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 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,0 + ,4 + ,1 + ,0 + ,5 + ,1 + ,8 + ,1 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,1 + ,4 + ,1 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,1 + ,0 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,1 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,5 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,1 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,1 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,5 + ,1 + ,8 + ,1 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,8 + ,1 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,1 + ,0 + ,5 + ,1 + ,7 + ,1 + ,4 + ,1 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,8 + ,1 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,1 + ,0 + ,5 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,7 + ,1 + ,4 + ,0 + ,0 + ,6 + ,1 + ,8 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,0 + ,5 + ,0 + ,7 + ,0 + ,4 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,0 + ,5 + ,0 + ,8 + ,1 + ,4 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,4 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,3 + ,5 + ,0 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,1 + ,8 + ,0 + ,2 + ,1 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,1 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,3 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,3 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,3 + ,5 + ,1 + ,8 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,5 + ,0 + ,8 + ,0 + ,2 + ,1 + ,3 + ,5 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,3 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,5 + ,1 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,1 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,5 + ,1 + ,7 + ,0 + ,2 + ,1 + ,3 + ,5 + ,1 + ,7 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,5 + ,0 + ,7 + ,1 + ,2 + ,0 + ,3 + ,5 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,1 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,1 + ,8 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,3 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,4 + ,6 + ,1 + ,7 + ,0 + ,2 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,4 + ,5 + ,0 + ,8 + ,1 + ,2 + ,1 + ,4 + ,5 + ,1 + ,8 + ,1 + ,2 + ,1 + ,4 + ,5 + ,0 + ,8 + ,0) + ,dim=c(7 + ,154) + ,dimnames=list(c('Weeks' + ,'UsedLimit' + ,'T20' + ,'Used' + ,'Useful' + ,'Outcome' + ,'CorrectAnalysis') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UsedLimit','T20','Used','Useful','Outcome','CorrectAnalysis'),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' > 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 UsedLimit T20 Used Useful Outcome 1 0 4 1 0 6 0 7 2 0 4 0 0 6 0 8 3 0 4 0 0 6 0 8 4 0 4 0 0 6 0 8 5 0 4 0 0 6 0 8 6 0 4 1 0 6 1 7 7 0 4 0 0 6 0 8 8 0 4 0 0 6 0 8 9 0 4 0 0 6 0 7 10 0 4 1 0 6 0 8 11 0 4 1 0 6 0 8 12 0 4 0 0 6 0 8 13 0 4 0 0 5 1 8 14 0 4 1 0 6 0 8 15 0 4 0 0 5 1 7 16 0 4 0 0 5 1 7 17 1 4 1 0 5 1 8 18 0 4 1 0 6 0 8 19 0 4 0 0 6 0 7 20 1 4 0 0 5 1 7 21 0 4 1 0 6 1 8 22 0 4 1 0 5 1 7 23 0 4 0 0 6 1 7 24 0 4 1 0 6 1 7 25 0 4 0 0 5 0 7 26 0 4 0 0 5 1 8 27 0 4 1 0 6 0 7 28 0 4 0 0 5 0 8 29 0 4 0 0 6 0 7 30 0 4 0 0 6 1 8 31 0 4 0 0 6 0 8 32 0 4 1 0 6 0 8 33 0 4 1 0 6 1 8 34 0 4 0 0 6 0 7 35 0 4 0 0 6 0 8 36 0 4 0 0 6 0 8 37 0 4 1 0 5 1 8 38 0 4 0 0 5 0 7 39 0 4 0 0 6 1 7 40 0 4 0 0 6 1 8 41 1 4 0 0 5 1 7 42 0 4 0 0 5 0 7 43 0 4 1 0 6 1 7 44 0 4 1 0 6 0 8 45 0 4 0 0 6 1 8 46 0 4 0 0 6 1 7 47 0 4 0 0 6 0 8 48 0 4 0 0 6 0 7 49 0 4 0 0 6 1 7 50 0 4 0 0 6 0 8 51 0 4 0 0 5 0 8 52 1 4 1 0 5 1 8 53 0 4 0 0 6 0 7 54 1 4 0 0 5 0 8 55 0 4 0 0 6 0 8 56 0 4 0 0 5 0 7 57 0 4 0 0 5 1 7 58 0 4 0 0 6 0 7 59 0 4 0 0 6 0 7 60 1 4 1 0 5 1 7 61 0 4 1 0 6 0 7 62 0 4 0 0 5 1 8 63 0 4 0 0 6 0 8 64 0 4 1 0 6 0 7 65 0 4 0 0 6 0 8 66 0 4 0 0 6 0 8 67 1 4 0 0 5 1 8 68 0 4 1 0 6 0 8 69 0 4 0 0 6 0 7 70 0 4 0 0 5 0 8 71 0 4 0 0 6 0 8 72 0 4 0 0 6 0 7 73 0 4 0 0 5 0 7 74 0 4 1 0 5 0 8 75 0 4 0 0 6 0 7 76 0 4 0 0 6 1 7 77 0 4 0 0 6 0 7 78 0 4 0 0 5 1 7 79 1 4 0 0 5 0 7 80 0 4 0 0 6 1 8 81 0 4 0 0 6 0 8 82 0 4 1 0 5 0 7 83 0 4 0 0 6 0 8 84 1 4 0 0 5 0 8 85 0 4 0 0 6 1 7 86 0 4 1 0 6 0 8 87 0 2 1 4 6 0 7 88 0 2 1 3 5 0 7 89 0 2 0 4 6 0 8 90 0 2 0 4 6 0 7 91 0 2 0 4 6 1 8 92 0 2 1 3 6 0 8 93 0 2 1 4 6 1 8 94 0 2 0 4 6 0 8 95 0 2 0 3 6 0 8 96 0 2 0 4 6 0 7 97 0 2 1 3 6 0 8 98 0 2 0 4 6 0 8 99 0 2 1 4 6 0 8 100 0 2 0 4 6 0 7 101 0 2 1 4 6 0 7 102 0 2 0 4 6 0 8 103 0 2 0 4 6 0 8 104 0 2 0 4 6 0 8 105 0 2 0 3 5 0 8 106 0 2 0 4 6 0 8 107 0 2 0 4 6 0 8 108 0 2 1 3 5 0 8 109 0 2 0 4 6 0 8 110 0 2 1 4 6 0 8 111 0 2 1 3 5 1 8 112 0 2 0 3 6 0 8 113 0 2 0 4 5 0 8 114 0 2 1 3 5 0 8 115 0 2 1 4 6 0 8 116 0 2 0 4 6 0 8 117 0 2 1 4 6 0 7 118 0 2 1 4 6 0 8 119 0 2 0 4 6 0 8 120 0 2 0 4 6 0 7 121 0 2 1 4 6 0 8 122 0 2 0 4 6 0 8 123 0 2 1 3 5 0 8 124 0 2 0 4 5 1 7 125 0 2 0 4 6 0 7 126 0 2 0 3 6 0 8 127 0 2 0 4 6 1 8 128 0 2 0 4 6 0 7 129 0 2 0 4 6 0 8 130 0 2 0 4 6 0 7 131 0 2 1 4 6 0 8 132 0 2 1 4 6 0 7 133 0 2 1 4 5 0 8 134 0 2 0 4 6 0 8 135 0 2 0 4 6 0 8 136 0 2 0 4 6 0 8 137 0 2 1 4 5 1 7 138 0 2 1 3 5 1 7 139 0 2 0 3 6 0 8 140 0 2 0 4 6 0 8 141 1 2 0 4 5 0 7 142 0 2 0 3 5 0 7 143 0 2 1 4 6 0 8 144 0 2 0 4 6 1 7 145 0 2 0 4 6 1 8 146 0 2 0 3 6 0 7 147 0 2 0 3 5 0 8 148 0 2 0 3 6 0 8 149 0 2 1 4 6 0 8 150 0 2 0 4 6 1 7 151 0 2 0 4 6 0 7 152 1 2 1 4 5 0 8 153 1 2 1 4 5 1 8 154 0 2 1 4 5 0 8 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UsedLimit T20 Used Useful 0.09101 0.32780 0.01121 0.16478 -0.27876 0.04529 Outcome 0.03522 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.34661 -0.05929 -0.01489 0.02033 0.74511 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.09101 0.62013 0.147 0.8835 Weeks 0.32780 0.13148 2.493 0.0138 * UsedLimit 0.01121 0.04167 0.269 0.7883 T20 0.16478 0.06915 2.383 0.0184 * Used -0.27876 0.04538 -6.143 7.22e-09 *** Useful 0.04529 0.04647 0.975 0.3314 Outcome 0.03522 0.04036 0.873 0.3843 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2378 on 147 degrees of freedom Multiple R-squared: 0.2489, Adjusted R-squared: 0.2182 F-statistic: 8.118 on 6 and 147 DF, p-value: 1.383e-07 > 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 1.0000000000 [2,] 0.000000000 0.000000000 1.0000000000 [3,] 0.000000000 0.000000000 1.0000000000 [4,] 0.000000000 0.000000000 1.0000000000 [5,] 0.000000000 0.000000000 1.0000000000 [6,] 0.000000000 0.000000000 1.0000000000 [7,] 0.000000000 0.000000000 1.0000000000 [8,] 0.434515961 0.869031921 0.5654840394 [9,] 0.378869942 0.757739883 0.6211300584 [10,] 0.341586834 0.683173668 0.6584131661 [11,] 0.883877041 0.232245917 0.1161229587 [12,] 0.841391786 0.317216428 0.1586082140 [13,] 0.896952587 0.206094825 0.1030474126 [14,] 0.863012126 0.273975747 0.1369878735 [15,] 0.819533950 0.360932101 0.1804660503 [16,] 0.807872872 0.384254257 0.1921271284 [17,] 0.832093094 0.335813811 0.1679069057 [18,] 0.786133813 0.427732373 0.2138661866 [19,] 0.773296328 0.453407344 0.2267036722 [20,] 0.724759061 0.550481878 0.2752409389 [21,] 0.670577035 0.658845929 0.3294229646 [22,] 0.611857601 0.776284797 0.3881423986 [23,] 0.551146771 0.897706458 0.4488532292 [24,] 0.497838798 0.995677596 0.5021612020 [25,] 0.439020787 0.878041575 0.5609792127 [26,] 0.380468041 0.760936081 0.6195319593 [27,] 0.324969738 0.649939476 0.6750302622 [28,] 0.352373554 0.704747108 0.6476264458 [29,] 0.328008586 0.656017173 0.6719914136 [30,] 0.277872120 0.555744239 0.7221278803 [31,] 0.233846020 0.467692040 0.7661539799 [32,] 0.689119740 0.621760520 0.3108802601 [33,] 0.676072406 0.647855189 0.3239275945 [34,] 0.629870537 0.740258927 0.3701294634 [35,] 0.578443799 0.843112403 0.4215562014 [36,] 0.529040335 0.941919330 0.4709596651 [37,] 0.477835106 0.955670211 0.5221648944 [38,] 0.426296648 0.852593297 0.5737033516 [39,] 0.375356109 0.750712218 0.6246438909 [40,] 0.328286172 0.656572345 0.6717138275 [41,] 0.283365527 0.566731054 0.7166344729 [42,] 0.283023928 0.566047855 0.7169760725 [43,] 0.618520633 0.762958734 0.3814793668 [44,] 0.571617306 0.856765387 0.4283826936 [45,] 0.878820752 0.242358496 0.1211792480 [46,] 0.852042585 0.295914831 0.1479574153 [47,] 0.852645303 0.294709393 0.1473546967 [48,] 0.865939975 0.268120050 0.1340600250 [49,] 0.839222482 0.321555036 0.1607775180 [50,] 0.809115806 0.381768388 0.1908841942 [51,] 0.954063408 0.091873184 0.0459365922 [52,] 0.941160451 0.117679099 0.0588395494 [53,] 0.952266725 0.095466550 0.0477332748 [54,] 0.939522728 0.120954543 0.0604772716 [55,] 0.923918476 0.152163049 0.0760815244 [56,] 0.905910314 0.188179372 0.0940896861 [57,] 0.885051458 0.229897083 0.1149485416 [58,] 0.977895717 0.044208566 0.0221042828 [59,] 0.970871179 0.058257642 0.0291288210 [60,] 0.962372646 0.075254707 0.0376273536 [61,] 0.967411627 0.065176745 0.0325883727 [62,] 0.958090284 0.083819432 0.0419097161 [63,] 0.946769788 0.106460424 0.0532302122 [64,] 0.952131978 0.095736045 0.0478680225 [65,] 0.962694320 0.074611360 0.0373056801 [66,] 0.953021153 0.093957694 0.0469788470 [67,] 0.940297445 0.119405111 0.0597025554 [68,] 0.926762255 0.146475490 0.0732377448 [69,] 0.944005492 0.111989017 0.0559945084 [70,] 0.993320775 0.013358451 0.0066792253 [71,] 0.990923941 0.018152118 0.0090760591 [72,] 0.988112490 0.023775019 0.0118875097 [73,] 0.992007941 0.015984119 0.0079920595 [74,] 0.991126119 0.017747761 0.0088738806 [75,] 0.999188621 0.001622759 0.0008113794 [76,] 0.998771632 0.002456736 0.0012283678 [77,] 0.998162661 0.003674677 0.0018373386 [78,] 0.997286814 0.005426372 0.0027131861 [79,] 0.996267757 0.007464486 0.0037322430 [80,] 0.994637988 0.010724024 0.0053620122 [81,] 0.992392847 0.015214305 0.0076071525 [82,] 0.989420655 0.021158690 0.0105793448 [83,] 0.986997313 0.026005373 0.0130026867 [84,] 0.982337969 0.035324062 0.0176620309 [85,] 0.976115149 0.047769701 0.0238848506 [86,] 0.970782052 0.058435897 0.0292179484 [87,] 0.961398525 0.077202950 0.0386014749 [88,] 0.953711193 0.092577615 0.0462888073 [89,] 0.939988700 0.120022600 0.0600113000 [90,] 0.923094154 0.153811691 0.0769058457 [91,] 0.902997046 0.194005907 0.0970029535 [92,] 0.878805127 0.242389747 0.1211948733 [93,] 0.850800378 0.298399244 0.1491996219 [94,] 0.818670676 0.362658648 0.1813293242 [95,] 0.782408368 0.435183264 0.2175916320 [96,] 0.756337391 0.487325218 0.2436626090 [97,] 0.713570326 0.572859347 0.2864296735 [98,] 0.667407575 0.665184850 0.3325924249 [99,] 0.632783386 0.734433228 0.3672166138 [100,] 0.581887576 0.836224847 0.4181124235 [101,] 0.528485424 0.943029151 0.4715145755 [102,] 0.491596966 0.983193932 0.5084030341 [103,] 0.455392665 0.910785330 0.5446073352 [104,] 0.506703679 0.986592643 0.4932963213 [105,] 0.470090010 0.940180020 0.5299099899 [106,] 0.414144136 0.828288271 0.5858558644 [107,] 0.362011229 0.724022458 0.6379887712 [108,] 0.310038718 0.620077435 0.6899612824 [109,] 0.260774326 0.521548651 0.7392256744 [110,] 0.217915994 0.435831988 0.7820840059 [111,] 0.177238464 0.354476927 0.8227615364 [112,] 0.141245427 0.282490854 0.8587545729 [113,] 0.112132130 0.224264259 0.8878678705 [114,] 0.096434501 0.192869003 0.9035654987 [115,] 0.119892480 0.239784960 0.8801075198 [116,] 0.091707425 0.183414850 0.9082925748 [117,] 0.075994135 0.151988271 0.9240058647 [118,] 0.056397937 0.112795875 0.9436020626 [119,] 0.040361137 0.080722273 0.9596388633 [120,] 0.028806268 0.057612537 0.9711937317 [121,] 0.019581105 0.039162209 0.9804188954 [122,] 0.012709079 0.025418159 0.9872909206 [123,] 0.008143962 0.016287924 0.9918560382 [124,] 0.015346173 0.030692346 0.9846538268 [125,] 0.010286650 0.020573299 0.9897133504 [126,] 0.006885498 0.013770997 0.9931145016 [127,] 0.004719633 0.009439265 0.9952803674 [128,] 0.008335562 0.016671124 0.9916644381 [129,] 0.006992506 0.013985012 0.9930074941 [130,] 0.005743929 0.011487857 0.9942560714 [131,] 0.002899058 0.005798115 0.9971009423 [132,] 0.031959003 0.063918006 0.9680409972 [133,] 0.025777253 0.051554507 0.9742227467 [134,] 0.014118248 0.028236496 0.9858817519 [135,] 0.006692859 0.013385717 0.9933071413 > postscript(file="/var/wessaorg/rcomp/tmp/1m8vc1355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/28kf71355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3v3u71355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4wjn81355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5wdd81355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 0.01266011 -0.01134305 -0.01134305 -0.01134305 -0.01134305 -0.03262865 7 8 9 10 11 12 -0.01134305 -0.01134305 0.02387272 -0.02255565 -0.02255565 -0.01134305 13 14 15 16 17 18 -0.33539478 -0.02255565 -0.30017901 -0.30017901 0.65339262 -0.02255565 19 20 21 22 23 24 0.02387272 0.69982099 -0.06784442 -0.31139162 -0.02141604 -0.03262865 25 26 27 28 29 30 -0.25489025 -0.33539478 0.01266011 -0.29010601 0.02387272 -0.05663181 31 32 33 34 35 36 -0.01134305 -0.02255565 -0.06784442 0.02387272 -0.01134305 -0.01134305 37 38 39 40 41 42 -0.34660738 -0.25489025 -0.02141604 -0.05663181 0.69982099 -0.25489025 43 44 45 46 47 48 -0.03262865 -0.02255565 -0.05663181 -0.02141604 -0.01134305 0.02387272 49 50 51 52 53 54 -0.02141604 -0.01134305 -0.29010601 0.65339262 0.02387272 0.70989399 55 56 57 58 59 60 -0.01134305 -0.25489025 -0.30017901 0.02387272 0.02387272 0.68860838 61 62 63 64 65 66 0.01266011 -0.33539478 -0.01134305 0.01266011 -0.01134305 -0.01134305 67 68 69 70 71 72 0.66460522 -0.02255565 0.02387272 -0.29010601 -0.01134305 0.02387272 73 74 75 76 77 78 -0.25489025 -0.30131862 0.02387272 -0.02141604 0.02387272 -0.30017901 79 80 81 82 83 84 0.74510975 -0.05663181 -0.01134305 -0.26610286 -0.01134305 0.70989399 85 86 87 88 89 90 -0.02141604 -0.02255565 0.00911809 -0.10486087 -0.01488507 0.02033070 91 92 93 94 95 96 -0.06017383 0.13868634 -0.07138644 -0.01488507 0.14989894 0.02033070 97 98 99 100 101 102 0.13868634 -0.01488507 -0.02609768 0.02033070 0.00911809 -0.01488507 103 104 105 106 107 108 -0.01488507 -0.01488507 -0.12886402 -0.01488507 -0.01488507 -0.14007663 109 110 111 112 113 114 -0.01488507 -0.02609768 -0.18536539 0.14989894 -0.29364804 -0.14007663 115 116 117 118 119 120 -0.02609768 -0.01488507 0.00911809 -0.02609768 -0.01488507 0.02033070 121 122 123 124 125 126 -0.02609768 -0.01488507 -0.14007663 -0.30372103 0.02033070 0.14989894 127 128 129 130 131 132 -0.06017383 0.02033070 -0.01488507 0.02033070 -0.02609768 0.00911809 133 134 135 136 137 138 -0.30486064 -0.01488507 -0.01488507 -0.01488507 -0.31493364 -0.15014963 139 140 141 142 143 144 0.14989894 -0.01488507 0.74156773 -0.09364826 -0.02609768 -0.02495806 145 146 147 148 149 150 -0.06017383 0.18511471 -0.12886402 0.14989894 -0.02609768 -0.02495806 151 152 153 154 0.02033070 0.69513936 0.64985060 -0.30486064 > postscript(file="/var/wessaorg/rcomp/tmp/6a3f51355681615.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 0.01266011 NA 1 -0.01134305 0.01266011 2 -0.01134305 -0.01134305 3 -0.01134305 -0.01134305 4 -0.01134305 -0.01134305 5 -0.03262865 -0.01134305 6 -0.01134305 -0.03262865 7 -0.01134305 -0.01134305 8 0.02387272 -0.01134305 9 -0.02255565 0.02387272 10 -0.02255565 -0.02255565 11 -0.01134305 -0.02255565 12 -0.33539478 -0.01134305 13 -0.02255565 -0.33539478 14 -0.30017901 -0.02255565 15 -0.30017901 -0.30017901 16 0.65339262 -0.30017901 17 -0.02255565 0.65339262 18 0.02387272 -0.02255565 19 0.69982099 0.02387272 20 -0.06784442 0.69982099 21 -0.31139162 -0.06784442 22 -0.02141604 -0.31139162 23 -0.03262865 -0.02141604 24 -0.25489025 -0.03262865 25 -0.33539478 -0.25489025 26 0.01266011 -0.33539478 27 -0.29010601 0.01266011 28 0.02387272 -0.29010601 29 -0.05663181 0.02387272 30 -0.01134305 -0.05663181 31 -0.02255565 -0.01134305 32 -0.06784442 -0.02255565 33 0.02387272 -0.06784442 34 -0.01134305 0.02387272 35 -0.01134305 -0.01134305 36 -0.34660738 -0.01134305 37 -0.25489025 -0.34660738 38 -0.02141604 -0.25489025 39 -0.05663181 -0.02141604 40 0.69982099 -0.05663181 41 -0.25489025 0.69982099 42 -0.03262865 -0.25489025 43 -0.02255565 -0.03262865 44 -0.05663181 -0.02255565 45 -0.02141604 -0.05663181 46 -0.01134305 -0.02141604 47 0.02387272 -0.01134305 48 -0.02141604 0.02387272 49 -0.01134305 -0.02141604 50 -0.29010601 -0.01134305 51 0.65339262 -0.29010601 52 0.02387272 0.65339262 53 0.70989399 0.02387272 54 -0.01134305 0.70989399 55 -0.25489025 -0.01134305 56 -0.30017901 -0.25489025 57 0.02387272 -0.30017901 58 0.02387272 0.02387272 59 0.68860838 0.02387272 60 0.01266011 0.68860838 61 -0.33539478 0.01266011 62 -0.01134305 -0.33539478 63 0.01266011 -0.01134305 64 -0.01134305 0.01266011 65 -0.01134305 -0.01134305 66 0.66460522 -0.01134305 67 -0.02255565 0.66460522 68 0.02387272 -0.02255565 69 -0.29010601 0.02387272 70 -0.01134305 -0.29010601 71 0.02387272 -0.01134305 72 -0.25489025 0.02387272 73 -0.30131862 -0.25489025 74 0.02387272 -0.30131862 75 -0.02141604 0.02387272 76 0.02387272 -0.02141604 77 -0.30017901 0.02387272 78 0.74510975 -0.30017901 79 -0.05663181 0.74510975 80 -0.01134305 -0.05663181 81 -0.26610286 -0.01134305 82 -0.01134305 -0.26610286 83 0.70989399 -0.01134305 84 -0.02141604 0.70989399 85 -0.02255565 -0.02141604 86 0.00911809 -0.02255565 87 -0.10486087 0.00911809 88 -0.01488507 -0.10486087 89 0.02033070 -0.01488507 90 -0.06017383 0.02033070 91 0.13868634 -0.06017383 92 -0.07138644 0.13868634 93 -0.01488507 -0.07138644 94 0.14989894 -0.01488507 95 0.02033070 0.14989894 96 0.13868634 0.02033070 97 -0.01488507 0.13868634 98 -0.02609768 -0.01488507 99 0.02033070 -0.02609768 100 0.00911809 0.02033070 101 -0.01488507 0.00911809 102 -0.01488507 -0.01488507 103 -0.01488507 -0.01488507 104 -0.12886402 -0.01488507 105 -0.01488507 -0.12886402 106 -0.01488507 -0.01488507 107 -0.14007663 -0.01488507 108 -0.01488507 -0.14007663 109 -0.02609768 -0.01488507 110 -0.18536539 -0.02609768 111 0.14989894 -0.18536539 112 -0.29364804 0.14989894 113 -0.14007663 -0.29364804 114 -0.02609768 -0.14007663 115 -0.01488507 -0.02609768 116 0.00911809 -0.01488507 117 -0.02609768 0.00911809 118 -0.01488507 -0.02609768 119 0.02033070 -0.01488507 120 -0.02609768 0.02033070 121 -0.01488507 -0.02609768 122 -0.14007663 -0.01488507 123 -0.30372103 -0.14007663 124 0.02033070 -0.30372103 125 0.14989894 0.02033070 126 -0.06017383 0.14989894 127 0.02033070 -0.06017383 128 -0.01488507 0.02033070 129 0.02033070 -0.01488507 130 -0.02609768 0.02033070 131 0.00911809 -0.02609768 132 -0.30486064 0.00911809 133 -0.01488507 -0.30486064 134 -0.01488507 -0.01488507 135 -0.01488507 -0.01488507 136 -0.31493364 -0.01488507 137 -0.15014963 -0.31493364 138 0.14989894 -0.15014963 139 -0.01488507 0.14989894 140 0.74156773 -0.01488507 141 -0.09364826 0.74156773 142 -0.02609768 -0.09364826 143 -0.02495806 -0.02609768 144 -0.06017383 -0.02495806 145 0.18511471 -0.06017383 146 -0.12886402 0.18511471 147 0.14989894 -0.12886402 148 -0.02609768 0.14989894 149 -0.02495806 -0.02609768 150 0.02033070 -0.02495806 151 0.69513936 0.02033070 152 0.64985060 0.69513936 153 -0.30486064 0.64985060 154 NA -0.30486064 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.01134305 0.01266011 [2,] -0.01134305 -0.01134305 [3,] -0.01134305 -0.01134305 [4,] -0.01134305 -0.01134305 [5,] -0.03262865 -0.01134305 [6,] -0.01134305 -0.03262865 [7,] -0.01134305 -0.01134305 [8,] 0.02387272 -0.01134305 [9,] -0.02255565 0.02387272 [10,] -0.02255565 -0.02255565 [11,] -0.01134305 -0.02255565 [12,] -0.33539478 -0.01134305 [13,] -0.02255565 -0.33539478 [14,] -0.30017901 -0.02255565 [15,] -0.30017901 -0.30017901 [16,] 0.65339262 -0.30017901 [17,] -0.02255565 0.65339262 [18,] 0.02387272 -0.02255565 [19,] 0.69982099 0.02387272 [20,] -0.06784442 0.69982099 [21,] -0.31139162 -0.06784442 [22,] -0.02141604 -0.31139162 [23,] -0.03262865 -0.02141604 [24,] -0.25489025 -0.03262865 [25,] -0.33539478 -0.25489025 [26,] 0.01266011 -0.33539478 [27,] -0.29010601 0.01266011 [28,] 0.02387272 -0.29010601 [29,] -0.05663181 0.02387272 [30,] -0.01134305 -0.05663181 [31,] -0.02255565 -0.01134305 [32,] -0.06784442 -0.02255565 [33,] 0.02387272 -0.06784442 [34,] -0.01134305 0.02387272 [35,] -0.01134305 -0.01134305 [36,] -0.34660738 -0.01134305 [37,] -0.25489025 -0.34660738 [38,] -0.02141604 -0.25489025 [39,] -0.05663181 -0.02141604 [40,] 0.69982099 -0.05663181 [41,] -0.25489025 0.69982099 [42,] -0.03262865 -0.25489025 [43,] -0.02255565 -0.03262865 [44,] -0.05663181 -0.02255565 [45,] -0.02141604 -0.05663181 [46,] -0.01134305 -0.02141604 [47,] 0.02387272 -0.01134305 [48,] -0.02141604 0.02387272 [49,] -0.01134305 -0.02141604 [50,] -0.29010601 -0.01134305 [51,] 0.65339262 -0.29010601 [52,] 0.02387272 0.65339262 [53,] 0.70989399 0.02387272 [54,] -0.01134305 0.70989399 [55,] -0.25489025 -0.01134305 [56,] -0.30017901 -0.25489025 [57,] 0.02387272 -0.30017901 [58,] 0.02387272 0.02387272 [59,] 0.68860838 0.02387272 [60,] 0.01266011 0.68860838 [61,] -0.33539478 0.01266011 [62,] -0.01134305 -0.33539478 [63,] 0.01266011 -0.01134305 [64,] -0.01134305 0.01266011 [65,] -0.01134305 -0.01134305 [66,] 0.66460522 -0.01134305 [67,] -0.02255565 0.66460522 [68,] 0.02387272 -0.02255565 [69,] -0.29010601 0.02387272 [70,] -0.01134305 -0.29010601 [71,] 0.02387272 -0.01134305 [72,] -0.25489025 0.02387272 [73,] -0.30131862 -0.25489025 [74,] 0.02387272 -0.30131862 [75,] -0.02141604 0.02387272 [76,] 0.02387272 -0.02141604 [77,] -0.30017901 0.02387272 [78,] 0.74510975 -0.30017901 [79,] -0.05663181 0.74510975 [80,] -0.01134305 -0.05663181 [81,] -0.26610286 -0.01134305 [82,] -0.01134305 -0.26610286 [83,] 0.70989399 -0.01134305 [84,] -0.02141604 0.70989399 [85,] -0.02255565 -0.02141604 [86,] 0.00911809 -0.02255565 [87,] -0.10486087 0.00911809 [88,] -0.01488507 -0.10486087 [89,] 0.02033070 -0.01488507 [90,] -0.06017383 0.02033070 [91,] 0.13868634 -0.06017383 [92,] -0.07138644 0.13868634 [93,] -0.01488507 -0.07138644 [94,] 0.14989894 -0.01488507 [95,] 0.02033070 0.14989894 [96,] 0.13868634 0.02033070 [97,] -0.01488507 0.13868634 [98,] -0.02609768 -0.01488507 [99,] 0.02033070 -0.02609768 [100,] 0.00911809 0.02033070 [101,] -0.01488507 0.00911809 [102,] -0.01488507 -0.01488507 [103,] -0.01488507 -0.01488507 [104,] -0.12886402 -0.01488507 [105,] -0.01488507 -0.12886402 [106,] -0.01488507 -0.01488507 [107,] -0.14007663 -0.01488507 [108,] -0.01488507 -0.14007663 [109,] -0.02609768 -0.01488507 [110,] -0.18536539 -0.02609768 [111,] 0.14989894 -0.18536539 [112,] -0.29364804 0.14989894 [113,] -0.14007663 -0.29364804 [114,] -0.02609768 -0.14007663 [115,] -0.01488507 -0.02609768 [116,] 0.00911809 -0.01488507 [117,] -0.02609768 0.00911809 [118,] -0.01488507 -0.02609768 [119,] 0.02033070 -0.01488507 [120,] -0.02609768 0.02033070 [121,] -0.01488507 -0.02609768 [122,] -0.14007663 -0.01488507 [123,] -0.30372103 -0.14007663 [124,] 0.02033070 -0.30372103 [125,] 0.14989894 0.02033070 [126,] -0.06017383 0.14989894 [127,] 0.02033070 -0.06017383 [128,] -0.01488507 0.02033070 [129,] 0.02033070 -0.01488507 [130,] -0.02609768 0.02033070 [131,] 0.00911809 -0.02609768 [132,] -0.30486064 0.00911809 [133,] -0.01488507 -0.30486064 [134,] -0.01488507 -0.01488507 [135,] -0.01488507 -0.01488507 [136,] -0.31493364 -0.01488507 [137,] -0.15014963 -0.31493364 [138,] 0.14989894 -0.15014963 [139,] -0.01488507 0.14989894 [140,] 0.74156773 -0.01488507 [141,] -0.09364826 0.74156773 [142,] -0.02609768 -0.09364826 [143,] -0.02495806 -0.02609768 [144,] -0.06017383 -0.02495806 [145,] 0.18511471 -0.06017383 [146,] -0.12886402 0.18511471 [147,] 0.14989894 -0.12886402 [148,] -0.02609768 0.14989894 [149,] -0.02495806 -0.02609768 [150,] 0.02033070 -0.02495806 [151,] 0.69513936 0.02033070 [152,] 0.64985060 0.69513936 [153,] -0.30486064 0.64985060 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.01134305 0.01266011 2 -0.01134305 -0.01134305 3 -0.01134305 -0.01134305 4 -0.01134305 -0.01134305 5 -0.03262865 -0.01134305 6 -0.01134305 -0.03262865 7 -0.01134305 -0.01134305 8 0.02387272 -0.01134305 9 -0.02255565 0.02387272 10 -0.02255565 -0.02255565 11 -0.01134305 -0.02255565 12 -0.33539478 -0.01134305 13 -0.02255565 -0.33539478 14 -0.30017901 -0.02255565 15 -0.30017901 -0.30017901 16 0.65339262 -0.30017901 17 -0.02255565 0.65339262 18 0.02387272 -0.02255565 19 0.69982099 0.02387272 20 -0.06784442 0.69982099 21 -0.31139162 -0.06784442 22 -0.02141604 -0.31139162 23 -0.03262865 -0.02141604 24 -0.25489025 -0.03262865 25 -0.33539478 -0.25489025 26 0.01266011 -0.33539478 27 -0.29010601 0.01266011 28 0.02387272 -0.29010601 29 -0.05663181 0.02387272 30 -0.01134305 -0.05663181 31 -0.02255565 -0.01134305 32 -0.06784442 -0.02255565 33 0.02387272 -0.06784442 34 -0.01134305 0.02387272 35 -0.01134305 -0.01134305 36 -0.34660738 -0.01134305 37 -0.25489025 -0.34660738 38 -0.02141604 -0.25489025 39 -0.05663181 -0.02141604 40 0.69982099 -0.05663181 41 -0.25489025 0.69982099 42 -0.03262865 -0.25489025 43 -0.02255565 -0.03262865 44 -0.05663181 -0.02255565 45 -0.02141604 -0.05663181 46 -0.01134305 -0.02141604 47 0.02387272 -0.01134305 48 -0.02141604 0.02387272 49 -0.01134305 -0.02141604 50 -0.29010601 -0.01134305 51 0.65339262 -0.29010601 52 0.02387272 0.65339262 53 0.70989399 0.02387272 54 -0.01134305 0.70989399 55 -0.25489025 -0.01134305 56 -0.30017901 -0.25489025 57 0.02387272 -0.30017901 58 0.02387272 0.02387272 59 0.68860838 0.02387272 60 0.01266011 0.68860838 61 -0.33539478 0.01266011 62 -0.01134305 -0.33539478 63 0.01266011 -0.01134305 64 -0.01134305 0.01266011 65 -0.01134305 -0.01134305 66 0.66460522 -0.01134305 67 -0.02255565 0.66460522 68 0.02387272 -0.02255565 69 -0.29010601 0.02387272 70 -0.01134305 -0.29010601 71 0.02387272 -0.01134305 72 -0.25489025 0.02387272 73 -0.30131862 -0.25489025 74 0.02387272 -0.30131862 75 -0.02141604 0.02387272 76 0.02387272 -0.02141604 77 -0.30017901 0.02387272 78 0.74510975 -0.30017901 79 -0.05663181 0.74510975 80 -0.01134305 -0.05663181 81 -0.26610286 -0.01134305 82 -0.01134305 -0.26610286 83 0.70989399 -0.01134305 84 -0.02141604 0.70989399 85 -0.02255565 -0.02141604 86 0.00911809 -0.02255565 87 -0.10486087 0.00911809 88 -0.01488507 -0.10486087 89 0.02033070 -0.01488507 90 -0.06017383 0.02033070 91 0.13868634 -0.06017383 92 -0.07138644 0.13868634 93 -0.01488507 -0.07138644 94 0.14989894 -0.01488507 95 0.02033070 0.14989894 96 0.13868634 0.02033070 97 -0.01488507 0.13868634 98 -0.02609768 -0.01488507 99 0.02033070 -0.02609768 100 0.00911809 0.02033070 101 -0.01488507 0.00911809 102 -0.01488507 -0.01488507 103 -0.01488507 -0.01488507 104 -0.12886402 -0.01488507 105 -0.01488507 -0.12886402 106 -0.01488507 -0.01488507 107 -0.14007663 -0.01488507 108 -0.01488507 -0.14007663 109 -0.02609768 -0.01488507 110 -0.18536539 -0.02609768 111 0.14989894 -0.18536539 112 -0.29364804 0.14989894 113 -0.14007663 -0.29364804 114 -0.02609768 -0.14007663 115 -0.01488507 -0.02609768 116 0.00911809 -0.01488507 117 -0.02609768 0.00911809 118 -0.01488507 -0.02609768 119 0.02033070 -0.01488507 120 -0.02609768 0.02033070 121 -0.01488507 -0.02609768 122 -0.14007663 -0.01488507 123 -0.30372103 -0.14007663 124 0.02033070 -0.30372103 125 0.14989894 0.02033070 126 -0.06017383 0.14989894 127 0.02033070 -0.06017383 128 -0.01488507 0.02033070 129 0.02033070 -0.01488507 130 -0.02609768 0.02033070 131 0.00911809 -0.02609768 132 -0.30486064 0.00911809 133 -0.01488507 -0.30486064 134 -0.01488507 -0.01488507 135 -0.01488507 -0.01488507 136 -0.31493364 -0.01488507 137 -0.15014963 -0.31493364 138 0.14989894 -0.15014963 139 -0.01488507 0.14989894 140 0.74156773 -0.01488507 141 -0.09364826 0.74156773 142 -0.02609768 -0.09364826 143 -0.02495806 -0.02609768 144 -0.06017383 -0.02495806 145 0.18511471 -0.06017383 146 -0.12886402 0.18511471 147 0.14989894 -0.12886402 148 -0.02609768 0.14989894 149 -0.02495806 -0.02609768 150 0.02033070 -0.02495806 151 0.69513936 0.02033070 152 0.64985060 0.69513936 153 -0.30486064 0.64985060 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/757941355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8fn5p1355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9gvfv1355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/105lze1355681615.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11hpgu1355681615.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12hktm1355681615.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ht1v1355681615.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14syxl1355681615.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15hp9b1355681615.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16sufa1355681615.tab") + } > > try(system("convert tmp/1m8vc1355681615.ps tmp/1m8vc1355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/28kf71355681615.ps tmp/28kf71355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/3v3u71355681615.ps tmp/3v3u71355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/4wjn81355681615.ps tmp/4wjn81355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/5wdd81355681615.ps tmp/5wdd81355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/6a3f51355681615.ps tmp/6a3f51355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/757941355681615.ps tmp/757941355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/8fn5p1355681615.ps tmp/8fn5p1355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/9gvfv1355681615.ps tmp/9gvfv1355681615.png",intern=TRUE)) character(0) > try(system("convert tmp/105lze1355681615.ps tmp/105lze1355681615.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.217 0.767 7.972