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. 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+ ,15 + ,0) + ,dim=c(14 + ,154) + ,dimnames=list(c('CM' + ,'CM_G' + ,'D' + ,'D_G' + ,'PE' + ,'PE_G' + ,'PC' + ,'PC_G' + ,'PS' + ,'PS_G' + ,'O' + ,'O_G' + ,'H' + ,'H_G') + ,1:154)) > y <- array(NA,dim=c(14,154),dimnames=list(c('CM','CM_G','D','D_G','PE','PE_G','PC','PC_G','PS','PS_G','O','O_G','H','H_G'),1:154)) > 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 = '9' > #'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 PS CM CM_G D D_G PE PE_G PC PC_G PS_G O O_G H H_G 1 24 24 24 14 14 11 11 12 12 24 26 26 10 10 2 25 25 0 11 0 7 0 8 0 0 23 0 14 0 3 30 17 0 6 0 17 0 8 0 0 25 0 18 0 4 19 18 18 12 12 10 10 8 8 19 23 23 15 15 5 22 18 0 8 0 12 0 9 0 0 19 0 18 0 6 22 16 0 10 0 12 0 7 0 0 29 0 11 0 7 25 20 0 10 0 11 0 4 0 0 25 0 17 0 8 23 16 0 11 0 11 0 11 0 0 21 0 19 0 9 17 18 0 16 0 12 0 7 0 0 22 0 7 0 10 21 17 0 11 0 13 0 7 0 0 25 0 12 0 11 19 23 23 13 13 14 14 12 12 19 24 24 13 13 12 19 30 0 12 0 16 0 10 0 0 18 0 15 0 13 15 23 0 8 0 11 0 10 0 0 22 0 14 0 14 16 18 0 12 0 10 0 8 0 0 15 0 14 0 15 23 15 15 11 11 11 11 8 8 23 22 22 16 16 16 27 12 12 4 4 15 15 4 4 27 28 28 16 16 17 22 21 0 9 0 9 0 9 0 0 20 0 12 0 18 14 15 15 8 8 11 11 8 8 14 12 12 12 12 19 22 20 20 8 8 17 17 7 7 22 24 24 13 13 20 23 31 0 14 0 17 0 11 0 0 20 0 16 0 21 23 27 0 15 0 11 0 9 0 0 21 0 9 0 22 19 21 0 9 0 14 0 13 0 0 21 0 11 0 23 18 31 31 14 14 10 10 8 8 18 23 23 12 12 24 20 19 19 11 11 11 11 8 8 20 28 28 11 11 25 23 16 0 8 0 15 0 9 0 0 24 0 14 0 26 25 20 0 9 0 15 0 6 0 0 24 0 18 0 27 19 21 21 9 9 13 13 9 9 19 24 24 11 11 28 24 22 22 9 9 16 16 9 9 24 23 23 14 14 29 22 17 0 9 0 13 0 6 0 0 23 0 17 0 30 26 25 0 16 0 18 0 16 0 0 24 0 12 0 31 29 26 0 11 0 18 0 5 0 0 18 0 14 0 32 32 25 0 8 0 12 0 7 0 0 25 0 14 0 33 25 17 0 9 0 17 0 9 0 0 21 0 15 0 34 29 32 32 16 16 9 9 6 6 29 26 26 11 11 35 28 33 33 11 11 9 9 6 6 28 22 22 15 15 36 17 13 13 16 16 12 12 5 5 17 22 22 14 14 37 28 32 0 12 0 18 0 12 0 0 22 0 11 0 38 29 25 25 12 12 12 12 7 7 29 23 23 12 12 39 26 29 29 14 14 18 18 10 10 26 30 30 17 17 40 25 22 0 9 0 14 0 9 0 0 23 0 15 0 41 14 18 18 10 10 15 15 8 8 14 17 17 9 9 42 25 17 0 9 0 16 0 5 0 0 23 0 16 0 43 26 20 20 10 10 10 10 8 8 26 23 23 13 13 44 20 15 15 12 12 11 11 8 8 20 25 25 15 15 45 18 20 0 14 0 14 0 10 0 0 24 0 11 0 46 32 33 33 14 14 9 9 6 6 32 24 24 10 10 47 25 29 0 10 0 12 0 8 0 0 23 0 16 0 48 25 23 0 14 0 17 0 7 0 0 21 0 13 0 49 23 26 26 16 16 5 5 4 4 23 24 24 9 9 50 21 18 18 9 9 12 12 8 8 21 24 24 14 14 51 20 20 0 10 0 12 0 8 0 0 28 0 16 0 52 15 11 0 6 0 6 0 4 0 0 16 0 15 0 53 30 28 28 8 8 24 24 20 20 30 20 20 14 14 54 24 26 0 13 0 12 0 8 0 0 29 0 13 0 55 26 22 0 10 0 12 0 8 0 0 27 0 14 0 56 24 17 17 8 8 14 14 6 6 24 22 22 16 16 57 22 12 12 7 7 7 7 4 4 22 28 28 15 15 58 14 14 0 15 0 13 0 8 0 0 16 0 16 0 59 24 17 17 9 9 12 12 9 9 24 25 25 15 15 60 24 21 21 10 10 13 13 6 6 24 24 24 13 13 61 24 19 0 12 0 14 0 7 0 0 28 0 11 0 62 24 18 18 13 13 8 8 9 9 24 24 24 16 16 63 19 10 10 10 10 11 11 5 5 19 23 23 17 17 64 31 29 29 11 11 9 9 5 5 31 30 30 10 10 65 22 31 31 8 8 11 11 8 8 22 24 24 17 17 66 27 19 19 9 9 13 13 8 8 27 21 21 11 11 67 19 9 9 13 13 10 10 6 6 19 25 25 14 14 68 25 20 0 11 0 11 0 8 0 0 25 0 15 0 69 20 28 0 8 0 12 0 7 0 0 22 0 16 0 70 21 19 0 9 0 9 0 7 0 0 23 0 15 0 71 27 30 0 9 0 15 0 9 0 0 26 0 16 0 72 23 29 0 15 0 18 0 11 0 0 23 0 15 0 73 25 26 0 9 0 15 0 6 0 0 25 0 14 0 74 20 23 0 10 0 12 0 8 0 0 21 0 17 0 75 22 21 0 12 0 14 0 9 0 0 24 0 12 0 76 23 19 19 12 12 10 10 8 8 23 29 29 12 12 77 25 28 0 11 0 13 0 6 0 0 22 0 9 0 78 25 23 0 14 0 13 0 10 0 0 27 0 12 0 79 17 18 0 6 0 11 0 8 0 0 26 0 17 0 80 19 21 21 12 12 13 13 8 8 19 22 22 11 11 81 25 20 0 8 0 16 0 10 0 0 24 0 16 0 82 19 23 23 14 14 8 8 5 5 19 27 27 9 9 83 20 21 21 11 11 16 16 7 7 20 24 24 15 15 84 26 21 0 10 0 11 0 5 0 0 24 0 17 0 85 23 15 15 14 14 9 9 8 8 23 29 29 17 17 86 27 28 0 12 0 16 0 14 0 0 22 0 12 0 87 17 19 19 10 10 12 12 7 7 17 21 21 15 15 88 17 26 26 14 14 14 14 8 8 17 24 24 18 18 89 17 16 16 11 11 9 9 5 5 17 23 23 13 13 90 22 22 0 10 0 15 0 6 0 0 20 0 15 0 91 21 19 19 9 9 11 11 10 10 21 27 27 16 16 92 32 31 0 10 0 21 0 12 0 0 26 0 17 0 93 21 31 31 16 16 14 14 9 9 21 25 25 15 15 94 21 29 0 13 0 18 0 12 0 0 21 0 13 0 95 18 19 19 9 9 12 12 7 7 18 21 21 12 12 96 18 22 0 10 0 13 0 8 0 0 19 0 11 0 97 23 23 0 10 0 15 0 10 0 0 21 0 15 0 98 19 15 15 7 7 12 12 6 6 19 21 21 15 15 99 20 20 0 9 0 19 0 10 0 0 16 0 15 0 100 21 18 0 8 0 15 0 10 0 0 22 0 18 0 101 20 23 23 14 14 11 11 10 10 20 29 29 16 16 102 17 25 0 14 0 11 0 5 0 0 15 0 12 0 103 18 21 0 8 0 10 0 7 0 0 17 0 16 0 104 19 24 0 9 0 13 0 10 0 0 15 0 15 0 105 22 25 0 14 0 15 0 11 0 0 21 0 15 0 106 15 17 17 14 14 12 12 6 6 15 21 21 15 15 107 14 13 0 8 0 12 0 7 0 0 19 0 17 0 108 18 28 0 8 0 16 0 12 0 0 24 0 15 0 109 24 21 21 8 8 9 9 11 11 24 20 20 13 13 110 35 25 0 7 0 18 0 11 0 0 17 0 16 0 111 29 9 0 6 0 8 0 11 0 0 23 0 13 0 112 21 16 0 8 0 13 0 5 0 0 24 0 13 0 113 20 17 17 11 11 9 9 6 6 20 19 19 15 15 114 22 25 25 14 14 15 15 9 9 22 24 24 13 13 115 13 20 20 11 11 8 8 4 4 13 13 13 16 16 116 26 29 0 11 0 7 0 4 0 0 22 0 14 0 117 17 14 0 11 0 12 0 7 0 0 16 0 15 0 118 25 22 0 14 0 14 0 11 0 0 19 0 11 0 119 20 15 0 8 0 6 0 6 0 0 25 0 15 0 120 19 19 19 20 20 8 8 7 7 19 25 25 14 14 121 21 20 20 11 11 17 17 8 8 21 23 23 14 14 122 22 15 0 8 0 10 0 4 0 0 24 0 17 0 123 24 20 0 11 0 11 0 8 0 0 26 0 15 0 124 21 18 0 10 0 14 0 9 0 0 26 0 14 0 125 26 33 0 14 0 11 0 8 0 0 25 0 15 0 126 24 22 0 11 0 13 0 11 0 0 18 0 13 0 127 16 16 0 9 0 12 0 8 0 0 21 0 15 0 128 23 17 17 9 9 11 11 5 5 23 26 26 16 16 129 18 16 0 8 0 9 0 4 0 0 23 0 12 0 130 16 21 21 10 10 12 12 8 8 16 23 23 14 14 131 26 26 26 13 13 20 20 10 10 26 22 22 12 12 132 19 18 0 13 0 12 0 6 0 0 20 0 14 0 133 21 18 0 12 0 13 0 9 0 0 13 0 14 0 134 21 17 0 8 0 12 0 9 0 0 24 0 15 0 135 22 22 0 13 0 12 0 13 0 0 15 0 13 0 136 23 30 0 14 0 9 0 9 0 0 14 0 15 0 137 29 30 0 12 0 15 0 10 0 0 22 0 16 0 138 21 24 0 14 0 24 0 20 0 0 10 0 10 0 139 21 21 21 15 15 7 7 5 5 21 24 24 8 8 140 23 21 0 13 0 17 0 11 0 0 22 0 15 0 141 27 29 0 16 0 11 0 6 0 0 24 0 14 0 142 25 31 0 9 0 17 0 9 0 0 19 0 13 0 143 21 20 0 9 0 11 0 7 0 0 20 0 15 0 144 10 16 0 9 0 12 0 9 0 0 13 0 13 0 145 20 22 0 8 0 14 0 10 0 0 20 0 14 0 146 26 20 0 7 0 11 0 9 0 0 22 0 19 0 147 24 28 0 16 0 16 0 8 0 0 24 0 17 0 148 29 38 0 11 0 21 0 7 0 0 29 0 16 0 149 19 22 0 9 0 14 0 6 0 0 12 0 16 0 150 24 20 0 11 0 20 0 13 0 0 20 0 14 0 151 19 17 0 9 0 13 0 6 0 0 21 0 12 0 152 24 28 28 14 14 11 11 8 8 24 24 24 13 13 153 22 22 0 13 0 15 0 10 0 0 22 0 14 0 154 17 31 0 16 0 19 0 16 0 0 20 0 15 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) CM CM_G D D_G PE 3.02312 0.28981 -0.29363 -0.19925 0.15354 0.25596 PE_G PC PC_G PS_G O O_G -0.28323 0.01327 -0.01175 0.97857 0.41592 -0.44454 H H_G 0.17814 -0.24798 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.45277 -0.70711 -0.02976 0.68570 11.45210 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.02312 2.61223 1.157 0.24912 CM 0.28981 0.06346 4.567 1.08e-05 *** CM_G -0.29363 0.10615 -2.766 0.00644 ** D -0.19925 0.13003 -1.532 0.12769 D_G 0.15354 0.18875 0.813 0.41734 PE 0.25596 0.11122 2.301 0.02285 * PE_G -0.28323 0.18093 -1.565 0.11974 PC 0.01327 0.13106 0.101 0.91947 PC_G -0.01175 0.22985 -0.051 0.95931 PS_G 0.97857 0.11379 8.600 1.45e-14 *** O 0.41592 0.07216 5.764 5.02e-08 *** O_G -0.44454 0.13941 -3.189 0.00176 ** H 0.17814 0.12584 1.416 0.15912 H_G -0.24798 0.17240 -1.438 0.15256 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.806 on 140 degrees of freedom Multiple R-squared: 0.6037, Adjusted R-squared: 0.5669 F-statistic: 16.4 on 13 and 140 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,] 9.781439e-01 4.371216e-02 0.02185608 [2,] 9.526787e-01 9.464257e-02 0.04732129 [3,] 9.119939e-01 1.760122e-01 0.08800609 [4,] 8.714543e-01 2.570913e-01 0.12854567 [5,] 9.180650e-01 1.638700e-01 0.08193498 [6,] 8.800292e-01 2.399417e-01 0.11997083 [7,] 8.248027e-01 3.503946e-01 0.17519728 [8,] 7.588942e-01 4.822116e-01 0.24110580 [9,] 6.871602e-01 6.256796e-01 0.31283981 [10,] 6.115839e-01 7.768323e-01 0.38841613 [11,] 5.277421e-01 9.445158e-01 0.47225789 [12,] 4.445735e-01 8.891471e-01 0.55542647 [13,] 3.927320e-01 7.854641e-01 0.60726796 [14,] 3.768796e-01 7.537592e-01 0.62312039 [15,] 6.751201e-01 6.497598e-01 0.32487988 [16,] 8.752954e-01 2.494092e-01 0.12470461 [17,] 8.647530e-01 2.704941e-01 0.13524703 [18,] 8.243113e-01 3.513773e-01 0.17568865 [19,] 7.775676e-01 4.448648e-01 0.22243242 [20,] 7.250672e-01 5.498656e-01 0.27493280 [21,] 6.917246e-01 6.165507e-01 0.30827537 [22,] 6.327188e-01 7.345625e-01 0.36728123 [23,] 5.745086e-01 8.509828e-01 0.42549142 [24,] 5.176399e-01 9.647201e-01 0.48236007 [25,] 4.577608e-01 9.155217e-01 0.54223915 [26,] 4.236864e-01 8.473728e-01 0.57631358 [27,] 3.646931e-01 7.293861e-01 0.63530693 [28,] 3.091777e-01 6.183554e-01 0.69082229 [29,] 3.468634e-01 6.937268e-01 0.65313659 [30,] 2.939793e-01 5.879587e-01 0.70602065 [31,] 2.578106e-01 5.156212e-01 0.74218939 [32,] 2.375128e-01 4.750257e-01 0.76248716 [33,] 1.951017e-01 3.902034e-01 0.80489828 [34,] 1.578545e-01 3.157089e-01 0.84214553 [35,] 2.568005e-01 5.136010e-01 0.74319952 [36,] 2.168651e-01 4.337302e-01 0.78313491 [37,] 1.780679e-01 3.561358e-01 0.82193209 [38,] 1.573029e-01 3.146058e-01 0.84269709 [39,] 1.346164e-01 2.692329e-01 0.86538356 [40,] 1.070486e-01 2.140971e-01 0.89295143 [41,] 8.386752e-02 1.677350e-01 0.91613248 [42,] 7.732036e-02 1.546407e-01 0.92267964 [43,] 5.947941e-02 1.189588e-01 0.94052059 [44,] 4.509179e-02 9.018359e-02 0.95490821 [45,] 3.404079e-02 6.808157e-02 0.96595921 [46,] 2.510087e-02 5.020173e-02 0.97489913 [47,] 1.821985e-02 3.643970e-02 0.98178015 [48,] 1.308016e-02 2.616032e-02 0.98691984 [49,] 9.251983e-03 1.850397e-02 0.99074802 [50,] 6.428341e-03 1.285668e-02 0.99357166 [51,] 4.404637e-03 8.809275e-03 0.99559536 [52,] 4.159153e-03 8.318306e-03 0.99584085 [53,] 1.316626e-02 2.633253e-02 0.98683374 [54,] 9.422168e-03 1.884434e-02 0.99057783 [55,] 7.197111e-03 1.439422e-02 0.99280289 [56,] 6.804866e-03 1.360973e-02 0.99319513 [57,] 5.471681e-03 1.094336e-02 0.99452832 [58,] 5.128019e-03 1.025604e-02 0.99487198 [59,] 3.563131e-03 7.126262e-03 0.99643687 [60,] 2.405889e-03 4.811777e-03 0.99759411 [61,] 1.881719e-03 3.763438e-03 0.99811828 [62,] 1.421058e-03 2.842115e-03 0.99857894 [63,] 9.688886e-03 1.937777e-02 0.99031111 [64,] 6.862045e-03 1.372409e-02 0.99313795 [65,] 4.899102e-03 9.798204e-03 0.99510090 [66,] 3.368559e-03 6.737119e-03 0.99663144 [67,] 2.280207e-03 4.560414e-03 0.99771979 [68,] 2.386758e-03 4.773517e-03 0.99761324 [69,] 1.606193e-03 3.212386e-03 0.99839381 [70,] 1.711732e-03 3.423465e-03 0.99828827 [71,] 1.129384e-03 2.258768e-03 0.99887062 [72,] 7.368061e-04 1.473612e-03 0.99926319 [73,] 4.725281e-04 9.450561e-04 0.99952747 [74,] 3.302821e-04 6.605642e-04 0.99966972 [75,] 2.053046e-04 4.106092e-04 0.99979470 [76,] 2.014602e-04 4.029204e-04 0.99979854 [77,] 1.249634e-04 2.499268e-04 0.99987504 [78,] 1.731660e-04 3.463320e-04 0.99982683 [79,] 1.066372e-04 2.132745e-04 0.99989336 [80,] 1.014902e-04 2.029804e-04 0.99989851 [81,] 6.043144e-05 1.208629e-04 0.99993957 [82,] 3.563988e-05 7.127976e-05 0.99996436 [83,] 2.314708e-05 4.629415e-05 0.99997685 [84,] 1.537497e-05 3.074995e-05 0.99998463 [85,] 8.752282e-06 1.750456e-05 0.99999125 [86,] 5.905854e-06 1.181171e-05 0.99999409 [87,] 4.490527e-06 8.981055e-06 0.99999551 [88,] 3.286879e-06 6.573758e-06 0.99999671 [89,] 1.795887e-06 3.591774e-06 0.99999820 [90,] 9.399380e-07 1.879876e-06 0.99999906 [91,] 3.379000e-06 6.757999e-06 0.99999662 [92,] 3.600745e-04 7.201491e-04 0.99963993 [93,] 2.191390e-04 4.382779e-04 0.99978086 [94,] 6.324852e-02 1.264970e-01 0.93675148 [95,] 4.649230e-01 9.298459e-01 0.53507704 [96,] 4.159092e-01 8.318184e-01 0.58409081 [97,] 3.615881e-01 7.231762e-01 0.63841191 [98,] 3.058755e-01 6.117509e-01 0.69412454 [99,] 2.540774e-01 5.081549e-01 0.74592256 [100,] 2.394513e-01 4.789026e-01 0.76054871 [101,] 1.937323e-01 3.874646e-01 0.80626769 [102,] 2.607950e-01 5.215900e-01 0.73920500 [103,] 2.119475e-01 4.238950e-01 0.78805248 [104,] 1.666870e-01 3.333740e-01 0.83331302 [105,] 1.276527e-01 2.553053e-01 0.87234735 [106,] 9.982137e-02 1.996427e-01 0.90017863 [107,] 7.773665e-02 1.554733e-01 0.92226335 [108,] 5.835869e-02 1.167174e-01 0.94164131 [109,] 4.141779e-02 8.283558e-02 0.95858221 [110,] 4.357352e-02 8.714704e-02 0.95642648 [111,] 5.253589e-02 1.050718e-01 0.94746411 [112,] 3.446458e-02 6.892916e-02 0.96553542 [113,] 2.398513e-02 4.797025e-02 0.97601487 [114,] 1.438357e-02 2.876714e-02 0.98561643 [115,] 8.152560e-03 1.630512e-02 0.99184744 [116,] 4.506496e-03 9.012992e-03 0.99549350 [117,] 4.213910e-03 8.427820e-03 0.99578609 [118,] 2.133098e-03 4.266197e-03 0.99786690 [119,] 1.398346e-03 2.796693e-03 0.99860165 [120,] 8.085160e-04 1.617032e-03 0.99919148 [121,] 9.569618e-04 1.913924e-03 0.99904304 > postscript(file="/var/www/html/rcomp/tmp/1vcjr1292167472.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/263ic1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/363ic1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4zdif1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5zdif1292167472.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 = 154 Frequency = 1 1 2 3 4 5 6 -0.05307656 2.96522024 5.18343267 -0.03241199 1.05419598 -0.85339235 7 8 9 10 11 12 1.87800225 2.45098493 -1.61348265 -0.71435776 0.02432376 -4.71329412 13 14 15 16 17 18 -7.68737453 -1.24735010 0.06460106 0.10571754 1.80482075 -0.83090205 19 20 21 22 23 24 -0.06200069 -1.88383059 1.86803443 -2.76586309 -0.12221143 -0.16187320 25 26 27 28 29 30 0.49887672 0.86614488 -0.32853822 0.04511885 -0.15843895 1.98006140 31 32 33 34 35 36 5.97928724 7.26909531 2.96602631 0.20054371 0.11925871 0.04911941 37 38 39 40 41 42 2.21748394 0.05518298 0.80614105 1.45301101 -0.68536885 2.26509447 43 44 45 46 47 48 -0.10586837 0.06204236 -3.68775802 0.05015580 -0.02935374 2.60624405 49 50 51 52 53 54 -0.25388825 -0.11338333 -4.50068740 -0.93130155 0.26641078 -1.52329949 55 56 57 58 59 60 1.69189978 0.04138915 -0.15224918 -3.03045270 0.04400379 -0.03143318 61 62 63 64 65 66 0.57967939 0.16283585 0.01708758 0.04951744 0.09429163 -0.24908661 67 68 69 70 71 72 -0.03066889 2.38043644 -4.70884347 -0.37120759 -0.54734186 -2.43055441 73 74 75 76 77 78 -0.57607317 -2.63678779 -0.54093277 0.01931097 1.89320571 1.27285930 79 80 81 82 83 84 -6.80839600 -0.24710651 0.71412026 -0.27637664 0.14851456 2.99084248 85 86 87 88 89 90 0.41735989 2.68395454 -0.16404402 0.39394368 -0.29099124 -0.31610479 91 92 93 94 95 96 0.08564639 2.60836910 0.40778069 -3.65419583 -0.39784057 -2.70224293 97 98 99 100 101 102 -0.07493611 -0.27209740 -1.34897719 -1.97473332 0.36531238 -1.73737566 103 104 105 106 107 108 -2.08863227 -1.55652774 -0.87083374 -0.03017072 -5.29206539 -8.45276524 109 110 111 112 113 114 -0.35401940 11.45209679 10.48828868 -0.75796343 -0.19920084 0.17381302 115 116 117 118 119 120 -0.46379910 3.27500276 -0.38007270 4.79896964 -0.46190980 0.27150466 121 122 123 124 125 126 0.09340461 0.60043794 0.96451215 -2.25813260 0.21065673 3.51682508 127 128 129 130 131 132 -4.45108613 0.09986781 -2.12678082 -0.19195393 0.22537011 -0.61309530 133 134 135 136 137 138 3.80334209 -1.20119215 3.39250814 3.15390038 3.70082818 1.46173419 139 140 141 142 143 144 -0.37840898 0.16131055 3.38900801 0.09682068 0.07483415 -6.78068365 145 146 147 148 149 150 -2.33359826 3.10537401 -1.16195796 -3.22431028 -0.11013996 1.26818128 151 152 153 154 -1.43588435 0.12060319 -0.42516229 -7.88548426 > postscript(file="/var/www/html/rcomp/tmp/6zdif1292167472.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.05307656 NA 1 2.96522024 -0.05307656 2 5.18343267 2.96522024 3 -0.03241199 5.18343267 4 1.05419598 -0.03241199 5 -0.85339235 1.05419598 6 1.87800225 -0.85339235 7 2.45098493 1.87800225 8 -1.61348265 2.45098493 9 -0.71435776 -1.61348265 10 0.02432376 -0.71435776 11 -4.71329412 0.02432376 12 -7.68737453 -4.71329412 13 -1.24735010 -7.68737453 14 0.06460106 -1.24735010 15 0.10571754 0.06460106 16 1.80482075 0.10571754 17 -0.83090205 1.80482075 18 -0.06200069 -0.83090205 19 -1.88383059 -0.06200069 20 1.86803443 -1.88383059 21 -2.76586309 1.86803443 22 -0.12221143 -2.76586309 23 -0.16187320 -0.12221143 24 0.49887672 -0.16187320 25 0.86614488 0.49887672 26 -0.32853822 0.86614488 27 0.04511885 -0.32853822 28 -0.15843895 0.04511885 29 1.98006140 -0.15843895 30 5.97928724 1.98006140 31 7.26909531 5.97928724 32 2.96602631 7.26909531 33 0.20054371 2.96602631 34 0.11925871 0.20054371 35 0.04911941 0.11925871 36 2.21748394 0.04911941 37 0.05518298 2.21748394 38 0.80614105 0.05518298 39 1.45301101 0.80614105 40 -0.68536885 1.45301101 41 2.26509447 -0.68536885 42 -0.10586837 2.26509447 43 0.06204236 -0.10586837 44 -3.68775802 0.06204236 45 0.05015580 -3.68775802 46 -0.02935374 0.05015580 47 2.60624405 -0.02935374 48 -0.25388825 2.60624405 49 -0.11338333 -0.25388825 50 -4.50068740 -0.11338333 51 -0.93130155 -4.50068740 52 0.26641078 -0.93130155 53 -1.52329949 0.26641078 54 1.69189978 -1.52329949 55 0.04138915 1.69189978 56 -0.15224918 0.04138915 57 -3.03045270 -0.15224918 58 0.04400379 -3.03045270 59 -0.03143318 0.04400379 60 0.57967939 -0.03143318 61 0.16283585 0.57967939 62 0.01708758 0.16283585 63 0.04951744 0.01708758 64 0.09429163 0.04951744 65 -0.24908661 0.09429163 66 -0.03066889 -0.24908661 67 2.38043644 -0.03066889 68 -4.70884347 2.38043644 69 -0.37120759 -4.70884347 70 -0.54734186 -0.37120759 71 -2.43055441 -0.54734186 72 -0.57607317 -2.43055441 73 -2.63678779 -0.57607317 74 -0.54093277 -2.63678779 75 0.01931097 -0.54093277 76 1.89320571 0.01931097 77 1.27285930 1.89320571 78 -6.80839600 1.27285930 79 -0.24710651 -6.80839600 80 0.71412026 -0.24710651 81 -0.27637664 0.71412026 82 0.14851456 -0.27637664 83 2.99084248 0.14851456 84 0.41735989 2.99084248 85 2.68395454 0.41735989 86 -0.16404402 2.68395454 87 0.39394368 -0.16404402 88 -0.29099124 0.39394368 89 -0.31610479 -0.29099124 90 0.08564639 -0.31610479 91 2.60836910 0.08564639 92 0.40778069 2.60836910 93 -3.65419583 0.40778069 94 -0.39784057 -3.65419583 95 -2.70224293 -0.39784057 96 -0.07493611 -2.70224293 97 -0.27209740 -0.07493611 98 -1.34897719 -0.27209740 99 -1.97473332 -1.34897719 100 0.36531238 -1.97473332 101 -1.73737566 0.36531238 102 -2.08863227 -1.73737566 103 -1.55652774 -2.08863227 104 -0.87083374 -1.55652774 105 -0.03017072 -0.87083374 106 -5.29206539 -0.03017072 107 -8.45276524 -5.29206539 108 -0.35401940 -8.45276524 109 11.45209679 -0.35401940 110 10.48828868 11.45209679 111 -0.75796343 10.48828868 112 -0.19920084 -0.75796343 113 0.17381302 -0.19920084 114 -0.46379910 0.17381302 115 3.27500276 -0.46379910 116 -0.38007270 3.27500276 117 4.79896964 -0.38007270 118 -0.46190980 4.79896964 119 0.27150466 -0.46190980 120 0.09340461 0.27150466 121 0.60043794 0.09340461 122 0.96451215 0.60043794 123 -2.25813260 0.96451215 124 0.21065673 -2.25813260 125 3.51682508 0.21065673 126 -4.45108613 3.51682508 127 0.09986781 -4.45108613 128 -2.12678082 0.09986781 129 -0.19195393 -2.12678082 130 0.22537011 -0.19195393 131 -0.61309530 0.22537011 132 3.80334209 -0.61309530 133 -1.20119215 3.80334209 134 3.39250814 -1.20119215 135 3.15390038 3.39250814 136 3.70082818 3.15390038 137 1.46173419 3.70082818 138 -0.37840898 1.46173419 139 0.16131055 -0.37840898 140 3.38900801 0.16131055 141 0.09682068 3.38900801 142 0.07483415 0.09682068 143 -6.78068365 0.07483415 144 -2.33359826 -6.78068365 145 3.10537401 -2.33359826 146 -1.16195796 3.10537401 147 -3.22431028 -1.16195796 148 -0.11013996 -3.22431028 149 1.26818128 -0.11013996 150 -1.43588435 1.26818128 151 0.12060319 -1.43588435 152 -0.42516229 0.12060319 153 -7.88548426 -0.42516229 154 NA -7.88548426 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.96522024 -0.05307656 [2,] 5.18343267 2.96522024 [3,] -0.03241199 5.18343267 [4,] 1.05419598 -0.03241199 [5,] -0.85339235 1.05419598 [6,] 1.87800225 -0.85339235 [7,] 2.45098493 1.87800225 [8,] -1.61348265 2.45098493 [9,] -0.71435776 -1.61348265 [10,] 0.02432376 -0.71435776 [11,] -4.71329412 0.02432376 [12,] -7.68737453 -4.71329412 [13,] -1.24735010 -7.68737453 [14,] 0.06460106 -1.24735010 [15,] 0.10571754 0.06460106 [16,] 1.80482075 0.10571754 [17,] -0.83090205 1.80482075 [18,] -0.06200069 -0.83090205 [19,] -1.88383059 -0.06200069 [20,] 1.86803443 -1.88383059 [21,] -2.76586309 1.86803443 [22,] -0.12221143 -2.76586309 [23,] -0.16187320 -0.12221143 [24,] 0.49887672 -0.16187320 [25,] 0.86614488 0.49887672 [26,] -0.32853822 0.86614488 [27,] 0.04511885 -0.32853822 [28,] -0.15843895 0.04511885 [29,] 1.98006140 -0.15843895 [30,] 5.97928724 1.98006140 [31,] 7.26909531 5.97928724 [32,] 2.96602631 7.26909531 [33,] 0.20054371 2.96602631 [34,] 0.11925871 0.20054371 [35,] 0.04911941 0.11925871 [36,] 2.21748394 0.04911941 [37,] 0.05518298 2.21748394 [38,] 0.80614105 0.05518298 [39,] 1.45301101 0.80614105 [40,] -0.68536885 1.45301101 [41,] 2.26509447 -0.68536885 [42,] -0.10586837 2.26509447 [43,] 0.06204236 -0.10586837 [44,] -3.68775802 0.06204236 [45,] 0.05015580 -3.68775802 [46,] -0.02935374 0.05015580 [47,] 2.60624405 -0.02935374 [48,] -0.25388825 2.60624405 [49,] -0.11338333 -0.25388825 [50,] -4.50068740 -0.11338333 [51,] -0.93130155 -4.50068740 [52,] 0.26641078 -0.93130155 [53,] -1.52329949 0.26641078 [54,] 1.69189978 -1.52329949 [55,] 0.04138915 1.69189978 [56,] -0.15224918 0.04138915 [57,] -3.03045270 -0.15224918 [58,] 0.04400379 -3.03045270 [59,] -0.03143318 0.04400379 [60,] 0.57967939 -0.03143318 [61,] 0.16283585 0.57967939 [62,] 0.01708758 0.16283585 [63,] 0.04951744 0.01708758 [64,] 0.09429163 0.04951744 [65,] -0.24908661 0.09429163 [66,] -0.03066889 -0.24908661 [67,] 2.38043644 -0.03066889 [68,] -4.70884347 2.38043644 [69,] -0.37120759 -4.70884347 [70,] -0.54734186 -0.37120759 [71,] -2.43055441 -0.54734186 [72,] -0.57607317 -2.43055441 [73,] -2.63678779 -0.57607317 [74,] -0.54093277 -2.63678779 [75,] 0.01931097 -0.54093277 [76,] 1.89320571 0.01931097 [77,] 1.27285930 1.89320571 [78,] -6.80839600 1.27285930 [79,] -0.24710651 -6.80839600 [80,] 0.71412026 -0.24710651 [81,] -0.27637664 0.71412026 [82,] 0.14851456 -0.27637664 [83,] 2.99084248 0.14851456 [84,] 0.41735989 2.99084248 [85,] 2.68395454 0.41735989 [86,] -0.16404402 2.68395454 [87,] 0.39394368 -0.16404402 [88,] -0.29099124 0.39394368 [89,] -0.31610479 -0.29099124 [90,] 0.08564639 -0.31610479 [91,] 2.60836910 0.08564639 [92,] 0.40778069 2.60836910 [93,] -3.65419583 0.40778069 [94,] -0.39784057 -3.65419583 [95,] -2.70224293 -0.39784057 [96,] -0.07493611 -2.70224293 [97,] -0.27209740 -0.07493611 [98,] -1.34897719 -0.27209740 [99,] -1.97473332 -1.34897719 [100,] 0.36531238 -1.97473332 [101,] -1.73737566 0.36531238 [102,] -2.08863227 -1.73737566 [103,] -1.55652774 -2.08863227 [104,] -0.87083374 -1.55652774 [105,] -0.03017072 -0.87083374 [106,] -5.29206539 -0.03017072 [107,] -8.45276524 -5.29206539 [108,] -0.35401940 -8.45276524 [109,] 11.45209679 -0.35401940 [110,] 10.48828868 11.45209679 [111,] -0.75796343 10.48828868 [112,] -0.19920084 -0.75796343 [113,] 0.17381302 -0.19920084 [114,] -0.46379910 0.17381302 [115,] 3.27500276 -0.46379910 [116,] -0.38007270 3.27500276 [117,] 4.79896964 -0.38007270 [118,] -0.46190980 4.79896964 [119,] 0.27150466 -0.46190980 [120,] 0.09340461 0.27150466 [121,] 0.60043794 0.09340461 [122,] 0.96451215 0.60043794 [123,] -2.25813260 0.96451215 [124,] 0.21065673 -2.25813260 [125,] 3.51682508 0.21065673 [126,] -4.45108613 3.51682508 [127,] 0.09986781 -4.45108613 [128,] -2.12678082 0.09986781 [129,] -0.19195393 -2.12678082 [130,] 0.22537011 -0.19195393 [131,] -0.61309530 0.22537011 [132,] 3.80334209 -0.61309530 [133,] -1.20119215 3.80334209 [134,] 3.39250814 -1.20119215 [135,] 3.15390038 3.39250814 [136,] 3.70082818 3.15390038 [137,] 1.46173419 3.70082818 [138,] -0.37840898 1.46173419 [139,] 0.16131055 -0.37840898 [140,] 3.38900801 0.16131055 [141,] 0.09682068 3.38900801 [142,] 0.07483415 0.09682068 [143,] -6.78068365 0.07483415 [144,] -2.33359826 -6.78068365 [145,] 3.10537401 -2.33359826 [146,] -1.16195796 3.10537401 [147,] -3.22431028 -1.16195796 [148,] -0.11013996 -3.22431028 [149,] 1.26818128 -0.11013996 [150,] -1.43588435 1.26818128 [151,] 0.12060319 -1.43588435 [152,] -0.42516229 0.12060319 [153,] -7.88548426 -0.42516229 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.96522024 -0.05307656 2 5.18343267 2.96522024 3 -0.03241199 5.18343267 4 1.05419598 -0.03241199 5 -0.85339235 1.05419598 6 1.87800225 -0.85339235 7 2.45098493 1.87800225 8 -1.61348265 2.45098493 9 -0.71435776 -1.61348265 10 0.02432376 -0.71435776 11 -4.71329412 0.02432376 12 -7.68737453 -4.71329412 13 -1.24735010 -7.68737453 14 0.06460106 -1.24735010 15 0.10571754 0.06460106 16 1.80482075 0.10571754 17 -0.83090205 1.80482075 18 -0.06200069 -0.83090205 19 -1.88383059 -0.06200069 20 1.86803443 -1.88383059 21 -2.76586309 1.86803443 22 -0.12221143 -2.76586309 23 -0.16187320 -0.12221143 24 0.49887672 -0.16187320 25 0.86614488 0.49887672 26 -0.32853822 0.86614488 27 0.04511885 -0.32853822 28 -0.15843895 0.04511885 29 1.98006140 -0.15843895 30 5.97928724 1.98006140 31 7.26909531 5.97928724 32 2.96602631 7.26909531 33 0.20054371 2.96602631 34 0.11925871 0.20054371 35 0.04911941 0.11925871 36 2.21748394 0.04911941 37 0.05518298 2.21748394 38 0.80614105 0.05518298 39 1.45301101 0.80614105 40 -0.68536885 1.45301101 41 2.26509447 -0.68536885 42 -0.10586837 2.26509447 43 0.06204236 -0.10586837 44 -3.68775802 0.06204236 45 0.05015580 -3.68775802 46 -0.02935374 0.05015580 47 2.60624405 -0.02935374 48 -0.25388825 2.60624405 49 -0.11338333 -0.25388825 50 -4.50068740 -0.11338333 51 -0.93130155 -4.50068740 52 0.26641078 -0.93130155 53 -1.52329949 0.26641078 54 1.69189978 -1.52329949 55 0.04138915 1.69189978 56 -0.15224918 0.04138915 57 -3.03045270 -0.15224918 58 0.04400379 -3.03045270 59 -0.03143318 0.04400379 60 0.57967939 -0.03143318 61 0.16283585 0.57967939 62 0.01708758 0.16283585 63 0.04951744 0.01708758 64 0.09429163 0.04951744 65 -0.24908661 0.09429163 66 -0.03066889 -0.24908661 67 2.38043644 -0.03066889 68 -4.70884347 2.38043644 69 -0.37120759 -4.70884347 70 -0.54734186 -0.37120759 71 -2.43055441 -0.54734186 72 -0.57607317 -2.43055441 73 -2.63678779 -0.57607317 74 -0.54093277 -2.63678779 75 0.01931097 -0.54093277 76 1.89320571 0.01931097 77 1.27285930 1.89320571 78 -6.80839600 1.27285930 79 -0.24710651 -6.80839600 80 0.71412026 -0.24710651 81 -0.27637664 0.71412026 82 0.14851456 -0.27637664 83 2.99084248 0.14851456 84 0.41735989 2.99084248 85 2.68395454 0.41735989 86 -0.16404402 2.68395454 87 0.39394368 -0.16404402 88 -0.29099124 0.39394368 89 -0.31610479 -0.29099124 90 0.08564639 -0.31610479 91 2.60836910 0.08564639 92 0.40778069 2.60836910 93 -3.65419583 0.40778069 94 -0.39784057 -3.65419583 95 -2.70224293 -0.39784057 96 -0.07493611 -2.70224293 97 -0.27209740 -0.07493611 98 -1.34897719 -0.27209740 99 -1.97473332 -1.34897719 100 0.36531238 -1.97473332 101 -1.73737566 0.36531238 102 -2.08863227 -1.73737566 103 -1.55652774 -2.08863227 104 -0.87083374 -1.55652774 105 -0.03017072 -0.87083374 106 -5.29206539 -0.03017072 107 -8.45276524 -5.29206539 108 -0.35401940 -8.45276524 109 11.45209679 -0.35401940 110 10.48828868 11.45209679 111 -0.75796343 10.48828868 112 -0.19920084 -0.75796343 113 0.17381302 -0.19920084 114 -0.46379910 0.17381302 115 3.27500276 -0.46379910 116 -0.38007270 3.27500276 117 4.79896964 -0.38007270 118 -0.46190980 4.79896964 119 0.27150466 -0.46190980 120 0.09340461 0.27150466 121 0.60043794 0.09340461 122 0.96451215 0.60043794 123 -2.25813260 0.96451215 124 0.21065673 -2.25813260 125 3.51682508 0.21065673 126 -4.45108613 3.51682508 127 0.09986781 -4.45108613 128 -2.12678082 0.09986781 129 -0.19195393 -2.12678082 130 0.22537011 -0.19195393 131 -0.61309530 0.22537011 132 3.80334209 -0.61309530 133 -1.20119215 3.80334209 134 3.39250814 -1.20119215 135 3.15390038 3.39250814 136 3.70082818 3.15390038 137 1.46173419 3.70082818 138 -0.37840898 1.46173419 139 0.16131055 -0.37840898 140 3.38900801 0.16131055 141 0.09682068 3.38900801 142 0.07483415 0.09682068 143 -6.78068365 0.07483415 144 -2.33359826 -6.78068365 145 3.10537401 -2.33359826 146 -1.16195796 3.10537401 147 -3.22431028 -1.16195796 148 -0.11013996 -3.22431028 149 1.26818128 -0.11013996 150 -1.43588435 1.26818128 151 0.12060319 -1.43588435 152 -0.42516229 0.12060319 153 -7.88548426 -0.42516229 > 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/794h01292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11ynwb1292167472.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/12j5dh1292167472.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/13q7d31292167473.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/14b8t91292167473.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/15mhtu1292167473.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/1609831292167473.tab") + } > > try(system("convert tmp/1vcjr1292167472.ps tmp/1vcjr1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/263ic1292167472.ps tmp/263ic1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/363ic1292167472.ps tmp/363ic1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/4zdif1292167472.ps tmp/4zdif1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/5zdif1292167472.ps tmp/5zdif1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/6zdif1292167472.ps tmp/6zdif1292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/794h01292167472.ps tmp/794h01292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/8kdy31292167472.ps tmp/8kdy31292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/9kdy31292167472.ps tmp/9kdy31292167472.png",intern=TRUE)) character(0) > try(system("convert tmp/10kdy31292167472.ps tmp/10kdy31292167472.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.832 1.784 12.167