R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(2 + ,9 + ,2 + ,1 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,9 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,9 + ,4 + ,2 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,9 + ,3 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,9 + ,3 + ,2 + ,3 + ,3 + ,2 + ,2 + ,2 + ,3 + ,9 + ,1 + ,2 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,9 + ,4 + ,4 + ,5 + ,4 + ,4 + ,5 + ,4 + ,3 + ,9 + ,2 + ,2 + ,4 + ,2 + ,2 + ,3 + ,2 + ,3 + ,9 + ,2 + ,2 + ,4 + ,4 + ,3 + ,2 + ,3 + ,4 + ,9 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,3 + ,9 + ,4 + ,2 + ,2 + ,3 + ,2 + ,4 + ,4 + ,3 + ,9 + ,3 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,2 + ,9 + ,3 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,9 + ,2 + ,2 + ,5 + ,3 + ,4 + ,2 + ,3 + ,9 + ,3 + ,3 + ,5 + ,3 + ,3 + ,4 + ,3 + ,3 + ,9 + ,2 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,3 + ,9 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,9 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,9 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,9 + ,2 + ,2 + ,2 + ,3 + ,2 + ,2 + ,3 + ,3 + ,9 + ,1 + ,1 + ,4 + ,3 + ,3 + ,3 + ,2 + ,2 + ,9 + ,4 + ,3 + 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,2 + ,4 + ,3 + ,2 + ,4 + ,3 + ,10 + ,3 + ,2 + ,4 + ,3 + ,2 + ,4 + ,3 + ,3 + ,10 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,10 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,10 + ,2 + ,4 + ,5 + ,3 + ,4 + ,4 + ,3 + ,2 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,2 + ,10 + ,3 + ,3 + ,4 + ,4 + ,2 + ,4 + ,3 + ,1 + ,10 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,10 + ,2 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,10 + ,2 + ,2 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,10 + ,2 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,10 + ,2 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,10 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,10 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,10 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,10 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,10 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,5 + ,10 + ,1 + ,3 + ,1 + ,1 + ,1 + ,1 + ,1 + ,2 + ,10 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,10 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,2 + ,1 + ,10 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,3 + ,10 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,10 + ,2 + ,2 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,10 + ,2 + ,1 + ,4 + ,3 + ,2 + ,2 + ,2 + ,1 + ,10 + ,3 + ,3 + ,4 + ,4 + ,5 + ,4 + ,4 + ,2 + ,10 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,10 + ,2 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,10 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,10 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,10 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,10 + ,2 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,1 + ,3 + ,3 + ,4 + ,10 + ,2 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,1 + ,10 + ,3 + ,3 + ,5 + ,3 + ,5 + ,5 + ,4 + ,2 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,10 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,10 + ,4 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,2 + ,10 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,10 + ,2 + ,2 + ,3 + ,3 + ,2 + ,3 + ,2 + ,3 + ,10 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,10 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,10 + ,1 + ,1 + ,3 + ,3 + ,3 + ,4 + ,2) + ,dim=c(9 + ,156) + ,dimnames=list(c('Y' + ,'month' + ,'X1t' + ,'X2t' + ,'X3t' + ,'X4t' + ,'X5t' + ,'X6t' + ,'X7t') + ,1:156)) > y <- array(NA,dim=c(9,156),dimnames=list(c('Y','month','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),1:156)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Y month X1t X2t X3t X4t X5t X6t X7t 1 2 9 2 1 4 3 3 3 3 2 3 9 2 3 4 3 3 4 3 3 3 9 4 2 3 4 4 4 3 4 3 9 3 3 2 3 3 3 3 5 3 9 3 2 3 3 2 2 2 6 3 9 1 2 4 3 3 2 2 7 2 9 4 4 5 4 4 5 4 8 3 9 2 2 4 2 2 3 2 9 3 9 2 2 4 4 3 2 3 10 4 9 2 2 2 2 2 2 2 11 3 9 4 2 2 3 2 4 4 12 3 9 3 3 4 3 2 3 3 13 2 9 3 2 4 4 4 3 3 14 3 9 2 2 5 3 4 2 3 15 9 3 3 5 3 3 4 3 3 16 9 2 2 4 3 2 2 2 3 17 9 3 3 3 3 3 3 3 3 18 9 3 3 4 4 4 4 3 2 19 9 2 2 4 2 2 2 2 4 20 9 2 2 2 3 2 2 3 3 21 9 1 1 4 3 3 3 2 2 22 9 4 3 4 4 4 4 3 3 23 9 3 2 4 3 3 2 3 3 24 9 2 2 4 3 3 2 2 2 25 9 3 3 4 3 4 3 3 2 26 9 3 3 4 4 4 4 3 4 27 9 4 3 4 4 2 4 4 2 28 9 3 2 3 4 3 3 3 3 29 9 3 3 3 4 3 3 3 2 30 9 2 2 4 4 4 4 2 4 31 9 2 2 3 2 4 2 2 3 32 9 4 3 4 3 3 3 4 2 33 9 4 3 4 4 3 4 4 3 34 9 2 2 4 3 2 3 3 3 35 9 2 2 4 3 2 2 3 1 36 9 3 3 4 4 4 4 4 3 37 9 3 3 4 3 3 4 3 3 38 9 3 2 3 2 2 2 2 3 39 9 3 3 4 3 3 3 3 2 40 9 4 3 4 4 4 4 4 3 41 9 3 3 4 3 4 4 3 9 42 1 2 3 2 2 3 3 5 9 43 2 1 5 2 1 4 2 4 9 44 2 2 4 3 2 3 2 3 9 45 3 3 4 3 2 3 3 2 9 46 4 3 4 4 4 3 4 2 9 47 3 2 4 4 4 3 4 3 9 48 2 2 5 2 2 2 2 4 9 49 2 3 4 3 3 4 3 2 9 50 3 3 4 4 3 4 3 3 9 51 3 3 4 3 2 4 3 4 10 52 4 2 3 3 1 2 2 3 10 53 3 2 4 4 3 3 4 4 10 54 2 2 4 3 2 3 3 3 10 55 2 3 5 3 4 3 4 3 10 56 2 3 4 3 3 3 3 4 10 57 2 2 3 3 4 2 3 2 10 58 3 3 3 4 4 4 4 4 10 59 1 1 4 3 4 4 1 2 10 60 5 3 4 4 4 4 4 4 10 61 2 1 4 3 1 3 2 2 10 62 3 3 4 4 4 4 3 3 10 63 4 2 3 3 4 3 3 2 10 64 4 2 3 4 4 4 3 3 10 65 2 3 3 3 1 3 3 3 10 66 3 2 4 3 4 3 4 3 10 67 3 3 4 3 3 3 2 3 10 68 3 2 4 3 3 3 2 2 10 69 3 3 4 3 4 4 4 4 10 70 1 1 5 2 1 1 1 2 10 71 3 2 3 3 4 4 4 3 10 72 3 2 4 3 3 4 4 4 10 73 3 2 3 4 3 3 3 2 10 74 4 2 2 4 2 5 2 3 10 75 3 3 4 3 3 3 3 3 10 76 4 2 4 3 3 3 3 3 10 77 3 2 5 3 3 3 3 3 10 78 3 2 2 3 4 4 3 2 10 79 2 2 4 4 3 4 4 4 10 80 1 1 4 2 1 3 2 4 10 81 2 2 4 3 3 3 2 10 3 82 3 3 3 3 3 2 10 3 3 83 4 4 4 4 3 4 10 2 3 84 3 3 3 2 2 3 10 2 1 85 4 3 4 4 2 3 10 3 3 86 5 3 3 3 3 3 10 2 2 87 2 3 2 2 2 3 10 3 2 88 2 4 3 4 3 2 10 4 4 89 4 4 4 4 3 4 10 2 2 90 4 3 3 3 3 3 10 3 3 91 3 4 4 4 3 4 10 3 3 92 4 4 3 4 4 2 10 4 3 93 4 4 4 4 4 2 10 3 3 94 4 3 4 3 4 3 10 2 3 95 4 3 3 3 3 3 10 2 2 96 4 2 2 4 2 3 10 3 3 97 2 3 1 5 3 4 10 2 1 98 4 3 2 3 2 2 10 3 2 99 4 4 2 4 3 3 10 3 3 100 4 3 3 4 3 2 10 4 3 101 4 4 4 4 4 2 10 4 3 102 4 3 4 4 3 3 10 3 3 103 5 3 5 5 3 3 10 1 2 104 4 3 2 4 2 5 10 1 1 105 4 3 1 3 1 2 10 4 4 106 4 4 3 4 3 4 10 2 1 107 3 3 3 4 3 3 10 4 4 108 4 4 4 4 4 4 10 2 1 109 4 3 2 4 2 4 10 2 2 110 4 3 2 4 3 10 3 2 4 111 3 2 4 3 3 10 4 3 3 112 3 3 3 4 3 10 3 3 4 113 3 3 4 3 3 10 3 3 4 114 3 4 4 4 2 10 4 3 4 115 4 4 4 4 2 10 2 4 5 116 3 4 4 3 2 10 3 3 4 117 3 4 4 3 2 10 3 3 4 118 4 2 4 3 1 10 3 3 4 119 3 3 4 3 2 10 2 2 4 120 3 3 3 3 3 10 2 2 4 121 3 3 4 3 3 10 2 3 4 122 3 4 3 3 4 10 2 2 4 123 3 3 3 3 3 10 4 2 4 124 3 4 4 4 2 10 3 3 4 125 4 4 4 3 3 10 2 3 4 126 3 3 4 2 2 10 4 4 4 127 4 4 4 4 3 10 3 3 4 128 3 3 3 3 3 10 2 3 3 129 4 3 3 3 5 10 1 3 1 130 1 1 1 1 2 10 4 4 4 131 4 4 4 4 2 10 3 3 4 132 3 4 3 3 3 10 2 2 4 133 4 2 4 2 1 10 2 4 4 134 4 2 4 2 3 10 3 3 4 135 4 4 3 4 3 10 2 2 4 136 3 3 4 3 3 10 3 3 4 137 3 4 4 4 4 10 2 1 4 138 3 2 2 2 1 10 3 3 4 139 4 5 4 4 2 10 3 2 3 140 3 3 3 4 4 10 2 2 4 141 3 4 3 3 2 10 3 3 4 142 3 3 4 3 2 10 2 2 4 143 4 4 4 3 4 10 2 2 4 144 2 2 2 2 3 10 2 2 4 145 3 3 3 3 3 10 3 3 4 146 3 1 3 3 4 10 2 3 4 147 3 2 3 3 1 10 3 3 5 148 3 5 5 4 2 10 3 3 4 149 3 4 4 4 2 10 4 4 4 150 4 4 4 3 3 10 4 3 4 151 3 3 3 3 2 10 4 3 4 152 4 4 4 4 3 10 2 2 3 153 3 2 3 2 3 10 3 3 3 154 4 4 4 3 3 10 3 3 4 155 3 4 4 4 3 10 1 1 3 156 3 3 4 2 2 9 2 1 4 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month X1t X2t X3t X4t 9.44285 -0.47492 -0.15118 1.09641 0.19319 -0.41887 X5t X6t X7t -0.49951 -0.05971 -0.59600 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.116335 -0.669034 -0.006143 0.685510 5.686900 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.44285 0.89761 10.520 < 2e-16 *** month -0.47492 0.06702 -7.086 5.33e-11 *** X1t -0.15118 0.14058 -1.075 0.284 X2t 1.09641 0.15341 7.147 3.83e-11 *** X3t 0.19319 0.12783 1.511 0.133 X4t -0.41887 0.04244 -9.871 < 2e-16 *** X5t -0.49951 0.04729 -10.562 < 2e-16 *** X6t -0.05971 0.11195 -0.533 0.595 X7t -0.59600 0.05143 -11.588 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.336 on 147 degrees of freedom Multiple R-squared: 0.6936, Adjusted R-squared: 0.677 F-statistic: 41.6 on 8 and 147 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.0533434299 1.066869e-01 9.466566e-01 [2,] 0.0174393670 3.487873e-02 9.825606e-01 [3,] 0.0248581712 4.971634e-02 9.751418e-01 [4,] 0.0087271420 1.745428e-02 9.912729e-01 [5,] 0.0055637814 1.112756e-02 9.944362e-01 [6,] 0.0062622124 1.252442e-02 9.937378e-01 [7,] 0.0027755631 5.551126e-03 9.972244e-01 [8,] 0.0012287968 2.457594e-03 9.987712e-01 [9,] 0.0004640898 9.281796e-04 9.995359e-01 [10,] 0.0020415213 4.083043e-03 9.979585e-01 [11,] 0.0103638980 2.072780e-02 9.896361e-01 [12,] 0.0086356724 1.727134e-02 9.913643e-01 [13,] 0.0061092350 1.221847e-02 9.938908e-01 [14,] 0.0033820740 6.764148e-03 9.966179e-01 [15,] 0.0030573899 6.114780e-03 9.969426e-01 [16,] 0.0023841218 4.768244e-03 9.976159e-01 [17,] 0.0020550450 4.110090e-03 9.979450e-01 [18,] 0.0011767710 2.353542e-03 9.988232e-01 [19,] 0.0008223644 1.644729e-03 9.991776e-01 [20,] 0.0005492792 1.098558e-03 9.994507e-01 [21,] 0.0005087985 1.017597e-03 9.994912e-01 [22,] 0.0006137396 1.227479e-03 9.993863e-01 [23,] 0.0005829679 1.165936e-03 9.994170e-01 [24,] 0.0007642253 1.528451e-03 9.992358e-01 [25,] 0.0007904625 1.580925e-03 9.992095e-01 [26,] 0.0008621940 1.724388e-03 9.991378e-01 [27,] 0.0013691359 2.738272e-03 9.986309e-01 [28,] 0.0030758770 6.151754e-03 9.969241e-01 [29,] 0.0326589731 6.531795e-02 9.673410e-01 [30,] 0.4008681663 8.017363e-01 5.991318e-01 [31,] 0.9997538086 4.923827e-04 2.461914e-04 [32,] 0.9999831517 3.369658e-05 1.684829e-05 [33,] 0.9999960992 7.801612e-06 3.900806e-06 [34,] 0.9999948056 1.038881e-05 5.194407e-06 [35,] 0.9999935073 1.298547e-05 6.492734e-06 [36,] 0.9999959869 8.026192e-06 4.013096e-06 [37,] 0.9999929005 1.419903e-05 7.099517e-06 [38,] 0.9999930205 1.395908e-05 6.979539e-06 [39,] 0.9999886452 2.270970e-05 1.135485e-05 [40,] 0.9999844733 3.105348e-05 1.552674e-05 [41,] 0.9999936196 1.276090e-05 6.380448e-06 [42,] 0.9999914881 1.702378e-05 8.511891e-06 [43,] 0.9999896296 2.074078e-05 1.037039e-05 [44,] 0.9999894399 2.112030e-05 1.056015e-05 [45,] 0.9999858644 2.827121e-05 1.413561e-05 [46,] 0.9999824322 3.513564e-05 1.756782e-05 [47,] 0.9999716215 5.675699e-05 2.837850e-05 [48,] 0.9999895298 2.094030e-05 1.047015e-05 [49,] 0.9999984492 3.101625e-06 1.550812e-06 [50,] 0.9999984465 3.107022e-06 1.553511e-06 [51,] 0.9999976191 4.761792e-06 2.380896e-06 [52,] 0.9999985123 2.975349e-06 1.487675e-06 [53,] 0.9999983561 3.287829e-06 1.643914e-06 [54,] 0.9999978598 4.280335e-06 2.140167e-06 [55,] 0.9999961863 7.627328e-06 3.813664e-06 [56,] 0.9999965706 6.858851e-06 3.429426e-06 [57,] 0.9999950155 9.968953e-06 4.984476e-06 [58,] 0.9999919536 1.609289e-05 8.046445e-06 [59,] 0.9999964326 7.134733e-06 3.567366e-06 [60,] 0.9999941496 1.170082e-05 5.850409e-06 [61,] 0.9999901387 1.972255e-05 9.861276e-06 [62,] 0.9999856577 2.868460e-05 1.434230e-05 [63,] 0.9999917267 1.654656e-05 8.273278e-06 [64,] 0.9999888144 2.237123e-05 1.118561e-05 [65,] 0.9999956074 8.785142e-06 4.392571e-06 [66,] 0.9999933591 1.328187e-05 6.640936e-06 [67,] 0.9999950081 9.983786e-06 4.991893e-06 [68,] 0.9999964493 7.101437e-06 3.550719e-06 [69,] 0.9999981957 3.608585e-06 1.804292e-06 [70,] 0.9999998607 2.786971e-07 1.393486e-07 [71,] 0.9999999989 2.116066e-09 1.058033e-09 [72,] 0.9999999993 1.396384e-09 6.981920e-10 [73,] 0.9999999995 1.012494e-09 5.062470e-10 [74,] 0.9999999991 1.887697e-09 9.438485e-10 [75,] 0.9999999995 9.130064e-10 4.565032e-10 [76,] 0.9999999999 2.518299e-10 1.259150e-10 [77,] 1.0000000000 1.011533e-11 5.057663e-12 [78,] 1.0000000000 1.873757e-11 9.368785e-12 [79,] 1.0000000000 4.012819e-11 2.006409e-11 [80,] 1.0000000000 2.689173e-11 1.344587e-11 [81,] 1.0000000000 6.783645e-11 3.391822e-11 [82,] 0.9999999999 1.491018e-10 7.455092e-11 [83,] 0.9999999998 3.628428e-10 1.814214e-10 [84,] 0.9999999996 8.235705e-10 4.117853e-10 [85,] 0.9999999993 1.336753e-09 6.683766e-10 [86,] 1.0000000000 1.663347e-12 8.316733e-13 [87,] 1.0000000000 4.426590e-12 2.213295e-12 [88,] 1.0000000000 1.085707e-11 5.428533e-12 [89,] 1.0000000000 2.945051e-11 1.472526e-11 [90,] 1.0000000000 7.070242e-11 3.535121e-11 [91,] 0.9999999999 1.821710e-10 9.108549e-11 [92,] 0.9999999998 3.141391e-10 1.570695e-10 [93,] 0.9999999998 4.741477e-10 2.370738e-10 [94,] 0.9999999998 3.837676e-10 1.918838e-10 [95,] 0.9999999996 8.947652e-10 4.473826e-10 [96,] 0.9999999995 1.046964e-09 5.234819e-10 [97,] 0.9999999991 1.842178e-09 9.210888e-10 [98,] 0.9999999978 4.302308e-09 2.151154e-09 [99,] 0.9999999997 5.376000e-10 2.688000e-10 [100,] 0.9999999994 1.144710e-09 5.723552e-10 [101,] 0.9999999987 2.626130e-09 1.313065e-09 [102,] 0.9999999972 5.524126e-09 2.762063e-09 [103,] 0.9999999941 1.181182e-08 5.905912e-09 [104,] 0.9999999876 2.484352e-08 1.242176e-08 [105,] 0.9999999752 4.965036e-08 2.482518e-08 [106,] 0.9999999528 9.446447e-08 4.723223e-08 [107,] 0.9999999418 1.164191e-07 5.820957e-08 [108,] 0.9999998744 2.511507e-07 1.255753e-07 [109,] 0.9999996941 6.117711e-07 3.058856e-07 [110,] 0.9999995319 9.361664e-07 4.680832e-07 [111,] 0.9999988955 2.209079e-06 1.104539e-06 [112,] 0.9999975651 4.869783e-06 2.434892e-06 [113,] 0.9999959294 8.141234e-06 4.070617e-06 [114,] 0.9999905357 1.892863e-05 9.464317e-06 [115,] 0.9999846194 3.076123e-05 1.538062e-05 [116,] 0.9999685250 6.295010e-05 3.147505e-05 [117,] 0.9999388867 1.222267e-04 6.111335e-05 [118,] 0.9998956453 2.087094e-04 1.043547e-04 [119,] 0.9999530670 9.386609e-05 4.693304e-05 [120,] 0.9999209310 1.581379e-04 7.906895e-05 [121,] 0.9998173163 3.653674e-04 1.826837e-04 [122,] 0.9997210449 5.579101e-04 2.789551e-04 [123,] 0.9997193688 5.612623e-04 2.806312e-04 [124,] 0.9997328289 5.343422e-04 2.671711e-04 [125,] 0.9994082663 1.183467e-03 5.917337e-04 [126,] 0.9989731955 2.053609e-03 1.026804e-03 [127,] 0.9986928651 2.614270e-03 1.307135e-03 [128,] 0.9975923134 4.815373e-03 2.407687e-03 [129,] 0.9942141465 1.157171e-02 5.785853e-03 [130,] 0.9875011152 2.499777e-02 1.249888e-02 [131,] 0.9683165192 6.336696e-02 3.168348e-02 [132,] 0.9244682364 1.510635e-01 7.553176e-02 [133,] 0.8800283608 2.399433e-01 1.199716e-01 > postscript(file="/var/www/html/freestat/rcomp/tmp/142fh1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/242fh1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3wbe21291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4wbe21291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5wbe21291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 156 Frequency = 1 1 2 3 4 5 -0.0130813809 -1.1461845772 1.3641538494 -0.6683362218 -0.9203330478 6 7 8 9 10 -0.9163788160 -1.5593284428 -1.6238661131 0.2496744592 -0.2971993944 11 12 13 14 15 0.7354550333 -1.5542197515 -0.0399270931 0.1371185796 0.5956299861 16 17 18 19 20 -0.4116640243 2.2889376334 1.3217233559 0.3775229044 1.8408604340 21 22 23 24 25 -0.7153872617 2.3926445213 0.5418429031 -0.5887888934 1.0154058507 26 27 28 29 30 2.5137198635 1.0186101290 1.9445673219 1.4997507048 1.8279046841 31 32 33 34 35 1.7156783337 1.1311660086 2.0334817675 0.1475527867 -1.5439499009 36 37 38 39 40 1.9774322406 1.6920368998 1.3528544759 0.5965324660 2.4523551523 41 42 43 44 45 5.6868998073 -1.2009789049 -0.3206932946 -1.7651316239 0.1495868368 46 47 48 49 50 0.1663087534 -1.2489035273 -0.8767058273 -0.6247284533 -0.6614247360 51 52 53 54 55 1.2838797373 0.4540002834 -0.4000059676 -0.6696271901 0.0696061885 56 57 58 59 60 -0.3281823223 -1.6857701922 0.1494200172 -3.1707770577 2.3006016539 61 62 63 64 65 -1.5105782378 -0.2586151571 0.7331031925 0.1152802946 -0.1526972402 66 67 68 69 70 0.4435016402 0.1126008667 -0.4220326759 1.3970085676 -2.6002426370 71 72 73 74 75 0.7111933883 1.1152743308 -1.1701150463 0.2698432124 0.6121070467 76 77 78 79 80 1.1371841351 0.2883657718 0.0007949406 -0.9811325829 -1.2947500622 81 82 83 84 85 -5.1163354046 -0.6333924913 0.6743412819 -0.1766306566 0.0334442914 86 87 88 89 90 1.1297720087 -0.6721034085 -1.5991676085 0.0783430281 0.7854808935 91 92 93 94 95 -0.2659480871 -0.3883545372 -0.2968835315 0.6837632243 0.1297720087 96 97 98 99 100 -0.7438418937 -4.5425299611 -0.1873837069 0.0128152548 -0.6700887740 101 102 103 104 105 -0.2371729005 -0.1597443835 -0.8203891763 -0.7425899821 1.1063304699 106 107 108 109 110 -0.6688368624 -0.6552171354 -0.7108439006 -0.5057544821 -0.4902496008 111 112 113 114 115 -0.6031837682 -1.2793573332 -0.0317687828 0.0394420701 0.6961385948 116 117 118 119 120 0.6363428037 0.6363428037 0.8796856553 -0.3977969189 -0.7421672305 121 122 123 124 125 -0.5312749628 -0.4604329937 0.2568451295 -0.4600641099 0.9436479488 126 127 128 129 130 1.8170436167 0.3467472152 -1.2784548533 -2.3563348906 -0.4899402030 131 132 133 134 135 0.5399358901 -0.2672443188 1.5362970199 1.5897152192 -0.3636512325 136 137 138 139 140 -0.0317687828 -1.4653689017 0.6737292956 0.3591499169 -2.0317628190 141 142 143 144 145 0.4851611670 -0.3977969189 0.6907486430 -1.2718648652 -0.1829504195 146 147 148 149 150 -1.8254910977 0.3245022724 0.1660404384 0.0991527010 1.9426603089 151 152 153 154 155 0.5097444354 -0.8084678496 -0.1574646713 1.4431541288 -2.3676846606 156 0.2200259790 > postscript(file="/var/www/html/freestat/rcomp/tmp/67kvn1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.0130813809 NA 1 -1.1461845772 -0.0130813809 2 1.3641538494 -1.1461845772 3 -0.6683362218 1.3641538494 4 -0.9203330478 -0.6683362218 5 -0.9163788160 -0.9203330478 6 -1.5593284428 -0.9163788160 7 -1.6238661131 -1.5593284428 8 0.2496744592 -1.6238661131 9 -0.2971993944 0.2496744592 10 0.7354550333 -0.2971993944 11 -1.5542197515 0.7354550333 12 -0.0399270931 -1.5542197515 13 0.1371185796 -0.0399270931 14 0.5956299861 0.1371185796 15 -0.4116640243 0.5956299861 16 2.2889376334 -0.4116640243 17 1.3217233559 2.2889376334 18 0.3775229044 1.3217233559 19 1.8408604340 0.3775229044 20 -0.7153872617 1.8408604340 21 2.3926445213 -0.7153872617 22 0.5418429031 2.3926445213 23 -0.5887888934 0.5418429031 24 1.0154058507 -0.5887888934 25 2.5137198635 1.0154058507 26 1.0186101290 2.5137198635 27 1.9445673219 1.0186101290 28 1.4997507048 1.9445673219 29 1.8279046841 1.4997507048 30 1.7156783337 1.8279046841 31 1.1311660086 1.7156783337 32 2.0334817675 1.1311660086 33 0.1475527867 2.0334817675 34 -1.5439499009 0.1475527867 35 1.9774322406 -1.5439499009 36 1.6920368998 1.9774322406 37 1.3528544759 1.6920368998 38 0.5965324660 1.3528544759 39 2.4523551523 0.5965324660 40 5.6868998073 2.4523551523 41 -1.2009789049 5.6868998073 42 -0.3206932946 -1.2009789049 43 -1.7651316239 -0.3206932946 44 0.1495868368 -1.7651316239 45 0.1663087534 0.1495868368 46 -1.2489035273 0.1663087534 47 -0.8767058273 -1.2489035273 48 -0.6247284533 -0.8767058273 49 -0.6614247360 -0.6247284533 50 1.2838797373 -0.6614247360 51 0.4540002834 1.2838797373 52 -0.4000059676 0.4540002834 53 -0.6696271901 -0.4000059676 54 0.0696061885 -0.6696271901 55 -0.3281823223 0.0696061885 56 -1.6857701922 -0.3281823223 57 0.1494200172 -1.6857701922 58 -3.1707770577 0.1494200172 59 2.3006016539 -3.1707770577 60 -1.5105782378 2.3006016539 61 -0.2586151571 -1.5105782378 62 0.7331031925 -0.2586151571 63 0.1152802946 0.7331031925 64 -0.1526972402 0.1152802946 65 0.4435016402 -0.1526972402 66 0.1126008667 0.4435016402 67 -0.4220326759 0.1126008667 68 1.3970085676 -0.4220326759 69 -2.6002426370 1.3970085676 70 0.7111933883 -2.6002426370 71 1.1152743308 0.7111933883 72 -1.1701150463 1.1152743308 73 0.2698432124 -1.1701150463 74 0.6121070467 0.2698432124 75 1.1371841351 0.6121070467 76 0.2883657718 1.1371841351 77 0.0007949406 0.2883657718 78 -0.9811325829 0.0007949406 79 -1.2947500622 -0.9811325829 80 -5.1163354046 -1.2947500622 81 -0.6333924913 -5.1163354046 82 0.6743412819 -0.6333924913 83 -0.1766306566 0.6743412819 84 0.0334442914 -0.1766306566 85 1.1297720087 0.0334442914 86 -0.6721034085 1.1297720087 87 -1.5991676085 -0.6721034085 88 0.0783430281 -1.5991676085 89 0.7854808935 0.0783430281 90 -0.2659480871 0.7854808935 91 -0.3883545372 -0.2659480871 92 -0.2968835315 -0.3883545372 93 0.6837632243 -0.2968835315 94 0.1297720087 0.6837632243 95 -0.7438418937 0.1297720087 96 -4.5425299611 -0.7438418937 97 -0.1873837069 -4.5425299611 98 0.0128152548 -0.1873837069 99 -0.6700887740 0.0128152548 100 -0.2371729005 -0.6700887740 101 -0.1597443835 -0.2371729005 102 -0.8203891763 -0.1597443835 103 -0.7425899821 -0.8203891763 104 1.1063304699 -0.7425899821 105 -0.6688368624 1.1063304699 106 -0.6552171354 -0.6688368624 107 -0.7108439006 -0.6552171354 108 -0.5057544821 -0.7108439006 109 -0.4902496008 -0.5057544821 110 -0.6031837682 -0.4902496008 111 -1.2793573332 -0.6031837682 112 -0.0317687828 -1.2793573332 113 0.0394420701 -0.0317687828 114 0.6961385948 0.0394420701 115 0.6363428037 0.6961385948 116 0.6363428037 0.6363428037 117 0.8796856553 0.6363428037 118 -0.3977969189 0.8796856553 119 -0.7421672305 -0.3977969189 120 -0.5312749628 -0.7421672305 121 -0.4604329937 -0.5312749628 122 0.2568451295 -0.4604329937 123 -0.4600641099 0.2568451295 124 0.9436479488 -0.4600641099 125 1.8170436167 0.9436479488 126 0.3467472152 1.8170436167 127 -1.2784548533 0.3467472152 128 -2.3563348906 -1.2784548533 129 -0.4899402030 -2.3563348906 130 0.5399358901 -0.4899402030 131 -0.2672443188 0.5399358901 132 1.5362970199 -0.2672443188 133 1.5897152192 1.5362970199 134 -0.3636512325 1.5897152192 135 -0.0317687828 -0.3636512325 136 -1.4653689017 -0.0317687828 137 0.6737292956 -1.4653689017 138 0.3591499169 0.6737292956 139 -2.0317628190 0.3591499169 140 0.4851611670 -2.0317628190 141 -0.3977969189 0.4851611670 142 0.6907486430 -0.3977969189 143 -1.2718648652 0.6907486430 144 -0.1829504195 -1.2718648652 145 -1.8254910977 -0.1829504195 146 0.3245022724 -1.8254910977 147 0.1660404384 0.3245022724 148 0.0991527010 0.1660404384 149 1.9426603089 0.0991527010 150 0.5097444354 1.9426603089 151 -0.8084678496 0.5097444354 152 -0.1574646713 -0.8084678496 153 1.4431541288 -0.1574646713 154 -2.3676846606 1.4431541288 155 0.2200259790 -2.3676846606 156 NA 0.2200259790 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.1461845772 -0.0130813809 [2,] 1.3641538494 -1.1461845772 [3,] -0.6683362218 1.3641538494 [4,] -0.9203330478 -0.6683362218 [5,] -0.9163788160 -0.9203330478 [6,] -1.5593284428 -0.9163788160 [7,] -1.6238661131 -1.5593284428 [8,] 0.2496744592 -1.6238661131 [9,] -0.2971993944 0.2496744592 [10,] 0.7354550333 -0.2971993944 [11,] -1.5542197515 0.7354550333 [12,] -0.0399270931 -1.5542197515 [13,] 0.1371185796 -0.0399270931 [14,] 0.5956299861 0.1371185796 [15,] -0.4116640243 0.5956299861 [16,] 2.2889376334 -0.4116640243 [17,] 1.3217233559 2.2889376334 [18,] 0.3775229044 1.3217233559 [19,] 1.8408604340 0.3775229044 [20,] -0.7153872617 1.8408604340 [21,] 2.3926445213 -0.7153872617 [22,] 0.5418429031 2.3926445213 [23,] -0.5887888934 0.5418429031 [24,] 1.0154058507 -0.5887888934 [25,] 2.5137198635 1.0154058507 [26,] 1.0186101290 2.5137198635 [27,] 1.9445673219 1.0186101290 [28,] 1.4997507048 1.9445673219 [29,] 1.8279046841 1.4997507048 [30,] 1.7156783337 1.8279046841 [31,] 1.1311660086 1.7156783337 [32,] 2.0334817675 1.1311660086 [33,] 0.1475527867 2.0334817675 [34,] -1.5439499009 0.1475527867 [35,] 1.9774322406 -1.5439499009 [36,] 1.6920368998 1.9774322406 [37,] 1.3528544759 1.6920368998 [38,] 0.5965324660 1.3528544759 [39,] 2.4523551523 0.5965324660 [40,] 5.6868998073 2.4523551523 [41,] -1.2009789049 5.6868998073 [42,] -0.3206932946 -1.2009789049 [43,] -1.7651316239 -0.3206932946 [44,] 0.1495868368 -1.7651316239 [45,] 0.1663087534 0.1495868368 [46,] -1.2489035273 0.1663087534 [47,] -0.8767058273 -1.2489035273 [48,] -0.6247284533 -0.8767058273 [49,] -0.6614247360 -0.6247284533 [50,] 1.2838797373 -0.6614247360 [51,] 0.4540002834 1.2838797373 [52,] -0.4000059676 0.4540002834 [53,] -0.6696271901 -0.4000059676 [54,] 0.0696061885 -0.6696271901 [55,] -0.3281823223 0.0696061885 [56,] -1.6857701922 -0.3281823223 [57,] 0.1494200172 -1.6857701922 [58,] -3.1707770577 0.1494200172 [59,] 2.3006016539 -3.1707770577 [60,] -1.5105782378 2.3006016539 [61,] -0.2586151571 -1.5105782378 [62,] 0.7331031925 -0.2586151571 [63,] 0.1152802946 0.7331031925 [64,] -0.1526972402 0.1152802946 [65,] 0.4435016402 -0.1526972402 [66,] 0.1126008667 0.4435016402 [67,] -0.4220326759 0.1126008667 [68,] 1.3970085676 -0.4220326759 [69,] -2.6002426370 1.3970085676 [70,] 0.7111933883 -2.6002426370 [71,] 1.1152743308 0.7111933883 [72,] -1.1701150463 1.1152743308 [73,] 0.2698432124 -1.1701150463 [74,] 0.6121070467 0.2698432124 [75,] 1.1371841351 0.6121070467 [76,] 0.2883657718 1.1371841351 [77,] 0.0007949406 0.2883657718 [78,] -0.9811325829 0.0007949406 [79,] -1.2947500622 -0.9811325829 [80,] -5.1163354046 -1.2947500622 [81,] -0.6333924913 -5.1163354046 [82,] 0.6743412819 -0.6333924913 [83,] -0.1766306566 0.6743412819 [84,] 0.0334442914 -0.1766306566 [85,] 1.1297720087 0.0334442914 [86,] -0.6721034085 1.1297720087 [87,] -1.5991676085 -0.6721034085 [88,] 0.0783430281 -1.5991676085 [89,] 0.7854808935 0.0783430281 [90,] -0.2659480871 0.7854808935 [91,] -0.3883545372 -0.2659480871 [92,] -0.2968835315 -0.3883545372 [93,] 0.6837632243 -0.2968835315 [94,] 0.1297720087 0.6837632243 [95,] -0.7438418937 0.1297720087 [96,] -4.5425299611 -0.7438418937 [97,] -0.1873837069 -4.5425299611 [98,] 0.0128152548 -0.1873837069 [99,] -0.6700887740 0.0128152548 [100,] -0.2371729005 -0.6700887740 [101,] -0.1597443835 -0.2371729005 [102,] -0.8203891763 -0.1597443835 [103,] -0.7425899821 -0.8203891763 [104,] 1.1063304699 -0.7425899821 [105,] -0.6688368624 1.1063304699 [106,] -0.6552171354 -0.6688368624 [107,] -0.7108439006 -0.6552171354 [108,] -0.5057544821 -0.7108439006 [109,] -0.4902496008 -0.5057544821 [110,] -0.6031837682 -0.4902496008 [111,] -1.2793573332 -0.6031837682 [112,] -0.0317687828 -1.2793573332 [113,] 0.0394420701 -0.0317687828 [114,] 0.6961385948 0.0394420701 [115,] 0.6363428037 0.6961385948 [116,] 0.6363428037 0.6363428037 [117,] 0.8796856553 0.6363428037 [118,] -0.3977969189 0.8796856553 [119,] -0.7421672305 -0.3977969189 [120,] -0.5312749628 -0.7421672305 [121,] -0.4604329937 -0.5312749628 [122,] 0.2568451295 -0.4604329937 [123,] -0.4600641099 0.2568451295 [124,] 0.9436479488 -0.4600641099 [125,] 1.8170436167 0.9436479488 [126,] 0.3467472152 1.8170436167 [127,] -1.2784548533 0.3467472152 [128,] -2.3563348906 -1.2784548533 [129,] -0.4899402030 -2.3563348906 [130,] 0.5399358901 -0.4899402030 [131,] -0.2672443188 0.5399358901 [132,] 1.5362970199 -0.2672443188 [133,] 1.5897152192 1.5362970199 [134,] -0.3636512325 1.5897152192 [135,] -0.0317687828 -0.3636512325 [136,] -1.4653689017 -0.0317687828 [137,] 0.6737292956 -1.4653689017 [138,] 0.3591499169 0.6737292956 [139,] -2.0317628190 0.3591499169 [140,] 0.4851611670 -2.0317628190 [141,] -0.3977969189 0.4851611670 [142,] 0.6907486430 -0.3977969189 [143,] -1.2718648652 0.6907486430 [144,] -0.1829504195 -1.2718648652 [145,] -1.8254910977 -0.1829504195 [146,] 0.3245022724 -1.8254910977 [147,] 0.1660404384 0.3245022724 [148,] 0.0991527010 0.1660404384 [149,] 1.9426603089 0.0991527010 [150,] 0.5097444354 1.9426603089 [151,] -0.8084678496 0.5097444354 [152,] -0.1574646713 -0.8084678496 [153,] 1.4431541288 -0.1574646713 [154,] -2.3676846606 1.4431541288 [155,] 0.2200259790 -2.3676846606 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.1461845772 -0.0130813809 2 1.3641538494 -1.1461845772 3 -0.6683362218 1.3641538494 4 -0.9203330478 -0.6683362218 5 -0.9163788160 -0.9203330478 6 -1.5593284428 -0.9163788160 7 -1.6238661131 -1.5593284428 8 0.2496744592 -1.6238661131 9 -0.2971993944 0.2496744592 10 0.7354550333 -0.2971993944 11 -1.5542197515 0.7354550333 12 -0.0399270931 -1.5542197515 13 0.1371185796 -0.0399270931 14 0.5956299861 0.1371185796 15 -0.4116640243 0.5956299861 16 2.2889376334 -0.4116640243 17 1.3217233559 2.2889376334 18 0.3775229044 1.3217233559 19 1.8408604340 0.3775229044 20 -0.7153872617 1.8408604340 21 2.3926445213 -0.7153872617 22 0.5418429031 2.3926445213 23 -0.5887888934 0.5418429031 24 1.0154058507 -0.5887888934 25 2.5137198635 1.0154058507 26 1.0186101290 2.5137198635 27 1.9445673219 1.0186101290 28 1.4997507048 1.9445673219 29 1.8279046841 1.4997507048 30 1.7156783337 1.8279046841 31 1.1311660086 1.7156783337 32 2.0334817675 1.1311660086 33 0.1475527867 2.0334817675 34 -1.5439499009 0.1475527867 35 1.9774322406 -1.5439499009 36 1.6920368998 1.9774322406 37 1.3528544759 1.6920368998 38 0.5965324660 1.3528544759 39 2.4523551523 0.5965324660 40 5.6868998073 2.4523551523 41 -1.2009789049 5.6868998073 42 -0.3206932946 -1.2009789049 43 -1.7651316239 -0.3206932946 44 0.1495868368 -1.7651316239 45 0.1663087534 0.1495868368 46 -1.2489035273 0.1663087534 47 -0.8767058273 -1.2489035273 48 -0.6247284533 -0.8767058273 49 -0.6614247360 -0.6247284533 50 1.2838797373 -0.6614247360 51 0.4540002834 1.2838797373 52 -0.4000059676 0.4540002834 53 -0.6696271901 -0.4000059676 54 0.0696061885 -0.6696271901 55 -0.3281823223 0.0696061885 56 -1.6857701922 -0.3281823223 57 0.1494200172 -1.6857701922 58 -3.1707770577 0.1494200172 59 2.3006016539 -3.1707770577 60 -1.5105782378 2.3006016539 61 -0.2586151571 -1.5105782378 62 0.7331031925 -0.2586151571 63 0.1152802946 0.7331031925 64 -0.1526972402 0.1152802946 65 0.4435016402 -0.1526972402 66 0.1126008667 0.4435016402 67 -0.4220326759 0.1126008667 68 1.3970085676 -0.4220326759 69 -2.6002426370 1.3970085676 70 0.7111933883 -2.6002426370 71 1.1152743308 0.7111933883 72 -1.1701150463 1.1152743308 73 0.2698432124 -1.1701150463 74 0.6121070467 0.2698432124 75 1.1371841351 0.6121070467 76 0.2883657718 1.1371841351 77 0.0007949406 0.2883657718 78 -0.9811325829 0.0007949406 79 -1.2947500622 -0.9811325829 80 -5.1163354046 -1.2947500622 81 -0.6333924913 -5.1163354046 82 0.6743412819 -0.6333924913 83 -0.1766306566 0.6743412819 84 0.0334442914 -0.1766306566 85 1.1297720087 0.0334442914 86 -0.6721034085 1.1297720087 87 -1.5991676085 -0.6721034085 88 0.0783430281 -1.5991676085 89 0.7854808935 0.0783430281 90 -0.2659480871 0.7854808935 91 -0.3883545372 -0.2659480871 92 -0.2968835315 -0.3883545372 93 0.6837632243 -0.2968835315 94 0.1297720087 0.6837632243 95 -0.7438418937 0.1297720087 96 -4.5425299611 -0.7438418937 97 -0.1873837069 -4.5425299611 98 0.0128152548 -0.1873837069 99 -0.6700887740 0.0128152548 100 -0.2371729005 -0.6700887740 101 -0.1597443835 -0.2371729005 102 -0.8203891763 -0.1597443835 103 -0.7425899821 -0.8203891763 104 1.1063304699 -0.7425899821 105 -0.6688368624 1.1063304699 106 -0.6552171354 -0.6688368624 107 -0.7108439006 -0.6552171354 108 -0.5057544821 -0.7108439006 109 -0.4902496008 -0.5057544821 110 -0.6031837682 -0.4902496008 111 -1.2793573332 -0.6031837682 112 -0.0317687828 -1.2793573332 113 0.0394420701 -0.0317687828 114 0.6961385948 0.0394420701 115 0.6363428037 0.6961385948 116 0.6363428037 0.6363428037 117 0.8796856553 0.6363428037 118 -0.3977969189 0.8796856553 119 -0.7421672305 -0.3977969189 120 -0.5312749628 -0.7421672305 121 -0.4604329937 -0.5312749628 122 0.2568451295 -0.4604329937 123 -0.4600641099 0.2568451295 124 0.9436479488 -0.4600641099 125 1.8170436167 0.9436479488 126 0.3467472152 1.8170436167 127 -1.2784548533 0.3467472152 128 -2.3563348906 -1.2784548533 129 -0.4899402030 -2.3563348906 130 0.5399358901 -0.4899402030 131 -0.2672443188 0.5399358901 132 1.5362970199 -0.2672443188 133 1.5897152192 1.5362970199 134 -0.3636512325 1.5897152192 135 -0.0317687828 -0.3636512325 136 -1.4653689017 -0.0317687828 137 0.6737292956 -1.4653689017 138 0.3591499169 0.6737292956 139 -2.0317628190 0.3591499169 140 0.4851611670 -2.0317628190 141 -0.3977969189 0.4851611670 142 0.6907486430 -0.3977969189 143 -1.2718648652 0.6907486430 144 -0.1829504195 -1.2718648652 145 -1.8254910977 -0.1829504195 146 0.3245022724 -1.8254910977 147 0.1660404384 0.3245022724 148 0.0991527010 0.1660404384 149 1.9426603089 0.0991527010 150 0.5097444354 1.9426603089 151 -0.8084678496 0.5097444354 152 -0.1574646713 -0.8084678496 153 1.4431541288 -0.1574646713 154 -2.3676846606 1.4431541288 155 0.2200259790 -2.3676846606 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/7icvq1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8icvq1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9icvq1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10b3cb1291333157.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/11w4az1291333157.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/12zm941291333157.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/13vw7v1291333157.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/freestat/rcomp/tmp/14henj1291333157.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/15dpp21291333158.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/16yq5q1291333158.tab") + } > > try(system("convert tmp/142fh1291333157.ps tmp/142fh1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/242fh1291333157.ps tmp/242fh1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/3wbe21291333157.ps tmp/3wbe21291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/4wbe21291333157.ps tmp/4wbe21291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/5wbe21291333157.ps tmp/5wbe21291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/67kvn1291333157.ps tmp/67kvn1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/7icvq1291333157.ps tmp/7icvq1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/8icvq1291333157.ps tmp/8icvq1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/9icvq1291333157.ps tmp/9icvq1291333157.png",intern=TRUE)) character(0) > try(system("convert tmp/10b3cb1291333157.ps tmp/10b3cb1291333157.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.961 2.634 6.351