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(17.848,19.592,21.092,20.899,25.890,24.965,22.225,20.977,22.897,22.785,22.769,19.637,20.203,20.450,23.083,21.738,26.766,25.280,22.574,22.729,21.378,22.902,24.989,21.116,15.169,15.846,20.927,18.273,22.538,15.596,14.034,11.366,14.861,15.149,13.577,13.026,13.190,13.196,15.826,14.733,16.307,15.703,14.589,12.043,15.057,14.053,12.698,10.888,10.045,11.549,13.767,12.434,13.116,14.211,12.266,12.602,15.714,13.742,12.745,10.491,10.057,10.900,11.771,11.992,11.933,14.504,11.727,11.477,13.578,11.555,11.846,11.397,10.066,10.269,14.279,13.870,13.695,14.420,11.424,9.704,12.464,14.301,13.464,9.893,11.572,12.380,16.692,16.052,16.459,14.761,13.654,13.480,18.068,16.560,14.530,10.650,11.651,13.735,13.360,17.818,20.613,16.231,13.862,12.004,17.734,15.034,12.609,12.320,10.833,11.350,13.648,14.890,16.325,18.045,15.616,11.926,16.855,15.083,12.520,12.355),dim=c(1,120),dimnames=list(c('Pas'),1:120)) > y <- array(NA,dim=c(1,120),dimnames=list(c('Pas'),1:120)) > 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 = '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 Pas M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 17.848 1 0 0 0 0 0 0 0 0 0 0 1 2 19.592 0 1 0 0 0 0 0 0 0 0 0 2 3 21.092 0 0 1 0 0 0 0 0 0 0 0 3 4 20.899 0 0 0 1 0 0 0 0 0 0 0 4 5 25.890 0 0 0 0 1 0 0 0 0 0 0 5 6 24.965 0 0 0 0 0 1 0 0 0 0 0 6 7 22.225 0 0 0 0 0 0 1 0 0 0 0 7 8 20.977 0 0 0 0 0 0 0 1 0 0 0 8 9 22.897 0 0 0 0 0 0 0 0 1 0 0 9 10 22.785 0 0 0 0 0 0 0 0 0 1 0 10 11 22.769 0 0 0 0 0 0 0 0 0 0 1 11 12 19.637 0 0 0 0 0 0 0 0 0 0 0 12 13 20.203 1 0 0 0 0 0 0 0 0 0 0 13 14 20.450 0 1 0 0 0 0 0 0 0 0 0 14 15 23.083 0 0 1 0 0 0 0 0 0 0 0 15 16 21.738 0 0 0 1 0 0 0 0 0 0 0 16 17 26.766 0 0 0 0 1 0 0 0 0 0 0 17 18 25.280 0 0 0 0 0 1 0 0 0 0 0 18 19 22.574 0 0 0 0 0 0 1 0 0 0 0 19 20 22.729 0 0 0 0 0 0 0 1 0 0 0 20 21 21.378 0 0 0 0 0 0 0 0 1 0 0 21 22 22.902 0 0 0 0 0 0 0 0 0 1 0 22 23 24.989 0 0 0 0 0 0 0 0 0 0 1 23 24 21.116 0 0 0 0 0 0 0 0 0 0 0 24 25 15.169 1 0 0 0 0 0 0 0 0 0 0 25 26 15.846 0 1 0 0 0 0 0 0 0 0 0 26 27 20.927 0 0 1 0 0 0 0 0 0 0 0 27 28 18.273 0 0 0 1 0 0 0 0 0 0 0 28 29 22.538 0 0 0 0 1 0 0 0 0 0 0 29 30 15.596 0 0 0 0 0 1 0 0 0 0 0 30 31 14.034 0 0 0 0 0 0 1 0 0 0 0 31 32 11.366 0 0 0 0 0 0 0 1 0 0 0 32 33 14.861 0 0 0 0 0 0 0 0 1 0 0 33 34 15.149 0 0 0 0 0 0 0 0 0 1 0 34 35 13.577 0 0 0 0 0 0 0 0 0 0 1 35 36 13.026 0 0 0 0 0 0 0 0 0 0 0 36 37 13.190 1 0 0 0 0 0 0 0 0 0 0 37 38 13.196 0 1 0 0 0 0 0 0 0 0 0 38 39 15.826 0 0 1 0 0 0 0 0 0 0 0 39 40 14.733 0 0 0 1 0 0 0 0 0 0 0 40 41 16.307 0 0 0 0 1 0 0 0 0 0 0 41 42 15.703 0 0 0 0 0 1 0 0 0 0 0 42 43 14.589 0 0 0 0 0 0 1 0 0 0 0 43 44 12.043 0 0 0 0 0 0 0 1 0 0 0 44 45 15.057 0 0 0 0 0 0 0 0 1 0 0 45 46 14.053 0 0 0 0 0 0 0 0 0 1 0 46 47 12.698 0 0 0 0 0 0 0 0 0 0 1 47 48 10.888 0 0 0 0 0 0 0 0 0 0 0 48 49 10.045 1 0 0 0 0 0 0 0 0 0 0 49 50 11.549 0 1 0 0 0 0 0 0 0 0 0 50 51 13.767 0 0 1 0 0 0 0 0 0 0 0 51 52 12.434 0 0 0 1 0 0 0 0 0 0 0 52 53 13.116 0 0 0 0 1 0 0 0 0 0 0 53 54 14.211 0 0 0 0 0 1 0 0 0 0 0 54 55 12.266 0 0 0 0 0 0 1 0 0 0 0 55 56 12.602 0 0 0 0 0 0 0 1 0 0 0 56 57 15.714 0 0 0 0 0 0 0 0 1 0 0 57 58 13.742 0 0 0 0 0 0 0 0 0 1 0 58 59 12.745 0 0 0 0 0 0 0 0 0 0 1 59 60 10.491 0 0 0 0 0 0 0 0 0 0 0 60 61 10.057 1 0 0 0 0 0 0 0 0 0 0 61 62 10.900 0 1 0 0 0 0 0 0 0 0 0 62 63 11.771 0 0 1 0 0 0 0 0 0 0 0 63 64 11.992 0 0 0 1 0 0 0 0 0 0 0 64 65 11.933 0 0 0 0 1 0 0 0 0 0 0 65 66 14.504 0 0 0 0 0 1 0 0 0 0 0 66 67 11.727 0 0 0 0 0 0 1 0 0 0 0 67 68 11.477 0 0 0 0 0 0 0 1 0 0 0 68 69 13.578 0 0 0 0 0 0 0 0 1 0 0 69 70 11.555 0 0 0 0 0 0 0 0 0 1 0 70 71 11.846 0 0 0 0 0 0 0 0 0 0 1 71 72 11.397 0 0 0 0 0 0 0 0 0 0 0 72 73 10.066 1 0 0 0 0 0 0 0 0 0 0 73 74 10.269 0 1 0 0 0 0 0 0 0 0 0 74 75 14.279 0 0 1 0 0 0 0 0 0 0 0 75 76 13.870 0 0 0 1 0 0 0 0 0 0 0 76 77 13.695 0 0 0 0 1 0 0 0 0 0 0 77 78 14.420 0 0 0 0 0 1 0 0 0 0 0 78 79 11.424 0 0 0 0 0 0 1 0 0 0 0 79 80 9.704 0 0 0 0 0 0 0 1 0 0 0 80 81 12.464 0 0 0 0 0 0 0 0 1 0 0 81 82 14.301 0 0 0 0 0 0 0 0 0 1 0 82 83 13.464 0 0 0 0 0 0 0 0 0 0 1 83 84 9.893 0 0 0 0 0 0 0 0 0 0 0 84 85 11.572 1 0 0 0 0 0 0 0 0 0 0 85 86 12.380 0 1 0 0 0 0 0 0 0 0 0 86 87 16.692 0 0 1 0 0 0 0 0 0 0 0 87 88 16.052 0 0 0 1 0 0 0 0 0 0 0 88 89 16.459 0 0 0 0 1 0 0 0 0 0 0 89 90 14.761 0 0 0 0 0 1 0 0 0 0 0 90 91 13.654 0 0 0 0 0 0 1 0 0 0 0 91 92 13.480 0 0 0 0 0 0 0 1 0 0 0 92 93 18.068 0 0 0 0 0 0 0 0 1 0 0 93 94 16.560 0 0 0 0 0 0 0 0 0 1 0 94 95 14.530 0 0 0 0 0 0 0 0 0 0 1 95 96 10.650 0 0 0 0 0 0 0 0 0 0 0 96 97 11.651 1 0 0 0 0 0 0 0 0 0 0 97 98 13.735 0 1 0 0 0 0 0 0 0 0 0 98 99 13.360 0 0 1 0 0 0 0 0 0 0 0 99 100 17.818 0 0 0 1 0 0 0 0 0 0 0 100 101 20.613 0 0 0 0 1 0 0 0 0 0 0 101 102 16.231 0 0 0 0 0 1 0 0 0 0 0 102 103 13.862 0 0 0 0 0 0 1 0 0 0 0 103 104 12.004 0 0 0 0 0 0 0 1 0 0 0 104 105 17.734 0 0 0 0 0 0 0 0 1 0 0 105 106 15.034 0 0 0 0 0 0 0 0 0 1 0 106 107 12.609 0 0 0 0 0 0 0 0 0 0 1 107 108 12.320 0 0 0 0 0 0 0 0 0 0 0 108 109 10.833 1 0 0 0 0 0 0 0 0 0 0 109 110 11.350 0 1 0 0 0 0 0 0 0 0 0 110 111 13.648 0 0 1 0 0 0 0 0 0 0 0 111 112 14.890 0 0 0 1 0 0 0 0 0 0 0 112 113 16.325 0 0 0 0 1 0 0 0 0 0 0 113 114 18.045 0 0 0 0 0 1 0 0 0 0 0 114 115 15.616 0 0 0 0 0 0 1 0 0 0 0 115 116 11.926 0 0 0 0 0 0 0 1 0 0 0 116 117 16.855 0 0 0 0 0 0 0 0 1 0 0 117 118 15.083 0 0 0 0 0 0 0 0 0 1 0 118 119 12.520 0 0 0 0 0 0 0 0 0 0 1 119 120 12.355 0 0 0 0 0 0 0 0 0 0 0 120 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) M1 M2 M3 M4 M5 17.76507 -0.87853 0.05428 2.64160 2.53651 4.70032 M6 M7 M8 M9 M10 M11 3.77723 1.67224 0.37545 3.47477 2.80008 1.92789 t -0.06951 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.014 -2.851 -0.031 2.380 6.895 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.765067 1.143005 15.542 < 2e-16 *** M1 -0.878528 1.417703 -0.620 0.53678 M2 0.054284 1.417183 0.038 0.96952 M3 2.641595 1.416712 1.865 0.06498 . M4 2.536507 1.416291 1.791 0.07613 . M5 4.700319 1.415919 3.320 0.00123 ** M6 3.777230 1.415597 2.668 0.00881 ** M7 1.672242 1.415324 1.182 0.24001 M8 0.375454 1.415101 0.265 0.79127 M9 3.474765 1.414927 2.456 0.01567 * M10 2.800077 1.414803 1.979 0.05037 . M11 1.927888 1.414729 1.363 0.17583 t -0.069512 0.008378 -8.297 3.49e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.163 on 107 degrees of freedom Multiple R-squared: 0.4856, Adjusted R-squared: 0.4279 F-statistic: 8.419 on 12 and 107 DF, p-value: 5.109e-11 > 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,] 0.0081843845 1.636877e-02 9.918156e-01 [2,] 0.0019187113 3.837423e-03 9.980813e-01 [3,] 0.0008436644 1.687329e-03 9.991563e-01 [4,] 0.0003041990 6.083980e-04 9.996958e-01 [5,] 0.0001182823 2.365645e-04 9.998817e-01 [6,] 0.0008768266 1.753653e-03 9.991232e-01 [7,] 0.0004455692 8.911383e-04 9.995544e-01 [8,] 0.0008004583 1.600917e-03 9.991995e-01 [9,] 0.0008605387 1.721077e-03 9.991395e-01 [10,] 0.0802174726 1.604349e-01 9.197825e-01 [11,] 0.2490160518 4.980321e-01 7.509839e-01 [12,] 0.2982822286 5.965645e-01 7.017178e-01 [13,] 0.3643403813 7.286808e-01 6.356596e-01 [14,] 0.6516880233 6.966240e-01 3.483120e-01 [15,] 0.9844014267 3.119715e-02 1.559857e-02 [16,] 0.9974140816 5.171837e-03 2.585918e-03 [17,] 0.9998326639 3.346723e-04 1.673361e-04 [18,] 0.9998620999 2.758001e-04 1.379001e-04 [19,] 0.9999045163 1.909674e-04 9.548368e-05 [20,] 0.9999777820 4.443608e-05 2.221804e-05 [21,] 0.9999840039 3.199224e-05 1.599612e-05 [22,] 0.9999880323 2.393533e-05 1.196767e-05 [23,] 0.9999857993 2.840147e-05 1.420074e-05 [24,] 0.9999869638 2.607232e-05 1.303616e-05 [25,] 0.9999785660 4.286793e-05 2.143397e-05 [26,] 0.9999826252 3.474954e-05 1.737477e-05 [27,] 0.9999739626 5.207485e-05 2.603743e-05 [28,] 0.9999781218 4.375633e-05 2.187816e-05 [29,] 0.9999684171 6.316573e-05 3.158287e-05 [30,] 0.9999492723 1.014555e-04 5.072774e-05 [31,] 0.9999203156 1.593687e-04 7.968437e-05 [32,] 0.9998969142 2.061715e-04 1.030858e-04 [33,] 0.9998543585 2.912830e-04 1.456415e-04 [34,] 0.9997659908 4.680183e-04 2.340092e-04 [35,] 0.9996987109 6.025782e-04 3.012891e-04 [36,] 0.9995809205 8.381590e-04 4.190795e-04 [37,] 0.9993502705 1.299459e-03 6.497295e-04 [38,] 0.9992324017 1.535197e-03 7.675983e-04 [39,] 0.9988078758 2.384248e-03 1.192124e-03 [40,] 0.9982256407 3.548719e-03 1.774359e-03 [41,] 0.9987795198 2.440960e-03 1.220480e-03 [42,] 0.9991860461 1.627908e-03 8.139539e-04 [43,] 0.9989287420 2.142516e-03 1.071258e-03 [44,] 0.9986689916 2.662017e-03 1.331008e-03 [45,] 0.9981550971 3.689806e-03 1.844903e-03 [46,] 0.9978067674 4.386465e-03 2.193233e-03 [47,] 0.9973019618 5.396076e-03 2.698038e-03 [48,] 0.9960711398 7.857720e-03 3.928860e-03 [49,] 0.9959401897 8.119621e-03 4.059810e-03 [50,] 0.9974150241 5.169952e-03 2.584976e-03 [51,] 0.9968221661 6.355668e-03 3.177834e-03 [52,] 0.9956265228 8.746954e-03 4.373477e-03 [53,] 0.9951250467 9.749907e-03 4.874953e-03 [54,] 0.9944979271 1.100415e-02 5.502073e-03 [55,] 0.9945417772 1.091645e-02 5.458223e-03 [56,] 0.9920586634 1.588267e-02 7.941337e-03 [57,] 0.9919915165 1.601697e-02 8.008483e-03 [58,] 0.9909208619 1.815828e-02 9.079138e-03 [59,] 0.9893643814 2.127124e-02 1.063562e-02 [60,] 0.9891108787 2.177824e-02 1.088912e-02 [61,] 0.9902291067 1.954179e-02 9.770893e-03 [62,] 0.9932006727 1.359865e-02 6.799327e-03 [63,] 0.9920278491 1.594430e-02 7.972151e-03 [64,] 0.9924679908 1.506402e-02 7.532009e-03 [65,] 0.9925456282 1.490874e-02 7.454372e-03 [66,] 0.9991514936 1.697013e-03 8.485064e-04 [67,] 0.9991610015 1.677997e-03 8.389985e-04 [68,] 0.9987725340 2.454932e-03 1.227466e-03 [69,] 0.9988912214 2.217557e-03 1.108779e-03 [70,] 0.9986294727 2.741055e-03 1.370527e-03 [71,] 0.9982576713 3.484657e-03 1.742329e-03 [72,] 0.9993243430 1.351314e-03 6.756570e-04 [73,] 0.9991967211 1.606558e-03 8.032789e-04 [74,] 0.9993450738 1.309852e-03 6.549262e-04 [75,] 0.9996587670 6.824660e-04 3.412330e-04 [76,] 0.9996432207 7.135586e-04 3.567793e-04 [77,] 0.9993976180 1.204764e-03 6.023820e-04 [78,] 0.9990992527 1.801495e-03 9.007473e-04 [79,] 0.9985062259 2.987548e-03 1.493774e-03 [80,] 0.9976908547 4.618291e-03 2.309145e-03 [81,] 0.9977823788 4.435242e-03 2.217621e-03 [82,] 0.9951798440 9.640312e-03 4.820156e-03 [83,] 0.9932743879 1.345122e-02 6.725612e-03 [84,] 0.9853985226 2.920295e-02 1.460148e-02 [85,] 0.9858839799 2.823204e-02 1.411602e-02 [86,] 0.9993727054 1.254589e-03 6.272946e-04 [87,] 0.9991684388 1.663122e-03 8.315612e-04 [88,] 0.9998803027 2.393947e-04 1.196973e-04 [89,] 0.9986169469 2.766106e-03 1.383053e-03 > postscript(file="/var/www/html/rcomp/tmp/18vvg1292063046.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/28vvg1292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/38vvg1292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/404c11292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/504c11292063046.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 = 120 Frequency = 1 1 2 3 4 5 6 1.03097273 1.91167273 0.89387273 0.87547273 3.77217273 3.83977273 7 8 9 10 11 12 3.27427273 3.39257273 2.28277273 2.91497273 3.84067273 2.70607273 13 14 15 16 17 18 4.22011212 3.60381212 3.71901212 2.54861212 5.48231212 4.98891212 19 20 21 22 23 24 4.45741212 5.97871212 1.59791212 3.86611212 6.89481212 5.01921212 25 26 27 28 29 30 0.02025152 -0.16604848 2.39715152 -0.08224848 2.08845152 -3.86094848 31 32 33 34 35 36 -3.24844848 -4.55014848 -4.08494848 -3.05274848 -3.68304848 -2.23664848 37 38 39 40 41 42 -1.12460909 -1.98190909 -1.86970909 -2.78810909 -3.30840909 -2.91980909 43 44 45 46 47 48 -1.85930909 -3.03900909 -3.05480909 -3.31460909 -3.72790909 -3.54050909 49 50 51 52 53 54 -3.43546970 -2.79476970 -3.09456970 -4.25296970 -5.66526970 -3.57766970 55 56 57 58 59 60 -3.34816970 -1.64586970 -1.56366970 -2.79146970 -2.84676970 -3.10336970 61 62 63 64 65 66 -2.58933030 -2.60963030 -4.25643030 -3.86083030 -6.01413030 -2.45053030 67 68 69 70 71 72 -3.05303030 -1.93673030 -2.86553030 -4.14433030 -2.91163030 -1.36323030 73 74 75 76 77 78 -1.74619091 -2.40649091 -0.91429091 -1.14869091 -3.41799091 -1.70039091 79 80 81 82 83 84 -2.52189091 -2.87559091 -3.14539091 -0.56419091 -0.45949091 -2.03309091 85 86 87 88 89 90 0.59394848 0.53864848 2.33284848 1.86744848 0.18014848 -0.52525152 91 92 93 94 95 96 0.54224848 1.73454848 3.29274848 2.52894848 1.44064848 -0.44195152 97 98 99 100 101 102 1.50708788 2.72778788 -0.16501212 4.46758788 5.16828788 1.77888788 103 104 105 106 107 108 1.58438788 1.09268788 3.79288788 1.83708788 0.35378788 2.06218788 109 110 111 112 113 114 1.52322727 1.17692727 0.95712727 2.37372727 1.71442727 4.42702727 115 116 117 118 119 120 4.17252727 1.84882727 3.74802727 2.72022727 1.09892727 2.93132727 > postscript(file="/var/www/html/rcomp/tmp/6tvc41292063046.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 = 120 Frequency = 1 lag(myerror, k = 1) myerror 0 1.03097273 NA 1 1.91167273 1.03097273 2 0.89387273 1.91167273 3 0.87547273 0.89387273 4 3.77217273 0.87547273 5 3.83977273 3.77217273 6 3.27427273 3.83977273 7 3.39257273 3.27427273 8 2.28277273 3.39257273 9 2.91497273 2.28277273 10 3.84067273 2.91497273 11 2.70607273 3.84067273 12 4.22011212 2.70607273 13 3.60381212 4.22011212 14 3.71901212 3.60381212 15 2.54861212 3.71901212 16 5.48231212 2.54861212 17 4.98891212 5.48231212 18 4.45741212 4.98891212 19 5.97871212 4.45741212 20 1.59791212 5.97871212 21 3.86611212 1.59791212 22 6.89481212 3.86611212 23 5.01921212 6.89481212 24 0.02025152 5.01921212 25 -0.16604848 0.02025152 26 2.39715152 -0.16604848 27 -0.08224848 2.39715152 28 2.08845152 -0.08224848 29 -3.86094848 2.08845152 30 -3.24844848 -3.86094848 31 -4.55014848 -3.24844848 32 -4.08494848 -4.55014848 33 -3.05274848 -4.08494848 34 -3.68304848 -3.05274848 35 -2.23664848 -3.68304848 36 -1.12460909 -2.23664848 37 -1.98190909 -1.12460909 38 -1.86970909 -1.98190909 39 -2.78810909 -1.86970909 40 -3.30840909 -2.78810909 41 -2.91980909 -3.30840909 42 -1.85930909 -2.91980909 43 -3.03900909 -1.85930909 44 -3.05480909 -3.03900909 45 -3.31460909 -3.05480909 46 -3.72790909 -3.31460909 47 -3.54050909 -3.72790909 48 -3.43546970 -3.54050909 49 -2.79476970 -3.43546970 50 -3.09456970 -2.79476970 51 -4.25296970 -3.09456970 52 -5.66526970 -4.25296970 53 -3.57766970 -5.66526970 54 -3.34816970 -3.57766970 55 -1.64586970 -3.34816970 56 -1.56366970 -1.64586970 57 -2.79146970 -1.56366970 58 -2.84676970 -2.79146970 59 -3.10336970 -2.84676970 60 -2.58933030 -3.10336970 61 -2.60963030 -2.58933030 62 -4.25643030 -2.60963030 63 -3.86083030 -4.25643030 64 -6.01413030 -3.86083030 65 -2.45053030 -6.01413030 66 -3.05303030 -2.45053030 67 -1.93673030 -3.05303030 68 -2.86553030 -1.93673030 69 -4.14433030 -2.86553030 70 -2.91163030 -4.14433030 71 -1.36323030 -2.91163030 72 -1.74619091 -1.36323030 73 -2.40649091 -1.74619091 74 -0.91429091 -2.40649091 75 -1.14869091 -0.91429091 76 -3.41799091 -1.14869091 77 -1.70039091 -3.41799091 78 -2.52189091 -1.70039091 79 -2.87559091 -2.52189091 80 -3.14539091 -2.87559091 81 -0.56419091 -3.14539091 82 -0.45949091 -0.56419091 83 -2.03309091 -0.45949091 84 0.59394848 -2.03309091 85 0.53864848 0.59394848 86 2.33284848 0.53864848 87 1.86744848 2.33284848 88 0.18014848 1.86744848 89 -0.52525152 0.18014848 90 0.54224848 -0.52525152 91 1.73454848 0.54224848 92 3.29274848 1.73454848 93 2.52894848 3.29274848 94 1.44064848 2.52894848 95 -0.44195152 1.44064848 96 1.50708788 -0.44195152 97 2.72778788 1.50708788 98 -0.16501212 2.72778788 99 4.46758788 -0.16501212 100 5.16828788 4.46758788 101 1.77888788 5.16828788 102 1.58438788 1.77888788 103 1.09268788 1.58438788 104 3.79288788 1.09268788 105 1.83708788 3.79288788 106 0.35378788 1.83708788 107 2.06218788 0.35378788 108 1.52322727 2.06218788 109 1.17692727 1.52322727 110 0.95712727 1.17692727 111 2.37372727 0.95712727 112 1.71442727 2.37372727 113 4.42702727 1.71442727 114 4.17252727 4.42702727 115 1.84882727 4.17252727 116 3.74802727 1.84882727 117 2.72022727 3.74802727 118 1.09892727 2.72022727 119 2.93132727 1.09892727 120 NA 2.93132727 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.91167273 1.03097273 [2,] 0.89387273 1.91167273 [3,] 0.87547273 0.89387273 [4,] 3.77217273 0.87547273 [5,] 3.83977273 3.77217273 [6,] 3.27427273 3.83977273 [7,] 3.39257273 3.27427273 [8,] 2.28277273 3.39257273 [9,] 2.91497273 2.28277273 [10,] 3.84067273 2.91497273 [11,] 2.70607273 3.84067273 [12,] 4.22011212 2.70607273 [13,] 3.60381212 4.22011212 [14,] 3.71901212 3.60381212 [15,] 2.54861212 3.71901212 [16,] 5.48231212 2.54861212 [17,] 4.98891212 5.48231212 [18,] 4.45741212 4.98891212 [19,] 5.97871212 4.45741212 [20,] 1.59791212 5.97871212 [21,] 3.86611212 1.59791212 [22,] 6.89481212 3.86611212 [23,] 5.01921212 6.89481212 [24,] 0.02025152 5.01921212 [25,] -0.16604848 0.02025152 [26,] 2.39715152 -0.16604848 [27,] -0.08224848 2.39715152 [28,] 2.08845152 -0.08224848 [29,] -3.86094848 2.08845152 [30,] -3.24844848 -3.86094848 [31,] -4.55014848 -3.24844848 [32,] -4.08494848 -4.55014848 [33,] -3.05274848 -4.08494848 [34,] -3.68304848 -3.05274848 [35,] -2.23664848 -3.68304848 [36,] -1.12460909 -2.23664848 [37,] -1.98190909 -1.12460909 [38,] -1.86970909 -1.98190909 [39,] -2.78810909 -1.86970909 [40,] -3.30840909 -2.78810909 [41,] -2.91980909 -3.30840909 [42,] -1.85930909 -2.91980909 [43,] -3.03900909 -1.85930909 [44,] -3.05480909 -3.03900909 [45,] -3.31460909 -3.05480909 [46,] -3.72790909 -3.31460909 [47,] -3.54050909 -3.72790909 [48,] -3.43546970 -3.54050909 [49,] -2.79476970 -3.43546970 [50,] -3.09456970 -2.79476970 [51,] -4.25296970 -3.09456970 [52,] -5.66526970 -4.25296970 [53,] -3.57766970 -5.66526970 [54,] -3.34816970 -3.57766970 [55,] -1.64586970 -3.34816970 [56,] -1.56366970 -1.64586970 [57,] -2.79146970 -1.56366970 [58,] -2.84676970 -2.79146970 [59,] -3.10336970 -2.84676970 [60,] -2.58933030 -3.10336970 [61,] -2.60963030 -2.58933030 [62,] -4.25643030 -2.60963030 [63,] -3.86083030 -4.25643030 [64,] -6.01413030 -3.86083030 [65,] -2.45053030 -6.01413030 [66,] -3.05303030 -2.45053030 [67,] -1.93673030 -3.05303030 [68,] -2.86553030 -1.93673030 [69,] -4.14433030 -2.86553030 [70,] -2.91163030 -4.14433030 [71,] -1.36323030 -2.91163030 [72,] -1.74619091 -1.36323030 [73,] -2.40649091 -1.74619091 [74,] -0.91429091 -2.40649091 [75,] -1.14869091 -0.91429091 [76,] -3.41799091 -1.14869091 [77,] -1.70039091 -3.41799091 [78,] -2.52189091 -1.70039091 [79,] -2.87559091 -2.52189091 [80,] -3.14539091 -2.87559091 [81,] -0.56419091 -3.14539091 [82,] -0.45949091 -0.56419091 [83,] -2.03309091 -0.45949091 [84,] 0.59394848 -2.03309091 [85,] 0.53864848 0.59394848 [86,] 2.33284848 0.53864848 [87,] 1.86744848 2.33284848 [88,] 0.18014848 1.86744848 [89,] -0.52525152 0.18014848 [90,] 0.54224848 -0.52525152 [91,] 1.73454848 0.54224848 [92,] 3.29274848 1.73454848 [93,] 2.52894848 3.29274848 [94,] 1.44064848 2.52894848 [95,] -0.44195152 1.44064848 [96,] 1.50708788 -0.44195152 [97,] 2.72778788 1.50708788 [98,] -0.16501212 2.72778788 [99,] 4.46758788 -0.16501212 [100,] 5.16828788 4.46758788 [101,] 1.77888788 5.16828788 [102,] 1.58438788 1.77888788 [103,] 1.09268788 1.58438788 [104,] 3.79288788 1.09268788 [105,] 1.83708788 3.79288788 [106,] 0.35378788 1.83708788 [107,] 2.06218788 0.35378788 [108,] 1.52322727 2.06218788 [109,] 1.17692727 1.52322727 [110,] 0.95712727 1.17692727 [111,] 2.37372727 0.95712727 [112,] 1.71442727 2.37372727 [113,] 4.42702727 1.71442727 [114,] 4.17252727 4.42702727 [115,] 1.84882727 4.17252727 [116,] 3.74802727 1.84882727 [117,] 2.72022727 3.74802727 [118,] 1.09892727 2.72022727 [119,] 2.93132727 1.09892727 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.91167273 1.03097273 2 0.89387273 1.91167273 3 0.87547273 0.89387273 4 3.77217273 0.87547273 5 3.83977273 3.77217273 6 3.27427273 3.83977273 7 3.39257273 3.27427273 8 2.28277273 3.39257273 9 2.91497273 2.28277273 10 3.84067273 2.91497273 11 2.70607273 3.84067273 12 4.22011212 2.70607273 13 3.60381212 4.22011212 14 3.71901212 3.60381212 15 2.54861212 3.71901212 16 5.48231212 2.54861212 17 4.98891212 5.48231212 18 4.45741212 4.98891212 19 5.97871212 4.45741212 20 1.59791212 5.97871212 21 3.86611212 1.59791212 22 6.89481212 3.86611212 23 5.01921212 6.89481212 24 0.02025152 5.01921212 25 -0.16604848 0.02025152 26 2.39715152 -0.16604848 27 -0.08224848 2.39715152 28 2.08845152 -0.08224848 29 -3.86094848 2.08845152 30 -3.24844848 -3.86094848 31 -4.55014848 -3.24844848 32 -4.08494848 -4.55014848 33 -3.05274848 -4.08494848 34 -3.68304848 -3.05274848 35 -2.23664848 -3.68304848 36 -1.12460909 -2.23664848 37 -1.98190909 -1.12460909 38 -1.86970909 -1.98190909 39 -2.78810909 -1.86970909 40 -3.30840909 -2.78810909 41 -2.91980909 -3.30840909 42 -1.85930909 -2.91980909 43 -3.03900909 -1.85930909 44 -3.05480909 -3.03900909 45 -3.31460909 -3.05480909 46 -3.72790909 -3.31460909 47 -3.54050909 -3.72790909 48 -3.43546970 -3.54050909 49 -2.79476970 -3.43546970 50 -3.09456970 -2.79476970 51 -4.25296970 -3.09456970 52 -5.66526970 -4.25296970 53 -3.57766970 -5.66526970 54 -3.34816970 -3.57766970 55 -1.64586970 -3.34816970 56 -1.56366970 -1.64586970 57 -2.79146970 -1.56366970 58 -2.84676970 -2.79146970 59 -3.10336970 -2.84676970 60 -2.58933030 -3.10336970 61 -2.60963030 -2.58933030 62 -4.25643030 -2.60963030 63 -3.86083030 -4.25643030 64 -6.01413030 -3.86083030 65 -2.45053030 -6.01413030 66 -3.05303030 -2.45053030 67 -1.93673030 -3.05303030 68 -2.86553030 -1.93673030 69 -4.14433030 -2.86553030 70 -2.91163030 -4.14433030 71 -1.36323030 -2.91163030 72 -1.74619091 -1.36323030 73 -2.40649091 -1.74619091 74 -0.91429091 -2.40649091 75 -1.14869091 -0.91429091 76 -3.41799091 -1.14869091 77 -1.70039091 -3.41799091 78 -2.52189091 -1.70039091 79 -2.87559091 -2.52189091 80 -3.14539091 -2.87559091 81 -0.56419091 -3.14539091 82 -0.45949091 -0.56419091 83 -2.03309091 -0.45949091 84 0.59394848 -2.03309091 85 0.53864848 0.59394848 86 2.33284848 0.53864848 87 1.86744848 2.33284848 88 0.18014848 1.86744848 89 -0.52525152 0.18014848 90 0.54224848 -0.52525152 91 1.73454848 0.54224848 92 3.29274848 1.73454848 93 2.52894848 3.29274848 94 1.44064848 2.52894848 95 -0.44195152 1.44064848 96 1.50708788 -0.44195152 97 2.72778788 1.50708788 98 -0.16501212 2.72778788 99 4.46758788 -0.16501212 100 5.16828788 4.46758788 101 1.77888788 5.16828788 102 1.58438788 1.77888788 103 1.09268788 1.58438788 104 3.79288788 1.09268788 105 1.83708788 3.79288788 106 0.35378788 1.83708788 107 2.06218788 0.35378788 108 1.52322727 2.06218788 109 1.17692727 1.52322727 110 0.95712727 1.17692727 111 2.37372727 0.95712727 112 1.71442727 2.37372727 113 4.42702727 1.71442727 114 4.17252727 4.42702727 115 1.84882727 4.17252727 116 3.74802727 1.84882727 117 2.72022727 3.74802727 118 1.09892727 2.72022727 119 2.93132727 1.09892727 > 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/7tvc41292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/845b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/945b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/1045b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11iw8f1292063046.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/12bo801292063046.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/13i75u1292063046.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/14ay4f1292063046.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/15wg3l1292063046.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/16sq0u1292063046.tab") + } > > try(system("convert tmp/18vvg1292063046.ps tmp/18vvg1292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/28vvg1292063046.ps tmp/28vvg1292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/38vvg1292063046.ps tmp/38vvg1292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/404c11292063046.ps tmp/404c11292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/504c11292063046.ps tmp/504c11292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/6tvc41292063046.ps tmp/6tvc41292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/7tvc41292063046.ps tmp/7tvc41292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/845b71292063046.ps tmp/845b71292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/945b71292063046.ps tmp/945b71292063046.png",intern=TRUE)) character(0) > try(system("convert tmp/1045b71292063046.ps tmp/1045b71292063046.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.359 1.682 7.811