Paper: faillissementen (crisis dummy)

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 19 Dec 2010 15:04:29 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t12927709750evjv0ksfza70w4.htm/, Retrieved Sun, 19 Dec 2010 16:02:56 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t12927709750evjv0ksfza70w4.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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608 0 651 0 691 0 627 0 634 0 731 0 475 0 337 0 803 0 722 0 590 0 724 0 627 0 696 0 825 0 677 0 656 0 785 0 412 0 352 0 839 0 729 0 696 0 641 0 695 0 638 0 762 0 635 0 721 0 854 0 418 0 367 0 824 0 687 0 601 0 676 0 740 0 691 0 683 0 594 0 729 0 731 0 386 0 331 0 706 0 715 0 657 0 653 0 642 0 643 0 718 0 654 0 632 0 731 0 392 1 344 1 792 1 852 1 649 1 629 1 685 1 617 1 715 1 715 1 629 1 916 1 531 1 357 1 917 1 828 1 708 1 858 1 775 1 785 1 1.006 1 789 1 734 1 906 1 532 1 387 1 991 1 841 1 892 1 782 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation faillissement[t] = + 648.925925925926 + 36.0409407407407crisis[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 648.925925925926 22.500188 28.8409 0 0 crisis 36.0409407407407 37.650016 0.9573 0.341249 0.170624 Multiple Linear Regression - Regression Statistics Multiple R 0.105126175431771 R-squared 0.0110515127609115 Adjusted R-squared -0.00100883464444324 F-TEST (value) 0.916351112406987 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.341248721960249 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 165.341939215305 Sum Squared Residuals 2241712.46280517 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 608 648.925925925926 -40.9259259259258 2 651 648.925925925926 2.07407407407411 3 691 648.925925925926 42.0740740740741 4 627 648.925925925926 -21.9259259259259 5 634 648.925925925926 -14.9259259259259 6 731 648.925925925926 82.074074074074 7 475 648.925925925926 -173.925925925926 8 337 648.925925925926 -311.925925925926 9 803 648.925925925926 154.074074074074 10 722 648.925925925926 73.0740740740741 11 590 648.925925925926 -58.9259259259259 12 724 648.925925925926 75.0740740740741 13 627 648.925925925926 -21.9259259259259 14 696 648.925925925926 47.0740740740741 15 825 648.925925925926 176.074074074074 16 677 648.925925925926 28.0740740740741 17 656 648.925925925926 7.07407407407407 18 785 648.925925925926 136.074074074074 19 412 648.925925925926 -236.925925925926 20 352 648.925925925926 -296.925925925926 21 839 648.925925925926 190.074074074074 22 729 648.925925925926 80.074074074074 23 696 648.925925925926 47.0740740740741 24 641 648.925925925926 -7.92592592592593 25 695 648.925925925926 46.0740740740741 26 638 648.925925925926 -10.9259259259259 27 762 648.925925925926 113.074074074074 28 635 648.925925925926 -13.9259259259259 29 721 648.925925925926 72.0740740740741 30 854 648.925925925926 205.074074074074 31 418 648.925925925926 -230.925925925926 32 367 648.925925925926 -281.925925925926 33 824 648.925925925926 175.074074074074 34 687 648.925925925926 38.0740740740741 35 601 648.925925925926 -47.9259259259259 36 676 648.925925925926 27.0740740740741 37 740 648.925925925926 91.074074074074 38 691 648.925925925926 42.0740740740741 39 683 648.925925925926 34.0740740740741 40 594 648.925925925926 -54.9259259259259 41 729 648.925925925926 80.074074074074 42 731 648.925925925926 82.074074074074 43 386 648.925925925926 -262.925925925926 44 331 648.925925925926 -317.925925925926 45 706 648.925925925926 57.0740740740741 46 715 648.925925925926 66.0740740740741 47 657 648.925925925926 8.07407407407407 48 653 648.925925925926 4.07407407407407 49 642 648.925925925926 -6.92592592592593 50 643 648.925925925926 -5.92592592592593 51 718 648.925925925926 69.0740740740741 52 654 648.925925925926 5.07407407407407 53 632 648.925925925926 -16.9259259259259 54 731 648.925925925926 82.074074074074 55 392 684.966866666667 -292.966866666667 56 344 684.966866666667 -340.966866666667 57 792 684.966866666667 107.033133333333 58 852 684.966866666667 167.033133333333 59 649 684.966866666667 -35.9668666666667 60 629 684.966866666667 -55.9668666666667 61 685 684.966866666667 0.0331333333333354 62 617 684.966866666667 -67.9668666666667 63 715 684.966866666667 30.0331333333333 64 715 684.966866666667 30.0331333333333 65 629 684.966866666667 -55.9668666666667 66 916 684.966866666667 231.033133333333 67 531 684.966866666667 -153.966866666667 68 357 684.966866666667 -327.966866666667 69 917 684.966866666667 232.033133333333 70 828 684.966866666667 143.033133333333 71 708 684.966866666667 23.0331333333333 72 858 684.966866666667 173.033133333333 73 775 684.966866666667 90.0331333333333 74 785 684.966866666667 100.033133333333 75 1.006 684.966866666667 -683.960866666667 76 789 684.966866666667 104.033133333333 77 734 684.966866666667 49.0331333333333 78 906 684.966866666667 221.033133333333 79 532 684.966866666667 -152.966866666667 80 387 684.966866666667 -297.966866666667 81 991 684.966866666667 306.033133333333 82 841 684.966866666667 156.033133333333 83 892 684.966866666667 207.033133333333 84 782 684.966866666667 97.0331333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0127921778101135 0.0255843556202270 0.987207822189886 6 0.0144925102049768 0.0289850204099536 0.985507489795023 7 0.0692497721225158 0.138499544245032 0.930750227877484 8 0.328165376321932 0.656330752643863 0.671834623678068 9 0.385127952971941 0.770255905943882 0.614872047028059 10 0.316482694444583 0.632965388889166 0.683517305555417 11 0.229614721215278 0.459229442430556 0.770385278784722 12 0.181821293706304 0.363642587412608 0.818178706293696 13 0.121781846240046 0.243563692480093 0.878218153759953 14 0.0845451625126134 0.169090325025227 0.915454837487387 15 0.104607100022929 0.209214200045857 0.895392899977071 16 0.0693361243350628 0.138672248670126 0.930663875664937 17 0.0438217379909078 0.0876434759818156 0.956178262009092 18 0.0400865242255213 0.0801730484510425 0.959913475774479 19 0.0795397370027972 0.159079474005594 0.920460262997203 20 0.182149994219741 0.364299988439482 0.817850005780259 21 0.212557417656781 0.425114835313563 0.787442582343219 22 0.174278038095541 0.348556076191083 0.825721961904459 23 0.133551508957688 0.267103017915375 0.866448491042312 24 0.09723159718111 0.19446319436222 0.90276840281889 25 0.0712010172206204 0.142402034441241 0.92879898277938 26 0.0494713193312292 0.0989426386624584 0.95052868066877 27 0.0412305685408978 0.0824611370817957 0.958769431459102 28 0.0277717557997230 0.0555435115994461 0.972228244200277 29 0.0198821365044372 0.0397642730088743 0.980117863495563 30 0.0265870049528680 0.0531740099057361 0.973412995047132 31 0.0431948482143988 0.0863896964287976 0.956805151785601 32 0.087958844146407 0.175917688292814 0.912041155853593 33 0.0928994137088111 0.185798827417622 0.907100586291189 34 0.0692934103705028 0.138586820741006 0.930706589629497 35 0.0512889763488979 0.102577952697796 0.948711023651102 36 0.0364587971861893 0.0729175943723787 0.96354120281381 37 0.0283755689695084 0.0567511379390169 0.971624431030492 38 0.0198015767554292 0.0396031535108584 0.98019842324457 39 0.0134301259568529 0.0268602519137058 0.986569874043147 40 0.00920647488224509 0.0184129497644902 0.990793525117755 41 0.00662007449699896 0.0132401489939979 0.993379925503001 42 0.00475719034693566 0.00951438069387133 0.995242809653064 43 0.0099724804744302 0.0199449609488604 0.99002751952557 44 0.0306117011789627 0.0612234023579253 0.969388298821037 45 0.0219208676578642 0.0438417353157283 0.978079132342136 46 0.0156449440252696 0.0312898880505391 0.98435505597473 47 0.0103600409327999 0.0207200818655998 0.9896399590672 48 0.00670369269451756 0.0134073853890351 0.993296307305482 49 0.00425453153847611 0.00850906307695222 0.995745468461524 50 0.00264395160228847 0.00528790320457695 0.997356048397712 51 0.00170518598726900 0.00341037197453801 0.998294814012731 52 0.00100679543226356 0.00201359086452711 0.998993204567736 53 0.000601708594498681 0.00120341718899736 0.999398291405501 54 0.00036831905717073 0.00073663811434146 0.99963168094283 55 0.000433472189719403 0.000866944379438805 0.99956652781028 56 0.000777810320936211 0.00155562064187242 0.999222189679064 57 0.00183287455300933 0.00366574910601867 0.99816712544699 58 0.0030292347366671 0.0060584694733342 0.996970765263333 59 0.00187607683970506 0.00375215367941011 0.998123923160295 60 0.00114097203439685 0.00228194406879371 0.998859027965603 61 0.000673322044123371 0.00134664408824674 0.999326677955877 62 0.000396822249273843 0.000793644498547686 0.999603177750726 63 0.000228235027856020 0.000456470055712041 0.999771764972144 64 0.000125351664030686 0.000250703328061371 0.99987464833597 65 6.68244166872383e-05 0.000133648833374477 0.999933175583313 66 0.000113668649619585 0.00022733729923917 0.99988633135038 67 9.16726190202616e-05 0.000183345238040523 0.99990832738098 68 0.000443704952297813 0.000887409904595626 0.999556295047702 69 0.000629346693726953 0.00125869338745391 0.999370653306273 70 0.000455331360546579 0.000910662721093158 0.999544668639453 71 0.000221936647862719 0.000443873295725439 0.999778063352137 72 0.000178699401959209 0.000357398803918419 0.99982130059804 73 9.12707225423477e-05 0.000182541445084695 0.999908729277458 74 4.63226524423919e-05 9.26453048847837e-05 0.999953677347558 75 0.142438014481562 0.284876028963125 0.857561985518438 76 0.0927496453420138 0.185499290684028 0.907250354657986 77 0.0537663959991938 0.107532791998388 0.946233604000806 78 0.0436309355583545 0.0872618711167091 0.956369064441645 79 0.0455996574248666 0.0911993148497332 0.954400342575133 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.36 NOK 5% type I error level 39 0.52 NOK 10% type I error level 51 0.68 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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='mytable5.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='mytable6.tab') }

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