R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(2 + ,5 + ,2 + ,3 + ,3 + ,4 + ,4 + ,2 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,5 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,3 + ,2 + ,4 + ,4 + ,5 + ,1 + ,3 + ,2 + ,4 + ,5 + ,3 + ,5 + ,1 + ,2 + ,1 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,2 + ,3 + ,2 + ,4 + ,4 + ,2 + ,4 + ,1 + ,3 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,3 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,4 + ,2 + ,4 + ,4 + ,1 + ,1 + ,3 + ,4 + ,3 + ,4 + ,5 + ,1 + ,1 + ,1 + ,4 + ,4 + ,3 + ,4 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,3 + ,4 + ,2 + ,2 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,4 + ,3 + ,2 + ,4 + ,1 + ,4 + ,2 + ,4 + ,3 + ,5 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,5 + ,2 + ,3 + ,2 + ,4 + ,2 + ,2 + ,2 + ,4 + ,3 + ,3 + ,5 + ,2 + ,3 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,3 + ,4 + ,2 + ,2 + ,2 + ,3 + ,4 + ,4 + ,4 + ,3 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,1 + ,4 + ,1 + ,2 + ,3 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,2 + ,1 + ,4 + ,1 + ,4 + ,4 + ,2 + ,4 + ,2 + ,5 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,3 + ,4 + ,3 + ,3 + ,5 + ,2 + ,4 + ,2 + ,5 + ,4 + ,2 + ,5 + ,2 + ,4 + ,1 + ,4 + ,3 + ,4 + ,4 + ,2 + ,2 + ,1 + ,2 + ,4 + ,5 + ,3 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,4 + ,3 + ,4 + ,5 + ,2 + ,2 + ,2 + ,5 + ,5 + ,4 + ,4 + ,2 + ,3 + ,1 + ,4 + ,4 + ,3 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,4 + ,5 + ,2 + ,4 + ,1 + ,4 + ,3 + ,2 + ,4 + ,2 + ,3 + ,2 + ,4 + ,3 + ,2 + ,5 + ,1 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,2 + ,4 + ,1 + ,5 + ,2 + ,5 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,3 + ,1 + ,4 + ,2 + ,4 + ,4 + ,1 + ,4 + ,1 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,4 + ,5 + ,1 + ,2 + ,1 + ,2 + ,1 + ,3 + ,3 + ,4 + ,3 + ,5 + ,4 + ,5 + ,5 + ,3 + ,3 + ,5 + ,2 + ,3 + ,2 + ,4 + ,5 + 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,4 + ,3 + ,5 + ,2 + ,5 + ,2 + ,2 + ,4 + ,2 + ,3 + ,5 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,1 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,2 + ,4 + ,2 + ,1 + ,1 + ,1 + ,2 + ,1 + ,3 + ,4 + ,5 + ,4 + ,3 + ,3 + ,2 + ,3 + ,4 + ,3 + ,3 + ,1 + ,2 + ,1 + ,2 + ,2 + ,3 + ,4 + ,1 + ,3 + ,1 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,2 + ,4 + ,3 + ,2 + ,5 + ,2 + ,2 + ,2 + ,5 + ,4 + ,2 + ,4 + ,1 + ,2 + ,3 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,2 + ,3 + ,4 + ,4 + ,4 + ,1 + ,4 + ,1 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,2 + ,3 + ,4 + ,3 + ,4 + ,1 + ,3 + ,2 + ,3 + ,4 + ,3 + ,4 + ,2 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,4 + ,2 + ,3 + ,4 + ,2 + ,2 + ,2 + ,3 + ,4 + ,4 + ,4 + ,1 + ,1 + ,2 + ,2 + ,5 + ,4 + ,4 + ,1 + ,3 + ,1 + ,3 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,2 + ,3 + ,1 + ,2 + ,2 + ,4 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,4 + ,1 + ,3 + ,4 + ,3 + ,3 + ,1 + ,4 + ,4 + ,3 + ,2 + ,4 + ,2 + ,3 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,5 + ,3 + ,5 + ,2 + ,5 + ,2 + ,5 + ,3 + ,1 + ,2 + ,4 + ,1 + ,2 + ,1 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,2 + ,4 + ,5 + ,3 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,2 + ,4 + ,2 + ,5 + ,3 + ,3 + ,2 + ,2 + ,4 + ,2 + ,3 + ,4 + ,3 + ,2 + ,1 + ,1 + ,3 + ,2 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,1 + ,3 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,1 + ,1 + ,5 + ,5 + ,4 + ,2 + ,1 + ,2 + ,2 + ,3 + ,2 + ,5 + ,5 + ,4 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,2 + ,2 + ,2 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,2 + ,5 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,2 + ,3) + ,dim=c(7 + ,159) + ,dimnames=list(c('standards' + ,'organization' + ,'punished' + ,'secondrate' + ,'mistakes' + ,'competent' + ,'neat') + ,1:159)) > y <- array(NA,dim=c(7,159),dimnames=list(c('standards','organization','punished','secondrate','mistakes','competent','neat'),1:159)) > 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 = '5' > #'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 mistakes standards organization punished secondrate competent neat 1 3 2 5 2 3 4 4 2 3 2 4 2 4 4 4 3 2 4 4 2 4 5 4 4 2 2 4 2 2 2 4 5 3 3 2 2 2 2 4 6 2 4 5 1 3 4 5 7 1 3 5 1 2 4 4 8 3 3 4 3 3 4 3 9 2 3 3 2 3 4 4 10 2 2 4 1 3 2 4 11 3 4 4 4 3 3 4 12 2 4 2 2 4 4 4 13 2 3 3 3 2 3 4 14 2 3 3 2 2 4 2 15 3 4 4 1 1 4 3 16 1 4 5 1 1 4 4 17 3 3 4 2 3 4 3 18 2 3 2 2 2 2 2 19 3 3 4 2 2 4 4 20 4 4 4 2 3 4 3 21 2 2 4 1 4 4 3 22 3 5 4 2 4 3 4 23 5 4 4 4 3 2 3 24 2 2 4 2 2 4 3 25 2 3 5 2 3 2 4 26 3 4 4 2 4 3 4 27 2 4 4 2 3 4 4 28 2 3 4 2 2 3 4 29 2 4 4 3 1 4 4 30 2 4 4 2 3 4 4 31 3 1 4 1 2 4 5 32 4 4 4 4 4 4 4 33 1 5 2 1 4 4 4 34 3 2 4 2 5 4 4 35 3 4 4 2 2 4 3 36 2 3 5 2 4 5 4 37 1 2 5 2 4 4 3 38 1 4 4 2 2 2 4 39 2 5 3 2 4 4 4 40 2 4 4 2 4 4 3 41 2 4 5 2 2 5 5 42 1 4 4 2 3 4 4 43 2 3 4 2 2 2 3 44 1 4 5 2 4 4 3 45 2 2 4 2 3 4 3 46 2 2 5 1 1 4 4 47 4 4 4 2 2 2 4 48 2 2 4 1 5 5 4 49 2 4 4 2 2 4 4 50 2 4 3 1 4 4 4 51 1 1 4 1 4 4 4 52 2 4 4 2 2 4 4 53 2 2 4 2 2 4 5 54 1 1 2 1 2 3 3 55 5 4 3 5 4 5 3 56 2 3 5 2 3 4 5 57 2 2 4 2 4 4 5 58 2 4 4 1 2 4 4 59 1 3 5 1 3 4 4 60 3 2 3 2 2 2 3 61 1 2 5 2 2 4 4 62 1 3 4 1 3 4 4 63 2 2 5 1 2 4 5 64 3 1 4 2 3 4 4 65 2 3 4 1 2 3 4 66 2 2 5 1 4 4 5 67 2 3 4 2 2 2 4 68 4 3 4 1 5 4 3 69 1 3 5 1 1 4 4 70 2 2 4 2 3 4 4 71 2 3 3 1 2 4 4 72 2 2 4 1 2 4 4 73 2 4 5 3 3 4 4 74 2 4 5 3 4 3 4 75 1 4 5 2 4 4 4 76 2 2 4 2 2 4 3 77 2 3 4 1 3 4 4 78 2 4 5 3 4 4 3 79 2 3 5 2 2 4 5 80 1 4 4 2 2 4 4 81 4 2 5 2 4 4 5 82 2 3 3 2 2 2 5 83 3 3 4 1 4 3 4 84 2 4 4 4 2 5 4 85 1 2 4 1 3 3 4 86 2 4 4 1 4 3 4 87 2 2 4 1 3 4 4 88 1 2 5 1 1 4 5 89 2 4 4 4 3 4 4 90 1 3 4 2 2 4 3 91 2 4 4 2 2 4 4 92 1 2 5 1 1 3 3 93 2 2 3 1 3 4 4 94 2 3 3 1 2 4 4 95 3 3 5 3 3 4 4 96 4 5 5 4 5 5 4 97 1 2 4 4 3 4 4 98 3 3 4 3 4 4 3 99 1 4 4 2 2 2 3 100 1 3 4 2 2 3 3 101 2 4 4 3 3 3 3 102 1 3 4 1 2 3 3 103 3 3 4 3 2 4 2 104 2 2 4 2 2 4 3 105 2 3 5 2 3 2 5 106 1 2 2 2 5 3 2 107 2 3 4 2 2 3 2 108 2 2 2 4 3 4 3 109 1 4 4 3 3 4 3 110 2 2 5 1 1 2 3 111 2 4 3 1 1 3 4 112 4 4 4 2 3 4 4 113 3 1 3 1 4 4 3 114 2 5 4 3 5 5 2 115 5 2 4 2 3 3 3 116 1 3 4 2 3 3 4 117 2 4 2 2 3 4 2 118 1 1 1 1 2 3 4 119 2 5 4 3 3 3 4 120 1 3 3 1 2 2 2 121 1 3 4 1 3 4 3 122 2 3 3 2 2 3 3 123 2 3 3 3 4 4 3 124 2 2 5 2 2 5 4 125 3 2 4 1 2 4 4 126 2 4 3 2 4 3 4 127 1 4 4 1 4 3 3 128 2 3 4 2 3 3 4 129 2 3 4 1 3 3 4 130 3 3 4 2 3 4 4 131 2 4 3 3 4 4 2 132 2 3 4 2 2 3 4 133 2 4 4 1 1 2 5 134 1 4 4 1 3 3 4 135 2 2 4 2 2 2 4 136 2 4 4 2 3 4 4 137 2 2 3 1 2 4 3 138 3 4 4 2 2 4 1 139 1 3 4 3 3 4 4 140 3 3 2 4 2 4 3 141 4 2 2 2 4 4 3 142 2 2 4 4 4 5 3 143 5 5 2 5 2 3 1 144 1 2 4 1 2 4 4 145 2 4 3 3 3 4 5 146 3 3 4 2 4 4 4 147 2 3 3 2 4 5 3 148 2 3 2 2 4 3 4 149 3 3 2 1 1 2 3 150 2 4 4 4 4 4 4 151 1 4 3 2 4 3 4 152 2 4 4 2 3 4 4 153 1 4 4 3 1 5 5 154 2 4 2 1 2 3 2 155 3 5 5 4 2 3 3 156 2 3 4 2 2 3 3 157 2 3 4 2 3 5 4 158 2 4 4 4 3 4 4 159 4 4 3 4 3 2 3 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) standards organization punished secondrate 1.98324 -0.04480 -0.03568 0.35102 0.11261 competent neat -0.09118 -0.07255 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.83792 -0.57860 -0.07685 0.41515 2.70038 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.98324 0.52311 3.791 0.000216 *** standards -0.04480 0.07529 -0.595 0.552697 organization -0.03568 0.08680 -0.411 0.681624 punished 0.35102 0.07988 4.394 2.08e-05 *** secondrate 0.11261 0.07157 1.573 0.117714 competent -0.09118 0.08973 -1.016 0.311209 neat -0.07255 0.09282 -0.782 0.435636 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.8482 on 152 degrees of freedom Multiple R-squared: 0.165, Adjusted R-squared: 0.1321 F-statistic: 5.008 on 6 and 152 DF, p-value: 0.0001038 > 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,] 0.35271598 0.70543195 0.6472840 [2,] 0.27796388 0.55592776 0.7220361 [3,] 0.19139294 0.38278588 0.8086071 [4,] 0.17156944 0.34313888 0.8284306 [5,] 0.10494939 0.20989878 0.8950506 [6,] 0.30996206 0.61992412 0.6900379 [7,] 0.28532563 0.57065126 0.7146744 [8,] 0.22896047 0.45792093 0.7710395 [9,] 0.20284912 0.40569824 0.7971509 [10,] 0.19890617 0.39781234 0.8010938 [11,] 0.38087420 0.76174839 0.6191258 [12,] 0.32379184 0.64758368 0.6762082 [13,] 0.26227583 0.52455165 0.7377242 [14,] 0.31553890 0.63107780 0.6844611 [15,] 0.25634593 0.51269186 0.7436541 [16,] 0.26573964 0.53147929 0.7342604 [17,] 0.21617245 0.43234490 0.7838276 [18,] 0.18372395 0.36744791 0.8162760 [19,] 0.14277291 0.28554582 0.8572271 [20,] 0.11560293 0.23120585 0.8843971 [21,] 0.09282292 0.18564583 0.9071771 [22,] 0.22724723 0.45449445 0.7727528 [23,] 0.20571075 0.41142149 0.7942893 [24,] 0.19617855 0.39235711 0.8038214 [25,] 0.16046743 0.32093486 0.8395326 [26,] 0.15157272 0.30314544 0.8484273 [27,] 0.14031928 0.28063855 0.8596807 [28,] 0.29182142 0.58364283 0.7081786 [29,] 0.37010168 0.74020336 0.6298983 [30,] 0.32272072 0.64544144 0.6772793 [31,] 0.28578833 0.57157666 0.7142117 [32,] 0.24177872 0.48355744 0.7582213 [33,] 0.29147530 0.58295061 0.7085247 [34,] 0.25311218 0.50622436 0.7468878 [35,] 0.31263279 0.62526557 0.6873672 [36,] 0.27603961 0.55207922 0.7239604 [37,] 0.24081573 0.48163146 0.7591843 [38,] 0.40687754 0.81375509 0.5931225 [39,] 0.35870819 0.71741639 0.6412918 [40,] 0.31294124 0.62588248 0.6870588 [41,] 0.27179556 0.54359112 0.7282044 [42,] 0.28158570 0.56317140 0.7184143 [43,] 0.24201319 0.48402639 0.7579868 [44,] 0.20768757 0.41537515 0.7923124 [45,] 0.20800415 0.41600830 0.7919959 [46,] 0.26710505 0.53421010 0.7328949 [47,] 0.23115057 0.46230115 0.7688494 [48,] 0.19847857 0.39695714 0.8015214 [49,] 0.17511091 0.35022181 0.8248891 [50,] 0.16264864 0.32529729 0.8373514 [51,] 0.15086019 0.30172038 0.8491398 [52,] 0.17659584 0.35319168 0.8234042 [53,] 0.16489318 0.32978636 0.8351068 [54,] 0.14649507 0.29299015 0.8535049 [55,] 0.14221174 0.28442348 0.8577883 [56,] 0.12087151 0.24174303 0.8791285 [57,] 0.10128988 0.20257977 0.8987101 [58,] 0.08434204 0.16868408 0.9156580 [59,] 0.22490379 0.44980759 0.7750962 [60,] 0.19970305 0.39940611 0.8002969 [61,] 0.17033021 0.34066043 0.8296698 [62,] 0.14775719 0.29551438 0.8522428 [63,] 0.12697779 0.25395558 0.8730222 [64,] 0.11647607 0.23295214 0.8835239 [65,] 0.11151385 0.22302769 0.8884862 [66,] 0.13129466 0.26258932 0.8687053 [67,] 0.10924563 0.21849126 0.8907544 [68,] 0.09091278 0.18182556 0.9090872 [69,] 0.08310387 0.16620774 0.9168961 [70,] 0.06695859 0.13391718 0.9330414 [71,] 0.07301430 0.14602860 0.9269857 [72,] 0.16159747 0.32319494 0.8384025 [73,] 0.13794674 0.27589349 0.8620533 [74,] 0.16164171 0.32328342 0.8383583 [75,] 0.15368977 0.30737954 0.8463102 [76,] 0.15184474 0.30368947 0.8481553 [77,] 0.12730557 0.25461114 0.8726944 [78,] 0.10676485 0.21352970 0.8932352 [79,] 0.09011175 0.18022349 0.9098883 [80,] 0.09030556 0.18061113 0.9096944 [81,] 0.10180860 0.20361721 0.8981914 [82,] 0.08232246 0.16464492 0.9176775 [83,] 0.07581983 0.15163965 0.9241802 [84,] 0.06122710 0.12245421 0.9387729 [85,] 0.04982624 0.09965249 0.9501738 [86,] 0.04470300 0.08940600 0.9552970 [87,] 0.06675114 0.13350229 0.9332489 [88,] 0.14377763 0.28755525 0.8562224 [89,] 0.12742739 0.25485477 0.8725726 [90,] 0.15162807 0.30325615 0.8483719 [91,] 0.17827833 0.35655666 0.8217217 [92,] 0.15858378 0.31716756 0.8414162 [93,] 0.15791495 0.31582990 0.8420851 [94,] 0.13795093 0.27590186 0.8620491 [95,] 0.11434297 0.22868595 0.8856570 [96,] 0.09283401 0.18566803 0.9071660 [97,] 0.15483211 0.30966422 0.8451679 [98,] 0.13595570 0.27191140 0.8640443 [99,] 0.15120380 0.30240760 0.8487962 [100,] 0.20757298 0.41514597 0.7924270 [101,] 0.18359711 0.36719421 0.8164029 [102,] 0.15877873 0.31755747 0.8412213 [103,] 0.39961724 0.79923448 0.6003828 [104,] 0.40296474 0.80592947 0.5970353 [105,] 0.36115249 0.72230497 0.6388475 [106,] 0.80150054 0.39699892 0.1984995 [107,] 0.81760389 0.36479222 0.1823961 [108,] 0.78676933 0.42646133 0.2132307 [109,] 0.83579525 0.32840949 0.1642047 [110,] 0.80089592 0.39820817 0.1991041 [111,] 0.88539658 0.22920684 0.1146034 [112,] 0.88260147 0.23479706 0.1173985 [113,] 0.87066584 0.25866833 0.1293342 [114,] 0.85539905 0.28920191 0.1446010 [115,] 0.82178328 0.35643343 0.1782167 [116,] 0.88796230 0.22407541 0.1120377 [117,] 0.85536063 0.28927873 0.1446394 [118,] 0.86079058 0.27841885 0.1392094 [119,] 0.82135924 0.35728153 0.1786408 [120,] 0.78194308 0.43611384 0.2180569 [121,] 0.85469218 0.29061564 0.1453078 [122,] 0.87383955 0.25232089 0.1261604 [123,] 0.83353363 0.33293274 0.1664664 [124,] 0.83633087 0.32733826 0.1636691 [125,] 0.80799643 0.38400715 0.1920036 [126,] 0.75241245 0.49517510 0.2475875 [127,] 0.70471571 0.59056858 0.2952843 [128,] 0.63406944 0.73186112 0.3659306 [129,] 0.57057958 0.85884083 0.4294204 [130,] 0.62984400 0.74031201 0.3701560 [131,] 0.56217354 0.87565293 0.4378265 [132,] 0.73403718 0.53192565 0.2659628 [133,] 0.75494227 0.49011547 0.2450577 [134,] 0.71739959 0.56520082 0.2826004 [135,] 0.74923458 0.50153083 0.2507654 [136,] 0.71007302 0.57985395 0.2899270 [137,] 0.79659753 0.40680494 0.2034025 [138,] 0.68724494 0.62551013 0.3127551 [139,] 0.54331103 0.91337794 0.4566890 [140,] 0.45648089 0.91296177 0.5435191 > postscript(file="/var/www/html/rcomp/tmp/13sj91292850509.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/23sj91292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/33sj91292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ej0u1292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ej0u1292850509.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 = 159 Frequency = 1 1 2 3 4 5 8.997866e-01 7.514974e-01 -6.772588e-02 -2.056314e-01 7.678143e-01 6 7 8 9 10 4.129557e-01 -5.917840e-01 4.853408e-01 -1.267677e-01 3.277377e-02 11 12 13 14 15 1.604990e-01 -2.302568e-01 -4.563493e-01 -1.592589e-01 1.457399e+00 16 17 18 19 20 -4.343718e-01 8.363580e-01 -3.772889e-01 1.021522e+00 1.881158e+00 21 22 23 24 25 2.996295e-02 7.947216e-01 1.996771e+00 -9.583020e-02 -2.377659e-01 26 27 28 29 30 7.499214e-01 -4.629026e-02 -6.965482e-02 -1.720834e-01 -4.629026e-02 31 32 33 34 35 1.355490e+00 1.139063e+00 -8.344394e-01 6.388854e-01 9.937702e-01 36 37 38 39 40 -7.684880e-02 -1.285377e+00 -1.116031e+00 -1.497793e-01 -2.314538e-01 41 42 43 44 45 2.657270e-01 -1.046290e+00 -2.333828e-01 -1.195777e+00 -2.084422e-01 46 47 48 49 50 4.760278e-01 1.883969e+00 8.107890e-02 6.632175e-02 1.564376e-01 51 52 53 54 55 -9.422857e-01 6.632175e-02 4.927294e-02 -9.521442e-01 1.770994e+00 56 57 58 59 60 1.713840e-02 -1.759511e-01 4.173389e-01 -7.043960e-01 6.861398e-01 61 62 63 64 65 -9.876014e-01 -7.400733e-01 4.359674e-01 8.193092e-01 2.813623e-01 66 67 68 69 70 2.107434e-01 -1.608312e-01 1.962151e+00 -4.791720e-01 -1.358906e-01 71 72 73 74 75 3.368615e-01 3.277385e-01 -3.616301e-01 -5.654185e-01 -1.123225e+00 76 77 78 79 80 -9.583020e-02 2.599267e-01 -5.467937e-01 1.297504e-01 -9.336783e-01 81 82 83 84 85 1.859726e+00 -1.239569e-01 1.056138e+00 -5.445362e-01 -8.760499e-01 86 87 88 89 90 1.009385e-01 2.151265e-01 -4.514206e-01 -7.483246e-01 -1.051030e+00 91 92 93 94 95 6.632175e-02 -6.877001e-01 1.794493e-01 3.368615e-01 5.935697e-01 96 97 98 99 100 1.198105e+00 -1.837925e+00 3.727288e-01 -1.188583e+00 -1.142206e+00 101 102 103 104 105 -5.610354e-01 -7.911892e-01 5.254013e-01 -9.583020e-02 -1.652144e-01 106 107 108 109 110 -1.668749e+00 -2.147580e-01 -9.818311e-01 -1.469859e+00 2.211235e-01 111 112 113 114 115 4.030973e-01 1.953710e+00 9.494855e-01 -6.316580e-01 2.700381e+00 116 117 118 119 120 -1.182267e+00 -2.627479e-01 -9.152698e-01 -4.436836e-01 -9.905944e-01 121 122 123 124 125 -8.126249e-01 -1.778837e-01 -6.629485e-01 1.035750e-01 1.327739e+00 126 127 128 129 130 -2.857559e-01 -9.716130e-01 -1.822668e-01 1.687503e-01 9.089096e-01 131 132 133 134 135 -6.906998e-01 -6.965482e-02 4.201497e-01 -7.864495e-01 -2.056314e-01 136 137 138 139 140 -4.629026e-02 2.195097e-01 8.486670e-01 -1.442108e+00 1.755811e-01 141 142 143 144 145 1.607591e+00 -9.319122e-01 1.677885e+00 -6.722615e-01 -3.604331e-01 146 147 148 149 150 7.962976e-01 -2.207549e-01 -3.662334e-01 1.158892e+00 -8.609366e-01 151 152 153 154 155 -1.285756e+00 -4.629026e-02 -1.008355e+00 1.097048e-01 2.810369e-01 156 157 158 159 -1.422064e-01 8.593336e-05 -7.483246e-01 9.610938e-01 > postscript(file="/var/www/html/rcomp/tmp/6ej0u1292850509.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 = 159 Frequency = 1 lag(myerror, k = 1) myerror 0 8.997866e-01 NA 1 7.514974e-01 8.997866e-01 2 -6.772588e-02 7.514974e-01 3 -2.056314e-01 -6.772588e-02 4 7.678143e-01 -2.056314e-01 5 4.129557e-01 7.678143e-01 6 -5.917840e-01 4.129557e-01 7 4.853408e-01 -5.917840e-01 8 -1.267677e-01 4.853408e-01 9 3.277377e-02 -1.267677e-01 10 1.604990e-01 3.277377e-02 11 -2.302568e-01 1.604990e-01 12 -4.563493e-01 -2.302568e-01 13 -1.592589e-01 -4.563493e-01 14 1.457399e+00 -1.592589e-01 15 -4.343718e-01 1.457399e+00 16 8.363580e-01 -4.343718e-01 17 -3.772889e-01 8.363580e-01 18 1.021522e+00 -3.772889e-01 19 1.881158e+00 1.021522e+00 20 2.996295e-02 1.881158e+00 21 7.947216e-01 2.996295e-02 22 1.996771e+00 7.947216e-01 23 -9.583020e-02 1.996771e+00 24 -2.377659e-01 -9.583020e-02 25 7.499214e-01 -2.377659e-01 26 -4.629026e-02 7.499214e-01 27 -6.965482e-02 -4.629026e-02 28 -1.720834e-01 -6.965482e-02 29 -4.629026e-02 -1.720834e-01 30 1.355490e+00 -4.629026e-02 31 1.139063e+00 1.355490e+00 32 -8.344394e-01 1.139063e+00 33 6.388854e-01 -8.344394e-01 34 9.937702e-01 6.388854e-01 35 -7.684880e-02 9.937702e-01 36 -1.285377e+00 -7.684880e-02 37 -1.116031e+00 -1.285377e+00 38 -1.497793e-01 -1.116031e+00 39 -2.314538e-01 -1.497793e-01 40 2.657270e-01 -2.314538e-01 41 -1.046290e+00 2.657270e-01 42 -2.333828e-01 -1.046290e+00 43 -1.195777e+00 -2.333828e-01 44 -2.084422e-01 -1.195777e+00 45 4.760278e-01 -2.084422e-01 46 1.883969e+00 4.760278e-01 47 8.107890e-02 1.883969e+00 48 6.632175e-02 8.107890e-02 49 1.564376e-01 6.632175e-02 50 -9.422857e-01 1.564376e-01 51 6.632175e-02 -9.422857e-01 52 4.927294e-02 6.632175e-02 53 -9.521442e-01 4.927294e-02 54 1.770994e+00 -9.521442e-01 55 1.713840e-02 1.770994e+00 56 -1.759511e-01 1.713840e-02 57 4.173389e-01 -1.759511e-01 58 -7.043960e-01 4.173389e-01 59 6.861398e-01 -7.043960e-01 60 -9.876014e-01 6.861398e-01 61 -7.400733e-01 -9.876014e-01 62 4.359674e-01 -7.400733e-01 63 8.193092e-01 4.359674e-01 64 2.813623e-01 8.193092e-01 65 2.107434e-01 2.813623e-01 66 -1.608312e-01 2.107434e-01 67 1.962151e+00 -1.608312e-01 68 -4.791720e-01 1.962151e+00 69 -1.358906e-01 -4.791720e-01 70 3.368615e-01 -1.358906e-01 71 3.277385e-01 3.368615e-01 72 -3.616301e-01 3.277385e-01 73 -5.654185e-01 -3.616301e-01 74 -1.123225e+00 -5.654185e-01 75 -9.583020e-02 -1.123225e+00 76 2.599267e-01 -9.583020e-02 77 -5.467937e-01 2.599267e-01 78 1.297504e-01 -5.467937e-01 79 -9.336783e-01 1.297504e-01 80 1.859726e+00 -9.336783e-01 81 -1.239569e-01 1.859726e+00 82 1.056138e+00 -1.239569e-01 83 -5.445362e-01 1.056138e+00 84 -8.760499e-01 -5.445362e-01 85 1.009385e-01 -8.760499e-01 86 2.151265e-01 1.009385e-01 87 -4.514206e-01 2.151265e-01 88 -7.483246e-01 -4.514206e-01 89 -1.051030e+00 -7.483246e-01 90 6.632175e-02 -1.051030e+00 91 -6.877001e-01 6.632175e-02 92 1.794493e-01 -6.877001e-01 93 3.368615e-01 1.794493e-01 94 5.935697e-01 3.368615e-01 95 1.198105e+00 5.935697e-01 96 -1.837925e+00 1.198105e+00 97 3.727288e-01 -1.837925e+00 98 -1.188583e+00 3.727288e-01 99 -1.142206e+00 -1.188583e+00 100 -5.610354e-01 -1.142206e+00 101 -7.911892e-01 -5.610354e-01 102 5.254013e-01 -7.911892e-01 103 -9.583020e-02 5.254013e-01 104 -1.652144e-01 -9.583020e-02 105 -1.668749e+00 -1.652144e-01 106 -2.147580e-01 -1.668749e+00 107 -9.818311e-01 -2.147580e-01 108 -1.469859e+00 -9.818311e-01 109 2.211235e-01 -1.469859e+00 110 4.030973e-01 2.211235e-01 111 1.953710e+00 4.030973e-01 112 9.494855e-01 1.953710e+00 113 -6.316580e-01 9.494855e-01 114 2.700381e+00 -6.316580e-01 115 -1.182267e+00 2.700381e+00 116 -2.627479e-01 -1.182267e+00 117 -9.152698e-01 -2.627479e-01 118 -4.436836e-01 -9.152698e-01 119 -9.905944e-01 -4.436836e-01 120 -8.126249e-01 -9.905944e-01 121 -1.778837e-01 -8.126249e-01 122 -6.629485e-01 -1.778837e-01 123 1.035750e-01 -6.629485e-01 124 1.327739e+00 1.035750e-01 125 -2.857559e-01 1.327739e+00 126 -9.716130e-01 -2.857559e-01 127 -1.822668e-01 -9.716130e-01 128 1.687503e-01 -1.822668e-01 129 9.089096e-01 1.687503e-01 130 -6.906998e-01 9.089096e-01 131 -6.965482e-02 -6.906998e-01 132 4.201497e-01 -6.965482e-02 133 -7.864495e-01 4.201497e-01 134 -2.056314e-01 -7.864495e-01 135 -4.629026e-02 -2.056314e-01 136 2.195097e-01 -4.629026e-02 137 8.486670e-01 2.195097e-01 138 -1.442108e+00 8.486670e-01 139 1.755811e-01 -1.442108e+00 140 1.607591e+00 1.755811e-01 141 -9.319122e-01 1.607591e+00 142 1.677885e+00 -9.319122e-01 143 -6.722615e-01 1.677885e+00 144 -3.604331e-01 -6.722615e-01 145 7.962976e-01 -3.604331e-01 146 -2.207549e-01 7.962976e-01 147 -3.662334e-01 -2.207549e-01 148 1.158892e+00 -3.662334e-01 149 -8.609366e-01 1.158892e+00 150 -1.285756e+00 -8.609366e-01 151 -4.629026e-02 -1.285756e+00 152 -1.008355e+00 -4.629026e-02 153 1.097048e-01 -1.008355e+00 154 2.810369e-01 1.097048e-01 155 -1.422064e-01 2.810369e-01 156 8.593336e-05 -1.422064e-01 157 -7.483246e-01 8.593336e-05 158 9.610938e-01 -7.483246e-01 159 NA 9.610938e-01 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 7.514974e-01 8.997866e-01 [2,] -6.772588e-02 7.514974e-01 [3,] -2.056314e-01 -6.772588e-02 [4,] 7.678143e-01 -2.056314e-01 [5,] 4.129557e-01 7.678143e-01 [6,] -5.917840e-01 4.129557e-01 [7,] 4.853408e-01 -5.917840e-01 [8,] -1.267677e-01 4.853408e-01 [9,] 3.277377e-02 -1.267677e-01 [10,] 1.604990e-01 3.277377e-02 [11,] -2.302568e-01 1.604990e-01 [12,] -4.563493e-01 -2.302568e-01 [13,] -1.592589e-01 -4.563493e-01 [14,] 1.457399e+00 -1.592589e-01 [15,] -4.343718e-01 1.457399e+00 [16,] 8.363580e-01 -4.343718e-01 [17,] -3.772889e-01 8.363580e-01 [18,] 1.021522e+00 -3.772889e-01 [19,] 1.881158e+00 1.021522e+00 [20,] 2.996295e-02 1.881158e+00 [21,] 7.947216e-01 2.996295e-02 [22,] 1.996771e+00 7.947216e-01 [23,] -9.583020e-02 1.996771e+00 [24,] -2.377659e-01 -9.583020e-02 [25,] 7.499214e-01 -2.377659e-01 [26,] -4.629026e-02 7.499214e-01 [27,] -6.965482e-02 -4.629026e-02 [28,] -1.720834e-01 -6.965482e-02 [29,] -4.629026e-02 -1.720834e-01 [30,] 1.355490e+00 -4.629026e-02 [31,] 1.139063e+00 1.355490e+00 [32,] -8.344394e-01 1.139063e+00 [33,] 6.388854e-01 -8.344394e-01 [34,] 9.937702e-01 6.388854e-01 [35,] -7.684880e-02 9.937702e-01 [36,] -1.285377e+00 -7.684880e-02 [37,] -1.116031e+00 -1.285377e+00 [38,] -1.497793e-01 -1.116031e+00 [39,] -2.314538e-01 -1.497793e-01 [40,] 2.657270e-01 -2.314538e-01 [41,] -1.046290e+00 2.657270e-01 [42,] -2.333828e-01 -1.046290e+00 [43,] -1.195777e+00 -2.333828e-01 [44,] -2.084422e-01 -1.195777e+00 [45,] 4.760278e-01 -2.084422e-01 [46,] 1.883969e+00 4.760278e-01 [47,] 8.107890e-02 1.883969e+00 [48,] 6.632175e-02 8.107890e-02 [49,] 1.564376e-01 6.632175e-02 [50,] -9.422857e-01 1.564376e-01 [51,] 6.632175e-02 -9.422857e-01 [52,] 4.927294e-02 6.632175e-02 [53,] -9.521442e-01 4.927294e-02 [54,] 1.770994e+00 -9.521442e-01 [55,] 1.713840e-02 1.770994e+00 [56,] -1.759511e-01 1.713840e-02 [57,] 4.173389e-01 -1.759511e-01 [58,] -7.043960e-01 4.173389e-01 [59,] 6.861398e-01 -7.043960e-01 [60,] -9.876014e-01 6.861398e-01 [61,] -7.400733e-01 -9.876014e-01 [62,] 4.359674e-01 -7.400733e-01 [63,] 8.193092e-01 4.359674e-01 [64,] 2.813623e-01 8.193092e-01 [65,] 2.107434e-01 2.813623e-01 [66,] -1.608312e-01 2.107434e-01 [67,] 1.962151e+00 -1.608312e-01 [68,] -4.791720e-01 1.962151e+00 [69,] -1.358906e-01 -4.791720e-01 [70,] 3.368615e-01 -1.358906e-01 [71,] 3.277385e-01 3.368615e-01 [72,] -3.616301e-01 3.277385e-01 [73,] -5.654185e-01 -3.616301e-01 [74,] -1.123225e+00 -5.654185e-01 [75,] -9.583020e-02 -1.123225e+00 [76,] 2.599267e-01 -9.583020e-02 [77,] -5.467937e-01 2.599267e-01 [78,] 1.297504e-01 -5.467937e-01 [79,] -9.336783e-01 1.297504e-01 [80,] 1.859726e+00 -9.336783e-01 [81,] -1.239569e-01 1.859726e+00 [82,] 1.056138e+00 -1.239569e-01 [83,] -5.445362e-01 1.056138e+00 [84,] -8.760499e-01 -5.445362e-01 [85,] 1.009385e-01 -8.760499e-01 [86,] 2.151265e-01 1.009385e-01 [87,] -4.514206e-01 2.151265e-01 [88,] -7.483246e-01 -4.514206e-01 [89,] -1.051030e+00 -7.483246e-01 [90,] 6.632175e-02 -1.051030e+00 [91,] -6.877001e-01 6.632175e-02 [92,] 1.794493e-01 -6.877001e-01 [93,] 3.368615e-01 1.794493e-01 [94,] 5.935697e-01 3.368615e-01 [95,] 1.198105e+00 5.935697e-01 [96,] -1.837925e+00 1.198105e+00 [97,] 3.727288e-01 -1.837925e+00 [98,] -1.188583e+00 3.727288e-01 [99,] -1.142206e+00 -1.188583e+00 [100,] -5.610354e-01 -1.142206e+00 [101,] -7.911892e-01 -5.610354e-01 [102,] 5.254013e-01 -7.911892e-01 [103,] -9.583020e-02 5.254013e-01 [104,] -1.652144e-01 -9.583020e-02 [105,] -1.668749e+00 -1.652144e-01 [106,] -2.147580e-01 -1.668749e+00 [107,] -9.818311e-01 -2.147580e-01 [108,] -1.469859e+00 -9.818311e-01 [109,] 2.211235e-01 -1.469859e+00 [110,] 4.030973e-01 2.211235e-01 [111,] 1.953710e+00 4.030973e-01 [112,] 9.494855e-01 1.953710e+00 [113,] -6.316580e-01 9.494855e-01 [114,] 2.700381e+00 -6.316580e-01 [115,] -1.182267e+00 2.700381e+00 [116,] -2.627479e-01 -1.182267e+00 [117,] -9.152698e-01 -2.627479e-01 [118,] -4.436836e-01 -9.152698e-01 [119,] -9.905944e-01 -4.436836e-01 [120,] -8.126249e-01 -9.905944e-01 [121,] -1.778837e-01 -8.126249e-01 [122,] -6.629485e-01 -1.778837e-01 [123,] 1.035750e-01 -6.629485e-01 [124,] 1.327739e+00 1.035750e-01 [125,] -2.857559e-01 1.327739e+00 [126,] -9.716130e-01 -2.857559e-01 [127,] -1.822668e-01 -9.716130e-01 [128,] 1.687503e-01 -1.822668e-01 [129,] 9.089096e-01 1.687503e-01 [130,] -6.906998e-01 9.089096e-01 [131,] -6.965482e-02 -6.906998e-01 [132,] 4.201497e-01 -6.965482e-02 [133,] -7.864495e-01 4.201497e-01 [134,] -2.056314e-01 -7.864495e-01 [135,] -4.629026e-02 -2.056314e-01 [136,] 2.195097e-01 -4.629026e-02 [137,] 8.486670e-01 2.195097e-01 [138,] -1.442108e+00 8.486670e-01 [139,] 1.755811e-01 -1.442108e+00 [140,] 1.607591e+00 1.755811e-01 [141,] -9.319122e-01 1.607591e+00 [142,] 1.677885e+00 -9.319122e-01 [143,] -6.722615e-01 1.677885e+00 [144,] -3.604331e-01 -6.722615e-01 [145,] 7.962976e-01 -3.604331e-01 [146,] -2.207549e-01 7.962976e-01 [147,] -3.662334e-01 -2.207549e-01 [148,] 1.158892e+00 -3.662334e-01 [149,] -8.609366e-01 1.158892e+00 [150,] -1.285756e+00 -8.609366e-01 [151,] -4.629026e-02 -1.285756e+00 [152,] -1.008355e+00 -4.629026e-02 [153,] 1.097048e-01 -1.008355e+00 [154,] 2.810369e-01 1.097048e-01 [155,] -1.422064e-01 2.810369e-01 [156,] 8.593336e-05 -1.422064e-01 [157,] -7.483246e-01 8.593336e-05 [158,] 9.610938e-01 -7.483246e-01 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 7.514974e-01 8.997866e-01 2 -6.772588e-02 7.514974e-01 3 -2.056314e-01 -6.772588e-02 4 7.678143e-01 -2.056314e-01 5 4.129557e-01 7.678143e-01 6 -5.917840e-01 4.129557e-01 7 4.853408e-01 -5.917840e-01 8 -1.267677e-01 4.853408e-01 9 3.277377e-02 -1.267677e-01 10 1.604990e-01 3.277377e-02 11 -2.302568e-01 1.604990e-01 12 -4.563493e-01 -2.302568e-01 13 -1.592589e-01 -4.563493e-01 14 1.457399e+00 -1.592589e-01 15 -4.343718e-01 1.457399e+00 16 8.363580e-01 -4.343718e-01 17 -3.772889e-01 8.363580e-01 18 1.021522e+00 -3.772889e-01 19 1.881158e+00 1.021522e+00 20 2.996295e-02 1.881158e+00 21 7.947216e-01 2.996295e-02 22 1.996771e+00 7.947216e-01 23 -9.583020e-02 1.996771e+00 24 -2.377659e-01 -9.583020e-02 25 7.499214e-01 -2.377659e-01 26 -4.629026e-02 7.499214e-01 27 -6.965482e-02 -4.629026e-02 28 -1.720834e-01 -6.965482e-02 29 -4.629026e-02 -1.720834e-01 30 1.355490e+00 -4.629026e-02 31 1.139063e+00 1.355490e+00 32 -8.344394e-01 1.139063e+00 33 6.388854e-01 -8.344394e-01 34 9.937702e-01 6.388854e-01 35 -7.684880e-02 9.937702e-01 36 -1.285377e+00 -7.684880e-02 37 -1.116031e+00 -1.285377e+00 38 -1.497793e-01 -1.116031e+00 39 -2.314538e-01 -1.497793e-01 40 2.657270e-01 -2.314538e-01 41 -1.046290e+00 2.657270e-01 42 -2.333828e-01 -1.046290e+00 43 -1.195777e+00 -2.333828e-01 44 -2.084422e-01 -1.195777e+00 45 4.760278e-01 -2.084422e-01 46 1.883969e+00 4.760278e-01 47 8.107890e-02 1.883969e+00 48 6.632175e-02 8.107890e-02 49 1.564376e-01 6.632175e-02 50 -9.422857e-01 1.564376e-01 51 6.632175e-02 -9.422857e-01 52 4.927294e-02 6.632175e-02 53 -9.521442e-01 4.927294e-02 54 1.770994e+00 -9.521442e-01 55 1.713840e-02 1.770994e+00 56 -1.759511e-01 1.713840e-02 57 4.173389e-01 -1.759511e-01 58 -7.043960e-01 4.173389e-01 59 6.861398e-01 -7.043960e-01 60 -9.876014e-01 6.861398e-01 61 -7.400733e-01 -9.876014e-01 62 4.359674e-01 -7.400733e-01 63 8.193092e-01 4.359674e-01 64 2.813623e-01 8.193092e-01 65 2.107434e-01 2.813623e-01 66 -1.608312e-01 2.107434e-01 67 1.962151e+00 -1.608312e-01 68 -4.791720e-01 1.962151e+00 69 -1.358906e-01 -4.791720e-01 70 3.368615e-01 -1.358906e-01 71 3.277385e-01 3.368615e-01 72 -3.616301e-01 3.277385e-01 73 -5.654185e-01 -3.616301e-01 74 -1.123225e+00 -5.654185e-01 75 -9.583020e-02 -1.123225e+00 76 2.599267e-01 -9.583020e-02 77 -5.467937e-01 2.599267e-01 78 1.297504e-01 -5.467937e-01 79 -9.336783e-01 1.297504e-01 80 1.859726e+00 -9.336783e-01 81 -1.239569e-01 1.859726e+00 82 1.056138e+00 -1.239569e-01 83 -5.445362e-01 1.056138e+00 84 -8.760499e-01 -5.445362e-01 85 1.009385e-01 -8.760499e-01 86 2.151265e-01 1.009385e-01 87 -4.514206e-01 2.151265e-01 88 -7.483246e-01 -4.514206e-01 89 -1.051030e+00 -7.483246e-01 90 6.632175e-02 -1.051030e+00 91 -6.877001e-01 6.632175e-02 92 1.794493e-01 -6.877001e-01 93 3.368615e-01 1.794493e-01 94 5.935697e-01 3.368615e-01 95 1.198105e+00 5.935697e-01 96 -1.837925e+00 1.198105e+00 97 3.727288e-01 -1.837925e+00 98 -1.188583e+00 3.727288e-01 99 -1.142206e+00 -1.188583e+00 100 -5.610354e-01 -1.142206e+00 101 -7.911892e-01 -5.610354e-01 102 5.254013e-01 -7.911892e-01 103 -9.583020e-02 5.254013e-01 104 -1.652144e-01 -9.583020e-02 105 -1.668749e+00 -1.652144e-01 106 -2.147580e-01 -1.668749e+00 107 -9.818311e-01 -2.147580e-01 108 -1.469859e+00 -9.818311e-01 109 2.211235e-01 -1.469859e+00 110 4.030973e-01 2.211235e-01 111 1.953710e+00 4.030973e-01 112 9.494855e-01 1.953710e+00 113 -6.316580e-01 9.494855e-01 114 2.700381e+00 -6.316580e-01 115 -1.182267e+00 2.700381e+00 116 -2.627479e-01 -1.182267e+00 117 -9.152698e-01 -2.627479e-01 118 -4.436836e-01 -9.152698e-01 119 -9.905944e-01 -4.436836e-01 120 -8.126249e-01 -9.905944e-01 121 -1.778837e-01 -8.126249e-01 122 -6.629485e-01 -1.778837e-01 123 1.035750e-01 -6.629485e-01 124 1.327739e+00 1.035750e-01 125 -2.857559e-01 1.327739e+00 126 -9.716130e-01 -2.857559e-01 127 -1.822668e-01 -9.716130e-01 128 1.687503e-01 -1.822668e-01 129 9.089096e-01 1.687503e-01 130 -6.906998e-01 9.089096e-01 131 -6.965482e-02 -6.906998e-01 132 4.201497e-01 -6.965482e-02 133 -7.864495e-01 4.201497e-01 134 -2.056314e-01 -7.864495e-01 135 -4.629026e-02 -2.056314e-01 136 2.195097e-01 -4.629026e-02 137 8.486670e-01 2.195097e-01 138 -1.442108e+00 8.486670e-01 139 1.755811e-01 -1.442108e+00 140 1.607591e+00 1.755811e-01 141 -9.319122e-01 1.607591e+00 142 1.677885e+00 -9.319122e-01 143 -6.722615e-01 1.677885e+00 144 -3.604331e-01 -6.722615e-01 145 7.962976e-01 -3.604331e-01 146 -2.207549e-01 7.962976e-01 147 -3.662334e-01 -2.207549e-01 148 1.158892e+00 -3.662334e-01 149 -8.609366e-01 1.158892e+00 150 -1.285756e+00 -8.609366e-01 151 -4.629026e-02 -1.285756e+00 152 -1.008355e+00 -4.629026e-02 153 1.097048e-01 -1.008355e+00 154 2.810369e-01 1.097048e-01 155 -1.422064e-01 2.810369e-01 156 8.593336e-05 -1.422064e-01 157 -7.483246e-01 8.593336e-05 158 9.610938e-01 -7.483246e-01 > 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/76shf1292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11dtwr1292850509.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/12hudx1292850509.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/13d4to1292850509.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/14l7y01292850509.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/15j58h1292850509.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/1655on1292850509.tab") + } > > try(system("convert tmp/13sj91292850509.ps tmp/13sj91292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/23sj91292850509.ps tmp/23sj91292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/33sj91292850509.ps tmp/33sj91292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/4ej0u1292850509.ps tmp/4ej0u1292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/5ej0u1292850509.ps tmp/5ej0u1292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/6ej0u1292850509.ps tmp/6ej0u1292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/76shf1292850509.ps tmp/76shf1292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/8z1z01292850509.ps tmp/8z1z01292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/9z1z01292850509.ps tmp/9z1z01292850509.png",intern=TRUE)) character(0) > try(system("convert tmp/10z1z01292850509.ps tmp/10z1z01292850509.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.128 1.907 10.019