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 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + 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,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,2 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0) + ,dim=c(8 + ,154) + ,dimnames=list(c('weeks' + ,'uselimit' + ,'T40' + ,'T20' + ,'Used' + ,'CorrectAnalysis' + ,'useful' + ,'outcome') + ,1:154)) > y <- array(NA,dim=c(8,154),dimnames=list(c('weeks','uselimit','T40','T20','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 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'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 weeks uselimit T40 T20 Used CorrectAnalysis useful outcome 1 4 1 1 0 0 0 0 1 2 4 0 0 0 0 0 0 0 3 4 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 5 4 0 0 0 0 0 0 0 6 4 1 0 0 0 0 1 1 7 4 0 0 0 0 0 0 0 8 4 0 1 0 0 0 0 0 9 4 0 0 0 0 0 0 1 10 4 1 0 0 0 0 0 0 11 4 1 1 0 0 0 0 0 12 4 0 0 0 0 0 0 0 13 4 0 0 0 1 0 1 0 14 4 1 1 0 0 0 0 0 15 4 0 0 0 1 0 1 1 16 4 0 1 0 1 0 1 1 17 4 1 1 0 1 1 1 0 18 4 1 1 0 0 0 0 0 19 4 0 0 0 0 0 0 1 20 4 0 1 0 1 1 1 1 21 4 1 0 0 0 0 1 0 22 4 1 0 0 1 0 1 1 23 4 0 0 0 0 0 1 1 24 4 1 0 0 0 0 1 1 25 4 0 1 0 1 0 0 1 26 4 0 0 0 1 0 1 0 27 4 1 0 0 0 0 0 1 28 4 0 0 0 1 0 0 0 29 4 0 0 0 0 0 0 1 30 4 0 0 0 0 0 1 0 31 4 0 0 0 0 0 0 0 32 4 1 0 0 0 0 0 0 33 4 1 0 0 0 0 1 0 34 4 0 1 0 0 0 0 1 35 4 0 0 0 0 0 0 0 36 4 0 0 0 0 0 0 0 37 4 1 1 0 1 0 1 0 38 4 0 0 0 1 0 0 1 39 4 0 0 0 0 0 1 1 40 4 0 1 0 0 0 1 0 41 4 0 0 0 1 1 1 1 42 4 0 0 0 1 0 0 1 43 4 1 0 0 0 0 1 1 44 4 1 1 0 0 0 0 0 45 4 0 0 0 0 0 1 0 46 4 0 0 0 0 0 1 1 47 4 0 0 0 0 0 0 0 48 4 0 0 0 0 0 0 1 49 4 0 0 0 0 0 1 1 50 4 0 0 0 0 0 0 0 51 4 0 1 0 1 0 0 0 52 4 1 1 0 1 1 1 0 53 4 0 0 0 0 0 0 1 54 4 0 0 0 1 1 0 0 55 4 0 0 0 0 0 0 0 56 4 0 1 0 1 0 0 1 57 4 0 0 0 1 0 1 1 58 4 0 0 0 0 0 0 1 59 4 0 0 0 0 0 0 1 60 4 1 1 0 1 1 1 1 61 4 1 1 0 0 0 0 1 62 4 0 0 0 1 0 1 0 63 4 0 0 0 0 0 0 0 64 4 1 1 0 0 0 0 1 65 4 0 0 0 0 0 0 0 66 4 0 0 0 0 0 0 0 67 4 0 1 0 1 1 1 0 68 4 1 0 0 0 0 0 0 69 4 0 0 0 0 0 0 1 70 4 0 0 0 1 0 0 0 71 4 0 0 0 0 0 0 0 72 4 0 0 0 0 0 0 1 73 4 0 0 0 1 0 0 1 74 4 1 0 0 1 0 0 0 75 4 0 0 0 0 0 0 1 76 4 0 1 0 0 0 1 1 77 4 0 0 0 0 0 0 1 78 4 0 0 0 1 0 1 1 79 4 0 1 0 1 1 0 1 80 4 0 1 0 0 0 1 0 81 4 0 0 0 0 0 0 0 82 4 1 0 0 1 0 0 1 83 4 0 0 0 0 0 0 0 84 4 0 0 0 1 1 0 0 85 4 0 0 0 0 0 1 1 86 4 1 0 0 0 0 0 0 87 2 1 0 1 0 0 0 1 88 2 1 0 0 1 0 0 1 89 2 0 0 1 0 0 0 0 90 2 0 0 1 0 0 0 1 91 2 0 0 1 0 0 1 0 92 2 1 0 0 0 0 0 0 93 2 1 0 1 0 0 1 0 94 2 0 0 1 0 0 0 0 95 2 0 0 0 0 0 0 0 96 2 0 0 1 0 0 0 1 97 2 1 0 0 0 0 0 0 98 2 0 0 1 0 0 0 0 99 2 1 0 1 0 0 0 0 100 2 0 0 1 0 0 0 1 101 2 1 0 1 0 0 0 1 102 2 0 0 1 0 0 0 0 103 2 0 0 1 0 0 0 0 104 2 0 0 1 0 0 0 0 105 2 0 0 0 1 0 0 0 106 2 0 0 1 0 0 0 0 107 2 0 0 1 0 0 0 0 108 2 1 0 0 1 0 0 0 109 2 0 0 1 0 0 0 0 110 2 1 0 1 0 0 0 0 111 2 1 0 0 1 0 1 0 112 2 0 0 0 0 0 0 0 113 2 0 0 1 1 0 0 0 114 2 1 0 0 1 0 0 0 115 2 1 0 1 0 0 0 0 116 2 0 0 1 0 0 0 0 117 2 1 0 1 0 0 0 1 118 2 1 0 1 0 0 0 0 119 2 0 0 1 0 0 0 0 120 2 0 0 1 0 0 0 1 121 2 1 0 1 0 0 0 0 122 2 0 0 1 0 0 0 0 123 2 1 0 0 1 0 0 0 124 2 0 0 1 1 0 1 1 125 2 0 0 1 0 0 0 1 126 2 0 0 0 0 0 0 0 127 2 0 0 1 0 0 1 0 128 2 0 0 1 0 0 0 1 129 2 0 0 1 0 0 0 0 130 2 0 0 1 0 0 0 1 131 2 1 0 1 0 0 0 0 132 2 1 0 1 0 0 0 1 133 2 1 0 1 1 0 0 0 134 2 0 0 1 0 0 0 0 135 2 0 0 1 0 0 0 0 136 2 0 0 1 0 0 0 0 137 2 1 0 1 1 0 1 1 138 2 1 0 0 1 0 1 1 139 2 0 0 0 0 0 0 0 140 2 0 0 1 0 0 0 0 141 2 0 0 1 1 1 0 1 142 2 0 0 0 1 0 0 1 143 2 1 0 1 0 0 0 0 144 2 0 0 1 0 0 1 1 145 2 0 0 1 0 0 1 0 146 2 0 0 0 0 0 0 1 147 2 0 0 0 1 0 0 0 148 2 0 0 0 0 0 0 0 149 2 1 0 1 0 0 0 0 150 2 0 0 1 0 0 1 1 151 2 0 0 1 0 0 0 1 152 2 1 0 1 1 1 0 0 153 2 1 0 1 1 1 1 0 154 2 1 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 T40 T20 3.6076 -0.1798 0.3992 -1.5938 Used CorrectAnalysis useful outcome -0.3584 0.3399 0.2132 0.1448 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.7524 -0.1116 0.1660 0.3924 0.9307 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.60756 0.08689 41.517 < 2e-16 *** uselimit -0.17979 0.10035 -1.792 0.07528 . T40 0.39921 0.14264 2.799 0.00582 ** T20 -1.59378 0.10527 -15.140 < 2e-16 *** Used -0.35844 0.11818 -3.033 0.00287 ** CorrectAnalysis 0.33991 0.20034 1.697 0.09189 . useful 0.21321 0.11027 1.934 0.05511 . outcome 0.14484 0.09618 1.506 0.13424 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5694 on 146 degrees of freedom Multiple R-squared: 0.6883, Adjusted R-squared: 0.6734 F-statistic: 46.07 on 7 and 146 DF, p-value: < 2.2e-16 > 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,] 5.822058e-49 1.164412e-48 1.000000e+00 [2,] 8.360088e-61 1.672018e-60 1.000000e+00 [3,] 2.981255e-86 5.962511e-86 1.000000e+00 [4,] 3.534644e-90 7.069288e-90 1.000000e+00 [5,] 5.534773e-105 1.106955e-104 1.000000e+00 [6,] 0.000000e+00 0.000000e+00 1.000000e+00 [7,] 2.806482e-145 5.612964e-145 1.000000e+00 [8,] 3.914057e-151 7.828114e-151 1.000000e+00 [9,] 7.114211e-165 1.422842e-164 1.000000e+00 [10,] 1.063469e-188 2.126938e-188 1.000000e+00 [11,] 6.762851e-221 1.352570e-220 1.000000e+00 [12,] 1.579212e-212 3.158424e-212 1.000000e+00 [13,] 6.071370e-224 1.214274e-223 1.000000e+00 [14,] 7.183946e-242 1.436789e-241 1.000000e+00 [15,] 5.174273e-260 1.034855e-259 1.000000e+00 [16,] 1.203398e-300 2.406795e-300 1.000000e+00 [17,] 8.075652e-289 1.615130e-288 1.000000e+00 [18,] 5.411977e-299 1.082395e-298 1.000000e+00 [19,] 6.764747e-319 1.352949e-318 1.000000e+00 [20,] 0.000000e+00 0.000000e+00 1.000000e+00 [21,] 0.000000e+00 0.000000e+00 1.000000e+00 [22,] 0.000000e+00 0.000000e+00 1.000000e+00 [23,] 0.000000e+00 0.000000e+00 1.000000e+00 [24,] 0.000000e+00 0.000000e+00 1.000000e+00 [25,] 0.000000e+00 0.000000e+00 1.000000e+00 [26,] 0.000000e+00 0.000000e+00 1.000000e+00 [27,] 0.000000e+00 0.000000e+00 1.000000e+00 [28,] 0.000000e+00 0.000000e+00 1.000000e+00 [29,] 0.000000e+00 0.000000e+00 1.000000e+00 [30,] 0.000000e+00 0.000000e+00 1.000000e+00 [31,] 0.000000e+00 0.000000e+00 1.000000e+00 [32,] 0.000000e+00 0.000000e+00 1.000000e+00 [33,] 0.000000e+00 0.000000e+00 1.000000e+00 [34,] 0.000000e+00 0.000000e+00 1.000000e+00 [35,] 0.000000e+00 0.000000e+00 1.000000e+00 [36,] 0.000000e+00 0.000000e+00 1.000000e+00 [37,] 0.000000e+00 0.000000e+00 1.000000e+00 [38,] 0.000000e+00 0.000000e+00 1.000000e+00 [39,] 0.000000e+00 0.000000e+00 1.000000e+00 [40,] 0.000000e+00 0.000000e+00 1.000000e+00 [41,] 0.000000e+00 0.000000e+00 1.000000e+00 [42,] 0.000000e+00 0.000000e+00 1.000000e+00 [43,] 0.000000e+00 0.000000e+00 1.000000e+00 [44,] 0.000000e+00 0.000000e+00 1.000000e+00 [45,] 0.000000e+00 0.000000e+00 1.000000e+00 [46,] 0.000000e+00 0.000000e+00 1.000000e+00 [47,] 0.000000e+00 0.000000e+00 1.000000e+00 [48,] 0.000000e+00 0.000000e+00 1.000000e+00 [49,] 0.000000e+00 0.000000e+00 1.000000e+00 [50,] 0.000000e+00 0.000000e+00 1.000000e+00 [51,] 0.000000e+00 0.000000e+00 1.000000e+00 [52,] 0.000000e+00 0.000000e+00 1.000000e+00 [53,] 0.000000e+00 0.000000e+00 1.000000e+00 [54,] 0.000000e+00 0.000000e+00 1.000000e+00 [55,] 0.000000e+00 0.000000e+00 1.000000e+00 [56,] 0.000000e+00 0.000000e+00 1.000000e+00 [57,] 0.000000e+00 0.000000e+00 1.000000e+00 [58,] 0.000000e+00 0.000000e+00 1.000000e+00 [59,] 0.000000e+00 0.000000e+00 1.000000e+00 [60,] 0.000000e+00 0.000000e+00 1.000000e+00 [61,] 0.000000e+00 0.000000e+00 1.000000e+00 [62,] 0.000000e+00 0.000000e+00 1.000000e+00 [63,] 0.000000e+00 0.000000e+00 1.000000e+00 [64,] 0.000000e+00 0.000000e+00 1.000000e+00 [65,] 0.000000e+00 0.000000e+00 1.000000e+00 [66,] 0.000000e+00 0.000000e+00 1.000000e+00 [67,] 0.000000e+00 0.000000e+00 1.000000e+00 [68,] 0.000000e+00 0.000000e+00 1.000000e+00 [69,] 0.000000e+00 0.000000e+00 1.000000e+00 [70,] 0.000000e+00 0.000000e+00 1.000000e+00 [71,] 0.000000e+00 0.000000e+00 1.000000e+00 [72,] 0.000000e+00 0.000000e+00 1.000000e+00 [73,] 0.000000e+00 0.000000e+00 1.000000e+00 [74,] 0.000000e+00 0.000000e+00 1.000000e+00 [75,] 0.000000e+00 0.000000e+00 1.000000e+00 [76,] 1.000000e+00 1.725567e-18 8.627835e-19 [77,] 2.719663e-08 5.439327e-08 1.000000e+00 [78,] 1.000000e+00 0.000000e+00 0.000000e+00 [79,] 1.000000e+00 0.000000e+00 0.000000e+00 [80,] 1.000000e+00 0.000000e+00 0.000000e+00 [81,] 1.000000e+00 0.000000e+00 0.000000e+00 [82,] 1.000000e+00 0.000000e+00 0.000000e+00 [83,] 1.000000e+00 0.000000e+00 0.000000e+00 [84,] 1.000000e+00 0.000000e+00 0.000000e+00 [85,] 1.000000e+00 0.000000e+00 0.000000e+00 [86,] 1.000000e+00 0.000000e+00 0.000000e+00 [87,] 1.000000e+00 0.000000e+00 0.000000e+00 [88,] 1.000000e+00 0.000000e+00 0.000000e+00 [89,] 1.000000e+00 0.000000e+00 0.000000e+00 [90,] 1.000000e+00 0.000000e+00 0.000000e+00 [91,] 1.000000e+00 0.000000e+00 0.000000e+00 [92,] 1.000000e+00 0.000000e+00 0.000000e+00 [93,] 1.000000e+00 0.000000e+00 0.000000e+00 [94,] 1.000000e+00 0.000000e+00 0.000000e+00 [95,] 1.000000e+00 0.000000e+00 0.000000e+00 [96,] 1.000000e+00 0.000000e+00 0.000000e+00 [97,] 1.000000e+00 0.000000e+00 0.000000e+00 [98,] 1.000000e+00 0.000000e+00 0.000000e+00 [99,] 1.000000e+00 0.000000e+00 0.000000e+00 [100,] 1.000000e+00 0.000000e+00 0.000000e+00 [101,] 1.000000e+00 0.000000e+00 0.000000e+00 [102,] 1.000000e+00 0.000000e+00 0.000000e+00 [103,] 1.000000e+00 0.000000e+00 0.000000e+00 [104,] 1.000000e+00 0.000000e+00 0.000000e+00 [105,] 1.000000e+00 0.000000e+00 0.000000e+00 [106,] 1.000000e+00 0.000000e+00 0.000000e+00 [107,] 1.000000e+00 0.000000e+00 0.000000e+00 [108,] 1.000000e+00 0.000000e+00 0.000000e+00 [109,] 1.000000e+00 0.000000e+00 0.000000e+00 [110,] 1.000000e+00 0.000000e+00 0.000000e+00 [111,] 1.000000e+00 0.000000e+00 0.000000e+00 [112,] 1.000000e+00 0.000000e+00 0.000000e+00 [113,] 1.000000e+00 0.000000e+00 0.000000e+00 [114,] 1.000000e+00 0.000000e+00 0.000000e+00 [115,] 1.000000e+00 8.893182e-323 4.446591e-323 [116,] 1.000000e+00 2.609495e-303 1.304747e-303 [117,] 1.000000e+00 2.406144e-292 1.203072e-292 [118,] 1.000000e+00 1.251262e-303 6.256309e-304 [119,] 1.000000e+00 6.293505e-263 3.146752e-263 [120,] 1.000000e+00 2.251000e-244 1.125500e-244 [121,] 1.000000e+00 3.316047e-227 1.658023e-227 [122,] 1.000000e+00 4.705588e-215 2.352794e-215 [123,] 1.000000e+00 1.427753e-222 7.138764e-223 [124,] 1.000000e+00 3.610384e-190 1.805192e-190 [125,] 1.000000e+00 2.802131e-166 1.401065e-166 [126,] 1.000000e+00 9.748056e-153 4.874028e-153 [127,] 1.000000e+00 7.891768e-148 3.945884e-148 [128,] 1.000000e+00 0.000000e+00 0.000000e+00 [129,] 1.000000e+00 3.005973e-106 1.502986e-106 [130,] 1.000000e+00 9.650293e-92 4.825147e-92 [131,] 1.000000e+00 6.551151e-87 3.275576e-87 [132,] 1.000000e+00 4.179806e-61 2.089903e-61 [133,] 1.000000e+00 4.828536e-49 2.414268e-49 > postscript(file="/var/wessaorg/rcomp/tmp/1ktpx1355926481.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2493f1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3hcd11355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4hsmp1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5hglc1355926481.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 0.028178157 0.392438107 0.392438107 0.392438107 0.392438107 0.214172426 7 8 9 10 11 12 0.392438107 -0.006770111 0.247599422 0.572225059 0.173016842 0.392438107 13 14 15 16 17 18 0.537667981 0.173016842 0.392829297 -0.006378921 -0.021663445 0.173016842 19 20 21 22 23 24 0.247599422 -0.346289082 0.359011110 0.572616249 0.034385473 0.214172426 25 26 27 28 29 30 0.206835028 0.537667981 0.427386375 0.750881930 0.247599422 0.179224158 31 32 33 34 35 36 0.392438107 0.572225059 0.359011110 -0.151608795 0.392438107 0.392438107 37 38 39 40 41 42 0.318246716 0.606043246 0.034385473 -0.219984060 0.052919135 0.606043246 43 44 45 46 47 48 0.214172426 0.173016842 0.179224158 0.034385473 0.392438107 0.247599422 49 50 51 52 53 54 0.034385473 0.392438107 0.351673712 -0.021663445 0.247599422 0.410971769 55 56 57 58 59 60 0.392438107 0.206835028 0.392829297 0.247599422 0.247599422 -0.166502130 61 62 63 64 65 66 0.028178157 0.537667981 0.392438107 0.028178157 0.392438107 0.392438107 67 68 69 70 71 72 -0.201450398 0.572225059 0.247599422 0.750881930 0.392438107 0.247599422 73 74 75 76 77 78 0.606043246 0.930668882 0.247599422 -0.364822744 0.247599422 0.392829297 79 80 81 82 83 84 -0.133075133 -0.219984060 0.392438107 0.785830198 0.392438107 0.410971769 85 86 87 88 89 90 0.034385473 0.572225059 0.021167835 -1.214169802 -0.013780433 -0.158619117 91 92 93 94 95 96 -0.226994382 -1.427774941 -0.047207429 -0.013780433 -1.607561893 -0.158619117 97 98 99 100 101 102 -1.427774941 -0.013780433 0.166006520 -0.158619117 0.021167835 -0.013780433 103 104 105 106 107 108 -0.013780433 -0.013780433 -1.249118070 -0.013780433 -0.013780433 -1.069331118 109 110 111 112 113 114 -0.013780433 0.166006520 -1.282545067 -1.607561893 0.344663390 -1.069331118 115 116 117 118 119 120 0.166006520 -0.013780433 0.021167835 0.166006520 -0.013780433 -0.158619117 121 122 123 124 125 126 0.166006520 -0.013780433 -1.069331118 -0.013389243 -0.158619117 -1.607561893 127 128 129 130 131 132 -0.226994382 -0.158619117 -0.013780433 -0.158619117 0.166006520 0.021167835 133 134 135 136 137 138 0.524450343 -0.013780433 -0.013780433 -0.013780433 0.166397710 -1.427383751 139 140 141 142 143 144 -1.607561893 -0.013780433 -0.140085455 -1.393956754 0.166006520 -0.371833066 145 146 147 148 149 150 -0.226994382 -1.752400578 -1.249118070 -1.607561893 0.166006520 -0.371833066 151 152 153 154 -0.158619117 0.184540182 -0.028673767 0.524450343 > postscript(file="/var/wessaorg/rcomp/tmp/6xv981355926481.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 0.028178157 NA 1 0.392438107 0.028178157 2 0.392438107 0.392438107 3 0.392438107 0.392438107 4 0.392438107 0.392438107 5 0.214172426 0.392438107 6 0.392438107 0.214172426 7 -0.006770111 0.392438107 8 0.247599422 -0.006770111 9 0.572225059 0.247599422 10 0.173016842 0.572225059 11 0.392438107 0.173016842 12 0.537667981 0.392438107 13 0.173016842 0.537667981 14 0.392829297 0.173016842 15 -0.006378921 0.392829297 16 -0.021663445 -0.006378921 17 0.173016842 -0.021663445 18 0.247599422 0.173016842 19 -0.346289082 0.247599422 20 0.359011110 -0.346289082 21 0.572616249 0.359011110 22 0.034385473 0.572616249 23 0.214172426 0.034385473 24 0.206835028 0.214172426 25 0.537667981 0.206835028 26 0.427386375 0.537667981 27 0.750881930 0.427386375 28 0.247599422 0.750881930 29 0.179224158 0.247599422 30 0.392438107 0.179224158 31 0.572225059 0.392438107 32 0.359011110 0.572225059 33 -0.151608795 0.359011110 34 0.392438107 -0.151608795 35 0.392438107 0.392438107 36 0.318246716 0.392438107 37 0.606043246 0.318246716 38 0.034385473 0.606043246 39 -0.219984060 0.034385473 40 0.052919135 -0.219984060 41 0.606043246 0.052919135 42 0.214172426 0.606043246 43 0.173016842 0.214172426 44 0.179224158 0.173016842 45 0.034385473 0.179224158 46 0.392438107 0.034385473 47 0.247599422 0.392438107 48 0.034385473 0.247599422 49 0.392438107 0.034385473 50 0.351673712 0.392438107 51 -0.021663445 0.351673712 52 0.247599422 -0.021663445 53 0.410971769 0.247599422 54 0.392438107 0.410971769 55 0.206835028 0.392438107 56 0.392829297 0.206835028 57 0.247599422 0.392829297 58 0.247599422 0.247599422 59 -0.166502130 0.247599422 60 0.028178157 -0.166502130 61 0.537667981 0.028178157 62 0.392438107 0.537667981 63 0.028178157 0.392438107 64 0.392438107 0.028178157 65 0.392438107 0.392438107 66 -0.201450398 0.392438107 67 0.572225059 -0.201450398 68 0.247599422 0.572225059 69 0.750881930 0.247599422 70 0.392438107 0.750881930 71 0.247599422 0.392438107 72 0.606043246 0.247599422 73 0.930668882 0.606043246 74 0.247599422 0.930668882 75 -0.364822744 0.247599422 76 0.247599422 -0.364822744 77 0.392829297 0.247599422 78 -0.133075133 0.392829297 79 -0.219984060 -0.133075133 80 0.392438107 -0.219984060 81 0.785830198 0.392438107 82 0.392438107 0.785830198 83 0.410971769 0.392438107 84 0.034385473 0.410971769 85 0.572225059 0.034385473 86 0.021167835 0.572225059 87 -1.214169802 0.021167835 88 -0.013780433 -1.214169802 89 -0.158619117 -0.013780433 90 -0.226994382 -0.158619117 91 -1.427774941 -0.226994382 92 -0.047207429 -1.427774941 93 -0.013780433 -0.047207429 94 -1.607561893 -0.013780433 95 -0.158619117 -1.607561893 96 -1.427774941 -0.158619117 97 -0.013780433 -1.427774941 98 0.166006520 -0.013780433 99 -0.158619117 0.166006520 100 0.021167835 -0.158619117 101 -0.013780433 0.021167835 102 -0.013780433 -0.013780433 103 -0.013780433 -0.013780433 104 -1.249118070 -0.013780433 105 -0.013780433 -1.249118070 106 -0.013780433 -0.013780433 107 -1.069331118 -0.013780433 108 -0.013780433 -1.069331118 109 0.166006520 -0.013780433 110 -1.282545067 0.166006520 111 -1.607561893 -1.282545067 112 0.344663390 -1.607561893 113 -1.069331118 0.344663390 114 0.166006520 -1.069331118 115 -0.013780433 0.166006520 116 0.021167835 -0.013780433 117 0.166006520 0.021167835 118 -0.013780433 0.166006520 119 -0.158619117 -0.013780433 120 0.166006520 -0.158619117 121 -0.013780433 0.166006520 122 -1.069331118 -0.013780433 123 -0.013389243 -1.069331118 124 -0.158619117 -0.013389243 125 -1.607561893 -0.158619117 126 -0.226994382 -1.607561893 127 -0.158619117 -0.226994382 128 -0.013780433 -0.158619117 129 -0.158619117 -0.013780433 130 0.166006520 -0.158619117 131 0.021167835 0.166006520 132 0.524450343 0.021167835 133 -0.013780433 0.524450343 134 -0.013780433 -0.013780433 135 -0.013780433 -0.013780433 136 0.166397710 -0.013780433 137 -1.427383751 0.166397710 138 -1.607561893 -1.427383751 139 -0.013780433 -1.607561893 140 -0.140085455 -0.013780433 141 -1.393956754 -0.140085455 142 0.166006520 -1.393956754 143 -0.371833066 0.166006520 144 -0.226994382 -0.371833066 145 -1.752400578 -0.226994382 146 -1.249118070 -1.752400578 147 -1.607561893 -1.249118070 148 0.166006520 -1.607561893 149 -0.371833066 0.166006520 150 -0.158619117 -0.371833066 151 0.184540182 -0.158619117 152 -0.028673767 0.184540182 153 0.524450343 -0.028673767 154 NA 0.524450343 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.392438107 0.028178157 [2,] 0.392438107 0.392438107 [3,] 0.392438107 0.392438107 [4,] 0.392438107 0.392438107 [5,] 0.214172426 0.392438107 [6,] 0.392438107 0.214172426 [7,] -0.006770111 0.392438107 [8,] 0.247599422 -0.006770111 [9,] 0.572225059 0.247599422 [10,] 0.173016842 0.572225059 [11,] 0.392438107 0.173016842 [12,] 0.537667981 0.392438107 [13,] 0.173016842 0.537667981 [14,] 0.392829297 0.173016842 [15,] -0.006378921 0.392829297 [16,] -0.021663445 -0.006378921 [17,] 0.173016842 -0.021663445 [18,] 0.247599422 0.173016842 [19,] -0.346289082 0.247599422 [20,] 0.359011110 -0.346289082 [21,] 0.572616249 0.359011110 [22,] 0.034385473 0.572616249 [23,] 0.214172426 0.034385473 [24,] 0.206835028 0.214172426 [25,] 0.537667981 0.206835028 [26,] 0.427386375 0.537667981 [27,] 0.750881930 0.427386375 [28,] 0.247599422 0.750881930 [29,] 0.179224158 0.247599422 [30,] 0.392438107 0.179224158 [31,] 0.572225059 0.392438107 [32,] 0.359011110 0.572225059 [33,] -0.151608795 0.359011110 [34,] 0.392438107 -0.151608795 [35,] 0.392438107 0.392438107 [36,] 0.318246716 0.392438107 [37,] 0.606043246 0.318246716 [38,] 0.034385473 0.606043246 [39,] -0.219984060 0.034385473 [40,] 0.052919135 -0.219984060 [41,] 0.606043246 0.052919135 [42,] 0.214172426 0.606043246 [43,] 0.173016842 0.214172426 [44,] 0.179224158 0.173016842 [45,] 0.034385473 0.179224158 [46,] 0.392438107 0.034385473 [47,] 0.247599422 0.392438107 [48,] 0.034385473 0.247599422 [49,] 0.392438107 0.034385473 [50,] 0.351673712 0.392438107 [51,] -0.021663445 0.351673712 [52,] 0.247599422 -0.021663445 [53,] 0.410971769 0.247599422 [54,] 0.392438107 0.410971769 [55,] 0.206835028 0.392438107 [56,] 0.392829297 0.206835028 [57,] 0.247599422 0.392829297 [58,] 0.247599422 0.247599422 [59,] -0.166502130 0.247599422 [60,] 0.028178157 -0.166502130 [61,] 0.537667981 0.028178157 [62,] 0.392438107 0.537667981 [63,] 0.028178157 0.392438107 [64,] 0.392438107 0.028178157 [65,] 0.392438107 0.392438107 [66,] -0.201450398 0.392438107 [67,] 0.572225059 -0.201450398 [68,] 0.247599422 0.572225059 [69,] 0.750881930 0.247599422 [70,] 0.392438107 0.750881930 [71,] 0.247599422 0.392438107 [72,] 0.606043246 0.247599422 [73,] 0.930668882 0.606043246 [74,] 0.247599422 0.930668882 [75,] -0.364822744 0.247599422 [76,] 0.247599422 -0.364822744 [77,] 0.392829297 0.247599422 [78,] -0.133075133 0.392829297 [79,] -0.219984060 -0.133075133 [80,] 0.392438107 -0.219984060 [81,] 0.785830198 0.392438107 [82,] 0.392438107 0.785830198 [83,] 0.410971769 0.392438107 [84,] 0.034385473 0.410971769 [85,] 0.572225059 0.034385473 [86,] 0.021167835 0.572225059 [87,] -1.214169802 0.021167835 [88,] -0.013780433 -1.214169802 [89,] -0.158619117 -0.013780433 [90,] -0.226994382 -0.158619117 [91,] -1.427774941 -0.226994382 [92,] -0.047207429 -1.427774941 [93,] -0.013780433 -0.047207429 [94,] -1.607561893 -0.013780433 [95,] -0.158619117 -1.607561893 [96,] -1.427774941 -0.158619117 [97,] -0.013780433 -1.427774941 [98,] 0.166006520 -0.013780433 [99,] -0.158619117 0.166006520 [100,] 0.021167835 -0.158619117 [101,] -0.013780433 0.021167835 [102,] -0.013780433 -0.013780433 [103,] -0.013780433 -0.013780433 [104,] -1.249118070 -0.013780433 [105,] -0.013780433 -1.249118070 [106,] -0.013780433 -0.013780433 [107,] -1.069331118 -0.013780433 [108,] -0.013780433 -1.069331118 [109,] 0.166006520 -0.013780433 [110,] -1.282545067 0.166006520 [111,] -1.607561893 -1.282545067 [112,] 0.344663390 -1.607561893 [113,] -1.069331118 0.344663390 [114,] 0.166006520 -1.069331118 [115,] -0.013780433 0.166006520 [116,] 0.021167835 -0.013780433 [117,] 0.166006520 0.021167835 [118,] -0.013780433 0.166006520 [119,] -0.158619117 -0.013780433 [120,] 0.166006520 -0.158619117 [121,] -0.013780433 0.166006520 [122,] -1.069331118 -0.013780433 [123,] -0.013389243 -1.069331118 [124,] -0.158619117 -0.013389243 [125,] -1.607561893 -0.158619117 [126,] -0.226994382 -1.607561893 [127,] -0.158619117 -0.226994382 [128,] -0.013780433 -0.158619117 [129,] -0.158619117 -0.013780433 [130,] 0.166006520 -0.158619117 [131,] 0.021167835 0.166006520 [132,] 0.524450343 0.021167835 [133,] -0.013780433 0.524450343 [134,] -0.013780433 -0.013780433 [135,] -0.013780433 -0.013780433 [136,] 0.166397710 -0.013780433 [137,] -1.427383751 0.166397710 [138,] -1.607561893 -1.427383751 [139,] -0.013780433 -1.607561893 [140,] -0.140085455 -0.013780433 [141,] -1.393956754 -0.140085455 [142,] 0.166006520 -1.393956754 [143,] -0.371833066 0.166006520 [144,] -0.226994382 -0.371833066 [145,] -1.752400578 -0.226994382 [146,] -1.249118070 -1.752400578 [147,] -1.607561893 -1.249118070 [148,] 0.166006520 -1.607561893 [149,] -0.371833066 0.166006520 [150,] -0.158619117 -0.371833066 [151,] 0.184540182 -0.158619117 [152,] -0.028673767 0.184540182 [153,] 0.524450343 -0.028673767 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.392438107 0.028178157 2 0.392438107 0.392438107 3 0.392438107 0.392438107 4 0.392438107 0.392438107 5 0.214172426 0.392438107 6 0.392438107 0.214172426 7 -0.006770111 0.392438107 8 0.247599422 -0.006770111 9 0.572225059 0.247599422 10 0.173016842 0.572225059 11 0.392438107 0.173016842 12 0.537667981 0.392438107 13 0.173016842 0.537667981 14 0.392829297 0.173016842 15 -0.006378921 0.392829297 16 -0.021663445 -0.006378921 17 0.173016842 -0.021663445 18 0.247599422 0.173016842 19 -0.346289082 0.247599422 20 0.359011110 -0.346289082 21 0.572616249 0.359011110 22 0.034385473 0.572616249 23 0.214172426 0.034385473 24 0.206835028 0.214172426 25 0.537667981 0.206835028 26 0.427386375 0.537667981 27 0.750881930 0.427386375 28 0.247599422 0.750881930 29 0.179224158 0.247599422 30 0.392438107 0.179224158 31 0.572225059 0.392438107 32 0.359011110 0.572225059 33 -0.151608795 0.359011110 34 0.392438107 -0.151608795 35 0.392438107 0.392438107 36 0.318246716 0.392438107 37 0.606043246 0.318246716 38 0.034385473 0.606043246 39 -0.219984060 0.034385473 40 0.052919135 -0.219984060 41 0.606043246 0.052919135 42 0.214172426 0.606043246 43 0.173016842 0.214172426 44 0.179224158 0.173016842 45 0.034385473 0.179224158 46 0.392438107 0.034385473 47 0.247599422 0.392438107 48 0.034385473 0.247599422 49 0.392438107 0.034385473 50 0.351673712 0.392438107 51 -0.021663445 0.351673712 52 0.247599422 -0.021663445 53 0.410971769 0.247599422 54 0.392438107 0.410971769 55 0.206835028 0.392438107 56 0.392829297 0.206835028 57 0.247599422 0.392829297 58 0.247599422 0.247599422 59 -0.166502130 0.247599422 60 0.028178157 -0.166502130 61 0.537667981 0.028178157 62 0.392438107 0.537667981 63 0.028178157 0.392438107 64 0.392438107 0.028178157 65 0.392438107 0.392438107 66 -0.201450398 0.392438107 67 0.572225059 -0.201450398 68 0.247599422 0.572225059 69 0.750881930 0.247599422 70 0.392438107 0.750881930 71 0.247599422 0.392438107 72 0.606043246 0.247599422 73 0.930668882 0.606043246 74 0.247599422 0.930668882 75 -0.364822744 0.247599422 76 0.247599422 -0.364822744 77 0.392829297 0.247599422 78 -0.133075133 0.392829297 79 -0.219984060 -0.133075133 80 0.392438107 -0.219984060 81 0.785830198 0.392438107 82 0.392438107 0.785830198 83 0.410971769 0.392438107 84 0.034385473 0.410971769 85 0.572225059 0.034385473 86 0.021167835 0.572225059 87 -1.214169802 0.021167835 88 -0.013780433 -1.214169802 89 -0.158619117 -0.013780433 90 -0.226994382 -0.158619117 91 -1.427774941 -0.226994382 92 -0.047207429 -1.427774941 93 -0.013780433 -0.047207429 94 -1.607561893 -0.013780433 95 -0.158619117 -1.607561893 96 -1.427774941 -0.158619117 97 -0.013780433 -1.427774941 98 0.166006520 -0.013780433 99 -0.158619117 0.166006520 100 0.021167835 -0.158619117 101 -0.013780433 0.021167835 102 -0.013780433 -0.013780433 103 -0.013780433 -0.013780433 104 -1.249118070 -0.013780433 105 -0.013780433 -1.249118070 106 -0.013780433 -0.013780433 107 -1.069331118 -0.013780433 108 -0.013780433 -1.069331118 109 0.166006520 -0.013780433 110 -1.282545067 0.166006520 111 -1.607561893 -1.282545067 112 0.344663390 -1.607561893 113 -1.069331118 0.344663390 114 0.166006520 -1.069331118 115 -0.013780433 0.166006520 116 0.021167835 -0.013780433 117 0.166006520 0.021167835 118 -0.013780433 0.166006520 119 -0.158619117 -0.013780433 120 0.166006520 -0.158619117 121 -0.013780433 0.166006520 122 -1.069331118 -0.013780433 123 -0.013389243 -1.069331118 124 -0.158619117 -0.013389243 125 -1.607561893 -0.158619117 126 -0.226994382 -1.607561893 127 -0.158619117 -0.226994382 128 -0.013780433 -0.158619117 129 -0.158619117 -0.013780433 130 0.166006520 -0.158619117 131 0.021167835 0.166006520 132 0.524450343 0.021167835 133 -0.013780433 0.524450343 134 -0.013780433 -0.013780433 135 -0.013780433 -0.013780433 136 0.166397710 -0.013780433 137 -1.427383751 0.166397710 138 -1.607561893 -1.427383751 139 -0.013780433 -1.607561893 140 -0.140085455 -0.013780433 141 -1.393956754 -0.140085455 142 0.166006520 -1.393956754 143 -0.371833066 0.166006520 144 -0.226994382 -0.371833066 145 -1.752400578 -0.226994382 146 -1.249118070 -1.752400578 147 -1.607561893 -1.249118070 148 0.166006520 -1.607561893 149 -0.371833066 0.166006520 150 -0.158619117 -0.371833066 151 0.184540182 -0.158619117 152 -0.028673767 0.184540182 153 0.524450343 -0.028673767 > 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/7dmhm1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8q9v81355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9lnze1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10kz9i1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/119t5a1355926481.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/1222nq1355926481.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/13f77e1355926481.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/14g8d21355926481.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/15x6p21355926481.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/16ftpn1355926481.tab") + } > > try(system("convert tmp/1ktpx1355926481.ps tmp/1ktpx1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/2493f1355926481.ps tmp/2493f1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/3hcd11355926481.ps tmp/3hcd11355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/4hsmp1355926481.ps tmp/4hsmp1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/5hglc1355926481.ps tmp/5hglc1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/6xv981355926481.ps tmp/6xv981355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/7dmhm1355926481.ps tmp/7dmhm1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/8q9v81355926481.ps tmp/8q9v81355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/9lnze1355926481.ps tmp/9lnze1355926481.png",intern=TRUE)) character(0) > try(system("convert tmp/10kz9i1355926481.ps tmp/10kz9i1355926481.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.641 1.121 10.829