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 + ,7 + ,7 + ,6 + ,1 + ,5 + ,7 + ,5 + ,5 + ,6 + ,4 + ,1 + ,4 + ,5 + ,4 + ,6 + ,6 + ,6 + ,2 + ,5 + ,5 + ,3 + ,4 + ,5 + ,4 + ,2 + ,4 + ,5 + ,6 + ,5 + ,6 + ,2 + ,2 + ,4 + ,5 + ,5 + ,6 + ,7 + ,5 + ,1 + ,6 + ,7 + ,5 + ,7 + ,7 + ,1 + ,1 + ,5 + ,7 + ,1 + ,6 + ,7 + ,6 + ,1 + ,3 + ,5 + ,4 + ,6 + ,7 + ,3 + ,1 + ,4 + ,3 + ,5 + ,6 + ,6 + ,4 + ,1 + ,4 + ,6 + ,6 + ,5 + ,4 + ,3 + ,1 + ,2 + ,7 + ,7 + ,5 + ,6 + ,2 + ,1 + ,5 + ,6 + ,5 + ,4 + ,6 + ,4 + ,1 + ,3 + ,5 + ,6 + ,6 + ,7 + ,3 + ,1 + ,5 + ,3 + ,5 + ,6 + ,6 + ,5 + ,1 + ,6 + ,7 + ,4 + ,5 + ,6 + ,3 + ,2 + ,4 + ,5 + ,7 + ,3 + ,4 + ,3 + ,1 + ,3 + ,7 + ,7 + ,7 + ,7 + ,6 + ,1 + ,6 + ,7 + ,6 + ,3 + ,7 + ,1 + ,1 + ,7 + ,7 + ,6 + ,5 + ,6 + ,1 + ,2 + ,2 + ,6 + ,2 + ,3 + ,3 + ,1 + ,1 + ,6 + ,5 + ,7 + ,5 + ,7 + ,5 + ,1 + ,4 + ,7 + ,5 + ,2 + ,5 + ,2 + ,1 + ,1 + ,4 + ,4 + ,6 + ,7 + ,3 + ,1 + ,4 + ,7 + ,7 + ,3 + ,6 + ,3 + ,1 + ,3 + ,7 + ,1 + ,6 + ,5 + ,5 + ,1 + ,6 + ,7 + ,1 + ,6 + ,5 + ,5 + ,1 + ,6 + ,7 + ,7 + ,5 + ,6 + ,1 + ,1 + ,3 + ,3 + ,4 + ,5 + ,5 + ,2 + ,1 + ,6 + ,7 + ,5 + ,7 + ,6 + ,3 + ,1 + ,4 + ,5 + ,5 + ,6 + ,6 + ,5 + ,1 + ,7 + ,7 + ,5 + ,5 + ,5 + ,3 + ,1 + ,4 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,4 + ,5 + ,5 + ,4 + ,5 + ,3 + ,3 + ,3 + ,4 + ,5 + ,4 + ,4 + ,2 + ,1 + ,4 + ,5 + ,4 + ,6 + ,6 + ,4 + ,2 + ,6 + ,6 + ,7 + ,5 + ,6 + ,3 + ,1 + ,3 + ,7 + ,7 + ,5 + ,7 + ,3 + ,1 + ,2 + ,5 + ,7 + ,7 + ,7 + ,6 + ,1 + ,7 + ,7 + ,4 + ,5 + ,7 + ,6 + ,1 + ,7 + ,7 + ,7 + ,5 + ,7 + ,3 + ,1 + ,5 + ,6 + ,7 + ,6 + ,5 + ,3 + ,1 + ,5 + ,6 + ,4 + ,5 + ,6 + ,4 + ,2 + ,6 + ,7 + ,3 + ,6 + ,6 + ,4 + ,1 + ,7 + ,7 + ,2 + ,7 + ,3 + ,2 + ,1 + ,6 + ,6 + ,6 + ,5 + ,6 + ,2 + ,4 + ,3 + ,6 + ,4 + ,5 + ,5 + ,3 + ,1 + ,4 + ,4 + ,5 + ,5 + ,4 + ,2 + ,3 + ,4 + ,7 + ,4 + ,6 + ,6 + ,5 + ,2 + ,4 + ,5 + ,7 + ,2 + ,6 + ,5 + ,3 + ,2 + ,6 + ,4 + ,4 + ,6 + ,6 + ,2 + ,4 + ,5 + ,6 + ,4 + ,5 + ,3 + ,1 + ,3 + ,3 + ,7 + ,6 + ,6 + ,3 + ,2 + ,5 + ,7 + ,1 + ,3 + ,5 + ,2 + ,1 + ,3 + ,6 + ,5 + ,6 + ,7 + ,5 + ,1 + ,5 + ,6 + ,4 + ,6 + ,6 + ,2 + ,1 + ,5 + ,5 + 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,3 + ,7 + ,6 + ,2 + ,7 + ,3 + ,1 + ,3 + ,7 + ,5 + ,5 + ,5 + ,3 + ,1 + ,4 + ,5 + ,6 + ,7 + ,7 + ,5 + ,1 + ,5 + ,5 + ,5 + ,4 + ,5 + ,3 + ,1 + ,5 + ,6 + ,5 + ,4 + ,6 + ,3 + ,2 + ,3 + ,5 + ,6 + ,7 + ,7 + ,4 + ,1 + ,6 + ,6 + ,5 + ,6 + ,6 + ,5 + ,1 + ,5 + ,6 + ,3 + ,5 + ,5 + ,5 + ,2 + ,4 + ,6 + ,6 + ,5 + ,6 + ,5 + ,1 + ,5 + ,5 + ,7 + ,5 + ,7 + ,5 + ,1 + ,4 + ,6 + ,4 + ,7 + ,6 + ,3 + ,1 + ,7 + ,7 + ,5 + ,6 + ,7 + ,4 + ,1 + ,5 + ,7 + ,4 + ,6 + ,7 + ,4 + ,1 + ,3 + ,6 + ,5 + ,5 + ,6 + ,3 + ,2 + ,5 + ,6 + ,2 + ,2 + ,6 + ,4 + ,2 + ,2 + ,6 + ,7 + ,4 + ,4 + ,4 + ,4 + ,4 + ,7 + ,5 + ,6 + ,7 + ,3 + ,1 + ,3 + ,6 + ,4 + ,5 + ,6 + ,2 + ,1 + ,4 + ,5 + ,2 + ,5 + ,4 + ,4 + ,1 + ,5 + ,5 + ,4 + ,5 + ,5 + ,4 + ,1 + ,4 + ,5) + ,dim=c(7 + ,162) + ,dimnames=list(c('Q1' + ,'Q2' + ,'Q3' + ,'Q4' + ,'Q5' + ,'Q6' + ,'Q7') + ,1:162)) > y <- array(NA,dim=c(7,162),dimnames=list(c('Q1','Q2','Q3','Q4','Q5','Q6','Q7'),1:162)) > 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 Q1 Q2 Q3 Q4 Q5 Q6 Q7 1 4 7 7 6 1 5 7 2 5 5 6 4 1 4 5 3 4 6 6 6 2 5 5 4 3 4 5 4 2 4 5 5 6 5 6 2 2 4 5 6 5 6 7 5 1 6 7 7 5 7 7 1 1 5 7 8 1 6 7 6 1 3 5 9 4 6 7 3 1 4 3 10 5 6 6 4 1 4 6 11 6 5 4 3 1 2 7 12 7 5 6 2 1 5 6 13 5 4 6 4 1 3 5 14 6 6 7 3 1 5 3 15 5 6 6 5 1 6 7 16 4 5 6 3 2 4 5 17 7 3 4 3 1 3 7 18 7 7 7 6 1 6 7 19 6 3 7 1 1 7 7 20 6 5 6 1 2 2 6 21 2 3 3 1 1 6 5 22 7 5 7 5 1 4 7 23 5 2 5 2 1 1 4 24 4 6 7 3 1 4 7 25 7 3 6 3 1 3 7 26 1 6 5 5 1 6 7 27 1 6 5 5 1 6 7 28 7 5 6 1 1 3 3 29 4 5 5 2 1 6 7 30 5 7 6 3 1 4 5 31 5 6 6 5 1 7 7 32 5 5 5 3 1 4 5 33 4 5 4 5 4 4 5 34 5 4 5 3 3 3 4 35 5 4 4 2 1 4 5 36 4 6 6 4 2 6 6 37 7 5 6 3 1 3 7 38 7 5 7 3 1 2 5 39 7 7 7 6 1 7 7 40 4 5 7 6 1 7 7 41 7 5 7 3 1 5 6 42 7 6 5 3 1 5 6 43 4 5 6 4 2 6 7 44 3 6 6 4 1 7 7 45 2 7 3 2 1 6 6 46 6 5 6 2 4 3 6 47 4 5 5 3 1 4 4 48 5 5 4 2 3 4 7 49 4 6 6 5 2 4 5 50 7 2 6 5 3 2 6 51 4 4 6 6 2 4 5 52 6 4 5 3 1 3 3 53 7 6 6 3 2 5 7 54 1 3 5 2 1 3 6 55 5 6 7 5 1 5 6 56 4 6 6 2 1 5 5 57 5 5 6 4 1 4 5 58 5 6 7 4 1 5 7 59 5 1 4 1 1 5 7 60 5 5 3 6 2 6 7 61 5 7 4 2 1 4 6 62 5 4 4 3 3 4 6 63 6 5 5 4 1 7 7 64 3 6 4 3 1 6 7 65 4 4 6 4 4 5 5 66 6 6 7 3 1 6 7 67 6 6 6 6 1 6 6 68 3 5 6 4 1 5 5 69 5 5 6 5 1 4 6 70 2 3 6 3 1 2 5 71 7 5 7 4 1 7 5 72 7 6 6 3 1 5 6 73 4 5 6 3 1 5 6 74 6 6 6 6 1 6 6 75 6 6 7 6 1 6 7 76 3 4 5 2 2 4 6 77 3 4 4 2 2 4 5 78 6 6 7 6 1 6 7 79 7 7 7 5 1 6 7 80 3 4 6 1 1 5 6 81 1 5 7 2 1 7 7 82 5 6 6 5 1 3 6 83 5 6 5 3 1 6 6 84 5 5 7 3 1 5 7 85 5 3 6 4 2 6 6 86 6 7 5 6 1 7 7 87 6 6 6 4 1 4 7 88 5 4 5 2 4 2 5 89 7 4 7 4 3 3 3 90 6 5 6 4 2 5 6 91 1 3 2 2 1 5 6 92 3 7 5 4 1 6 5 93 5 6 7 3 1 3 6 94 1 6 7 6 1 3 6 95 6 4 7 4 2 5 6 96 4 5 7 4 1 5 7 97 5 6 6 3 1 3 6 98 5 5 5 3 2 4 6 99 6 6 6 3 1 1 6 100 5 6 6 4 3 7 7 101 5 4 5 2 1 4 6 102 4 5 7 2 1 7 5 103 6 6 5 3 2 4 5 104 6 5 6 3 1 5 6 105 4 5 5 4 1 5 6 106 5 4 5 4 2 6 6 107 5 4 5 2 2 4 5 108 2 6 5 5 1 4 6 109 7 5 7 5 1 4 4 110 5 6 6 4 1 6 6 111 5 5 7 4 1 4 7 112 2 6 6 3 1 3 7 113 3 5 5 4 1 3 5 114 5 4 5 4 2 5 5 115 5 6 7 5 1 5 7 116 5 4 6 4 1 3 3 117 6 5 5 5 2 4 7 118 6 5 7 3 2 5 5 119 4 6 4 2 1 5 7 120 6 3 3 1 2 3 5 121 3 5 7 5 2 3 3 122 6 4 5 5 2 5 6 123 4 5 6 3 2 3 5 124 3 5 4 4 4 4 4 125 4 7 7 4 1 7 7 126 6 5 7 5 2 6 6 127 4 7 5 5 1 5 7 128 6 5 7 3 1 2 2 129 5 4 3 4 1 4 5 130 5 6 6 6 1 6 6 131 5 4 5 4 3 6 6 132 3 4 5 5 2 5 6 133 5 4 6 4 1 4 2 134 4 4 5 4 1 4 6 135 5 6 6 5 1 5 7 136 5 6 6 4 2 3 4 137 1 5 7 3 5 5 7 138 4 3 5 3 1 5 7 139 7 6 7 6 1 5 6 140 4 5 6 5 2 6 6 141 6 4 6 2 1 4 2 142 7 5 7 4 2 3 7 143 6 2 7 3 1 3 7 144 5 5 5 3 1 4 5 145 6 7 7 5 1 5 5 146 5 4 5 3 1 5 6 147 5 4 6 3 2 3 5 148 6 7 7 4 1 6 6 149 5 6 6 5 1 5 6 150 3 5 5 5 2 4 6 151 6 5 6 5 1 5 5 152 7 5 7 5 1 4 6 153 4 7 6 3 1 7 7 154 5 6 7 4 1 5 7 155 4 6 7 4 1 3 6 156 5 5 6 3 2 5 6 157 2 2 6 4 2 2 6 158 7 4 4 4 4 4 7 159 5 6 7 3 1 3 6 160 4 5 6 2 1 4 5 161 2 5 4 4 1 5 5 162 4 5 5 4 1 4 5 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Q2 Q3 Q4 Q5 Q6 2.94044 -0.04287 0.39836 -0.01665 0.01450 -0.07914 Q7 0.02904 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.3447 -0.8898 0.1439 0.9265 2.7595 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.94044 0.98798 2.976 0.00339 ** Q2 -0.04287 0.11885 -0.361 0.71878 Q3 0.39836 0.11883 3.352 0.00101 ** Q4 -0.01665 0.10033 -0.166 0.86840 Q5 0.01450 0.15718 0.092 0.92662 Q6 -0.07914 0.10202 -0.776 0.43908 Q7 0.02904 0.11252 0.258 0.79668 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.515 on 155 degrees of freedom Multiple R-squared: 0.07587, Adjusted R-squared: 0.0401 F-statistic: 2.121 on 6 and 155 DF, p-value: 0.05389 > 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,] 0.5702241 0.85955170 0.42977585 [2,] 0.4373412 0.87468239 0.56265880 [3,] 0.3224549 0.64490983 0.67754509 [4,] 0.2842904 0.56858085 0.71570958 [5,] 0.2127285 0.42545706 0.78727147 [6,] 0.1680921 0.33618427 0.83190787 [7,] 0.1089831 0.21796613 0.89101694 [8,] 0.0735871 0.14717420 0.92641290 [9,] 0.2167231 0.43344630 0.78327685 [10,] 0.1926182 0.38523637 0.80738181 [11,] 0.2581291 0.51625827 0.74187086 [12,] 0.6872638 0.62547237 0.31273619 [13,] 0.6657255 0.66854904 0.33427452 [14,] 0.5939359 0.81212811 0.40606406 [15,] 0.6456837 0.70863259 0.35431629 [16,] 0.6076212 0.78475765 0.39237882 [17,] 0.7795562 0.44088767 0.22044384 [18,] 0.8539561 0.29208773 0.14604387 [19,] 0.8750584 0.24988310 0.12494155 [20,] 0.8414645 0.31707092 0.15853546 [21,] 0.8061125 0.38777491 0.19388746 [22,] 0.7907981 0.41840376 0.20920188 [23,] 0.7548310 0.49033801 0.24516901 [24,] 0.7316669 0.53666626 0.26833313 [25,] 0.6809187 0.63816258 0.31908129 [26,] 0.6442959 0.71140829 0.35570414 [27,] 0.5931751 0.81364990 0.40682495 [28,] 0.5871727 0.82565451 0.41282726 [29,] 0.5586604 0.88267924 0.44133962 [30,] 0.6809195 0.63816102 0.31908051 [31,] 0.6470366 0.70592683 0.35296342 [32,] 0.6409840 0.71803199 0.35901599 [33,] 0.7455178 0.50896448 0.25448224 [34,] 0.7153444 0.56931115 0.28465558 [35,] 0.7146854 0.57062911 0.28531455 [36,] 0.6930717 0.61385660 0.30692830 [37,] 0.6566153 0.68676938 0.34338469 [38,] 0.6088370 0.78232604 0.39116302 [39,] 0.5677236 0.86455282 0.43227641 [40,] 0.5261212 0.94775757 0.47387879 [41,] 0.5222719 0.95545624 0.47772812 [42,] 0.4840641 0.96812813 0.51593594 [43,] 0.4916619 0.98332378 0.50833811 [44,] 0.5338815 0.93223705 0.46611853 [45,] 0.8300752 0.33984969 0.16992484 [46,] 0.7974483 0.40510342 0.20255171 [47,] 0.7728373 0.45432545 0.22716272 [48,] 0.7347750 0.53044993 0.26522497 [49,] 0.6971852 0.60562963 0.30281482 [50,] 0.6691898 0.66162045 0.33081023 [51,] 0.7084945 0.58301090 0.29150545 [52,] 0.6833222 0.63335558 0.31667779 [53,] 0.6506079 0.69878420 0.34939210 [54,] 0.6732730 0.65345407 0.32672704 [55,] 0.6447339 0.71053214 0.35526607 [56,] 0.6191710 0.76165795 0.38082898 [57,] 0.5873928 0.82521445 0.41260723 [58,] 0.5916051 0.81678985 0.40839492 [59,] 0.6075188 0.78496243 0.39248121 [60,] 0.5614491 0.87710179 0.43855090 [61,] 0.7164493 0.56710130 0.28355065 [62,] 0.7486740 0.50265195 0.25132598 [63,] 0.7896316 0.42073671 0.21036836 [64,] 0.7661504 0.46769918 0.23384959 [65,] 0.7599429 0.48011410 0.24005705 [66,] 0.7329293 0.53414149 0.26707075 [67,] 0.7388507 0.52229851 0.26114926 [68,] 0.7197591 0.56048173 0.28024086 [69,] 0.6905104 0.61897911 0.30948955 [70,] 0.7118820 0.57623607 0.28811803 [71,] 0.7330930 0.53381397 0.26690699 [72,] 0.9111481 0.17770389 0.08885194 [73,] 0.8921350 0.21573009 0.10786504 [74,] 0.8735724 0.25285516 0.12642758 [75,] 0.8491119 0.30177628 0.15088814 [76,] 0.8210982 0.35780354 0.17890177 [77,] 0.8343488 0.33130247 0.16565124 [78,] 0.8248854 0.35022920 0.17511460 [79,] 0.7951005 0.40979898 0.20489949 [80,] 0.7995541 0.40089189 0.20044594 [81,] 0.7863247 0.42735069 0.21367535 [82,] 0.8484902 0.30301962 0.15150981 [83,] 0.8425709 0.31485814 0.15742907 [84,] 0.8168830 0.36623393 0.18311697 [85,] 0.9546639 0.09067214 0.04533607 [86,] 0.9459232 0.10815365 0.05407682 [87,] 0.9413428 0.11731436 0.05865718 [88,] 0.9262761 0.14744790 0.07372395 [89,] 0.9104047 0.17919069 0.08959534 [90,] 0.9077784 0.18444317 0.09222158 [91,] 0.8882484 0.22350313 0.11175156 [92,] 0.8651451 0.26970971 0.13485485 [93,] 0.8627082 0.27458355 0.13729177 [94,] 0.8752188 0.24956230 0.12478115 [95,] 0.8629050 0.27419003 0.13709502 [96,] 0.8384858 0.32302835 0.16151418 [97,] 0.8080207 0.38395860 0.19197930 [98,] 0.7763635 0.44727293 0.22363647 [99,] 0.8325886 0.33482286 0.16741143 [100,] 0.8415160 0.31696791 0.15848395 [101,] 0.8088662 0.38226755 0.19113378 [102,] 0.7730355 0.45392906 0.22696453 [103,] 0.8517569 0.29648616 0.14824308 [104,] 0.8617031 0.27659386 0.13829693 [105,] 0.8334134 0.33317325 0.16658662 [106,] 0.7986152 0.40276956 0.20138478 [107,] 0.7594299 0.48114019 0.24057009 [108,] 0.7584275 0.48314495 0.24157247 [109,] 0.7386085 0.52278307 0.26139153 [110,] 0.6944397 0.61112070 0.30556035 [111,] 0.7515832 0.49683365 0.24841683 [112,] 0.8189280 0.36214394 0.18107197 [113,] 0.8171987 0.36560257 0.18280129 [114,] 0.7855092 0.42898156 0.21449078 [115,] 0.7524024 0.49519522 0.24759761 [116,] 0.7264784 0.54704314 0.27352157 [117,] 0.6907579 0.61848430 0.30924215 [118,] 0.6471255 0.70574902 0.35287451 [119,] 0.5963490 0.80730199 0.40365099 [120,] 0.5690081 0.86198381 0.43099190 [121,] 0.5103753 0.97924944 0.48962472 [122,] 0.4871552 0.97431038 0.51284481 [123,] 0.4741652 0.94833048 0.52583476 [124,] 0.4129160 0.82583203 0.58708399 [125,] 0.3598870 0.71977394 0.64011303 [126,] 0.3001526 0.60030518 0.69984741 [127,] 0.2439257 0.48785132 0.75607434 [128,] 0.7193226 0.56135490 0.28067745 [129,] 0.6583398 0.68332034 0.34166017 [130,] 0.6532909 0.69341813 0.34670907 [131,] 0.7213197 0.55736060 0.27868030 [132,] 0.6628977 0.67420456 0.33710228 [133,] 0.6510896 0.69782089 0.34891044 [134,] 0.6676825 0.66463496 0.33231748 [135,] 0.6799761 0.64004779 0.32002390 [136,] 0.6000624 0.79987523 0.39993762 [137,] 0.7916063 0.41678738 0.20839369 [138,] 0.7101087 0.57978267 0.28989134 [139,] 0.6807909 0.63841814 0.31920907 [140,] 0.5639802 0.87203966 0.43601983 [141,] 0.7414991 0.51700178 0.25850089 [142,] 0.6260236 0.74795290 0.37397645 [143,] 0.7310712 0.53785753 0.26892876 > postscript(file="/var/fisher/rcomp/tmp/14rdp1353352249.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2amcd1353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3z6yx1353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4wxl41353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/53o0y1353352249.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 = 162 Frequency = 1 1 2 3 4 5 6 -1.15097018 0.10727277 -0.75190621 -1.55174415 1.05947064 -0.13135110 7 8 9 10 11 12 -0.23422712 -4.29405092 -1.20678016 0.12110589 1.67096442 2.12407358 13 14 15 16 17 18 -0.01474482 0.87236387 0.26700489 -0.92387797 2.66436133 1.92817385 19 20 21 22 23 24 0.75256669 0.85549075 -1.67507248 1.66748727 0.13531366 -1.32294194 25 26 27 28 29 30 1.86764934 -3.33463912 -3.33463912 2.03625547 -0.42746684 0.17636851 31 32 33 34 35 36 0.34614892 0.48897738 -0.12286191 0.36700153 0.82780842 -0.73510540 37 38 39 40 41 42 1.95339646 1.53397733 2.00731788 -1.07842925 1.74236897 2.58195452 43 44 45 46 47 48 -0.80701941 -1.67050247 -1.51596728 0.92228747 -0.48198218 0.78360239 49 50 51 52 53 54 -0.84770163 1.77897626 -0.91679737 1.42504067 2.14005873 -3.72160561 55 56 57 58 59 60 -0.18145469 -0.80401241 0.10727277 -0.22714652 0.70359948 1.42135135 61 62 63 64 65 66 0.92738866 0.78642066 1.68497996 -0.96958590 -0.89995482 0.83534612 67 68 69 70 71 72 1.31269672 -1.81358320 0.09488371 -3.15341380 1.94634886 2.18359853 73 74 75 76 77 78 -0.85927503 1.31269672 0.88530028 -1.61408737 -1.18669093 0.88530028 79 80 81 82 83 84 1.91152246 -1.93545137 -4.14503480 0.05861325 0.66109855 -0.28667147 85 86 87 88 89 90 0.13627391 1.80402986 1.09206544 0.22766632 1.61598137 1.14287700 91 92 93 94 95 96 -2.36824957 -1.25033605 -0.37304552 -4.32309136 0.70164745 -1.27002008 97 98 99 100 101 102 0.02531047 0.44543758 0.86702241 0.30049883 0.40041198 -1.08695391 103 104 105 106 107 108 1.51735159 1.14072497 -0.44426765 0.57750346 0.41495307 -2.46388673 109 110 111 112 113 114 1.75460861 0.27939395 -0.34916411 -3.00372997 -1.57351527 0.52739988 115 116 117 118 119 120 -0.21049513 0.04333607 1.44969991 0.75691007 -0.06538131 2.07299608 121 122 123 124 125 126 -2.30999433 1.51501082 -1.00302200 -1.11047285 -1.02598490 0.84031643 127 128 129 130 131 132 -0.37090958 0.62109866 1.25946719 0.31269672 0.56300411 -1.48498918 133 134 135 136 137 138 0.15152054 -0.56628524 0.18786086 0.08554340 -4.34466887 -0.57570661 139 140 141 142 143 144 1.83519670 -0.76132758 1.11821776 1.55719251 0.42641978 0.48897738 145 146 147 148 149 150 0.89045932 0.49620740 -0.04589556 0.92391152 0.21690130 -1.52125964 151 152 153 154 155 156 1.20306819 1.69652772 -0.64428029 -0.22714652 -1.35639414 0.12622562 157 158 159 160 161 162 -3.22317577 2.75953226 -0.37304552 -0.92603001 -2.01687121 -0.49437124 > postscript(file="/var/fisher/rcomp/tmp/6x22p1353352249.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.15097018 NA 1 0.10727277 -1.15097018 2 -0.75190621 0.10727277 3 -1.55174415 -0.75190621 4 1.05947064 -1.55174415 5 -0.13135110 1.05947064 6 -0.23422712 -0.13135110 7 -4.29405092 -0.23422712 8 -1.20678016 -4.29405092 9 0.12110589 -1.20678016 10 1.67096442 0.12110589 11 2.12407358 1.67096442 12 -0.01474482 2.12407358 13 0.87236387 -0.01474482 14 0.26700489 0.87236387 15 -0.92387797 0.26700489 16 2.66436133 -0.92387797 17 1.92817385 2.66436133 18 0.75256669 1.92817385 19 0.85549075 0.75256669 20 -1.67507248 0.85549075 21 1.66748727 -1.67507248 22 0.13531366 1.66748727 23 -1.32294194 0.13531366 24 1.86764934 -1.32294194 25 -3.33463912 1.86764934 26 -3.33463912 -3.33463912 27 2.03625547 -3.33463912 28 -0.42746684 2.03625547 29 0.17636851 -0.42746684 30 0.34614892 0.17636851 31 0.48897738 0.34614892 32 -0.12286191 0.48897738 33 0.36700153 -0.12286191 34 0.82780842 0.36700153 35 -0.73510540 0.82780842 36 1.95339646 -0.73510540 37 1.53397733 1.95339646 38 2.00731788 1.53397733 39 -1.07842925 2.00731788 40 1.74236897 -1.07842925 41 2.58195452 1.74236897 42 -0.80701941 2.58195452 43 -1.67050247 -0.80701941 44 -1.51596728 -1.67050247 45 0.92228747 -1.51596728 46 -0.48198218 0.92228747 47 0.78360239 -0.48198218 48 -0.84770163 0.78360239 49 1.77897626 -0.84770163 50 -0.91679737 1.77897626 51 1.42504067 -0.91679737 52 2.14005873 1.42504067 53 -3.72160561 2.14005873 54 -0.18145469 -3.72160561 55 -0.80401241 -0.18145469 56 0.10727277 -0.80401241 57 -0.22714652 0.10727277 58 0.70359948 -0.22714652 59 1.42135135 0.70359948 60 0.92738866 1.42135135 61 0.78642066 0.92738866 62 1.68497996 0.78642066 63 -0.96958590 1.68497996 64 -0.89995482 -0.96958590 65 0.83534612 -0.89995482 66 1.31269672 0.83534612 67 -1.81358320 1.31269672 68 0.09488371 -1.81358320 69 -3.15341380 0.09488371 70 1.94634886 -3.15341380 71 2.18359853 1.94634886 72 -0.85927503 2.18359853 73 1.31269672 -0.85927503 74 0.88530028 1.31269672 75 -1.61408737 0.88530028 76 -1.18669093 -1.61408737 77 0.88530028 -1.18669093 78 1.91152246 0.88530028 79 -1.93545137 1.91152246 80 -4.14503480 -1.93545137 81 0.05861325 -4.14503480 82 0.66109855 0.05861325 83 -0.28667147 0.66109855 84 0.13627391 -0.28667147 85 1.80402986 0.13627391 86 1.09206544 1.80402986 87 0.22766632 1.09206544 88 1.61598137 0.22766632 89 1.14287700 1.61598137 90 -2.36824957 1.14287700 91 -1.25033605 -2.36824957 92 -0.37304552 -1.25033605 93 -4.32309136 -0.37304552 94 0.70164745 -4.32309136 95 -1.27002008 0.70164745 96 0.02531047 -1.27002008 97 0.44543758 0.02531047 98 0.86702241 0.44543758 99 0.30049883 0.86702241 100 0.40041198 0.30049883 101 -1.08695391 0.40041198 102 1.51735159 -1.08695391 103 1.14072497 1.51735159 104 -0.44426765 1.14072497 105 0.57750346 -0.44426765 106 0.41495307 0.57750346 107 -2.46388673 0.41495307 108 1.75460861 -2.46388673 109 0.27939395 1.75460861 110 -0.34916411 0.27939395 111 -3.00372997 -0.34916411 112 -1.57351527 -3.00372997 113 0.52739988 -1.57351527 114 -0.21049513 0.52739988 115 0.04333607 -0.21049513 116 1.44969991 0.04333607 117 0.75691007 1.44969991 118 -0.06538131 0.75691007 119 2.07299608 -0.06538131 120 -2.30999433 2.07299608 121 1.51501082 -2.30999433 122 -1.00302200 1.51501082 123 -1.11047285 -1.00302200 124 -1.02598490 -1.11047285 125 0.84031643 -1.02598490 126 -0.37090958 0.84031643 127 0.62109866 -0.37090958 128 1.25946719 0.62109866 129 0.31269672 1.25946719 130 0.56300411 0.31269672 131 -1.48498918 0.56300411 132 0.15152054 -1.48498918 133 -0.56628524 0.15152054 134 0.18786086 -0.56628524 135 0.08554340 0.18786086 136 -4.34466887 0.08554340 137 -0.57570661 -4.34466887 138 1.83519670 -0.57570661 139 -0.76132758 1.83519670 140 1.11821776 -0.76132758 141 1.55719251 1.11821776 142 0.42641978 1.55719251 143 0.48897738 0.42641978 144 0.89045932 0.48897738 145 0.49620740 0.89045932 146 -0.04589556 0.49620740 147 0.92391152 -0.04589556 148 0.21690130 0.92391152 149 -1.52125964 0.21690130 150 1.20306819 -1.52125964 151 1.69652772 1.20306819 152 -0.64428029 1.69652772 153 -0.22714652 -0.64428029 154 -1.35639414 -0.22714652 155 0.12622562 -1.35639414 156 -3.22317577 0.12622562 157 2.75953226 -3.22317577 158 -0.37304552 2.75953226 159 -0.92603001 -0.37304552 160 -2.01687121 -0.92603001 161 -0.49437124 -2.01687121 162 NA -0.49437124 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.10727277 -1.15097018 [2,] -0.75190621 0.10727277 [3,] -1.55174415 -0.75190621 [4,] 1.05947064 -1.55174415 [5,] -0.13135110 1.05947064 [6,] -0.23422712 -0.13135110 [7,] -4.29405092 -0.23422712 [8,] -1.20678016 -4.29405092 [9,] 0.12110589 -1.20678016 [10,] 1.67096442 0.12110589 [11,] 2.12407358 1.67096442 [12,] -0.01474482 2.12407358 [13,] 0.87236387 -0.01474482 [14,] 0.26700489 0.87236387 [15,] -0.92387797 0.26700489 [16,] 2.66436133 -0.92387797 [17,] 1.92817385 2.66436133 [18,] 0.75256669 1.92817385 [19,] 0.85549075 0.75256669 [20,] -1.67507248 0.85549075 [21,] 1.66748727 -1.67507248 [22,] 0.13531366 1.66748727 [23,] -1.32294194 0.13531366 [24,] 1.86764934 -1.32294194 [25,] -3.33463912 1.86764934 [26,] -3.33463912 -3.33463912 [27,] 2.03625547 -3.33463912 [28,] -0.42746684 2.03625547 [29,] 0.17636851 -0.42746684 [30,] 0.34614892 0.17636851 [31,] 0.48897738 0.34614892 [32,] -0.12286191 0.48897738 [33,] 0.36700153 -0.12286191 [34,] 0.82780842 0.36700153 [35,] -0.73510540 0.82780842 [36,] 1.95339646 -0.73510540 [37,] 1.53397733 1.95339646 [38,] 2.00731788 1.53397733 [39,] -1.07842925 2.00731788 [40,] 1.74236897 -1.07842925 [41,] 2.58195452 1.74236897 [42,] -0.80701941 2.58195452 [43,] -1.67050247 -0.80701941 [44,] -1.51596728 -1.67050247 [45,] 0.92228747 -1.51596728 [46,] -0.48198218 0.92228747 [47,] 0.78360239 -0.48198218 [48,] -0.84770163 0.78360239 [49,] 1.77897626 -0.84770163 [50,] -0.91679737 1.77897626 [51,] 1.42504067 -0.91679737 [52,] 2.14005873 1.42504067 [53,] -3.72160561 2.14005873 [54,] -0.18145469 -3.72160561 [55,] -0.80401241 -0.18145469 [56,] 0.10727277 -0.80401241 [57,] -0.22714652 0.10727277 [58,] 0.70359948 -0.22714652 [59,] 1.42135135 0.70359948 [60,] 0.92738866 1.42135135 [61,] 0.78642066 0.92738866 [62,] 1.68497996 0.78642066 [63,] -0.96958590 1.68497996 [64,] -0.89995482 -0.96958590 [65,] 0.83534612 -0.89995482 [66,] 1.31269672 0.83534612 [67,] -1.81358320 1.31269672 [68,] 0.09488371 -1.81358320 [69,] -3.15341380 0.09488371 [70,] 1.94634886 -3.15341380 [71,] 2.18359853 1.94634886 [72,] -0.85927503 2.18359853 [73,] 1.31269672 -0.85927503 [74,] 0.88530028 1.31269672 [75,] -1.61408737 0.88530028 [76,] -1.18669093 -1.61408737 [77,] 0.88530028 -1.18669093 [78,] 1.91152246 0.88530028 [79,] -1.93545137 1.91152246 [80,] -4.14503480 -1.93545137 [81,] 0.05861325 -4.14503480 [82,] 0.66109855 0.05861325 [83,] -0.28667147 0.66109855 [84,] 0.13627391 -0.28667147 [85,] 1.80402986 0.13627391 [86,] 1.09206544 1.80402986 [87,] 0.22766632 1.09206544 [88,] 1.61598137 0.22766632 [89,] 1.14287700 1.61598137 [90,] -2.36824957 1.14287700 [91,] -1.25033605 -2.36824957 [92,] -0.37304552 -1.25033605 [93,] -4.32309136 -0.37304552 [94,] 0.70164745 -4.32309136 [95,] -1.27002008 0.70164745 [96,] 0.02531047 -1.27002008 [97,] 0.44543758 0.02531047 [98,] 0.86702241 0.44543758 [99,] 0.30049883 0.86702241 [100,] 0.40041198 0.30049883 [101,] -1.08695391 0.40041198 [102,] 1.51735159 -1.08695391 [103,] 1.14072497 1.51735159 [104,] -0.44426765 1.14072497 [105,] 0.57750346 -0.44426765 [106,] 0.41495307 0.57750346 [107,] -2.46388673 0.41495307 [108,] 1.75460861 -2.46388673 [109,] 0.27939395 1.75460861 [110,] -0.34916411 0.27939395 [111,] -3.00372997 -0.34916411 [112,] -1.57351527 -3.00372997 [113,] 0.52739988 -1.57351527 [114,] -0.21049513 0.52739988 [115,] 0.04333607 -0.21049513 [116,] 1.44969991 0.04333607 [117,] 0.75691007 1.44969991 [118,] -0.06538131 0.75691007 [119,] 2.07299608 -0.06538131 [120,] -2.30999433 2.07299608 [121,] 1.51501082 -2.30999433 [122,] -1.00302200 1.51501082 [123,] -1.11047285 -1.00302200 [124,] -1.02598490 -1.11047285 [125,] 0.84031643 -1.02598490 [126,] -0.37090958 0.84031643 [127,] 0.62109866 -0.37090958 [128,] 1.25946719 0.62109866 [129,] 0.31269672 1.25946719 [130,] 0.56300411 0.31269672 [131,] -1.48498918 0.56300411 [132,] 0.15152054 -1.48498918 [133,] -0.56628524 0.15152054 [134,] 0.18786086 -0.56628524 [135,] 0.08554340 0.18786086 [136,] -4.34466887 0.08554340 [137,] -0.57570661 -4.34466887 [138,] 1.83519670 -0.57570661 [139,] -0.76132758 1.83519670 [140,] 1.11821776 -0.76132758 [141,] 1.55719251 1.11821776 [142,] 0.42641978 1.55719251 [143,] 0.48897738 0.42641978 [144,] 0.89045932 0.48897738 [145,] 0.49620740 0.89045932 [146,] -0.04589556 0.49620740 [147,] 0.92391152 -0.04589556 [148,] 0.21690130 0.92391152 [149,] -1.52125964 0.21690130 [150,] 1.20306819 -1.52125964 [151,] 1.69652772 1.20306819 [152,] -0.64428029 1.69652772 [153,] -0.22714652 -0.64428029 [154,] -1.35639414 -0.22714652 [155,] 0.12622562 -1.35639414 [156,] -3.22317577 0.12622562 [157,] 2.75953226 -3.22317577 [158,] -0.37304552 2.75953226 [159,] -0.92603001 -0.37304552 [160,] -2.01687121 -0.92603001 [161,] -0.49437124 -2.01687121 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.10727277 -1.15097018 2 -0.75190621 0.10727277 3 -1.55174415 -0.75190621 4 1.05947064 -1.55174415 5 -0.13135110 1.05947064 6 -0.23422712 -0.13135110 7 -4.29405092 -0.23422712 8 -1.20678016 -4.29405092 9 0.12110589 -1.20678016 10 1.67096442 0.12110589 11 2.12407358 1.67096442 12 -0.01474482 2.12407358 13 0.87236387 -0.01474482 14 0.26700489 0.87236387 15 -0.92387797 0.26700489 16 2.66436133 -0.92387797 17 1.92817385 2.66436133 18 0.75256669 1.92817385 19 0.85549075 0.75256669 20 -1.67507248 0.85549075 21 1.66748727 -1.67507248 22 0.13531366 1.66748727 23 -1.32294194 0.13531366 24 1.86764934 -1.32294194 25 -3.33463912 1.86764934 26 -3.33463912 -3.33463912 27 2.03625547 -3.33463912 28 -0.42746684 2.03625547 29 0.17636851 -0.42746684 30 0.34614892 0.17636851 31 0.48897738 0.34614892 32 -0.12286191 0.48897738 33 0.36700153 -0.12286191 34 0.82780842 0.36700153 35 -0.73510540 0.82780842 36 1.95339646 -0.73510540 37 1.53397733 1.95339646 38 2.00731788 1.53397733 39 -1.07842925 2.00731788 40 1.74236897 -1.07842925 41 2.58195452 1.74236897 42 -0.80701941 2.58195452 43 -1.67050247 -0.80701941 44 -1.51596728 -1.67050247 45 0.92228747 -1.51596728 46 -0.48198218 0.92228747 47 0.78360239 -0.48198218 48 -0.84770163 0.78360239 49 1.77897626 -0.84770163 50 -0.91679737 1.77897626 51 1.42504067 -0.91679737 52 2.14005873 1.42504067 53 -3.72160561 2.14005873 54 -0.18145469 -3.72160561 55 -0.80401241 -0.18145469 56 0.10727277 -0.80401241 57 -0.22714652 0.10727277 58 0.70359948 -0.22714652 59 1.42135135 0.70359948 60 0.92738866 1.42135135 61 0.78642066 0.92738866 62 1.68497996 0.78642066 63 -0.96958590 1.68497996 64 -0.89995482 -0.96958590 65 0.83534612 -0.89995482 66 1.31269672 0.83534612 67 -1.81358320 1.31269672 68 0.09488371 -1.81358320 69 -3.15341380 0.09488371 70 1.94634886 -3.15341380 71 2.18359853 1.94634886 72 -0.85927503 2.18359853 73 1.31269672 -0.85927503 74 0.88530028 1.31269672 75 -1.61408737 0.88530028 76 -1.18669093 -1.61408737 77 0.88530028 -1.18669093 78 1.91152246 0.88530028 79 -1.93545137 1.91152246 80 -4.14503480 -1.93545137 81 0.05861325 -4.14503480 82 0.66109855 0.05861325 83 -0.28667147 0.66109855 84 0.13627391 -0.28667147 85 1.80402986 0.13627391 86 1.09206544 1.80402986 87 0.22766632 1.09206544 88 1.61598137 0.22766632 89 1.14287700 1.61598137 90 -2.36824957 1.14287700 91 -1.25033605 -2.36824957 92 -0.37304552 -1.25033605 93 -4.32309136 -0.37304552 94 0.70164745 -4.32309136 95 -1.27002008 0.70164745 96 0.02531047 -1.27002008 97 0.44543758 0.02531047 98 0.86702241 0.44543758 99 0.30049883 0.86702241 100 0.40041198 0.30049883 101 -1.08695391 0.40041198 102 1.51735159 -1.08695391 103 1.14072497 1.51735159 104 -0.44426765 1.14072497 105 0.57750346 -0.44426765 106 0.41495307 0.57750346 107 -2.46388673 0.41495307 108 1.75460861 -2.46388673 109 0.27939395 1.75460861 110 -0.34916411 0.27939395 111 -3.00372997 -0.34916411 112 -1.57351527 -3.00372997 113 0.52739988 -1.57351527 114 -0.21049513 0.52739988 115 0.04333607 -0.21049513 116 1.44969991 0.04333607 117 0.75691007 1.44969991 118 -0.06538131 0.75691007 119 2.07299608 -0.06538131 120 -2.30999433 2.07299608 121 1.51501082 -2.30999433 122 -1.00302200 1.51501082 123 -1.11047285 -1.00302200 124 -1.02598490 -1.11047285 125 0.84031643 -1.02598490 126 -0.37090958 0.84031643 127 0.62109866 -0.37090958 128 1.25946719 0.62109866 129 0.31269672 1.25946719 130 0.56300411 0.31269672 131 -1.48498918 0.56300411 132 0.15152054 -1.48498918 133 -0.56628524 0.15152054 134 0.18786086 -0.56628524 135 0.08554340 0.18786086 136 -4.34466887 0.08554340 137 -0.57570661 -4.34466887 138 1.83519670 -0.57570661 139 -0.76132758 1.83519670 140 1.11821776 -0.76132758 141 1.55719251 1.11821776 142 0.42641978 1.55719251 143 0.48897738 0.42641978 144 0.89045932 0.48897738 145 0.49620740 0.89045932 146 -0.04589556 0.49620740 147 0.92391152 -0.04589556 148 0.21690130 0.92391152 149 -1.52125964 0.21690130 150 1.20306819 -1.52125964 151 1.69652772 1.20306819 152 -0.64428029 1.69652772 153 -0.22714652 -0.64428029 154 -1.35639414 -0.22714652 155 0.12622562 -1.35639414 156 -3.22317577 0.12622562 157 2.75953226 -3.22317577 158 -0.37304552 2.75953226 159 -0.92603001 -0.37304552 160 -2.01687121 -0.92603001 161 -0.49437124 -2.01687121 > 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/7xv4t1353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8f7xt1353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9sjk01353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/100h4i1353352249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11mjmn1353352249.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/12xj9a1353352250.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/13pftb1353352250.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/142g221353352250.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/15yobd1353352250.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/16knhk1353352250.tab") + } > > try(system("convert tmp/14rdp1353352249.ps tmp/14rdp1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/2amcd1353352249.ps tmp/2amcd1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/3z6yx1353352249.ps tmp/3z6yx1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/4wxl41353352249.ps tmp/4wxl41353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/53o0y1353352249.ps tmp/53o0y1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/6x22p1353352249.ps tmp/6x22p1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/7xv4t1353352249.ps tmp/7xv4t1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/8f7xt1353352249.ps tmp/8f7xt1353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/9sjk01353352249.ps tmp/9sjk01353352249.png",intern=TRUE)) character(0) > try(system("convert tmp/100h4i1353352249.ps tmp/100h4i1353352249.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.955 1.324 9.282