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(1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0) + ,dim=c(6 + ,154) + ,dimnames=list(c('UseLimit' + ,'Used' + ,'Useful' + ,'Outcome' + ,'CorrectAnalysis' + ,'T40') + ,1:154)) > y <- array(NA,dim=c(6,154),dimnames=list(c('UseLimit','Used','Useful','Outcome','CorrectAnalysis','T40'),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 UseLimit Used Useful Outcome T40 1 0 1 0 0 1 1 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 1 0 1 1 0 7 0 0 0 0 0 0 8 0 0 0 0 0 1 9 0 0 0 0 1 0 10 0 1 0 0 0 0 11 0 1 0 0 0 1 12 0 0 0 0 0 0 13 0 0 1 1 0 0 14 0 1 0 0 0 1 15 0 0 1 1 1 0 16 0 0 1 1 1 1 17 1 1 1 1 0 1 18 0 1 0 0 0 1 19 0 0 0 0 1 0 20 1 0 1 1 1 1 21 0 1 0 1 0 0 22 0 1 1 1 1 0 23 0 0 0 1 1 0 24 0 1 0 1 1 0 25 0 0 1 0 1 1 26 0 0 1 1 0 0 27 0 1 0 0 1 0 28 0 0 1 0 0 0 29 0 0 0 0 1 0 30 0 0 0 1 0 0 31 0 0 0 0 0 0 32 0 1 0 0 0 0 33 0 1 0 1 0 0 34 0 0 0 0 1 1 35 0 0 0 0 0 0 36 0 0 0 0 0 0 37 0 1 1 1 0 1 38 0 0 1 0 1 0 39 0 0 0 1 1 0 40 0 0 0 1 0 1 41 1 0 1 1 1 0 42 0 0 1 0 1 0 43 0 1 0 1 1 0 44 0 1 0 0 0 1 45 0 0 0 1 0 0 46 0 0 0 1 1 0 47 0 0 0 0 0 0 48 0 0 0 0 1 0 49 0 0 0 1 1 0 50 0 0 0 0 0 0 51 0 0 1 0 0 1 52 1 1 1 1 0 1 53 0 0 0 0 1 0 54 1 0 1 0 0 0 55 0 0 0 0 0 0 56 0 0 1 0 1 1 57 0 0 1 1 1 0 58 0 0 0 0 1 0 59 0 0 0 0 1 0 60 1 1 1 1 1 1 61 0 1 0 0 1 1 62 0 0 1 1 0 0 63 0 0 0 0 0 0 64 0 1 0 0 1 1 65 0 0 0 0 0 0 66 0 0 0 0 0 0 67 1 0 1 1 0 1 68 0 1 0 0 0 0 69 0 0 0 0 1 0 70 0 0 1 0 0 0 71 0 0 0 0 0 0 72 0 0 0 0 1 0 73 0 0 1 0 1 0 74 0 1 1 0 0 0 75 0 0 0 0 1 0 76 0 0 0 1 1 1 77 0 0 0 0 1 0 78 0 0 1 1 1 0 79 1 0 1 0 1 1 80 0 0 0 1 0 1 81 0 0 0 0 0 0 82 0 1 1 0 1 0 83 0 0 0 0 0 0 84 1 0 1 0 0 0 85 0 0 0 1 1 0 86 0 1 0 0 0 0 87 0 1 0 0 1 0 88 0 1 1 0 1 0 89 0 0 0 0 0 0 90 0 0 0 0 1 0 91 0 0 0 1 0 0 92 0 1 0 0 0 0 93 0 1 0 1 0 0 94 0 0 0 0 0 0 95 0 0 0 0 0 0 96 0 0 0 0 1 0 97 0 1 0 0 0 0 98 0 0 0 0 0 0 99 0 1 0 0 0 0 100 0 0 0 0 1 0 101 0 1 0 0 1 0 102 0 0 0 0 0 0 103 0 0 0 0 0 0 104 0 0 0 0 0 0 105 0 0 1 0 0 0 106 0 0 0 0 0 0 107 0 0 0 0 0 0 108 0 1 1 0 0 0 109 0 0 0 0 0 0 110 0 1 0 0 0 0 111 0 1 1 1 0 0 112 0 0 0 0 0 0 113 0 0 1 0 0 0 114 0 1 1 0 0 0 115 0 1 0 0 0 0 116 0 0 0 0 0 0 117 0 1 0 0 1 0 118 0 1 0 0 0 0 119 0 0 0 0 0 0 120 0 0 0 0 1 0 121 0 1 0 0 0 0 122 0 0 0 0 0 0 123 0 1 1 0 0 0 124 0 0 1 1 1 0 125 0 0 0 0 1 0 126 0 0 0 0 0 0 127 0 0 0 1 0 0 128 0 0 0 0 1 0 129 0 0 0 0 0 0 130 0 0 0 0 1 0 131 0 1 0 0 0 0 132 0 1 0 0 1 0 133 0 1 1 0 0 0 134 0 0 0 0 0 0 135 0 0 0 0 0 0 136 0 0 0 0 0 0 137 0 1 1 1 1 0 138 0 1 1 1 1 0 139 0 0 0 0 0 0 140 0 0 0 0 0 0 141 1 0 1 0 1 0 142 0 0 1 0 1 0 143 0 1 0 0 0 0 144 0 0 0 1 1 0 145 0 0 0 1 0 0 146 0 0 0 0 1 0 147 0 0 1 0 0 0 148 0 0 0 0 0 0 149 0 1 0 0 0 0 150 0 0 0 1 1 0 151 0 0 0 0 1 0 152 1 1 1 0 0 0 153 1 1 1 1 0 0 154 0 1 1 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) UseLimit Used Useful Outcome T40 -0.01201 -0.01274 0.23733 0.04935 -0.03058 0.15863 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.42057 -0.13389 0.01201 0.02475 0.80526 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.01201 0.03056 -0.393 0.69487 UseLimit -0.01274 0.04128 -0.308 0.75814 Used 0.23733 0.04382 5.416 2.41e-07 *** Useful 0.04935 0.04533 1.089 0.27810 Outcome -0.03058 0.03969 -0.770 0.44225 T40 0.15863 0.05493 2.888 0.00446 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2356 on 148 degrees of freedom Multiple R-squared: 0.2575, Adjusted R-squared: 0.2324 F-statistic: 10.27 on 5 and 148 DF, p-value: 1.831e-08 > 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,] 0.0000000000 0.0000000000 1.00000000 [2,] 0.0000000000 0.0000000000 1.00000000 [3,] 0.0000000000 0.0000000000 1.00000000 [4,] 0.0000000000 0.0000000000 1.00000000 [5,] 0.0000000000 0.0000000000 1.00000000 [6,] 0.0000000000 0.0000000000 1.00000000 [7,] 0.0000000000 0.0000000000 1.00000000 [8,] 0.0000000000 0.0000000000 1.00000000 [9,] 0.3551299835 0.7102599670 0.64487002 [10,] 0.3131552940 0.6263105880 0.68684471 [11,] 0.2844006003 0.5688012006 0.71559940 [12,] 0.8024570428 0.3950859145 0.19754296 [13,] 0.7456116272 0.5087767456 0.25438837 [14,] 0.7324541921 0.5350916159 0.26754581 [15,] 0.6685031727 0.6629936546 0.33149683 [16,] 0.6016875627 0.7966248745 0.39831244 [17,] 0.6062879631 0.7874240738 0.39371204 [18,] 0.6094704289 0.7810591421 0.39052957 [19,] 0.5678687624 0.8642624752 0.43213124 [20,] 0.5129674850 0.9740650300 0.48703252 [21,] 0.4604264133 0.9208528266 0.53957359 [22,] 0.4040857548 0.8081715095 0.59591425 [23,] 0.3471751417 0.6943502834 0.65282486 [24,] 0.2943534809 0.5887069617 0.70564652 [25,] 0.2462451933 0.4924903865 0.75375481 [26,] 0.2092965125 0.4185930250 0.79070349 [27,] 0.1701059664 0.3402119328 0.82989403 [28,] 0.1359898594 0.2719797189 0.86401014 [29,] 0.1947990124 0.3895980249 0.80520099 [30,] 0.1631189266 0.3262378532 0.83688107 [31,] 0.1303150783 0.2606301566 0.86968492 [32,] 0.1268348944 0.2536697888 0.87316511 [33,] 0.6390408545 0.7219182911 0.36095915 [34,] 0.6074225812 0.7851548376 0.39257742 [35,] 0.5562625746 0.8874748508 0.44373743 [36,] 0.5183732669 0.9632534662 0.48162673 [37,] 0.4669319144 0.9338638287 0.53306809 [38,] 0.4166001874 0.8332003747 0.58339981 [39,] 0.3676832348 0.7353664696 0.63231677 [40,] 0.3208304836 0.6416609673 0.67916952 [41,] 0.2770616177 0.5541232354 0.72293838 [42,] 0.2364145641 0.4728291282 0.76358544 [43,] 0.2921552184 0.5843104368 0.70784478 [44,] 0.5665433223 0.8669133553 0.43345668 [45,] 0.5205199650 0.9589600700 0.47948004 [46,] 0.9003924566 0.1992150868 0.09960754 [47,] 0.8771440535 0.2457118930 0.12285595 [48,] 0.9155419336 0.1689161328 0.08445807 [49,] 0.9152376959 0.1695246082 0.08476230 [50,] 0.8962790641 0.2074418719 0.10372094 [51,] 0.8742679757 0.2514640486 0.12573202 [52,] 0.9572510859 0.0854978281 0.04274891 [53,] 0.9520262973 0.0959474054 0.04797370 [54,] 0.9548421847 0.0903156307 0.04515782 [55,] 0.9425297858 0.1149404284 0.05747021 [56,] 0.9409996406 0.1180007188 0.05900036 [57,] 0.9258815321 0.1482369358 0.07411847 [58,] 0.9079562005 0.1840875990 0.09204380 [59,] 0.9622233059 0.0755533881 0.03777669 [60,] 0.9513305332 0.0973389337 0.04866947 [61,] 0.9390075448 0.1219849103 0.06099246 [62,] 0.9375987373 0.1248025253 0.06240126 [63,] 0.9218788651 0.1562422698 0.07812113 [64,] 0.9041683151 0.1916633699 0.09583168 [65,] 0.8987105472 0.2025789056 0.10128945 [66,] 0.8944679066 0.2110641869 0.10553209 [67,] 0.8724707198 0.2550585604 0.12752928 [68,] 0.8731464152 0.2537071697 0.12685358 [69,] 0.8481922816 0.3036154367 0.15180772 [70,] 0.8462578532 0.3074842936 0.15374215 [71,] 0.9540564558 0.0918870884 0.04594354 [72,] 0.9451611640 0.1096776719 0.05483884 [73,] 0.9308236369 0.1383527262 0.06917636 [74,] 0.9241941717 0.1516116567 0.07580583 [75,] 0.9058161304 0.1883677393 0.09418387 [76,] 0.9948015205 0.0103969590 0.00519848 [77,] 0.9926607802 0.0146784396 0.00733922 [78,] 0.9897911554 0.0204176891 0.01020884 [79,] 0.9860960462 0.0278079075 0.01390395 [80,] 0.9844942400 0.0310115200 0.01550576 [81,] 0.9790240480 0.0419519040 0.02097595 [82,] 0.9721793657 0.0556412686 0.02782063 [83,] 0.9633777530 0.0732444939 0.03662225 [84,] 0.9523430905 0.0953138190 0.04765691 [85,] 0.9387394552 0.1225210895 0.06126054 [86,] 0.9221588130 0.1556823740 0.07784119 [87,] 0.9023108269 0.1953783461 0.09768917 [88,] 0.8794518560 0.2410962879 0.12054814 [89,] 0.8524679965 0.2950640069 0.14753200 [90,] 0.8214529787 0.3570940425 0.17854702 [91,] 0.7866356696 0.4267286608 0.21336433 [92,] 0.7486499601 0.5027000798 0.25135004 [93,] 0.7076113116 0.5847773769 0.29238869 [94,] 0.6621557508 0.6756884984 0.33784425 [95,] 0.6140012687 0.7719974625 0.38599873 [96,] 0.5638061775 0.8723876449 0.43619382 [97,] 0.5499619471 0.9000761057 0.45003805 [98,] 0.4980401535 0.9960803069 0.50195985 [99,] 0.4459001753 0.8918003506 0.55409982 [100,] 0.4336994155 0.8673988310 0.56630058 [101,] 0.3822205754 0.7644411507 0.61777942 [102,] 0.3327613483 0.6655226965 0.66723865 [103,] 0.3320351259 0.6640702518 0.66796487 [104,] 0.2844534466 0.5689068931 0.71554655 [105,] 0.2802304880 0.5604609760 0.71976951 [106,] 0.2824588328 0.5649176656 0.71754117 [107,] 0.2380593716 0.4761187432 0.76194063 [108,] 0.1971168115 0.3942336229 0.80288319 [109,] 0.1614368293 0.3228736585 0.83856317 [110,] 0.1294544146 0.2589088293 0.87054559 [111,] 0.1017655805 0.2035311610 0.89823442 [112,] 0.0787557028 0.1575114056 0.92124430 [113,] 0.0596780790 0.1193561580 0.94032192 [114,] 0.0442068448 0.0884136896 0.95579316 [115,] 0.0479264176 0.0958528351 0.95207358 [116,] 0.0496117754 0.0992235509 0.95038822 [117,] 0.0360355238 0.0720710475 0.96396448 [118,] 0.0254294817 0.0508589634 0.97457052 [119,] 0.0176141338 0.0352282677 0.98238587 [120,] 0.0118884601 0.0237769203 0.98811154 [121,] 0.0077521244 0.0155042488 0.99224788 [122,] 0.0049542508 0.0099085016 0.99504575 [123,] 0.0030416424 0.0060832847 0.99695836 [124,] 0.0018786122 0.0037572245 0.99812139 [125,] 0.0025468670 0.0050937340 0.99745313 [126,] 0.0014653967 0.0029307934 0.99853460 [127,] 0.0008114924 0.0016229847 0.99918851 [128,] 0.0004316462 0.0008632923 0.99956835 [129,] 0.0005869765 0.0011739531 0.99941302 [130,] 0.0034381038 0.0068762075 0.99656190 [131,] 0.0020754217 0.0041508434 0.99792458 [132,] 0.0013524214 0.0027048429 0.99864758 [133,] 0.0396281219 0.0792562438 0.96037188 [134,] 0.0316042423 0.0632084846 0.96839576 [135,] 0.0186702381 0.0373404763 0.98132976 [136,] 0.0100470963 0.0200941925 0.98995290 [137,] 0.0042106432 0.0084212864 0.99578936 > postscript(file="/var/fisher/rcomp/tmp/1yel61356098805.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/29khu1356098805.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3do9b1356098805.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4tx7h1356098805.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5iin71356098805.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 -0.103306428 0.012009900 0.012009900 0.012009900 0.012009900 0.005980329 7 8 9 10 11 12 0.012009900 -0.146624275 0.042591863 0.024745783 -0.133888392 0.012009900 13 14 15 16 17 18 -0.274670592 -0.133888392 -0.244088628 -0.402722803 0.579431117 -0.133888392 19 20 21 22 23 24 0.042591863 0.597277197 -0.024601634 -0.231352745 -0.006755554 0.005980329 25 26 27 28 29 30 -0.353375386 -0.274670592 0.055327746 -0.225323175 0.042591863 -0.037337517 31 32 33 34 35 36 0.012009900 0.024745783 -0.024601634 -0.116042312 0.012009900 0.012009900 37 38 39 40 41 42 -0.420568883 -0.194741212 -0.006755554 -0.195971692 0.755911372 -0.194741212 43 44 45 46 47 48 0.005980329 -0.133888392 -0.037337517 -0.006755554 0.012009900 0.042591863 49 50 51 52 53 54 -0.006755554 0.012009900 -0.383957349 0.579431117 0.042591863 0.774676825 55 56 57 58 59 60 0.012009900 -0.353375386 -0.244088628 0.042591863 0.042591863 0.610013080 61 62 63 64 65 66 -0.103306428 -0.274670592 0.012009900 -0.103306428 0.012009900 0.012009900 67 68 69 70 71 72 0.566695234 0.024745783 0.042591863 -0.225323175 0.012009900 0.042591863 73 74 75 76 77 78 -0.194741212 -0.212587292 0.042591863 -0.165389728 0.042591863 -0.244088628 79 80 81 82 83 84 0.646624614 -0.195971692 0.012009900 -0.182005328 0.012009900 0.774676825 85 86 87 88 89 90 -0.006755554 0.024745783 0.055327746 -0.182005328 0.012009900 0.042591863 91 92 93 94 95 96 -0.037337517 0.024745783 -0.024601634 0.012009900 0.012009900 0.042591863 97 98 99 100 101 102 0.024745783 0.012009900 0.024745783 0.042591863 0.055327746 0.012009900 103 104 105 106 107 108 0.012009900 0.012009900 -0.225323175 0.012009900 0.012009900 -0.212587292 109 110 111 112 113 114 0.012009900 0.024745783 -0.261934709 0.012009900 -0.225323175 -0.212587292 115 116 117 118 119 120 0.024745783 0.012009900 0.055327746 0.024745783 0.012009900 0.042591863 121 122 123 124 125 126 0.024745783 0.012009900 -0.212587292 -0.244088628 0.042591863 0.012009900 127 128 129 130 131 132 -0.037337517 0.042591863 0.012009900 0.042591863 0.024745783 0.055327746 133 134 135 136 137 138 -0.212587292 0.012009900 0.012009900 0.012009900 -0.231352745 -0.231352745 139 140 141 142 143 144 0.012009900 0.012009900 0.805258788 -0.194741212 0.024745783 -0.006755554 145 146 147 148 149 150 -0.037337517 0.042591863 -0.225323175 0.012009900 0.024745783 -0.006755554 151 152 153 154 0.042591863 0.787412708 0.738065291 -0.212587292 > postscript(file="/var/fisher/rcomp/tmp/6xjoj1356098805.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 -0.103306428 NA 1 0.012009900 -0.103306428 2 0.012009900 0.012009900 3 0.012009900 0.012009900 4 0.012009900 0.012009900 5 0.005980329 0.012009900 6 0.012009900 0.005980329 7 -0.146624275 0.012009900 8 0.042591863 -0.146624275 9 0.024745783 0.042591863 10 -0.133888392 0.024745783 11 0.012009900 -0.133888392 12 -0.274670592 0.012009900 13 -0.133888392 -0.274670592 14 -0.244088628 -0.133888392 15 -0.402722803 -0.244088628 16 0.579431117 -0.402722803 17 -0.133888392 0.579431117 18 0.042591863 -0.133888392 19 0.597277197 0.042591863 20 -0.024601634 0.597277197 21 -0.231352745 -0.024601634 22 -0.006755554 -0.231352745 23 0.005980329 -0.006755554 24 -0.353375386 0.005980329 25 -0.274670592 -0.353375386 26 0.055327746 -0.274670592 27 -0.225323175 0.055327746 28 0.042591863 -0.225323175 29 -0.037337517 0.042591863 30 0.012009900 -0.037337517 31 0.024745783 0.012009900 32 -0.024601634 0.024745783 33 -0.116042312 -0.024601634 34 0.012009900 -0.116042312 35 0.012009900 0.012009900 36 -0.420568883 0.012009900 37 -0.194741212 -0.420568883 38 -0.006755554 -0.194741212 39 -0.195971692 -0.006755554 40 0.755911372 -0.195971692 41 -0.194741212 0.755911372 42 0.005980329 -0.194741212 43 -0.133888392 0.005980329 44 -0.037337517 -0.133888392 45 -0.006755554 -0.037337517 46 0.012009900 -0.006755554 47 0.042591863 0.012009900 48 -0.006755554 0.042591863 49 0.012009900 -0.006755554 50 -0.383957349 0.012009900 51 0.579431117 -0.383957349 52 0.042591863 0.579431117 53 0.774676825 0.042591863 54 0.012009900 0.774676825 55 -0.353375386 0.012009900 56 -0.244088628 -0.353375386 57 0.042591863 -0.244088628 58 0.042591863 0.042591863 59 0.610013080 0.042591863 60 -0.103306428 0.610013080 61 -0.274670592 -0.103306428 62 0.012009900 -0.274670592 63 -0.103306428 0.012009900 64 0.012009900 -0.103306428 65 0.012009900 0.012009900 66 0.566695234 0.012009900 67 0.024745783 0.566695234 68 0.042591863 0.024745783 69 -0.225323175 0.042591863 70 0.012009900 -0.225323175 71 0.042591863 0.012009900 72 -0.194741212 0.042591863 73 -0.212587292 -0.194741212 74 0.042591863 -0.212587292 75 -0.165389728 0.042591863 76 0.042591863 -0.165389728 77 -0.244088628 0.042591863 78 0.646624614 -0.244088628 79 -0.195971692 0.646624614 80 0.012009900 -0.195971692 81 -0.182005328 0.012009900 82 0.012009900 -0.182005328 83 0.774676825 0.012009900 84 -0.006755554 0.774676825 85 0.024745783 -0.006755554 86 0.055327746 0.024745783 87 -0.182005328 0.055327746 88 0.012009900 -0.182005328 89 0.042591863 0.012009900 90 -0.037337517 0.042591863 91 0.024745783 -0.037337517 92 -0.024601634 0.024745783 93 0.012009900 -0.024601634 94 0.012009900 0.012009900 95 0.042591863 0.012009900 96 0.024745783 0.042591863 97 0.012009900 0.024745783 98 0.024745783 0.012009900 99 0.042591863 0.024745783 100 0.055327746 0.042591863 101 0.012009900 0.055327746 102 0.012009900 0.012009900 103 0.012009900 0.012009900 104 -0.225323175 0.012009900 105 0.012009900 -0.225323175 106 0.012009900 0.012009900 107 -0.212587292 0.012009900 108 0.012009900 -0.212587292 109 0.024745783 0.012009900 110 -0.261934709 0.024745783 111 0.012009900 -0.261934709 112 -0.225323175 0.012009900 113 -0.212587292 -0.225323175 114 0.024745783 -0.212587292 115 0.012009900 0.024745783 116 0.055327746 0.012009900 117 0.024745783 0.055327746 118 0.012009900 0.024745783 119 0.042591863 0.012009900 120 0.024745783 0.042591863 121 0.012009900 0.024745783 122 -0.212587292 0.012009900 123 -0.244088628 -0.212587292 124 0.042591863 -0.244088628 125 0.012009900 0.042591863 126 -0.037337517 0.012009900 127 0.042591863 -0.037337517 128 0.012009900 0.042591863 129 0.042591863 0.012009900 130 0.024745783 0.042591863 131 0.055327746 0.024745783 132 -0.212587292 0.055327746 133 0.012009900 -0.212587292 134 0.012009900 0.012009900 135 0.012009900 0.012009900 136 -0.231352745 0.012009900 137 -0.231352745 -0.231352745 138 0.012009900 -0.231352745 139 0.012009900 0.012009900 140 0.805258788 0.012009900 141 -0.194741212 0.805258788 142 0.024745783 -0.194741212 143 -0.006755554 0.024745783 144 -0.037337517 -0.006755554 145 0.042591863 -0.037337517 146 -0.225323175 0.042591863 147 0.012009900 -0.225323175 148 0.024745783 0.012009900 149 -0.006755554 0.024745783 150 0.042591863 -0.006755554 151 0.787412708 0.042591863 152 0.738065291 0.787412708 153 -0.212587292 0.738065291 154 NA -0.212587292 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.012009900 -0.103306428 [2,] 0.012009900 0.012009900 [3,] 0.012009900 0.012009900 [4,] 0.012009900 0.012009900 [5,] 0.005980329 0.012009900 [6,] 0.012009900 0.005980329 [7,] -0.146624275 0.012009900 [8,] 0.042591863 -0.146624275 [9,] 0.024745783 0.042591863 [10,] -0.133888392 0.024745783 [11,] 0.012009900 -0.133888392 [12,] -0.274670592 0.012009900 [13,] -0.133888392 -0.274670592 [14,] -0.244088628 -0.133888392 [15,] -0.402722803 -0.244088628 [16,] 0.579431117 -0.402722803 [17,] -0.133888392 0.579431117 [18,] 0.042591863 -0.133888392 [19,] 0.597277197 0.042591863 [20,] -0.024601634 0.597277197 [21,] -0.231352745 -0.024601634 [22,] -0.006755554 -0.231352745 [23,] 0.005980329 -0.006755554 [24,] -0.353375386 0.005980329 [25,] -0.274670592 -0.353375386 [26,] 0.055327746 -0.274670592 [27,] -0.225323175 0.055327746 [28,] 0.042591863 -0.225323175 [29,] -0.037337517 0.042591863 [30,] 0.012009900 -0.037337517 [31,] 0.024745783 0.012009900 [32,] -0.024601634 0.024745783 [33,] -0.116042312 -0.024601634 [34,] 0.012009900 -0.116042312 [35,] 0.012009900 0.012009900 [36,] -0.420568883 0.012009900 [37,] -0.194741212 -0.420568883 [38,] -0.006755554 -0.194741212 [39,] -0.195971692 -0.006755554 [40,] 0.755911372 -0.195971692 [41,] -0.194741212 0.755911372 [42,] 0.005980329 -0.194741212 [43,] -0.133888392 0.005980329 [44,] -0.037337517 -0.133888392 [45,] -0.006755554 -0.037337517 [46,] 0.012009900 -0.006755554 [47,] 0.042591863 0.012009900 [48,] -0.006755554 0.042591863 [49,] 0.012009900 -0.006755554 [50,] -0.383957349 0.012009900 [51,] 0.579431117 -0.383957349 [52,] 0.042591863 0.579431117 [53,] 0.774676825 0.042591863 [54,] 0.012009900 0.774676825 [55,] -0.353375386 0.012009900 [56,] -0.244088628 -0.353375386 [57,] 0.042591863 -0.244088628 [58,] 0.042591863 0.042591863 [59,] 0.610013080 0.042591863 [60,] -0.103306428 0.610013080 [61,] -0.274670592 -0.103306428 [62,] 0.012009900 -0.274670592 [63,] -0.103306428 0.012009900 [64,] 0.012009900 -0.103306428 [65,] 0.012009900 0.012009900 [66,] 0.566695234 0.012009900 [67,] 0.024745783 0.566695234 [68,] 0.042591863 0.024745783 [69,] -0.225323175 0.042591863 [70,] 0.012009900 -0.225323175 [71,] 0.042591863 0.012009900 [72,] -0.194741212 0.042591863 [73,] -0.212587292 -0.194741212 [74,] 0.042591863 -0.212587292 [75,] -0.165389728 0.042591863 [76,] 0.042591863 -0.165389728 [77,] -0.244088628 0.042591863 [78,] 0.646624614 -0.244088628 [79,] -0.195971692 0.646624614 [80,] 0.012009900 -0.195971692 [81,] -0.182005328 0.012009900 [82,] 0.012009900 -0.182005328 [83,] 0.774676825 0.012009900 [84,] -0.006755554 0.774676825 [85,] 0.024745783 -0.006755554 [86,] 0.055327746 0.024745783 [87,] -0.182005328 0.055327746 [88,] 0.012009900 -0.182005328 [89,] 0.042591863 0.012009900 [90,] -0.037337517 0.042591863 [91,] 0.024745783 -0.037337517 [92,] -0.024601634 0.024745783 [93,] 0.012009900 -0.024601634 [94,] 0.012009900 0.012009900 [95,] 0.042591863 0.012009900 [96,] 0.024745783 0.042591863 [97,] 0.012009900 0.024745783 [98,] 0.024745783 0.012009900 [99,] 0.042591863 0.024745783 [100,] 0.055327746 0.042591863 [101,] 0.012009900 0.055327746 [102,] 0.012009900 0.012009900 [103,] 0.012009900 0.012009900 [104,] -0.225323175 0.012009900 [105,] 0.012009900 -0.225323175 [106,] 0.012009900 0.012009900 [107,] -0.212587292 0.012009900 [108,] 0.012009900 -0.212587292 [109,] 0.024745783 0.012009900 [110,] -0.261934709 0.024745783 [111,] 0.012009900 -0.261934709 [112,] -0.225323175 0.012009900 [113,] -0.212587292 -0.225323175 [114,] 0.024745783 -0.212587292 [115,] 0.012009900 0.024745783 [116,] 0.055327746 0.012009900 [117,] 0.024745783 0.055327746 [118,] 0.012009900 0.024745783 [119,] 0.042591863 0.012009900 [120,] 0.024745783 0.042591863 [121,] 0.012009900 0.024745783 [122,] -0.212587292 0.012009900 [123,] -0.244088628 -0.212587292 [124,] 0.042591863 -0.244088628 [125,] 0.012009900 0.042591863 [126,] -0.037337517 0.012009900 [127,] 0.042591863 -0.037337517 [128,] 0.012009900 0.042591863 [129,] 0.042591863 0.012009900 [130,] 0.024745783 0.042591863 [131,] 0.055327746 0.024745783 [132,] -0.212587292 0.055327746 [133,] 0.012009900 -0.212587292 [134,] 0.012009900 0.012009900 [135,] 0.012009900 0.012009900 [136,] -0.231352745 0.012009900 [137,] -0.231352745 -0.231352745 [138,] 0.012009900 -0.231352745 [139,] 0.012009900 0.012009900 [140,] 0.805258788 0.012009900 [141,] -0.194741212 0.805258788 [142,] 0.024745783 -0.194741212 [143,] -0.006755554 0.024745783 [144,] -0.037337517 -0.006755554 [145,] 0.042591863 -0.037337517 [146,] -0.225323175 0.042591863 [147,] 0.012009900 -0.225323175 [148,] 0.024745783 0.012009900 [149,] -0.006755554 0.024745783 [150,] 0.042591863 -0.006755554 [151,] 0.787412708 0.042591863 [152,] 0.738065291 0.787412708 [153,] -0.212587292 0.738065291 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.012009900 -0.103306428 2 0.012009900 0.012009900 3 0.012009900 0.012009900 4 0.012009900 0.012009900 5 0.005980329 0.012009900 6 0.012009900 0.005980329 7 -0.146624275 0.012009900 8 0.042591863 -0.146624275 9 0.024745783 0.042591863 10 -0.133888392 0.024745783 11 0.012009900 -0.133888392 12 -0.274670592 0.012009900 13 -0.133888392 -0.274670592 14 -0.244088628 -0.133888392 15 -0.402722803 -0.244088628 16 0.579431117 -0.402722803 17 -0.133888392 0.579431117 18 0.042591863 -0.133888392 19 0.597277197 0.042591863 20 -0.024601634 0.597277197 21 -0.231352745 -0.024601634 22 -0.006755554 -0.231352745 23 0.005980329 -0.006755554 24 -0.353375386 0.005980329 25 -0.274670592 -0.353375386 26 0.055327746 -0.274670592 27 -0.225323175 0.055327746 28 0.042591863 -0.225323175 29 -0.037337517 0.042591863 30 0.012009900 -0.037337517 31 0.024745783 0.012009900 32 -0.024601634 0.024745783 33 -0.116042312 -0.024601634 34 0.012009900 -0.116042312 35 0.012009900 0.012009900 36 -0.420568883 0.012009900 37 -0.194741212 -0.420568883 38 -0.006755554 -0.194741212 39 -0.195971692 -0.006755554 40 0.755911372 -0.195971692 41 -0.194741212 0.755911372 42 0.005980329 -0.194741212 43 -0.133888392 0.005980329 44 -0.037337517 -0.133888392 45 -0.006755554 -0.037337517 46 0.012009900 -0.006755554 47 0.042591863 0.012009900 48 -0.006755554 0.042591863 49 0.012009900 -0.006755554 50 -0.383957349 0.012009900 51 0.579431117 -0.383957349 52 0.042591863 0.579431117 53 0.774676825 0.042591863 54 0.012009900 0.774676825 55 -0.353375386 0.012009900 56 -0.244088628 -0.353375386 57 0.042591863 -0.244088628 58 0.042591863 0.042591863 59 0.610013080 0.042591863 60 -0.103306428 0.610013080 61 -0.274670592 -0.103306428 62 0.012009900 -0.274670592 63 -0.103306428 0.012009900 64 0.012009900 -0.103306428 65 0.012009900 0.012009900 66 0.566695234 0.012009900 67 0.024745783 0.566695234 68 0.042591863 0.024745783 69 -0.225323175 0.042591863 70 0.012009900 -0.225323175 71 0.042591863 0.012009900 72 -0.194741212 0.042591863 73 -0.212587292 -0.194741212 74 0.042591863 -0.212587292 75 -0.165389728 0.042591863 76 0.042591863 -0.165389728 77 -0.244088628 0.042591863 78 0.646624614 -0.244088628 79 -0.195971692 0.646624614 80 0.012009900 -0.195971692 81 -0.182005328 0.012009900 82 0.012009900 -0.182005328 83 0.774676825 0.012009900 84 -0.006755554 0.774676825 85 0.024745783 -0.006755554 86 0.055327746 0.024745783 87 -0.182005328 0.055327746 88 0.012009900 -0.182005328 89 0.042591863 0.012009900 90 -0.037337517 0.042591863 91 0.024745783 -0.037337517 92 -0.024601634 0.024745783 93 0.012009900 -0.024601634 94 0.012009900 0.012009900 95 0.042591863 0.012009900 96 0.024745783 0.042591863 97 0.012009900 0.024745783 98 0.024745783 0.012009900 99 0.042591863 0.024745783 100 0.055327746 0.042591863 101 0.012009900 0.055327746 102 0.012009900 0.012009900 103 0.012009900 0.012009900 104 -0.225323175 0.012009900 105 0.012009900 -0.225323175 106 0.012009900 0.012009900 107 -0.212587292 0.012009900 108 0.012009900 -0.212587292 109 0.024745783 0.012009900 110 -0.261934709 0.024745783 111 0.012009900 -0.261934709 112 -0.225323175 0.012009900 113 -0.212587292 -0.225323175 114 0.024745783 -0.212587292 115 0.012009900 0.024745783 116 0.055327746 0.012009900 117 0.024745783 0.055327746 118 0.012009900 0.024745783 119 0.042591863 0.012009900 120 0.024745783 0.042591863 121 0.012009900 0.024745783 122 -0.212587292 0.012009900 123 -0.244088628 -0.212587292 124 0.042591863 -0.244088628 125 0.012009900 0.042591863 126 -0.037337517 0.012009900 127 0.042591863 -0.037337517 128 0.012009900 0.042591863 129 0.042591863 0.012009900 130 0.024745783 0.042591863 131 0.055327746 0.024745783 132 -0.212587292 0.055327746 133 0.012009900 -0.212587292 134 0.012009900 0.012009900 135 0.012009900 0.012009900 136 -0.231352745 0.012009900 137 -0.231352745 -0.231352745 138 0.012009900 -0.231352745 139 0.012009900 0.012009900 140 0.805258788 0.012009900 141 -0.194741212 0.805258788 142 0.024745783 -0.194741212 143 -0.006755554 0.024745783 144 -0.037337517 -0.006755554 145 0.042591863 -0.037337517 146 -0.225323175 0.042591863 147 0.012009900 -0.225323175 148 0.024745783 0.012009900 149 -0.006755554 0.024745783 150 0.042591863 -0.006755554 151 0.787412708 0.042591863 152 0.738065291 0.787412708 153 -0.212587292 0.738065291 > 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/7bapi1356098806.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/855n61356098806.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9wvgl1356098806.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/104abv1356098806.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11angv1356098806.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/12u3oz1356098806.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/13dbkq1356098806.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/14jlns1356098806.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/15vnxl1356098806.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/166d3r1356098806.tab") + } > > try(system("convert tmp/1yel61356098805.ps tmp/1yel61356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/29khu1356098805.ps tmp/29khu1356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/3do9b1356098805.ps tmp/3do9b1356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/4tx7h1356098805.ps tmp/4tx7h1356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/5iin71356098805.ps tmp/5iin71356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/6xjoj1356098805.ps tmp/6xjoj1356098805.png",intern=TRUE)) character(0) > try(system("convert tmp/7bapi1356098806.ps tmp/7bapi1356098806.png",intern=TRUE)) character(0) > try(system("convert tmp/855n61356098806.ps tmp/855n61356098806.png",intern=TRUE)) character(0) > try(system("convert tmp/9wvgl1356098806.ps tmp/9wvgl1356098806.png",intern=TRUE)) character(0) > try(system("convert tmp/104abv1356098806.ps tmp/104abv1356098806.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.776 1.733 9.503