R version 2.11.1 (2010-05-31) Copyright (C) 2010 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(22.5 + ,20 + ,12 + ,0 + ,0 + ,1 + ,3 + ,0 + ,2 + ,2 + ,3 + ,0 + ,3 + ,0 + ,4 + ,4.8 + ,0 + ,0 + ,0.9 + ,12 + ,0 + ,0 + ,4 + ,5 + ,6 + ,0 + ,0 + ,22.5 + ,28 + ,18 + ,0 + ,3 + ,6 + ,12 + ,0 + ,2 + ,6 + ,0 + ,7 + ,30 + ,2 + ,6 + ,24 + ,0 + ,1 + ,1 + ,0 + ,0 + ,3 + ,0 + ,0 + ,22.4 + ,0 + ,9 + ,4 + ,0 + ,0 + ,1.6 + ,2 + ,1 + ,12 + ,0 + ,1 + ,24 + ,2 + ,20 + ,0 + ,8 + ,9 + ,0 + ,0 + ,6 + ,22.5 + ,0 + ,11 + ,18 + ,17 + ,18 + ,2.2 + ,0 + ,3 + ,33 + ,0 + ,5 + ,2.5 + ,3 + ,10 + ,4 + ,0 + ,2 + ,75 + ,6 + ,7 + ,1.2 + ,0 + ,0 + ,18 + ,0 + ,8 + ,1.6 + ,0 + ,5 + ,4 + ,0 + ,9 + ,3 + ,0 + ,4 + ,2 + ,0 + ,0 + ,16.8 + ,7 + ,0 + ,90 + ,5 + ,1 + ,19.2 + ,4 + ,0 + ,6 + ,2 + ,6 + ,4.2 + ,15 + ,9 + ,2 + ,0 + ,5 + ,42.5 + ,15 + ,38 + ,7.5 + ,0 + ,10 + ,0 + ,0 + ,3 + ,3.9 + ,0 + ,8 + ,4 + ,8 + ,28 + ,30 + ,2 + ,20 + ,0 + ,0 + ,0 + ,8 + ,0 + ,10 + ,15 + ,3 + ,8 + ,4 + ,0 + ,10 + ,0 + ,2 + ,8 + ,6 + ,4 + ,8 + ,4.4 + ,0 + ,8 + ,20 + ,6 + ,6 + ,0 + ,7 + ,32 + ,0 + ,0 + ,3 + ,0 + ,0 + ,15 + ,0 + ,1 + ,12 + ,0 + ,0 + ,5 + ,0 + ,4 + ,8 + ,0 + ,8 + ,14 + ,7 + ,0 + ,2 + ,6 + ,4 + ,19 + ,18 + ,8 + ,22 + ,9 + ,0 + ,9 + ,18 + ,1 + ,24 + ,15 + ,0 + ,18 + ,4.5 + ,1 + ,1 + ,12 + ,10 + ,0 + ,0 + ,0 + ,0 + ,32 + ,0 + ,20 + ,5 + ,0 + ,19 + ,0 + ,0 + ,20 + ,0 + ,0 + ,1 + ,3 + ,0 + ,0 + ,15 + ,0 + ,57 + ,15 + ,2 + ,28 + ,42 + ,0 + ,0 + ,18 + ,12 + ,6 + ,24 + ,8 + ,20 + ,18 + ,12 + ,4 + ,30 + ,1 + ,0 + ,0 + ,15 + ,4 + ,6 + ,3 + ,10 + ,4.5 + ,0 + ,6 + ,0 + ,0 + ,1 + ,21 + ,0 + ,13 + ,3.6 + ,0 + ,3 + ,1.2 + ,0 + ,5 + ,0 + ,0 + ,3 + ,24 + ,0 + ,0 + ,19.2 + ,0 + ,4 + ,22.5 + ,0 + ,5 + ,0 + ,0 + ,0 + ,10.4 + ,0 + ,46 + ,6 + ,0 + ,0 + ,28 + ,4 + ,24 + ,2.5 + ,0 + ,0 + ,20 + ,0 + ,0 + ,32 + ,9 + ,53 + ,6 + ,0 + ,38 + ,0 + ,0 + ,0 + ,8 + ,10 + ,5 + ,18 + ,0 + ,7 + ,9 + ,0 + ,5 + ,2 + ,4 + ,1 + ,20 + ,30 + ,16 + ,0 + ,0 + ,1 + ,26 + ,7 + ,31 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,12 + ,2 + ,9 + ,12 + ,25 + ,30 + ,32 + ,0 + ,4 + ,6 + ,2 + ,8 + ,0 + ,0 + ,11 + ,0 + ,0 + ,16 + ,4 + ,0 + ,0 + ,12.6 + ,11 + ,1 + ,25.5 + ,1 + ,15 + ,4.8 + ,0 + ,0 + ,4.5 + ,5 + ,8 + ,4.8 + ,0 + ,5 + ,16 + ,1 + ,4 + ,3 + ,8 + ,4 + ,7 + ,9 + ,2 + ,0 + ,5 + ,6 + ,20 + ,24 + ,7 + ,4.8 + ,0 + ,3 + ,0 + ,0 + ,4 + ,4.8 + ,1 + ,6 + ,0 + ,0 + ,7 + ,3.2 + ,0 + ,5 + ,29.9 + ,0 + ,5 + ,24 + ,2 + ,0 + ,35.2 + ,5 + ,9 + ,30 + ,0 + ,13 + ,26 + ,0 + ,0 + ,58.8 + ,4 + ,6 + ,15 + ,7 + ,16 + ,14 + ,0 + ,4 + ,4.8 + ,15 + ,61 + ,30 + ,0 + ,0 + ,14.4 + ,0 + ,0 + ,10 + ,0 + ,1 + ,9.6 + ,0 + ,9 + ,0 + ,0 + ,18 + ,26 + ,4 + ,35 + ,0 + ,0 + ,20 + ,31.5 + ,10 + ,16 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,24 + ,0 + ,4 + ,3.6 + ,0 + ,3 + ,3 + ,0 + ,16) + ,dim=c(3 + ,160) + ,dimnames=list(c('Sport' + ,'Event' + ,'Tv ') + ,1:160)) > y <- array(NA,dim=c(3,160),dimnames=list(c('Sport','Event','Tv '),1:160)) > 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 Event Tv\r 1 22.5 20 12 2 0.0 0 1 3 3.0 0 2 4 2.0 3 0 5 3.0 0 4 6 4.8 0 0 7 0.9 12 0 8 0.0 4 5 9 6.0 0 0 10 22.5 28 18 11 0.0 3 6 12 12.0 0 2 13 6.0 0 7 14 30.0 2 6 15 24.0 0 1 16 1.0 0 0 17 3.0 0 0 18 22.4 0 9 19 4.0 0 0 20 1.6 2 1 21 12.0 0 1 22 24.0 2 20 23 0.0 8 9 24 0.0 0 6 25 22.5 0 11 26 18.0 17 18 27 2.2 0 3 28 33.0 0 5 29 2.5 3 10 30 4.0 0 2 31 75.0 6 7 32 1.2 0 0 33 18.0 0 8 34 1.6 0 5 35 4.0 0 9 36 3.0 0 4 37 2.0 0 0 38 16.8 7 0 39 90.0 5 1 40 19.2 4 0 41 6.0 2 6 42 4.2 15 9 43 2.0 0 5 44 42.5 15 38 45 7.5 0 10 46 0.0 0 3 47 3.9 0 8 48 4.0 8 28 49 30.0 2 20 50 0.0 0 0 51 8.0 0 10 52 15.0 3 8 53 4.0 0 10 54 0.0 2 8 55 6.0 4 8 56 4.4 0 8 57 20.0 6 6 58 0.0 7 32 59 0.0 0 3 60 0.0 0 15 61 0.0 1 12 62 0.0 0 5 63 0.0 4 8 64 0.0 8 14 65 7.0 0 2 66 6.0 4 19 67 18.0 8 22 68 9.0 0 9 69 18.0 1 24 70 15.0 0 18 71 4.5 1 1 72 12.0 10 0 73 0.0 0 0 74 32.0 0 20 75 5.0 0 19 76 0.0 0 20 77 0.0 0 1 78 3.0 0 0 79 15.0 0 57 80 15.0 2 28 81 42.0 0 0 82 18.0 12 6 83 24.0 8 20 84 18.0 12 4 85 30.0 1 0 86 0.0 15 4 87 6.0 3 10 88 4.5 0 6 89 0.0 0 1 90 21.0 0 13 91 3.6 0 3 92 1.2 0 5 93 0.0 0 3 94 24.0 0 0 95 19.2 0 4 96 22.5 0 5 97 0.0 0 0 98 10.4 0 46 99 6.0 0 0 100 28.0 4 24 101 2.5 0 0 102 20.0 0 0 103 32.0 9 53 104 6.0 0 38 105 0.0 0 0 106 8.0 10 5 107 18.0 0 7 108 9.0 0 5 109 2.0 4 1 110 20.0 30 16 111 0.0 0 1 112 26.0 7 31 113 0.0 0 4 114 0.0 0 0 115 0.0 0 1 116 0.0 0 0 117 12.0 2 9 118 12.0 25 30 119 32.0 0 4 120 6.0 2 8 121 0.0 0 11 122 0.0 0 16 123 4.0 0 0 124 12.6 11 1 125 25.5 1 15 126 4.8 0 0 127 4.5 5 8 128 4.8 0 5 129 16.0 1 4 130 3.0 8 4 131 7.0 9 2 132 0.0 5 6 133 20.0 24 7 134 4.8 0 3 135 0.0 0 4 136 4.8 1 6 137 0.0 0 7 138 3.2 0 5 139 29.9 0 5 140 24.0 2 0 141 35.2 5 9 142 30.0 0 13 143 26.0 0 0 144 58.8 4 6 145 15.0 7 16 146 14.0 0 4 147 4.8 15 61 148 30.0 0 0 149 14.4 0 0 150 10.0 0 1 151 9.6 0 9 152 0.0 0 18 153 26.0 4 35 154 0.0 0 20 155 31.5 10 16 156 0.0 0 0 157 1.0 4 1 158 24.0 0 4 159 3.6 0 3 160 3.0 0 16 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Event `Tv\r` 8.9386 0.4174 0.1343 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -18.595 -8.939 -4.964 5.430 78.840 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.9386 1.4218 6.287 3.07e-09 *** Event 0.4174 0.2021 2.065 0.0406 * `Tv\r` 0.1343 0.1018 1.320 0.1888 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.64 on 157 degrees of freedom Multiple R-squared: 0.05072, Adjusted R-squared: 0.03863 F-statistic: 4.194 on 2 and 157 DF, p-value: 0.01680 > 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,] 0.0077125811 1.542516e-02 9.922874e-01 [2,] 0.0046044216 9.208843e-03 9.953956e-01 [3,] 0.0068543915 1.370878e-02 9.931456e-01 [4,] 0.0045107522 9.021504e-03 9.954892e-01 [5,] 0.0016024745 3.204949e-03 9.983975e-01 [6,] 0.0011282616 2.256523e-03 9.988717e-01 [7,] 0.0019686055 3.937211e-03 9.980314e-01 [8,] 0.0006941224 1.388245e-03 9.993059e-01 [9,] 0.0258442954 5.168859e-02 9.741557e-01 [10,] 0.0716896444 1.433793e-01 9.283104e-01 [11,] 0.0469532094 9.390642e-02 9.530468e-01 [12,] 0.0286819865 5.736397e-02 9.713180e-01 [13,] 0.0209893796 4.197876e-02 9.790106e-01 [14,] 0.0122142901 2.442858e-02 9.877857e-01 [15,] 0.0073860035 1.477201e-02 9.926140e-01 [16,] 0.0050479543 1.009591e-02 9.949520e-01 [17,] 0.0032475696 6.495139e-03 9.967524e-01 [18,] 0.0047814722 9.562944e-03 9.952185e-01 [19,] 0.0045466051 9.093210e-03 9.954534e-01 [20,] 0.0033270432 6.654086e-03 9.966730e-01 [21,] 0.0020044677 4.008935e-03 9.979955e-01 [22,] 0.0012796241 2.559248e-03 9.987204e-01 [23,] 0.0076572989 1.531460e-02 9.923427e-01 [24,] 0.0081515910 1.630318e-02 9.918484e-01 [25,] 0.0052621587 1.052432e-02 9.947378e-01 [26,] 0.8100369587 3.799261e-01 1.899630e-01 [27,] 0.7751263236 4.497474e-01 2.248737e-01 [28,] 0.7352835835 5.294328e-01 2.647164e-01 [29,] 0.7087227731 5.825545e-01 2.912772e-01 [30,] 0.6853671655 6.292657e-01 3.146328e-01 [31,] 0.6456336074 7.087328e-01 3.543664e-01 [32,] 0.5986523311 8.026953e-01 4.013477e-01 [33,] 0.5714576024 8.570848e-01 4.285424e-01 [34,] 0.9999762286 4.754286e-05 2.377143e-05 [35,] 0.9999663336 6.733272e-05 3.366636e-05 [36,] 0.9999475769 1.048461e-04 5.242305e-05 [37,] 0.9999431823 1.136354e-04 5.681768e-05 [38,] 0.9999203807 1.592387e-04 7.961935e-05 [39,] 0.9999394452 1.211095e-04 6.055476e-05 [40,] 0.9999067609 1.864783e-04 9.323914e-05 [41,] 0.9998785593 2.428814e-04 1.214407e-04 [42,] 0.9998286806 3.426388e-04 1.713194e-04 [43,] 0.9998480259 3.039482e-04 1.519741e-04 [44,] 0.9998651071 2.697859e-04 1.348929e-04 [45,] 0.9998199426 3.601148e-04 1.800574e-04 [46,] 0.9997251408 5.497183e-04 2.748592e-04 [47,] 0.9995859631 8.280738e-04 4.140369e-04 [48,] 0.9994342605 1.131479e-03 5.657395e-04 [49,] 0.9993462906 1.307419e-03 6.537094e-04 [50,] 0.9990915263 1.816947e-03 9.084737e-04 [51,] 0.9987472849 2.505430e-03 1.252715e-03 [52,] 0.9983668969 3.266206e-03 1.633103e-03 [53,] 0.9987268292 2.546342e-03 1.273171e-03 [54,] 0.9984243014 3.151397e-03 1.575699e-03 [55,] 0.9981851042 3.629792e-03 1.814896e-03 [56,] 0.9979084568 4.183086e-03 2.091543e-03 [57,] 0.9974675292 5.064942e-03 2.532471e-03 [58,] 0.9971966124 5.606775e-03 2.803388e-03 [59,] 0.9972602212 5.479558e-03 2.739779e-03 [60,] 0.9961567833 7.686433e-03 3.843217e-03 [61,] 0.9950542100 9.891580e-03 4.945790e-03 [62,] 0.9932384088 1.352318e-02 6.761591e-03 [63,] 0.9907701023 1.845980e-02 9.229898e-03 [64,] 0.9881528431 2.369431e-02 1.184716e-02 [65,] 0.9844732997 3.105340e-02 1.552670e-02 [66,] 0.9801697641 3.966047e-02 1.983024e-02 [67,] 0.9740227350 5.195453e-02 2.597726e-02 [68,] 0.9698486853 6.030263e-02 3.015131e-02 [69,] 0.9779341663 4.413167e-02 2.206583e-02 [70,] 0.9730107908 5.397842e-02 2.698921e-02 [71,] 0.9712970924 5.740582e-02 2.870291e-02 [72,] 0.9670377955 6.592441e-02 3.296220e-02 [73,] 0.9598528806 8.029424e-02 4.014712e-02 [74,] 0.9492064544 1.015871e-01 5.079355e-02 [75,] 0.9361595417 1.276809e-01 6.384046e-02 [76,] 0.9814969436 3.700611e-02 1.850306e-02 [77,] 0.9760484538 4.790309e-02 2.395155e-02 [78,] 0.9722325157 5.553497e-02 2.776748e-02 [79,] 0.9648164797 7.036704e-02 3.518352e-02 [80,] 0.9752205058 4.955899e-02 2.477949e-02 [81,] 0.9771736992 4.565260e-02 2.282630e-02 [82,] 0.9714593843 5.708123e-02 2.854062e-02 [83,] 0.9645239698 7.095206e-02 3.547603e-02 [84,] 0.9595875443 8.082491e-02 4.041246e-02 [85,] 0.9548964852 9.020703e-02 4.510351e-02 [86,] 0.9453475674 1.093049e-01 5.465243e-02 [87,] 0.9375455545 1.249089e-01 6.245445e-02 [88,] 0.9306017069 1.387966e-01 6.939829e-02 [89,] 0.9334496100 1.331008e-01 6.655039e-02 [90,] 0.9252313328 1.495373e-01 7.476867e-02 [91,] 0.9231564148 1.536872e-01 7.684359e-02 [92,] 0.9137921360 1.724157e-01 8.620786e-02 [93,] 0.8965683881 2.068632e-01 1.034316e-01 [94,] 0.8750018068 2.499964e-01 1.249982e-01 [95,] 0.8770379648 2.459241e-01 1.229620e-01 [96,] 0.8581089688 2.837821e-01 1.418910e-01 [97,] 0.8475778575 3.048443e-01 1.524221e-01 [98,] 0.8500498871 2.999002e-01 1.499501e-01 [99,] 0.8279734245 3.440532e-01 1.720266e-01 [100,] 0.8125550682 3.748899e-01 1.874449e-01 [101,] 0.7845236887 4.309526e-01 2.154763e-01 [102,] 0.7593694214 4.812612e-01 2.406306e-01 [103,] 0.7195256776 5.609486e-01 2.804743e-01 [104,] 0.6975591103 6.048818e-01 3.024409e-01 [105,] 0.6553418658 6.893163e-01 3.446581e-01 [106,] 0.6347806202 7.304388e-01 3.652194e-01 [107,] 0.6247756883 7.504486e-01 3.752243e-01 [108,] 0.6052573328 7.894853e-01 3.947427e-01 [109,] 0.5869116329 8.261767e-01 4.130884e-01 [110,] 0.5701315363 8.597369e-01 4.298685e-01 [111,] 0.5555940767 8.888118e-01 4.444059e-01 [112,] 0.5038739882 9.922520e-01 4.961260e-01 [113,] 0.4723747569 9.447495e-01 5.276252e-01 [114,] 0.5427569163 9.144862e-01 4.572431e-01 [115,] 0.4990194666 9.980389e-01 5.009805e-01 [116,] 0.4803994701 9.607989e-01 5.196005e-01 [117,] 0.4638109164 9.276218e-01 5.361891e-01 [118,] 0.4277705932 8.555412e-01 5.722294e-01 [119,] 0.3778159615 7.556319e-01 6.221840e-01 [120,] 0.3756756232 7.513512e-01 6.243244e-01 [121,] 0.3380751669 6.761503e-01 6.619248e-01 [122,] 0.3102829176 6.205658e-01 6.897171e-01 [123,] 0.2753611014 5.507222e-01 7.246389e-01 [124,] 0.2322516334 4.645033e-01 7.677484e-01 [125,] 0.2263921553 4.527843e-01 7.736078e-01 [126,] 0.2097069648 4.194139e-01 7.902930e-01 [127,] 0.2229624189 4.459248e-01 7.770376e-01 [128,] 0.2708028156 5.416056e-01 7.291972e-01 [129,] 0.2411776386 4.823553e-01 7.588224e-01 [130,] 0.2398426529 4.796853e-01 7.601573e-01 [131,] 0.2190093564 4.380187e-01 7.809906e-01 [132,] 0.2165133601 4.330267e-01 7.834866e-01 [133,] 0.2045015311 4.090031e-01 7.954985e-01 [134,] 0.2126548177 4.253096e-01 7.873452e-01 [135,] 0.1736919527 3.473839e-01 8.263080e-01 [136,] 0.1770935853 3.541872e-01 8.229064e-01 [137,] 0.2203908213 4.407816e-01 7.796092e-01 [138,] 0.1987243492 3.974487e-01 8.012757e-01 [139,] 0.8048164657 3.903671e-01 1.951835e-01 [140,] 0.7375276908 5.249446e-01 2.624723e-01 [141,] 0.6616438539 6.767123e-01 3.383561e-01 [142,] 0.7209019729 5.581961e-01 2.790980e-01 [143,] 0.8813930992 2.372138e-01 1.186069e-01 [144,] 0.8584826586 2.830347e-01 1.415173e-01 [145,] 0.8029185775 3.941628e-01 1.970814e-01 [146,] 0.7194645167 5.610710e-01 2.805355e-01 [147,] 0.6391770287 7.216459e-01 3.608230e-01 [148,] 0.5313426529 9.373147e-01 4.686573e-01 [149,] 0.4221102530 8.442205e-01 5.778897e-01 > postscript(file="/var/www/rcomp/tmp/1kmxp1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2vvwa1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3vvwa1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4vvwa1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5n4vd1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 160 Frequency = 1 1 2 3 4 5 6 3.6003200 -9.0728974 -6.2072431 -8.1908987 -6.4759345 -4.1385517 7 8 9 10 11 12 -13.0479397 -11.2800762 -2.9385517 -0.5453462 -10.9969729 2.7927569 13 14 15 16 17 18 -3.8789716 19.4204761 14.9271026 -7.9385517 -5.9385517 12.2523370 19 20 21 22 23 24 -4.9385517 -8.3077954 2.9271026 11.5396363 -13.4872550 -9.7446259 25 26 27 28 29 30 12.0836456 -0.4534072 -7.1415888 23.3897198 -9.0343557 -5.2072431 31 32 33 34 35 36 62.6163344 -7.7385517 7.9866827 -8.0102802 -6.1476630 -6.4759345 37 38 39 40 41 42 -6.9385517 4.9393053 78.8398576 8.5916523 -4.5795239 -12.2093979 43 44 45 46 47 48 -7.6102802 22.1945767 -2.7820087 -9.3415888 -6.1133173 -12.0398233 49 50 51 52 53 54 17.5396363 -8.9385517 -2.2820087 3.7343357 -6.2820087 -10.8482153 55 56 57 58 59 60 -5.6831133 -5.6133173 7.7506801 -16.1597571 -9.3415888 -10.9537372 61 62 63 64 65 66 -10.9681491 -9.6102802 -11.6831133 -14.1589835 -2.2072431 -7.1609160 67 68 69 70 71 72 2.7662509 -1.1476630 5.4197025 3.6432257 -4.9903464 -1.1130417 73 74 75 76 77 78 -8.9385517 20.3745343 -6.4911200 -11.6254657 -9.0728974 -5.9385517 79 80 81 82 83 84 -1.5962567 1.4648707 33.0614483 3.2459861 9.0349423 3.5146775 85 86 87 88 89 90 20.6439993 -15.7376694 -5.5343557 -5.2446259 -9.0728974 10.3149542 91 92 93 94 95 96 -5.7415888 -8.4102802 -9.3415888 15.0614483 9.7240655 12.8897198 97 98 99 100 101 102 -8.9385517 -4.7184540 -2.9385517 14.1673555 -6.4385517 11.0614483 103 104 105 106 107 108 12.1840852 -8.0436884 -8.9385517 -5.7847702 8.1210284 -0.6102802 109 110 111 112 113 114 -8.7426934 -3.6115528 -9.0728974 9.9745886 -9.4759345 -8.9385517 115 116 117 118 119 120 -9.0728974 -8.9385517 1.0174390 -11.4051476 22.5240655 -4.8482153 121 122 123 124 125 126 -10.4163544 -11.0880829 -4.9385517 -1.0648364 14.1288138 -4.1385517 127 128 129 130 131 132 -7.6005623 -4.8102802 6.1066165 -9.8155265 -5.9642841 -11.8318709 133 134 135 136 137 138 0.1022525 -4.5415888 -9.4759345 -5.3620749 -9.8789716 -6.4102802 139 140 141 142 143 144 20.2897198 14.2265503 22.9650920 19.3149542 17.0614483 47.3855781 145 146 147 148 149 150 0.9897741 4.5240655 -18.5953744 21.0614483 5.4614483 0.9271026 151 152 153 154 155 156 -0.5476630 -11.3567743 10.6895528 -11.6254657 16.2374271 -8.9385517 157 158 159 160 -9.7426934 14.5240655 -5.7415888 -8.0880829 > postscript(file="/var/www/rcomp/tmp/6n4vd1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 160 Frequency = 1 lag(myerror, k = 1) myerror 0 3.6003200 NA 1 -9.0728974 3.6003200 2 -6.2072431 -9.0728974 3 -8.1908987 -6.2072431 4 -6.4759345 -8.1908987 5 -4.1385517 -6.4759345 6 -13.0479397 -4.1385517 7 -11.2800762 -13.0479397 8 -2.9385517 -11.2800762 9 -0.5453462 -2.9385517 10 -10.9969729 -0.5453462 11 2.7927569 -10.9969729 12 -3.8789716 2.7927569 13 19.4204761 -3.8789716 14 14.9271026 19.4204761 15 -7.9385517 14.9271026 16 -5.9385517 -7.9385517 17 12.2523370 -5.9385517 18 -4.9385517 12.2523370 19 -8.3077954 -4.9385517 20 2.9271026 -8.3077954 21 11.5396363 2.9271026 22 -13.4872550 11.5396363 23 -9.7446259 -13.4872550 24 12.0836456 -9.7446259 25 -0.4534072 12.0836456 26 -7.1415888 -0.4534072 27 23.3897198 -7.1415888 28 -9.0343557 23.3897198 29 -5.2072431 -9.0343557 30 62.6163344 -5.2072431 31 -7.7385517 62.6163344 32 7.9866827 -7.7385517 33 -8.0102802 7.9866827 34 -6.1476630 -8.0102802 35 -6.4759345 -6.1476630 36 -6.9385517 -6.4759345 37 4.9393053 -6.9385517 38 78.8398576 4.9393053 39 8.5916523 78.8398576 40 -4.5795239 8.5916523 41 -12.2093979 -4.5795239 42 -7.6102802 -12.2093979 43 22.1945767 -7.6102802 44 -2.7820087 22.1945767 45 -9.3415888 -2.7820087 46 -6.1133173 -9.3415888 47 -12.0398233 -6.1133173 48 17.5396363 -12.0398233 49 -8.9385517 17.5396363 50 -2.2820087 -8.9385517 51 3.7343357 -2.2820087 52 -6.2820087 3.7343357 53 -10.8482153 -6.2820087 54 -5.6831133 -10.8482153 55 -5.6133173 -5.6831133 56 7.7506801 -5.6133173 57 -16.1597571 7.7506801 58 -9.3415888 -16.1597571 59 -10.9537372 -9.3415888 60 -10.9681491 -10.9537372 61 -9.6102802 -10.9681491 62 -11.6831133 -9.6102802 63 -14.1589835 -11.6831133 64 -2.2072431 -14.1589835 65 -7.1609160 -2.2072431 66 2.7662509 -7.1609160 67 -1.1476630 2.7662509 68 5.4197025 -1.1476630 69 3.6432257 5.4197025 70 -4.9903464 3.6432257 71 -1.1130417 -4.9903464 72 -8.9385517 -1.1130417 73 20.3745343 -8.9385517 74 -6.4911200 20.3745343 75 -11.6254657 -6.4911200 76 -9.0728974 -11.6254657 77 -5.9385517 -9.0728974 78 -1.5962567 -5.9385517 79 1.4648707 -1.5962567 80 33.0614483 1.4648707 81 3.2459861 33.0614483 82 9.0349423 3.2459861 83 3.5146775 9.0349423 84 20.6439993 3.5146775 85 -15.7376694 20.6439993 86 -5.5343557 -15.7376694 87 -5.2446259 -5.5343557 88 -9.0728974 -5.2446259 89 10.3149542 -9.0728974 90 -5.7415888 10.3149542 91 -8.4102802 -5.7415888 92 -9.3415888 -8.4102802 93 15.0614483 -9.3415888 94 9.7240655 15.0614483 95 12.8897198 9.7240655 96 -8.9385517 12.8897198 97 -4.7184540 -8.9385517 98 -2.9385517 -4.7184540 99 14.1673555 -2.9385517 100 -6.4385517 14.1673555 101 11.0614483 -6.4385517 102 12.1840852 11.0614483 103 -8.0436884 12.1840852 104 -8.9385517 -8.0436884 105 -5.7847702 -8.9385517 106 8.1210284 -5.7847702 107 -0.6102802 8.1210284 108 -8.7426934 -0.6102802 109 -3.6115528 -8.7426934 110 -9.0728974 -3.6115528 111 9.9745886 -9.0728974 112 -9.4759345 9.9745886 113 -8.9385517 -9.4759345 114 -9.0728974 -8.9385517 115 -8.9385517 -9.0728974 116 1.0174390 -8.9385517 117 -11.4051476 1.0174390 118 22.5240655 -11.4051476 119 -4.8482153 22.5240655 120 -10.4163544 -4.8482153 121 -11.0880829 -10.4163544 122 -4.9385517 -11.0880829 123 -1.0648364 -4.9385517 124 14.1288138 -1.0648364 125 -4.1385517 14.1288138 126 -7.6005623 -4.1385517 127 -4.8102802 -7.6005623 128 6.1066165 -4.8102802 129 -9.8155265 6.1066165 130 -5.9642841 -9.8155265 131 -11.8318709 -5.9642841 132 0.1022525 -11.8318709 133 -4.5415888 0.1022525 134 -9.4759345 -4.5415888 135 -5.3620749 -9.4759345 136 -9.8789716 -5.3620749 137 -6.4102802 -9.8789716 138 20.2897198 -6.4102802 139 14.2265503 20.2897198 140 22.9650920 14.2265503 141 19.3149542 22.9650920 142 17.0614483 19.3149542 143 47.3855781 17.0614483 144 0.9897741 47.3855781 145 4.5240655 0.9897741 146 -18.5953744 4.5240655 147 21.0614483 -18.5953744 148 5.4614483 21.0614483 149 0.9271026 5.4614483 150 -0.5476630 0.9271026 151 -11.3567743 -0.5476630 152 10.6895528 -11.3567743 153 -11.6254657 10.6895528 154 16.2374271 -11.6254657 155 -8.9385517 16.2374271 156 -9.7426934 -8.9385517 157 14.5240655 -9.7426934 158 -5.7415888 14.5240655 159 -8.0880829 -5.7415888 160 NA -8.0880829 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -9.0728974 3.6003200 [2,] -6.2072431 -9.0728974 [3,] -8.1908987 -6.2072431 [4,] -6.4759345 -8.1908987 [5,] -4.1385517 -6.4759345 [6,] -13.0479397 -4.1385517 [7,] -11.2800762 -13.0479397 [8,] -2.9385517 -11.2800762 [9,] -0.5453462 -2.9385517 [10,] -10.9969729 -0.5453462 [11,] 2.7927569 -10.9969729 [12,] -3.8789716 2.7927569 [13,] 19.4204761 -3.8789716 [14,] 14.9271026 19.4204761 [15,] -7.9385517 14.9271026 [16,] -5.9385517 -7.9385517 [17,] 12.2523370 -5.9385517 [18,] -4.9385517 12.2523370 [19,] -8.3077954 -4.9385517 [20,] 2.9271026 -8.3077954 [21,] 11.5396363 2.9271026 [22,] -13.4872550 11.5396363 [23,] -9.7446259 -13.4872550 [24,] 12.0836456 -9.7446259 [25,] -0.4534072 12.0836456 [26,] -7.1415888 -0.4534072 [27,] 23.3897198 -7.1415888 [28,] -9.0343557 23.3897198 [29,] -5.2072431 -9.0343557 [30,] 62.6163344 -5.2072431 [31,] -7.7385517 62.6163344 [32,] 7.9866827 -7.7385517 [33,] -8.0102802 7.9866827 [34,] -6.1476630 -8.0102802 [35,] -6.4759345 -6.1476630 [36,] -6.9385517 -6.4759345 [37,] 4.9393053 -6.9385517 [38,] 78.8398576 4.9393053 [39,] 8.5916523 78.8398576 [40,] -4.5795239 8.5916523 [41,] -12.2093979 -4.5795239 [42,] -7.6102802 -12.2093979 [43,] 22.1945767 -7.6102802 [44,] -2.7820087 22.1945767 [45,] -9.3415888 -2.7820087 [46,] -6.1133173 -9.3415888 [47,] -12.0398233 -6.1133173 [48,] 17.5396363 -12.0398233 [49,] -8.9385517 17.5396363 [50,] -2.2820087 -8.9385517 [51,] 3.7343357 -2.2820087 [52,] -6.2820087 3.7343357 [53,] -10.8482153 -6.2820087 [54,] -5.6831133 -10.8482153 [55,] -5.6133173 -5.6831133 [56,] 7.7506801 -5.6133173 [57,] -16.1597571 7.7506801 [58,] -9.3415888 -16.1597571 [59,] -10.9537372 -9.3415888 [60,] -10.9681491 -10.9537372 [61,] -9.6102802 -10.9681491 [62,] -11.6831133 -9.6102802 [63,] -14.1589835 -11.6831133 [64,] -2.2072431 -14.1589835 [65,] -7.1609160 -2.2072431 [66,] 2.7662509 -7.1609160 [67,] -1.1476630 2.7662509 [68,] 5.4197025 -1.1476630 [69,] 3.6432257 5.4197025 [70,] -4.9903464 3.6432257 [71,] -1.1130417 -4.9903464 [72,] -8.9385517 -1.1130417 [73,] 20.3745343 -8.9385517 [74,] -6.4911200 20.3745343 [75,] -11.6254657 -6.4911200 [76,] -9.0728974 -11.6254657 [77,] -5.9385517 -9.0728974 [78,] -1.5962567 -5.9385517 [79,] 1.4648707 -1.5962567 [80,] 33.0614483 1.4648707 [81,] 3.2459861 33.0614483 [82,] 9.0349423 3.2459861 [83,] 3.5146775 9.0349423 [84,] 20.6439993 3.5146775 [85,] -15.7376694 20.6439993 [86,] -5.5343557 -15.7376694 [87,] -5.2446259 -5.5343557 [88,] -9.0728974 -5.2446259 [89,] 10.3149542 -9.0728974 [90,] -5.7415888 10.3149542 [91,] -8.4102802 -5.7415888 [92,] -9.3415888 -8.4102802 [93,] 15.0614483 -9.3415888 [94,] 9.7240655 15.0614483 [95,] 12.8897198 9.7240655 [96,] -8.9385517 12.8897198 [97,] -4.7184540 -8.9385517 [98,] -2.9385517 -4.7184540 [99,] 14.1673555 -2.9385517 [100,] -6.4385517 14.1673555 [101,] 11.0614483 -6.4385517 [102,] 12.1840852 11.0614483 [103,] -8.0436884 12.1840852 [104,] -8.9385517 -8.0436884 [105,] -5.7847702 -8.9385517 [106,] 8.1210284 -5.7847702 [107,] -0.6102802 8.1210284 [108,] -8.7426934 -0.6102802 [109,] -3.6115528 -8.7426934 [110,] -9.0728974 -3.6115528 [111,] 9.9745886 -9.0728974 [112,] -9.4759345 9.9745886 [113,] -8.9385517 -9.4759345 [114,] -9.0728974 -8.9385517 [115,] -8.9385517 -9.0728974 [116,] 1.0174390 -8.9385517 [117,] -11.4051476 1.0174390 [118,] 22.5240655 -11.4051476 [119,] -4.8482153 22.5240655 [120,] -10.4163544 -4.8482153 [121,] -11.0880829 -10.4163544 [122,] -4.9385517 -11.0880829 [123,] -1.0648364 -4.9385517 [124,] 14.1288138 -1.0648364 [125,] -4.1385517 14.1288138 [126,] -7.6005623 -4.1385517 [127,] -4.8102802 -7.6005623 [128,] 6.1066165 -4.8102802 [129,] -9.8155265 6.1066165 [130,] -5.9642841 -9.8155265 [131,] -11.8318709 -5.9642841 [132,] 0.1022525 -11.8318709 [133,] -4.5415888 0.1022525 [134,] -9.4759345 -4.5415888 [135,] -5.3620749 -9.4759345 [136,] -9.8789716 -5.3620749 [137,] -6.4102802 -9.8789716 [138,] 20.2897198 -6.4102802 [139,] 14.2265503 20.2897198 [140,] 22.9650920 14.2265503 [141,] 19.3149542 22.9650920 [142,] 17.0614483 19.3149542 [143,] 47.3855781 17.0614483 [144,] 0.9897741 47.3855781 [145,] 4.5240655 0.9897741 [146,] -18.5953744 4.5240655 [147,] 21.0614483 -18.5953744 [148,] 5.4614483 21.0614483 [149,] 0.9271026 5.4614483 [150,] -0.5476630 0.9271026 [151,] -11.3567743 -0.5476630 [152,] 10.6895528 -11.3567743 [153,] -11.6254657 10.6895528 [154,] 16.2374271 -11.6254657 [155,] -8.9385517 16.2374271 [156,] -9.7426934 -8.9385517 [157,] 14.5240655 -9.7426934 [158,] -5.7415888 14.5240655 [159,] -8.0880829 -5.7415888 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -9.0728974 3.6003200 2 -6.2072431 -9.0728974 3 -8.1908987 -6.2072431 4 -6.4759345 -8.1908987 5 -4.1385517 -6.4759345 6 -13.0479397 -4.1385517 7 -11.2800762 -13.0479397 8 -2.9385517 -11.2800762 9 -0.5453462 -2.9385517 10 -10.9969729 -0.5453462 11 2.7927569 -10.9969729 12 -3.8789716 2.7927569 13 19.4204761 -3.8789716 14 14.9271026 19.4204761 15 -7.9385517 14.9271026 16 -5.9385517 -7.9385517 17 12.2523370 -5.9385517 18 -4.9385517 12.2523370 19 -8.3077954 -4.9385517 20 2.9271026 -8.3077954 21 11.5396363 2.9271026 22 -13.4872550 11.5396363 23 -9.7446259 -13.4872550 24 12.0836456 -9.7446259 25 -0.4534072 12.0836456 26 -7.1415888 -0.4534072 27 23.3897198 -7.1415888 28 -9.0343557 23.3897198 29 -5.2072431 -9.0343557 30 62.6163344 -5.2072431 31 -7.7385517 62.6163344 32 7.9866827 -7.7385517 33 -8.0102802 7.9866827 34 -6.1476630 -8.0102802 35 -6.4759345 -6.1476630 36 -6.9385517 -6.4759345 37 4.9393053 -6.9385517 38 78.8398576 4.9393053 39 8.5916523 78.8398576 40 -4.5795239 8.5916523 41 -12.2093979 -4.5795239 42 -7.6102802 -12.2093979 43 22.1945767 -7.6102802 44 -2.7820087 22.1945767 45 -9.3415888 -2.7820087 46 -6.1133173 -9.3415888 47 -12.0398233 -6.1133173 48 17.5396363 -12.0398233 49 -8.9385517 17.5396363 50 -2.2820087 -8.9385517 51 3.7343357 -2.2820087 52 -6.2820087 3.7343357 53 -10.8482153 -6.2820087 54 -5.6831133 -10.8482153 55 -5.6133173 -5.6831133 56 7.7506801 -5.6133173 57 -16.1597571 7.7506801 58 -9.3415888 -16.1597571 59 -10.9537372 -9.3415888 60 -10.9681491 -10.9537372 61 -9.6102802 -10.9681491 62 -11.6831133 -9.6102802 63 -14.1589835 -11.6831133 64 -2.2072431 -14.1589835 65 -7.1609160 -2.2072431 66 2.7662509 -7.1609160 67 -1.1476630 2.7662509 68 5.4197025 -1.1476630 69 3.6432257 5.4197025 70 -4.9903464 3.6432257 71 -1.1130417 -4.9903464 72 -8.9385517 -1.1130417 73 20.3745343 -8.9385517 74 -6.4911200 20.3745343 75 -11.6254657 -6.4911200 76 -9.0728974 -11.6254657 77 -5.9385517 -9.0728974 78 -1.5962567 -5.9385517 79 1.4648707 -1.5962567 80 33.0614483 1.4648707 81 3.2459861 33.0614483 82 9.0349423 3.2459861 83 3.5146775 9.0349423 84 20.6439993 3.5146775 85 -15.7376694 20.6439993 86 -5.5343557 -15.7376694 87 -5.2446259 -5.5343557 88 -9.0728974 -5.2446259 89 10.3149542 -9.0728974 90 -5.7415888 10.3149542 91 -8.4102802 -5.7415888 92 -9.3415888 -8.4102802 93 15.0614483 -9.3415888 94 9.7240655 15.0614483 95 12.8897198 9.7240655 96 -8.9385517 12.8897198 97 -4.7184540 -8.9385517 98 -2.9385517 -4.7184540 99 14.1673555 -2.9385517 100 -6.4385517 14.1673555 101 11.0614483 -6.4385517 102 12.1840852 11.0614483 103 -8.0436884 12.1840852 104 -8.9385517 -8.0436884 105 -5.7847702 -8.9385517 106 8.1210284 -5.7847702 107 -0.6102802 8.1210284 108 -8.7426934 -0.6102802 109 -3.6115528 -8.7426934 110 -9.0728974 -3.6115528 111 9.9745886 -9.0728974 112 -9.4759345 9.9745886 113 -8.9385517 -9.4759345 114 -9.0728974 -8.9385517 115 -8.9385517 -9.0728974 116 1.0174390 -8.9385517 117 -11.4051476 1.0174390 118 22.5240655 -11.4051476 119 -4.8482153 22.5240655 120 -10.4163544 -4.8482153 121 -11.0880829 -10.4163544 122 -4.9385517 -11.0880829 123 -1.0648364 -4.9385517 124 14.1288138 -1.0648364 125 -4.1385517 14.1288138 126 -7.6005623 -4.1385517 127 -4.8102802 -7.6005623 128 6.1066165 -4.8102802 129 -9.8155265 6.1066165 130 -5.9642841 -9.8155265 131 -11.8318709 -5.9642841 132 0.1022525 -11.8318709 133 -4.5415888 0.1022525 134 -9.4759345 -4.5415888 135 -5.3620749 -9.4759345 136 -9.8789716 -5.3620749 137 -6.4102802 -9.8789716 138 20.2897198 -6.4102802 139 14.2265503 20.2897198 140 22.9650920 14.2265503 141 19.3149542 22.9650920 142 17.0614483 19.3149542 143 47.3855781 17.0614483 144 0.9897741 47.3855781 145 4.5240655 0.9897741 146 -18.5953744 4.5240655 147 21.0614483 -18.5953744 148 5.4614483 21.0614483 149 0.9271026 5.4614483 150 -0.5476630 0.9271026 151 -11.3567743 -0.5476630 152 10.6895528 -11.3567743 153 -11.6254657 10.6895528 154 16.2374271 -11.6254657 155 -8.9385517 16.2374271 156 -9.7426934 -8.9385517 157 14.5240655 -9.7426934 158 -5.7415888 14.5240655 159 -8.0880829 -5.7415888 > 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/73zkn1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/83zkn1290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/995u11290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/1095u11290515710.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11unap1290515710.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/12gorv1290515710.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/13m7671290515710.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/14xgna1290515710.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/150z4g1290515710.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/16xr161290515710.tab") + } > > try(system("convert tmp/1kmxp1290515710.ps tmp/1kmxp1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/2vvwa1290515710.ps tmp/2vvwa1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/3vvwa1290515710.ps tmp/3vvwa1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/4vvwa1290515710.ps tmp/4vvwa1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/5n4vd1290515710.ps tmp/5n4vd1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/6n4vd1290515710.ps tmp/6n4vd1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/73zkn1290515710.ps tmp/73zkn1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/83zkn1290515710.ps tmp/83zkn1290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/995u11290515710.ps tmp/995u11290515710.png",intern=TRUE)) character(0) > try(system("convert tmp/1095u11290515710.ps tmp/1095u11290515710.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.120 2.290 7.346