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: Mon, 23 Nov 2009 01:30:59 -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/23/t12589651132xjd6q97i1845j3.htm/, Retrieved Mon, 23 Nov 2009 09:32:06 +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/23/t12589651132xjd6q97i1845j3.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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286602 326011 283042 328282 276687 317480 277915 317539 277128 313737 277103 312276 275037 309391 270150 302950 267140 300316 264993 304035 287259 333476 291186 337698 292300 335932 288186 323931 281477 313927 282656 314485 280190 313218 280408 309664 276836 302963 275216 298989 274352 298423 271311 301631 289802 329765 290726 335083 292300 327616 278506 309119 269826 295916 265861 291413 269034 291542 264176 284678 255198 276475 253353 272566 246057 264981 235372 263290 258556 296806 260993 303598 254663 286994 250643 276427 243422 266424 247105 267153 248541 268381 245039 262522 237080 255542 237085 253158 225554 243803 226839 250741 247934 280445 248333 285257 246969 270976 245098 261076 246263 255603 255765 260376 264319 263903 268347 264291 273046 263276 273963 262572 267430 256167 271993 264221 292710 293860 295881 300713 293299 287224

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -225475.408018723 + 1.46214500504937X[t] + 16420.7919349884M1[t] + 23028.7163256820M2[t] + 30661.1473651015M3[t] + 31235.5640239155M4[t] + 31993.2453135483M5[t] + 34960.6704055155M6[t] + 37647.041692M7[t] + 39974.3973820299M8[t] + 40611.7071382829M9[t] + 31413.0352303011M10[t] + 7293.95281682754M11[t] + 1278.41807555408t + 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) -225475.408018723 24007.470271 -9.3919 0 0 X 1.46214500504937 0.067228 21.7491 0 0 M1 16420.7919349884 3124.551765 5.2554 4e-06 2e-06 M2 23028.7163256820 3561.627751 6.4658 0 0 M3 30661.1473651015 3864.592908 7.9339 0 0 M4 31235.5640239155 3803.963406 8.2113 0 0 M5 31993.2453135483 3758.640978 8.5119 0 0 M6 34960.6704055155 3842.677284 9.098 0 0 M7 37647.041692 4000.497449 9.4106 0 0 M8 39974.3973820299 4096.582564 9.758 0 0 M9 40611.7071382829 4279.017839 9.4909 0 0 M10 31413.0352303011 4047.414021 7.7613 0 0 M11 7293.95281682754 3168.837757 2.3018 0.025826 0.012913 t 1278.41807555408 87.511796 14.6085 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.970362601330797 R-squared 0.941603578061471 Adjusted R-squared 0.925451376248687 F-TEST (value) 58.2956793739531 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4957.90285228592 Sum Squared Residuals 1155297632.55713 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 268899.157232969 17702.8427670306 2 283042 280106.031005685 2935.968994315 3 276687 273222.789776115 3464.21022388462 4 277915 275161.891065781 2753.10893421868 5 277128 271638.915121770 5489.08487822956 6 277103 273748.564436915 3354.43556308533 7 275037 273495.065459386 1541.93454061418 8 270150 267683.163247447 2466.83675255327 9 267140 265747.601135954 1392.39886404620 10 264993 263265.064577305 1727.93542269529 11 287259 283471.411333044 3787.58866695619 12 291186 283629.052803089 7556.9471969112 13 292300 298746.114734714 -6446.11473471408 14 288186 289085.254995364 -899.254995364174 15 281477 283368.805479824 -1891.80547982388 16 282656 286037.517127009 -3381.51712700947 17 280190 286221.078770799 -6031.07877079874 18 280408 285270.458590375 -4862.45859037459 19 276836 279437.414273577 -2601.41427357734 20 275216 277232.623789095 -2016.62378909508 21 274352 278320.777548044 -3968.77754804426 22 271311 275091.084891815 -3780.08489181489 23 289802 293386.408125954 -3584.40812595447 24 290726 295146.560521534 -4420.56052153358 25 292300 301927.933779372 -9627.9337793724 26 278506 282768.980087222 -4262.98008722176 27 269826 272375.128700529 -2549.12870052852 28 265861 267643.924477159 -1782.92447715923 29 269034 269868.640547997 -834.640547997416 30 264176 264078.32040086 97.6795991401582 31 255198 256049.134286478 -851.134286478446 32 253353 253939.383227324 -586.38322732438 33 246057 244764.741195832 1292.25880416799 34 235372 234372.000159866 999.999840134242 35 258556 260536.587811181 -1980.58781118108 36 260993 264451.941944203 -3458.94194420296 37 254663 257873.696290906 -3210.69629090564 38 250643 250309.552488797 333.447511203453 39 243422 244594.565118261 -1172.56511826129 40 247105 247513.303561310 -408.303561310337 41 248541 251344.916992698 -2803.91699269779 42 245039 247024.052575635 -1985.05257563483 43 237080 240783.069802429 -3703.06980242881 44 237085 240903.089875975 -3818.08987597506 45 225554 229140.451185545 -3586.45118554529 46 226839 231364.55939815 -4525.55939815009 47 247934 251955.450290217 -4021.45029021719 48 248333 252975.757313241 -4642.7573132413 49 246969 249794.074506674 -2825.07450667369 50 245098 243205.181422933 1892.81857706748 51 246263 244113.710925271 2149.28907472906 52 255765 252945.363768740 2819.63623126036 53 264319 260138.448566736 4180.55143326438 54 268347 264951.603996216 3395.39600378393 55 273046 267432.316178130 5613.68382187043 56 273963 270008.739860159 3954.26013984125 57 267430 262559.428934625 4870.57106537536 58 271993 266415.290972865 5577.70902713545 59 292710 286911.142439603 5798.85756039656 60 295881 290915.687417933 4965.31258206665 61 293299 288892.023455365 4406.97654463518 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0417475844947872 0.0834951689895745 0.958252415505213 18 0.0193212781011057 0.0386425562022114 0.980678721898894 19 0.0185852602448914 0.0371705204897828 0.981414739755109 20 0.00976423046470019 0.0195284609294004 0.9902357695353 21 0.0198591132574878 0.0397182265149755 0.980140886742512 22 0.0135419274872913 0.0270838549745825 0.986458072512709 23 0.0093199126548001 0.0186398253096002 0.9906800873452 24 0.0402091949850402 0.0804183899700804 0.95979080501496 25 0.121910901702447 0.243821803404895 0.878089098297553 26 0.67854748069699 0.642905038606019 0.321452519303010 27 0.825931657446617 0.348136685106765 0.174068342553383 28 0.906051149264926 0.187897701470148 0.0939488507350738 29 0.901874462767496 0.196251074465008 0.0981255372325042 30 0.863899080857174 0.272201838285652 0.136100919142826 31 0.86978610697274 0.260427786054519 0.130213893027260 32 0.84064916030485 0.318701679390299 0.159350839695150 33 0.837919047712921 0.324161904574158 0.162080952287079 34 0.99326910245892 0.0134617950821591 0.00673089754107956 35 0.996579451597459 0.00684109680508242 0.00342054840254121 36 0.997746919895384 0.00450616020923294 0.00225308010461647 37 0.999119048817595 0.00176190236480989 0.000880951182404943 38 0.997836777788617 0.00432644442276608 0.00216322221138304 39 0.994200211933712 0.0115995761325767 0.00579978806628837 40 0.993727215866376 0.0125455682672485 0.00627278413362423 41 0.986167035230524 0.0276659295389527 0.0138329647694764 42 0.985986169987599 0.0280276600248023 0.0140138300124011 43 0.961475792643625 0.0770484147127509 0.0385242073563754 44 0.937443333272573 0.125113333454854 0.0625566667274269 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.142857142857143 NOK 5% type I error level 15 0.535714285714286 NOK 10% type I error level 18 0.642857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/104sjq1258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/104sjq1258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/1ernp1258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/1ernp1258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/22lha1258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/22lha1258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/3ovo11258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/3ovo11258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/4ypof1258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/4ypof1258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/5xbzl1258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/5xbzl1258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/6ool51258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/6ool51258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/7qyo01258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/7qyo01258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/8xrf01258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/8xrf01258965052.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/94a401258965052.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589651132xjd6q97i1845j3/94a401258965052.ps (open in new window)

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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