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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 19 Nov 2009 02:48:41 -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/19/t1258624171n1m1l1oplu8ky3v.htm/, Retrieved Thu, 19 Nov 2009 10:49:43 +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/19/t1258624171n1m1l1oplu8ky3v.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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23 25.7 19 24.7 18 24.2 19 23.6 19 24.4 22 22.5 23 19.4 20 18.1 14 18.1 14 20.7 14 19.1 15 18.3 11 16.9 17 17.9 16 20.2 20 21.2 24 23.8 23 24 20 26.6 21 25.3 19 27.6 23 24.7 23 26.6 23 24.4 23 24.6 27 26 26 24.8 17 24 24 22.7 26 23 24 24.1 27 24 27 22.7 26 22.6 24 23.1 23 24.4 23 23 24 22 17 21.3 21 21.5 19 21.3 22 23.2 22 21.8 18 23.3 16 21 14 22.4 12 20.4 14 19.9 16 21.3 8 18.9 3 15.6 0 12.5 5 7.8 1 5.5 1 4 3 3.3 6 3.7 7 3.1 8 5 14 6.3

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 Y[t] = + 0.601780378749309 + 0.921662359123831X[t] -1.95485098721075M1[t] -1.78618604356121M2[t] -4.15945563935701M3[t] -4.15115848233528M4[t] -0.835027561225937M5[t] + 0.0967708880586427M6[t] -0.279264426744397M7[t] -0.129032730277341M8[t] -1.36313350563505M9[t] -1.03686649436496M10[t] -1.76589922464229M11[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) 0.601780378749309 2.584339 0.2329 0.816884 0.408442 X 0.921662359123831 0.089166 10.3365 0 0 M1 -1.95485098721075 2.815365 -0.6944 0.49088 0.24544 M2 -1.78618604356121 2.811476 -0.6353 0.528301 0.264151 M3 -4.15945563935701 2.805894 -1.4824 0.144909 0.072454 M4 -4.15115848233528 2.801721 -1.4816 0.145108 0.072554 M5 -0.835027561225937 2.799145 -0.2983 0.766777 0.383389 M6 0.0967708880586427 2.797959 0.0346 0.972556 0.486278 M7 -0.279264426744397 2.796978 -0.0998 0.920892 0.460446 M8 -0.129032730277341 2.796622 -0.0461 0.963395 0.481698 M9 -1.36313350563505 2.796596 -0.4874 0.628222 0.314111 M10 -1.03686649436496 2.796596 -0.3708 0.712482 0.356241 M11 -1.76589922464229 2.79664 -0.6314 0.530815 0.265407 Multiple Linear Regression - Regression Statistics Multiple R 0.838610587508146 R-squared 0.703267717480758 Adjusted R-squared 0.627506283646058 F-TEST (value) 9.28266113620792 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 8.0980888661486e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.4218030807938 Sum Squared Residuals 918.960096809924 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 23 22.3336520210210 0.66634797897896 2 19 21.5806546055467 -2.58065460554671 3 18 18.746553830189 -0.746553830189013 4 19 18.2018535717364 0.798146428263554 5 19 22.2553143801449 -3.25531438014486 6 22 21.4359543470942 0.564045652905844 7 23 18.2027657190072 4.79723428099276 8 20 17.1548363486133 2.84516365138668 9 14 15.9207355732556 -1.92073557325561 10 14 18.6433247182477 -4.64332471824766 11 14 16.4396322133722 -2.43963221337219 12 15 17.4682015507154 -2.46820155071542 13 11 14.2230232607313 -3.22302326073131 14 17 15.3133505635047 1.68664943649532 15 16 15.0599043936937 0.940095606306308 16 20 15.9898639098393 4.01013609016075 17 24 21.7023169646706 2.29768303532944 18 23 22.8184478857799 0.181552114220096 19 20 24.8387347046988 -4.83873470469883 20 21 23.7908053343049 -2.7908053343049 21 19 24.676527984932 -5.676527984932 22 23 22.329974154743 0.670025845257014 23 23 23.3520999068009 -0.352099906800930 24 23 23.0903419413708 -0.090341941370793 25 23 21.3198234259848 1.68017657401519 26 27 22.7788156724077 4.22118432759229 27 26 19.2995512456633 6.70044875433668 28 17 18.570518515386 -1.57051851538598 29 24 20.6884883696343 3.31151163036566 30 26 21.8967855266561 4.10321447334392 31 24 22.5345788068893 1.46542119311075 32 27 22.5926442674439 4.40735573255608 33 27 20.1603824252252 6.83961757477477 34 26 20.3944832005829 5.60551679941706 35 24 20.1262816498675 3.87371835013248 36 23 23.0903419413708 -0.090341941370793 37 23 19.8451636513867 3.15483634861332 38 24 19.0921662359124 4.90783376408761 39 17 16.0737329887299 0.926267011270093 40 21 16.2663626175764 4.7336373824236 41 19 19.3981610668610 -0.398161066860982 42 22 22.0811179984808 -0.0811179984808388 43 22 20.4147553809044 1.58524461909556 44 18 21.9474806160572 -3.94748061605724 45 16 18.5935564147147 -2.59355641471472 46 14 20.2101507287582 -6.21015072875817 47 12 17.6377932802332 -5.63779328023318 48 14 18.9428613253136 -4.94286132531355 49 16 18.2783376408762 -2.27833764087617 50 8 16.2350129226285 -8.2350129226285 51 3 10.8202575417241 -7.82025754172407 52 0 7.97140138546192 -7.97140138546192 53 5 6.95571921868926 -1.95571921868926 54 1 5.76769424198903 -4.76769424198903 55 1 4.00916538850024 -3.00916538850024 56 3 3.51423343358061 -0.514233433580615 57 6 2.64879760187244 3.35120239812756 58 7 2.42206719766823 4.57793280233177 59 8 3.44419294972618 4.55580705027382 60 14 6.40825324122945 7.59174675877055 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.180327272469191 0.360654544938383 0.819672727530809 17 0.173450644978236 0.346901289956472 0.826549355021764 18 0.084296420889624 0.168592841779248 0.915703579110376 19 0.143328427135142 0.286656854270283 0.856671572864859 20 0.0871920413428489 0.174384082685698 0.912807958657151 21 0.0567626913987818 0.113525382797564 0.943237308601218 22 0.0705475092761683 0.141095018552337 0.929452490723832 23 0.0541568692763768 0.108313738552754 0.945843130723623 24 0.0375955213426798 0.0751910426853595 0.96240447865732 25 0.0263652968259499 0.0527305936518999 0.97363470317405 26 0.0280328421725541 0.0560656843451081 0.971967157827446 27 0.0503010459342524 0.100602091868505 0.949698954065748 28 0.0357189104872345 0.0714378209744689 0.964281089512766 29 0.026433094681791 0.052866189363582 0.97356690531821 30 0.0220165819182519 0.0440331638365038 0.977983418081748 31 0.0125428835269783 0.0250857670539567 0.987457116473022 32 0.0122004968889114 0.0244009937778229 0.987799503111089 33 0.0369404806556752 0.0738809613113504 0.963059519344325 34 0.0501923511710016 0.100384702342003 0.949807648828998 35 0.0454182682865794 0.0908365365731588 0.95458173171342 36 0.0263017328496061 0.0526034656992122 0.973698267150394 37 0.0192300651381266 0.0384601302762532 0.980769934861873 38 0.0372764374326566 0.0745528748653132 0.962723562567343 39 0.040365541364092 0.080731082728184 0.959634458635908 40 0.133965096606606 0.267930193213212 0.866034903393394 41 0.0996230239831672 0.199246047966334 0.900376976016833 42 0.160733746850279 0.321467493700557 0.839266253149721 43 0.493524623465633 0.987049246931267 0.506475376534367 44 0.680771593337239 0.638456813325523 0.319228406662761 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 4 0.137931034482759 NOK 10% type I error level 14 0.482758620689655 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/10atr31258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/10atr31258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/13q8x1258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/13q8x1258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/2gfo51258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/2gfo51258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/3m2ky1258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/3m2ky1258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/4j5be1258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/4j5be1258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/51z241258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/51z241258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/6b2f61258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/6b2f61258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/788u11258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/788u11258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/89f061258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/89f061258624117.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/9zar21258624117.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v/9zar21258624117.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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