Multiple Regression met monthly dummies, lineaire trend en een autoregressie van 2 lags

*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 11:33:52 -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/t1258742098c6lqiq8uwt6yzfg.htm/, Retrieved Fri, 20 Nov 2009 19:35:10 +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/t1258742098c6lqiq8uwt6yzfg.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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7.9 9.1 7.6 7.5 7.9 9 7.9 7.6 8.1 9.3 7.9 7.9 8.2 9.9 8.1 7.9 8 9.8 8.2 8.1 7.5 9.3 8 8.2 6.8 8.3 7.5 8 6.5 8 6.8 7.5 6.6 8.5 6.5 6.8 7.6 10.4 6.6 6.5 8 11.1 7.6 6.6 8.1 10.9 8 7.6 7.7 10 8.1 8 7.5 9.2 7.7 8.1 7.6 9.2 7.5 7.7 7.8 9.5 7.6 7.5 7.8 9.6 7.8 7.6 7.8 9.5 7.8 7.8 7.5 9.1 7.8 7.8 7.5 8.9 7.5 7.8 7.1 9 7.5 7.5 7.5 10.1 7.1 7.5 7.5 10.3 7.5 7.1 7.6 10.2 7.5 7.5 7.7 9.6 7.6 7.5 7.7 9.2 7.7 7.6 7.9 9.3 7.7 7.7 8.1 9.4 7.9 7.7 8.2 9.4 8.1 7.9 8.2 9.2 8.2 8.1 8.2 9 8.2 8.2 7.9 9 8.2 8.2 7.3 9 7.9 8.2 6.9 9.8 7.3 7.9 6.6 10 6.9 7.3 6.7 9.8 6.6 6.9 6.9 9.3 6.7 6.6 7 9 6.9 6.7 7.1 9 7 6.9 7.2 9.1 7.1 7 7.1 9.1 7.2 7.1 6.9 9.1 7.1 7.2 7 9.2 6.9 7.1 6.8 8.8 7 6.9 6.4 8.3 6.8 7 6.7 8.4 6.4 6.8 6.6 8.1 6.7 6.4 6.4 7.7 6.6 6.7 6.3 7.9 6.4 6.6 6.2 7.9 6.3 6.4 6.5 8 6.2 6.3 6.8 7.9 6.5 6.2 6.8 7.6 6.8 6.5 6.4 7.1 6.8 6.8 6.1 6.8 6.4 6.8 5.8 6.5 6.1 6.4 6.1 6.9 5.8 6.1 7.2 8.2 6.1 5.8 7.3 8.7 7.2 6.1 6.9 8.3 7.3 7.2 6.1 7.9 6.9 7.3 5.8 7.5 6.1 6.9 6.2 7.8 etc...

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 Y[t] = + 1.10312509344711 + 0.104087521816943X[t] + 1.38413536249161Y1[t] -0.67705851157981Y2[t] + 0.0212423842191513M1[t] + 0.112739859643006M2[t] + 0.35310377017092M3[t] + 0.271985478125367M4[t] + 0.0747982571942579M5[t] + 0.0628831265365121M6[t] + 0.123428674625584M7[t] + 0.142214073231639M8[t] + 0.148011045842880M9[t] + 0.540049045509759M10[t] -0.265589260227732M11[t] -0.00122112624684944t + 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) 1.10312509344711 0.61133 1.8045 0.076841 0.038421 X 0.104087521816943 0.056235 1.8509 0.069754 0.034877 Y1 1.38413536249161 0.107835 12.8356 0 0 Y2 -0.67705851157981 0.103939 -6.514 0 0 M1 0.0212423842191513 0.137612 0.1544 0.877909 0.438954 M2 0.112739859643006 0.14153 0.7966 0.429248 0.214624 M3 0.35310377017092 0.138677 2.5462 0.013832 0.006916 M4 0.271985478125367 0.13833 1.9662 0.054522 0.027261 M5 0.0747982571942579 0.141903 0.5271 0.600317 0.300159 M6 0.0628831265365121 0.143958 0.4368 0.664018 0.332009 M7 0.123428674625584 0.149545 0.8254 0.412867 0.206433 M8 0.142214073231639 0.152619 0.9318 0.355653 0.177826 M9 0.148011045842880 0.146823 1.0081 0.317991 0.158996 M10 0.540049045509759 0.144237 3.7442 0.000447 0.000224 M11 -0.265589260227732 0.148222 -1.7918 0.078869 0.039435 t -0.00122112624684944 0.002247 -0.5434 0.589166 0.294583 Multiple Linear Regression - Regression Statistics Multiple R 0.955139201380093 R-squared 0.912290894013003 Adjusted R-squared 0.88746756212989 F-TEST (value) 36.7513474141497 F-TEST (DF numerator) 15 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.221380152278365 Sum Squared Residuals 2.59748610660797 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.9 7.51183271804123 0.388167281958774 2 7.9 7.93923507262606 -0.039235072626056 3 8.1 8.00648655997826 0.0935134400217406 4 8.2 8.26342672727434 -0.0634267272743444 5 8 8.05761146184789 -0.0576114618478894 6 7.5 7.64789852037852 -0.147898520378522 7 6.8 7.04647944147396 -0.246479441473960 8 6.5 6.40245195933386 0.0975480406661404 9 6.6 6.51777191596511 0.0822280840348927 10 7.6 7.44788617056043 0.152113829439569 11 8 8.03031751518158 -0.0303175151815784 12 8.1 8.1504637782159 -0.0504637782159069 13 7.7 7.9443963981702 -0.244396398170196 14 7.5 7.33004273373902 0.169957266260976 15 7.6 7.56318185015369 0.0368181498463102 16 7.8 7.7858939269715 0.0141060730285068 17 7.8 7.80701555331557 -0.00701555331556997 18 7.8 7.64805884191332 0.151941158086682 19 7.5 7.66574825502876 -0.165748255028764 20 7.5 7.2472544142771 0.252745585722902 21 7.1 7.46535656629713 -0.365356566297127 22 7.5 7.41701556871915 0.0829844312808508 23 7.5 7.45545119072677 0.0445488092732339 24 7.6 7.43858716789403 0.16141283210597 25 7.7 7.53456944902533 0.165430550974674 26 7.7 7.65391847456674 0.0460815254332645 27 7.9 7.83576415987151 0.0642358401284878 28 8.1 8.04066056625913 0.0593394337408725 29 8.2 7.98366758926353 0.216332410736472 30 8.2 7.95271566192874 0.247284338071257 31 8.2 7.9235167282496 0.276483271750404 32 7.9 7.9410810006088 -0.0410810006088001 33 7.3 7.53041623822571 -0.230416238225711 34 6.9 7.37713946507827 -0.477139465078271 35 6.6 6.44367849940856 0.156321500591436 36 6.7 6.5428119249105 0.157188075089503 37 6.9 6.85232051169743 0.0476794883025676 38 7 7.1204918256697 -0.120491825669696 39 7.1 7.36263644388396 -0.262636443883958 40 7.2 7.36141346286443 -0.16141346286443 41 7.1 7.23371280077765 -0.133712800777653 42 6.9 7.01445715646591 -0.114457156465914 43 7 6.87506910914949 0.124930890850509 44 6.8 7.12482361134704 -0.324823611347042 45 6.4 6.73282277314666 -0.332822773146659 46 6.7 6.7158059560677 -0.0158059560677022 47 6.6 6.56378428091769 0.0362157190823141 48 6.4 6.44498631644869 -0.0449863164486863 49 6.3 6.27670385744404 0.0232961425559623 50 6.2 6.36397837268784 -0.163978372687843 51 6.5 6.54282222405942 -0.0428222240594221 52 6.8 6.93302051349079 -0.133020513490789 53 6.8 6.91550896504129 -0.115508965041287 54 6.4 6.64721139375428 -0.247211393754277 55 6.1 6.12165541405477 -0.0216554140547755 56 5.8 5.96357622575334 -0.163576225753338 57 6.1 5.79766402557097 0.302335974429033 58 7.2 6.94215283957445 0.257847160425553 59 7.3 7.5067685137654 -0.206768513765406 60 6.9 7.12315081253088 -0.22315081253088 61 6.1 6.48017706562178 -0.380177065621782 62 5.8 5.69233352071065 0.107666479289354 63 6.2 6.08910876205316 0.110891237946842 64 7.1 6.81558480313982 0.284415196860185 65 7.7 7.60248362975407 0.0975163702459272 66 7.9 7.78965842555923 0.110341574440774 67 7.7 7.66753105204341 0.0324689479565864 68 7.4 7.22081278867986 0.179187211320137 69 7.5 6.95596848079443 0.544031519205571 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.202899351740548 0.405798703481097 0.797100648259452 20 0.576239056456999 0.847521887086002 0.423760943543001 21 0.460977090563427 0.921954181126854 0.539022909436573 22 0.391999439979005 0.783998879958011 0.608000560020995 23 0.277031587419743 0.554063174839485 0.722968412580257 24 0.190832853924902 0.381665707849803 0.809167146075098 25 0.132296210452335 0.264592420904669 0.867703789547665 26 0.0865137338034053 0.173027467606811 0.913486266196595 27 0.0531462432634167 0.106292486526833 0.946853756736583 28 0.0315110228985738 0.0630220457971476 0.968488977101426 29 0.032849346011344 0.065698692022688 0.967150653988656 30 0.0439709367977439 0.0879418735954878 0.956029063202256 31 0.191132604181237 0.382265208362474 0.808867395818763 32 0.225796422560088 0.451592845120176 0.774203577439912 33 0.160573794275787 0.321147588551574 0.839426205724213 34 0.38449939485368 0.76899878970736 0.61550060514632 35 0.324587537162582 0.649175074325163 0.675412462837418 36 0.321397837265785 0.64279567453157 0.678602162734215 37 0.443750385441205 0.88750077088241 0.556249614558795 38 0.482047619154762 0.964095238309523 0.517952380845238 39 0.491668280847826 0.98333656169565 0.508331719152174 40 0.417141671452322 0.834283342904645 0.582858328547678 41 0.343566164124738 0.687132328249476 0.656433835875262 42 0.27385840173725 0.5477168034745 0.72614159826275 43 0.334955275969812 0.669910551939623 0.665044724030188 44 0.337794932280802 0.675589864561604 0.662205067719198 45 0.413076891786667 0.826153783573334 0.586923108213333 46 0.613451871872878 0.773096256254243 0.386548128127122 47 0.485704198468398 0.971408396936796 0.514295801531602 48 0.654498430443604 0.691003139112791 0.345501569556396 49 0.994394810455065 0.0112103790898699 0.00560518954493494 50 0.9956530471557 0.00869390568859886 0.00434695284429943 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.03125 NOK 5% type I error level 2 0.0625 NOK 10% type I error level 5 0.15625 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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