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(5 + ,22 + ,0 + ,15 + ,4 + ,2 + ,1 + ,0 + ,7 + ,0 + ,0 + ,3 + ,7 + ,4 + ,0 + ,2 + ,5 + ,14 + ,5 + ,3 + ,5 + ,2 + ,0 + ,12 + ,4 + ,0 + ,4 + ,3 + ,4 + ,4 + ,4 + ,0 + ,6 + ,6 + ,0 + ,12 + ,5 + ,25 + ,0 + ,15 + ,1 + ,0 + ,0 + ,0 + ,5 + ,25 + ,5 + ,10 + ,4 + ,0 + ,0 + ,12 + ,6 + ,2 + ,2 + ,20 + ,7 + ,30 + ,3 + ,20 + ,7 + ,1 + ,0 + ,2 + ,2 + ,0 + ,0 + ,3 + ,6 + ,0 + ,0 + ,16 + ,4 + ,8 + ,0 + ,4 + ,3 + ,0 + ,4 + ,2 + ,6 + ,0 + ,0 + ,4 + ,6 + ,0 + ,8 + ,16 + ,5 + ,6 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,6 + ,6 + ,0 + ,15 + ,4 + ,12 + ,3 + ,9 + ,3 + ,1 + ,0 + ,1 + ,4 + ,20 + ,24 + ,15 + ,5 + ,5 + ,15 + ,5 + ,6 + ,0 + ,0 + ,4 + ,6 + ,21 + ,12 + ,15 + ,4 + ,3 + ,0 + ,4 + ,6 + ,5 + ,0 + ,12 + ,6 + ,8 + ,0 + ,2 + ,5 + ,10 + ,4 + ,4 + ,6 + ,5 + ,1 + ,2 + ,4 + ,8 + ,0 + ,4 + ,6 + ,6 + ,16 + ,8 + ,7 + ,15 + ,9 + ,30 + ,5 + ,9 + ,0 + ,6 + ,6 + ,14 + ,8 + ,6 + ,6 + ,9 + ,10 + ,7 + ,5 + ,5 + ,0 + ,4 + ,7 + ,9 + ,6 + ,17 + ,6 + ,10 + ,0 + ,5 + ,3 + ,12 + ,0 + ,0 + ,4 + ,9 + ,15 + ,3 + ,5 + ,7 + ,0 + ,4 + ,4 + ,15 + ,0 + ,15 + ,3 + ,14 + ,0 + ,0 + ,5 + ,16 + ,0 + ,8 + ,5 + ,6 + ,0 + ,10 + ,4 + ,6 + ,0 + ,4 + ,5 + ,2 + ,0 + ,0 + ,1 + ,8 + ,10 + ,6 + ,2 + ,0 + ,7 + ,11 + ,3 + ,6 + ,2 + ,10 + ,4 + ,4 + ,0 + ,0 + ,3 + ,15 + ,2 + ,0 + ,7 + ,0 + ,0 + ,0 + ,2 + ,12 + ,3 + ,0 + ,4 + ,0 + ,12 + ,0 + ,2 + ,13 + ,0 + ,0 + ,5 + ,18 + ,3 + ,0 + ,6 + ,4 + ,0 + ,7 + ,6 + ,9 + ,0 + ,4 + ,6 + ,12 + ,0 + ,12 + ,6 + ,14 + ,8 + ,6 + ,6 + ,0 + ,0 + ,12 + ,6 + ,4 + ,7 + ,10 + ,6 + ,12 + ,0 + ,9 + ,4 + ,15 + ,18 + ,6 + ,4 + ,0 + ,0 + ,0 + ,5 + ,30 + ,13 + ,16 + ,6 + ,0 + ,0 + ,2 + ,6 + ,0 + ,0 + ,0 + ,7 + ,3 + ,0 + ,0 + ,4 + ,2 + ,0 + ,1 + ,6 + ,15 + ,0 + ,10 + ,6 + ,3 + ,2 + ,10 + ,6 + ,4 + ,0 + ,14 + ,3 + ,12 + ,9 + ,12 + ,5 + ,8 + ,16 + ,12 + ,6 + ,12 + ,10 + ,12 + ,4 + ,18 + ,0 + ,5 + ,5 + ,15 + ,7 + ,0 + ,6 + ,3 + ,8 + ,4 + ,6 + ,0 + ,0 + ,3 + ,3 + ,0 + ,0 + ,0 + ,6 + ,21 + ,0 + ,14 + ,5 + ,10 + ,0 + ,4 + ,6 + ,5 + ,1 + ,3 + ,4 + ,0 + ,0 + ,0 + ,7 + ,1 + ,0 + ,12 + ,5 + ,0 + ,0 + ,12 + ,6 + ,6 + ,0 + ,15 + ,6 + ,12 + ,0 + ,0 + ,6 + ,10 + ,20 + ,8 + ,7 + ,0 + ,9 + ,6 + ,6 + ,25 + ,0 + ,14 + ,6 + ,3 + ,0 + ,5 + ,6 + ,15 + ,0 + ,10 + ,6 + ,10 + ,0 + ,16 + ,2 + ,15 + ,4 + ,4 + ,4 + ,4 + ,0 + ,0 + ,4 + ,10 + ,2 + ,8 + ,6 + ,2 + ,0 + ,12 + ,5 + ,12 + ,0 + ,6 + ,6 + ,9 + ,0 + ,4 + ,6 + ,1 + ,28 + ,20 + ,2 + ,4 + ,0 + ,0 + ,7 + ,2 + ,0 + ,13 + ,1 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,6 + ,0 + ,0 + ,0 + ,6 + ,0 + ,0 + ,10 + ,6 + ,18 + ,10 + ,6 + ,7 + ,3 + ,0 + ,16 + ,6 + ,6 + ,0 + ,6 + ,4 + ,0 + ,16 + ,0 + ,4 + ,2 + ,1 + ,0 + ,6 + ,4 + ,10 + ,4 + ,5 + ,15 + ,0 + ,9 + ,7 + ,6 + ,0 + ,17 + ,4 + ,30 + ,15 + ,12 + ,4 + ,3 + ,10 + ,3 + ,6 + ,18 + ,0 + ,6 + ,7 + ,10 + ,0 + ,8 + ,5 + ,0 + ,0 + ,3 + ,6 + ,7 + ,2 + ,7 + ,6 + ,0 + ,3 + ,0 + ,6 + ,22 + ,4 + ,10 + ,5 + ,7 + ,1 + ,3 + ,7 + ,4 + ,4 + ,0 + ,4 + ,15 + ,0 + ,8 + ,6 + ,5 + ,0 + ,0 + ,6 + ,14 + ,8 + ,4 + ,7 + ,11 + ,0 + ,13 + ,6 + ,24 + ,0 + ,12 + ,6 + ,24 + ,6 + ,16 + ,5 + ,0 + ,0 + ,20 + ,5 + ,20 + ,2 + ,20 + ,5 + ,12 + ,0 + ,21 + ,6 + ,7 + ,0 + ,10 + ,6 + ,0 + ,0 + ,14 + ,7 + ,28 + ,0 + ,12 + ,4 + ,12 + ,0 + ,15 + ,6 + ,15 + ,27 + ,9 + ,6 + ,0 + ,0 + ,4 + ,7 + ,7 + ,4 + ,8 + ,6 + ,8 + ,0 + ,0 + ,7 + ,30 + ,0 + ,13 + ,4 + ,14 + ,0 + ,0 + ,6 + ,3 + ,0 + ,21 + ,4 + ,3 + ,1 + ,0 + ,4 + ,0 + ,0 + ,1 + ,7 + ,15 + ,4 + ,16 + ,4 + ,0 + ,0 + ,12 + ,7 + ,11 + ,0 + ,2) + ,dim=c(4 + ,160) + ,dimnames=list(c('satisfaction' + ,'Walked' + ,'Cycled' + ,'Other ') + ,1:160)) > y <- array(NA,dim=c(4,160),dimnames=list(c('satisfaction','Walked','Cycled','Other '),1:160)) > 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 satisfaction Walked Cycled Other\r\r 1 5 22 0 15 2 4 2 1 0 3 7 0 0 3 4 7 4 0 2 5 5 14 5 3 6 5 2 0 12 7 4 0 4 3 8 4 4 4 0 9 6 6 0 12 10 5 25 0 15 11 1 0 0 0 12 5 25 5 10 13 4 0 0 12 14 6 2 2 20 15 7 30 3 20 16 7 1 0 2 17 2 0 0 3 18 6 0 0 16 19 4 8 0 4 20 3 0 4 2 21 6 0 0 4 22 6 0 8 16 23 5 6 0 0 24 4 0 0 0 25 6 6 0 15 26 4 12 3 9 27 3 1 0 1 28 4 20 24 15 29 5 5 15 5 30 6 0 0 4 31 6 21 12 15 32 4 3 0 4 33 6 5 0 12 34 6 8 0 2 35 5 10 4 4 36 6 5 1 2 37 4 8 0 4 38 6 6 16 8 39 7 15 9 30 40 5 9 0 6 41 6 14 8 6 42 6 9 10 7 43 5 5 0 4 44 7 9 6 17 45 6 10 0 5 46 3 12 0 0 47 4 9 15 3 48 5 7 0 4 49 4 15 0 15 50 3 14 0 0 51 5 16 0 8 52 5 6 0 10 53 4 6 0 4 54 5 2 0 0 55 1 8 10 6 56 2 0 7 11 57 3 6 2 10 58 4 4 0 0 59 3 15 2 0 60 7 0 0 0 61 2 12 3 0 62 4 0 12 0 63 2 13 0 0 64 5 18 3 0 65 6 4 0 7 66 6 9 0 4 67 6 12 0 12 68 6 14 8 6 69 6 0 0 12 70 6 4 7 10 71 6 12 0 9 72 4 15 18 6 73 4 0 0 0 74 5 30 13 16 75 6 0 0 2 76 6 0 0 0 77 7 3 0 0 78 4 2 0 1 79 6 15 0 10 80 6 3 2 10 81 6 4 0 14 82 3 12 9 12 83 5 8 16 12 84 6 12 10 12 85 4 18 0 5 86 5 15 7 0 87 6 3 8 4 88 6 0 0 3 89 3 0 0 0 90 6 21 0 14 91 5 10 0 4 92 6 5 1 3 93 4 0 0 0 94 7 1 0 12 95 5 0 0 12 96 6 6 0 15 97 6 12 0 0 98 6 10 20 8 99 7 0 9 6 100 6 25 0 14 101 6 3 0 5 102 6 15 0 10 103 6 10 0 16 104 2 15 4 4 105 4 4 0 0 106 4 10 2 8 107 6 2 0 12 108 5 12 0 6 109 6 9 0 4 110 6 1 28 20 111 2 4 0 0 112 7 2 0 13 113 1 0 0 0 114 4 1 0 0 115 1 0 0 0 116 6 0 0 0 117 6 0 0 10 118 6 18 10 6 119 7 3 0 16 120 6 6 0 6 121 4 0 16 0 122 4 2 1 0 123 6 4 10 4 124 5 15 0 9 125 7 6 0 17 126 4 30 15 12 127 4 3 10 3 128 6 18 0 6 129 7 10 0 8 130 5 0 0 3 131 6 7 2 7 132 6 0 3 0 133 6 22 4 10 134 5 7 1 3 135 7 4 4 0 136 4 15 0 8 137 6 5 0 0 138 6 14 8 4 139 7 11 0 13 140 6 24 0 12 141 6 24 6 16 142 5 0 0 20 143 5 20 2 20 144 5 12 0 21 145 6 7 0 10 146 6 0 0 14 147 7 28 0 12 148 4 12 0 15 149 6 15 27 9 150 6 0 0 4 151 7 7 4 8 152 6 8 0 0 153 7 30 0 13 154 4 14 0 0 155 6 3 0 21 156 4 3 1 0 157 4 0 0 1 158 7 15 4 16 159 4 0 0 12 160 7 11 0 2 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Walked Cycled `Other\r\r` 4.574087 -0.002062 -0.018280 0.087193 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.8980 -0.6634 0.1930 1.0732 2.5073 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.574087 0.179503 25.482 < 2e-16 *** Walked -0.002062 0.014809 -0.139 0.889 Cycled -0.018280 0.019634 -0.931 0.353 `Other\r\r` 0.087193 0.018162 4.801 3.67e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.34 on 156 degrees of freedom Multiple R-squared: 0.1399, Adjusted R-squared: 0.1234 F-statistic: 8.461 on 3 and 156 DF, p-value: 3.039e-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,] 0.6630732 0.67385351 0.33692676 [2,] 0.5349661 0.93006786 0.46503393 [3,] 0.4228732 0.84574644 0.57712678 [4,] 0.3138176 0.62763525 0.68618237 [5,] 0.9453560 0.10928800 0.05464400 [6,] 0.9124913 0.17501743 0.08750871 [7,] 0.8981281 0.20374390 0.10187195 [8,] 0.8578055 0.28438899 0.14219449 [9,] 0.8282008 0.34359849 0.17179924 [10,] 0.8993711 0.20125779 0.10062889 [11,] 0.9550635 0.08987293 0.04493646 [12,] 0.9363438 0.12731231 0.06365616 [13,] 0.9159790 0.16804201 0.08402100 [14,] 0.9098485 0.18030299 0.09015150 [15,] 0.9066836 0.18663278 0.09331639 [16,] 0.8808564 0.23828728 0.11914364 [17,] 0.8521010 0.29579805 0.14789903 [18,] 0.8127749 0.37445023 0.18722512 [19,] 0.7673259 0.46534823 0.23267412 [20,] 0.7473504 0.50529928 0.25264964 [21,] 0.7456495 0.50870093 0.25435047 [22,] 0.7074076 0.58518488 0.29259244 [23,] 0.6817695 0.63646097 0.31823048 [24,] 0.6763079 0.64738427 0.32369214 [25,] 0.6356659 0.72866813 0.36433406 [26,] 0.5943250 0.81134996 0.40567498 [27,] 0.5451677 0.90966467 0.45483233 [28,] 0.5522371 0.89552584 0.44776292 [29,] 0.4989932 0.99798640 0.50100680 [30,] 0.5031273 0.99374546 0.49687273 [31,] 0.4683620 0.93672397 0.53163801 [32,] 0.4649822 0.92996446 0.53501777 [33,] 0.4108804 0.82176075 0.58911962 [34,] 0.3584296 0.71685926 0.64157037 [35,] 0.3450718 0.69014368 0.65492816 [36,] 0.3264857 0.65297137 0.67351431 [37,] 0.2803937 0.56078734 0.71960633 [38,] 0.2672621 0.53452411 0.73273795 [39,] 0.2510995 0.50219906 0.74890047 [40,] 0.2622587 0.52451734 0.73774133 [41,] 0.2276701 0.45534025 0.77232988 [42,] 0.1911363 0.38227260 0.80886370 [43,] 0.2215448 0.44308957 0.77845521 [44,] 0.2250329 0.45006590 0.77496705 [45,] 0.1893406 0.37868130 0.81065935 [46,] 0.1591257 0.31825141 0.84087429 [47,] 0.1402116 0.28042312 0.85978844 [48,] 0.1190408 0.23808151 0.88095924 [49,] 0.3874385 0.77487700 0.61256150 [50,] 0.6255272 0.74894557 0.37447278 [51,] 0.7071936 0.58561271 0.29280635 [52,] 0.6705363 0.65892738 0.32946369 [53,] 0.6739567 0.65208655 0.32604328 [54,] 0.7756407 0.44871856 0.22435928 [55,] 0.8433520 0.31329590 0.15664795 [56,] 0.8168123 0.36637548 0.18318774 [57,] 0.8806722 0.23865556 0.11932778 [58,] 0.8652284 0.26954330 0.13477165 [59,] 0.8511230 0.29775394 0.14887697 [60,] 0.8453974 0.30920514 0.15460257 [61,] 0.8202605 0.35947892 0.17973946 [62,] 0.8145577 0.37088457 0.18544229 [63,] 0.7852043 0.42959137 0.21479568 [64,] 0.7614908 0.47701843 0.23850921 [65,] 0.7353674 0.52926523 0.26463262 [66,] 0.7100400 0.57992007 0.28996003 [67,] 0.6783144 0.64337121 0.32168560 [68,] 0.6481646 0.70367080 0.35183540 [69,] 0.6442232 0.71155369 0.35577685 [70,] 0.6505027 0.69899461 0.34949730 [71,] 0.7419527 0.51609461 0.25804731 [72,] 0.7135322 0.57293557 0.28646779 [73,] 0.6816830 0.63663394 0.31831697 [74,] 0.6478756 0.70424875 0.35212437 [75,] 0.6052451 0.78950975 0.39475488 [76,] 0.7062562 0.58748760 0.29374380 [77,] 0.6739176 0.65216489 0.32608244 [78,] 0.6416530 0.71669403 0.35834701 [79,] 0.6247936 0.75041270 0.37520635 [80,] 0.5918695 0.81626094 0.40813047 [81,] 0.5822453 0.83550949 0.41775475 [82,] 0.5700263 0.85994740 0.42997370 [83,] 0.5903047 0.81939063 0.40969531 [84,] 0.5473719 0.90525616 0.45262808 [85,] 0.5019778 0.99604439 0.49802220 [86,] 0.4896233 0.97924659 0.51037671 [87,] 0.4530989 0.90619778 0.54690111 [88,] 0.4553715 0.91074303 0.54462848 [89,] 0.4191202 0.83824039 0.58087981 [90,] 0.3741709 0.74834173 0.62582914 [91,] 0.3766975 0.75339491 0.62330254 [92,] 0.3587989 0.71759781 0.64120110 [93,] 0.4120003 0.82400051 0.58799974 [94,] 0.3695889 0.73917778 0.63041111 [95,] 0.3485751 0.69715021 0.65142489 [96,] 0.3121642 0.62432848 0.68783576 [97,] 0.2710272 0.54205450 0.72897275 [98,] 0.4409271 0.88185429 0.55907286 [99,] 0.4043064 0.80861273 0.59569364 [100,] 0.4029866 0.80597325 0.59701338 [101,] 0.3607879 0.72157578 0.63921211 [102,] 0.3180406 0.63608121 0.68195939 [103,] 0.2978278 0.59565564 0.70217218 [104,] 0.2566194 0.51323874 0.74338063 [105,] 0.3901345 0.78026904 0.60986548 [106,] 0.3919863 0.78397254 0.60801373 [107,] 0.7394895 0.52102108 0.26051054 [108,] 0.7191926 0.56161478 0.28080739 [109,] 0.9663746 0.06725072 0.03362536 [110,] 0.9614772 0.07704555 0.03852278 [111,] 0.9505025 0.09899491 0.04949746 [112,] 0.9408706 0.11825885 0.05912943 [113,] 0.9414890 0.11702208 0.05851104 [114,] 0.9279301 0.14413970 0.07206985 [115,] 0.9187079 0.16258419 0.08129209 [116,] 0.9209473 0.15810541 0.07905270 [117,] 0.9080339 0.18393211 0.09196606 [118,] 0.8913256 0.21734885 0.10867442 [119,] 0.8948491 0.21030179 0.10515089 [120,] 0.9363761 0.12724772 0.06362386 [121,] 0.9459225 0.10815504 0.05407752 [122,] 0.9293703 0.14125949 0.07062975 [123,] 0.9380244 0.12395111 0.06197555 [124,] 0.9174492 0.16510154 0.08255077 [125,] 0.8949379 0.21012422 0.10506211 [126,] 0.8770232 0.24595365 0.12297683 [127,] 0.8422064 0.31558724 0.15779362 [128,] 0.8060198 0.38796031 0.19398016 [129,] 0.8467553 0.30648938 0.15324469 [130,] 0.8868873 0.22622536 0.11311268 [131,] 0.8644126 0.27117477 0.13558739 [132,] 0.8260142 0.34797159 0.17398580 [133,] 0.8366857 0.32662865 0.16331432 [134,] 0.7855233 0.42895338 0.21447669 [135,] 0.7274413 0.54511736 0.27255868 [136,] 0.6655438 0.66891243 0.33445622 [137,] 0.6666263 0.66674731 0.33337366 [138,] 0.6523112 0.69537765 0.34768883 [139,] 0.5754235 0.84915299 0.42457650 [140,] 0.5117989 0.97640224 0.48820112 [141,] 0.4251111 0.85022213 0.57488894 [142,] 0.5687531 0.86249371 0.43124686 [143,] 0.5671099 0.86578017 0.43289009 [144,] 0.5782299 0.84354028 0.42177014 [145,] 0.5075892 0.98482156 0.49241078 [146,] 0.4905266 0.98105329 0.50947335 [147,] 0.3666072 0.73321430 0.63339285 > postscript(file="/var/www/html/rcomp/tmp/114cm1291200690.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2cdbp1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3cdbp1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4cdbp1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5m4ta1291200690.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 = 160 Frequency = 1 1 2 3 4 5 6 -0.836626074 -0.551683953 2.164333039 2.259773292 0.284597584 -0.616281966 7 8 9 10 11 12 -0.762547153 -0.492720557 0.391965115 -0.830440762 -3.574087446 -0.303075144 13 14 15 16 17 18 -1.620405507 -0.277267436 0.798742087 2.253587981 -2.835666961 0.030821806 19 20 21 22 23 24 -0.906365970 -1.675353982 1.077139867 0.177061422 0.438283176 -0.574087446 25 26 27 28 29 30 0.130385600 -1.279244891 -1.659218847 -1.402030767 0.274454827 1.077139867 31 32 33 34 35 36 0.380671580 -0.916674822 0.389903345 1.268020374 0.170877379 1.280115014 37 38 39 40 41 42 -0.906365970 1.033217034 0.005563525 -0.078690543 1.077857925 1.016915805 43 44 45 46 47 48 0.087448719 1.071864280 1.010564399 -1.549346201 -0.542911748 0.091572260 49 50 51 52 53 54 -1.851058466 -1.545222660 -0.238644494 -0.433648541 -0.910489511 0.430036095 55 56 57 58 59 60 -3.897952794 -3.405252672 -2.397088637 -0.565840364 -1.506600986 2.425912554 61 62 63 64 65 66 -2.494506345 -0.354728022 -2.547284431 0.517864277 0.823807433 1.095695801 67 68 69 70 71 72 0.404335738 1.077857925 0.379594493 0.690187582 0.665915253 -0.737280785 73 74 75 76 77 78 -0.574087446 -0.669685706 1.251526210 1.425912554 2.432097865 -0.657157077 79 80 81 82 83 84 0.584907392 0.596726052 0.213455231 -2.431144695 -0.311432112 0.587135257 85 86 87 88 89 90 -0.972941438 0.584798774 1.229564794 1.164333039 -1.574087446 0.248505328 91 92 93 94 95 96 0.097757571 1.192921843 -0.574087446 1.381656263 -0.620405507 0.130385600 97 98 99 100 101 102 1.450653799 1.114583923 2.067273091 0.256752409 0.996132006 0.584907392 103 104 105 106 107 108 0.051439510 -2.818813769 -0.565840364 -1.214455212 0.383718034 -0.072505232 109 110 111 112 113 114 1.095695801 0.195949544 -2.565840364 1.296524862 -3.574087446 -0.572025676 115 116 117 118 119 120 -3.574087446 1.425912554 0.553980836 1.122664910 1.037007117 0.915124146 121 122 123 124 125 126 -0.281608215 -0.551683953 1.268186468 -0.327899436 0.955999257 -1.284353115 127 128 129 130 131 132 -0.646682130 0.939865391 1.748984884 0.164333039 0.866552648 1.480752410 133 134 135 136 137 138 0.672459593 0.197045383 2.507279443 -1.240706264 1.436221406 1.252244268 139 140 141 142 143 144 1.315080796 0.429076983 0.189984007 -1.317950881 -1.240155569 -1.380402808 145 146 147 148 149 150 0.568413229 0.205208149 1.437324064 -1.857243778 1.165659267 1.077139867 151 152 153 154 155 156 1.815919381 1.442406717 1.354254433 -0.545222660 -0.398958742 -0.549622183 157 158 159 160 -0.661280618 1.134868170 -1.620405507 2.274205685 > postscript(file="/var/www/html/rcomp/tmp/6m4ta1291200690.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 = 160 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.836626074 NA 1 -0.551683953 -0.836626074 2 2.164333039 -0.551683953 3 2.259773292 2.164333039 4 0.284597584 2.259773292 5 -0.616281966 0.284597584 6 -0.762547153 -0.616281966 7 -0.492720557 -0.762547153 8 0.391965115 -0.492720557 9 -0.830440762 0.391965115 10 -3.574087446 -0.830440762 11 -0.303075144 -3.574087446 12 -1.620405507 -0.303075144 13 -0.277267436 -1.620405507 14 0.798742087 -0.277267436 15 2.253587981 0.798742087 16 -2.835666961 2.253587981 17 0.030821806 -2.835666961 18 -0.906365970 0.030821806 19 -1.675353982 -0.906365970 20 1.077139867 -1.675353982 21 0.177061422 1.077139867 22 0.438283176 0.177061422 23 -0.574087446 0.438283176 24 0.130385600 -0.574087446 25 -1.279244891 0.130385600 26 -1.659218847 -1.279244891 27 -1.402030767 -1.659218847 28 0.274454827 -1.402030767 29 1.077139867 0.274454827 30 0.380671580 1.077139867 31 -0.916674822 0.380671580 32 0.389903345 -0.916674822 33 1.268020374 0.389903345 34 0.170877379 1.268020374 35 1.280115014 0.170877379 36 -0.906365970 1.280115014 37 1.033217034 -0.906365970 38 0.005563525 1.033217034 39 -0.078690543 0.005563525 40 1.077857925 -0.078690543 41 1.016915805 1.077857925 42 0.087448719 1.016915805 43 1.071864280 0.087448719 44 1.010564399 1.071864280 45 -1.549346201 1.010564399 46 -0.542911748 -1.549346201 47 0.091572260 -0.542911748 48 -1.851058466 0.091572260 49 -1.545222660 -1.851058466 50 -0.238644494 -1.545222660 51 -0.433648541 -0.238644494 52 -0.910489511 -0.433648541 53 0.430036095 -0.910489511 54 -3.897952794 0.430036095 55 -3.405252672 -3.897952794 56 -2.397088637 -3.405252672 57 -0.565840364 -2.397088637 58 -1.506600986 -0.565840364 59 2.425912554 -1.506600986 60 -2.494506345 2.425912554 61 -0.354728022 -2.494506345 62 -2.547284431 -0.354728022 63 0.517864277 -2.547284431 64 0.823807433 0.517864277 65 1.095695801 0.823807433 66 0.404335738 1.095695801 67 1.077857925 0.404335738 68 0.379594493 1.077857925 69 0.690187582 0.379594493 70 0.665915253 0.690187582 71 -0.737280785 0.665915253 72 -0.574087446 -0.737280785 73 -0.669685706 -0.574087446 74 1.251526210 -0.669685706 75 1.425912554 1.251526210 76 2.432097865 1.425912554 77 -0.657157077 2.432097865 78 0.584907392 -0.657157077 79 0.596726052 0.584907392 80 0.213455231 0.596726052 81 -2.431144695 0.213455231 82 -0.311432112 -2.431144695 83 0.587135257 -0.311432112 84 -0.972941438 0.587135257 85 0.584798774 -0.972941438 86 1.229564794 0.584798774 87 1.164333039 1.229564794 88 -1.574087446 1.164333039 89 0.248505328 -1.574087446 90 0.097757571 0.248505328 91 1.192921843 0.097757571 92 -0.574087446 1.192921843 93 1.381656263 -0.574087446 94 -0.620405507 1.381656263 95 0.130385600 -0.620405507 96 1.450653799 0.130385600 97 1.114583923 1.450653799 98 2.067273091 1.114583923 99 0.256752409 2.067273091 100 0.996132006 0.256752409 101 0.584907392 0.996132006 102 0.051439510 0.584907392 103 -2.818813769 0.051439510 104 -0.565840364 -2.818813769 105 -1.214455212 -0.565840364 106 0.383718034 -1.214455212 107 -0.072505232 0.383718034 108 1.095695801 -0.072505232 109 0.195949544 1.095695801 110 -2.565840364 0.195949544 111 1.296524862 -2.565840364 112 -3.574087446 1.296524862 113 -0.572025676 -3.574087446 114 -3.574087446 -0.572025676 115 1.425912554 -3.574087446 116 0.553980836 1.425912554 117 1.122664910 0.553980836 118 1.037007117 1.122664910 119 0.915124146 1.037007117 120 -0.281608215 0.915124146 121 -0.551683953 -0.281608215 122 1.268186468 -0.551683953 123 -0.327899436 1.268186468 124 0.955999257 -0.327899436 125 -1.284353115 0.955999257 126 -0.646682130 -1.284353115 127 0.939865391 -0.646682130 128 1.748984884 0.939865391 129 0.164333039 1.748984884 130 0.866552648 0.164333039 131 1.480752410 0.866552648 132 0.672459593 1.480752410 133 0.197045383 0.672459593 134 2.507279443 0.197045383 135 -1.240706264 2.507279443 136 1.436221406 -1.240706264 137 1.252244268 1.436221406 138 1.315080796 1.252244268 139 0.429076983 1.315080796 140 0.189984007 0.429076983 141 -1.317950881 0.189984007 142 -1.240155569 -1.317950881 143 -1.380402808 -1.240155569 144 0.568413229 -1.380402808 145 0.205208149 0.568413229 146 1.437324064 0.205208149 147 -1.857243778 1.437324064 148 1.165659267 -1.857243778 149 1.077139867 1.165659267 150 1.815919381 1.077139867 151 1.442406717 1.815919381 152 1.354254433 1.442406717 153 -0.545222660 1.354254433 154 -0.398958742 -0.545222660 155 -0.549622183 -0.398958742 156 -0.661280618 -0.549622183 157 1.134868170 -0.661280618 158 -1.620405507 1.134868170 159 2.274205685 -1.620405507 160 NA 2.274205685 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.551683953 -0.836626074 [2,] 2.164333039 -0.551683953 [3,] 2.259773292 2.164333039 [4,] 0.284597584 2.259773292 [5,] -0.616281966 0.284597584 [6,] -0.762547153 -0.616281966 [7,] -0.492720557 -0.762547153 [8,] 0.391965115 -0.492720557 [9,] -0.830440762 0.391965115 [10,] -3.574087446 -0.830440762 [11,] -0.303075144 -3.574087446 [12,] -1.620405507 -0.303075144 [13,] -0.277267436 -1.620405507 [14,] 0.798742087 -0.277267436 [15,] 2.253587981 0.798742087 [16,] -2.835666961 2.253587981 [17,] 0.030821806 -2.835666961 [18,] -0.906365970 0.030821806 [19,] -1.675353982 -0.906365970 [20,] 1.077139867 -1.675353982 [21,] 0.177061422 1.077139867 [22,] 0.438283176 0.177061422 [23,] -0.574087446 0.438283176 [24,] 0.130385600 -0.574087446 [25,] -1.279244891 0.130385600 [26,] -1.659218847 -1.279244891 [27,] -1.402030767 -1.659218847 [28,] 0.274454827 -1.402030767 [29,] 1.077139867 0.274454827 [30,] 0.380671580 1.077139867 [31,] -0.916674822 0.380671580 [32,] 0.389903345 -0.916674822 [33,] 1.268020374 0.389903345 [34,] 0.170877379 1.268020374 [35,] 1.280115014 0.170877379 [36,] -0.906365970 1.280115014 [37,] 1.033217034 -0.906365970 [38,] 0.005563525 1.033217034 [39,] -0.078690543 0.005563525 [40,] 1.077857925 -0.078690543 [41,] 1.016915805 1.077857925 [42,] 0.087448719 1.016915805 [43,] 1.071864280 0.087448719 [44,] 1.010564399 1.071864280 [45,] -1.549346201 1.010564399 [46,] -0.542911748 -1.549346201 [47,] 0.091572260 -0.542911748 [48,] -1.851058466 0.091572260 [49,] -1.545222660 -1.851058466 [50,] -0.238644494 -1.545222660 [51,] -0.433648541 -0.238644494 [52,] -0.910489511 -0.433648541 [53,] 0.430036095 -0.910489511 [54,] -3.897952794 0.430036095 [55,] -3.405252672 -3.897952794 [56,] -2.397088637 -3.405252672 [57,] -0.565840364 -2.397088637 [58,] -1.506600986 -0.565840364 [59,] 2.425912554 -1.506600986 [60,] -2.494506345 2.425912554 [61,] -0.354728022 -2.494506345 [62,] -2.547284431 -0.354728022 [63,] 0.517864277 -2.547284431 [64,] 0.823807433 0.517864277 [65,] 1.095695801 0.823807433 [66,] 0.404335738 1.095695801 [67,] 1.077857925 0.404335738 [68,] 0.379594493 1.077857925 [69,] 0.690187582 0.379594493 [70,] 0.665915253 0.690187582 [71,] -0.737280785 0.665915253 [72,] -0.574087446 -0.737280785 [73,] -0.669685706 -0.574087446 [74,] 1.251526210 -0.669685706 [75,] 1.425912554 1.251526210 [76,] 2.432097865 1.425912554 [77,] -0.657157077 2.432097865 [78,] 0.584907392 -0.657157077 [79,] 0.596726052 0.584907392 [80,] 0.213455231 0.596726052 [81,] -2.431144695 0.213455231 [82,] -0.311432112 -2.431144695 [83,] 0.587135257 -0.311432112 [84,] -0.972941438 0.587135257 [85,] 0.584798774 -0.972941438 [86,] 1.229564794 0.584798774 [87,] 1.164333039 1.229564794 [88,] -1.574087446 1.164333039 [89,] 0.248505328 -1.574087446 [90,] 0.097757571 0.248505328 [91,] 1.192921843 0.097757571 [92,] -0.574087446 1.192921843 [93,] 1.381656263 -0.574087446 [94,] -0.620405507 1.381656263 [95,] 0.130385600 -0.620405507 [96,] 1.450653799 0.130385600 [97,] 1.114583923 1.450653799 [98,] 2.067273091 1.114583923 [99,] 0.256752409 2.067273091 [100,] 0.996132006 0.256752409 [101,] 0.584907392 0.996132006 [102,] 0.051439510 0.584907392 [103,] -2.818813769 0.051439510 [104,] -0.565840364 -2.818813769 [105,] -1.214455212 -0.565840364 [106,] 0.383718034 -1.214455212 [107,] -0.072505232 0.383718034 [108,] 1.095695801 -0.072505232 [109,] 0.195949544 1.095695801 [110,] -2.565840364 0.195949544 [111,] 1.296524862 -2.565840364 [112,] -3.574087446 1.296524862 [113,] -0.572025676 -3.574087446 [114,] -3.574087446 -0.572025676 [115,] 1.425912554 -3.574087446 [116,] 0.553980836 1.425912554 [117,] 1.122664910 0.553980836 [118,] 1.037007117 1.122664910 [119,] 0.915124146 1.037007117 [120,] -0.281608215 0.915124146 [121,] -0.551683953 -0.281608215 [122,] 1.268186468 -0.551683953 [123,] -0.327899436 1.268186468 [124,] 0.955999257 -0.327899436 [125,] -1.284353115 0.955999257 [126,] -0.646682130 -1.284353115 [127,] 0.939865391 -0.646682130 [128,] 1.748984884 0.939865391 [129,] 0.164333039 1.748984884 [130,] 0.866552648 0.164333039 [131,] 1.480752410 0.866552648 [132,] 0.672459593 1.480752410 [133,] 0.197045383 0.672459593 [134,] 2.507279443 0.197045383 [135,] -1.240706264 2.507279443 [136,] 1.436221406 -1.240706264 [137,] 1.252244268 1.436221406 [138,] 1.315080796 1.252244268 [139,] 0.429076983 1.315080796 [140,] 0.189984007 0.429076983 [141,] -1.317950881 0.189984007 [142,] -1.240155569 -1.317950881 [143,] -1.380402808 -1.240155569 [144,] 0.568413229 -1.380402808 [145,] 0.205208149 0.568413229 [146,] 1.437324064 0.205208149 [147,] -1.857243778 1.437324064 [148,] 1.165659267 -1.857243778 [149,] 1.077139867 1.165659267 [150,] 1.815919381 1.077139867 [151,] 1.442406717 1.815919381 [152,] 1.354254433 1.442406717 [153,] -0.545222660 1.354254433 [154,] -0.398958742 -0.545222660 [155,] -0.549622183 -0.398958742 [156,] -0.661280618 -0.549622183 [157,] 1.134868170 -0.661280618 [158,] -1.620405507 1.134868170 [159,] 2.274205685 -1.620405507 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.551683953 -0.836626074 2 2.164333039 -0.551683953 3 2.259773292 2.164333039 4 0.284597584 2.259773292 5 -0.616281966 0.284597584 6 -0.762547153 -0.616281966 7 -0.492720557 -0.762547153 8 0.391965115 -0.492720557 9 -0.830440762 0.391965115 10 -3.574087446 -0.830440762 11 -0.303075144 -3.574087446 12 -1.620405507 -0.303075144 13 -0.277267436 -1.620405507 14 0.798742087 -0.277267436 15 2.253587981 0.798742087 16 -2.835666961 2.253587981 17 0.030821806 -2.835666961 18 -0.906365970 0.030821806 19 -1.675353982 -0.906365970 20 1.077139867 -1.675353982 21 0.177061422 1.077139867 22 0.438283176 0.177061422 23 -0.574087446 0.438283176 24 0.130385600 -0.574087446 25 -1.279244891 0.130385600 26 -1.659218847 -1.279244891 27 -1.402030767 -1.659218847 28 0.274454827 -1.402030767 29 1.077139867 0.274454827 30 0.380671580 1.077139867 31 -0.916674822 0.380671580 32 0.389903345 -0.916674822 33 1.268020374 0.389903345 34 0.170877379 1.268020374 35 1.280115014 0.170877379 36 -0.906365970 1.280115014 37 1.033217034 -0.906365970 38 0.005563525 1.033217034 39 -0.078690543 0.005563525 40 1.077857925 -0.078690543 41 1.016915805 1.077857925 42 0.087448719 1.016915805 43 1.071864280 0.087448719 44 1.010564399 1.071864280 45 -1.549346201 1.010564399 46 -0.542911748 -1.549346201 47 0.091572260 -0.542911748 48 -1.851058466 0.091572260 49 -1.545222660 -1.851058466 50 -0.238644494 -1.545222660 51 -0.433648541 -0.238644494 52 -0.910489511 -0.433648541 53 0.430036095 -0.910489511 54 -3.897952794 0.430036095 55 -3.405252672 -3.897952794 56 -2.397088637 -3.405252672 57 -0.565840364 -2.397088637 58 -1.506600986 -0.565840364 59 2.425912554 -1.506600986 60 -2.494506345 2.425912554 61 -0.354728022 -2.494506345 62 -2.547284431 -0.354728022 63 0.517864277 -2.547284431 64 0.823807433 0.517864277 65 1.095695801 0.823807433 66 0.404335738 1.095695801 67 1.077857925 0.404335738 68 0.379594493 1.077857925 69 0.690187582 0.379594493 70 0.665915253 0.690187582 71 -0.737280785 0.665915253 72 -0.574087446 -0.737280785 73 -0.669685706 -0.574087446 74 1.251526210 -0.669685706 75 1.425912554 1.251526210 76 2.432097865 1.425912554 77 -0.657157077 2.432097865 78 0.584907392 -0.657157077 79 0.596726052 0.584907392 80 0.213455231 0.596726052 81 -2.431144695 0.213455231 82 -0.311432112 -2.431144695 83 0.587135257 -0.311432112 84 -0.972941438 0.587135257 85 0.584798774 -0.972941438 86 1.229564794 0.584798774 87 1.164333039 1.229564794 88 -1.574087446 1.164333039 89 0.248505328 -1.574087446 90 0.097757571 0.248505328 91 1.192921843 0.097757571 92 -0.574087446 1.192921843 93 1.381656263 -0.574087446 94 -0.620405507 1.381656263 95 0.130385600 -0.620405507 96 1.450653799 0.130385600 97 1.114583923 1.450653799 98 2.067273091 1.114583923 99 0.256752409 2.067273091 100 0.996132006 0.256752409 101 0.584907392 0.996132006 102 0.051439510 0.584907392 103 -2.818813769 0.051439510 104 -0.565840364 -2.818813769 105 -1.214455212 -0.565840364 106 0.383718034 -1.214455212 107 -0.072505232 0.383718034 108 1.095695801 -0.072505232 109 0.195949544 1.095695801 110 -2.565840364 0.195949544 111 1.296524862 -2.565840364 112 -3.574087446 1.296524862 113 -0.572025676 -3.574087446 114 -3.574087446 -0.572025676 115 1.425912554 -3.574087446 116 0.553980836 1.425912554 117 1.122664910 0.553980836 118 1.037007117 1.122664910 119 0.915124146 1.037007117 120 -0.281608215 0.915124146 121 -0.551683953 -0.281608215 122 1.268186468 -0.551683953 123 -0.327899436 1.268186468 124 0.955999257 -0.327899436 125 -1.284353115 0.955999257 126 -0.646682130 -1.284353115 127 0.939865391 -0.646682130 128 1.748984884 0.939865391 129 0.164333039 1.748984884 130 0.866552648 0.164333039 131 1.480752410 0.866552648 132 0.672459593 1.480752410 133 0.197045383 0.672459593 134 2.507279443 0.197045383 135 -1.240706264 2.507279443 136 1.436221406 -1.240706264 137 1.252244268 1.436221406 138 1.315080796 1.252244268 139 0.429076983 1.315080796 140 0.189984007 0.429076983 141 -1.317950881 0.189984007 142 -1.240155569 -1.317950881 143 -1.380402808 -1.240155569 144 0.568413229 -1.380402808 145 0.205208149 0.568413229 146 1.437324064 0.205208149 147 -1.857243778 1.437324064 148 1.165659267 -1.857243778 149 1.077139867 1.165659267 150 1.815919381 1.077139867 151 1.442406717 1.815919381 152 1.354254433 1.442406717 153 -0.545222660 1.354254433 154 -0.398958742 -0.545222660 155 -0.549622183 -0.398958742 156 -0.661280618 -0.549622183 157 1.134868170 -0.661280618 158 -1.620405507 1.134868170 159 2.274205685 -1.620405507 > 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/7fesv1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8fesv1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9859g1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10859g1291200690.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11t58m1291200690.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/12wooa1291200690.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/133pll1291200690.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/14eyk61291200690.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/15hh1u1291200690.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/16vqzl1291200690.tab") + } > > try(system("convert tmp/114cm1291200690.ps tmp/114cm1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/2cdbp1291200690.ps tmp/2cdbp1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/3cdbp1291200690.ps tmp/3cdbp1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/4cdbp1291200690.ps tmp/4cdbp1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/5m4ta1291200690.ps tmp/5m4ta1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/6m4ta1291200690.ps tmp/6m4ta1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/7fesv1291200690.ps tmp/7fesv1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/8fesv1291200690.ps tmp/8fesv1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/9859g1291200690.ps tmp/9859g1291200690.png",intern=TRUE)) character(0) > try(system("convert tmp/10859g1291200690.ps tmp/10859g1291200690.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.104 1.786 9.325