*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, 21 Dec 2010 10:13:52 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa.htm/, Retrieved Tue, 21 Dec 2010 11:11:49 +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/2010/Dec/21/t1292926306bhb3hrhrrcg64oa.htm/}, year = {2010}, } @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 = {2010}, 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 «

695 0 638 0 762 0 635 0 721 0 854 0 418 0 367 0 824 0 687 0 601 0 676 0 740 0 691 0 683 0 594 0 729 0 731 0 386 0 331 0 707 0 715 0 657 0 653 0 642 0 643 0 718 0 654 0 632 0 731 0 392 1 344 1 792 1 852 1 649 1 629 1 685 1 617 1 715 1 715 1 629 1 916 1 531 1 357 1 917 1 828 1 708 1 858 1 775 1 785 1 1006 1 789 1 734 1 906 1 532 1 387 1 991 1 841 1 892 1 782 1 813 1 793 1 978 1 775 1 797 1 946 1 594 1 438 1 1022 1 868 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Y[t] = + 650.5 + 88.95X[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) 650.5 29.360583 22.1556 0 0 X 88.95 38.8404 2.2901 0.025121 0.01256 Multiple Linear Regression - Regression Statistics Multiple R 0.267592548273763 R-squared 0.0716057718916461 Adjusted R-squared 0.0579529155959351 F-TEST (value) 5.24474661863542 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 0.0251208221970607 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 160.814535120064 Sum Squared Residuals 1758569.4 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 695 650.5 44.5000000000002 2 638 650.5 -12.4999999999999 3 762 650.5 111.5 4 635 650.5 -15.5 5 721 650.5 70.5 6 854 650.5 203.5 7 418 650.5 -232.5 8 367 650.5 -283.5 9 824 650.5 173.5 10 687 650.5 36.5 11 601 650.5 -49.5 12 676 650.5 25.5 13 740 650.5 89.5 14 691 650.5 40.5 15 683 650.5 32.5 16 594 650.5 -56.5 17 729 650.5 78.5 18 731 650.5 80.5 19 386 650.5 -264.5 20 331 650.5 -319.5 21 707 650.5 56.5 22 715 650.5 64.5 23 657 650.5 6.49999999999999 24 653 650.5 2.49999999999999 25 642 650.5 -8.50000000000001 26 643 650.5 -7.50000000000001 27 718 650.5 67.5 28 654 650.5 3.49999999999999 29 632 650.5 -18.5 30 731 650.5 80.5 31 392 739.45 -347.45 32 344 739.45 -395.45 33 792 739.45 52.55 34 852 739.45 112.55 35 649 739.45 -90.45 36 629 739.45 -110.45 37 685 739.45 -54.45 38 617 739.45 -122.45 39 715 739.45 -24.45 40 715 739.45 -24.45 41 629 739.45 -110.45 42 916 739.45 176.55 43 531 739.45 -208.45 44 357 739.45 -382.45 45 917 739.45 177.55 46 828 739.45 88.55 47 708 739.45 -31.45 48 858 739.45 118.55 49 775 739.45 35.55 50 785 739.45 45.55 51 1006 739.45 266.55 52 789 739.45 49.55 53 734 739.45 -5.45 54 906 739.45 166.55 55 532 739.45 -207.45 56 387 739.45 -352.45 57 991 739.45 251.55 58 841 739.45 101.55 59 892 739.45 152.55 60 782 739.45 42.55 61 813 739.45 73.55 62 793 739.45 53.55 63 978 739.45 238.55 64 775 739.45 35.55 65 797 739.45 57.55 66 946 739.45 206.55 67 594 739.45 -145.45 68 438 739.45 -301.45 69 1022 739.45 282.55 70 868 739.45 128.55 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0667461226818037 0.133492245363607 0.933253877318196 6 0.132481957631438 0.264963915262876 0.867518042368562 7 0.463728775457814 0.927457550915628 0.536271224542186 8 0.701821130309581 0.596357739380839 0.298178869690419 9 0.703589702119169 0.592820595761662 0.296410297880831 10 0.6039326437188 0.7921347125624 0.3960673562812 11 0.50964346188801 0.98071307622398 0.49035653811199 12 0.408912718784838 0.817825437569675 0.591087281215163 13 0.339326560294685 0.67865312058937 0.660673439705315 14 0.258527575607394 0.517055151214788 0.741472424392606 15 0.189525317482151 0.379050634964302 0.810474682517849 16 0.143053351055901 0.286106702111802 0.8569466489441 17 0.106783691601876 0.213567383203751 0.893216308398124 18 0.078498982728701 0.156997965457402 0.921501017271299 19 0.172561217424683 0.345122434849367 0.827438782575317 20 0.372313199317091 0.744626398634181 0.62768680068291 21 0.309502414060185 0.61900482812037 0.690497585939815 22 0.254407988539369 0.508815977078738 0.745592011460631 23 0.196329225240765 0.392658450481529 0.803670774759235 24 0.147501636426309 0.295003272852619 0.85249836357369 25 0.108087618268355 0.21617523653671 0.891912381731645 26 0.0772263859820329 0.154452771964066 0.922773614017967 27 0.05713030068572 0.11426060137144 0.94286969931428 28 0.0387790152597549 0.0775580305195099 0.961220984740245 29 0.0262213595389415 0.052442719077883 0.973778640461059 30 0.0185577144232384 0.0371154288464768 0.981442285576762 31 0.0224798614034813 0.0449597228069626 0.977520138596519 32 0.0384671014418965 0.076934202883793 0.961532898558104 33 0.0979522641180817 0.195904528236163 0.902047735881918 34 0.148521709792558 0.297043419585117 0.851478290207442 35 0.119581118893062 0.239162237786123 0.880418881106938 36 0.0963473946452147 0.192694789290429 0.903652605354785 37 0.0748339565221914 0.149667913044383 0.925166043477809 38 0.0604347186650297 0.120869437330059 0.93956528133497 39 0.0460198682712485 0.092039736542497 0.953980131728752 40 0.0340043875631109 0.0680087751262219 0.965995612436889 41 0.0263940484640116 0.0527880969280232 0.973605951535988 42 0.0382799180045637 0.0765598360091275 0.961720081995436 43 0.0452210736274269 0.0904421472548538 0.954778926372573 44 0.18938894254218 0.378777885084361 0.81061105745782 45 0.224611144787415 0.44922228957483 0.775388855212585 46 0.198567930169859 0.397135860339717 0.801432069830141 47 0.160114789325275 0.320229578650549 0.839885210674725 48 0.143999176206817 0.287998352413634 0.856000823793183 49 0.11053682863917 0.221073657278339 0.88946317136083 50 0.0829119778302128 0.165823955660426 0.917088022169787 51 0.130774247291724 0.261548494583448 0.869225752708276 52 0.0963911175048472 0.192782235009694 0.903608882495153 53 0.068174562715215 0.13634912543043 0.931825437284785 54 0.0623032950339447 0.124606590067889 0.937696704966055 55 0.0857090995618945 0.171418199123789 0.914290900438105 56 0.362878951035762 0.725757902071523 0.637121048964238 57 0.410525259506232 0.821050519012465 0.589474740493768 58 0.332451410426451 0.664902820852901 0.667548589573549 59 0.281092898602127 0.562185797204254 0.718907101397873 60 0.203236525354067 0.406473050708134 0.796763474645933 61 0.138588764400271 0.277177528800542 0.86141123559973 62 0.0866767606933932 0.173353521386786 0.913323239306607 63 0.0917201325441508 0.183440265088302 0.908279867455849 64 0.0489980500239908 0.0979961000479816 0.95100194997601 65 0.0225589902187073 0.0451179804374147 0.977441009781293 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 3 0.0491803278688525 OK 10% type I error level 12 0.19672131147541 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/10n1801292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/10n1801292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/1ratr1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/1ratr1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/2ratr1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/2ratr1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/3ratr1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/3ratr1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/4jjac1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/4jjac1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/5jjac1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/5jjac1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/6jjac1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/6jjac1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/7csrx1292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/7csrx1292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/8n1801292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/8n1801292926424.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/9n1801292926424.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292926306bhb3hrhrrcg64oa/9n1801292926424.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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