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Type 'q()' to quit R. > x <- array(list(100 + ,0 + ,108.1560276 + ,0 + ,114.0150276 + ,0 + ,102.1880309 + ,0 + ,110.3672031 + ,0 + ,96.8602511 + ,0 + ,94.1944583 + ,0 + ,99.51621961 + ,0 + ,94.06333487 + ,0 + ,97.5541476 + ,0 + ,78.15062422 + ,0 + ,81.2434643 + ,0 + ,92.36262465 + ,0 + ,96.06324371 + ,0 + ,114.0523777 + ,0 + ,110.6616666 + ,0 + ,104.9171949 + ,0 + ,90.00187193 + ,0 + ,95.7008067 + ,0 + ,86.02741157 + ,0 + ,84.85287668 + ,0 + ,100.04328 + ,0 + ,80.91713823 + ,0 + ,74.06539709 + ,0 + ,77.30281369 + ,0 + ,97.23043249 + ,0 + ,90.75515676 + ,0 + ,100.5614455 + ,0 + ,92.01293267 + ,0 + ,99.24012138 + ,0 + ,105.8672755 + ,0 + ,90.9920463 + ,0 + ,93.30624423 + ,0 + ,91.17419413 + ,0 + ,77.33295039 + ,0 + ,91.1277721 + ,0 + ,85.01249943 + ,0 + ,83.90390242 + ,0 + ,104.8626302 + ,0 + ,110.9039108 + ,0 + ,95.43714373 + ,0 + ,111.6238727 + ,0 + ,108.8925403 + ,0 + ,96.17511682 + ,0 + ,101.9740205 + ,0 + ,99.11953031 + ,0 + ,86.78158147 + ,0 + ,118.4195003 + ,0 + ,118.7441447 + ,0 + ,106.5296192 + ,0 + ,134.7772694 + ,0 + ,104.6778714 + ,0 + ,105.2954304 + ,0 + ,139.4139849 + ,0 + ,103.6060491 + ,0 + ,99.78182974 + ,0 + ,103.4610301 + ,0 + ,120.0594945 + ,0 + ,96.71377168 + ,0 + ,107.1308929 + ,0 + ,105.3608372 + ,0 + ,111.6942359 + ,0 + ,132.0519998 + ,0 + ,126.8037879 + ,0 + ,154.4824253 + ,0 + ,141.5570984 + ,0 + ,109.9506882 + ,0 + ,127.904198 + ,0 + ,133.0888617 + ,0 + ,120.0796299 + ,0 + ,117.5557142 + ,0 + ,143.0362309 + ,0 + ,159.982927 + ,1 + ,128.5991124 + ,1 + ,149.7373327 + ,1 + ,126.8169313 + ,1 + ,140.9639674 + ,1 + ,137.6691981 + ,1 + ,117.9402337 + ,1 + ,122.3095247 + ,1 + ,127.7804207 + ,1 + ,136.1677176 + ,1 + ,116.2405856 + ,1 + ,123.1576893 + ,1 + ,116.3400234 + ,1 + ,108.6119282 + ,1 + ,125.8982264 + ,1 + ,112.8003105 + ,1 + ,107.5182447 + ,1 + ,135.0955413 + ,1 + ,115.5096488 + ,1 + ,115.8640759 + ,1 + ,104.5883906 + ,1 + ,163.7213386 + ,1 + ,113.4482275 + ,1 + ,98.0428844 + ,1 + ,116.7868521 + ,1 + ,126.5330444 + ,1 + ,113.0336597 + ,1 + ,124.3392163 + ,1 + ,109.8298759 + ,1 + ,124.4434777 + ,1 + ,111.5039454 + ,1 + ,102.0350019 + ,1 + ,116.8726598 + ,1 + ,112.2073122 + ,1 + ,101.1513902 + ,1 + ,124.4255108 + ,1) + ,dim=c(2 + ,108) + ,dimnames=list(c('BouwV' + ,'X') + ,1:108)) > y <- array(NA,dim=c(2,108),dimnames=list(c('BouwV','X'),1:108)) > 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 = 'Include Monthly 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 BouwV X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 100.00000 0 1 0 0 0 0 0 0 0 0 0 0 2 108.15603 0 0 1 0 0 0 0 0 0 0 0 0 3 114.01503 0 0 0 1 0 0 0 0 0 0 0 0 4 102.18803 0 0 0 0 1 0 0 0 0 0 0 0 5 110.36720 0 0 0 0 0 1 0 0 0 0 0 0 6 96.86025 0 0 0 0 0 0 1 0 0 0 0 0 7 94.19446 0 0 0 0 0 0 0 1 0 0 0 0 8 99.51622 0 0 0 0 0 0 0 0 1 0 0 0 9 94.06333 0 0 0 0 0 0 0 0 0 1 0 0 10 97.55415 0 0 0 0 0 0 0 0 0 0 1 0 11 78.15062 0 0 0 0 0 0 0 0 0 0 0 1 12 81.24346 0 0 0 0 0 0 0 0 0 0 0 0 13 92.36262 0 1 0 0 0 0 0 0 0 0 0 0 14 96.06324 0 0 1 0 0 0 0 0 0 0 0 0 15 114.05238 0 0 0 1 0 0 0 0 0 0 0 0 16 110.66167 0 0 0 0 1 0 0 0 0 0 0 0 17 104.91719 0 0 0 0 0 1 0 0 0 0 0 0 18 90.00187 0 0 0 0 0 0 1 0 0 0 0 0 19 95.70081 0 0 0 0 0 0 0 1 0 0 0 0 20 86.02741 0 0 0 0 0 0 0 0 1 0 0 0 21 84.85288 0 0 0 0 0 0 0 0 0 1 0 0 22 100.04328 0 0 0 0 0 0 0 0 0 0 1 0 23 80.91714 0 0 0 0 0 0 0 0 0 0 0 1 24 74.06540 0 0 0 0 0 0 0 0 0 0 0 0 25 77.30281 0 1 0 0 0 0 0 0 0 0 0 0 26 97.23043 0 0 1 0 0 0 0 0 0 0 0 0 27 90.75516 0 0 0 1 0 0 0 0 0 0 0 0 28 100.56145 0 0 0 0 1 0 0 0 0 0 0 0 29 92.01293 0 0 0 0 0 1 0 0 0 0 0 0 30 99.24012 0 0 0 0 0 0 1 0 0 0 0 0 31 105.86728 0 0 0 0 0 0 0 1 0 0 0 0 32 90.99205 0 0 0 0 0 0 0 0 1 0 0 0 33 93.30624 0 0 0 0 0 0 0 0 0 1 0 0 34 91.17419 0 0 0 0 0 0 0 0 0 0 1 0 35 77.33295 0 0 0 0 0 0 0 0 0 0 0 1 36 91.12777 0 0 0 0 0 0 0 0 0 0 0 0 37 85.01250 0 1 0 0 0 0 0 0 0 0 0 0 38 83.90390 0 0 1 0 0 0 0 0 0 0 0 0 39 104.86263 0 0 0 1 0 0 0 0 0 0 0 0 40 110.90391 0 0 0 0 1 0 0 0 0 0 0 0 41 95.43714 0 0 0 0 0 1 0 0 0 0 0 0 42 111.62387 0 0 0 0 0 0 1 0 0 0 0 0 43 108.89254 0 0 0 0 0 0 0 1 0 0 0 0 44 96.17512 0 0 0 0 0 0 0 0 1 0 0 0 45 101.97402 0 0 0 0 0 0 0 0 0 1 0 0 46 99.11953 0 0 0 0 0 0 0 0 0 0 1 0 47 86.78158 0 0 0 0 0 0 0 0 0 0 0 1 48 118.41950 0 0 0 0 0 0 0 0 0 0 0 0 49 118.74414 0 1 0 0 0 0 0 0 0 0 0 0 50 106.52962 0 0 1 0 0 0 0 0 0 0 0 0 51 134.77727 0 0 0 1 0 0 0 0 0 0 0 0 52 104.67787 0 0 0 0 1 0 0 0 0 0 0 0 53 105.29543 0 0 0 0 0 1 0 0 0 0 0 0 54 139.41398 0 0 0 0 0 0 1 0 0 0 0 0 55 103.60605 0 0 0 0 0 0 0 1 0 0 0 0 56 99.78183 0 0 0 0 0 0 0 0 1 0 0 0 57 103.46103 0 0 0 0 0 0 0 0 0 1 0 0 58 120.05949 0 0 0 0 0 0 0 0 0 0 1 0 59 96.71377 0 0 0 0 0 0 0 0 0 0 0 1 60 107.13089 0 0 0 0 0 0 0 0 0 0 0 0 61 105.36084 0 1 0 0 0 0 0 0 0 0 0 0 62 111.69424 0 0 1 0 0 0 0 0 0 0 0 0 63 132.05200 0 0 0 1 0 0 0 0 0 0 0 0 64 126.80379 0 0 0 0 1 0 0 0 0 0 0 0 65 154.48243 0 0 0 0 0 1 0 0 0 0 0 0 66 141.55710 0 0 0 0 0 0 1 0 0 0 0 0 67 109.95069 0 0 0 0 0 0 0 1 0 0 0 0 68 127.90420 0 0 0 0 0 0 0 0 1 0 0 0 69 133.08886 0 0 0 0 0 0 0 0 0 1 0 0 70 120.07963 0 0 0 0 0 0 0 0 0 0 1 0 71 117.55571 0 0 0 0 0 0 0 0 0 0 0 1 72 143.03623 0 0 0 0 0 0 0 0 0 0 0 0 73 159.98293 1 1 0 0 0 0 0 0 0 0 0 0 74 128.59911 1 0 1 0 0 0 0 0 0 0 0 0 75 149.73733 1 0 0 1 0 0 0 0 0 0 0 0 76 126.81693 1 0 0 0 1 0 0 0 0 0 0 0 77 140.96397 1 0 0 0 0 1 0 0 0 0 0 0 78 137.66920 1 0 0 0 0 0 1 0 0 0 0 0 79 117.94023 1 0 0 0 0 0 0 1 0 0 0 0 80 122.30952 1 0 0 0 0 0 0 0 1 0 0 0 81 127.78042 1 0 0 0 0 0 0 0 0 1 0 0 82 136.16772 1 0 0 0 0 0 0 0 0 0 1 0 83 116.24059 1 0 0 0 0 0 0 0 0 0 0 1 84 123.15769 1 0 0 0 0 0 0 0 0 0 0 0 85 116.34002 1 1 0 0 0 0 0 0 0 0 0 0 86 108.61193 1 0 1 0 0 0 0 0 0 0 0 0 87 125.89823 1 0 0 1 0 0 0 0 0 0 0 0 88 112.80031 1 0 0 0 1 0 0 0 0 0 0 0 89 107.51824 1 0 0 0 0 1 0 0 0 0 0 0 90 135.09554 1 0 0 0 0 0 1 0 0 0 0 0 91 115.50965 1 0 0 0 0 0 0 1 0 0 0 0 92 115.86408 1 0 0 0 0 0 0 0 1 0 0 0 93 104.58839 1 0 0 0 0 0 0 0 0 1 0 0 94 163.72134 1 0 0 0 0 0 0 0 0 0 1 0 95 113.44823 1 0 0 0 0 0 0 0 0 0 0 1 96 98.04288 1 0 0 0 0 0 0 0 0 0 0 0 97 116.78685 1 1 0 0 0 0 0 0 0 0 0 0 98 126.53304 1 0 1 0 0 0 0 0 0 0 0 0 99 113.03366 1 0 0 1 0 0 0 0 0 0 0 0 100 124.33922 1 0 0 0 1 0 0 0 0 0 0 0 101 109.82988 1 0 0 0 0 1 0 0 0 0 0 0 102 124.44348 1 0 0 0 0 0 1 0 0 0 0 0 103 111.50395 1 0 0 0 0 0 0 1 0 0 0 0 104 102.03500 1 0 0 0 0 0 0 0 1 0 0 0 105 116.87266 1 0 0 0 0 0 0 0 0 1 0 0 106 112.20731 1 0 0 0 0 0 0 0 0 0 1 0 107 101.15139 1 0 0 0 0 0 0 0 0 0 0 1 108 124.42551 1 0 0 0 0 0 0 0 0 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X M1 M2 M3 M4 100.7379 18.0027 1.2493 0.7414 13.1705 6.5671 M5 M6 M7 M8 M9 M10 6.6861 12.8062 0.2796 -2.2271 -0.0735 8.8308 M11 -10.2619 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -26.673 -9.831 -1.954 6.464 47.058 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 100.7379 5.3448 18.848 < 2e-16 *** X 18.0027 3.2069 5.614 1.96e-07 *** M1 1.2493 7.4059 0.169 0.8664 M2 0.7414 7.4059 0.100 0.9205 M3 13.1705 7.4059 1.778 0.0785 . M4 6.5671 7.4059 0.887 0.3775 M5 6.6861 7.4059 0.903 0.3689 M6 12.8062 7.4059 1.729 0.0870 . M7 0.2796 7.4059 0.038 0.9700 M8 -2.2271 7.4059 -0.301 0.7643 M9 -0.0735 7.4059 -0.010 0.9921 M10 8.8308 7.4059 1.192 0.2361 M11 -10.2619 7.4059 -1.386 0.1691 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 15.71 on 95 degrees of freedom Multiple R-squared: 0.3432, Adjusted R-squared: 0.2602 F-statistic: 4.136 on 12 and 95 DF, p-value: 3.417e-05 > 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,] 0.0806756219 0.1613512438 0.91932438 [2,] 0.0313450064 0.0626900128 0.96865499 [3,] 0.0144620332 0.0289240665 0.98553797 [4,] 0.0045474934 0.0090949868 0.99545251 [5,] 0.0049778539 0.0099557077 0.99502215 [6,] 0.0029292034 0.0058584068 0.99707080 [7,] 0.0010562074 0.0021124149 0.99894379 [8,] 0.0003710188 0.0007420377 0.99962898 [9,] 0.0002184950 0.0004369901 0.99978150 [10,] 0.0013450311 0.0026900621 0.99865497 [11,] 0.0006246607 0.0012493214 0.99937534 [12,] 0.0042166904 0.0084333809 0.99578331 [13,] 0.0023696396 0.0047392792 0.99763036 [14,] 0.0031174766 0.0062349532 0.99688252 [15,] 0.0021503030 0.0043006061 0.99784970 [16,] 0.0016052503 0.0032105007 0.99839475 [17,] 0.0008467389 0.0016934778 0.99915326 [18,] 0.0004683795 0.0009367590 0.99953162 [19,] 0.0003905552 0.0007811103 0.99960944 [20,] 0.0002376594 0.0004753187 0.99976234 [21,] 0.0003001785 0.0006003569 0.99969982 [22,] 0.0002801133 0.0005602267 0.99971989 [23,] 0.0006496402 0.0012992804 0.99935036 [24,] 0.0004426426 0.0008852852 0.99955736 [25,] 0.0002677847 0.0005355695 0.99973222 [26,] 0.0002405517 0.0004811033 0.99975945 [27,] 0.0004516968 0.0009033936 0.99954830 [28,] 0.0003470685 0.0006941369 0.99965293 [29,] 0.0002220429 0.0004440859 0.99977796 [30,] 0.0002040217 0.0004080435 0.99979598 [31,] 0.0002111883 0.0004223766 0.99978881 [32,] 0.0001973652 0.0003947304 0.99980263 [33,] 0.0040219729 0.0080439458 0.99597803 [34,] 0.0138560778 0.0277121556 0.98614392 [35,] 0.0115108572 0.0230217143 0.98848914 [36,] 0.0253922025 0.0507844049 0.97460780 [37,] 0.0204731365 0.0409462730 0.97952686 [38,] 0.0194268713 0.0388537427 0.98057313 [39,] 0.0723486063 0.1446972125 0.92765139 [40,] 0.0561258969 0.1122517938 0.94387410 [41,] 0.0501576630 0.1003153261 0.94984234 [42,] 0.0477977338 0.0955954675 0.95220227 [43,] 0.0611689261 0.1223378522 0.93883107 [44,] 0.0656068614 0.1312137227 0.93439314 [45,] 0.0710862365 0.1421724729 0.92891376 [46,] 0.1033284854 0.2066569709 0.89667151 [47,] 0.1066653898 0.2133307796 0.89333461 [48,] 0.1084710492 0.2169420983 0.89152895 [49,] 0.1086808306 0.2173616612 0.89131917 [50,] 0.3631852562 0.7263705125 0.63681474 [51,] 0.4083505272 0.8167010544 0.59164947 [52,] 0.3760095475 0.7520190949 0.62399045 [53,] 0.4087907849 0.8175815697 0.59120922 [54,] 0.4635528036 0.9271056071 0.53644720 [55,] 0.5394035616 0.9211928769 0.46059644 [56,] 0.5793758660 0.8412482680 0.42062413 [57,] 0.6494458264 0.7011083473 0.35055417 [58,] 0.8182686298 0.3634627404 0.18173137 [59,] 0.8049100467 0.3901799067 0.19508995 [60,] 0.8615635281 0.2768729438 0.13843647 [61,] 0.8369118871 0.3261762258 0.16308811 [62,] 0.9063850368 0.1872299264 0.09361496 [63,] 0.8768273245 0.2463453509 0.12317268 [64,] 0.8398241839 0.3203516322 0.16017582 [65,] 0.8125359209 0.3749281581 0.18746408 [66,] 0.7990781346 0.4018437309 0.20092187 [67,] 0.7312389706 0.5375220588 0.26876103 [68,] 0.6679948194 0.6640103612 0.33200518 [69,] 0.6135450105 0.7729099789 0.38645499 [70,] 0.5244832041 0.9510335918 0.47551680 [71,] 0.4944127311 0.9888254623 0.50558727 [72,] 0.4284906763 0.8569813527 0.57150932 [73,] 0.3582511334 0.7165022668 0.64174887 [74,] 0.2758597592 0.5517195184 0.72414024 [75,] 0.1953857384 0.3907714769 0.80461426 [76,] 0.1184017621 0.2368035242 0.88159824 [77,] 0.0707532422 0.1415064844 0.92924676 > postscript(file="/var/www/html/rcomp/tmp/13ts81258645696.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2er971258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3tj561258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4h6r91258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ozxo1258645696.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 = 108 Frequency = 1 1 2 3 4 5 6 -1.9871939 6.6767421 0.1066161 -5.1169907 2.9431541 -16.6839090 7 8 9 10 11 12 -6.8230605 1.0053921 -6.6010943 -12.0145933 -12.3253765 -19.4944652 13 14 15 16 17 18 -9.6245693 -5.4160418 0.1439662 3.3566450 -2.5068541 -23.5422881 19 20 21 22 23 24 -5.3167121 -12.4834159 -15.8115525 -9.5254609 -9.5588625 -26.6725324 25 26 27 28 29 30 -24.6843802 -4.2488530 -23.1532547 -6.7435761 -15.4111164 -14.3040387 31 32 33 34 35 36 4.8497567 -7.5187812 -7.3581849 -18.3945468 -13.1430503 -9.6101574 37 38 39 40 41 42 -16.9746945 -17.5753831 -9.0457813 3.5988892 -11.9869053 -1.9202874 43 44 45 46 47 48 7.8750215 -2.3357107 1.3095914 -10.4492106 -3.6944193 17.6815708 49 50 51 52 53 54 16.7569508 5.0503337 20.8688579 -2.6271502 -2.1286186 25.8698248 55 56 57 58 59 60 2.5885303 1.2710022 2.7966010 10.4907536 6.2377709 6.3929634 61 62 63 64 65 66 3.3736433 10.2149504 18.1435883 19.4987663 47.0583763 28.0129383 67 68 69 70 71 72 8.9331694 29.3933705 32.4244326 10.5108890 27.0797135 42.2983014 73 74 75 76 77 78 39.9930741 9.1171679 17.8262622 1.5092507 15.5372594 6.1223791 79 80 81 82 83 84 -1.0799441 5.7960382 9.1133326 8.5963177 7.7619259 4.4171009 85 86 87 88 89 90 -3.6498295 -10.8700163 -6.0128441 -12.5073701 -17.9084633 3.5487223 91 92 93 94 95 96 -3.5105290 -0.6494106 -14.0786975 36.1499387 4.9695678 -20.6977040 97 98 99 100 101 102 -3.2030008 7.0510999 -18.8774108 -0.9684643 -15.5968321 -7.1033413 103 104 105 106 107 108 -7.5162324 -14.4784846 -1.7944283 -15.3640877 -7.3272695 5.6849224 > postscript(file="/var/www/html/rcomp/tmp/6dd371258645696.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 = 108 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.9871939 NA 1 6.6767421 -1.9871939 2 0.1066161 6.6767421 3 -5.1169907 0.1066161 4 2.9431541 -5.1169907 5 -16.6839090 2.9431541 6 -6.8230605 -16.6839090 7 1.0053921 -6.8230605 8 -6.6010943 1.0053921 9 -12.0145933 -6.6010943 10 -12.3253765 -12.0145933 11 -19.4944652 -12.3253765 12 -9.6245693 -19.4944652 13 -5.4160418 -9.6245693 14 0.1439662 -5.4160418 15 3.3566450 0.1439662 16 -2.5068541 3.3566450 17 -23.5422881 -2.5068541 18 -5.3167121 -23.5422881 19 -12.4834159 -5.3167121 20 -15.8115525 -12.4834159 21 -9.5254609 -15.8115525 22 -9.5588625 -9.5254609 23 -26.6725324 -9.5588625 24 -24.6843802 -26.6725324 25 -4.2488530 -24.6843802 26 -23.1532547 -4.2488530 27 -6.7435761 -23.1532547 28 -15.4111164 -6.7435761 29 -14.3040387 -15.4111164 30 4.8497567 -14.3040387 31 -7.5187812 4.8497567 32 -7.3581849 -7.5187812 33 -18.3945468 -7.3581849 34 -13.1430503 -18.3945468 35 -9.6101574 -13.1430503 36 -16.9746945 -9.6101574 37 -17.5753831 -16.9746945 38 -9.0457813 -17.5753831 39 3.5988892 -9.0457813 40 -11.9869053 3.5988892 41 -1.9202874 -11.9869053 42 7.8750215 -1.9202874 43 -2.3357107 7.8750215 44 1.3095914 -2.3357107 45 -10.4492106 1.3095914 46 -3.6944193 -10.4492106 47 17.6815708 -3.6944193 48 16.7569508 17.6815708 49 5.0503337 16.7569508 50 20.8688579 5.0503337 51 -2.6271502 20.8688579 52 -2.1286186 -2.6271502 53 25.8698248 -2.1286186 54 2.5885303 25.8698248 55 1.2710022 2.5885303 56 2.7966010 1.2710022 57 10.4907536 2.7966010 58 6.2377709 10.4907536 59 6.3929634 6.2377709 60 3.3736433 6.3929634 61 10.2149504 3.3736433 62 18.1435883 10.2149504 63 19.4987663 18.1435883 64 47.0583763 19.4987663 65 28.0129383 47.0583763 66 8.9331694 28.0129383 67 29.3933705 8.9331694 68 32.4244326 29.3933705 69 10.5108890 32.4244326 70 27.0797135 10.5108890 71 42.2983014 27.0797135 72 39.9930741 42.2983014 73 9.1171679 39.9930741 74 17.8262622 9.1171679 75 1.5092507 17.8262622 76 15.5372594 1.5092507 77 6.1223791 15.5372594 78 -1.0799441 6.1223791 79 5.7960382 -1.0799441 80 9.1133326 5.7960382 81 8.5963177 9.1133326 82 7.7619259 8.5963177 83 4.4171009 7.7619259 84 -3.6498295 4.4171009 85 -10.8700163 -3.6498295 86 -6.0128441 -10.8700163 87 -12.5073701 -6.0128441 88 -17.9084633 -12.5073701 89 3.5487223 -17.9084633 90 -3.5105290 3.5487223 91 -0.6494106 -3.5105290 92 -14.0786975 -0.6494106 93 36.1499387 -14.0786975 94 4.9695678 36.1499387 95 -20.6977040 4.9695678 96 -3.2030008 -20.6977040 97 7.0510999 -3.2030008 98 -18.8774108 7.0510999 99 -0.9684643 -18.8774108 100 -15.5968321 -0.9684643 101 -7.1033413 -15.5968321 102 -7.5162324 -7.1033413 103 -14.4784846 -7.5162324 104 -1.7944283 -14.4784846 105 -15.3640877 -1.7944283 106 -7.3272695 -15.3640877 107 5.6849224 -7.3272695 108 NA 5.6849224 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 6.6767421 -1.9871939 [2,] 0.1066161 6.6767421 [3,] -5.1169907 0.1066161 [4,] 2.9431541 -5.1169907 [5,] -16.6839090 2.9431541 [6,] -6.8230605 -16.6839090 [7,] 1.0053921 -6.8230605 [8,] -6.6010943 1.0053921 [9,] -12.0145933 -6.6010943 [10,] -12.3253765 -12.0145933 [11,] -19.4944652 -12.3253765 [12,] -9.6245693 -19.4944652 [13,] -5.4160418 -9.6245693 [14,] 0.1439662 -5.4160418 [15,] 3.3566450 0.1439662 [16,] -2.5068541 3.3566450 [17,] -23.5422881 -2.5068541 [18,] -5.3167121 -23.5422881 [19,] -12.4834159 -5.3167121 [20,] -15.8115525 -12.4834159 [21,] -9.5254609 -15.8115525 [22,] -9.5588625 -9.5254609 [23,] -26.6725324 -9.5588625 [24,] -24.6843802 -26.6725324 [25,] -4.2488530 -24.6843802 [26,] -23.1532547 -4.2488530 [27,] -6.7435761 -23.1532547 [28,] -15.4111164 -6.7435761 [29,] -14.3040387 -15.4111164 [30,] 4.8497567 -14.3040387 [31,] -7.5187812 4.8497567 [32,] -7.3581849 -7.5187812 [33,] -18.3945468 -7.3581849 [34,] -13.1430503 -18.3945468 [35,] -9.6101574 -13.1430503 [36,] -16.9746945 -9.6101574 [37,] -17.5753831 -16.9746945 [38,] -9.0457813 -17.5753831 [39,] 3.5988892 -9.0457813 [40,] -11.9869053 3.5988892 [41,] -1.9202874 -11.9869053 [42,] 7.8750215 -1.9202874 [43,] -2.3357107 7.8750215 [44,] 1.3095914 -2.3357107 [45,] -10.4492106 1.3095914 [46,] -3.6944193 -10.4492106 [47,] 17.6815708 -3.6944193 [48,] 16.7569508 17.6815708 [49,] 5.0503337 16.7569508 [50,] 20.8688579 5.0503337 [51,] -2.6271502 20.8688579 [52,] -2.1286186 -2.6271502 [53,] 25.8698248 -2.1286186 [54,] 2.5885303 25.8698248 [55,] 1.2710022 2.5885303 [56,] 2.7966010 1.2710022 [57,] 10.4907536 2.7966010 [58,] 6.2377709 10.4907536 [59,] 6.3929634 6.2377709 [60,] 3.3736433 6.3929634 [61,] 10.2149504 3.3736433 [62,] 18.1435883 10.2149504 [63,] 19.4987663 18.1435883 [64,] 47.0583763 19.4987663 [65,] 28.0129383 47.0583763 [66,] 8.9331694 28.0129383 [67,] 29.3933705 8.9331694 [68,] 32.4244326 29.3933705 [69,] 10.5108890 32.4244326 [70,] 27.0797135 10.5108890 [71,] 42.2983014 27.0797135 [72,] 39.9930741 42.2983014 [73,] 9.1171679 39.9930741 [74,] 17.8262622 9.1171679 [75,] 1.5092507 17.8262622 [76,] 15.5372594 1.5092507 [77,] 6.1223791 15.5372594 [78,] -1.0799441 6.1223791 [79,] 5.7960382 -1.0799441 [80,] 9.1133326 5.7960382 [81,] 8.5963177 9.1133326 [82,] 7.7619259 8.5963177 [83,] 4.4171009 7.7619259 [84,] -3.6498295 4.4171009 [85,] -10.8700163 -3.6498295 [86,] -6.0128441 -10.8700163 [87,] -12.5073701 -6.0128441 [88,] -17.9084633 -12.5073701 [89,] 3.5487223 -17.9084633 [90,] -3.5105290 3.5487223 [91,] -0.6494106 -3.5105290 [92,] -14.0786975 -0.6494106 [93,] 36.1499387 -14.0786975 [94,] 4.9695678 36.1499387 [95,] -20.6977040 4.9695678 [96,] -3.2030008 -20.6977040 [97,] 7.0510999 -3.2030008 [98,] -18.8774108 7.0510999 [99,] -0.9684643 -18.8774108 [100,] -15.5968321 -0.9684643 [101,] -7.1033413 -15.5968321 [102,] -7.5162324 -7.1033413 [103,] -14.4784846 -7.5162324 [104,] -1.7944283 -14.4784846 [105,] -15.3640877 -1.7944283 [106,] -7.3272695 -15.3640877 [107,] 5.6849224 -7.3272695 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 6.6767421 -1.9871939 2 0.1066161 6.6767421 3 -5.1169907 0.1066161 4 2.9431541 -5.1169907 5 -16.6839090 2.9431541 6 -6.8230605 -16.6839090 7 1.0053921 -6.8230605 8 -6.6010943 1.0053921 9 -12.0145933 -6.6010943 10 -12.3253765 -12.0145933 11 -19.4944652 -12.3253765 12 -9.6245693 -19.4944652 13 -5.4160418 -9.6245693 14 0.1439662 -5.4160418 15 3.3566450 0.1439662 16 -2.5068541 3.3566450 17 -23.5422881 -2.5068541 18 -5.3167121 -23.5422881 19 -12.4834159 -5.3167121 20 -15.8115525 -12.4834159 21 -9.5254609 -15.8115525 22 -9.5588625 -9.5254609 23 -26.6725324 -9.5588625 24 -24.6843802 -26.6725324 25 -4.2488530 -24.6843802 26 -23.1532547 -4.2488530 27 -6.7435761 -23.1532547 28 -15.4111164 -6.7435761 29 -14.3040387 -15.4111164 30 4.8497567 -14.3040387 31 -7.5187812 4.8497567 32 -7.3581849 -7.5187812 33 -18.3945468 -7.3581849 34 -13.1430503 -18.3945468 35 -9.6101574 -13.1430503 36 -16.9746945 -9.6101574 37 -17.5753831 -16.9746945 38 -9.0457813 -17.5753831 39 3.5988892 -9.0457813 40 -11.9869053 3.5988892 41 -1.9202874 -11.9869053 42 7.8750215 -1.9202874 43 -2.3357107 7.8750215 44 1.3095914 -2.3357107 45 -10.4492106 1.3095914 46 -3.6944193 -10.4492106 47 17.6815708 -3.6944193 48 16.7569508 17.6815708 49 5.0503337 16.7569508 50 20.8688579 5.0503337 51 -2.6271502 20.8688579 52 -2.1286186 -2.6271502 53 25.8698248 -2.1286186 54 2.5885303 25.8698248 55 1.2710022 2.5885303 56 2.7966010 1.2710022 57 10.4907536 2.7966010 58 6.2377709 10.4907536 59 6.3929634 6.2377709 60 3.3736433 6.3929634 61 10.2149504 3.3736433 62 18.1435883 10.2149504 63 19.4987663 18.1435883 64 47.0583763 19.4987663 65 28.0129383 47.0583763 66 8.9331694 28.0129383 67 29.3933705 8.9331694 68 32.4244326 29.3933705 69 10.5108890 32.4244326 70 27.0797135 10.5108890 71 42.2983014 27.0797135 72 39.9930741 42.2983014 73 9.1171679 39.9930741 74 17.8262622 9.1171679 75 1.5092507 17.8262622 76 15.5372594 1.5092507 77 6.1223791 15.5372594 78 -1.0799441 6.1223791 79 5.7960382 -1.0799441 80 9.1133326 5.7960382 81 8.5963177 9.1133326 82 7.7619259 8.5963177 83 4.4171009 7.7619259 84 -3.6498295 4.4171009 85 -10.8700163 -3.6498295 86 -6.0128441 -10.8700163 87 -12.5073701 -6.0128441 88 -17.9084633 -12.5073701 89 3.5487223 -17.9084633 90 -3.5105290 3.5487223 91 -0.6494106 -3.5105290 92 -14.0786975 -0.6494106 93 36.1499387 -14.0786975 94 4.9695678 36.1499387 95 -20.6977040 4.9695678 96 -3.2030008 -20.6977040 97 7.0510999 -3.2030008 98 -18.8774108 7.0510999 99 -0.9684643 -18.8774108 100 -15.5968321 -0.9684643 101 -7.1033413 -15.5968321 102 -7.5162324 -7.1033413 103 -14.4784846 -7.5162324 104 -1.7944283 -14.4784846 105 -15.3640877 -1.7944283 106 -7.3272695 -15.3640877 107 5.6849224 -7.3272695 > 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/777hu1258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8gvlt1258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9zkic1258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10eblv1258645696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11nuod1258645696.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/12vsfw1258645696.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/13hngj1258645696.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/1487sy1258645696.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/15ualr1258645696.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/16n0xm1258645696.tab") + } > > system("convert tmp/13ts81258645696.ps tmp/13ts81258645696.png") > system("convert tmp/2er971258645696.ps tmp/2er971258645696.png") > system("convert tmp/3tj561258645696.ps tmp/3tj561258645696.png") > system("convert tmp/4h6r91258645696.ps tmp/4h6r91258645696.png") > system("convert tmp/5ozxo1258645696.ps tmp/5ozxo1258645696.png") > system("convert tmp/6dd371258645696.ps tmp/6dd371258645696.png") > system("convert tmp/777hu1258645696.ps tmp/777hu1258645696.png") > system("convert tmp/8gvlt1258645696.ps tmp/8gvlt1258645696.png") > system("convert tmp/9zkic1258645696.ps tmp/9zkic1258645696.png") > system("convert tmp/10eblv1258645696.ps tmp/10eblv1258645696.png") > > > proc.time() user system elapsed 3.087 1.604 4.049