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 + ,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 + ,2 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,2 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,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 + ,2 + ,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 + ,2 + ,1 + ,0 + ,0 + ,0 + ,4 + ,1 + ,2 + ,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 + ,2 + ,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 + ,2 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,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 + ,2 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,1 + ,1 + ,0 + ,1 + ,4 + ,0 + ,2 + ,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 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,3 + ,1 + ,0 + ,0 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,2 + ,1 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,3 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,3 + ,1 + ,0 + ,1 + ,0 + ,2 + ,0 + ,3 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,2 + ,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' + ,'T' + ,'used' + ,'Correctanalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','Uselimit','T','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 T used Useful Outcome 1 0 4 1 2 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 2 0 0 0 9 0 4 0 1 0 0 1 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 1 0 14 0 4 1 2 0 0 0 15 0 4 0 1 1 1 1 16 0 4 0 2 1 1 1 17 1 4 1 2 1 1 0 18 0 4 1 2 0 0 0 19 0 4 0 1 0 0 1 20 1 4 0 2 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 2 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 2 0 0 1 35 0 4 0 1 0 0 0 36 0 4 0 1 0 0 0 37 0 4 1 2 1 1 0 38 0 4 0 1 1 0 1 39 0 4 0 1 0 1 1 40 0 4 0 2 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 2 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 2 1 0 0 52 1 4 1 2 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 2 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 2 1 1 1 61 0 4 1 2 0 0 1 62 0 4 0 1 1 1 0 63 0 4 0 1 0 0 0 64 0 4 1 2 0 0 1 65 0 4 0 1 0 0 0 66 0 4 0 1 0 0 0 67 1 4 0 2 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 2 0 1 1 77 0 4 0 1 0 0 1 78 0 4 0 1 1 1 1 79 1 4 0 2 1 0 1 80 0 4 0 2 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 4 0 0 1 88 0 2 1 3 1 0 1 89 0 2 0 4 0 0 0 90 0 2 0 4 0 0 1 91 0 2 0 4 0 1 0 92 0 2 1 3 0 0 0 93 0 2 1 4 0 1 0 94 0 2 0 4 0 0 0 95 0 2 0 3 0 0 0 96 0 2 0 4 0 0 1 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 0 2 0 4 0 0 1 101 0 2 1 4 0 0 1 102 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 1 0 112 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 0 2 1 4 0 0 1 118 0 2 1 4 0 0 0 119 0 2 0 4 0 0 0 120 0 2 0 4 0 0 1 121 0 2 1 4 0 0 0 122 0 2 0 4 0 0 0 123 0 2 1 3 1 0 0 124 0 2 0 4 1 1 1 125 0 2 0 4 0 0 1 126 0 2 0 3 0 0 0 127 0 2 0 4 0 1 0 128 0 2 0 4 0 0 1 129 0 2 0 4 0 0 0 130 0 2 0 4 0 0 1 131 0 2 1 4 0 0 0 132 0 2 1 4 0 0 1 133 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 0 2 1 4 1 1 1 138 0 2 1 3 1 1 1 139 0 2 0 3 0 0 0 140 0 2 0 4 0 0 0 141 1 2 0 4 1 0 1 142 0 2 0 3 1 0 1 143 0 2 1 4 0 0 0 144 0 2 0 4 0 1 1 145 0 2 0 4 0 1 0 146 0 2 0 3 0 0 1 147 0 2 0 3 1 0 0 148 0 2 0 3 0 0 0 149 0 2 1 4 0 0 0 150 0 2 0 4 0 1 1 151 0 2 0 4 0 0 1 152 1 2 1 4 1 0 0 153 1 2 1 4 1 1 0 154 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 T used Useful -1.02909 0.21352 -0.00869 0.15682 0.26368 0.04040 Outcome -0.03569 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.43400 -0.11827 -0.01652 0.02143 0.75452 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.02909 0.27556 -3.735 0.000268 *** Weeks 0.21352 0.05728 3.727 0.000275 *** Uselimit -0.00869 0.04085 -0.213 0.831847 T 0.15682 0.04321 3.629 0.000391 *** used 0.26368 0.04301 6.131 7.65e-09 *** Useful 0.04040 0.04536 0.891 0.374617 Outcome -0.03569 0.03936 -0.907 0.366023 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2321 on 147 degrees of freedom Multiple R-squared: 0.284, Adjusted R-squared: 0.2548 F-statistic: 9.719 on 6 and 147 DF, p-value: 5.253e-09 > 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,] 0.466622464 0.933244927 0.533377536 [9,] 0.417745999 0.835491999 0.582254001 [10,] 0.383170323 0.766340645 0.616829677 [11,] 0.867089004 0.265821991 0.132910996 [12,] 0.820991007 0.358017985 0.179008993 [13,] 0.810001091 0.379997817 0.189998909 [14,] 0.754999208 0.490001585 0.245000792 [15,] 0.694788000 0.610424000 0.305212000 [16,] 0.700508359 0.598983283 0.299491641 [17,] 0.704748507 0.590502985 0.295251493 [18,] 0.666079497 0.667841006 0.333920503 [19,] 0.617186403 0.765627194 0.382813597 [20,] 0.565405686 0.869188627 0.434594314 [21,] 0.507724220 0.984551560 0.492275780 [22,] 0.447356543 0.894713086 0.552643457 [23,] 0.389066560 0.778133120 0.610933440 [24,] 0.334035136 0.668070273 0.665964864 [25,] 0.289832341 0.579664682 0.710167659 [26,] 0.241966388 0.483932776 0.758033612 [27,] 0.198794323 0.397588645 0.801205677 [28,] 0.276004716 0.552009433 0.723995284 [29,] 0.239912832 0.479825665 0.760087168 [30,] 0.197453749 0.394907498 0.802546251 [31,] 0.190621762 0.381243525 0.809378238 [32,] 0.730185260 0.539629480 0.269814740 [33,] 0.706617240 0.586765520 0.293382760 [34,] 0.659768207 0.680463586 0.340231793 [35,] 0.619962949 0.760074102 0.380037051 [36,] 0.569696687 0.860606625 0.430303313 [37,] 0.518949514 0.962100972 0.481050486 [38,] 0.467953592 0.935907185 0.532046408 [39,] 0.417344351 0.834688703 0.582655649 [40,] 0.368728781 0.737457562 0.631271219 [41,] 0.322029687 0.644059374 0.677970313 [42,] 0.382611376 0.765222752 0.617388624 [43,] 0.671475379 0.657049241 0.328524621 [44,] 0.628262434 0.743475131 0.371737566 [45,] 0.934698601 0.130602799 0.065301399 [46,] 0.917429237 0.165141525 0.082570763 [47,] 0.940382446 0.119235108 0.059617554 [48,] 0.942753769 0.114492463 0.057246231 [49,] 0.928493595 0.143012810 0.071506405 [50,] 0.911552452 0.176895096 0.088447548 [51,] 0.977413949 0.045172103 0.022586051 [52,] 0.971463168 0.057073664 0.028536832 [53,] 0.975187799 0.049624403 0.024812201 [54,] 0.967492206 0.065015589 0.032507794 [55,] 0.960100277 0.079799446 0.039899723 [56,] 0.948872854 0.102254293 0.051127146 [57,] 0.935265242 0.129469516 0.064734758 [58,] 0.980845582 0.038308836 0.019154418 [59,] 0.974532220 0.050935559 0.025467780 [60,] 0.967133190 0.065733619 0.032866810 [61,] 0.969283337 0.061433325 0.030716663 [62,] 0.960189852 0.079620295 0.039810148 [63,] 0.949487793 0.101024414 0.050512207 [64,] 0.950868538 0.098262924 0.049131462 [65,] 0.955725738 0.088548525 0.044274262 [66,] 0.944191626 0.111616748 0.055808374 [67,] 0.937555755 0.124888491 0.062444245 [68,] 0.922837707 0.154324586 0.077162293 [69,] 0.933356966 0.133286067 0.066643034 [70,] 0.981886302 0.036227397 0.018113698 [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,] 0.992964390 0.014071219 0.007035610 [80,] 0.990148492 0.019703016 0.009851508 [81,] 0.986397042 0.027205915 0.013602958 [82,] 0.981522983 0.036954034 0.018477017 [83,] 0.977327649 0.045344702 0.022672351 [84,] 0.969862818 0.060274364 0.030137182 [85,] 0.960269998 0.079460004 0.039730002 [86,] 0.952417976 0.095164048 0.047582024 [87,] 0.938497587 0.123004826 0.061502413 [88,] 0.928423960 0.143152080 0.071576040 [89,] 0.909550880 0.180898240 0.090449120 [90,] 0.886904531 0.226190939 0.113095469 [91,] 0.860368880 0.279262241 0.139631120 [92,] 0.829611316 0.340777368 0.170388684 [93,] 0.795183142 0.409633716 0.204816858 [94,] 0.756776777 0.486446445 0.243223223 [95,] 0.714627068 0.570745864 0.285372932 [96,] 0.682750459 0.634499082 0.317249541 [97,] 0.635111649 0.729776702 0.364888351 [98,] 0.585148799 0.829702402 0.414851201 [99,] 0.545774161 0.908451679 0.454225839 [100,] 0.493304741 0.986609481 0.506695259 [101,] 0.439714295 0.879428590 0.560285705 [102,] 0.401491344 0.802982687 0.598508656 [103,] 0.365737538 0.731475075 0.634262462 [104,] 0.417034963 0.834069925 0.582965037 [105,] 0.380414286 0.760828571 0.619585714 [106,] 0.328015015 0.656030029 0.671984985 [107,] 0.280679115 0.561358230 0.719320885 [108,] 0.235114078 0.470228156 0.764885922 [109,] 0.193123401 0.386246801 0.806876599 [110,] 0.157725059 0.315450117 0.842274941 [111,] 0.125026507 0.250053015 0.874973493 [112,] 0.097132038 0.194264076 0.902867962 [113,] 0.075278869 0.150557739 0.924721131 [114,] 0.063576569 0.127153138 0.936423431 [115,] 0.081559580 0.163119161 0.918440420 [116,] 0.060692919 0.121385839 0.939307081 [117,] 0.049518584 0.099037168 0.950481416 [118,] 0.035866411 0.071732823 0.964133589 [119,] 0.024943197 0.049886394 0.975056803 [120,] 0.017356316 0.034712632 0.982643684 [121,] 0.011462539 0.022925077 0.988537461 [122,] 0.007226843 0.014453686 0.992773157 [123,] 0.004510036 0.009020073 0.995489964 [124,] 0.009036534 0.018073069 0.990963466 [125,] 0.005920796 0.011841592 0.994079204 [126,] 0.003883751 0.007767502 0.996116249 [127,] 0.002620684 0.005241367 0.997379316 [128,] 0.004884305 0.009768610 0.995115695 [129,] 0.004067610 0.008135220 0.995932390 [130,] 0.003375991 0.006751982 0.996624009 [131,] 0.001652064 0.003304128 0.998347936 [132,] 0.023132622 0.046265243 0.976867378 [133,] 0.018722515 0.037445030 0.981277485 [134,] 0.010076608 0.020153217 0.989923392 [135,] 0.004692206 0.009384412 0.995307794 > postscript(file="/var/fisher/rcomp/tmp/1l9mq1356109641.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/26fzl1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3psow1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4xtmp1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5d4od1356109641.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 -0.09423128 0.01820060 0.01820060 0.01820060 0.01820060 0.02218933 7 8 9 10 11 12 0.01820060 -0.13861605 0.05389521 0.02689076 -0.12992589 0.01820060 13 14 15 16 17 18 -0.28587171 -0.12992589 -0.25017710 -0.40699375 0.56600180 -0.12992589 19 20 21 22 23 24 0.05389521 0.59300625 -0.01350528 -0.24148694 0.01349916 0.02218933 25 26 27 28 29 30 -0.36659771 -0.28587171 0.06258537 -0.24547567 0.05389521 -0.02219545 31 32 33 34 35 36 0.01820060 0.02689076 -0.01350528 -0.10292144 0.01820060 0.01820060 37 38 39 40 41 42 -0.43399820 -0.20978106 0.01349916 -0.17901209 0.74982290 -0.20978106 43 44 45 46 47 48 0.02218933 -0.12992589 -0.02219545 0.01349916 0.01820060 0.05389521 49 50 51 52 53 54 0.01349916 0.01820060 -0.40229232 0.56600180 0.05389521 0.75452433 55 56 57 58 59 60 0.01820060 -0.36659771 -0.25017710 0.05389521 0.05389521 0.60169641 61 62 63 64 65 66 -0.09423128 -0.28587171 0.01820060 -0.09423128 0.01820060 0.01820060 67 68 69 70 71 72 0.55731164 0.02689076 0.05389521 -0.24547567 0.01820060 0.05389521 73 74 75 76 77 78 -0.20978106 -0.23678551 0.05389521 -0.14331748 0.05389521 -0.25017710 79 80 81 82 83 84 0.63340229 -0.17901209 0.01820060 -0.20109090 0.01820060 0.75452433 85 86 87 88 89 90 0.01349916 0.02689076 0.01917064 -0.08768898 -0.02521413 0.01048048 91 92 93 94 95 96 -0.06561018 0.14029268 -0.05692001 -0.02521413 0.13160251 0.01048048 97 98 99 100 101 102 0.14029268 -0.02521413 -0.01652397 0.01048048 0.01917064 -0.02521413 103 104 105 106 107 108 -0.02521413 -0.02521413 -0.13207375 -0.02521413 -0.02521413 -0.12338359 109 110 111 112 113 114 -0.02521413 -0.01652397 -0.16377963 0.13160251 -0.28889040 -0.12338359 115 116 117 118 119 120 -0.01652397 -0.02521413 0.01917064 -0.01652397 -0.02521413 0.01048048 121 122 123 124 125 126 -0.01652397 -0.02521413 -0.12338359 -0.29359183 0.01048048 0.13160251 127 128 129 130 131 132 -0.06561018 0.01048048 -0.02521413 0.01048048 -0.01652397 0.01917064 133 134 135 136 137 138 -0.28020024 -0.02521413 -0.02521413 -0.02521413 -0.28490167 -0.12808502 139 140 141 142 143 144 0.13160251 -0.02521413 0.74680421 -0.09637914 -0.01652397 -0.02991557 145 146 147 148 149 150 -0.06561018 0.16729712 -0.13207375 0.13160251 -0.01652397 -0.02991557 151 152 153 154 0.01048048 0.71979976 0.67940372 -0.28020024 > postscript(file="/var/fisher/rcomp/tmp/6eefd1356109641.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 -0.09423128 NA 1 0.01820060 -0.09423128 2 0.01820060 0.01820060 3 0.01820060 0.01820060 4 0.01820060 0.01820060 5 0.02218933 0.01820060 6 0.01820060 0.02218933 7 -0.13861605 0.01820060 8 0.05389521 -0.13861605 9 0.02689076 0.05389521 10 -0.12992589 0.02689076 11 0.01820060 -0.12992589 12 -0.28587171 0.01820060 13 -0.12992589 -0.28587171 14 -0.25017710 -0.12992589 15 -0.40699375 -0.25017710 16 0.56600180 -0.40699375 17 -0.12992589 0.56600180 18 0.05389521 -0.12992589 19 0.59300625 0.05389521 20 -0.01350528 0.59300625 21 -0.24148694 -0.01350528 22 0.01349916 -0.24148694 23 0.02218933 0.01349916 24 -0.36659771 0.02218933 25 -0.28587171 -0.36659771 26 0.06258537 -0.28587171 27 -0.24547567 0.06258537 28 0.05389521 -0.24547567 29 -0.02219545 0.05389521 30 0.01820060 -0.02219545 31 0.02689076 0.01820060 32 -0.01350528 0.02689076 33 -0.10292144 -0.01350528 34 0.01820060 -0.10292144 35 0.01820060 0.01820060 36 -0.43399820 0.01820060 37 -0.20978106 -0.43399820 38 0.01349916 -0.20978106 39 -0.17901209 0.01349916 40 0.74982290 -0.17901209 41 -0.20978106 0.74982290 42 0.02218933 -0.20978106 43 -0.12992589 0.02218933 44 -0.02219545 -0.12992589 45 0.01349916 -0.02219545 46 0.01820060 0.01349916 47 0.05389521 0.01820060 48 0.01349916 0.05389521 49 0.01820060 0.01349916 50 -0.40229232 0.01820060 51 0.56600180 -0.40229232 52 0.05389521 0.56600180 53 0.75452433 0.05389521 54 0.01820060 0.75452433 55 -0.36659771 0.01820060 56 -0.25017710 -0.36659771 57 0.05389521 -0.25017710 58 0.05389521 0.05389521 59 0.60169641 0.05389521 60 -0.09423128 0.60169641 61 -0.28587171 -0.09423128 62 0.01820060 -0.28587171 63 -0.09423128 0.01820060 64 0.01820060 -0.09423128 65 0.01820060 0.01820060 66 0.55731164 0.01820060 67 0.02689076 0.55731164 68 0.05389521 0.02689076 69 -0.24547567 0.05389521 70 0.01820060 -0.24547567 71 0.05389521 0.01820060 72 -0.20978106 0.05389521 73 -0.23678551 -0.20978106 74 0.05389521 -0.23678551 75 -0.14331748 0.05389521 76 0.05389521 -0.14331748 77 -0.25017710 0.05389521 78 0.63340229 -0.25017710 79 -0.17901209 0.63340229 80 0.01820060 -0.17901209 81 -0.20109090 0.01820060 82 0.01820060 -0.20109090 83 0.75452433 0.01820060 84 0.01349916 0.75452433 85 0.02689076 0.01349916 86 0.01917064 0.02689076 87 -0.08768898 0.01917064 88 -0.02521413 -0.08768898 89 0.01048048 -0.02521413 90 -0.06561018 0.01048048 91 0.14029268 -0.06561018 92 -0.05692001 0.14029268 93 -0.02521413 -0.05692001 94 0.13160251 -0.02521413 95 0.01048048 0.13160251 96 0.14029268 0.01048048 97 -0.02521413 0.14029268 98 -0.01652397 -0.02521413 99 0.01048048 -0.01652397 100 0.01917064 0.01048048 101 -0.02521413 0.01917064 102 -0.02521413 -0.02521413 103 -0.02521413 -0.02521413 104 -0.13207375 -0.02521413 105 -0.02521413 -0.13207375 106 -0.02521413 -0.02521413 107 -0.12338359 -0.02521413 108 -0.02521413 -0.12338359 109 -0.01652397 -0.02521413 110 -0.16377963 -0.01652397 111 0.13160251 -0.16377963 112 -0.28889040 0.13160251 113 -0.12338359 -0.28889040 114 -0.01652397 -0.12338359 115 -0.02521413 -0.01652397 116 0.01917064 -0.02521413 117 -0.01652397 0.01917064 118 -0.02521413 -0.01652397 119 0.01048048 -0.02521413 120 -0.01652397 0.01048048 121 -0.02521413 -0.01652397 122 -0.12338359 -0.02521413 123 -0.29359183 -0.12338359 124 0.01048048 -0.29359183 125 0.13160251 0.01048048 126 -0.06561018 0.13160251 127 0.01048048 -0.06561018 128 -0.02521413 0.01048048 129 0.01048048 -0.02521413 130 -0.01652397 0.01048048 131 0.01917064 -0.01652397 132 -0.28020024 0.01917064 133 -0.02521413 -0.28020024 134 -0.02521413 -0.02521413 135 -0.02521413 -0.02521413 136 -0.28490167 -0.02521413 137 -0.12808502 -0.28490167 138 0.13160251 -0.12808502 139 -0.02521413 0.13160251 140 0.74680421 -0.02521413 141 -0.09637914 0.74680421 142 -0.01652397 -0.09637914 143 -0.02991557 -0.01652397 144 -0.06561018 -0.02991557 145 0.16729712 -0.06561018 146 -0.13207375 0.16729712 147 0.13160251 -0.13207375 148 -0.01652397 0.13160251 149 -0.02991557 -0.01652397 150 0.01048048 -0.02991557 151 0.71979976 0.01048048 152 0.67940372 0.71979976 153 -0.28020024 0.67940372 154 NA -0.28020024 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.01820060 -0.09423128 [2,] 0.01820060 0.01820060 [3,] 0.01820060 0.01820060 [4,] 0.01820060 0.01820060 [5,] 0.02218933 0.01820060 [6,] 0.01820060 0.02218933 [7,] -0.13861605 0.01820060 [8,] 0.05389521 -0.13861605 [9,] 0.02689076 0.05389521 [10,] -0.12992589 0.02689076 [11,] 0.01820060 -0.12992589 [12,] -0.28587171 0.01820060 [13,] -0.12992589 -0.28587171 [14,] -0.25017710 -0.12992589 [15,] -0.40699375 -0.25017710 [16,] 0.56600180 -0.40699375 [17,] -0.12992589 0.56600180 [18,] 0.05389521 -0.12992589 [19,] 0.59300625 0.05389521 [20,] -0.01350528 0.59300625 [21,] -0.24148694 -0.01350528 [22,] 0.01349916 -0.24148694 [23,] 0.02218933 0.01349916 [24,] -0.36659771 0.02218933 [25,] -0.28587171 -0.36659771 [26,] 0.06258537 -0.28587171 [27,] -0.24547567 0.06258537 [28,] 0.05389521 -0.24547567 [29,] -0.02219545 0.05389521 [30,] 0.01820060 -0.02219545 [31,] 0.02689076 0.01820060 [32,] -0.01350528 0.02689076 [33,] -0.10292144 -0.01350528 [34,] 0.01820060 -0.10292144 [35,] 0.01820060 0.01820060 [36,] -0.43399820 0.01820060 [37,] -0.20978106 -0.43399820 [38,] 0.01349916 -0.20978106 [39,] -0.17901209 0.01349916 [40,] 0.74982290 -0.17901209 [41,] -0.20978106 0.74982290 [42,] 0.02218933 -0.20978106 [43,] -0.12992589 0.02218933 [44,] -0.02219545 -0.12992589 [45,] 0.01349916 -0.02219545 [46,] 0.01820060 0.01349916 [47,] 0.05389521 0.01820060 [48,] 0.01349916 0.05389521 [49,] 0.01820060 0.01349916 [50,] -0.40229232 0.01820060 [51,] 0.56600180 -0.40229232 [52,] 0.05389521 0.56600180 [53,] 0.75452433 0.05389521 [54,] 0.01820060 0.75452433 [55,] -0.36659771 0.01820060 [56,] -0.25017710 -0.36659771 [57,] 0.05389521 -0.25017710 [58,] 0.05389521 0.05389521 [59,] 0.60169641 0.05389521 [60,] -0.09423128 0.60169641 [61,] -0.28587171 -0.09423128 [62,] 0.01820060 -0.28587171 [63,] -0.09423128 0.01820060 [64,] 0.01820060 -0.09423128 [65,] 0.01820060 0.01820060 [66,] 0.55731164 0.01820060 [67,] 0.02689076 0.55731164 [68,] 0.05389521 0.02689076 [69,] -0.24547567 0.05389521 [70,] 0.01820060 -0.24547567 [71,] 0.05389521 0.01820060 [72,] -0.20978106 0.05389521 [73,] -0.23678551 -0.20978106 [74,] 0.05389521 -0.23678551 [75,] -0.14331748 0.05389521 [76,] 0.05389521 -0.14331748 [77,] -0.25017710 0.05389521 [78,] 0.63340229 -0.25017710 [79,] -0.17901209 0.63340229 [80,] 0.01820060 -0.17901209 [81,] -0.20109090 0.01820060 [82,] 0.01820060 -0.20109090 [83,] 0.75452433 0.01820060 [84,] 0.01349916 0.75452433 [85,] 0.02689076 0.01349916 [86,] 0.01917064 0.02689076 [87,] -0.08768898 0.01917064 [88,] -0.02521413 -0.08768898 [89,] 0.01048048 -0.02521413 [90,] -0.06561018 0.01048048 [91,] 0.14029268 -0.06561018 [92,] -0.05692001 0.14029268 [93,] -0.02521413 -0.05692001 [94,] 0.13160251 -0.02521413 [95,] 0.01048048 0.13160251 [96,] 0.14029268 0.01048048 [97,] -0.02521413 0.14029268 [98,] -0.01652397 -0.02521413 [99,] 0.01048048 -0.01652397 [100,] 0.01917064 0.01048048 [101,] -0.02521413 0.01917064 [102,] -0.02521413 -0.02521413 [103,] -0.02521413 -0.02521413 [104,] -0.13207375 -0.02521413 [105,] -0.02521413 -0.13207375 [106,] -0.02521413 -0.02521413 [107,] -0.12338359 -0.02521413 [108,] -0.02521413 -0.12338359 [109,] -0.01652397 -0.02521413 [110,] -0.16377963 -0.01652397 [111,] 0.13160251 -0.16377963 [112,] -0.28889040 0.13160251 [113,] -0.12338359 -0.28889040 [114,] -0.01652397 -0.12338359 [115,] -0.02521413 -0.01652397 [116,] 0.01917064 -0.02521413 [117,] -0.01652397 0.01917064 [118,] -0.02521413 -0.01652397 [119,] 0.01048048 -0.02521413 [120,] -0.01652397 0.01048048 [121,] -0.02521413 -0.01652397 [122,] -0.12338359 -0.02521413 [123,] -0.29359183 -0.12338359 [124,] 0.01048048 -0.29359183 [125,] 0.13160251 0.01048048 [126,] -0.06561018 0.13160251 [127,] 0.01048048 -0.06561018 [128,] -0.02521413 0.01048048 [129,] 0.01048048 -0.02521413 [130,] -0.01652397 0.01048048 [131,] 0.01917064 -0.01652397 [132,] -0.28020024 0.01917064 [133,] -0.02521413 -0.28020024 [134,] -0.02521413 -0.02521413 [135,] -0.02521413 -0.02521413 [136,] -0.28490167 -0.02521413 [137,] -0.12808502 -0.28490167 [138,] 0.13160251 -0.12808502 [139,] -0.02521413 0.13160251 [140,] 0.74680421 -0.02521413 [141,] -0.09637914 0.74680421 [142,] -0.01652397 -0.09637914 [143,] -0.02991557 -0.01652397 [144,] -0.06561018 -0.02991557 [145,] 0.16729712 -0.06561018 [146,] -0.13207375 0.16729712 [147,] 0.13160251 -0.13207375 [148,] -0.01652397 0.13160251 [149,] -0.02991557 -0.01652397 [150,] 0.01048048 -0.02991557 [151,] 0.71979976 0.01048048 [152,] 0.67940372 0.71979976 [153,] -0.28020024 0.67940372 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.01820060 -0.09423128 2 0.01820060 0.01820060 3 0.01820060 0.01820060 4 0.01820060 0.01820060 5 0.02218933 0.01820060 6 0.01820060 0.02218933 7 -0.13861605 0.01820060 8 0.05389521 -0.13861605 9 0.02689076 0.05389521 10 -0.12992589 0.02689076 11 0.01820060 -0.12992589 12 -0.28587171 0.01820060 13 -0.12992589 -0.28587171 14 -0.25017710 -0.12992589 15 -0.40699375 -0.25017710 16 0.56600180 -0.40699375 17 -0.12992589 0.56600180 18 0.05389521 -0.12992589 19 0.59300625 0.05389521 20 -0.01350528 0.59300625 21 -0.24148694 -0.01350528 22 0.01349916 -0.24148694 23 0.02218933 0.01349916 24 -0.36659771 0.02218933 25 -0.28587171 -0.36659771 26 0.06258537 -0.28587171 27 -0.24547567 0.06258537 28 0.05389521 -0.24547567 29 -0.02219545 0.05389521 30 0.01820060 -0.02219545 31 0.02689076 0.01820060 32 -0.01350528 0.02689076 33 -0.10292144 -0.01350528 34 0.01820060 -0.10292144 35 0.01820060 0.01820060 36 -0.43399820 0.01820060 37 -0.20978106 -0.43399820 38 0.01349916 -0.20978106 39 -0.17901209 0.01349916 40 0.74982290 -0.17901209 41 -0.20978106 0.74982290 42 0.02218933 -0.20978106 43 -0.12992589 0.02218933 44 -0.02219545 -0.12992589 45 0.01349916 -0.02219545 46 0.01820060 0.01349916 47 0.05389521 0.01820060 48 0.01349916 0.05389521 49 0.01820060 0.01349916 50 -0.40229232 0.01820060 51 0.56600180 -0.40229232 52 0.05389521 0.56600180 53 0.75452433 0.05389521 54 0.01820060 0.75452433 55 -0.36659771 0.01820060 56 -0.25017710 -0.36659771 57 0.05389521 -0.25017710 58 0.05389521 0.05389521 59 0.60169641 0.05389521 60 -0.09423128 0.60169641 61 -0.28587171 -0.09423128 62 0.01820060 -0.28587171 63 -0.09423128 0.01820060 64 0.01820060 -0.09423128 65 0.01820060 0.01820060 66 0.55731164 0.01820060 67 0.02689076 0.55731164 68 0.05389521 0.02689076 69 -0.24547567 0.05389521 70 0.01820060 -0.24547567 71 0.05389521 0.01820060 72 -0.20978106 0.05389521 73 -0.23678551 -0.20978106 74 0.05389521 -0.23678551 75 -0.14331748 0.05389521 76 0.05389521 -0.14331748 77 -0.25017710 0.05389521 78 0.63340229 -0.25017710 79 -0.17901209 0.63340229 80 0.01820060 -0.17901209 81 -0.20109090 0.01820060 82 0.01820060 -0.20109090 83 0.75452433 0.01820060 84 0.01349916 0.75452433 85 0.02689076 0.01349916 86 0.01917064 0.02689076 87 -0.08768898 0.01917064 88 -0.02521413 -0.08768898 89 0.01048048 -0.02521413 90 -0.06561018 0.01048048 91 0.14029268 -0.06561018 92 -0.05692001 0.14029268 93 -0.02521413 -0.05692001 94 0.13160251 -0.02521413 95 0.01048048 0.13160251 96 0.14029268 0.01048048 97 -0.02521413 0.14029268 98 -0.01652397 -0.02521413 99 0.01048048 -0.01652397 100 0.01917064 0.01048048 101 -0.02521413 0.01917064 102 -0.02521413 -0.02521413 103 -0.02521413 -0.02521413 104 -0.13207375 -0.02521413 105 -0.02521413 -0.13207375 106 -0.02521413 -0.02521413 107 -0.12338359 -0.02521413 108 -0.02521413 -0.12338359 109 -0.01652397 -0.02521413 110 -0.16377963 -0.01652397 111 0.13160251 -0.16377963 112 -0.28889040 0.13160251 113 -0.12338359 -0.28889040 114 -0.01652397 -0.12338359 115 -0.02521413 -0.01652397 116 0.01917064 -0.02521413 117 -0.01652397 0.01917064 118 -0.02521413 -0.01652397 119 0.01048048 -0.02521413 120 -0.01652397 0.01048048 121 -0.02521413 -0.01652397 122 -0.12338359 -0.02521413 123 -0.29359183 -0.12338359 124 0.01048048 -0.29359183 125 0.13160251 0.01048048 126 -0.06561018 0.13160251 127 0.01048048 -0.06561018 128 -0.02521413 0.01048048 129 0.01048048 -0.02521413 130 -0.01652397 0.01048048 131 0.01917064 -0.01652397 132 -0.28020024 0.01917064 133 -0.02521413 -0.28020024 134 -0.02521413 -0.02521413 135 -0.02521413 -0.02521413 136 -0.28490167 -0.02521413 137 -0.12808502 -0.28490167 138 0.13160251 -0.12808502 139 -0.02521413 0.13160251 140 0.74680421 -0.02521413 141 -0.09637914 0.74680421 142 -0.01652397 -0.09637914 143 -0.02991557 -0.01652397 144 -0.06561018 -0.02991557 145 0.16729712 -0.06561018 146 -0.13207375 0.16729712 147 0.13160251 -0.13207375 148 -0.01652397 0.13160251 149 -0.02991557 -0.01652397 150 0.01048048 -0.02991557 151 0.71979976 0.01048048 152 0.67940372 0.71979976 153 -0.28020024 0.67940372 > 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/7he4v1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8nzwq1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/96vhj1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/108mpq1356109641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/114cqf1356109641.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/127gna1356109641.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/13g3n21356109642.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/14ktw91356109642.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/15xukq1356109642.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/16kcdb1356109642.tab") + } > > try(system("convert tmp/1l9mq1356109641.ps tmp/1l9mq1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/26fzl1356109641.ps tmp/26fzl1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/3psow1356109641.ps tmp/3psow1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/4xtmp1356109641.ps tmp/4xtmp1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/5d4od1356109641.ps tmp/5d4od1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/6eefd1356109641.ps tmp/6eefd1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/7he4v1356109641.ps tmp/7he4v1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/8nzwq1356109641.ps tmp/8nzwq1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/96vhj1356109641.ps tmp/96vhj1356109641.png",intern=TRUE)) character(0) > try(system("convert tmp/108mpq1356109641.ps tmp/108mpq1356109641.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.149 1.836 9.988