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. 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,dimnames=list(c('Concernovermistakes' + ,'DoubtsaboutactionsFemale' + ,'DoubtsaboutactionsMale' + ,'ParentalexpectationsFemale' + ,'ParentalexpectationsMale' + ,'ParentalcritismFemale' + ,'ParentalcritismMale' + ,'PersonalstandardsFemale' + ,'PersonalstandarsMale' + ,'OrganizationFemale' + ,'OrganizationMale') + ,1:159)) > y <- array(NA,dim=c(11,159),dimnames=list(c('Concernovermistakes','DoubtsaboutactionsFemale','DoubtsaboutactionsMale','ParentalexpectationsFemale','ParentalexpectationsMale','ParentalcritismFemale','ParentalcritismMale','PersonalstandardsFemale','PersonalstandarsMale','OrganizationFemale','OrganizationMale'),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 Concernovermistakes DoubtsaboutactionsFemale DoubtsaboutactionsMale 1 24 14 0 2 25 11 0 3 17 6 0 4 18 12 12 5 18 8 8 6 16 10 10 7 20 10 10 8 16 11 11 9 18 16 16 10 17 11 11 11 23 13 0 12 30 12 0 13 23 8 8 14 18 12 12 15 15 11 11 16 12 4 4 17 21 9 0 18 15 8 8 19 20 8 8 20 31 14 0 21 27 15 0 22 34 16 16 23 21 9 9 24 31 14 14 25 19 11 11 26 16 8 0 27 20 9 9 28 21 9 9 29 22 9 9 30 17 9 9 31 24 10 10 32 25 16 0 33 26 11 0 34 25 8 8 35 17 9 9 36 32 16 16 37 33 11 11 38 13 16 16 39 32 12 12 40 25 12 12 41 29 14 14 42 22 9 9 43 18 10 10 44 17 9 9 45 20 10 0 46 15 12 12 47 20 14 14 48 33 14 14 49 29 10 0 50 23 14 14 51 26 16 0 52 18 9 9 53 20 10 0 54 11 6 0 55 28 8 8 56 26 13 13 57 22 10 0 58 17 8 8 59 12 7 0 60 14 15 15 61 17 9 9 62 21 10 10 63 19 12 12 64 18 13 13 65 10 10 0 66 29 11 0 67 31 8 8 68 19 9 0 69 9 13 13 70 20 11 11 71 28 8 8 72 19 9 0 73 30 9 0 74 29 15 0 75 26 9 0 76 23 10 0 77 13 14 14 78 21 12 12 79 19 12 12 80 28 11 11 81 23 14 14 82 18 6 6 83 21 12 0 84 20 8 8 85 23 14 14 86 21 11 11 87 21 10 10 88 15 14 14 89 28 12 12 90 19 10 10 91 26 14 14 92 10 5 5 93 16 11 0 94 22 10 10 95 19 9 9 96 31 10 10 97 31 16 0 98 29 13 13 99 19 9 0 100 22 10 10 101 23 10 10 102 15 7 0 103 20 9 0 104 18 8 8 105 23 14 14 106 25 14 14 107 21 8 8 108 24 9 9 109 25 14 14 110 17 14 14 111 13 8 8 112 28 8 8 113 21 8 0 114 25 7 7 115 9 6 0 116 16 8 8 117 19 6 6 118 17 11 11 119 25 14 14 120 20 11 11 121 29 11 11 122 14 11 11 123 22 14 14 124 15 8 8 125 19 20 0 126 20 11 11 127 15 8 0 128 20 11 11 129 18 10 10 130 33 14 14 131 22 11 11 132 16 9 9 133 17 9 9 134 16 8 8 135 21 10 0 136 26 13 0 137 18 13 13 138 18 12 12 139 17 8 8 140 22 13 13 141 30 14 14 142 30 12 0 143 24 14 14 144 21 15 15 145 21 13 13 146 29 16 16 147 31 9 9 148 20 9 9 149 16 9 0 150 22 8 0 151 20 7 7 152 28 16 16 153 38 11 11 154 22 9 0 155 20 11 11 156 17 9 0 157 28 14 14 158 22 13 13 159 31 16 0 ParentalexpectationsFemale ParentalexpectationsMale ParentalcritismFemale 1 11 0 12 2 7 0 8 3 17 0 8 4 10 10 8 5 12 12 9 6 12 12 7 7 11 11 4 8 11 11 11 9 12 12 7 10 13 13 7 11 14 0 12 12 16 0 10 13 11 11 10 14 10 10 8 15 11 11 8 16 15 15 4 17 9 0 9 18 11 11 8 19 17 17 7 20 17 0 11 21 11 0 9 22 18 18 11 23 14 14 13 24 10 10 8 25 11 11 8 26 15 0 9 27 15 15 6 28 13 13 9 29 16 16 9 30 13 13 6 31 9 9 6 32 18 0 16 33 18 0 5 34 12 12 7 35 17 17 9 36 9 9 6 37 9 9 6 38 12 12 5 39 18 18 12 40 12 12 7 41 18 18 10 42 14 14 9 43 15 15 8 44 16 16 5 45 10 0 8 46 11 11 8 47 14 14 10 48 9 9 6 49 12 0 8 50 17 17 7 51 5 0 4 52 12 12 8 53 12 0 8 54 6 0 4 55 24 24 20 56 12 12 8 57 12 0 8 58 14 14 6 59 7 0 4 60 13 13 8 61 12 12 9 62 13 13 6 63 14 14 7 64 8 8 9 65 11 0 5 66 9 0 5 67 11 11 8 68 13 0 8 69 10 10 6 70 11 11 8 71 12 12 7 72 9 0 7 73 15 0 9 74 18 0 11 75 15 0 6 76 12 0 8 77 13 13 6 78 14 14 9 79 10 10 8 80 13 13 6 81 13 13 10 82 11 11 8 83 13 0 8 84 16 16 10 85 8 8 5 86 16 16 7 87 11 11 5 88 9 9 8 89 16 16 14 90 12 12 7 91 14 14 8 92 8 8 6 93 9 0 5 94 15 15 6 95 11 11 10 96 21 21 12 97 14 0 9 98 18 18 12 99 12 0 7 100 13 13 8 101 15 15 10 102 12 0 6 103 19 0 10 104 15 15 10 105 11 11 10 106 11 11 5 107 10 10 7 108 13 13 10 109 15 15 11 110 12 12 6 111 12 12 7 112 16 16 12 113 9 0 11 114 18 18 11 115 8 0 11 116 13 13 5 117 17 17 8 118 9 9 6 119 15 15 9 120 8 8 4 121 7 7 4 122 12 12 7 123 14 14 11 124 6 6 6 125 8 0 7 126 17 17 8 127 10 0 4 128 11 11 8 129 14 14 9 130 11 11 8 131 13 13 11 132 12 12 8 133 11 11 5 134 9 9 4 135 12 0 8 136 20 0 10 137 12 12 6 138 13 13 9 139 12 12 9 140 12 12 13 141 9 9 9 142 15 0 10 143 24 24 20 144 7 7 5 145 17 17 11 146 11 11 6 147 17 17 9 148 11 11 7 149 12 0 9 150 14 0 10 151 11 11 9 152 16 16 8 153 21 21 7 154 14 0 6 155 20 20 13 156 13 0 6 157 11 11 8 158 15 15 10 159 19 0 16 ParentalcritismMale PersonalstandardsFemale PersonalstandarsMale 1 0 24 0 2 0 25 0 3 0 30 0 4 8 19 19 5 9 22 22 6 7 22 22 7 4 25 25 8 11 23 23 9 7 17 17 10 7 21 21 11 0 19 0 12 0 19 0 13 10 15 15 14 8 16 16 15 8 23 23 16 4 27 27 17 0 22 0 18 8 14 14 19 7 22 22 20 0 23 0 21 0 23 0 22 11 21 21 23 13 19 19 24 8 18 18 25 8 20 20 26 0 23 0 27 6 25 25 28 9 19 19 29 9 24 24 30 6 22 22 31 6 25 25 32 0 26 0 33 0 29 0 34 7 32 32 35 9 25 25 36 6 29 29 37 6 28 28 38 5 17 17 39 12 28 28 40 7 29 29 41 10 26 26 42 9 25 25 43 8 14 14 44 5 25 25 45 0 26 0 46 8 20 20 47 10 18 18 48 6 32 32 49 0 25 0 50 7 25 25 51 0 23 0 52 8 21 21 53 0 20 0 54 0 15 0 55 20 30 30 56 8 24 24 57 0 26 0 58 6 24 24 59 0 22 0 60 8 14 14 61 9 24 24 62 6 24 24 63 7 24 24 64 9 24 24 65 0 19 0 66 0 31 0 67 8 22 22 68 0 27 0 69 6 19 19 70 8 25 25 71 7 20 20 72 0 21 0 73 0 27 0 74 0 23 0 75 0 25 0 76 0 20 0 77 6 21 21 78 9 22 22 79 8 23 23 80 6 25 25 81 10 25 25 82 8 17 17 83 0 19 0 84 10 25 25 85 5 19 19 86 7 20 20 87 5 26 26 88 8 23 23 89 14 27 27 90 7 17 17 91 8 17 17 92 6 19 19 93 0 17 0 94 6 22 22 95 10 21 21 96 12 32 32 97 0 21 0 98 12 21 21 99 0 18 0 100 8 18 18 101 10 23 23 102 0 19 0 103 0 20 0 104 10 21 21 105 10 20 20 106 5 17 17 107 7 18 18 108 10 19 19 109 11 22 22 110 6 15 15 111 7 14 14 112 12 18 18 113 0 24 0 114 11 35 35 115 0 29 0 116 5 21 21 117 8 25 25 118 6 20 20 119 9 22 22 120 4 13 13 121 4 26 26 122 7 17 17 123 11 25 25 124 6 20 20 125 0 19 0 126 8 21 21 127 0 22 0 128 8 24 24 129 9 21 21 130 8 26 26 131 11 24 24 132 8 16 16 133 5 23 23 134 4 18 18 135 0 16 0 136 0 26 0 137 6 19 19 138 9 21 21 139 9 21 21 140 13 22 22 141 9 23 23 142 0 29 0 143 20 21 21 144 5 21 21 145 11 23 23 146 6 27 27 147 9 25 25 148 7 21 21 149 0 10 0 150 0 20 0 151 9 26 26 152 8 24 24 153 7 29 29 154 0 19 0 155 13 24 24 156 0 19 0 157 8 24 24 158 10 22 22 159 0 17 0 OrganizationFemale OrganizationMale 1 26 0 2 23 0 3 25 0 4 23 23 5 19 19 6 29 29 7 25 25 8 21 21 9 22 22 10 25 25 11 24 0 12 18 0 13 22 22 14 15 15 15 22 22 16 28 28 17 20 0 18 12 12 19 24 24 20 20 0 21 21 0 22 20 20 23 21 21 24 23 23 25 28 28 26 24 0 27 24 24 28 24 24 29 23 23 30 23 23 31 29 29 32 24 0 33 18 0 34 25 25 35 21 21 36 26 26 37 22 22 38 22 22 39 22 22 40 23 23 41 30 30 42 23 23 43 17 17 44 23 23 45 23 0 46 25 25 47 24 24 48 24 24 49 23 0 50 21 21 51 24 0 52 24 24 53 28 0 54 16 0 55 20 20 56 29 29 57 27 0 58 22 22 59 28 0 60 16 16 61 25 25 62 24 24 63 28 28 64 24 24 65 23 0 66 30 0 67 24 24 68 21 0 69 25 25 70 25 25 71 22 22 72 23 0 73 26 0 74 23 0 75 25 0 76 21 0 77 25 25 78 24 24 79 29 29 80 22 22 81 27 27 82 26 26 83 22 0 84 24 24 85 27 27 86 24 24 87 24 24 88 29 29 89 22 22 90 21 21 91 24 24 92 24 24 93 23 0 94 20 20 95 27 27 96 26 26 97 25 0 98 21 21 99 21 0 100 19 19 101 21 21 102 21 0 103 16 0 104 22 22 105 29 29 106 15 15 107 17 17 108 15 15 109 21 21 110 21 21 111 19 19 112 24 24 113 20 0 114 17 17 115 23 0 116 24 24 117 14 14 118 19 19 119 24 24 120 13 13 121 22 22 122 16 16 123 19 19 124 25 25 125 25 0 126 23 23 127 24 0 128 26 26 129 26 26 130 25 25 131 18 18 132 21 21 133 26 26 134 23 23 135 23 0 136 22 0 137 20 20 138 13 13 139 24 24 140 15 15 141 14 14 142 22 0 143 10 10 144 24 24 145 22 22 146 24 24 147 19 19 148 20 20 149 13 0 150 20 0 151 22 22 152 24 24 153 29 29 154 12 0 155 20 20 156 21 0 157 24 24 158 22 22 159 20 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) DoubtsaboutactionsFemale -1.39516 1.06624 DoubtsaboutactionsMale ParentalexpectationsFemale -0.39034 0.44439 ParentalexpectationsMale ParentalcritismFemale -0.32676 0.05124 ParentalcritismMale PersonalstandardsFemale 0.18976 0.43572 PersonalstandarsMale OrganizationFemale 0.20857 -0.17450 OrganizationMale 0.07267 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -10.709 -2.495 -0.601 2.734 12.072 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.39516 3.09607 -0.451 0.6529 DoubtsaboutactionsFemale 1.06624 0.24107 4.423 1.88e-05 *** DoubtsaboutactionsMale -0.39034 0.28101 -1.389 0.1669 ParentalexpectationsFemale 0.44439 0.21820 2.037 0.0435 * ParentalexpectationsMale -0.32676 0.26812 -1.219 0.2249 ParentalcritismFemale 0.05124 0.30707 0.167 0.8677 ParentalcritismMale 0.18976 0.36796 0.516 0.6068 PersonalstandardsFemale 0.43572 0.18500 2.355 0.0198 * PersonalstandarsMale 0.20857 0.21422 0.974 0.3318 OrganizationFemale -0.17450 0.20566 -0.848 0.3975 OrganizationMale 0.07267 0.21510 0.338 0.7360 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.473 on 148 degrees of freedom Multiple R-squared: 0.4278, Adjusted R-squared: 0.3892 F-statistic: 11.07 on 10 and 148 DF, p-value: 5.854e-14 > 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,] 0.13618891 0.27237782 0.86381109 [2,] 0.07253538 0.14507076 0.92746462 [3,] 0.09419798 0.18839596 0.90580202 [4,] 0.06407793 0.12815586 0.93592207 [5,] 0.05510652 0.11021305 0.94489348 [6,] 0.09179379 0.18358759 0.90820621 [7,] 0.05835800 0.11671600 0.94164200 [8,] 0.06623623 0.13247246 0.93376377 [9,] 0.29341849 0.58683697 0.70658151 [10,] 0.22625535 0.45251070 0.77374465 [11,] 0.62913168 0.74173664 0.37086832 [12,] 0.55156273 0.89687455 0.44843727 [13,] 0.48177997 0.96355995 0.51822003 [14,] 0.42050115 0.84100231 0.57949885 [15,] 0.35460438 0.70920875 0.64539562 [16,] 0.29338956 0.58677911 0.70661044 [17,] 0.23752280 0.47504561 0.76247720 [18,] 0.36399891 0.72799782 0.63600109 [19,] 0.44237062 0.88474124 0.55762938 [20,] 0.42437932 0.84875864 0.57562068 [21,] 0.51177359 0.97645281 0.48822641 [22,] 0.51021882 0.97956235 0.48978118 [23,] 0.53973468 0.92053063 0.46026532 [24,] 0.70607041 0.58785919 0.29392959 [25,] 0.80845865 0.38308270 0.19154135 [26,] 0.80182373 0.39635255 0.19817627 [27,] 0.76282070 0.47435860 0.23717930 [28,] 0.73420026 0.53159949 0.26579974 [29,] 0.68515127 0.62969746 0.31484873 [30,] 0.64554515 0.70890969 0.35445485 [31,] 0.62133801 0.75732398 0.37866199 [32,] 0.57132776 0.85734448 0.42867224 [33,] 0.60319926 0.79360147 0.39680074 [34,] 0.55927779 0.88144441 0.44072221 [35,] 0.53513166 0.92973668 0.46486834 [36,] 0.60471760 0.79056479 0.39528240 [37,] 0.56745688 0.86508625 0.43254312 [38,] 0.57565194 0.84869612 0.42434806 [39,] 0.52561627 0.94876745 0.47438373 [40,] 0.47328141 0.94656281 0.52671859 [41,] 0.42230615 0.84461231 0.57769385 [42,] 0.38895524 0.77791048 0.61104476 [43,] 0.35535581 0.71071162 0.64464419 [44,] 0.30829419 0.61658838 0.69170581 [45,] 0.28268917 0.56537833 0.71731083 [46,] 0.25419434 0.50838869 0.74580566 [47,] 0.27786381 0.55572762 0.72213619 [48,] 0.27837607 0.55675214 0.72162393 [49,] 0.24073859 0.48147719 0.75926141 [50,] 0.22611686 0.45223373 0.77388314 [51,] 0.27641862 0.55283724 0.72358138 [52,] 0.39513085 0.79026171 0.60486915 [53,] 0.43226344 0.86452687 0.56773656 [54,] 0.72957827 0.54084346 0.27042173 [55,] 0.70725264 0.58549473 0.29274736 [56,] 0.86647800 0.26704401 0.13352200 [57,] 0.85093006 0.29813989 0.14906994 [58,] 0.94255889 0.11488222 0.05744111 [59,] 0.92941470 0.14117061 0.07058530 [60,] 0.95499999 0.09000002 0.04500001 [61,] 0.94242077 0.11515847 0.05757923 [62,] 0.94355205 0.11289590 0.05644795 [63,] 0.93762363 0.12475275 0.06237637 [64,] 0.97604845 0.04790310 0.02395155 [65,] 0.96943138 0.06113724 0.03056862 [66,] 0.96413611 0.07172777 0.03586389 [67,] 0.96630511 0.06738977 0.03369489 [68,] 0.95986885 0.08026230 0.04013115 [69,] 0.95811442 0.08377115 0.04188558 [70,] 0.94629770 0.10740460 0.05370230 [71,] 0.93499055 0.13001890 0.06500945 [72,] 0.92680596 0.14638807 0.07319404 [73,] 0.91299671 0.17400658 0.08700329 [74,] 0.89661837 0.20676326 0.10338163 [75,] 0.94461726 0.11076549 0.05538274 [76,] 0.93119374 0.13761253 0.06880626 [77,] 0.91565082 0.16869836 0.08434918 [78,] 0.92385874 0.15228252 0.07614126 [79,] 0.92206922 0.15586156 0.07793078 [80,] 0.90405528 0.19188943 0.09594472 [81,] 0.88343961 0.23312078 0.11656039 [82,] 0.85690678 0.28618643 0.14309322 [83,] 0.83081985 0.33836029 0.16918015 [84,] 0.84077417 0.31845167 0.15922583 [85,] 0.85096783 0.29806435 0.14903217 [86,] 0.82345319 0.35309362 0.17654681 [87,] 0.80864517 0.38270966 0.19135483 [88,] 0.77253992 0.45492017 0.22746008 [89,] 0.73287672 0.53424656 0.26712328 [90,] 0.73895912 0.52208176 0.26104088 [91,] 0.69957212 0.60085576 0.30042788 [92,] 0.65552015 0.68895970 0.34447985 [93,] 0.65516728 0.68966544 0.34483272 [94,] 0.65414119 0.69171762 0.34585881 [95,] 0.68538876 0.62922248 0.31461124 [96,] 0.63764012 0.72471976 0.36235988 [97,] 0.59815166 0.80369669 0.40184834 [98,] 0.54699758 0.90600484 0.45300242 [99,] 0.86082723 0.27834555 0.13917277 [100,] 0.87916355 0.24167290 0.12083645 [101,] 0.90252847 0.19494307 0.09747153 [102,] 0.94756914 0.10486173 0.05243086 [103,] 0.94067318 0.11865364 0.05932682 [104,] 0.96019520 0.07960960 0.03980480 [105,] 0.95608901 0.08782197 0.04391099 [106,] 0.94472048 0.11055905 0.05527952 [107,] 0.95683601 0.08632798 0.04316399 [108,] 0.95038467 0.09923066 0.04961533 [109,] 0.94934455 0.10131091 0.05065545 [110,] 0.95651246 0.08697508 0.04348754 [111,] 0.93925282 0.12149436 0.06074718 [112,] 0.93865336 0.12269328 0.06134664 [113,] 0.92028148 0.15943704 0.07971852 [114,] 0.89815085 0.20369830 0.10184915 [115,] 0.86996177 0.26007645 0.13003823 [116,] 0.82795712 0.34408575 0.17204288 [117,] 0.89989092 0.20021816 0.10010908 [118,] 0.86619926 0.26760148 0.13380074 [119,] 0.85612323 0.28775354 0.14387677 [120,] 0.86753005 0.26493991 0.13246995 [121,] 0.81693596 0.36612807 0.18306404 [122,] 0.79192382 0.41615236 0.20807618 [123,] 0.81112038 0.37775925 0.18887962 [124,] 0.75898594 0.48202811 0.24101406 [125,] 0.89981034 0.20037931 0.10018966 [126,] 0.88533130 0.22933739 0.11466870 [127,] 0.83451539 0.33096921 0.16548461 [128,] 0.80313001 0.39373998 0.19686999 [129,] 0.71477905 0.57044190 0.28522095 [130,] 0.77308721 0.45382557 0.22691279 [131,] 0.66189549 0.67620903 0.33810451 [132,] 0.50597304 0.98805391 0.49402696 > postscript(file="/var/www/html/rcomp/tmp/18mbm1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/28mbm1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/31dtp1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/41dtp1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/51dtp1290612933.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 -0.95563174 4.26638656 -4.67597186 -1.71921566 -1.83206567 -3.68356051 7 8 9 10 11 12 -1.18313296 -6.66474281 -3.23033162 -3.24012699 -0.39297669 5.83995483 13 14 15 16 17 18 7.86007448 -0.60100401 -6.83992820 -6.58134787 2.24249643 -0.03196178 19 20 21 22 23 24 0.57092616 2.81797026 0.69507086 8.31909544 1.42932114 10.57327540 25 26 27 28 29 30 -0.29607926 -4.09535565 -1.56157767 2.81642333 0.14026425 -2.49528606 31 32 33 34 35 36 2.97746437 -6.62424731 -2.08360501 -0.18196111 -5.82531605 4.03943361 37 38 39 40 41 42 8.65588614 -7.74834270 4.47534545 -1.15635428 2.70876248 -0.26876162 43 44 45 46 47 48 1.65487566 -4.54004510 -1.43627845 -5.27747102 -1.27740490 4.25470573 49 50 51 52 53 54 7.11065337 -2.72281743 2.07488109 -1.11352591 1.16174430 -0.61738390 55 56 57 58 59 60 -0.94703874 2.75917650 0.37292835 -3.32742425 -2.08406719 -5.59118456 61 62 63 64 65 66 -4.18555067 -0.35792724 -3.66102353 -5.52045152 -8.67692308 6.13848430 67 68 69 70 71 72 12.03571562 -3.48794072 -10.70946233 -2.82300885 10.24399131 1.30418679 73 74 75 76 77 78 7.44452726 -0.16916386 4.29518009 2.94025386 -9.02682670 -1.26176303 79 80 81 82 83 84 -2.68537696 5.11822506 -2.36429078 3.81261009 -1.02639412 -1.96728810 85 86 87 88 89 90 3.29455785 0.94943699 -1.17024583 -7.91954242 0.87290501 1.72322549 91 92 93 94 95 96 5.84887120 -4.16884779 -1.98293239 1.28806087 -0.17239047 2.30442583 97 98 99 100 101 102 3.86508674 5.20762639 0.92916658 3.51665073 0.78162709 -1.32284322 103 104 105 106 107 108 -3.07923514 -1.47617120 1.29606889 5.00826542 4.25867080 4.65894775 109 110 111 112 113 114 0.48132758 -1.45079793 -1.19577933 10.06073323 2.33481857 -3.92333622 115 116 117 118 119 120 -8.74341712 -1.83227513 -2.26944607 -2.49531015 1.26881020 5.00333225 121 122 123 124 125 126 6.66171072 -4.46182874 -4.53756577 -1.50373660 -8.75958064 -1.15530653 127 128 129 130 131 132 -2.18147948 -2.07689044 -2.06201727 7.50500980 -1.84978500 -0.19758373 133 134 135 136 137 138 -2.35782377 0.71027224 3.03212934 -3.35589913 -2.45387981 -4.61998879 139 140 141 142 143 144 -1.67862231 -2.58285873 5.31199459 2.62514770 -4.22215484 -0.85777762 145 146 147 148 149 150 -3.62049161 1.88908419 7.97102147 0.83777426 -0.08354098 2.90696489 151 152 153 154 155 156 -1.31019196 1.75180353 12.07185530 1.08540904 -4.95152602 -1.89970939 157 158 159 3.69175292 -1.49994867 2.15484026 > postscript(file="/var/www/html/rcomp/tmp/6b4aa1290612933.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 -0.95563174 NA 1 4.26638656 -0.95563174 2 -4.67597186 4.26638656 3 -1.71921566 -4.67597186 4 -1.83206567 -1.71921566 5 -3.68356051 -1.83206567 6 -1.18313296 -3.68356051 7 -6.66474281 -1.18313296 8 -3.23033162 -6.66474281 9 -3.24012699 -3.23033162 10 -0.39297669 -3.24012699 11 5.83995483 -0.39297669 12 7.86007448 5.83995483 13 -0.60100401 7.86007448 14 -6.83992820 -0.60100401 15 -6.58134787 -6.83992820 16 2.24249643 -6.58134787 17 -0.03196178 2.24249643 18 0.57092616 -0.03196178 19 2.81797026 0.57092616 20 0.69507086 2.81797026 21 8.31909544 0.69507086 22 1.42932114 8.31909544 23 10.57327540 1.42932114 24 -0.29607926 10.57327540 25 -4.09535565 -0.29607926 26 -1.56157767 -4.09535565 27 2.81642333 -1.56157767 28 0.14026425 2.81642333 29 -2.49528606 0.14026425 30 2.97746437 -2.49528606 31 -6.62424731 2.97746437 32 -2.08360501 -6.62424731 33 -0.18196111 -2.08360501 34 -5.82531605 -0.18196111 35 4.03943361 -5.82531605 36 8.65588614 4.03943361 37 -7.74834270 8.65588614 38 4.47534545 -7.74834270 39 -1.15635428 4.47534545 40 2.70876248 -1.15635428 41 -0.26876162 2.70876248 42 1.65487566 -0.26876162 43 -4.54004510 1.65487566 44 -1.43627845 -4.54004510 45 -5.27747102 -1.43627845 46 -1.27740490 -5.27747102 47 4.25470573 -1.27740490 48 7.11065337 4.25470573 49 -2.72281743 7.11065337 50 2.07488109 -2.72281743 51 -1.11352591 2.07488109 52 1.16174430 -1.11352591 53 -0.61738390 1.16174430 54 -0.94703874 -0.61738390 55 2.75917650 -0.94703874 56 0.37292835 2.75917650 57 -3.32742425 0.37292835 58 -2.08406719 -3.32742425 59 -5.59118456 -2.08406719 60 -4.18555067 -5.59118456 61 -0.35792724 -4.18555067 62 -3.66102353 -0.35792724 63 -5.52045152 -3.66102353 64 -8.67692308 -5.52045152 65 6.13848430 -8.67692308 66 12.03571562 6.13848430 67 -3.48794072 12.03571562 68 -10.70946233 -3.48794072 69 -2.82300885 -10.70946233 70 10.24399131 -2.82300885 71 1.30418679 10.24399131 72 7.44452726 1.30418679 73 -0.16916386 7.44452726 74 4.29518009 -0.16916386 75 2.94025386 4.29518009 76 -9.02682670 2.94025386 77 -1.26176303 -9.02682670 78 -2.68537696 -1.26176303 79 5.11822506 -2.68537696 80 -2.36429078 5.11822506 81 3.81261009 -2.36429078 82 -1.02639412 3.81261009 83 -1.96728810 -1.02639412 84 3.29455785 -1.96728810 85 0.94943699 3.29455785 86 -1.17024583 0.94943699 87 -7.91954242 -1.17024583 88 0.87290501 -7.91954242 89 1.72322549 0.87290501 90 5.84887120 1.72322549 91 -4.16884779 5.84887120 92 -1.98293239 -4.16884779 93 1.28806087 -1.98293239 94 -0.17239047 1.28806087 95 2.30442583 -0.17239047 96 3.86508674 2.30442583 97 5.20762639 3.86508674 98 0.92916658 5.20762639 99 3.51665073 0.92916658 100 0.78162709 3.51665073 101 -1.32284322 0.78162709 102 -3.07923514 -1.32284322 103 -1.47617120 -3.07923514 104 1.29606889 -1.47617120 105 5.00826542 1.29606889 106 4.25867080 5.00826542 107 4.65894775 4.25867080 108 0.48132758 4.65894775 109 -1.45079793 0.48132758 110 -1.19577933 -1.45079793 111 10.06073323 -1.19577933 112 2.33481857 10.06073323 113 -3.92333622 2.33481857 114 -8.74341712 -3.92333622 115 -1.83227513 -8.74341712 116 -2.26944607 -1.83227513 117 -2.49531015 -2.26944607 118 1.26881020 -2.49531015 119 5.00333225 1.26881020 120 6.66171072 5.00333225 121 -4.46182874 6.66171072 122 -4.53756577 -4.46182874 123 -1.50373660 -4.53756577 124 -8.75958064 -1.50373660 125 -1.15530653 -8.75958064 126 -2.18147948 -1.15530653 127 -2.07689044 -2.18147948 128 -2.06201727 -2.07689044 129 7.50500980 -2.06201727 130 -1.84978500 7.50500980 131 -0.19758373 -1.84978500 132 -2.35782377 -0.19758373 133 0.71027224 -2.35782377 134 3.03212934 0.71027224 135 -3.35589913 3.03212934 136 -2.45387981 -3.35589913 137 -4.61998879 -2.45387981 138 -1.67862231 -4.61998879 139 -2.58285873 -1.67862231 140 5.31199459 -2.58285873 141 2.62514770 5.31199459 142 -4.22215484 2.62514770 143 -0.85777762 -4.22215484 144 -3.62049161 -0.85777762 145 1.88908419 -3.62049161 146 7.97102147 1.88908419 147 0.83777426 7.97102147 148 -0.08354098 0.83777426 149 2.90696489 -0.08354098 150 -1.31019196 2.90696489 151 1.75180353 -1.31019196 152 12.07185530 1.75180353 153 1.08540904 12.07185530 154 -4.95152602 1.08540904 155 -1.89970939 -4.95152602 156 3.69175292 -1.89970939 157 -1.49994867 3.69175292 158 2.15484026 -1.49994867 159 NA 2.15484026 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 4.26638656 -0.95563174 [2,] -4.67597186 4.26638656 [3,] -1.71921566 -4.67597186 [4,] -1.83206567 -1.71921566 [5,] -3.68356051 -1.83206567 [6,] -1.18313296 -3.68356051 [7,] -6.66474281 -1.18313296 [8,] -3.23033162 -6.66474281 [9,] -3.24012699 -3.23033162 [10,] -0.39297669 -3.24012699 [11,] 5.83995483 -0.39297669 [12,] 7.86007448 5.83995483 [13,] -0.60100401 7.86007448 [14,] -6.83992820 -0.60100401 [15,] -6.58134787 -6.83992820 [16,] 2.24249643 -6.58134787 [17,] -0.03196178 2.24249643 [18,] 0.57092616 -0.03196178 [19,] 2.81797026 0.57092616 [20,] 0.69507086 2.81797026 [21,] 8.31909544 0.69507086 [22,] 1.42932114 8.31909544 [23,] 10.57327540 1.42932114 [24,] -0.29607926 10.57327540 [25,] -4.09535565 -0.29607926 [26,] -1.56157767 -4.09535565 [27,] 2.81642333 -1.56157767 [28,] 0.14026425 2.81642333 [29,] -2.49528606 0.14026425 [30,] 2.97746437 -2.49528606 [31,] -6.62424731 2.97746437 [32,] -2.08360501 -6.62424731 [33,] -0.18196111 -2.08360501 [34,] -5.82531605 -0.18196111 [35,] 4.03943361 -5.82531605 [36,] 8.65588614 4.03943361 [37,] -7.74834270 8.65588614 [38,] 4.47534545 -7.74834270 [39,] -1.15635428 4.47534545 [40,] 2.70876248 -1.15635428 [41,] -0.26876162 2.70876248 [42,] 1.65487566 -0.26876162 [43,] -4.54004510 1.65487566 [44,] -1.43627845 -4.54004510 [45,] -5.27747102 -1.43627845 [46,] -1.27740490 -5.27747102 [47,] 4.25470573 -1.27740490 [48,] 7.11065337 4.25470573 [49,] -2.72281743 7.11065337 [50,] 2.07488109 -2.72281743 [51,] -1.11352591 2.07488109 [52,] 1.16174430 -1.11352591 [53,] -0.61738390 1.16174430 [54,] -0.94703874 -0.61738390 [55,] 2.75917650 -0.94703874 [56,] 0.37292835 2.75917650 [57,] -3.32742425 0.37292835 [58,] -2.08406719 -3.32742425 [59,] -5.59118456 -2.08406719 [60,] -4.18555067 -5.59118456 [61,] -0.35792724 -4.18555067 [62,] -3.66102353 -0.35792724 [63,] -5.52045152 -3.66102353 [64,] -8.67692308 -5.52045152 [65,] 6.13848430 -8.67692308 [66,] 12.03571562 6.13848430 [67,] -3.48794072 12.03571562 [68,] -10.70946233 -3.48794072 [69,] -2.82300885 -10.70946233 [70,] 10.24399131 -2.82300885 [71,] 1.30418679 10.24399131 [72,] 7.44452726 1.30418679 [73,] -0.16916386 7.44452726 [74,] 4.29518009 -0.16916386 [75,] 2.94025386 4.29518009 [76,] -9.02682670 2.94025386 [77,] -1.26176303 -9.02682670 [78,] -2.68537696 -1.26176303 [79,] 5.11822506 -2.68537696 [80,] -2.36429078 5.11822506 [81,] 3.81261009 -2.36429078 [82,] -1.02639412 3.81261009 [83,] -1.96728810 -1.02639412 [84,] 3.29455785 -1.96728810 [85,] 0.94943699 3.29455785 [86,] -1.17024583 0.94943699 [87,] -7.91954242 -1.17024583 [88,] 0.87290501 -7.91954242 [89,] 1.72322549 0.87290501 [90,] 5.84887120 1.72322549 [91,] -4.16884779 5.84887120 [92,] -1.98293239 -4.16884779 [93,] 1.28806087 -1.98293239 [94,] -0.17239047 1.28806087 [95,] 2.30442583 -0.17239047 [96,] 3.86508674 2.30442583 [97,] 5.20762639 3.86508674 [98,] 0.92916658 5.20762639 [99,] 3.51665073 0.92916658 [100,] 0.78162709 3.51665073 [101,] -1.32284322 0.78162709 [102,] -3.07923514 -1.32284322 [103,] -1.47617120 -3.07923514 [104,] 1.29606889 -1.47617120 [105,] 5.00826542 1.29606889 [106,] 4.25867080 5.00826542 [107,] 4.65894775 4.25867080 [108,] 0.48132758 4.65894775 [109,] -1.45079793 0.48132758 [110,] -1.19577933 -1.45079793 [111,] 10.06073323 -1.19577933 [112,] 2.33481857 10.06073323 [113,] -3.92333622 2.33481857 [114,] -8.74341712 -3.92333622 [115,] -1.83227513 -8.74341712 [116,] -2.26944607 -1.83227513 [117,] -2.49531015 -2.26944607 [118,] 1.26881020 -2.49531015 [119,] 5.00333225 1.26881020 [120,] 6.66171072 5.00333225 [121,] -4.46182874 6.66171072 [122,] -4.53756577 -4.46182874 [123,] -1.50373660 -4.53756577 [124,] -8.75958064 -1.50373660 [125,] -1.15530653 -8.75958064 [126,] -2.18147948 -1.15530653 [127,] -2.07689044 -2.18147948 [128,] -2.06201727 -2.07689044 [129,] 7.50500980 -2.06201727 [130,] -1.84978500 7.50500980 [131,] -0.19758373 -1.84978500 [132,] -2.35782377 -0.19758373 [133,] 0.71027224 -2.35782377 [134,] 3.03212934 0.71027224 [135,] -3.35589913 3.03212934 [136,] -2.45387981 -3.35589913 [137,] -4.61998879 -2.45387981 [138,] -1.67862231 -4.61998879 [139,] -2.58285873 -1.67862231 [140,] 5.31199459 -2.58285873 [141,] 2.62514770 5.31199459 [142,] -4.22215484 2.62514770 [143,] -0.85777762 -4.22215484 [144,] -3.62049161 -0.85777762 [145,] 1.88908419 -3.62049161 [146,] 7.97102147 1.88908419 [147,] 0.83777426 7.97102147 [148,] -0.08354098 0.83777426 [149,] 2.90696489 -0.08354098 [150,] -1.31019196 2.90696489 [151,] 1.75180353 -1.31019196 [152,] 12.07185530 1.75180353 [153,] 1.08540904 12.07185530 [154,] -4.95152602 1.08540904 [155,] -1.89970939 -4.95152602 [156,] 3.69175292 -1.89970939 [157,] -1.49994867 3.69175292 [158,] 2.15484026 -1.49994867 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 4.26638656 -0.95563174 2 -4.67597186 4.26638656 3 -1.71921566 -4.67597186 4 -1.83206567 -1.71921566 5 -3.68356051 -1.83206567 6 -1.18313296 -3.68356051 7 -6.66474281 -1.18313296 8 -3.23033162 -6.66474281 9 -3.24012699 -3.23033162 10 -0.39297669 -3.24012699 11 5.83995483 -0.39297669 12 7.86007448 5.83995483 13 -0.60100401 7.86007448 14 -6.83992820 -0.60100401 15 -6.58134787 -6.83992820 16 2.24249643 -6.58134787 17 -0.03196178 2.24249643 18 0.57092616 -0.03196178 19 2.81797026 0.57092616 20 0.69507086 2.81797026 21 8.31909544 0.69507086 22 1.42932114 8.31909544 23 10.57327540 1.42932114 24 -0.29607926 10.57327540 25 -4.09535565 -0.29607926 26 -1.56157767 -4.09535565 27 2.81642333 -1.56157767 28 0.14026425 2.81642333 29 -2.49528606 0.14026425 30 2.97746437 -2.49528606 31 -6.62424731 2.97746437 32 -2.08360501 -6.62424731 33 -0.18196111 -2.08360501 34 -5.82531605 -0.18196111 35 4.03943361 -5.82531605 36 8.65588614 4.03943361 37 -7.74834270 8.65588614 38 4.47534545 -7.74834270 39 -1.15635428 4.47534545 40 2.70876248 -1.15635428 41 -0.26876162 2.70876248 42 1.65487566 -0.26876162 43 -4.54004510 1.65487566 44 -1.43627845 -4.54004510 45 -5.27747102 -1.43627845 46 -1.27740490 -5.27747102 47 4.25470573 -1.27740490 48 7.11065337 4.25470573 49 -2.72281743 7.11065337 50 2.07488109 -2.72281743 51 -1.11352591 2.07488109 52 1.16174430 -1.11352591 53 -0.61738390 1.16174430 54 -0.94703874 -0.61738390 55 2.75917650 -0.94703874 56 0.37292835 2.75917650 57 -3.32742425 0.37292835 58 -2.08406719 -3.32742425 59 -5.59118456 -2.08406719 60 -4.18555067 -5.59118456 61 -0.35792724 -4.18555067 62 -3.66102353 -0.35792724 63 -5.52045152 -3.66102353 64 -8.67692308 -5.52045152 65 6.13848430 -8.67692308 66 12.03571562 6.13848430 67 -3.48794072 12.03571562 68 -10.70946233 -3.48794072 69 -2.82300885 -10.70946233 70 10.24399131 -2.82300885 71 1.30418679 10.24399131 72 7.44452726 1.30418679 73 -0.16916386 7.44452726 74 4.29518009 -0.16916386 75 2.94025386 4.29518009 76 -9.02682670 2.94025386 77 -1.26176303 -9.02682670 78 -2.68537696 -1.26176303 79 5.11822506 -2.68537696 80 -2.36429078 5.11822506 81 3.81261009 -2.36429078 82 -1.02639412 3.81261009 83 -1.96728810 -1.02639412 84 3.29455785 -1.96728810 85 0.94943699 3.29455785 86 -1.17024583 0.94943699 87 -7.91954242 -1.17024583 88 0.87290501 -7.91954242 89 1.72322549 0.87290501 90 5.84887120 1.72322549 91 -4.16884779 5.84887120 92 -1.98293239 -4.16884779 93 1.28806087 -1.98293239 94 -0.17239047 1.28806087 95 2.30442583 -0.17239047 96 3.86508674 2.30442583 97 5.20762639 3.86508674 98 0.92916658 5.20762639 99 3.51665073 0.92916658 100 0.78162709 3.51665073 101 -1.32284322 0.78162709 102 -3.07923514 -1.32284322 103 -1.47617120 -3.07923514 104 1.29606889 -1.47617120 105 5.00826542 1.29606889 106 4.25867080 5.00826542 107 4.65894775 4.25867080 108 0.48132758 4.65894775 109 -1.45079793 0.48132758 110 -1.19577933 -1.45079793 111 10.06073323 -1.19577933 112 2.33481857 10.06073323 113 -3.92333622 2.33481857 114 -8.74341712 -3.92333622 115 -1.83227513 -8.74341712 116 -2.26944607 -1.83227513 117 -2.49531015 -2.26944607 118 1.26881020 -2.49531015 119 5.00333225 1.26881020 120 6.66171072 5.00333225 121 -4.46182874 6.66171072 122 -4.53756577 -4.46182874 123 -1.50373660 -4.53756577 124 -8.75958064 -1.50373660 125 -1.15530653 -8.75958064 126 -2.18147948 -1.15530653 127 -2.07689044 -2.18147948 128 -2.06201727 -2.07689044 129 7.50500980 -2.06201727 130 -1.84978500 7.50500980 131 -0.19758373 -1.84978500 132 -2.35782377 -0.19758373 133 0.71027224 -2.35782377 134 3.03212934 0.71027224 135 -3.35589913 3.03212934 136 -2.45387981 -3.35589913 137 -4.61998879 -2.45387981 138 -1.67862231 -4.61998879 139 -2.58285873 -1.67862231 140 5.31199459 -2.58285873 141 2.62514770 5.31199459 142 -4.22215484 2.62514770 143 -0.85777762 -4.22215484 144 -3.62049161 -0.85777762 145 1.88908419 -3.62049161 146 7.97102147 1.88908419 147 0.83777426 7.97102147 148 -0.08354098 0.83777426 149 2.90696489 -0.08354098 150 -1.31019196 2.90696489 151 1.75180353 -1.31019196 152 12.07185530 1.75180353 153 1.08540904 12.07185530 154 -4.95152602 1.08540904 155 -1.89970939 -4.95152602 156 3.69175292 -1.89970939 157 -1.49994867 3.69175292 158 2.15484026 -1.49994867 > 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/7mw9d1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8mw9d1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9xn8g1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10xn8g1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11i6731290612933.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/12l6n91290612933.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/13a72l1290612933.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/143g2o1290612933.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/15z8zf1290612933.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/16l9g31290612933.tab") + } > > try(system("convert tmp/18mbm1290612933.ps tmp/18mbm1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/28mbm1290612933.ps tmp/28mbm1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/31dtp1290612933.ps tmp/31dtp1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/41dtp1290612933.ps tmp/41dtp1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/51dtp1290612933.ps tmp/51dtp1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/6b4aa1290612933.ps tmp/6b4aa1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/7mw9d1290612933.ps tmp/7mw9d1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/8mw9d1290612933.ps tmp/8mw9d1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/9xn8g1290612933.ps tmp/9xn8g1290612933.png",intern=TRUE)) character(0) > try(system("convert tmp/10xn8g1290612933.ps tmp/10xn8g1290612933.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.486 1.728 10.282