Model 3

*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: Wed, 18 Nov 2009 11:55:42 -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/18/t12585706402g5xvzyzblg1gf1.htm/, Retrieved Wed, 18 Nov 2009 19:57:33 +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/18/t12585706402g5xvzyzblg1gf1.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:

» Textbox « » Textfile « » CSV «

562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 1 510 1 514 1 517 1 508 1 493 1 490 1 469 1 478 1 528 1 534 1 518 1 506 1 502 1 516 1 528 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 603.15761589404 -47.1084437086093X[t] -4.23420713760115M1[t] -13.742310522443M2[t] -23.5502483443709M3[t] -30.1581861662987M4[t] -39.3661239882266M5[t] -37.5740618101545M6[t] + 14.4180003679176M7[t] + 24.6100625459897M8[t] + 16.6021247240618M9[t] + 11.4158756438558M10[t] -3.19206217807213M11[t] -0.792062178072112t + 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) 603.15761589404 11.113403 54.273 0 0 X -47.1084437086093 9.524173 -4.9462 1e-05 5e-06 M1 -4.23420713760115 12.578523 -0.3366 0.7379 0.36895 M2 -13.742310522443 13.199876 -1.0411 0.303158 0.151579 M3 -23.5502483443709 13.185621 -1.7861 0.080542 0.040271 M4 -30.1581861662987 13.1756 -2.2889 0.026622 0.013311 M5 -39.3661239882266 13.169824 -2.9891 0.00444 0.00222 M6 -37.5740618101545 13.168298 -2.8534 0.006413 0.003207 M7 14.4180003679176 13.171024 1.0947 0.279237 0.139619 M8 24.6100625459897 13.178 1.8675 0.068073 0.034037 M9 16.6021247240618 13.189217 1.2588 0.214332 0.107166 M10 11.4158756438558 13.119081 0.8702 0.388628 0.194314 M11 -3.19206217807213 13.112678 -0.2434 0.808729 0.404365 t -0.792062178072112 0.23663 -3.3473 0.001613 0.000807 Multiple Linear Regression - Regression Statistics Multiple R 0.898611226493912 R-squared 0.807502136380892 Adjusted R-squared 0.754258046443692 F-TEST (value) 15.1660426036640 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.21302967670545e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 20.7295874110486 Sum Squared Residuals 20196.6423289183 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562 598.131346578366 -36.1313465783663 2 561 587.831181015452 -26.8311810154525 3 555 577.231181015453 -22.2311810154525 4 544 569.831181015453 -25.8311810154526 5 537 559.831181015452 -22.8311810154525 6 543 560.831181015453 -17.8311810154526 7 594 612.031181015453 -18.0311810154526 8 611 621.431181015453 -10.4311810154526 9 613 612.631181015453 0.368818984547442 10 611 606.652869757174 4.34713024282562 11 594 591.252869757174 2.74713024282559 12 595 593.652869757174 1.34713024282558 13 591 588.626600441501 2.37339955849886 14 589 578.326434878587 10.6735651214128 15 584 567.726434878587 16.2735651214128 16 573 560.326434878587 12.6735651214128 17 567 550.326434878587 16.6735651214128 18 569 551.326434878587 17.6735651214128 19 621 602.526434878587 18.4735651214128 20 629 611.926434878587 17.0735651214128 21 628 603.126434878587 24.8735651214128 22 612 597.148123620309 14.8518763796910 23 595 581.748123620309 13.2518763796910 24 597 584.148123620309 12.8518763796909 25 593 579.121854304636 13.8781456953642 26 590 568.821688741722 21.1783112582781 27 580 558.221688741722 21.7783112582782 28 574 550.821688741722 23.1783112582782 29 573 540.821688741722 32.1783112582781 30 573 541.821688741722 31.1783112582782 31 620 593.021688741722 26.9783112582782 32 626 602.421688741722 23.5783112582782 33 620 593.621688741722 26.3783112582781 34 588 587.643377483444 0.356622516556293 35 566 572.243377483444 -6.2433774834437 36 557 574.643377483444 -17.6433774834437 37 561 569.61710816777 -8.61710816777045 38 549 559.316942604857 -10.3169426048565 39 532 548.716942604857 -16.7169426048565 40 526 541.316942604857 -15.3169426048565 41 511 531.316942604857 -20.3169426048565 42 499 532.316942604856 -33.3169426048565 43 555 583.516942604856 -28.5169426048565 44 565 592.916942604857 -27.9169426048565 45 542 584.116942604856 -42.1169426048565 46 527 531.030187637969 -4.0301876379691 47 510 515.630187637969 -5.63018763796909 48 514 518.030187637969 -4.03018763796912 49 517 513.003918322296 3.99608167770415 50 508 502.703752759382 5.29624724061811 51 493 492.103752759382 0.896247240618112 52 490 484.703752759382 5.2962472406181 53 469 474.703752759382 -5.70375275938191 54 478 475.703752759382 2.29624724061811 55 528 526.903752759382 1.09624724061811 56 534 536.303752759382 -2.30375275938188 57 518 527.503752759382 -9.5037527593819 58 506 521.525441501104 -15.5254415011038 59 502 506.125441501104 -4.12544150110375 60 516 508.525441501104 7.47455849889623 61 528 503.499172185430 24.5008278145695 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 7.91941746080157e-05 0.000158388349216031 0.999920805825392 18 5.68226199005055e-05 0.000113645239801011 0.9999431773801 19 5.78908937844706e-06 1.15781787568941e-05 0.999994210910622 20 0.000267736823388096 0.000535473646776192 0.999732263176612 21 0.00066362916933069 0.00132725833866138 0.99933637083067 22 0.0112902805942641 0.0225805611885282 0.988709719405736 23 0.0220959141224432 0.0441918282448863 0.977904085877557 24 0.0248965747868562 0.0497931495737125 0.975103425213144 25 0.0222534059974510 0.0445068119949021 0.97774659400255 26 0.0152857687152432 0.0305715374304864 0.984714231284757 27 0.0124991596738726 0.0249983193477453 0.987500840326127 28 0.00696143210363536 0.0139228642072707 0.993038567896365 29 0.00476599364084249 0.00953198728168498 0.995234006359157 30 0.00447205926542283 0.00894411853084566 0.995527940734577 31 0.00508094209714535 0.0101618841942907 0.994919057902855 32 0.0110054375395079 0.0220108750790157 0.988994562460492 33 0.302298570772225 0.604597141544451 0.697701429227775 34 0.891603025577416 0.216793948845169 0.108396974422584 35 0.991181099497261 0.0176378010054771 0.00881890050273855 36 0.996394377182247 0.00721124563550575 0.00360562281775287 37 0.994963351594647 0.0100732968107067 0.00503664840535333 38 0.994822399795409 0.0103552004091826 0.00517760020459128 39 0.994290812510157 0.0114183749796859 0.00570918748984294 40 0.990430850688893 0.0191382986222143 0.00956914931110716 41 0.991668142741982 0.0166637145160356 0.00833185725801782 42 0.985154867977143 0.0296902640457146 0.0148451320228573 43 0.962811907741871 0.0743761845162572 0.0371880922581286 44 0.910271439772343 0.179457120455314 0.0897285602276571 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.285714285714286 NOK 5% type I error level 24 0.857142857142857 NOK 10% type I error level 25 0.892857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/109glc1258570538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/109glc1258570538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/13hhj1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/13hhj1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/2m7kl1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/2m7kl1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/3vzkc1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/3vzkc1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/4k31b1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/4k31b1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/54wwr1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/54wwr1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/666as1258570537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/666as1258570537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/7pbtr1258570538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/7pbtr1258570538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/8vr0s1258570538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/8vr0s1258570538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/9c98x1258570538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585706402g5xvzyzblg1gf1/9c98x1258570538.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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