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 + ,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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+ ,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('Yt' + ,'month' + ,'X1t' + ,'X2t' + ,'X3t' + ,'X4t' + ,'X5t' + ,'X6t' + ,'X7t') + ,1:156)) > y <- array(NA,dim=c(9,156),dimnames=list(c('Yt','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 = '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 month X1t X2t X3t X4t X5t X6t X7t t 1 2 9 2 1 4 3 3 3 3 1 2 3 9 2 3 4 3 3 4 3 2 3 3 9 4 2 3 4 4 4 3 3 4 3 9 3 3 2 3 3 3 3 4 5 3 9 3 2 3 3 2 2 2 5 6 3 9 1 2 4 3 3 2 2 6 7 2 9 4 4 5 4 4 5 4 7 8 3 9 2 2 4 2 2 3 2 8 9 3 9 2 2 4 4 3 2 3 9 10 4 9 2 2 2 2 2 2 2 10 11 3 9 4 2 2 3 2 4 4 11 12 3 9 3 3 4 3 2 3 3 12 13 2 9 3 2 4 4 4 3 3 13 14 3 9 2 2 5 3 4 2 3 14 15 9 3 3 5 3 3 4 3 3 15 16 9 2 2 4 3 2 2 2 3 16 17 9 3 3 3 3 3 3 3 3 17 18 9 3 3 4 4 4 4 3 2 18 19 9 2 2 4 2 2 2 2 4 19 20 9 2 2 2 3 2 2 3 3 20 21 9 1 1 4 3 3 3 2 2 21 22 9 4 3 4 4 4 4 3 3 22 23 9 3 2 4 3 3 2 3 3 23 24 9 2 2 4 3 3 2 2 2 24 25 9 3 3 4 3 4 3 3 2 25 26 9 3 3 4 4 4 4 3 4 26 27 9 4 3 4 4 2 4 4 2 27 28 9 3 2 3 4 3 3 3 3 28 29 9 3 3 3 4 3 3 3 2 29 30 9 2 2 4 4 4 4 2 4 30 31 9 2 2 3 2 4 2 2 3 31 32 9 4 3 4 3 3 3 4 2 32 33 9 4 3 4 4 3 4 4 3 33 34 9 2 2 4 3 2 3 3 3 34 35 9 2 2 4 3 2 2 3 1 35 36 9 3 3 4 4 4 4 4 3 36 37 9 3 3 4 3 3 4 3 3 37 38 9 3 2 3 2 2 2 2 3 38 39 9 3 3 4 3 3 3 3 2 39 40 9 4 3 4 4 4 4 4 3 40 41 9 3 3 4 3 4 4 3 9 41 42 1 2 3 2 2 3 3 5 9 42 43 2 1 5 2 1 4 2 4 9 43 44 2 2 4 3 2 3 2 3 9 44 45 3 3 4 3 2 3 3 2 9 45 46 4 3 4 4 4 3 4 2 9 46 47 3 2 4 4 4 3 4 3 9 47 48 2 2 5 2 2 2 2 4 9 48 49 2 3 4 3 3 4 3 2 9 49 50 3 3 4 4 3 4 3 3 9 50 51 3 3 4 3 2 4 3 4 10 51 52 4 2 3 3 1 2 2 3 10 52 53 3 2 4 4 3 3 4 4 10 53 54 2 2 4 3 2 3 3 3 10 54 55 2 3 5 3 4 3 4 3 10 55 56 2 3 4 3 3 3 3 4 10 56 57 2 2 3 3 4 2 3 2 10 57 58 3 3 3 4 4 4 4 4 10 58 59 1 1 4 3 4 4 1 2 10 59 60 5 3 4 4 4 4 4 4 10 60 61 2 1 4 3 1 3 2 2 10 61 62 3 3 4 4 4 4 3 3 10 62 63 4 2 3 3 4 3 3 2 10 63 64 4 2 3 4 4 4 3 3 10 64 65 2 3 3 3 1 3 3 3 10 65 66 3 2 4 3 4 3 4 3 10 66 67 3 3 4 3 3 3 2 3 10 67 68 3 2 4 3 3 3 2 2 10 68 69 3 3 4 3 4 4 4 4 10 69 70 1 1 5 2 1 1 1 2 10 70 71 3 2 3 3 4 4 4 3 10 71 72 3 2 4 3 3 4 4 4 10 72 73 3 2 3 4 3 3 3 2 10 73 74 4 2 2 4 2 5 2 3 10 74 75 3 3 4 3 3 3 3 3 10 75 76 4 2 4 3 3 3 3 3 10 76 77 3 2 5 3 3 3 3 3 10 77 78 3 2 2 3 4 4 3 2 10 78 79 2 2 4 4 3 4 4 4 10 79 80 1 1 4 2 1 3 2 4 10 80 81 2 2 4 3 3 3 2 10 3 81 82 3 3 3 3 3 2 10 3 3 82 83 4 4 4 4 3 4 10 2 3 83 84 3 3 3 2 2 3 10 2 1 84 85 4 3 4 4 2 3 10 3 3 85 86 5 3 3 3 3 3 10 2 2 86 87 2 3 2 2 2 3 10 3 2 87 88 2 4 3 4 3 2 10 4 4 88 89 4 4 4 4 3 4 10 2 2 89 90 4 3 3 3 3 3 10 3 3 90 91 3 4 4 4 3 4 10 3 3 91 92 4 4 3 4 4 2 10 4 3 92 93 4 4 4 4 4 2 10 3 3 93 94 4 3 4 3 4 3 10 2 3 94 95 4 3 3 3 3 3 10 2 2 95 96 4 2 2 4 2 3 10 3 3 96 97 2 3 1 5 3 4 10 2 1 97 98 4 3 2 3 2 2 10 3 2 98 99 4 4 2 4 3 3 10 3 3 99 100 4 3 3 4 3 2 10 4 3 100 101 4 4 4 4 4 2 10 4 3 101 102 4 3 4 4 3 3 10 3 3 102 103 5 3 5 5 3 3 10 1 2 103 104 4 3 2 4 2 5 10 1 1 104 105 4 3 1 3 1 2 10 4 4 105 106 4 4 3 4 3 4 10 2 1 106 107 3 3 3 4 3 3 10 4 4 107 108 4 4 4 4 4 4 10 2 1 108 109 4 3 2 4 2 4 10 2 2 109 110 4 3 2 4 3 10 3 2 4 110 111 3 2 4 3 3 10 4 3 3 111 112 3 3 3 4 3 10 3 3 4 112 113 3 3 4 3 3 10 3 3 4 113 114 3 4 4 4 2 10 4 3 4 114 115 4 4 4 4 2 10 2 4 5 115 116 3 4 4 3 2 10 3 3 4 116 117 3 4 4 3 2 10 3 3 4 117 118 4 2 4 3 1 10 3 3 4 118 119 3 3 4 3 2 10 2 2 4 119 120 3 3 3 3 3 10 2 2 4 120 121 3 3 4 3 3 10 2 3 4 121 122 3 4 3 3 4 10 2 2 4 122 123 3 3 3 3 3 10 4 2 4 123 124 3 4 4 4 2 10 3 3 4 124 125 4 4 4 3 3 10 2 3 4 125 126 3 3 4 2 2 10 4 4 4 126 127 4 4 4 4 3 10 3 3 4 127 128 3 3 3 3 3 10 2 3 3 128 129 4 3 3 3 5 10 1 3 1 129 130 1 1 1 1 2 10 4 4 4 130 131 4 4 4 4 2 10 3 3 4 131 132 3 4 3 3 3 10 2 2 4 132 133 4 2 4 2 1 10 2 4 4 133 134 4 2 4 2 3 10 3 3 4 134 135 4 4 3 4 3 10 2 2 4 135 136 3 3 4 3 3 10 3 3 4 136 137 3 4 4 4 4 10 2 1 4 137 138 3 2 2 2 1 10 3 3 4 138 139 4 5 4 4 2 10 3 2 3 139 140 3 3 3 4 4 10 2 2 4 140 141 3 4 3 3 2 10 3 3 4 141 142 3 3 4 3 2 10 2 2 4 142 143 4 4 4 3 4 10 2 2 4 143 144 2 2 2 2 3 10 2 2 4 144 145 3 3 3 3 3 10 3 3 4 145 146 3 1 3 3 4 10 2 3 4 146 147 3 2 3 3 1 10 3 3 5 147 148 3 5 5 4 2 10 3 3 4 148 149 3 4 4 4 2 10 4 4 4 149 150 4 4 4 3 3 10 4 3 4 150 151 3 3 3 3 2 10 4 3 4 151 152 4 4 4 4 3 10 2 2 3 152 153 3 2 3 2 3 10 3 3 3 153 154 4 4 4 3 3 10 3 3 4 154 155 3 4 4 4 3 10 1 1 3 155 156 3 3 4 2 2 9 2 1 4 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month X1t X2t X3t X4t 9.352518 -0.640088 -0.029074 0.995492 0.114643 0.008803 X5t X6t X7t t -0.243180 -0.104458 -0.542201 -0.033146 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.36174 -0.79352 0.08065 0.72533 4.81873 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.352518 0.819016 11.419 < 2e-16 *** month -0.640088 0.068035 -9.408 < 2e-16 *** X1t -0.029074 0.130130 -0.223 0.823518 X2t 0.995492 0.141128 7.054 6.47e-11 *** X3t 0.114643 0.117472 0.976 0.330722 X4t 0.008803 0.086423 0.102 0.919011 X5t -0.243180 0.063292 -3.842 0.000181 *** X6t -0.104458 0.102443 -1.020 0.309572 X7t -0.542201 0.047916 -11.316 < 2e-16 *** t -0.033146 0.005988 -5.535 1.40e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.219 on 146 degrees of freedom Multiple R-squared: 0.7468, Adjusted R-squared: 0.7312 F-statistic: 47.84 on 9 and 146 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,] 0.0616555127 1.233110e-01 9.383445e-01 [2,] 0.1002594976 2.005190e-01 8.997405e-01 [3,] 0.0425289853 8.505797e-02 9.574710e-01 [4,] 0.0271964549 5.439291e-02 9.728035e-01 [5,] 0.0210415892 4.208318e-02 9.789584e-01 [6,] 0.0088196537 1.763931e-02 9.911803e-01 [7,] 0.0045378290 9.075658e-03 9.954622e-01 [8,] 0.0021209900 4.241980e-03 9.978790e-01 [9,] 0.0148316772 2.966335e-02 9.851683e-01 [10,] 0.0410983106 8.219662e-02 9.589017e-01 [11,] 0.0281514497 5.630290e-02 9.718486e-01 [12,] 0.0246002460 4.920049e-02 9.753998e-01 [13,] 0.0141628252 2.832565e-02 9.858372e-01 [14,] 0.0096977290 1.939546e-02 9.903023e-01 [15,] 0.0053465673 1.069313e-02 9.946534e-01 [16,] 0.0031473743 6.294749e-03 9.968526e-01 [17,] 0.0018174047 3.634809e-03 9.981826e-01 [18,] 0.0012518662 2.503732e-03 9.987481e-01 [19,] 0.0008164270 1.632854e-03 9.991836e-01 [20,] 0.0004249305 8.498610e-04 9.995751e-01 [21,] 0.0002471954 4.943908e-04 9.997528e-01 [22,] 0.0006645935 1.329187e-03 9.993354e-01 [23,] 0.0016820401 3.364080e-03 9.983180e-01 [24,] 0.0013011025 2.602205e-03 9.986989e-01 [25,] 0.0013251481 2.650296e-03 9.986749e-01 [26,] 0.0014448754 2.889751e-03 9.985551e-01 [27,] 0.0026623519 5.324704e-03 9.973376e-01 [28,] 0.0138973979 2.779480e-02 9.861026e-01 [29,] 0.2114866285 4.229733e-01 7.885134e-01 [30,] 0.9995821799 8.356402e-04 4.178201e-04 [31,] 0.9999728399 5.432020e-05 2.716010e-05 [32,] 0.9999951771 9.645752e-06 4.822876e-06 [33,] 0.9999943277 1.134457e-05 5.672284e-06 [34,] 0.9999933965 1.320691e-05 6.603455e-06 [35,] 0.9999959292 8.141526e-06 4.070763e-06 [36,] 0.9999927035 1.459308e-05 7.296541e-06 [37,] 0.9999929908 1.401844e-05 7.009219e-06 [38,] 0.9999888787 2.224267e-05 1.112133e-05 [39,] 0.9999841427 3.171466e-05 1.585733e-05 [40,] 0.9999934436 1.311279e-05 6.556394e-06 [41,] 0.9999913839 1.723224e-05 8.616119e-06 [42,] 0.9999895866 2.082682e-05 1.041341e-05 [43,] 0.9999888646 2.227072e-05 1.113536e-05 [44,] 0.9999845888 3.082244e-05 1.541122e-05 [45,] 0.9999810365 3.792709e-05 1.896354e-05 [46,] 0.9999696026 6.079485e-05 3.039743e-05 [47,] 0.9999885623 2.287541e-05 1.143771e-05 [48,] 0.9999980319 3.936187e-06 1.968094e-06 [49,] 0.9999981929 3.614174e-06 1.807087e-06 [50,] 0.9999971113 5.777316e-06 2.888658e-06 [51,] 0.9999980070 3.985992e-06 1.992996e-06 [52,] 0.9999977760 4.448079e-06 2.224039e-06 [53,] 0.9999974485 5.103083e-06 2.551542e-06 [54,] 0.9999954538 9.092430e-06 4.546215e-06 [55,] 0.9999952110 9.577903e-06 4.788952e-06 [56,] 0.9999925558 1.488833e-05 7.444164e-06 [57,] 0.9999873503 2.529948e-05 1.264974e-05 [58,] 0.9999945348 1.093048e-05 5.465239e-06 [59,] 0.9999917186 1.656274e-05 8.281369e-06 [60,] 0.9999867036 2.659280e-05 1.329640e-05 [61,] 0.9999822191 3.556174e-05 1.778087e-05 [62,] 0.9999905090 1.898195e-05 9.490975e-06 [63,] 0.9999859427 2.811453e-05 1.405726e-05 [64,] 0.9999938374 1.232529e-05 6.162644e-06 [65,] 0.9999902500 1.949991e-05 9.749954e-06 [66,] 0.9999937679 1.246412e-05 6.232061e-06 [67,] 0.9999964083 7.183458e-06 3.591729e-06 [68,] 0.9999980288 3.942380e-06 1.971190e-06 [69,] 0.9999998226 3.547342e-07 1.773671e-07 [70,] 0.9999999941 1.187040e-08 5.935201e-09 [71,] 0.9999999923 1.538738e-08 7.693688e-09 [72,] 0.9999999923 1.536929e-08 7.684647e-09 [73,] 0.9999999850 2.992288e-08 1.496144e-08 [74,] 0.9999999932 1.361762e-08 6.808811e-09 [75,] 0.9999999978 4.426806e-09 2.213403e-09 [76,] 0.9999999999 2.220999e-10 1.110500e-10 [77,] 0.9999999998 4.990562e-10 2.495281e-10 [78,] 0.9999999995 9.767713e-10 4.883856e-10 [79,] 0.9999999996 7.449598e-10 3.724799e-10 [80,] 0.9999999992 1.688610e-09 8.443050e-10 [81,] 0.9999999983 3.340287e-09 1.670143e-09 [82,] 0.9999999963 7.382897e-09 3.691448e-09 [83,] 0.9999999918 1.630322e-08 8.151608e-09 [84,] 0.9999999870 2.594843e-08 1.297422e-08 [85,] 0.9999999999 1.017521e-10 5.087603e-11 [86,] 0.9999999999 2.296366e-10 1.148183e-10 [87,] 0.9999999998 4.746061e-10 2.373031e-10 [88,] 0.9999999994 1.156936e-09 5.784680e-10 [89,] 0.9999999988 2.340708e-09 1.170354e-09 [90,] 0.9999999972 5.536585e-09 2.768292e-09 [91,] 0.9999999946 1.079254e-08 5.396272e-09 [92,] 0.9999999902 1.964213e-08 9.821067e-09 [93,] 0.9999999927 1.455331e-08 7.276654e-09 [94,] 0.9999999826 3.474980e-08 1.737490e-08 [95,] 0.9999999803 3.936162e-08 1.968081e-08 [96,] 0.9999999633 7.336385e-08 3.668193e-08 [97,] 0.9999999193 1.614464e-07 8.072321e-08 [98,] 0.9999999863 2.736140e-08 1.368070e-08 [99,] 0.9999999675 6.500828e-08 3.250414e-08 [100,] 0.9999999196 1.608777e-07 8.043887e-08 [101,] 0.9999998443 3.113818e-07 1.556909e-07 [102,] 0.9999997021 5.957641e-07 2.978821e-07 [103,] 0.9999995667 8.665800e-07 4.332900e-07 [104,] 0.9999992635 1.473054e-06 7.365272e-07 [105,] 0.9999988175 2.364991e-06 1.182495e-06 [106,] 0.9999986579 2.684207e-06 1.342104e-06 [107,] 0.9999971998 5.600499e-06 2.800249e-06 [108,] 0.9999934127 1.317461e-05 6.587305e-06 [109,] 0.9999905259 1.894821e-05 9.474106e-06 [110,] 0.9999796996 4.060076e-05 2.030038e-05 [111,] 0.9999584438 8.311235e-05 4.155618e-05 [112,] 0.9999399711 1.200579e-04 6.002893e-05 [113,] 0.9998889158 2.221684e-04 1.110842e-04 [114,] 0.9998502726 2.994547e-04 1.497274e-04 [115,] 0.9997047388 5.905223e-04 2.952612e-04 [116,] 0.9994727051 1.054590e-03 5.272949e-04 [117,] 0.9989579451 2.084110e-03 1.042055e-03 [118,] 0.9997179003 5.641994e-04 2.820997e-04 [119,] 0.9994434576 1.113085e-03 5.565424e-04 [120,] 0.9989873056 2.025389e-03 1.012694e-03 [121,] 0.9982976122 3.404776e-03 1.702388e-03 [122,] 0.9979644192 4.071162e-03 2.035581e-03 [123,] 0.9981433384 3.713323e-03 1.856662e-03 [124,] 0.9962409280 7.518144e-03 3.759072e-03 [125,] 0.9942276346 1.154473e-02 5.772365e-03 [126,] 0.9922813436 1.543731e-02 7.718656e-03 [127,] 0.9897734016 2.045320e-02 1.022660e-02 [128,] 0.9751215963 4.975681e-02 2.487840e-02 [129,] 0.9624745567 7.505089e-02 3.752544e-02 [130,] 0.9115326935 1.769346e-01 8.846731e-02 [131,] 0.8496636117 3.006728e-01 1.503364e-01 > postscript(file="/var/www/html/rcomp/tmp/10bmq1291333662.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/20bmq1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 156 Frequency = 1 1 2 3 4 5 6 -0.31139230 -1.16477246 0.27103288 -0.94457929 -0.92042283 -0.81688857 7 8 9 10 11 12 -2.16999512 -0.85144256 -0.15497951 0.33967645 0.71548454 -1.15187959 13 14 15 16 17 18 -0.64568560 0.14808817 -0.28294974 -0.50548853 1.53114518 0.14633256 19 20 21 22 23 24 0.25079180 1.72253494 -1.31674607 1.46120338 0.46227326 -0.79132718 25 26 27 28 29 30 0.24981483 1.49589898 1.20679365 1.75202995 1.27204892 0.85486198 31 32 33 34 35 36 1.08422457 1.23518200 1.93906536 0.43876968 -0.85566599 1.38961187 37 38 39 40 41 42 1.44174525 1.97393680 0.72265598 2.16228190 4.81872971 -1.70804787 43 44 45 46 47 48 -1.49863887 -2.06026918 -0.24831392 -0.19676578 -1.69925014 -0.78986057 49 50 51 52 53 54 -1.23917693 -1.09706538 0.69287308 0.84146770 -0.73907536 -0.94343266 55 56 57 58 59 60 -0.22723105 -0.24723893 -1.19801040 -0.08577931 -3.24669741 2.00958606 61 62 63 64 65 66 -1.58449565 -0.27176007 0.99206053 0.12536938 0.14682515 0.46820883 67 68 69 70 71 72 0.76972539 0.05832574 1.30338820 -1.48719365 0.59606000 0.87738012 73 74 75 76 77 78 -0.55733241 0.40505432 1.27806991 1.67112790 0.73334767 0.45136774 79 80 81 82 83 84 -0.88609235 -0.75032219 -3.47052580 -0.60333004 -0.01857741 -0.62456633 85 86 87 88 89 90 -0.36447075 0.87379125 -0.90754524 -1.11320208 -0.36190457 0.65303210 91 92 93 94 95 96 -0.64895493 0.36253683 0.32029895 0.59558831 0.17210168 -0.69810503 97 98 99 100 101 102 -4.36174232 0.47036740 0.56686428 0.10225677 0.68992142 0.08436180 103 104 105 106 107 108 -0.60002622 -1.10377485 1.97681441 -0.36970425 -0.13232591 -0.38898161 109 110 111 112 113 114 -0.28258570 -1.03475523 -1.78262067 -1.83493222 -0.77722076 -0.74165684 115 116 117 118 119 120 0.45178783 0.07694639 0.11009199 -0.02229490 -0.81134174 -0.92191301 121 122 123 124 125 126 -0.75523561 -0.33017692 -0.33611683 -0.65338048 1.01743441 1.11144385 127 128 129 130 131 132 0.33141361 -1.09449133 -1.61821244 -1.12787972 0.57863875 0.11592184 133 134 135 136 137 138 1.33165882 1.27424102 0.21986695 -0.01487186 -0.90386805 0.57796057 139 140 141 142 143 144 0.83723275 -1.36913536 0.87651233 -0.04899285 1.39495492 -0.80008858 145 146 147 148 149 150 0.25436442 -1.35048761 0.45205425 0.81127578 0.52289695 2.33243389 151 152 153 154 155 156 0.81106045 0.27021559 0.33273255 2.22183662 -0.97798493 1.31488237 > postscript(file="/var/www/html/rcomp/tmp/63clv1291333662.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 -0.31139230 NA 1 -1.16477246 -0.31139230 2 0.27103288 -1.16477246 3 -0.94457929 0.27103288 4 -0.92042283 -0.94457929 5 -0.81688857 -0.92042283 6 -2.16999512 -0.81688857 7 -0.85144256 -2.16999512 8 -0.15497951 -0.85144256 9 0.33967645 -0.15497951 10 0.71548454 0.33967645 11 -1.15187959 0.71548454 12 -0.64568560 -1.15187959 13 0.14808817 -0.64568560 14 -0.28294974 0.14808817 15 -0.50548853 -0.28294974 16 1.53114518 -0.50548853 17 0.14633256 1.53114518 18 0.25079180 0.14633256 19 1.72253494 0.25079180 20 -1.31674607 1.72253494 21 1.46120338 -1.31674607 22 0.46227326 1.46120338 23 -0.79132718 0.46227326 24 0.24981483 -0.79132718 25 1.49589898 0.24981483 26 1.20679365 1.49589898 27 1.75202995 1.20679365 28 1.27204892 1.75202995 29 0.85486198 1.27204892 30 1.08422457 0.85486198 31 1.23518200 1.08422457 32 1.93906536 1.23518200 33 0.43876968 1.93906536 34 -0.85566599 0.43876968 35 1.38961187 -0.85566599 36 1.44174525 1.38961187 37 1.97393680 1.44174525 38 0.72265598 1.97393680 39 2.16228190 0.72265598 40 4.81872971 2.16228190 41 -1.70804787 4.81872971 42 -1.49863887 -1.70804787 43 -2.06026918 -1.49863887 44 -0.24831392 -2.06026918 45 -0.19676578 -0.24831392 46 -1.69925014 -0.19676578 47 -0.78986057 -1.69925014 48 -1.23917693 -0.78986057 49 -1.09706538 -1.23917693 50 0.69287308 -1.09706538 51 0.84146770 0.69287308 52 -0.73907536 0.84146770 53 -0.94343266 -0.73907536 54 -0.22723105 -0.94343266 55 -0.24723893 -0.22723105 56 -1.19801040 -0.24723893 57 -0.08577931 -1.19801040 58 -3.24669741 -0.08577931 59 2.00958606 -3.24669741 60 -1.58449565 2.00958606 61 -0.27176007 -1.58449565 62 0.99206053 -0.27176007 63 0.12536938 0.99206053 64 0.14682515 0.12536938 65 0.46820883 0.14682515 66 0.76972539 0.46820883 67 0.05832574 0.76972539 68 1.30338820 0.05832574 69 -1.48719365 1.30338820 70 0.59606000 -1.48719365 71 0.87738012 0.59606000 72 -0.55733241 0.87738012 73 0.40505432 -0.55733241 74 1.27806991 0.40505432 75 1.67112790 1.27806991 76 0.73334767 1.67112790 77 0.45136774 0.73334767 78 -0.88609235 0.45136774 79 -0.75032219 -0.88609235 80 -3.47052580 -0.75032219 81 -0.60333004 -3.47052580 82 -0.01857741 -0.60333004 83 -0.62456633 -0.01857741 84 -0.36447075 -0.62456633 85 0.87379125 -0.36447075 86 -0.90754524 0.87379125 87 -1.11320208 -0.90754524 88 -0.36190457 -1.11320208 89 0.65303210 -0.36190457 90 -0.64895493 0.65303210 91 0.36253683 -0.64895493 92 0.32029895 0.36253683 93 0.59558831 0.32029895 94 0.17210168 0.59558831 95 -0.69810503 0.17210168 96 -4.36174232 -0.69810503 97 0.47036740 -4.36174232 98 0.56686428 0.47036740 99 0.10225677 0.56686428 100 0.68992142 0.10225677 101 0.08436180 0.68992142 102 -0.60002622 0.08436180 103 -1.10377485 -0.60002622 104 1.97681441 -1.10377485 105 -0.36970425 1.97681441 106 -0.13232591 -0.36970425 107 -0.38898161 -0.13232591 108 -0.28258570 -0.38898161 109 -1.03475523 -0.28258570 110 -1.78262067 -1.03475523 111 -1.83493222 -1.78262067 112 -0.77722076 -1.83493222 113 -0.74165684 -0.77722076 114 0.45178783 -0.74165684 115 0.07694639 0.45178783 116 0.11009199 0.07694639 117 -0.02229490 0.11009199 118 -0.81134174 -0.02229490 119 -0.92191301 -0.81134174 120 -0.75523561 -0.92191301 121 -0.33017692 -0.75523561 122 -0.33611683 -0.33017692 123 -0.65338048 -0.33611683 124 1.01743441 -0.65338048 125 1.11144385 1.01743441 126 0.33141361 1.11144385 127 -1.09449133 0.33141361 128 -1.61821244 -1.09449133 129 -1.12787972 -1.61821244 130 0.57863875 -1.12787972 131 0.11592184 0.57863875 132 1.33165882 0.11592184 133 1.27424102 1.33165882 134 0.21986695 1.27424102 135 -0.01487186 0.21986695 136 -0.90386805 -0.01487186 137 0.57796057 -0.90386805 138 0.83723275 0.57796057 139 -1.36913536 0.83723275 140 0.87651233 -1.36913536 141 -0.04899285 0.87651233 142 1.39495492 -0.04899285 143 -0.80008858 1.39495492 144 0.25436442 -0.80008858 145 -1.35048761 0.25436442 146 0.45205425 -1.35048761 147 0.81127578 0.45205425 148 0.52289695 0.81127578 149 2.33243389 0.52289695 150 0.81106045 2.33243389 151 0.27021559 0.81106045 152 0.33273255 0.27021559 153 2.22183662 0.33273255 154 -0.97798493 2.22183662 155 1.31488237 -0.97798493 156 NA 1.31488237 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.16477246 -0.31139230 [2,] 0.27103288 -1.16477246 [3,] -0.94457929 0.27103288 [4,] -0.92042283 -0.94457929 [5,] -0.81688857 -0.92042283 [6,] -2.16999512 -0.81688857 [7,] -0.85144256 -2.16999512 [8,] -0.15497951 -0.85144256 [9,] 0.33967645 -0.15497951 [10,] 0.71548454 0.33967645 [11,] -1.15187959 0.71548454 [12,] -0.64568560 -1.15187959 [13,] 0.14808817 -0.64568560 [14,] -0.28294974 0.14808817 [15,] -0.50548853 -0.28294974 [16,] 1.53114518 -0.50548853 [17,] 0.14633256 1.53114518 [18,] 0.25079180 0.14633256 [19,] 1.72253494 0.25079180 [20,] -1.31674607 1.72253494 [21,] 1.46120338 -1.31674607 [22,] 0.46227326 1.46120338 [23,] -0.79132718 0.46227326 [24,] 0.24981483 -0.79132718 [25,] 1.49589898 0.24981483 [26,] 1.20679365 1.49589898 [27,] 1.75202995 1.20679365 [28,] 1.27204892 1.75202995 [29,] 0.85486198 1.27204892 [30,] 1.08422457 0.85486198 [31,] 1.23518200 1.08422457 [32,] 1.93906536 1.23518200 [33,] 0.43876968 1.93906536 [34,] -0.85566599 0.43876968 [35,] 1.38961187 -0.85566599 [36,] 1.44174525 1.38961187 [37,] 1.97393680 1.44174525 [38,] 0.72265598 1.97393680 [39,] 2.16228190 0.72265598 [40,] 4.81872971 2.16228190 [41,] -1.70804787 4.81872971 [42,] -1.49863887 -1.70804787 [43,] -2.06026918 -1.49863887 [44,] -0.24831392 -2.06026918 [45,] -0.19676578 -0.24831392 [46,] -1.69925014 -0.19676578 [47,] -0.78986057 -1.69925014 [48,] -1.23917693 -0.78986057 [49,] -1.09706538 -1.23917693 [50,] 0.69287308 -1.09706538 [51,] 0.84146770 0.69287308 [52,] -0.73907536 0.84146770 [53,] -0.94343266 -0.73907536 [54,] -0.22723105 -0.94343266 [55,] -0.24723893 -0.22723105 [56,] -1.19801040 -0.24723893 [57,] -0.08577931 -1.19801040 [58,] -3.24669741 -0.08577931 [59,] 2.00958606 -3.24669741 [60,] -1.58449565 2.00958606 [61,] -0.27176007 -1.58449565 [62,] 0.99206053 -0.27176007 [63,] 0.12536938 0.99206053 [64,] 0.14682515 0.12536938 [65,] 0.46820883 0.14682515 [66,] 0.76972539 0.46820883 [67,] 0.05832574 0.76972539 [68,] 1.30338820 0.05832574 [69,] -1.48719365 1.30338820 [70,] 0.59606000 -1.48719365 [71,] 0.87738012 0.59606000 [72,] -0.55733241 0.87738012 [73,] 0.40505432 -0.55733241 [74,] 1.27806991 0.40505432 [75,] 1.67112790 1.27806991 [76,] 0.73334767 1.67112790 [77,] 0.45136774 0.73334767 [78,] -0.88609235 0.45136774 [79,] -0.75032219 -0.88609235 [80,] -3.47052580 -0.75032219 [81,] -0.60333004 -3.47052580 [82,] -0.01857741 -0.60333004 [83,] -0.62456633 -0.01857741 [84,] -0.36447075 -0.62456633 [85,] 0.87379125 -0.36447075 [86,] -0.90754524 0.87379125 [87,] -1.11320208 -0.90754524 [88,] -0.36190457 -1.11320208 [89,] 0.65303210 -0.36190457 [90,] -0.64895493 0.65303210 [91,] 0.36253683 -0.64895493 [92,] 0.32029895 0.36253683 [93,] 0.59558831 0.32029895 [94,] 0.17210168 0.59558831 [95,] -0.69810503 0.17210168 [96,] -4.36174232 -0.69810503 [97,] 0.47036740 -4.36174232 [98,] 0.56686428 0.47036740 [99,] 0.10225677 0.56686428 [100,] 0.68992142 0.10225677 [101,] 0.08436180 0.68992142 [102,] -0.60002622 0.08436180 [103,] -1.10377485 -0.60002622 [104,] 1.97681441 -1.10377485 [105,] -0.36970425 1.97681441 [106,] -0.13232591 -0.36970425 [107,] -0.38898161 -0.13232591 [108,] -0.28258570 -0.38898161 [109,] -1.03475523 -0.28258570 [110,] -1.78262067 -1.03475523 [111,] -1.83493222 -1.78262067 [112,] -0.77722076 -1.83493222 [113,] -0.74165684 -0.77722076 [114,] 0.45178783 -0.74165684 [115,] 0.07694639 0.45178783 [116,] 0.11009199 0.07694639 [117,] -0.02229490 0.11009199 [118,] -0.81134174 -0.02229490 [119,] -0.92191301 -0.81134174 [120,] -0.75523561 -0.92191301 [121,] -0.33017692 -0.75523561 [122,] -0.33611683 -0.33017692 [123,] -0.65338048 -0.33611683 [124,] 1.01743441 -0.65338048 [125,] 1.11144385 1.01743441 [126,] 0.33141361 1.11144385 [127,] -1.09449133 0.33141361 [128,] -1.61821244 -1.09449133 [129,] -1.12787972 -1.61821244 [130,] 0.57863875 -1.12787972 [131,] 0.11592184 0.57863875 [132,] 1.33165882 0.11592184 [133,] 1.27424102 1.33165882 [134,] 0.21986695 1.27424102 [135,] -0.01487186 0.21986695 [136,] -0.90386805 -0.01487186 [137,] 0.57796057 -0.90386805 [138,] 0.83723275 0.57796057 [139,] -1.36913536 0.83723275 [140,] 0.87651233 -1.36913536 [141,] -0.04899285 0.87651233 [142,] 1.39495492 -0.04899285 [143,] -0.80008858 1.39495492 [144,] 0.25436442 -0.80008858 [145,] -1.35048761 0.25436442 [146,] 0.45205425 -1.35048761 [147,] 0.81127578 0.45205425 [148,] 0.52289695 0.81127578 [149,] 2.33243389 0.52289695 [150,] 0.81106045 2.33243389 [151,] 0.27021559 0.81106045 [152,] 0.33273255 0.27021559 [153,] 2.22183662 0.33273255 [154,] -0.97798493 2.22183662 [155,] 1.31488237 -0.97798493 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.16477246 -0.31139230 2 0.27103288 -1.16477246 3 -0.94457929 0.27103288 4 -0.92042283 -0.94457929 5 -0.81688857 -0.92042283 6 -2.16999512 -0.81688857 7 -0.85144256 -2.16999512 8 -0.15497951 -0.85144256 9 0.33967645 -0.15497951 10 0.71548454 0.33967645 11 -1.15187959 0.71548454 12 -0.64568560 -1.15187959 13 0.14808817 -0.64568560 14 -0.28294974 0.14808817 15 -0.50548853 -0.28294974 16 1.53114518 -0.50548853 17 0.14633256 1.53114518 18 0.25079180 0.14633256 19 1.72253494 0.25079180 20 -1.31674607 1.72253494 21 1.46120338 -1.31674607 22 0.46227326 1.46120338 23 -0.79132718 0.46227326 24 0.24981483 -0.79132718 25 1.49589898 0.24981483 26 1.20679365 1.49589898 27 1.75202995 1.20679365 28 1.27204892 1.75202995 29 0.85486198 1.27204892 30 1.08422457 0.85486198 31 1.23518200 1.08422457 32 1.93906536 1.23518200 33 0.43876968 1.93906536 34 -0.85566599 0.43876968 35 1.38961187 -0.85566599 36 1.44174525 1.38961187 37 1.97393680 1.44174525 38 0.72265598 1.97393680 39 2.16228190 0.72265598 40 4.81872971 2.16228190 41 -1.70804787 4.81872971 42 -1.49863887 -1.70804787 43 -2.06026918 -1.49863887 44 -0.24831392 -2.06026918 45 -0.19676578 -0.24831392 46 -1.69925014 -0.19676578 47 -0.78986057 -1.69925014 48 -1.23917693 -0.78986057 49 -1.09706538 -1.23917693 50 0.69287308 -1.09706538 51 0.84146770 0.69287308 52 -0.73907536 0.84146770 53 -0.94343266 -0.73907536 54 -0.22723105 -0.94343266 55 -0.24723893 -0.22723105 56 -1.19801040 -0.24723893 57 -0.08577931 -1.19801040 58 -3.24669741 -0.08577931 59 2.00958606 -3.24669741 60 -1.58449565 2.00958606 61 -0.27176007 -1.58449565 62 0.99206053 -0.27176007 63 0.12536938 0.99206053 64 0.14682515 0.12536938 65 0.46820883 0.14682515 66 0.76972539 0.46820883 67 0.05832574 0.76972539 68 1.30338820 0.05832574 69 -1.48719365 1.30338820 70 0.59606000 -1.48719365 71 0.87738012 0.59606000 72 -0.55733241 0.87738012 73 0.40505432 -0.55733241 74 1.27806991 0.40505432 75 1.67112790 1.27806991 76 0.73334767 1.67112790 77 0.45136774 0.73334767 78 -0.88609235 0.45136774 79 -0.75032219 -0.88609235 80 -3.47052580 -0.75032219 81 -0.60333004 -3.47052580 82 -0.01857741 -0.60333004 83 -0.62456633 -0.01857741 84 -0.36447075 -0.62456633 85 0.87379125 -0.36447075 86 -0.90754524 0.87379125 87 -1.11320208 -0.90754524 88 -0.36190457 -1.11320208 89 0.65303210 -0.36190457 90 -0.64895493 0.65303210 91 0.36253683 -0.64895493 92 0.32029895 0.36253683 93 0.59558831 0.32029895 94 0.17210168 0.59558831 95 -0.69810503 0.17210168 96 -4.36174232 -0.69810503 97 0.47036740 -4.36174232 98 0.56686428 0.47036740 99 0.10225677 0.56686428 100 0.68992142 0.10225677 101 0.08436180 0.68992142 102 -0.60002622 0.08436180 103 -1.10377485 -0.60002622 104 1.97681441 -1.10377485 105 -0.36970425 1.97681441 106 -0.13232591 -0.36970425 107 -0.38898161 -0.13232591 108 -0.28258570 -0.38898161 109 -1.03475523 -0.28258570 110 -1.78262067 -1.03475523 111 -1.83493222 -1.78262067 112 -0.77722076 -1.83493222 113 -0.74165684 -0.77722076 114 0.45178783 -0.74165684 115 0.07694639 0.45178783 116 0.11009199 0.07694639 117 -0.02229490 0.11009199 118 -0.81134174 -0.02229490 119 -0.92191301 -0.81134174 120 -0.75523561 -0.92191301 121 -0.33017692 -0.75523561 122 -0.33611683 -0.33017692 123 -0.65338048 -0.33611683 124 1.01743441 -0.65338048 125 1.11144385 1.01743441 126 0.33141361 1.11144385 127 -1.09449133 0.33141361 128 -1.61821244 -1.09449133 129 -1.12787972 -1.61821244 130 0.57863875 -1.12787972 131 0.11592184 0.57863875 132 1.33165882 0.11592184 133 1.27424102 1.33165882 134 0.21986695 1.27424102 135 -0.01487186 0.21986695 136 -0.90386805 -0.01487186 137 0.57796057 -0.90386805 138 0.83723275 0.57796057 139 -1.36913536 0.83723275 140 0.87651233 -1.36913536 141 -0.04899285 0.87651233 142 1.39495492 -0.04899285 143 -0.80008858 1.39495492 144 0.25436442 -0.80008858 145 -1.35048761 0.25436442 146 0.45205425 -1.35048761 147 0.81127578 0.45205425 148 0.52289695 0.81127578 149 2.33243389 0.52289695 150 0.81106045 2.33243389 151 0.27021559 0.81106045 152 0.33273255 0.27021559 153 2.22183662 0.33273255 154 -0.97798493 2.22183662 155 1.31488237 -0.97798493 > 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/7elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10puj11291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11sd071291333662.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/12dvyv1291333662.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/13snwm1291333662.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/14d6vs1291333662.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/15g6bg1291333662.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/162pa31291333662.tab") + } > > try(system("convert tmp/10bmq1291333662.ps tmp/10bmq1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/20bmq1291333662.ps tmp/20bmq1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/3blmt1291333662.ps tmp/3blmt1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/4blmt1291333662.ps tmp/4blmt1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/5blmt1291333662.ps tmp/5blmt1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/63clv1291333662.ps tmp/63clv1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/7elky1291333662.ps tmp/7elky1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/8elky1291333662.ps tmp/8elky1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/9elky1291333662.ps tmp/9elky1291333662.png",intern=TRUE)) character(0) > try(system("convert tmp/10puj11291333662.ps tmp/10puj11291333662.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.208 1.753 9.887