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. Type 'q()' to quit R. > x <- array(list(2 + ,2 + ,1 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,2 + ,3 + ,3 + ,2 + ,2 + ,2 + ,3 + ,1 + ,2 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,4 + ,4 + ,5 + ,4 + ,4 + ,5 + ,4 + ,3 + ,2 + ,2 + ,4 + ,2 + ,2 + ,3 + ,2 + ,3 + ,2 + ,2 + ,4 + ,4 + ,3 + ,2 + ,3 + ,4 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,3 + ,4 + ,2 + ,2 + ,3 + ,2 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,5 + ,3 + ,4 + ,2 + ,3 + ,3 + ,3 + ,5 + ,3 + ,3 + ,4 + ,3 + ,3 + ,2 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,3 + ,2 + ,2 + ,3 + ,3 + ,1 + ,1 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,2 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,2 + ,2 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + 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,4 + ,4 + ,3 + ,3 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,5 + ,1 + ,3 + ,1 + ,1 + ,1 + ,1 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,2 + ,1 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,2 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,1 + ,4 + ,3 + ,2 + ,2 + ,2 + ,1 + ,3 + ,3 + ,4 + ,4 + ,5 + ,4 + ,4 + ,2 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,2 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,2 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,1 + ,3 + ,3 + ,4 + ,2 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,1 + ,3 + ,3 + ,5 + ,3 + ,5 + ,5 + ,4 + ,2 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,2 + ,3 + ,3 + ,2 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,1 + ,1 + ,3 + ,3 + ,3 + ,4 + ,2) + ,dim=c(8 + ,156) + ,dimnames=list(c('Y' + ,'X1t' + ,'X2t' + ,'X3t' + ,'X4t' + ,'X5t' + ,'X6t' + ,'X7t') + ,1:156)) > y <- array(NA,dim=c(8,156),dimnames=list(c('Y','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),1:156)) > 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 = '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.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 X1t X2t X3t X4t X5t X6t X7t 1 2 2 1 4 3 3 3 3 2 3 2 3 4 3 3 4 3 3 3 4 2 3 4 4 4 3 4 3 3 3 2 3 3 3 3 5 3 3 2 3 3 2 2 2 6 3 1 2 4 3 3 2 2 7 2 4 4 5 4 4 5 4 8 3 2 2 4 2 2 3 2 9 3 2 2 4 4 3 2 3 10 4 2 2 2 2 2 2 2 11 3 4 2 2 3 2 4 4 12 3 3 3 4 3 2 3 3 13 2 3 2 4 4 4 3 3 14 3 2 2 5 3 4 2 3 15 3 3 5 3 3 4 3 3 16 2 2 4 3 2 2 2 3 17 3 3 3 3 3 3 3 3 18 3 3 4 4 4 4 3 2 19 2 2 4 2 2 2 2 4 20 2 2 2 3 2 2 3 3 21 1 1 4 3 3 3 2 2 22 4 3 4 4 4 4 3 3 23 3 2 4 3 3 2 3 3 24 2 2 4 3 3 2 2 2 25 3 3 4 3 4 3 3 2 26 3 3 4 4 4 4 3 4 27 4 3 4 4 2 4 4 2 28 3 2 3 4 3 3 3 3 29 3 3 3 4 3 3 3 2 30 2 2 4 4 4 4 2 4 31 2 2 3 2 4 2 2 3 32 4 3 4 3 3 3 4 2 33 4 3 4 4 3 4 4 3 34 2 2 4 3 2 3 3 3 35 2 2 4 3 2 2 3 1 36 3 3 4 4 4 4 4 3 37 3 3 4 3 3 4 3 3 38 3 2 3 2 2 2 2 3 39 3 3 4 3 3 3 3 2 40 4 3 4 4 4 4 4 3 41 3 3 4 3 4 4 3 1 42 2 3 2 2 3 3 5 2 43 1 5 2 1 4 2 4 2 44 2 4 3 2 3 2 3 3 45 3 4 3 2 3 3 2 4 46 3 4 4 4 3 4 2 3 47 2 4 4 4 3 4 3 2 48 2 5 2 2 2 2 4 2 49 3 4 3 3 4 3 2 3 50 3 4 4 3 4 3 3 3 51 3 4 3 2 4 3 4 4 52 2 3 3 1 2 2 3 3 53 2 4 4 3 3 4 4 2 54 2 4 3 2 3 3 3 2 55 3 5 3 4 3 4 3 2 56 3 4 3 3 3 3 4 2 57 2 3 3 4 2 3 2 3 58 3 3 4 4 4 4 4 1 59 1 4 3 4 4 1 2 5 60 3 4 4 4 4 4 4 2 61 1 4 3 1 3 2 2 3 62 3 4 4 4 4 3 3 4 63 2 3 3 4 3 3 2 4 64 2 3 4 4 4 3 3 2 65 3 3 3 1 3 3 3 3 66 2 4 3 4 3 4 3 3 67 3 4 3 3 3 2 3 3 68 2 4 3 3 3 2 2 3 69 3 4 3 4 4 4 4 1 70 1 5 2 1 1 1 2 3 71 2 3 3 4 4 4 3 3 72 2 4 3 3 4 4 4 3 73 2 3 4 3 3 3 2 4 74 2 2 4 2 5 2 3 3 75 3 4 3 3 3 3 3 4 76 2 4 3 3 3 3 3 3 77 2 5 3 3 3 3 3 3 78 2 2 3 4 4 3 2 2 79 2 4 4 3 4 4 4 1 80 1 4 2 1 3 2 4 2 81 2 4 3 3 3 2 3 3 82 3 3 3 3 2 3 3 4 83 4 4 4 3 4 2 3 3 84 3 3 2 2 3 2 1 4 85 3 4 4 2 3 3 3 5 86 3 3 3 3 3 2 2 2 87 3 2 2 2 3 3 2 2 88 4 3 4 3 2 4 4 4 89 4 4 4 3 4 2 2 4 90 3 3 3 3 3 3 3 3 91 4 4 4 3 4 3 3 4 92 4 3 4 4 2 4 3 4 93 4 4 4 4 2 3 3 4 94 3 4 3 4 3 2 3 4 95 3 3 3 3 3 2 2 4 96 2 2 4 2 3 3 3 2 97 3 1 5 3 4 2 1 4 98 3 2 3 2 2 3 2 4 99 4 2 4 3 3 3 3 4 100 3 3 4 3 2 4 3 4 101 4 4 4 4 2 4 3 4 102 3 4 4 3 3 3 3 5 103 3 5 5 3 3 1 2 4 104 3 2 4 2 5 1 1 4 105 3 1 3 1 2 4 4 4 106 4 3 4 3 4 2 1 3 107 3 3 4 3 3 4 4 4 108 4 4 4 4 4 2 1 4 109 3 2 4 2 4 2 2 4 110 3 2 4 3 3 2 4 3 111 2 4 3 3 4 3 3 3 112 3 3 4 3 3 3 4 3 113 3 4 3 3 3 3 4 3 114 4 4 4 2 4 3 4 4 115 4 4 4 2 2 4 5 3 116 4 4 3 2 3 3 4 3 117 4 4 3 2 3 3 4 4 118 2 4 3 1 3 3 4 3 119 3 4 3 2 2 2 4 3 120 3 3 3 3 2 2 4 3 121 3 4 3 3 2 3 4 3 122 4 3 3 4 2 2 4 3 123 3 3 3 3 4 2 4 3 124 4 4 4 2 3 3 4 4 125 4 4 3 3 2 3 4 3 126 3 4 2 2 4 4 4 4 127 4 4 4 3 3 3 4 3 128 3 3 3 3 2 3 3 4 129 3 3 3 5 1 3 1 1 130 1 1 1 2 4 4 4 4 131 4 4 4 2 3 3 4 3 132 4 3 3 3 2 2 4 4 133 2 4 2 1 2 4 4 4 134 2 4 2 3 3 3 4 4 135 4 3 4 3 2 2 4 3 136 3 4 3 3 3 3 4 3 137 4 4 4 4 2 1 4 3 138 2 2 2 1 3 3 4 4 139 5 4 4 2 3 2 3 3 140 3 3 4 4 2 2 4 3 141 4 3 3 2 3 3 4 3 142 3 4 3 2 2 2 4 4 143 4 4 3 4 2 2 4 2 144 2 2 2 3 2 2 4 3 145 3 3 3 3 3 3 4 3 146 1 3 3 4 2 3 4 3 147 2 3 3 1 3 3 5 3 148 5 5 4 2 3 3 4 3 149 4 4 4 2 4 4 4 4 150 4 4 3 3 4 3 4 3 151 3 3 3 2 4 3 4 4 152 4 4 4 3 2 2 3 3 153 2 3 2 3 3 3 3 4 154 4 4 3 3 3 3 4 3 155 4 4 4 3 1 1 3 3 156 3 4 2 2 2 1 4 3 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X1t X2t X3t X4t X5t 0.53018 0.08203 0.38222 0.12542 -0.12083 -0.04125 X6t X7t 0.16539 0.13966 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.13973 -0.57708 0.04524 0.54741 2.00454 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.53018 0.54282 0.977 0.3303 X1t 0.08203 0.07292 1.125 0.2624 X2t 0.38222 0.08267 4.623 8.16e-06 *** X3t 0.12542 0.07484 1.676 0.0959 . X4t -0.12083 0.08220 -1.470 0.1437 X5t -0.04125 0.08737 -0.472 0.6375 X6t 0.16539 0.08064 2.051 0.0420 * X7t 0.13966 0.07695 1.815 0.0716 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7812 on 148 degrees of freedom Multiple R-squared: 0.2072, Adjusted R-squared: 0.1697 F-statistic: 5.526 on 7 and 148 DF, p-value: 1.154e-05 > 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,] 0.21906137 0.4381227 0.78093863 [2,] 0.11359460 0.2271892 0.88640540 [3,] 0.05660164 0.1132033 0.94339836 [4,] 0.11906497 0.2381299 0.88093503 [5,] 0.08562226 0.1712445 0.91437774 [6,] 0.23382727 0.4676545 0.76617273 [7,] 0.16040112 0.3208022 0.83959888 [8,] 0.10381220 0.2076244 0.89618780 [9,] 0.08451044 0.1690209 0.91548956 [10,] 0.09373237 0.1874647 0.90626763 [11,] 0.35233158 0.7046632 0.64766842 [12,] 0.55170886 0.8965823 0.44829114 [13,] 0.53673202 0.9265360 0.46326798 [14,] 0.54587822 0.9082436 0.45412178 [15,] 0.47082303 0.9416461 0.52917697 [16,] 0.42777229 0.8555446 0.57222771 [17,] 0.40744845 0.8148969 0.59255155 [18,] 0.36936957 0.7387391 0.63063043 [19,] 0.31749389 0.6349878 0.68250611 [20,] 0.27507365 0.5501473 0.72492635 [21,] 0.22226722 0.4445344 0.77773278 [22,] 0.23300902 0.4660180 0.76699098 [23,] 0.24425325 0.4885065 0.75574675 [24,] 0.26783273 0.5356655 0.73216727 [25,] 0.33925919 0.6785184 0.66074081 [26,] 0.28708059 0.5741612 0.71291941 [27,] 0.23885119 0.4777024 0.76114881 [28,] 0.21548449 0.4309690 0.78451551 [29,] 0.17567501 0.3513500 0.82432499 [30,] 0.21265120 0.4253024 0.78734880 [31,] 0.18485606 0.3697121 0.81514394 [32,] 0.21281526 0.4256305 0.78718474 [33,] 0.40599941 0.8119988 0.59400059 [34,] 0.38625670 0.7725134 0.61374330 [35,] 0.34123854 0.6824771 0.65876146 [36,] 0.31182514 0.6236503 0.68817486 [37,] 0.41582422 0.8316484 0.58417578 [38,] 0.39606070 0.7921214 0.60393930 [39,] 0.36824210 0.7364842 0.63175790 [40,] 0.32811452 0.6562290 0.67188548 [41,] 0.29490393 0.5898079 0.70509607 [42,] 0.27063879 0.5412776 0.72936121 [43,] 0.32474464 0.6494893 0.67525536 [44,] 0.30365022 0.6073004 0.69634978 [45,] 0.26209690 0.5241938 0.73790310 [46,] 0.22689804 0.4537961 0.77310196 [47,] 0.23113633 0.4622727 0.76886367 [48,] 0.19441596 0.3888319 0.80558404 [49,] 0.29071455 0.5814291 0.70928545 [50,] 0.25078781 0.5015756 0.74921219 [51,] 0.33029554 0.6605911 0.66970446 [52,] 0.30405938 0.6081188 0.69594062 [53,] 0.29391318 0.5878264 0.70608682 [54,] 0.30948725 0.6189745 0.69051275 [55,] 0.28377917 0.5675583 0.71622083 [56,] 0.30279616 0.6055923 0.69720384 [57,] 0.28956086 0.5791217 0.71043914 [58,] 0.26916783 0.5383357 0.73083217 [59,] 0.23425846 0.4685169 0.76574154 [60,] 0.31073479 0.6214696 0.68926521 [61,] 0.31579456 0.6315891 0.68420544 [62,] 0.32836100 0.6567220 0.67163900 [63,] 0.37036733 0.7407347 0.62963267 [64,] 0.36385504 0.7277101 0.63614496 [65,] 0.33627041 0.6725408 0.66372959 [66,] 0.34490268 0.6898054 0.65509732 [67,] 0.37801923 0.7560385 0.62198077 [68,] 0.35611306 0.7122261 0.64388694 [69,] 0.45387140 0.9077428 0.54612860 [70,] 0.61328730 0.7734254 0.38671270 [71,] 0.65973975 0.6805205 0.34026025 [72,] 0.61837108 0.7632578 0.38162892 [73,] 0.71429815 0.5714037 0.28570185 [74,] 0.73343001 0.5331400 0.26656999 [75,] 0.72281013 0.5543797 0.27718987 [76,] 0.70305420 0.5938916 0.29694580 [77,] 0.71502988 0.5699402 0.28497012 [78,] 0.70859658 0.5828068 0.29140342 [79,] 0.76580663 0.4683867 0.23419337 [80,] 0.73091169 0.5381766 0.26908831 [81,] 0.74449641 0.5110072 0.25550359 [82,] 0.73717991 0.5256402 0.26282009 [83,] 0.72682247 0.5463551 0.27317753 [84,] 0.69039239 0.6192152 0.30960761 [85,] 0.65314677 0.6937065 0.34685323 [86,] 0.72135627 0.5572875 0.27864373 [87,] 0.69013761 0.6197248 0.30986239 [88,] 0.65745267 0.6850947 0.34254733 [89,] 0.68465494 0.6306901 0.31534506 [90,] 0.64670024 0.7065995 0.35329976 [91,] 0.62655044 0.7468991 0.37344956 [92,] 0.59058341 0.8188332 0.40941659 [93,] 0.77048162 0.4590368 0.22951838 [94,] 0.75567911 0.4886418 0.24432089 [95,] 0.78180200 0.4363960 0.21819800 [96,] 0.79281442 0.4143712 0.20718558 [97,] 0.75939157 0.4812169 0.24060843 [98,] 0.74474767 0.5105047 0.25525233 [99,] 0.70972270 0.5805546 0.29027730 [100,] 0.67114758 0.6577048 0.32885242 [101,] 0.81146171 0.3770766 0.18853829 [102,] 0.80005126 0.3998975 0.19994874 [103,] 0.77385157 0.4522969 0.22614843 [104,] 0.76239487 0.4752103 0.23760513 [105,] 0.76497352 0.4700530 0.23502648 [106,] 0.78413484 0.4317303 0.21586516 [107,] 0.79423898 0.4115220 0.20576102 [108,] 0.88909200 0.2218160 0.11090800 [109,] 0.87403042 0.2519392 0.12596958 [110,] 0.83958573 0.3208285 0.16041427 [111,] 0.79989213 0.4002157 0.20010787 [112,] 0.87815335 0.2436933 0.12184665 [113,] 0.87200883 0.2559823 0.12799117 [114,] 0.84282470 0.3143506 0.15717530 [115,] 0.90149025 0.1970195 0.09850975 [116,] 0.87526373 0.2494725 0.12473627 [117,] 0.84388832 0.3122234 0.15611168 [118,] 0.81905385 0.3618923 0.18094615 [119,] 0.77170662 0.4565868 0.22829338 [120,] 0.74414815 0.5117037 0.25585185 [121,] 0.69891373 0.6021725 0.30108627 [122,] 0.93735368 0.1252926 0.06264632 [123,] 0.92187813 0.1562437 0.07812187 [124,] 0.88870514 0.2225897 0.11129486 [125,] 0.89930814 0.2013837 0.10069186 [126,] 0.87560568 0.2487886 0.12439432 [127,] 0.82627752 0.3474450 0.17372248 [128,] 0.78306637 0.4338673 0.21693363 [129,] 0.76106598 0.4778680 0.23893402 [130,] 0.68376168 0.6324766 0.31623832 [131,] 0.76902845 0.4619431 0.23097155 [132,] 0.71987252 0.5602550 0.28012748 [133,] 0.61883604 0.7623279 0.38116396 [134,] 0.78376074 0.4324785 0.21623926 [135,] 0.77481948 0.4503610 0.22518052 > postscript(file="/var/www/html/rcomp/tmp/1mpsv1291322444.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2mpsv1291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3mpsv1291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4fy9y1291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5fy9y1291322444.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 = 156 Frequency = 1 1 2 3 4 5 6 -0.00704189 0.06312856 0.56877450 0.39731815 0.91791458 0.99781725 7 8 9 10 11 12 -1.75154644 0.58831612 0.89695326 2.00454053 0.35120822 0.10523178 13 14 15 16 17 18 -0.30921962 0.69195961 -0.45128955 -1.02497378 0.27190036 0.06599550 19 20 21 22 23 24 -1.03921181 -0.42592191 -1.64120567 0.92633968 -0.06953596 -0.76449130 25 26 27 28 29 30 0.15016249 -0.21331614 0.65895334 0.22851740 0.28613839 -0.96589247 31 32 33 34 35 36 -0.27568232 0.86394699 0.64012418 -1.14911182 -0.91105098 -0.23904916 37 38 39 40 41 42 -0.06906919 0.48266436 0.02933583 0.76095084 0.33106910 -0.41158336 43 44 45 46 47 48 -1.20527053 -0.72596748 0.34101634 -0.11113297 -1.13686599 -0.57234164 49 50 51 52 53 54 0.47608103 -0.07152816 0.13106532 -0.63934152 -1.17683705 -0.54506086 55 56 57 58 59 60 0.16331953 0.16413251 -0.80895524 0.04026247 -2.01114998 -0.18142818 61 62 63 64 65 66 -1.43516085 -0.33660177 -0.82778440 -0.97525530 0.52273593 -0.89430146 67 68 69 70 71 72 0.14861473 -0.68599642 0.34044799 -1.41787944 -0.69143997 -0.81344585 73 74 75 76 77 78 -1.08458697 -0.70246485 0.05020971 -0.81013447 -0.89216930 -0.34561128 79 80 81 82 83 84 -0.91635457 -1.24406236 -0.85138527 0.01141788 0.88722104 0.92940957 85 86 87 88 89 90 -0.34624867 0.53569422 1.16661799 0.50505949 0.91295406 0.27190036 91 92 93 94 95 96 0.78881602 0.54503054 0.42174492 -0.11645887 0.25638259 -0.76321156 97 98 99 100 101 102 -0.05777296 0.38425934 0.83205902 -0.32955167 0.46299571 -0.47166646 103 104 105 106 107 108 -0.71337857 0.44740621 0.30218507 1.30003355 -0.37411385 0.95292512 109 110 111 112 113 114 0.20244151 -0.23492480 -0.68930781 -0.27570883 0.02447669 0.74884497 115 116 117 118 119 120 0.52270942 1.14989448 1.01023866 -0.72468774 -0.01218298 -0.05556594 121 122 123 124 125 126 -0.09634997 0.81901628 0.18608738 0.62801831 0.90365003 0.55453647 127 128 129 130 131 132 0.64225634 0.01141788 0.38950079 -0.81713869 0.76767412 0.80477825 133 134 135 136 137 138 -0.56169906 -0.73295877 0.56221371 0.02447669 0.31351030 -0.31805354 139 140 141 142 143 144 1.89181217 -0.56320407 1.23192931 -0.15183880 0.87663727 -0.59131075 145 146 147 148 149 150 0.10651152 -2.13973293 -0.80804175 1.68563929 0.79009576 1.14530335 151 152 153 154 155 156 0.21310015 0.64556772 -0.48553510 1.02447669 0.48349027 0.32878658 > postscript(file="/var/www/html/rcomp/tmp/6fy9y1291322444.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.00704189 NA 1 0.06312856 -0.00704189 2 0.56877450 0.06312856 3 0.39731815 0.56877450 4 0.91791458 0.39731815 5 0.99781725 0.91791458 6 -1.75154644 0.99781725 7 0.58831612 -1.75154644 8 0.89695326 0.58831612 9 2.00454053 0.89695326 10 0.35120822 2.00454053 11 0.10523178 0.35120822 12 -0.30921962 0.10523178 13 0.69195961 -0.30921962 14 -0.45128955 0.69195961 15 -1.02497378 -0.45128955 16 0.27190036 -1.02497378 17 0.06599550 0.27190036 18 -1.03921181 0.06599550 19 -0.42592191 -1.03921181 20 -1.64120567 -0.42592191 21 0.92633968 -1.64120567 22 -0.06953596 0.92633968 23 -0.76449130 -0.06953596 24 0.15016249 -0.76449130 25 -0.21331614 0.15016249 26 0.65895334 -0.21331614 27 0.22851740 0.65895334 28 0.28613839 0.22851740 29 -0.96589247 0.28613839 30 -0.27568232 -0.96589247 31 0.86394699 -0.27568232 32 0.64012418 0.86394699 33 -1.14911182 0.64012418 34 -0.91105098 -1.14911182 35 -0.23904916 -0.91105098 36 -0.06906919 -0.23904916 37 0.48266436 -0.06906919 38 0.02933583 0.48266436 39 0.76095084 0.02933583 40 0.33106910 0.76095084 41 -0.41158336 0.33106910 42 -1.20527053 -0.41158336 43 -0.72596748 -1.20527053 44 0.34101634 -0.72596748 45 -0.11113297 0.34101634 46 -1.13686599 -0.11113297 47 -0.57234164 -1.13686599 48 0.47608103 -0.57234164 49 -0.07152816 0.47608103 50 0.13106532 -0.07152816 51 -0.63934152 0.13106532 52 -1.17683705 -0.63934152 53 -0.54506086 -1.17683705 54 0.16331953 -0.54506086 55 0.16413251 0.16331953 56 -0.80895524 0.16413251 57 0.04026247 -0.80895524 58 -2.01114998 0.04026247 59 -0.18142818 -2.01114998 60 -1.43516085 -0.18142818 61 -0.33660177 -1.43516085 62 -0.82778440 -0.33660177 63 -0.97525530 -0.82778440 64 0.52273593 -0.97525530 65 -0.89430146 0.52273593 66 0.14861473 -0.89430146 67 -0.68599642 0.14861473 68 0.34044799 -0.68599642 69 -1.41787944 0.34044799 70 -0.69143997 -1.41787944 71 -0.81344585 -0.69143997 72 -1.08458697 -0.81344585 73 -0.70246485 -1.08458697 74 0.05020971 -0.70246485 75 -0.81013447 0.05020971 76 -0.89216930 -0.81013447 77 -0.34561128 -0.89216930 78 -0.91635457 -0.34561128 79 -1.24406236 -0.91635457 80 -0.85138527 -1.24406236 81 0.01141788 -0.85138527 82 0.88722104 0.01141788 83 0.92940957 0.88722104 84 -0.34624867 0.92940957 85 0.53569422 -0.34624867 86 1.16661799 0.53569422 87 0.50505949 1.16661799 88 0.91295406 0.50505949 89 0.27190036 0.91295406 90 0.78881602 0.27190036 91 0.54503054 0.78881602 92 0.42174492 0.54503054 93 -0.11645887 0.42174492 94 0.25638259 -0.11645887 95 -0.76321156 0.25638259 96 -0.05777296 -0.76321156 97 0.38425934 -0.05777296 98 0.83205902 0.38425934 99 -0.32955167 0.83205902 100 0.46299571 -0.32955167 101 -0.47166646 0.46299571 102 -0.71337857 -0.47166646 103 0.44740621 -0.71337857 104 0.30218507 0.44740621 105 1.30003355 0.30218507 106 -0.37411385 1.30003355 107 0.95292512 -0.37411385 108 0.20244151 0.95292512 109 -0.23492480 0.20244151 110 -0.68930781 -0.23492480 111 -0.27570883 -0.68930781 112 0.02447669 -0.27570883 113 0.74884497 0.02447669 114 0.52270942 0.74884497 115 1.14989448 0.52270942 116 1.01023866 1.14989448 117 -0.72468774 1.01023866 118 -0.01218298 -0.72468774 119 -0.05556594 -0.01218298 120 -0.09634997 -0.05556594 121 0.81901628 -0.09634997 122 0.18608738 0.81901628 123 0.62801831 0.18608738 124 0.90365003 0.62801831 125 0.55453647 0.90365003 126 0.64225634 0.55453647 127 0.01141788 0.64225634 128 0.38950079 0.01141788 129 -0.81713869 0.38950079 130 0.76767412 -0.81713869 131 0.80477825 0.76767412 132 -0.56169906 0.80477825 133 -0.73295877 -0.56169906 134 0.56221371 -0.73295877 135 0.02447669 0.56221371 136 0.31351030 0.02447669 137 -0.31805354 0.31351030 138 1.89181217 -0.31805354 139 -0.56320407 1.89181217 140 1.23192931 -0.56320407 141 -0.15183880 1.23192931 142 0.87663727 -0.15183880 143 -0.59131075 0.87663727 144 0.10651152 -0.59131075 145 -2.13973293 0.10651152 146 -0.80804175 -2.13973293 147 1.68563929 -0.80804175 148 0.79009576 1.68563929 149 1.14530335 0.79009576 150 0.21310015 1.14530335 151 0.64556772 0.21310015 152 -0.48553510 0.64556772 153 1.02447669 -0.48553510 154 0.48349027 1.02447669 155 0.32878658 0.48349027 156 NA 0.32878658 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.06312856 -0.00704189 [2,] 0.56877450 0.06312856 [3,] 0.39731815 0.56877450 [4,] 0.91791458 0.39731815 [5,] 0.99781725 0.91791458 [6,] -1.75154644 0.99781725 [7,] 0.58831612 -1.75154644 [8,] 0.89695326 0.58831612 [9,] 2.00454053 0.89695326 [10,] 0.35120822 2.00454053 [11,] 0.10523178 0.35120822 [12,] -0.30921962 0.10523178 [13,] 0.69195961 -0.30921962 [14,] -0.45128955 0.69195961 [15,] -1.02497378 -0.45128955 [16,] 0.27190036 -1.02497378 [17,] 0.06599550 0.27190036 [18,] -1.03921181 0.06599550 [19,] -0.42592191 -1.03921181 [20,] -1.64120567 -0.42592191 [21,] 0.92633968 -1.64120567 [22,] -0.06953596 0.92633968 [23,] -0.76449130 -0.06953596 [24,] 0.15016249 -0.76449130 [25,] -0.21331614 0.15016249 [26,] 0.65895334 -0.21331614 [27,] 0.22851740 0.65895334 [28,] 0.28613839 0.22851740 [29,] -0.96589247 0.28613839 [30,] -0.27568232 -0.96589247 [31,] 0.86394699 -0.27568232 [32,] 0.64012418 0.86394699 [33,] -1.14911182 0.64012418 [34,] -0.91105098 -1.14911182 [35,] -0.23904916 -0.91105098 [36,] -0.06906919 -0.23904916 [37,] 0.48266436 -0.06906919 [38,] 0.02933583 0.48266436 [39,] 0.76095084 0.02933583 [40,] 0.33106910 0.76095084 [41,] -0.41158336 0.33106910 [42,] -1.20527053 -0.41158336 [43,] -0.72596748 -1.20527053 [44,] 0.34101634 -0.72596748 [45,] -0.11113297 0.34101634 [46,] -1.13686599 -0.11113297 [47,] -0.57234164 -1.13686599 [48,] 0.47608103 -0.57234164 [49,] -0.07152816 0.47608103 [50,] 0.13106532 -0.07152816 [51,] -0.63934152 0.13106532 [52,] -1.17683705 -0.63934152 [53,] -0.54506086 -1.17683705 [54,] 0.16331953 -0.54506086 [55,] 0.16413251 0.16331953 [56,] -0.80895524 0.16413251 [57,] 0.04026247 -0.80895524 [58,] -2.01114998 0.04026247 [59,] -0.18142818 -2.01114998 [60,] -1.43516085 -0.18142818 [61,] -0.33660177 -1.43516085 [62,] -0.82778440 -0.33660177 [63,] -0.97525530 -0.82778440 [64,] 0.52273593 -0.97525530 [65,] -0.89430146 0.52273593 [66,] 0.14861473 -0.89430146 [67,] -0.68599642 0.14861473 [68,] 0.34044799 -0.68599642 [69,] -1.41787944 0.34044799 [70,] -0.69143997 -1.41787944 [71,] -0.81344585 -0.69143997 [72,] -1.08458697 -0.81344585 [73,] -0.70246485 -1.08458697 [74,] 0.05020971 -0.70246485 [75,] -0.81013447 0.05020971 [76,] -0.89216930 -0.81013447 [77,] -0.34561128 -0.89216930 [78,] -0.91635457 -0.34561128 [79,] -1.24406236 -0.91635457 [80,] -0.85138527 -1.24406236 [81,] 0.01141788 -0.85138527 [82,] 0.88722104 0.01141788 [83,] 0.92940957 0.88722104 [84,] -0.34624867 0.92940957 [85,] 0.53569422 -0.34624867 [86,] 1.16661799 0.53569422 [87,] 0.50505949 1.16661799 [88,] 0.91295406 0.50505949 [89,] 0.27190036 0.91295406 [90,] 0.78881602 0.27190036 [91,] 0.54503054 0.78881602 [92,] 0.42174492 0.54503054 [93,] -0.11645887 0.42174492 [94,] 0.25638259 -0.11645887 [95,] -0.76321156 0.25638259 [96,] -0.05777296 -0.76321156 [97,] 0.38425934 -0.05777296 [98,] 0.83205902 0.38425934 [99,] -0.32955167 0.83205902 [100,] 0.46299571 -0.32955167 [101,] -0.47166646 0.46299571 [102,] -0.71337857 -0.47166646 [103,] 0.44740621 -0.71337857 [104,] 0.30218507 0.44740621 [105,] 1.30003355 0.30218507 [106,] -0.37411385 1.30003355 [107,] 0.95292512 -0.37411385 [108,] 0.20244151 0.95292512 [109,] -0.23492480 0.20244151 [110,] -0.68930781 -0.23492480 [111,] -0.27570883 -0.68930781 [112,] 0.02447669 -0.27570883 [113,] 0.74884497 0.02447669 [114,] 0.52270942 0.74884497 [115,] 1.14989448 0.52270942 [116,] 1.01023866 1.14989448 [117,] -0.72468774 1.01023866 [118,] -0.01218298 -0.72468774 [119,] -0.05556594 -0.01218298 [120,] -0.09634997 -0.05556594 [121,] 0.81901628 -0.09634997 [122,] 0.18608738 0.81901628 [123,] 0.62801831 0.18608738 [124,] 0.90365003 0.62801831 [125,] 0.55453647 0.90365003 [126,] 0.64225634 0.55453647 [127,] 0.01141788 0.64225634 [128,] 0.38950079 0.01141788 [129,] -0.81713869 0.38950079 [130,] 0.76767412 -0.81713869 [131,] 0.80477825 0.76767412 [132,] -0.56169906 0.80477825 [133,] -0.73295877 -0.56169906 [134,] 0.56221371 -0.73295877 [135,] 0.02447669 0.56221371 [136,] 0.31351030 0.02447669 [137,] -0.31805354 0.31351030 [138,] 1.89181217 -0.31805354 [139,] -0.56320407 1.89181217 [140,] 1.23192931 -0.56320407 [141,] -0.15183880 1.23192931 [142,] 0.87663727 -0.15183880 [143,] -0.59131075 0.87663727 [144,] 0.10651152 -0.59131075 [145,] -2.13973293 0.10651152 [146,] -0.80804175 -2.13973293 [147,] 1.68563929 -0.80804175 [148,] 0.79009576 1.68563929 [149,] 1.14530335 0.79009576 [150,] 0.21310015 1.14530335 [151,] 0.64556772 0.21310015 [152,] -0.48553510 0.64556772 [153,] 1.02447669 -0.48553510 [154,] 0.48349027 1.02447669 [155,] 0.32878658 0.48349027 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.06312856 -0.00704189 2 0.56877450 0.06312856 3 0.39731815 0.56877450 4 0.91791458 0.39731815 5 0.99781725 0.91791458 6 -1.75154644 0.99781725 7 0.58831612 -1.75154644 8 0.89695326 0.58831612 9 2.00454053 0.89695326 10 0.35120822 2.00454053 11 0.10523178 0.35120822 12 -0.30921962 0.10523178 13 0.69195961 -0.30921962 14 -0.45128955 0.69195961 15 -1.02497378 -0.45128955 16 0.27190036 -1.02497378 17 0.06599550 0.27190036 18 -1.03921181 0.06599550 19 -0.42592191 -1.03921181 20 -1.64120567 -0.42592191 21 0.92633968 -1.64120567 22 -0.06953596 0.92633968 23 -0.76449130 -0.06953596 24 0.15016249 -0.76449130 25 -0.21331614 0.15016249 26 0.65895334 -0.21331614 27 0.22851740 0.65895334 28 0.28613839 0.22851740 29 -0.96589247 0.28613839 30 -0.27568232 -0.96589247 31 0.86394699 -0.27568232 32 0.64012418 0.86394699 33 -1.14911182 0.64012418 34 -0.91105098 -1.14911182 35 -0.23904916 -0.91105098 36 -0.06906919 -0.23904916 37 0.48266436 -0.06906919 38 0.02933583 0.48266436 39 0.76095084 0.02933583 40 0.33106910 0.76095084 41 -0.41158336 0.33106910 42 -1.20527053 -0.41158336 43 -0.72596748 -1.20527053 44 0.34101634 -0.72596748 45 -0.11113297 0.34101634 46 -1.13686599 -0.11113297 47 -0.57234164 -1.13686599 48 0.47608103 -0.57234164 49 -0.07152816 0.47608103 50 0.13106532 -0.07152816 51 -0.63934152 0.13106532 52 -1.17683705 -0.63934152 53 -0.54506086 -1.17683705 54 0.16331953 -0.54506086 55 0.16413251 0.16331953 56 -0.80895524 0.16413251 57 0.04026247 -0.80895524 58 -2.01114998 0.04026247 59 -0.18142818 -2.01114998 60 -1.43516085 -0.18142818 61 -0.33660177 -1.43516085 62 -0.82778440 -0.33660177 63 -0.97525530 -0.82778440 64 0.52273593 -0.97525530 65 -0.89430146 0.52273593 66 0.14861473 -0.89430146 67 -0.68599642 0.14861473 68 0.34044799 -0.68599642 69 -1.41787944 0.34044799 70 -0.69143997 -1.41787944 71 -0.81344585 -0.69143997 72 -1.08458697 -0.81344585 73 -0.70246485 -1.08458697 74 0.05020971 -0.70246485 75 -0.81013447 0.05020971 76 -0.89216930 -0.81013447 77 -0.34561128 -0.89216930 78 -0.91635457 -0.34561128 79 -1.24406236 -0.91635457 80 -0.85138527 -1.24406236 81 0.01141788 -0.85138527 82 0.88722104 0.01141788 83 0.92940957 0.88722104 84 -0.34624867 0.92940957 85 0.53569422 -0.34624867 86 1.16661799 0.53569422 87 0.50505949 1.16661799 88 0.91295406 0.50505949 89 0.27190036 0.91295406 90 0.78881602 0.27190036 91 0.54503054 0.78881602 92 0.42174492 0.54503054 93 -0.11645887 0.42174492 94 0.25638259 -0.11645887 95 -0.76321156 0.25638259 96 -0.05777296 -0.76321156 97 0.38425934 -0.05777296 98 0.83205902 0.38425934 99 -0.32955167 0.83205902 100 0.46299571 -0.32955167 101 -0.47166646 0.46299571 102 -0.71337857 -0.47166646 103 0.44740621 -0.71337857 104 0.30218507 0.44740621 105 1.30003355 0.30218507 106 -0.37411385 1.30003355 107 0.95292512 -0.37411385 108 0.20244151 0.95292512 109 -0.23492480 0.20244151 110 -0.68930781 -0.23492480 111 -0.27570883 -0.68930781 112 0.02447669 -0.27570883 113 0.74884497 0.02447669 114 0.52270942 0.74884497 115 1.14989448 0.52270942 116 1.01023866 1.14989448 117 -0.72468774 1.01023866 118 -0.01218298 -0.72468774 119 -0.05556594 -0.01218298 120 -0.09634997 -0.05556594 121 0.81901628 -0.09634997 122 0.18608738 0.81901628 123 0.62801831 0.18608738 124 0.90365003 0.62801831 125 0.55453647 0.90365003 126 0.64225634 0.55453647 127 0.01141788 0.64225634 128 0.38950079 0.01141788 129 -0.81713869 0.38950079 130 0.76767412 -0.81713869 131 0.80477825 0.76767412 132 -0.56169906 0.80477825 133 -0.73295877 -0.56169906 134 0.56221371 -0.73295877 135 0.02447669 0.56221371 136 0.31351030 0.02447669 137 -0.31805354 0.31351030 138 1.89181217 -0.31805354 139 -0.56320407 1.89181217 140 1.23192931 -0.56320407 141 -0.15183880 1.23192931 142 0.87663727 -0.15183880 143 -0.59131075 0.87663727 144 0.10651152 -0.59131075 145 -2.13973293 0.10651152 146 -0.80804175 -2.13973293 147 1.68563929 -0.80804175 148 0.79009576 1.68563929 149 1.14530335 0.79009576 150 0.21310015 1.14530335 151 0.64556772 0.21310015 152 -0.48553510 0.64556772 153 1.02447669 -0.48553510 154 0.48349027 1.02447669 155 0.32878658 0.48349027 > 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/78q811291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/81h741291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/91h741291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/101h741291322444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11xrnv1291322444.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/120r4j1291322444.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13e12s1291322444.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14i10g1291322444.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/15l2h41291322444.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/166lfr1291322444.tab") + } > > try(system("convert tmp/1mpsv1291322444.ps tmp/1mpsv1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/2mpsv1291322444.ps tmp/2mpsv1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/3mpsv1291322444.ps tmp/3mpsv1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/4fy9y1291322444.ps tmp/4fy9y1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/5fy9y1291322444.ps tmp/5fy9y1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/6fy9y1291322444.ps tmp/6fy9y1291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/78q811291322444.ps tmp/78q811291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/81h741291322444.ps tmp/81h741291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/91h741291322444.ps tmp/91h741291322444.png",intern=TRUE)) character(0) > try(system("convert tmp/101h741291322444.ps tmp/101h741291322444.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.127 1.771 9.853