WS 7 Model 4

*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: Sun, 22 Nov 2009 09:57:43 -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/22/t1258909485qiq6vfna16yu739.htm/, Retrieved Sun, 22 Nov 2009 18:04:57 +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/Nov/22/t1258909485qiq6vfna16yu739.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:

WS 7 Model 4

Dataseries X:

» Textbox « » Textfile « » CSV «

277128 0 277915 276687 283042 286602 277103 0 277128 277915 276687 283042 275037 0 277103 277128 277915 276687 270150 0 275037 277103 277128 277915 267140 0 270150 275037 277103 277128 264993 0 267140 270150 275037 277103 287259 0 264993 267140 270150 275037 291186 0 287259 264993 267140 270150 292300 0 291186 287259 264993 267140 288186 0 292300 291186 287259 264993 281477 0 288186 292300 291186 287259 282656 0 281477 288186 292300 291186 280190 0 282656 281477 288186 292300 280408 0 280190 282656 281477 288186 276836 0 280408 280190 282656 281477 275216 0 276836 280408 280190 282656 274352 0 275216 276836 280408 280190 271311 0 274352 275216 276836 280408 289802 0 271311 274352 275216 276836 290726 0 289802 271311 274352 275216 292300 0 290726 289802 271311 274352 278506 0 292300 290726 289802 271311 269826 0 278506 292300 290726 289802 265861 0 269826 278506 292300 290726 269034 0 265861 269826 278506 292300 264176 0 269034 265861 269826 278506 255198 0 264176 269034 265861 269826 etc...

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation nwwmb[t] = + 19383.0482520845 + 5208.18468261152dummy_variable[t] + 0.925680076204517`y[t-1]`[t] + 0.170923414732768`y[t-2]`[t] + 0.155111372936063`y[t-3]`[t] -0.305318747860637`y[t-4] `[t] + 837.730352246995M1[t] -2931.44490665555M2[t] -8038.39352053068M3[t] -5571.84238247885M4[t] -8599.4415923363M5[t] -5578.99689219737M6[t] + 17664.8784464577M7[t] + 1152.70424096104M8[t] -6679.57569678684M9[t] -16111.3074526013M10[t] -9459.3839340622M11[t] -99.011851773791t + 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) 19383.0482520845 12923.97714 1.4998 0.141936 0.070968 dummy_variable 5208.18468261152 2029.574277 2.5661 0.01435 0.007175 `y[t-1]` 0.925680076204517 0.146303 6.3272 0 0 `y[t-2]` 0.170923414732768 0.208523 0.8197 0.417506 0.208753 `y[t-3]` 0.155111372936063 0.214173 0.7242 0.473358 0.236679 `y[t-4] ` -0.305318747860637 0.156157 -1.9552 0.057942 0.028971 M1 837.730352246995 2718.786025 0.3081 0.759669 0.379834 M2 -2931.44490665555 2997.827815 -0.9779 0.33433 0.167165 M3 -8038.39352053068 2788.968873 -2.8822 0.006463 0.003232 M4 -5571.84238247885 2547.179392 -2.1875 0.034932 0.017466 M5 -8599.4415923363 2448.908336 -3.5115 0.001166 0.000583 M6 -5578.99689219737 2466.297078 -2.2621 0.029494 0.014747 M7 17664.8784464577 2403.344745 7.3501 0 0 M8 1152.70424096104 4660.838654 0.2473 0.805994 0.402997 M9 -6679.57569678684 5008.747669 -1.3336 0.19028 0.09514 M10 -16111.3074526013 4385.432377 -3.6738 0.000733 0.000367 M11 -9459.3839340622 2597.248662 -3.6421 0.000804 0.000402 t -99.011851773791 54.96703 -1.8013 0.079596 0.039798 Multiple Linear Regression - Regression Statistics Multiple R 0.987230121934677 R-squared 0.974623313655158 Adjusted R-squared 0.963270585553518 F-TEST (value) 85.8492606296439 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3474.57074208821 Sum Squared Residuals 458760389.987466 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 277128 281072.501427316 -3944.50142731563 2 277103 276786.900017334 316.099982666263 3 275037 273554.058231005 1482.94176899508 4 270150 273507.865321602 -3357.86532160251 5 267140 265740.736022965 1399.26397703518 6 264993 264727.741986366 265.258013634064 7 287259 285243.450124832 2015.54987516802 8 291186 289901.691561157 1284.30843884250 9 292300 289997.311496698 2302.68850330244 10 288186 286278.220925108 1907.77907489212 11 281477 283024.188564035 -1547.18856403507 12 282656 284444.801433459 -1788.80143345883 13 280190 284149.918280959 -3959.9182809593 14 280408 278415.961936003 1992.03806399682 15 276836 275221.562374324 1614.43762567553 16 275216 273577.358283424 1638.64171657631 17 274352 269127.33737244 5224.66262755993 18 271311 270351.469391936 959.53060806381 19 289802 291372.980079952 -1570.98007995215 20 290726 291719.366352895 -993.366352894611 21 292300 287596.049528663 4703.95047133746 22 278506 283476.898305438 -4970.89830543822 23 269826 272027.686397730 -2201.68639772965 24 265861 270957.468613717 -5096.4686137172 25 269034 263922.072384746 5111.92761525363 26 264176 265178.556907356 -1002.55690735615 27 255198 258053.132764192 -2855.1327641916 28 253353 252982.327599127 370.672400872754 29 246057 244890.978942743 1166.02105725731 30 235372 240833.944825825 -5461.94482582471 31 258556 255295.830699796 3260.16930020383 32 260993 257750.915355693 3242.08464430695 33 254663 256608.434923616 -1945.43492361564 34 250643 248493.109686397 2149.89031360285 35 243422 243542.338797006 -120.338797006029 36 247105 243805.346142574 3299.65385742575 37 248541 248028.226340678 512.773659321764 38 245039 246226.148898321 -1187.14889832082 39 237080 240799.884694185 -3719.88469418512 40 237085 234299.613438723 2785.38656127730 41 225554 228835.613569664 -3281.61356966443 42 226839 226126.763596810 712.236403189713 43 247934 250921.015557418 -2987.01555741829 44 248333 252266.571460549 -3933.57146054875 45 246969 252030.204051024 -5061.20405102425 46 245098 244184.771083057 913.228916943247 47 246263 242393.786241229 3869.21375877075 48 255765 252179.383810250 3585.61618975027 49 264319 262039.281566300 2279.71843369953 50 268347 268465.432240986 -118.432240986103 51 273046 269568.361936294 3477.63806370610 52 273963 275399.835357124 -1436.83535712385 53 267430 271938.334092188 -4508.33409218799 54 271993 268468.080199063 3524.91980093712 55 292710 293427.723538001 -717.723538001405 56 295881 295480.455269706 400.544730293901 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0256304187190940 0.0512608374381879 0.974369581280906 22 0.595590481970641 0.808819036058718 0.404409518029359 23 0.44733857301763 0.89467714603526 0.55266142698237 24 0.480834115666041 0.961668231332082 0.519165884333959 25 0.467373972254989 0.934747944509978 0.532626027745011 26 0.368250505000644 0.736501010001288 0.631749494999356 27 0.346188173485305 0.692376346970609 0.653811826514695 28 0.453457046416626 0.906914092833252 0.546542953583374 29 0.362020975913306 0.724041951826612 0.637979024086694 30 0.481170748870694 0.962341497741389 0.518829251129306 31 0.566458876750354 0.867082246499291 0.433541123249646 32 0.745218594986899 0.509562810026202 0.254781405013101 33 0.68220302071082 0.63559395857836 0.31779697928918 34 0.636277327443966 0.727445345112068 0.363722672556034 35 0.464982110526308 0.929964221052616 0.535017889473692 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 0 0 OK 10% type I error level 1 0.0666666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/103ak21258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/103ak21258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/1eith1258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/1eith1258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/29hc51258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/29hc51258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/3aua81258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/3aua81258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/494nk1258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/494nk1258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/55lkw1258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/55lkw1258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/6l6sv1258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/6l6sv1258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/7dg491258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/7dg491258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/87hm91258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/87hm91258909059.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/9uduu1258909059.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258909485qiq6vfna16yu739/9uduu1258909059.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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