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 + ,0 + ,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 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,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 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,2 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,2 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,2 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0) + ,dim=c(7 + ,154) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'Treatment' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UseLimit','Treatment','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 = '5' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '5' > #'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 CorrectAnalysis Weeks UseLimit Treatment Used Useful Outcome 1 0 4 1 0 0 0 1 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 0 4 1 1 0 1 1 7 0 4 0 1 0 0 0 8 0 4 0 0 0 0 0 9 0 4 0 1 0 0 1 10 0 4 1 1 0 0 0 11 0 4 1 0 0 0 0 12 0 4 0 1 0 0 0 13 0 4 0 1 1 1 0 14 0 4 1 0 0 0 0 15 0 4 0 1 1 1 1 16 0 4 0 0 1 1 1 17 1 4 1 0 1 1 0 18 0 4 1 0 0 0 0 19 0 4 0 1 0 0 1 20 1 4 0 0 1 1 1 21 0 4 1 1 0 1 0 22 0 4 1 1 1 1 1 23 0 4 0 1 0 1 1 24 0 4 1 1 0 1 1 25 0 4 0 0 1 0 1 26 0 4 0 1 1 1 0 27 0 4 1 1 0 0 1 28 0 4 0 1 1 0 0 29 0 4 0 1 0 0 1 30 0 4 0 1 0 1 0 31 0 4 0 1 0 0 0 32 0 4 1 1 0 0 0 33 0 4 1 1 0 1 0 34 0 4 0 0 0 0 1 35 0 4 0 1 0 0 0 36 0 4 0 1 0 0 0 37 0 4 1 0 1 1 0 38 0 4 0 1 1 0 1 39 0 4 0 1 0 1 1 40 0 4 0 0 0 1 0 41 1 4 0 1 1 1 1 42 0 4 0 1 1 0 1 43 0 4 1 1 0 1 1 44 0 4 1 0 0 0 0 45 0 4 0 1 0 1 0 46 0 4 0 1 0 1 1 47 0 4 0 1 0 0 0 48 0 4 0 1 0 0 1 49 0 4 0 1 0 1 1 50 0 4 0 1 0 0 0 51 0 4 0 0 1 0 0 52 1 4 1 0 1 1 0 53 0 4 0 1 0 0 1 54 1 4 0 1 1 0 0 55 0 4 0 1 0 0 0 56 0 4 0 0 1 0 1 57 0 4 0 1 1 1 1 58 0 4 0 1 0 0 1 59 0 4 0 1 0 0 1 60 1 4 1 0 1 1 1 61 0 4 1 0 0 0 1 62 0 4 0 1 1 1 0 63 0 4 0 1 0 0 0 64 0 4 1 0 0 0 1 65 0 4 0 1 0 0 0 66 0 4 0 1 0 0 0 67 1 4 0 0 1 1 0 68 0 4 1 1 0 0 0 69 0 4 0 1 0 0 1 70 0 4 0 1 1 0 0 71 0 4 0 1 0 0 0 72 0 4 0 1 0 0 1 73 0 4 0 1 1 0 1 74 0 4 1 1 1 0 0 75 0 4 0 1 0 0 1 76 0 4 0 0 0 1 1 77 0 4 0 1 0 0 1 78 0 4 0 1 1 1 1 79 1 4 0 0 1 0 1 80 0 4 0 0 0 1 0 81 0 4 0 1 0 0 0 82 0 4 1 1 1 0 1 83 0 4 0 1 0 0 0 84 1 4 0 1 1 0 0 85 0 4 0 1 0 1 1 86 0 4 1 1 0 0 0 87 0 2 1 1 0 0 1 88 0 2 1 0 1 0 1 89 0 2 0 1 0 0 0 90 0 2 0 1 0 0 1 91 0 2 0 1 0 1 0 92 0 2 1 0 0 0 0 93 0 2 1 1 0 1 0 94 0 2 0 1 0 0 0 95 0 2 0 0 0 0 0 96 0 2 0 1 0 0 1 97 0 2 1 0 0 0 0 98 0 2 0 1 0 0 0 99 0 2 1 1 0 0 0 100 0 2 0 1 0 0 1 101 0 2 1 1 0 0 1 102 0 2 0 1 0 0 0 103 0 2 0 1 0 0 0 104 0 2 0 1 0 0 0 105 0 2 0 0 1 0 0 106 0 2 0 1 0 0 0 107 0 2 0 1 0 0 0 108 0 2 1 0 1 0 0 109 0 2 0 1 0 0 0 110 0 2 1 1 0 0 0 111 0 2 1 0 1 1 0 112 0 2 0 0 0 0 0 113 0 2 0 1 1 0 0 114 0 2 1 0 1 0 0 115 0 2 1 1 0 0 0 116 0 2 0 1 0 0 0 117 0 2 1 1 0 0 1 118 0 2 1 1 0 0 0 119 0 2 0 1 0 0 0 120 0 2 0 1 0 0 1 121 0 2 1 1 0 0 0 122 0 2 0 1 0 0 0 123 0 2 1 0 1 0 0 124 0 2 0 1 1 1 1 125 0 2 0 1 0 0 1 126 0 2 0 0 0 0 0 127 0 2 0 1 0 1 0 128 0 2 0 1 0 0 1 129 0 2 0 1 0 0 0 130 0 2 0 1 0 0 1 131 0 2 1 1 0 0 0 132 0 2 1 1 0 0 1 133 0 2 1 1 1 0 0 134 0 2 0 1 0 0 0 135 0 2 0 1 0 0 0 136 0 2 0 1 0 0 0 137 0 2 1 1 1 1 1 138 0 2 1 0 1 1 1 139 0 2 0 0 0 0 0 140 0 2 0 1 0 0 0 141 1 2 0 1 1 0 1 142 0 2 0 0 1 0 1 143 0 2 1 1 0 0 0 144 0 2 0 1 0 1 1 145 0 2 0 1 0 1 0 146 0 2 0 0 0 0 1 147 0 2 0 0 1 0 0 148 0 2 0 0 0 0 0 149 0 2 1 1 0 0 0 150 0 2 0 1 0 1 1 151 0 2 0 1 0 0 1 152 1 2 1 1 1 0 0 153 1 2 1 1 1 1 0 154 0 2 1 1 1 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit Treatment Used Useful -0.036634 0.017825 0.002774 -0.023698 0.245491 0.056832 Outcome -0.028338 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.33976 -0.06664 -0.01097 0.02468 0.80753 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.036634 0.079782 -0.459 0.647 Weeks 0.017825 0.020411 0.873 0.384 UseLimit 0.002774 0.043003 0.065 0.949 Treatment -0.023698 0.046967 -0.505 0.615 Used 0.245491 0.046272 5.305 4.06e-07 *** Useful 0.056832 0.047115 1.206 0.230 Outcome -0.028338 0.041040 -0.690 0.491 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2421 on 147 degrees of freedom Multiple R-squared: 0.2212, Adjusted R-squared: 0.1894 F-statistic: 6.959 on 6 and 147 DF, p-value: 1.581e-06 > 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,] 0.000000000 0.000000000 1.000000000 [2,] 0.000000000 0.000000000 1.000000000 [3,] 0.000000000 0.000000000 1.000000000 [4,] 0.000000000 0.000000000 1.000000000 [5,] 0.000000000 0.000000000 1.000000000 [6,] 0.000000000 0.000000000 1.000000000 [7,] 0.000000000 0.000000000 1.000000000 [8,] 0.395288480 0.790576961 0.604711520 [9,] 0.344539865 0.689079731 0.655460135 [10,] 0.309787639 0.619575277 0.690212361 [11,] 0.819097174 0.361805652 0.180902826 [12,] 0.763505717 0.472988566 0.236494283 [13,] 0.751676142 0.496647716 0.248323858 [14,] 0.688417830 0.623164340 0.311582170 [15,] 0.621692346 0.756615307 0.378307654 [16,] 0.610971376 0.778057249 0.389028624 [17,] 0.616136704 0.767726593 0.383863296 [18,] 0.572213868 0.855572264 0.427786132 [19,] 0.519769925 0.960460150 0.480230075 [20,] 0.465429530 0.930859060 0.534570470 [21,] 0.408770847 0.817541694 0.591229153 [22,] 0.350675374 0.701350747 0.649324626 [23,] 0.296531092 0.593062185 0.703468908 [24,] 0.248507185 0.497014369 0.751492815 [25,] 0.207996694 0.415993387 0.792003306 [26,] 0.168464296 0.336928592 0.831535704 [27,] 0.134188756 0.268377513 0.865811244 [28,] 0.177833286 0.355666572 0.822166714 [29,] 0.150187851 0.300375703 0.849812149 [30,] 0.119329464 0.238658928 0.880670536 [31,] 0.109190068 0.218380135 0.890809932 [32,] 0.560072793 0.879854415 0.439927207 [33,] 0.532278332 0.935443337 0.467721668 [34,] 0.479694505 0.959389011 0.520305495 [35,] 0.426651354 0.853302709 0.573348646 [36,] 0.378394543 0.756789087 0.621605457 [37,] 0.331083102 0.662166204 0.668916898 [38,] 0.286320044 0.572640089 0.713679956 [39,] 0.243554893 0.487109786 0.756445107 [40,] 0.206127470 0.412254940 0.793872530 [41,] 0.172206421 0.344412843 0.827793579 [42,] 0.174910518 0.349821035 0.825089482 [43,] 0.453905032 0.907810064 0.546094968 [44,] 0.407376059 0.814752118 0.592623941 [45,] 0.810321290 0.379357419 0.189678710 [46,] 0.775153647 0.449692706 0.224846353 [47,] 0.777325830 0.445348339 0.222674170 [48,] 0.788230003 0.423539995 0.211769997 [49,] 0.753657299 0.492685402 0.246342701 [50,] 0.716083070 0.567833860 0.283916930 [51,] 0.910167143 0.179665715 0.089832857 [52,] 0.889661397 0.220677207 0.110338603 [53,] 0.903177472 0.193645056 0.096822528 [54,] 0.882149004 0.235701992 0.117850996 [55,] 0.858413702 0.283172597 0.141586298 [56,] 0.831380899 0.337238203 0.168619101 [57,] 0.801442369 0.397115261 0.198557631 [58,] 0.948151400 0.103697199 0.051848600 [59,] 0.934090305 0.131819391 0.065909695 [60,] 0.918632940 0.162734119 0.081367060 [61,] 0.922778427 0.154443146 0.077221573 [62,] 0.905365185 0.189269630 0.094634815 [63,] 0.885542346 0.228915309 0.114457654 [64,] 0.891063259 0.217873482 0.108936741 [65,] 0.901780101 0.196439798 0.098219899 [66,] 0.883794450 0.232411100 0.116205550 [67,] 0.866270447 0.267459106 0.133729553 [68,] 0.844881530 0.310236939 0.155118470 [69,] 0.875426884 0.249146232 0.124573116 [70,] 0.984459182 0.031081637 0.015540818 [71,] 0.981015383 0.037969234 0.018984617 [72,] 0.975873601 0.048252797 0.024126399 [73,] 0.981737456 0.036525088 0.018262544 [74,] 0.980049789 0.039900422 0.019950211 [75,] 0.998275794 0.003448413 0.001724206 [76,] 0.997458350 0.005083300 0.002541650 [77,] 0.996303488 0.007393024 0.003696512 [78,] 0.994685971 0.010628057 0.005314029 [79,] 0.993973848 0.012052304 0.006026152 [80,] 0.991732973 0.016534055 0.008267027 [81,] 0.988770725 0.022458550 0.011229275 [82,] 0.984512310 0.030975379 0.015487690 [83,] 0.980250089 0.039499822 0.019749911 [84,] 0.973389900 0.053220201 0.026610100 [85,] 0.964904262 0.070191477 0.035095738 [86,] 0.957036172 0.085927657 0.042963828 [87,] 0.944970718 0.110058564 0.055029282 [88,] 0.934805148 0.130389704 0.065194852 [89,] 0.917397867 0.165204265 0.082602133 [90,] 0.896614008 0.206771984 0.103385992 [91,] 0.872672557 0.254654886 0.127327443 [92,] 0.845053021 0.309893959 0.154946979 [93,] 0.812587162 0.374825675 0.187412838 [94,] 0.776068623 0.447862753 0.223931377 [95,] 0.735656145 0.528687711 0.264343855 [96,] 0.724026707 0.551946587 0.275973293 [97,] 0.679325348 0.641349304 0.320674652 [98,] 0.631663472 0.736673057 0.368336528 [99,] 0.609223589 0.781552822 0.390776411 [100,] 0.558278531 0.883442938 0.441721469 [101,] 0.505475347 0.989049305 0.494524653 [102,] 0.485875285 0.971750570 0.514124715 [103,] 0.443564535 0.887129071 0.556435465 [104,] 0.485501291 0.971002582 0.514498709 [105,] 0.457325599 0.914651198 0.542674401 [106,] 0.403485310 0.806970619 0.596514690 [107,] 0.352668371 0.705336742 0.647331629 [108,] 0.303379879 0.606759758 0.696620121 [109,] 0.255742738 0.511485477 0.744257262 [110,] 0.213901053 0.427802107 0.786098947 [111,] 0.174307152 0.348614304 0.825692848 [112,] 0.139330173 0.278660347 0.860669827 [113,] 0.110774750 0.221549499 0.889225250 [114,] 0.098721042 0.197442083 0.901278958 [115,] 0.118983667 0.237967333 0.881016333 [116,] 0.091271685 0.182543371 0.908728315 [117,] 0.073898013 0.147796025 0.926101987 [118,] 0.054661548 0.109323096 0.945338452 [119,] 0.039190751 0.078381502 0.960809249 [120,] 0.028014130 0.056028260 0.971985870 [121,] 0.019082466 0.038164931 0.980917534 [122,] 0.012446094 0.024892188 0.987553906 [123,] 0.008036704 0.016073408 0.991963296 [124,] 0.014464848 0.028929696 0.985535152 [125,] 0.009697640 0.019395280 0.990302360 [126,] 0.006494518 0.012989035 0.993505482 [127,] 0.004456045 0.008912090 0.995543955 [128,] 0.007620317 0.015240633 0.992379683 [129,] 0.006715535 0.013431070 0.993284465 [130,] 0.005417705 0.010835411 0.994582295 [131,] 0.002731591 0.005463182 0.997268409 [132,] 0.033031266 0.066062533 0.966968734 [133,] 0.027451948 0.054903897 0.972548052 [134,] 0.015136927 0.030273854 0.984863073 [135,] 0.007203530 0.014407060 0.992796470 > postscript(file="/var/fisher/rcomp/tmp/1zfo21356089675.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/24bba1356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3zp2h1356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4dkm21356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5zixg1356089675.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 -0.0091012198 -0.0109672286 -0.0109672286 -0.0109672286 -0.0109672286 6 7 8 9 10 -0.0422356175 -0.0109672286 -0.0346651147 0.0173704453 -0.0137410075 11 12 13 14 15 -0.0374388936 -0.0109672286 -0.3132900376 -0.0374388936 -0.2849523638 16 17 18 19 20 -0.3086502499 0.6602382974 -0.0374388936 0.0173704453 0.6913497501 21 22 23 24 25 -0.0705732913 -0.2877261427 -0.0394618386 -0.0422356175 -0.2518179661 26 27 28 29 30 -0.3132900376 0.0145966663 -0.2564577538 0.0173704453 -0.0677995124 31 32 33 34 35 -0.0109672286 -0.0137410075 -0.0705732913 -0.0063274409 -0.0109672286 36 37 38 39 40 -0.0109672286 -0.3397617026 -0.2281200800 -0.0394618386 -0.0914973985 41 42 43 44 45 0.7150476362 -0.2281200800 -0.0422356175 -0.0374388936 -0.0677995124 46 47 48 49 50 -0.0394618386 -0.0109672286 0.0173704453 -0.0394618386 -0.0109672286 51 52 53 54 55 -0.2801556399 0.6602382974 0.0173704453 0.7435422462 -0.0109672286 56 57 58 59 60 -0.2518179661 -0.2849523638 0.0173704453 0.0173704453 0.6885759712 61 62 63 64 65 -0.0091012198 -0.3132900376 -0.0109672286 -0.0091012198 -0.0109672286 66 67 68 69 70 -0.0109672286 0.6630120763 -0.0137410075 0.0173704453 -0.2564577538 71 72 73 74 75 -0.0109672286 0.0173704453 -0.2281200800 -0.2592315327 0.0173704453 76 77 78 79 80 -0.0631597247 0.0173704453 -0.2849523638 0.7481820339 -0.0914973985 81 82 83 84 85 -0.0109672286 -0.2308938589 -0.0109672286 0.7435422462 -0.0394618386 86 87 88 89 90 -0.0137410075 0.0502460305 -0.2189423809 0.0246821355 0.0530198094 91 92 93 94 95 -0.0321501483 -0.0017895295 -0.0349239272 0.0246821355 0.0009842494 96 97 98 99 100 0.0530198094 -0.0017895295 0.0246821355 0.0219083566 0.0530198094 101 102 103 104 105 0.0502460305 0.0246821355 0.0246821355 0.0246821355 -0.2445062758 106 107 108 109 110 0.0246821355 0.0246821355 -0.2472800547 0.0246821355 0.0219083566 111 112 113 114 115 -0.3041123385 0.0009842494 -0.2208083897 -0.2472800547 0.0219083566 116 117 118 119 120 0.0246821355 0.0502460305 0.0219083566 0.0246821355 0.0530198094 121 122 123 124 125 0.0219083566 0.0246821355 -0.2472800547 -0.2493029997 0.0530198094 126 127 128 129 130 0.0009842494 -0.0321501483 0.0530198094 0.0246821355 0.0530198094 131 132 133 134 135 0.0219083566 0.0502460305 -0.2235821686 0.0246821355 0.0246821355 136 137 138 139 140 0.0246821355 -0.2520767786 -0.2757746647 0.0009842494 0.0246821355 141 142 143 144 145 0.8075292841 -0.2161686020 0.0219083566 -0.0038124744 -0.0321501483 146 147 148 149 150 0.0293219233 -0.2445062758 0.0009842494 0.0219083566 -0.0038124744 151 152 153 154 0.0530198094 0.7764178314 0.7195855476 -0.2235821686 > postscript(file="/var/fisher/rcomp/tmp/6ehvv1356089675.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 -0.0091012198 NA 1 -0.0109672286 -0.0091012198 2 -0.0109672286 -0.0109672286 3 -0.0109672286 -0.0109672286 4 -0.0109672286 -0.0109672286 5 -0.0422356175 -0.0109672286 6 -0.0109672286 -0.0422356175 7 -0.0346651147 -0.0109672286 8 0.0173704453 -0.0346651147 9 -0.0137410075 0.0173704453 10 -0.0374388936 -0.0137410075 11 -0.0109672286 -0.0374388936 12 -0.3132900376 -0.0109672286 13 -0.0374388936 -0.3132900376 14 -0.2849523638 -0.0374388936 15 -0.3086502499 -0.2849523638 16 0.6602382974 -0.3086502499 17 -0.0374388936 0.6602382974 18 0.0173704453 -0.0374388936 19 0.6913497501 0.0173704453 20 -0.0705732913 0.6913497501 21 -0.2877261427 -0.0705732913 22 -0.0394618386 -0.2877261427 23 -0.0422356175 -0.0394618386 24 -0.2518179661 -0.0422356175 25 -0.3132900376 -0.2518179661 26 0.0145966663 -0.3132900376 27 -0.2564577538 0.0145966663 28 0.0173704453 -0.2564577538 29 -0.0677995124 0.0173704453 30 -0.0109672286 -0.0677995124 31 -0.0137410075 -0.0109672286 32 -0.0705732913 -0.0137410075 33 -0.0063274409 -0.0705732913 34 -0.0109672286 -0.0063274409 35 -0.0109672286 -0.0109672286 36 -0.3397617026 -0.0109672286 37 -0.2281200800 -0.3397617026 38 -0.0394618386 -0.2281200800 39 -0.0914973985 -0.0394618386 40 0.7150476362 -0.0914973985 41 -0.2281200800 0.7150476362 42 -0.0422356175 -0.2281200800 43 -0.0374388936 -0.0422356175 44 -0.0677995124 -0.0374388936 45 -0.0394618386 -0.0677995124 46 -0.0109672286 -0.0394618386 47 0.0173704453 -0.0109672286 48 -0.0394618386 0.0173704453 49 -0.0109672286 -0.0394618386 50 -0.2801556399 -0.0109672286 51 0.6602382974 -0.2801556399 52 0.0173704453 0.6602382974 53 0.7435422462 0.0173704453 54 -0.0109672286 0.7435422462 55 -0.2518179661 -0.0109672286 56 -0.2849523638 -0.2518179661 57 0.0173704453 -0.2849523638 58 0.0173704453 0.0173704453 59 0.6885759712 0.0173704453 60 -0.0091012198 0.6885759712 61 -0.3132900376 -0.0091012198 62 -0.0109672286 -0.3132900376 63 -0.0091012198 -0.0109672286 64 -0.0109672286 -0.0091012198 65 -0.0109672286 -0.0109672286 66 0.6630120763 -0.0109672286 67 -0.0137410075 0.6630120763 68 0.0173704453 -0.0137410075 69 -0.2564577538 0.0173704453 70 -0.0109672286 -0.2564577538 71 0.0173704453 -0.0109672286 72 -0.2281200800 0.0173704453 73 -0.2592315327 -0.2281200800 74 0.0173704453 -0.2592315327 75 -0.0631597247 0.0173704453 76 0.0173704453 -0.0631597247 77 -0.2849523638 0.0173704453 78 0.7481820339 -0.2849523638 79 -0.0914973985 0.7481820339 80 -0.0109672286 -0.0914973985 81 -0.2308938589 -0.0109672286 82 -0.0109672286 -0.2308938589 83 0.7435422462 -0.0109672286 84 -0.0394618386 0.7435422462 85 -0.0137410075 -0.0394618386 86 0.0502460305 -0.0137410075 87 -0.2189423809 0.0502460305 88 0.0246821355 -0.2189423809 89 0.0530198094 0.0246821355 90 -0.0321501483 0.0530198094 91 -0.0017895295 -0.0321501483 92 -0.0349239272 -0.0017895295 93 0.0246821355 -0.0349239272 94 0.0009842494 0.0246821355 95 0.0530198094 0.0009842494 96 -0.0017895295 0.0530198094 97 0.0246821355 -0.0017895295 98 0.0219083566 0.0246821355 99 0.0530198094 0.0219083566 100 0.0502460305 0.0530198094 101 0.0246821355 0.0502460305 102 0.0246821355 0.0246821355 103 0.0246821355 0.0246821355 104 -0.2445062758 0.0246821355 105 0.0246821355 -0.2445062758 106 0.0246821355 0.0246821355 107 -0.2472800547 0.0246821355 108 0.0246821355 -0.2472800547 109 0.0219083566 0.0246821355 110 -0.3041123385 0.0219083566 111 0.0009842494 -0.3041123385 112 -0.2208083897 0.0009842494 113 -0.2472800547 -0.2208083897 114 0.0219083566 -0.2472800547 115 0.0246821355 0.0219083566 116 0.0502460305 0.0246821355 117 0.0219083566 0.0502460305 118 0.0246821355 0.0219083566 119 0.0530198094 0.0246821355 120 0.0219083566 0.0530198094 121 0.0246821355 0.0219083566 122 -0.2472800547 0.0246821355 123 -0.2493029997 -0.2472800547 124 0.0530198094 -0.2493029997 125 0.0009842494 0.0530198094 126 -0.0321501483 0.0009842494 127 0.0530198094 -0.0321501483 128 0.0246821355 0.0530198094 129 0.0530198094 0.0246821355 130 0.0219083566 0.0530198094 131 0.0502460305 0.0219083566 132 -0.2235821686 0.0502460305 133 0.0246821355 -0.2235821686 134 0.0246821355 0.0246821355 135 0.0246821355 0.0246821355 136 -0.2520767786 0.0246821355 137 -0.2757746647 -0.2520767786 138 0.0009842494 -0.2757746647 139 0.0246821355 0.0009842494 140 0.8075292841 0.0246821355 141 -0.2161686020 0.8075292841 142 0.0219083566 -0.2161686020 143 -0.0038124744 0.0219083566 144 -0.0321501483 -0.0038124744 145 0.0293219233 -0.0321501483 146 -0.2445062758 0.0293219233 147 0.0009842494 -0.2445062758 148 0.0219083566 0.0009842494 149 -0.0038124744 0.0219083566 150 0.0530198094 -0.0038124744 151 0.7764178314 0.0530198094 152 0.7195855476 0.7764178314 153 -0.2235821686 0.7195855476 154 NA -0.2235821686 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.0109672286 -0.0091012198 [2,] -0.0109672286 -0.0109672286 [3,] -0.0109672286 -0.0109672286 [4,] -0.0109672286 -0.0109672286 [5,] -0.0422356175 -0.0109672286 [6,] -0.0109672286 -0.0422356175 [7,] -0.0346651147 -0.0109672286 [8,] 0.0173704453 -0.0346651147 [9,] -0.0137410075 0.0173704453 [10,] -0.0374388936 -0.0137410075 [11,] -0.0109672286 -0.0374388936 [12,] -0.3132900376 -0.0109672286 [13,] -0.0374388936 -0.3132900376 [14,] -0.2849523638 -0.0374388936 [15,] -0.3086502499 -0.2849523638 [16,] 0.6602382974 -0.3086502499 [17,] -0.0374388936 0.6602382974 [18,] 0.0173704453 -0.0374388936 [19,] 0.6913497501 0.0173704453 [20,] -0.0705732913 0.6913497501 [21,] -0.2877261427 -0.0705732913 [22,] -0.0394618386 -0.2877261427 [23,] -0.0422356175 -0.0394618386 [24,] -0.2518179661 -0.0422356175 [25,] -0.3132900376 -0.2518179661 [26,] 0.0145966663 -0.3132900376 [27,] -0.2564577538 0.0145966663 [28,] 0.0173704453 -0.2564577538 [29,] -0.0677995124 0.0173704453 [30,] -0.0109672286 -0.0677995124 [31,] -0.0137410075 -0.0109672286 [32,] -0.0705732913 -0.0137410075 [33,] -0.0063274409 -0.0705732913 [34,] -0.0109672286 -0.0063274409 [35,] -0.0109672286 -0.0109672286 [36,] -0.3397617026 -0.0109672286 [37,] -0.2281200800 -0.3397617026 [38,] -0.0394618386 -0.2281200800 [39,] -0.0914973985 -0.0394618386 [40,] 0.7150476362 -0.0914973985 [41,] -0.2281200800 0.7150476362 [42,] -0.0422356175 -0.2281200800 [43,] -0.0374388936 -0.0422356175 [44,] -0.0677995124 -0.0374388936 [45,] -0.0394618386 -0.0677995124 [46,] -0.0109672286 -0.0394618386 [47,] 0.0173704453 -0.0109672286 [48,] -0.0394618386 0.0173704453 [49,] -0.0109672286 -0.0394618386 [50,] -0.2801556399 -0.0109672286 [51,] 0.6602382974 -0.2801556399 [52,] 0.0173704453 0.6602382974 [53,] 0.7435422462 0.0173704453 [54,] -0.0109672286 0.7435422462 [55,] -0.2518179661 -0.0109672286 [56,] -0.2849523638 -0.2518179661 [57,] 0.0173704453 -0.2849523638 [58,] 0.0173704453 0.0173704453 [59,] 0.6885759712 0.0173704453 [60,] -0.0091012198 0.6885759712 [61,] -0.3132900376 -0.0091012198 [62,] -0.0109672286 -0.3132900376 [63,] -0.0091012198 -0.0109672286 [64,] -0.0109672286 -0.0091012198 [65,] -0.0109672286 -0.0109672286 [66,] 0.6630120763 -0.0109672286 [67,] -0.0137410075 0.6630120763 [68,] 0.0173704453 -0.0137410075 [69,] -0.2564577538 0.0173704453 [70,] -0.0109672286 -0.2564577538 [71,] 0.0173704453 -0.0109672286 [72,] -0.2281200800 0.0173704453 [73,] -0.2592315327 -0.2281200800 [74,] 0.0173704453 -0.2592315327 [75,] -0.0631597247 0.0173704453 [76,] 0.0173704453 -0.0631597247 [77,] -0.2849523638 0.0173704453 [78,] 0.7481820339 -0.2849523638 [79,] -0.0914973985 0.7481820339 [80,] -0.0109672286 -0.0914973985 [81,] -0.2308938589 -0.0109672286 [82,] -0.0109672286 -0.2308938589 [83,] 0.7435422462 -0.0109672286 [84,] -0.0394618386 0.7435422462 [85,] -0.0137410075 -0.0394618386 [86,] 0.0502460305 -0.0137410075 [87,] -0.2189423809 0.0502460305 [88,] 0.0246821355 -0.2189423809 [89,] 0.0530198094 0.0246821355 [90,] -0.0321501483 0.0530198094 [91,] -0.0017895295 -0.0321501483 [92,] -0.0349239272 -0.0017895295 [93,] 0.0246821355 -0.0349239272 [94,] 0.0009842494 0.0246821355 [95,] 0.0530198094 0.0009842494 [96,] -0.0017895295 0.0530198094 [97,] 0.0246821355 -0.0017895295 [98,] 0.0219083566 0.0246821355 [99,] 0.0530198094 0.0219083566 [100,] 0.0502460305 0.0530198094 [101,] 0.0246821355 0.0502460305 [102,] 0.0246821355 0.0246821355 [103,] 0.0246821355 0.0246821355 [104,] -0.2445062758 0.0246821355 [105,] 0.0246821355 -0.2445062758 [106,] 0.0246821355 0.0246821355 [107,] -0.2472800547 0.0246821355 [108,] 0.0246821355 -0.2472800547 [109,] 0.0219083566 0.0246821355 [110,] -0.3041123385 0.0219083566 [111,] 0.0009842494 -0.3041123385 [112,] -0.2208083897 0.0009842494 [113,] -0.2472800547 -0.2208083897 [114,] 0.0219083566 -0.2472800547 [115,] 0.0246821355 0.0219083566 [116,] 0.0502460305 0.0246821355 [117,] 0.0219083566 0.0502460305 [118,] 0.0246821355 0.0219083566 [119,] 0.0530198094 0.0246821355 [120,] 0.0219083566 0.0530198094 [121,] 0.0246821355 0.0219083566 [122,] -0.2472800547 0.0246821355 [123,] -0.2493029997 -0.2472800547 [124,] 0.0530198094 -0.2493029997 [125,] 0.0009842494 0.0530198094 [126,] -0.0321501483 0.0009842494 [127,] 0.0530198094 -0.0321501483 [128,] 0.0246821355 0.0530198094 [129,] 0.0530198094 0.0246821355 [130,] 0.0219083566 0.0530198094 [131,] 0.0502460305 0.0219083566 [132,] -0.2235821686 0.0502460305 [133,] 0.0246821355 -0.2235821686 [134,] 0.0246821355 0.0246821355 [135,] 0.0246821355 0.0246821355 [136,] -0.2520767786 0.0246821355 [137,] -0.2757746647 -0.2520767786 [138,] 0.0009842494 -0.2757746647 [139,] 0.0246821355 0.0009842494 [140,] 0.8075292841 0.0246821355 [141,] -0.2161686020 0.8075292841 [142,] 0.0219083566 -0.2161686020 [143,] -0.0038124744 0.0219083566 [144,] -0.0321501483 -0.0038124744 [145,] 0.0293219233 -0.0321501483 [146,] -0.2445062758 0.0293219233 [147,] 0.0009842494 -0.2445062758 [148,] 0.0219083566 0.0009842494 [149,] -0.0038124744 0.0219083566 [150,] 0.0530198094 -0.0038124744 [151,] 0.7764178314 0.0530198094 [152,] 0.7195855476 0.7764178314 [153,] -0.2235821686 0.7195855476 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.0109672286 -0.0091012198 2 -0.0109672286 -0.0109672286 3 -0.0109672286 -0.0109672286 4 -0.0109672286 -0.0109672286 5 -0.0422356175 -0.0109672286 6 -0.0109672286 -0.0422356175 7 -0.0346651147 -0.0109672286 8 0.0173704453 -0.0346651147 9 -0.0137410075 0.0173704453 10 -0.0374388936 -0.0137410075 11 -0.0109672286 -0.0374388936 12 -0.3132900376 -0.0109672286 13 -0.0374388936 -0.3132900376 14 -0.2849523638 -0.0374388936 15 -0.3086502499 -0.2849523638 16 0.6602382974 -0.3086502499 17 -0.0374388936 0.6602382974 18 0.0173704453 -0.0374388936 19 0.6913497501 0.0173704453 20 -0.0705732913 0.6913497501 21 -0.2877261427 -0.0705732913 22 -0.0394618386 -0.2877261427 23 -0.0422356175 -0.0394618386 24 -0.2518179661 -0.0422356175 25 -0.3132900376 -0.2518179661 26 0.0145966663 -0.3132900376 27 -0.2564577538 0.0145966663 28 0.0173704453 -0.2564577538 29 -0.0677995124 0.0173704453 30 -0.0109672286 -0.0677995124 31 -0.0137410075 -0.0109672286 32 -0.0705732913 -0.0137410075 33 -0.0063274409 -0.0705732913 34 -0.0109672286 -0.0063274409 35 -0.0109672286 -0.0109672286 36 -0.3397617026 -0.0109672286 37 -0.2281200800 -0.3397617026 38 -0.0394618386 -0.2281200800 39 -0.0914973985 -0.0394618386 40 0.7150476362 -0.0914973985 41 -0.2281200800 0.7150476362 42 -0.0422356175 -0.2281200800 43 -0.0374388936 -0.0422356175 44 -0.0677995124 -0.0374388936 45 -0.0394618386 -0.0677995124 46 -0.0109672286 -0.0394618386 47 0.0173704453 -0.0109672286 48 -0.0394618386 0.0173704453 49 -0.0109672286 -0.0394618386 50 -0.2801556399 -0.0109672286 51 0.6602382974 -0.2801556399 52 0.0173704453 0.6602382974 53 0.7435422462 0.0173704453 54 -0.0109672286 0.7435422462 55 -0.2518179661 -0.0109672286 56 -0.2849523638 -0.2518179661 57 0.0173704453 -0.2849523638 58 0.0173704453 0.0173704453 59 0.6885759712 0.0173704453 60 -0.0091012198 0.6885759712 61 -0.3132900376 -0.0091012198 62 -0.0109672286 -0.3132900376 63 -0.0091012198 -0.0109672286 64 -0.0109672286 -0.0091012198 65 -0.0109672286 -0.0109672286 66 0.6630120763 -0.0109672286 67 -0.0137410075 0.6630120763 68 0.0173704453 -0.0137410075 69 -0.2564577538 0.0173704453 70 -0.0109672286 -0.2564577538 71 0.0173704453 -0.0109672286 72 -0.2281200800 0.0173704453 73 -0.2592315327 -0.2281200800 74 0.0173704453 -0.2592315327 75 -0.0631597247 0.0173704453 76 0.0173704453 -0.0631597247 77 -0.2849523638 0.0173704453 78 0.7481820339 -0.2849523638 79 -0.0914973985 0.7481820339 80 -0.0109672286 -0.0914973985 81 -0.2308938589 -0.0109672286 82 -0.0109672286 -0.2308938589 83 0.7435422462 -0.0109672286 84 -0.0394618386 0.7435422462 85 -0.0137410075 -0.0394618386 86 0.0502460305 -0.0137410075 87 -0.2189423809 0.0502460305 88 0.0246821355 -0.2189423809 89 0.0530198094 0.0246821355 90 -0.0321501483 0.0530198094 91 -0.0017895295 -0.0321501483 92 -0.0349239272 -0.0017895295 93 0.0246821355 -0.0349239272 94 0.0009842494 0.0246821355 95 0.0530198094 0.0009842494 96 -0.0017895295 0.0530198094 97 0.0246821355 -0.0017895295 98 0.0219083566 0.0246821355 99 0.0530198094 0.0219083566 100 0.0502460305 0.0530198094 101 0.0246821355 0.0502460305 102 0.0246821355 0.0246821355 103 0.0246821355 0.0246821355 104 -0.2445062758 0.0246821355 105 0.0246821355 -0.2445062758 106 0.0246821355 0.0246821355 107 -0.2472800547 0.0246821355 108 0.0246821355 -0.2472800547 109 0.0219083566 0.0246821355 110 -0.3041123385 0.0219083566 111 0.0009842494 -0.3041123385 112 -0.2208083897 0.0009842494 113 -0.2472800547 -0.2208083897 114 0.0219083566 -0.2472800547 115 0.0246821355 0.0219083566 116 0.0502460305 0.0246821355 117 0.0219083566 0.0502460305 118 0.0246821355 0.0219083566 119 0.0530198094 0.0246821355 120 0.0219083566 0.0530198094 121 0.0246821355 0.0219083566 122 -0.2472800547 0.0246821355 123 -0.2493029997 -0.2472800547 124 0.0530198094 -0.2493029997 125 0.0009842494 0.0530198094 126 -0.0321501483 0.0009842494 127 0.0530198094 -0.0321501483 128 0.0246821355 0.0530198094 129 0.0530198094 0.0246821355 130 0.0219083566 0.0530198094 131 0.0502460305 0.0219083566 132 -0.2235821686 0.0502460305 133 0.0246821355 -0.2235821686 134 0.0246821355 0.0246821355 135 0.0246821355 0.0246821355 136 -0.2520767786 0.0246821355 137 -0.2757746647 -0.2520767786 138 0.0009842494 -0.2757746647 139 0.0246821355 0.0009842494 140 0.8075292841 0.0246821355 141 -0.2161686020 0.8075292841 142 0.0219083566 -0.2161686020 143 -0.0038124744 0.0219083566 144 -0.0321501483 -0.0038124744 145 0.0293219233 -0.0321501483 146 -0.2445062758 0.0293219233 147 0.0009842494 -0.2445062758 148 0.0219083566 0.0009842494 149 -0.0038124744 0.0219083566 150 0.0530198094 -0.0038124744 151 0.7764178314 0.0530198094 152 0.7195855476 0.7764178314 153 -0.2235821686 0.7195855476 > 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/7y26s1356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8gvb81356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9nh4m1356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10l67q1356089675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11yvpk1356089675.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/12b8so1356089675.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/139q621356089675.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/14hl251356089675.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/150wv81356089675.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/16q0dl1356089675.tab") + } > > try(system("convert tmp/1zfo21356089675.ps tmp/1zfo21356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/24bba1356089675.ps tmp/24bba1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/3zp2h1356089675.ps tmp/3zp2h1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/4dkm21356089675.ps tmp/4dkm21356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/5zixg1356089675.ps tmp/5zixg1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/6ehvv1356089675.ps tmp/6ehvv1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/7y26s1356089675.ps tmp/7y26s1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/8gvb81356089675.ps tmp/8gvb81356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/9nh4m1356089675.ps tmp/9nh4m1356089675.png",intern=TRUE)) character(0) > try(system("convert tmp/10l67q1356089675.ps tmp/10l67q1356089675.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.892 1.744 9.646