workshop 7 data 2

*Unverified author*

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 11:14:50 -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/t1258741016sykxmfxq11fztdq.htm/, Retrieved Fri, 20 Nov 2009 19:17:08 +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/t1258741016sykxmfxq11fztdq.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 «

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 0 510 0 514 0 517 0 508 0 493 0 490 0 469 1 478 1 528 1 534 1 518 1 506 1 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 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 Yt[t] = + 616.551900582166 + 7.38444103238997X[t] -1.95296039659737t + 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) 616.551900582166 8.113104 75.9946 0 0 X 7.38444103238997 12.282856 0.6012 0.55005 0.275025 t -1.95296039659737 0.29336 -6.6572 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.766800045961603 R-squared 0.587982310486717 Adjusted R-squared 0.573774803951776 F-TEST (value) 41.3853274704243 F-TEST (DF numerator) 2 F-TEST (DF denominator) 58 p-value 6.80078215964386e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27.6374538885241 Sum Squared Residuals 44302.0737315372 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 543 614.598940185571 -71.5989401855713 2 594 612.645979788972 -18.6459797889719 3 611 610.693019392375 0.306980607625428 4 613 608.740058995777 4.25994100422288 5 611 606.78709859918 4.21290140082024 6 594 604.834138202582 -10.8341382025824 7 595 602.881177805985 -7.88117780598502 8 591 600.928217409388 -9.92821740938765 9 589 598.97525701279 -9.97525701279028 10 584 597.022296616193 -13.0222966161929 11 573 595.069336219596 -22.0693362195955 12 567 593.116375822998 -26.1163758229982 13 569 591.1634154264 -22.1634154264008 14 621 589.210455029803 31.7895449701966 15 629 587.257494633206 41.7425053667939 16 628 585.304534236609 42.6954657633913 17 612 583.351573840011 28.6484261599887 18 595 581.398613443414 13.6013865565860 19 597 579.445653046817 17.5543469531834 20 593 577.492692650219 15.5073073497808 21 590 575.539732253622 14.4602677463782 22 580 573.586771857024 6.41322814297552 23 574 571.633811460427 2.36618853957289 24 573 569.68085106383 3.31914893617026 25 573 567.727890667232 5.27210933276763 26 620 565.774930270635 54.225069729365 27 626 563.821969874038 62.1780301259624 28 620 561.86900947744 58.1309905225597 29 588 559.916049080843 28.0839509191571 30 566 557.963088684246 8.03691131575447 31 557 556.010128287648 0.989871712351839 32 561 554.057167891051 6.94283210894921 33 549 552.104207494453 -3.10420749445342 34 532 550.151247097856 -18.1512470978561 35 526 548.198286701259 -22.1982867012587 36 511 546.245326304661 -35.2453263046613 37 499 544.292365908064 -45.2923659080639 38 555 542.339405511467 12.6605944885334 39 565 540.386445114869 24.6135548851308 40 542 538.433484718272 3.56651528172816 41 527 536.480524321674 -9.48052432167447 42 510 534.527563925077 -24.5275639250771 43 514 532.57460352848 -18.5746035284797 44 517 530.621643131882 -13.6216431318824 45 508 528.668682735285 -20.668682735285 46 493 526.715722338688 -33.7157223386876 47 490 524.76276194209 -34.7627619420903 48 469 530.194242577883 -61.1942425778829 49 478 528.241282181286 -50.2412821812855 50 528 526.288321784688 1.71167821531184 51 534 524.335361388091 9.6646386119092 52 518 522.382400991493 -4.38240099149343 53 506 520.429440594896 -14.4294405948961 54 502 518.476480198299 -16.4764801982987 55 516 516.523519801701 -0.523519801701317 56 528 514.570559405104 13.4294405948960 57 533 512.617599008507 20.3824009914934 58 536 510.664638611909 25.3353613880908 59 537 508.711678215312 28.2883217846882 60 524 506.758717818714 17.2412821812855 61 536 504.805757422117 31.1942425778829 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.603561770978399 0.792876458043202 0.396438229021601 7 0.538078111590127 0.923843776819746 0.461921888409873 8 0.467799372745983 0.935598745491965 0.532200627254017 9 0.388082449515054 0.776164899030108 0.611917550484946 10 0.324665599914699 0.649331199829399 0.6753344000853 11 0.311013458419598 0.622026916839195 0.688986541580402 12 0.310510171813140 0.621020343626281 0.68948982818686 13 0.286624517661377 0.573249035322753 0.713375482338623 14 0.387756837133948 0.775513674267896 0.612243162866052 15 0.454202499114023 0.908404998228047 0.545797500885977 16 0.446626770586996 0.893253541173992 0.553373229413004 17 0.362271234363814 0.724542468727628 0.637728765636186 18 0.298573506241571 0.597147012483143 0.701426493758429 19 0.234733347170628 0.469466694341255 0.765266652829372 20 0.184649842491473 0.369299684982945 0.815350157508527 21 0.144505739725926 0.289011479451851 0.855494260274074 22 0.125893986750005 0.251787973500011 0.874106013249994 23 0.115439046264839 0.230878092529679 0.884560953735161 24 0.0997836545878486 0.199567309175697 0.900216345412151 25 0.0805701281233201 0.161140256246640 0.91942987187668 26 0.104306272087560 0.208612544175121 0.89569372791244 27 0.183048483326768 0.366096966653536 0.816951516673232 28 0.315855383236049 0.631710766472097 0.684144616763951 29 0.363237527903543 0.726475055807085 0.636762472096457 30 0.421424621192812 0.842849242385624 0.578575378807188 31 0.487533481940195 0.97506696388039 0.512466518059805 32 0.55824253511251 0.88351492977498 0.44175746488749 33 0.62895452774544 0.742090944509121 0.371045472254561 34 0.693175044570413 0.613649910859174 0.306824955429587 35 0.73013066073095 0.539738678538101 0.269869339269050 36 0.773451241817027 0.453097516365946 0.226548758182973 37 0.829238237411674 0.341523525176651 0.170761762588325 38 0.859021392846621 0.281957214306758 0.140978607153379 39 0.958018286565789 0.0839634268684222 0.0419817134342111 40 0.981189363311185 0.0376212733776306 0.0188106366888153 41 0.987407271158773 0.0251854576824537 0.0125927288412268 42 0.98498310691223 0.0300337861755380 0.0150168930877690 43 0.983142998418913 0.0337140031621744 0.0168570015810872 44 0.984842009676813 0.0303159806463732 0.0151579903231866 45 0.98281939187737 0.0343612162452577 0.0171806081226288 46 0.973416333128793 0.0531673337424149 0.0265836668712074 47 0.957415585912193 0.0851688281756137 0.0425844140878068 48 0.973867111175451 0.0522657776490978 0.0261328888245489 49 0.993401209803974 0.0131975803920518 0.00659879019602592 50 0.99164621384879 0.0167075723024208 0.00835378615121039 51 0.99729652424907 0.00540695150185796 0.00270347575092898 52 0.995327072489265 0.00934585502147 0.004672927510735 53 0.98549800619353 0.0290039876129407 0.0145019938064704 54 0.986790848724658 0.0264183025506830 0.0132091512753415 55 0.980320916718475 0.0393581665630502 0.0196790832815251 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.04 NOK 5% type I error level 13 0.26 NOK 10% type I error level 17 0.34 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/10zn5u1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/10zn5u1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/1snmy1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/1snmy1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/2axol1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/2axol1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/3u0ab1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/3u0ab1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/48slz1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/48slz1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/5hr961258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/5hr961258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/6dk901258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/6dk901258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/7n3a51258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/7n3a51258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/8pkrj1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/8pkrj1258740885.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/9813w1258740885.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741016sykxmfxq11fztdq/9813w1258740885.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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