*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: Tue, 15 Dec 2009 09:06:53 -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/Dec/15/t1260893264xm8nghwprz9kgcc.htm/, Retrieved Tue, 15 Dec 2009 17:07:57 +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/Dec/15/t1260893264xm8nghwprz9kgcc.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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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 0 510 0 514 0 517 0 508 0 493 0 490 0 469 0 478 0 528 0 534 0 518 1 506 1 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 1 587 1 597 1 581 1 564 1 558 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WklBe[t] = + 568.346341463414 -22.3658536585366X[t] -6.72439024390259M1[t] -8.07317073170728M2[t] -5.87317073170727M3[t] -10.0731707317073M4[t] -18.8731707317073M5[t] -23.8731707317073M6[t] -35.0731707317073M7[t] -32.8731707317073M8[t] + 18.3268292682927M9[t] + 26.3268292682927M10[t] + 18.4000000000000M11[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) 568.346341463414 18.211093 31.2088 0 0 X -22.3658536585366 11.888041 -1.8814 0.065993 0.032997 M1 -6.72439024390259 23.815677 -0.2824 0.778888 0.389444 M2 -8.07317073170728 24.97432 -0.3233 0.747903 0.373952 M3 -5.87317073170727 24.97432 -0.2352 0.815078 0.407539 M4 -10.0731707317073 24.97432 -0.4033 0.688488 0.344244 M5 -18.8731707317073 24.97432 -0.7557 0.45352 0.22676 M6 -23.8731707317073 24.97432 -0.9559 0.343908 0.171954 M7 -35.0731707317073 24.97432 -1.4044 0.166647 0.083323 M8 -32.8731707317073 24.97432 -1.3163 0.194332 0.097166 M9 18.3268292682927 24.97432 0.7338 0.466624 0.233312 M10 26.3268292682927 24.97432 1.0542 0.297089 0.148545 M11 18.4000000000000 24.860886 0.7401 0.462833 0.231417 Multiple Linear Regression - Regression Statistics Multiple R 0.509122565271679 R-squared 0.259205786468815 Adjusted R-squared 0.0740072330860191 F-TEST (value) 1.39961021149582 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.199092966448552 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 39.3085117440034 Sum Squared Residuals 74167.6365853658 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 594 561.621951219513 32.3780487804869 2 595 560.273170731707 34.7268292682927 3 591 562.473170731707 28.5268292682927 4 589 558.273170731707 30.7268292682927 5 584 549.473170731707 34.5268292682927 6 573 544.473170731707 28.5268292682927 7 567 533.273170731707 33.7268292682927 8 569 535.473170731707 33.5268292682927 9 621 586.673170731707 34.3268292682927 10 629 594.673170731707 34.3268292682927 11 628 586.746341463415 41.2536585365854 12 612 568.346341463415 43.6536585365854 13 595 561.621951219512 33.378048780488 14 597 560.273170731707 36.7268292682927 15 593 562.473170731707 30.5268292682927 16 590 558.273170731707 31.7268292682927 17 580 549.473170731707 30.5268292682927 18 574 544.473170731707 29.5268292682927 19 573 533.273170731707 39.7268292682927 20 573 535.473170731707 37.5268292682927 21 620 586.673170731707 33.3268292682927 22 626 594.673170731707 31.3268292682927 23 620 586.746341463415 33.2536585365854 24 588 568.346341463415 19.6536585365854 25 566 561.621951219512 4.378048780488 26 557 560.273170731707 -3.27317073170733 27 561 562.473170731707 -1.47317073170731 28 549 558.273170731707 -9.27317073170733 29 532 549.473170731707 -17.4731707317073 30 526 544.473170731707 -18.4731707317073 31 511 533.273170731707 -22.2731707317073 32 499 535.473170731707 -36.4731707317073 33 555 586.673170731707 -31.6731707317073 34 565 594.673170731707 -29.6731707317073 35 542 586.746341463415 -44.7463414634146 36 527 568.346341463415 -41.3463414634146 37 510 561.621951219512 -51.621951219512 38 514 560.273170731707 -46.2731707317073 39 517 562.473170731707 -45.4731707317073 40 508 558.273170731707 -50.2731707317073 41 493 549.473170731707 -56.4731707317073 42 490 544.473170731707 -54.4731707317073 43 469 533.273170731707 -64.2731707317073 44 478 535.473170731707 -57.4731707317073 45 528 586.673170731707 -58.6731707317073 46 534 594.673170731707 -60.6731707317073 47 518 564.380487804878 -46.3804878048781 48 506 545.980487804878 -39.980487804878 49 502 539.256097560975 -37.2560975609754 50 516 537.907317073171 -21.9073170731708 51 528 540.107317073171 -12.1073170731708 52 533 535.907317073171 -2.90731707317078 53 536 527.107317073171 8.89268292682924 54 537 522.107317073171 14.8926829268292 55 524 510.907317073171 13.0926829268293 56 536 513.107317073171 22.8926829268292 57 587 564.307317073171 22.6926829268292 58 597 572.307317073171 24.6926829268292 59 581 564.380487804878 16.6195121951219 60 564 545.980487804878 18.0195121951220 61 558 539.256097560975 18.7439024390246 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 5.97189506083504e-05 0.000119437901216701 0.999940281049392 17 1.26098801767863e-05 2.52197603535725e-05 0.999987390119823 18 6.58380064075557e-07 1.31676012815111e-06 0.999999341619936 19 4.43923853722702e-07 8.87847707445403e-07 0.999999556076146 20 8.22659840659948e-08 1.64531968131990e-07 0.999999917734016 21 8.28178721129484e-09 1.65635744225897e-08 0.999999991718213 22 1.35694083684402e-09 2.71388167368803e-09 0.99999999864306 23 3.62917683675478e-09 7.25835367350955e-09 0.999999996370823 24 3.35977955664699e-06 6.71955911329397e-06 0.999996640220443 25 0.000202471680383882 0.000404943360767764 0.999797528319616 26 0.00601688441449387 0.0120337688289877 0.993983115585506 27 0.0196183995620030 0.0392367991240060 0.980381600437997 28 0.0697896688304588 0.139579337660918 0.930210331169541 29 0.19250399963042 0.38500799926084 0.80749600036958 30 0.304750117452173 0.609500234904346 0.695249882547827 31 0.490204862551215 0.98040972510243 0.509795137448785 32 0.653724902022921 0.692550195954157 0.346275097977079 33 0.72104578266651 0.557908434666981 0.278954217333490 34 0.751094142835486 0.497811714329029 0.248905857164514 35 0.828292564602725 0.343414870794549 0.171707435397275 36 0.859319650760368 0.281360698479265 0.140680349239632 37 0.870224577395054 0.259550845209892 0.129775422604946 38 0.891829357086893 0.216341285826214 0.108170642913107 39 0.895384734480529 0.209230531038943 0.104615265519471 40 0.880749366907859 0.238501266184281 0.119250633092141 41 0.842366822692594 0.315266354614813 0.157633177307406 42 0.780604267082116 0.438791465835769 0.219395732917884 43 0.703555962629808 0.592888074740383 0.296444037370192 44 0.584807527332678 0.830384945334645 0.415192472667322 45 0.438203582847041 0.876407165694083 0.561796417152959 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.333333333333333 NOK 5% type I error level 12 0.4 NOK 10% type I error level 12 0.4 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/10pywa1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/10pywa1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/1yxt01260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/1yxt01260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/2nyjz1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/2nyjz1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/3ynkl1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/3ynkl1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/40tfg1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/40tfg1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/5q7hk1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/5q7hk1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/6jyce1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/6jyce1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/7j8mo1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/7j8mo1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/860tx1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/860tx1260893208.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/9h80c1260893208.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260893264xm8nghwprz9kgcc/9h80c1260893208.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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