Workshop 7 data 3

*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 11:40:21 -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/t12587425281sev99q6hy0vo6x.htm/, Retrieved Fri, 20 Nov 2009 19:42:20 +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/t12587425281sev99q6hy0vo6x.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)

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Dataseries X:

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613 0 611 594 543 611 0 613 611 594 594 0 611 613 611 595 0 594 611 613 591 0 595 594 611 589 0 591 595 594 584 0 589 591 595 573 0 584 589 591 567 0 573 584 589 569 0 567 573 584 621 0 569 567 573 629 0 621 569 567 628 0 629 621 569 612 0 628 629 621 595 0 612 628 629 597 0 595 612 628 593 0 597 595 612 590 0 593 597 595 580 0 590 593 597 574 0 580 590 593 573 0 574 580 590 573 0 573 574 580 620 0 573 573 574 626 0 620 573 573 620 0 626 620 573 588 0 620 626 620 566 0 588 620 626 557 0 566 588 620 561 0 557 566 588 549 0 561 557 566 532 0 549 561 557 526 0 532 549 561 511 0 526 532 549 499 0 511 526 532 555 0 499 511 526 565 0 555 499 511 542 0 565 555 499 527 0 542 565 555 510 0 527 542 565 514 0 510 527 542 517 0 514 510 527 508 0 517 514 510 493 0 508 517 514 490 0 493 508 517 469 1 490 493 508 478 1 469 490 493 528 1 478 469 490 534 1 528 478 469 518 1 534 528 478 506 1 518 534 528 502 1 506 518 534 516 1 502 506 518 528 1 516 502 506 533 1 528 516 502 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 27.2884150780906 + 7.47895824192816X[t] + 1.07957881958415Y1[t] + 0.0219700856362730Y2[t] -0.140078156472438Y3[t] -18.6096792509571M1[t] -17.3675468737958M2[t] -14.4556056693470M3[t] + 3.85191565250290M4[t] + 1.81841815303683M5[t] -6.79023110683807M6[t] -10.5917907190378M7[t] -5.5146948443727M8[t] -13.0312279400098M9[t] + 0.313573448969398M10[t] + 49.1524062059444M11[t] -0.176355958412839t + 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) 27.2884150780906 32.454672 0.8408 0.405328 0.202664 X 7.47895824192816 3.932394 1.9019 0.064228 0.032114 Y1 1.07957881958415 0.159332 6.7757 0 0 Y2 0.0219700856362730 0.235354 0.0933 0.926081 0.46304 Y3 -0.140078156472438 0.16043 -0.8731 0.38767 0.193835 M1 -18.6096792509571 12.046999 -1.5448 0.13009 0.065045 M2 -17.3675468737958 10.959982 -1.5846 0.120734 0.060367 M3 -14.4556056693470 11.530624 -1.2537 0.217063 0.108532 M4 3.85191565250290 11.502559 0.3349 0.739427 0.369713 M5 1.81841815303683 9.083038 0.2002 0.842314 0.421157 M6 -6.79023110683807 9.113171 -0.7451 0.460461 0.23023 M7 -10.5917907190378 9.953327 -1.0641 0.29349 0.146745 M8 -5.5146948443727 10.450702 -0.5277 0.600561 0.300281 M9 -13.0312279400098 10.161493 -1.2824 0.206901 0.103451 M10 0.313573448969398 11.010339 0.0285 0.977418 0.488709 M11 49.1524062059444 9.593202 5.1237 8e-06 4e-06 t -0.176355958412839 0.13019 -1.3546 0.182962 0.091481 Multiple Linear Regression - Regression Statistics Multiple R 0.989562151332385 R-squared 0.979233251349577 Adjusted R-squared 0.971129154315266 F-TEST (value) 120.831876420492 F-TEST (DF numerator) 16 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.2247844893341 Sum Squared Residuals 2140.09794761022 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 613 605.112830538047 7.88716946195325 2 611 601.567270071685 9.43272992831453 3 594 599.806309189794 -5.80630918979425 4 595 599.260538136083 -4.26053813608336 5 591 598.036928354917 -7.03692835491685 6 589 587.33690660396 1.66309339603974 7 584 580.971874895162 3.02812510483808 8 573 580.99109316811 -7.99109316811058 9 567 561.593142983399 5.40685701660145 10 569 568.742835336823 0.257164663176796 11 621 620.973508981933 0.0264910180672184 12 629 628.667254546058 0.332745453941555 13 628 619.380138033503 8.61986196649697 14 612 612.258032181191 -0.258032181190727 15 595 596.577760976465 -1.57776097646458 16 597 596.144643193263 0.85535680673684 17 593 597.961706422295 -4.96170642229492 18 590 587.283654756975 2.71634524302540 19 580 579.69896607212 0.301033927880349 20 574 574.298320161511 -0.298320161511326 21 573 560.328491803011 12.6715081969889 22 573 573.6863194649 -0.686319464900039 23 620 623.167295116661 -3.16729511666052 24 626 624.718815629231 1.28118437076930 25 620 613.44284736227 6.55715263772948 26 588 601.579298023127 -13.5792980231271 27 566 568.796071589818 -2.7960715898181 28 557 563.313929120878 -6.31392912087777 29 561 555.387025409862 5.61297459013844 30 549 553.804324141578 -4.80432414157761 31 532 538.220046486752 -6.22004648675236 32 526 523.943992816549 2.05600718345097 33 511 511.081077266847 -0.0810772668468236 34 499 510.305348549865 -11.3053485498648 35 555 546.523797167708 8.47620283229231 36 565 559.488980219514 5.51101978048596 37 542 554.409995879286 -12.4099958792862 38 527 523.020783541505 3.97921645849463 39 510 507.65659295942 2.3434070405795 40 514 510.327164704249 3.67283529575101 41 517 514.163307415977 2.83669258402338 42 508 511.086247659018 -3.08624765901787 43 493 496.897720343167 -3.89772034316707 44 490 484.986812725513 5.01318727448669 45 469 482.465297578347 -13.4652975783470 46 478 474.997849887824 3.00215011217601 47 528 533.335398733699 -5.33539873369901 48 534 541.124949605197 -7.12494960519685 49 518 528.654188186894 -10.6541881868935 50 506 505.574616182491 0.425383817508676 51 502 494.163265284503 7.83673471549744 52 516 509.953724845527 6.04627515447328 53 528 524.45103239695 3.54896760304995 54 533 529.48886683847 3.51113316153034 55 536 529.211392202799 6.788607797201 56 537 535.779781128316 1.22021887168424 57 524 528.531990368397 -4.53199036839658 58 536 527.267646760588 8.73235323941201 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.129758041783772 0.259516083567544 0.870241958216228 21 0.145175921385500 0.290351842771001 0.8548240786145 22 0.0738035900525588 0.147607180105118 0.926196409947441 23 0.0419930714755745 0.083986142951149 0.958006928524425 24 0.0209600806215535 0.0419201612431069 0.979039919378446 25 0.0452541040056122 0.0905082080112245 0.954745895994388 26 0.511750230933191 0.976499538133619 0.488249769066809 27 0.40477671669805 0.8095534333961 0.59522328330195 28 0.437175126404128 0.874350252808255 0.562824873595872 29 0.370362468609078 0.740724937218156 0.629637531390922 30 0.370612393317022 0.741224786634045 0.629387606682978 31 0.457810856798340 0.915621713596679 0.54218914320166 32 0.473001017566528 0.946002035133055 0.526998982433472 33 0.488265684686765 0.97653136937353 0.511734315313235 34 0.625760518948731 0.748478962102537 0.374239481051269 35 0.699142712998317 0.601714574003367 0.300857287001683 36 0.836655049040567 0.326689901918867 0.163344950959433 37 0.784191066276361 0.431617867447278 0.215808933723639 38 0.758015362251213 0.483969275497573 0.241984637748787 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 1 0.0526315789473684 NOK 10% type I error level 3 0.157894736842105 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/105iag1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/105iag1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/1zvjy1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/1zvjy1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/2pt2h1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/2pt2h1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/313qc1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/313qc1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/4824o1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/4824o1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/58jsw1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/58jsw1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/64chy1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/64chy1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/71sty1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/71sty1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/8kzzl1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/8kzzl1258742417.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/95t9g1258742417.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587425281sev99q6hy0vo6x/95t9g1258742417.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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