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(4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,2 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + 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,1 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,1 + ,0 + ,1 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,1 + ,0 + ,1 + ,1 + ,2 + ,1 + ,3 + ,1 + ,0 + ,1 + ,1 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,1 + ,1 + ,0 + ,1 + ,2 + ,0 + ,3 + ,1 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,2 + ,1 + ,4 + ,1 + ,1 + ,1 + ,0 + ,2 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0) + ,dim=c(7 + ,154) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'T40enT20' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UseLimit','T40enT20','Used','CorrectAnalysis','Useful','Outcome'),1:154)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '7' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '7' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, 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 Outcome Weeks UseLimit T40enT20 Used CorrectAnalysis Useful 1 1 4 1 2 0 0 0 2 0 4 0 1 0 0 0 3 0 4 0 1 0 0 0 4 0 4 0 1 0 0 0 5 0 4 0 1 0 0 0 6 1 4 1 1 0 0 1 7 0 4 0 1 0 0 0 8 0 4 0 2 0 0 0 9 1 4 0 1 0 0 0 10 0 4 1 1 0 0 0 11 0 4 1 2 0 0 0 12 0 4 0 1 0 0 0 13 0 4 0 1 1 0 1 14 0 4 1 2 0 0 0 15 1 4 0 1 1 0 1 16 1 4 0 2 1 0 1 17 0 4 1 2 1 1 1 18 0 4 1 2 0 0 0 19 1 4 0 1 0 0 0 20 1 4 0 2 1 1 1 21 0 4 1 1 0 0 1 22 1 4 1 1 1 0 1 23 1 4 0 1 0 0 1 24 1 4 1 1 0 0 1 25 1 4 0 2 1 0 0 26 0 4 0 1 1 0 1 27 1 4 1 1 0 0 0 28 0 4 0 1 1 0 0 29 1 4 0 1 0 0 0 30 0 4 0 1 0 0 1 31 0 4 0 1 0 0 0 32 0 4 1 1 0 0 0 33 0 4 1 1 0 0 1 34 1 4 0 2 0 0 0 35 0 4 0 1 0 0 0 36 0 4 0 1 0 0 0 37 0 4 1 2 1 0 1 38 1 4 0 1 1 0 0 39 1 4 0 1 0 0 1 40 0 4 0 2 0 0 1 41 1 4 0 1 1 1 1 42 1 4 0 1 1 0 0 43 1 4 1 1 0 0 1 44 0 4 1 2 0 0 0 45 0 4 0 1 0 0 1 46 1 4 0 1 0 0 1 47 0 4 0 1 0 0 0 48 1 4 0 1 0 0 0 49 1 4 0 1 0 0 1 50 0 4 0 1 0 0 0 51 0 4 0 2 1 0 0 52 0 4 1 2 1 1 1 53 1 4 0 1 0 0 0 54 0 4 0 1 1 1 0 55 0 4 0 1 0 0 0 56 1 4 0 2 1 0 0 57 1 4 0 1 1 0 1 58 1 4 0 1 0 0 0 59 1 4 0 1 0 0 0 60 1 4 1 2 1 1 1 61 1 4 1 2 0 0 0 62 0 4 0 1 1 0 1 63 0 4 0 1 0 0 0 64 1 4 1 2 0 0 0 65 0 4 0 1 0 0 0 66 0 4 0 1 0 0 0 67 0 4 0 2 1 1 1 68 0 4 1 1 0 0 0 69 1 4 0 1 0 0 0 70 0 4 0 1 1 0 0 71 0 4 0 1 0 0 0 72 1 4 0 1 0 0 0 73 1 4 0 1 1 0 0 74 0 4 1 1 1 0 0 75 1 4 0 1 0 0 0 76 1 4 0 2 0 0 1 77 1 4 0 1 0 0 0 78 1 4 0 1 1 0 1 79 1 4 0 2 1 1 0 80 0 4 0 2 0 0 1 81 0 4 0 1 0 0 0 82 1 4 1 1 1 0 0 83 0 4 0 1 0 0 0 84 0 4 0 1 1 1 0 85 1 4 0 1 0 0 1 86 0 4 1 1 0 0 0 87 1 2 1 4 0 0 0 88 1 2 1 3 1 0 0 89 0 2 0 4 0 0 0 90 1 2 0 4 0 0 0 91 0 2 0 4 0 0 1 92 0 2 1 3 0 0 0 93 0 2 1 4 0 0 1 94 0 2 0 4 0 0 0 95 0 2 0 3 0 0 0 96 1 2 0 4 0 0 0 97 0 2 1 3 0 0 0 98 0 2 0 4 0 0 0 99 0 2 1 4 0 0 0 100 1 2 0 4 0 0 0 101 1 2 1 4 0 0 0 102 0 2 0 4 0 0 0 103 0 2 0 4 0 0 0 104 0 2 0 4 0 0 0 105 0 2 0 3 1 0 0 106 0 2 0 4 0 0 0 107 0 2 0 4 0 0 0 108 0 2 1 3 1 0 0 109 0 2 0 4 0 0 0 110 0 2 1 4 0 0 0 111 0 2 1 3 1 0 1 112 0 2 0 3 0 0 0 113 0 2 0 4 1 0 0 114 0 2 1 3 1 0 0 115 0 2 1 4 0 0 0 116 0 2 0 4 0 0 0 117 1 2 1 4 0 0 0 118 0 2 1 4 0 0 0 119 0 2 0 4 0 0 0 120 1 2 0 4 0 0 0 121 0 2 1 4 0 0 0 122 0 2 0 4 0 0 0 123 0 2 1 3 1 0 0 124 1 2 0 4 1 0 1 125 1 2 0 4 0 0 0 126 0 2 0 3 0 0 0 127 0 2 0 4 0 0 1 128 1 2 0 4 0 0 0 129 0 2 0 4 0 0 0 130 1 2 0 4 0 0 0 131 0 2 1 4 0 0 0 132 1 2 1 4 0 0 0 133 0 2 1 4 1 0 0 134 0 2 0 4 0 0 0 135 0 2 0 4 0 0 0 136 0 2 0 4 0 0 0 137 1 2 1 4 1 0 1 138 1 2 1 3 1 0 1 139 0 2 0 3 0 0 0 140 0 2 0 4 0 0 0 141 1 2 0 4 1 1 0 142 1 2 0 3 1 0 0 143 0 2 1 4 0 0 0 144 1 2 0 4 0 0 1 145 0 2 0 4 0 0 1 146 1 2 0 3 0 0 0 147 0 2 0 3 1 0 0 148 0 2 0 3 0 0 0 149 0 2 1 4 0 0 0 150 1 2 0 4 0 0 1 151 1 2 0 4 0 0 0 152 0 2 1 4 1 1 0 153 0 2 1 4 1 1 1 154 0 2 1 4 1 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit T40enT20 -0.28741 0.15286 -0.08900 0.07482 Used CorrectAnalysis Useful 0.10423 -0.15582 0.15326 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.6563 -0.3988 -0.2504 0.5284 0.7714 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.28741 0.60196 -0.477 0.634 Weeks 0.15286 0.12457 1.227 0.222 UseLimit -0.08900 0.08506 -1.046 0.297 T40enT20 0.07482 0.09403 0.796 0.428 Used 0.10423 0.10032 1.039 0.301 CorrectAnalysis -0.15582 0.17185 -0.907 0.366 Useful 0.15326 0.09418 1.627 0.106 Residual standard error: 0.485 on 147 degrees of freedom Multiple R-squared: 0.06116, Adjusted R-squared: 0.02284 F-statistic: 1.596 on 6 and 147 DF, p-value: 0.1522 > 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,] 0.8466588 0.3066823 0.1533412 [2,] 0.8178586 0.3642829 0.1821414 [3,] 0.7224935 0.5550129 0.2775065 [4,] 0.6171145 0.7657710 0.3828855 [5,] 0.5500165 0.8999671 0.4499835 [6,] 0.6434051 0.7131899 0.3565949 [7,] 0.5773041 0.8453918 0.4226959 [8,] 0.4862213 0.9724426 0.5137787 [9,] 0.4245026 0.8490052 0.5754974 [10,] 0.5868179 0.8263642 0.4131821 [11,] 0.6022796 0.7954409 0.3977204 [12,] 0.6667376 0.6665247 0.3332624 [13,] 0.6633806 0.6732389 0.3366194 [14,] 0.6187336 0.7625329 0.3812664 [15,] 0.5857529 0.8284942 0.4142471 [16,] 0.6083164 0.7833671 0.3916836 [17,] 0.6884371 0.6231258 0.3115629 [18,] 0.7658555 0.4682889 0.2341445 [19,] 0.7375468 0.5249063 0.2624532 [20,] 0.7768263 0.4463475 0.2231737 [21,] 0.7948181 0.4103638 0.2051819 [22,] 0.7661625 0.4676750 0.2338375 [23,] 0.7324917 0.5350166 0.2675083 [24,] 0.7306102 0.5387796 0.2693898 [25,] 0.7367665 0.5264669 0.2632335 [26,] 0.7069018 0.5861964 0.2930982 [27,] 0.6751925 0.6496150 0.3248075 [28,] 0.7133293 0.5733413 0.2866707 [29,] 0.7309015 0.5381970 0.2690985 [30,] 0.7233424 0.5533152 0.2766576 [31,] 0.7434078 0.5131844 0.2565922 [32,] 0.7295553 0.5408894 0.2704447 [33,] 0.7227658 0.5544683 0.2772342 [34,] 0.7332209 0.5335583 0.2667791 [35,] 0.7083121 0.5833757 0.2916879 [36,] 0.7101182 0.5797635 0.2898818 [37,] 0.7082338 0.5835323 0.2917662 [38,] 0.6893670 0.6212661 0.3106330 [39,] 0.7123615 0.5752771 0.2876385 [40,] 0.7052129 0.5895743 0.2947871 [41,] 0.6883102 0.6233796 0.3116898 [42,] 0.6982857 0.6034286 0.3017143 [43,] 0.7007084 0.5985832 0.2992916 [44,] 0.7196151 0.5607697 0.2803849 [45,] 0.7004268 0.5991465 0.2995732 [46,] 0.6852940 0.6294121 0.3147060 [47,] 0.6808201 0.6383597 0.3191799 [48,] 0.6506564 0.6986872 0.3493436 [49,] 0.6710256 0.6579488 0.3289744 [50,] 0.6897457 0.6205086 0.3102543 [51,] 0.6988270 0.6023459 0.3011730 [52,] 0.7272257 0.5455487 0.2727743 [53,] 0.7579915 0.4840170 0.2420085 [54,] 0.7445197 0.5109607 0.2554803 [55,] 0.7625663 0.4748674 0.2374337 [56,] 0.7487694 0.5024611 0.2512306 [57,] 0.7353521 0.5292958 0.2646479 [58,] 0.7498321 0.5003357 0.2501679 [59,] 0.7300932 0.5398137 0.2699068 [60,] 0.7446769 0.5106463 0.2553231 [61,] 0.7527896 0.4944208 0.2472104 [62,] 0.7442666 0.5114668 0.2557334 [63,] 0.7559157 0.4881686 0.2440843 [64,] 0.7497306 0.5005389 0.2502694 [65,] 0.7471942 0.5056116 0.2528058 [66,] 0.7591624 0.4816753 0.2408376 [67,] 0.7374840 0.5250319 0.2625160 [68,] 0.7573783 0.4852435 0.2426217 [69,] 0.7385524 0.5228953 0.2614476 [70,] 0.7549721 0.4900558 0.2450279 [71,] 0.7717196 0.4565607 0.2282804 [72,] 0.7567426 0.4865148 0.2432574 [73,] 0.7736347 0.4527306 0.2263653 [74,] 0.7542891 0.4914218 0.2457109 [75,] 0.7366070 0.5267860 0.2633930 [76,] 0.7361282 0.5277437 0.2638718 [77,] 0.7028354 0.5943291 0.2971646 [78,] 0.7251065 0.5497871 0.2748935 [79,] 0.7542729 0.4914541 0.2457271 [80,] 0.7681094 0.4637812 0.2318906 [81,] 0.7813990 0.4372020 0.2186010 [82,] 0.8050876 0.3898248 0.1949124 [83,] 0.7815553 0.4368895 0.2184447 [84,] 0.7783480 0.4433040 0.2216520 [85,] 0.7592566 0.4814868 0.2407434 [86,] 0.7288426 0.5423149 0.2711574 [87,] 0.7635630 0.4728739 0.2364370 [88,] 0.7283169 0.5433661 0.2716831 [89,] 0.7045963 0.5908075 0.2954037 [90,] 0.6689516 0.6620969 0.3310484 [91,] 0.7103151 0.5793697 0.2896849 [92,] 0.7775300 0.4449399 0.2224700 [93,] 0.7557306 0.4885389 0.2442694 [94,] 0.7323118 0.5353765 0.2676882 [95,] 0.7076910 0.5846180 0.2923090 [96,] 0.6840384 0.6319232 0.3159616 [97,] 0.6573912 0.6852175 0.3426088 [98,] 0.6307785 0.7384431 0.3692215 [99,] 0.5879199 0.8241603 0.4120801 [100,] 0.5603851 0.8792297 0.4396149 [101,] 0.5131251 0.9737498 0.4868749 [102,] 0.4987233 0.9974467 0.5012767 [103,] 0.4596847 0.9193695 0.5403153 [104,] 0.4537801 0.9075602 0.5462199 [105,] 0.4113273 0.8226545 0.5886727 [106,] 0.3631556 0.7263113 0.6368444 [107,] 0.3377698 0.6755396 0.6622302 [108,] 0.4464035 0.8928069 0.5535965 [109,] 0.3922648 0.7845297 0.6077352 [110,] 0.3679894 0.7359788 0.6320106 [111,] 0.4040131 0.8080262 0.5959869 [112,] 0.3495206 0.6990412 0.6504794 [113,] 0.3239853 0.6479705 0.6760147 [114,] 0.2833628 0.5667256 0.7166372 [115,] 0.2458833 0.4917666 0.7541167 [116,] 0.2771482 0.5542964 0.7228518 [117,] 0.2467844 0.4935688 0.7532156 [118,] 0.2731675 0.5463349 0.7268325 [119,] 0.3130824 0.6261648 0.6869176 [120,] 0.2779483 0.5558966 0.7220517 [121,] 0.3281083 0.6562166 0.6718917 [122,] 0.2683829 0.5367658 0.7316171 [123,] 0.4613304 0.9226607 0.5386696 [124,] 0.3984103 0.7968206 0.6015897 [125,] 0.3432673 0.6865346 0.6567327 [126,] 0.2951977 0.5903953 0.7048023 [127,] 0.2576096 0.5152192 0.7423904 [128,] 0.2322166 0.4644333 0.7677834 [129,] 0.3342845 0.6685690 0.6657155 [130,] 0.2972309 0.5944618 0.7027691 [131,] 0.4069163 0.8138326 0.5930837 [132,] 0.3105483 0.6210966 0.6894517 [133,] 0.3745632 0.7491265 0.6254368 [134,] 0.2567330 0.5134659 0.7432670 [135,] 0.1906328 0.3812657 0.8093672 > postscript(file="/var/fisher/rcomp/tmp/1vtpq1356085985.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2z5md1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3xh011356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4705a1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5lkpk1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 154 Frequency = 1 1 2 3 4 5 6 7 0.6153284 -0.3988542 -0.3988542 -0.3988542 -0.3988542 0.5368855 -0.3988542 8 9 10 11 12 13 14 -0.4736709 0.6011458 -0.3098550 -0.3846716 -0.3988542 -0.6563409 -0.3846716 15 16 17 18 19 20 21 0.3436591 0.2688424 -0.4863348 -0.3846716 0.6011458 0.4246659 -0.4631145 22 23 24 25 26 27 28 0.4326583 0.4478862 0.5368855 0.4221020 -0.6563409 0.6901450 -0.5030814 29 30 31 32 33 34 35 0.6011458 -0.5521138 -0.3988542 -0.3098550 -0.4631145 0.5263291 -0.3988542 36 37 38 39 40 41 42 -0.3988542 -0.6421583 0.4969186 0.4478862 -0.6269304 0.4994826 0.4969186 43 44 45 46 47 48 49 0.5368855 -0.3846716 -0.5521138 0.4478862 -0.3988542 0.6011458 0.4478862 50 51 52 53 54 55 56 -0.3988542 -0.5778980 -0.4863348 0.6011458 -0.3472579 -0.3988542 0.4221020 57 58 59 60 61 62 63 0.3436591 0.6011458 0.6011458 0.5136652 0.6153284 -0.6563409 -0.3988542 64 65 66 67 68 69 70 0.6153284 -0.3988542 -0.3988542 -0.5753341 -0.3098550 0.6011458 -0.5030814 71 72 73 74 75 76 77 -0.3988542 0.6011458 0.4969186 -0.4140821 0.6011458 0.3730696 0.6011458 78 79 80 81 82 83 84 0.3436591 0.5779255 -0.6269304 -0.3988542 0.5859179 -0.3988542 -0.3472579 85 86 87 88 89 90 91 0.4478862 -0.3098550 0.7714167 0.7420061 -0.3175826 0.6824174 -0.4708422 92 93 94 95 96 97 98 -0.1537667 -0.3818429 -0.3175826 -0.2427660 0.6824174 -0.1537667 -0.3175826 99 100 101 102 103 104 105 -0.2285833 0.6824174 0.7714167 -0.3175826 -0.3175826 -0.3175826 -0.3469931 106 107 108 109 110 111 112 -0.3175826 -0.3175826 -0.2579939 -0.3175826 -0.2285833 -0.4112534 -0.2427660 113 114 115 116 117 118 119 -0.4218098 -0.2579939 -0.2285833 -0.3175826 0.7714167 -0.2285833 -0.3175826 120 121 122 123 124 125 126 0.6824174 -0.2285833 -0.3175826 -0.2579939 0.4249307 0.6824174 -0.2427660 127 128 129 130 131 132 133 -0.4708422 0.6824174 -0.3175826 0.6824174 -0.2285833 0.7714167 -0.3328105 134 135 136 137 138 139 140 -0.3175826 -0.3175826 -0.3175826 0.5139300 0.5887466 -0.2427660 -0.3175826 141 142 143 144 145 146 147 0.7340137 0.6530069 -0.2285833 0.5291578 -0.4708422 0.7572340 -0.3469931 148 149 150 151 152 153 154 -0.2427660 -0.2285833 0.5291578 0.6824174 -0.1769870 -0.3302465 -0.3328105 > postscript(file="/var/fisher/rcomp/tmp/6o5t91356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 0.6153284 NA 1 -0.3988542 0.6153284 2 -0.3988542 -0.3988542 3 -0.3988542 -0.3988542 4 -0.3988542 -0.3988542 5 0.5368855 -0.3988542 6 -0.3988542 0.5368855 7 -0.4736709 -0.3988542 8 0.6011458 -0.4736709 9 -0.3098550 0.6011458 10 -0.3846716 -0.3098550 11 -0.3988542 -0.3846716 12 -0.6563409 -0.3988542 13 -0.3846716 -0.6563409 14 0.3436591 -0.3846716 15 0.2688424 0.3436591 16 -0.4863348 0.2688424 17 -0.3846716 -0.4863348 18 0.6011458 -0.3846716 19 0.4246659 0.6011458 20 -0.4631145 0.4246659 21 0.4326583 -0.4631145 22 0.4478862 0.4326583 23 0.5368855 0.4478862 24 0.4221020 0.5368855 25 -0.6563409 0.4221020 26 0.6901450 -0.6563409 27 -0.5030814 0.6901450 28 0.6011458 -0.5030814 29 -0.5521138 0.6011458 30 -0.3988542 -0.5521138 31 -0.3098550 -0.3988542 32 -0.4631145 -0.3098550 33 0.5263291 -0.4631145 34 -0.3988542 0.5263291 35 -0.3988542 -0.3988542 36 -0.6421583 -0.3988542 37 0.4969186 -0.6421583 38 0.4478862 0.4969186 39 -0.6269304 0.4478862 40 0.4994826 -0.6269304 41 0.4969186 0.4994826 42 0.5368855 0.4969186 43 -0.3846716 0.5368855 44 -0.5521138 -0.3846716 45 0.4478862 -0.5521138 46 -0.3988542 0.4478862 47 0.6011458 -0.3988542 48 0.4478862 0.6011458 49 -0.3988542 0.4478862 50 -0.5778980 -0.3988542 51 -0.4863348 -0.5778980 52 0.6011458 -0.4863348 53 -0.3472579 0.6011458 54 -0.3988542 -0.3472579 55 0.4221020 -0.3988542 56 0.3436591 0.4221020 57 0.6011458 0.3436591 58 0.6011458 0.6011458 59 0.5136652 0.6011458 60 0.6153284 0.5136652 61 -0.6563409 0.6153284 62 -0.3988542 -0.6563409 63 0.6153284 -0.3988542 64 -0.3988542 0.6153284 65 -0.3988542 -0.3988542 66 -0.5753341 -0.3988542 67 -0.3098550 -0.5753341 68 0.6011458 -0.3098550 69 -0.5030814 0.6011458 70 -0.3988542 -0.5030814 71 0.6011458 -0.3988542 72 0.4969186 0.6011458 73 -0.4140821 0.4969186 74 0.6011458 -0.4140821 75 0.3730696 0.6011458 76 0.6011458 0.3730696 77 0.3436591 0.6011458 78 0.5779255 0.3436591 79 -0.6269304 0.5779255 80 -0.3988542 -0.6269304 81 0.5859179 -0.3988542 82 -0.3988542 0.5859179 83 -0.3472579 -0.3988542 84 0.4478862 -0.3472579 85 -0.3098550 0.4478862 86 0.7714167 -0.3098550 87 0.7420061 0.7714167 88 -0.3175826 0.7420061 89 0.6824174 -0.3175826 90 -0.4708422 0.6824174 91 -0.1537667 -0.4708422 92 -0.3818429 -0.1537667 93 -0.3175826 -0.3818429 94 -0.2427660 -0.3175826 95 0.6824174 -0.2427660 96 -0.1537667 0.6824174 97 -0.3175826 -0.1537667 98 -0.2285833 -0.3175826 99 0.6824174 -0.2285833 100 0.7714167 0.6824174 101 -0.3175826 0.7714167 102 -0.3175826 -0.3175826 103 -0.3175826 -0.3175826 104 -0.3469931 -0.3175826 105 -0.3175826 -0.3469931 106 -0.3175826 -0.3175826 107 -0.2579939 -0.3175826 108 -0.3175826 -0.2579939 109 -0.2285833 -0.3175826 110 -0.4112534 -0.2285833 111 -0.2427660 -0.4112534 112 -0.4218098 -0.2427660 113 -0.2579939 -0.4218098 114 -0.2285833 -0.2579939 115 -0.3175826 -0.2285833 116 0.7714167 -0.3175826 117 -0.2285833 0.7714167 118 -0.3175826 -0.2285833 119 0.6824174 -0.3175826 120 -0.2285833 0.6824174 121 -0.3175826 -0.2285833 122 -0.2579939 -0.3175826 123 0.4249307 -0.2579939 124 0.6824174 0.4249307 125 -0.2427660 0.6824174 126 -0.4708422 -0.2427660 127 0.6824174 -0.4708422 128 -0.3175826 0.6824174 129 0.6824174 -0.3175826 130 -0.2285833 0.6824174 131 0.7714167 -0.2285833 132 -0.3328105 0.7714167 133 -0.3175826 -0.3328105 134 -0.3175826 -0.3175826 135 -0.3175826 -0.3175826 136 0.5139300 -0.3175826 137 0.5887466 0.5139300 138 -0.2427660 0.5887466 139 -0.3175826 -0.2427660 140 0.7340137 -0.3175826 141 0.6530069 0.7340137 142 -0.2285833 0.6530069 143 0.5291578 -0.2285833 144 -0.4708422 0.5291578 145 0.7572340 -0.4708422 146 -0.3469931 0.7572340 147 -0.2427660 -0.3469931 148 -0.2285833 -0.2427660 149 0.5291578 -0.2285833 150 0.6824174 0.5291578 151 -0.1769870 0.6824174 152 -0.3302465 -0.1769870 153 -0.3328105 -0.3302465 154 NA -0.3328105 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.3988542 0.6153284 [2,] -0.3988542 -0.3988542 [3,] -0.3988542 -0.3988542 [4,] -0.3988542 -0.3988542 [5,] 0.5368855 -0.3988542 [6,] -0.3988542 0.5368855 [7,] -0.4736709 -0.3988542 [8,] 0.6011458 -0.4736709 [9,] -0.3098550 0.6011458 [10,] -0.3846716 -0.3098550 [11,] -0.3988542 -0.3846716 [12,] -0.6563409 -0.3988542 [13,] -0.3846716 -0.6563409 [14,] 0.3436591 -0.3846716 [15,] 0.2688424 0.3436591 [16,] -0.4863348 0.2688424 [17,] -0.3846716 -0.4863348 [18,] 0.6011458 -0.3846716 [19,] 0.4246659 0.6011458 [20,] -0.4631145 0.4246659 [21,] 0.4326583 -0.4631145 [22,] 0.4478862 0.4326583 [23,] 0.5368855 0.4478862 [24,] 0.4221020 0.5368855 [25,] -0.6563409 0.4221020 [26,] 0.6901450 -0.6563409 [27,] -0.5030814 0.6901450 [28,] 0.6011458 -0.5030814 [29,] -0.5521138 0.6011458 [30,] -0.3988542 -0.5521138 [31,] -0.3098550 -0.3988542 [32,] -0.4631145 -0.3098550 [33,] 0.5263291 -0.4631145 [34,] -0.3988542 0.5263291 [35,] -0.3988542 -0.3988542 [36,] -0.6421583 -0.3988542 [37,] 0.4969186 -0.6421583 [38,] 0.4478862 0.4969186 [39,] -0.6269304 0.4478862 [40,] 0.4994826 -0.6269304 [41,] 0.4969186 0.4994826 [42,] 0.5368855 0.4969186 [43,] -0.3846716 0.5368855 [44,] -0.5521138 -0.3846716 [45,] 0.4478862 -0.5521138 [46,] -0.3988542 0.4478862 [47,] 0.6011458 -0.3988542 [48,] 0.4478862 0.6011458 [49,] -0.3988542 0.4478862 [50,] -0.5778980 -0.3988542 [51,] -0.4863348 -0.5778980 [52,] 0.6011458 -0.4863348 [53,] -0.3472579 0.6011458 [54,] -0.3988542 -0.3472579 [55,] 0.4221020 -0.3988542 [56,] 0.3436591 0.4221020 [57,] 0.6011458 0.3436591 [58,] 0.6011458 0.6011458 [59,] 0.5136652 0.6011458 [60,] 0.6153284 0.5136652 [61,] -0.6563409 0.6153284 [62,] -0.3988542 -0.6563409 [63,] 0.6153284 -0.3988542 [64,] -0.3988542 0.6153284 [65,] -0.3988542 -0.3988542 [66,] -0.5753341 -0.3988542 [67,] -0.3098550 -0.5753341 [68,] 0.6011458 -0.3098550 [69,] -0.5030814 0.6011458 [70,] -0.3988542 -0.5030814 [71,] 0.6011458 -0.3988542 [72,] 0.4969186 0.6011458 [73,] -0.4140821 0.4969186 [74,] 0.6011458 -0.4140821 [75,] 0.3730696 0.6011458 [76,] 0.6011458 0.3730696 [77,] 0.3436591 0.6011458 [78,] 0.5779255 0.3436591 [79,] -0.6269304 0.5779255 [80,] -0.3988542 -0.6269304 [81,] 0.5859179 -0.3988542 [82,] -0.3988542 0.5859179 [83,] -0.3472579 -0.3988542 [84,] 0.4478862 -0.3472579 [85,] -0.3098550 0.4478862 [86,] 0.7714167 -0.3098550 [87,] 0.7420061 0.7714167 [88,] -0.3175826 0.7420061 [89,] 0.6824174 -0.3175826 [90,] -0.4708422 0.6824174 [91,] -0.1537667 -0.4708422 [92,] -0.3818429 -0.1537667 [93,] -0.3175826 -0.3818429 [94,] -0.2427660 -0.3175826 [95,] 0.6824174 -0.2427660 [96,] -0.1537667 0.6824174 [97,] -0.3175826 -0.1537667 [98,] -0.2285833 -0.3175826 [99,] 0.6824174 -0.2285833 [100,] 0.7714167 0.6824174 [101,] -0.3175826 0.7714167 [102,] -0.3175826 -0.3175826 [103,] -0.3175826 -0.3175826 [104,] -0.3469931 -0.3175826 [105,] -0.3175826 -0.3469931 [106,] -0.3175826 -0.3175826 [107,] -0.2579939 -0.3175826 [108,] -0.3175826 -0.2579939 [109,] -0.2285833 -0.3175826 [110,] -0.4112534 -0.2285833 [111,] -0.2427660 -0.4112534 [112,] -0.4218098 -0.2427660 [113,] -0.2579939 -0.4218098 [114,] -0.2285833 -0.2579939 [115,] -0.3175826 -0.2285833 [116,] 0.7714167 -0.3175826 [117,] -0.2285833 0.7714167 [118,] -0.3175826 -0.2285833 [119,] 0.6824174 -0.3175826 [120,] -0.2285833 0.6824174 [121,] -0.3175826 -0.2285833 [122,] -0.2579939 -0.3175826 [123,] 0.4249307 -0.2579939 [124,] 0.6824174 0.4249307 [125,] -0.2427660 0.6824174 [126,] -0.4708422 -0.2427660 [127,] 0.6824174 -0.4708422 [128,] -0.3175826 0.6824174 [129,] 0.6824174 -0.3175826 [130,] -0.2285833 0.6824174 [131,] 0.7714167 -0.2285833 [132,] -0.3328105 0.7714167 [133,] -0.3175826 -0.3328105 [134,] -0.3175826 -0.3175826 [135,] -0.3175826 -0.3175826 [136,] 0.5139300 -0.3175826 [137,] 0.5887466 0.5139300 [138,] -0.2427660 0.5887466 [139,] -0.3175826 -0.2427660 [140,] 0.7340137 -0.3175826 [141,] 0.6530069 0.7340137 [142,] -0.2285833 0.6530069 [143,] 0.5291578 -0.2285833 [144,] -0.4708422 0.5291578 [145,] 0.7572340 -0.4708422 [146,] -0.3469931 0.7572340 [147,] -0.2427660 -0.3469931 [148,] -0.2285833 -0.2427660 [149,] 0.5291578 -0.2285833 [150,] 0.6824174 0.5291578 [151,] -0.1769870 0.6824174 [152,] -0.3302465 -0.1769870 [153,] -0.3328105 -0.3302465 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.3988542 0.6153284 2 -0.3988542 -0.3988542 3 -0.3988542 -0.3988542 4 -0.3988542 -0.3988542 5 0.5368855 -0.3988542 6 -0.3988542 0.5368855 7 -0.4736709 -0.3988542 8 0.6011458 -0.4736709 9 -0.3098550 0.6011458 10 -0.3846716 -0.3098550 11 -0.3988542 -0.3846716 12 -0.6563409 -0.3988542 13 -0.3846716 -0.6563409 14 0.3436591 -0.3846716 15 0.2688424 0.3436591 16 -0.4863348 0.2688424 17 -0.3846716 -0.4863348 18 0.6011458 -0.3846716 19 0.4246659 0.6011458 20 -0.4631145 0.4246659 21 0.4326583 -0.4631145 22 0.4478862 0.4326583 23 0.5368855 0.4478862 24 0.4221020 0.5368855 25 -0.6563409 0.4221020 26 0.6901450 -0.6563409 27 -0.5030814 0.6901450 28 0.6011458 -0.5030814 29 -0.5521138 0.6011458 30 -0.3988542 -0.5521138 31 -0.3098550 -0.3988542 32 -0.4631145 -0.3098550 33 0.5263291 -0.4631145 34 -0.3988542 0.5263291 35 -0.3988542 -0.3988542 36 -0.6421583 -0.3988542 37 0.4969186 -0.6421583 38 0.4478862 0.4969186 39 -0.6269304 0.4478862 40 0.4994826 -0.6269304 41 0.4969186 0.4994826 42 0.5368855 0.4969186 43 -0.3846716 0.5368855 44 -0.5521138 -0.3846716 45 0.4478862 -0.5521138 46 -0.3988542 0.4478862 47 0.6011458 -0.3988542 48 0.4478862 0.6011458 49 -0.3988542 0.4478862 50 -0.5778980 -0.3988542 51 -0.4863348 -0.5778980 52 0.6011458 -0.4863348 53 -0.3472579 0.6011458 54 -0.3988542 -0.3472579 55 0.4221020 -0.3988542 56 0.3436591 0.4221020 57 0.6011458 0.3436591 58 0.6011458 0.6011458 59 0.5136652 0.6011458 60 0.6153284 0.5136652 61 -0.6563409 0.6153284 62 -0.3988542 -0.6563409 63 0.6153284 -0.3988542 64 -0.3988542 0.6153284 65 -0.3988542 -0.3988542 66 -0.5753341 -0.3988542 67 -0.3098550 -0.5753341 68 0.6011458 -0.3098550 69 -0.5030814 0.6011458 70 -0.3988542 -0.5030814 71 0.6011458 -0.3988542 72 0.4969186 0.6011458 73 -0.4140821 0.4969186 74 0.6011458 -0.4140821 75 0.3730696 0.6011458 76 0.6011458 0.3730696 77 0.3436591 0.6011458 78 0.5779255 0.3436591 79 -0.6269304 0.5779255 80 -0.3988542 -0.6269304 81 0.5859179 -0.3988542 82 -0.3988542 0.5859179 83 -0.3472579 -0.3988542 84 0.4478862 -0.3472579 85 -0.3098550 0.4478862 86 0.7714167 -0.3098550 87 0.7420061 0.7714167 88 -0.3175826 0.7420061 89 0.6824174 -0.3175826 90 -0.4708422 0.6824174 91 -0.1537667 -0.4708422 92 -0.3818429 -0.1537667 93 -0.3175826 -0.3818429 94 -0.2427660 -0.3175826 95 0.6824174 -0.2427660 96 -0.1537667 0.6824174 97 -0.3175826 -0.1537667 98 -0.2285833 -0.3175826 99 0.6824174 -0.2285833 100 0.7714167 0.6824174 101 -0.3175826 0.7714167 102 -0.3175826 -0.3175826 103 -0.3175826 -0.3175826 104 -0.3469931 -0.3175826 105 -0.3175826 -0.3469931 106 -0.3175826 -0.3175826 107 -0.2579939 -0.3175826 108 -0.3175826 -0.2579939 109 -0.2285833 -0.3175826 110 -0.4112534 -0.2285833 111 -0.2427660 -0.4112534 112 -0.4218098 -0.2427660 113 -0.2579939 -0.4218098 114 -0.2285833 -0.2579939 115 -0.3175826 -0.2285833 116 0.7714167 -0.3175826 117 -0.2285833 0.7714167 118 -0.3175826 -0.2285833 119 0.6824174 -0.3175826 120 -0.2285833 0.6824174 121 -0.3175826 -0.2285833 122 -0.2579939 -0.3175826 123 0.4249307 -0.2579939 124 0.6824174 0.4249307 125 -0.2427660 0.6824174 126 -0.4708422 -0.2427660 127 0.6824174 -0.4708422 128 -0.3175826 0.6824174 129 0.6824174 -0.3175826 130 -0.2285833 0.6824174 131 0.7714167 -0.2285833 132 -0.3328105 0.7714167 133 -0.3175826 -0.3328105 134 -0.3175826 -0.3175826 135 -0.3175826 -0.3175826 136 0.5139300 -0.3175826 137 0.5887466 0.5139300 138 -0.2427660 0.5887466 139 -0.3175826 -0.2427660 140 0.7340137 -0.3175826 141 0.6530069 0.7340137 142 -0.2285833 0.6530069 143 0.5291578 -0.2285833 144 -0.4708422 0.5291578 145 0.7572340 -0.4708422 146 -0.3469931 0.7572340 147 -0.2427660 -0.3469931 148 -0.2285833 -0.2427660 149 0.5291578 -0.2285833 150 0.6824174 0.5291578 151 -0.1769870 0.6824174 152 -0.3302465 -0.1769870 153 -0.3328105 -0.3302465 > 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/fisher/rcomp/tmp/7rywi1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8lbub1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/93bvp1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10yd4o1356085985.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/118edg1356085985.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/fisher/rcomp/tmp/12m6uv1356085985.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/fisher/rcomp/tmp/13cwuo1356085985.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/fisher/rcomp/tmp/14p5k91356085985.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/fisher/rcomp/tmp/15mmmr1356085985.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/fisher/rcomp/tmp/16nrdx1356085985.tab") + } > > try(system("convert tmp/1vtpq1356085985.ps tmp/1vtpq1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/2z5md1356085985.ps tmp/2z5md1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/3xh011356085985.ps tmp/3xh011356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/4705a1356085985.ps tmp/4705a1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/5lkpk1356085985.ps tmp/5lkpk1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/6o5t91356085985.ps tmp/6o5t91356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/7rywi1356085985.ps tmp/7rywi1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/8lbub1356085985.ps tmp/8lbub1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/93bvp1356085985.ps tmp/93bvp1356085985.png",intern=TRUE)) character(0) > try(system("convert tmp/10yd4o1356085985.ps tmp/10yd4o1356085985.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.116 1.777 9.888