R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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(5.3 + ,6.0 + ,5.5 + ,12 + ,5.6 + ,4.0 + ,3.5 + ,11 + ,3.8 + ,4.0 + ,8.5 + ,14 + ,4.0 + ,4.0 + ,5.0 + ,12 + ,4.0 + ,4.5 + ,6.0 + ,21 + ,3.6 + ,3.5 + ,6.0 + ,12 + ,4.4 + ,2.0 + ,5.5 + ,22 + ,3.6 + ,5.5 + ,5.5 + ,11 + ,4.0 + ,3.5 + ,6.0 + ,10 + ,3.8 + ,3.5 + ,6.5 + ,13 + ,5.1 + ,6.0 + ,7.0 + ,10 + ,6.7 + ,5.0 + ,8.0 + ,8 + ,5.1 + ,5.0 + ,5.5 + ,15 + ,4.0 + ,4.0 + ,5.0 + ,14 + ,3.3 + ,4.0 + ,5.5 + ,10 + ,2.7 + ,2.0 + ,7.5 + ,14 + ,4.7 + ,4.5 + ,4.5 + ,14 + ,3.3 + ,4.0 + ,5.5 + ,11 + ,4.4 + ,3.5 + ,8.5 + ,10 + ,6.9 + ,5.5 + ,8.5 + ,13 + ,6.0 + ,4.5 + ,5.5 + ,7 + ,7.6 + ,5.5 + ,9.0 + ,14 + ,4.7 + ,6.5 + ,7.0 + ,12 + ,6.9 + ,4.0 + ,5.0 + ,14 + ,4.2 + ,4.0 + ,5.5 + ,11 + ,3.6 + ,4.5 + ,7.5 + ,9 + ,4.4 + ,3.0 + ,7.5 + ,11 + ,4.7 + ,4.5 + ,6.5 + ,15 + ,4.9 + ,4.5 + ,8.0 + ,14 + ,3.8 + ,3.0 + ,6.5 + ,13 + ,5.3 + ,3.0 + ,4.5 + ,9 + ,5.6 + ,8.0 + ,9.0 + ,15 + ,5.8 + ,2.5 + ,9.0 + ,10 + ,5.6 + ,3.5 + ,6.0 + ,11 + ,3.8 + ,4.5 + ,8.5 + ,13 + ,7.1 + ,3.0 + ,4.5 + ,8 + ,7.3 + ,3.0 + ,4.5 + ,20 + ,2.9 + ,2.5 + ,6.0 + ,12 + ,7.1 + ,6.0 + ,9.0 + ,10 + ,5.6 + ,3.5 + ,6.0 + ,10 + ,6.4 + ,5.0 + ,9.0 + ,9 + ,4.9 + ,4.5 + ,7.0 + ,14 + ,4.0 + ,4.0 + ,7.5 + ,8 + ,3.8 + ,2.5 + ,8.0 + ,14 + ,4.4 + ,4.0 + ,5.0 + ,11 + ,3.3 + ,4.0 + ,5.5 + ,13 + ,4.4 + ,5.0 + ,7.0 + ,9 + ,7.3 + ,3.0 + ,4.5 + ,11 + ,6.4 + ,4.0 + ,6.0 + ,15 + ,5.1 + ,3.5 + ,8.5 + ,11 + ,5.8 + ,2.0 + ,2.5 + ,10 + ,4.0 + ,4.0 + ,6.0 + ,14 + ,4.4 + ,4.0 + ,6.0 + ,18 + ,2.4 + ,2.0 + ,3.0 + ,14 + ,6.2 + ,10.0 + ,12.0 + ,11 + ,5.8 + ,4.0 + ,6.0 + ,12 + ,4.9 + ,4.0 + ,6.0 + ,13 + ,3.8 + ,3.0 + ,7.0 + ,9 + ,2.7 + ,2.0 + ,3.5 + ,10 + ,3.1 + ,4.0 + ,6.5 + ,15 + ,3.8 + ,4.5 + ,6.0 + ,20 + ,4.7 + ,3.0 + ,6.5 + ,12 + ,4.2 + ,3.5 + ,7.0 + ,12 + ,4.0 + ,4.5 + ,4.0 + ,14 + ,2.2 + ,2.5 + ,5.5 + ,13 + ,6.4 + ,2.5 + ,4.5 + ,11 + ,6.9 + ,4.0 + ,5.5 + ,17 + ,4.2 + ,4.0 + ,6.5 + ,12 + ,2.0 + ,3.0 + ,5.0 + ,13 + ,4.4 + ,4.0 + ,5.5 + ,14 + ,6.2 + ,3.5 + ,6.0 + ,13 + ,4.2 + ,3.5 + ,4.5 + ,15 + ,6.7 + ,4.5 + ,7.5 + ,13 + ,6.4 + ,5.5 + ,9.0 + ,10 + ,5.8 + ,3.0 + ,7.5 + ,11 + ,5.1 + ,4.0 + ,6.0 + ,19 + ,2.9 + ,3.0 + ,6.5 + ,13 + ,4.7 + ,4.5 + ,7.0 + ,17 + ,4.2 + ,4.0 + ,5.0 + ,13 + ,6.2 + ,3.0 + ,6.5 + ,9 + ,5.1 + ,5.0 + ,6.5 + ,11 + ,4.0 + ,4.0 + ,5.5 + ,10 + ,4.7 + ,4.0 + ,6.5 + ,9 + ,4.4 + ,5.0 + ,8.0 + ,12 + ,5.1 + ,2.5 + ,4.0 + ,12 + ,4.7 + ,3.5 + ,8.0 + ,13 + ,4.7 + ,2.5 + ,5.5 + ,13 + ,3.3 + ,4.0 + ,4.5 + ,12 + ,6.2 + ,7.0 + ,8.0 + ,15 + ,4.2 + ,3.5 + ,6.0 + ,22 + ,5.8 + ,4.0 + ,7.0 + ,13 + ,2.2 + ,3.0 + ,4.0 + ,15 + ,3.6 + ,2.5 + ,4.5 + ,13 + ,4.9 + ,3.0 + ,7.5 + ,15 + ,4.2 + ,5.0 + ,5.5 + ,10 + ,6.9 + ,6.0 + ,10.5 + ,11 + ,6.9 + ,4.5 + ,7.0 + ,16 + ,6.4 + ,6.0 + ,9.0 + ,11 + ,4.2 + ,3.5 + ,6.0 + ,11 + ,4.9 + ,4.0 + ,6.5 + ,10 + ,5.1 + ,5.0 + ,7.5 + ,10 + ,3.3 + ,3.0 + ,6.0 + ,16 + ,4.4 + ,5.0 + ,9.5 + ,12 + ,4.0 + ,5.0 + ,7.5 + ,11 + ,5.1 + ,5.0 + ,5.5 + ,16 + ,5.6 + ,2.5 + ,5.5 + ,19 + ,4.7 + ,3.5 + ,5.0 + ,11 + ,5.3 + ,5.0 + ,6.5 + ,16 + ,5.6 + ,5.5 + ,7.5 + ,15 + ,3.8 + ,3.0 + ,6.0 + ,24 + ,2.9 + ,3.5 + ,6.0 + ,14 + ,6.2 + ,6.0 + ,8.0 + ,15 + ,4.7 + ,5.5 + ,4.5 + ,11 + ,5.6 + ,5.5 + ,9.0 + ,15 + ,2.0 + ,5.5 + ,4.0 + ,12 + ,3.6 + ,2.5 + ,6.5 + ,10 + ,4.2 + ,4.0 + ,8.5 + ,14 + ,3.8 + ,3.0 + ,4.5 + ,13 + ,5.6 + ,4.5 + ,7.5 + ,9 + ,4.4 + ,2.0 + ,4.0 + ,15 + ,6.4 + ,2.0 + ,3.5 + ,15 + ,3.1 + ,3.5 + ,6.0 + ,14 + ,4.9 + ,5.5 + ,7.0 + ,11 + ,3.3 + ,3.0 + ,3.0 + ,8 + ,4.2 + ,3.5 + ,4.0 + ,11 + ,4.4 + ,4.0 + ,8.5 + ,11 + ,3.3 + ,2.0 + ,5.0 + ,8 + ,4.4 + ,4.0 + ,5.5 + ,10 + ,4.0 + ,4.5 + ,7.0 + ,11 + ,7.3 + ,4.0 + ,5.5 + ,13 + ,4.9 + ,5.5 + ,6.5 + ,11 + ,3.6 + ,4.0 + ,6.0 + ,20 + ,3.8 + ,2.5 + ,5.5 + ,10 + ,3.6 + ,2.0 + ,4.5 + ,15 + ,4.7 + ,4.0 + ,6.0 + ,12 + ,5.8 + ,5.0 + ,10.0 + ,14 + ,4.0 + ,3.0 + ,6.0 + ,23 + ,4.0 + ,4.5 + ,6.5 + ,14 + ,3.8 + ,4.5 + ,6.0 + ,16 + ,4.9 + ,6.5 + ,6.0 + ,11 + ,6.7 + ,4.5 + ,4.5 + ,12 + ,6.7 + ,5.0 + ,7.5 + ,10 + ,5.3 + ,10.0 + ,12.0 + ,14 + ,4.7 + ,2.5 + ,3.5 + ,12 + ,4.7 + ,5.5 + ,8.5 + ,12 + ,6.4 + ,3.0 + ,5.5 + ,11 + ,6.9 + ,4.5 + ,8.5 + ,12 + ,4.4 + ,3.5 + ,5.5 + ,13 + ,3.6 + ,4.5 + ,6.0 + ,11 + ,4.9 + ,5.0 + ,7.0 + ,19 + ,4.4 + ,4.5 + ,5.5 + ,12 + ,6.2 + ,4.0 + ,8.0 + ,17 + ,8.4 + ,3.5 + ,10.5 + ,9 + ,4.9 + ,3.0 + ,7.0 + ,12 + ,4.4 + ,6.5 + ,10.0 + ,19 + ,3.8 + ,3.0 + ,6.5 + ,18 + ,6.2 + ,4.0 + ,5.5 + ,15 + ,4.9 + ,5.0 + ,7.5 + ,14 + ,6.9 + ,8.0 + ,9.5 + ,11) + ,dim=c(4 + ,159) + ,dimnames=list(c('Concerns' + ,'Criticism' + ,'Expectat' + ,'Depression') + ,1:159)) > y <- array(NA,dim=c(4,159),dimnames=list(c('Concerns','Criticism','Expectat','Depression'),1:159)) > 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 = '4' > #'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.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 Depression Concerns Criticism Expectat 1 12 5.3 6.0 5.5 2 11 5.6 4.0 3.5 3 14 3.8 4.0 8.5 4 12 4.0 4.0 5.0 5 21 4.0 4.5 6.0 6 12 3.6 3.5 6.0 7 22 4.4 2.0 5.5 8 11 3.6 5.5 5.5 9 10 4.0 3.5 6.0 10 13 3.8 3.5 6.5 11 10 5.1 6.0 7.0 12 8 6.7 5.0 8.0 13 15 5.1 5.0 5.5 14 14 4.0 4.0 5.0 15 10 3.3 4.0 5.5 16 14 2.7 2.0 7.5 17 14 4.7 4.5 4.5 18 11 3.3 4.0 5.5 19 10 4.4 3.5 8.5 20 13 6.9 5.5 8.5 21 7 6.0 4.5 5.5 22 14 7.6 5.5 9.0 23 12 4.7 6.5 7.0 24 14 6.9 4.0 5.0 25 11 4.2 4.0 5.5 26 9 3.6 4.5 7.5 27 11 4.4 3.0 7.5 28 15 4.7 4.5 6.5 29 14 4.9 4.5 8.0 30 13 3.8 3.0 6.5 31 9 5.3 3.0 4.5 32 15 5.6 8.0 9.0 33 10 5.8 2.5 9.0 34 11 5.6 3.5 6.0 35 13 3.8 4.5 8.5 36 8 7.1 3.0 4.5 37 20 7.3 3.0 4.5 38 12 2.9 2.5 6.0 39 10 7.1 6.0 9.0 40 10 5.6 3.5 6.0 41 9 6.4 5.0 9.0 42 14 4.9 4.5 7.0 43 8 4.0 4.0 7.5 44 14 3.8 2.5 8.0 45 11 4.4 4.0 5.0 46 13 3.3 4.0 5.5 47 9 4.4 5.0 7.0 48 11 7.3 3.0 4.5 49 15 6.4 4.0 6.0 50 11 5.1 3.5 8.5 51 10 5.8 2.0 2.5 52 14 4.0 4.0 6.0 53 18 4.4 4.0 6.0 54 14 2.4 2.0 3.0 55 11 6.2 10.0 12.0 56 12 5.8 4.0 6.0 57 13 4.9 4.0 6.0 58 9 3.8 3.0 7.0 59 10 2.7 2.0 3.5 60 15 3.1 4.0 6.5 61 20 3.8 4.5 6.0 62 12 4.7 3.0 6.5 63 12 4.2 3.5 7.0 64 14 4.0 4.5 4.0 65 13 2.2 2.5 5.5 66 11 6.4 2.5 4.5 67 17 6.9 4.0 5.5 68 12 4.2 4.0 6.5 69 13 2.0 3.0 5.0 70 14 4.4 4.0 5.5 71 13 6.2 3.5 6.0 72 15 4.2 3.5 4.5 73 13 6.7 4.5 7.5 74 10 6.4 5.5 9.0 75 11 5.8 3.0 7.5 76 19 5.1 4.0 6.0 77 13 2.9 3.0 6.5 78 17 4.7 4.5 7.0 79 13 4.2 4.0 5.0 80 9 6.2 3.0 6.5 81 11 5.1 5.0 6.5 82 10 4.0 4.0 5.5 83 9 4.7 4.0 6.5 84 12 4.4 5.0 8.0 85 12 5.1 2.5 4.0 86 13 4.7 3.5 8.0 87 13 4.7 2.5 5.5 88 12 3.3 4.0 4.5 89 15 6.2 7.0 8.0 90 22 4.2 3.5 6.0 91 13 5.8 4.0 7.0 92 15 2.2 3.0 4.0 93 13 3.6 2.5 4.5 94 15 4.9 3.0 7.5 95 10 4.2 5.0 5.5 96 11 6.9 6.0 10.5 97 16 6.9 4.5 7.0 98 11 6.4 6.0 9.0 99 11 4.2 3.5 6.0 100 10 4.9 4.0 6.5 101 10 5.1 5.0 7.5 102 16 3.3 3.0 6.0 103 12 4.4 5.0 9.5 104 11 4.0 5.0 7.5 105 16 5.1 5.0 5.5 106 19 5.6 2.5 5.5 107 11 4.7 3.5 5.0 108 16 5.3 5.0 6.5 109 15 5.6 5.5 7.5 110 24 3.8 3.0 6.0 111 14 2.9 3.5 6.0 112 15 6.2 6.0 8.0 113 11 4.7 5.5 4.5 114 15 5.6 5.5 9.0 115 12 2.0 5.5 4.0 116 10 3.6 2.5 6.5 117 14 4.2 4.0 8.5 118 13 3.8 3.0 4.5 119 9 5.6 4.5 7.5 120 15 4.4 2.0 4.0 121 15 6.4 2.0 3.5 122 14 3.1 3.5 6.0 123 11 4.9 5.5 7.0 124 8 3.3 3.0 3.0 125 11 4.2 3.5 4.0 126 11 4.4 4.0 8.5 127 8 3.3 2.0 5.0 128 10 4.4 4.0 5.5 129 11 4.0 4.5 7.0 130 13 7.3 4.0 5.5 131 11 4.9 5.5 6.5 132 20 3.6 4.0 6.0 133 10 3.8 2.5 5.5 134 15 3.6 2.0 4.5 135 12 4.7 4.0 6.0 136 14 5.8 5.0 10.0 137 23 4.0 3.0 6.0 138 14 4.0 4.5 6.5 139 16 3.8 4.5 6.0 140 11 4.9 6.5 6.0 141 12 6.7 4.5 4.5 142 10 6.7 5.0 7.5 143 14 5.3 10.0 12.0 144 12 4.7 2.5 3.5 145 12 4.7 5.5 8.5 146 11 6.4 3.0 5.5 147 12 6.9 4.5 8.5 148 13 4.4 3.5 5.5 149 11 3.6 4.5 6.0 150 19 4.9 5.0 7.0 151 12 4.4 4.5 5.5 152 17 6.2 4.0 8.0 153 9 8.4 3.5 10.5 154 12 4.9 3.0 7.0 155 19 4.4 6.5 10.0 156 18 3.8 3.0 6.5 157 15 6.2 4.0 5.5 158 14 4.9 5.0 7.5 159 11 6.9 8.0 9.5 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Concerns Criticism Expectat 14.164941 -0.229536 -0.035846 -0.003658 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.6063 -2.0388 -0.6683 1.4581 10.8368 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 14.164941 1.194343 11.860 <2e-16 *** Concerns -0.229536 0.212105 -1.082 0.281 Criticism -0.035846 0.233418 -0.154 0.878 Expectat -0.003658 0.184625 -0.020 0.984 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.16 on 155 degrees of freedom Multiple R-squared: 0.009883, Adjusted R-squared: -0.00928 F-statistic: 0.5157 on 3 and 155 DF, p-value: 0.672 > 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,] 0.97967882 0.04064236 0.02032118 [2,] 0.95622576 0.08754849 0.04377424 [3,] 0.97340850 0.05318299 0.02659150 [4,] 0.95717263 0.08565474 0.04282737 [5,] 0.93524585 0.12950830 0.06475415 [6,] 0.92930347 0.14139306 0.07069653 [7,] 0.92208450 0.15583099 0.07791550 [8,] 0.88417213 0.23165573 0.11582787 [9,] 0.89990220 0.20019561 0.10009780 [10,] 0.87297692 0.25404616 0.12702308 [11,] 0.82925584 0.34148831 0.17074416 [12,] 0.80389752 0.39220497 0.19610248 [13,] 0.78349728 0.43300543 0.21650272 [14,] 0.75552317 0.48895366 0.24447683 [15,] 0.84577126 0.30845748 0.15422874 [16,] 0.83836504 0.32326991 0.16163496 [17,] 0.80210866 0.39578268 0.19789134 [18,] 0.75625627 0.48748745 0.24374373 [19,] 0.72172512 0.55654976 0.27827488 [20,] 0.72176653 0.55646694 0.27823347 [21,] 0.69441556 0.61116888 0.30558444 [22,] 0.67455855 0.65088291 0.32544145 [23,] 0.63398198 0.73203603 0.36601802 [24,] 0.57563673 0.84872654 0.42436327 [25,] 0.62856445 0.74287111 0.37143555 [26,] 0.64011947 0.71976107 0.35988053 [27,] 0.62215258 0.75569483 0.37784742 [28,] 0.57625225 0.84749550 0.42374775 [29,] 0.51958637 0.96082726 0.48041363 [30,] 0.53510772 0.92978457 0.46489228 [31,] 0.78906791 0.42186417 0.21093209 [32,] 0.75183469 0.49633062 0.24816531 [33,] 0.72451542 0.55096915 0.27548458 [34,] 0.70714220 0.58571560 0.29285780 [35,] 0.70039848 0.59920304 0.29960152 [36,] 0.66462142 0.67075716 0.33537858 [37,] 0.71191603 0.57616795 0.28808397 [38,] 0.67561876 0.64876248 0.32438124 [39,] 0.64236081 0.71527837 0.35763919 [40,] 0.59396701 0.81206597 0.40603299 [41,] 0.60274436 0.79451128 0.39725564 [42,] 0.56224115 0.87551770 0.43775885 [43,] 0.54868853 0.90262293 0.45131147 [44,] 0.50977096 0.98045808 0.49022904 [45,] 0.49895224 0.99790447 0.50104776 [46,] 0.45887957 0.91775914 0.54112043 [47,] 0.54867585 0.90264831 0.45132415 [48,] 0.50090735 0.99818530 0.49909265 [49,] 0.45847560 0.91695119 0.54152440 [50,] 0.41173126 0.82346253 0.58826874 [51,] 0.36620159 0.73240317 0.63379841 [52,] 0.38981741 0.77963482 0.61018259 [53,] 0.39376423 0.78752846 0.60623577 [54,] 0.36901054 0.73802108 0.63098946 [55,] 0.55720032 0.88559936 0.44279968 [56,] 0.51365362 0.97269277 0.48634638 [57,] 0.47103064 0.94206128 0.52896936 [58,] 0.42826375 0.85652750 0.57173625 [59,] 0.38446121 0.76892243 0.61553879 [60,] 0.34910343 0.69820686 0.65089657 [61,] 0.40595308 0.81190615 0.59404692 [62,] 0.36564737 0.73129473 0.63435263 [63,] 0.32501538 0.65003076 0.67498462 [64,] 0.28901103 0.57802206 0.71098897 [65,] 0.25184068 0.50368135 0.74815932 [66,] 0.22953744 0.45907489 0.77046256 [67,] 0.19800969 0.39601939 0.80199031 [68,] 0.18311143 0.36622286 0.81688857 [69,] 0.16208942 0.32417884 0.83791058 [70,] 0.25908658 0.51817316 0.74091342 [71,] 0.22553181 0.45106362 0.77446819 [72,] 0.25184644 0.50369288 0.74815356 [73,] 0.21638069 0.43276138 0.78361931 [74,] 0.22548426 0.45096852 0.77451574 [75,] 0.20202170 0.40404340 0.79797830 [76,] 0.20091302 0.40182604 0.79908698 [77,] 0.21941656 0.43883312 0.78058344 [78,] 0.19104899 0.38209798 0.80895101 [79,] 0.16356857 0.32713714 0.83643143 [80,] 0.13940767 0.27881534 0.86059233 [81,] 0.11613326 0.23226653 0.88386674 [82,] 0.09845743 0.19691486 0.90154257 [83,] 0.09309166 0.18618332 0.90690834 [84,] 0.29566225 0.59132451 0.70433775 [85,] 0.25771392 0.51542784 0.74228608 [86,] 0.22731721 0.45463443 0.77268279 [87,] 0.19429267 0.38858535 0.80570733 [88,] 0.17585312 0.35170625 0.82414688 [89,] 0.17177446 0.34354892 0.82822554 [90,] 0.15066914 0.30133828 0.84933086 [91,] 0.15798902 0.31597803 0.84201098 [92,] 0.13728807 0.27457614 0.86271193 [93,] 0.12457990 0.24915980 0.87542010 [94,] 0.12224883 0.24449767 0.87775117 [95,] 0.11911933 0.23823865 0.88088067 [96,] 0.10961062 0.21922124 0.89038938 [97,] 0.09505561 0.19011123 0.90494439 [98,] 0.08694646 0.17389292 0.91305354 [99,] 0.08768505 0.17537010 0.91231495 [100,] 0.14879786 0.29759573 0.85120214 [101,] 0.13150153 0.26300306 0.86849847 [102,] 0.13168515 0.26337029 0.86831485 [103,] 0.11888800 0.23777599 0.88111200 [104,] 0.51396476 0.97207048 0.48603524 [105,] 0.46431518 0.92863036 0.53568482 [106,] 0.44363349 0.88726697 0.55636651 [107,] 0.40424344 0.80848687 0.59575656 [108,] 0.37486472 0.74972944 0.62513528 [109,] 0.33606377 0.67212755 0.66393623 [110,] 0.35287841 0.70575683 0.64712159 [111,] 0.30790052 0.61580104 0.69209948 [112,] 0.26308332 0.52616664 0.73691668 [113,] 0.28290950 0.56581900 0.71709050 [114,] 0.25564214 0.51128429 0.74435786 [115,] 0.26372356 0.52744713 0.73627644 [116,] 0.22172871 0.44345742 0.77827129 [117,] 0.19669567 0.39339133 0.80330433 [118,] 0.25713131 0.51426262 0.74286869 [119,] 0.23018315 0.46036630 0.76981685 [120,] 0.22860583 0.45721167 0.77139417 [121,] 0.39624002 0.79248005 0.60375998 [122,] 0.41837869 0.83675738 0.58162131 [123,] 0.44668200 0.89336400 0.55331800 [124,] 0.42868361 0.85736722 0.57131639 [125,] 0.40096759 0.80193518 0.59903241 [126,] 0.49936369 0.99872739 0.50063631 [127,] 0.62355268 0.75289464 0.37644732 [128,] 0.56701907 0.86596186 0.43298093 [129,] 0.53223151 0.93553698 0.46776849 [130,] 0.46763596 0.93527193 0.53236404 [131,] 0.82402506 0.35194988 0.17597494 [132,] 0.77513806 0.44972388 0.22486194 [133,] 0.72806703 0.54386595 0.27193297 [134,] 0.68961039 0.62077922 0.31038961 [135,] 0.63263063 0.73473874 0.36736937 [136,] 0.57872193 0.84255613 0.42127807 [137,] 0.50938765 0.98122469 0.49061235 [138,] 0.42608041 0.85216082 0.57391959 [139,] 0.42166605 0.84333210 0.57833395 [140,] 0.33312313 0.66624625 0.66687687 [141,] 0.24925264 0.49850529 0.75074736 [142,] 0.18509003 0.37018007 0.81490997 [143,] 0.32484905 0.64969809 0.67515095 [144,] 0.37441534 0.74883068 0.62558466 [145,] 0.41121203 0.82242407 0.58878797 [146,] 0.48945320 0.97890639 0.51054680 > postscript(file="/var/www/html/rcomp/tmp/1ygiz1290462421.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2wboq1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3wboq1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4wboq1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5wboq1290462421.ps",horizontal=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 = 159 Frequency = 1 1 2 3 4 5 6 -0.71320632 -1.72335402 0.88177087 -1.08512431 7.93645669 -1.19120396 7 8 9 10 11 12 8.93682605 -2.12134007 -3.09938971 -0.14346793 -2.75362674 -4.41855834 13 14 15 16 17 18 2.20504017 0.91487569 -3.24397035 0.55393109 1.09164492 -2.24397035 19 20 21 22 23 24 -2.99843096 0.64710088 -5.60630097 1.80960472 -0.82751779 1.58052899 25 26 27 28 29 30 -2.03738829 -4.14987086 -2.02001195 2.09896052 1.15035435 -0.16139113 31 32 33 34 35 36 -3.82440330 2.44014946 -2.71109858 -1.73213271 -0.10030594 -4.41123918 37 38 39 40 41 42 7.63466795 -1.38772529 -2.28723989 -2.73213271 -3.48376122 1.14669655 43 44 45 46 47 48 -5.07597981 0.82617238 -1.99331006 -0.24397035 -3.95014807 -1.36533205 49 50 51 52 53 54 2.46941898 -1.83775602 -2.75279748 0.91853349 5.01034774 0.46861031 55 56 57 58 59 60 -1.33946298 -0.66830239 0.12511555 -4.15956223 -3.46070011 1.71378033 61 62 63 64 65 66 6.89054956 -0.95480907 -1.04982478 0.92914108 -0.55022912 -1.58983731 67 68 69 70 71 72 4.58235789 -1.03373049 -0.58004195 1.00851884 0.40558866 1.94103072 73 74 75 76 77 78 0.56168957 -2.46583803 -1.69866208 6.17102267 -0.36797319 4.10078942 79 80 81 82 83 84 -0.03921719 -3.61050563 -1.79130203 -3.08329541 -3.91896268 -0.94649027 85 86 87 88 89 90 -0.89006252 0.06860083 0.02360993 -1.24762815 2.53836664 8.94651742 91 92 93 94 95 96 0.33535541 1.46220737 -0.23253705 2.09475586 -3.00154189 -1.32766032 97 98 99 100 101 102 3.60576779 -1.44791483 -2.05348258 -2.87305555 -2.78764423 2.72201216 103 104 105 106 107 108 -0.94100357 -2.04013342 3.20504017 6.23019199 -1.94237257 3.25460509 109 110 111 112 113 114 2.34504677 10.83677997 0.64812111 2.50252025 -1.87250869 2.35053348 115 116 117 118 119 120 -1.49408377 -3.22522145 0.97358511 -0.16870673 -3.69079962 1.93133935 121 122 123 124 125 126 2.38858170 0.69402823 -1.81745706 -5.28896124 -2.06079818 -1.98050776 127 128 129 130 131 132 -5.31749203 -2.99148116 -2.05988551 0.67417214 -1.81928596 6.82671924 133 134 135 136 137 138 -3.18297213 1.74953975 -0.92079158 1.38217520 9.88268710 0.93828559 139 140 141 142 143 144 2.89054956 -1.78526847 -0.44928383 -2.42038724 1.45395496 -0.98370567 145 146 147 148 149 150 -0.85787748 -1.56825631 -0.38874551 -0.00940436 -2.15535756 6.16461974 151 152 153 154 155 156 -0.97355797 4.43082746 -3.07297287 -0.90707304 6.11459492 4.83860887 157 158 159 2.42168296 1.16644864 -1.25962533 > postscript(file="/var/www/html/rcomp/tmp/6khyn1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 159 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.71320632 NA 1 -1.72335402 -0.71320632 2 0.88177087 -1.72335402 3 -1.08512431 0.88177087 4 7.93645669 -1.08512431 5 -1.19120396 7.93645669 6 8.93682605 -1.19120396 7 -2.12134007 8.93682605 8 -3.09938971 -2.12134007 9 -0.14346793 -3.09938971 10 -2.75362674 -0.14346793 11 -4.41855834 -2.75362674 12 2.20504017 -4.41855834 13 0.91487569 2.20504017 14 -3.24397035 0.91487569 15 0.55393109 -3.24397035 16 1.09164492 0.55393109 17 -2.24397035 1.09164492 18 -2.99843096 -2.24397035 19 0.64710088 -2.99843096 20 -5.60630097 0.64710088 21 1.80960472 -5.60630097 22 -0.82751779 1.80960472 23 1.58052899 -0.82751779 24 -2.03738829 1.58052899 25 -4.14987086 -2.03738829 26 -2.02001195 -4.14987086 27 2.09896052 -2.02001195 28 1.15035435 2.09896052 29 -0.16139113 1.15035435 30 -3.82440330 -0.16139113 31 2.44014946 -3.82440330 32 -2.71109858 2.44014946 33 -1.73213271 -2.71109858 34 -0.10030594 -1.73213271 35 -4.41123918 -0.10030594 36 7.63466795 -4.41123918 37 -1.38772529 7.63466795 38 -2.28723989 -1.38772529 39 -2.73213271 -2.28723989 40 -3.48376122 -2.73213271 41 1.14669655 -3.48376122 42 -5.07597981 1.14669655 43 0.82617238 -5.07597981 44 -1.99331006 0.82617238 45 -0.24397035 -1.99331006 46 -3.95014807 -0.24397035 47 -1.36533205 -3.95014807 48 2.46941898 -1.36533205 49 -1.83775602 2.46941898 50 -2.75279748 -1.83775602 51 0.91853349 -2.75279748 52 5.01034774 0.91853349 53 0.46861031 5.01034774 54 -1.33946298 0.46861031 55 -0.66830239 -1.33946298 56 0.12511555 -0.66830239 57 -4.15956223 0.12511555 58 -3.46070011 -4.15956223 59 1.71378033 -3.46070011 60 6.89054956 1.71378033 61 -0.95480907 6.89054956 62 -1.04982478 -0.95480907 63 0.92914108 -1.04982478 64 -0.55022912 0.92914108 65 -1.58983731 -0.55022912 66 4.58235789 -1.58983731 67 -1.03373049 4.58235789 68 -0.58004195 -1.03373049 69 1.00851884 -0.58004195 70 0.40558866 1.00851884 71 1.94103072 0.40558866 72 0.56168957 1.94103072 73 -2.46583803 0.56168957 74 -1.69866208 -2.46583803 75 6.17102267 -1.69866208 76 -0.36797319 6.17102267 77 4.10078942 -0.36797319 78 -0.03921719 4.10078942 79 -3.61050563 -0.03921719 80 -1.79130203 -3.61050563 81 -3.08329541 -1.79130203 82 -3.91896268 -3.08329541 83 -0.94649027 -3.91896268 84 -0.89006252 -0.94649027 85 0.06860083 -0.89006252 86 0.02360993 0.06860083 87 -1.24762815 0.02360993 88 2.53836664 -1.24762815 89 8.94651742 2.53836664 90 0.33535541 8.94651742 91 1.46220737 0.33535541 92 -0.23253705 1.46220737 93 2.09475586 -0.23253705 94 -3.00154189 2.09475586 95 -1.32766032 -3.00154189 96 3.60576779 -1.32766032 97 -1.44791483 3.60576779 98 -2.05348258 -1.44791483 99 -2.87305555 -2.05348258 100 -2.78764423 -2.87305555 101 2.72201216 -2.78764423 102 -0.94100357 2.72201216 103 -2.04013342 -0.94100357 104 3.20504017 -2.04013342 105 6.23019199 3.20504017 106 -1.94237257 6.23019199 107 3.25460509 -1.94237257 108 2.34504677 3.25460509 109 10.83677997 2.34504677 110 0.64812111 10.83677997 111 2.50252025 0.64812111 112 -1.87250869 2.50252025 113 2.35053348 -1.87250869 114 -1.49408377 2.35053348 115 -3.22522145 -1.49408377 116 0.97358511 -3.22522145 117 -0.16870673 0.97358511 118 -3.69079962 -0.16870673 119 1.93133935 -3.69079962 120 2.38858170 1.93133935 121 0.69402823 2.38858170 122 -1.81745706 0.69402823 123 -5.28896124 -1.81745706 124 -2.06079818 -5.28896124 125 -1.98050776 -2.06079818 126 -5.31749203 -1.98050776 127 -2.99148116 -5.31749203 128 -2.05988551 -2.99148116 129 0.67417214 -2.05988551 130 -1.81928596 0.67417214 131 6.82671924 -1.81928596 132 -3.18297213 6.82671924 133 1.74953975 -3.18297213 134 -0.92079158 1.74953975 135 1.38217520 -0.92079158 136 9.88268710 1.38217520 137 0.93828559 9.88268710 138 2.89054956 0.93828559 139 -1.78526847 2.89054956 140 -0.44928383 -1.78526847 141 -2.42038724 -0.44928383 142 1.45395496 -2.42038724 143 -0.98370567 1.45395496 144 -0.85787748 -0.98370567 145 -1.56825631 -0.85787748 146 -0.38874551 -1.56825631 147 -0.00940436 -0.38874551 148 -2.15535756 -0.00940436 149 6.16461974 -2.15535756 150 -0.97355797 6.16461974 151 4.43082746 -0.97355797 152 -3.07297287 4.43082746 153 -0.90707304 -3.07297287 154 6.11459492 -0.90707304 155 4.83860887 6.11459492 156 2.42168296 4.83860887 157 1.16644864 2.42168296 158 -1.25962533 1.16644864 159 NA -1.25962533 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.72335402 -0.71320632 [2,] 0.88177087 -1.72335402 [3,] -1.08512431 0.88177087 [4,] 7.93645669 -1.08512431 [5,] -1.19120396 7.93645669 [6,] 8.93682605 -1.19120396 [7,] -2.12134007 8.93682605 [8,] -3.09938971 -2.12134007 [9,] -0.14346793 -3.09938971 [10,] -2.75362674 -0.14346793 [11,] -4.41855834 -2.75362674 [12,] 2.20504017 -4.41855834 [13,] 0.91487569 2.20504017 [14,] -3.24397035 0.91487569 [15,] 0.55393109 -3.24397035 [16,] 1.09164492 0.55393109 [17,] -2.24397035 1.09164492 [18,] -2.99843096 -2.24397035 [19,] 0.64710088 -2.99843096 [20,] -5.60630097 0.64710088 [21,] 1.80960472 -5.60630097 [22,] -0.82751779 1.80960472 [23,] 1.58052899 -0.82751779 [24,] -2.03738829 1.58052899 [25,] -4.14987086 -2.03738829 [26,] -2.02001195 -4.14987086 [27,] 2.09896052 -2.02001195 [28,] 1.15035435 2.09896052 [29,] -0.16139113 1.15035435 [30,] -3.82440330 -0.16139113 [31,] 2.44014946 -3.82440330 [32,] -2.71109858 2.44014946 [33,] -1.73213271 -2.71109858 [34,] -0.10030594 -1.73213271 [35,] -4.41123918 -0.10030594 [36,] 7.63466795 -4.41123918 [37,] -1.38772529 7.63466795 [38,] -2.28723989 -1.38772529 [39,] -2.73213271 -2.28723989 [40,] -3.48376122 -2.73213271 [41,] 1.14669655 -3.48376122 [42,] -5.07597981 1.14669655 [43,] 0.82617238 -5.07597981 [44,] -1.99331006 0.82617238 [45,] -0.24397035 -1.99331006 [46,] -3.95014807 -0.24397035 [47,] -1.36533205 -3.95014807 [48,] 2.46941898 -1.36533205 [49,] -1.83775602 2.46941898 [50,] -2.75279748 -1.83775602 [51,] 0.91853349 -2.75279748 [52,] 5.01034774 0.91853349 [53,] 0.46861031 5.01034774 [54,] -1.33946298 0.46861031 [55,] -0.66830239 -1.33946298 [56,] 0.12511555 -0.66830239 [57,] -4.15956223 0.12511555 [58,] -3.46070011 -4.15956223 [59,] 1.71378033 -3.46070011 [60,] 6.89054956 1.71378033 [61,] -0.95480907 6.89054956 [62,] -1.04982478 -0.95480907 [63,] 0.92914108 -1.04982478 [64,] -0.55022912 0.92914108 [65,] -1.58983731 -0.55022912 [66,] 4.58235789 -1.58983731 [67,] -1.03373049 4.58235789 [68,] -0.58004195 -1.03373049 [69,] 1.00851884 -0.58004195 [70,] 0.40558866 1.00851884 [71,] 1.94103072 0.40558866 [72,] 0.56168957 1.94103072 [73,] -2.46583803 0.56168957 [74,] -1.69866208 -2.46583803 [75,] 6.17102267 -1.69866208 [76,] -0.36797319 6.17102267 [77,] 4.10078942 -0.36797319 [78,] -0.03921719 4.10078942 [79,] -3.61050563 -0.03921719 [80,] -1.79130203 -3.61050563 [81,] -3.08329541 -1.79130203 [82,] -3.91896268 -3.08329541 [83,] -0.94649027 -3.91896268 [84,] -0.89006252 -0.94649027 [85,] 0.06860083 -0.89006252 [86,] 0.02360993 0.06860083 [87,] -1.24762815 0.02360993 [88,] 2.53836664 -1.24762815 [89,] 8.94651742 2.53836664 [90,] 0.33535541 8.94651742 [91,] 1.46220737 0.33535541 [92,] -0.23253705 1.46220737 [93,] 2.09475586 -0.23253705 [94,] -3.00154189 2.09475586 [95,] -1.32766032 -3.00154189 [96,] 3.60576779 -1.32766032 [97,] -1.44791483 3.60576779 [98,] -2.05348258 -1.44791483 [99,] -2.87305555 -2.05348258 [100,] -2.78764423 -2.87305555 [101,] 2.72201216 -2.78764423 [102,] -0.94100357 2.72201216 [103,] -2.04013342 -0.94100357 [104,] 3.20504017 -2.04013342 [105,] 6.23019199 3.20504017 [106,] -1.94237257 6.23019199 [107,] 3.25460509 -1.94237257 [108,] 2.34504677 3.25460509 [109,] 10.83677997 2.34504677 [110,] 0.64812111 10.83677997 [111,] 2.50252025 0.64812111 [112,] -1.87250869 2.50252025 [113,] 2.35053348 -1.87250869 [114,] -1.49408377 2.35053348 [115,] -3.22522145 -1.49408377 [116,] 0.97358511 -3.22522145 [117,] -0.16870673 0.97358511 [118,] -3.69079962 -0.16870673 [119,] 1.93133935 -3.69079962 [120,] 2.38858170 1.93133935 [121,] 0.69402823 2.38858170 [122,] -1.81745706 0.69402823 [123,] -5.28896124 -1.81745706 [124,] -2.06079818 -5.28896124 [125,] -1.98050776 -2.06079818 [126,] -5.31749203 -1.98050776 [127,] -2.99148116 -5.31749203 [128,] -2.05988551 -2.99148116 [129,] 0.67417214 -2.05988551 [130,] -1.81928596 0.67417214 [131,] 6.82671924 -1.81928596 [132,] -3.18297213 6.82671924 [133,] 1.74953975 -3.18297213 [134,] -0.92079158 1.74953975 [135,] 1.38217520 -0.92079158 [136,] 9.88268710 1.38217520 [137,] 0.93828559 9.88268710 [138,] 2.89054956 0.93828559 [139,] -1.78526847 2.89054956 [140,] -0.44928383 -1.78526847 [141,] -2.42038724 -0.44928383 [142,] 1.45395496 -2.42038724 [143,] -0.98370567 1.45395496 [144,] -0.85787748 -0.98370567 [145,] -1.56825631 -0.85787748 [146,] -0.38874551 -1.56825631 [147,] -0.00940436 -0.38874551 [148,] -2.15535756 -0.00940436 [149,] 6.16461974 -2.15535756 [150,] -0.97355797 6.16461974 [151,] 4.43082746 -0.97355797 [152,] -3.07297287 4.43082746 [153,] -0.90707304 -3.07297287 [154,] 6.11459492 -0.90707304 [155,] 4.83860887 6.11459492 [156,] 2.42168296 4.83860887 [157,] 1.16644864 2.42168296 [158,] -1.25962533 1.16644864 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.72335402 -0.71320632 2 0.88177087 -1.72335402 3 -1.08512431 0.88177087 4 7.93645669 -1.08512431 5 -1.19120396 7.93645669 6 8.93682605 -1.19120396 7 -2.12134007 8.93682605 8 -3.09938971 -2.12134007 9 -0.14346793 -3.09938971 10 -2.75362674 -0.14346793 11 -4.41855834 -2.75362674 12 2.20504017 -4.41855834 13 0.91487569 2.20504017 14 -3.24397035 0.91487569 15 0.55393109 -3.24397035 16 1.09164492 0.55393109 17 -2.24397035 1.09164492 18 -2.99843096 -2.24397035 19 0.64710088 -2.99843096 20 -5.60630097 0.64710088 21 1.80960472 -5.60630097 22 -0.82751779 1.80960472 23 1.58052899 -0.82751779 24 -2.03738829 1.58052899 25 -4.14987086 -2.03738829 26 -2.02001195 -4.14987086 27 2.09896052 -2.02001195 28 1.15035435 2.09896052 29 -0.16139113 1.15035435 30 -3.82440330 -0.16139113 31 2.44014946 -3.82440330 32 -2.71109858 2.44014946 33 -1.73213271 -2.71109858 34 -0.10030594 -1.73213271 35 -4.41123918 -0.10030594 36 7.63466795 -4.41123918 37 -1.38772529 7.63466795 38 -2.28723989 -1.38772529 39 -2.73213271 -2.28723989 40 -3.48376122 -2.73213271 41 1.14669655 -3.48376122 42 -5.07597981 1.14669655 43 0.82617238 -5.07597981 44 -1.99331006 0.82617238 45 -0.24397035 -1.99331006 46 -3.95014807 -0.24397035 47 -1.36533205 -3.95014807 48 2.46941898 -1.36533205 49 -1.83775602 2.46941898 50 -2.75279748 -1.83775602 51 0.91853349 -2.75279748 52 5.01034774 0.91853349 53 0.46861031 5.01034774 54 -1.33946298 0.46861031 55 -0.66830239 -1.33946298 56 0.12511555 -0.66830239 57 -4.15956223 0.12511555 58 -3.46070011 -4.15956223 59 1.71378033 -3.46070011 60 6.89054956 1.71378033 61 -0.95480907 6.89054956 62 -1.04982478 -0.95480907 63 0.92914108 -1.04982478 64 -0.55022912 0.92914108 65 -1.58983731 -0.55022912 66 4.58235789 -1.58983731 67 -1.03373049 4.58235789 68 -0.58004195 -1.03373049 69 1.00851884 -0.58004195 70 0.40558866 1.00851884 71 1.94103072 0.40558866 72 0.56168957 1.94103072 73 -2.46583803 0.56168957 74 -1.69866208 -2.46583803 75 6.17102267 -1.69866208 76 -0.36797319 6.17102267 77 4.10078942 -0.36797319 78 -0.03921719 4.10078942 79 -3.61050563 -0.03921719 80 -1.79130203 -3.61050563 81 -3.08329541 -1.79130203 82 -3.91896268 -3.08329541 83 -0.94649027 -3.91896268 84 -0.89006252 -0.94649027 85 0.06860083 -0.89006252 86 0.02360993 0.06860083 87 -1.24762815 0.02360993 88 2.53836664 -1.24762815 89 8.94651742 2.53836664 90 0.33535541 8.94651742 91 1.46220737 0.33535541 92 -0.23253705 1.46220737 93 2.09475586 -0.23253705 94 -3.00154189 2.09475586 95 -1.32766032 -3.00154189 96 3.60576779 -1.32766032 97 -1.44791483 3.60576779 98 -2.05348258 -1.44791483 99 -2.87305555 -2.05348258 100 -2.78764423 -2.87305555 101 2.72201216 -2.78764423 102 -0.94100357 2.72201216 103 -2.04013342 -0.94100357 104 3.20504017 -2.04013342 105 6.23019199 3.20504017 106 -1.94237257 6.23019199 107 3.25460509 -1.94237257 108 2.34504677 3.25460509 109 10.83677997 2.34504677 110 0.64812111 10.83677997 111 2.50252025 0.64812111 112 -1.87250869 2.50252025 113 2.35053348 -1.87250869 114 -1.49408377 2.35053348 115 -3.22522145 -1.49408377 116 0.97358511 -3.22522145 117 -0.16870673 0.97358511 118 -3.69079962 -0.16870673 119 1.93133935 -3.69079962 120 2.38858170 1.93133935 121 0.69402823 2.38858170 122 -1.81745706 0.69402823 123 -5.28896124 -1.81745706 124 -2.06079818 -5.28896124 125 -1.98050776 -2.06079818 126 -5.31749203 -1.98050776 127 -2.99148116 -5.31749203 128 -2.05988551 -2.99148116 129 0.67417214 -2.05988551 130 -1.81928596 0.67417214 131 6.82671924 -1.81928596 132 -3.18297213 6.82671924 133 1.74953975 -3.18297213 134 -0.92079158 1.74953975 135 1.38217520 -0.92079158 136 9.88268710 1.38217520 137 0.93828559 9.88268710 138 2.89054956 0.93828559 139 -1.78526847 2.89054956 140 -0.44928383 -1.78526847 141 -2.42038724 -0.44928383 142 1.45395496 -2.42038724 143 -0.98370567 1.45395496 144 -0.85787748 -0.98370567 145 -1.56825631 -0.85787748 146 -0.38874551 -1.56825631 147 -0.00940436 -0.38874551 148 -2.15535756 -0.00940436 149 6.16461974 -2.15535756 150 -0.97355797 6.16461974 151 4.43082746 -0.97355797 152 -3.07297287 4.43082746 153 -0.90707304 -3.07297287 154 6.11459492 -0.90707304 155 4.83860887 6.11459492 156 2.42168296 4.83860887 157 1.16644864 2.42168296 158 -1.25962533 1.16644864 > 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/www/html/rcomp/tmp/7c8gp1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8c8gp1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9c8gp1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10nhfs1290462421.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/html/rcomp/tmp/118iwg1290462421.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/www/html/rcomp/tmp/12u0u41290462421.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/www/html/rcomp/tmp/13j19g1290462421.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/www/html/rcomp/tmp/14mkq41290462421.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/www/html/rcomp/tmp/15pkoa1290462421.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/www/html/rcomp/tmp/16t34x1290462421.tab") + } > > try(system("convert tmp/1ygiz1290462421.ps tmp/1ygiz1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/2wboq1290462421.ps tmp/2wboq1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/3wboq1290462421.ps tmp/3wboq1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/4wboq1290462421.ps tmp/4wboq1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/5wboq1290462421.ps tmp/5wboq1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/6khyn1290462421.ps tmp/6khyn1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/7c8gp1290462421.ps tmp/7c8gp1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/8c8gp1290462421.ps tmp/8c8gp1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/9c8gp1290462421.ps tmp/9c8gp1290462421.png",intern=TRUE)) character(0) > try(system("convert tmp/10nhfs1290462421.ps tmp/10nhfs1290462421.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.978 1.736 8.885