Regressie Sterftes

*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, 26 Nov 2010 18:08:07 +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/Nov/26/t1290794875wrca3sge5qvbmt2.htm/, Retrieved Fri, 26 Nov 2010 19:08:04 +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/Nov/26/t1290794875wrca3sge5qvbmt2.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 «

12008 9169 8788 8417 8247 8197 8236 8253 7733 8366 8626 8863 10102 8463 9114 8563 8872 8301 8301 8278 7736 7973 8268 9476 11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630

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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Sterftes[t] = + 9332 + 1006.87500000000M1[t] -432.25M2[t] -41.6250000000006M3[t] -779.5M4[t] -984.5M5[t] -1251M6[t] -1078.87500000000M7[t] -1318.75M8[t] -1572.5M9[t] -1002.75M10[t] -966.625M11[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) 9332 149.273738 62.516 0 0 M1 1006.87500000000 211.104945 4.7695 8e-06 4e-06 M2 -432.25 211.104945 -2.0476 0.043728 0.021864 M3 -41.6250000000006 211.104945 -0.1972 0.844166 0.422083 M4 -779.5 211.104945 -3.6925 0.000394 0.000197 M5 -984.5 211.104945 -4.6636 1.2e-05 6e-06 M6 -1251 211.104945 -5.926 0 0 M7 -1078.87500000000 211.104945 -5.1106 2e-06 1e-06 M8 -1318.75 211.104945 -6.2469 0 0 M9 -1572.5 211.104945 -7.4489 0 0 M10 -1002.75 211.104945 -4.75 8e-06 4e-06 M11 -966.625 211.104945 -4.5789 1.6e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.868570091926711 R-squared 0.754414004589576 Adjusted R-squared 0.72225393376202 F-TEST (value) 23.4580952459587 F-TEST (DF numerator) 11 F-TEST (DF denominator) 84 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 422.209889126477 Sum Squared Residuals 14973940.0000001 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12008 10338.8750000000 1669.12500000003 2 9169 8899.75 269.250000000000 3 8788 9290.375 -502.374999999999 4 8417 8552.5 -135.499999999997 5 8247 8347.5 -100.499999999997 6 8197 8081 116.000000000001 7 8236 8253.125 -17.1249999999997 8 8253 8013.25 239.749999999996 9 7733 7759.5 -26.4999999999984 10 8366 8329.25 36.7499999999997 11 8626 8365.375 260.624999999999 12 8863 9332 -468.999999999999 13 10102 10338.875 -236.875000000004 14 8463 8899.75 -436.75 15 9114 9290.375 -176.375000000000 16 8563 8552.5 10.4999999999999 17 8872 8347.5 524.5 18 8301 8081 220 19 8301 8253.125 47.875 20 8278 8013.25 264.750000000001 21 7736 7759.5 -23.5000000000002 22 7973 8329.25 -356.25 23 8268 8365.375 -97.3749999999998 24 9476 9332 144.000000000000 25 11100 10338.875 761.124999999996 26 8962 8899.75 62.2499999999998 27 9173 9290.375 -117.375000000000 28 8738 8552.5 185.5 29 8459 8347.5 111.500000000000 30 8078 8081 -3.00000000000017 31 8411 8253.125 157.875 32 8291 8013.25 277.750000000001 33 7810 7759.5 50.4999999999998 34 8616 8329.25 286.75 35 8312 8365.375 -53.3749999999998 36 9692 9332 360 37 9911 10338.875 -427.875000000004 38 8915 8899.75 15.2499999999998 39 9452 9290.375 161.625000000000 40 9112 8552.5 559.5 41 8472 8347.5 124.500000000000 42 8230 8081 149.000000000000 43 8384 8253.125 130.875 44 8625 8013.25 611.750000000001 45 8221 7759.5 461.5 46 8649 8329.25 319.75 47 8625 8365.375 259.625 48 10443 9332 1111 49 10357 10338.875 18.1249999999959 50 8586 8899.75 -313.75 51 8892 9290.375 -398.375 52 8329 8552.5 -223.5 53 8101 8347.5 -246.500000000000 54 7922 8081 -159.000000000000 55 8120 8253.125 -133.125 56 7838 8013.25 -175.249999999999 57 7735 7759.5 -24.5000000000002 58 8406 8329.25 76.75 59 8209 8365.375 -156.375000000000 60 9451 9332 119.000000000000 61 10041 10338.875 -297.875000000004 62 9411 8899.75 511.25 63 10405 9290.375 1114.625 64 8467 8552.5 -85.5000000000001 65 8464 8347.5 116.500000000000 66 8102 8081 20.9999999999998 67 7627 8253.125 -626.125 68 7513 8013.25 -500.249999999999 69 7510 7759.5 -249.500000000000 70 8291 8329.25 -38.2499999999999 71 8064 8365.375 -301.375 72 9383 9332 51.0000000000002 73 9706 10338.875 -632.875000000004 74 8579 8899.75 -320.75 75 9474 9290.375 183.625000000000 76 8318 8552.5 -234.5 77 8213 8347.5 -134.500000000000 78 8059 8081 -22.0000000000002 79 9111 8253.125 857.875 80 7708 8013.25 -305.249999999999 81 7680 7759.5 -79.5000000000002 82 8014 8329.25 -315.25 83 8007 8365.375 -358.375 84 8718 9332 -614 85 9486 10338.875 -852.875000000004 86 9113 8899.75 213.250000000000 87 9025 9290.375 -265.375 88 8476 8552.5 -76.5000000000001 89 7952 8347.5 -395.500000000000 90 7759 8081 -322 91 7835 8253.125 -418.125 92 7600 8013.25 -413.249999999999 93 7651 7759.5 -108.500000000000 94 8319 8329.25 -10.2499999999999 95 8812 8365.375 446.625 96 8630 9332 -702 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.994246696762887 0.0115066064742250 0.00575330323711252 16 0.986015125171147 0.0279697496577052 0.0139848748288526 17 0.983044108719314 0.0339117825613727 0.0169558912806864 18 0.968044544220693 0.0639109115586145 0.0319554557793072 19 0.943391903780632 0.113216192438735 0.0566080962193675 20 0.910931028552956 0.178137942894088 0.0890689714470438 21 0.863690553238134 0.272618893523731 0.136309446761866 22 0.831480671972424 0.337038656055152 0.168519328027576 23 0.784530211452123 0.430939577095754 0.215469788547877 24 0.76831043107353 0.463379137852941 0.231689568926470 25 0.793702735821181 0.412594528357638 0.206297264178819 26 0.732503872313221 0.534992255373558 0.267496127686779 27 0.677318035014208 0.645363929971584 0.322681964985792 28 0.618951402868913 0.762097194262174 0.381048597131087 29 0.547551513582256 0.904896972835489 0.452448486417744 30 0.476271049144759 0.952542098289519 0.523728950855241 31 0.407944193159787 0.815888386319574 0.592055806840213 32 0.352740753231901 0.705481506463801 0.6472592467681 33 0.286371736238321 0.572743472476642 0.713628263761679 34 0.271134782984877 0.542269565969754 0.728865217015123 35 0.216566486425282 0.433132972850564 0.783433513574718 36 0.220550701864743 0.441101403729486 0.779449298135257 37 0.429305345080718 0.858610690161435 0.570694654919282 38 0.361549531367502 0.723099062735004 0.638450468632498 39 0.330565176504 0.661130353008 0.669434823496 40 0.371585394182431 0.743170788364861 0.62841460581757 41 0.317256281516675 0.63451256303335 0.682743718483325 42 0.265647830838964 0.531295661677928 0.734352169161036 43 0.216930217288269 0.433860434576538 0.783069782711731 44 0.272644458377460 0.545288916754919 0.72735554162254 45 0.287208253065034 0.574416506130068 0.712791746934966 46 0.265360469603432 0.530720939206863 0.734639530396568 47 0.232957164156766 0.465914328313532 0.767042835843234 48 0.661543518342458 0.676912963315083 0.338456481657542 49 0.688798354024492 0.622403291951017 0.311201645975508 50 0.67478045679763 0.65043908640474 0.32521954320237 51 0.720913869675807 0.558172260648386 0.279086130324193 52 0.6827165320911 0.6345669358178 0.3172834679089 53 0.646148938049801 0.707702123900398 0.353851061950199 54 0.592550878782699 0.814898242434602 0.407449121217301 55 0.532507888017244 0.934984223965513 0.467492111982756 56 0.51412957444742 0.97174085110516 0.48587042555258 57 0.450834683261487 0.901669366522974 0.549165316738513 58 0.390762091761886 0.781524183523772 0.609237908238114 59 0.334462304205090 0.668924608410181 0.66553769579491 60 0.331283498521555 0.66256699704311 0.668716501478445 61 0.356073454410119 0.712146908820237 0.643926545589881 62 0.392857006911261 0.785714013822522 0.607142993088739 63 0.791484025113908 0.417031949772184 0.208515974886092 64 0.735544501618507 0.528910996762986 0.264455498381493 65 0.702658467872898 0.594683064254204 0.297341532127102 66 0.640718481814066 0.718563036371867 0.359281518185934 67 0.781455655513035 0.437088688973931 0.218544344486965 68 0.757840937954954 0.484318124090092 0.242159062045046 69 0.701259770594684 0.597480458810631 0.298740229405316 70 0.627617376347304 0.744765247305392 0.372382623652696 71 0.586627728020647 0.826744543958706 0.413372271979353 72 0.662039310024068 0.675921379951865 0.337960689975932 73 0.635096490290453 0.729807019419094 0.364903509709547 74 0.621590044020635 0.75681991195873 0.378409955979365 75 0.578781033962708 0.842437932074585 0.421218966037292 76 0.483312363682361 0.966624727364722 0.516687636317639 77 0.39406104589593 0.78812209179186 0.60593895410407 78 0.308044171017020 0.616088342034039 0.69195582898298 79 0.817243004935286 0.365513990129428 0.182756995064714 80 0.702931515570734 0.594136968858532 0.297068484429266 81 0.533862632193923 0.932274735612153 0.466137367806077 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 0.0447761194029851 OK 10% type I error level 4 0.0597014925373134 OK

Charts produced by software:

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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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