R version 2.8.0 (2008-10-20) Copyright (C) 2008 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. Natural language support but running in an English locale 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(15 + ,10 + ,12 + ,16 + ,6 + ,1 + ,1 + ,3 + ,12 + ,9 + ,7 + ,12 + ,6 + ,1 + ,0 + ,0 + ,9 + ,12 + ,11 + ,11 + ,4 + ,1 + ,0 + ,3 + ,10 + ,12 + ,11 + ,12 + ,6 + ,1 + ,3 + ,0 + ,13 + ,9 + ,14 + ,14 + ,6 + ,1 + ,1 + ,3 + ,16 + ,11 + ,16 + ,16 + ,7 + ,1 + ,1 + ,0 + ,14 + ,12 + ,13 + ,13 + ,6 + ,1 + ,2 + ,0 + ,16 + ,11 + ,13 + ,14 + ,7 + ,2 + ,0 + ,1 + ,10 + ,12 + ,5 + ,13 + ,6 + ,1 + ,1 + ,1 + ,8 + ,12 + ,8 + ,13 + ,4 + ,0 + ,0 + ,0 + ,12 + ,11 + ,14 + ,13 + ,5 + ,2 + ,1 + ,0 + ,15 + ,11 + ,15 + ,15 + ,8 + ,1 + ,0 + ,2 + ,14 + ,12 + ,8 + ,14 + ,4 + ,1 + ,0 + ,0 + ,14 + ,6 + ,13 + ,12 + ,6 + ,1 + ,0 + ,0 + ,12 + ,13 + ,12 + ,12 + ,6 + ,1 + ,0 + ,1 + ,12 + ,11 + ,11 + ,12 + ,5 + ,0 + ,2 + ,1 + ,10 + ,12 + ,8 + ,11 + ,4 + ,0 + ,3 + ,1 + ,4 + ,10 + ,4 + ,10 + ,2 + ,0 + ,2 + ,0 + ,14 + ,11 + ,15 + ,15 + ,8 + ,0 + ,2 + ,1 + ,15 + ,12 + ,12 + ,16 + ,7 + ,0 + ,0 + ,0 + ,16 + ,12 + ,14 + ,14 + ,6 + ,1 + ,0 + ,0 + ,12 + ,12 + ,9 + ,13 + ,4 + ,2 + ,0 + ,0 + ,12 + 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+ ,0 + ,10 + ,13 + ,4 + ,13 + ,6 + ,0 + ,0 + ,1 + ,15 + ,11 + ,16 + ,14 + ,6 + ,1 + ,0 + ,0 + ,16 + ,12 + ,12 + ,15 + ,8 + ,0 + ,1 + ,0 + ,16 + ,12 + ,15 + ,16 + ,7 + ,1 + ,0 + ,0 + ,14 + ,10 + ,12 + ,15 + ,6 + ,0 + ,0 + ,0 + ,14 + ,11 + ,14 + ,12 + ,6 + ,1 + ,0 + ,0 + ,12 + ,11 + ,11 + ,14 + ,2 + ,0 + ,0 + ,0 + ,15 + ,11 + ,16 + ,11 + ,5 + ,0 + ,0 + ,0 + ,13 + ,8 + ,14 + ,14 + ,5 + ,0 + ,0 + ,1 + ,16 + ,11 + ,14 + ,14 + ,6 + ,1 + ,0 + ,0 + ,14 + ,12 + ,15 + ,14 + ,6 + ,0 + ,0 + ,1 + ,8 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,16 + ,12 + ,15 + ,14 + ,6 + ,0 + ,0 + ,0 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,0 + ,1 + ,12 + ,12 + ,15 + ,13 + ,6 + ,0 + ,1 + ,0 + ,11 + ,8 + ,10 + ,14 + ,5 + ,0 + ,0 + ,0 + ,16 + ,12 + ,14 + ,16 + ,8 + ,0 + ,0 + ,0 + ,9 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0) + ,dim=c(8 + ,156) + ,dimnames=list(c('Popularity' + ,'FindingFriends' + ,'KnowingPeople' + ,'Liked' + ,'Celebrity' + ,'B' + ,'2B' + ,'3B') + ,1:156)) > y <- array(NA,dim=c(8,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B'),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 Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B 1 15 10 12 16 6 1 1 3 2 12 9 7 12 6 1 0 0 3 9 12 11 11 4 1 0 3 4 10 12 11 12 6 1 3 0 5 13 9 14 14 6 1 1 3 6 16 11 16 16 7 1 1 0 7 14 12 13 13 6 1 2 0 8 16 11 13 14 7 2 0 1 9 10 12 5 13 6 1 1 1 10 8 12 8 13 4 0 0 0 11 12 11 14 13 5 2 1 0 12 15 11 15 15 8 1 0 2 13 14 12 8 14 4 1 0 0 14 14 6 13 12 6 1 0 0 15 12 13 12 12 6 1 0 1 16 12 11 11 12 5 0 2 1 17 10 12 8 11 4 0 3 1 18 4 10 4 10 2 0 2 0 19 14 11 15 15 8 0 2 1 20 15 12 12 16 7 0 0 0 21 16 12 14 14 6 1 0 0 22 12 12 9 13 4 2 0 0 23 12 11 16 13 4 0 0 0 24 12 12 10 13 4 0 1 0 25 12 12 8 13 5 0 2 0 26 12 12 14 14 4 1 0 0 27 11 6 6 9 4 1 1 0 28 11 5 16 14 6 3 0 0 29 11 12 11 12 6 0 1 3 30 11 14 7 13 6 0 1 2 31 11 12 13 11 4 1 0 0 32 11 9 7 13 2 2 0 1 33 15 11 14 15 7 1 0 0 34 15 11 17 16 6 1 0 1 35 9 11 15 15 7 0 2 2 36 16 12 8 14 4 0 2 1 37 13 10 8 8 4 0 0 1 38 9 12 11 11 4 2 2 0 39 16 11 16 15 6 1 2 0 40 12 12 10 15 6 1 0 0 41 15 9 5 11 3 2 1 0 42 5 15 8 12 3 0 3 0 43 11 11 8 12 6 1 2 0 44 17 11 15 14 5 2 0 0 45 9 15 6 8 4 0 2 1 46 13 12 16 16 6 2 0 0 47 16 9 16 16 6 0 1 1 48 16 12 16 14 6 0 1 0 49 14 9 19 12 6 1 1 0 50 16 11 14 15 6 0 1 1 51 11 12 15 12 6 1 0 0 52 11 11 11 14 5 0 1 2 53 11 6 14 17 6 1 2 1 54 12 10 12 13 6 1 0 0 55 12 12 15 13 6 1 1 1 56 12 13 14 12 5 1 1 0 57 14 11 13 16 6 1 1 1 58 10 10 11 12 5 1 0 2 59 9 11 8 10 4 0 1 0 60 12 7 11 15 5 0 1 0 61 10 11 9 12 4 1 0 0 62 14 11 10 16 6 2 2 0 63 8 7 4 13 6 1 0 0 64 16 12 15 15 7 0 2 1 65 14 14 17 18 6 0 1 3 66 14 11 12 12 4 0 0 1 67 12 12 12 13 4 0 1 0 68 14 11 15 14 6 0 1 0 69 7 12 13 12 3 0 1 0 70 19 12 15 15 6 2 1 0 71 15 12 14 16 4 0 0 2 72 8 12 8 14 5 0 0 0 73 10 15 15 15 6 1 0 0 74 13 11 12 13 7 1 1 0 75 13 13 14 13 3 1 0 3 76 10 10 10 11 5 1 0 1 77 12 12 7 12 3 1 0 0 78 15 13 16 18 8 0 0 1 79 7 14 12 12 4 0 0 1 80 14 11 15 16 6 0 0 1 81 10 11 7 9 4 1 1 1 82 6 7 9 11 4 2 0 0 83 11 11 15 10 5 1 1 0 84 12 12 7 11 4 2 2 0 85 14 12 15 13 6 3 1 0 86 12 10 14 13 7 1 2 0 87 14 12 14 15 7 0 1 2 88 11 8 8 13 4 2 1 1 89 10 7 8 9 5 1 0 0 90 13 11 14 13 6 0 1 2 91 8 11 10 12 4 0 0 1 92 9 11 12 13 5 2 0 4 93 6 9 15 11 6 1 1 0 94 12 12 12 14 5 1 0 0 95 14 13 13 13 5 0 0 0 96 11 9 12 12 4 2 2 0 97 8 11 10 15 2 0 0 1 98 7 12 8 12 3 1 0 0 99 9 9 6 12 5 0 2 0 100 14 12 13 13 5 3 1 0 101 13 12 7 12 5 0 0 0 102 15 12 13 13 6 0 1 2 103 5 14 4 5 2 1 1 2 104 15 11 14 13 5 0 2 2 105 13 12 13 13 5 0 1 0 106 12 8 13 13 5 0 0 1 107 6 12 6 11 2 1 0 0 108 7 12 7 12 4 1 0 0 109 13 12 5 12 3 3 0 0 110 16 11 14 15 8 2 0 0 111 10 11 13 15 6 0 1 0 112 16 12 16 16 7 1 0 0 113 15 10 16 13 6 1 0 0 114 8 13 7 10 3 1 0 1 115 11 8 14 15 5 1 0 0 116 13 12 11 13 6 1 1 2 117 16 11 17 16 7 0 2 1 118 11 10 5 13 3 0 1 3 119 14 13 10 16 8 0 1 1 120 9 10 11 13 3 0 0 2 121 8 10 10 14 3 0 1 0 122 8 7 9 15 4 2 0 0 123 11 10 12 14 5 1 3 0 124 12 8 15 13 7 0 2 0 125 11 12 7 13 6 2 1 0 126 14 12 13 15 6 1 0 0 127 11 12 8 16 6 1 0 0 128 14 11 16 12 5 1 1 0 129 13 13 15 14 6 0 0 1 130 12 12 6 14 5 1 1 0 131 4 8 6 4 4 1 0 0 132 15 11 12 13 6 0 0 0 133 10 12 8 16 4 0 0 1 134 13 13 11 15 6 0 0 0 135 15 12 13 14 6 0 0 2 136 12 10 14 14 5 0 0 0 137 13 12 14 14 6 1 0 0 138 8 10 10 6 4 0 0 0 139 10 13 4 13 6 0 0 1 140 15 11 16 14 6 1 0 0 141 16 12 12 15 8 0 1 0 142 16 12 15 16 7 1 0 0 143 14 10 12 15 6 0 0 0 144 14 11 14 12 6 1 0 0 145 12 11 11 14 2 0 0 0 146 15 11 16 11 5 0 0 0 147 13 8 14 14 5 0 0 1 148 16 11 14 14 6 1 0 0 149 14 12 15 14 6 0 0 1 150 8 11 9 12 4 0 0 0 151 16 12 15 14 6 0 0 0 152 16 12 14 16 8 1 0 1 153 12 12 15 13 6 0 1 0 154 11 8 10 14 5 0 0 0 155 16 12 14 16 8 0 0 0 156 9 11 9 12 4 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) FindingFriends KnowingPeople Liked Celebrity -0.23026 0.12442 0.24245 0.35248 0.63332 B `2B` `3B` 0.32079 -0.11246 -0.02646 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.41179 -1.12493 -0.05509 1.27452 6.59187 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.23026 1.49695 -0.154 0.877960 FindingFriends 0.12442 0.09824 1.267 0.207313 KnowingPeople 0.24245 0.06161 3.935 0.000128 *** Liked 0.35248 0.09736 3.620 0.000403 *** Celebrity 0.63332 0.15756 4.020 9.25e-05 *** B 0.32079 0.22771 1.409 0.161003 `2B` -0.11246 0.21191 -0.531 0.596422 `3B` -0.02646 0.19725 -0.134 0.893463 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.109 on 148 degrees of freedom Multiple R-squared: 0.5076, Adjusted R-squared: 0.4843 F-statistic: 21.79 on 7 and 148 DF, p-value: < 2.2e-16 > 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,] 0.27842126 0.5568425213 0.7215787393 [2,] 0.24316503 0.4863300520 0.7568349740 [3,] 0.34040270 0.6808053986 0.6595973007 [4,] 0.24231903 0.4846380604 0.7576809698 [5,] 0.17202341 0.3440468252 0.8279765874 [6,] 0.22539947 0.4507989306 0.7746005347 [7,] 0.20020203 0.4004040554 0.7997979723 [8,] 0.23835304 0.4767060854 0.7616469573 [9,] 0.18874656 0.3774931203 0.8112534399 [10,] 0.13242326 0.2648465231 0.8675767385 [11,] 0.12819686 0.2563937228 0.8718031386 [12,] 0.08759866 0.1751973108 0.9124013446 [13,] 0.05885796 0.1177159112 0.9411420444 [14,] 0.04898931 0.0979786271 0.9510106864 [15,] 0.04065573 0.0813114670 0.9593442665 [16,] 0.03128717 0.0625743419 0.9687128291 [17,] 0.03772714 0.0754542716 0.9622728642 [18,] 0.15041427 0.3008285475 0.8495857263 [19,] 0.11589755 0.2317950982 0.8841024509 [20,] 0.08799280 0.1759856036 0.9120071982 [21,] 0.06328089 0.1265617775 0.9367191113 [22,] 0.05667238 0.1133447580 0.9433276210 [23,] 0.04003389 0.0800677885 0.9599661058 [24,] 0.02768266 0.0553653211 0.9723173394 [25,] 0.12237112 0.2447422430 0.8776288785 [26,] 0.37375689 0.7475137897 0.6262431052 [27,] 0.56048717 0.8790256562 0.4395128281 [28,] 0.52042869 0.9591426220 0.4795713110 [29,] 0.53490441 0.9301911821 0.4650955910 [30,] 0.51703002 0.9659399655 0.4829699828 [31,] 0.81364430 0.3727114037 0.1863557019 [32,] 0.89822317 0.2035536529 0.1017768265 [33,] 0.87325532 0.2534893700 0.1267446850 [34,] 0.91291854 0.1741629228 0.0870814614 [35,] 0.89627916 0.2074416863 0.1037208432 [36,] 0.89428683 0.2114263309 0.1057131655 [37,] 0.88770734 0.2245853218 0.1122926609 [38,] 0.89262677 0.2147464597 0.1073732299 [39,] 0.86720046 0.2655990806 0.1327995403 [40,] 0.87212026 0.2557594726 0.1278797363 [41,] 0.87467023 0.2506595341 0.1253297670 [42,] 0.85489650 0.2902069935 0.1451034967 [43,] 0.88461301 0.2307739751 0.1153869876 [44,] 0.86774510 0.2645098001 0.1322549000 [45,] 0.84975667 0.3004866700 0.1502433350 [46,] 0.81983779 0.3603244149 0.1801622074 [47,] 0.78549466 0.4290106701 0.2145053350 [48,] 0.77107270 0.4578545955 0.2289272978 [49,] 0.74006009 0.5198798227 0.2599399113 [50,] 0.70963491 0.5807301888 0.2903650944 [51,] 0.68057651 0.6388469706 0.3194234853 [52,] 0.64061385 0.7187723089 0.3593861545 [53,] 0.69496552 0.6100689531 0.3050344766 [54,] 0.68558346 0.6288330706 0.3144165353 [55,] 0.67136753 0.6572649490 0.3286324745 [56,] 0.70862712 0.5827457688 0.2913728844 [57,] 0.67019731 0.6596053786 0.3298026893 [58,] 0.62757265 0.7448547048 0.3724273524 [59,] 0.72111057 0.5577788512 0.2788894256 [60,] 0.84432816 0.3113436780 0.1556718390 [61,] 0.84166579 0.3166684243 0.1583342121 [62,] 0.88614356 0.2277128864 0.1138564432 [63,] 0.95604011 0.0879197704 0.0439598852 [64,] 0.94382460 0.1123507900 0.0561753950 [65,] 0.93445179 0.1310964261 0.0655482131 [66,] 0.92094313 0.1581137432 0.0790568716 [67,] 0.92974565 0.1405087069 0.0702543534 [68,] 0.92626601 0.1474679840 0.0737339920 [69,] 0.97070015 0.0585996935 0.0292998467 [70,] 0.96185337 0.0762932504 0.0381466252 [71,] 0.95595009 0.0880998278 0.0440499139 [72,] 0.97452069 0.0509586115 0.0254793058 [73,] 0.96765156 0.0646968803 0.0323484402 [74,] 0.97022060 0.0595587949 0.0297793974 [75,] 0.96105075 0.0778984956 0.0389492478 [76,] 0.95549300 0.0890139929 0.0445069965 [77,] 0.94472454 0.1105509137 0.0552754569 [78,] 0.94011387 0.1197722677 0.0598861338 [79,] 0.93868220 0.1226355955 0.0613177977 [80,] 0.92277702 0.1544459506 0.0772229753 [81,] 0.92685832 0.1462833681 0.0731416840 [82,] 0.96327863 0.0734427396 0.0367213698 [83,] 0.99864611 0.0027077862 0.0013538931 [84,] 0.99805935 0.0038812915 0.0019406458 [85,] 0.99770554 0.0045889139 0.0022944570 [86,] 0.99664325 0.0067134941 0.0033567471 [87,] 0.99676234 0.0064753265 0.0032376632 [88,] 0.99737742 0.0052451684 0.0026225842 [89,] 0.99658727 0.0068254659 0.0034127330 [90,] 0.99512357 0.0097528572 0.0048764286 [91,] 0.99780620 0.0043876053 0.0021938026 [92,] 0.99744826 0.0051034761 0.0025517381 [93,] 0.99687548 0.0062490434 0.0031245217 [94,] 0.99717935 0.0056412982 0.0028206491 [95,] 0.99614044 0.0077191215 0.0038595608 [96,] 0.99436496 0.0112700717 0.0056350358 [97,] 0.99413925 0.0117214973 0.0058607487 [98,] 0.99647439 0.0070512114 0.0035256057 [99,] 0.99927779 0.0014444176 0.0007222088 [100,] 0.99885542 0.0022891535 0.0011445767 [101,] 0.99965567 0.0006886573 0.0003443286 [102,] 0.99942389 0.0011522149 0.0005761075 [103,] 0.99921438 0.0015712352 0.0007856176 [104,] 0.99880952 0.0023809685 0.0011904843 [105,] 0.99855804 0.0028839288 0.0014419644 [106,] 0.99764613 0.0047077401 0.0023538701 [107,] 0.99630134 0.0073973233 0.0036986616 [108,] 0.99928918 0.0014216436 0.0007108218 [109,] 0.99878429 0.0024314266 0.0012157133 [110,] 0.99802136 0.0039572701 0.0019786351 [111,] 0.99781866 0.0043626843 0.0021813422 [112,] 0.99814330 0.0037134042 0.0018567021 [113,] 0.99689207 0.0062158662 0.0031079331 [114,] 0.99709515 0.0058096926 0.0029048463 [115,] 0.99509923 0.0098015307 0.0049007654 [116,] 0.99186931 0.0162613887 0.0081306943 [117,] 0.99110395 0.0177921075 0.0088960537 [118,] 0.98578932 0.0284213615 0.0142106808 [119,] 0.98547052 0.0290589549 0.0145294775 [120,] 0.99132720 0.0173456045 0.0086728023 [121,] 0.98693603 0.0261279321 0.0130639661 [122,] 0.98951106 0.0209778774 0.0104889387 [123,] 0.98447549 0.0310490278 0.0155245139 [124,] 0.97448738 0.0510252378 0.0255126189 [125,] 0.96516448 0.0696710302 0.0348355151 [126,] 0.96100292 0.0779941565 0.0389970782 [127,] 0.95768971 0.0846205726 0.0423102863 [128,] 0.92864508 0.1427098499 0.0713549249 [129,] 0.92593959 0.1481208274 0.0740604137 [130,] 0.90269559 0.1946088236 0.0973044118 [131,] 0.99476428 0.0104714421 0.0052357210 [132,] 0.99962754 0.0007449143 0.0003724571 [133,] 0.99860231 0.0027953834 0.0013976917 [134,] 0.99707568 0.0058486312 0.0029243156 [135,] 0.98551420 0.0289716031 0.0144858016 > postscript(file="/var/www/html/freestat/rcomp/tmp/1fmh91293203680.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2fmh91293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/freestat/rcomp/tmp/3fmh91293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4pvyu1293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5pvyu1293203680.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 1.508116028 1.062851244 -1.581693625 -1.942823715 -0.147395860 0.701196370 7 8 9 10 11 12 1.107340636 1.726716092 -1.039077541 -2.317912868 -0.810609567 -0.396732928 13 14 15 16 17 18 3.008820441 1.981431708 -0.620611004 1.049707778 0.750890753 -2.550274171 19 20 21 22 23 24 -0.877488887 0.754890468 2.287492392 0.798067571 -0.133070861 1.309652259 25 26 27 28 29 30 1.273685645 -0.445864374 3.115109227 -2.968022323 -0.767571394 -0.425567817 31 32 33 34 35 36 -0.145974969 1.949333157 0.426111888 0.006072588 -5.217705135 5.580988752 37 38 39 40 41 42 4.719796013 -1.756945946 1.799458698 -1.095198377 6.591865699 -4.367995688 43 44 45 46 47 48 -0.203519613 3.482002253 0.807502285 -2.223149883 1.930609687 2.235843564 49 50 51 52 53 54 0.265941680 2.519141752 -2.249993787 -0.741251443 -2.772036723 -0.626288506 55 56 57 58 59 60 -1.463552232 -0.386186395 0.088322529 -1.345114504 -0.023589107 0.351030679 61 62 63 64 65 66 -0.404243978 0.580876821 -2.313443475 1.631410971 -1.585983600 3.215661795 67 68 69 70 71 72 0.824757321 0.602712793 -3.431887886 4.484238296 2.222884651 -3.303715130 73 74 75 76 77 78 -4.680701001 -0.271571817 1.494902534 -0.776648525 2.589550818 -1.651141941 79 80 81 82 83 84 -4.157603482 -0.188246428 1.277015097 -3.874862341 -0.674829054 2.212843931 85 86 87 88 89 90 -0.131586459 -1.519584931 -0.212139490 0.677124278 0.760010847 0.250565177 91 92 93 94 95 96 -2.299443266 -3.332326154 -6.411787798 -0.594291053 1.712106410 0.021391217 97 98 99 100 101 102 -2.090241968 -2.652896651 -0.515673493 0.986630097 2.643693630 2.368590887 103 104 105 106 107 108 -0.665879823 2.996346860 0.948988235 0.360677342 -2.182199450 -3.043770799 109 110 111 112 113 114 3.432873664 0.472004225 -3.264872914 0.464314545 1.403921617 -0.803447515 115 116 117 118 119 120 -1.933979599 0.532699779 0.918457147 2.483441146 -0.379035770 -1.110165869 121 122 123 124 125 126 -2.160663250 -3.284784923 -1.008067339 -1.192402836 -0.871220660 0.177459215 127 128 129 130 131 132 -1.962784084 1.377762186 -0.732128656 0.972853827 -2.483791129 2.570075781 133 134 135 136 137 138 -1.348892669 -0.141281560 1.903650177 -0.509556426 -0.712507608 -0.086599769 139 140 141 142 143 144 -0.712725850 0.927019213 1.586509561 0.706762014 0.989536249 1.116875442 145 146 147 148 149 150 1.993329073 2.938568812 0.765749227 2.411914151 0.392293103 -2.083457932 151 152 153 154 155 156 2.365830969 0.342350001 -1.169228321 -0.290923031 0.636673912 -1.083457932 > postscript(file="/var/www/html/freestat/rcomp/tmp/6pvyu1293203680.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 1.508116028 NA 1 1.062851244 1.508116028 2 -1.581693625 1.062851244 3 -1.942823715 -1.581693625 4 -0.147395860 -1.942823715 5 0.701196370 -0.147395860 6 1.107340636 0.701196370 7 1.726716092 1.107340636 8 -1.039077541 1.726716092 9 -2.317912868 -1.039077541 10 -0.810609567 -2.317912868 11 -0.396732928 -0.810609567 12 3.008820441 -0.396732928 13 1.981431708 3.008820441 14 -0.620611004 1.981431708 15 1.049707778 -0.620611004 16 0.750890753 1.049707778 17 -2.550274171 0.750890753 18 -0.877488887 -2.550274171 19 0.754890468 -0.877488887 20 2.287492392 0.754890468 21 0.798067571 2.287492392 22 -0.133070861 0.798067571 23 1.309652259 -0.133070861 24 1.273685645 1.309652259 25 -0.445864374 1.273685645 26 3.115109227 -0.445864374 27 -2.968022323 3.115109227 28 -0.767571394 -2.968022323 29 -0.425567817 -0.767571394 30 -0.145974969 -0.425567817 31 1.949333157 -0.145974969 32 0.426111888 1.949333157 33 0.006072588 0.426111888 34 -5.217705135 0.006072588 35 5.580988752 -5.217705135 36 4.719796013 5.580988752 37 -1.756945946 4.719796013 38 1.799458698 -1.756945946 39 -1.095198377 1.799458698 40 6.591865699 -1.095198377 41 -4.367995688 6.591865699 42 -0.203519613 -4.367995688 43 3.482002253 -0.203519613 44 0.807502285 3.482002253 45 -2.223149883 0.807502285 46 1.930609687 -2.223149883 47 2.235843564 1.930609687 48 0.265941680 2.235843564 49 2.519141752 0.265941680 50 -2.249993787 2.519141752 51 -0.741251443 -2.249993787 52 -2.772036723 -0.741251443 53 -0.626288506 -2.772036723 54 -1.463552232 -0.626288506 55 -0.386186395 -1.463552232 56 0.088322529 -0.386186395 57 -1.345114504 0.088322529 58 -0.023589107 -1.345114504 59 0.351030679 -0.023589107 60 -0.404243978 0.351030679 61 0.580876821 -0.404243978 62 -2.313443475 0.580876821 63 1.631410971 -2.313443475 64 -1.585983600 1.631410971 65 3.215661795 -1.585983600 66 0.824757321 3.215661795 67 0.602712793 0.824757321 68 -3.431887886 0.602712793 69 4.484238296 -3.431887886 70 2.222884651 4.484238296 71 -3.303715130 2.222884651 72 -4.680701001 -3.303715130 73 -0.271571817 -4.680701001 74 1.494902534 -0.271571817 75 -0.776648525 1.494902534 76 2.589550818 -0.776648525 77 -1.651141941 2.589550818 78 -4.157603482 -1.651141941 79 -0.188246428 -4.157603482 80 1.277015097 -0.188246428 81 -3.874862341 1.277015097 82 -0.674829054 -3.874862341 83 2.212843931 -0.674829054 84 -0.131586459 2.212843931 85 -1.519584931 -0.131586459 86 -0.212139490 -1.519584931 87 0.677124278 -0.212139490 88 0.760010847 0.677124278 89 0.250565177 0.760010847 90 -2.299443266 0.250565177 91 -3.332326154 -2.299443266 92 -6.411787798 -3.332326154 93 -0.594291053 -6.411787798 94 1.712106410 -0.594291053 95 0.021391217 1.712106410 96 -2.090241968 0.021391217 97 -2.652896651 -2.090241968 98 -0.515673493 -2.652896651 99 0.986630097 -0.515673493 100 2.643693630 0.986630097 101 2.368590887 2.643693630 102 -0.665879823 2.368590887 103 2.996346860 -0.665879823 104 0.948988235 2.996346860 105 0.360677342 0.948988235 106 -2.182199450 0.360677342 107 -3.043770799 -2.182199450 108 3.432873664 -3.043770799 109 0.472004225 3.432873664 110 -3.264872914 0.472004225 111 0.464314545 -3.264872914 112 1.403921617 0.464314545 113 -0.803447515 1.403921617 114 -1.933979599 -0.803447515 115 0.532699779 -1.933979599 116 0.918457147 0.532699779 117 2.483441146 0.918457147 118 -0.379035770 2.483441146 119 -1.110165869 -0.379035770 120 -2.160663250 -1.110165869 121 -3.284784923 -2.160663250 122 -1.008067339 -3.284784923 123 -1.192402836 -1.008067339 124 -0.871220660 -1.192402836 125 0.177459215 -0.871220660 126 -1.962784084 0.177459215 127 1.377762186 -1.962784084 128 -0.732128656 1.377762186 129 0.972853827 -0.732128656 130 -2.483791129 0.972853827 131 2.570075781 -2.483791129 132 -1.348892669 2.570075781 133 -0.141281560 -1.348892669 134 1.903650177 -0.141281560 135 -0.509556426 1.903650177 136 -0.712507608 -0.509556426 137 -0.086599769 -0.712507608 138 -0.712725850 -0.086599769 139 0.927019213 -0.712725850 140 1.586509561 0.927019213 141 0.706762014 1.586509561 142 0.989536249 0.706762014 143 1.116875442 0.989536249 144 1.993329073 1.116875442 145 2.938568812 1.993329073 146 0.765749227 2.938568812 147 2.411914151 0.765749227 148 0.392293103 2.411914151 149 -2.083457932 0.392293103 150 2.365830969 -2.083457932 151 0.342350001 2.365830969 152 -1.169228321 0.342350001 153 -0.290923031 -1.169228321 154 0.636673912 -0.290923031 155 -1.083457932 0.636673912 156 NA -1.083457932 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.062851244 1.508116028 [2,] -1.581693625 1.062851244 [3,] -1.942823715 -1.581693625 [4,] -0.147395860 -1.942823715 [5,] 0.701196370 -0.147395860 [6,] 1.107340636 0.701196370 [7,] 1.726716092 1.107340636 [8,] -1.039077541 1.726716092 [9,] -2.317912868 -1.039077541 [10,] -0.810609567 -2.317912868 [11,] -0.396732928 -0.810609567 [12,] 3.008820441 -0.396732928 [13,] 1.981431708 3.008820441 [14,] -0.620611004 1.981431708 [15,] 1.049707778 -0.620611004 [16,] 0.750890753 1.049707778 [17,] -2.550274171 0.750890753 [18,] -0.877488887 -2.550274171 [19,] 0.754890468 -0.877488887 [20,] 2.287492392 0.754890468 [21,] 0.798067571 2.287492392 [22,] -0.133070861 0.798067571 [23,] 1.309652259 -0.133070861 [24,] 1.273685645 1.309652259 [25,] -0.445864374 1.273685645 [26,] 3.115109227 -0.445864374 [27,] -2.968022323 3.115109227 [28,] -0.767571394 -2.968022323 [29,] -0.425567817 -0.767571394 [30,] -0.145974969 -0.425567817 [31,] 1.949333157 -0.145974969 [32,] 0.426111888 1.949333157 [33,] 0.006072588 0.426111888 [34,] -5.217705135 0.006072588 [35,] 5.580988752 -5.217705135 [36,] 4.719796013 5.580988752 [37,] -1.756945946 4.719796013 [38,] 1.799458698 -1.756945946 [39,] -1.095198377 1.799458698 [40,] 6.591865699 -1.095198377 [41,] -4.367995688 6.591865699 [42,] -0.203519613 -4.367995688 [43,] 3.482002253 -0.203519613 [44,] 0.807502285 3.482002253 [45,] -2.223149883 0.807502285 [46,] 1.930609687 -2.223149883 [47,] 2.235843564 1.930609687 [48,] 0.265941680 2.235843564 [49,] 2.519141752 0.265941680 [50,] -2.249993787 2.519141752 [51,] -0.741251443 -2.249993787 [52,] -2.772036723 -0.741251443 [53,] -0.626288506 -2.772036723 [54,] -1.463552232 -0.626288506 [55,] -0.386186395 -1.463552232 [56,] 0.088322529 -0.386186395 [57,] -1.345114504 0.088322529 [58,] -0.023589107 -1.345114504 [59,] 0.351030679 -0.023589107 [60,] -0.404243978 0.351030679 [61,] 0.580876821 -0.404243978 [62,] -2.313443475 0.580876821 [63,] 1.631410971 -2.313443475 [64,] -1.585983600 1.631410971 [65,] 3.215661795 -1.585983600 [66,] 0.824757321 3.215661795 [67,] 0.602712793 0.824757321 [68,] -3.431887886 0.602712793 [69,] 4.484238296 -3.431887886 [70,] 2.222884651 4.484238296 [71,] -3.303715130 2.222884651 [72,] -4.680701001 -3.303715130 [73,] -0.271571817 -4.680701001 [74,] 1.494902534 -0.271571817 [75,] -0.776648525 1.494902534 [76,] 2.589550818 -0.776648525 [77,] -1.651141941 2.589550818 [78,] -4.157603482 -1.651141941 [79,] -0.188246428 -4.157603482 [80,] 1.277015097 -0.188246428 [81,] -3.874862341 1.277015097 [82,] -0.674829054 -3.874862341 [83,] 2.212843931 -0.674829054 [84,] -0.131586459 2.212843931 [85,] -1.519584931 -0.131586459 [86,] -0.212139490 -1.519584931 [87,] 0.677124278 -0.212139490 [88,] 0.760010847 0.677124278 [89,] 0.250565177 0.760010847 [90,] -2.299443266 0.250565177 [91,] -3.332326154 -2.299443266 [92,] -6.411787798 -3.332326154 [93,] -0.594291053 -6.411787798 [94,] 1.712106410 -0.594291053 [95,] 0.021391217 1.712106410 [96,] -2.090241968 0.021391217 [97,] -2.652896651 -2.090241968 [98,] -0.515673493 -2.652896651 [99,] 0.986630097 -0.515673493 [100,] 2.643693630 0.986630097 [101,] 2.368590887 2.643693630 [102,] -0.665879823 2.368590887 [103,] 2.996346860 -0.665879823 [104,] 0.948988235 2.996346860 [105,] 0.360677342 0.948988235 [106,] -2.182199450 0.360677342 [107,] -3.043770799 -2.182199450 [108,] 3.432873664 -3.043770799 [109,] 0.472004225 3.432873664 [110,] -3.264872914 0.472004225 [111,] 0.464314545 -3.264872914 [112,] 1.403921617 0.464314545 [113,] -0.803447515 1.403921617 [114,] -1.933979599 -0.803447515 [115,] 0.532699779 -1.933979599 [116,] 0.918457147 0.532699779 [117,] 2.483441146 0.918457147 [118,] -0.379035770 2.483441146 [119,] -1.110165869 -0.379035770 [120,] -2.160663250 -1.110165869 [121,] -3.284784923 -2.160663250 [122,] -1.008067339 -3.284784923 [123,] -1.192402836 -1.008067339 [124,] -0.871220660 -1.192402836 [125,] 0.177459215 -0.871220660 [126,] -1.962784084 0.177459215 [127,] 1.377762186 -1.962784084 [128,] -0.732128656 1.377762186 [129,] 0.972853827 -0.732128656 [130,] -2.483791129 0.972853827 [131,] 2.570075781 -2.483791129 [132,] -1.348892669 2.570075781 [133,] -0.141281560 -1.348892669 [134,] 1.903650177 -0.141281560 [135,] -0.509556426 1.903650177 [136,] -0.712507608 -0.509556426 [137,] -0.086599769 -0.712507608 [138,] -0.712725850 -0.086599769 [139,] 0.927019213 -0.712725850 [140,] 1.586509561 0.927019213 [141,] 0.706762014 1.586509561 [142,] 0.989536249 0.706762014 [143,] 1.116875442 0.989536249 [144,] 1.993329073 1.116875442 [145,] 2.938568812 1.993329073 [146,] 0.765749227 2.938568812 [147,] 2.411914151 0.765749227 [148,] 0.392293103 2.411914151 [149,] -2.083457932 0.392293103 [150,] 2.365830969 -2.083457932 [151,] 0.342350001 2.365830969 [152,] -1.169228321 0.342350001 [153,] -0.290923031 -1.169228321 [154,] 0.636673912 -0.290923031 [155,] -1.083457932 0.636673912 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.062851244 1.508116028 2 -1.581693625 1.062851244 3 -1.942823715 -1.581693625 4 -0.147395860 -1.942823715 5 0.701196370 -0.147395860 6 1.107340636 0.701196370 7 1.726716092 1.107340636 8 -1.039077541 1.726716092 9 -2.317912868 -1.039077541 10 -0.810609567 -2.317912868 11 -0.396732928 -0.810609567 12 3.008820441 -0.396732928 13 1.981431708 3.008820441 14 -0.620611004 1.981431708 15 1.049707778 -0.620611004 16 0.750890753 1.049707778 17 -2.550274171 0.750890753 18 -0.877488887 -2.550274171 19 0.754890468 -0.877488887 20 2.287492392 0.754890468 21 0.798067571 2.287492392 22 -0.133070861 0.798067571 23 1.309652259 -0.133070861 24 1.273685645 1.309652259 25 -0.445864374 1.273685645 26 3.115109227 -0.445864374 27 -2.968022323 3.115109227 28 -0.767571394 -2.968022323 29 -0.425567817 -0.767571394 30 -0.145974969 -0.425567817 31 1.949333157 -0.145974969 32 0.426111888 1.949333157 33 0.006072588 0.426111888 34 -5.217705135 0.006072588 35 5.580988752 -5.217705135 36 4.719796013 5.580988752 37 -1.756945946 4.719796013 38 1.799458698 -1.756945946 39 -1.095198377 1.799458698 40 6.591865699 -1.095198377 41 -4.367995688 6.591865699 42 -0.203519613 -4.367995688 43 3.482002253 -0.203519613 44 0.807502285 3.482002253 45 -2.223149883 0.807502285 46 1.930609687 -2.223149883 47 2.235843564 1.930609687 48 0.265941680 2.235843564 49 2.519141752 0.265941680 50 -2.249993787 2.519141752 51 -0.741251443 -2.249993787 52 -2.772036723 -0.741251443 53 -0.626288506 -2.772036723 54 -1.463552232 -0.626288506 55 -0.386186395 -1.463552232 56 0.088322529 -0.386186395 57 -1.345114504 0.088322529 58 -0.023589107 -1.345114504 59 0.351030679 -0.023589107 60 -0.404243978 0.351030679 61 0.580876821 -0.404243978 62 -2.313443475 0.580876821 63 1.631410971 -2.313443475 64 -1.585983600 1.631410971 65 3.215661795 -1.585983600 66 0.824757321 3.215661795 67 0.602712793 0.824757321 68 -3.431887886 0.602712793 69 4.484238296 -3.431887886 70 2.222884651 4.484238296 71 -3.303715130 2.222884651 72 -4.680701001 -3.303715130 73 -0.271571817 -4.680701001 74 1.494902534 -0.271571817 75 -0.776648525 1.494902534 76 2.589550818 -0.776648525 77 -1.651141941 2.589550818 78 -4.157603482 -1.651141941 79 -0.188246428 -4.157603482 80 1.277015097 -0.188246428 81 -3.874862341 1.277015097 82 -0.674829054 -3.874862341 83 2.212843931 -0.674829054 84 -0.131586459 2.212843931 85 -1.519584931 -0.131586459 86 -0.212139490 -1.519584931 87 0.677124278 -0.212139490 88 0.760010847 0.677124278 89 0.250565177 0.760010847 90 -2.299443266 0.250565177 91 -3.332326154 -2.299443266 92 -6.411787798 -3.332326154 93 -0.594291053 -6.411787798 94 1.712106410 -0.594291053 95 0.021391217 1.712106410 96 -2.090241968 0.021391217 97 -2.652896651 -2.090241968 98 -0.515673493 -2.652896651 99 0.986630097 -0.515673493 100 2.643693630 0.986630097 101 2.368590887 2.643693630 102 -0.665879823 2.368590887 103 2.996346860 -0.665879823 104 0.948988235 2.996346860 105 0.360677342 0.948988235 106 -2.182199450 0.360677342 107 -3.043770799 -2.182199450 108 3.432873664 -3.043770799 109 0.472004225 3.432873664 110 -3.264872914 0.472004225 111 0.464314545 -3.264872914 112 1.403921617 0.464314545 113 -0.803447515 1.403921617 114 -1.933979599 -0.803447515 115 0.532699779 -1.933979599 116 0.918457147 0.532699779 117 2.483441146 0.918457147 118 -0.379035770 2.483441146 119 -1.110165869 -0.379035770 120 -2.160663250 -1.110165869 121 -3.284784923 -2.160663250 122 -1.008067339 -3.284784923 123 -1.192402836 -1.008067339 124 -0.871220660 -1.192402836 125 0.177459215 -0.871220660 126 -1.962784084 0.177459215 127 1.377762186 -1.962784084 128 -0.732128656 1.377762186 129 0.972853827 -0.732128656 130 -2.483791129 0.972853827 131 2.570075781 -2.483791129 132 -1.348892669 2.570075781 133 -0.141281560 -1.348892669 134 1.903650177 -0.141281560 135 -0.509556426 1.903650177 136 -0.712507608 -0.509556426 137 -0.086599769 -0.712507608 138 -0.712725850 -0.086599769 139 0.927019213 -0.712725850 140 1.586509561 0.927019213 141 0.706762014 1.586509561 142 0.989536249 0.706762014 143 1.116875442 0.989536249 144 1.993329073 1.116875442 145 2.938568812 1.993329073 146 0.765749227 2.938568812 147 2.411914151 0.765749227 148 0.392293103 2.411914151 149 -2.083457932 0.392293103 150 2.365830969 -2.083457932 151 0.342350001 2.365830969 152 -1.169228321 0.342350001 153 -0.290923031 -1.169228321 154 0.636673912 -0.290923031 155 -1.083457932 0.636673912 > 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/freestat/rcomp/tmp/7i4yx1293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8i4yx1293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9bex01293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10bex01293203680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/freestat/rcomp/tmp/11eew51293203680.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/freestat/rcomp/tmp/120xct1293203680.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/freestat/rcomp/tmp/136yr51293203680.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/freestat/rcomp/tmp/144afe1293203680.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/freestat/rcomp/tmp/15kqpw1293203680.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/freestat/rcomp/tmp/16yh441293203680.tab") + } > > try(system("convert tmp/1fmh91293203680.ps tmp/1fmh91293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/2fmh91293203680.ps tmp/2fmh91293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/3fmh91293203680.ps tmp/3fmh91293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/4pvyu1293203680.ps tmp/4pvyu1293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/5pvyu1293203680.ps tmp/5pvyu1293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/6pvyu1293203680.ps tmp/6pvyu1293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/7i4yx1293203680.ps tmp/7i4yx1293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/8i4yx1293203680.ps tmp/8i4yx1293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/9bex01293203680.ps tmp/9bex01293203680.png",intern=TRUE)) character(0) > try(system("convert tmp/10bex01293203680.ps tmp/10bex01293203680.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.896 2.674 6.227