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Type 'q()' to quit R. > x <- array(list(32.68 + ,10967.87 + ,0 + ,31.54 + ,10433.56 + ,0 + ,32.43 + ,10665.78 + ,0 + ,26.54 + ,10666.71 + ,0 + ,25.85 + ,10682.74 + ,0 + ,27.6 + ,10777.22 + ,0 + ,25.71 + ,10052.6 + ,0 + ,25.38 + ,10213.97 + ,0 + ,28.57 + ,10546.82 + ,0 + ,27.64 + ,10767.2 + ,0 + ,25.36 + ,10444.5 + ,0 + ,25.9 + ,10314.68 + ,0 + ,26.29 + ,9042.56 + ,0 + ,21.74 + ,9220.75 + ,0 + ,19.2 + ,9721.84 + ,0 + ,19.32 + ,9978.53 + ,0 + ,19.82 + ,9923.81 + ,0 + ,20.36 + ,9892.56 + ,0 + ,24.31 + ,10500.98 + ,0 + ,25.97 + ,10179.35 + ,0 + ,25.61 + ,10080.48 + ,0 + ,24.67 + ,9492.44 + ,0 + ,25.59 + ,8616.49 + ,0 + ,26.09 + ,8685.4 + ,0 + ,28.37 + ,8160.67 + ,0 + ,27.34 + ,8048.1 + ,0 + ,24.46 + ,8641.21 + ,0 + ,27.46 + ,8526.63 + ,0 + ,30.23 + ,8474.21 + ,0 + ,32.33 + ,7916.13 + ,0 + ,29.87 + ,7977.64 + ,0 + ,24.87 + ,8334.59 + ,0 + ,25.48 + ,8623.36 + ,0 + ,27.28 + ,9098.03 + ,0 + ,28.24 + ,9154.34 + ,0 + ,29.58 + ,9284.73 + ,0 + ,26.95 + ,9492.49 + ,0 + ,29.08 + ,9682.35 + ,0 + ,28.76 + ,9762.12 + ,0 + ,29.59 + ,10124.63 + ,0 + ,30.7 + ,10540.05 + ,0 + ,30.52 + ,10601.61 + ,0 + ,32.67 + ,10323.73 + ,0 + ,33.19 + ,10418.4 + ,0 + ,37.13 + ,10092.96 + ,0 + ,35.54 + ,10364.91 + ,0 + ,37.75 + ,10152.09 + ,0 + ,41.84 + ,10032.8 + ,0 + ,42.94 + ,10204.59 + ,0 + ,49.14 + ,10001.6 + ,0 + ,44.61 + ,10411.75 + ,0 + ,40.22 + ,10673.38 + ,0 + ,44.23 + ,10539.51 + ,0 + ,45.85 + ,10723.78 + ,0 + ,53.38 + ,10682.06 + ,0 + ,53.26 + ,10283.19 + ,0 + ,51.8 + ,10377.18 + ,0 + ,55.3 + ,10486.64 + ,0 + ,57.81 + ,10545.38 + ,0 + ,63.96 + ,10554.27 + ,0 + ,63.77 + ,10532.54 + ,0 + ,59.15 + ,10324.31 + ,0 + ,56.12 + ,10695.25 + ,0 + ,57.42 + ,10827.81 + ,0 + ,63.52 + ,10872.48 + ,0 + ,61.71 + ,10971.19 + ,0 + ,63.01 + ,11145.65 + ,0 + ,68.18 + ,11234.68 + ,0 + ,72.03 + ,11333.88 + ,0 + ,69.75 + ,10997.97 + ,0 + ,74.41 + ,11036.89 + ,0 + ,74.33 + ,11257.35 + ,0 + ,64.24 + ,11533.59 + ,0 + ,60.03 + ,11963.12 + ,0 + ,59.44 + ,12185.15 + ,0 + ,62.5 + ,12377.62 + ,0 + ,55.04 + ,12512.89 + ,0 + ,58.34 + ,12631.48 + ,0 + ,61.92 + ,12268.53 + ,0 + ,67.65 + ,12754.8 + ,0 + ,67.68 + ,13407.75 + ,0 + ,70.3 + ,13480.21 + ,0 + ,75.26 + ,13673.28 + ,1 + ,71.44 + ,13239.71 + ,1 + ,76.36 + ,13557.69 + ,1 + ,81.71 + ,13901.28 + ,1 + ,92.6 + ,13200.58 + ,1 + ,90.6 + ,13406.97 + ,1 + ,92.23 + ,12538.12 + ,1 + ,94.09 + ,12419.57 + ,1 + ,102.79 + ,12193.88 + ,1 + ,109.65 + ,12656.63 + ,1 + ,124.05 + ,12812.48 + ,1 + ,132.69 + ,12056.67 + ,1 + ,135.81 + ,11322.38 + ,1 + ,116.07 + ,11530.75 + ,1 + ,101.42 + ,11114.08 + ,1 + ,75.73 + ,9181.73 + ,1 + ,55.48 + ,8614.55 + ,1) + ,dim=c(3 + ,99) + ,dimnames=list(c('Olieprijs' + ,'DowJones' + ,'Dummy(kredietcrisis)') + ,1:99)) > y <- array(NA,dim=c(3,99),dimnames=list(c('Olieprijs','DowJones','Dummy(kredietcrisis)'),1:99)) > 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 Olieprijs DowJones Dummy(kredietcrisis) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 32.68 10967.87 0 1 0 0 0 0 0 0 0 0 0 0 2 31.54 10433.56 0 0 1 0 0 0 0 0 0 0 0 0 3 32.43 10665.78 0 0 0 1 0 0 0 0 0 0 0 0 4 26.54 10666.71 0 0 0 0 1 0 0 0 0 0 0 0 5 25.85 10682.74 0 0 0 0 0 1 0 0 0 0 0 0 6 27.60 10777.22 0 0 0 0 0 0 1 0 0 0 0 0 7 25.71 10052.60 0 0 0 0 0 0 0 1 0 0 0 0 8 25.38 10213.97 0 0 0 0 0 0 0 0 1 0 0 0 9 28.57 10546.82 0 0 0 0 0 0 0 0 0 1 0 0 10 27.64 10767.20 0 0 0 0 0 0 0 0 0 0 1 0 11 25.36 10444.50 0 0 0 0 0 0 0 0 0 0 0 1 12 25.90 10314.68 0 0 0 0 0 0 0 0 0 0 0 0 13 26.29 9042.56 0 1 0 0 0 0 0 0 0 0 0 0 14 21.74 9220.75 0 0 1 0 0 0 0 0 0 0 0 0 15 19.20 9721.84 0 0 0 1 0 0 0 0 0 0 0 0 16 19.32 9978.53 0 0 0 0 1 0 0 0 0 0 0 0 17 19.82 9923.81 0 0 0 0 0 1 0 0 0 0 0 0 18 20.36 9892.56 0 0 0 0 0 0 1 0 0 0 0 0 19 24.31 10500.98 0 0 0 0 0 0 0 1 0 0 0 0 20 25.97 10179.35 0 0 0 0 0 0 0 0 1 0 0 0 21 25.61 10080.48 0 0 0 0 0 0 0 0 0 1 0 0 22 24.67 9492.44 0 0 0 0 0 0 0 0 0 0 1 0 23 25.59 8616.49 0 0 0 0 0 0 0 0 0 0 0 1 24 26.09 8685.40 0 0 0 0 0 0 0 0 0 0 0 0 25 28.37 8160.67 0 1 0 0 0 0 0 0 0 0 0 0 26 27.34 8048.10 0 0 1 0 0 0 0 0 0 0 0 0 27 24.46 8641.21 0 0 0 1 0 0 0 0 0 0 0 0 28 27.46 8526.63 0 0 0 0 1 0 0 0 0 0 0 0 29 30.23 8474.21 0 0 0 0 0 1 0 0 0 0 0 0 30 32.33 7916.13 0 0 0 0 0 0 1 0 0 0 0 0 31 29.87 7977.64 0 0 0 0 0 0 0 1 0 0 0 0 32 24.87 8334.59 0 0 0 0 0 0 0 0 1 0 0 0 33 25.48 8623.36 0 0 0 0 0 0 0 0 0 1 0 0 34 27.28 9098.03 0 0 0 0 0 0 0 0 0 0 1 0 35 28.24 9154.34 0 0 0 0 0 0 0 0 0 0 0 1 36 29.58 9284.73 0 0 0 0 0 0 0 0 0 0 0 0 37 26.95 9492.49 0 1 0 0 0 0 0 0 0 0 0 0 38 29.08 9682.35 0 0 1 0 0 0 0 0 0 0 0 0 39 28.76 9762.12 0 0 0 1 0 0 0 0 0 0 0 0 40 29.59 10124.63 0 0 0 0 1 0 0 0 0 0 0 0 41 30.70 10540.05 0 0 0 0 0 1 0 0 0 0 0 0 42 30.52 10601.61 0 0 0 0 0 0 1 0 0 0 0 0 43 32.67 10323.73 0 0 0 0 0 0 0 1 0 0 0 0 44 33.19 10418.40 0 0 0 0 0 0 0 0 1 0 0 0 45 37.13 10092.96 0 0 0 0 0 0 0 0 0 1 0 0 46 35.54 10364.91 0 0 0 0 0 0 0 0 0 0 1 0 47 37.75 10152.09 0 0 0 0 0 0 0 0 0 0 0 1 48 41.84 10032.80 0 0 0 0 0 0 0 0 0 0 0 0 49 42.94 10204.59 0 1 0 0 0 0 0 0 0 0 0 0 50 49.14 10001.60 0 0 1 0 0 0 0 0 0 0 0 0 51 44.61 10411.75 0 0 0 1 0 0 0 0 0 0 0 0 52 40.22 10673.38 0 0 0 0 1 0 0 0 0 0 0 0 53 44.23 10539.51 0 0 0 0 0 1 0 0 0 0 0 0 54 45.85 10723.78 0 0 0 0 0 0 1 0 0 0 0 0 55 53.38 10682.06 0 0 0 0 0 0 0 1 0 0 0 0 56 53.26 10283.19 0 0 0 0 0 0 0 0 1 0 0 0 57 51.80 10377.18 0 0 0 0 0 0 0 0 0 1 0 0 58 55.30 10486.64 0 0 0 0 0 0 0 0 0 0 1 0 59 57.81 10545.38 0 0 0 0 0 0 0 0 0 0 0 1 60 63.96 10554.27 0 0 0 0 0 0 0 0 0 0 0 0 61 63.77 10532.54 0 1 0 0 0 0 0 0 0 0 0 0 62 59.15 10324.31 0 0 1 0 0 0 0 0 0 0 0 0 63 56.12 10695.25 0 0 0 1 0 0 0 0 0 0 0 0 64 57.42 10827.81 0 0 0 0 1 0 0 0 0 0 0 0 65 63.52 10872.48 0 0 0 0 0 1 0 0 0 0 0 0 66 61.71 10971.19 0 0 0 0 0 0 1 0 0 0 0 0 67 63.01 11145.65 0 0 0 0 0 0 0 1 0 0 0 0 68 68.18 11234.68 0 0 0 0 0 0 0 0 1 0 0 0 69 72.03 11333.88 0 0 0 0 0 0 0 0 0 1 0 0 70 69.75 10997.97 0 0 0 0 0 0 0 0 0 0 1 0 71 74.41 11036.89 0 0 0 0 0 0 0 0 0 0 0 1 72 74.33 11257.35 0 0 0 0 0 0 0 0 0 0 0 0 73 64.24 11533.59 0 1 0 0 0 0 0 0 0 0 0 0 74 60.03 11963.12 0 0 1 0 0 0 0 0 0 0 0 0 75 59.44 12185.15 0 0 0 1 0 0 0 0 0 0 0 0 76 62.50 12377.62 0 0 0 0 1 0 0 0 0 0 0 0 77 55.04 12512.89 0 0 0 0 0 1 0 0 0 0 0 0 78 58.34 12631.48 0 0 0 0 0 0 1 0 0 0 0 0 79 61.92 12268.53 0 0 0 0 0 0 0 1 0 0 0 0 80 67.65 12754.80 0 0 0 0 0 0 0 0 1 0 0 0 81 67.68 13407.75 0 0 0 0 0 0 0 0 0 1 0 0 82 70.30 13480.21 0 0 0 0 0 0 0 0 0 0 1 0 83 75.26 13673.28 1 0 0 0 0 0 0 0 0 0 0 1 84 71.44 13239.71 1 0 0 0 0 0 0 0 0 0 0 0 85 76.36 13557.69 1 1 0 0 0 0 0 0 0 0 0 0 86 81.71 13901.28 1 0 1 0 0 0 0 0 0 0 0 0 87 92.60 13200.58 1 0 0 1 0 0 0 0 0 0 0 0 88 90.60 13406.97 1 0 0 0 1 0 0 0 0 0 0 0 89 92.23 12538.12 1 0 0 0 0 1 0 0 0 0 0 0 90 94.09 12419.57 1 0 0 0 0 0 1 0 0 0 0 0 91 102.79 12193.88 1 0 0 0 0 0 0 1 0 0 0 0 92 109.65 12656.63 1 0 0 0 0 0 0 0 1 0 0 0 93 124.05 12812.48 1 0 0 0 0 0 0 0 0 1 0 0 94 132.69 12056.67 1 0 0 0 0 0 0 0 0 0 1 0 95 135.81 11322.38 1 0 0 0 0 0 0 0 0 0 0 1 96 116.07 11530.75 1 0 0 0 0 0 0 0 0 0 0 0 97 101.42 11114.08 1 1 0 0 0 0 0 0 0 0 0 0 98 75.73 9181.73 1 0 1 0 0 0 0 0 0 0 0 0 99 55.48 8614.55 1 0 0 1 0 0 0 0 0 0 0 0 t 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) DowJones `Dummy(kredietcrisis)` -11.45403 0.00305 21.81492 M1 M2 M3 -1.03364 -4.02079 -7.44361 M4 M5 M6 -5.44688 -4.81219 -4.15899 M7 M8 M9 -1.55249 -0.64758 1.36864 M10 M11 t 2.37192 1.91168 0.55159 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -28.3170 -6.2743 -0.2689 6.2647 36.6057 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -11.454026 10.479944 -1.093 0.27754 DowJones 0.003050 0.001051 2.901 0.00475 ** `Dummy(kredietcrisis)` 21.814920 3.952547 5.519 3.71e-07 *** M1 -1.033640 5.316734 -0.194 0.84632 M2 -4.020791 5.318398 -0.756 0.45175 M3 -7.443615 5.314612 -1.401 0.16502 M4 -5.446881 5.505062 -0.989 0.32529 M5 -4.812189 5.496302 -0.876 0.38378 M6 -4.158990 5.492622 -0.757 0.45105 M7 -1.552493 5.485305 -0.283 0.77785 M8 -0.647575 5.491312 -0.118 0.90641 M9 1.368635 5.502356 0.249 0.80417 M10 2.371917 5.496506 0.432 0.66719 M11 1.911681 5.467013 0.350 0.72746 t 0.551587 0.058264 9.467 6.82e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 10.93 on 84 degrees of freedom Multiple R-squared: 0.8635, Adjusted R-squared: 0.8408 F-statistic: 37.96 on 14 and 84 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,] 1.528241e-02 3.056481e-02 0.9847176 [2,] 6.146415e-03 1.229283e-02 0.9938536 [3,] 2.875303e-03 5.750606e-03 0.9971247 [4,] 7.338861e-04 1.467772e-03 0.9992661 [5,] 3.393310e-04 6.786621e-04 0.9996607 [6,] 4.356374e-04 8.712748e-04 0.9995644 [7,] 2.433711e-04 4.867421e-04 0.9997566 [8,] 1.511191e-04 3.022382e-04 0.9998489 [9,] 1.086021e-04 2.172042e-04 0.9998914 [10,] 4.344605e-05 8.689210e-05 0.9999566 [11,] 5.623740e-05 1.124748e-04 0.9999438 [12,] 1.025251e-04 2.050502e-04 0.9998975 [13,] 1.122700e-04 2.245399e-04 0.9998877 [14,] 5.189692e-05 1.037938e-04 0.9999481 [15,] 1.852943e-05 3.705885e-05 0.9999815 [16,] 6.636308e-06 1.327262e-05 0.9999934 [17,] 2.844762e-06 5.689524e-06 0.9999972 [18,] 1.674650e-06 3.349301e-06 0.9999983 [19,] 8.565713e-07 1.713143e-06 0.9999991 [20,] 2.813970e-07 5.627939e-07 0.9999997 [21,] 1.113071e-07 2.226141e-07 0.9999999 [22,] 4.535237e-08 9.070474e-08 1.0000000 [23,] 1.992355e-08 3.984710e-08 1.0000000 [24,] 7.577319e-09 1.515464e-08 1.0000000 [25,] 2.287237e-09 4.574473e-09 1.0000000 [26,] 8.605437e-10 1.721087e-09 1.0000000 [27,] 4.381293e-10 8.762587e-10 1.0000000 [28,] 4.584199e-10 9.168399e-10 1.0000000 [29,] 3.599420e-10 7.198839e-10 1.0000000 [30,] 3.806813e-10 7.613626e-10 1.0000000 [31,] 6.513570e-10 1.302714e-09 1.0000000 [32,] 4.582148e-10 9.164297e-10 1.0000000 [33,] 2.954588e-09 5.909176e-09 1.0000000 [34,] 3.288794e-09 6.577588e-09 1.0000000 [35,] 1.439073e-09 2.878147e-09 1.0000000 [36,] 9.526527e-10 1.905305e-09 1.0000000 [37,] 5.323484e-10 1.064697e-09 1.0000000 [38,] 1.279850e-09 2.559701e-09 1.0000000 [39,] 4.084116e-09 8.168232e-09 1.0000000 [40,] 6.294338e-09 1.258868e-08 1.0000000 [41,] 2.206697e-08 4.413394e-08 1.0000000 [42,] 4.726511e-08 9.453022e-08 1.0000000 [43,] 1.301782e-07 2.603564e-07 0.9999999 [44,] 1.495389e-07 2.990779e-07 0.9999999 [45,] 1.036927e-07 2.073855e-07 0.9999999 [46,] 7.373186e-08 1.474637e-07 0.9999999 [47,] 4.088034e-08 8.176069e-08 1.0000000 [48,] 4.690830e-08 9.381661e-08 1.0000000 [49,] 2.813872e-08 5.627744e-08 1.0000000 [50,] 1.255184e-08 2.510367e-08 1.0000000 [51,] 8.627264e-09 1.725453e-08 1.0000000 [52,] 6.989530e-09 1.397906e-08 1.0000000 [53,] 5.200941e-09 1.040188e-08 1.0000000 [54,] 4.262422e-09 8.524843e-09 1.0000000 [55,] 4.875527e-09 9.751055e-09 1.0000000 [56,] 7.928601e-09 1.585720e-08 1.0000000 [57,] 1.161545e-07 2.323090e-07 0.9999999 [58,] 5.532355e-06 1.106471e-05 0.9999945 [59,] 8.083218e-05 1.616644e-04 0.9999192 [60,] 1.500857e-04 3.001714e-04 0.9998499 [61,] 2.371974e-04 4.743948e-04 0.9997628 [62,] 4.654230e-04 9.308459e-04 0.9995346 [63,] 4.563980e-03 9.127960e-03 0.9954360 [64,] 6.943132e-03 1.388626e-02 0.9930569 > postscript(file="/var/www/html/freestat/rcomp/tmp/12zfm1229554134.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2ybah1229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/freestat/rcomp/tmp/3vvd91229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4plah1229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5spw71229554134.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 = 99 Frequency = 1 1 2 3 4 5 6 11.1663228 14.0914227 17.1444357 8.7032781 6.7781110 7.0351798 7 8 9 10 11 12 4.1970383 1.9183874 1.5254655 -1.6315175 -3.0186995 -0.7226818 13 14 15 16 17 18 4.0290763 1.3711971 0.1742117 -3.0369614 -3.5563555 -4.1258360 19 20 21 22 23 24 -5.1894763 -4.0050762 -6.6313405 -7.3328082 -3.8326911 -2.1827592 25 26 27 28 29 30 2.1796125 3.9284919 2.1108643 2.9119886 4.6555799 7.2528230 31 32 33 34 35 36 1.4471461 -6.0979837 -8.9364706 -10.1389860 -9.4420709 -7.1396404 37 38 39 40 41 42 -9.9212131 -5.9346833 -3.6267291 -6.4506315 -7.7938563 -9.3663883 43 44 45 46 47 48 -9.5269952 -10.7522248 -8.3874971 -12.3617580 -9.5940515 -3.7801482 49 50 51 52 53 54 -2.7220197 6.5326220 3.6229850 -4.1132541 -0.8812570 -1.0280293 55 56 57 58 59 60 3.4711242 3.1110905 -1.2033573 0.4079429 2.6474470 10.1304277 61 62 63 64 65 66 10.4887524 8.9393751 7.6493205 5.9967185 10.7742052 7.4583734 67 68 69 70 71 72 5.0682221 8.5101934 9.4898561 6.6794437 11.1293947 11.7371302 73 74 75 76 77 78 1.2867075 -1.7977071 -0.1936167 -0.2689320 -9.3277567 -7.5942185 79 80 81 82 83 84 -6.0653793 -3.2749087 -7.8040700 -6.9599273 -24.4950223 -25.6326293 85 86 87 88 89 90 -21.2003505 -14.4626657 1.4355622 -3.7422063 -0.6486707 0.3680959 91 92 93 94 95 96 6.5983202 10.5905220 21.9474139 31.3376104 36.6056935 17.5903010 97 98 99 4.6931118 -12.6680527 -28.3170335 > postscript(file="/var/www/html/freestat/rcomp/tmp/644281229554134.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 = 99 Frequency = 1 lag(myerror, k = 1) myerror 0 11.1663228 NA 1 14.0914227 11.1663228 2 17.1444357 14.0914227 3 8.7032781 17.1444357 4 6.7781110 8.7032781 5 7.0351798 6.7781110 6 4.1970383 7.0351798 7 1.9183874 4.1970383 8 1.5254655 1.9183874 9 -1.6315175 1.5254655 10 -3.0186995 -1.6315175 11 -0.7226818 -3.0186995 12 4.0290763 -0.7226818 13 1.3711971 4.0290763 14 0.1742117 1.3711971 15 -3.0369614 0.1742117 16 -3.5563555 -3.0369614 17 -4.1258360 -3.5563555 18 -5.1894763 -4.1258360 19 -4.0050762 -5.1894763 20 -6.6313405 -4.0050762 21 -7.3328082 -6.6313405 22 -3.8326911 -7.3328082 23 -2.1827592 -3.8326911 24 2.1796125 -2.1827592 25 3.9284919 2.1796125 26 2.1108643 3.9284919 27 2.9119886 2.1108643 28 4.6555799 2.9119886 29 7.2528230 4.6555799 30 1.4471461 7.2528230 31 -6.0979837 1.4471461 32 -8.9364706 -6.0979837 33 -10.1389860 -8.9364706 34 -9.4420709 -10.1389860 35 -7.1396404 -9.4420709 36 -9.9212131 -7.1396404 37 -5.9346833 -9.9212131 38 -3.6267291 -5.9346833 39 -6.4506315 -3.6267291 40 -7.7938563 -6.4506315 41 -9.3663883 -7.7938563 42 -9.5269952 -9.3663883 43 -10.7522248 -9.5269952 44 -8.3874971 -10.7522248 45 -12.3617580 -8.3874971 46 -9.5940515 -12.3617580 47 -3.7801482 -9.5940515 48 -2.7220197 -3.7801482 49 6.5326220 -2.7220197 50 3.6229850 6.5326220 51 -4.1132541 3.6229850 52 -0.8812570 -4.1132541 53 -1.0280293 -0.8812570 54 3.4711242 -1.0280293 55 3.1110905 3.4711242 56 -1.2033573 3.1110905 57 0.4079429 -1.2033573 58 2.6474470 0.4079429 59 10.1304277 2.6474470 60 10.4887524 10.1304277 61 8.9393751 10.4887524 62 7.6493205 8.9393751 63 5.9967185 7.6493205 64 10.7742052 5.9967185 65 7.4583734 10.7742052 66 5.0682221 7.4583734 67 8.5101934 5.0682221 68 9.4898561 8.5101934 69 6.6794437 9.4898561 70 11.1293947 6.6794437 71 11.7371302 11.1293947 72 1.2867075 11.7371302 73 -1.7977071 1.2867075 74 -0.1936167 -1.7977071 75 -0.2689320 -0.1936167 76 -9.3277567 -0.2689320 77 -7.5942185 -9.3277567 78 -6.0653793 -7.5942185 79 -3.2749087 -6.0653793 80 -7.8040700 -3.2749087 81 -6.9599273 -7.8040700 82 -24.4950223 -6.9599273 83 -25.6326293 -24.4950223 84 -21.2003505 -25.6326293 85 -14.4626657 -21.2003505 86 1.4355622 -14.4626657 87 -3.7422063 1.4355622 88 -0.6486707 -3.7422063 89 0.3680959 -0.6486707 90 6.5983202 0.3680959 91 10.5905220 6.5983202 92 21.9474139 10.5905220 93 31.3376104 21.9474139 94 36.6056935 31.3376104 95 17.5903010 36.6056935 96 4.6931118 17.5903010 97 -12.6680527 4.6931118 98 -28.3170335 -12.6680527 99 NA -28.3170335 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 14.0914227 11.1663228 [2,] 17.1444357 14.0914227 [3,] 8.7032781 17.1444357 [4,] 6.7781110 8.7032781 [5,] 7.0351798 6.7781110 [6,] 4.1970383 7.0351798 [7,] 1.9183874 4.1970383 [8,] 1.5254655 1.9183874 [9,] -1.6315175 1.5254655 [10,] -3.0186995 -1.6315175 [11,] -0.7226818 -3.0186995 [12,] 4.0290763 -0.7226818 [13,] 1.3711971 4.0290763 [14,] 0.1742117 1.3711971 [15,] -3.0369614 0.1742117 [16,] -3.5563555 -3.0369614 [17,] -4.1258360 -3.5563555 [18,] -5.1894763 -4.1258360 [19,] -4.0050762 -5.1894763 [20,] -6.6313405 -4.0050762 [21,] -7.3328082 -6.6313405 [22,] -3.8326911 -7.3328082 [23,] -2.1827592 -3.8326911 [24,] 2.1796125 -2.1827592 [25,] 3.9284919 2.1796125 [26,] 2.1108643 3.9284919 [27,] 2.9119886 2.1108643 [28,] 4.6555799 2.9119886 [29,] 7.2528230 4.6555799 [30,] 1.4471461 7.2528230 [31,] -6.0979837 1.4471461 [32,] -8.9364706 -6.0979837 [33,] -10.1389860 -8.9364706 [34,] -9.4420709 -10.1389860 [35,] -7.1396404 -9.4420709 [36,] -9.9212131 -7.1396404 [37,] -5.9346833 -9.9212131 [38,] -3.6267291 -5.9346833 [39,] -6.4506315 -3.6267291 [40,] -7.7938563 -6.4506315 [41,] -9.3663883 -7.7938563 [42,] -9.5269952 -9.3663883 [43,] -10.7522248 -9.5269952 [44,] -8.3874971 -10.7522248 [45,] -12.3617580 -8.3874971 [46,] -9.5940515 -12.3617580 [47,] -3.7801482 -9.5940515 [48,] -2.7220197 -3.7801482 [49,] 6.5326220 -2.7220197 [50,] 3.6229850 6.5326220 [51,] -4.1132541 3.6229850 [52,] -0.8812570 -4.1132541 [53,] -1.0280293 -0.8812570 [54,] 3.4711242 -1.0280293 [55,] 3.1110905 3.4711242 [56,] -1.2033573 3.1110905 [57,] 0.4079429 -1.2033573 [58,] 2.6474470 0.4079429 [59,] 10.1304277 2.6474470 [60,] 10.4887524 10.1304277 [61,] 8.9393751 10.4887524 [62,] 7.6493205 8.9393751 [63,] 5.9967185 7.6493205 [64,] 10.7742052 5.9967185 [65,] 7.4583734 10.7742052 [66,] 5.0682221 7.4583734 [67,] 8.5101934 5.0682221 [68,] 9.4898561 8.5101934 [69,] 6.6794437 9.4898561 [70,] 11.1293947 6.6794437 [71,] 11.7371302 11.1293947 [72,] 1.2867075 11.7371302 [73,] -1.7977071 1.2867075 [74,] -0.1936167 -1.7977071 [75,] -0.2689320 -0.1936167 [76,] -9.3277567 -0.2689320 [77,] -7.5942185 -9.3277567 [78,] -6.0653793 -7.5942185 [79,] -3.2749087 -6.0653793 [80,] -7.8040700 -3.2749087 [81,] -6.9599273 -7.8040700 [82,] -24.4950223 -6.9599273 [83,] -25.6326293 -24.4950223 [84,] -21.2003505 -25.6326293 [85,] -14.4626657 -21.2003505 [86,] 1.4355622 -14.4626657 [87,] -3.7422063 1.4355622 [88,] -0.6486707 -3.7422063 [89,] 0.3680959 -0.6486707 [90,] 6.5983202 0.3680959 [91,] 10.5905220 6.5983202 [92,] 21.9474139 10.5905220 [93,] 31.3376104 21.9474139 [94,] 36.6056935 31.3376104 [95,] 17.5903010 36.6056935 [96,] 4.6931118 17.5903010 [97,] -12.6680527 4.6931118 [98,] -28.3170335 -12.6680527 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 14.0914227 11.1663228 2 17.1444357 14.0914227 3 8.7032781 17.1444357 4 6.7781110 8.7032781 5 7.0351798 6.7781110 6 4.1970383 7.0351798 7 1.9183874 4.1970383 8 1.5254655 1.9183874 9 -1.6315175 1.5254655 10 -3.0186995 -1.6315175 11 -0.7226818 -3.0186995 12 4.0290763 -0.7226818 13 1.3711971 4.0290763 14 0.1742117 1.3711971 15 -3.0369614 0.1742117 16 -3.5563555 -3.0369614 17 -4.1258360 -3.5563555 18 -5.1894763 -4.1258360 19 -4.0050762 -5.1894763 20 -6.6313405 -4.0050762 21 -7.3328082 -6.6313405 22 -3.8326911 -7.3328082 23 -2.1827592 -3.8326911 24 2.1796125 -2.1827592 25 3.9284919 2.1796125 26 2.1108643 3.9284919 27 2.9119886 2.1108643 28 4.6555799 2.9119886 29 7.2528230 4.6555799 30 1.4471461 7.2528230 31 -6.0979837 1.4471461 32 -8.9364706 -6.0979837 33 -10.1389860 -8.9364706 34 -9.4420709 -10.1389860 35 -7.1396404 -9.4420709 36 -9.9212131 -7.1396404 37 -5.9346833 -9.9212131 38 -3.6267291 -5.9346833 39 -6.4506315 -3.6267291 40 -7.7938563 -6.4506315 41 -9.3663883 -7.7938563 42 -9.5269952 -9.3663883 43 -10.7522248 -9.5269952 44 -8.3874971 -10.7522248 45 -12.3617580 -8.3874971 46 -9.5940515 -12.3617580 47 -3.7801482 -9.5940515 48 -2.7220197 -3.7801482 49 6.5326220 -2.7220197 50 3.6229850 6.5326220 51 -4.1132541 3.6229850 52 -0.8812570 -4.1132541 53 -1.0280293 -0.8812570 54 3.4711242 -1.0280293 55 3.1110905 3.4711242 56 -1.2033573 3.1110905 57 0.4079429 -1.2033573 58 2.6474470 0.4079429 59 10.1304277 2.6474470 60 10.4887524 10.1304277 61 8.9393751 10.4887524 62 7.6493205 8.9393751 63 5.9967185 7.6493205 64 10.7742052 5.9967185 65 7.4583734 10.7742052 66 5.0682221 7.4583734 67 8.5101934 5.0682221 68 9.4898561 8.5101934 69 6.6794437 9.4898561 70 11.1293947 6.6794437 71 11.7371302 11.1293947 72 1.2867075 11.7371302 73 -1.7977071 1.2867075 74 -0.1936167 -1.7977071 75 -0.2689320 -0.1936167 76 -9.3277567 -0.2689320 77 -7.5942185 -9.3277567 78 -6.0653793 -7.5942185 79 -3.2749087 -6.0653793 80 -7.8040700 -3.2749087 81 -6.9599273 -7.8040700 82 -24.4950223 -6.9599273 83 -25.6326293 -24.4950223 84 -21.2003505 -25.6326293 85 -14.4626657 -21.2003505 86 1.4355622 -14.4626657 87 -3.7422063 1.4355622 88 -0.6486707 -3.7422063 89 0.3680959 -0.6486707 90 6.5983202 0.3680959 91 10.5905220 6.5983202 92 21.9474139 10.5905220 93 31.3376104 21.9474139 94 36.6056935 31.3376104 95 17.5903010 36.6056935 96 4.6931118 17.5903010 97 -12.6680527 4.6931118 98 -28.3170335 -12.6680527 > 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/freestat/rcomp/tmp/73hqv1229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/80w911229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9ddbt1229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/102v4i1229554134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/freestat/rcomp/tmp/1111to1229554135.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/freestat/rcomp/tmp/12gogn1229554135.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/freestat/rcomp/tmp/13xilv1229554135.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/freestat/rcomp/tmp/14tnla1229554135.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/freestat/rcomp/tmp/15o4ad1229554135.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/freestat/rcomp/tmp/16m8v71229554135.tab") + } > > system("convert tmp/12zfm1229554134.ps tmp/12zfm1229554134.png") > system("convert tmp/2ybah1229554134.ps tmp/2ybah1229554134.png") > system("convert tmp/3vvd91229554134.ps tmp/3vvd91229554134.png") > system("convert tmp/4plah1229554134.ps tmp/4plah1229554134.png") > system("convert tmp/5spw71229554134.ps tmp/5spw71229554134.png") > system("convert tmp/644281229554134.ps tmp/644281229554134.png") > system("convert tmp/73hqv1229554134.ps tmp/73hqv1229554134.png") > system("convert tmp/80w911229554134.ps tmp/80w911229554134.png") > system("convert tmp/9ddbt1229554134.ps tmp/9ddbt1229554134.png") > system("convert tmp/102v4i1229554134.ps tmp/102v4i1229554134.png") > > > proc.time() user system elapsed 4.423 2.608 5.471