R version 2.11.1 (2010-05-31) Copyright (C) 2010 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(5.6 + ,5.5 + ,6 + ,4.8 + ,4.4 + ,3.5 + ,4 + ,4.4 + ,2.4 + ,8.5 + ,4 + ,5.6 + ,4.8 + ,5 + ,4 + ,4.8 + ,3.2 + ,6 + ,4.5 + ,8.4 + ,4 + ,6 + ,3.5 + ,4.8 + ,4 + ,5.5 + ,2 + ,8.8 + ,4.4 + ,5.5 + ,5.5 + ,4.4 + ,6.4 + ,6 + ,3.5 + ,4 + ,4.4 + ,6.5 + ,3.5 + ,5.2 + ,5.2 + ,7 + ,6 + ,4 + ,4.8 + ,8 + ,5 + ,3.2 + ,3.2 + ,5.5 + ,5 + ,6 + ,4.8 + ,5 + ,4 + ,5.6 + ,4.4 + ,5.5 + ,4 + ,4 + ,1.6 + ,7.5 + ,2 + ,5.6 + ,3.6 + ,4.5 + ,4.5 + ,5.6 + ,3.2 + ,5.5 + ,4 + ,4.4 + ,3.2 + ,8.5 + ,3.5 + ,4 + ,5.6 + ,8.5 + ,5.5 + ,5.2 + ,6 + ,5.5 + ,4.5 + ,2.8 + ,6.4 + ,9 + ,5.5 + ,5.6 + ,3.6 + ,7 + ,6.5 + ,4.8 + ,5.6 + ,5 + ,4 + ,5.6 + ,4.4 + ,5.5 + ,4 + ,4.4 + ,3.2 + ,7.5 + ,4.5 + ,3.6 + ,3.6 + ,7.5 + ,3 + ,4.4 + ,3.6 + ,6.5 + ,4.5 + ,6 + ,3.6 + ,8 + ,4.5 + ,5.6 + ,3.6 + ,6.5 + ,3 + ,5.2 + ,4 + ,4.5 + ,3 + ,3.6 + ,6.4 + ,9 + ,8 + ,6 + ,4.4 + ,9 + ,2.5 + ,4 + ,3.2 + ,6 + ,3.5 + ,4.4 + ,3.6 + ,8.5 + ,4.5 + ,5.2 + ,6.4 + ,4.5 + ,3 + ,3.2 + ,4.4 + ,4.5 + ,3 + ,8 + ,6.4 + ,6 + ,2.5 + ,4.8 + ,4.8 + ,9 + ,6 + ,4 + ,4.8 + ,6 + ,3.5 + ,4 + ,5.6 + ,9 + ,5 + ,3.6 + ,3.6 + ,7 + ,4.5 + ,5.6 + ,4 + ,7.5 + ,4 + ,3.2 + ,3.6 + ,8 + ,2.5 + ,5.6 + ,4 + ,5 + ,4 + ,4.4 + ,4.8 + ,5.5 + ,4 + ,5.2 + ,5.6 + ,7 + ,5 + ,3.6 + ,5.6 + ,4.5 + ,3 + ,4.4 + ,4 + ,6 + ,4 + ,6 + ,5.6 + ,8.5 + ,3.5 + ,4.4 + ,6.4 + ,2.5 + ,2 + ,4 + ,3.6 + ,6 + ,4 + ,5.6 + ,4 + ,6 + ,4 + ,7.2 + ,2.4 + ,3 + ,2 + ,5.6 + ,3.2 + ,12 + ,10 + ,4.4 + ,5.2 + ,6 + ,4 + ,4.8 + ,4 + ,6 + ,4 + ,5.2 + ,3.2 + ,7 + ,3 + ,3.6 + ,2.8 + ,3.5 + ,2 + ,4 + ,6 + ,6.5 + ,4 + ,6 + ,3.6 + ,6 + ,4.5 + ,8 + ,4 + ,6.5 + ,3 + ,4.8 + ,4.8 + ,7 + ,3.5 + ,4.8 + ,5.2 + ,4 + ,4.5 + ,5.6 + ,4 + ,5.5 + ,2.5 + ,5.2 + ,4.4 + ,4.5 + ,2.5 + ,4.4 + ,3.2 + ,5.5 + ,4 + ,6.8 + ,3.6 + ,6.5 + ,4 + ,4.8 + ,5.2 + ,5 + ,3 + ,5.2 + ,4.4 + ,5.5 + ,4 + ,5.6 + ,3.2 + ,6 + ,3.5 + ,5.2 + ,3.6 + ,4.5 + ,3.5 + ,6 + ,3.6 + ,7.5 + ,4.5 + ,5.2 + ,6 + ,9 + ,5.5 + ,4 + ,3.6 + ,7.5 + ,3 + ,4.4 + ,4 + ,6 + ,4 + ,7.6 + ,5.6 + ,6.5 + ,3 + ,5.2 + ,4.8 + ,7 + ,4.5 + ,6.8 + ,4.8 + ,5 + ,4 + ,5.2 + ,4.4 + ,6.5 + ,3 + ,3.6 + ,5.6 + ,6.5 + ,5 + ,4.4 + ,2.4 + ,5.5 + ,4 + ,4 + ,4.8 + ,6.5 + ,4 + ,3.6 + ,3.2 + ,8 + ,5 + ,4.8 + ,5.6 + ,4 + ,2.5 + ,4.8 + ,4.4 + ,8 + ,3.5 + ,5.2 + ,4 + ,5.5 + ,2.5 + ,5.2 + ,5.6 + ,4.5 + ,4 + ,4.8 + ,4.8 + ,8 + ,7 + ,6 + ,4 + ,6 + ,3.5 + ,8.8 + ,5.6 + ,7 + ,4 + ,5.2 + ,2 + ,4 + ,3 + ,6 + ,4.4 + ,4.5 + ,2.5 + ,5.2 + ,4 + ,7.5 + ,3 + ,6 + ,3.6 + ,5.5 + ,5 + ,4 + ,4 + ,10.5 + ,6 + ,4.4 + ,6.4 + ,7 + ,4.5 + ,6.4 + ,5.2 + ,9 + ,6 + ,4.4 + ,3.6 + ,6 + ,3.5 + ,4.4 + ,4 + ,6.5 + ,4 + ,4 + ,4 + ,7.5 + ,5 + ,4 + ,2.8 + ,6 + ,3 + ,6.4 + ,3.6 + ,9.5 + ,5 + ,4.8 + ,3.2 + ,7.5 + ,5 + ,4.4 + ,5.6 + ,5.5 + ,5 + ,6.4 + ,5.6 + ,5.5 + ,2.5 + ,7.6 + ,3.2 + ,5 + ,3.5 + ,4.4 + ,3.6 + ,6.5 + ,5 + ,6.4 + ,5.6 + ,7.5 + ,5.5 + ,6 + ,5.6 + ,6 + ,3 + ,9.6 + ,3.2 + ,6 + ,3.5 + ,5.6 + ,3.2 + ,8 + ,6 + ,6 + ,3.2 + ,4.5 + ,5.5 + ,4.4 + ,2.8 + ,9 + ,5.5 + ,6 + ,2.4 + ,4 + ,5.5 + ,4.8 + ,3.2 + ,6.5 + ,2.5 + ,4 + ,2.4 + ,8.5 + ,4 + ,5.6 + ,4.4 + ,4.5 + ,3 + ,5.2 + ,5.6 + ,7.5 + ,4.5 + ,3.6 + ,4.4 + ,4 + ,2 + ,6 + ,4.4 + ,3.5 + ,2 + ,6 + ,4.4 + ,6 + ,3.5 + ,5.6 + ,5.6 + ,7 + ,5.5 + ,4.4 + ,3.2 + ,3 + ,3 + ,3.2 + ,8 + ,4 + ,3.5 + ,4.4 + ,4.4 + ,8.5 + ,4 + ,4.4 + ,3.2 + ,5 + ,2 + ,3.2 + ,4.4 + ,5.5 + ,4 + ,4 + ,4 + ,7 + ,4.5 + ,4.4 + ,5.6 + ,5.5 + ,4 + ,5.2 + ,4.4 + ,6.5 + ,5.5 + ,4.4 + ,3.6 + ,6 + ,4 + ,8 + ,3.6 + ,5.5 + ,2.5 + ,4 + ,3.2 + ,4.5 + ,2 + ,6 + ,4 + ,6 + ,4 + ,4.8 + ,5.2 + ,10 + ,5 + ,5.6 + ,5.2 + ,6 + ,3 + ,9.2 + ,4.8 + ,6.5 + ,4.5 + ,5.6 + ,3.2 + ,6 + ,4.5 + ,6.4 + ,5.2 + ,6 + ,6.5 + ,4.4 + ,5.6 + ,4.5 + ,4.5 + ,4.8 + ,4.8 + ,7.5 + ,5 + ,4 + ,5.6 + ,12 + ,10 + ,5.6 + ,6 + ,3.5 + ,2.5 + ,4.8 + ,5.2 + ,8.5 + ,5.5 + ,4.8 + ,6.4 + ,5.5 + ,3 + ,4.4 + ,3.6 + ,8.5 + ,4.5 + ,4.8 + ,3.6 + ,5.5 + ,3.5 + ,5.2 + ,3.6 + ,6 + ,4.5 + ,4.4 + ,3.2 + ,7 + ,5 + ,7.6 + ,2.8 + ,5.5 + ,4.5 + ,4.8 + ,6.4 + ,8 + ,4 + ,6.8 + ,4.4 + ,10.5 + ,3.5 + ,3.6 + ,3.6 + ,7 + ,3 + ,4.8 + ,4.4 + ,10 + ,6.5 + ,7.6 + ,3.6 + ,6.5 + ,3 + ,7.2 + ,5.6 + ,5.5 + ,4 + ,6 + ,5.2 + ,7.5 + ,5 + ,5.6 + ,6.4 + ,9.5 + ,8 + ,4.4) + ,dim=c(4 + ,159) + ,dimnames=list(c('Doubts' + ,'Expectat' + ,'Criticism' + ,'Depression') + ,1:159)) > y <- array(NA,dim=c(4,159),dimnames=list(c('Doubts','Expectat','Criticism','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 > 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 Doubts Expectat Criticism 1 4.8 5.6 5.5 6.0 2 4.4 4.4 3.5 4.0 3 5.6 2.4 8.5 4.0 4 4.8 4.8 5.0 4.0 5 8.4 3.2 6.0 4.5 6 4.8 4.0 6.0 3.5 7 8.8 4.0 5.5 2.0 8 4.4 4.4 5.5 5.5 9 4.0 6.4 6.0 3.5 10 5.2 4.4 6.5 3.5 11 4.0 5.2 7.0 6.0 12 3.2 4.8 8.0 5.0 13 6.0 3.2 5.5 5.0 14 5.6 4.8 5.0 4.0 15 4.0 4.4 5.5 4.0 16 5.6 1.6 7.5 2.0 17 5.6 3.6 4.5 4.5 18 4.4 3.2 5.5 4.0 19 4.0 3.2 8.5 3.5 20 5.2 5.6 8.5 5.5 21 2.8 6.0 5.5 4.5 22 5.6 6.4 9.0 5.5 23 4.8 3.6 7.0 6.5 24 5.6 5.6 5.0 4.0 25 4.4 4.4 5.5 4.0 26 3.6 3.2 7.5 4.5 27 4.4 3.6 7.5 3.0 28 6.0 3.6 6.5 4.5 29 5.6 3.6 8.0 4.5 30 5.2 3.6 6.5 3.0 31 3.6 4.0 4.5 3.0 32 6.0 6.4 9.0 8.0 33 4.0 4.4 9.0 2.5 34 4.4 3.2 6.0 3.5 35 5.2 3.6 8.5 4.5 36 3.2 6.4 4.5 3.0 37 8.0 4.4 4.5 3.0 38 4.8 6.4 6.0 2.5 39 4.0 4.8 9.0 6.0 40 4.0 4.8 6.0 3.5 41 3.6 5.6 9.0 5.0 42 5.6 3.6 7.0 4.5 43 3.2 4.0 7.5 4.0 44 5.6 3.6 8.0 2.5 45 4.4 4.0 5.0 4.0 46 5.2 4.8 5.5 4.0 47 3.6 5.6 7.0 5.0 48 4.4 5.6 4.5 3.0 49 6.0 4.0 6.0 4.0 50 4.4 5.6 8.5 3.5 51 4.0 6.4 2.5 2.0 52 5.6 3.6 6.0 4.0 53 7.2 4.0 6.0 4.0 54 5.6 2.4 3.0 2.0 55 4.4 3.2 12.0 10.0 56 4.8 5.2 6.0 4.0 57 5.2 4.0 6.0 4.0 58 3.6 3.2 7.0 3.0 59 4.0 2.8 3.5 2.0 60 6.0 6.0 6.5 4.0 61 8.0 3.6 6.0 4.5 62 4.8 4.0 6.5 3.0 63 4.8 4.8 7.0 3.5 64 5.6 5.2 4.0 4.5 65 5.2 4.0 5.5 2.5 66 4.4 4.4 4.5 2.5 67 6.8 3.2 5.5 4.0 68 4.8 3.6 6.5 4.0 69 5.2 5.2 5.0 3.0 70 5.6 4.4 5.5 4.0 71 5.2 3.2 6.0 3.5 72 6.0 3.6 4.5 3.5 73 5.2 3.6 7.5 4.5 74 4.0 6.0 9.0 5.5 75 4.4 3.6 7.5 3.0 76 7.6 4.0 6.0 4.0 77 5.2 5.6 6.5 3.0 78 6.8 4.8 7.0 4.5 79 5.2 4.8 5.0 4.0 80 3.6 4.4 6.5 3.0 81 4.4 5.6 6.5 5.0 82 4.0 2.4 5.5 4.0 83 3.6 4.8 6.5 4.0 84 4.8 3.2 8.0 5.0 85 4.8 5.6 4.0 2.5 86 5.2 4.4 8.0 3.5 87 5.2 4.0 5.5 2.5 88 4.8 5.6 4.5 4.0 89 6.0 4.8 8.0 7.0 90 8.8 4.0 6.0 3.5 91 5.2 5.6 7.0 4.0 92 6.0 2.0 4.0 3.0 93 5.2 4.4 4.5 2.5 94 6.0 4.0 7.5 3.0 95 4.0 3.6 5.5 5.0 96 4.4 4.0 10.5 6.0 97 6.4 6.4 7.0 4.5 98 4.4 5.2 9.0 6.0 99 4.4 3.6 6.0 3.5 100 4.0 4.0 6.5 4.0 101 4.0 4.0 7.5 5.0 102 6.4 2.8 6.0 3.0 103 4.8 3.6 9.5 5.0 104 4.4 3.2 7.5 5.0 105 6.4 5.6 5.5 5.0 106 7.6 5.6 5.5 2.5 107 4.4 3.2 5.0 3.5 108 6.4 3.6 6.5 5.0 109 6.0 5.6 7.5 5.5 110 9.6 5.6 6.0 3.0 111 5.6 3.2 6.0 3.5 112 6.0 3.2 8.0 6.0 113 4.4 3.2 4.5 5.5 114 6.0 2.8 9.0 5.5 115 4.8 2.4 4.0 5.5 116 4.0 3.2 6.5 2.5 117 5.6 2.4 8.5 4.0 118 5.2 4.4 4.5 3.0 119 3.6 5.6 7.5 4.5 120 6.0 4.4 4.0 2.0 121 6.0 4.4 3.5 2.0 122 5.6 4.4 6.0 3.5 123 4.4 5.6 7.0 5.5 124 3.2 3.2 3.0 3.0 125 4.4 8.0 4.0 3.5 126 4.4 4.4 8.5 4.0 127 3.2 3.2 5.0 2.0 128 4.0 4.4 5.5 4.0 129 4.4 4.0 7.0 4.5 130 5.2 5.6 5.5 4.0 131 4.4 4.4 6.5 5.5 132 8.0 3.6 6.0 4.0 133 4.0 3.6 5.5 2.5 134 6.0 3.2 4.5 2.0 135 4.8 4.0 6.0 4.0 136 5.6 5.2 10.0 5.0 137 9.2 5.2 6.0 3.0 138 5.6 4.8 6.5 4.5 139 6.4 3.2 6.0 4.5 140 4.4 5.2 6.0 6.5 141 4.8 5.6 4.5 4.5 142 4.0 4.8 7.5 5.0 143 5.6 5.6 12.0 10.0 144 4.8 6.0 3.5 2.5 145 4.8 5.2 8.5 5.5 146 4.4 6.4 5.5 3.0 147 4.8 3.6 8.5 4.5 148 5.2 3.6 5.5 3.5 149 4.4 3.6 6.0 4.5 150 7.6 3.2 7.0 5.0 151 4.8 2.8 5.5 4.5 152 6.8 6.4 8.0 4.0 153 3.6 4.4 10.5 3.5 154 4.8 3.6 7.0 3.0 155 7.6 4.4 10.0 6.5 156 7.2 3.6 6.5 3.0 157 6.0 5.6 5.5 4.0 158 5.6 5.2 7.5 5.0 159 4.4 6.4 9.5 8.0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Doubts Expectat Criticism 5.70985 -0.07996 -0.02180 -0.01558 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.2401 -0.8202 -0.2487 0.5929 4.5155 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.70985 0.54612 10.455 <2e-16 *** Doubts -0.07996 0.09127 -0.876 0.382 Expectat -0.02180 0.07284 -0.299 0.765 Criticism -0.01558 0.09380 -0.166 0.868 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.266 on 155 degrees of freedom Multiple R-squared: 0.007318, Adjusted R-squared: -0.0119 F-statistic: 0.3809 on 3 and 155 DF, p-value: 0.7669 > 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,] 0.97639453 0.04721093 0.02360547 [2,] 0.95190339 0.09619323 0.04809661 [3,] 0.91986285 0.16027430 0.08013715 [4,] 0.87349813 0.25300373 0.12650187 [5,] 0.81206607 0.37586786 0.18793393 [6,] 0.78758805 0.42482391 0.21241195 [7,] 0.71078555 0.57842891 0.28921445 [8,] 0.62751399 0.74497201 0.37248601 [9,] 0.65500430 0.68999139 0.34499570 [10,] 0.72318559 0.55362883 0.27681441 [11,] 0.65482027 0.69035945 0.34517973 [12,] 0.67176193 0.65647614 0.32823807 [13,] 0.64816428 0.70367144 0.35183572 [14,] 0.68010436 0.63979127 0.31989564 [15,] 0.72398311 0.55203378 0.27601689 [16,] 0.77571352 0.44857296 0.22428648 [17,] 0.72137841 0.55724318 0.27862159 [18,] 0.68092381 0.63815237 0.31907619 [19,] 0.64095470 0.71809060 0.35904530 [20,] 0.67098840 0.65802319 0.32901160 [21,] 0.63973861 0.72052277 0.36026139 [22,] 0.60749071 0.78501858 0.39250929 [23,] 0.55908749 0.88182501 0.44091251 [24,] 0.49882721 0.99765441 0.50117279 [25,] 0.55211562 0.89576875 0.44788438 [26,] 0.58754794 0.82490413 0.41245206 [27,] 0.55927067 0.88145866 0.44072933 [28,] 0.52483688 0.95032625 0.47516312 [29,] 0.46740839 0.93481678 0.53259161 [30,] 0.47138657 0.94277313 0.52861343 [31,] 0.71100033 0.57799933 0.28899967 [32,] 0.66710474 0.66579053 0.33289526 [33,] 0.63806477 0.72387046 0.36193523 [34,] 0.61564790 0.76870420 0.38435210 [35,] 0.59806912 0.80386175 0.40193088 [36,] 0.55208748 0.89582504 0.44791252 [37,] 0.60418592 0.79162815 0.39581408 [38,] 0.56492158 0.87015683 0.43507842 [39,] 0.53313897 0.93372205 0.46686103 [40,] 0.48394022 0.96788043 0.51605978 [41,] 0.47562958 0.95125916 0.52437042 [42,] 0.43420539 0.86841078 0.56579461 [43,] 0.40928013 0.81856026 0.59071987 [44,] 0.36870953 0.73741906 0.63129047 [45,] 0.34611500 0.69222999 0.65388500 [46,] 0.30519130 0.61038261 0.69480870 [47,] 0.38451586 0.76903172 0.61548414 [48,] 0.34087107 0.68174214 0.65912893 [49,] 0.30643839 0.61287677 0.69356161 [50,] 0.26716110 0.53432220 0.73283890 [51,] 0.22858810 0.45717620 0.77141190 [52,] 0.25688318 0.51376636 0.74311682 [53,] 0.27458806 0.54917612 0.72541194 [54,] 0.27915633 0.55831265 0.72084367 [55,] 0.45541149 0.91082298 0.54458851 [56,] 0.41229321 0.82458642 0.58770679 [57,] 0.37012746 0.74025493 0.62987254 [58,] 0.33342856 0.66685712 0.66657144 [59,] 0.29211983 0.58423966 0.70788017 [60,] 0.26811591 0.53623183 0.73188409 [61,] 0.28348640 0.56697281 0.71651360 [62,] 0.24905565 0.49811130 0.75094435 [63,] 0.21571054 0.43142107 0.78428946 [64,] 0.18721421 0.37442842 0.81278579 [65,] 0.15754815 0.31509631 0.84245185 [66,] 0.13840713 0.27681426 0.86159287 [67,] 0.11430331 0.22860662 0.88569669 [68,] 0.10394748 0.20789496 0.89605252 [69,] 0.09184988 0.18369975 0.90815012 [70,] 0.15678948 0.31357895 0.84321052 [71,] 0.13565871 0.27131743 0.86434129 [72,] 0.16248413 0.32496826 0.83751587 [73,] 0.13569138 0.27138277 0.86430862 [74,] 0.15048636 0.30097272 0.84951364 [75,] 0.13151561 0.26303121 0.86848439 [76,] 0.13984272 0.27968544 0.86015728 [77,] 0.15271226 0.30542452 0.84728774 [78,] 0.12952343 0.25904686 0.87047657 [79,] 0.10982775 0.21965549 0.89017225 [80,] 0.09196942 0.18393885 0.90803058 [81,] 0.07484782 0.14969564 0.92515218 [82,] 0.06110176 0.12220353 0.93889824 [83,] 0.05618714 0.11237428 0.94381286 [84,] 0.22061928 0.44123856 0.77938072 [85,] 0.19173465 0.38346929 0.80826535 [86,] 0.17153398 0.34306796 0.82846602 [87,] 0.14382110 0.28764219 0.85617890 [88,] 0.12887709 0.25775419 0.87112291 [89,] 0.12683484 0.25366967 0.87316516 [90,] 0.11139440 0.22278880 0.88860560 [91,] 0.11662385 0.23324769 0.88337615 [92,] 0.10226809 0.20453617 0.89773191 [93,] 0.09135894 0.18271789 0.90864106 [94,] 0.09028123 0.18056246 0.90971877 [95,] 0.08822235 0.17644469 0.91177765 [96,] 0.08243453 0.16486906 0.91756547 [97,] 0.06839922 0.13679844 0.93160078 [98,] 0.05994715 0.11989431 0.94005285 [99,] 0.06023543 0.12047086 0.93976457 [100,] 0.10322936 0.20645871 0.89677064 [101,] 0.09201326 0.18402652 0.90798674 [102,] 0.08872884 0.17745767 0.91127116 [103,] 0.07911388 0.15822777 0.92088612 [104,] 0.46949552 0.93899103 0.53050448 [105,] 0.42260121 0.84520243 0.57739879 [106,] 0.39011858 0.78023715 0.60988142 [107,] 0.35820489 0.71640978 0.64179511 [108,] 0.32443090 0.64886181 0.67556910 [109,] 0.28410119 0.56820238 0.71589881 [110,] 0.28655584 0.57311169 0.71344416 [111,] 0.24489463 0.48978925 0.75510537 [112,] 0.20539278 0.41078556 0.79460722 [113,] 0.22206114 0.44412227 0.77793886 [114,] 0.19818496 0.39636992 0.80181504 [115,] 0.17914787 0.35829574 0.82085213 [116,] 0.14888228 0.29776457 0.85111772 [117,] 0.12740581 0.25481163 0.87259419 [118,] 0.16585525 0.33171050 0.83414475 [119,] 0.13799033 0.27598067 0.86200967 [120,] 0.12272003 0.24544006 0.87727997 [121,] 0.18549876 0.37099751 0.81450124 [122,] 0.18708553 0.37417106 0.81291447 [123,] 0.17323128 0.34646256 0.82676872 [124,] 0.13781181 0.27562361 0.86218819 [125,] 0.12345249 0.24690497 0.87654751 [126,] 0.22167263 0.44334526 0.77832737 [127,] 0.24164862 0.48329724 0.75835138 [128,] 0.19733569 0.39467138 0.80266431 [129,] 0.16641408 0.33282816 0.83358592 [130,] 0.13012321 0.26024642 0.86987679 [131,] 0.59495868 0.81008264 0.40504132 [132,] 0.52755139 0.94489721 0.47244861 [133,] 0.48853509 0.97707017 0.51146491 [134,] 0.44797632 0.89595265 0.55202368 [135,] 0.37925800 0.75851599 0.62074200 [136,] 0.38725794 0.77451588 0.61274206 [137,] 0.31659231 0.63318461 0.68340769 [138,] 0.24863128 0.49726257 0.75136872 [139,] 0.19821003 0.39642005 0.80178997 [140,] 0.16885831 0.33771663 0.83114169 [141,] 0.12588863 0.25177727 0.87411137 [142,] 0.08647370 0.17294741 0.91352630 [143,] 0.09387708 0.18775415 0.90612292 [144,] 0.11592257 0.23184514 0.88407743 [145,] 0.11270127 0.22540254 0.88729873 [146,] 0.35459812 0.70919624 0.64540188 > postscript(file="/var/www/rcomp/tmp/1f11s1290461686.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2qb0w1290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3qb0w1290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4qb0w1290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5qb0w1290461686.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 -0.248689928 -0.819401736 0.329679930 -0.354716514 3.146936377 -0.404673894 7 8 9 10 11 12 3.561057523 -0.752432219 -1.012768159 0.038210707 -1.047973283 -1.873735798 13 14 15 16 17 18 0.743825489 0.445283486 -1.175800492 0.212753033 0.346219732 -0.871753359 19 20 21 22 23 24 -1.214140916 0.208922515 -2.240073911 0.683791405 -0.368121015 0.509252065 25 26 27 28 29 30 -0.775800492 -1.620362690 -0.811746673 0.789820977 0.422521910 -0.033547296 31 32 33 34 35 36 -1.645164251 1.122738525 -1.122866586 -0.868642472 0.033422222 -1.853258517 37 38 39 40 41 42 2.786820038 -0.228347007 -1.036356328 -1.140705315 -1.387966598 0.400721288 43 44 45 46 47 48 -1.964183536 0.391364214 -0.818685092 0.056183798 -1.431567842 -0.717227095 49 50 51 52 53 54 0.803115531 -0.622235181 -1.112438610 0.371131241 2.003115531 0.178618811 55 56 57 58 59 60 -0.636576224 -0.300931602 0.003115531 -1.654631274 -1.378496589 0.973937287 61 62 63 64 65 66 2.778920666 -0.401563007 -0.318904693 0.463256577 -0.031153053 -0.820969386 67 68 69 70 71 72 1.528246641 -0.417968447 0.061688927 0.424199508 -0.068642472 0.730640884 73 74 75 76 77 78 0.011621599 -0.948192884 -0.811746673 2.403115531 0.126374150 1.696674155 79 80 81 82 83 84 0.045283486 -1.569578717 -0.642468154 -1.335721937 -1.522015580 -0.401672955 85 86 87 88 89 90 -0.335916830 0.070911640 -0.031153053 -0.301648247 0.957421898 3.595326106 91 92 93 94 95 96 0.152853309 0.584013992 -0.020969386 0.820237616 -1.224190221 -0.667623972 97 98 99 100 101 102 1.424611312 -0.604372039 -0.836658183 -1.185984158 -1.148604688 1.091583815 103 104 105 106 107 108 -0.336987732 -0.812573266 1.335731224 2.496784103 -0.890443094 1.197610401 109 110 111 112 113 114 0.987121893 4.515473839 0.331357528 0.813905894 -0.870185709 0.795932803 115 116 117 118 119 120 -0.545054598 -1.273321009 0.329679930 -0.013179962 -1.428456955 0.760340878 121 122 123 124 125 126 0.749440567 0.427310396 -0.623778418 -2.141833763 -0.528432247 -0.710398624 127 128 129 130 131 132 -2.113811367 -1.175800492 -0.767294423 0.120152376 -0.730631597 2.771131241 133 134 135 136 137 138 -1.263137342 0.675288322 -0.396884469 0.601849736 4.083489550 0.485773844 139 140 141 142 143 144 1.146936377 -0.661984481 -0.293858822 -1.084636109 0.755329511 -0.314832852 145 146 147 148 149 150 -0.223061774 -0.631457894 -0.366577778 -0.047558494 -0.821079334 2.376526423 151 152 153 154 155 156 -0.495948224 1.838622510 -1.474586804 -0.422646984 2.561249430 1.966452704 157 158 159 0.920152376 0.547348180 -0.466361163 > postscript(file="/var/www/rcomp/tmp/6i2hh1290461686.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 -0.248689928 NA 1 -0.819401736 -0.248689928 2 0.329679930 -0.819401736 3 -0.354716514 0.329679930 4 3.146936377 -0.354716514 5 -0.404673894 3.146936377 6 3.561057523 -0.404673894 7 -0.752432219 3.561057523 8 -1.012768159 -0.752432219 9 0.038210707 -1.012768159 10 -1.047973283 0.038210707 11 -1.873735798 -1.047973283 12 0.743825489 -1.873735798 13 0.445283486 0.743825489 14 -1.175800492 0.445283486 15 0.212753033 -1.175800492 16 0.346219732 0.212753033 17 -0.871753359 0.346219732 18 -1.214140916 -0.871753359 19 0.208922515 -1.214140916 20 -2.240073911 0.208922515 21 0.683791405 -2.240073911 22 -0.368121015 0.683791405 23 0.509252065 -0.368121015 24 -0.775800492 0.509252065 25 -1.620362690 -0.775800492 26 -0.811746673 -1.620362690 27 0.789820977 -0.811746673 28 0.422521910 0.789820977 29 -0.033547296 0.422521910 30 -1.645164251 -0.033547296 31 1.122738525 -1.645164251 32 -1.122866586 1.122738525 33 -0.868642472 -1.122866586 34 0.033422222 -0.868642472 35 -1.853258517 0.033422222 36 2.786820038 -1.853258517 37 -0.228347007 2.786820038 38 -1.036356328 -0.228347007 39 -1.140705315 -1.036356328 40 -1.387966598 -1.140705315 41 0.400721288 -1.387966598 42 -1.964183536 0.400721288 43 0.391364214 -1.964183536 44 -0.818685092 0.391364214 45 0.056183798 -0.818685092 46 -1.431567842 0.056183798 47 -0.717227095 -1.431567842 48 0.803115531 -0.717227095 49 -0.622235181 0.803115531 50 -1.112438610 -0.622235181 51 0.371131241 -1.112438610 52 2.003115531 0.371131241 53 0.178618811 2.003115531 54 -0.636576224 0.178618811 55 -0.300931602 -0.636576224 56 0.003115531 -0.300931602 57 -1.654631274 0.003115531 58 -1.378496589 -1.654631274 59 0.973937287 -1.378496589 60 2.778920666 0.973937287 61 -0.401563007 2.778920666 62 -0.318904693 -0.401563007 63 0.463256577 -0.318904693 64 -0.031153053 0.463256577 65 -0.820969386 -0.031153053 66 1.528246641 -0.820969386 67 -0.417968447 1.528246641 68 0.061688927 -0.417968447 69 0.424199508 0.061688927 70 -0.068642472 0.424199508 71 0.730640884 -0.068642472 72 0.011621599 0.730640884 73 -0.948192884 0.011621599 74 -0.811746673 -0.948192884 75 2.403115531 -0.811746673 76 0.126374150 2.403115531 77 1.696674155 0.126374150 78 0.045283486 1.696674155 79 -1.569578717 0.045283486 80 -0.642468154 -1.569578717 81 -1.335721937 -0.642468154 82 -1.522015580 -1.335721937 83 -0.401672955 -1.522015580 84 -0.335916830 -0.401672955 85 0.070911640 -0.335916830 86 -0.031153053 0.070911640 87 -0.301648247 -0.031153053 88 0.957421898 -0.301648247 89 3.595326106 0.957421898 90 0.152853309 3.595326106 91 0.584013992 0.152853309 92 -0.020969386 0.584013992 93 0.820237616 -0.020969386 94 -1.224190221 0.820237616 95 -0.667623972 -1.224190221 96 1.424611312 -0.667623972 97 -0.604372039 1.424611312 98 -0.836658183 -0.604372039 99 -1.185984158 -0.836658183 100 -1.148604688 -1.185984158 101 1.091583815 -1.148604688 102 -0.336987732 1.091583815 103 -0.812573266 -0.336987732 104 1.335731224 -0.812573266 105 2.496784103 1.335731224 106 -0.890443094 2.496784103 107 1.197610401 -0.890443094 108 0.987121893 1.197610401 109 4.515473839 0.987121893 110 0.331357528 4.515473839 111 0.813905894 0.331357528 112 -0.870185709 0.813905894 113 0.795932803 -0.870185709 114 -0.545054598 0.795932803 115 -1.273321009 -0.545054598 116 0.329679930 -1.273321009 117 -0.013179962 0.329679930 118 -1.428456955 -0.013179962 119 0.760340878 -1.428456955 120 0.749440567 0.760340878 121 0.427310396 0.749440567 122 -0.623778418 0.427310396 123 -2.141833763 -0.623778418 124 -0.528432247 -2.141833763 125 -0.710398624 -0.528432247 126 -2.113811367 -0.710398624 127 -1.175800492 -2.113811367 128 -0.767294423 -1.175800492 129 0.120152376 -0.767294423 130 -0.730631597 0.120152376 131 2.771131241 -0.730631597 132 -1.263137342 2.771131241 133 0.675288322 -1.263137342 134 -0.396884469 0.675288322 135 0.601849736 -0.396884469 136 4.083489550 0.601849736 137 0.485773844 4.083489550 138 1.146936377 0.485773844 139 -0.661984481 1.146936377 140 -0.293858822 -0.661984481 141 -1.084636109 -0.293858822 142 0.755329511 -1.084636109 143 -0.314832852 0.755329511 144 -0.223061774 -0.314832852 145 -0.631457894 -0.223061774 146 -0.366577778 -0.631457894 147 -0.047558494 -0.366577778 148 -0.821079334 -0.047558494 149 2.376526423 -0.821079334 150 -0.495948224 2.376526423 151 1.838622510 -0.495948224 152 -1.474586804 1.838622510 153 -0.422646984 -1.474586804 154 2.561249430 -0.422646984 155 1.966452704 2.561249430 156 0.920152376 1.966452704 157 0.547348180 0.920152376 158 -0.466361163 0.547348180 159 NA -0.466361163 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.819401736 -0.248689928 [2,] 0.329679930 -0.819401736 [3,] -0.354716514 0.329679930 [4,] 3.146936377 -0.354716514 [5,] -0.404673894 3.146936377 [6,] 3.561057523 -0.404673894 [7,] -0.752432219 3.561057523 [8,] -1.012768159 -0.752432219 [9,] 0.038210707 -1.012768159 [10,] -1.047973283 0.038210707 [11,] -1.873735798 -1.047973283 [12,] 0.743825489 -1.873735798 [13,] 0.445283486 0.743825489 [14,] -1.175800492 0.445283486 [15,] 0.212753033 -1.175800492 [16,] 0.346219732 0.212753033 [17,] -0.871753359 0.346219732 [18,] -1.214140916 -0.871753359 [19,] 0.208922515 -1.214140916 [20,] -2.240073911 0.208922515 [21,] 0.683791405 -2.240073911 [22,] -0.368121015 0.683791405 [23,] 0.509252065 -0.368121015 [24,] -0.775800492 0.509252065 [25,] -1.620362690 -0.775800492 [26,] -0.811746673 -1.620362690 [27,] 0.789820977 -0.811746673 [28,] 0.422521910 0.789820977 [29,] -0.033547296 0.422521910 [30,] -1.645164251 -0.033547296 [31,] 1.122738525 -1.645164251 [32,] -1.122866586 1.122738525 [33,] -0.868642472 -1.122866586 [34,] 0.033422222 -0.868642472 [35,] -1.853258517 0.033422222 [36,] 2.786820038 -1.853258517 [37,] -0.228347007 2.786820038 [38,] -1.036356328 -0.228347007 [39,] -1.140705315 -1.036356328 [40,] -1.387966598 -1.140705315 [41,] 0.400721288 -1.387966598 [42,] -1.964183536 0.400721288 [43,] 0.391364214 -1.964183536 [44,] -0.818685092 0.391364214 [45,] 0.056183798 -0.818685092 [46,] -1.431567842 0.056183798 [47,] -0.717227095 -1.431567842 [48,] 0.803115531 -0.717227095 [49,] -0.622235181 0.803115531 [50,] -1.112438610 -0.622235181 [51,] 0.371131241 -1.112438610 [52,] 2.003115531 0.371131241 [53,] 0.178618811 2.003115531 [54,] -0.636576224 0.178618811 [55,] -0.300931602 -0.636576224 [56,] 0.003115531 -0.300931602 [57,] -1.654631274 0.003115531 [58,] -1.378496589 -1.654631274 [59,] 0.973937287 -1.378496589 [60,] 2.778920666 0.973937287 [61,] -0.401563007 2.778920666 [62,] -0.318904693 -0.401563007 [63,] 0.463256577 -0.318904693 [64,] -0.031153053 0.463256577 [65,] -0.820969386 -0.031153053 [66,] 1.528246641 -0.820969386 [67,] -0.417968447 1.528246641 [68,] 0.061688927 -0.417968447 [69,] 0.424199508 0.061688927 [70,] -0.068642472 0.424199508 [71,] 0.730640884 -0.068642472 [72,] 0.011621599 0.730640884 [73,] -0.948192884 0.011621599 [74,] -0.811746673 -0.948192884 [75,] 2.403115531 -0.811746673 [76,] 0.126374150 2.403115531 [77,] 1.696674155 0.126374150 [78,] 0.045283486 1.696674155 [79,] -1.569578717 0.045283486 [80,] -0.642468154 -1.569578717 [81,] -1.335721937 -0.642468154 [82,] -1.522015580 -1.335721937 [83,] -0.401672955 -1.522015580 [84,] -0.335916830 -0.401672955 [85,] 0.070911640 -0.335916830 [86,] -0.031153053 0.070911640 [87,] -0.301648247 -0.031153053 [88,] 0.957421898 -0.301648247 [89,] 3.595326106 0.957421898 [90,] 0.152853309 3.595326106 [91,] 0.584013992 0.152853309 [92,] -0.020969386 0.584013992 [93,] 0.820237616 -0.020969386 [94,] -1.224190221 0.820237616 [95,] -0.667623972 -1.224190221 [96,] 1.424611312 -0.667623972 [97,] -0.604372039 1.424611312 [98,] -0.836658183 -0.604372039 [99,] -1.185984158 -0.836658183 [100,] -1.148604688 -1.185984158 [101,] 1.091583815 -1.148604688 [102,] -0.336987732 1.091583815 [103,] -0.812573266 -0.336987732 [104,] 1.335731224 -0.812573266 [105,] 2.496784103 1.335731224 [106,] -0.890443094 2.496784103 [107,] 1.197610401 -0.890443094 [108,] 0.987121893 1.197610401 [109,] 4.515473839 0.987121893 [110,] 0.331357528 4.515473839 [111,] 0.813905894 0.331357528 [112,] -0.870185709 0.813905894 [113,] 0.795932803 -0.870185709 [114,] -0.545054598 0.795932803 [115,] -1.273321009 -0.545054598 [116,] 0.329679930 -1.273321009 [117,] -0.013179962 0.329679930 [118,] -1.428456955 -0.013179962 [119,] 0.760340878 -1.428456955 [120,] 0.749440567 0.760340878 [121,] 0.427310396 0.749440567 [122,] -0.623778418 0.427310396 [123,] -2.141833763 -0.623778418 [124,] -0.528432247 -2.141833763 [125,] -0.710398624 -0.528432247 [126,] -2.113811367 -0.710398624 [127,] -1.175800492 -2.113811367 [128,] -0.767294423 -1.175800492 [129,] 0.120152376 -0.767294423 [130,] -0.730631597 0.120152376 [131,] 2.771131241 -0.730631597 [132,] -1.263137342 2.771131241 [133,] 0.675288322 -1.263137342 [134,] -0.396884469 0.675288322 [135,] 0.601849736 -0.396884469 [136,] 4.083489550 0.601849736 [137,] 0.485773844 4.083489550 [138,] 1.146936377 0.485773844 [139,] -0.661984481 1.146936377 [140,] -0.293858822 -0.661984481 [141,] -1.084636109 -0.293858822 [142,] 0.755329511 -1.084636109 [143,] -0.314832852 0.755329511 [144,] -0.223061774 -0.314832852 [145,] -0.631457894 -0.223061774 [146,] -0.366577778 -0.631457894 [147,] -0.047558494 -0.366577778 [148,] -0.821079334 -0.047558494 [149,] 2.376526423 -0.821079334 [150,] -0.495948224 2.376526423 [151,] 1.838622510 -0.495948224 [152,] -1.474586804 1.838622510 [153,] -0.422646984 -1.474586804 [154,] 2.561249430 -0.422646984 [155,] 1.966452704 2.561249430 [156,] 0.920152376 1.966452704 [157,] 0.547348180 0.920152376 [158,] -0.466361163 0.547348180 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.819401736 -0.248689928 2 0.329679930 -0.819401736 3 -0.354716514 0.329679930 4 3.146936377 -0.354716514 5 -0.404673894 3.146936377 6 3.561057523 -0.404673894 7 -0.752432219 3.561057523 8 -1.012768159 -0.752432219 9 0.038210707 -1.012768159 10 -1.047973283 0.038210707 11 -1.873735798 -1.047973283 12 0.743825489 -1.873735798 13 0.445283486 0.743825489 14 -1.175800492 0.445283486 15 0.212753033 -1.175800492 16 0.346219732 0.212753033 17 -0.871753359 0.346219732 18 -1.214140916 -0.871753359 19 0.208922515 -1.214140916 20 -2.240073911 0.208922515 21 0.683791405 -2.240073911 22 -0.368121015 0.683791405 23 0.509252065 -0.368121015 24 -0.775800492 0.509252065 25 -1.620362690 -0.775800492 26 -0.811746673 -1.620362690 27 0.789820977 -0.811746673 28 0.422521910 0.789820977 29 -0.033547296 0.422521910 30 -1.645164251 -0.033547296 31 1.122738525 -1.645164251 32 -1.122866586 1.122738525 33 -0.868642472 -1.122866586 34 0.033422222 -0.868642472 35 -1.853258517 0.033422222 36 2.786820038 -1.853258517 37 -0.228347007 2.786820038 38 -1.036356328 -0.228347007 39 -1.140705315 -1.036356328 40 -1.387966598 -1.140705315 41 0.400721288 -1.387966598 42 -1.964183536 0.400721288 43 0.391364214 -1.964183536 44 -0.818685092 0.391364214 45 0.056183798 -0.818685092 46 -1.431567842 0.056183798 47 -0.717227095 -1.431567842 48 0.803115531 -0.717227095 49 -0.622235181 0.803115531 50 -1.112438610 -0.622235181 51 0.371131241 -1.112438610 52 2.003115531 0.371131241 53 0.178618811 2.003115531 54 -0.636576224 0.178618811 55 -0.300931602 -0.636576224 56 0.003115531 -0.300931602 57 -1.654631274 0.003115531 58 -1.378496589 -1.654631274 59 0.973937287 -1.378496589 60 2.778920666 0.973937287 61 -0.401563007 2.778920666 62 -0.318904693 -0.401563007 63 0.463256577 -0.318904693 64 -0.031153053 0.463256577 65 -0.820969386 -0.031153053 66 1.528246641 -0.820969386 67 -0.417968447 1.528246641 68 0.061688927 -0.417968447 69 0.424199508 0.061688927 70 -0.068642472 0.424199508 71 0.730640884 -0.068642472 72 0.011621599 0.730640884 73 -0.948192884 0.011621599 74 -0.811746673 -0.948192884 75 2.403115531 -0.811746673 76 0.126374150 2.403115531 77 1.696674155 0.126374150 78 0.045283486 1.696674155 79 -1.569578717 0.045283486 80 -0.642468154 -1.569578717 81 -1.335721937 -0.642468154 82 -1.522015580 -1.335721937 83 -0.401672955 -1.522015580 84 -0.335916830 -0.401672955 85 0.070911640 -0.335916830 86 -0.031153053 0.070911640 87 -0.301648247 -0.031153053 88 0.957421898 -0.301648247 89 3.595326106 0.957421898 90 0.152853309 3.595326106 91 0.584013992 0.152853309 92 -0.020969386 0.584013992 93 0.820237616 -0.020969386 94 -1.224190221 0.820237616 95 -0.667623972 -1.224190221 96 1.424611312 -0.667623972 97 -0.604372039 1.424611312 98 -0.836658183 -0.604372039 99 -1.185984158 -0.836658183 100 -1.148604688 -1.185984158 101 1.091583815 -1.148604688 102 -0.336987732 1.091583815 103 -0.812573266 -0.336987732 104 1.335731224 -0.812573266 105 2.496784103 1.335731224 106 -0.890443094 2.496784103 107 1.197610401 -0.890443094 108 0.987121893 1.197610401 109 4.515473839 0.987121893 110 0.331357528 4.515473839 111 0.813905894 0.331357528 112 -0.870185709 0.813905894 113 0.795932803 -0.870185709 114 -0.545054598 0.795932803 115 -1.273321009 -0.545054598 116 0.329679930 -1.273321009 117 -0.013179962 0.329679930 118 -1.428456955 -0.013179962 119 0.760340878 -1.428456955 120 0.749440567 0.760340878 121 0.427310396 0.749440567 122 -0.623778418 0.427310396 123 -2.141833763 -0.623778418 124 -0.528432247 -2.141833763 125 -0.710398624 -0.528432247 126 -2.113811367 -0.710398624 127 -1.175800492 -2.113811367 128 -0.767294423 -1.175800492 129 0.120152376 -0.767294423 130 -0.730631597 0.120152376 131 2.771131241 -0.730631597 132 -1.263137342 2.771131241 133 0.675288322 -1.263137342 134 -0.396884469 0.675288322 135 0.601849736 -0.396884469 136 4.083489550 0.601849736 137 0.485773844 4.083489550 138 1.146936377 0.485773844 139 -0.661984481 1.146936377 140 -0.293858822 -0.661984481 141 -1.084636109 -0.293858822 142 0.755329511 -1.084636109 143 -0.314832852 0.755329511 144 -0.223061774 -0.314832852 145 -0.631457894 -0.223061774 146 -0.366577778 -0.631457894 147 -0.047558494 -0.366577778 148 -0.821079334 -0.047558494 149 2.376526423 -0.821079334 150 -0.495948224 2.376526423 151 1.838622510 -0.495948224 152 -1.474586804 1.838622510 153 -0.422646984 -1.474586804 154 2.561249430 -0.422646984 155 1.966452704 2.561249430 156 0.920152376 1.966452704 157 0.547348180 0.920152376 158 -0.466361163 0.547348180 > 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/rcomp/tmp/7btz11290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8btz11290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9btz11290461686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10ev1z1290461687.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11hvzn1290461687.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/rcomp/tmp/123wgb1290461687.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/rcomp/tmp/13afd51290461687.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/rcomp/tmp/1426u71290461687.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/rcomp/tmp/1566sv1290461687.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/rcomp/tmp/162yq41290461687.tab") + } > > try(system("convert tmp/1f11s1290461686.ps tmp/1f11s1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/2qb0w1290461686.ps tmp/2qb0w1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/3qb0w1290461686.ps tmp/3qb0w1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/4qb0w1290461686.ps tmp/4qb0w1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/5qb0w1290461686.ps tmp/5qb0w1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/6i2hh1290461686.ps tmp/6i2hh1290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/7btz11290461686.ps tmp/7btz11290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/8btz11290461686.ps tmp/8btz11290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/9btz11290461686.ps tmp/9btz11290461686.png",intern=TRUE)) character(0) > try(system("convert tmp/10ev1z1290461687.ps tmp/10ev1z1290461687.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.310 2.090 7.414