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(24 + ,3 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,25 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,30 + ,4 + ,5 + ,5 + ,4 + ,4 + ,4 + ,4 + ,19 + ,2 + ,2 + ,3 + ,4 + ,2 + ,3 + ,3 + ,22 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,22 + ,3 + ,4 + ,3 + ,4 + ,2 + ,2 + ,4 + ,25 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,23 + ,3 + ,4 + ,3 + ,4 + ,4 + ,2 + ,3 + ,17 + ,3 + ,4 + ,2 + ,2 + ,1 + ,3 + ,2 + ,21 + ,3 + ,2 + ,4 + ,4 + ,2 + ,3 + ,3 + ,19 + ,3 + ,3 + ,2 + ,3 + ,2 + ,3 + ,3 + ,19 + ,4 + ,4 + ,2 + ,2 + ,2 + ,3 + ,2 + ,15 + ,2 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,16 + ,2 + ,4 + ,1 + ,3 + ,1 + ,3 + ,2 + ,23 + ,1 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,27 + ,1 + ,4 + ,4 + ,5 + ,4 + ,5 + ,4 + ,22 + ,3 + ,4 + ,2 + ,4 + ,2 + ,4 + ,3 + ,14 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,22 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,23 + ,3 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,23 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,3 + ,21 + ,4 + ,3 + ,4 + ,2 + ,2 + ,3 + ,3 + ,19 + ,3 + ,2 + ,3 + ,2 + ,3 + ,2 + ,4 + ,18 + ,2 + ,4 + ,2 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,2 + ,3 + ,3 + ,21 + ,NA + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,18 + ,2 + ,2 + ,3 + ,3 + ,3 + ,3 + ,2 + ,18 + ,2 + ,3 + ,2 + ,4 + ,3 + ,2 + ,2 + ,23 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,19 + ,2 + ,3 + ,3 + ,3 + ,2 + ,3 + ,3 + ,20 + ,2 + ,4 + ,3 + ,2 + ,3 + ,3 + ,3 + ,21 + ,2 + ,4 + ,3 + ,4 + ,2 + ,3 + ,3 + ,20 + ,3 + ,2 + ,4 + ,3 + ,2 + ,2 + ,4 + ,17 + ,5 + ,3 + ,1 + ,2 + ,1 + ,3 + ,2 + ,18 + ,2 + ,3 + ,3 + ,3 + ,2 + ,3 + ,2 + ,19 + ,3 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,22 + ,3 + ,4 + ,3 + ,4 + ,3 + ,2 + ,3 + ,15 + ,1 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,14 + ,1 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,18 + ,3 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,24 + ,4 + ,4 + ,4 + ,3 + ,2 + ,3 + ,4 + ,35 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,29 + ,3 + ,3 + ,5 + ,4 + ,4 + ,5 + ,5 + ,21 + ,3 + ,3 + ,3 + ,4 + ,3 + ,2 + ,3 + ,25 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,20 + ,2 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,22 + ,3 + ,3 + ,3 + ,4 + ,2 + ,4 + ,3 + ,13 + ,2 + ,2 + ,2 + ,3 + ,1 + ,2 + ,1 + ,26 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,17 + ,2 + ,3 + ,2 + ,4 + ,2 + ,2 + ,2 + ,25 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,20 + ,2 + ,5 + ,2 + ,4 + ,1 + ,4 + ,2 + ,19 + ,2 + ,4 + ,2 + ,3 + ,2 + ,4 + ,2 + ,21 + ,4 + ,3 + ,2 + ,4 + ,3 + ,3 + ,2 + ,22 + ,4 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,24 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,21 + ,3 + ,3 + ,4 + ,4 + ,2 + ,2 + ,3 + ,26 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,24 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,16 + ,2 + ,3 + ,2 + ,4 + ,2 + ,1 + ,2 + ,23 + ,1 + ,2 + ,4 + ,5 + ,4 + ,3 + ,4 + ,18 + ,3 + ,3 + ,3 + ,2 + ,3 + ,1 + ,3 + ,16 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,26 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,19 + ,2 + ,4 + ,3 + ,3 + ,1 + ,4 + ,2 + ,21 + ,2 + ,4 + ,3 + ,3 + ,2 + ,4 + ,3 + ,21 + ,3 + ,4 + ,3 + ,4 + ,1 + ,3 + ,3 + ,22 + ,2 + ,4 + ,3 + ,4 + ,2 + ,4 + ,3 + ,23 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,2 + ,29 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,21 + ,2 + ,3 + ,5 + ,1 + ,4 + ,3 + ,3 + ,21 + ,2 + ,4 + ,2 + ,3 + ,4 + ,2 + ,4 + ,23 + ,3 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,27 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,25 + ,4 + ,5 + ,3 + ,4 + ,4 + ,3 + ,2 + ,21 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,2 + ,10 + ,1 + ,2 + ,1 + ,3 + ,1 + ,1 + ,1 + ,20 + ,4 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,26 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,24 + ,3 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,29 + ,1 + ,5 + ,5 + ,4 + ,5 + ,4 + ,5 + ,19 + ,2 + ,3 + ,2 + ,4 + ,3 + ,3 + ,2 + ,24 + ,2 + ,3 + ,4 + ,3 + ,4 + ,5 + ,3 + ,19 + ,2 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3 + ,24 + ,3 + ,5 + ,2 + ,4 + ,4 + ,3 + ,3 + ,22 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,2 + ,17 + ,3 + ,2 + ,2 + ,3 + ,3 + ,2 + ,2) + ,dim=c(8 + ,159) + ,dimnames=list(c('Yt' + ,'X1t' + ,'X2t' + ,'X3t' + ,'X4t' + ,'X5t' + ,'X6t' + ,'X7t') + ,1:159)) > y <- array(NA,dim=c(8,159),dimnames=list(c('Yt','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),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 = '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 Yt X1t X2t X3t X4t X5t X6t X7t 1 24 3 4 4 4 3 2 4 2 25 4 4 4 3 2 4 4 3 30 4 5 5 4 4 4 4 4 19 2 2 3 4 2 3 3 5 22 2 2 4 4 4 4 2 6 22 3 4 3 4 2 2 4 7 25 2 4 4 4 3 4 4 8 23 3 4 3 4 4 2 3 9 17 3 4 2 2 1 3 2 10 21 3 2 4 4 2 3 3 11 19 3 3 2 3 2 3 3 12 19 4 4 2 2 2 3 2 13 15 2 3 2 2 2 2 2 14 16 2 4 1 3 1 3 2 15 23 1 4 4 4 2 4 4 16 27 1 4 4 5 4 5 4 17 22 3 4 2 4 2 4 3 18 14 2 2 2 2 2 2 2 19 22 2 4 3 4 3 3 3 20 23 3 4 3 3 2 4 4 21 23 4 4 3 4 3 2 3 22 21 4 3 4 2 2 3 3 23 19 3 2 3 2 3 2 4 24 18 2 4 2 4 2 2 2 25 20 3 2 2 4 3 2 4 26 23 4 3 4 4 3 3 2 27 25 3 4 4 4 4 3 3 28 19 2 3 3 3 2 3 3 29 24 1 4 4 4 4 3 4 30 22 3 4 2 5 3 3 2 31 25 2 4 4 4 4 3 4 32 26 4 4 4 4 3 3 4 33 29 4 4 5 4 4 4 4 34 32 5 4 5 4 4 5 5 35 25 2 4 4 4 3 4 4 36 29 4 5 4 4 5 4 3 37 28 4 4 4 4 4 4 4 38 17 2 2 2 4 2 3 2 39 28 4 4 4 4 4 4 4 40 29 4 4 4 5 4 4 4 41 26 2 5 4 4 5 2 4 42 25 3 4 3 4 4 4 3 43 14 2 2 2 2 2 2 2 44 25 4 4 3 4 4 3 3 45 26 3 4 4 4 4 3 4 46 20 1 4 3 4 1 4 3 47 18 2 2 2 4 2 2 4 48 32 5 5 4 4 5 4 5 49 25 2 4 4 4 4 3 4 50 25 4 4 3 4 2 4 4 51 23 4 4 4 3 3 2 3 52 21 2 4 3 4 4 2 2 53 20 2 4 4 4 2 2 2 54 15 2 3 2 4 1 1 2 55 30 4 5 4 3 4 5 5 56 24 3 4 4 3 2 4 4 57 26 4 4 4 4 4 2 4 58 24 2 4 4 4 2 4 4 59 22 3 4 3 5 3 2 2 60 14 2 2 1 3 2 2 2 61 24 2 4 3 4 3 4 4 62 24 3 4 3 4 2 4 4 63 24 2 4 4 4 2 4 4 64 24 3 4 2 4 3 4 4 65 19 2 3 3 4 1 3 3 66 31 4 4 5 4 5 4 5 67 22 2 2 4 4 4 3 3 68 27 5 4 4 4 4 4 2 69 19 1 4 3 4 1 3 3 70 25 3 4 4 4 2 4 4 71 20 2 4 2 4 2 4 2 72 21 2 4 3 4 2 3 3 73 27 3 4 4 4 4 4 4 74 23 4 3 3 3 2 4 4 75 25 4 4 4 4 3 3 3 76 20 2 4 4 4 2 2 2 77 21 3 4 3 3 2 3 3 78 22 4 4 3 4 2 3 2 79 23 2 4 2 4 4 3 4 80 25 2 4 4 4 3 4 4 81 25 4 4 3 4 3 3 4 82 17 2 2 2 4 2 3 2 83 19 4 3 2 2 2 4 2 84 25 2 5 3 4 3 4 4 85 19 3 3 2 3 2 3 3 86 20 4 3 2 3 2 3 3 87 26 3 4 4 4 3 4 4 88 23 1 4 4 4 4 3 3 89 27 3 4 4 4 4 4 4 90 17 2 4 2 3 2 2 2 91 17 2 4 2 2 2 3 2 92 19 1 3 4 5 2 2 2 93 17 3 4 2 2 2 2 2 94 22 2 4 3 4 3 3 3 95 21 3 4 3 4 2 2 3 96 32 5 5 5 4 5 4 4 97 21 3 4 3 3 2 3 3 98 21 NA 4 4 4 4 2 3 99 18 2 2 3 3 3 3 2 100 18 2 3 2 4 3 2 2 101 23 3 3 4 4 3 3 3 102 19 2 3 3 3 2 3 3 103 20 2 4 3 2 3 3 3 104 21 2 4 3 4 2 3 3 105 20 3 2 4 3 2 2 4 106 17 5 3 1 2 1 3 2 107 18 2 3 3 3 2 3 2 108 19 3 4 2 4 2 2 2 109 22 3 4 3 4 3 2 3 110 15 1 2 2 4 2 2 2 111 14 1 3 2 2 2 2 2 112 18 3 4 2 2 2 2 3 113 24 4 4 4 3 2 3 4 114 35 5 5 5 5 5 5 5 115 29 3 3 5 4 4 5 5 116 21 3 3 3 4 3 2 3 117 25 3 4 4 4 4 2 4 118 20 2 3 4 4 2 2 3 119 22 3 3 3 4 2 4 3 120 13 2 2 2 3 1 2 1 121 26 3 4 4 4 4 4 3 122 17 2 3 2 4 2 2 2 123 25 4 4 3 3 4 4 3 124 20 2 5 2 4 1 4 2 125 19 2 4 2 3 2 4 2 126 21 4 3 2 4 3 3 2 127 22 4 3 4 4 2 2 3 128 24 3 3 4 4 3 3 4 129 21 3 3 4 4 2 2 3 130 26 3 4 4 4 4 3 4 131 24 4 4 3 4 3 3 3 132 16 2 3 2 4 2 1 2 133 23 1 2 4 5 4 3 4 134 18 3 3 3 2 3 1 3 135 16 2 2 2 4 2 2 2 136 26 3 4 4 4 4 3 4 137 19 2 4 3 3 1 4 2 138 21 2 4 3 3 2 4 3 139 21 3 4 3 4 1 3 3 140 22 2 4 3 4 2 4 3 141 23 4 4 3 3 3 4 2 142 29 4 5 4 4 4 4 4 143 21 2 3 5 1 4 3 3 144 21 2 4 2 3 4 2 4 145 23 3 4 2 4 3 3 4 146 27 4 4 4 4 4 4 3 147 25 4 5 3 4 4 3 2 148 21 4 3 3 4 3 2 2 149 10 1 2 1 3 1 1 1 150 20 4 4 2 4 2 2 2 151 26 4 3 4 4 4 4 3 152 24 3 4 4 4 3 2 4 153 29 1 5 5 4 5 4 5 154 19 2 3 2 4 3 3 2 155 24 2 3 4 3 4 5 3 156 19 2 3 3 4 2 2 3 157 24 3 5 2 4 4 3 3 158 22 3 4 4 3 3 3 2 159 17 3 2 2 3 3 2 2 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X1t X2t X3t X4t X5t 5.653e-15 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00 X6t X7t 1.000e+00 1.000e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.345e-14 -8.791e-16 -1.987e-16 4.388e-16 5.468e-14 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.653e-15 2.603e-15 2.171e+00 0.0315 * X1t 1.000e+00 4.357e-16 2.295e+15 <2e-16 *** X2t 1.000e+00 5.757e-16 1.737e+15 <2e-16 *** X3t 1.000e+00 5.949e-16 1.681e+15 <2e-16 *** X4t 1.000e+00 5.915e-16 1.691e+15 <2e-16 *** X5t 1.000e+00 5.051e-16 1.980e+15 <2e-16 *** X6t 1.000e+00 5.243e-16 1.907e+15 <2e-16 *** X7t 1.000e+00 6.025e-16 1.660e+15 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 5.023e-15 on 150 degrees of freedom (1 observation deleted due to missingness) Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.59e+31 on 7 and 150 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,] 1.987273e-01 3.974546e-01 8.012727e-01 [2,] 4.904915e-04 9.809830e-04 9.995095e-01 [3,] 1.946889e-06 3.893778e-06 9.999981e-01 [4,] 5.768266e-02 1.153653e-01 9.423173e-01 [5,] 1.329720e-04 2.659441e-04 9.998670e-01 [6,] 2.709063e-08 5.418125e-08 1.000000e+00 [7,] 7.822915e-13 1.564583e-12 1.000000e+00 [8,] 5.435888e-11 1.087178e-10 1.000000e+00 [9,] 4.865498e-08 9.730995e-08 1.000000e+00 [10,] 2.268730e-11 4.537459e-11 1.000000e+00 [11,] 1.216590e-07 2.433180e-07 9.999999e-01 [12,] 4.711272e-09 9.422545e-09 1.000000e+00 [13,] 9.214189e-01 1.571622e-01 7.858111e-02 [14,] 8.159317e-06 1.631863e-05 9.999918e-01 [15,] 2.339365e-07 4.678731e-07 9.999998e-01 [16,] 1.306950e-04 2.613900e-04 9.998693e-01 [17,] 6.877790e-01 6.244419e-01 3.122210e-01 [18,] 4.755555e-17 9.511110e-17 1.000000e+00 [19,] 9.969439e-01 6.112180e-03 3.056090e-03 [20,] 1.186408e-06 2.372817e-06 9.999988e-01 [21,] 2.379794e-03 4.759587e-03 9.976202e-01 [22,] 1.000000e+00 2.170322e-36 1.085161e-36 [23,] 1.213468e-05 2.426937e-05 9.999879e-01 [24,] 7.225372e-03 1.445074e-02 9.927746e-01 [25,] 1.025438e-03 2.050875e-03 9.989746e-01 [26,] 3.477981e-24 6.955963e-24 1.000000e+00 [27,] 3.565287e-15 7.130574e-15 1.000000e+00 [28,] 3.488542e-21 6.977084e-21 1.000000e+00 [29,] 7.641857e-20 1.528371e-19 1.000000e+00 [30,] 9.958180e-01 8.364073e-03 4.182036e-03 [31,] 1.267715e-01 2.535430e-01 8.732285e-01 [32,] 8.615209e-01 2.769581e-01 1.384791e-01 [33,] 1.522601e-06 3.045201e-06 9.999985e-01 [34,] 4.882631e-01 9.765261e-01 5.117369e-01 [35,] 6.051057e-15 1.210211e-14 1.000000e+00 [36,] 1.442731e-03 2.885463e-03 9.985573e-01 [37,] 5.910425e-17 1.182085e-16 1.000000e+00 [38,] 4.264117e-23 8.528233e-23 1.000000e+00 [39,] 1.951436e-21 3.902872e-21 1.000000e+00 [40,] 9.397784e-01 1.204431e-01 6.022157e-02 [41,] 9.994308e-01 1.138411e-03 5.692057e-04 [42,] 1.121496e-02 2.242992e-02 9.887850e-01 [43,] 2.030102e-06 4.060203e-06 9.999980e-01 [44,] 2.053097e-46 4.106194e-46 1.000000e+00 [45,] 1.000000e+00 7.145051e-17 3.572526e-17 [46,] 3.094158e-02 6.188317e-02 9.690584e-01 [47,] 1.000000e+00 1.003279e-32 5.016396e-33 [48,] 5.456169e-21 1.091234e-20 1.000000e+00 [49,] 9.999263e-01 1.473856e-04 7.369280e-05 [50,] 7.782531e-01 4.434937e-01 2.217469e-01 [51,] 1.000000e+00 6.576389e-33 3.288194e-33 [52,] 1.730561e-06 3.461122e-06 9.999983e-01 [53,] 2.483100e-36 4.966201e-36 1.000000e+00 [54,] 2.153168e-17 4.306337e-17 1.000000e+00 [55,] 9.137406e-01 1.725188e-01 8.625939e-02 [56,] 1.000000e+00 1.193019e-36 5.965097e-37 [57,] 9.978002e-01 4.399585e-03 2.199792e-03 [58,] 1.000000e+00 2.655264e-38 1.327632e-38 [59,] 1.598880e-19 3.197759e-19 1.000000e+00 [60,] 2.965203e-01 5.930405e-01 7.034797e-01 [61,] 1.466240e-01 2.932481e-01 8.533760e-01 [62,] 1.000000e+00 1.179933e-69 5.899667e-70 [63,] 4.723417e-45 9.446835e-45 1.000000e+00 [64,] 4.011248e-13 8.022496e-13 1.000000e+00 [65,] 2.734219e-23 5.468438e-23 1.000000e+00 [66,] 3.062980e-15 6.125961e-15 1.000000e+00 [67,] 1.058032e-44 2.116063e-44 1.000000e+00 [68,] 1.000000e+00 1.527309e-12 7.636547e-13 [69,] 1.491914e-37 2.983829e-37 1.000000e+00 [70,] 3.907895e-07 7.815790e-07 9.999996e-01 [71,] 1.000000e+00 1.994306e-95 9.971532e-96 [72,] 1.000000e+00 2.016489e-08 1.008244e-08 [73,] 1.403099e-33 2.806198e-33 1.000000e+00 [74,] 1.000000e+00 2.966745e-16 1.483373e-16 [75,] 1.000000e+00 2.806269e-09 1.403134e-09 [76,] 9.998946e-01 2.107132e-04 1.053566e-04 [77,] 1.000000e+00 5.467410e-29 2.733705e-29 [78,] 3.156334e-05 6.312669e-05 9.999684e-01 [79,] 1.569737e-02 3.139474e-02 9.843026e-01 [80,] 3.372008e-08 6.744015e-08 1.000000e+00 [81,] 1.000000e+00 6.017068e-25 3.008534e-25 [82,] 9.994481e-01 1.103789e-03 5.518946e-04 [83,] 1.000000e+00 4.589008e-11 2.294504e-11 [84,] 1.000000e+00 2.570984e-10 1.285492e-10 [85,] 9.999975e-01 5.036832e-06 2.518416e-06 [86,] 1.000000e+00 2.068458e-11 1.034229e-11 [87,] 9.720180e-04 1.944036e-03 9.990280e-01 [88,] 1.000000e+00 7.490595e-38 3.745298e-38 [89,] 2.536533e-07 5.073067e-07 9.999997e-01 [90,] 1.000000e+00 8.495983e-56 4.247991e-56 [91,] 1.000000e+00 2.941952e-08 1.470976e-08 [92,] 1.000000e+00 9.765393e-18 4.882696e-18 [93,] 1.370440e-17 2.740881e-17 1.000000e+00 [94,] 1.000000e+00 7.177167e-45 3.588584e-45 [95,] 1.000000e+00 1.316311e-15 6.581554e-16 [96,] 1.083180e-16 2.166361e-16 1.000000e+00 [97,] 9.974583e-01 5.083456e-03 2.541728e-03 [98,] 7.935177e-04 1.587035e-03 9.992065e-01 [99,] 1.000000e+00 8.953108e-18 4.476554e-18 [100,] 5.310618e-30 1.062124e-29 1.000000e+00 [101,] 1.000000e+00 1.283903e-18 6.419516e-19 [102,] 1.000000e+00 1.104006e-18 5.520028e-19 [103,] 1.000000e+00 6.214930e-17 3.107465e-17 [104,] 1.727472e-18 3.454944e-18 1.000000e+00 [105,] 5.420401e-41 1.084080e-40 1.000000e+00 [106,] 9.489738e-01 1.020524e-01 5.102619e-02 [107,] 2.823153e-03 5.646306e-03 9.971768e-01 [108,] 1.283675e-01 2.567350e-01 8.716325e-01 [109,] 1.000000e+00 1.813874e-10 9.069371e-11 [110,] 6.160049e-01 7.679902e-01 3.839951e-01 [111,] 1.000000e+00 3.813187e-29 1.906594e-29 [112,] 1.000000e+00 3.277133e-27 1.638567e-27 [113,] 9.999102e-01 1.795128e-04 8.975640e-05 [114,] 9.998559e-01 2.881685e-04 1.440842e-04 [115,] 9.999983e-01 3.332614e-06 1.666307e-06 [116,] 1.000000e+00 4.922317e-29 2.461159e-29 [117,] 1.000000e+00 1.774143e-11 8.870714e-12 [118,] 1.000000e+00 1.064175e-23 5.320874e-24 [119,] 9.933193e-01 1.336132e-02 6.680662e-03 [120,] 9.999998e-01 3.053487e-07 1.526744e-07 [121,] 1.781945e-05 3.563891e-05 9.999822e-01 [122,] 7.654053e-01 4.691894e-01 2.345947e-01 [123,] 1.000000e+00 2.686581e-10 1.343291e-10 [124,] 1.000000e+00 7.309045e-16 3.654522e-16 [125,] 9.999999e-01 1.367663e-07 6.838315e-08 [126,] 9.999999e-01 2.575518e-07 1.287759e-07 [127,] 9.999998e-01 4.750836e-07 2.375418e-07 [128,] 1.000000e+00 1.689548e-18 8.447742e-19 [129,] 1.000000e+00 3.641956e-14 1.820978e-14 [130,] 1.000000e+00 7.749172e-08 3.874586e-08 [131,] 1.000000e+00 5.485089e-09 2.742545e-09 [132,] 1.000000e+00 5.681398e-09 2.840699e-09 [133,] 1.000000e+00 2.254561e-10 1.127280e-10 [134,] 8.666271e-01 2.667459e-01 1.333729e-01 [135,] 9.996205e-01 7.589097e-04 3.794549e-04 [136,] 9.999999e-01 1.266066e-07 6.330331e-08 [137,] 9.967429e-01 6.514136e-03 3.257068e-03 [138,] 3.827862e-01 7.655724e-01 6.172138e-01 > postscript(file="/var/www/html/rcomp/tmp/1u6q81290506270.ps",horizontal=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$resid) Warning message: In x[, 1] - mysum$resid : longer object length is not a multiple of shorter object length > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 158 Frequency = 1 1 2 3 4 5 5.467923e-14 -2.344667e-14 4.400606e-15 -6.162446e-15 -6.950011e-16 6 7 8 9 10 -5.186692e-15 2.070999e-15 2.120195e-15 9.694495e-16 7.774362e-16 11 12 13 14 15 4.381414e-16 7.123155e-16 -3.736534e-16 1.340927e-15 -2.953421e-16 16 17 18 19 20 4.632170e-16 6.564393e-16 5.400822e-16 -4.674533e-16 2.165393e-16 21 22 23 24 25 -1.166677e-15 3.669429e-16 -1.385017e-16 -3.622269e-16 -3.462854e-16 26 27 28 29 30 1.022143e-15 -1.201465e-15 8.717172e-17 -1.754603e-15 -4.310169e-16 31 32 33 34 35 -1.047843e-15 -7.243436e-16 -1.356389e-16 -2.582699e-16 -5.287565e-17 36 37 38 39 40 -1.763253e-16 1.191941e-16 1.329785e-15 1.191941e-16 1.654415e-16 41 42 43 44 45 -3.123080e-15 -2.737924e-17 5.400822e-16 -2.259947e-16 -1.284773e-15 46 47 48 49 50 6.458702e-16 -3.111961e-16 -1.134984e-15 -1.047843e-15 8.585238e-16 51 52 53 54 55 -1.606535e-15 -7.129786e-16 -1.815582e-15 -1.228780e-15 1.160889e-15 56 57 58 59 60 7.111068e-16 -2.163400e-15 2.283926e-17 -2.049192e-15 3.970734e-16 61 62 63 64 65 6.460465e-16 3.182978e-16 2.283926e-17 2.198601e-16 4.866898e-16 66 67 68 69 70 -6.277292e-16 -3.027976e-16 3.819478e-16 -6.064493e-16 3.965318e-16 71 72 73 74 75 1.531789e-15 -3.431662e-16 3.561242e-16 1.281923e-15 -5.300128e-16 76 77 78 79 80 -1.815582e-15 -3.696047e-16 4.389551e-16 1.834677e-16 -5.287565e-17 81 82 83 84 85 -6.360441e-16 1.329785e-15 2.378770e-15 -2.121779e-16 4.381414e-16 86 87 88 89 90 8.673451e-16 2.375501e-16 -1.615783e-15 3.561242e-16 -2.974520e-16 91 92 93 94 95 1.075154e-15 -1.062758e-15 -6.361407e-16 -4.674533e-16 -1.880988e-15 96 97 99 100 101 -1.250997e-15 -3.696047e-16 9.529898e-16 -6.899404e-16 -1.565032e-16 102 103 104 105 106 8.717172e-17 -6.034779e-17 -3.431662e-16 -2.713723e-16 3.874012e-16 107 108 109 110 111 7.811029e-16 -1.265291e-15 -2.012214e-15 -1.852047e-16 -8.583682e-16 112 113 114 115 116 -8.859827e-16 -4.173204e-16 -9.239173e-16 7.461417e-17 -1.098479e-15 117 118 119 120 121 -2.370559e-15 -1.484756e-15 1.398609e-15 1.355976e-15 1.618770e-16 122 123 124 125 126 -5.587143e-16 1.077222e-15 2.496796e-16 1.874120e-15 8.101638e-16 127 128 129 130 131 -9.316597e-16 -4.618563e-16 -1.194330e-15 -1.284773e-15 -1.364020e-16 132 133 134 135 136 -1.700012e-15 -7.135512e-16 -1.277559e-15 2.856324e-16 -1.284773e-15 137 138 139 140 141 1.972558e-15 8.143337e-16 -7.160177e-16 8.883367e-16 1.680335e-15 142 143 144 145 146 -2.671856e-16 -3.333519e-16 -1.725722e-15 -1.143482e-15 2.580137e-16 147 148 149 150 151 -6.817215e-16 -5.304553e-16 -7.368483e-16 -6.140427e-16 1.227261e-15 152 153 154 155 156 -2.128311e-15 -1.330275e-15 3.403345e-16 2.185548e-15 -1.174412e-15 157 158 159 -1.050423e-15 7.704534e-17 4.124624e-16 > postscript(file="/var/www/html/rcomp/tmp/6g77d1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 158 Frequency = 1 lag(myerror, k = 1) myerror 0 5.467923e-14 NA 1 -2.344667e-14 5.467923e-14 2 4.400606e-15 -2.344667e-14 3 -6.162446e-15 4.400606e-15 4 -6.950011e-16 -6.162446e-15 5 -5.186692e-15 -6.950011e-16 6 2.070999e-15 -5.186692e-15 7 2.120195e-15 2.070999e-15 8 9.694495e-16 2.120195e-15 9 7.774362e-16 9.694495e-16 10 4.381414e-16 7.774362e-16 11 7.123155e-16 4.381414e-16 12 -3.736534e-16 7.123155e-16 13 1.340927e-15 -3.736534e-16 14 -2.953421e-16 1.340927e-15 15 4.632170e-16 -2.953421e-16 16 6.564393e-16 4.632170e-16 17 5.400822e-16 6.564393e-16 18 -4.674533e-16 5.400822e-16 19 2.165393e-16 -4.674533e-16 20 -1.166677e-15 2.165393e-16 21 3.669429e-16 -1.166677e-15 22 -1.385017e-16 3.669429e-16 23 -3.622269e-16 -1.385017e-16 24 -3.462854e-16 -3.622269e-16 25 1.022143e-15 -3.462854e-16 26 -1.201465e-15 1.022143e-15 27 8.717172e-17 -1.201465e-15 28 -1.754603e-15 8.717172e-17 29 -4.310169e-16 -1.754603e-15 30 -1.047843e-15 -4.310169e-16 31 -7.243436e-16 -1.047843e-15 32 -1.356389e-16 -7.243436e-16 33 -2.582699e-16 -1.356389e-16 34 -5.287565e-17 -2.582699e-16 35 -1.763253e-16 -5.287565e-17 36 1.191941e-16 -1.763253e-16 37 1.329785e-15 1.191941e-16 38 1.191941e-16 1.329785e-15 39 1.654415e-16 1.191941e-16 40 -3.123080e-15 1.654415e-16 41 -2.737924e-17 -3.123080e-15 42 5.400822e-16 -2.737924e-17 43 -2.259947e-16 5.400822e-16 44 -1.284773e-15 -2.259947e-16 45 6.458702e-16 -1.284773e-15 46 -3.111961e-16 6.458702e-16 47 -1.134984e-15 -3.111961e-16 48 -1.047843e-15 -1.134984e-15 49 8.585238e-16 -1.047843e-15 50 -1.606535e-15 8.585238e-16 51 -7.129786e-16 -1.606535e-15 52 -1.815582e-15 -7.129786e-16 53 -1.228780e-15 -1.815582e-15 54 1.160889e-15 -1.228780e-15 55 7.111068e-16 1.160889e-15 56 -2.163400e-15 7.111068e-16 57 2.283926e-17 -2.163400e-15 58 -2.049192e-15 2.283926e-17 59 3.970734e-16 -2.049192e-15 60 6.460465e-16 3.970734e-16 61 3.182978e-16 6.460465e-16 62 2.283926e-17 3.182978e-16 63 2.198601e-16 2.283926e-17 64 4.866898e-16 2.198601e-16 65 -6.277292e-16 4.866898e-16 66 -3.027976e-16 -6.277292e-16 67 3.819478e-16 -3.027976e-16 68 -6.064493e-16 3.819478e-16 69 3.965318e-16 -6.064493e-16 70 1.531789e-15 3.965318e-16 71 -3.431662e-16 1.531789e-15 72 3.561242e-16 -3.431662e-16 73 1.281923e-15 3.561242e-16 74 -5.300128e-16 1.281923e-15 75 -1.815582e-15 -5.300128e-16 76 -3.696047e-16 -1.815582e-15 77 4.389551e-16 -3.696047e-16 78 1.834677e-16 4.389551e-16 79 -5.287565e-17 1.834677e-16 80 -6.360441e-16 -5.287565e-17 81 1.329785e-15 -6.360441e-16 82 2.378770e-15 1.329785e-15 83 -2.121779e-16 2.378770e-15 84 4.381414e-16 -2.121779e-16 85 8.673451e-16 4.381414e-16 86 2.375501e-16 8.673451e-16 87 -1.615783e-15 2.375501e-16 88 3.561242e-16 -1.615783e-15 89 -2.974520e-16 3.561242e-16 90 1.075154e-15 -2.974520e-16 91 -1.062758e-15 1.075154e-15 92 -6.361407e-16 -1.062758e-15 93 -4.674533e-16 -6.361407e-16 94 -1.880988e-15 -4.674533e-16 95 -1.250997e-15 -1.880988e-15 96 -3.696047e-16 -1.250997e-15 97 9.529898e-16 -3.696047e-16 98 -6.899404e-16 9.529898e-16 99 -1.565032e-16 -6.899404e-16 100 8.717172e-17 -1.565032e-16 101 -6.034779e-17 8.717172e-17 102 -3.431662e-16 -6.034779e-17 103 -2.713723e-16 -3.431662e-16 104 3.874012e-16 -2.713723e-16 105 7.811029e-16 3.874012e-16 106 -1.265291e-15 7.811029e-16 107 -2.012214e-15 -1.265291e-15 108 -1.852047e-16 -2.012214e-15 109 -8.583682e-16 -1.852047e-16 110 -8.859827e-16 -8.583682e-16 111 -4.173204e-16 -8.859827e-16 112 -9.239173e-16 -4.173204e-16 113 7.461417e-17 -9.239173e-16 114 -1.098479e-15 7.461417e-17 115 -2.370559e-15 -1.098479e-15 116 -1.484756e-15 -2.370559e-15 117 1.398609e-15 -1.484756e-15 118 1.355976e-15 1.398609e-15 119 1.618770e-16 1.355976e-15 120 -5.587143e-16 1.618770e-16 121 1.077222e-15 -5.587143e-16 122 2.496796e-16 1.077222e-15 123 1.874120e-15 2.496796e-16 124 8.101638e-16 1.874120e-15 125 -9.316597e-16 8.101638e-16 126 -4.618563e-16 -9.316597e-16 127 -1.194330e-15 -4.618563e-16 128 -1.284773e-15 -1.194330e-15 129 -1.364020e-16 -1.284773e-15 130 -1.700012e-15 -1.364020e-16 131 -7.135512e-16 -1.700012e-15 132 -1.277559e-15 -7.135512e-16 133 2.856324e-16 -1.277559e-15 134 -1.284773e-15 2.856324e-16 135 1.972558e-15 -1.284773e-15 136 8.143337e-16 1.972558e-15 137 -7.160177e-16 8.143337e-16 138 8.883367e-16 -7.160177e-16 139 1.680335e-15 8.883367e-16 140 -2.671856e-16 1.680335e-15 141 -3.333519e-16 -2.671856e-16 142 -1.725722e-15 -3.333519e-16 143 -1.143482e-15 -1.725722e-15 144 2.580137e-16 -1.143482e-15 145 -6.817215e-16 2.580137e-16 146 -5.304553e-16 -6.817215e-16 147 -7.368483e-16 -5.304553e-16 148 -6.140427e-16 -7.368483e-16 149 1.227261e-15 -6.140427e-16 150 -2.128311e-15 1.227261e-15 151 -1.330275e-15 -2.128311e-15 152 3.403345e-16 -1.330275e-15 153 2.185548e-15 3.403345e-16 154 -1.174412e-15 2.185548e-15 155 -1.050423e-15 -1.174412e-15 156 7.704534e-17 -1.050423e-15 157 4.124624e-16 7.704534e-17 158 NA 4.124624e-16 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.344667e-14 5.467923e-14 [2,] 4.400606e-15 -2.344667e-14 [3,] -6.162446e-15 4.400606e-15 [4,] -6.950011e-16 -6.162446e-15 [5,] -5.186692e-15 -6.950011e-16 [6,] 2.070999e-15 -5.186692e-15 [7,] 2.120195e-15 2.070999e-15 [8,] 9.694495e-16 2.120195e-15 [9,] 7.774362e-16 9.694495e-16 [10,] 4.381414e-16 7.774362e-16 [11,] 7.123155e-16 4.381414e-16 [12,] -3.736534e-16 7.123155e-16 [13,] 1.340927e-15 -3.736534e-16 [14,] -2.953421e-16 1.340927e-15 [15,] 4.632170e-16 -2.953421e-16 [16,] 6.564393e-16 4.632170e-16 [17,] 5.400822e-16 6.564393e-16 [18,] -4.674533e-16 5.400822e-16 [19,] 2.165393e-16 -4.674533e-16 [20,] -1.166677e-15 2.165393e-16 [21,] 3.669429e-16 -1.166677e-15 [22,] -1.385017e-16 3.669429e-16 [23,] -3.622269e-16 -1.385017e-16 [24,] -3.462854e-16 -3.622269e-16 [25,] 1.022143e-15 -3.462854e-16 [26,] -1.201465e-15 1.022143e-15 [27,] 8.717172e-17 -1.201465e-15 [28,] -1.754603e-15 8.717172e-17 [29,] -4.310169e-16 -1.754603e-15 [30,] -1.047843e-15 -4.310169e-16 [31,] -7.243436e-16 -1.047843e-15 [32,] -1.356389e-16 -7.243436e-16 [33,] -2.582699e-16 -1.356389e-16 [34,] -5.287565e-17 -2.582699e-16 [35,] -1.763253e-16 -5.287565e-17 [36,] 1.191941e-16 -1.763253e-16 [37,] 1.329785e-15 1.191941e-16 [38,] 1.191941e-16 1.329785e-15 [39,] 1.654415e-16 1.191941e-16 [40,] -3.123080e-15 1.654415e-16 [41,] -2.737924e-17 -3.123080e-15 [42,] 5.400822e-16 -2.737924e-17 [43,] -2.259947e-16 5.400822e-16 [44,] -1.284773e-15 -2.259947e-16 [45,] 6.458702e-16 -1.284773e-15 [46,] -3.111961e-16 6.458702e-16 [47,] -1.134984e-15 -3.111961e-16 [48,] -1.047843e-15 -1.134984e-15 [49,] 8.585238e-16 -1.047843e-15 [50,] -1.606535e-15 8.585238e-16 [51,] -7.129786e-16 -1.606535e-15 [52,] -1.815582e-15 -7.129786e-16 [53,] -1.228780e-15 -1.815582e-15 [54,] 1.160889e-15 -1.228780e-15 [55,] 7.111068e-16 1.160889e-15 [56,] -2.163400e-15 7.111068e-16 [57,] 2.283926e-17 -2.163400e-15 [58,] -2.049192e-15 2.283926e-17 [59,] 3.970734e-16 -2.049192e-15 [60,] 6.460465e-16 3.970734e-16 [61,] 3.182978e-16 6.460465e-16 [62,] 2.283926e-17 3.182978e-16 [63,] 2.198601e-16 2.283926e-17 [64,] 4.866898e-16 2.198601e-16 [65,] -6.277292e-16 4.866898e-16 [66,] -3.027976e-16 -6.277292e-16 [67,] 3.819478e-16 -3.027976e-16 [68,] -6.064493e-16 3.819478e-16 [69,] 3.965318e-16 -6.064493e-16 [70,] 1.531789e-15 3.965318e-16 [71,] -3.431662e-16 1.531789e-15 [72,] 3.561242e-16 -3.431662e-16 [73,] 1.281923e-15 3.561242e-16 [74,] -5.300128e-16 1.281923e-15 [75,] -1.815582e-15 -5.300128e-16 [76,] -3.696047e-16 -1.815582e-15 [77,] 4.389551e-16 -3.696047e-16 [78,] 1.834677e-16 4.389551e-16 [79,] -5.287565e-17 1.834677e-16 [80,] -6.360441e-16 -5.287565e-17 [81,] 1.329785e-15 -6.360441e-16 [82,] 2.378770e-15 1.329785e-15 [83,] -2.121779e-16 2.378770e-15 [84,] 4.381414e-16 -2.121779e-16 [85,] 8.673451e-16 4.381414e-16 [86,] 2.375501e-16 8.673451e-16 [87,] -1.615783e-15 2.375501e-16 [88,] 3.561242e-16 -1.615783e-15 [89,] -2.974520e-16 3.561242e-16 [90,] 1.075154e-15 -2.974520e-16 [91,] -1.062758e-15 1.075154e-15 [92,] -6.361407e-16 -1.062758e-15 [93,] -4.674533e-16 -6.361407e-16 [94,] -1.880988e-15 -4.674533e-16 [95,] -1.250997e-15 -1.880988e-15 [96,] -3.696047e-16 -1.250997e-15 [97,] 9.529898e-16 -3.696047e-16 [98,] -6.899404e-16 9.529898e-16 [99,] -1.565032e-16 -6.899404e-16 [100,] 8.717172e-17 -1.565032e-16 [101,] -6.034779e-17 8.717172e-17 [102,] -3.431662e-16 -6.034779e-17 [103,] -2.713723e-16 -3.431662e-16 [104,] 3.874012e-16 -2.713723e-16 [105,] 7.811029e-16 3.874012e-16 [106,] -1.265291e-15 7.811029e-16 [107,] -2.012214e-15 -1.265291e-15 [108,] -1.852047e-16 -2.012214e-15 [109,] -8.583682e-16 -1.852047e-16 [110,] -8.859827e-16 -8.583682e-16 [111,] -4.173204e-16 -8.859827e-16 [112,] -9.239173e-16 -4.173204e-16 [113,] 7.461417e-17 -9.239173e-16 [114,] -1.098479e-15 7.461417e-17 [115,] -2.370559e-15 -1.098479e-15 [116,] -1.484756e-15 -2.370559e-15 [117,] 1.398609e-15 -1.484756e-15 [118,] 1.355976e-15 1.398609e-15 [119,] 1.618770e-16 1.355976e-15 [120,] -5.587143e-16 1.618770e-16 [121,] 1.077222e-15 -5.587143e-16 [122,] 2.496796e-16 1.077222e-15 [123,] 1.874120e-15 2.496796e-16 [124,] 8.101638e-16 1.874120e-15 [125,] -9.316597e-16 8.101638e-16 [126,] -4.618563e-16 -9.316597e-16 [127,] -1.194330e-15 -4.618563e-16 [128,] -1.284773e-15 -1.194330e-15 [129,] -1.364020e-16 -1.284773e-15 [130,] -1.700012e-15 -1.364020e-16 [131,] -7.135512e-16 -1.700012e-15 [132,] -1.277559e-15 -7.135512e-16 [133,] 2.856324e-16 -1.277559e-15 [134,] -1.284773e-15 2.856324e-16 [135,] 1.972558e-15 -1.284773e-15 [136,] 8.143337e-16 1.972558e-15 [137,] -7.160177e-16 8.143337e-16 [138,] 8.883367e-16 -7.160177e-16 [139,] 1.680335e-15 8.883367e-16 [140,] -2.671856e-16 1.680335e-15 [141,] -3.333519e-16 -2.671856e-16 [142,] -1.725722e-15 -3.333519e-16 [143,] -1.143482e-15 -1.725722e-15 [144,] 2.580137e-16 -1.143482e-15 [145,] -6.817215e-16 2.580137e-16 [146,] -5.304553e-16 -6.817215e-16 [147,] -7.368483e-16 -5.304553e-16 [148,] -6.140427e-16 -7.368483e-16 [149,] 1.227261e-15 -6.140427e-16 [150,] -2.128311e-15 1.227261e-15 [151,] -1.330275e-15 -2.128311e-15 [152,] 3.403345e-16 -1.330275e-15 [153,] 2.185548e-15 3.403345e-16 [154,] -1.174412e-15 2.185548e-15 [155,] -1.050423e-15 -1.174412e-15 [156,] 7.704534e-17 -1.050423e-15 [157,] 4.124624e-16 7.704534e-17 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.344667e-14 5.467923e-14 2 4.400606e-15 -2.344667e-14 3 -6.162446e-15 4.400606e-15 4 -6.950011e-16 -6.162446e-15 5 -5.186692e-15 -6.950011e-16 6 2.070999e-15 -5.186692e-15 7 2.120195e-15 2.070999e-15 8 9.694495e-16 2.120195e-15 9 7.774362e-16 9.694495e-16 10 4.381414e-16 7.774362e-16 11 7.123155e-16 4.381414e-16 12 -3.736534e-16 7.123155e-16 13 1.340927e-15 -3.736534e-16 14 -2.953421e-16 1.340927e-15 15 4.632170e-16 -2.953421e-16 16 6.564393e-16 4.632170e-16 17 5.400822e-16 6.564393e-16 18 -4.674533e-16 5.400822e-16 19 2.165393e-16 -4.674533e-16 20 -1.166677e-15 2.165393e-16 21 3.669429e-16 -1.166677e-15 22 -1.385017e-16 3.669429e-16 23 -3.622269e-16 -1.385017e-16 24 -3.462854e-16 -3.622269e-16 25 1.022143e-15 -3.462854e-16 26 -1.201465e-15 1.022143e-15 27 8.717172e-17 -1.201465e-15 28 -1.754603e-15 8.717172e-17 29 -4.310169e-16 -1.754603e-15 30 -1.047843e-15 -4.310169e-16 31 -7.243436e-16 -1.047843e-15 32 -1.356389e-16 -7.243436e-16 33 -2.582699e-16 -1.356389e-16 34 -5.287565e-17 -2.582699e-16 35 -1.763253e-16 -5.287565e-17 36 1.191941e-16 -1.763253e-16 37 1.329785e-15 1.191941e-16 38 1.191941e-16 1.329785e-15 39 1.654415e-16 1.191941e-16 40 -3.123080e-15 1.654415e-16 41 -2.737924e-17 -3.123080e-15 42 5.400822e-16 -2.737924e-17 43 -2.259947e-16 5.400822e-16 44 -1.284773e-15 -2.259947e-16 45 6.458702e-16 -1.284773e-15 46 -3.111961e-16 6.458702e-16 47 -1.134984e-15 -3.111961e-16 48 -1.047843e-15 -1.134984e-15 49 8.585238e-16 -1.047843e-15 50 -1.606535e-15 8.585238e-16 51 -7.129786e-16 -1.606535e-15 52 -1.815582e-15 -7.129786e-16 53 -1.228780e-15 -1.815582e-15 54 1.160889e-15 -1.228780e-15 55 7.111068e-16 1.160889e-15 56 -2.163400e-15 7.111068e-16 57 2.283926e-17 -2.163400e-15 58 -2.049192e-15 2.283926e-17 59 3.970734e-16 -2.049192e-15 60 6.460465e-16 3.970734e-16 61 3.182978e-16 6.460465e-16 62 2.283926e-17 3.182978e-16 63 2.198601e-16 2.283926e-17 64 4.866898e-16 2.198601e-16 65 -6.277292e-16 4.866898e-16 66 -3.027976e-16 -6.277292e-16 67 3.819478e-16 -3.027976e-16 68 -6.064493e-16 3.819478e-16 69 3.965318e-16 -6.064493e-16 70 1.531789e-15 3.965318e-16 71 -3.431662e-16 1.531789e-15 72 3.561242e-16 -3.431662e-16 73 1.281923e-15 3.561242e-16 74 -5.300128e-16 1.281923e-15 75 -1.815582e-15 -5.300128e-16 76 -3.696047e-16 -1.815582e-15 77 4.389551e-16 -3.696047e-16 78 1.834677e-16 4.389551e-16 79 -5.287565e-17 1.834677e-16 80 -6.360441e-16 -5.287565e-17 81 1.329785e-15 -6.360441e-16 82 2.378770e-15 1.329785e-15 83 -2.121779e-16 2.378770e-15 84 4.381414e-16 -2.121779e-16 85 8.673451e-16 4.381414e-16 86 2.375501e-16 8.673451e-16 87 -1.615783e-15 2.375501e-16 88 3.561242e-16 -1.615783e-15 89 -2.974520e-16 3.561242e-16 90 1.075154e-15 -2.974520e-16 91 -1.062758e-15 1.075154e-15 92 -6.361407e-16 -1.062758e-15 93 -4.674533e-16 -6.361407e-16 94 -1.880988e-15 -4.674533e-16 95 -1.250997e-15 -1.880988e-15 96 -3.696047e-16 -1.250997e-15 97 9.529898e-16 -3.696047e-16 98 -6.899404e-16 9.529898e-16 99 -1.565032e-16 -6.899404e-16 100 8.717172e-17 -1.565032e-16 101 -6.034779e-17 8.717172e-17 102 -3.431662e-16 -6.034779e-17 103 -2.713723e-16 -3.431662e-16 104 3.874012e-16 -2.713723e-16 105 7.811029e-16 3.874012e-16 106 -1.265291e-15 7.811029e-16 107 -2.012214e-15 -1.265291e-15 108 -1.852047e-16 -2.012214e-15 109 -8.583682e-16 -1.852047e-16 110 -8.859827e-16 -8.583682e-16 111 -4.173204e-16 -8.859827e-16 112 -9.239173e-16 -4.173204e-16 113 7.461417e-17 -9.239173e-16 114 -1.098479e-15 7.461417e-17 115 -2.370559e-15 -1.098479e-15 116 -1.484756e-15 -2.370559e-15 117 1.398609e-15 -1.484756e-15 118 1.355976e-15 1.398609e-15 119 1.618770e-16 1.355976e-15 120 -5.587143e-16 1.618770e-16 121 1.077222e-15 -5.587143e-16 122 2.496796e-16 1.077222e-15 123 1.874120e-15 2.496796e-16 124 8.101638e-16 1.874120e-15 125 -9.316597e-16 8.101638e-16 126 -4.618563e-16 -9.316597e-16 127 -1.194330e-15 -4.618563e-16 128 -1.284773e-15 -1.194330e-15 129 -1.364020e-16 -1.284773e-15 130 -1.700012e-15 -1.364020e-16 131 -7.135512e-16 -1.700012e-15 132 -1.277559e-15 -7.135512e-16 133 2.856324e-16 -1.277559e-15 134 -1.284773e-15 2.856324e-16 135 1.972558e-15 -1.284773e-15 136 8.143337e-16 1.972558e-15 137 -7.160177e-16 8.143337e-16 138 8.883367e-16 -7.160177e-16 139 1.680335e-15 8.883367e-16 140 -2.671856e-16 1.680335e-15 141 -3.333519e-16 -2.671856e-16 142 -1.725722e-15 -3.333519e-16 143 -1.143482e-15 -1.725722e-15 144 2.580137e-16 -1.143482e-15 145 -6.817215e-16 2.580137e-16 146 -5.304553e-16 -6.817215e-16 147 -7.368483e-16 -5.304553e-16 148 -6.140427e-16 -7.368483e-16 149 1.227261e-15 -6.140427e-16 150 -2.128311e-15 1.227261e-15 151 -1.330275e-15 -2.128311e-15 152 3.403345e-16 -1.330275e-15 153 2.185548e-15 3.403345e-16 154 -1.174412e-15 2.185548e-15 155 -1.050423e-15 -1.174412e-15 156 7.704534e-17 -1.050423e-15 157 4.124624e-16 7.704534e-17 > 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/7ry6h1290506270.ps",horizontal=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/8ry6h1290506270.ps",horizontal=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/9ry6h1290506270.ps",horizontal=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/10j7521290506270.ps",horizontal=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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/1158mp1290506270.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12q8kd1290506270.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13m00m1290506270.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14qjza1290506270.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/15b1xg1290506270.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/16fkw41290506270.tab") + } > > try(system("convert tmp/1u6q81290506270.ps tmp/1u6q81290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/2ngqb1290506270.ps tmp/2ngqb1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/3ngqb1290506270.ps tmp/3ngqb1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/4ngqb1290506270.ps tmp/4ngqb1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/5ngqb1290506270.ps tmp/5ngqb1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/6g77d1290506270.ps tmp/6g77d1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/7ry6h1290506270.ps tmp/7ry6h1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/8ry6h1290506270.ps tmp/8ry6h1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/9ry6h1290506270.ps tmp/9ry6h1290506270.png",intern=TRUE)) character(0) > try(system("convert tmp/10j7521290506270.ps tmp/10j7521290506270.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.665 1.997 29.352