R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows" 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(14 + ,12 + ,38 + ,41 + ,7 + ,2 + ,53 + ,18 + ,11 + ,32 + ,39 + ,5 + ,2 + ,86 + ,11 + ,14 + ,35 + ,30 + ,5 + ,2 + ,66 + ,12 + ,12 + ,33 + ,31 + ,5 + ,1 + ,67 + ,16 + ,21 + ,37 + ,34 + ,8 + ,2 + ,76 + ,18 + ,12 + ,29 + ,35 + ,6 + ,2 + ,78 + ,14 + ,22 + ,31 + ,39 + ,5 + ,2 + ,53 + ,14 + ,11 + ,36 + ,34 + ,6 + ,2 + ,80 + ,15 + ,10 + ,35 + ,36 + ,5 + ,2 + ,74 + ,15 + ,13 + ,38 + ,37 + ,4 + ,2 + ,76 + ,17 + ,10 + ,31 + ,38 + ,6 + ,1 + ,79 + ,19 + ,8 + ,34 + ,36 + ,5 + ,2 + ,54 + ,10 + ,15 + ,35 + ,38 + ,5 + ,1 + ,67 + ,16 + ,14 + ,38 + ,39 + ,6 + ,2 + ,54 + ,18 + ,10 + ,37 + ,33 + ,7 + ,2 + ,87 + ,14 + ,14 + ,33 + ,32 + ,6 + ,1 + ,58 + ,14 + ,14 + ,32 + ,36 + ,7 + ,1 + ,75 + ,17 + ,11 + ,38 + ,38 + ,6 + ,2 + ,88 + ,14 + ,10 + ,38 + ,39 + ,8 + ,1 + ,64 + ,16 + ,13 + ,32 + ,32 + ,7 + ,2 + ,57 + ,18 + ,7 + ,33 + ,32 + ,5 + ,1 + ,66 + ,11 + ,14 + ,31 + ,31 + ,5 + ,2 + ,68 + ,14 + ,12 + ,38 + ,39 + ,7 + ,2 + ,54 + ,12 + ,14 + ,39 + ,37 + ,7 + ,2 + ,56 + ,17 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+ ,7 + ,1 + ,73 + ,16 + ,12 + ,39 + ,38 + ,6 + ,2 + ,85 + ,13 + ,17 + ,37 + ,42 + ,7 + ,2 + ,79 + ,16 + ,9 + ,38 + ,34 + ,10 + ,1 + ,71 + ,12 + ,12 + ,39 + ,35 + ,6 + ,2 + ,72 + ,9 + ,19 + ,34 + ,35 + ,8 + ,2 + ,69 + ,13 + ,18 + ,31 + ,33 + ,4 + ,2 + ,78 + ,13 + ,15 + ,32 + ,36 + ,5 + ,2 + ,54 + ,14 + ,14 + ,37 + ,32 + ,6 + ,2 + ,69 + ,19 + ,11 + ,36 + ,33 + ,7 + ,2 + ,81 + ,13 + ,9 + ,32 + ,34 + ,7 + ,2 + ,84 + ,12 + ,18 + ,35 + ,32 + ,6 + ,2 + ,84 + ,13 + ,16 + ,36 + ,34 + ,6 + ,2 + ,69) + ,dim=c(7 + ,162) + ,dimnames=list(c('Y_t' + ,'b_1' + ,'b_2' + ,'b_3' + ,'b_4' + ,'b_5' + ,'b_6') + ,1:162)) > y <- array(NA,dim=c(7,162),dimnames=list(c('Y_t','b_1','b_2','b_3','b_4','b_5','b_6'),1:162)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'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 Y_t b_1 b_2 b_3 b_4 b_5 b_6 t 1 14 12 38 41 7 2 53 1 2 18 11 32 39 5 2 86 2 3 11 14 35 30 5 2 66 3 4 12 12 33 31 5 1 67 4 5 16 21 37 34 8 2 76 5 6 18 12 29 35 6 2 78 6 7 14 22 31 39 5 2 53 7 8 14 11 36 34 6 2 80 8 9 15 10 35 36 5 2 74 9 10 15 13 38 37 4 2 76 10 11 17 10 31 38 6 1 79 11 12 19 8 34 36 5 2 54 12 13 10 15 35 38 5 1 67 13 14 16 14 38 39 6 2 54 14 15 18 10 37 33 7 2 87 15 16 14 14 33 32 6 1 58 16 17 14 14 32 36 7 1 75 17 18 17 11 38 38 6 2 88 18 19 14 10 38 39 8 1 64 19 20 16 13 32 32 7 2 57 20 21 18 7 33 32 5 1 66 21 22 11 14 31 31 5 2 68 22 23 14 12 38 39 7 2 54 23 24 12 14 39 37 7 2 56 24 25 17 11 32 39 5 1 86 25 26 9 9 32 41 4 2 80 26 27 16 11 35 36 10 1 76 27 28 14 15 37 33 6 2 69 28 29 15 14 33 33 5 2 78 29 30 11 13 33 34 5 1 67 30 31 16 9 28 31 5 2 80 31 32 13 15 32 27 5 1 54 32 33 17 10 31 37 6 2 71 33 34 15 11 37 34 5 2 84 34 35 14 13 30 34 5 1 74 35 36 16 8 33 32 5 1 71 36 37 9 20 31 29 5 1 63 37 38 15 12 33 36 5 1 71 38 39 17 10 31 29 5 2 76 39 40 13 10 33 35 5 1 69 40 41 15 9 32 37 5 1 74 41 42 16 14 33 34 7 2 75 42 43 16 8 32 38 5 1 54 43 44 12 14 33 35 6 1 52 44 45 12 11 28 38 7 2 69 45 46 11 13 35 37 7 2 68 46 47 15 9 39 38 5 2 65 47 48 15 11 34 33 5 2 75 48 49 17 15 38 36 4 2 74 49 50 13 11 32 38 5 1 75 50 51 16 10 38 32 4 2 72 51 52 14 14 30 32 5 1 67 52 53 11 18 33 32 5 1 63 53 54 12 14 38 34 7 2 62 54 55 12 11 32 32 5 1 63 55 56 15 12 32 37 5 2 76 56 57 16 13 34 39 6 2 74 57 58 15 9 34 29 4 2 67 58 59 12 10 36 37 6 1 73 59 60 12 15 34 35 6 2 70 60 61 8 20 28 30 5 1 53 61 62 13 12 34 38 7 1 77 62 63 11 12 35 34 6 2 77 63 64 14 14 35 31 8 2 52 64 65 15 13 31 34 7 2 54 65 66 10 11 37 35 5 1 80 66 67 11 17 35 36 6 2 66 67 68 12 12 27 30 6 1 73 68 69 15 13 40 39 5 2 63 69 70 15 14 37 35 5 1 69 70 71 14 13 36 38 5 1 67 71 72 16 15 38 31 5 2 54 72 73 15 13 39 34 4 2 81 73 74 15 10 41 38 6 1 69 74 75 13 11 27 34 6 1 84 75 76 12 19 30 39 6 2 80 76 77 17 13 37 37 6 2 70 77 78 13 17 31 34 7 2 69 78 79 15 13 31 28 5 1 77 79 80 13 9 27 37 7 1 54 80 81 15 11 36 33 6 1 79 81 82 16 10 38 37 5 1 30 82 83 15 9 37 35 5 2 71 83 84 16 12 33 37 4 1 73 84 85 15 12 34 32 8 2 72 85 86 14 13 31 33 8 2 77 86 87 15 13 39 38 5 1 75 87 88 14 12 34 33 5 2 69 88 89 13 15 32 29 6 2 54 89 90 7 22 33 33 4 2 70 90 91 17 13 36 31 5 2 73 91 92 13 15 32 36 5 2 54 92 93 15 13 41 35 5 2 77 93 94 14 15 28 32 5 2 82 94 95 13 10 30 29 6 2 80 95 96 16 11 36 39 6 2 80 96 97 12 16 35 37 5 2 69 97 98 14 11 31 35 6 2 78 98 99 17 11 34 37 5 1 81 99 100 15 10 36 32 7 1 76 100 101 17 10 36 38 5 2 76 101 102 12 16 35 37 6 1 73 102 103 16 12 37 36 6 2 85 103 104 11 11 28 32 6 1 66 104 105 15 16 39 33 4 2 79 105 106 9 19 32 40 5 1 68 106 107 16 11 35 38 5 2 76 107 108 15 16 39 41 7 1 71 108 109 10 15 35 36 6 1 54 109 110 10 24 42 43 9 2 46 110 111 15 14 34 30 6 2 82 111 112 11 15 33 31 6 2 74 112 113 13 11 41 32 5 2 88 113 114 14 15 33 32 6 1 38 114 115 18 12 34 37 5 2 76 115 116 16 10 32 37 8 1 86 116 117 14 14 40 33 7 2 54 117 118 14 13 40 34 5 2 70 118 119 14 9 35 33 7 2 69 119 120 14 15 36 38 6 2 90 120 121 12 15 37 33 6 2 54 121 122 14 14 27 31 9 2 76 122 123 15 11 39 38 7 2 89 123 124 15 8 38 37 6 2 76 124 125 15 11 31 33 5 2 73 125 126 13 11 33 31 5 2 79 126 127 17 8 32 39 6 1 90 127 128 17 10 39 44 6 2 74 128 129 19 11 36 33 7 2 81 129 130 15 13 33 35 5 2 72 130 131 13 11 33 32 5 1 71 131 132 9 20 32 28 5 1 66 132 133 15 10 37 40 6 2 77 133 134 15 15 30 27 4 1 65 134 135 15 12 38 37 5 1 74 135 136 16 14 29 32 7 2 82 136 137 11 23 22 28 5 1 54 137 138 14 14 35 34 7 1 63 138 139 11 16 35 30 7 2 54 139 140 15 11 34 35 6 2 64 140 141 13 12 35 31 5 1 69 141 142 15 10 34 32 8 2 54 142 143 16 14 34 30 5 1 84 143 144 14 12 35 30 5 2 86 144 145 15 12 23 31 5 1 77 145 146 16 11 31 40 6 2 89 146 147 16 12 27 32 4 2 76 147 148 11 13 36 36 5 1 60 148 149 12 11 31 32 5 1 75 149 150 9 19 32 35 7 1 73 150 151 16 12 39 38 6 2 85 151 152 13 17 37 42 7 2 79 152 153 16 9 38 34 10 1 71 153 154 12 12 39 35 6 2 72 154 155 9 19 34 35 8 2 69 155 156 13 18 31 33 4 2 78 156 157 13 15 32 36 5 2 54 157 158 14 14 37 32 6 2 69 158 159 19 11 36 33 7 2 81 159 160 13 9 32 34 7 2 84 160 161 12 18 35 32 6 2 84 161 162 13 16 36 34 6 2 69 162 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) b_1 b_2 b_3 b_4 b_5 13.290728 -0.368426 0.043743 0.017464 0.048110 0.813842 b_6 t 0.029349 -0.003249 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.1743 -1.3222 0.2075 1.1845 4.0071 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.290728 2.390117 5.561 1.16e-07 *** b_1 -0.368426 0.050782 -7.255 1.84e-11 *** b_2 0.043743 0.047380 0.923 0.3573 b_3 0.017464 0.049358 0.354 0.7240 b_4 0.048110 0.133285 0.361 0.7186 b_5 0.813842 0.325885 2.497 0.0136 * b_6 0.029349 0.015242 1.926 0.0560 . t -0.003249 0.003354 -0.969 0.3342 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.91 on 154 degrees of freedom Multiple R-squared: 0.3616, Adjusted R-squared: 0.3326 F-statistic: 12.46 on 7 and 154 DF, p-value: 1.357e-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,] 0.19538427 0.390768545 0.804615727 [2,] 0.72007677 0.559846458 0.279923229 [3,] 0.82793843 0.344123141 0.172061570 [4,] 0.75121717 0.497565656 0.248782828 [5,] 0.66044912 0.679101759 0.339550879 [6,] 0.56566404 0.868671923 0.434335961 [7,] 0.58428384 0.831432321 0.415716161 [8,] 0.49946607 0.998932150 0.500533925 [9,] 0.43380643 0.867612853 0.566193573 [10,] 0.46344734 0.926894682 0.536552659 [11,] 0.59174954 0.816500915 0.408250458 [12,] 0.89127608 0.217447838 0.108723919 [13,] 0.88279808 0.234403839 0.117201920 [14,] 0.87719940 0.245601204 0.122800602 [15,] 0.85788890 0.284222204 0.142111102 [16,] 0.99771816 0.004563672 0.002281836 [17,] 0.99709853 0.005802936 0.002901468 [18,] 0.99611862 0.007762756 0.003881378 [19,] 0.99505268 0.009894639 0.004947319 [20,] 0.99394537 0.012109252 0.006054626 [21,] 0.99083431 0.018331383 0.009165691 [22,] 0.98914963 0.021700738 0.010850369 [23,] 0.98711277 0.025774454 0.012887227 [24,] 0.98266980 0.034660399 0.017330200 [25,] 0.97655727 0.046885451 0.023442726 [26,] 0.97342541 0.053149186 0.026574593 [27,] 0.96882456 0.062350872 0.031175436 [28,] 0.96722596 0.065548077 0.032774039 [29,] 0.96404703 0.071905935 0.035952967 [30,] 0.95473171 0.090536587 0.045268294 [31,] 0.94130291 0.117394180 0.058697090 [32,] 0.93652535 0.126949290 0.063474645 [33,] 0.93077084 0.138458313 0.069229157 [34,] 0.91294144 0.174117116 0.087058558 [35,] 0.95716984 0.085660312 0.042830156 [36,] 0.96915564 0.061688718 0.030844359 [37,] 0.96318434 0.073631316 0.036815658 [38,] 0.95314634 0.093707319 0.046853659 [39,] 0.98204476 0.035910470 0.017955235 [40,] 0.97726323 0.045473536 0.022736768 [41,] 0.97194320 0.056113600 0.028056800 [42,] 0.96766305 0.064673906 0.032336953 [43,] 0.95774667 0.084506666 0.042253333 [44,] 0.95331531 0.093369374 0.046684687 [45,] 0.94800675 0.103986507 0.051993254 [46,] 0.93526294 0.129474112 0.064737056 [47,] 0.93361069 0.132778614 0.066389307 [48,] 0.91708705 0.165825896 0.082912948 [49,] 0.92470079 0.150598416 0.075299208 [50,] 0.91392317 0.172153657 0.086076828 [51,] 0.91053226 0.178935480 0.089467740 [52,] 0.89280585 0.214388303 0.107194152 [53,] 0.93361621 0.132767586 0.066383793 [54,] 0.92174297 0.156514068 0.078257034 [55,] 0.91577900 0.168441996 0.084220998 [56,] 0.96383563 0.072328738 0.036164369 [57,] 0.95886505 0.082269904 0.041134952 [58,] 0.95526363 0.089472746 0.044736373 [59,] 0.95411473 0.091770532 0.045885266 [60,] 0.96266681 0.074666371 0.037333185 [61,] 0.95747419 0.085051627 0.042525813 [62,] 0.97265453 0.054690931 0.027345465 [63,] 0.96587498 0.068250045 0.034125023 [64,] 0.95847384 0.083052320 0.041526160 [65,] 0.95382740 0.092345198 0.046172599 [66,] 0.94152187 0.116956267 0.058478134 [67,] 0.95509713 0.089805737 0.044902868 [68,] 0.94398369 0.112032629 0.056016314 [69,] 0.94188464 0.116230721 0.058115360 [70,] 0.93732226 0.125355488 0.062677744 [71,] 0.92398004 0.152039926 0.076019963 [72,] 0.93283440 0.134331202 0.067165601 [73,] 0.91952538 0.160949232 0.080474616 [74,] 0.92471766 0.150564672 0.075282336 [75,] 0.90759164 0.184816712 0.092408356 [76,] 0.88669032 0.226619355 0.113309678 [77,] 0.87175915 0.256481710 0.128240855 [78,] 0.84655851 0.306882975 0.153441488 [79,] 0.81820605 0.363587900 0.181793950 [80,] 0.88697640 0.226047192 0.113023596 [81,] 0.91187916 0.176241674 0.088120837 [82,] 0.89141108 0.217177836 0.108588918 [83,] 0.86890947 0.262181051 0.131090526 [84,] 0.84521760 0.309564799 0.154782400 [85,] 0.85824334 0.283513321 0.141756660 [86,] 0.83264092 0.334718154 0.167359077 [87,] 0.81218402 0.375631954 0.187815977 [88,] 0.79534124 0.409317527 0.204658763 [89,] 0.81380297 0.372394053 0.186197027 [90,] 0.77958030 0.440839397 0.220419699 [91,] 0.76027877 0.479442450 0.239721225 [92,] 0.72386367 0.552272656 0.276136328 [93,] 0.68847254 0.623054928 0.311527464 [94,] 0.75791710 0.484165791 0.242082896 [95,] 0.75269527 0.494609454 0.247304727 [96,] 0.78899772 0.422004562 0.211002281 [97,] 0.75497223 0.490055543 0.245027772 [98,] 0.76624808 0.467503831 0.233751916 [99,] 0.79969042 0.400619169 0.200309584 [100,] 0.76518618 0.469627647 0.234813823 [101,] 0.73532909 0.529341827 0.264670914 [102,] 0.77082676 0.458346483 0.229173241 [103,] 0.80162930 0.396741391 0.198370696 [104,] 0.81563714 0.368725727 0.184362864 [105,] 0.86705225 0.265895504 0.132947752 [106,] 0.83927459 0.321450824 0.160725412 [107,] 0.81861009 0.362779811 0.181389905 [108,] 0.78097484 0.438050317 0.219025159 [109,] 0.77324195 0.453516092 0.226758046 [110,] 0.73009365 0.539812700 0.269906350 [111,] 0.68720294 0.625594117 0.312797058 [112,] 0.64802137 0.703957261 0.351978630 [113,] 0.61023294 0.779534126 0.389767063 [114,] 0.59729662 0.805406750 0.402703375 [115,] 0.55150278 0.896994436 0.448497218 [116,] 0.67069075 0.658618504 0.329309252 [117,] 0.62422974 0.751540523 0.375770262 [118,] 0.58411544 0.831769122 0.415884561 [119,] 0.65391375 0.692172498 0.346086249 [120,] 0.59753804 0.804923920 0.402461960 [121,] 0.58388755 0.832224907 0.416112453 [122,] 0.61121002 0.777579952 0.388789976 [123,] 0.56269356 0.874612881 0.437306440 [124,] 0.55891168 0.882176640 0.441088320 [125,] 0.50447478 0.991050444 0.495525222 [126,] 0.45139436 0.902788724 0.548605638 [127,] 0.46518718 0.930374353 0.534812823 [128,] 0.44810195 0.896203899 0.551898050 [129,] 0.39309348 0.786186967 0.606906517 [130,] 0.32097052 0.641941034 0.679029483 [131,] 0.25899308 0.517986169 0.741006915 [132,] 0.19984821 0.399696426 0.800151787 [133,] 0.25775177 0.515503536 0.742248232 [134,] 0.22010660 0.440213200 0.779893400 [135,] 0.19946276 0.398925523 0.800537238 [136,] 0.14011686 0.280233729 0.859883135 [137,] 0.15329291 0.306585814 0.846707093 [138,] 0.12058681 0.241173611 0.879413194 [139,] 0.08510128 0.170202570 0.914898715 [140,] 0.07004018 0.140080367 0.929959817 [141,] 0.03630916 0.072618315 0.963690842 > postscript(file="/var/fisher/rcomp/tmp/19eyl1351884343.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2xkqq1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/300ij1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4qlbz1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5zz6s1351884343.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 = 162 Frequency = 1 1 2 3 4 5 6 -0.76459695 2.29530109 -2.98323601 -1.86232442 4.00707678 3.06449294 7 8 9 10 11 12 3.37650116 -1.64486915 -0.77702553 0.17221893 1.98850066 3.12660022 13 14 15 16 17 18 -2.93754177 2.06817905 1.72961353 1.11208463 0.54217047 0.99548266 19 20 21 22 23 24 -0.96515040 1.96779497 2.36266370 -2.82269835 -0.68754185 -2.01495470 25 26 27 28 29 30 2.18386824 -7.17429876 1.16447560 0.19037667 0.78413628 -2.46181939 31 32 33 34 35 36 0.14344903 0.82906387 1.49839395 -0.67342800 0.48020790 0.63307625 37 38 39 40 41 42 -1.56789288 1.04342033 1.55896356 -1.61077037 -0.11387964 1.80073680 43 44 45 46 47 48 1.09371596 -0.67324194 -2.96984002 -3.48912569 -0.96774755 -0.21510616 49 50 51 52 53 54 3.11194103 -1.39460149 0.40486565 1.14423922 -0.39263980 -1.99744822 55 56 57 58 59 60 -1.92137929 0.16759043 1.42744022 -0.56670705 -2.88070492 -1.63870736 61 62 63 64 65 66 -2.08266085 -1.22959250 -3.96596212 0.46404599 1.21085878 -4.65568725 67 68 69 70 71 72 -1.82292292 -1.59867884 0.57492331 1.78542883 0.47030107 2.81286492 73 74 75 76 77 78 0.23880340 0.04924818 -1.33706205 -0.30140109 2.51351601 0.28655686 79 80 81 82 83 84 1.59615425 -1.27768963 0.45295733 2.41666770 -0.88700479 2.16481719 85 86 87 88 89 90 0.23471272 -0.42659532 1.15626025 -0.54062751 0.11737195 -3.78736927 91 92 93 94 95 96 2.66759017 0.05297919 0.26811979 0.48252216 -2.38086159 0.55371457 97 98 99 100 101 102 -1.15128382 -1.09251816 2.51847765 0.20366363 1.38450480 -0.48670437 103 104 105 106 107 108 0.80678550 -2.72336906 1.52420883 -2.09473902 0.81616682 2.29855034 109 110 111 112 113 114 -2.25728524 -0.09002959 0.89368985 -2.47356180 -2.67420407 2.38589175 115 116 117 118 119 120 3.27179085 1.00169312 0.37200709 -0.38400466 -1.68515048 -0.17063820 121 122 123 124 125 126 -1.06723343 0.24993061 -0.78458307 -1.39575272 0.22498750 -2.00041715 127 128 129 130 131 132 1.24447322 1.24680119 3.68825473 0.88501899 -0.95299958 -1.37357269 133 134 135 136 137 138 -0.66766011 2.97320532 1.03433882 2.11058872 2.53756969 1.19118179 139 140 141 142 143 144 -1.54855880 0.32359926 -0.56340735 0.21133834 2.80090816 -0.84897805 145 146 147 148 149 150 1.73970684 0.65326573 1.81738627 -2.03915841 -1.92443103 -2.10743217 151 152 153 154 155 156 0.84031951 -0.16868738 0.88743436 -2.71599925 -2.92322743 0.80604677 157 158 159 160 161 162 0.26415870 0.26177413 3.78572220 -2.87842147 -0.60753135 0.02043572 > postscript(file="/var/fisher/rcomp/tmp/606bd1351884343.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.76459695 NA 1 2.29530109 -0.76459695 2 -2.98323601 2.29530109 3 -1.86232442 -2.98323601 4 4.00707678 -1.86232442 5 3.06449294 4.00707678 6 3.37650116 3.06449294 7 -1.64486915 3.37650116 8 -0.77702553 -1.64486915 9 0.17221893 -0.77702553 10 1.98850066 0.17221893 11 3.12660022 1.98850066 12 -2.93754177 3.12660022 13 2.06817905 -2.93754177 14 1.72961353 2.06817905 15 1.11208463 1.72961353 16 0.54217047 1.11208463 17 0.99548266 0.54217047 18 -0.96515040 0.99548266 19 1.96779497 -0.96515040 20 2.36266370 1.96779497 21 -2.82269835 2.36266370 22 -0.68754185 -2.82269835 23 -2.01495470 -0.68754185 24 2.18386824 -2.01495470 25 -7.17429876 2.18386824 26 1.16447560 -7.17429876 27 0.19037667 1.16447560 28 0.78413628 0.19037667 29 -2.46181939 0.78413628 30 0.14344903 -2.46181939 31 0.82906387 0.14344903 32 1.49839395 0.82906387 33 -0.67342800 1.49839395 34 0.48020790 -0.67342800 35 0.63307625 0.48020790 36 -1.56789288 0.63307625 37 1.04342033 -1.56789288 38 1.55896356 1.04342033 39 -1.61077037 1.55896356 40 -0.11387964 -1.61077037 41 1.80073680 -0.11387964 42 1.09371596 1.80073680 43 -0.67324194 1.09371596 44 -2.96984002 -0.67324194 45 -3.48912569 -2.96984002 46 -0.96774755 -3.48912569 47 -0.21510616 -0.96774755 48 3.11194103 -0.21510616 49 -1.39460149 3.11194103 50 0.40486565 -1.39460149 51 1.14423922 0.40486565 52 -0.39263980 1.14423922 53 -1.99744822 -0.39263980 54 -1.92137929 -1.99744822 55 0.16759043 -1.92137929 56 1.42744022 0.16759043 57 -0.56670705 1.42744022 58 -2.88070492 -0.56670705 59 -1.63870736 -2.88070492 60 -2.08266085 -1.63870736 61 -1.22959250 -2.08266085 62 -3.96596212 -1.22959250 63 0.46404599 -3.96596212 64 1.21085878 0.46404599 65 -4.65568725 1.21085878 66 -1.82292292 -4.65568725 67 -1.59867884 -1.82292292 68 0.57492331 -1.59867884 69 1.78542883 0.57492331 70 0.47030107 1.78542883 71 2.81286492 0.47030107 72 0.23880340 2.81286492 73 0.04924818 0.23880340 74 -1.33706205 0.04924818 75 -0.30140109 -1.33706205 76 2.51351601 -0.30140109 77 0.28655686 2.51351601 78 1.59615425 0.28655686 79 -1.27768963 1.59615425 80 0.45295733 -1.27768963 81 2.41666770 0.45295733 82 -0.88700479 2.41666770 83 2.16481719 -0.88700479 84 0.23471272 2.16481719 85 -0.42659532 0.23471272 86 1.15626025 -0.42659532 87 -0.54062751 1.15626025 88 0.11737195 -0.54062751 89 -3.78736927 0.11737195 90 2.66759017 -3.78736927 91 0.05297919 2.66759017 92 0.26811979 0.05297919 93 0.48252216 0.26811979 94 -2.38086159 0.48252216 95 0.55371457 -2.38086159 96 -1.15128382 0.55371457 97 -1.09251816 -1.15128382 98 2.51847765 -1.09251816 99 0.20366363 2.51847765 100 1.38450480 0.20366363 101 -0.48670437 1.38450480 102 0.80678550 -0.48670437 103 -2.72336906 0.80678550 104 1.52420883 -2.72336906 105 -2.09473902 1.52420883 106 0.81616682 -2.09473902 107 2.29855034 0.81616682 108 -2.25728524 2.29855034 109 -0.09002959 -2.25728524 110 0.89368985 -0.09002959 111 -2.47356180 0.89368985 112 -2.67420407 -2.47356180 113 2.38589175 -2.67420407 114 3.27179085 2.38589175 115 1.00169312 3.27179085 116 0.37200709 1.00169312 117 -0.38400466 0.37200709 118 -1.68515048 -0.38400466 119 -0.17063820 -1.68515048 120 -1.06723343 -0.17063820 121 0.24993061 -1.06723343 122 -0.78458307 0.24993061 123 -1.39575272 -0.78458307 124 0.22498750 -1.39575272 125 -2.00041715 0.22498750 126 1.24447322 -2.00041715 127 1.24680119 1.24447322 128 3.68825473 1.24680119 129 0.88501899 3.68825473 130 -0.95299958 0.88501899 131 -1.37357269 -0.95299958 132 -0.66766011 -1.37357269 133 2.97320532 -0.66766011 134 1.03433882 2.97320532 135 2.11058872 1.03433882 136 2.53756969 2.11058872 137 1.19118179 2.53756969 138 -1.54855880 1.19118179 139 0.32359926 -1.54855880 140 -0.56340735 0.32359926 141 0.21133834 -0.56340735 142 2.80090816 0.21133834 143 -0.84897805 2.80090816 144 1.73970684 -0.84897805 145 0.65326573 1.73970684 146 1.81738627 0.65326573 147 -2.03915841 1.81738627 148 -1.92443103 -2.03915841 149 -2.10743217 -1.92443103 150 0.84031951 -2.10743217 151 -0.16868738 0.84031951 152 0.88743436 -0.16868738 153 -2.71599925 0.88743436 154 -2.92322743 -2.71599925 155 0.80604677 -2.92322743 156 0.26415870 0.80604677 157 0.26177413 0.26415870 158 3.78572220 0.26177413 159 -2.87842147 3.78572220 160 -0.60753135 -2.87842147 161 0.02043572 -0.60753135 162 NA 0.02043572 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.29530109 -0.76459695 [2,] -2.98323601 2.29530109 [3,] -1.86232442 -2.98323601 [4,] 4.00707678 -1.86232442 [5,] 3.06449294 4.00707678 [6,] 3.37650116 3.06449294 [7,] -1.64486915 3.37650116 [8,] -0.77702553 -1.64486915 [9,] 0.17221893 -0.77702553 [10,] 1.98850066 0.17221893 [11,] 3.12660022 1.98850066 [12,] -2.93754177 3.12660022 [13,] 2.06817905 -2.93754177 [14,] 1.72961353 2.06817905 [15,] 1.11208463 1.72961353 [16,] 0.54217047 1.11208463 [17,] 0.99548266 0.54217047 [18,] -0.96515040 0.99548266 [19,] 1.96779497 -0.96515040 [20,] 2.36266370 1.96779497 [21,] -2.82269835 2.36266370 [22,] -0.68754185 -2.82269835 [23,] -2.01495470 -0.68754185 [24,] 2.18386824 -2.01495470 [25,] -7.17429876 2.18386824 [26,] 1.16447560 -7.17429876 [27,] 0.19037667 1.16447560 [28,] 0.78413628 0.19037667 [29,] -2.46181939 0.78413628 [30,] 0.14344903 -2.46181939 [31,] 0.82906387 0.14344903 [32,] 1.49839395 0.82906387 [33,] -0.67342800 1.49839395 [34,] 0.48020790 -0.67342800 [35,] 0.63307625 0.48020790 [36,] -1.56789288 0.63307625 [37,] 1.04342033 -1.56789288 [38,] 1.55896356 1.04342033 [39,] -1.61077037 1.55896356 [40,] -0.11387964 -1.61077037 [41,] 1.80073680 -0.11387964 [42,] 1.09371596 1.80073680 [43,] -0.67324194 1.09371596 [44,] -2.96984002 -0.67324194 [45,] -3.48912569 -2.96984002 [46,] -0.96774755 -3.48912569 [47,] -0.21510616 -0.96774755 [48,] 3.11194103 -0.21510616 [49,] -1.39460149 3.11194103 [50,] 0.40486565 -1.39460149 [51,] 1.14423922 0.40486565 [52,] -0.39263980 1.14423922 [53,] -1.99744822 -0.39263980 [54,] -1.92137929 -1.99744822 [55,] 0.16759043 -1.92137929 [56,] 1.42744022 0.16759043 [57,] -0.56670705 1.42744022 [58,] -2.88070492 -0.56670705 [59,] -1.63870736 -2.88070492 [60,] -2.08266085 -1.63870736 [61,] -1.22959250 -2.08266085 [62,] -3.96596212 -1.22959250 [63,] 0.46404599 -3.96596212 [64,] 1.21085878 0.46404599 [65,] -4.65568725 1.21085878 [66,] -1.82292292 -4.65568725 [67,] -1.59867884 -1.82292292 [68,] 0.57492331 -1.59867884 [69,] 1.78542883 0.57492331 [70,] 0.47030107 1.78542883 [71,] 2.81286492 0.47030107 [72,] 0.23880340 2.81286492 [73,] 0.04924818 0.23880340 [74,] -1.33706205 0.04924818 [75,] -0.30140109 -1.33706205 [76,] 2.51351601 -0.30140109 [77,] 0.28655686 2.51351601 [78,] 1.59615425 0.28655686 [79,] -1.27768963 1.59615425 [80,] 0.45295733 -1.27768963 [81,] 2.41666770 0.45295733 [82,] -0.88700479 2.41666770 [83,] 2.16481719 -0.88700479 [84,] 0.23471272 2.16481719 [85,] -0.42659532 0.23471272 [86,] 1.15626025 -0.42659532 [87,] -0.54062751 1.15626025 [88,] 0.11737195 -0.54062751 [89,] -3.78736927 0.11737195 [90,] 2.66759017 -3.78736927 [91,] 0.05297919 2.66759017 [92,] 0.26811979 0.05297919 [93,] 0.48252216 0.26811979 [94,] -2.38086159 0.48252216 [95,] 0.55371457 -2.38086159 [96,] -1.15128382 0.55371457 [97,] -1.09251816 -1.15128382 [98,] 2.51847765 -1.09251816 [99,] 0.20366363 2.51847765 [100,] 1.38450480 0.20366363 [101,] -0.48670437 1.38450480 [102,] 0.80678550 -0.48670437 [103,] -2.72336906 0.80678550 [104,] 1.52420883 -2.72336906 [105,] -2.09473902 1.52420883 [106,] 0.81616682 -2.09473902 [107,] 2.29855034 0.81616682 [108,] -2.25728524 2.29855034 [109,] -0.09002959 -2.25728524 [110,] 0.89368985 -0.09002959 [111,] -2.47356180 0.89368985 [112,] -2.67420407 -2.47356180 [113,] 2.38589175 -2.67420407 [114,] 3.27179085 2.38589175 [115,] 1.00169312 3.27179085 [116,] 0.37200709 1.00169312 [117,] -0.38400466 0.37200709 [118,] -1.68515048 -0.38400466 [119,] -0.17063820 -1.68515048 [120,] -1.06723343 -0.17063820 [121,] 0.24993061 -1.06723343 [122,] -0.78458307 0.24993061 [123,] -1.39575272 -0.78458307 [124,] 0.22498750 -1.39575272 [125,] -2.00041715 0.22498750 [126,] 1.24447322 -2.00041715 [127,] 1.24680119 1.24447322 [128,] 3.68825473 1.24680119 [129,] 0.88501899 3.68825473 [130,] -0.95299958 0.88501899 [131,] -1.37357269 -0.95299958 [132,] -0.66766011 -1.37357269 [133,] 2.97320532 -0.66766011 [134,] 1.03433882 2.97320532 [135,] 2.11058872 1.03433882 [136,] 2.53756969 2.11058872 [137,] 1.19118179 2.53756969 [138,] -1.54855880 1.19118179 [139,] 0.32359926 -1.54855880 [140,] -0.56340735 0.32359926 [141,] 0.21133834 -0.56340735 [142,] 2.80090816 0.21133834 [143,] -0.84897805 2.80090816 [144,] 1.73970684 -0.84897805 [145,] 0.65326573 1.73970684 [146,] 1.81738627 0.65326573 [147,] -2.03915841 1.81738627 [148,] -1.92443103 -2.03915841 [149,] -2.10743217 -1.92443103 [150,] 0.84031951 -2.10743217 [151,] -0.16868738 0.84031951 [152,] 0.88743436 -0.16868738 [153,] -2.71599925 0.88743436 [154,] -2.92322743 -2.71599925 [155,] 0.80604677 -2.92322743 [156,] 0.26415870 0.80604677 [157,] 0.26177413 0.26415870 [158,] 3.78572220 0.26177413 [159,] -2.87842147 3.78572220 [160,] -0.60753135 -2.87842147 [161,] 0.02043572 -0.60753135 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.29530109 -0.76459695 2 -2.98323601 2.29530109 3 -1.86232442 -2.98323601 4 4.00707678 -1.86232442 5 3.06449294 4.00707678 6 3.37650116 3.06449294 7 -1.64486915 3.37650116 8 -0.77702553 -1.64486915 9 0.17221893 -0.77702553 10 1.98850066 0.17221893 11 3.12660022 1.98850066 12 -2.93754177 3.12660022 13 2.06817905 -2.93754177 14 1.72961353 2.06817905 15 1.11208463 1.72961353 16 0.54217047 1.11208463 17 0.99548266 0.54217047 18 -0.96515040 0.99548266 19 1.96779497 -0.96515040 20 2.36266370 1.96779497 21 -2.82269835 2.36266370 22 -0.68754185 -2.82269835 23 -2.01495470 -0.68754185 24 2.18386824 -2.01495470 25 -7.17429876 2.18386824 26 1.16447560 -7.17429876 27 0.19037667 1.16447560 28 0.78413628 0.19037667 29 -2.46181939 0.78413628 30 0.14344903 -2.46181939 31 0.82906387 0.14344903 32 1.49839395 0.82906387 33 -0.67342800 1.49839395 34 0.48020790 -0.67342800 35 0.63307625 0.48020790 36 -1.56789288 0.63307625 37 1.04342033 -1.56789288 38 1.55896356 1.04342033 39 -1.61077037 1.55896356 40 -0.11387964 -1.61077037 41 1.80073680 -0.11387964 42 1.09371596 1.80073680 43 -0.67324194 1.09371596 44 -2.96984002 -0.67324194 45 -3.48912569 -2.96984002 46 -0.96774755 -3.48912569 47 -0.21510616 -0.96774755 48 3.11194103 -0.21510616 49 -1.39460149 3.11194103 50 0.40486565 -1.39460149 51 1.14423922 0.40486565 52 -0.39263980 1.14423922 53 -1.99744822 -0.39263980 54 -1.92137929 -1.99744822 55 0.16759043 -1.92137929 56 1.42744022 0.16759043 57 -0.56670705 1.42744022 58 -2.88070492 -0.56670705 59 -1.63870736 -2.88070492 60 -2.08266085 -1.63870736 61 -1.22959250 -2.08266085 62 -3.96596212 -1.22959250 63 0.46404599 -3.96596212 64 1.21085878 0.46404599 65 -4.65568725 1.21085878 66 -1.82292292 -4.65568725 67 -1.59867884 -1.82292292 68 0.57492331 -1.59867884 69 1.78542883 0.57492331 70 0.47030107 1.78542883 71 2.81286492 0.47030107 72 0.23880340 2.81286492 73 0.04924818 0.23880340 74 -1.33706205 0.04924818 75 -0.30140109 -1.33706205 76 2.51351601 -0.30140109 77 0.28655686 2.51351601 78 1.59615425 0.28655686 79 -1.27768963 1.59615425 80 0.45295733 -1.27768963 81 2.41666770 0.45295733 82 -0.88700479 2.41666770 83 2.16481719 -0.88700479 84 0.23471272 2.16481719 85 -0.42659532 0.23471272 86 1.15626025 -0.42659532 87 -0.54062751 1.15626025 88 0.11737195 -0.54062751 89 -3.78736927 0.11737195 90 2.66759017 -3.78736927 91 0.05297919 2.66759017 92 0.26811979 0.05297919 93 0.48252216 0.26811979 94 -2.38086159 0.48252216 95 0.55371457 -2.38086159 96 -1.15128382 0.55371457 97 -1.09251816 -1.15128382 98 2.51847765 -1.09251816 99 0.20366363 2.51847765 100 1.38450480 0.20366363 101 -0.48670437 1.38450480 102 0.80678550 -0.48670437 103 -2.72336906 0.80678550 104 1.52420883 -2.72336906 105 -2.09473902 1.52420883 106 0.81616682 -2.09473902 107 2.29855034 0.81616682 108 -2.25728524 2.29855034 109 -0.09002959 -2.25728524 110 0.89368985 -0.09002959 111 -2.47356180 0.89368985 112 -2.67420407 -2.47356180 113 2.38589175 -2.67420407 114 3.27179085 2.38589175 115 1.00169312 3.27179085 116 0.37200709 1.00169312 117 -0.38400466 0.37200709 118 -1.68515048 -0.38400466 119 -0.17063820 -1.68515048 120 -1.06723343 -0.17063820 121 0.24993061 -1.06723343 122 -0.78458307 0.24993061 123 -1.39575272 -0.78458307 124 0.22498750 -1.39575272 125 -2.00041715 0.22498750 126 1.24447322 -2.00041715 127 1.24680119 1.24447322 128 3.68825473 1.24680119 129 0.88501899 3.68825473 130 -0.95299958 0.88501899 131 -1.37357269 -0.95299958 132 -0.66766011 -1.37357269 133 2.97320532 -0.66766011 134 1.03433882 2.97320532 135 2.11058872 1.03433882 136 2.53756969 2.11058872 137 1.19118179 2.53756969 138 -1.54855880 1.19118179 139 0.32359926 -1.54855880 140 -0.56340735 0.32359926 141 0.21133834 -0.56340735 142 2.80090816 0.21133834 143 -0.84897805 2.80090816 144 1.73970684 -0.84897805 145 0.65326573 1.73970684 146 1.81738627 0.65326573 147 -2.03915841 1.81738627 148 -1.92443103 -2.03915841 149 -2.10743217 -1.92443103 150 0.84031951 -2.10743217 151 -0.16868738 0.84031951 152 0.88743436 -0.16868738 153 -2.71599925 0.88743436 154 -2.92322743 -2.71599925 155 0.80604677 -2.92322743 156 0.26415870 0.80604677 157 0.26177413 0.26415870 158 3.78572220 0.26177413 159 -2.87842147 3.78572220 160 -0.60753135 -2.87842147 161 0.02043572 -0.60753135 > 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/7qxp11351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8jpri1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9z5mw1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10e8ic1351884343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/119bq01351884343.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/12wy6y1351884343.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/13rdtc1351884343.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/14is131351884343.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/15qizk1351884343.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/16pp2t1351884343.tab") + } > > try(system("convert tmp/19eyl1351884343.ps tmp/19eyl1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/2xkqq1351884343.ps tmp/2xkqq1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/300ij1351884343.ps tmp/300ij1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/4qlbz1351884343.ps tmp/4qlbz1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/5zz6s1351884343.ps tmp/5zz6s1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/606bd1351884343.ps tmp/606bd1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/7qxp11351884343.ps tmp/7qxp11351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/8jpri1351884343.ps tmp/8jpri1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/9z5mw1351884343.ps tmp/9z5mw1351884343.png",intern=TRUE)) character(0) > try(system("convert tmp/10e8ic1351884343.ps tmp/10e8ic1351884343.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.067 1.175 9.242