*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: Fri, 20 Nov 2009 09:31:50 -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/t1258734791oyod5csklij6oo4.htm/, Retrieved Fri, 20 Nov 2009 17:33:23 +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/t1258734791oyod5csklij6oo4.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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111.4 0 87.4 0 96.8 0 114.1 0 110.3 0 103.9 0 101.6 0 94.6 0 95.9 0 104.7 0 102.8 0 98.1 0 113.9 0 80.9 0 95.7 0 113.2 0 105.9 0 108.8 0 102.3 0 99 0 100.7 0 115.5 0 100.7 0 109.9 0 114.6 0 85.4 0 100.5 0 114.8 0 116.5 0 112.9 0 102 0 106 0 105.3 0 118.8 0 106.1 0 109.3 0 117.2 0 92.5 0 104.2 0 112.5 0 122.4 0 113.3 0 100 0 110.7 0 112.8 0 109.8 0 117.3 0 109.1 0 115.9 0 96 0 99.8 0 116.8 1 115.7 1 99.4 1 94.3 1 91 1 93.2 1 103.1 1 94.1 1 91.8 1 102.7 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] = + 105.300995850622 -8.30497925311204X[t] + 8.69983402489626M1[t] -16.8609958506224M2[t] -5.9009958506224M3[t] + 10.64M4[t] + 10.52M5[t] + 4.02M6[t] -3.59999999999999M7[t] -3.38M8[t] -2.06000000000000M9[t] + 6.74M10[t] + 0.560000000000001M11[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) 105.300995850622 2.424293 43.4357 0 0 X -8.30497925311204 1.889219 -4.396 6.1e-05 3e-05 M1 8.69983402489626 3.243007 2.6826 0.009989 0.004994 M2 -16.8609958506224 3.407584 -4.9481 1e-05 5e-06 M3 -5.9009958506224 3.407584 -1.7317 0.089746 0.044873 M4 10.64 3.386571 3.1418 0.002875 0.001437 M5 10.52 3.386571 3.1064 0.003177 0.001588 M6 4.02 3.386571 1.187 0.241053 0.120526 M7 -3.59999999999999 3.386571 -1.063 0.293091 0.146545 M8 -3.38 3.386571 -0.9981 0.323255 0.161628 M9 -2.06000000000000 3.386571 -0.6083 0.545866 0.272933 M10 6.74 3.386571 1.9902 0.052279 0.02614 M11 0.560000000000001 3.386571 0.1654 0.869356 0.434678 Multiple Linear Regression - Regression Statistics Multiple R 0.855759430330213 R-squared 0.73232420259909 Adjusted R-squared 0.665405253248862 F-TEST (value) 10.9434503934961 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 4.93353691233267e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.35463934521169 Sum Squared Residuals 1376.26380082988 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 111.4 114.000829875519 -2.60082987551872 2 87.4 88.44 -1.03999999999997 3 96.8 99.4 -2.60000000000000 4 114.1 115.940995850622 -1.84099585062243 5 110.3 115.820995850622 -5.52099585062241 6 103.9 109.320995850622 -5.4209958506224 7 101.6 101.700995850622 -0.100995850622399 8 94.6 101.920995850622 -7.32099585062241 9 95.9 103.240995850622 -7.3409958506224 10 104.7 112.040995850622 -7.3409958506224 11 102.8 105.860995850622 -3.06099585062241 12 98.1 105.300995850622 -7.20099585062241 13 113.9 114.000829875519 -0.100829875518659 14 80.9 88.44 -7.54 15 95.7 99.4 -3.7 16 113.2 115.940995850622 -2.74099585062240 17 105.9 115.820995850622 -9.9209958506224 18 108.8 109.320995850622 -0.52099585062241 19 102.3 101.700995850622 0.599004149377588 20 99 101.920995850622 -2.9209958506224 21 100.7 103.240995850622 -2.54099585062241 22 115.5 112.040995850622 3.45900414937759 23 100.7 105.860995850622 -5.1609958506224 24 109.9 105.300995850622 4.59900414937760 25 114.6 114.000829875519 0.59917012448133 26 85.4 88.44 -3.04000000000001 27 100.5 99.4 1.1 28 114.8 115.940995850622 -1.14099585062240 29 116.5 115.820995850622 0.679004149377588 30 112.9 109.320995850622 3.5790041493776 31 102 101.700995850622 0.299004149377590 32 106 101.920995850622 4.0790041493776 33 105.3 103.240995850622 2.05900414937759 34 118.8 112.040995850622 6.75900414937759 35 106.1 105.860995850622 0.239004149377590 36 109.3 105.300995850622 3.9990041493776 37 117.2 114.000829875519 3.19917012448134 38 92.5 88.44 4.05999999999999 39 104.2 99.4 4.8 40 112.5 115.940995850622 -3.4409958506224 41 122.4 115.820995850622 6.5790041493776 42 113.3 109.320995850622 3.97900414937759 43 100 101.700995850622 -1.70099585062241 44 110.7 101.920995850622 8.7790041493776 45 112.8 103.240995850622 9.55900414937759 46 109.8 112.040995850622 -2.24099585062241 47 117.3 105.860995850622 11.4390041493776 48 109.1 105.300995850622 3.79900414937759 49 115.9 114.000829875519 1.89917012448134 50 96 88.44 7.55999999999999 51 99.8 99.4 0.399999999999997 52 116.8 107.636016597510 9.16398340248963 53 115.7 107.516016597510 8.18398340248962 54 99.4 101.016016597510 -1.61601659751037 55 94.3 93.3960165975104 0.903983402489621 56 91 93.6160165975104 -2.61601659751037 57 93.2 94.9360165975104 -1.73601659751037 58 103.1 103.736016597510 -0.636016597510378 59 94.1 97.5560165975104 -3.45601659751038 60 91.8 96.9960165975104 -5.19601659751037 61 102.7 105.695850622407 -2.99585062240663 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.185006406914785 0.370012813829569 0.814993593085215 17 0.195092461390920 0.390184922781841 0.80490753860908 18 0.172038157508224 0.344076315016449 0.827961842491776 19 0.0922168555423761 0.184433711084752 0.907783144457624 20 0.0845475898051151 0.169095179610230 0.915452410194885 21 0.0840093897414679 0.168018779482936 0.915990610258532 22 0.231446609764244 0.462893219528488 0.768553390235756 23 0.207575829645887 0.415151659291774 0.792424170354113 24 0.382868876120226 0.765737752240453 0.617131123879774 25 0.295788774151401 0.591577548302802 0.704211225848599 26 0.27859935830253 0.55719871660506 0.72140064169747 27 0.234392451351187 0.468784902702375 0.765607548648813 28 0.189838047377991 0.379676094755983 0.810161952622009 29 0.307794331185789 0.615588662371577 0.692205668814211 30 0.295893244258341 0.591786488516683 0.704106755741658 31 0.218748027074013 0.437496054148025 0.781251972925987 32 0.256718444661232 0.513436889322464 0.743281555338768 33 0.254211539882628 0.508423079765256 0.745788460117372 34 0.304272039301198 0.608544078602395 0.695727960698802 35 0.282298201899428 0.564596403798857 0.717701798100572 36 0.236660263574739 0.473320527149478 0.763339736425261 37 0.180848858742064 0.361697717484128 0.819151141257936 38 0.171370419750368 0.342740839500737 0.828629580249632 39 0.148557895746487 0.297115791492975 0.851442104253513 40 0.443662012219723 0.887324024439446 0.556337987780277 41 0.561135272118202 0.877729455763596 0.438864727881798 42 0.450676668884733 0.901353337769465 0.549323331115267 43 0.580445284284846 0.839109431430307 0.419554715715154 44 0.523090438492251 0.953819123015497 0.476909561507749 45 0.459603421268021 0.919206842536042 0.540396578731979 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/10cwzu1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/10cwzu1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/16jlm1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/16jlm1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/2hm6y1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/2hm6y1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/3rxxa1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/3rxxa1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/4f99k1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/4f99k1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/5m6aq1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/5m6aq1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/6g30a1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/6g30a1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/7eopr1258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/7eopr1258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/8s5p61258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/8s5p61258734706.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/9ak641258734706.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734791oyod5csklij6oo4/9ak641258734706.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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