R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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(12 + ,65 + ,22 + ,114468 + ,2 + ,13 + ,54 + ,20 + ,88594 + ,4 + ,11 + ,58 + ,24 + ,74151 + ,9 + ,12 + ,77 + ,21 + ,77921 + ,2 + ,8 + ,41 + ,15 + ,53212 + ,1 + ,7 + ,0 + ,16 + ,34956 + ,2 + ,18 + ,111 + ,20 + ,149703 + ,0 + ,0 + ,1 + ,18 + ,6853 + ,0 + ,9 + ,36 + ,19 + ,58907 + ,5 + ,11 + ,60 + ,20 + ,67067 + ,0 + ,13 + ,63 + ,25 + ,110563 + ,0 + ,13 + ,71 + ,37 + ,58126 + ,7 + ,9 + ,38 + ,23 + ,57113 + ,6 + ,12 + ,76 + ,28 + ,77993 + ,3 + ,11 + ,61 + ,25 + ,68091 + ,4 + ,17 + ,125 + ,35 + ,124676 + ,0 + ,14 + ,84 + ,20 + ,109522 + ,4 + ,15 + ,69 + ,22 + ,75865 + ,3 + ,13 + ,77 + ,19 + ,79746 + ,0 + ,15 + ,100 + ,26 + ,77844 + ,5 + ,13 + ,78 + ,27 + ,98681 + ,0 + ,13 + ,76 + ,22 + ,105531 + ,1 + ,8 + ,40 + ,15 + ,51428 + ,3 + ,16 + ,81 + ,26 + ,65703 + ,5 + ,14 + ,102 + ,24 + ,72562 + ,0 + ,14 + ,70 + ,22 + ,81728 + ,0 + ,14 + ,75 + ,21 + ,95580 + ,4 + ,14 + ,93 + ,23 + ,98278 + ,0 + ,12 + ,42 + ,21 + ,46629 + ,0 + ,14 + ,95 + ,25 + ,115189 + ,0 + ,2 + ,8 + ,4 + ,15049 + ,0 + ,12 + ,87 + ,30 + ,109011 + ,5 + ,13 + ,87 + ,20 + ,134245 + ,5 + ,16 + ,112 + ,26 + ,136692 + ,0 + ,15 + ,96 + ,27 + ,149510 + ,6 + ,16 + ,93 + ,18 + ,147888 + ,6 + ,15 + ,98 + ,20 + ,79169 + ,2 + ,16 + ,99 + ,17 + ,65469 + ,5 + ,14 + ,94 + ,22 + ,56756 + ,0 + ,17 + ,98 + ,25 + ,81399 + ,3 + ,18 + ,109 + ,30 + ,104953 + ,0 + ,16 + ,108 + ,26 + ,59633 + ,1 + ,10 + ,42 + ,20 + ,63249 + ,1 + ,15 + ,108 + ,25 + ,82928 + ,2 + ,10 + ,27 + ,21 + ,50000 + ,4 + ,16 + ,115 + ,23 + ,139357 + ,0 + ,17 + ,92 + ,33 + ,110044 + ,7 + ,17 + ,106 + ,19 + ,155118 + ,7 + ,13 + ,73 + ,31 + ,83061 + ,6 + ,14 + ,105 + ,25 + ,127122 + ,0 + ,12 + ,30 + ,20 + ,45653 + ,0 + ,7 + ,13 + ,19 + ,19630 + ,4 + ,14 + ,69 + ,15 + ,67229 + ,4 + ,12 + ,72 + ,21 + ,86060 + ,0 + ,16 + ,80 + ,22 + ,88003 + ,0 + ,14 + ,106 + ,24 + ,95815 + ,0 + ,8 + ,28 + ,19 + ,85499 + ,0 + ,14 + ,70 + ,20 + ,27220 + ,0 + ,15 + ,51 + ,23 + ,109882 + ,4 + ,16 + ,90 + ,27 + ,72579 + ,0 + ,0 + ,12 + ,1 + ,5841 + ,0 + ,12 + ,84 + ,20 + ,68369 + ,0 + ,8 + ,23 + ,11 + ,24610 + ,4 + ,12 + ,57 + ,27 + ,30995 + ,0 + ,15 + ,84 + ,22 + ,150662 + ,1 + ,0 + ,4 + ,0 + ,6622 + ,0 + ,11 + ,56 + ,17 + ,93694 + ,5 + ,15 + ,18 + ,8 + ,13155 + ,0 + ,17 + ,86 + ,23 + ,111908 + ,1 + ,13 + ,39 + ,26 + ,57550 + ,7 + ,8 + ,16 + ,20 + ,16356 + ,5 + ,15 + ,18 + ,16 + ,40174 + ,2 + ,12 + ,16 + ,8 + ,13983 + ,0 + ,10 + ,42 + ,22 + ,52316 + ,1 + ,13 + ,77 + ,33 + ,99585 + ,0 + ,17 + ,30 + ,28 + ,86271 + ,0 + ,17 + ,104 + ,26 + ,131012 + ,2 + ,16 + ,121 + ,27 + ,130274 + ,0 + ,18 + ,109 + ,35 + ,159051 + ,2 + ,14 + ,57 + ,21 + ,76506 + ,0 + ,9 + ,28 + ,20 + ,49145 + ,0 + ,10 + ,56 + ,24 + ,66398 + ,4 + ,15 + ,81 + ,26 + ,127546 + ,4 + ,2 + ,2 + ,20 + ,6802 + ,8 + ,11 + ,88 + ,22 + ,99509 + ,0 + ,15 + ,41 + ,24 + ,43106 + ,4 + ,14 + ,83 + ,23 + ,108303 + ,0 + ,13 + ,55 + ,22 + ,64167 + ,1 + ,4 + ,3 + ,12 + ,8579 + ,0 + ,12 + ,54 + ,21 + ,97811 + ,9 + ,11 + ,89 + ,24 + ,84365 + ,0 + ,9 + ,41 + ,21 + ,10901 + ,3 + ,15 + ,94 + ,25 + ,91346 + ,7 + ,16 + ,101 + ,32 + ,33660 + ,5 + ,14 + ,70 + ,24 + ,93634 + ,2 + ,16 + ,111 + ,29 + ,109348 + ,1 + ,0 + ,0 + ,0 + ,0 + ,9 + ,0 + ,4 + ,0 + ,7953 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,10 + ,42 + ,20 + ,63538 + ,2 + ,12 + ,97 + ,27 + ,108281 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,7 + ,0 + ,4245 + ,0 + ,4 + ,12 + ,5 + ,21509 + ,0 + ,0 + ,0 + ,1 + ,7670 + ,0 + ,5 + ,37 + ,23 + ,10641 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,3 + ,39 + ,16 + ,41243 + ,2) + ,dim=c(5 + ,112) + ,dimnames=list(c('Score_op_20' + ,'Blogs' + ,'Reviews' + ,'Compendium_Writing' + ,'Gedeelde_compendia') + ,1:112)) > y <- array(NA,dim=c(5,112),dimnames=list(c('Score_op_20','Blogs','Reviews','Compendium_Writing','Gedeelde_compendia'),1:112)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Score_op_20 Blogs Reviews Compendium_Writing Gedeelde_compendia t 1 12 65 22 114468 2 1 2 13 54 20 88594 4 2 3 11 58 24 74151 9 3 4 12 77 21 77921 2 4 5 8 41 15 53212 1 5 6 7 0 16 34956 2 6 7 18 111 20 149703 0 7 8 0 1 18 6853 0 8 9 9 36 19 58907 5 9 10 11 60 20 67067 0 10 11 13 63 25 110563 0 11 12 13 71 37 58126 7 12 13 9 38 23 57113 6 13 14 12 76 28 77993 3 14 15 11 61 25 68091 4 15 16 17 125 35 124676 0 16 17 14 84 20 109522 4 17 18 15 69 22 75865 3 18 19 13 77 19 79746 0 19 20 15 100 26 77844 5 20 21 13 78 27 98681 0 21 22 13 76 22 105531 1 22 23 8 40 15 51428 3 23 24 16 81 26 65703 5 24 25 14 102 24 72562 0 25 26 14 70 22 81728 0 26 27 14 75 21 95580 4 27 28 14 93 23 98278 0 28 29 12 42 21 46629 0 29 30 14 95 25 115189 0 30 31 2 8 4 15049 0 31 32 12 87 30 109011 5 32 33 13 87 20 134245 5 33 34 16 112 26 136692 0 34 35 15 96 27 149510 6 35 36 16 93 18 147888 6 36 37 15 98 20 79169 2 37 38 16 99 17 65469 5 38 39 14 94 22 56756 0 39 40 17 98 25 81399 3 40 41 18 109 30 104953 0 41 42 16 108 26 59633 1 42 43 10 42 20 63249 1 43 44 15 108 25 82928 2 44 45 10 27 21 50000 4 45 46 16 115 23 139357 0 46 47 17 92 33 110044 7 47 48 17 106 19 155118 7 48 49 13 73 31 83061 6 49 50 14 105 25 127122 0 50 51 12 30 20 45653 0 51 52 7 13 19 19630 4 52 53 14 69 15 67229 4 53 54 12 72 21 86060 0 54 55 16 80 22 88003 0 55 56 14 106 24 95815 0 56 57 8 28 19 85499 0 57 58 14 70 20 27220 0 58 59 15 51 23 109882 4 59 60 16 90 27 72579 0 60 61 0 12 1 5841 0 61 62 12 84 20 68369 0 62 63 8 23 11 24610 4 63 64 12 57 27 30995 0 64 65 15 84 22 150662 1 65 66 0 4 0 6622 0 66 67 11 56 17 93694 5 67 68 15 18 8 13155 0 68 69 17 86 23 111908 1 69 70 13 39 26 57550 7 70 71 8 16 20 16356 5 71 72 15 18 16 40174 2 72 73 12 16 8 13983 0 73 74 10 42 22 52316 1 74 75 13 77 33 99585 0 75 76 17 30 28 86271 0 76 77 17 104 26 131012 2 77 78 16 121 27 130274 0 78 79 18 109 35 159051 2 79 80 14 57 21 76506 0 80 81 9 28 20 49145 0 81 82 10 56 24 66398 4 82 83 15 81 26 127546 4 83 84 2 2 20 6802 8 84 85 11 88 22 99509 0 85 86 15 41 24 43106 4 86 87 14 83 23 108303 0 87 88 13 55 22 64167 1 88 89 4 3 12 8579 0 89 90 12 54 21 97811 9 90 91 11 89 24 84365 0 91 92 9 41 21 10901 3 92 93 15 94 25 91346 7 93 94 16 101 32 33660 5 94 95 14 70 24 93634 2 95 96 16 111 29 109348 1 96 97 0 0 0 0 9 97 98 0 4 0 7953 0 98 99 0 0 0 0 0 99 100 0 0 0 0 0 100 101 0 0 0 0 1 101 102 0 0 0 0 0 102 103 10 42 20 63538 2 103 104 12 97 27 108281 1 104 105 0 0 0 0 0 105 106 0 0 0 0 0 106 107 2 7 0 4245 0 107 108 4 12 5 21509 0 108 109 0 0 1 7670 0 109 110 5 37 23 10641 0 110 111 0 0 0 0 0 111 112 3 39 16 41243 2 112 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Blogs Reviews Compendium_Writing 2.183e+00 5.874e-02 2.129e-01 2.271e-05 Gedeelde_compendia t 1.024e-02 -7.080e-03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.1728 -1.4762 -0.3431 0.9818 10.2391 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.183e+00 8.899e-01 2.453 0.0158 * Blogs 5.874e-02 1.342e-02 4.377 2.83e-05 *** Reviews 2.129e-01 4.256e-02 5.002 2.26e-06 *** Compendium_Writing 2.271e-05 1.011e-05 2.245 0.0268 * Gedeelde_compendia 1.024e-02 9.725e-02 0.105 0.9163 t -7.080e-03 7.974e-03 -0.888 0.3766 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.478 on 106 degrees of freedom Multiple R-squared: 0.7939, Adjusted R-squared: 0.7842 F-statistic: 81.68 on 5 and 106 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,] 0.2784944205 0.5569888410 0.721505580 [2,] 0.2992316192 0.5984632384 0.700768381 [3,] 0.1937923851 0.3875847702 0.806207615 [4,] 0.1918838690 0.3837677380 0.808116131 [5,] 0.1375731842 0.2751463684 0.862426816 [6,] 0.0895765944 0.1791531888 0.910423406 [7,] 0.0533934699 0.1067869397 0.946606530 [8,] 0.0329183174 0.0658366349 0.967081683 [9,] 0.0201129867 0.0402259733 0.979887013 [10,] 0.0454354093 0.0908708186 0.954564591 [11,] 0.0270313037 0.0540626073 0.972968696 [12,] 0.0153725413 0.0307450825 0.984627459 [13,] 0.0097490755 0.0194981510 0.990250925 [14,] 0.0066997584 0.0133995169 0.993300242 [15,] 0.0046828402 0.0093656803 0.995317160 [16,] 0.0080215037 0.0160430074 0.991978496 [17,] 0.0046452837 0.0092905674 0.995354716 [18,] 0.0037150270 0.0074300540 0.996284973 [19,] 0.0021765563 0.0043531126 0.997823444 [20,] 0.0014091154 0.0028182308 0.998590885 [21,] 0.0025774058 0.0051548116 0.997422594 [22,] 0.0029427692 0.0058855385 0.997057231 [23,] 0.0051697663 0.0103395326 0.994830234 [24,] 0.0194394417 0.0388788834 0.980560558 [25,] 0.0204372242 0.0408744484 0.979562776 [26,] 0.0153202993 0.0306405986 0.984679701 [27,] 0.0126348506 0.0252697013 0.987365149 [28,] 0.0085587047 0.0171174094 0.991441295 [29,] 0.0056525991 0.0113051982 0.994347401 [30,] 0.0045181277 0.0090362554 0.995481872 [31,] 0.0028188499 0.0056376998 0.997181150 [32,] 0.0026633893 0.0053267785 0.997336611 [33,] 0.0021608888 0.0043217775 0.997839111 [34,] 0.0012997895 0.0025995790 0.998700210 [35,] 0.0009653897 0.0019307793 0.999034610 [36,] 0.0006893073 0.0013786146 0.999310693 [37,] 0.0006531242 0.0013062485 0.999346876 [38,] 0.0004799401 0.0009598803 0.999520060 [39,] 0.0003223719 0.0006447438 0.999677628 [40,] 0.0001862859 0.0003725719 0.999813714 [41,] 0.0001704807 0.0003409613 0.999829519 [42,] 0.0002320022 0.0004640043 0.999767998 [43,] 0.0005516516 0.0011033032 0.999448348 [44,] 0.0004540975 0.0009081951 0.999545902 [45,] 0.0003672573 0.0007345146 0.999632743 [46,] 0.0002761864 0.0005523728 0.999723814 [47,] 0.0002718292 0.0005436584 0.999728171 [48,] 0.0003056911 0.0006113821 0.999694309 [49,] 0.0004054591 0.0008109182 0.999594541 [50,] 0.0003450613 0.0006901226 0.999654939 [51,] 0.0004225123 0.0008450247 0.999577488 [52,] 0.0002717225 0.0005434451 0.999728277 [53,] 0.0032783136 0.0065566273 0.996721686 [54,] 0.0030564636 0.0061129272 0.996943536 [55,] 0.0020939619 0.0041879238 0.997906038 [56,] 0.0018461298 0.0036922596 0.998153870 [57,] 0.0013221743 0.0026443485 0.998677826 [58,] 0.0089886471 0.0179772941 0.991011353 [59,] 0.0087788044 0.0175576087 0.991221196 [60,] 0.2205898036 0.4411796072 0.779410196 [61,] 0.1922300394 0.3844600789 0.807769961 [62,] 0.1588334115 0.3176668230 0.841166588 [63,] 0.1469015831 0.2938031662 0.853098417 [64,] 0.3961916814 0.7923833628 0.603808319 [65,] 0.6854001403 0.6291997195 0.314599860 [66,] 0.6481858047 0.7036283906 0.351814195 [67,] 0.7399816578 0.5200366844 0.260018342 [68,] 0.8643385004 0.2713229993 0.135661500 [69,] 0.8291625591 0.3416748817 0.170837441 [70,] 0.8304600705 0.3390798590 0.169539930 [71,] 0.8137513737 0.3724972525 0.186248626 [72,] 0.8097662779 0.3804674442 0.190233722 [73,] 0.7670100879 0.4659798242 0.232989912 [74,] 0.7701152815 0.4597694371 0.229884719 [75,] 0.7176991858 0.5646016284 0.282300814 [76,] 0.9641744827 0.0716510346 0.035825517 [77,] 0.9794969139 0.0410061722 0.020503086 [78,] 0.9931627820 0.0136744361 0.006837218 [79,] 0.9884741294 0.0230517413 0.011525871 [80,] 0.9877277894 0.0245444211 0.012272211 [81,] 0.9818657863 0.0362684275 0.018134214 [82,] 0.9701637423 0.0596725153 0.029836258 [83,] 0.9857488735 0.0285022530 0.014251127 [84,] 0.9753360806 0.0493278389 0.024663919 [85,] 0.9605539123 0.0788921755 0.039446088 [86,] 0.9859609697 0.0280780605 0.014039030 [87,] 0.9780456784 0.0439086432 0.021954322 [88,] 0.9854828792 0.0290342417 0.014517121 [89,] 0.9879125817 0.0241748365 0.012087418 [90,] 0.9845355169 0.0309289663 0.015464483 [91,] 0.9729748164 0.0540503672 0.027025184 [92,] 0.9571362923 0.0857274154 0.042863708 [93,] 0.9108201088 0.1783597824 0.089179891 [94,] 0.8699010805 0.2601978389 0.130098919 [95,] 0.8732416891 0.2535166218 0.126758311 > postscript(file="/var/www/rcomp/tmp/1bx9z1321705340.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/www/rcomp/tmp/2twsa1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3cyk01321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/48ygy1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/51t6a1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 112 Frequency = 1 1 2 3 4 5 6 -1.29721905 1.34878411 -1.45380482 -0.93799974 -0.96783918 0.63884653 7 8 9 10 11 12 1.68952212 -6.17282227 -0.66763510 -0.41718347 -0.63842346 -2.53665460 13 14 15 16 17 18 -1.57779375 -2.31047740 -1.56911716 -2.69387360 0.21766740 2.45456990 19 20 21 22 23 24 0.57298315 -0.26899674 -1.60458941 -0.58145229 -0.76164078 2.15100046 25 26 27 28 29 30 -0.75416914 1.35005142 0.92080893 -0.57540881 2.02580814 -1.48849778 31 32 33 34 35 36 -1.62699481 -3.97974390 -1.41693110 -1.15986161 -1.77841129 1.35759338 37 38 39 40 41 42 1.24671546 3.11407433 0.59951228 2.14269279 0.93514451 0.87137651 43 44 45 46 47 48 -0.04985921 -0.44082498 0.90260080 -0.67300359 0.15020447 1.29168689 49 50 51 52 53 54 -1.67092399 -2.20524346 3.12142339 -0.11013387 3.37835747 -0.45469007 55 56 57 58 59 60 2.82550666 -1.29768816 -1.41059828 3.24014790 2.80647522 1.55942580 61 62 63 64 65 66 -2.80182176 -0.48826604 1.97030662 0.47032846 0.22822287 -2.10139946 67 68 69 70 71 72 0.20405704 10.23909845 2.80624210 2.10821160 0.69940214 7.93036354 73 74 75 76 77 78 7.37316438 -0.00787314 -2.46134467 5.67302546 0.72294611 -1.44411408 79 80 81 82 83 84 -1.10918344 2.82736780 0.37198197 -1.54978932 0.17456664 -4.20003054 85 86 87 88 89 90 -2.69327617 4.88848929 0.20198494 2.05855963 -0.47878925 0.49839990 91 92 93 94 95 96 -2.79138806 0.31116506 0.48600617 0.92225203 1.12193772 -0.69012063 97 98 99 100 101 102 -1.58877925 -1.90507956 -1.48245676 -1.47537723 -1.47853808 -1.46121817 103 104 105 106 107 108 0.35810955 -3.36120522 -1.43997957 -1.43290004 0.06663318 0.32361364 109 110 111 112 -1.79871239 -3.71558835 -1.39750238 -5.04417586 > postscript(file="/var/www/rcomp/tmp/67m0e1321705340.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 = 112 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.29721905 NA 1 1.34878411 -1.29721905 2 -1.45380482 1.34878411 3 -0.93799974 -1.45380482 4 -0.96783918 -0.93799974 5 0.63884653 -0.96783918 6 1.68952212 0.63884653 7 -6.17282227 1.68952212 8 -0.66763510 -6.17282227 9 -0.41718347 -0.66763510 10 -0.63842346 -0.41718347 11 -2.53665460 -0.63842346 12 -1.57779375 -2.53665460 13 -2.31047740 -1.57779375 14 -1.56911716 -2.31047740 15 -2.69387360 -1.56911716 16 0.21766740 -2.69387360 17 2.45456990 0.21766740 18 0.57298315 2.45456990 19 -0.26899674 0.57298315 20 -1.60458941 -0.26899674 21 -0.58145229 -1.60458941 22 -0.76164078 -0.58145229 23 2.15100046 -0.76164078 24 -0.75416914 2.15100046 25 1.35005142 -0.75416914 26 0.92080893 1.35005142 27 -0.57540881 0.92080893 28 2.02580814 -0.57540881 29 -1.48849778 2.02580814 30 -1.62699481 -1.48849778 31 -3.97974390 -1.62699481 32 -1.41693110 -3.97974390 33 -1.15986161 -1.41693110 34 -1.77841129 -1.15986161 35 1.35759338 -1.77841129 36 1.24671546 1.35759338 37 3.11407433 1.24671546 38 0.59951228 3.11407433 39 2.14269279 0.59951228 40 0.93514451 2.14269279 41 0.87137651 0.93514451 42 -0.04985921 0.87137651 43 -0.44082498 -0.04985921 44 0.90260080 -0.44082498 45 -0.67300359 0.90260080 46 0.15020447 -0.67300359 47 1.29168689 0.15020447 48 -1.67092399 1.29168689 49 -2.20524346 -1.67092399 50 3.12142339 -2.20524346 51 -0.11013387 3.12142339 52 3.37835747 -0.11013387 53 -0.45469007 3.37835747 54 2.82550666 -0.45469007 55 -1.29768816 2.82550666 56 -1.41059828 -1.29768816 57 3.24014790 -1.41059828 58 2.80647522 3.24014790 59 1.55942580 2.80647522 60 -2.80182176 1.55942580 61 -0.48826604 -2.80182176 62 1.97030662 -0.48826604 63 0.47032846 1.97030662 64 0.22822287 0.47032846 65 -2.10139946 0.22822287 66 0.20405704 -2.10139946 67 10.23909845 0.20405704 68 2.80624210 10.23909845 69 2.10821160 2.80624210 70 0.69940214 2.10821160 71 7.93036354 0.69940214 72 7.37316438 7.93036354 73 -0.00787314 7.37316438 74 -2.46134467 -0.00787314 75 5.67302546 -2.46134467 76 0.72294611 5.67302546 77 -1.44411408 0.72294611 78 -1.10918344 -1.44411408 79 2.82736780 -1.10918344 80 0.37198197 2.82736780 81 -1.54978932 0.37198197 82 0.17456664 -1.54978932 83 -4.20003054 0.17456664 84 -2.69327617 -4.20003054 85 4.88848929 -2.69327617 86 0.20198494 4.88848929 87 2.05855963 0.20198494 88 -0.47878925 2.05855963 89 0.49839990 -0.47878925 90 -2.79138806 0.49839990 91 0.31116506 -2.79138806 92 0.48600617 0.31116506 93 0.92225203 0.48600617 94 1.12193772 0.92225203 95 -0.69012063 1.12193772 96 -1.58877925 -0.69012063 97 -1.90507956 -1.58877925 98 -1.48245676 -1.90507956 99 -1.47537723 -1.48245676 100 -1.47853808 -1.47537723 101 -1.46121817 -1.47853808 102 0.35810955 -1.46121817 103 -3.36120522 0.35810955 104 -1.43997957 -3.36120522 105 -1.43290004 -1.43997957 106 0.06663318 -1.43290004 107 0.32361364 0.06663318 108 -1.79871239 0.32361364 109 -3.71558835 -1.79871239 110 -1.39750238 -3.71558835 111 -5.04417586 -1.39750238 112 NA -5.04417586 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.34878411 -1.29721905 [2,] -1.45380482 1.34878411 [3,] -0.93799974 -1.45380482 [4,] -0.96783918 -0.93799974 [5,] 0.63884653 -0.96783918 [6,] 1.68952212 0.63884653 [7,] -6.17282227 1.68952212 [8,] -0.66763510 -6.17282227 [9,] -0.41718347 -0.66763510 [10,] -0.63842346 -0.41718347 [11,] -2.53665460 -0.63842346 [12,] -1.57779375 -2.53665460 [13,] -2.31047740 -1.57779375 [14,] -1.56911716 -2.31047740 [15,] -2.69387360 -1.56911716 [16,] 0.21766740 -2.69387360 [17,] 2.45456990 0.21766740 [18,] 0.57298315 2.45456990 [19,] -0.26899674 0.57298315 [20,] -1.60458941 -0.26899674 [21,] -0.58145229 -1.60458941 [22,] -0.76164078 -0.58145229 [23,] 2.15100046 -0.76164078 [24,] -0.75416914 2.15100046 [25,] 1.35005142 -0.75416914 [26,] 0.92080893 1.35005142 [27,] -0.57540881 0.92080893 [28,] 2.02580814 -0.57540881 [29,] -1.48849778 2.02580814 [30,] -1.62699481 -1.48849778 [31,] -3.97974390 -1.62699481 [32,] -1.41693110 -3.97974390 [33,] -1.15986161 -1.41693110 [34,] -1.77841129 -1.15986161 [35,] 1.35759338 -1.77841129 [36,] 1.24671546 1.35759338 [37,] 3.11407433 1.24671546 [38,] 0.59951228 3.11407433 [39,] 2.14269279 0.59951228 [40,] 0.93514451 2.14269279 [41,] 0.87137651 0.93514451 [42,] -0.04985921 0.87137651 [43,] -0.44082498 -0.04985921 [44,] 0.90260080 -0.44082498 [45,] -0.67300359 0.90260080 [46,] 0.15020447 -0.67300359 [47,] 1.29168689 0.15020447 [48,] -1.67092399 1.29168689 [49,] -2.20524346 -1.67092399 [50,] 3.12142339 -2.20524346 [51,] -0.11013387 3.12142339 [52,] 3.37835747 -0.11013387 [53,] -0.45469007 3.37835747 [54,] 2.82550666 -0.45469007 [55,] -1.29768816 2.82550666 [56,] -1.41059828 -1.29768816 [57,] 3.24014790 -1.41059828 [58,] 2.80647522 3.24014790 [59,] 1.55942580 2.80647522 [60,] -2.80182176 1.55942580 [61,] -0.48826604 -2.80182176 [62,] 1.97030662 -0.48826604 [63,] 0.47032846 1.97030662 [64,] 0.22822287 0.47032846 [65,] -2.10139946 0.22822287 [66,] 0.20405704 -2.10139946 [67,] 10.23909845 0.20405704 [68,] 2.80624210 10.23909845 [69,] 2.10821160 2.80624210 [70,] 0.69940214 2.10821160 [71,] 7.93036354 0.69940214 [72,] 7.37316438 7.93036354 [73,] -0.00787314 7.37316438 [74,] -2.46134467 -0.00787314 [75,] 5.67302546 -2.46134467 [76,] 0.72294611 5.67302546 [77,] -1.44411408 0.72294611 [78,] -1.10918344 -1.44411408 [79,] 2.82736780 -1.10918344 [80,] 0.37198197 2.82736780 [81,] -1.54978932 0.37198197 [82,] 0.17456664 -1.54978932 [83,] -4.20003054 0.17456664 [84,] -2.69327617 -4.20003054 [85,] 4.88848929 -2.69327617 [86,] 0.20198494 4.88848929 [87,] 2.05855963 0.20198494 [88,] -0.47878925 2.05855963 [89,] 0.49839990 -0.47878925 [90,] -2.79138806 0.49839990 [91,] 0.31116506 -2.79138806 [92,] 0.48600617 0.31116506 [93,] 0.92225203 0.48600617 [94,] 1.12193772 0.92225203 [95,] -0.69012063 1.12193772 [96,] -1.58877925 -0.69012063 [97,] -1.90507956 -1.58877925 [98,] -1.48245676 -1.90507956 [99,] -1.47537723 -1.48245676 [100,] -1.47853808 -1.47537723 [101,] -1.46121817 -1.47853808 [102,] 0.35810955 -1.46121817 [103,] -3.36120522 0.35810955 [104,] -1.43997957 -3.36120522 [105,] -1.43290004 -1.43997957 [106,] 0.06663318 -1.43290004 [107,] 0.32361364 0.06663318 [108,] -1.79871239 0.32361364 [109,] -3.71558835 -1.79871239 [110,] -1.39750238 -3.71558835 [111,] -5.04417586 -1.39750238 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.34878411 -1.29721905 2 -1.45380482 1.34878411 3 -0.93799974 -1.45380482 4 -0.96783918 -0.93799974 5 0.63884653 -0.96783918 6 1.68952212 0.63884653 7 -6.17282227 1.68952212 8 -0.66763510 -6.17282227 9 -0.41718347 -0.66763510 10 -0.63842346 -0.41718347 11 -2.53665460 -0.63842346 12 -1.57779375 -2.53665460 13 -2.31047740 -1.57779375 14 -1.56911716 -2.31047740 15 -2.69387360 -1.56911716 16 0.21766740 -2.69387360 17 2.45456990 0.21766740 18 0.57298315 2.45456990 19 -0.26899674 0.57298315 20 -1.60458941 -0.26899674 21 -0.58145229 -1.60458941 22 -0.76164078 -0.58145229 23 2.15100046 -0.76164078 24 -0.75416914 2.15100046 25 1.35005142 -0.75416914 26 0.92080893 1.35005142 27 -0.57540881 0.92080893 28 2.02580814 -0.57540881 29 -1.48849778 2.02580814 30 -1.62699481 -1.48849778 31 -3.97974390 -1.62699481 32 -1.41693110 -3.97974390 33 -1.15986161 -1.41693110 34 -1.77841129 -1.15986161 35 1.35759338 -1.77841129 36 1.24671546 1.35759338 37 3.11407433 1.24671546 38 0.59951228 3.11407433 39 2.14269279 0.59951228 40 0.93514451 2.14269279 41 0.87137651 0.93514451 42 -0.04985921 0.87137651 43 -0.44082498 -0.04985921 44 0.90260080 -0.44082498 45 -0.67300359 0.90260080 46 0.15020447 -0.67300359 47 1.29168689 0.15020447 48 -1.67092399 1.29168689 49 -2.20524346 -1.67092399 50 3.12142339 -2.20524346 51 -0.11013387 3.12142339 52 3.37835747 -0.11013387 53 -0.45469007 3.37835747 54 2.82550666 -0.45469007 55 -1.29768816 2.82550666 56 -1.41059828 -1.29768816 57 3.24014790 -1.41059828 58 2.80647522 3.24014790 59 1.55942580 2.80647522 60 -2.80182176 1.55942580 61 -0.48826604 -2.80182176 62 1.97030662 -0.48826604 63 0.47032846 1.97030662 64 0.22822287 0.47032846 65 -2.10139946 0.22822287 66 0.20405704 -2.10139946 67 10.23909845 0.20405704 68 2.80624210 10.23909845 69 2.10821160 2.80624210 70 0.69940214 2.10821160 71 7.93036354 0.69940214 72 7.37316438 7.93036354 73 -0.00787314 7.37316438 74 -2.46134467 -0.00787314 75 5.67302546 -2.46134467 76 0.72294611 5.67302546 77 -1.44411408 0.72294611 78 -1.10918344 -1.44411408 79 2.82736780 -1.10918344 80 0.37198197 2.82736780 81 -1.54978932 0.37198197 82 0.17456664 -1.54978932 83 -4.20003054 0.17456664 84 -2.69327617 -4.20003054 85 4.88848929 -2.69327617 86 0.20198494 4.88848929 87 2.05855963 0.20198494 88 -0.47878925 2.05855963 89 0.49839990 -0.47878925 90 -2.79138806 0.49839990 91 0.31116506 -2.79138806 92 0.48600617 0.31116506 93 0.92225203 0.48600617 94 1.12193772 0.92225203 95 -0.69012063 1.12193772 96 -1.58877925 -0.69012063 97 -1.90507956 -1.58877925 98 -1.48245676 -1.90507956 99 -1.47537723 -1.48245676 100 -1.47853808 -1.47537723 101 -1.46121817 -1.47853808 102 0.35810955 -1.46121817 103 -3.36120522 0.35810955 104 -1.43997957 -3.36120522 105 -1.43290004 -1.43997957 106 0.06663318 -1.43290004 107 0.32361364 0.06663318 108 -1.79871239 0.32361364 109 -3.71558835 -1.79871239 110 -1.39750238 -3.71558835 111 -5.04417586 -1.39750238 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/7lkf41321705340.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/www/rcomp/tmp/8q4o21321705340.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/www/rcomp/tmp/91y1u1321705340.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/www/rcomp/tmp/10ut571321705340.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/11sq711321705340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/12oozp1321705340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/13i60p1321705340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/14t13e1321705340.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/15liib1321705340.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/16m7eo1321705340.tab") + } > > try(system("convert tmp/1bx9z1321705340.ps tmp/1bx9z1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/2twsa1321705340.ps tmp/2twsa1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/3cyk01321705340.ps tmp/3cyk01321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/48ygy1321705340.ps tmp/48ygy1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/51t6a1321705340.ps tmp/51t6a1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/67m0e1321705340.ps tmp/67m0e1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/7lkf41321705340.ps tmp/7lkf41321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/8q4o21321705340.ps tmp/8q4o21321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/91y1u1321705340.ps tmp/91y1u1321705340.png",intern=TRUE)) character(0) > try(system("convert tmp/10ut571321705340.ps tmp/10ut571321705340.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.610 0.210 3.806