -0.752530574 2.077958470 [50,] -4.413903507 -0.752530574 [51,] -1.532925747 -4.413903507 [52,] 4.026787064 -1.532925747 [53,] -1.896086897 4.026787064 [54,] 1.308173403 -1.896086897 [55,] 2.903888961 1.308173403 [56,] 0.785258605 2.903888961 [57,] -1.734685538 0.785258605 [58,] 2.137169043 -1.734685538 [59,] 1.474224827 2.137169043 [60,] 0.173675495 1.474224827 [61,] 4.243063126 0.173675495 [62,] 0.988251133 4.243063126 [63,] 4.174209498 0.988251133 [64,] -4.140568543 4.174209498 [65,] 5.978748353 -4.140568543 [66,] 1.509622073 5.978748353 [67,] 2.351923382 1.509622073 [68,] -5.096739957 2.351923382 [69,] -0.770995048 -5.096739957 [70,] -0.695756827 -0.770995048 [71,] -1.631141082 -0.695756827 [72,] -0.894823554 -1.631141082 [73,] -2.353574445 -0.894823554 [74,] -0.798875561 -2.353574445 [75,] 0.030853822 -0.798875561 [76,] 0.605301672 0.030853822 [77,] 1.041324211 0.605301672 [78,] -7.044504337 1.041324211 [79,] -2.108503947 -7.044504337 [80,] 0.939380601 -2.108503947 [81,] -3.410980117 0.939380601 [82,] -2.653089247 -3.410980117 [83,] 3.269198040 -2.653089247 [84,] 2.305572419 3.269198040 [85,] 2.270702034 2.305572419 [86,] -3.392839477 2.270702034 [87,] -5.936103314 -3.392839477 [88,] -2.437189221 -5.936103314 [89,] -0.054737421 -2.437189221 [90,] -2.235111233 -0.054737421 [91,] 3.276230963 -2.235111233 [92,] -3.456487401 3.276230963 [93,] -3.520146107 -3.456487401 [94,] -2.803145281 -3.520146107 [95,] -3.470098498 -2.803145281 [96,] 0.050456067 -3.470098498 [97,] -1.044750118 0.050456067 [98,] -0.773244221 -1.044750118 [99,] -1.326594367 -0.773244221 [100,] -3.744485995 -1.326594367 [101,] -1.947507272 -3.744485995 [102,] -2.307534103 -1.947507272 [103,] -1.702084074 -2.307534103 [104,] -0.303015406 -1.702084074 [105,] -3.360406709 -0.303015406 [106,] -4.827141629 -3.360406709 [107,] -8.791164302 -4.827141629 [108,] 3.005949788 -8.791164302 [109,] 11.627228656 3.005949788 [110,] 9.499781825 11.627228656 [111,] -1.171267342 9.499781825 [112,] 1.978483196 -1.171267342 [113,] -0.924916675 1.978483196 [114,] -3.167137254 -0.924916675 [115,] 2.426103207 -3.167137254 [116,] 0.093649753 2.426103207 [117,] 4.610012203 0.093649753 [118,] -1.138125223 4.610012203 [119,] 0.841283701 -1.138125223 [120,] -1.109206689 0.841283701 [121,] 0.771662040 -1.109206689 [122,] 0.919808320 0.771662040 [123,] -2.292933995 0.919808320 [124,] 0.068173664 -2.292933995 [125,] 3.247563963 0.068173664 [126,] -4.429618001 3.247563963 [127,] 1.022773538 -4.429618001 [128,] -3.092066075 1.022773538 [129,] -5.975166936 -3.092066075 [130,] 2.417436037 -5.975166936 [131,] -0.276506436 2.417436037 [132,] 4.154058890 -0.276506436 [133,] -1.423911608 4.154058890 [134,] 3.306997434 -1.423911608 [135,] 3.097370045 3.306997434 [136,] 4.004109510 3.097370045 [137,] 1.917747696 4.004109510 [138,] 1.014594496 1.917747696 [139,] 0.929981307 1.014594496 [140,] 3.530788077 0.929981307 [141,] -0.395774408 3.530788077 [142,] -0.124037234 -0.395774408 [143,] -7.056192330 -0.124037234 [144,] -2.722683791 -7.056192330 [145,] 3.376164380 -2.722683791 [146,] 0.070905010 3.376164380 [147,] -2.853484323 0.070905010 [148,] 0.252214883 -2.853484323 [149,] 1.883520694 0.252214883 [150,] -1.928275467 1.883520694 [151,] 0.757693158 -1.928275467 [152,] -0.072319861 0.757693158 [153,] -6.985452198 -0.072319861 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.180666005 1.004705251 2 5.784089268 2.180666005 3 -0.971146301 5.784089268 4 1.482225482 -0.971146301 5 -1.619044795 1.482225482 6 2.200161236 -1.619044795 7 3.400846743 2.200161236 8 -2.345346969 3.400846743 9 -1.020199788 -2.345346969 10 -3.539895549 -1.020199788 11 -4.452332212 -3.539895549 12 -8.437517511 -4.452332212 13 -1.197696571 -8.437517511 14 3.981108643 -1.197696571 15 3.527739290 3.981108643 16 0.723757978 3.527739290 17 -1.802932079 0.723757978 18 -1.537873054 -1.802932079 19 -1.144511812 -1.537873054 20 0.926671564 -1.144511812 21 -3.665103663 0.926671564 22 -5.665201824 -3.665103663 23 -3.056803247 -5.665201824 24 0.401546773 -3.056803247 25 1.617115535 0.401546773 26 -4.010548954 1.617115535 27 0.687857622 -4.010548954 28 0.333220479 0.687857622 29 2.349651838 0.333220479 30 6.353656334 2.349651838 31 6.546246015 6.353656334 32 3.408519963 6.546246015 33 4.579339787 3.408519963 34 3.404156220 4.579339787 35 0.204773184 3.404156220 36 1.672956656 0.204773184 37 6.357991910 1.672956656 38 -1.248996302 6.357991910 39 1.374190795 -1.248996302 40 -4.997851314 1.374190795 41 2.862009396 -4.997851314 42 4.649075223 2.862009396 43 -0.005511595 4.649075223 44 -3.857610987 -0.005511595 45 7.418810512 -3.857610987 46 -0.222192954 7.418810512 47 3.119745967 -0.222192954 48 2.077958470 3.119745967 49 -0.752530574 2.077958470 50 -4.413903507 -0.752530574 51 -1.532925747 -4.413903507 52 4.026787064 -1.532925747 53 -1.896086897 4.026787064 54 1.308173403 -1.896086897 55 2.903888961 1.308173403 56 0.785258605 2.903888961 57 -1.734685538 0.785258605 58 2.137169043 -1.734685538 59 1.474224827 2.137169043 60 0.173675495 1.474224827 61 4.243063126 0.173675495 62 0.988251133 4.243063126 63 4.174209498 0.988251133 64 -4.140568543 4.174209498 65 5.978748353 -4.140568543 66 1.509622073 5.978748353 67 2.351923382 1.509622073 68 -5.096739957 2.351923382 69 -0.770995048 -5.096739957 70 -0.695756827 -0.770995048 71 -1.631141082 -0.695756827 72 -0.894823554 -1.631141082 73 -2.353574445 -0.894823554 74 -0.798875561 -2.353574445 75 0.030853822 -0.798875561 76 0.605301672 0.030853822 77 1.041324211 0.605301672 78 -7.044504337 1.041324211 79 -2.108503947 -7.044504337 80 0.939380601 -2.108503947 81 -3.410980117 0.939380601 82 -2.653089247 -3.410980117 83 3.269198040 -2.653089247 84 2.305572419 3.269198040 85 2.270702034 2.305572419 86 -3.392839477 2.270702034 87 -5.936103314 -3.392839477 88 -2.437189221 -5.936103314 89 -0.054737421 -2.437189221 90 -2.235111233 -0.054737421 91 3.276230963 -2.235111233 92 -3.456487401 3.276230963 93 -3.520146107 -3.456487401 94 -2.803145281 -3.520146107 95 -3.470098498 -2.803145281 96 0.050456067 -3.470098498 97 -1.044750118 0.050456067 98 -0.773244221 -1.044750118 99 -1.326594367 -0.773244221 100 -3.744485995 -1.326594367 101 -1.947507272 -3.744485995 102 -2.307534103 -1.947507272 103 -1.702084074 -2.307534103 104 -0.303015406 -1.702084074 105 -3.360406709 -0.303015406 106 -4.827141629 -3.360406709 107 -8.791164302 -4.827141629 108 3.005949788 -8.791164302 109 11.627228656 3.005949788 110 9.499781825 11.627228656 111 -1.171267342 9.499781825 112 1.978483196 -1.171267342 113 -0.924916675 1.978483196 114 -3.167137254 -0.924916675 115 2.426103207 -3.167137254 116 0.093649753 2.426103207 117 4.610012203 0.093649753 118 -1.138125223 4.610012203 119 0.841283701 -1.138125223 120 -1.109206689 0.841283701 121 0.771662040 -1.109206689 122 0.919808320 0.771662040 123 -2.292933995 0.919808320 124 0.068173664 -2.292933995 125 3.247563963 0.068173664 126 -4.429618001 3.247563963 127 1.022773538 -4.429618001 128 -3.092066075 1.022773538 129 -5.975166936 -3.092066075 130 2.417436037 -5.975166936 131 -0.276506436 2.417436037 132 4.154058890 -0.276506436 133 -1.423911608 4.154058890 134 3.306997434 -1.423911608 135 3.097370045 3.306997434 136 4.004109510 3.097370045 137 1.917747696 4.004109510 138 1.014594496 1.917747696 139 0.929981307 1.014594496 140 3.530788077 0.929981307 141 -0.395774408 3.530788077 142 -0.124037234 -0.395774408 143 -7.056192330 -0.124037234 144 -2.722683791 -7.056192330 145 3.376164380 -2.722683791 146 0.070905010 3.376164380 147 -2.853484323 0.070905010 148 0.252214883 -2.853484323 149 1.883520694 0.252214883 150 -1.928275467 1.883520694 151 0.757693158 -1.928275467 152 -0.072319861 0.757693158 153 -6.985452198 -0.072319861 > 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/html/rcomp/tmp/76mbw1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/86mbw1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/96mbw1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10hvsz1291969915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } Error: subscript out of bounds Execution halted