WS7

*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: Fri, 20 Nov 2009 09:30:09 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit.htm/, Retrieved Fri, 20 Nov 2009 17:33:07 +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/2009/Nov/20/t12587347755p4r74pktfedjit.htm/}, year = {2009}, } @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 = {2009}, 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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363 14,3 364 14,2 363 15,9 358 15,3 357 15,5 357 15,1 380 15 378 12,1 376 15,8 380 16,9 379 15,1 384 13,7 392 14,8 394 14,7 392 16 396 15,4 392 15 396 15,5 419 15,1 421 11,7 420 16,3 418 16,7 410 15 418 14,9 426 14,6 428 15,3 430 17,9 424 16,4 423 15,4 427 17,9 441 15,9 449 13,9 452 17,8 462 17,9 455 17,4 461 16,7 461 16 463 16,6 462 19,1 456 17,8 455 17,2 456 18,6 472 16,3 472 15,1 471 19,2 465 17,7 459 19,1 465 18 468 17,5 467 17,8 463 21,1 460 17,2 462 19,4 461 19,8 476 17,6 476 16,2 471 19,5 453 19,9 443 20 442 17,3

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WK>25j[t] = + 109.356227758007 + 20.1391918264263ExpBE[t] + 1.69465044197025M1[t] -2.74432326942943M2[t] -49.8616806336816M3[t] -21.2417575479279M4[t] -23.852892894042M5[t] -39.9753817012972M6[t] + 6.41948685569967M7[t] + 51.9229250373091M8[t] -28.2227069222822M9[t] -32.6366261049248M10[t] -28.9670301917116M11[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) 109.356227758007 32.210234 3.3951 0.001403 0.000702 ExpBE 20.1391918264263 1.898898 10.6057 0 0 M1 1.69465044197025 14.237024 0.119 0.905758 0.452879 M2 -2.74432326942943 14.198678 -0.1933 0.847572 0.423786 M3 -49.8616806336816 14.620873 -3.4103 0.001342 0.000671 M4 -21.2417575479279 14.189787 -1.497 0.141086 0.070543 M5 -23.852892894042 14.196697 -1.6802 0.09956 0.04978 M6 -39.9753817012972 14.378808 -2.7802 0.00779 0.003895 M7 6.41948685569967 14.180839 0.4527 0.652857 0.326428 M8 51.9229250373091 14.847002 3.4972 0.001038 0.000519 M9 -28.2227069222822 14.500221 -1.9464 0.057603 0.028801 M10 -32.6366261049248 14.541194 -2.2444 0.029552 0.014776 M11 -28.9670301917116 14.360289 -2.0172 0.04941 0.024705 Multiple Linear Regression - Regression Statistics Multiple R 0.847826266166205 R-squared 0.718809377601329 Adjusted R-squared 0.647016027201668 F-TEST (value) 10.0121999265927 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 2.5348314558471e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 22.4179352962809 Sum Squared Residuals 23620.4996785672 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 363 399.041321317873 -36.0413213178727 2 364 392.588428423832 -28.5884284238318 3 363 379.707697164505 -16.7076971645047 4 358 396.244105154403 -38.2441051544025 5 357 397.660808173574 -40.6608081735736 6 357 373.482642635748 -16.4826426357479 7 380 417.863592010102 -37.8635920101022 8 378 404.963373895075 -26.9633738950752 9 376 399.332751693261 -23.3327516932614 10 380 417.071943519688 -37.0719435196877 11 379 384.490994145334 -5.49099414533349 12 384 385.263155780048 -1.26315578004820 13 392 409.110917231087 -17.1109172310875 14 394 402.658024337045 -8.65802433704512 15 392 381.721616347147 10.2783836528527 16 396 398.258024337045 -2.25802433704512 17 392 387.591212260360 4.40878773963953 18 396 381.538319366318 14.4616806336815 19 419 419.877511192745 -0.877511192744778 20 421 396.907697164505 24.0923028354953 21 420 409.402347606475 10.5976523935254 22 418 413.044105154402 4.95589484559751 23 410 382.477074962691 27.5229250373092 24 418 409.43018597176 8.56981402824016 25 426 405.083078865802 20.9169211341978 26 428 414.741539432901 13.2584605670990 27 430 419.986080817357 10.0139191826427 28 424 418.397216163471 5.60278383652856 29 423 395.646888990931 27.353111009069 30 427 429.872379749742 -2.87237974974167 31 441 435.988864653886 5.01113534611412 32 449 441.213919182643 7.78608081735736 33 452 439.611135346114 12.3888646538859 34 462 437.211135346114 24.7888646538859 35 455 430.811135346114 24.1888646538859 36 461 445.680731259327 15.3192687406727 37 461 433.277947422799 27.7220525772009 38 463 440.922488807255 22.0775111927448 39 462 444.153111009069 17.8468889909310 40 456 446.592084720468 9.40791527953162 41 455 431.897434278498 23.1025657215015 42 456 443.96981402824 12.0301859717598 43 472 444.044541384456 27.9554586155436 44 472 465.380949374354 6.61905062564574 45 471 467.806003903111 3.19399609688900 46 465 433.183296980829 31.8167030191712 47 459 465.047761451039 -6.04776145103894 48 465 471.861680633682 -6.86168063368154 49 468 463.486735162439 4.51326483756139 50 467 465.089518998967 1.91048100103314 51 463 484.431494661922 -21.4314946619217 52 460 434.508569624613 25.4914303753875 53 462 476.203656296636 -14.2036562966364 54 461 468.136844219952 -7.13684421995179 55 476 470.225490758811 5.7745092411893 56 476 487.534060383423 -11.5340603834232 57 471 473.847761451039 -2.84776145103891 58 453 477.489518998967 -24.4895189989668 59 443 483.173034094823 -40.1730340948226 60 442 457.764246355183 -15.7642463551831 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.735972928818482 0.528054142363035 0.264027071181518 17 0.981754365411545 0.0364912691769099 0.0182456345884550 18 0.97935232053374 0.0412953589325189 0.0206476794662594 19 0.98954424177301 0.0209115164539771 0.0104557582269886 20 0.996997157678044 0.00600568464391195 0.00300284232195598 21 0.997059686598547 0.0058806268029056 0.0029403134014528 22 0.998706619077193 0.00258676184561459 0.00129338092280729 23 0.99829217012879 0.00341565974242024 0.00170782987121012 24 0.997086629643525 0.00582674071295021 0.00291337035647511 25 0.998774549219954 0.00245090156009149 0.00122545078004574 26 0.998814871565743 0.00237025686851346 0.00118512843425673 27 0.99822896729288 0.00354206541424046 0.00177103270712023 28 0.998771255665196 0.00245748866960761 0.00122874433480381 29 0.999368774214136 0.00126245157172747 0.000631225785863735 30 0.999721977160076 0.000556045679848304 0.000278022839924152 31 0.99995307931428 9.38413714405145e-05 4.69206857202573e-05 32 0.999981862053227 3.62758935469469e-05 1.81379467734735e-05 33 0.999990947865985 1.81042680293711e-05 9.05213401468556e-06 34 0.999977709525403 4.45809491942825e-05 2.22904745971412e-05 35 0.999930367566821 0.000139264866357063 6.96324331785316e-05 36 0.999839742177451 0.000320515645097844 0.000160257822548922 37 0.999618958666454 0.000762082667091595 0.000381041333545797 38 0.998866512507234 0.00226697498553168 0.00113348749276584 39 0.996575113579644 0.00684977284071161 0.00342488642035580 40 0.99073548113313 0.0185290377337405 0.00926451886687027 41 0.978731480712336 0.0425370385753285 0.0212685192876642 42 0.951764120138474 0.096471759723052 0.048235879861526 43 0.904600093561522 0.190799812876955 0.0953999064384775 44 0.80993492385613 0.380130152287739 0.190065076143869 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.689655172413793 NOK 5% type I error level 25 0.862068965517241 NOK 10% type I error level 26 0.896551724137931 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/10vzbp1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/10vzbp1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/1jpuq1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/1jpuq1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/2u1ey1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/2u1ey1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/3sz5m1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/3sz5m1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/45k4l1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/45k4l1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/5e05n1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/5e05n1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/6p5ky1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/6p5ky1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/7sxcj1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/7sxcj1258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/833m31258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/833m31258734605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/90rma1258734605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587347755p4r74pktfedjit/90rma1258734605.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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