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(15 + ,10 + ,12 + ,16 + ,6 + ,2 + ,0 + ,0 + ,12 + ,9 + ,7 + ,12 + ,6 + ,1 + ,1 + ,2 + ,9 + ,12 + ,11 + ,11 + ,4 + ,1 + ,2 + ,1 + ,10 + ,12 + ,11 + ,12 + ,6 + ,0 + ,0 + ,0 + ,13 + ,9 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,16 + ,11 + ,16 + ,16 + ,7 + ,1 + ,0 + ,0 + ,14 + ,12 + ,13 + ,13 + ,6 + ,0 + ,0 + ,0 + ,16 + ,11 + ,13 + ,14 + ,7 + ,1 + ,1 + ,0 + ,10 + ,12 + ,5 + ,13 + ,6 + ,0 + ,0 + ,0 + ,8 + ,12 + ,8 + ,13 + ,4 + ,2 + ,0 + ,1 + ,12 + ,11 + ,14 + ,13 + ,5 + ,1 + ,0 + ,0 + ,15 + ,11 + ,15 + ,15 + ,8 + ,0 + ,0 + ,0 + ,14 + ,12 + ,8 + ,14 + ,4 + ,0 + ,1 + ,0 + ,14 + ,6 + ,13 + ,12 + ,6 + ,1 + ,1 + ,2 + ,12 + ,13 + ,12 + ,12 + ,6 + ,1 + ,2 + ,1 + ,12 + ,11 + ,11 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,12 + ,8 + ,11 + ,4 + ,0 + ,0 + ,0 + ,4 + ,10 + ,4 + ,10 + ,2 + ,0 + ,0 + ,0 + ,14 + ,11 + ,15 + ,15 + ,8 + ,0 + ,1 + ,0 + ,15 + ,12 + ,12 + ,16 + ,7 + ,0 + ,0 + ,0 + ,16 + ,12 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,12 + ,12 + ,9 + ,13 + ,4 + ,0 + ,1 + ,0 + ,12 + 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+ ,0 + ,10 + ,13 + ,4 + ,13 + ,6 + ,2 + ,0 + ,0 + ,15 + ,11 + ,16 + ,14 + ,6 + ,0 + ,0 + ,0 + ,16 + ,12 + ,12 + ,15 + ,8 + ,0 + ,0 + ,0 + ,16 + ,12 + ,15 + ,16 + ,7 + ,0 + ,0 + ,0 + ,14 + ,10 + ,12 + ,15 + ,6 + ,0 + ,0 + ,0 + ,14 + ,11 + ,14 + ,12 + ,6 + ,1 + ,1 + ,1 + ,12 + ,11 + ,11 + ,14 + ,2 + ,1 + ,1 + ,1 + ,15 + ,11 + ,16 + ,11 + ,5 + ,0 + ,1 + ,2 + ,13 + ,8 + ,14 + ,14 + ,5 + ,1 + ,1 + ,1 + ,16 + ,11 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,14 + ,12 + ,15 + ,14 + ,6 + ,0 + ,0 + ,0 + ,8 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,16 + ,12 + ,15 + ,14 + ,6 + ,0 + ,1 + ,0 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,12 + ,12 + ,15 + ,13 + ,6 + ,0 + ,0 + ,0 + ,11 + ,8 + ,10 + ,14 + ,5 + ,0 + ,3 + ,1 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,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 2 0 0 2 12 9 7 12 6 1 1 2 3 9 12 11 11 4 1 2 1 4 10 12 11 12 6 0 0 0 5 13 9 14 14 6 0 0 0 6 16 11 16 16 7 1 0 0 7 14 12 13 13 6 0 0 0 8 16 11 13 14 7 1 1 0 9 10 12 5 13 6 0 0 0 10 8 12 8 13 4 2 0 1 11 12 11 14 13 5 1 0 0 12 15 11 15 15 8 0 0 0 13 14 12 8 14 4 0 1 0 14 14 6 13 12 6 1 1 2 15 12 13 12 12 6 1 2 1 16 12 11 11 12 5 0 0 0 17 10 12 8 11 4 0 0 0 18 4 10 4 10 2 0 0 0 19 14 11 15 15 8 0 1 0 20 15 12 12 16 7 0 0 0 21 16 12 14 14 6 0 0 0 22 12 12 9 13 4 0 1 0 23 12 11 16 13 4 0 0 0 24 12 12 10 13 4 0 0 1 25 12 12 8 13 5 1 0 1 26 12 12 14 14 4 0 0 0 27 11 6 6 9 4 3 2 1 28 11 5 16 14 6 1 0 0 29 11 12 11 12 6 1 1 0 30 11 14 7 13 6 1 1 0 31 11 12 13 11 4 3 1 1 32 11 9 7 13 2 0 0 0 33 15 11 14 15 7 0 0 0 34 15 11 17 16 6 0 0 0 35 9 11 15 15 7 0 0 0 36 16 12 8 14 4 0 0 0 37 13 10 8 8 4 0 2 1 38 9 12 11 11 4 1 0 0 39 16 11 16 15 6 0 0 0 40 12 12 10 15 6 0 0 0 41 15 9 5 11 3 0 0 2 42 5 15 8 12 3 0 0 0 43 11 11 8 12 6 2 2 0 44 17 11 15 14 5 2 2 0 45 9 15 6 8 4 0 1 1 46 13 12 16 16 6 0 0 0 47 16 9 16 16 6 0 0 0 48 16 12 16 14 6 0 0 0 49 14 9 19 12 6 2 0 2 50 16 11 14 15 6 1 0 0 51 11 12 15 12 6 0 0 0 52 11 11 11 14 5 0 0 0 53 11 6 14 17 6 0 0 0 54 12 10 12 13 6 0 0 0 55 12 12 15 13 6 1 1 1 56 12 13 14 12 5 0 0 0 57 14 11 13 16 6 0 0 0 58 10 10 11 12 5 2 0 0 59 9 11 8 10 4 0 2 0 60 12 7 11 15 5 0 0 1 61 10 11 9 12 4 0 0 0 62 14 11 10 16 6 0 0 0 63 8 7 4 13 6 0 0 0 64 16 12 15 15 7 1 0 0 65 14 14 17 18 6 1 0 0 66 14 11 12 12 4 0 0 0 67 12 12 12 13 4 0 0 0 68 14 11 15 14 6 1 0 0 69 7 12 13 12 3 1 1 1 70 19 12 15 15 6 0 0 0 71 15 12 14 16 4 0 0 0 72 8 12 8 14 5 0 0 0 73 10 15 15 15 6 0 0 0 74 13 11 12 13 7 0 0 0 75 13 13 14 13 3 0 0 0 76 10 10 10 11 5 0 0 0 77 12 12 7 12 3 0 0 0 78 15 13 16 18 8 0 1 1 79 7 14 12 12 4 1 0 0 80 14 11 15 16 6 0 0 0 81 10 11 7 9 4 0 0 0 82 6 7 9 11 4 0 3 0 83 11 11 15 10 5 2 0 0 84 12 12 7 11 4 0 0 0 85 14 12 15 13 6 0 0 2 86 12 10 14 13 7 0 0 0 87 14 12 14 15 7 0 0 0 88 11 8 8 13 4 2 2 0 89 10 7 8 9 5 1 0 1 90 13 11 14 13 6 0 0 1 91 8 11 10 12 4 0 0 0 92 9 11 12 13 5 0 0 0 93 6 9 15 11 6 0 0 0 94 12 12 12 14 5 1 0 2 95 14 13 13 13 5 0 0 0 96 11 9 12 12 4 0 0 0 97 8 11 10 15 2 1 0 1 98 7 12 8 12 3 0 0 0 99 9 9 6 12 5 0 2 1 100 14 12 13 13 5 2 1 0 101 13 12 7 12 5 0 0 0 102 15 12 13 13 6 0 0 0 103 5 14 4 5 2 0 0 0 104 15 11 14 13 5 3 1 0 105 13 12 13 13 5 0 1 0 106 12 8 13 13 5 0 0 0 107 6 12 6 11 2 1 0 0 108 7 12 7 12 4 0 0 0 109 13 12 5 12 3 0 0 0 110 16 11 14 15 8 1 1 0 111 10 11 13 15 6 0 0 0 112 16 12 16 16 7 0 0 0 113 15 10 16 13 6 0 0 0 114 8 13 7 10 3 0 0 0 115 11 8 14 15 5 0 0 0 116 13 12 11 13 6 0 3 1 117 16 11 17 16 7 1 0 0 118 11 10 5 13 3 0 0 0 119 14 13 10 16 8 0 0 0 120 9 10 11 13 3 2 1 0 121 8 10 10 14 3 0 0 0 122 8 7 9 15 4 1 0 1 123 11 10 12 14 5 2 0 0 124 12 8 15 13 7 0 0 0 125 11 12 7 13 6 4 0 0 126 14 12 13 15 6 0 1 2 127 11 12 8 16 6 2 1 0 128 14 11 16 12 5 0 0 0 129 13 13 15 14 6 2 1 2 130 12 12 6 14 5 0 0 0 131 4 8 6 4 4 0 0 0 132 15 11 12 13 6 2 1 1 133 10 12 8 16 4 0 0 0 134 13 13 11 15 6 1 2 1 135 15 12 13 14 6 1 1 2 136 12 10 14 14 5 1 2 1 137 13 12 14 14 6 0 0 0 138 8 10 10 6 4 0 0 0 139 10 13 4 13 6 2 0 0 140 15 11 16 14 6 0 0 0 141 16 12 12 15 8 0 0 0 142 16 12 15 16 7 0 0 0 143 14 10 12 15 6 0 0 0 144 14 11 14 12 6 1 1 1 145 12 11 11 14 2 1 1 1 146 15 11 16 11 5 0 1 2 147 13 8 14 14 5 1 1 1 148 16 11 14 14 6 0 0 0 149 14 12 15 14 6 0 0 0 150 8 11 9 12 4 0 0 0 151 16 12 15 14 6 0 1 0 152 16 12 14 16 8 1 1 1 153 12 12 15 13 6 0 0 0 154 11 8 10 14 5 0 3 1 155 16 12 14 16 8 1 1 1 156 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 -0.34096 0.11785 0.24088 0.37207 0.61092 B `2B` `3B` -0.04365 0.17183 0.50219 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.09123 -1.27976 -0.08508 1.29622 6.14596 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.34096 1.46415 -0.233 0.816184 FindingFriends 0.11785 0.09637 1.223 0.223335 KnowingPeople 0.24088 0.06160 3.910 0.000140 *** Liked 0.37207 0.09686 3.841 0.000181 *** Celebrity 0.61092 0.15633 3.908 0.000141 *** B -0.04365 0.22313 -0.196 0.845159 `2B` 0.17183 0.26854 0.640 0.523248 `3B` 0.50219 0.31640 1.587 0.114600 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.097 on 148 degrees of freedom Multiple R-squared: 0.5129, Adjusted R-squared: 0.4899 F-statistic: 22.27 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,] 0.28405131 0.5681026142 0.7159486929 [2,] 0.24572743 0.4914548611 0.7542725695 [3,] 0.29353173 0.5870634609 0.7064682695 [4,] 0.20030187 0.4006037476 0.7996981262 [5,] 0.12247104 0.2449420738 0.8775289631 [6,] 0.10024194 0.2004838739 0.8997580631 [7,] 0.07879206 0.1575841230 0.9212079385 [8,] 0.12240480 0.2448096024 0.8775951988 [9,] 0.21772666 0.4354533202 0.7822733399 [10,] 0.15770574 0.3154114812 0.8422942594 [11,] 0.18072914 0.3614582878 0.8192708561 [12,] 0.13218322 0.2643664363 0.8678167819 [13,] 0.10039876 0.2007975253 0.8996012373 [14,] 0.06930459 0.1386091831 0.9306954085 [15,] 0.05245507 0.1049101496 0.9475449252 [16,] 0.03998449 0.0799689703 0.9600155149 [17,] 0.05982729 0.1196545794 0.9401727103 [18,] 0.11787353 0.2357470660 0.8821264670 [19,] 0.09246158 0.1849231561 0.9075384219 [20,] 0.07104353 0.1420870592 0.9289564704 [21,] 0.05116021 0.1023204199 0.9488397900 [22,] 0.04454485 0.0890896977 0.9554551512 [23,] 0.03142902 0.0628580412 0.9685709794 [24,] 0.02216364 0.0443272895 0.9778363552 [25,] 0.18267389 0.3653477839 0.8173261081 [26,] 0.37052036 0.7410407296 0.6294796352 [27,] 0.51737782 0.9652443513 0.4826221757 [28,] 0.46312899 0.9262579855 0.5368710073 [29,] 0.44821666 0.8964333278 0.5517833361 [30,] 0.41082175 0.8216434933 0.5891782534 [31,] 0.67543708 0.6491258478 0.3245629239 [32,] 0.83048370 0.3390326022 0.1695163011 [33,] 0.79462739 0.4107452285 0.2053726142 [34,] 0.83868794 0.3226241123 0.1613120562 [35,] 0.81475541 0.3704891751 0.1852445875 [36,] 0.80776641 0.3844671833 0.1922335916 [37,] 0.78254813 0.4349037490 0.2174518745 [38,] 0.80099088 0.3980182382 0.1990091191 [39,] 0.76309585 0.4738082995 0.2369041497 [40,] 0.78097321 0.4380535891 0.2190267945 [41,] 0.75708285 0.4858342973 0.2429171487 [42,] 0.73212818 0.5357436389 0.2678718194 [43,] 0.84031241 0.3193751883 0.1596875941 [44,] 0.80763907 0.3847218600 0.1923609300 [45,] 0.80437985 0.3912403002 0.1956201501 [46,] 0.77127525 0.4574494918 0.2287247459 [47,] 0.73226895 0.5354620982 0.2677310491 [48,] 0.69219270 0.6156145979 0.3078072989 [49,] 0.66119496 0.6776100716 0.3388050358 [50,] 0.64331134 0.7133773142 0.3566886571 [51,] 0.59641816 0.8071636857 0.4035818429 [52,] 0.55601333 0.8879733337 0.4439866669 [53,] 0.54296789 0.9140642219 0.4570321110 [54,] 0.52928514 0.9414297247 0.4707148623 [55,] 0.53201740 0.9359652064 0.4679826032 [56,] 0.59681332 0.8063733699 0.4031866850 [57,] 0.55328945 0.8934210974 0.4467105487 [58,] 0.51164577 0.9767084515 0.4883542257 [59,] 0.68391140 0.6321771998 0.3160885999 [60,] 0.84808273 0.3038345373 0.1519172686 [61,] 0.84459873 0.3108025478 0.1554012739 [62,] 0.88082926 0.2383414781 0.1191707391 [63,] 0.94545968 0.1090806351 0.0545403176 [64,] 0.93120991 0.1375801792 0.0687900896 [65,] 0.92354053 0.1529189346 0.0764594673 [66,] 0.90501195 0.1899760974 0.0949880487 [67,] 0.92413494 0.1517301230 0.0758650615 [68,] 0.93176781 0.1364643750 0.0682321875 [69,] 0.97124566 0.0575086830 0.0287543415 [70,] 0.96252747 0.0749450657 0.0374725329 [71,] 0.95939744 0.0812051222 0.0406025611 [72,] 0.97970514 0.0405897108 0.0202948554 [73,] 0.97366612 0.0526677540 0.0263338770 [74,] 0.97911814 0.0417637209 0.0208818604 [75,] 0.97228802 0.0554239552 0.0277119776 [76,] 0.96707085 0.0658582925 0.0329291462 [77,] 0.95781481 0.0843703793 0.0421851896 [78,] 0.95110407 0.0977918678 0.0488959339 [79,] 0.95168556 0.0966288809 0.0483144404 [80,] 0.93812715 0.1237456921 0.0618728460 [81,] 0.94100263 0.1179947354 0.0589973677 [82,] 0.95273532 0.0945293634 0.0472646817 [83,] 0.99689501 0.0062099873 0.0031049937 [84,] 0.99588101 0.0082379855 0.0041189927 [85,] 0.99494139 0.0101172190 0.0050586095 [86,] 0.99307954 0.0138409163 0.0069204581 [87,] 0.99398754 0.0120249293 0.0060124647 [88,] 0.99484427 0.0103114648 0.0051557324 [89,] 0.99312713 0.0137457347 0.0068728674 [90,] 0.99169441 0.0166111702 0.0083055851 [91,] 0.99447398 0.0110520370 0.0055260185 [92,] 0.99440656 0.0111868832 0.0055934416 [93,] 0.99221801 0.0155639709 0.0077819855 [94,] 0.99401935 0.0119613018 0.0059806509 [95,] 0.99144142 0.0171171697 0.0085585848 [96,] 0.98849767 0.0230046560 0.0115023280 [97,] 0.98740006 0.0251998760 0.0125999380 [98,] 0.99163489 0.0167302128 0.0083651064 [99,] 0.99907421 0.0018515749 0.0009257874 [100,] 0.99867367 0.0026526682 0.0013263341 [101,] 0.99967487 0.0006502628 0.0003251314 [102,] 0.99945649 0.0010870271 0.0005435136 [103,] 0.99934363 0.0013127402 0.0006563701 [104,] 0.99897546 0.0020490769 0.0010245384 [105,] 0.99862705 0.0027459021 0.0013729510 [106,] 0.99775706 0.0044858777 0.0022429389 [107,] 0.99642791 0.0071441723 0.0035720861 [108,] 0.99923340 0.0015332018 0.0007666009 [109,] 0.99878932 0.0024213662 0.0012106831 [110,] 0.99809216 0.0038156831 0.0019078415 [111,] 0.99799260 0.0040148082 0.0020074041 [112,] 0.99797816 0.0040436780 0.0020218390 [113,] 0.99708216 0.0058356757 0.0029178379 [114,] 0.99771141 0.0045771877 0.0022885938 [115,] 0.99613409 0.0077318161 0.0038659081 [116,] 0.99442637 0.0111472548 0.0055736274 [117,] 0.99219413 0.0156117443 0.0078058722 [118,] 0.98849186 0.0230162805 0.0115081403 [119,] 0.99468801 0.0106239834 0.0053119917 [120,] 0.99655263 0.0068947329 0.0034473665 [121,] 0.99461720 0.0107655965 0.0053827982 [122,] 0.99395792 0.0120841540 0.0060420770 [123,] 0.99080626 0.0183874713 0.0091937357 [124,] 0.98402299 0.0319540203 0.0159770101 [125,] 0.97216222 0.0556755564 0.0278377782 [126,] 0.96854283 0.0629143393 0.0314571697 [127,] 0.95735279 0.0852944248 0.0426472124 [128,] 0.93483513 0.1303297375 0.0651648688 [129,] 0.92147888 0.1570422453 0.0785211226 [130,] 0.87192986 0.2561402754 0.1280701377 [131,] 0.87711486 0.2457702900 0.1228851450 [132,] 0.80285413 0.3942917459 0.1971458730 [133,] 0.73551679 0.5289664196 0.2644832098 [134,] 0.71361285 0.5727743027 0.2863871514 [135,] 0.58090993 0.8381801453 0.4190900727 > postscript(file="/var/www/html/rcomp/tmp/1uabe1293203553.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5gar21293203553.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 1.74051170 0.33119764 -2.06161883 -1.85332033 0.03343624 1.00456323 7 8 9 10 11 12 1.29284170 2.29952450 -0.78009940 -2.69580001 -0.17562164 -0.03705014 13 14 15 16 17 18 3.17517867 1.23945361 -1.01425517 0.87544506 0.46323068 -2.74363585 19 20 21 22 23 24 -1.20888244 0.80658900 2.67988610 1.30636955 -0.09012465 0.73512801 25 26 27 28 29 30 0.64963102 -0.09828321 2.68134699 -1.93327468 -0.98149900 -0.62574288 31 32 33 34 35 36 -0.28424400 2.53534739 0.81474756 0.33094258 -5.42613480 5.34701097 37 38 39 40 41 42 3.96929443 -1.21576277 1.94389818 -0.72865769 6.14596031 -4.65147735 43 44 45 46 47 48 -0.26918053 3.91141180 0.03364123 -1.54602510 1.80752503 2.19812138 49 50 51 52 53 54 -0.34390478 2.46931654 -1.81684978 -0.86870142 -2.72923334 -0.23057584 55 56 57 58 59 60 -1.81929316 -0.08290212 0.29447203 -0.91939764 -0.39051062 -0.27156595 61 62 63 64 65 66 -0.03187487 1.01711912 -1.94996681 1.49966879 -1.72310040 3.24547804 67 68 69 70 71 72 0.75555476 0.60050742 -4.13270916 5.06693050 2.15757031 -3.26390438 73 74 75 76 77 78 -4.28661963 0.04065877 1.76685533 -0.39374929 2.94295515 -2.30387608 79 80 81 82 83 84 -4.06441846 -0.18729269 1.56610957 -3.70389833 -0.25663065 2.70411305 85 86 87 88 89 90 -0.19330596 -1.32325591 -0.30310248 0.93412706 0.72717420 -0.33238208 91 92 93 94 95 96 -2.27275723 -2.73751054 -6.09122640 -1.18816314 1.78590700 0.48117813 97 98 99 100 101 102 -2.62568411 -2.29792721 -1.53029910 1.81923201 2.72112446 2.29284170 103 104 105 106 107 108 -0.35466983 2.73985333 0.73192476 0.37515723 -1.78952027 -2.66796019 109 110 111 112 113 114 4.42471988 1.07565355 -3.33345473 0.84305955 1.80589471 -0.43074841 115 116 117 118 119 120 -1.60987161 -0.24308192 0.76368087 2.28834673 -0.44041166 -1.24147248 121 122 123 124 125 126 -2.28813832 -3.13523225 -0.90442648 -1.32843818 -0.08724961 -0.62752001 127 128 129 130 131 132 -1.70349124 1.67103325 -1.76775428 1.21786034 -1.97909173 2.06485761 133 134 135 136 137 138 -1.39713551 -0.88959253 0.78820686 -1.27570089 -0.32011390 0.07753225 139 140 141 142 143 144 -0.56975982 1.31597142 1.56774690 1.08394192 1.02527768 0.91151249 145 146 147 148 149 150 1.33367448 1.86689125 0.13183149 2.79773615 0.43900374 -2.03187487 151 152 153 154 155 156 2.26717145 0.08353880 -1.18892302 -1.29195728 0.08353880 -1.03187487 > postscript(file="/var/www/html/rcomp/tmp/6gar21293203553.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 1.74051170 NA 1 0.33119764 1.74051170 2 -2.06161883 0.33119764 3 -1.85332033 -2.06161883 4 0.03343624 -1.85332033 5 1.00456323 0.03343624 6 1.29284170 1.00456323 7 2.29952450 1.29284170 8 -0.78009940 2.29952450 9 -2.69580001 -0.78009940 10 -0.17562164 -2.69580001 11 -0.03705014 -0.17562164 12 3.17517867 -0.03705014 13 1.23945361 3.17517867 14 -1.01425517 1.23945361 15 0.87544506 -1.01425517 16 0.46323068 0.87544506 17 -2.74363585 0.46323068 18 -1.20888244 -2.74363585 19 0.80658900 -1.20888244 20 2.67988610 0.80658900 21 1.30636955 2.67988610 22 -0.09012465 1.30636955 23 0.73512801 -0.09012465 24 0.64963102 0.73512801 25 -0.09828321 0.64963102 26 2.68134699 -0.09828321 27 -1.93327468 2.68134699 28 -0.98149900 -1.93327468 29 -0.62574288 -0.98149900 30 -0.28424400 -0.62574288 31 2.53534739 -0.28424400 32 0.81474756 2.53534739 33 0.33094258 0.81474756 34 -5.42613480 0.33094258 35 5.34701097 -5.42613480 36 3.96929443 5.34701097 37 -1.21576277 3.96929443 38 1.94389818 -1.21576277 39 -0.72865769 1.94389818 40 6.14596031 -0.72865769 41 -4.65147735 6.14596031 42 -0.26918053 -4.65147735 43 3.91141180 -0.26918053 44 0.03364123 3.91141180 45 -1.54602510 0.03364123 46 1.80752503 -1.54602510 47 2.19812138 1.80752503 48 -0.34390478 2.19812138 49 2.46931654 -0.34390478 50 -1.81684978 2.46931654 51 -0.86870142 -1.81684978 52 -2.72923334 -0.86870142 53 -0.23057584 -2.72923334 54 -1.81929316 -0.23057584 55 -0.08290212 -1.81929316 56 0.29447203 -0.08290212 57 -0.91939764 0.29447203 58 -0.39051062 -0.91939764 59 -0.27156595 -0.39051062 60 -0.03187487 -0.27156595 61 1.01711912 -0.03187487 62 -1.94996681 1.01711912 63 1.49966879 -1.94996681 64 -1.72310040 1.49966879 65 3.24547804 -1.72310040 66 0.75555476 3.24547804 67 0.60050742 0.75555476 68 -4.13270916 0.60050742 69 5.06693050 -4.13270916 70 2.15757031 5.06693050 71 -3.26390438 2.15757031 72 -4.28661963 -3.26390438 73 0.04065877 -4.28661963 74 1.76685533 0.04065877 75 -0.39374929 1.76685533 76 2.94295515 -0.39374929 77 -2.30387608 2.94295515 78 -4.06441846 -2.30387608 79 -0.18729269 -4.06441846 80 1.56610957 -0.18729269 81 -3.70389833 1.56610957 82 -0.25663065 -3.70389833 83 2.70411305 -0.25663065 84 -0.19330596 2.70411305 85 -1.32325591 -0.19330596 86 -0.30310248 -1.32325591 87 0.93412706 -0.30310248 88 0.72717420 0.93412706 89 -0.33238208 0.72717420 90 -2.27275723 -0.33238208 91 -2.73751054 -2.27275723 92 -6.09122640 -2.73751054 93 -1.18816314 -6.09122640 94 1.78590700 -1.18816314 95 0.48117813 1.78590700 96 -2.62568411 0.48117813 97 -2.29792721 -2.62568411 98 -1.53029910 -2.29792721 99 1.81923201 -1.53029910 100 2.72112446 1.81923201 101 2.29284170 2.72112446 102 -0.35466983 2.29284170 103 2.73985333 -0.35466983 104 0.73192476 2.73985333 105 0.37515723 0.73192476 106 -1.78952027 0.37515723 107 -2.66796019 -1.78952027 108 4.42471988 -2.66796019 109 1.07565355 4.42471988 110 -3.33345473 1.07565355 111 0.84305955 -3.33345473 112 1.80589471 0.84305955 113 -0.43074841 1.80589471 114 -1.60987161 -0.43074841 115 -0.24308192 -1.60987161 116 0.76368087 -0.24308192 117 2.28834673 0.76368087 118 -0.44041166 2.28834673 119 -1.24147248 -0.44041166 120 -2.28813832 -1.24147248 121 -3.13523225 -2.28813832 122 -0.90442648 -3.13523225 123 -1.32843818 -0.90442648 124 -0.08724961 -1.32843818 125 -0.62752001 -0.08724961 126 -1.70349124 -0.62752001 127 1.67103325 -1.70349124 128 -1.76775428 1.67103325 129 1.21786034 -1.76775428 130 -1.97909173 1.21786034 131 2.06485761 -1.97909173 132 -1.39713551 2.06485761 133 -0.88959253 -1.39713551 134 0.78820686 -0.88959253 135 -1.27570089 0.78820686 136 -0.32011390 -1.27570089 137 0.07753225 -0.32011390 138 -0.56975982 0.07753225 139 1.31597142 -0.56975982 140 1.56774690 1.31597142 141 1.08394192 1.56774690 142 1.02527768 1.08394192 143 0.91151249 1.02527768 144 1.33367448 0.91151249 145 1.86689125 1.33367448 146 0.13183149 1.86689125 147 2.79773615 0.13183149 148 0.43900374 2.79773615 149 -2.03187487 0.43900374 150 2.26717145 -2.03187487 151 0.08353880 2.26717145 152 -1.18892302 0.08353880 153 -1.29195728 -1.18892302 154 0.08353880 -1.29195728 155 -1.03187487 0.08353880 156 NA -1.03187487 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.33119764 1.74051170 [2,] -2.06161883 0.33119764 [3,] -1.85332033 -2.06161883 [4,] 0.03343624 -1.85332033 [5,] 1.00456323 0.03343624 [6,] 1.29284170 1.00456323 [7,] 2.29952450 1.29284170 [8,] -0.78009940 2.29952450 [9,] -2.69580001 -0.78009940 [10,] -0.17562164 -2.69580001 [11,] -0.03705014 -0.17562164 [12,] 3.17517867 -0.03705014 [13,] 1.23945361 3.17517867 [14,] -1.01425517 1.23945361 [15,] 0.87544506 -1.01425517 [16,] 0.46323068 0.87544506 [17,] -2.74363585 0.46323068 [18,] -1.20888244 -2.74363585 [19,] 0.80658900 -1.20888244 [20,] 2.67988610 0.80658900 [21,] 1.30636955 2.67988610 [22,] -0.09012465 1.30636955 [23,] 0.73512801 -0.09012465 [24,] 0.64963102 0.73512801 [25,] -0.09828321 0.64963102 [26,] 2.68134699 -0.09828321 [27,] -1.93327468 2.68134699 [28,] -0.98149900 -1.93327468 [29,] -0.62574288 -0.98149900 [30,] -0.28424400 -0.62574288 [31,] 2.53534739 -0.28424400 [32,] 0.81474756 2.53534739 [33,] 0.33094258 0.81474756 [34,] -5.42613480 0.33094258 [35,] 5.34701097 -5.42613480 [36,] 3.96929443 5.34701097 [37,] -1.21576277 3.96929443 [38,] 1.94389818 -1.21576277 [39,] -0.72865769 1.94389818 [40,] 6.14596031 -0.72865769 [41,] -4.65147735 6.14596031 [42,] -0.26918053 -4.65147735 [43,] 3.91141180 -0.26918053 [44,] 0.03364123 3.91141180 [45,] -1.54602510 0.03364123 [46,] 1.80752503 -1.54602510 [47,] 2.19812138 1.80752503 [48,] -0.34390478 2.19812138 [49,] 2.46931654 -0.34390478 [50,] -1.81684978 2.46931654 [51,] -0.86870142 -1.81684978 [52,] -2.72923334 -0.86870142 [53,] -0.23057584 -2.72923334 [54,] -1.81929316 -0.23057584 [55,] -0.08290212 -1.81929316 [56,] 0.29447203 -0.08290212 [57,] -0.91939764 0.29447203 [58,] -0.39051062 -0.91939764 [59,] -0.27156595 -0.39051062 [60,] -0.03187487 -0.27156595 [61,] 1.01711912 -0.03187487 [62,] -1.94996681 1.01711912 [63,] 1.49966879 -1.94996681 [64,] -1.72310040 1.49966879 [65,] 3.24547804 -1.72310040 [66,] 0.75555476 3.24547804 [67,] 0.60050742 0.75555476 [68,] -4.13270916 0.60050742 [69,] 5.06693050 -4.13270916 [70,] 2.15757031 5.06693050 [71,] -3.26390438 2.15757031 [72,] -4.28661963 -3.26390438 [73,] 0.04065877 -4.28661963 [74,] 1.76685533 0.04065877 [75,] -0.39374929 1.76685533 [76,] 2.94295515 -0.39374929 [77,] -2.30387608 2.94295515 [78,] -4.06441846 -2.30387608 [79,] -0.18729269 -4.06441846 [80,] 1.56610957 -0.18729269 [81,] -3.70389833 1.56610957 [82,] -0.25663065 -3.70389833 [83,] 2.70411305 -0.25663065 [84,] -0.19330596 2.70411305 [85,] -1.32325591 -0.19330596 [86,] -0.30310248 -1.32325591 [87,] 0.93412706 -0.30310248 [88,] 0.72717420 0.93412706 [89,] -0.33238208 0.72717420 [90,] -2.27275723 -0.33238208 [91,] -2.73751054 -2.27275723 [92,] -6.09122640 -2.73751054 [93,] -1.18816314 -6.09122640 [94,] 1.78590700 -1.18816314 [95,] 0.48117813 1.78590700 [96,] -2.62568411 0.48117813 [97,] -2.29792721 -2.62568411 [98,] -1.53029910 -2.29792721 [99,] 1.81923201 -1.53029910 [100,] 2.72112446 1.81923201 [101,] 2.29284170 2.72112446 [102,] -0.35466983 2.29284170 [103,] 2.73985333 -0.35466983 [104,] 0.73192476 2.73985333 [105,] 0.37515723 0.73192476 [106,] -1.78952027 0.37515723 [107,] -2.66796019 -1.78952027 [108,] 4.42471988 -2.66796019 [109,] 1.07565355 4.42471988 [110,] -3.33345473 1.07565355 [111,] 0.84305955 -3.33345473 [112,] 1.80589471 0.84305955 [113,] -0.43074841 1.80589471 [114,] -1.60987161 -0.43074841 [115,] -0.24308192 -1.60987161 [116,] 0.76368087 -0.24308192 [117,] 2.28834673 0.76368087 [118,] -0.44041166 2.28834673 [119,] -1.24147248 -0.44041166 [120,] -2.28813832 -1.24147248 [121,] -3.13523225 -2.28813832 [122,] -0.90442648 -3.13523225 [123,] -1.32843818 -0.90442648 [124,] -0.08724961 -1.32843818 [125,] -0.62752001 -0.08724961 [126,] -1.70349124 -0.62752001 [127,] 1.67103325 -1.70349124 [128,] -1.76775428 1.67103325 [129,] 1.21786034 -1.76775428 [130,] -1.97909173 1.21786034 [131,] 2.06485761 -1.97909173 [132,] -1.39713551 2.06485761 [133,] -0.88959253 -1.39713551 [134,] 0.78820686 -0.88959253 [135,] -1.27570089 0.78820686 [136,] -0.32011390 -1.27570089 [137,] 0.07753225 -0.32011390 [138,] -0.56975982 0.07753225 [139,] 1.31597142 -0.56975982 [140,] 1.56774690 1.31597142 [141,] 1.08394192 1.56774690 [142,] 1.02527768 1.08394192 [143,] 0.91151249 1.02527768 [144,] 1.33367448 0.91151249 [145,] 1.86689125 1.33367448 [146,] 0.13183149 1.86689125 [147,] 2.79773615 0.13183149 [148,] 0.43900374 2.79773615 [149,] -2.03187487 0.43900374 [150,] 2.26717145 -2.03187487 [151,] 0.08353880 2.26717145 [152,] -1.18892302 0.08353880 [153,] -1.29195728 -1.18892302 [154,] 0.08353880 -1.29195728 [155,] -1.03187487 0.08353880 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.33119764 1.74051170 2 -2.06161883 0.33119764 3 -1.85332033 -2.06161883 4 0.03343624 -1.85332033 5 1.00456323 0.03343624 6 1.29284170 1.00456323 7 2.29952450 1.29284170 8 -0.78009940 2.29952450 9 -2.69580001 -0.78009940 10 -0.17562164 -2.69580001 11 -0.03705014 -0.17562164 12 3.17517867 -0.03705014 13 1.23945361 3.17517867 14 -1.01425517 1.23945361 15 0.87544506 -1.01425517 16 0.46323068 0.87544506 17 -2.74363585 0.46323068 18 -1.20888244 -2.74363585 19 0.80658900 -1.20888244 20 2.67988610 0.80658900 21 1.30636955 2.67988610 22 -0.09012465 1.30636955 23 0.73512801 -0.09012465 24 0.64963102 0.73512801 25 -0.09828321 0.64963102 26 2.68134699 -0.09828321 27 -1.93327468 2.68134699 28 -0.98149900 -1.93327468 29 -0.62574288 -0.98149900 30 -0.28424400 -0.62574288 31 2.53534739 -0.28424400 32 0.81474756 2.53534739 33 0.33094258 0.81474756 34 -5.42613480 0.33094258 35 5.34701097 -5.42613480 36 3.96929443 5.34701097 37 -1.21576277 3.96929443 38 1.94389818 -1.21576277 39 -0.72865769 1.94389818 40 6.14596031 -0.72865769 41 -4.65147735 6.14596031 42 -0.26918053 -4.65147735 43 3.91141180 -0.26918053 44 0.03364123 3.91141180 45 -1.54602510 0.03364123 46 1.80752503 -1.54602510 47 2.19812138 1.80752503 48 -0.34390478 2.19812138 49 2.46931654 -0.34390478 50 -1.81684978 2.46931654 51 -0.86870142 -1.81684978 52 -2.72923334 -0.86870142 53 -0.23057584 -2.72923334 54 -1.81929316 -0.23057584 55 -0.08290212 -1.81929316 56 0.29447203 -0.08290212 57 -0.91939764 0.29447203 58 -0.39051062 -0.91939764 59 -0.27156595 -0.39051062 60 -0.03187487 -0.27156595 61 1.01711912 -0.03187487 62 -1.94996681 1.01711912 63 1.49966879 -1.94996681 64 -1.72310040 1.49966879 65 3.24547804 -1.72310040 66 0.75555476 3.24547804 67 0.60050742 0.75555476 68 -4.13270916 0.60050742 69 5.06693050 -4.13270916 70 2.15757031 5.06693050 71 -3.26390438 2.15757031 72 -4.28661963 -3.26390438 73 0.04065877 -4.28661963 74 1.76685533 0.04065877 75 -0.39374929 1.76685533 76 2.94295515 -0.39374929 77 -2.30387608 2.94295515 78 -4.06441846 -2.30387608 79 -0.18729269 -4.06441846 80 1.56610957 -0.18729269 81 -3.70389833 1.56610957 82 -0.25663065 -3.70389833 83 2.70411305 -0.25663065 84 -0.19330596 2.70411305 85 -1.32325591 -0.19330596 86 -0.30310248 -1.32325591 87 0.93412706 -0.30310248 88 0.72717420 0.93412706 89 -0.33238208 0.72717420 90 -2.27275723 -0.33238208 91 -2.73751054 -2.27275723 92 -6.09122640 -2.73751054 93 -1.18816314 -6.09122640 94 1.78590700 -1.18816314 95 0.48117813 1.78590700 96 -2.62568411 0.48117813 97 -2.29792721 -2.62568411 98 -1.53029910 -2.29792721 99 1.81923201 -1.53029910 100 2.72112446 1.81923201 101 2.29284170 2.72112446 102 -0.35466983 2.29284170 103 2.73985333 -0.35466983 104 0.73192476 2.73985333 105 0.37515723 0.73192476 106 -1.78952027 0.37515723 107 -2.66796019 -1.78952027 108 4.42471988 -2.66796019 109 1.07565355 4.42471988 110 -3.33345473 1.07565355 111 0.84305955 -3.33345473 112 1.80589471 0.84305955 113 -0.43074841 1.80589471 114 -1.60987161 -0.43074841 115 -0.24308192 -1.60987161 116 0.76368087 -0.24308192 117 2.28834673 0.76368087 118 -0.44041166 2.28834673 119 -1.24147248 -0.44041166 120 -2.28813832 -1.24147248 121 -3.13523225 -2.28813832 122 -0.90442648 -3.13523225 123 -1.32843818 -0.90442648 124 -0.08724961 -1.32843818 125 -0.62752001 -0.08724961 126 -1.70349124 -0.62752001 127 1.67103325 -1.70349124 128 -1.76775428 1.67103325 129 1.21786034 -1.76775428 130 -1.97909173 1.21786034 131 2.06485761 -1.97909173 132 -1.39713551 2.06485761 133 -0.88959253 -1.39713551 134 0.78820686 -0.88959253 135 -1.27570089 0.78820686 136 -0.32011390 -1.27570089 137 0.07753225 -0.32011390 138 -0.56975982 0.07753225 139 1.31597142 -0.56975982 140 1.56774690 1.31597142 141 1.08394192 1.56774690 142 1.02527768 1.08394192 143 0.91151249 1.02527768 144 1.33367448 0.91151249 145 1.86689125 1.33367448 146 0.13183149 1.86689125 147 2.79773615 0.13183149 148 0.43900374 2.79773615 149 -2.03187487 0.43900374 150 2.26717145 -2.03187487 151 0.08353880 2.26717145 152 -1.18892302 0.08353880 153 -1.29195728 -1.18892302 154 0.08353880 -1.29195728 155 -1.03187487 0.08353880 > 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/7qj8m1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8qj8m1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9jb8p1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10jb8p1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11mt6d1293203553.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/12qu511293203553.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/134m2a1293203553.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/14pm1y1293203553.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/15tmz41293203553.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/16w5g91293203553.tab") + } > > try(system("convert tmp/1uabe1293203553.ps tmp/1uabe1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/2njsz1293203553.ps tmp/2njsz1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/3njsz1293203553.ps tmp/3njsz1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/4njsz1293203553.ps tmp/4njsz1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/5gar21293203553.ps tmp/5gar21293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/6gar21293203553.ps tmp/6gar21293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/7qj8m1293203553.ps tmp/7qj8m1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/8qj8m1293203553.ps tmp/8qj8m1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/9jb8p1293203553.ps tmp/9jb8p1293203553.png",intern=TRUE)) character(0) > try(system("convert tmp/10jb8p1293203553.ps tmp/10jb8p1293203553.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.156 1.849 11.281