R version 2.12.1 (2010-12-16) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) 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(3 + ,2 + ,3 + ,3 + ,3 + ,7 + ,6 + ,5 + ,6 + ,0 + ,7 + ,7 + ,2 + ,7 + ,6 + ,6 + ,0 + ,6 + ,8 + ,3 + ,8 + ,6 + ,6 + ,6 + ,6 + ,9 + ,8 + ,8 + ,7 + ,8 + ,5 + ,5 + ,5 + ,7 + ,9 + ,3 + ,1 + ,0 + ,7 + ,7 + ,7 + ,8 + ,8 + ,9 + ,8 + ,8 + ,8 + ,9 + ,8 + ,4 + ,4 + ,0 + ,2 + ,3 + ,2 + ,7 + ,7 + ,7 + ,0 + ,4 + ,8 + ,4 + ,7 + ,4 + ,4 + ,9 + ,9 + ,4 + ,4 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,6 + ,6 + ,4 + ,4 + ,7 + ,7 + ,7 + ,5 + ,5 + ,8 + ,9 + ,5 + ,4 + ,5 + ,4 + ,4 + ,8 + ,8 + ,8 + ,6 + ,6 + ,0 + ,2 + ,2 + ,7 + ,5 + ,5 + ,5 + ,0 + ,4 + ,9 + ,4 + ,4 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,9 + ,9 + ,9 + ,6 + ,6 + ,8 + ,8 + ,8 + ,4 + ,4 + ,0 + ,4 + ,8 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,6 + ,2 + ,5 + ,5 + ,5 + ,5 + ,2 + ,6 + ,7 + ,7 + ,7 + ,7 + ,7 + ,9 + ,7 + ,5 + ,5 + ,5 + ,5 + ,3 + ,3 + ,3 + ,9 + ,9 + ,4 + ,4 + ,4 + ,4 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,7 + ,3 + ,0 + ,7 + ,9 + ,7 + ,7 + ,3 + ,3 + ,1 + 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,6 + ,6 + ,1 + ,1 + ,0 + ,5 + ,9 + ,5 + ,6 + ,4 + ,5 + ,4 + ,4 + ,3 + ,3 + ,6 + ,4 + ,2 + ,7 + ,7 + ,7 + ,2 + ,7 + ,6 + ,6 + ,0 + ,6 + ,6 + ,6 + ,7 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,9 + ,9 + ,2 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,9 + ,6 + ,6 + ,8 + ,8 + ,8 + ,8 + ,8 + ,9 + ,6 + ,7 + ,7 + ,2 + ,2 + ,4 + ,4 + ,4 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,0 + ,9 + ,0 + ,4 + ,4 + ,4 + ,8 + ,6 + ,2 + ,0 + ,6 + ,8 + ,7 + ,7 + ,6 + ,6 + ,5 + ,5 + ,5 + ,5 + ,9 + ,5 + ,5 + ,0 + ,2 + ,9 + ,2 + ,6) + ,dim=c(7 + ,156) + ,dimnames=list(c('Sport' + ,'GoingOut' + ,'Relation' + ,'Family' + ,'Friends' + ,'Coach' + ,'Job') + ,1:156)) > y <- array(NA,dim=c(7,156),dimnames=list(c('Sport','GoingOut','Relation','Family','Friends','Coach','Job'),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 > 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 Sport GoingOut Relation Family Friends Coach Job 1 3 2 3 3 3 7 6 2 5 6 0 7 7 2 7 3 6 6 0 6 8 3 8 4 6 6 6 6 9 8 8 5 7 8 5 5 5 7 9 6 3 1 0 7 7 7 8 7 8 9 8 8 8 9 8 8 4 4 0 2 3 2 7 9 7 7 0 4 8 4 7 10 4 4 9 9 4 4 4 11 6 6 6 6 6 6 6 12 6 5 6 6 4 4 7 13 7 7 5 5 8 9 5 14 4 5 4 4 8 8 8 15 6 6 0 2 2 7 5 16 5 5 0 4 9 4 4 17 0 2 2 2 2 2 9 18 9 9 6 6 8 8 8 19 4 4 0 4 8 4 4 20 4 4 4 4 4 4 6 21 2 5 5 5 5 2 6 22 7 7 7 7 7 9 7 23 5 5 5 5 3 3 3 24 9 9 4 4 4 4 4 25 6 6 6 6 6 6 6 26 6 6 6 6 6 6 6 27 7 3 0 7 9 7 7 28 3 3 1 2 2 2 5 29 6 5 0 6 6 6 8 30 6 5 4 4 4 4 6 31 4 4 4 4 8 2 4 32 7 7 7 7 3 9 9 33 7 6 7 7 7 7 7 34 7 7 0 4 4 4 4 35 4 4 4 4 4 4 6 36 5 5 5 5 8 7 8 37 6 6 0 6 6 6 6 38 5 5 5 5 5 5 5 39 6 0 1 6 6 6 6 40 6 6 2 2 9 2 6 41 6 5 0 6 4 2 4 42 3 3 9 9 7 7 7 43 3 3 3 3 3 3 9 44 3 3 0 4 4 4 8 45 6 7 6 6 6 6 6 46 7 7 1 5 8 5 6 47 5 1 5 5 5 7 5 48 5 5 0 4 4 4 7 49 5 5 0 2 2 2 5 50 6 6 0 6 9 6 8 51 6 2 6 6 6 9 6 52 6 6 7 7 8 8 8 53 5 5 0 5 5 5 5 54 4 2 4 4 4 4 4 55 7 7 5 5 5 2 5 56 5 5 1 5 9 9 6 57 3 3 4 4 4 4 4 58 6 6 9 9 8 6 6 59 2 2 2 2 2 2 9 60 8 8 8 8 8 8 7 61 3 5 3 3 3 3 3 62 0 2 1 6 3 3 6 63 6 6 0 6 6 7 6 64 8 2 6 6 6 2 6 65 4 1 0 5 5 9 5 66 5 5 0 5 5 5 5 67 6 6 6 6 4 4 5 68 5 2 2 2 9 2 9 69 6 6 1 6 6 6 8 70 2 2 5 5 5 5 5 71 6 6 5 5 5 5 6 72 5 5 5 5 3 9 7 73 5 0 5 5 8 2 5 74 6 2 6 6 9 6 6 75 4 4 6 6 6 6 6 76 6 1 0 9 6 6 6 77 5 5 0 5 5 5 6 78 5 5 1 5 3 3 9 79 4 2 7 7 4 2 7 80 2 2 2 2 9 2 9 81 7 7 4 4 4 4 4 82 5 5 0 6 8 8 8 83 6 2 5 5 5 5 5 84 5 5 5 5 5 9 8 85 3 3 3 3 8 2 9 86 6 6 0 6 6 6 6 87 4 1 4 4 9 4 4 88 5 5 9 9 5 5 7 89 7 7 0 8 8 8 8 90 4 2 4 4 3 3 9 91 6 6 2 2 2 2 9 92 8 8 7 7 7 7 7 93 7 7 7 7 7 7 8 94 6 6 6 6 4 9 4 95 7 7 0 5 5 5 6 96 4 4 5 5 9 5 7 97 0 5 6 6 6 2 6 98 3 2 0 3 3 3 7 99 5 5 5 5 5 5 5 100 6 2 9 9 2 2 9 101 5 5 0 7 7 7 7 102 7 7 7 7 7 7 7 103 6 5 1 6 6 6 6 104 8 8 3 3 8 3 6 105 7 2 7 7 9 3 9 106 8 8 8 8 8 2 9 107 3 3 0 3 3 3 8 108 8 2 5 5 5 5 8 109 3 3 3 3 3 3 3 110 4 5 0 4 4 4 6 111 2 2 5 5 5 5 5 112 7 2 7 7 9 7 7 113 6 6 0 6 6 6 6 114 2 2 0 7 7 7 7 115 7 7 0 9 7 2 7 116 6 6 6 6 6 6 6 117 6 2 0 6 3 9 8 118 6 2 6 6 9 4 9 119 6 5 6 6 6 6 6 120 6 6 2 2 2 2 9 121 4 4 5 5 5 2 5 122 5 5 0 5 5 5 6 123 7 7 4 4 9 4 4 124 6 6 0 7 7 7 7 125 6 6 6 6 6 6 6 126 5 5 5 5 8 7 8 127 8 2 8 8 8 8 8 128 6 6 6 6 6 6 9 129 0 3 5 5 3 3 8 130 4 2 0 4 4 4 4 131 8 8 8 8 9 8 6 132 6 6 0 6 6 9 6 133 4 4 9 9 4 2 7 134 6 6 5 5 5 5 9 135 2 5 0 6 6 6 8 136 4 4 0 4 4 4 4 137 6 2 0 6 6 6 6 138 3 3 3 3 3 3 9 139 6 6 6 6 6 6 6 140 5 5 0 5 5 5 5 141 4 4 4 4 9 8 8 142 6 6 6 6 6 6 6 143 1 1 0 5 9 5 6 144 4 5 4 4 3 3 6 145 4 2 7 7 7 2 7 146 6 6 0 6 6 6 7 147 5 5 5 5 5 5 9 148 9 2 6 6 6 6 6 149 6 6 6 6 9 6 6 150 8 8 8 8 8 9 6 151 7 7 2 2 4 4 4 152 7 7 7 7 7 7 7 153 0 9 0 4 4 4 8 154 6 2 0 6 8 7 7 155 6 6 5 5 5 5 9 156 5 5 0 2 9 2 6 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) GoingOut Relation Family Friends Coach 1.39978 0.36802 0.06355 0.14513 0.15296 0.12801 Job -0.06668 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.8829 -0.6032 -0.0059 0.7751 4.3264 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.39978 0.66606 2.102 0.0373 * GoingOut 0.36802 0.05937 6.199 5.31e-09 *** Relation 0.06355 0.04636 1.371 0.1725 Family 0.14513 0.08704 1.667 0.0975 . Friends 0.15296 0.06294 2.430 0.0163 * Coach 0.12801 0.06366 2.011 0.0462 * Job -0.06668 0.07735 -0.862 0.3901 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.496 on 149 degrees of freedom Multiple R-squared: 0.383, Adjusted R-squared: 0.3581 F-statistic: 15.41 on 6 and 149 DF, p-value: 1.057e-13 > 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,] 3.100928e-02 6.201855e-02 0.9689907 [2,] 8.198958e-03 1.639792e-02 0.9918010 [3,] 2.024892e-02 4.049785e-02 0.9797511 [4,] 8.239205e-03 1.647841e-02 0.9917608 [5,] 2.271443e-02 4.542886e-02 0.9772856 [6,] 9.883672e-03 1.976734e-02 0.9901163 [7,] 4.008534e-03 8.017067e-03 0.9959915 [8,] 1.745416e-02 3.490831e-02 0.9825458 [9,] 1.268034e-02 2.536069e-02 0.9873197 [10,] 6.747602e-03 1.349520e-02 0.9932524 [11,] 3.411032e-03 6.822065e-03 0.9965890 [12,] 1.371751e-02 2.743501e-02 0.9862825 [13,] 7.559548e-03 1.511910e-02 0.9924405 [14,] 4.288497e-03 8.576994e-03 0.9957115 [15,] 3.624009e-03 7.248019e-03 0.9963760 [16,] 1.951467e-03 3.902935e-03 0.9980485 [17,] 1.021335e-03 2.042669e-03 0.9989787 [18,] 5.491051e-03 1.098210e-02 0.9945089 [19,] 3.367736e-03 6.735471e-03 0.9966323 [20,] 1.949739e-03 3.899477e-03 0.9980503 [21,] 3.069395e-03 6.138790e-03 0.9969306 [22,] 2.405566e-03 4.811133e-03 0.9975944 [23,] 1.388499e-03 2.776999e-03 0.9986115 [24,] 1.170230e-03 2.340460e-03 0.9988298 [25,] 7.573090e-04 1.514618e-03 0.9992427 [26,] 4.289117e-04 8.578234e-04 0.9995711 [27,] 2.386973e-04 4.773945e-04 0.9997613 [28,] 2.098446e-04 4.196892e-04 0.9997902 [29,] 1.116982e-04 2.233964e-04 0.9998883 [30,] 2.997351e-03 5.994702e-03 0.9970026 [31,] 3.687575e-03 7.375149e-03 0.9963124 [32,] 2.553708e-03 5.107416e-03 0.9974463 [33,] 3.345367e-03 6.690734e-03 0.9966546 [34,] 2.365421e-03 4.730842e-03 0.9976346 [35,] 1.697085e-03 3.394170e-03 0.9983029 [36,] 1.152943e-03 2.305886e-03 0.9988471 [37,] 7.406707e-04 1.481341e-03 0.9992593 [38,] 1.058510e-03 2.117019e-03 0.9989415 [39,] 6.680093e-04 1.336019e-03 0.9993320 [40,] 4.779727e-04 9.559454e-04 0.9995220 [41,] 3.125949e-04 6.251897e-04 0.9996874 [42,] 3.357108e-04 6.714216e-04 0.9996643 [43,] 2.116121e-04 4.232242e-04 0.9997884 [44,] 1.782105e-04 3.564210e-04 0.9998218 [45,] 1.129239e-04 2.258478e-04 0.9998871 [46,] 1.356341e-04 2.712682e-04 0.9998644 [47,] 1.999379e-04 3.998758e-04 0.9998001 [48,] 1.713370e-04 3.426740e-04 0.9998287 [49,] 1.138896e-04 2.277792e-04 0.9998861 [50,] 7.056467e-05 1.411293e-04 0.9999294 [51,] 4.287552e-05 8.575105e-05 0.9999571 [52,] 7.786411e-05 1.557282e-04 0.9999221 [53,] 9.982755e-04 1.996551e-03 0.9990017 [54,] 7.352139e-04 1.470428e-03 0.9992648 [55,] 3.157201e-02 6.314402e-02 0.9684280 [56,] 2.382591e-02 4.765182e-02 0.9761741 [57,] 1.810418e-02 3.620837e-02 0.9818958 [58,] 1.355463e-02 2.710925e-02 0.9864454 [59,] 1.597351e-02 3.194703e-02 0.9840265 [60,] 1.180906e-02 2.361811e-02 0.9881909 [61,] 1.602300e-02 3.204601e-02 0.9839770 [62,] 1.194544e-02 2.389088e-02 0.9880546 [63,] 9.025426e-03 1.805085e-02 0.9909746 [64,] 1.091869e-02 2.183738e-02 0.9890813 [65,] 9.403825e-03 1.880765e-02 0.9905962 [66,] 8.932694e-03 1.786539e-02 0.9910673 [67,] 1.043088e-02 2.086176e-02 0.9895691 [68,] 7.620030e-03 1.524006e-02 0.9923800 [69,] 5.980293e-03 1.196059e-02 0.9940197 [70,] 4.378393e-03 8.756785e-03 0.9956216 [71,] 4.373084e-03 8.746169e-03 0.9956269 [72,] 4.039307e-03 8.078615e-03 0.9959607 [73,] 3.240396e-03 6.480793e-03 0.9967596 [74,] 4.001629e-03 8.003258e-03 0.9959984 [75,] 3.123633e-03 6.247267e-03 0.9968764 [76,] 2.494755e-03 4.989509e-03 0.9975052 [77,] 1.770933e-03 3.541867e-03 0.9982291 [78,] 1.206622e-03 2.413244e-03 0.9987934 [79,] 9.513900e-04 1.902780e-03 0.9990486 [80,] 6.438852e-04 1.287770e-03 0.9993561 [81,] 5.186840e-04 1.037368e-03 0.9994813 [82,] 7.415918e-04 1.483184e-03 0.9992584 [83,] 5.365642e-04 1.073128e-03 0.9994634 [84,] 3.517294e-04 7.034588e-04 0.9996483 [85,] 2.352944e-04 4.705887e-04 0.9997647 [86,] 2.287995e-04 4.575989e-04 0.9997712 [87,] 2.268084e-04 4.536168e-04 0.9997732 [88,] 1.528180e-02 3.056359e-02 0.9847182 [89,] 1.119441e-02 2.238883e-02 0.9888056 [90,] 8.271388e-03 1.654278e-02 0.9917286 [91,] 1.124276e-02 2.248553e-02 0.9887572 [92,] 8.739069e-03 1.747814e-02 0.9912609 [93,] 6.246055e-03 1.249211e-02 0.9937539 [94,] 4.685580e-03 9.371159e-03 0.9953144 [95,] 6.029572e-03 1.205914e-02 0.9939704 [96,] 8.638720e-03 1.727744e-02 0.9913613 [97,] 8.291579e-03 1.658316e-02 0.9917084 [98,] 5.924466e-03 1.184893e-02 0.9940755 [99,] 3.095794e-02 6.191588e-02 0.9690421 [100,] 2.556240e-02 5.112480e-02 0.9744376 [101,] 1.944233e-02 3.888467e-02 0.9805577 [102,] 3.616851e-02 7.233702e-02 0.9638315 [103,] 3.349129e-02 6.698258e-02 0.9665087 [104,] 2.609139e-02 5.218278e-02 0.9739086 [105,] 4.801486e-02 9.602972e-02 0.9519851 [106,] 1.021749e-01 2.043499e-01 0.8978251 [107,] 8.120280e-02 1.624056e-01 0.9187972 [108,] 7.516763e-02 1.503353e-01 0.9248324 [109,] 7.781943e-02 1.556389e-01 0.9221806 [110,] 6.055186e-02 1.211037e-01 0.9394481 [111,] 1.441374e-01 2.882748e-01 0.8558626 [112,] 1.175270e-01 2.350540e-01 0.8824730 [113,] 9.690616e-02 1.938123e-01 0.9030938 [114,] 7.920206e-02 1.584041e-01 0.9207979 [115,] 7.443462e-02 1.488692e-01 0.9255654 [116,] 5.708291e-02 1.141658e-01 0.9429171 [117,] 4.799580e-02 9.599160e-02 0.9520042 [118,] 4.740796e-02 9.481592e-02 0.9525920 [119,] 3.808138e-02 7.616276e-02 0.9619186 [120,] 1.759541e-01 3.519082e-01 0.8240459 [121,] 1.521614e-01 3.043228e-01 0.8478386 [122,] 1.173289e-01 2.346579e-01 0.8826711 [123,] 8.661349e-02 1.732270e-01 0.9133865 [124,] 7.001829e-02 1.400366e-01 0.9299817 [125,] 6.758827e-02 1.351765e-01 0.9324117 [126,] 7.683351e-02 1.536670e-01 0.9231665 [127,] 6.963908e-02 1.392782e-01 0.9303609 [128,] 5.592501e-02 1.118500e-01 0.9440750 [129,] 3.754666e-02 7.509332e-02 0.9624533 [130,] 2.535082e-02 5.070163e-02 0.9746492 [131,] 1.499819e-02 2.999639e-02 0.9850018 [132,] 3.603794e-02 7.207589e-02 0.9639621 [133,] 2.309186e-02 4.618372e-02 0.9769081 [134,] 4.394532e-01 8.789065e-01 0.5605468 [135,] 3.708668e-01 7.417336e-01 0.6291332 [136,] 2.795462e-01 5.590923e-01 0.7204538 [137,] 6.540815e-01 6.918371e-01 0.3459185 > postscript(file="/var/www/rcomp/tmp/1eapj1321957431.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/21l6e1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3j2bj1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4n3uz1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5xly61321957431.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 -0.716737138 -0.483779130 0.447063397 -0.727259089 0.551911792 -1.217063541 7 8 9 10 11 12 -0.223733121 0.589748741 1.174616454 -1.607156300 -0.145722638 0.850915400 13 14 15 16 17 18 -0.061689153 -1.788916715 1.233252460 -0.442346453 -2.514997797 1.321644000 19 20 21 22 23 24 -0.921369842 -0.430380936 -2.904017712 -0.192735727 0.073845954 2.596166204 25 26 27 28 29 30 -0.145722638 -0.145722638 1.674302757 -0.086186850 0.736964164 1.201600684 31 32 33 34 35 36 -0.919552100 0.552458154 0.431305370 1.586407940 -0.430380936 -0.869588238 37 38 39 40 41 42 0.235584825 -0.354732266 2.380143864 0.742179629 1.288204143 -2.882005251 43 44 45 46 47 48 -0.372668648 -0.674796622 -0.513741019 0.771241735 0.861318539 0.522486138 49 50 51 52 53 54 1.241327633 -0.089928909 0.942316809 -0.782983740 -0.036976047 0.172294867 55 56 57 58 59 60 1.293265048 -1.157725169 -1.195723514 -1.077687743 -0.514997797 0.205616139 61 62 63 64 65 66 -1.508788283 -3.512984129 0.107573467 3.838396318 -0.076947959 -0.036976047 67 68 69 70 71 72 0.349536061 1.414294588 0.305394539 -2.250677125 0.343929833 -0.427500280 73 74 75 76 77 78 1.410519020 0.867476192 -1.409685877 1.640281815 0.029704432 0.728133804 79 80 81 82 83 84 0.002310377 -1.585705412 1.332202965 -0.824975015 1.749322875 -0.666736263 85 86 87 88 89 90 -1.009448443 0.235584825 -0.224477906 -1.056102833 0.148724949 0.786666851 91 92 93 94 95 96 2.012928681 0.695268609 0.129967469 -0.357201210 1.293667671 -1.465185851 97 98 99 100 101 102 -5.265658824 0.052642506 -0.354732266 2.024222034 -0.755817542 0.063286990 103 104 105 106 107 108 0.540051962 1.822406859 2.242868823 1.107045248 -0.248695395 3.949364313 109 110 111 112 113 114 -0.772751522 -0.544194341 -2.250677125 1.597462431 0.235584825 -2.651762401 115 116 117 118 119 120 0.857939214 -0.145722638 1.915859922 1.323540346 0.222295742 2.012928681 121 122 123 124 125 126 -0.602679810 0.029704432 0.567411811 -0.123835923 -0.145722638 -0.869588238 127 128 129 130 131 132 2.480406901 0.054318799 -3.856714889 0.426499842 -0.014022571 -0.148449250 133 134 135 136 137 138 -1.151092146 0.543971270 -3.263035836 -0.309536919 1.707658347 -0.372668648 139 140 141 142 143 144 -0.145722638 -0.036976047 -1.573856566 -0.145722638 -3.110054969 -0.517429727 145 146 147 148 149 150 -0.456564315 0.302265304 -0.088010350 4.326350884 -0.604597330 0.010924301 151 152 153 154 155 156 1.749568727 0.063286990 -5.882906905 1.340411006 0.543971270 0.237300497 > postscript(file="/var/www/rcomp/tmp/6y3gk1321957431.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 -0.716737138 NA 1 -0.483779130 -0.716737138 2 0.447063397 -0.483779130 3 -0.727259089 0.447063397 4 0.551911792 -0.727259089 5 -1.217063541 0.551911792 6 -0.223733121 -1.217063541 7 0.589748741 -0.223733121 8 1.174616454 0.589748741 9 -1.607156300 1.174616454 10 -0.145722638 -1.607156300 11 0.850915400 -0.145722638 12 -0.061689153 0.850915400 13 -1.788916715 -0.061689153 14 1.233252460 -1.788916715 15 -0.442346453 1.233252460 16 -2.514997797 -0.442346453 17 1.321644000 -2.514997797 18 -0.921369842 1.321644000 19 -0.430380936 -0.921369842 20 -2.904017712 -0.430380936 21 -0.192735727 -2.904017712 22 0.073845954 -0.192735727 23 2.596166204 0.073845954 24 -0.145722638 2.596166204 25 -0.145722638 -0.145722638 26 1.674302757 -0.145722638 27 -0.086186850 1.674302757 28 0.736964164 -0.086186850 29 1.201600684 0.736964164 30 -0.919552100 1.201600684 31 0.552458154 -0.919552100 32 0.431305370 0.552458154 33 1.586407940 0.431305370 34 -0.430380936 1.586407940 35 -0.869588238 -0.430380936 36 0.235584825 -0.869588238 37 -0.354732266 0.235584825 38 2.380143864 -0.354732266 39 0.742179629 2.380143864 40 1.288204143 0.742179629 41 -2.882005251 1.288204143 42 -0.372668648 -2.882005251 43 -0.674796622 -0.372668648 44 -0.513741019 -0.674796622 45 0.771241735 -0.513741019 46 0.861318539 0.771241735 47 0.522486138 0.861318539 48 1.241327633 0.522486138 49 -0.089928909 1.241327633 50 0.942316809 -0.089928909 51 -0.782983740 0.942316809 52 -0.036976047 -0.782983740 53 0.172294867 -0.036976047 54 1.293265048 0.172294867 55 -1.157725169 1.293265048 56 -1.195723514 -1.157725169 57 -1.077687743 -1.195723514 58 -0.514997797 -1.077687743 59 0.205616139 -0.514997797 60 -1.508788283 0.205616139 61 -3.512984129 -1.508788283 62 0.107573467 -3.512984129 63 3.838396318 0.107573467 64 -0.076947959 3.838396318 65 -0.036976047 -0.076947959 66 0.349536061 -0.036976047 67 1.414294588 0.349536061 68 0.305394539 1.414294588 69 -2.250677125 0.305394539 70 0.343929833 -2.250677125 71 -0.427500280 0.343929833 72 1.410519020 -0.427500280 73 0.867476192 1.410519020 74 -1.409685877 0.867476192 75 1.640281815 -1.409685877 76 0.029704432 1.640281815 77 0.728133804 0.029704432 78 0.002310377 0.728133804 79 -1.585705412 0.002310377 80 1.332202965 -1.585705412 81 -0.824975015 1.332202965 82 1.749322875 -0.824975015 83 -0.666736263 1.749322875 84 -1.009448443 -0.666736263 85 0.235584825 -1.009448443 86 -0.224477906 0.235584825 87 -1.056102833 -0.224477906 88 0.148724949 -1.056102833 89 0.786666851 0.148724949 90 2.012928681 0.786666851 91 0.695268609 2.012928681 92 0.129967469 0.695268609 93 -0.357201210 0.129967469 94 1.293667671 -0.357201210 95 -1.465185851 1.293667671 96 -5.265658824 -1.465185851 97 0.052642506 -5.265658824 98 -0.354732266 0.052642506 99 2.024222034 -0.354732266 100 -0.755817542 2.024222034 101 0.063286990 -0.755817542 102 0.540051962 0.063286990 103 1.822406859 0.540051962 104 2.242868823 1.822406859 105 1.107045248 2.242868823 106 -0.248695395 1.107045248 107 3.949364313 -0.248695395 108 -0.772751522 3.949364313 109 -0.544194341 -0.772751522 110 -2.250677125 -0.544194341 111 1.597462431 -2.250677125 112 0.235584825 1.597462431 113 -2.651762401 0.235584825 114 0.857939214 -2.651762401 115 -0.145722638 0.857939214 116 1.915859922 -0.145722638 117 1.323540346 1.915859922 118 0.222295742 1.323540346 119 2.012928681 0.222295742 120 -0.602679810 2.012928681 121 0.029704432 -0.602679810 122 0.567411811 0.029704432 123 -0.123835923 0.567411811 124 -0.145722638 -0.123835923 125 -0.869588238 -0.145722638 126 2.480406901 -0.869588238 127 0.054318799 2.480406901 128 -3.856714889 0.054318799 129 0.426499842 -3.856714889 130 -0.014022571 0.426499842 131 -0.148449250 -0.014022571 132 -1.151092146 -0.148449250 133 0.543971270 -1.151092146 134 -3.263035836 0.543971270 135 -0.309536919 -3.263035836 136 1.707658347 -0.309536919 137 -0.372668648 1.707658347 138 -0.145722638 -0.372668648 139 -0.036976047 -0.145722638 140 -1.573856566 -0.036976047 141 -0.145722638 -1.573856566 142 -3.110054969 -0.145722638 143 -0.517429727 -3.110054969 144 -0.456564315 -0.517429727 145 0.302265304 -0.456564315 146 -0.088010350 0.302265304 147 4.326350884 -0.088010350 148 -0.604597330 4.326350884 149 0.010924301 -0.604597330 150 1.749568727 0.010924301 151 0.063286990 1.749568727 152 -5.882906905 0.063286990 153 1.340411006 -5.882906905 154 0.543971270 1.340411006 155 0.237300497 0.543971270 156 NA 0.237300497 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.483779130 -0.716737138 [2,] 0.447063397 -0.483779130 [3,] -0.727259089 0.447063397 [4,] 0.551911792 -0.727259089 [5,] -1.217063541 0.551911792 [6,] -0.223733121 -1.217063541 [7,] 0.589748741 -0.223733121 [8,] 1.174616454 0.589748741 [9,] -1.607156300 1.174616454 [10,] -0.145722638 -1.607156300 [11,] 0.850915400 -0.145722638 [12,] -0.061689153 0.850915400 [13,] -1.788916715 -0.061689153 [14,] 1.233252460 -1.788916715 [15,] -0.442346453 1.233252460 [16,] -2.514997797 -0.442346453 [17,] 1.321644000 -2.514997797 [18,] -0.921369842 1.321644000 [19,] -0.430380936 -0.921369842 [20,] -2.904017712 -0.430380936 [21,] -0.192735727 -2.904017712 [22,] 0.073845954 -0.192735727 [23,] 2.596166204 0.073845954 [24,] -0.145722638 2.596166204 [25,] -0.145722638 -0.145722638 [26,] 1.674302757 -0.145722638 [27,] -0.086186850 1.674302757 [28,] 0.736964164 -0.086186850 [29,] 1.201600684 0.736964164 [30,] -0.919552100 1.201600684 [31,] 0.552458154 -0.919552100 [32,] 0.431305370 0.552458154 [33,] 1.586407940 0.431305370 [34,] -0.430380936 1.586407940 [35,] -0.869588238 -0.430380936 [36,] 0.235584825 -0.869588238 [37,] -0.354732266 0.235584825 [38,] 2.380143864 -0.354732266 [39,] 0.742179629 2.380143864 [40,] 1.288204143 0.742179629 [41,] -2.882005251 1.288204143 [42,] -0.372668648 -2.882005251 [43,] -0.674796622 -0.372668648 [44,] -0.513741019 -0.674796622 [45,] 0.771241735 -0.513741019 [46,] 0.861318539 0.771241735 [47,] 0.522486138 0.861318539 [48,] 1.241327633 0.522486138 [49,] -0.089928909 1.241327633 [50,] 0.942316809 -0.089928909 [51,] -0.782983740 0.942316809 [52,] -0.036976047 -0.782983740 [53,] 0.172294867 -0.036976047 [54,] 1.293265048 0.172294867 [55,] -1.157725169 1.293265048 [56,] -1.195723514 -1.157725169 [57,] -1.077687743 -1.195723514 [58,] -0.514997797 -1.077687743 [59,] 0.205616139 -0.514997797 [60,] -1.508788283 0.205616139 [61,] -3.512984129 -1.508788283 [62,] 0.107573467 -3.512984129 [63,] 3.838396318 0.107573467 [64,] -0.076947959 3.838396318 [65,] -0.036976047 -0.076947959 [66,] 0.349536061 -0.036976047 [67,] 1.414294588 0.349536061 [68,] 0.305394539 1.414294588 [69,] -2.250677125 0.305394539 [70,] 0.343929833 -2.250677125 [71,] -0.427500280 0.343929833 [72,] 1.410519020 -0.427500280 [73,] 0.867476192 1.410519020 [74,] -1.409685877 0.867476192 [75,] 1.640281815 -1.409685877 [76,] 0.029704432 1.640281815 [77,] 0.728133804 0.029704432 [78,] 0.002310377 0.728133804 [79,] -1.585705412 0.002310377 [80,] 1.332202965 -1.585705412 [81,] -0.824975015 1.332202965 [82,] 1.749322875 -0.824975015 [83,] -0.666736263 1.749322875 [84,] -1.009448443 -0.666736263 [85,] 0.235584825 -1.009448443 [86,] -0.224477906 0.235584825 [87,] -1.056102833 -0.224477906 [88,] 0.148724949 -1.056102833 [89,] 0.786666851 0.148724949 [90,] 2.012928681 0.786666851 [91,] 0.695268609 2.012928681 [92,] 0.129967469 0.695268609 [93,] -0.357201210 0.129967469 [94,] 1.293667671 -0.357201210 [95,] -1.465185851 1.293667671 [96,] -5.265658824 -1.465185851 [97,] 0.052642506 -5.265658824 [98,] -0.354732266 0.052642506 [99,] 2.024222034 -0.354732266 [100,] -0.755817542 2.024222034 [101,] 0.063286990 -0.755817542 [102,] 0.540051962 0.063286990 [103,] 1.822406859 0.540051962 [104,] 2.242868823 1.822406859 [105,] 1.107045248 2.242868823 [106,] -0.248695395 1.107045248 [107,] 3.949364313 -0.248695395 [108,] -0.772751522 3.949364313 [109,] -0.544194341 -0.772751522 [110,] -2.250677125 -0.544194341 [111,] 1.597462431 -2.250677125 [112,] 0.235584825 1.597462431 [113,] -2.651762401 0.235584825 [114,] 0.857939214 -2.651762401 [115,] -0.145722638 0.857939214 [116,] 1.915859922 -0.145722638 [117,] 1.323540346 1.915859922 [118,] 0.222295742 1.323540346 [119,] 2.012928681 0.222295742 [120,] -0.602679810 2.012928681 [121,] 0.029704432 -0.602679810 [122,] 0.567411811 0.029704432 [123,] -0.123835923 0.567411811 [124,] -0.145722638 -0.123835923 [125,] -0.869588238 -0.145722638 [126,] 2.480406901 -0.869588238 [127,] 0.054318799 2.480406901 [128,] -3.856714889 0.054318799 [129,] 0.426499842 -3.856714889 [130,] -0.014022571 0.426499842 [131,] -0.148449250 -0.014022571 [132,] -1.151092146 -0.148449250 [133,] 0.543971270 -1.151092146 [134,] -3.263035836 0.543971270 [135,] -0.309536919 -3.263035836 [136,] 1.707658347 -0.309536919 [137,] -0.372668648 1.707658347 [138,] -0.145722638 -0.372668648 [139,] -0.036976047 -0.145722638 [140,] -1.573856566 -0.036976047 [141,] -0.145722638 -1.573856566 [142,] -3.110054969 -0.145722638 [143,] -0.517429727 -3.110054969 [144,] -0.456564315 -0.517429727 [145,] 0.302265304 -0.456564315 [146,] -0.088010350 0.302265304 [147,] 4.326350884 -0.088010350 [148,] -0.604597330 4.326350884 [149,] 0.010924301 -0.604597330 [150,] 1.749568727 0.010924301 [151,] 0.063286990 1.749568727 [152,] -5.882906905 0.063286990 [153,] 1.340411006 -5.882906905 [154,] 0.543971270 1.340411006 [155,] 0.237300497 0.543971270 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.483779130 -0.716737138 2 0.447063397 -0.483779130 3 -0.727259089 0.447063397 4 0.551911792 -0.727259089 5 -1.217063541 0.551911792 6 -0.223733121 -1.217063541 7 0.589748741 -0.223733121 8 1.174616454 0.589748741 9 -1.607156300 1.174616454 10 -0.145722638 -1.607156300 11 0.850915400 -0.145722638 12 -0.061689153 0.850915400 13 -1.788916715 -0.061689153 14 1.233252460 -1.788916715 15 -0.442346453 1.233252460 16 -2.514997797 -0.442346453 17 1.321644000 -2.514997797 18 -0.921369842 1.321644000 19 -0.430380936 -0.921369842 20 -2.904017712 -0.430380936 21 -0.192735727 -2.904017712 22 0.073845954 -0.192735727 23 2.596166204 0.073845954 24 -0.145722638 2.596166204 25 -0.145722638 -0.145722638 26 1.674302757 -0.145722638 27 -0.086186850 1.674302757 28 0.736964164 -0.086186850 29 1.201600684 0.736964164 30 -0.919552100 1.201600684 31 0.552458154 -0.919552100 32 0.431305370 0.552458154 33 1.586407940 0.431305370 34 -0.430380936 1.586407940 35 -0.869588238 -0.430380936 36 0.235584825 -0.869588238 37 -0.354732266 0.235584825 38 2.380143864 -0.354732266 39 0.742179629 2.380143864 40 1.288204143 0.742179629 41 -2.882005251 1.288204143 42 -0.372668648 -2.882005251 43 -0.674796622 -0.372668648 44 -0.513741019 -0.674796622 45 0.771241735 -0.513741019 46 0.861318539 0.771241735 47 0.522486138 0.861318539 48 1.241327633 0.522486138 49 -0.089928909 1.241327633 50 0.942316809 -0.089928909 51 -0.782983740 0.942316809 52 -0.036976047 -0.782983740 53 0.172294867 -0.036976047 54 1.293265048 0.172294867 55 -1.157725169 1.293265048 56 -1.195723514 -1.157725169 57 -1.077687743 -1.195723514 58 -0.514997797 -1.077687743 59 0.205616139 -0.514997797 60 -1.508788283 0.205616139 61 -3.512984129 -1.508788283 62 0.107573467 -3.512984129 63 3.838396318 0.107573467 64 -0.076947959 3.838396318 65 -0.036976047 -0.076947959 66 0.349536061 -0.036976047 67 1.414294588 0.349536061 68 0.305394539 1.414294588 69 -2.250677125 0.305394539 70 0.343929833 -2.250677125 71 -0.427500280 0.343929833 72 1.410519020 -0.427500280 73 0.867476192 1.410519020 74 -1.409685877 0.867476192 75 1.640281815 -1.409685877 76 0.029704432 1.640281815 77 0.728133804 0.029704432 78 0.002310377 0.728133804 79 -1.585705412 0.002310377 80 1.332202965 -1.585705412 81 -0.824975015 1.332202965 82 1.749322875 -0.824975015 83 -0.666736263 1.749322875 84 -1.009448443 -0.666736263 85 0.235584825 -1.009448443 86 -0.224477906 0.235584825 87 -1.056102833 -0.224477906 88 0.148724949 -1.056102833 89 0.786666851 0.148724949 90 2.012928681 0.786666851 91 0.695268609 2.012928681 92 0.129967469 0.695268609 93 -0.357201210 0.129967469 94 1.293667671 -0.357201210 95 -1.465185851 1.293667671 96 -5.265658824 -1.465185851 97 0.052642506 -5.265658824 98 -0.354732266 0.052642506 99 2.024222034 -0.354732266 100 -0.755817542 2.024222034 101 0.063286990 -0.755817542 102 0.540051962 0.063286990 103 1.822406859 0.540051962 104 2.242868823 1.822406859 105 1.107045248 2.242868823 106 -0.248695395 1.107045248 107 3.949364313 -0.248695395 108 -0.772751522 3.949364313 109 -0.544194341 -0.772751522 110 -2.250677125 -0.544194341 111 1.597462431 -2.250677125 112 0.235584825 1.597462431 113 -2.651762401 0.235584825 114 0.857939214 -2.651762401 115 -0.145722638 0.857939214 116 1.915859922 -0.145722638 117 1.323540346 1.915859922 118 0.222295742 1.323540346 119 2.012928681 0.222295742 120 -0.602679810 2.012928681 121 0.029704432 -0.602679810 122 0.567411811 0.029704432 123 -0.123835923 0.567411811 124 -0.145722638 -0.123835923 125 -0.869588238 -0.145722638 126 2.480406901 -0.869588238 127 0.054318799 2.480406901 128 -3.856714889 0.054318799 129 0.426499842 -3.856714889 130 -0.014022571 0.426499842 131 -0.148449250 -0.014022571 132 -1.151092146 -0.148449250 133 0.543971270 -1.151092146 134 -3.263035836 0.543971270 135 -0.309536919 -3.263035836 136 1.707658347 -0.309536919 137 -0.372668648 1.707658347 138 -0.145722638 -0.372668648 139 -0.036976047 -0.145722638 140 -1.573856566 -0.036976047 141 -0.145722638 -1.573856566 142 -3.110054969 -0.145722638 143 -0.517429727 -3.110054969 144 -0.456564315 -0.517429727 145 0.302265304 -0.456564315 146 -0.088010350 0.302265304 147 4.326350884 -0.088010350 148 -0.604597330 4.326350884 149 0.010924301 -0.604597330 150 1.749568727 0.010924301 151 0.063286990 1.749568727 152 -5.882906905 0.063286990 153 1.340411006 -5.882906905 154 0.543971270 1.340411006 155 0.237300497 0.543971270 > 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/rcomp/tmp/7t91b1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8bupq1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/95fa71321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/101glk1321957431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11jx371321957431.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/rcomp/tmp/120y7x1321957431.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/rcomp/tmp/13j64i1321957431.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/rcomp/tmp/149k231321957431.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/rcomp/tmp/15t4e51321957431.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/rcomp/tmp/16z3v51321957431.tab") + } > > try(system("convert tmp/1eapj1321957431.ps tmp/1eapj1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/21l6e1321957431.ps tmp/21l6e1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/3j2bj1321957431.ps tmp/3j2bj1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/4n3uz1321957431.ps tmp/4n3uz1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/5xly61321957431.ps tmp/5xly61321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/6y3gk1321957431.ps tmp/6y3gk1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/7t91b1321957431.ps tmp/7t91b1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/8bupq1321957431.ps tmp/8bupq1321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/95fa71321957431.ps tmp/95fa71321957431.png",intern=TRUE)) character(0) > try(system("convert tmp/101glk1321957431.ps tmp/101glk1321957431.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.744 0.656 7.398