R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(13 + ,13 + ,0 + ,14 + ,0 + ,13 + ,0 + ,3 + ,0 + ,0 + ,12 + ,12 + ,12 + ,8 + ,8 + ,13 + ,13 + ,5 + ,5 + ,1 + ,10 + ,10 + ,0 + ,10 + ,0 + ,11 + ,0 + ,5 + ,0 + ,0 + ,15 + ,13 + ,13 + ,16 + ,16 + ,18 + ,18 + ,8 + ,8 + ,1 + ,9 + ,12 + ,12 + ,11 + ,11 + ,11 + ,11 + ,4 + ,4 + ,1 + ,12 + ,12 + ,12 + ,14 + ,14 + ,14 + ,14 + ,4 + ,4 + ,1 + ,11 + ,5 + ,0 + ,16 + ,0 + ,14 + ,0 + ,6 + ,0 + ,0 + ,11 + ,12 + ,12 + ,11 + ,11 + ,12 + ,12 + ,6 + ,6 + ,1 + ,15 + ,11 + ,11 + ,16 + ,16 + ,11 + ,11 + ,5 + ,5 + ,1 + ,7 + ,14 + ,0 + ,12 + ,0 + ,12 + ,0 + ,4 + ,0 + ,0 + ,11 + ,14 + ,0 + ,7 + ,0 + ,13 + ,0 + ,6 + ,0 + ,0 + ,11 + ,12 + ,12 + ,13 + ,13 + ,11 + ,11 + ,4 + ,4 + ,1 + ,10 + ,12 + ,12 + ,11 + ,11 + ,12 + ,12 + ,6 + ,6 + ,1 + ,14 + ,11 + ,0 + ,15 + ,0 + ,16 + ,0 + ,6 + ,0 + ,0 + ,10 + ,11 + ,11 + ,7 + ,7 + ,9 + ,9 + ,4 + ,4 + ,1 + ,6 + ,7 + ,0 + ,9 + ,0 + ,11 + ,0 + ,4 + ,0 + ,0 + ,11 + ,9 + ,9 + ,7 + ,7 + ,13 + ,13 + ,2 + ,2 + ,1 + ,15 + ,11 + ,0 + ,14 + ,0 + 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+ ,14 + ,6 + ,6 + ,1 + ,14 + ,12 + ,0 + ,15 + ,0 + ,14 + ,0 + ,6 + ,0 + ,0 + ,14 + ,11 + ,0 + ,12 + ,0 + ,12 + ,0 + ,4 + ,0 + ,0 + ,14 + ,11 + ,0 + ,16 + ,0 + ,12 + ,0 + ,5 + ,0 + ,0 + ,8 + ,11 + ,0 + ,9 + ,0 + ,12 + ,0 + ,4 + ,0 + ,0 + ,16 + ,12 + ,0 + ,15 + ,0 + ,14 + ,0 + ,6 + ,0 + ,0 + ,12 + ,12 + ,12 + ,6 + ,6 + ,14 + ,14 + ,5 + ,5 + ,1 + ,12 + ,12 + ,12 + ,15 + ,15 + ,13 + ,13 + ,6 + ,6 + ,1 + ,16 + ,12 + ,0 + ,14 + ,0 + ,16 + ,0 + ,8 + ,0 + ,0 + ,15 + ,11 + ,11 + ,12 + ,12 + ,13 + ,13 + ,6 + ,6 + ,1 + ,10 + ,12 + ,12 + ,8 + ,8 + ,16 + ,16 + ,4 + ,4 + ,1 + ,12 + ,12 + ,12 + ,9 + ,9 + ,13 + ,13 + ,4 + ,4 + ,1 + ,14 + ,11 + ,0 + ,15 + ,0 + ,14 + ,0 + ,6 + ,0 + ,0 + ,19 + ,12 + ,12 + ,15 + ,15 + ,15 + ,15 + ,6 + ,6 + ,1 + ,15 + ,12 + ,12 + ,14 + ,14 + ,16 + ,16 + ,4 + ,4 + ,1 + ,8 + ,10 + ,0 + ,10 + ,0 + ,6 + ,0 + ,4 + ,0 + ,0 + ,8 + ,12 + ,0 + ,8 + ,0 + ,14 + ,0 + ,5 + ,0 + ,0 + ,10 + ,15 + ,0 + ,15 + ,0 + ,15 + ,0 + ,6 + ,0 + ,0 + ,15 + ,11 + ,0 + ,16 + ,0 + ,14 + ,0 + ,6 + ,0 + ,0 + ,16 + ,12 + ,12 + ,12 + ,12 + ,15 + ,15 + ,8 + ,8 + ,1 + ,13 + ,11 + ,11 + ,12 + ,12 + ,13 + ,13 + ,7 + ,7 + ,1) + ,dim=c(10 + ,111) + ,dimnames=list(c('populair' + ,'vrienden' + ,'vrienden_G' + ,'kennen' + ,'kennen_G' + ,'geliefd' + ,'geliefd_G' + ,'celebrity' + ,'celebrity_G' + ,'geslacht(dummy)') + ,1:111)) > y <- array(NA,dim=c(10,111),dimnames=list(c('populair','vrienden','vrienden_G','kennen','kennen_G','geliefd','geliefd_G','celebrity','celebrity_G','geslacht(dummy)'),1:111)) > 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 populair vrienden vrienden_G kennen kennen_G geliefd geliefd_G celebrity 1 13 13 0 14 0 13 0 3 2 12 12 12 8 8 13 13 5 3 10 10 0 10 0 11 0 5 4 15 13 13 16 16 18 18 8 5 9 12 12 11 11 11 11 4 6 12 12 12 14 14 14 14 4 7 11 5 0 16 0 14 0 6 8 11 12 12 11 11 12 12 6 9 15 11 11 16 16 11 11 5 10 7 14 0 12 0 12 0 4 11 11 14 0 7 0 13 0 6 12 11 12 12 13 13 11 11 4 13 10 12 12 11 11 12 12 6 14 14 11 0 15 0 16 0 6 15 10 11 11 7 7 9 9 4 16 6 7 0 9 0 11 0 4 17 11 9 9 7 7 13 13 2 18 15 11 0 14 0 15 0 7 19 11 11 11 15 15 10 10 5 20 12 12 0 7 0 11 0 4 21 14 12 12 15 15 13 13 6 22 15 11 0 17 0 16 0 6 23 9 11 0 15 0 15 0 7 24 13 8 8 14 14 14 14 5 25 13 9 0 14 0 14 0 6 26 13 10 10 8 8 8 8 4 27 12 10 0 14 0 13 0 7 28 14 12 12 14 14 15 15 7 29 11 8 0 8 0 13 0 4 30 9 12 12 11 11 11 11 4 31 16 11 0 16 0 15 0 6 32 13 11 11 14 14 13 13 6 33 16 11 11 16 16 16 16 7 34 15 9 9 5 5 11 11 3 35 5 15 15 8 8 12 12 3 36 11 11 11 8 8 12 12 6 37 16 11 0 13 0 14 0 7 38 17 11 11 15 15 14 14 5 39 9 15 0 6 0 8 0 4 40 9 11 11 12 12 13 13 5 41 13 12 12 16 16 16 16 6 42 6 9 0 15 0 11 0 6 43 12 12 0 12 0 14 0 5 44 8 12 0 8 0 13 0 4 45 14 13 0 13 0 13 0 5 46 12 11 11 14 14 13 13 5 47 16 9 9 16 16 16 16 6 48 8 11 0 10 0 15 0 2 49 15 11 11 15 15 15 15 8 50 7 12 0 8 0 12 0 3 51 16 12 0 16 0 14 0 6 52 14 9 9 19 19 12 12 6 53 16 11 11 14 14 15 15 6 54 9 9 9 6 6 12 12 5 55 11 12 0 15 0 12 0 6 56 5 14 0 4 0 5 0 2 57 15 11 11 14 14 13 13 5 58 13 12 12 13 13 13 13 5 59 11 11 0 11 0 14 0 5 60 11 6 0 14 0 17 0 6 61 12 13 13 14 14 12 12 5 62 14 12 12 8 8 14 14 4 63 6 12 12 6 6 11 11 2 64 7 12 0 7 0 12 0 4 65 14 6 6 13 13 12 12 6 66 14 11 11 13 13 16 16 6 67 10 10 10 11 11 12 12 5 68 13 12 0 5 0 12 0 3 69 12 13 0 12 0 12 0 6 70 9 11 0 8 0 10 0 4 71 12 7 7 11 11 15 15 5 72 10 11 0 9 0 12 0 4 73 10 11 11 13 13 15 15 6 74 16 12 12 16 16 16 16 7 75 15 10 10 16 16 13 13 6 76 8 7 7 4 4 13 13 6 77 8 13 0 7 0 10 0 3 78 13 12 0 11 0 13 0 6 79 16 11 11 17 17 16 16 7 80 16 12 12 15 15 15 15 7 81 14 14 0 17 0 18 0 6 82 11 10 10 5 5 13 13 3 83 14 13 13 10 10 16 16 8 84 9 10 10 11 11 13 13 3 85 8 10 10 10 10 14 14 3 86 8 7 7 9 9 15 15 4 87 11 10 10 12 12 14 14 5 88 12 8 8 15 15 13 13 7 89 14 12 12 13 13 15 15 6 90 16 11 0 14 0 14 0 6 91 16 12 12 14 14 14 14 6 92 14 12 0 15 0 14 0 6 93 14 11 0 12 0 12 0 4 94 14 11 0 16 0 12 0 5 95 8 11 0 9 0 12 0 4 96 16 12 0 15 0 14 0 6 97 12 12 12 6 6 14 14 5 98 12 12 12 15 15 13 13 6 99 16 12 0 14 0 16 0 8 100 15 11 11 12 12 13 13 6 101 10 12 12 8 8 16 16 4 102 12 12 12 9 9 13 13 4 103 14 11 0 15 0 14 0 6 104 19 12 12 15 15 15 15 6 105 15 12 12 14 14 16 16 4 106 8 10 0 10 0 6 0 4 107 8 12 0 8 0 14 0 5 108 10 15 0 15 0 15 0 6 109 15 11 0 16 0 14 0 6 110 16 12 12 12 12 15 15 8 111 13 11 11 12 12 13 13 7 celebrity_G geslacht(dummy) 1 0 0 2 5 1 3 0 0 4 8 1 5 4 1 6 4 1 7 0 0 8 6 1 9 5 1 10 0 0 11 0 0 12 4 1 13 6 1 14 0 0 15 4 1 16 0 0 17 2 1 18 0 0 19 5 1 20 0 0 21 6 1 22 0 0 23 0 0 24 5 1 25 0 0 26 4 1 27 0 0 28 7 1 29 0 0 30 4 1 31 0 0 32 6 1 33 7 1 34 3 1 35 3 1 36 6 1 37 0 0 38 5 1 39 0 0 40 5 1 41 6 1 42 0 0 43 0 0 44 0 0 45 0 0 46 5 1 47 6 1 48 0 0 49 8 1 50 0 0 51 0 0 52 6 1 53 6 1 54 5 1 55 0 0 56 0 0 57 5 1 58 5 1 59 0 0 60 0 0 61 5 1 62 4 1 63 2 1 64 0 0 65 6 1 66 6 1 67 5 1 68 0 0 69 0 0 70 0 0 71 5 1 72 0 0 73 6 1 74 7 1 75 6 1 76 6 1 77 0 0 78 0 0 79 7 1 80 7 1 81 0 0 82 3 1 83 8 1 84 3 1 85 3 1 86 4 1 87 5 1 88 7 1 89 6 1 90 0 0 91 6 1 92 0 0 93 0 0 94 0 0 95 0 0 96 0 0 97 5 1 98 6 1 99 0 0 100 6 1 101 4 1 102 4 1 103 0 0 104 6 1 105 4 1 106 0 0 107 0 0 108 0 0 109 0 0 110 8 1 111 7 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) vrienden vrienden_G kennen -1.89772 0.25941 -0.29981 0.28096 kennen_G geliefd geliefd_G celebrity 0.02931 0.30943 -0.07447 0.60058 celebrity_G `geslacht(dummy)` -0.03257 4.98633 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.6585 -1.5686 0.0784 1.3412 6.4351 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.89772 2.80986 -0.675 0.5010 vrienden 0.25941 0.16746 1.549 0.1245 vrienden_G -0.29981 0.24183 -1.240 0.2179 kennen 0.28096 0.14473 1.941 0.0550 . kennen_G 0.02931 0.17644 0.166 0.8684 geliefd 0.30943 0.18162 1.704 0.0915 . geliefd_G -0.07447 0.25090 -0.297 0.7672 celebrity 0.60058 0.35150 1.709 0.0906 . celebrity_G -0.03257 0.43768 -0.074 0.9408 `geslacht(dummy)` 4.98633 3.85462 1.294 0.1988 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.248 on 101 degrees of freedom Multiple R-squared: 0.4939, Adjusted R-squared: 0.4488 F-statistic: 10.95 on 9 and 101 DF, p-value: 9.68e-12 > 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,] 0.1655973 0.3311946 0.83440271 [2,] 0.2103511 0.4207022 0.78964889 [3,] 0.1143487 0.2286973 0.88565133 [4,] 0.3582348 0.7164696 0.64176520 [5,] 0.3628063 0.7256126 0.63719371 [6,] 0.2950140 0.5900280 0.70498601 [7,] 0.2600264 0.5200528 0.73997362 [8,] 0.3956683 0.7913365 0.60433174 [9,] 0.3206838 0.6413676 0.67931619 [10,] 0.2435309 0.4870619 0.75646905 [11,] 0.4106534 0.8213067 0.58934664 [12,] 0.3671185 0.7342370 0.63288148 [13,] 0.3255534 0.6511069 0.67444656 [14,] 0.3853846 0.7707692 0.61461541 [15,] 0.3695971 0.7391943 0.63040286 [16,] 0.3036829 0.6073657 0.69631714 [17,] 0.2489686 0.4979372 0.75103139 [18,] 0.2238224 0.4476448 0.77617758 [19,] 0.2498217 0.4996435 0.75017826 [20,] 0.1967101 0.3934203 0.80328986 [21,] 0.1611405 0.3222809 0.83885955 [22,] 0.3745618 0.7491236 0.62543821 [23,] 0.4108177 0.8216354 0.58918228 [24,] 0.3772179 0.7544358 0.62278209 [25,] 0.4631941 0.9263882 0.53680589 [26,] 0.5739727 0.8520547 0.42602734 [27,] 0.5333261 0.9333477 0.46667386 [28,] 0.6291032 0.7417936 0.37089679 [29,] 0.6030832 0.7938335 0.39691677 [30,] 0.8048841 0.3902319 0.19511593 [31,] 0.7650114 0.4699772 0.23498860 [32,] 0.8036975 0.3926051 0.19630255 [33,] 0.8015938 0.3968125 0.19840625 [34,] 0.7659253 0.4681493 0.23407466 [35,] 0.7275215 0.5449569 0.27247845 [36,] 0.7424090 0.5151820 0.25759099 [37,] 0.6978120 0.6043760 0.30218799 [38,] 0.6890647 0.6218707 0.31093533 [39,] 0.7037957 0.5924086 0.29620431 [40,] 0.6718776 0.6562448 0.32812238 [41,] 0.6611847 0.6776307 0.33881533 [42,] 0.6876569 0.6246861 0.31234307 [43,] 0.6697455 0.6605089 0.33025446 [44,] 0.6303103 0.7393795 0.36968974 [45,] 0.6265577 0.7468846 0.37344229 [46,] 0.5762098 0.8475805 0.42379023 [47,] 0.5206024 0.9587953 0.47939765 [48,] 0.6439826 0.7120349 0.35601745 [49,] 0.6076046 0.7847907 0.39239536 [50,] 0.6843585 0.6312831 0.31564154 [51,] 0.6958462 0.6083076 0.30415381 [52,] 0.7133072 0.5733857 0.28669283 [53,] 0.7616201 0.4767597 0.23837987 [54,] 0.7112612 0.5774775 0.28873876 [55,] 0.6972489 0.6055022 0.30275108 [56,] 0.9053517 0.1892967 0.09464834 [57,] 0.8813750 0.2372500 0.11862500 [58,] 0.8482401 0.3035198 0.15175988 [59,] 0.8707878 0.2584243 0.12921216 [60,] 0.8337500 0.3325000 0.16625001 [61,] 0.9109821 0.1780358 0.08901788 [62,] 0.8859168 0.2281665 0.11408323 [63,] 0.8619343 0.2761314 0.13806571 [64,] 0.8496877 0.3006246 0.15031229 [65,] 0.8822777 0.2354445 0.11772227 [66,] 0.8775571 0.2448857 0.12244287 [67,] 0.8385616 0.3228768 0.16143839 [68,] 0.7998273 0.4003454 0.20017271 [69,] 0.7629767 0.4740466 0.23702331 [70,] 0.8432393 0.3135214 0.15676069 [71,] 0.8580424 0.2839152 0.14195760 [72,] 0.8206238 0.3587524 0.17937619 [73,] 0.8055364 0.3889272 0.19446361 [74,] 0.7767645 0.4464710 0.22323549 [75,] 0.7201739 0.5596522 0.27982609 [76,] 0.6765908 0.6468184 0.32340921 [77,] 0.6194161 0.7611678 0.38058392 [78,] 0.5826161 0.8347678 0.41738390 [79,] 0.5163726 0.9672548 0.48362738 [80,] 0.4187886 0.8375772 0.58121139 [81,] 0.5458706 0.9082588 0.45412940 [82,] 0.4442333 0.8884666 0.55576672 [83,] 0.3350501 0.6701001 0.66494993 [84,] 0.3507977 0.7015955 0.64920227 [85,] 0.2828483 0.5656965 0.71715174 [86,] 0.8216777 0.3566446 0.17832231 > postscript(file="/var/www/html/rcomp/tmp/1e7jy1290172702.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ph0j1290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3ph0j1290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ph0j1290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/50qhm1290172702.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 = 111 Frequency = 1 1 2 3 4 5 6 1.76765449 1.01957159 0.08742228 -1.30097891 -1.87331686 -0.50899330 7 8 9 10 11 12 -0.83011928 -1.24428405 1.96693333 -4.22099453 -0.32678412 -0.49385476 13 14 15 16 17 18 -2.24428405 0.27550937 0.79727226 -2.25280081 1.91265731 1.26531484 19 20 21 22 23 24 -1.48784119 3.01205072 0.27968362 0.71359201 -5.01564384 -0.23859758 25 26 27 28 29 30 0.69414542 3.68156010 -0.85642096 -0.44796582 2.14989366 -1.87331686 31 32 33 34 35 36 2.30397621 -0.45044717 0.65614001 6.43510294 -4.48826201 -0.35387695 37 38 39 40 41 42 2.85569904 3.57233268 0.44304647 -3.26190394 -1.73545492 -5.65853670 43 44 45 46 47 48 0.07840202 -1.88775900 1.84745570 -0.88244184 1.14334587 -1.60795676 49 50 51 52 53 54 -0.36663984 -1.97775474 2.35398857 -0.84763485 2.07963977 -1.24613319 55 56 57 58 59 60 -1.74620171 -0.60618897 2.11755816 0.46822685 -0.38122613 -1.45589164 61 62 63 64 65 66 -0.56668583 3.35262039 -2.18596146 -2.29737480 0.89277963 0.15495219 67 68 69 70 71 72 -1.75707820 4.86512130 -0.16273884 0.29993073 -0.58314700 0.40012101 73 74 75 76 77 78 -3.61009128 0.69653975 0.88861520 -2.50935663 -0.33735818 1.06820749 79 80 81 82 83 84 0.34587107 1.24176523 -1.68349853 2.00558962 0.03054784 -1.85602407 85 86 87 88 89 90 -2.78071165 -3.39460378 -1.53726021 -2.44992065 0.43030845 3.17531909 91 92 93 94 95 96 2.35499604 0.63494725 3.55724497 1.83283151 -1.59987899 2.63494725 97 98 99 100 101 102 1.40515295 -1.72031638 1.09589741 2.17009073 -1.11729268 1.27730797 103 104 105 106 107 108 0.89436041 4.80977056 2.02109363 0.23512861 -2.79776325 -4.45271777 109 110 111 1.61340173 1.60456674 -0.39791460 > postscript(file="/var/www/html/rcomp/tmp/60qhm1290172702.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 = 111 Frequency = 1 lag(myerror, k = 1) myerror 0 1.76765449 NA 1 1.01957159 1.76765449 2 0.08742228 1.01957159 3 -1.30097891 0.08742228 4 -1.87331686 -1.30097891 5 -0.50899330 -1.87331686 6 -0.83011928 -0.50899330 7 -1.24428405 -0.83011928 8 1.96693333 -1.24428405 9 -4.22099453 1.96693333 10 -0.32678412 -4.22099453 11 -0.49385476 -0.32678412 12 -2.24428405 -0.49385476 13 0.27550937 -2.24428405 14 0.79727226 0.27550937 15 -2.25280081 0.79727226 16 1.91265731 -2.25280081 17 1.26531484 1.91265731 18 -1.48784119 1.26531484 19 3.01205072 -1.48784119 20 0.27968362 3.01205072 21 0.71359201 0.27968362 22 -5.01564384 0.71359201 23 -0.23859758 -5.01564384 24 0.69414542 -0.23859758 25 3.68156010 0.69414542 26 -0.85642096 3.68156010 27 -0.44796582 -0.85642096 28 2.14989366 -0.44796582 29 -1.87331686 2.14989366 30 2.30397621 -1.87331686 31 -0.45044717 2.30397621 32 0.65614001 -0.45044717 33 6.43510294 0.65614001 34 -4.48826201 6.43510294 35 -0.35387695 -4.48826201 36 2.85569904 -0.35387695 37 3.57233268 2.85569904 38 0.44304647 3.57233268 39 -3.26190394 0.44304647 40 -1.73545492 -3.26190394 41 -5.65853670 -1.73545492 42 0.07840202 -5.65853670 43 -1.88775900 0.07840202 44 1.84745570 -1.88775900 45 -0.88244184 1.84745570 46 1.14334587 -0.88244184 47 -1.60795676 1.14334587 48 -0.36663984 -1.60795676 49 -1.97775474 -0.36663984 50 2.35398857 -1.97775474 51 -0.84763485 2.35398857 52 2.07963977 -0.84763485 53 -1.24613319 2.07963977 54 -1.74620171 -1.24613319 55 -0.60618897 -1.74620171 56 2.11755816 -0.60618897 57 0.46822685 2.11755816 58 -0.38122613 0.46822685 59 -1.45589164 -0.38122613 60 -0.56668583 -1.45589164 61 3.35262039 -0.56668583 62 -2.18596146 3.35262039 63 -2.29737480 -2.18596146 64 0.89277963 -2.29737480 65 0.15495219 0.89277963 66 -1.75707820 0.15495219 67 4.86512130 -1.75707820 68 -0.16273884 4.86512130 69 0.29993073 -0.16273884 70 -0.58314700 0.29993073 71 0.40012101 -0.58314700 72 -3.61009128 0.40012101 73 0.69653975 -3.61009128 74 0.88861520 0.69653975 75 -2.50935663 0.88861520 76 -0.33735818 -2.50935663 77 1.06820749 -0.33735818 78 0.34587107 1.06820749 79 1.24176523 0.34587107 80 -1.68349853 1.24176523 81 2.00558962 -1.68349853 82 0.03054784 2.00558962 83 -1.85602407 0.03054784 84 -2.78071165 -1.85602407 85 -3.39460378 -2.78071165 86 -1.53726021 -3.39460378 87 -2.44992065 -1.53726021 88 0.43030845 -2.44992065 89 3.17531909 0.43030845 90 2.35499604 3.17531909 91 0.63494725 2.35499604 92 3.55724497 0.63494725 93 1.83283151 3.55724497 94 -1.59987899 1.83283151 95 2.63494725 -1.59987899 96 1.40515295 2.63494725 97 -1.72031638 1.40515295 98 1.09589741 -1.72031638 99 2.17009073 1.09589741 100 -1.11729268 2.17009073 101 1.27730797 -1.11729268 102 0.89436041 1.27730797 103 4.80977056 0.89436041 104 2.02109363 4.80977056 105 0.23512861 2.02109363 106 -2.79776325 0.23512861 107 -4.45271777 -2.79776325 108 1.61340173 -4.45271777 109 1.60456674 1.61340173 110 -0.39791460 1.60456674 111 NA -0.39791460 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.01957159 1.76765449 [2,] 0.08742228 1.01957159 [3,] -1.30097891 0.08742228 [4,] -1.87331686 -1.30097891 [5,] -0.50899330 -1.87331686 [6,] -0.83011928 -0.50899330 [7,] -1.24428405 -0.83011928 [8,] 1.96693333 -1.24428405 [9,] -4.22099453 1.96693333 [10,] -0.32678412 -4.22099453 [11,] -0.49385476 -0.32678412 [12,] -2.24428405 -0.49385476 [13,] 0.27550937 -2.24428405 [14,] 0.79727226 0.27550937 [15,] -2.25280081 0.79727226 [16,] 1.91265731 -2.25280081 [17,] 1.26531484 1.91265731 [18,] -1.48784119 1.26531484 [19,] 3.01205072 -1.48784119 [20,] 0.27968362 3.01205072 [21,] 0.71359201 0.27968362 [22,] -5.01564384 0.71359201 [23,] -0.23859758 -5.01564384 [24,] 0.69414542 -0.23859758 [25,] 3.68156010 0.69414542 [26,] -0.85642096 3.68156010 [27,] -0.44796582 -0.85642096 [28,] 2.14989366 -0.44796582 [29,] -1.87331686 2.14989366 [30,] 2.30397621 -1.87331686 [31,] -0.45044717 2.30397621 [32,] 0.65614001 -0.45044717 [33,] 6.43510294 0.65614001 [34,] -4.48826201 6.43510294 [35,] -0.35387695 -4.48826201 [36,] 2.85569904 -0.35387695 [37,] 3.57233268 2.85569904 [38,] 0.44304647 3.57233268 [39,] -3.26190394 0.44304647 [40,] -1.73545492 -3.26190394 [41,] -5.65853670 -1.73545492 [42,] 0.07840202 -5.65853670 [43,] -1.88775900 0.07840202 [44,] 1.84745570 -1.88775900 [45,] -0.88244184 1.84745570 [46,] 1.14334587 -0.88244184 [47,] -1.60795676 1.14334587 [48,] -0.36663984 -1.60795676 [49,] -1.97775474 -0.36663984 [50,] 2.35398857 -1.97775474 [51,] -0.84763485 2.35398857 [52,] 2.07963977 -0.84763485 [53,] -1.24613319 2.07963977 [54,] -1.74620171 -1.24613319 [55,] -0.60618897 -1.74620171 [56,] 2.11755816 -0.60618897 [57,] 0.46822685 2.11755816 [58,] -0.38122613 0.46822685 [59,] -1.45589164 -0.38122613 [60,] -0.56668583 -1.45589164 [61,] 3.35262039 -0.56668583 [62,] -2.18596146 3.35262039 [63,] -2.29737480 -2.18596146 [64,] 0.89277963 -2.29737480 [65,] 0.15495219 0.89277963 [66,] -1.75707820 0.15495219 [67,] 4.86512130 -1.75707820 [68,] -0.16273884 4.86512130 [69,] 0.29993073 -0.16273884 [70,] -0.58314700 0.29993073 [71,] 0.40012101 -0.58314700 [72,] -3.61009128 0.40012101 [73,] 0.69653975 -3.61009128 [74,] 0.88861520 0.69653975 [75,] -2.50935663 0.88861520 [76,] -0.33735818 -2.50935663 [77,] 1.06820749 -0.33735818 [78,] 0.34587107 1.06820749 [79,] 1.24176523 0.34587107 [80,] -1.68349853 1.24176523 [81,] 2.00558962 -1.68349853 [82,] 0.03054784 2.00558962 [83,] -1.85602407 0.03054784 [84,] -2.78071165 -1.85602407 [85,] -3.39460378 -2.78071165 [86,] -1.53726021 -3.39460378 [87,] -2.44992065 -1.53726021 [88,] 0.43030845 -2.44992065 [89,] 3.17531909 0.43030845 [90,] 2.35499604 3.17531909 [91,] 0.63494725 2.35499604 [92,] 3.55724497 0.63494725 [93,] 1.83283151 3.55724497 [94,] -1.59987899 1.83283151 [95,] 2.63494725 -1.59987899 [96,] 1.40515295 2.63494725 [97,] -1.72031638 1.40515295 [98,] 1.09589741 -1.72031638 [99,] 2.17009073 1.09589741 [100,] -1.11729268 2.17009073 [101,] 1.27730797 -1.11729268 [102,] 0.89436041 1.27730797 [103,] 4.80977056 0.89436041 [104,] 2.02109363 4.80977056 [105,] 0.23512861 2.02109363 [106,] -2.79776325 0.23512861 [107,] -4.45271777 -2.79776325 [108,] 1.61340173 -4.45271777 [109,] 1.60456674 1.61340173 [110,] -0.39791460 1.60456674 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.01957159 1.76765449 2 0.08742228 1.01957159 3 -1.30097891 0.08742228 4 -1.87331686 -1.30097891 5 -0.50899330 -1.87331686 6 -0.83011928 -0.50899330 7 -1.24428405 -0.83011928 8 1.96693333 -1.24428405 9 -4.22099453 1.96693333 10 -0.32678412 -4.22099453 11 -0.49385476 -0.32678412 12 -2.24428405 -0.49385476 13 0.27550937 -2.24428405 14 0.79727226 0.27550937 15 -2.25280081 0.79727226 16 1.91265731 -2.25280081 17 1.26531484 1.91265731 18 -1.48784119 1.26531484 19 3.01205072 -1.48784119 20 0.27968362 3.01205072 21 0.71359201 0.27968362 22 -5.01564384 0.71359201 23 -0.23859758 -5.01564384 24 0.69414542 -0.23859758 25 3.68156010 0.69414542 26 -0.85642096 3.68156010 27 -0.44796582 -0.85642096 28 2.14989366 -0.44796582 29 -1.87331686 2.14989366 30 2.30397621 -1.87331686 31 -0.45044717 2.30397621 32 0.65614001 -0.45044717 33 6.43510294 0.65614001 34 -4.48826201 6.43510294 35 -0.35387695 -4.48826201 36 2.85569904 -0.35387695 37 3.57233268 2.85569904 38 0.44304647 3.57233268 39 -3.26190394 0.44304647 40 -1.73545492 -3.26190394 41 -5.65853670 -1.73545492 42 0.07840202 -5.65853670 43 -1.88775900 0.07840202 44 1.84745570 -1.88775900 45 -0.88244184 1.84745570 46 1.14334587 -0.88244184 47 -1.60795676 1.14334587 48 -0.36663984 -1.60795676 49 -1.97775474 -0.36663984 50 2.35398857 -1.97775474 51 -0.84763485 2.35398857 52 2.07963977 -0.84763485 53 -1.24613319 2.07963977 54 -1.74620171 -1.24613319 55 -0.60618897 -1.74620171 56 2.11755816 -0.60618897 57 0.46822685 2.11755816 58 -0.38122613 0.46822685 59 -1.45589164 -0.38122613 60 -0.56668583 -1.45589164 61 3.35262039 -0.56668583 62 -2.18596146 3.35262039 63 -2.29737480 -2.18596146 64 0.89277963 -2.29737480 65 0.15495219 0.89277963 66 -1.75707820 0.15495219 67 4.86512130 -1.75707820 68 -0.16273884 4.86512130 69 0.29993073 -0.16273884 70 -0.58314700 0.29993073 71 0.40012101 -0.58314700 72 -3.61009128 0.40012101 73 0.69653975 -3.61009128 74 0.88861520 0.69653975 75 -2.50935663 0.88861520 76 -0.33735818 -2.50935663 77 1.06820749 -0.33735818 78 0.34587107 1.06820749 79 1.24176523 0.34587107 80 -1.68349853 1.24176523 81 2.00558962 -1.68349853 82 0.03054784 2.00558962 83 -1.85602407 0.03054784 84 -2.78071165 -1.85602407 85 -3.39460378 -2.78071165 86 -1.53726021 -3.39460378 87 -2.44992065 -1.53726021 88 0.43030845 -2.44992065 89 3.17531909 0.43030845 90 2.35499604 3.17531909 91 0.63494725 2.35499604 92 3.55724497 0.63494725 93 1.83283151 3.55724497 94 -1.59987899 1.83283151 95 2.63494725 -1.59987899 96 1.40515295 2.63494725 97 -1.72031638 1.40515295 98 1.09589741 -1.72031638 99 2.17009073 1.09589741 100 -1.11729268 2.17009073 101 1.27730797 -1.11729268 102 0.89436041 1.27730797 103 4.80977056 0.89436041 104 2.02109363 4.80977056 105 0.23512861 2.02109363 106 -2.79776325 0.23512861 107 -4.45271777 -2.79776325 108 1.61340173 -4.45271777 109 1.60456674 1.61340173 110 -0.39791460 1.60456674 > 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/7shy71290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8shy71290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/93qfr1290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/103qfr1290172702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/1169wx1290172702.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/12arvl1290172702.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/13yssx1290172702.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/142t831290172702.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/155tp91290172702.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/169c5f1290172702.tab") + } > > try(system("convert tmp/1e7jy1290172702.ps tmp/1e7jy1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/2ph0j1290172702.ps tmp/2ph0j1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/3ph0j1290172702.ps tmp/3ph0j1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/4ph0j1290172702.ps tmp/4ph0j1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/50qhm1290172702.ps tmp/50qhm1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/60qhm1290172702.ps tmp/60qhm1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/7shy71290172702.ps tmp/7shy71290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/8shy71290172702.ps tmp/8shy71290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/93qfr1290172702.ps tmp/93qfr1290172702.png",intern=TRUE)) character(0) > try(system("convert tmp/103qfr1290172702.ps tmp/103qfr1290172702.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.311 1.648 7.840