R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-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(7 + ,7 + ,7 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,5 + ,4 + ,4 + ,4 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,6 + ,6 + ,7 + ,7 + ,4 + ,7 + ,7 + ,6 + ,5 + ,5 + ,6 + ,6 + ,7 + ,7 + ,6 + ,6 + ,5 + ,5 + ,6 + ,5 + ,2 + ,4 + ,6 + ,5 + ,5 + ,6 + ,6 + ,4 + ,5 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,7 + ,7 + ,5 + ,5 + ,6 + ,5 + ,3 + ,3 + ,4 + ,3 + ,7 + ,6 + ,6 + ,7 + ,3 + ,5 + ,6 + ,6 + ,5 + ,5 + ,6 + ,6 + ,3 + ,3 + ,4 + ,4 + ,5 + ,5 + ,5 + ,6 + ,2 + ,1 + ,2 + ,2 + ,6 + ,5 + ,6 + ,6 + ,3 + ,4 + ,5 + ,5 + ,6 + ,6 + ,6 + ,7 + ,6 + ,6 + ,6 + ,7 + ,5 + ,4 + ,5 + ,5 + ,5 + ,4 + ,5 + ,6 + ,7 + ,5 + ,5 + ,6 + ,6 + ,4 + ,6 + ,6 + ,5 + ,5 + ,6 + ,6 + ,5 + ,6 + ,5 + ,5 + ,4 + ,3 + ,4 + ,4 + ,4 + ,5 + ,6 + ,5 + ,6 + ,5 + ,5 + ,6 + ,5 + ,3 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,7 + ,7 + ,7 + ,7 + ,5 + ,6 + ,5 + ,6 + ,5 + ,4 + ,5 + ,4 + ,6 + ,5 + ,7 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,6 + ,7 + ,6 + ,7 + ,7 + ,6 + ,6 + ,5 + ,3 + ,3 + ,4 + ,5 + ,4 + ,4 + ,4 + ,5 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,5 + ,2 + ,2 + ,4 + ,5 + ,4 + ,4 + ,4 + ,6 + ,4 + ,4 + ,6 + ,5 + ,6 + ,5 + ,5 + ,6 + ,3 + ,4 + ,4 + ,5 + ,6 + ,6 + ,6 + ,6 + ,6 + ,2 + ,5 + ,5 + ,5 + ,4 + ,5 + ,6 + ,6 + ,6 + ,6 + ,6 + ,1 + ,4 + ,6 + ,3 + ,5 + ,5 + ,6 + ,6 + ,7 + ,6 + ,5 + ,6 + ,4 + ,4 + ,5 + ,5 + ,5 + ,6 + ,5 + ,5 + ,6 + ,6 + ,5 + ,6 + ,4 + ,5 + ,4 + ,4 + ,6 + ,6 + ,5 + ,5 + ,6 + ,6 + ,6 + ,6 + ,5 + ,4 + ,6 + ,6 + ,5 + ,6 + ,5 + ,6 + ,3 + ,3 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,6 + ,5 + ,6 + ,6 + ,5 + ,5 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,6 + ,5 + ,5 + ,5 + ,7 + ,6 + ,6 + ,7 + ,4 + ,3 + ,3 + ,5 + ,5 + ,6 + ,7 + ,7 + ,6 + ,2 + ,5 + ,5 + ,6 + ,5 + ,6 + ,6 + ,5 + ,3 + ,6 + ,6 + ,3 + ,4 + ,5 + ,5 + ,7 + ,6 + ,6 + ,6 + ,6 + ,5 + ,6 + ,7 + ,4 + ,4 + ,4 + ,5 + ,4 + ,5 + ,6 + ,4 + ,5 + ,5 + ,5 + ,5 + ,3 + ,4 + ,4 + ,4 + ,7 + ,7 + ,7 + ,6 + ,6 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,5 + ,5 + ,6 + ,6 + ,6 + ,7 + ,5 + ,3 + ,5 + ,6 + ,6 + ,4 + ,4 + ,5 + ,6 + ,7 + ,7 + ,6 + ,4 + ,5 + ,6 + ,6 + ,5 + ,5 + ,5 + ,5 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,6 + ,6 + ,5 + ,5 + ,5 + ,5 + ,4 + ,4 + ,5 + ,5 + ,4 + ,5 + ,5 + ,4 + ,6 + ,6 + ,5 + ,7 + ,5 + ,5 + ,7 + ,7 + ,6 + ,5 + ,6 + ,5 + ,5 + ,4 + ,7 + ,7 + ,6 + ,4 + ,6 + ,6 + ,5 + ,5 + ,5 + ,5 + ,4 + ,5 + ,4 + ,5 + ,6 + ,6 + ,6 + ,7 + ,4 + ,5 + ,4 + ,5 + ,5 + ,3 + ,3 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,3 + ,5 + ,5 + ,3 + ,4 + ,5 + ,3 + ,5 + ,4 + ,5 + ,5 + ,4 + ,5 + ,5 + ,5 + ,5 + ,2 + ,5 + ,4 + ,5 + ,5 + ,3 + ,4 + ,7 + ,7 + ,7 + ,7 + ,5 + ,6 + ,6 + ,6 + ,7 + ,6 + ,6 + ,6 + ,5 + ,5 + ,4 + ,6 + ,4 + ,4 + ,4 + ,5 + ,6 + ,6 + ,6 + ,5 + ,4 + ,3 + ,4 + ,5 + ,4 + ,7 + ,6 + ,6 + ,4 + ,3 + ,2 + ,2 + ,4 + ,4 + ,5 + ,5 + ,6 + ,6 + ,5 + ,7 + ,6 + ,5 + ,6 + ,6 + ,5 + ,6 + ,5 + ,5 + ,3 + ,4 + ,4 + ,5 + ,6 + ,6 + ,6 + ,6 + ,5 + ,6 + ,6 + ,6 + ,4 + ,4 + ,5 + ,5 + ,5 + ,5 + ,5 + ,5 + ,2 + ,3 + ,2 + ,2 + ,5 + ,6 + ,6 + ,7 + ,7 + ,5 + ,6 + ,7 + ,4 + ,6 + ,5 + ,5 + ,4 + ,5 + ,6 + ,6 + ,7 + ,6 + ,7 + ,5 + ,6 + ,5 + ,5 + ,6 + ,5 + ,6 + ,5 + ,5 + ,5 + ,5 + ,5 + ,6 + ,5 + ,5 + ,6 + ,6 + ,7 + ,6 + ,7 + ,7 + ,6 + ,5 + ,7 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,5 + ,6 + ,2 + ,4 + ,6 + ,6 + ,4 + ,4 + ,4 + ,4 + ,6 + ,4 + ,6 + ,6 + ,5 + ,5 + ,6 + ,4 + ,5 + ,4 + ,4 + ,5 + ,5 + ,5 + ,5 + ,5) + ,dim=c(4 + ,162) + ,dimnames=list(c('Q2' + ,'Q9' + ,'Q16' + ,'Q23') + ,1:162)) > y <- array(NA,dim=c(4,162),dimnames=list(c('Q2','Q9','Q16','Q23'),1:162)) > 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' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, 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 Q2 Q9 Q16 Q23 1 7 7 7 5 2 5 5 5 5 3 6 5 4 4 4 4 5 5 5 5 5 5 5 5 6 6 6 6 7 7 7 4 7 7 8 6 5 5 6 9 6 7 7 6 10 6 5 5 6 11 5 2 4 6 12 5 5 6 6 13 4 5 4 6 14 6 6 6 6 15 6 6 7 7 16 5 5 6 5 17 3 3 4 3 18 7 6 6 7 19 3 5 6 6 20 5 5 6 6 21 3 3 4 4 22 5 5 5 6 23 2 1 2 2 24 6 5 6 6 25 3 4 5 5 26 6 6 6 7 27 6 6 6 7 28 5 4 5 5 29 5 4 5 6 30 7 5 5 6 31 6 4 6 6 32 5 5 6 6 33 5 6 5 5 34 4 3 4 4 35 4 5 6 5 36 6 5 5 6 37 5 3 5 5 38 5 5 5 5 39 7 7 7 7 40 5 6 5 6 41 5 4 5 4 42 6 5 7 5 43 5 5 5 5 44 6 6 7 6 45 7 7 6 6 46 5 3 3 4 47 5 4 4 4 48 5 6 6 6 49 6 5 5 5 50 2 2 4 5 51 4 4 4 6 52 4 4 6 5 53 6 5 5 6 54 3 4 4 5 55 6 6 6 6 56 6 2 5 5 57 5 4 5 6 58 6 6 6 6 59 1 4 6 3 60 5 5 6 6 61 7 6 5 6 62 4 4 5 5 63 5 6 5 5 64 6 6 5 6 65 4 5 4 4 66 6 6 5 5 67 6 6 6 6 68 5 4 6 6 69 5 6 5 6 70 3 3 5 5 71 5 5 5 6 72 6 5 6 6 73 5 5 6 6 74 6 6 6 6 75 6 6 6 6 76 4 4 4 4 77 4 4 4 4 78 6 5 5 5 79 7 6 6 7 80 4 3 3 5 81 5 6 7 7 82 6 2 5 5 83 6 5 6 6 84 5 3 6 6 85 3 4 5 5 86 7 6 6 6 87 6 5 6 7 88 4 4 4 5 89 4 5 6 4 90 5 5 5 5 91 3 4 4 4 92 7 7 7 6 93 6 4 6 6 94 6 6 6 5 95 4 4 4 4 96 5 4 5 5 97 6 6 6 7 98 5 3 5 6 99 6 4 4 5 100 6 7 7 6 101 4 5 6 6 102 5 5 5 5 103 6 6 6 6 104 5 5 6 6 105 5 5 5 5 106 4 4 5 5 107 4 5 5 4 108 6 6 5 7 109 5 5 7 7 110 6 5 6 5 111 5 4 7 7 112 6 4 6 6 113 5 5 5 5 114 4 5 4 5 115 6 6 6 7 116 4 5 4 5 117 5 3 3 5 118 5 5 5 5 119 6 3 5 5 120 3 4 5 3 121 5 4 5 5 122 4 5 5 5 123 5 2 5 4 124 5 5 3 4 125 7 7 7 7 126 5 6 6 6 127 7 6 6 6 128 5 5 4 6 129 4 4 4 5 130 6 6 6 5 131 4 3 4 5 132 4 7 6 6 133 4 3 2 2 134 4 4 5 5 135 6 6 5 7 136 6 5 6 6 137 5 6 5 5 138 3 4 4 5 139 6 6 6 6 140 5 6 6 6 141 4 4 5 5 142 5 5 5 5 143 2 3 2 2 144 5 6 6 7 145 7 5 6 7 146 4 6 5 5 147 4 5 6 6 148 7 6 7 5 149 6 5 5 6 150 5 6 5 5 151 5 5 5 6 152 5 5 6 6 153 7 6 7 7 154 6 5 7 6 155 6 6 6 6 156 5 5 5 6 157 2 4 6 6 158 4 4 4 4 159 6 4 6 6 160 5 5 6 4 161 5 4 4 5 162 5 5 5 5 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Q9 Q16 Q23 0.6441 0.2754 0.1359 0.4386 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.1928 -0.4788 0.0265 0.6651 1.9325 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.64415 0.41541 1.551 0.122992 Q9 0.27537 0.07764 3.547 0.000513 *** Q16 0.13594 0.09914 1.371 0.172239 Q23 0.43858 0.09543 4.596 8.77e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9016 on 158 degrees of freedom Multiple R-squared: 0.4366, Adjusted R-squared: 0.4259 F-statistic: 40.81 on 3 and 158 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.81132364 0.37735272 0.1886764 [2,] 0.74673675 0.50652650 0.2532632 [3,] 0.66496199 0.67007603 0.3350380 [4,] 0.58011835 0.83976331 0.4198817 [5,] 0.46619281 0.93238563 0.5338072 [6,] 0.47642232 0.95284464 0.5235777 [7,] 0.46196462 0.92392923 0.5380354 [8,] 0.36899792 0.73799585 0.6310021 [9,] 0.29411850 0.58823699 0.7058815 [10,] 0.29747803 0.59495606 0.7025220 [11,] 0.36319480 0.72638960 0.6368052 [12,] 0.34545582 0.69091165 0.6545442 [13,] 0.83527132 0.32945736 0.1647287 [14,] 0.80058516 0.39882968 0.1994148 [15,] 0.78143926 0.43712148 0.2185607 [16,] 0.73115048 0.53769903 0.2688495 [17,] 0.67140513 0.65718975 0.3285949 [18,] 0.62651696 0.74696608 0.3734830 [19,] 0.73361239 0.53277522 0.2663876 [20,] 0.67919039 0.64161922 0.3208096 [21,] 0.62111715 0.75776570 0.3788829 [22,] 0.57424110 0.85151781 0.4257589 [23,] 0.51236158 0.97527683 0.4876384 [24,] 0.65232915 0.69534170 0.3476708 [25,] 0.63908310 0.72183380 0.3609169 [26,] 0.59941358 0.80117284 0.4005864 [27,] 0.54407434 0.91185132 0.4559257 [28,] 0.49103692 0.98207383 0.5089631 [29,] 0.50630894 0.98738213 0.4936911 [30,] 0.47673467 0.95346934 0.5232653 [31,] 0.45129114 0.90258229 0.5487089 [32,] 0.39713138 0.79426276 0.6028686 [33,] 0.35239891 0.70479782 0.6476011 [34,] 0.32684037 0.65368074 0.6731596 [35,] 0.31790218 0.63580436 0.6820978 [36,] 0.29873589 0.59747178 0.7012641 [37,] 0.25424793 0.50849585 0.7457521 [38,] 0.21386067 0.42772134 0.7861393 [39,] 0.21892775 0.43785549 0.7810723 [40,] 0.27243151 0.54486303 0.7275685 [41,] 0.26851961 0.53703922 0.7314804 [42,] 0.26117585 0.52235171 0.7388241 [43,] 0.27316419 0.54632839 0.7268358 [44,] 0.43745322 0.87490645 0.5625468 [45,] 0.43319870 0.86639740 0.5668013 [46,] 0.42054737 0.84109475 0.5794526 [47,] 0.39742724 0.79485449 0.6025728 [48,] 0.48039537 0.96079074 0.5196046 [49,] 0.43450804 0.86901608 0.5654920 [50,] 0.61169745 0.77660511 0.3883026 [51,] 0.56492035 0.87015931 0.4350797 [52,] 0.52006879 0.95986243 0.4799312 [53,] 0.86964096 0.26071809 0.1303590 [54,] 0.85038794 0.29922412 0.1496121 [55,] 0.87907343 0.24185314 0.1209266 [56,] 0.86560161 0.26879678 0.1343984 [57,] 0.84037698 0.31924604 0.1596230 [58,] 0.81605468 0.36789063 0.1839453 [59,] 0.78810467 0.42379066 0.2118953 [60,] 0.78070507 0.43858985 0.2192949 [61,] 0.74784702 0.50430596 0.2521530 [62,] 0.71119727 0.57760545 0.2888027 [63,] 0.69336453 0.61327095 0.3066355 [64,] 0.73918096 0.52163808 0.2608190 [65,] 0.70668098 0.58663803 0.2933190 [66,] 0.68017517 0.63964967 0.3198248 [67,] 0.64931514 0.70136972 0.3506849 [68,] 0.60985563 0.78028873 0.3901444 [69,] 0.56940088 0.86119824 0.4305991 [70,] 0.52427779 0.95144441 0.4757222 [71,] 0.47889137 0.95778275 0.5211086 [72,] 0.50088322 0.99823355 0.4991168 [73,] 0.49610249 0.99220498 0.5038975 [74,] 0.45108390 0.90216780 0.5489161 [75,] 0.49907430 0.99814860 0.5009257 [76,] 0.65494511 0.69010978 0.3450549 [77,] 0.62715355 0.74569291 0.3728465 [78,] 0.58347123 0.83305755 0.4165288 [79,] 0.67905190 0.64189619 0.3209481 [80,] 0.71987425 0.56025151 0.2801258 [81,] 0.68163547 0.63672906 0.3183645 [82,] 0.65166400 0.69667201 0.3483360 [83,] 0.63184570 0.73630861 0.3681543 [84,] 0.58827425 0.82345150 0.4117258 [85,] 0.60704078 0.78591844 0.3929592 [86,] 0.60696879 0.78606242 0.3930312 [87,] 0.59553496 0.80893008 0.4044650 [88,] 0.57926963 0.84146075 0.4207304 [89,] 0.53421626 0.93156748 0.4657837 [90,] 0.49481740 0.98963480 0.5051826 [91,] 0.45279751 0.90559503 0.5472025 [92,] 0.40869155 0.81738311 0.5913084 [93,] 0.49504116 0.99008232 0.5049588 [94,] 0.44947817 0.89895634 0.5505218 [95,] 0.52076129 0.95847743 0.4792387 [96,] 0.47436253 0.94872506 0.5256375 [97,] 0.43473167 0.86946334 0.5652683 [98,] 0.39890620 0.79781240 0.6010938 [99,] 0.35443290 0.70886580 0.6455671 [100,] 0.33266701 0.66533401 0.6673330 [101,] 0.30290197 0.60580394 0.6970980 [102,] 0.27053513 0.54107025 0.7294649 [103,] 0.28021393 0.56042786 0.7197861 [104,] 0.28037702 0.56075404 0.7196230 [105,] 0.27713432 0.55426865 0.7228657 [106,] 0.26118352 0.52236705 0.7388165 [107,] 0.22330685 0.44661370 0.7766931 [108,] 0.20600855 0.41201710 0.7939915 [109,] 0.17373732 0.34747465 0.8262627 [110,] 0.15853218 0.31706435 0.8414678 [111,] 0.16210793 0.32421586 0.8378921 [112,] 0.13332471 0.26664943 0.8666753 [113,] 0.19584777 0.39169553 0.8041522 [114,] 0.20745771 0.41491543 0.7925423 [115,] 0.17752555 0.35505110 0.8224745 [116,] 0.17422157 0.34844314 0.8257784 [117,] 0.20638064 0.41276129 0.7936194 [118,] 0.20397707 0.40795415 0.7960229 [119,] 0.17708727 0.35417453 0.8229127 [120,] 0.16060840 0.32121680 0.8393916 [121,] 0.19893418 0.39786836 0.8010658 [122,] 0.16878067 0.33756133 0.8312193 [123,] 0.13847961 0.27695922 0.8615204 [124,] 0.12178868 0.24357735 0.8782113 [125,] 0.09550902 0.19101805 0.9044910 [126,] 0.21282216 0.42564432 0.7871778 [127,] 0.32014150 0.64028299 0.6798585 [128,] 0.27430473 0.54860946 0.7256953 [129,] 0.23216343 0.46432686 0.7678366 [130,] 0.20775019 0.41550037 0.7922498 [131,] 0.16604538 0.33209077 0.8339546 [132,] 0.17202340 0.34404680 0.8279766 [133,] 0.13497798 0.26995596 0.8650220 [134,] 0.12266867 0.24533734 0.8773313 [135,] 0.09521357 0.19042714 0.9047864 [136,] 0.06959878 0.13919757 0.9304012 [137,] 0.05027849 0.10055699 0.9497215 [138,] 0.05632311 0.11264622 0.9436769 [139,] 0.07684916 0.15369831 0.9231508 [140,] 0.13252013 0.26504026 0.8674799 [141,] 0.17714024 0.35428048 0.8228598 [142,] 0.17335175 0.34670350 0.8266482 [143,] 0.15135424 0.30270849 0.8486458 [144,] 0.13488694 0.26977389 0.8651131 [145,] 0.09062570 0.18125139 0.9093743 [146,] 0.05818097 0.11636193 0.9418190 [147,] 0.03804742 0.07609484 0.9619526 [148,] 0.03057119 0.06114239 0.9694288 [149,] 0.01367090 0.02734180 0.9863291 > postscript(file="/var/wessaorg/rcomp/tmp/10vw01353318648.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/wessaorg/rcomp/tmp/2zyje1353318648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/391lf1353318648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/447vc1353318648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5k5s21353318648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 162 Frequency = 1 1 2 3 4 5 6 1.28375006 0.10637694 1.68090185 -0.89362306 0.10637694 -0.18209891 7 8 9 10 11 12 1.23269682 0.66779573 -0.15483115 0.66779573 0.62984862 -0.46814798 13 14 15 16 17 18 -1.19626056 0.25648229 -0.31804263 -0.02956678 -0.32977749 0.81790109 19 20 21 22 23 24 -2.46814798 -0.46814798 -0.76835870 -0.33220427 -0.06856941 0.53185202 25 26 27 28 29 30 -1.61825334 -0.18209891 -0.18209891 0.38174666 -0.05683455 1.66779573 31 32 33 34 35 36 0.80722174 -0.46814798 -0.16899279 0.23164130 -1.02956678 0.66779573 37 38 39 40 41 42 0.65711639 0.10637694 0.40658765 -0.60757400 0.82032787 0.83448951 43 44 45 46 47 48 0.10637694 0.12053858 0.98111257 1.36758502 0.95627158 -0.74351771 49 50 51 52 53 54 1.10637694 -1.93157018 -0.92089083 -0.75419705 0.66779573 -1.48230963 55 56 57 58 59 60 0.25648229 1.93248611 -0.05683455 0.25648229 -2.87703464 -0.46814798 61 62 63 64 65 66 1.39242600 -0.61825334 -0.16899279 0.39242600 -0.31909815 0.83100721 67 68 69 70 71 72 0.25648229 -0.19277826 -0.60757400 -1.34288361 -0.33220427 0.53185202 73 74 75 76 77 78 -0.46814798 0.25648229 0.25648229 -0.04372842 -0.04372842 1.10637694 79 80 81 82 83 84 0.81790109 -0.07099619 -1.31804263 1.93248611 0.53185202 0.08259147 85 86 87 88 89 90 -1.61825334 1.25648229 0.09327081 -0.48230963 -0.59098557 0.10637694 91 92 93 94 95 96 -1.04372842 0.84516885 0.80722174 0.69506350 -0.04372842 0.38174666 97 98 99 100 101 102 -0.18209891 0.21853518 1.51769037 -0.15483115 -1.46814798 0.10637694 103 104 105 106 107 108 0.25648229 -0.46814798 0.10637694 -0.61825334 -0.45504186 -0.04615520 109 110 111 112 113 114 -1.04267290 0.97043322 -0.76730318 0.80722174 0.10637694 -0.75767935 115 116 117 118 119 120 -0.18209891 -0.75767935 0.92900381 0.10637694 1.65711639 -0.74109093 121 122 123 124 125 126 0.38174666 -0.89362306 1.37106732 0.81684557 0.40658765 -0.74351771 127 128 129 130 131 132 1.25648229 -0.19626056 -0.48230963 0.69506350 -0.20693990 -2.01888743 133 134 135 136 137 138 1.38069114 -0.61825334 -0.04615520 0.53185202 -0.16899279 -1.48230963 139 140 141 142 143 144 0.25648229 -0.74351771 -0.61825334 0.10637694 -0.61930886 -1.18209891 145 146 147 148 149 150 1.09327081 -1.16899279 -1.46814798 1.55911979 0.66779573 -0.16899279 151 152 153 154 155 156 -0.33220427 -0.46814798 0.68195737 0.39590830 0.25648229 -0.33220427 157 158 159 160 161 162 -3.19277826 -0.04372842 0.80722174 0.40901443 0.51769037 0.10637694 > postscript(file="/var/wessaorg/rcomp/tmp/68g8u1353318648.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 1.28375006 NA 1 0.10637694 1.28375006 2 1.68090185 0.10637694 3 -0.89362306 1.68090185 4 0.10637694 -0.89362306 5 -0.18209891 0.10637694 6 1.23269682 -0.18209891 7 0.66779573 1.23269682 8 -0.15483115 0.66779573 9 0.66779573 -0.15483115 10 0.62984862 0.66779573 11 -0.46814798 0.62984862 12 -1.19626056 -0.46814798 13 0.25648229 -1.19626056 14 -0.31804263 0.25648229 15 -0.02956678 -0.31804263 16 -0.32977749 -0.02956678 17 0.81790109 -0.32977749 18 -2.46814798 0.81790109 19 -0.46814798 -2.46814798 20 -0.76835870 -0.46814798 21 -0.33220427 -0.76835870 22 -0.06856941 -0.33220427 23 0.53185202 -0.06856941 24 -1.61825334 0.53185202 25 -0.18209891 -1.61825334 26 -0.18209891 -0.18209891 27 0.38174666 -0.18209891 28 -0.05683455 0.38174666 29 1.66779573 -0.05683455 30 0.80722174 1.66779573 31 -0.46814798 0.80722174 32 -0.16899279 -0.46814798 33 0.23164130 -0.16899279 34 -1.02956678 0.23164130 35 0.66779573 -1.02956678 36 0.65711639 0.66779573 37 0.10637694 0.65711639 38 0.40658765 0.10637694 39 -0.60757400 0.40658765 40 0.82032787 -0.60757400 41 0.83448951 0.82032787 42 0.10637694 0.83448951 43 0.12053858 0.10637694 44 0.98111257 0.12053858 45 1.36758502 0.98111257 46 0.95627158 1.36758502 47 -0.74351771 0.95627158 48 1.10637694 -0.74351771 49 -1.93157018 1.10637694 50 -0.92089083 -1.93157018 51 -0.75419705 -0.92089083 52 0.66779573 -0.75419705 53 -1.48230963 0.66779573 54 0.25648229 -1.48230963 55 1.93248611 0.25648229 56 -0.05683455 1.93248611 57 0.25648229 -0.05683455 58 -2.87703464 0.25648229 59 -0.46814798 -2.87703464 60 1.39242600 -0.46814798 61 -0.61825334 1.39242600 62 -0.16899279 -0.61825334 63 0.39242600 -0.16899279 64 -0.31909815 0.39242600 65 0.83100721 -0.31909815 66 0.25648229 0.83100721 67 -0.19277826 0.25648229 68 -0.60757400 -0.19277826 69 -1.34288361 -0.60757400 70 -0.33220427 -1.34288361 71 0.53185202 -0.33220427 72 -0.46814798 0.53185202 73 0.25648229 -0.46814798 74 0.25648229 0.25648229 75 -0.04372842 0.25648229 76 -0.04372842 -0.04372842 77 1.10637694 -0.04372842 78 0.81790109 1.10637694 79 -0.07099619 0.81790109 80 -1.31804263 -0.07099619 81 1.93248611 -1.31804263 82 0.53185202 1.93248611 83 0.08259147 0.53185202 84 -1.61825334 0.08259147 85 1.25648229 -1.61825334 86 0.09327081 1.25648229 87 -0.48230963 0.09327081 88 -0.59098557 -0.48230963 89 0.10637694 -0.59098557 90 -1.04372842 0.10637694 91 0.84516885 -1.04372842 92 0.80722174 0.84516885 93 0.69506350 0.80722174 94 -0.04372842 0.69506350 95 0.38174666 -0.04372842 96 -0.18209891 0.38174666 97 0.21853518 -0.18209891 98 1.51769037 0.21853518 99 -0.15483115 1.51769037 100 -1.46814798 -0.15483115 101 0.10637694 -1.46814798 102 0.25648229 0.10637694 103 -0.46814798 0.25648229 104 0.10637694 -0.46814798 105 -0.61825334 0.10637694 106 -0.45504186 -0.61825334 107 -0.04615520 -0.45504186 108 -1.04267290 -0.04615520 109 0.97043322 -1.04267290 110 -0.76730318 0.97043322 111 0.80722174 -0.76730318 112 0.10637694 0.80722174 113 -0.75767935 0.10637694 114 -0.18209891 -0.75767935 115 -0.75767935 -0.18209891 116 0.92900381 -0.75767935 117 0.10637694 0.92900381 118 1.65711639 0.10637694 119 -0.74109093 1.65711639 120 0.38174666 -0.74109093 121 -0.89362306 0.38174666 122 1.37106732 -0.89362306 123 0.81684557 1.37106732 124 0.40658765 0.81684557 125 -0.74351771 0.40658765 126 1.25648229 -0.74351771 127 -0.19626056 1.25648229 128 -0.48230963 -0.19626056 129 0.69506350 -0.48230963 130 -0.20693990 0.69506350 131 -2.01888743 -0.20693990 132 1.38069114 -2.01888743 133 -0.61825334 1.38069114 134 -0.04615520 -0.61825334 135 0.53185202 -0.04615520 136 -0.16899279 0.53185202 137 -1.48230963 -0.16899279 138 0.25648229 -1.48230963 139 -0.74351771 0.25648229 140 -0.61825334 -0.74351771 141 0.10637694 -0.61825334 142 -0.61930886 0.10637694 143 -1.18209891 -0.61930886 144 1.09327081 -1.18209891 145 -1.16899279 1.09327081 146 -1.46814798 -1.16899279 147 1.55911979 -1.46814798 148 0.66779573 1.55911979 149 -0.16899279 0.66779573 150 -0.33220427 -0.16899279 151 -0.46814798 -0.33220427 152 0.68195737 -0.46814798 153 0.39590830 0.68195737 154 0.25648229 0.39590830 155 -0.33220427 0.25648229 156 -3.19277826 -0.33220427 157 -0.04372842 -3.19277826 158 0.80722174 -0.04372842 159 0.40901443 0.80722174 160 0.51769037 0.40901443 161 0.10637694 0.51769037 162 NA 0.10637694 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.10637694 1.28375006 [2,] 1.68090185 0.10637694 [3,] -0.89362306 1.68090185 [4,] 0.10637694 -0.89362306 [5,] -0.18209891 0.10637694 [6,] 1.23269682 -0.18209891 [7,] 0.66779573 1.23269682 [8,] -0.15483115 0.66779573 [9,] 0.66779573 -0.15483115 [10,] 0.62984862 0.66779573 [11,] -0.46814798 0.62984862 [12,] -1.19626056 -0.46814798 [13,] 0.25648229 -1.19626056 [14,] -0.31804263 0.25648229 [15,] -0.02956678 -0.31804263 [16,] -0.32977749 -0.02956678 [17,] 0.81790109 -0.32977749 [18,] -2.46814798 0.81790109 [19,] -0.46814798 -2.46814798 [20,] -0.76835870 -0.46814798 [21,] -0.33220427 -0.76835870 [22,] -0.06856941 -0.33220427 [23,] 0.53185202 -0.06856941 [24,] -1.61825334 0.53185202 [25,] -0.18209891 -1.61825334 [26,] -0.18209891 -0.18209891 [27,] 0.38174666 -0.18209891 [28,] -0.05683455 0.38174666 [29,] 1.66779573 -0.05683455 [30,] 0.80722174 1.66779573 [31,] -0.46814798 0.80722174 [32,] -0.16899279 -0.46814798 [33,] 0.23164130 -0.16899279 [34,] -1.02956678 0.23164130 [35,] 0.66779573 -1.02956678 [36,] 0.65711639 0.66779573 [37,] 0.10637694 0.65711639 [38,] 0.40658765 0.10637694 [39,] -0.60757400 0.40658765 [40,] 0.82032787 -0.60757400 [41,] 0.83448951 0.82032787 [42,] 0.10637694 0.83448951 [43,] 0.12053858 0.10637694 [44,] 0.98111257 0.12053858 [45,] 1.36758502 0.98111257 [46,] 0.95627158 1.36758502 [47,] -0.74351771 0.95627158 [48,] 1.10637694 -0.74351771 [49,] -1.93157018 1.10637694 [50,] -0.92089083 -1.93157018 [51,] -0.75419705 -0.92089083 [52,] 0.66779573 -0.75419705 [53,] -1.48230963 0.66779573 [54,] 0.25648229 -1.48230963 [55,] 1.93248611 0.25648229 [56,] -0.05683455 1.93248611 [57,] 0.25648229 -0.05683455 [58,] -2.87703464 0.25648229 [59,] -0.46814798 -2.87703464 [60,] 1.39242600 -0.46814798 [61,] -0.61825334 1.39242600 [62,] -0.16899279 -0.61825334 [63,] 0.39242600 -0.16899279 [64,] -0.31909815 0.39242600 [65,] 0.83100721 -0.31909815 [66,] 0.25648229 0.83100721 [67,] -0.19277826 0.25648229 [68,] -0.60757400 -0.19277826 [69,] -1.34288361 -0.60757400 [70,] -0.33220427 -1.34288361 [71,] 0.53185202 -0.33220427 [72,] -0.46814798 0.53185202 [73,] 0.25648229 -0.46814798 [74,] 0.25648229 0.25648229 [75,] -0.04372842 0.25648229 [76,] -0.04372842 -0.04372842 [77,] 1.10637694 -0.04372842 [78,] 0.81790109 1.10637694 [79,] -0.07099619 0.81790109 [80,] -1.31804263 -0.07099619 [81,] 1.93248611 -1.31804263 [82,] 0.53185202 1.93248611 [83,] 0.08259147 0.53185202 [84,] -1.61825334 0.08259147 [85,] 1.25648229 -1.61825334 [86,] 0.09327081 1.25648229 [87,] -0.48230963 0.09327081 [88,] -0.59098557 -0.48230963 [89,] 0.10637694 -0.59098557 [90,] -1.04372842 0.10637694 [91,] 0.84516885 -1.04372842 [92,] 0.80722174 0.84516885 [93,] 0.69506350 0.80722174 [94,] -0.04372842 0.69506350 [95,] 0.38174666 -0.04372842 [96,] -0.18209891 0.38174666 [97,] 0.21853518 -0.18209891 [98,] 1.51769037 0.21853518 [99,] -0.15483115 1.51769037 [100,] -1.46814798 -0.15483115 [101,] 0.10637694 -1.46814798 [102,] 0.25648229 0.10637694 [103,] -0.46814798 0.25648229 [104,] 0.10637694 -0.46814798 [105,] -0.61825334 0.10637694 [106,] -0.45504186 -0.61825334 [107,] -0.04615520 -0.45504186 [108,] -1.04267290 -0.04615520 [109,] 0.97043322 -1.04267290 [110,] -0.76730318 0.97043322 [111,] 0.80722174 -0.76730318 [112,] 0.10637694 0.80722174 [113,] -0.75767935 0.10637694 [114,] -0.18209891 -0.75767935 [115,] -0.75767935 -0.18209891 [116,] 0.92900381 -0.75767935 [117,] 0.10637694 0.92900381 [118,] 1.65711639 0.10637694 [119,] -0.74109093 1.65711639 [120,] 0.38174666 -0.74109093 [121,] -0.89362306 0.38174666 [122,] 1.37106732 -0.89362306 [123,] 0.81684557 1.37106732 [124,] 0.40658765 0.81684557 [125,] -0.74351771 0.40658765 [126,] 1.25648229 -0.74351771 [127,] -0.19626056 1.25648229 [128,] -0.48230963 -0.19626056 [129,] 0.69506350 -0.48230963 [130,] -0.20693990 0.69506350 [131,] -2.01888743 -0.20693990 [132,] 1.38069114 -2.01888743 [133,] -0.61825334 1.38069114 [134,] -0.04615520 -0.61825334 [135,] 0.53185202 -0.04615520 [136,] -0.16899279 0.53185202 [137,] -1.48230963 -0.16899279 [138,] 0.25648229 -1.48230963 [139,] -0.74351771 0.25648229 [140,] -0.61825334 -0.74351771 [141,] 0.10637694 -0.61825334 [142,] -0.61930886 0.10637694 [143,] -1.18209891 -0.61930886 [144,] 1.09327081 -1.18209891 [145,] -1.16899279 1.09327081 [146,] -1.46814798 -1.16899279 [147,] 1.55911979 -1.46814798 [148,] 0.66779573 1.55911979 [149,] -0.16899279 0.66779573 [150,] -0.33220427 -0.16899279 [151,] -0.46814798 -0.33220427 [152,] 0.68195737 -0.46814798 [153,] 0.39590830 0.68195737 [154,] 0.25648229 0.39590830 [155,] -0.33220427 0.25648229 [156,] -3.19277826 -0.33220427 [157,] -0.04372842 -3.19277826 [158,] 0.80722174 -0.04372842 [159,] 0.40901443 0.80722174 [160,] 0.51769037 0.40901443 [161,] 0.10637694 0.51769037 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.10637694 1.28375006 2 1.68090185 0.10637694 3 -0.89362306 1.68090185 4 0.10637694 -0.89362306 5 -0.18209891 0.10637694 6 1.23269682 -0.18209891 7 0.66779573 1.23269682 8 -0.15483115 0.66779573 9 0.66779573 -0.15483115 10 0.62984862 0.66779573 11 -0.46814798 0.62984862 12 -1.19626056 -0.46814798 13 0.25648229 -1.19626056 14 -0.31804263 0.25648229 15 -0.02956678 -0.31804263 16 -0.32977749 -0.02956678 17 0.81790109 -0.32977749 18 -2.46814798 0.81790109 19 -0.46814798 -2.46814798 20 -0.76835870 -0.46814798 21 -0.33220427 -0.76835870 22 -0.06856941 -0.33220427 23 0.53185202 -0.06856941 24 -1.61825334 0.53185202 25 -0.18209891 -1.61825334 26 -0.18209891 -0.18209891 27 0.38174666 -0.18209891 28 -0.05683455 0.38174666 29 1.66779573 -0.05683455 30 0.80722174 1.66779573 31 -0.46814798 0.80722174 32 -0.16899279 -0.46814798 33 0.23164130 -0.16899279 34 -1.02956678 0.23164130 35 0.66779573 -1.02956678 36 0.65711639 0.66779573 37 0.10637694 0.65711639 38 0.40658765 0.10637694 39 -0.60757400 0.40658765 40 0.82032787 -0.60757400 41 0.83448951 0.82032787 42 0.10637694 0.83448951 43 0.12053858 0.10637694 44 0.98111257 0.12053858 45 1.36758502 0.98111257 46 0.95627158 1.36758502 47 -0.74351771 0.95627158 48 1.10637694 -0.74351771 49 -1.93157018 1.10637694 50 -0.92089083 -1.93157018 51 -0.75419705 -0.92089083 52 0.66779573 -0.75419705 53 -1.48230963 0.66779573 54 0.25648229 -1.48230963 55 1.93248611 0.25648229 56 -0.05683455 1.93248611 57 0.25648229 -0.05683455 58 -2.87703464 0.25648229 59 -0.46814798 -2.87703464 60 1.39242600 -0.46814798 61 -0.61825334 1.39242600 62 -0.16899279 -0.61825334 63 0.39242600 -0.16899279 64 -0.31909815 0.39242600 65 0.83100721 -0.31909815 66 0.25648229 0.83100721 67 -0.19277826 0.25648229 68 -0.60757400 -0.19277826 69 -1.34288361 -0.60757400 70 -0.33220427 -1.34288361 71 0.53185202 -0.33220427 72 -0.46814798 0.53185202 73 0.25648229 -0.46814798 74 0.25648229 0.25648229 75 -0.04372842 0.25648229 76 -0.04372842 -0.04372842 77 1.10637694 -0.04372842 78 0.81790109 1.10637694 79 -0.07099619 0.81790109 80 -1.31804263 -0.07099619 81 1.93248611 -1.31804263 82 0.53185202 1.93248611 83 0.08259147 0.53185202 84 -1.61825334 0.08259147 85 1.25648229 -1.61825334 86 0.09327081 1.25648229 87 -0.48230963 0.09327081 88 -0.59098557 -0.48230963 89 0.10637694 -0.59098557 90 -1.04372842 0.10637694 91 0.84516885 -1.04372842 92 0.80722174 0.84516885 93 0.69506350 0.80722174 94 -0.04372842 0.69506350 95 0.38174666 -0.04372842 96 -0.18209891 0.38174666 97 0.21853518 -0.18209891 98 1.51769037 0.21853518 99 -0.15483115 1.51769037 100 -1.46814798 -0.15483115 101 0.10637694 -1.46814798 102 0.25648229 0.10637694 103 -0.46814798 0.25648229 104 0.10637694 -0.46814798 105 -0.61825334 0.10637694 106 -0.45504186 -0.61825334 107 -0.04615520 -0.45504186 108 -1.04267290 -0.04615520 109 0.97043322 -1.04267290 110 -0.76730318 0.97043322 111 0.80722174 -0.76730318 112 0.10637694 0.80722174 113 -0.75767935 0.10637694 114 -0.18209891 -0.75767935 115 -0.75767935 -0.18209891 116 0.92900381 -0.75767935 117 0.10637694 0.92900381 118 1.65711639 0.10637694 119 -0.74109093 1.65711639 120 0.38174666 -0.74109093 121 -0.89362306 0.38174666 122 1.37106732 -0.89362306 123 0.81684557 1.37106732 124 0.40658765 0.81684557 125 -0.74351771 0.40658765 126 1.25648229 -0.74351771 127 -0.19626056 1.25648229 128 -0.48230963 -0.19626056 129 0.69506350 -0.48230963 130 -0.20693990 0.69506350 131 -2.01888743 -0.20693990 132 1.38069114 -2.01888743 133 -0.61825334 1.38069114 134 -0.04615520 -0.61825334 135 0.53185202 -0.04615520 136 -0.16899279 0.53185202 137 -1.48230963 -0.16899279 138 0.25648229 -1.48230963 139 -0.74351771 0.25648229 140 -0.61825334 -0.74351771 141 0.10637694 -0.61825334 142 -0.61930886 0.10637694 143 -1.18209891 -0.61930886 144 1.09327081 -1.18209891 145 -1.16899279 1.09327081 146 -1.46814798 -1.16899279 147 1.55911979 -1.46814798 148 0.66779573 1.55911979 149 -0.16899279 0.66779573 150 -0.33220427 -0.16899279 151 -0.46814798 -0.33220427 152 0.68195737 -0.46814798 153 0.39590830 0.68195737 154 0.25648229 0.39590830 155 -0.33220427 0.25648229 156 -3.19277826 -0.33220427 157 -0.04372842 -3.19277826 158 0.80722174 -0.04372842 159 0.40901443 0.80722174 160 0.51769037 0.40901443 161 0.10637694 0.51769037 > 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/wessaorg/rcomp/tmp/7vvsc1353318648.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/wessaorg/rcomp/tmp/86kc71353318648.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/wessaorg/rcomp/tmp/9u1sx1353318648.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/wessaorg/rcomp/tmp/107o8p1353318648.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1155ac1353318648.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12b69t1353318648.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13nrwj1353318648.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14pr061353318649.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/wessaorg/rcomp/tmp/15tln11353318649.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/wessaorg/rcomp/tmp/16wkms1353318649.tab") + } > > try(system("convert tmp/10vw01353318648.ps tmp/10vw01353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/2zyje1353318648.ps tmp/2zyje1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/391lf1353318648.ps tmp/391lf1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/447vc1353318648.ps tmp/447vc1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/5k5s21353318648.ps tmp/5k5s21353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/68g8u1353318648.ps tmp/68g8u1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/7vvsc1353318648.ps tmp/7vvsc1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/86kc71353318648.ps tmp/86kc71353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/9u1sx1353318648.ps tmp/9u1sx1353318648.png",intern=TRUE)) character(0) > try(system("convert tmp/107o8p1353318648.ps tmp/107o8p1353318648.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.308 1.292 10.595