R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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(75.5 + ,78.4 + ,67.3 + ,75.3 + ,106.1 + ,125.7 + ,101.6 + ,83.2 + ,79.3 + ,75.2 + ,83.6 + ,112.7 + ,153.8 + ,113.4 + ,94.5 + ,84.3 + ,91.1 + ,91.2 + ,123.2 + ,134.9 + ,122.2 + ,83.3 + ,81.2 + ,83.7 + ,85.2 + ,101.7 + ,95.3 + ,102.2 + ,92.7 + ,88.4 + ,105.0 + ,100.0 + ,118.7 + ,96.6 + ,113.2 + ,89.8 + ,83.1 + ,106.2 + ,89.8 + ,107.1 + ,100.5 + ,115.3 + ,74.8 + ,76.6 + ,88.5 + ,88.9 + ,93.6 + ,106.2 + ,87.4 + ,81.5 + ,82.6 + ,100.1 + ,85.6 + ,77.5 + ,153.4 + ,98.7 + ,92.8 + ,84.4 + ,90.3 + ,83.2 + ,117.2 + ,132.1 + ,117.3 + ,92.8 + ,94.6 + ,85.3 + ,97.1 + ,124.5 + ,110.9 + ,121.2 + ,91.7 + ,91.8 + ,81.9 + ,85.8 + ,120.8 + ,94.3 + ,118.7 + ,83.5 + ,89.3 + ,77.2 + ,80.9 + ,97.0 + ,91.7 + ,112.1 + ,92.8 + ,87.7 + ,78.6 + ,81.3 + ,115.1 + ,138.6 + ,102.9 + ,91.3 + ,83.1 + ,75.1 + ,83.2 + ,112.9 + ,154.3 + ,108.8 + ,99.5 + ,93.6 + ,90.3 + ,90.7 + ,122.7 + ,149.8 + ,118.6 + ,87.6 + ,85.1 + ,88.5 + ,88.4 + ,106.9 + ,99.2 + ,99.2 + ,95.3 + ,90.8 + ,112.5 + 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,146.8 + ,71.3 + ,49.6 + ,81.6) + ,dim=c(7 + ,151) + ,dimnames=list(c('Totaal' + ,'Voeding' + ,'Dranken' + ,'Tabaksproducten' + ,'Textiel' + ,'Kleding' + ,'Apparatuur ') + ,1:151)) > y <- array(NA,dim=c(7,151),dimnames=list(c('Totaal','Voeding','Dranken','Tabaksproducten','Textiel','Kleding','Apparatuur '),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' > 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, 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 Totaal Voeding Dranken Tabaksproducten Textiel Kleding Apparatuur\r 1 75.5 78.4 67.3 75.3 106.1 125.7 101.6 2 83.2 79.3 75.2 83.6 112.7 153.8 113.4 3 94.5 84.3 91.1 91.2 123.2 134.9 122.2 4 83.3 81.2 83.7 85.2 101.7 95.3 102.2 5 92.7 88.4 105.0 100.0 118.7 96.6 113.2 6 89.8 83.1 106.2 89.8 107.1 100.5 115.3 7 74.8 76.6 88.5 88.9 93.6 106.2 87.4 8 81.5 82.6 100.1 85.6 77.5 153.4 98.7 9 92.8 84.4 90.3 83.2 117.2 132.1 117.3 10 92.8 94.6 85.3 97.1 124.5 110.9 121.2 11 91.7 91.8 81.9 85.8 120.8 94.3 118.7 12 83.5 89.3 77.2 80.9 97.0 91.7 112.1 13 92.8 87.7 78.6 81.3 115.1 138.6 102.9 14 91.3 83.1 75.1 83.2 112.9 154.3 108.8 15 99.5 93.6 90.3 90.7 122.7 149.8 118.6 16 87.6 85.1 88.5 88.4 106.9 99.2 99.2 17 95.3 90.8 112.5 94.1 115.0 97.7 102.2 18 98.5 90.5 101.1 92.0 114.9 107.7 108.8 19 80.1 86.1 114.0 92.0 103.1 120.1 94.0 20 84.2 93.3 107.7 89.3 80.8 164.5 96.2 21 92.4 94.9 77.8 87.0 118.2 136.1 118.4 22 98.0 102.6 101.4 97.7 129.6 117.5 120.0 23 92.2 98.3 87.2 82.5 118.7 98.2 117.5 24 80.0 93.4 75.9 96.5 88.4 91.9 102.6 25 88.7 92.8 78.8 86.2 113.1 141.8 92.8 26 87.4 86.5 82.3 84.9 109.8 154.2 100.3 27 96.1 93.8 89.1 100.0 116.1 138.6 106.3 28 94.1 90.4 100.1 92.7 113.6 97.9 103.9 29 91.9 91.0 101.8 96.7 107.9 90.3 102.4 30 93.6 89.1 98.5 105.8 107.4 90.9 114.5 31 83.5 89.6 106.6 88.5 102.7 127.0 89.0 32 80.8 89.3 101.8 78.7 78.3 156.8 94.3 33 96.3 95.3 92.4 99.9 121.0 127.2 115.7 34 101.5 104.1 94.4 107.8 132.2 111.3 120.2 35 91.6 94.7 81.0 102.4 113.2 93.0 109.5 36 84.0 97.6 94.6 106.0 89.2 89.5 99.4 37 91.8 96.8 83.8 87.3 113.2 141.8 86.4 38 90.4 92.8 79.4 93.3 107.6 152.0 95.1 39 98.0 94.7 95.6 98.2 107.3 120.2 101.5 40 95.5 95.8 106.0 102.0 110.9 88.8 92.9 41 90.5 88.9 106.2 93.9 96.4 82.8 90.8 42 97.1 91.2 115.0 106.6 101.2 82.8 100.4 43 87.9 91.6 122.4 92.9 94.0 121.7 82.2 44 79.8 87.3 113.7 78.0 70.5 147.1 75.3 45 102.0 97.8 98.0 104.2 116.4 132.5 110.3 46 104.3 105.1 105.8 115.9 121.9 107.5 113.5 47 92.1 93.8 88.3 99.9 109.5 77.9 94.9 48 95.9 99.0 95.7 103.9 91.1 85.5 95.7 49 89.1 91.4 85.8 93.5 104.0 126.5 85.3 50 92.2 89.0 83.9 101.7 101.2 135.4 92.5 51 107.5 101.4 114.1 124.6 118.4 122.5 107.7 52 99.7 95.4 102.0 124.2 106.9 79.2 97.9 53 92.2 90.5 108.1 103.3 95.6 66.1 93.9 54 108.9 98.7 125.4 120.5 114.2 77.9 111.5 55 89.8 91.2 108.1 98.0 92.4 109.6 88.6 56 89.4 91.7 110.4 100.4 75.3 142.9 82.5 57 107.6 102.9 102.4 126.8 120.4 120.5 108.6 58 105.6 105.5 89.6 120.2 115.9 96.3 113.8 59 100.9 102.6 95.0 114.0 109.8 82.6 103.4 60 102.9 107.2 93.7 109.1 94.9 78.4 99.0 61 96.2 96.9 77.7 94.2 97.5 104.5 89.9 62 94.7 88.9 80.1 86.0 101.3 137.9 97.9 63 107.3 99.6 103.6 112.9 108.7 125.8 107.8 64 103.0 96.7 103.1 99.7 105.1 78.0 103.7 65 96.1 93.8 112.4 104.5 94.9 67.7 98.2 66 109.8 101.9 119.2 111.6 108.9 78.4 111.7 67 85.4 87.6 105.3 99.2 87.5 101.7 82.6 68 89.9 100.0 107.2 90.9 73.0 154.1 86.1 69 109.3 105.8 108.7 111.4 115.2 107.3 111.2 70 101.2 105.5 93.7 98.2 107.5 86.5 105.3 71 104.7 111.3 96.1 101.7 109.8 82.1 106.3 72 102.4 112.1 92.9 89.7 90.7 76.1 99.4 73 97.7 102.0 81.1 89.5 97.6 115.5 91.9 74 98.9 93.2 83.2 85.1 98.7 129.6 96.2 75 115.0 108.4 99.7 95.9 113.9 121.6 105.4 76 97.5 97.9 96.8 88.9 96.6 64.0 95.0 77 107.3 106.4 108.7 98.1 104.4 58.1 100.5 78 112.3 102.8 120.9 109.7 115.1 79.7 111.6 79 88.5 96.3 114.8 92.0 91.4 108.9 88.5 80 92.9 105.7 108.7 74.3 76.2 138.5 83.7 81 108.8 108.4 97.4 96.9 117.4 117.9 113.9 82 112.3 115.8 98.6 100.3 122.0 96.7 115.2 83 107.3 113.8 91.7 97.1 120.2 78.6 111.0 84 101.8 106.4 91.2 86.0 93.6 64.1 96.9 85 105.0 107.9 83.5 97.3 106.6 112.0 102.1 86 103.4 98.2 82.4 86.4 108.4 139.4 101.5 87 116.7 111.1 103.1 97.7 121.4 116.2 115.0 88 103.6 99.8 110.3 90.6 104.8 63.4 105.0 89 108.8 103.5 115.8 99.2 104.2 61.1 105.4 90 117.0 105.4 120.1 107.4 115.0 65.5 119.7 91 100.9 102.6 105.1 107.1 99.0 90.9 91.8 92 100.8 107.4 108.6 78.9 82.8 115.3 89.1 93 109.7 108.2 95.7 92.8 112.5 85.2 106.2 94 121.0 121.7 103.2 106.2 127.9 87.0 119.9 95 114.1 118.0 96.9 97.2 114.4 62.6 111.6 96 105.5 109.6 95.7 80.0 83.7 62.7 95.1 97 112.5 116.7 92.7 109.3 108.5 91.6 101.3 98 113.8 110.6 81.3 111.3 109.7 104.3 118.3 99 115.3 109.6 94.5 119.5 104.7 88.1 126.2 100 120.4 117.4 105.6 119.8 112.2 62.3 113.2 101 111.1 109.2 112.9 112.5 96.9 50.3 103.6 102 120.1 110.8 102.6 125.6 103.8 64.1 116.2 103 106.1 112.8 116.2 105.1 95.1 75.7 98.3 104 95.9 106.5 104.9 91.9 66.7 85.5 84.2 105 119.4 119.6 100.4 128.2 103.4 71.9 118.3 106 117.4 127.2 97.1 122.6 105.4 66.9 117.4 107 98.6 113.9 90.2 109.6 89.2 50.5 94.5 108 99.7 120.0 100.5 120.4 72.5 57.9 93.3 109 87.4 107.6 81.1 103.8 78.0 84.1 90.2 110 90.8 105.2 87.2 96.6 77.3 87.0 88.5 111 101.3 115.3 102.0 110.7 85.1 71.9 101.0 112 93.2 113.9 107.0 111.7 80.9 45.0 87.0 113 95.1 106.1 107.6 111.9 72.5 39.5 81.2 114 101.9 114.3 123.5 131.5 82.1 53.8 98.1 115 87.0 112.0 116.6 122.8 78.3 59.5 75.5 116 86.2 109.0 103.2 98.3 57.8 68.4 70.7 117 105.0 119.1 103.9 133.7 89.3 56.9 103.7 118 104.1 124.4 95.4 120.0 91.4 61.9 100.4 119 99.2 116.6 93.6 119.6 84.2 40.4 91.3 120 95.2 118.5 102.1 108.7 72.5 49.4 97.2 121 92.7 108.9 69.0 112.5 74.6 65.2 85.4 122 99.3 107.5 88.9 102.7 80.3 82.1 86.5 123 113.5 125.9 106.2 123.4 92.6 69.0 105.3 124 104.7 117.7 103.0 116.5 86.3 45.9 97.7 125 100.5 109.2 103.5 102.3 80.3 39.1 84.3 126 116.2 118.8 124.5 148.4 93.6 56.9 109.8 127 94.1 108.1 117.9 126.6 79.5 51.6 79.1 128 94.8 112.1 104.2 106.6 61.8 62.9 83.4 129 115.1 117.8 99.9 144.4 94.8 58.3 101.9 130 110.0 121.8 89.4 132.4 91.6 56.9 113.0 131 108.4 121.0 93.5 136.2 89.2 41.3 98.6 132 103.9 121.7 89.6 121.6 74.1 46.9 94.7 133 102.9 114.2 85.0 135.1 78.6 61.9 94.5 134 107.7 109.8 90.0 124.7 78.2 74.8 90.7 135 126.7 124.1 113.7 148.8 95.1 67.0 113.0 136 108.8 112.9 112.1 145.6 78.7 53.3 89.9 137 117.1 118.7 129.8 140.3 85.9 51.4 98.7 138 112.2 113.3 119.1 138.5 81.2 50.3 102.2 139 94.7 106.8 103.5 127.3 73.1 52.7 74.3 140 102.7 119.3 105.5 117.9 58.7 70.3 84.5 141 119.1 126.4 111.7 145.3 85.7 59.7 110.1 142 110.6 126.6 98.6 120.7 81.8 52.0 100.4 143 109.1 127.2 102.8 134.7 79.6 36.1 92.8 144 105.3 123.8 101.1 124.4 70.7 39.7 92.2 145 103.4 116.8 94.2 128.3 74.5 67.6 94.0 146 103.7 113.8 92.6 128.4 84.8 72.8 100.7 147 117.0 130.4 112.0 134.1 80.7 53.8 111.9 148 101.2 112.8 108.6 133.3 69.9 39.6 95.9 149 105.4 119.4 125.8 130.6 74.1 39.4 88.8 150 110.3 117.5 138.7 165.7 76.1 41.2 102.0 151 97.7 117.5 115.2 146.8 71.3 49.6 81.6 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Voeding Dranken Tabaksproducten -28.281883 0.648098 0.143334 0.062337 Textiel Kleding `Apparatuur\\r` 0.211282 0.009738 0.183562 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -12.6545 -2.6855 -0.1293 3.4389 10.2475 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -28.281883 6.955720 -4.066 7.84e-05 *** Voeding 0.648098 0.049335 13.137 < 2e-16 *** Dranken 0.143334 0.032429 4.420 1.93e-05 *** Tabaksproducten 0.062337 0.033087 1.884 0.06158 . Textiel 0.211282 0.041112 5.139 8.83e-07 *** Kleding 0.009738 0.017620 0.553 0.58134 `Apparatuur\\r` 0.183562 0.054785 3.351 0.00103 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.54 on 144 degrees of freedom Multiple R-squared: 0.8221, Adjusted R-squared: 0.8147 F-statistic: 110.9 on 6 and 144 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,] 0.18634530 0.37269060 0.81365470 [2,] 0.08459675 0.16919350 0.91540325 [3,] 0.03530377 0.07060754 0.96469623 [4,] 0.04824401 0.09648802 0.95175599 [5,] 0.08229503 0.16459006 0.91770497 [6,] 0.04504096 0.09008192 0.95495904 [7,] 0.02899389 0.05798779 0.97100611 [8,] 0.02152776 0.04305551 0.97847224 [9,] 0.02095862 0.04191723 0.97904138 [10,] 0.30460680 0.60921361 0.69539320 [11,] 0.26743530 0.53487061 0.73256470 [12,] 0.23685280 0.47370560 0.76314720 [13,] 0.30556517 0.61113033 0.69443483 [14,] 0.31440206 0.62880412 0.68559794 [15,] 0.29519675 0.59039350 0.70480325 [16,] 0.23886563 0.47773125 0.76113437 [17,] 0.18796766 0.37593532 0.81203234 [18,] 0.17461639 0.34923279 0.82538361 [19,] 0.16334184 0.32668369 0.83665816 [20,] 0.14330815 0.28661629 0.85669185 [21,] 0.13406762 0.26813524 0.86593238 [22,] 0.14007822 0.28015644 0.85992178 [23,] 0.12571621 0.25143242 0.87428379 [24,] 0.11769675 0.23539350 0.88230325 [25,] 0.13776207 0.27552413 0.86223793 [26,] 0.13902805 0.27805610 0.86097195 [27,] 0.19787799 0.39575598 0.80212201 [28,] 0.17996671 0.35993342 0.82003329 [29,] 0.15902857 0.31805715 0.84097143 [30,] 0.25157275 0.50314549 0.74842725 [31,] 0.23797971 0.47595941 0.76202029 [32,] 0.27099738 0.54199477 0.72900262 [33,] 0.31266790 0.62533579 0.68733210 [34,] 0.27272729 0.54545458 0.72727271 [35,] 0.24255935 0.48511870 0.75744065 [36,] 0.25538704 0.51077408 0.74461296 [37,] 0.26581285 0.53162570 0.73418715 [38,] 0.24562570 0.49125141 0.75437430 [39,] 0.32775655 0.65551311 0.67224345 [40,] 0.28594335 0.57188670 0.71405665 [41,] 0.27139308 0.54278616 0.72860692 [42,] 0.23866882 0.47733764 0.76133118 [43,] 0.20468777 0.40937555 0.79531223 [44,] 0.19151756 0.38303512 0.80848244 [45,] 0.19834352 0.39668703 0.80165648 [46,] 0.17412284 0.34824568 0.82587716 [47,] 0.15753927 0.31507853 0.84246073 [48,] 0.13514917 0.27029833 0.86485083 [49,] 0.13868631 0.27737261 0.86131369 [50,] 0.13064299 0.26128599 0.86935701 [51,] 0.17248185 0.34496371 0.82751815 [52,] 0.24082264 0.48164528 0.75917736 [53,] 0.33522243 0.67044486 0.66477757 [54,] 0.36439360 0.72878720 0.63560640 [55,] 0.45349674 0.90699348 0.54650326 [56,] 0.43628804 0.87257607 0.56371196 [57,] 0.47217222 0.94434444 0.52782778 [58,] 0.45470612 0.90941223 0.54529388 [59,] 0.42756132 0.85512264 0.57243868 [60,] 0.41821025 0.83642050 0.58178975 [61,] 0.41409021 0.82818042 0.58590979 [62,] 0.39750169 0.79500337 0.60249831 [63,] 0.38825340 0.77650679 0.61174660 [64,] 0.37920563 0.75841126 0.62079437 [65,] 0.51099696 0.97800607 0.48900304 [66,] 0.66980719 0.66038561 0.33019281 [67,] 0.65301853 0.69396294 0.34698147 [68,] 0.63810484 0.72379031 0.36189516 [69,] 0.63337919 0.73324161 0.36662081 [70,] 0.71618367 0.56763266 0.28381633 [71,] 0.67551766 0.64896468 0.32448234 [72,] 0.66866752 0.66266497 0.33133248 [73,] 0.65433509 0.69132982 0.34566491 [74,] 0.66584464 0.66831072 0.33415536 [75,] 0.64956497 0.70087005 0.35043503 [76,] 0.61333909 0.77332183 0.38666091 [77,] 0.64794761 0.70410479 0.35205239 [78,] 0.63976315 0.72047370 0.36023685 [79,] 0.62157091 0.75685817 0.37842909 [80,] 0.60505613 0.78988773 0.39494387 [81,] 0.61285073 0.77429853 0.38714927 [82,] 0.56706860 0.86586280 0.43293140 [83,] 0.53684624 0.92630752 0.46315376 [84,] 0.50169142 0.99661717 0.49830858 [85,] 0.45723421 0.91446842 0.54276579 [86,] 0.40787494 0.81574987 0.59212506 [87,] 0.51567927 0.96864146 0.48432073 [88,] 0.48371866 0.96743733 0.51628134 [89,] 0.46347357 0.92694713 0.53652643 [90,] 0.45788701 0.91577401 0.54211299 [91,] 0.44534715 0.89069429 0.55465285 [92,] 0.43417171 0.86834342 0.56582829 [93,] 0.50460225 0.99079550 0.49539775 [94,] 0.47218661 0.94437322 0.52781339 [95,] 0.47176291 0.94352582 0.52823709 [96,] 0.43491051 0.86982103 0.56508949 [97,] 0.42179642 0.84359284 0.57820358 [98,] 0.42067853 0.84135706 0.57932147 [99,] 0.45545733 0.91091467 0.54454267 [100,] 0.58398000 0.83204000 0.41602000 [101,] 0.55717068 0.88565865 0.44282932 [102,] 0.56244485 0.87511030 0.43755515 [103,] 0.62731140 0.74537720 0.37268860 [104,] 0.59036315 0.81927370 0.40963685 [105,] 0.63720375 0.72559249 0.36279625 [106,] 0.88628974 0.22742052 0.11371026 [107,] 0.87278588 0.25442824 0.12721412 [108,] 0.90567152 0.18865696 0.09432848 [109,] 0.95054354 0.09891292 0.04945646 [110,] 0.93928919 0.12142163 0.06071081 [111,] 0.97638535 0.04722930 0.02361465 [112,] 0.96781324 0.06437352 0.03218676 [113,] 0.95607775 0.08784451 0.04392225 [114,] 0.95732326 0.08535348 0.04267674 [115,] 0.94884033 0.10231933 0.05115967 [116,] 0.94497203 0.11005594 0.05502797 [117,] 0.93570957 0.12858086 0.06429043 [118,] 0.97181258 0.05637483 0.02818742 [119,] 0.96616293 0.06767413 0.03383707 [120,] 0.95236182 0.09527636 0.04763818 [121,] 0.96422656 0.07154688 0.03577344 [122,] 0.94295418 0.11409165 0.05704582 [123,] 0.91136717 0.17726566 0.08863283 [124,] 0.86746980 0.26506040 0.13253020 [125,] 0.91025299 0.17949402 0.08974701 [126,] 0.92608600 0.14782801 0.07391400 [127,] 0.96914823 0.06170353 0.03085177 [128,] 0.95975369 0.08049262 0.04024631 [129,] 0.96815485 0.06369030 0.03184515 [130,] 0.97094809 0.05810382 0.02905191 [131,] 0.95421909 0.09156181 0.04578091 [132,] 0.95277591 0.09444818 0.04722409 > postscript(file="/var/wessaorg/rcomp/tmp/120831351609529.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/23su61351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3nsed1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4l5z11351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5q1y91351609529.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 -3.66034277 -2.02750089 -0.37051382 0.47271239 -4.39282471 -1.36666139 7 8 9 10 11 12 -1.64273240 0.57945394 0.67244960 -8.13976329 -4.83104407 -4.16634732 13 14 15 16 17 18 3.35286026 4.44625417 -0.63062678 0.77169104 -1.26526478 2.60631599 19 20 21 22 23 24 -9.70197263 -5.32160021 -5.42994003 -11.39116223 -8.47165410 -7.55075830 25 26 27 28 29 30 -2.14118054 -0.57903290 -0.80663909 -0.35962132 -1.88783728 -1.17201792 31 32 33 34 35 36 -6.35633627 -3.67080919 -4.69530263 -9.01524223 -4.40915057 -9.10357217 37 38 39 40 41 42 -1.26514484 -0.32924704 2.61016693 -1.20649484 2.24913769 2.52914608 43 44 45 46 47 48 -2.65350789 0.19348519 0.22525063 -5.55920572 -0.60756271 2.17902804 49 50 51 52 53 54 1.15613204 4.75601086 -0.03517319 2.46300325 1.81648970 2.37477019 55 56 57 58 59 60 0.51857460 3.72360985 0.06425329 -1.14279444 -1.01954739 2.48765491 61 62 63 64 65 66 6.55214642 7.80747066 5.16469150 5.61739547 2.22962404 3.72253603 67 68 69 70 71 72 0.99184318 -0.38867977 0.69170008 -1.32857399 -2.77638615 0.97233280 73 74 75 76 77 78 4.05712078 9.77463089 8.16294271 4.44513898 3.85699750 4.20977081 79 80 81 82 83 84 -4.43658803 -0.34668362 -0.03345791 -2.71740706 -3.90519871 4.50385434 85 86 87 88 89 90 2.96332791 7.95002416 4.21931396 3.71048389 5.26382789 6.15531536 91 92 93 94 95 96 2.29323814 4.01936576 4.26255816 -0.88315331 0.69232548 8.29469139 97 98 99 100 101 102 2.63742177 5.90238599 5.41133911 4.89940571 5.43419525 10.15185198 103 104 105 106 107 108 -0.80483193 4.01389601 3.52492048 -2.78720335 -3.38207237 -4.70843656 109 110 111 112 113 114 -6.00469331 -1.04305716 -3.88461842 -8.13707810 1.61260579 -5.67237270 115 116 117 118 119 120 -12.65454562 -2.93662060 -5.59040272 -7.73959599 -3.90048000 -8.36939889 121 122 123 124 125 126 1.42829650 5.12339055 -2.29382939 -1.93953189 3.97646170 -0.09324575 127 128 129 130 131 132 -4.28760656 -0.12926026 4.41318544 -2.37396213 -0.97774732 -0.61058452 133 134 135 136 137 138 2.00781227 10.24750426 7.49222896 5.11845717 4.33479045 4.94166917 139 140 141 142 143 144 1.39788277 2.59468279 1.49591598 -1.04300538 -2.39185522 -1.14709742 145 146 147 148 149 150 0.83050295 -0.15880649 -1.75785203 -0.25700107 -2.21362845 -2.98238792 151 -6.35885408 > postscript(file="/var/wessaorg/rcomp/tmp/62bw01351609529.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 -3.66034277 NA 1 -2.02750089 -3.66034277 2 -0.37051382 -2.02750089 3 0.47271239 -0.37051382 4 -4.39282471 0.47271239 5 -1.36666139 -4.39282471 6 -1.64273240 -1.36666139 7 0.57945394 -1.64273240 8 0.67244960 0.57945394 9 -8.13976329 0.67244960 10 -4.83104407 -8.13976329 11 -4.16634732 -4.83104407 12 3.35286026 -4.16634732 13 4.44625417 3.35286026 14 -0.63062678 4.44625417 15 0.77169104 -0.63062678 16 -1.26526478 0.77169104 17 2.60631599 -1.26526478 18 -9.70197263 2.60631599 19 -5.32160021 -9.70197263 20 -5.42994003 -5.32160021 21 -11.39116223 -5.42994003 22 -8.47165410 -11.39116223 23 -7.55075830 -8.47165410 24 -2.14118054 -7.55075830 25 -0.57903290 -2.14118054 26 -0.80663909 -0.57903290 27 -0.35962132 -0.80663909 28 -1.88783728 -0.35962132 29 -1.17201792 -1.88783728 30 -6.35633627 -1.17201792 31 -3.67080919 -6.35633627 32 -4.69530263 -3.67080919 33 -9.01524223 -4.69530263 34 -4.40915057 -9.01524223 35 -9.10357217 -4.40915057 36 -1.26514484 -9.10357217 37 -0.32924704 -1.26514484 38 2.61016693 -0.32924704 39 -1.20649484 2.61016693 40 2.24913769 -1.20649484 41 2.52914608 2.24913769 42 -2.65350789 2.52914608 43 0.19348519 -2.65350789 44 0.22525063 0.19348519 45 -5.55920572 0.22525063 46 -0.60756271 -5.55920572 47 2.17902804 -0.60756271 48 1.15613204 2.17902804 49 4.75601086 1.15613204 50 -0.03517319 4.75601086 51 2.46300325 -0.03517319 52 1.81648970 2.46300325 53 2.37477019 1.81648970 54 0.51857460 2.37477019 55 3.72360985 0.51857460 56 0.06425329 3.72360985 57 -1.14279444 0.06425329 58 -1.01954739 -1.14279444 59 2.48765491 -1.01954739 60 6.55214642 2.48765491 61 7.80747066 6.55214642 62 5.16469150 7.80747066 63 5.61739547 5.16469150 64 2.22962404 5.61739547 65 3.72253603 2.22962404 66 0.99184318 3.72253603 67 -0.38867977 0.99184318 68 0.69170008 -0.38867977 69 -1.32857399 0.69170008 70 -2.77638615 -1.32857399 71 0.97233280 -2.77638615 72 4.05712078 0.97233280 73 9.77463089 4.05712078 74 8.16294271 9.77463089 75 4.44513898 8.16294271 76 3.85699750 4.44513898 77 4.20977081 3.85699750 78 -4.43658803 4.20977081 79 -0.34668362 -4.43658803 80 -0.03345791 -0.34668362 81 -2.71740706 -0.03345791 82 -3.90519871 -2.71740706 83 4.50385434 -3.90519871 84 2.96332791 4.50385434 85 7.95002416 2.96332791 86 4.21931396 7.95002416 87 3.71048389 4.21931396 88 5.26382789 3.71048389 89 6.15531536 5.26382789 90 2.29323814 6.15531536 91 4.01936576 2.29323814 92 4.26255816 4.01936576 93 -0.88315331 4.26255816 94 0.69232548 -0.88315331 95 8.29469139 0.69232548 96 2.63742177 8.29469139 97 5.90238599 2.63742177 98 5.41133911 5.90238599 99 4.89940571 5.41133911 100 5.43419525 4.89940571 101 10.15185198 5.43419525 102 -0.80483193 10.15185198 103 4.01389601 -0.80483193 104 3.52492048 4.01389601 105 -2.78720335 3.52492048 106 -3.38207237 -2.78720335 107 -4.70843656 -3.38207237 108 -6.00469331 -4.70843656 109 -1.04305716 -6.00469331 110 -3.88461842 -1.04305716 111 -8.13707810 -3.88461842 112 1.61260579 -8.13707810 113 -5.67237270 1.61260579 114 -12.65454562 -5.67237270 115 -2.93662060 -12.65454562 116 -5.59040272 -2.93662060 117 -7.73959599 -5.59040272 118 -3.90048000 -7.73959599 119 -8.36939889 -3.90048000 120 1.42829650 -8.36939889 121 5.12339055 1.42829650 122 -2.29382939 5.12339055 123 -1.93953189 -2.29382939 124 3.97646170 -1.93953189 125 -0.09324575 3.97646170 126 -4.28760656 -0.09324575 127 -0.12926026 -4.28760656 128 4.41318544 -0.12926026 129 -2.37396213 4.41318544 130 -0.97774732 -2.37396213 131 -0.61058452 -0.97774732 132 2.00781227 -0.61058452 133 10.24750426 2.00781227 134 7.49222896 10.24750426 135 5.11845717 7.49222896 136 4.33479045 5.11845717 137 4.94166917 4.33479045 138 1.39788277 4.94166917 139 2.59468279 1.39788277 140 1.49591598 2.59468279 141 -1.04300538 1.49591598 142 -2.39185522 -1.04300538 143 -1.14709742 -2.39185522 144 0.83050295 -1.14709742 145 -0.15880649 0.83050295 146 -1.75785203 -0.15880649 147 -0.25700107 -1.75785203 148 -2.21362845 -0.25700107 149 -2.98238792 -2.21362845 150 -6.35885408 -2.98238792 151 NA -6.35885408 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.02750089 -3.66034277 [2,] -0.37051382 -2.02750089 [3,] 0.47271239 -0.37051382 [4,] -4.39282471 0.47271239 [5,] -1.36666139 -4.39282471 [6,] -1.64273240 -1.36666139 [7,] 0.57945394 -1.64273240 [8,] 0.67244960 0.57945394 [9,] -8.13976329 0.67244960 [10,] -4.83104407 -8.13976329 [11,] -4.16634732 -4.83104407 [12,] 3.35286026 -4.16634732 [13,] 4.44625417 3.35286026 [14,] -0.63062678 4.44625417 [15,] 0.77169104 -0.63062678 [16,] -1.26526478 0.77169104 [17,] 2.60631599 -1.26526478 [18,] -9.70197263 2.60631599 [19,] -5.32160021 -9.70197263 [20,] -5.42994003 -5.32160021 [21,] -11.39116223 -5.42994003 [22,] -8.47165410 -11.39116223 [23,] -7.55075830 -8.47165410 [24,] -2.14118054 -7.55075830 [25,] -0.57903290 -2.14118054 [26,] -0.80663909 -0.57903290 [27,] -0.35962132 -0.80663909 [28,] -1.88783728 -0.35962132 [29,] -1.17201792 -1.88783728 [30,] -6.35633627 -1.17201792 [31,] -3.67080919 -6.35633627 [32,] -4.69530263 -3.67080919 [33,] -9.01524223 -4.69530263 [34,] -4.40915057 -9.01524223 [35,] -9.10357217 -4.40915057 [36,] -1.26514484 -9.10357217 [37,] -0.32924704 -1.26514484 [38,] 2.61016693 -0.32924704 [39,] -1.20649484 2.61016693 [40,] 2.24913769 -1.20649484 [41,] 2.52914608 2.24913769 [42,] -2.65350789 2.52914608 [43,] 0.19348519 -2.65350789 [44,] 0.22525063 0.19348519 [45,] -5.55920572 0.22525063 [46,] -0.60756271 -5.55920572 [47,] 2.17902804 -0.60756271 [48,] 1.15613204 2.17902804 [49,] 4.75601086 1.15613204 [50,] -0.03517319 4.75601086 [51,] 2.46300325 -0.03517319 [52,] 1.81648970 2.46300325 [53,] 2.37477019 1.81648970 [54,] 0.51857460 2.37477019 [55,] 3.72360985 0.51857460 [56,] 0.06425329 3.72360985 [57,] -1.14279444 0.06425329 [58,] -1.01954739 -1.14279444 [59,] 2.48765491 -1.01954739 [60,] 6.55214642 2.48765491 [61,] 7.80747066 6.55214642 [62,] 5.16469150 7.80747066 [63,] 5.61739547 5.16469150 [64,] 2.22962404 5.61739547 [65,] 3.72253603 2.22962404 [66,] 0.99184318 3.72253603 [67,] -0.38867977 0.99184318 [68,] 0.69170008 -0.38867977 [69,] -1.32857399 0.69170008 [70,] -2.77638615 -1.32857399 [71,] 0.97233280 -2.77638615 [72,] 4.05712078 0.97233280 [73,] 9.77463089 4.05712078 [74,] 8.16294271 9.77463089 [75,] 4.44513898 8.16294271 [76,] 3.85699750 4.44513898 [77,] 4.20977081 3.85699750 [78,] -4.43658803 4.20977081 [79,] -0.34668362 -4.43658803 [80,] -0.03345791 -0.34668362 [81,] -2.71740706 -0.03345791 [82,] -3.90519871 -2.71740706 [83,] 4.50385434 -3.90519871 [84,] 2.96332791 4.50385434 [85,] 7.95002416 2.96332791 [86,] 4.21931396 7.95002416 [87,] 3.71048389 4.21931396 [88,] 5.26382789 3.71048389 [89,] 6.15531536 5.26382789 [90,] 2.29323814 6.15531536 [91,] 4.01936576 2.29323814 [92,] 4.26255816 4.01936576 [93,] -0.88315331 4.26255816 [94,] 0.69232548 -0.88315331 [95,] 8.29469139 0.69232548 [96,] 2.63742177 8.29469139 [97,] 5.90238599 2.63742177 [98,] 5.41133911 5.90238599 [99,] 4.89940571 5.41133911 [100,] 5.43419525 4.89940571 [101,] 10.15185198 5.43419525 [102,] -0.80483193 10.15185198 [103,] 4.01389601 -0.80483193 [104,] 3.52492048 4.01389601 [105,] -2.78720335 3.52492048 [106,] -3.38207237 -2.78720335 [107,] -4.70843656 -3.38207237 [108,] -6.00469331 -4.70843656 [109,] -1.04305716 -6.00469331 [110,] -3.88461842 -1.04305716 [111,] -8.13707810 -3.88461842 [112,] 1.61260579 -8.13707810 [113,] -5.67237270 1.61260579 [114,] -12.65454562 -5.67237270 [115,] -2.93662060 -12.65454562 [116,] -5.59040272 -2.93662060 [117,] -7.73959599 -5.59040272 [118,] -3.90048000 -7.73959599 [119,] -8.36939889 -3.90048000 [120,] 1.42829650 -8.36939889 [121,] 5.12339055 1.42829650 [122,] -2.29382939 5.12339055 [123,] -1.93953189 -2.29382939 [124,] 3.97646170 -1.93953189 [125,] -0.09324575 3.97646170 [126,] -4.28760656 -0.09324575 [127,] -0.12926026 -4.28760656 [128,] 4.41318544 -0.12926026 [129,] -2.37396213 4.41318544 [130,] -0.97774732 -2.37396213 [131,] -0.61058452 -0.97774732 [132,] 2.00781227 -0.61058452 [133,] 10.24750426 2.00781227 [134,] 7.49222896 10.24750426 [135,] 5.11845717 7.49222896 [136,] 4.33479045 5.11845717 [137,] 4.94166917 4.33479045 [138,] 1.39788277 4.94166917 [139,] 2.59468279 1.39788277 [140,] 1.49591598 2.59468279 [141,] -1.04300538 1.49591598 [142,] -2.39185522 -1.04300538 [143,] -1.14709742 -2.39185522 [144,] 0.83050295 -1.14709742 [145,] -0.15880649 0.83050295 [146,] -1.75785203 -0.15880649 [147,] -0.25700107 -1.75785203 [148,] -2.21362845 -0.25700107 [149,] -2.98238792 -2.21362845 [150,] -6.35885408 -2.98238792 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.02750089 -3.66034277 2 -0.37051382 -2.02750089 3 0.47271239 -0.37051382 4 -4.39282471 0.47271239 5 -1.36666139 -4.39282471 6 -1.64273240 -1.36666139 7 0.57945394 -1.64273240 8 0.67244960 0.57945394 9 -8.13976329 0.67244960 10 -4.83104407 -8.13976329 11 -4.16634732 -4.83104407 12 3.35286026 -4.16634732 13 4.44625417 3.35286026 14 -0.63062678 4.44625417 15 0.77169104 -0.63062678 16 -1.26526478 0.77169104 17 2.60631599 -1.26526478 18 -9.70197263 2.60631599 19 -5.32160021 -9.70197263 20 -5.42994003 -5.32160021 21 -11.39116223 -5.42994003 22 -8.47165410 -11.39116223 23 -7.55075830 -8.47165410 24 -2.14118054 -7.55075830 25 -0.57903290 -2.14118054 26 -0.80663909 -0.57903290 27 -0.35962132 -0.80663909 28 -1.88783728 -0.35962132 29 -1.17201792 -1.88783728 30 -6.35633627 -1.17201792 31 -3.67080919 -6.35633627 32 -4.69530263 -3.67080919 33 -9.01524223 -4.69530263 34 -4.40915057 -9.01524223 35 -9.10357217 -4.40915057 36 -1.26514484 -9.10357217 37 -0.32924704 -1.26514484 38 2.61016693 -0.32924704 39 -1.20649484 2.61016693 40 2.24913769 -1.20649484 41 2.52914608 2.24913769 42 -2.65350789 2.52914608 43 0.19348519 -2.65350789 44 0.22525063 0.19348519 45 -5.55920572 0.22525063 46 -0.60756271 -5.55920572 47 2.17902804 -0.60756271 48 1.15613204 2.17902804 49 4.75601086 1.15613204 50 -0.03517319 4.75601086 51 2.46300325 -0.03517319 52 1.81648970 2.46300325 53 2.37477019 1.81648970 54 0.51857460 2.37477019 55 3.72360985 0.51857460 56 0.06425329 3.72360985 57 -1.14279444 0.06425329 58 -1.01954739 -1.14279444 59 2.48765491 -1.01954739 60 6.55214642 2.48765491 61 7.80747066 6.55214642 62 5.16469150 7.80747066 63 5.61739547 5.16469150 64 2.22962404 5.61739547 65 3.72253603 2.22962404 66 0.99184318 3.72253603 67 -0.38867977 0.99184318 68 0.69170008 -0.38867977 69 -1.32857399 0.69170008 70 -2.77638615 -1.32857399 71 0.97233280 -2.77638615 72 4.05712078 0.97233280 73 9.77463089 4.05712078 74 8.16294271 9.77463089 75 4.44513898 8.16294271 76 3.85699750 4.44513898 77 4.20977081 3.85699750 78 -4.43658803 4.20977081 79 -0.34668362 -4.43658803 80 -0.03345791 -0.34668362 81 -2.71740706 -0.03345791 82 -3.90519871 -2.71740706 83 4.50385434 -3.90519871 84 2.96332791 4.50385434 85 7.95002416 2.96332791 86 4.21931396 7.95002416 87 3.71048389 4.21931396 88 5.26382789 3.71048389 89 6.15531536 5.26382789 90 2.29323814 6.15531536 91 4.01936576 2.29323814 92 4.26255816 4.01936576 93 -0.88315331 4.26255816 94 0.69232548 -0.88315331 95 8.29469139 0.69232548 96 2.63742177 8.29469139 97 5.90238599 2.63742177 98 5.41133911 5.90238599 99 4.89940571 5.41133911 100 5.43419525 4.89940571 101 10.15185198 5.43419525 102 -0.80483193 10.15185198 103 4.01389601 -0.80483193 104 3.52492048 4.01389601 105 -2.78720335 3.52492048 106 -3.38207237 -2.78720335 107 -4.70843656 -3.38207237 108 -6.00469331 -4.70843656 109 -1.04305716 -6.00469331 110 -3.88461842 -1.04305716 111 -8.13707810 -3.88461842 112 1.61260579 -8.13707810 113 -5.67237270 1.61260579 114 -12.65454562 -5.67237270 115 -2.93662060 -12.65454562 116 -5.59040272 -2.93662060 117 -7.73959599 -5.59040272 118 -3.90048000 -7.73959599 119 -8.36939889 -3.90048000 120 1.42829650 -8.36939889 121 5.12339055 1.42829650 122 -2.29382939 5.12339055 123 -1.93953189 -2.29382939 124 3.97646170 -1.93953189 125 -0.09324575 3.97646170 126 -4.28760656 -0.09324575 127 -0.12926026 -4.28760656 128 4.41318544 -0.12926026 129 -2.37396213 4.41318544 130 -0.97774732 -2.37396213 131 -0.61058452 -0.97774732 132 2.00781227 -0.61058452 133 10.24750426 2.00781227 134 7.49222896 10.24750426 135 5.11845717 7.49222896 136 4.33479045 5.11845717 137 4.94166917 4.33479045 138 1.39788277 4.94166917 139 2.59468279 1.39788277 140 1.49591598 2.59468279 141 -1.04300538 1.49591598 142 -2.39185522 -1.04300538 143 -1.14709742 -2.39185522 144 0.83050295 -1.14709742 145 -0.15880649 0.83050295 146 -1.75785203 -0.15880649 147 -0.25700107 -1.75785203 148 -2.21362845 -0.25700107 149 -2.98238792 -2.21362845 150 -6.35885408 -2.98238792 > 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/wessaorg/rcomp/tmp/7n8fv1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/877341351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9s0ih1351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/101hj41351609529.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/wessaorg/rcomp/tmp/11h25c1351609529.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/wessaorg/rcomp/tmp/12ioj81351609529.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/wessaorg/rcomp/tmp/13eeib1351609530.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/wessaorg/rcomp/tmp/14gfpx1351609530.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/wessaorg/rcomp/tmp/15dki11351609530.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/wessaorg/rcomp/tmp/164tin1351609530.tab") + } > > try(system("convert tmp/120831351609529.ps tmp/120831351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/23su61351609529.ps tmp/23su61351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/3nsed1351609529.ps tmp/3nsed1351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/4l5z11351609529.ps tmp/4l5z11351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/5q1y91351609529.ps tmp/5q1y91351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/62bw01351609529.ps tmp/62bw01351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/7n8fv1351609529.ps tmp/7n8fv1351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/877341351609529.ps tmp/877341351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/9s0ih1351609529.ps tmp/9s0ih1351609529.png",intern=TRUE)) character(0) > try(system("convert tmp/101hj41351609529.ps tmp/101hj41351609529.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.285 1.512 14.548