*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, 15 Dec 2009 12:02:02 -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/Dec/15/t1260904285tcfendpy3xdn1o1.htm/, Retrieved Tue, 15 Dec 2009 20:11:39 +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/Dec/15/t1260904285tcfendpy3xdn1o1.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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564 -0.9 581 -1 597 -0.7 587 -1.7 536 -1 524 -0.2 537 0.7 536 0.6 533 1.9 528 2.1 516 2.7 502 3.2 506 4.8 518 5.5 534 5.4 528 5.9 478 5.8 469 5.1 490 4.1 493 4.4 508 3.6 517 3.5 514 3.1 510 2.9 527 2.2 542 1.4 565 1.2 555 1.3 499 1.3 511 1.3 526 1.8 532 1.8 549 1.8 561 1.7 557 2.1 566 2 588 1.7 620 1.9 626 2.3 620 2.4 573 2.5 573 2.8 574 2.6 580 2.2 590 2.8 593 2.8 597 2.8 595 2.3 612 2.2 628 3 629 2.9 621 2.7 569 2.7 567 2.3 573 2.4 584 2.8 589 2.3 591 2 595 1.9 594 2.3

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 BouwV[t] = + 518.747264117247 -11.3910198062011X[t] + 19.2778823073052M1[t] + 37.7341697487835M2[t] + 49.0513552096416M3[t] + 38.1459775015075M4[t] -13.2255554531384M5[t] -17.1918311806524M6[t] -7.0746457197943M7[t] -3.38528065506023M8[t] + 5.01536599416992M9[t] + 6.76562907828386M10[t] + 3.93845533138997M11[t] + 1.76627572751399t + 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) 518.747264117247 10.657189 48.6758 0 0 X -11.3910198062011 1.612509 -7.0642 0 0 M1 19.2778823073052 12.386812 1.5563 0.126485 0.063243 M2 37.7341697487835 12.359388 3.0531 0.003758 0.001879 M3 49.0513552096416 12.340771 3.9747 0.000247 0.000123 M4 38.1459775015075 12.331543 3.0934 0.00336 0.00168 M5 -13.2255554531384 12.312201 -1.0742 0.288343 0.144172 M6 -17.1918311806524 12.301295 -1.3976 0.168949 0.084474 M7 -7.0746457197943 12.290025 -0.5756 0.567663 0.283832 M8 -3.38528065506023 12.281387 -0.2756 0.784057 0.392028 M9 5.01536599416992 12.273745 0.4086 0.684711 0.342355 M10 6.76562907828386 12.270125 0.5514 0.584035 0.292017 M11 3.93845533138997 12.266633 0.3211 0.749611 0.374806 t 1.76627572751399 0.149689 11.7997 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.909497131849277 R-squared 0.82718503284206 Adjusted R-squared 0.778346020384382 F-TEST (value) 16.9369729488052 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 2.36588526547621e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.3938442355615 Sum Squared Residuals 17301.5749347282 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 564 550.043339977648 13.9566600223522 2 581 571.40500512726 9.59499487274032 3 597 581.071160373771 15.9288396262285 4 587 583.323078199352 3.6769218006476 5 536 525.74410710788 10.2558928921202 6 524 514.431291262919 9.56870873708105 7 537 516.06283462571 20.9371653742900 8 536 522.657577398578 13.3424226014218 9 533 518.016174027261 14.9838259727391 10 528 519.254508877649 8.7454911223514 11 516 511.358998974548 4.64100102545195 12 502 503.491309467572 -1.49130946757155 13 506 506.309835812469 -0.309835812468962 14 518 518.55868511712 -0.55868511712047 15 534 532.781248286113 1.21875171388738 16 528 517.946636402392 10.0533635976080 17 478 469.48048115588 8.51951884411976 18 469 475.254195020221 -6.25419502022099 19 490 498.528676014794 -8.52867601479417 20 493 500.567010865182 -7.56701086518187 21 508 519.846749086887 -11.8467490868869 22 517 524.502389879135 -7.50238987913494 23 514 527.997899782235 -13.9978997822355 24 510 528.1039241396 -18.1039241395997 25 527 557.12179603876 -30.1217960387597 26 542 586.457175052713 -44.4571750527128 27 565 601.818840202325 -36.8188402023251 28 555 591.540636241085 -36.5406362410849 29 499 541.935379013953 -42.935379013953 30 511 539.735379013953 -28.7353790139531 31 526 545.923330299225 -19.9233302992246 32 532 551.378971091473 -19.3789710914726 33 549 561.545893468217 -12.5458934682168 34 561 566.201534260465 -5.20153426046479 35 557 560.584228318604 -3.58422831860446 36 566 559.551150695349 6.4488493046514 37 588 584.012614672028 3.98738532797186 38 620 601.95697387978 18.0430261202198 39 626 610.484027145672 15.5159728543282 40 620 600.205823184432 19.7941768155684 41 573 549.46146397668 23.5385360233204 42 573 543.844158034819 29.1558419651807 43 574 558.005823184432 15.9941768155684 44 580 568.01787189916 11.9821281008399 45 590 571.350182392184 18.6498176078165 46 593 574.866721203811 18.1332787961885 47 597 573.805823184432 23.1941768155684 48 595 577.329153483656 17.6708465163439 49 612 599.512413499095 12.4875865009045 50 628 610.622160823127 17.3778391768732 51 629 624.844723992119 4.15527600788097 52 621 617.983825972739 3.01617402726085 53 569 568.378568745607 0.621431254392733 54 567 570.734976668088 -3.73497666808771 55 573 581.47933587584 -8.47933587583966 56 584 582.378568745607 1.62143125439271 57 589 598.241001025452 -9.24100102545196 58 591 605.17484577894 -14.1748457789402 59 595 605.25304974018 -10.2530497401804 60 594 598.524462213824 -4.52446221382403 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00241895901723311 0.00483791803446621 0.997581040982767 18 0.000725555881337732 0.00145111176267546 0.999274444118662 19 0.000217401274492083 0.000434802548984167 0.999782598725508 20 0.000116146245552312 0.000232292491104624 0.999883853754448 21 0.000157207953710994 0.000314415907421988 0.99984279204629 22 0.000566551975826148 0.00113310395165230 0.999433448024174 23 0.00052568941155096 0.00105137882310192 0.99947431058845 24 0.000960493907449871 0.00192098781489974 0.99903950609255 25 0.0027736480285569 0.0055472960571138 0.997226351971443 26 0.00584070066453306 0.0116814013290661 0.994159299335467 27 0.00264491419789270 0.00528982839578539 0.997355085802107 28 0.00143521812783954 0.00287043625567908 0.99856478187216 29 0.00243247952849115 0.0048649590569823 0.997567520471509 30 0.00769761801255042 0.0153952360251008 0.99230238198745 31 0.0219533499175337 0.0439066998350673 0.978046650082466 32 0.0951793349519692 0.190358669903938 0.904820665048031 33 0.286484286717076 0.572968573434152 0.713515713282924 34 0.522078930509451 0.955842138981099 0.477921069490549 35 0.937955341127696 0.124089317744607 0.0620446588723037 36 0.998893339586427 0.00221332082714679 0.00110666041357340 37 0.999999847515663 3.04968674122329e-07 1.52484337061164e-07 38 0.999999882484408 2.35031184252051e-07 1.17515592126026e-07 39 0.999999409671168 1.18065766486405e-06 5.90328832432025e-07 40 0.999996028955268 7.9420894632852e-06 3.9710447316426e-06 41 0.99999198246126 1.60350774780667e-05 8.01753873903334e-06 42 0.999995763099204 8.47380159274221e-06 4.23690079637111e-06 43 0.999912251287406 0.000175497425188443 8.77487125942217e-05 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.740740740740741 NOK 5% type I error level 23 0.851851851851852 NOK 10% type I error level 23 0.851851851851852 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/107v891260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/107v891260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/1k7uw1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/1k7uw1260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/2xwam1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/2xwam1260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/3030x1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/3030x1260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/4twxs1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/4twxs1260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/52qk31260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/52qk31260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/6eh071260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/6eh071260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/7hdnw1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/7hdnw1260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/8ct061260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/8ct061260903717.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/9seeo1260903717.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260904285tcfendpy3xdn1o1/9seeo1260903717.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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