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(4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,5 + ,5 + ,4 + ,4 + ,5 + ,5 + ,3 + ,3 + ,2 + ,3 + ,4 + ,4 + ,2 + ,3 + ,2 + ,3 + ,2 + ,4 + ,5 + ,4 + ,3 + ,3 + ,4 + ,5 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,5 + ,4 + ,3 + ,2 + ,3 + ,2 + ,2 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,4 + ,2 + ,3 + ,5 + ,4 + ,2 + ,5 + ,5 + ,5 + ,3 + ,4 + ,2 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,5 + ,3 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,2 + ,2 + ,4 + ,4 + ,2 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,3 + ,2 + ,3 + ,3 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,2 + ,3 + ,2 + ,3 + ,2 + ,3 + ,2 + ,2 + ,4 + ,2 + ,2 + ,5 + ,4 + ,3 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,5 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,4 + ,2 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,2 + ,2 + ,5 + ,2 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,2 + ,3 + ,2 + ,3 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,5 + ,5 + ,5 + ,4 + ,4 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,2 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,2 + ,4 + ,2 + ,3 + ,3 + ,3 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,2 + ,4 + ,3 + ,4 + ,2 + ,4 + ,4 + ,5 + ,5 + ,4 + ,2 + ,4 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,4 + ,5 + ,4 + ,3 + ,4 + ,2 + ,2 + ,3 + ,5 + ,5 + ,4 + ,4 + ,5 + ,4 + ,4 + ,3 + ,2 + ,4 + ,2 + ,4 + ,3 + ,2 + ,2 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,2 + ,3 + ,4 + ,4 + ,3 + ,2 + ,2 + ,2 + ,1 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,5 + ,3 + ,4 + ,3 + ,4 + ,2 + ,4 + ,2 + ,3 + ,2 + ,2 + ,3 + ,4 + ,5 + ,4 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,4 + ,2 + ,2 + ,5 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,2 + ,2 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,5 + ,2 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,5 + ,4 + ,3 + ,3 + ,4 + ,3 + ,5 + ,5 + ,4 + ,4 + ,3 + ,3 + ,4 + ,5 + ,4 + ,3 + ,4 + ,4 + ,5 + ,5 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,4 + ,3 + ,4 + ,2 + ,3 + ,2 + ,4 + ,3 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,5 + ,2 + ,2 + ,3 + ,3 + ,1 + ,4 + ,3 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,2 + ,3 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,4 + ,3 + ,3 + ,5 + ,4 + ,4 + ,3 + ,4 + ,4 + ,5 + ,2 + ,2 + ,3 + ,2 + ,3 + ,3 + ,5 + ,4 + ,4 + ,3 + ,3 + ,5 + ,5 + ,3 + ,2 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,3 + ,3 + ,5 + ,1 + ,5 + ,5 + ,2 + ,2 + ,4 + ,2 + ,2 + ,2 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,2 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,5 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,3 + ,5 + ,2 + ,2 + ,2 + ,2 + ,4 + ,3 + ,5 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,2 + ,4 + ,3 + ,4 + ,5 + ,3 + ,5 + ,5 + ,5 + ,5) + ,dim=c(6 + ,151) + ,dimnames=list(c('y' + ,'x1' + ,'x2' + ,'x3' + ,'x4' + ,'x5') + ,1:151)) > y <- array(NA,dim=c(6,151),dimnames=list(c('y','x1','x2','x3','x4','x5'),1:151)) > 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 y x1 x2 x3 x4 x5 1 4 4 5 4 4 4 2 4 4 4 4 3 4 3 5 5 4 4 5 5 4 3 3 2 3 4 4 5 2 3 2 3 2 4 6 5 4 3 3 4 5 7 4 3 3 3 3 4 8 2 3 4 4 2 4 9 4 4 3 4 4 5 10 4 3 2 3 2 2 11 4 3 2 4 4 4 12 2 3 2 4 2 3 13 5 4 2 5 5 5 14 3 4 2 3 3 4 15 4 3 4 4 4 4 16 4 3 3 4 4 5 17 3 2 3 3 3 3 18 4 4 4 4 4 4 19 2 3 2 2 2 4 20 4 2 4 4 3 4 21 3 3 2 4 4 4 22 3 2 4 4 2 3 23 4 4 2 4 4 4 24 4 4 3 4 4 4 25 4 4 4 4 4 4 26 4 3 3 4 3 4 27 5 4 4 4 4 4 28 4 1 4 4 4 4 29 4 4 2 4 4 4 30 4 4 2 4 4 4 31 4 3 4 3 2 4 32 4 3 2 4 4 4 33 4 4 5 4 4 5 34 4 4 4 4 4 4 35 4 4 4 4 4 4 36 5 3 2 3 3 5 37 4 4 2 4 4 4 38 4 3 3 3 3 4 39 4 4 3 4 3 4 40 3 3 4 4 3 3 41 4 4 4 4 3 4 42 2 3 2 3 2 3 43 2 2 4 2 2 5 44 4 3 4 4 4 5 45 4 4 4 4 2 4 46 5 4 4 4 4 5 47 4 3 2 4 4 4 48 4 3 3 4 3 4 49 4 4 2 4 4 4 50 5 4 2 4 4 4 51 3 3 4 3 3 4 52 2 2 4 2 1 4 53 4 4 4 4 4 4 54 4 4 3 4 4 4 55 2 3 4 4 2 4 56 2 2 5 2 2 4 57 4 4 4 4 4 4 58 4 3 4 4 4 4 59 4 3 4 4 3 4 60 3 4 4 4 3 4 61 2 3 2 3 1 4 62 4 4 4 4 4 4 63 5 3 4 4 2 4 64 4 4 3 4 4 4 65 5 4 4 5 5 5 66 4 4 2 4 3 4 67 3 3 2 3 3 4 68 3 3 2 3 2 3 69 4 3 4 4 4 4 70 3 4 4 3 2 4 71 2 3 3 3 2 2 72 4 2 2 2 2 4 73 3 4 2 4 4 5 74 5 4 2 4 5 4 75 5 4 5 4 4 5 76 4 3 4 2 2 3 77 5 5 4 4 5 4 78 4 3 2 4 2 4 79 3 2 2 3 3 3 80 3 3 4 3 4 4 81 4 3 4 3 3 4 82 4 4 4 4 2 4 83 3 4 4 3 3 4 84 4 3 2 3 4 4 85 3 2 2 2 1 4 86 3 4 4 4 2 5 87 3 4 3 4 2 4 88 2 3 2 2 3 4 89 5 4 2 4 3 4 90 3 3 4 3 2 4 91 4 2 4 2 2 5 92 4 3 3 4 4 4 93 3 3 4 3 3 4 94 3 3 3 3 3 3 95 4 4 3 3 4 4 96 4 4 4 5 4 4 97 3 4 4 4 2 4 98 3 3 4 2 2 5 99 4 4 4 4 4 5 100 2 4 3 3 3 4 101 4 3 4 2 2 4 102 4 4 2 4 4 5 103 4 3 3 4 3 5 104 5 4 4 3 3 4 105 5 4 3 4 4 5 106 5 3 3 4 3 4 107 4 3 2 4 4 4 108 4 3 2 4 3 4 109 3 3 2 4 3 4 110 2 3 2 4 3 2 111 2 2 4 2 2 4 112 4 4 2 4 2 5 113 2 2 3 3 1 4 114 3 3 4 3 2 4 115 4 3 3 4 3 4 116 4 3 3 3 3 4 117 4 4 4 4 3 4 118 4 4 3 3 3 3 119 3 3 2 3 2 4 120 4 4 3 4 4 4 121 3 3 2 3 2 3 122 4 3 3 4 3 4 123 4 3 4 3 3 5 124 4 4 3 4 4 5 125 2 2 3 2 3 3 126 5 4 4 3 3 5 127 5 3 2 4 3 4 128 3 3 2 3 4 4 129 3 4 3 4 4 3 130 3 4 3 3 3 3 131 4 4 3 4 4 4 132 3 3 5 1 5 5 133 2 2 4 2 2 2 134 5 4 4 4 4 4 135 4 2 4 4 4 4 136 2 3 3 3 3 4 137 4 4 4 4 3 5 138 3 3 3 4 4 4 139 2 3 2 2 3 4 140 3 3 4 4 2 4 141 3 3 4 4 4 4 142 4 4 4 4 4 4 143 4 3 2 4 4 4 144 4 3 4 4 3 5 145 2 2 2 2 4 3 146 5 2 4 4 4 4 147 4 3 3 3 4 4 148 4 4 2 4 4 4 149 3 3 3 3 3 4 150 3 4 2 4 3 4 151 5 3 5 5 5 5 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) x1 x2 x3 x4 x5 -0.37757 0.13456 0.05101 0.33632 0.27231 0.33092 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.46326 -0.42798 -0.02087 0.33499 1.65646 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.37757 0.43184 -0.874 0.383382 x1 0.13456 0.09190 1.464 0.145301 x2 0.05101 0.06133 0.832 0.406935 x3 0.33632 0.08973 3.748 0.000257 *** x4 0.27231 0.07050 3.863 0.000169 *** x5 0.33092 0.09618 3.441 0.000759 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.6749 on 145 degrees of freedom Multiple R-squared: 0.4473, Adjusted R-squared: 0.4282 F-statistic: 23.47 on 5 and 145 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.62287112 0.7542578 0.3771289 [2,] 0.69925730 0.6014854 0.3007427 [3,] 0.77397513 0.4520497 0.2260249 [4,] 0.74613623 0.5077275 0.2538638 [5,] 0.75450442 0.4909912 0.2454956 [6,] 0.78069010 0.4386198 0.2193099 [7,] 0.70031803 0.5993639 0.2996820 [8,] 0.64846551 0.7030690 0.3515345 [9,] 0.57029169 0.8594166 0.4297083 [10,] 0.50716016 0.9856797 0.4928398 [11,] 0.46051859 0.9210372 0.5394814 [12,] 0.54246221 0.9150756 0.4575378 [13,] 0.59883818 0.8023236 0.4011618 [14,] 0.53604862 0.9279028 0.4639514 [15,] 0.46062714 0.9212543 0.5393729 [16,] 0.39230426 0.7846085 0.6076957 [17,] 0.34000802 0.6800160 0.6599920 [18,] 0.34695945 0.6939189 0.6530405 [19,] 0.35120414 0.7024083 0.6487959 [20,] 0.29158561 0.5831712 0.7084144 [21,] 0.23635906 0.4727181 0.7636409 [22,] 0.18801297 0.3760259 0.8119870 [23,] 0.27536625 0.5507325 0.7246337 [24,] 0.22967498 0.4593500 0.7703250 [25,] 0.20871290 0.4174258 0.7912871 [26,] 0.17749828 0.3549966 0.8225017 [27,] 0.14805413 0.2961083 0.8519459 [28,] 0.39495987 0.7899197 0.6050401 [29,] 0.33967550 0.6793510 0.6603245 [30,] 0.31693397 0.6338679 0.6830660 [31,] 0.28631455 0.5726291 0.7136855 [32,] 0.25924119 0.5184824 0.7407588 [33,] 0.22303609 0.4460722 0.7769639 [34,] 0.22771437 0.4554287 0.7722856 [35,] 0.29100522 0.5820104 0.7089948 [36,] 0.25090818 0.5018164 0.7490918 [37,] 0.25570333 0.5114067 0.7442967 [38,] 0.24617637 0.4923527 0.7538236 [39,] 0.20707221 0.4141444 0.7929278 [40,] 0.18595892 0.3719178 0.8140411 [41,] 0.15252285 0.3050457 0.8474772 [42,] 0.18555875 0.3711175 0.8144413 [43,] 0.16628414 0.3325683 0.8337159 [44,] 0.14061916 0.2812383 0.8593808 [45,] 0.11661783 0.2332357 0.8833822 [46,] 0.09441739 0.1888348 0.9055826 [47,] 0.16080798 0.3216160 0.8391920 [48,] 0.16330581 0.3266116 0.8366942 [49,] 0.13638975 0.2727795 0.8636102 [50,] 0.11062595 0.2212519 0.8893741 [51,] 0.09628850 0.1925770 0.9037115 [52,] 0.10473942 0.2094788 0.8952606 [53,] 0.09774395 0.1954879 0.9022561 [54,] 0.07930587 0.1586117 0.9206941 [55,] 0.24575663 0.4915133 0.7542434 [56,] 0.20989795 0.4197959 0.7901020 [57,] 0.17664228 0.3532846 0.8233577 [58,] 0.15214555 0.3042911 0.8478544 [59,] 0.12890729 0.2578146 0.8710927 [60,] 0.11021045 0.2204209 0.8897895 [61,] 0.08892375 0.1778475 0.9110762 [62,] 0.07257260 0.1451452 0.9274274 [63,] 0.06429218 0.1285844 0.9357078 [64,] 0.14473057 0.2894611 0.8552694 [65,] 0.23054309 0.4610862 0.7694569 [66,] 0.23011919 0.4602384 0.7698808 [67,] 0.21637410 0.4327482 0.7836259 [68,] 0.35705983 0.7141197 0.6429402 [69,] 0.33578740 0.6715748 0.6642126 [70,] 0.35219949 0.7043990 0.6478005 [71,] 0.31238785 0.6247757 0.6876122 [72,] 0.32017391 0.6403478 0.6798261 [73,] 0.30976699 0.6195340 0.6902330 [74,] 0.28979497 0.5795899 0.7102050 [75,] 0.27312896 0.5462579 0.7268710 [76,] 0.25146119 0.5029224 0.7485388 [77,] 0.25869458 0.5173892 0.7413054 [78,] 0.29163545 0.5832709 0.7083646 [79,] 0.27654541 0.5530908 0.7234546 [80,] 0.31783636 0.6356727 0.6821636 [81,] 0.43623559 0.8724712 0.5637644 [82,] 0.39172557 0.7834511 0.6082744 [83,] 0.44766999 0.8953400 0.5523300 [84,] 0.40069401 0.8013880 0.5993060 [85,] 0.37012636 0.7402527 0.6298736 [86,] 0.32703033 0.6540607 0.6729697 [87,] 0.29342346 0.5868469 0.7065765 [88,] 0.27564772 0.5512954 0.7243523 [89,] 0.28339572 0.5667914 0.7166043 [90,] 0.24436407 0.4887281 0.7556359 [91,] 0.23079235 0.4615847 0.7692076 [92,] 0.40686208 0.8137242 0.5931379 [93,] 0.50592445 0.9881511 0.4940755 [94,] 0.46716016 0.9343203 0.5328398 [95,] 0.41758473 0.8351695 0.5824153 [96,] 0.59505706 0.8098859 0.4049429 [97,] 0.57842938 0.8431412 0.4215706 [98,] 0.72438383 0.5512323 0.2756162 [99,] 0.68336704 0.6332659 0.3166330 [100,] 0.65932679 0.6813464 0.3406732 [101,] 0.63985531 0.7202894 0.3601447 [102,] 0.68143662 0.6371268 0.3185634 [103,] 0.66333867 0.6733227 0.3366613 [104,] 0.61478697 0.7704261 0.3852130 [105,] 0.62443654 0.7511269 0.3755635 [106,] 0.57742630 0.8451474 0.4225737 [107,] 0.52856078 0.9428784 0.4714392 [108,] 0.53756853 0.9248629 0.4624315 [109,] 0.47897403 0.9579481 0.5210260 [110,] 0.55370462 0.8925908 0.4462954 [111,] 0.49229876 0.9845975 0.5077012 [112,] 0.43180060 0.8636012 0.5681994 [113,] 0.40159282 0.8031856 0.5984072 [114,] 0.35454794 0.7090959 0.6454521 [115,] 0.30619778 0.6123956 0.6938022 [116,] 0.26330852 0.5266170 0.7366915 [117,] 0.22407664 0.4481533 0.7759234 [118,] 0.40650357 0.8130071 0.5934964 [119,] 0.74422370 0.5115526 0.2557763 [120,] 0.68842081 0.6231584 0.3115792 [121,] 0.72000823 0.5599835 0.2799918 [122,] 0.65174915 0.6965017 0.3482508 [123,] 0.57442678 0.8511464 0.4255732 [124,] 0.50837305 0.9832539 0.4916270 [125,] 0.44403871 0.8880774 0.5559613 [126,] 0.58808281 0.8238344 0.4119172 [127,] 0.49476125 0.9895225 0.5052388 [128,] 0.55969519 0.8806096 0.4403048 [129,] 0.46285419 0.9257084 0.5371458 [130,] 0.54067574 0.9186485 0.4593243 [131,] 0.46064221 0.9212844 0.5393578 [132,] 0.33886072 0.6777214 0.6611393 [133,] 0.49685078 0.9937016 0.5031492 [134,] 0.33562470 0.6712494 0.6643753 > postscript(file="/var/www/html/rcomp/tmp/1lmoa1291375391.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2wvnd1291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3wvnd1291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4wvnd1291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/564mg1291375391.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 = 151 Frequency = 1 1 2 3 4 5 6 -0.173900599 0.149414747 0.139318458 -0.549992799 -1.005381830 0.933512021 7 8 9 10 11 12 0.671302825 -1.443717187 -0.402803615 1.656463646 0.113691565 -1.010774728 13 14 15 16 17 18 0.039585125 -0.412249895 0.011671843 -0.268241034 0.136788143 -0.122890738 19 20 21 22 23 24 -0.669066194 0.418539909 -0.886308435 0.021768131 -0.020871016 -0.071880877 25 26 27 28 29 30 -0.122890738 0.334987189 0.877109262 0.280797005 -0.020871016 -0.020871016 31 32 33 34 35 36 0.892598449 0.113691565 -0.504823337 -0.122890738 -0.122890738 1.391389948 37 38 39 40 41 42 -0.020871016 0.671302825 0.200424608 -0.385099935 0.149414747 -0.674459092 43 44 45 46 47 48 -0.967446072 -0.319250895 0.421720231 0.546186524 0.113691565 0.334987189 49 50 51 52 53 54 -0.020871016 0.979128984 -0.379707036 -0.364217849 -0.122890738 -0.071880877 55 56 57 58 59 60 -1.443717187 -0.687533195 -0.122890738 0.011671843 0.283977328 -0.850585253 61 62 63 64 65 66 -0.733076345 -0.122890738 1.556282813 -0.071880877 -0.062434597 0.251434469 67 68 69 70 71 72 -0.277687314 0.325540908 0.011671843 -0.241964133 -0.394546215 1.465496388 73 74 75 76 77 78 -1.351793754 0.706823499 0.495176663 1.559836822 0.470241196 0.658302534 79 80 81 82 83 84 0.187798004 -0.652012521 0.620292964 0.421720231 -0.514269617 0.450007201 85 86 87 88 89 90 0.737801872 -0.909202506 -0.527269908 -0.941371678 1.251434469 -0.107401551 91 92 93 94 95 96 1.032553928 0.062681704 -0.379707036 0.002225562 0.264434759 -0.459206374 97 98 99 100 101 102 -0.578279769 -0.102008653 -0.453813476 -1.463259756 1.228914085 -0.351793754 103 104 105 106 107 108 0.004064451 1.485730383 0.597196385 1.334987189 0.113691565 0.385997050 109 110 111 112 113 114 -0.614002950 -0.952157475 -0.636523334 0.192817216 -0.649523625 -0.107401551 115 116 117 118 119 120 0.334987189 0.671302825 0.149414747 0.867662981 -0.005381830 -0.071880877 121 122 123 124 125 126 0.325540908 0.334987189 0.289370226 -0.402803615 -0.526896221 1.154807645 127 128 129 130 131 132 1.385997050 -0.549992799 -0.740958140 -0.132337019 -0.071880877 -0.633619333 133 134 135 136 137 138 0.025322141 0.877109262 0.146234424 -1.328697175 -0.181507991 -0.937318296 139 140 141 142 143 144 -0.941371678 -0.443717187 -0.988328157 -0.122890738 0.113691565 -0.046945410 145 146 147 148 149 150 -0.748191845 1.146234424 0.398997340 -0.020871016 -0.328697175 -0.748565531 151 0.021118123 > postscript(file="/var/www/html/rcomp/tmp/664mg1291375391.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 = 151 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.173900599 NA 1 0.149414747 -0.173900599 2 0.139318458 0.149414747 3 -0.549992799 0.139318458 4 -1.005381830 -0.549992799 5 0.933512021 -1.005381830 6 0.671302825 0.933512021 7 -1.443717187 0.671302825 8 -0.402803615 -1.443717187 9 1.656463646 -0.402803615 10 0.113691565 1.656463646 11 -1.010774728 0.113691565 12 0.039585125 -1.010774728 13 -0.412249895 0.039585125 14 0.011671843 -0.412249895 15 -0.268241034 0.011671843 16 0.136788143 -0.268241034 17 -0.122890738 0.136788143 18 -0.669066194 -0.122890738 19 0.418539909 -0.669066194 20 -0.886308435 0.418539909 21 0.021768131 -0.886308435 22 -0.020871016 0.021768131 23 -0.071880877 -0.020871016 24 -0.122890738 -0.071880877 25 0.334987189 -0.122890738 26 0.877109262 0.334987189 27 0.280797005 0.877109262 28 -0.020871016 0.280797005 29 -0.020871016 -0.020871016 30 0.892598449 -0.020871016 31 0.113691565 0.892598449 32 -0.504823337 0.113691565 33 -0.122890738 -0.504823337 34 -0.122890738 -0.122890738 35 1.391389948 -0.122890738 36 -0.020871016 1.391389948 37 0.671302825 -0.020871016 38 0.200424608 0.671302825 39 -0.385099935 0.200424608 40 0.149414747 -0.385099935 41 -0.674459092 0.149414747 42 -0.967446072 -0.674459092 43 -0.319250895 -0.967446072 44 0.421720231 -0.319250895 45 0.546186524 0.421720231 46 0.113691565 0.546186524 47 0.334987189 0.113691565 48 -0.020871016 0.334987189 49 0.979128984 -0.020871016 50 -0.379707036 0.979128984 51 -0.364217849 -0.379707036 52 -0.122890738 -0.364217849 53 -0.071880877 -0.122890738 54 -1.443717187 -0.071880877 55 -0.687533195 -1.443717187 56 -0.122890738 -0.687533195 57 0.011671843 -0.122890738 58 0.283977328 0.011671843 59 -0.850585253 0.283977328 60 -0.733076345 -0.850585253 61 -0.122890738 -0.733076345 62 1.556282813 -0.122890738 63 -0.071880877 1.556282813 64 -0.062434597 -0.071880877 65 0.251434469 -0.062434597 66 -0.277687314 0.251434469 67 0.325540908 -0.277687314 68 0.011671843 0.325540908 69 -0.241964133 0.011671843 70 -0.394546215 -0.241964133 71 1.465496388 -0.394546215 72 -1.351793754 1.465496388 73 0.706823499 -1.351793754 74 0.495176663 0.706823499 75 1.559836822 0.495176663 76 0.470241196 1.559836822 77 0.658302534 0.470241196 78 0.187798004 0.658302534 79 -0.652012521 0.187798004 80 0.620292964 -0.652012521 81 0.421720231 0.620292964 82 -0.514269617 0.421720231 83 0.450007201 -0.514269617 84 0.737801872 0.450007201 85 -0.909202506 0.737801872 86 -0.527269908 -0.909202506 87 -0.941371678 -0.527269908 88 1.251434469 -0.941371678 89 -0.107401551 1.251434469 90 1.032553928 -0.107401551 91 0.062681704 1.032553928 92 -0.379707036 0.062681704 93 0.002225562 -0.379707036 94 0.264434759 0.002225562 95 -0.459206374 0.264434759 96 -0.578279769 -0.459206374 97 -0.102008653 -0.578279769 98 -0.453813476 -0.102008653 99 -1.463259756 -0.453813476 100 1.228914085 -1.463259756 101 -0.351793754 1.228914085 102 0.004064451 -0.351793754 103 1.485730383 0.004064451 104 0.597196385 1.485730383 105 1.334987189 0.597196385 106 0.113691565 1.334987189 107 0.385997050 0.113691565 108 -0.614002950 0.385997050 109 -0.952157475 -0.614002950 110 -0.636523334 -0.952157475 111 0.192817216 -0.636523334 112 -0.649523625 0.192817216 113 -0.107401551 -0.649523625 114 0.334987189 -0.107401551 115 0.671302825 0.334987189 116 0.149414747 0.671302825 117 0.867662981 0.149414747 118 -0.005381830 0.867662981 119 -0.071880877 -0.005381830 120 0.325540908 -0.071880877 121 0.334987189 0.325540908 122 0.289370226 0.334987189 123 -0.402803615 0.289370226 124 -0.526896221 -0.402803615 125 1.154807645 -0.526896221 126 1.385997050 1.154807645 127 -0.549992799 1.385997050 128 -0.740958140 -0.549992799 129 -0.132337019 -0.740958140 130 -0.071880877 -0.132337019 131 -0.633619333 -0.071880877 132 0.025322141 -0.633619333 133 0.877109262 0.025322141 134 0.146234424 0.877109262 135 -1.328697175 0.146234424 136 -0.181507991 -1.328697175 137 -0.937318296 -0.181507991 138 -0.941371678 -0.937318296 139 -0.443717187 -0.941371678 140 -0.988328157 -0.443717187 141 -0.122890738 -0.988328157 142 0.113691565 -0.122890738 143 -0.046945410 0.113691565 144 -0.748191845 -0.046945410 145 1.146234424 -0.748191845 146 0.398997340 1.146234424 147 -0.020871016 0.398997340 148 -0.328697175 -0.020871016 149 -0.748565531 -0.328697175 150 0.021118123 -0.748565531 151 NA 0.021118123 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.149414747 -0.173900599 [2,] 0.139318458 0.149414747 [3,] -0.549992799 0.139318458 [4,] -1.005381830 -0.549992799 [5,] 0.933512021 -1.005381830 [6,] 0.671302825 0.933512021 [7,] -1.443717187 0.671302825 [8,] -0.402803615 -1.443717187 [9,] 1.656463646 -0.402803615 [10,] 0.113691565 1.656463646 [11,] -1.010774728 0.113691565 [12,] 0.039585125 -1.010774728 [13,] -0.412249895 0.039585125 [14,] 0.011671843 -0.412249895 [15,] -0.268241034 0.011671843 [16,] 0.136788143 -0.268241034 [17,] -0.122890738 0.136788143 [18,] -0.669066194 -0.122890738 [19,] 0.418539909 -0.669066194 [20,] -0.886308435 0.418539909 [21,] 0.021768131 -0.886308435 [22,] -0.020871016 0.021768131 [23,] -0.071880877 -0.020871016 [24,] -0.122890738 -0.071880877 [25,] 0.334987189 -0.122890738 [26,] 0.877109262 0.334987189 [27,] 0.280797005 0.877109262 [28,] -0.020871016 0.280797005 [29,] -0.020871016 -0.020871016 [30,] 0.892598449 -0.020871016 [31,] 0.113691565 0.892598449 [32,] -0.504823337 0.113691565 [33,] -0.122890738 -0.504823337 [34,] -0.122890738 -0.122890738 [35,] 1.391389948 -0.122890738 [36,] -0.020871016 1.391389948 [37,] 0.671302825 -0.020871016 [38,] 0.200424608 0.671302825 [39,] -0.385099935 0.200424608 [40,] 0.149414747 -0.385099935 [41,] -0.674459092 0.149414747 [42,] -0.967446072 -0.674459092 [43,] -0.319250895 -0.967446072 [44,] 0.421720231 -0.319250895 [45,] 0.546186524 0.421720231 [46,] 0.113691565 0.546186524 [47,] 0.334987189 0.113691565 [48,] -0.020871016 0.334987189 [49,] 0.979128984 -0.020871016 [50,] -0.379707036 0.979128984 [51,] -0.364217849 -0.379707036 [52,] -0.122890738 -0.364217849 [53,] -0.071880877 -0.122890738 [54,] -1.443717187 -0.071880877 [55,] -0.687533195 -1.443717187 [56,] -0.122890738 -0.687533195 [57,] 0.011671843 -0.122890738 [58,] 0.283977328 0.011671843 [59,] -0.850585253 0.283977328 [60,] -0.733076345 -0.850585253 [61,] -0.122890738 -0.733076345 [62,] 1.556282813 -0.122890738 [63,] -0.071880877 1.556282813 [64,] -0.062434597 -0.071880877 [65,] 0.251434469 -0.062434597 [66,] -0.277687314 0.251434469 [67,] 0.325540908 -0.277687314 [68,] 0.011671843 0.325540908 [69,] -0.241964133 0.011671843 [70,] -0.394546215 -0.241964133 [71,] 1.465496388 -0.394546215 [72,] -1.351793754 1.465496388 [73,] 0.706823499 -1.351793754 [74,] 0.495176663 0.706823499 [75,] 1.559836822 0.495176663 [76,] 0.470241196 1.559836822 [77,] 0.658302534 0.470241196 [78,] 0.187798004 0.658302534 [79,] -0.652012521 0.187798004 [80,] 0.620292964 -0.652012521 [81,] 0.421720231 0.620292964 [82,] -0.514269617 0.421720231 [83,] 0.450007201 -0.514269617 [84,] 0.737801872 0.450007201 [85,] -0.909202506 0.737801872 [86,] -0.527269908 -0.909202506 [87,] -0.941371678 -0.527269908 [88,] 1.251434469 -0.941371678 [89,] -0.107401551 1.251434469 [90,] 1.032553928 -0.107401551 [91,] 0.062681704 1.032553928 [92,] -0.379707036 0.062681704 [93,] 0.002225562 -0.379707036 [94,] 0.264434759 0.002225562 [95,] -0.459206374 0.264434759 [96,] -0.578279769 -0.459206374 [97,] -0.102008653 -0.578279769 [98,] -0.453813476 -0.102008653 [99,] -1.463259756 -0.453813476 [100,] 1.228914085 -1.463259756 [101,] -0.351793754 1.228914085 [102,] 0.004064451 -0.351793754 [103,] 1.485730383 0.004064451 [104,] 0.597196385 1.485730383 [105,] 1.334987189 0.597196385 [106,] 0.113691565 1.334987189 [107,] 0.385997050 0.113691565 [108,] -0.614002950 0.385997050 [109,] -0.952157475 -0.614002950 [110,] -0.636523334 -0.952157475 [111,] 0.192817216 -0.636523334 [112,] -0.649523625 0.192817216 [113,] -0.107401551 -0.649523625 [114,] 0.334987189 -0.107401551 [115,] 0.671302825 0.334987189 [116,] 0.149414747 0.671302825 [117,] 0.867662981 0.149414747 [118,] -0.005381830 0.867662981 [119,] -0.071880877 -0.005381830 [120,] 0.325540908 -0.071880877 [121,] 0.334987189 0.325540908 [122,] 0.289370226 0.334987189 [123,] -0.402803615 0.289370226 [124,] -0.526896221 -0.402803615 [125,] 1.154807645 -0.526896221 [126,] 1.385997050 1.154807645 [127,] -0.549992799 1.385997050 [128,] -0.740958140 -0.549992799 [129,] -0.132337019 -0.740958140 [130,] -0.071880877 -0.132337019 [131,] -0.633619333 -0.071880877 [132,] 0.025322141 -0.633619333 [133,] 0.877109262 0.025322141 [134,] 0.146234424 0.877109262 [135,] -1.328697175 0.146234424 [136,] -0.181507991 -1.328697175 [137,] -0.937318296 -0.181507991 [138,] -0.941371678 -0.937318296 [139,] -0.443717187 -0.941371678 [140,] -0.988328157 -0.443717187 [141,] -0.122890738 -0.988328157 [142,] 0.113691565 -0.122890738 [143,] -0.046945410 0.113691565 [144,] -0.748191845 -0.046945410 [145,] 1.146234424 -0.748191845 [146,] 0.398997340 1.146234424 [147,] -0.020871016 0.398997340 [148,] -0.328697175 -0.020871016 [149,] -0.748565531 -0.328697175 [150,] 0.021118123 -0.748565531 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.149414747 -0.173900599 2 0.139318458 0.149414747 3 -0.549992799 0.139318458 4 -1.005381830 -0.549992799 5 0.933512021 -1.005381830 6 0.671302825 0.933512021 7 -1.443717187 0.671302825 8 -0.402803615 -1.443717187 9 1.656463646 -0.402803615 10 0.113691565 1.656463646 11 -1.010774728 0.113691565 12 0.039585125 -1.010774728 13 -0.412249895 0.039585125 14 0.011671843 -0.412249895 15 -0.268241034 0.011671843 16 0.136788143 -0.268241034 17 -0.122890738 0.136788143 18 -0.669066194 -0.122890738 19 0.418539909 -0.669066194 20 -0.886308435 0.418539909 21 0.021768131 -0.886308435 22 -0.020871016 0.021768131 23 -0.071880877 -0.020871016 24 -0.122890738 -0.071880877 25 0.334987189 -0.122890738 26 0.877109262 0.334987189 27 0.280797005 0.877109262 28 -0.020871016 0.280797005 29 -0.020871016 -0.020871016 30 0.892598449 -0.020871016 31 0.113691565 0.892598449 32 -0.504823337 0.113691565 33 -0.122890738 -0.504823337 34 -0.122890738 -0.122890738 35 1.391389948 -0.122890738 36 -0.020871016 1.391389948 37 0.671302825 -0.020871016 38 0.200424608 0.671302825 39 -0.385099935 0.200424608 40 0.149414747 -0.385099935 41 -0.674459092 0.149414747 42 -0.967446072 -0.674459092 43 -0.319250895 -0.967446072 44 0.421720231 -0.319250895 45 0.546186524 0.421720231 46 0.113691565 0.546186524 47 0.334987189 0.113691565 48 -0.020871016 0.334987189 49 0.979128984 -0.020871016 50 -0.379707036 0.979128984 51 -0.364217849 -0.379707036 52 -0.122890738 -0.364217849 53 -0.071880877 -0.122890738 54 -1.443717187 -0.071880877 55 -0.687533195 -1.443717187 56 -0.122890738 -0.687533195 57 0.011671843 -0.122890738 58 0.283977328 0.011671843 59 -0.850585253 0.283977328 60 -0.733076345 -0.850585253 61 -0.122890738 -0.733076345 62 1.556282813 -0.122890738 63 -0.071880877 1.556282813 64 -0.062434597 -0.071880877 65 0.251434469 -0.062434597 66 -0.277687314 0.251434469 67 0.325540908 -0.277687314 68 0.011671843 0.325540908 69 -0.241964133 0.011671843 70 -0.394546215 -0.241964133 71 1.465496388 -0.394546215 72 -1.351793754 1.465496388 73 0.706823499 -1.351793754 74 0.495176663 0.706823499 75 1.559836822 0.495176663 76 0.470241196 1.559836822 77 0.658302534 0.470241196 78 0.187798004 0.658302534 79 -0.652012521 0.187798004 80 0.620292964 -0.652012521 81 0.421720231 0.620292964 82 -0.514269617 0.421720231 83 0.450007201 -0.514269617 84 0.737801872 0.450007201 85 -0.909202506 0.737801872 86 -0.527269908 -0.909202506 87 -0.941371678 -0.527269908 88 1.251434469 -0.941371678 89 -0.107401551 1.251434469 90 1.032553928 -0.107401551 91 0.062681704 1.032553928 92 -0.379707036 0.062681704 93 0.002225562 -0.379707036 94 0.264434759 0.002225562 95 -0.459206374 0.264434759 96 -0.578279769 -0.459206374 97 -0.102008653 -0.578279769 98 -0.453813476 -0.102008653 99 -1.463259756 -0.453813476 100 1.228914085 -1.463259756 101 -0.351793754 1.228914085 102 0.004064451 -0.351793754 103 1.485730383 0.004064451 104 0.597196385 1.485730383 105 1.334987189 0.597196385 106 0.113691565 1.334987189 107 0.385997050 0.113691565 108 -0.614002950 0.385997050 109 -0.952157475 -0.614002950 110 -0.636523334 -0.952157475 111 0.192817216 -0.636523334 112 -0.649523625 0.192817216 113 -0.107401551 -0.649523625 114 0.334987189 -0.107401551 115 0.671302825 0.334987189 116 0.149414747 0.671302825 117 0.867662981 0.149414747 118 -0.005381830 0.867662981 119 -0.071880877 -0.005381830 120 0.325540908 -0.071880877 121 0.334987189 0.325540908 122 0.289370226 0.334987189 123 -0.402803615 0.289370226 124 -0.526896221 -0.402803615 125 1.154807645 -0.526896221 126 1.385997050 1.154807645 127 -0.549992799 1.385997050 128 -0.740958140 -0.549992799 129 -0.132337019 -0.740958140 130 -0.071880877 -0.132337019 131 -0.633619333 -0.071880877 132 0.025322141 -0.633619333 133 0.877109262 0.025322141 134 0.146234424 0.877109262 135 -1.328697175 0.146234424 136 -0.181507991 -1.328697175 137 -0.937318296 -0.181507991 138 -0.941371678 -0.937318296 139 -0.443717187 -0.941371678 140 -0.988328157 -0.443717187 141 -0.122890738 -0.988328157 142 0.113691565 -0.122890738 143 -0.046945410 0.113691565 144 -0.748191845 -0.046945410 145 1.146234424 -0.748191845 146 0.398997340 1.146234424 147 -0.020871016 0.398997340 148 -0.328697175 -0.020871016 149 -0.748565531 -0.328697175 150 0.021118123 -0.748565531 > 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/7hw411291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8hw411291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9anlm1291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10anlm1291375391.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11vo2a1291375391.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/12go0g1291375391.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/13n7fr1291375391.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/14ygwv1291375391.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/151zv01291375391.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/16g9t91291375391.tab") + } > > try(system("convert tmp/1lmoa1291375391.ps tmp/1lmoa1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/2wvnd1291375391.ps tmp/2wvnd1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/3wvnd1291375391.ps tmp/3wvnd1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/4wvnd1291375391.ps tmp/4wvnd1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/564mg1291375391.ps tmp/564mg1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/664mg1291375391.ps tmp/664mg1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/7hw411291375391.ps tmp/7hw411291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/8hw411291375391.ps tmp/8hw411291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/9anlm1291375391.ps tmp/9anlm1291375391.png",intern=TRUE)) character(0) > try(system("convert tmp/10anlm1291375391.ps tmp/10anlm1291375391.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.969 1.762 17.057