R version 2.8.0 (2008-10-20) Copyright (C) 2008 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. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1 + ,4 + ,4 + ,1 + ,4 + ,2 + ,4 + ,4 + ,2 + ,1 + ,4 + ,2 + ,1 + ,4 + ,3 + ,2 + ,5 + ,2 + ,2 + ,4 + ,2 + ,1 + ,3 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,5 + ,2 + ,1 + ,3 + ,2 + ,1 + ,4 + ,1 + ,3 + ,4 + ,3 + ,3 + ,3 + ,1 + ,1 + ,3 + ,3 + ,4 + ,4 + ,1 + ,1 + ,2 + ,4 + ,2 + ,4 + ,2 + ,1 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,1 + ,3 + ,3 + ,3 + ,1 + ,1 + ,4 + ,2 + ,2 + ,4 + ,3 + ,3 + ,4 + ,1 + ,1 + ,3 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,3 + ,3 + ,2 + ,4 + ,3 + ,2 + ,3 + ,3 + ,2 + ,3 + ,3 + ,3 + ,1 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,1 + ,3 + ,2 + ,2 + ,3 + ,3 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,2 + ,4 + ,2 + ,2 + ,2 + ,3 + ,2 + ,4 + ,1 + ,3 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3 + ,2 + ,3 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,5 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,2 + ,5 + ,4 + ,2 + ,4 + ,3 + ,2 + ,5 + ,2 + ,1 + ,3 + ,1 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,2 + ,4 + ,2 + ,1 + ,4 + ,2 + ,1 + ,2 + ,3 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,2 + ,1 + ,4 + ,4 + ,2 + ,5 + ,3 + ,2 + ,4 + ,5 + ,2 + ,4 + ,3 + ,2 + ,3 + ,3 + ,2 + ,3 + ,2 + ,2 + ,2 + ,2 + ,3 + ,3 + ,1 + ,2 + ,3 + ,2 + ,1 + ,3 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,1 + ,3 + ,3 + ,3 + ,4 + ,4 + ,2 + ,2 + ,2 + ,3 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,3 + ,4 + ,2 + ,4 + ,3 + ,4 + ,2 + ,4 + ,2 + ,1 + ,4 + ,4 + ,4 + ,4 + ,2 + ,2 + ,3 + ,2 + ,2 + ,5 + ,2 + ,2 + ,4 + ,2 + ,1 + ,3 + ,1 + ,1 + ,2 + ,1 + ,3 + ,3 + ,2 + ,5 + ,4 + ,2 + ,4 + ,5 + ,3 + ,2 + ,4 + ,3 + ,2 + ,5 + ,2 + ,2 + ,4 + ,2 + ,3 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,1 + ,1 + ,3 + ,2 + ,1 + ,3 + ,1 + ,2 + ,1 + ,4 + ,1 + ,4 + ,2 + ,2 + ,3 + ,3 + ,2 + ,4 + ,2 + ,2 + ,3 + ,2 + ,1 + ,5 + ,1 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,4 + ,1 + ,4 + ,1 + ,1 + ,3 + ,2 + ,4 + ,5 + ,4 + ,1 + ,5 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,3 + ,1 + ,2 + ,4 + ,2 + ,1 + ,4 + ,1 + ,1 + ,3 + ,2 + ,3 + ,4 + ,3 + ,2 + ,4 + ,2 + ,2 + ,4 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,1 + ,3 + ,2 + ,3 + ,4 + ,4 + ,3 + ,4 + ,2 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3 + ,4 + ,2 + ,4 + ,2 + ,1 + ,4 + ,2 + ,2 + ,3 + ,4 + ,2 + ,3 + ,2 + ,2 + ,3 + ,3 + ,2 + ,5 + ,2 + ,1 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,4 + ,2 + ,1 + ,5 + ,2 + ,3 + ,3 + ,3 + ,2 + ,5 + ,2 + ,2 + ,2 + ,1 + ,2 + ,4 + ,2 + ,2 + ,2 + ,4 + ,1 + ,4 + ,1 + ,2 + ,3 + ,2 + ,4 + ,4 + ,3 + ,1 + ,2 + ,4 + ,2 + ,4 + ,3 + ,2 + ,2 + ,3 + ,2 + ,4 + ,2 + ,3 + ,4 + ,3 + ,1 + ,5 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,2 + ,3 + ,2 + ,2 + ,2 + ,2 + ,4 + ,4 + ,2 + ,2 + ,2 + ,4 + ,1 + ,3 + ,1 + ,1 + ,4 + ,1 + ,2 + ,4 + ,1 + ,1 + ,2 + ,3 + ,4 + ,4 + ,1 + ,2 + ,3 + ,2 + ,3 + ,4 + ,2 + ,2 + ,3 + ,2 + ,4 + ,4 + ,2 + ,4 + ,5 + ,2 + ,4 + ,4 + ,3 + ,2 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,2 + ,3 + ,2 + ,1 + ,3 + ,2 + ,2 + ,3 + ,2 + ,3 + ,2 + ,2 + ,2 + ,3 + ,4 + ,2 + ,4 + ,3 + ,2 + ,3 + ,2 + ,2 + ,3 + ,2 + ,2 + ,2 + ,3 + ,4 + ,3 + ,2 + ,3 + ,3 + ,2 + ,3 + ,3 + ,2 + ,2 + ,5 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,1 + ,1 + ,4 + ,4 + ,2 + ,2 + ,1 + ,3 + ,2 + ,2 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,3 + ,2 + ,3 + ,3 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,1 + ,4 + ,2 + ,1 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,2 + ,3 + ,1 + ,3 + ,3 + ,2 + ,4 + ,2 + ,4 + ,2 + ,1 + ,2 + ,5 + ,2 + ,1 + ,3 + ,1 + ,1 + ,5 + ,3 + ,2 + ,4 + ,1 + ,2 + ,3 + ,2 + ,1 + ,2 + ,2 + ,3 + ,4 + ,2 + ,1 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,4 + ,3 + ,2 + ,3 + ,3 + ,4 + ,2 + ,1 + ,1 + ,2 + ,4 + ,1 + ,3 + ,3 + ,1 + ,4 + ,4 + ,1 + ,3 + ,1 + ,2 + ,2 + ,3 + ,2 + ,3 + ,2 + ,2 + ,3 + ,3 + ,1 + ,4 + ,1 + ,1 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,5 + ,2 + ,4 + ,2 + ,3 + ,2 + ,2 + ,2 + ,3 + ,3 + ,1 + ,4 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,3 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,4 + ,2 + ,2 + ,2 + ,3 + ,1 + ,1 + ,4 + ,3 + ,2 + ,2 + ,2 + ,1 + ,5 + ,2 + ,1 + ,4 + ,4 + ,1 + ,4 + ,1 + ,1 + ,3 + ,1 + ,4 + ,4 + ,2 + ,2 + ,2 + ,3 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,1 + ,2 + ,3 + ,2 + ,1 + ,1 + ,2 + ,2 + ,3 + ,3 + ,2 + ,4 + ,2 + ,2 + ,3 + ,2 + ,1 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,2 + ,4 + ,2 + ,3 + ,1 + ,4 + ,5 + ,5 + ,5 + ,3 + ,4 + ,4 + ,1 + ,2 + ,4 + ,2 + ,5 + ,2 + ,4 + ,2 + ,3 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,2 + ,2 + ,3 + ,2 + ,2 + ,3 + ,1 + ,2 + ,1 + ,3 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,3 + ,4 + ,2 + ,1 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,5 + ,5 + ,4 + ,5 + ,5 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,3 + ,3 + ,3 + ,4 + ,3 + ,2 + ,3 + ,2 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,2 + ,2 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,2 + ,4) + ,dim=c(6 + ,159) + ,dimnames=list(c('failure' + ,'neat' + ,'failure' + ,'performance' + ,'goals' + ,'right ') + ,1:159)) > y <- array(NA,dim=c(6,159),dimnames=list(c('failure','neat','failure','performance','goals','right '),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 = '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.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 failure neat failure performance goals right\r 1 1 4 4 1 4 2 2 4 4 2 1 4 2 3 1 4 3 2 5 2 4 2 4 2 1 3 2 5 2 4 2 2 4 2 6 2 5 2 1 3 2 7 1 4 1 3 4 3 8 3 3 1 1 3 3 9 4 4 1 1 2 4 10 2 4 2 1 4 2 11 2 4 2 2 2 3 12 4 4 2 4 2 4 13 4 4 2 2 2 2 14 2 2 2 1 1 3 15 3 3 1 1 4 2 16 2 4 3 3 4 1 17 1 3 2 2 2 2 18 2 2 2 2 2 2 19 2 4 2 3 3 2 20 4 3 2 3 3 2 21 3 3 3 1 3 4 22 4 4 4 2 4 4 23 1 3 2 2 3 3 24 4 3 2 2 2 4 25 2 4 2 2 2 3 26 2 4 1 3 4 2 27 2 4 2 2 4 3 28 3 4 2 2 3 2 29 3 4 2 2 4 2 30 2 4 2 2 2 2 31 3 5 4 2 4 3 32 3 4 2 3 4 4 33 2 4 4 2 5 4 34 2 4 3 2 5 2 35 1 3 1 2 4 2 36 4 4 4 2 4 4 37 4 3 3 2 4 2 38 1 4 2 1 2 3 39 5 4 4 2 4 4 40 3 3 2 1 4 4 41 2 5 3 2 4 5 42 2 4 3 2 3 3 43 2 3 2 2 2 2 44 3 3 1 2 3 2 45 1 3 2 2 4 2 46 2 4 1 3 3 3 47 4 4 2 2 2 3 48 5 4 4 2 4 4 49 2 4 2 2 4 2 50 3 4 2 4 3 4 51 2 4 2 1 4 4 52 4 4 2 2 3 2 53 2 5 2 2 4 2 54 1 3 1 1 2 1 55 3 3 2 5 4 2 56 4 5 3 2 4 3 57 2 5 2 2 4 2 58 3 4 2 2 4 2 59 2 4 1 1 3 2 60 1 3 1 2 1 4 61 1 4 2 2 3 3 62 2 4 2 2 3 2 63 1 5 1 2 4 2 64 2 4 2 2 2 4 65 1 4 1 1 3 2 66 4 5 4 1 5 2 67 4 4 4 2 4 3 68 3 3 1 2 4 2 69 1 4 1 1 3 2 70 3 4 3 2 4 2 71 2 4 4 2 2 2 72 2 4 2 1 3 2 73 3 4 4 3 4 2 74 3 4 4 3 3 4 75 4 4 3 3 4 2 76 2 3 4 2 4 2 77 1 4 2 2 3 4 78 2 3 2 2 3 3 79 2 5 2 1 2 2 80 4 4 2 4 4 2 81 1 5 2 3 3 3 82 2 5 2 2 2 1 83 2 4 2 2 2 4 84 1 4 1 2 3 2 85 4 4 3 1 2 4 86 2 4 3 2 2 3 87 2 4 2 3 4 3 88 1 5 4 1 4 4 89 4 4 4 2 4 4 90 2 3 2 2 2 2 91 4 4 2 2 2 4 92 1 3 1 1 4 1 93 2 4 1 1 2 3 94 4 4 1 2 3 2 95 3 4 2 2 3 2 96 4 4 2 4 5 2 97 4 4 3 2 3 4 98 4 3 4 4 4 3 99 2 3 2 1 3 2 100 2 3 2 3 2 2 101 2 3 4 2 4 3 102 2 3 2 2 3 2 103 2 2 3 4 3 2 104 3 3 2 3 3 2 105 2 5 2 2 4 4 106 4 2 4 1 1 4 107 4 2 2 1 3 2 108 2 3 3 2 2 2 109 2 3 2 3 3 4 110 2 2 2 2 4 1 111 4 2 1 2 2 4 112 4 4 2 2 3 1 113 3 3 2 4 2 4 114 2 1 2 5 2 1 115 3 1 1 5 3 2 116 4 1 2 3 2 1 117 2 2 3 4 2 1 118 4 2 2 4 2 4 119 4 3 2 3 3 4 120 2 1 1 2 4 1 121 3 3 1 4 4 1 122 3 1 2 2 3 2 123 3 2 2 3 3 1 124 4 1 1 2 2 2 125 4 2 2 2 5 2 126 4 2 3 2 2 2 127 3 3 1 4 2 2 128 4 2 2 4 3 2 129 4 2 2 4 2 4 130 4 4 2 4 3 4 131 2 2 2 3 1 1 132 4 3 2 2 2 1 133 5 2 1 4 4 1 134 4 1 1 3 1 4 135 4 2 2 2 3 2 136 4 3 4 4 4 3 137 3 1 2 3 2 1 138 1 2 2 3 3 2 139 4 2 2 3 2 1 140 3 3 2 3 3 4 141 3 3 2 3 3 4 142 3 3 2 4 2 3 143 1 4 5 5 5 3 144 4 4 1 2 4 2 145 5 2 4 2 3 3 146 4 3 2 4 4 3 147 3 3 3 3 3 3 148 4 2 2 3 2 2 149 3 1 2 1 3 2 150 4 2 2 2 2 3 151 4 2 1 4 2 4 152 4 4 2 4 5 5 153 5 4 5 5 2 2 154 2 2 2 2 1 2 155 3 3 3 4 3 2 156 3 2 2 3 2 4 157 4 4 2 2 4 2 158 4 2 2 4 2 4 159 3 4 4 2 4 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) neat performance goals `right\r` 2.24878 -0.26071 0.20301 0.09735 0.24164 Warning messages: 1: In model.matrix.default(mt, mf, contrasts) : the response appeared on the right-hand side and was dropped 2: In model.matrix.default(mt, mf, contrasts) : problem with term 2 in model.matrix: no columns are assigned > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.43270 -0.74901 -0.09268 0.85865 2.10099 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.24878 0.45120 4.984 1.66e-06 *** neat -0.26071 0.09518 -2.739 0.00689 ** performance 0.20301 0.08534 2.379 0.01859 * goals 0.09735 0.09388 1.037 0.30138 `right\r` 0.24164 0.08538 2.830 0.00528 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.051 on 154 degrees of freedom Multiple R-squared: 0.1358, Adjusted R-squared: 0.1133 F-statistic: 6.048 on 4 and 154 DF, p-value: 0.0001513 > 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,] 0.7022554 0.5954892 0.2977446 [2,] 0.5784541 0.8430918 0.4215459 [3,] 0.4507188 0.9014376 0.5492812 [4,] 0.6262469 0.7475061 0.3737531 [5,] 0.6341294 0.7317413 0.3658706 [6,] 0.7160598 0.5678804 0.2839402 [7,] 0.7377326 0.5245348 0.2622674 [8,] 0.6664914 0.6670172 0.3335086 [9,] 0.7122304 0.5755392 0.2877696 [10,] 0.6421698 0.7156604 0.3578302 [11,] 0.5668555 0.8662890 0.4331445 [12,] 0.7146084 0.5707833 0.2853916 [13,] 0.6476771 0.7046457 0.3523229 [14,] 0.6335151 0.7329698 0.3664849 [15,] 0.7167266 0.5665468 0.2832734 [16,] 0.6899745 0.6200510 0.3100255 [17,] 0.6679507 0.6640986 0.3320493 [18,] 0.6103215 0.7793570 0.3896785 [19,] 0.5668615 0.8662770 0.4331385 [20,] 0.5323425 0.9353150 0.4676575 [21,] 0.5058915 0.9882170 0.4941085 [22,] 0.4521387 0.9042773 0.5478613 [23,] 0.3953211 0.7906422 0.6046789 [24,] 0.3377864 0.6755728 0.6622136 [25,] 0.3242067 0.6484135 0.6757933 [26,] 0.2763434 0.5526869 0.7236566 [27,] 0.2879921 0.5759842 0.7120079 [28,] 0.2867359 0.5734717 0.7132641 [29,] 0.4128333 0.8256666 0.5871667 [30,] 0.4939327 0.9878654 0.5060673 [31,] 0.6140158 0.7719684 0.3859842 [32,] 0.5607013 0.8785975 0.4392987 [33,] 0.6028286 0.7943428 0.3971714 [34,] 0.5685301 0.8629399 0.4314699 [35,] 0.5222410 0.9555179 0.4777590 [36,] 0.4875409 0.9750818 0.5124591 [37,] 0.5331045 0.9337909 0.4668955 [38,] 0.5064713 0.9870575 0.4935287 [39,] 0.5422319 0.9155362 0.4577681 [40,] 0.6613462 0.6773076 0.3386538 [41,] 0.6193865 0.7612269 0.3806135 [42,] 0.5732941 0.8534118 0.4267059 [43,] 0.5636224 0.8727552 0.4363776 [44,] 0.6368373 0.7263253 0.3631627 [45,] 0.5921969 0.8156062 0.4078031 [46,] 0.5828700 0.8342600 0.4171300 [47,] 0.5424092 0.9151816 0.4575908 [48,] 0.5767645 0.8464710 0.4232355 [49,] 0.5323128 0.9353744 0.4676872 [50,] 0.5023638 0.9952724 0.4976362 [51,] 0.4555959 0.9111918 0.5444041 [52,] 0.5733138 0.8533725 0.4266862 [53,] 0.6394640 0.7210720 0.3605360 [54,] 0.5993105 0.8013790 0.4006895 [55,] 0.6258017 0.7483967 0.3741983 [56,] 0.6103075 0.7793849 0.3896925 [57,] 0.6237087 0.7525826 0.3762913 [58,] 0.6952590 0.6094820 0.3047410 [59,] 0.7126460 0.5747079 0.2873540 [60,] 0.6807618 0.6384763 0.3192382 [61,] 0.6940097 0.6119806 0.3059903 [62,] 0.6633296 0.6733407 0.3366704 [63,] 0.6245617 0.7508765 0.3754383 [64,] 0.5828959 0.8342082 0.4171041 [65,] 0.5431029 0.9137942 0.4568971 [66,] 0.4969165 0.9938330 0.5030835 [67,] 0.5274936 0.9450128 0.4725064 [68,] 0.4990961 0.9981921 0.5009039 [69,] 0.6085826 0.7828348 0.3914174 [70,] 0.5926878 0.8146245 0.4073122 [71,] 0.5483698 0.9032604 0.4516302 [72,] 0.5575103 0.8849795 0.4424897 [73,] 0.6345857 0.7308285 0.3654143 [74,] 0.5934236 0.8131529 0.4065764 [75,] 0.5829307 0.8341385 0.4170693 [76,] 0.6362746 0.7274508 0.3637254 [77,] 0.6562928 0.6874143 0.3437072 [78,] 0.6374646 0.7250709 0.3625354 [79,] 0.6353198 0.7293603 0.3646802 [80,] 0.7441507 0.5116986 0.2558493 [81,] 0.7302598 0.5394803 0.2697402 [82,] 0.7165651 0.5668698 0.2834349 [83,] 0.7126672 0.5746656 0.2873328 [84,] 0.7639023 0.4721954 0.2360977 [85,] 0.7632898 0.4734204 0.2367102 [86,] 0.7849176 0.4301647 0.2150824 [87,] 0.7564458 0.4871083 0.2435542 [88,] 0.7527089 0.4945823 0.2472911 [89,] 0.7389481 0.5221037 0.2610519 [90,] 0.7201174 0.5597652 0.2798826 [91,] 0.7147555 0.5704891 0.2852445 [92,] 0.7149200 0.5701600 0.2850800 [93,] 0.7286524 0.5426952 0.2713476 [94,] 0.7342910 0.5314180 0.2657090 [95,] 0.7395183 0.5209635 0.2604817 [96,] 0.7031307 0.5937387 0.2968693 [97,] 0.7563079 0.4873842 0.2436921 [98,] 0.7440660 0.5118680 0.2559340 [99,] 0.7473591 0.5052818 0.2526409 [100,] 0.7727469 0.4545062 0.2272531 [101,] 0.8301548 0.3396904 0.1698452 [102,] 0.8356333 0.3287333 0.1643667 [103,] 0.8109035 0.3781930 0.1890965 [104,] 0.8194310 0.3611379 0.1805690 [105,] 0.7963815 0.4072371 0.2036185 [106,] 0.7864478 0.4271043 0.2135522 [107,] 0.7461033 0.5077933 0.2538967 [108,] 0.7525266 0.4949467 0.2474734 [109,] 0.7564012 0.4871976 0.2435988 [110,] 0.7195914 0.5608172 0.2804086 [111,] 0.6812074 0.6375851 0.3187926 [112,] 0.6989389 0.6021222 0.3010611 [113,] 0.6544714 0.6910571 0.3455286 [114,] 0.6151377 0.7697247 0.3848623 [115,] 0.5711999 0.8576002 0.4288001 [116,] 0.5415094 0.9169813 0.4584906 [117,] 0.5025367 0.9949267 0.4974633 [118,] 0.4782825 0.9565650 0.5217175 [119,] 0.4284415 0.8568831 0.5715585 [120,] 0.3910402 0.7820804 0.6089598 [121,] 0.3431255 0.6862511 0.6568745 [122,] 0.2998918 0.5997836 0.7001082 [123,] 0.3360252 0.6720504 0.6639748 [124,] 0.3174786 0.6349572 0.6825214 [125,] 0.4686243 0.9372487 0.5313757 [126,] 0.4089205 0.8178411 0.5910795 [127,] 0.3847291 0.7694582 0.6152709 [128,] 0.3683761 0.7367522 0.6316239 [129,] 0.3063381 0.6126763 0.6936619 [130,] 0.5084807 0.9830386 0.4915193 [131,] 0.4698467 0.9396935 0.5301533 [132,] 0.4215086 0.8430173 0.5784914 [133,] 0.3864835 0.7729669 0.6135165 [134,] 0.3431723 0.6863445 0.6568277 [135,] 0.7888622 0.4222757 0.2111378 [136,] 0.7377102 0.5245796 0.2622898 [137,] 0.8923032 0.2153936 0.1076968 [138,] 0.8312257 0.3375487 0.1687743 [139,] 0.7841819 0.4316362 0.2158181 [140,] 0.7297867 0.5404267 0.2702133 [141,] 0.7094233 0.5811534 0.2905767 [142,] 0.8030283 0.3939433 0.1969717 There were 50 or more warnings (use warnings() to see the first 50) > postscript(file="/var/www/html/freestat/rcomp/tmp/1pxaf1291381448.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2z6r01291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3z6r01291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4z6r01291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5sgq31291381448.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 = 159 Frequency = 1 1 2 3 4 5 6 -1.28165481 1.71834519 -1.58202130 -0.18430255 -0.48466905 0.07640358 7 8 9 10 11 12 -1.92932155 0.31335304 1.42977317 -0.28165481 -0.53160280 0.82073046 13 14 15 16 17 18 1.71003546 -0.75264859 0.45763905 -0.44604502 -1.55067068 -0.81137681 19 20 21 22 23 24 -0.59033103 1.14896283 0.07171478 1.03205442 -1.88966119 0.96605279 25 26 27 28 29 30 -0.53160280 -0.68768328 -0.72630731 0.61268321 0.51533095 -0.28996454 31 32 33 34 35 36 0.53439883 -0.17095981 -1.06529783 -0.58202130 -1.74537518 1.03205442 37 38 39 40 41 42 1.25462482 -1.32858857 2.03205442 -0.02563748 -0.94887770 -0.62895506 43 44 45 46 47 48 -0.55067068 0.35197707 -1.74537518 -0.83196929 1.46839720 2.03205442 49 50 51 52 53 54 -0.48466905 -0.27662180 -0.76493134 1.61268321 -0.22396291 -1.10601817 55 56 57 58 59 60 -0.35441789 1.53439883 -0.22396291 0.51533095 -0.18430255 -1.93659495 61 62 63 64 65 66 -1.62895506 -0.38731679 -1.22396291 -0.77324107 -1.18430255 1.88169908 67 68 69 70 71 72 1.27369269 0.25462482 -1.18430255 0.51533095 -0.28996454 -0.18430255 73 74 75 76 77 78 0.31231672 -0.07360756 1.31231672 -0.74537518 -1.87059332 -0.88966119 79 80 81 82 83 84 0.17375584 1.10930248 -1.57126316 0.21237986 -0.77324107 -1.38731679 85 86 87 88 89 90 1.42977317 -0.53160280 -0.92932155 -1.50422520 1.03205442 -0.55067068 91 92 93 94 95 96 1.22675893 -1.30072268 -0.32858857 1.61268321 0.61268321 1.01195023 97 98 99 100 101 102 1.12940668 0.60695808 -0.44500869 -0.75368491 -0.98701345 -0.64802293 103 104 105 106 107 108 -1.31475754 0.14896283 -0.70723944 1.00571315 1.29428517 -0.55067068 109 110 111 112 113 114 -1.33431370 -0.76444306 0.70534666 1.85432147 -0.43997568 -1.43948740 115 116 117 118 119 120 -0.77847792 0.96654108 -0.97576702 0.29931818 0.66568630 -1.02514919 121 122 123 124 125 126 0.09023461 -0.16943520 0.12989496 0.92791705 0.89656643 1.18862319 127 128 129 130 131 132 0.04330085 0.68524246 0.29931818 0.72337820 -0.67540053 1.69096759 133 134 135 136 137 138 1.82952847 0.33897854 1.09127093 0.60695808 -0.03345892 -2.11174330 139 140 141 142 143 144 1.22724721 -0.33431370 -0.33431370 -0.19833741 -2.43270227 1.51533095 145 146 147 148 149 150 1.84963267 0.60695808 -0.09267543 0.98560895 0.03357903 0.94698492 151 152 153 154 155 156 0.29931818 0.28703543 2.10099275 -0.71402456 -0.05405140 -0.49766758 157 158 159 1.51533095 0.29931818 0.75696922 > postscript(file="/var/www/html/freestat/rcomp/tmp/6sgq31291381448.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 = 159 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.28165481 NA 1 1.71834519 -1.28165481 2 -1.58202130 1.71834519 3 -0.18430255 -1.58202130 4 -0.48466905 -0.18430255 5 0.07640358 -0.48466905 6 -1.92932155 0.07640358 7 0.31335304 -1.92932155 8 1.42977317 0.31335304 9 -0.28165481 1.42977317 10 -0.53160280 -0.28165481 11 0.82073046 -0.53160280 12 1.71003546 0.82073046 13 -0.75264859 1.71003546 14 0.45763905 -0.75264859 15 -0.44604502 0.45763905 16 -1.55067068 -0.44604502 17 -0.81137681 -1.55067068 18 -0.59033103 -0.81137681 19 1.14896283 -0.59033103 20 0.07171478 1.14896283 21 1.03205442 0.07171478 22 -1.88966119 1.03205442 23 0.96605279 -1.88966119 24 -0.53160280 0.96605279 25 -0.68768328 -0.53160280 26 -0.72630731 -0.68768328 27 0.61268321 -0.72630731 28 0.51533095 0.61268321 29 -0.28996454 0.51533095 30 0.53439883 -0.28996454 31 -0.17095981 0.53439883 32 -1.06529783 -0.17095981 33 -0.58202130 -1.06529783 34 -1.74537518 -0.58202130 35 1.03205442 -1.74537518 36 1.25462482 1.03205442 37 -1.32858857 1.25462482 38 2.03205442 -1.32858857 39 -0.02563748 2.03205442 40 -0.94887770 -0.02563748 41 -0.62895506 -0.94887770 42 -0.55067068 -0.62895506 43 0.35197707 -0.55067068 44 -1.74537518 0.35197707 45 -0.83196929 -1.74537518 46 1.46839720 -0.83196929 47 2.03205442 1.46839720 48 -0.48466905 2.03205442 49 -0.27662180 -0.48466905 50 -0.76493134 -0.27662180 51 1.61268321 -0.76493134 52 -0.22396291 1.61268321 53 -1.10601817 -0.22396291 54 -0.35441789 -1.10601817 55 1.53439883 -0.35441789 56 -0.22396291 1.53439883 57 0.51533095 -0.22396291 58 -0.18430255 0.51533095 59 -1.93659495 -0.18430255 60 -1.62895506 -1.93659495 61 -0.38731679 -1.62895506 62 -1.22396291 -0.38731679 63 -0.77324107 -1.22396291 64 -1.18430255 -0.77324107 65 1.88169908 -1.18430255 66 1.27369269 1.88169908 67 0.25462482 1.27369269 68 -1.18430255 0.25462482 69 0.51533095 -1.18430255 70 -0.28996454 0.51533095 71 -0.18430255 -0.28996454 72 0.31231672 -0.18430255 73 -0.07360756 0.31231672 74 1.31231672 -0.07360756 75 -0.74537518 1.31231672 76 -1.87059332 -0.74537518 77 -0.88966119 -1.87059332 78 0.17375584 -0.88966119 79 1.10930248 0.17375584 80 -1.57126316 1.10930248 81 0.21237986 -1.57126316 82 -0.77324107 0.21237986 83 -1.38731679 -0.77324107 84 1.42977317 -1.38731679 85 -0.53160280 1.42977317 86 -0.92932155 -0.53160280 87 -1.50422520 -0.92932155 88 1.03205442 -1.50422520 89 -0.55067068 1.03205442 90 1.22675893 -0.55067068 91 -1.30072268 1.22675893 92 -0.32858857 -1.30072268 93 1.61268321 -0.32858857 94 0.61268321 1.61268321 95 1.01195023 0.61268321 96 1.12940668 1.01195023 97 0.60695808 1.12940668 98 -0.44500869 0.60695808 99 -0.75368491 -0.44500869 100 -0.98701345 -0.75368491 101 -0.64802293 -0.98701345 102 -1.31475754 -0.64802293 103 0.14896283 -1.31475754 104 -0.70723944 0.14896283 105 1.00571315 -0.70723944 106 1.29428517 1.00571315 107 -0.55067068 1.29428517 108 -1.33431370 -0.55067068 109 -0.76444306 -1.33431370 110 0.70534666 -0.76444306 111 1.85432147 0.70534666 112 -0.43997568 1.85432147 113 -1.43948740 -0.43997568 114 -0.77847792 -1.43948740 115 0.96654108 -0.77847792 116 -0.97576702 0.96654108 117 0.29931818 -0.97576702 118 0.66568630 0.29931818 119 -1.02514919 0.66568630 120 0.09023461 -1.02514919 121 -0.16943520 0.09023461 122 0.12989496 -0.16943520 123 0.92791705 0.12989496 124 0.89656643 0.92791705 125 1.18862319 0.89656643 126 0.04330085 1.18862319 127 0.68524246 0.04330085 128 0.29931818 0.68524246 129 0.72337820 0.29931818 130 -0.67540053 0.72337820 131 1.69096759 -0.67540053 132 1.82952847 1.69096759 133 0.33897854 1.82952847 134 1.09127093 0.33897854 135 0.60695808 1.09127093 136 -0.03345892 0.60695808 137 -2.11174330 -0.03345892 138 1.22724721 -2.11174330 139 -0.33431370 1.22724721 140 -0.33431370 -0.33431370 141 -0.19833741 -0.33431370 142 -2.43270227 -0.19833741 143 1.51533095 -2.43270227 144 1.84963267 1.51533095 145 0.60695808 1.84963267 146 -0.09267543 0.60695808 147 0.98560895 -0.09267543 148 0.03357903 0.98560895 149 0.94698492 0.03357903 150 0.29931818 0.94698492 151 0.28703543 0.29931818 152 2.10099275 0.28703543 153 -0.71402456 2.10099275 154 -0.05405140 -0.71402456 155 -0.49766758 -0.05405140 156 1.51533095 -0.49766758 157 0.29931818 1.51533095 158 0.75696922 0.29931818 159 NA 0.75696922 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.71834519 -1.28165481 [2,] -1.58202130 1.71834519 [3,] -0.18430255 -1.58202130 [4,] -0.48466905 -0.18430255 [5,] 0.07640358 -0.48466905 [6,] -1.92932155 0.07640358 [7,] 0.31335304 -1.92932155 [8,] 1.42977317 0.31335304 [9,] -0.28165481 1.42977317 [10,] -0.53160280 -0.28165481 [11,] 0.82073046 -0.53160280 [12,] 1.71003546 0.82073046 [13,] -0.75264859 1.71003546 [14,] 0.45763905 -0.75264859 [15,] -0.44604502 0.45763905 [16,] -1.55067068 -0.44604502 [17,] -0.81137681 -1.55067068 [18,] -0.59033103 -0.81137681 [19,] 1.14896283 -0.59033103 [20,] 0.07171478 1.14896283 [21,] 1.03205442 0.07171478 [22,] -1.88966119 1.03205442 [23,] 0.96605279 -1.88966119 [24,] -0.53160280 0.96605279 [25,] -0.68768328 -0.53160280 [26,] -0.72630731 -0.68768328 [27,] 0.61268321 -0.72630731 [28,] 0.51533095 0.61268321 [29,] -0.28996454 0.51533095 [30,] 0.53439883 -0.28996454 [31,] -0.17095981 0.53439883 [32,] -1.06529783 -0.17095981 [33,] -0.58202130 -1.06529783 [34,] -1.74537518 -0.58202130 [35,] 1.03205442 -1.74537518 [36,] 1.25462482 1.03205442 [37,] -1.32858857 1.25462482 [38,] 2.03205442 -1.32858857 [39,] -0.02563748 2.03205442 [40,] -0.94887770 -0.02563748 [41,] -0.62895506 -0.94887770 [42,] -0.55067068 -0.62895506 [43,] 0.35197707 -0.55067068 [44,] -1.74537518 0.35197707 [45,] -0.83196929 -1.74537518 [46,] 1.46839720 -0.83196929 [47,] 2.03205442 1.46839720 [48,] -0.48466905 2.03205442 [49,] -0.27662180 -0.48466905 [50,] -0.76493134 -0.27662180 [51,] 1.61268321 -0.76493134 [52,] -0.22396291 1.61268321 [53,] -1.10601817 -0.22396291 [54,] -0.35441789 -1.10601817 [55,] 1.53439883 -0.35441789 [56,] -0.22396291 1.53439883 [57,] 0.51533095 -0.22396291 [58,] -0.18430255 0.51533095 [59,] -1.93659495 -0.18430255 [60,] -1.62895506 -1.93659495 [61,] -0.38731679 -1.62895506 [62,] -1.22396291 -0.38731679 [63,] -0.77324107 -1.22396291 [64,] -1.18430255 -0.77324107 [65,] 1.88169908 -1.18430255 [66,] 1.27369269 1.88169908 [67,] 0.25462482 1.27369269 [68,] -1.18430255 0.25462482 [69,] 0.51533095 -1.18430255 [70,] -0.28996454 0.51533095 [71,] -0.18430255 -0.28996454 [72,] 0.31231672 -0.18430255 [73,] -0.07360756 0.31231672 [74,] 1.31231672 -0.07360756 [75,] -0.74537518 1.31231672 [76,] -1.87059332 -0.74537518 [77,] -0.88966119 -1.87059332 [78,] 0.17375584 -0.88966119 [79,] 1.10930248 0.17375584 [80,] -1.57126316 1.10930248 [81,] 0.21237986 -1.57126316 [82,] -0.77324107 0.21237986 [83,] -1.38731679 -0.77324107 [84,] 1.42977317 -1.38731679 [85,] -0.53160280 1.42977317 [86,] -0.92932155 -0.53160280 [87,] -1.50422520 -0.92932155 [88,] 1.03205442 -1.50422520 [89,] -0.55067068 1.03205442 [90,] 1.22675893 -0.55067068 [91,] -1.30072268 1.22675893 [92,] -0.32858857 -1.30072268 [93,] 1.61268321 -0.32858857 [94,] 0.61268321 1.61268321 [95,] 1.01195023 0.61268321 [96,] 1.12940668 1.01195023 [97,] 0.60695808 1.12940668 [98,] -0.44500869 0.60695808 [99,] -0.75368491 -0.44500869 [100,] -0.98701345 -0.75368491 [101,] -0.64802293 -0.98701345 [102,] -1.31475754 -0.64802293 [103,] 0.14896283 -1.31475754 [104,] -0.70723944 0.14896283 [105,] 1.00571315 -0.70723944 [106,] 1.29428517 1.00571315 [107,] -0.55067068 1.29428517 [108,] -1.33431370 -0.55067068 [109,] -0.76444306 -1.33431370 [110,] 0.70534666 -0.76444306 [111,] 1.85432147 0.70534666 [112,] -0.43997568 1.85432147 [113,] -1.43948740 -0.43997568 [114,] -0.77847792 -1.43948740 [115,] 0.96654108 -0.77847792 [116,] -0.97576702 0.96654108 [117,] 0.29931818 -0.97576702 [118,] 0.66568630 0.29931818 [119,] -1.02514919 0.66568630 [120,] 0.09023461 -1.02514919 [121,] -0.16943520 0.09023461 [122,] 0.12989496 -0.16943520 [123,] 0.92791705 0.12989496 [124,] 0.89656643 0.92791705 [125,] 1.18862319 0.89656643 [126,] 0.04330085 1.18862319 [127,] 0.68524246 0.04330085 [128,] 0.29931818 0.68524246 [129,] 0.72337820 0.29931818 [130,] -0.67540053 0.72337820 [131,] 1.69096759 -0.67540053 [132,] 1.82952847 1.69096759 [133,] 0.33897854 1.82952847 [134,] 1.09127093 0.33897854 [135,] 0.60695808 1.09127093 [136,] -0.03345892 0.60695808 [137,] -2.11174330 -0.03345892 [138,] 1.22724721 -2.11174330 [139,] -0.33431370 1.22724721 [140,] -0.33431370 -0.33431370 [141,] -0.19833741 -0.33431370 [142,] -2.43270227 -0.19833741 [143,] 1.51533095 -2.43270227 [144,] 1.84963267 1.51533095 [145,] 0.60695808 1.84963267 [146,] -0.09267543 0.60695808 [147,] 0.98560895 -0.09267543 [148,] 0.03357903 0.98560895 [149,] 0.94698492 0.03357903 [150,] 0.29931818 0.94698492 [151,] 0.28703543 0.29931818 [152,] 2.10099275 0.28703543 [153,] -0.71402456 2.10099275 [154,] -0.05405140 -0.71402456 [155,] -0.49766758 -0.05405140 [156,] 1.51533095 -0.49766758 [157,] 0.29931818 1.51533095 [158,] 0.75696922 0.29931818 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.71834519 -1.28165481 2 -1.58202130 1.71834519 3 -0.18430255 -1.58202130 4 -0.48466905 -0.18430255 5 0.07640358 -0.48466905 6 -1.92932155 0.07640358 7 0.31335304 -1.92932155 8 1.42977317 0.31335304 9 -0.28165481 1.42977317 10 -0.53160280 -0.28165481 11 0.82073046 -0.53160280 12 1.71003546 0.82073046 13 -0.75264859 1.71003546 14 0.45763905 -0.75264859 15 -0.44604502 0.45763905 16 -1.55067068 -0.44604502 17 -0.81137681 -1.55067068 18 -0.59033103 -0.81137681 19 1.14896283 -0.59033103 20 0.07171478 1.14896283 21 1.03205442 0.07171478 22 -1.88966119 1.03205442 23 0.96605279 -1.88966119 24 -0.53160280 0.96605279 25 -0.68768328 -0.53160280 26 -0.72630731 -0.68768328 27 0.61268321 -0.72630731 28 0.51533095 0.61268321 29 -0.28996454 0.51533095 30 0.53439883 -0.28996454 31 -0.17095981 0.53439883 32 -1.06529783 -0.17095981 33 -0.58202130 -1.06529783 34 -1.74537518 -0.58202130 35 1.03205442 -1.74537518 36 1.25462482 1.03205442 37 -1.32858857 1.25462482 38 2.03205442 -1.32858857 39 -0.02563748 2.03205442 40 -0.94887770 -0.02563748 41 -0.62895506 -0.94887770 42 -0.55067068 -0.62895506 43 0.35197707 -0.55067068 44 -1.74537518 0.35197707 45 -0.83196929 -1.74537518 46 1.46839720 -0.83196929 47 2.03205442 1.46839720 48 -0.48466905 2.03205442 49 -0.27662180 -0.48466905 50 -0.76493134 -0.27662180 51 1.61268321 -0.76493134 52 -0.22396291 1.61268321 53 -1.10601817 -0.22396291 54 -0.35441789 -1.10601817 55 1.53439883 -0.35441789 56 -0.22396291 1.53439883 57 0.51533095 -0.22396291 58 -0.18430255 0.51533095 59 -1.93659495 -0.18430255 60 -1.62895506 -1.93659495 61 -0.38731679 -1.62895506 62 -1.22396291 -0.38731679 63 -0.77324107 -1.22396291 64 -1.18430255 -0.77324107 65 1.88169908 -1.18430255 66 1.27369269 1.88169908 67 0.25462482 1.27369269 68 -1.18430255 0.25462482 69 0.51533095 -1.18430255 70 -0.28996454 0.51533095 71 -0.18430255 -0.28996454 72 0.31231672 -0.18430255 73 -0.07360756 0.31231672 74 1.31231672 -0.07360756 75 -0.74537518 1.31231672 76 -1.87059332 -0.74537518 77 -0.88966119 -1.87059332 78 0.17375584 -0.88966119 79 1.10930248 0.17375584 80 -1.57126316 1.10930248 81 0.21237986 -1.57126316 82 -0.77324107 0.21237986 83 -1.38731679 -0.77324107 84 1.42977317 -1.38731679 85 -0.53160280 1.42977317 86 -0.92932155 -0.53160280 87 -1.50422520 -0.92932155 88 1.03205442 -1.50422520 89 -0.55067068 1.03205442 90 1.22675893 -0.55067068 91 -1.30072268 1.22675893 92 -0.32858857 -1.30072268 93 1.61268321 -0.32858857 94 0.61268321 1.61268321 95 1.01195023 0.61268321 96 1.12940668 1.01195023 97 0.60695808 1.12940668 98 -0.44500869 0.60695808 99 -0.75368491 -0.44500869 100 -0.98701345 -0.75368491 101 -0.64802293 -0.98701345 102 -1.31475754 -0.64802293 103 0.14896283 -1.31475754 104 -0.70723944 0.14896283 105 1.00571315 -0.70723944 106 1.29428517 1.00571315 107 -0.55067068 1.29428517 108 -1.33431370 -0.55067068 109 -0.76444306 -1.33431370 110 0.70534666 -0.76444306 111 1.85432147 0.70534666 112 -0.43997568 1.85432147 113 -1.43948740 -0.43997568 114 -0.77847792 -1.43948740 115 0.96654108 -0.77847792 116 -0.97576702 0.96654108 117 0.29931818 -0.97576702 118 0.66568630 0.29931818 119 -1.02514919 0.66568630 120 0.09023461 -1.02514919 121 -0.16943520 0.09023461 122 0.12989496 -0.16943520 123 0.92791705 0.12989496 124 0.89656643 0.92791705 125 1.18862319 0.89656643 126 0.04330085 1.18862319 127 0.68524246 0.04330085 128 0.29931818 0.68524246 129 0.72337820 0.29931818 130 -0.67540053 0.72337820 131 1.69096759 -0.67540053 132 1.82952847 1.69096759 133 0.33897854 1.82952847 134 1.09127093 0.33897854 135 0.60695808 1.09127093 136 -0.03345892 0.60695808 137 -2.11174330 -0.03345892 138 1.22724721 -2.11174330 139 -0.33431370 1.22724721 140 -0.33431370 -0.33431370 141 -0.19833741 -0.33431370 142 -2.43270227 -0.19833741 143 1.51533095 -2.43270227 144 1.84963267 1.51533095 145 0.60695808 1.84963267 146 -0.09267543 0.60695808 147 0.98560895 -0.09267543 148 0.03357903 0.98560895 149 0.94698492 0.03357903 150 0.29931818 0.94698492 151 0.28703543 0.29931818 152 2.10099275 0.28703543 153 -0.71402456 2.10099275 154 -0.05405140 -0.71402456 155 -0.49766758 -0.05405140 156 1.51533095 -0.49766758 157 0.29931818 1.51533095 158 0.75696922 0.29931818 > 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/freestat/rcomp/tmp/7lpqo1291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8lpqo1291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9wg7r1291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: In model.matrix.default(object, data = list(failure = c(1, 4, 1, : the response appeared on the right-hand side and was dropped 2: In model.matrix.default(object, data = list(failure = c(1, 4, 1, : problem with term 2 in model.matrix: no columns are assigned > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10wg7r1291381448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } Error: subscript out of bounds Execution halted