WS 7 (1)

*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, 23 Nov 2010 08:36:41 +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/23/t129050131371j1ktvzq5p2h25.htm/, Retrieved Tue, 23 Nov 2010 09:35:23 +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/2010/Nov/23/t129050131371j1ktvzq5p2h25.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:

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1.39 1.08 1.34 1.12 1.33 1.12 1.3 1.16 1.28 1.16 1.29 1.16 1.29 1.16 1.28 1.15 1.27 1.17 1.26 1.16 1.29 1.19 1.36 1.13 1.33 1.14 1.35 1.13 1.31 1.16 1.3 1.17 1.32 1.14 1.33 1.14 1.36 1.11 1.35 1.12 1.4 1.08 1.41 1.07 1.4 1.09 1.4 1.08 1.4 1.08 1.41 1.08 1.4 1.09 1.39 1.08 1.41 1.07 1.42 1.07 1.43 1.07 1.42 1.08 1.42 1.07 1.43 1.06 1.43 1.06 1.43 1.06 1.46 1.04 1.47 1.03 1.47 1.03 1.46 1.04 1.47 1.03 1.49 1.02 1.5 1.01 1.47 1.03 1.48 1.02 1.49 1.01 1.49 1.02 1.5 1.01 1.48 1.02 1.46 1.03 1.43 1.04 1.44 1.04 1.43 1.03

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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation us/ch[t] = + 2.10935943686758 -0.734389206628538`eu/us`[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) 2.10935943686758 0.028181 74.851 0 0 `eu/us` -0.734389206628538 0.02018 -36.3916 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.981284059913598 R-squared 0.962918406240514 Adjusted R-squared 0.962191316166798 F-TEST (value) 1324.34541613257 F-TEST (DF numerator) 1 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0102367726314117 Sum Squared Residuals 0.00534436720926817 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.08 1.08855843965391 -0.00855843965390507 2 1.12 1.12527789998534 -0.00527789998533506 3 1.12 1.13262179205162 -0.0126217920516204 4 1.16 1.15465346825048 0.0053465317495233 5 1.16 1.16934125238305 -0.00934125238304747 6 1.16 1.16199736031676 -0.00199736031676209 7 1.16 1.16199736031676 -0.00199736031676209 8 1.15 1.16934125238305 -0.0193412523830475 9 1.17 1.17668514444933 -0.00668514444933285 10 1.16 1.18402903651562 -0.0240290365156182 11 1.19 1.16199736031676 0.0280026396832379 12 1.13 1.11059011585276 0.0194098841472356 13 1.14 1.13262179205162 0.00737820794837943 14 1.13 1.11793400791905 0.0120659920809502 15 1.16 1.14730957618419 0.0126904238158087 16 1.17 1.15465346825048 0.0153465317495233 17 1.14 1.13996568411791 3.43158820940498e-05 18 1.14 1.13262179205162 0.00737820794837943 19 1.11 1.11059011585276 -0.000590115852764216 20 1.12 1.11793400791905 0.00206599208095041 21 1.08 1.08121454758762 -0.00121454758762287 22 1.07 1.07387065552134 -0.0038706555213375 23 1.09 1.08121454758762 0.00878545241237714 24 1.08 1.08121454758762 -0.00121454758762287 25 1.08 1.08121454758762 -0.00121454758762287 26 1.08 1.07387065552134 0.00612934447866251 27 1.09 1.08121454758762 0.00878545241237714 28 1.08 1.08855843965391 -0.00855843965390826 29 1.07 1.07387065552134 -0.0038706555213375 30 1.07 1.06652676345505 0.00347323654494789 31 1.07 1.05918287138877 0.0108171286112333 32 1.08 1.06652676345505 0.0134732365449479 33 1.07 1.06652676345505 0.00347323654494789 34 1.06 1.05918287138877 0.000817128611233264 35 1.06 1.05918287138877 0.000817128611233264 36 1.06 1.05918287138877 0.000817128611233264 37 1.04 1.03715119518991 0.00284880481008939 38 1.03 1.02980730312363 0.000192696876374768 39 1.03 1.02980730312363 0.000192696876374768 40 1.04 1.03715119518991 0.00284880481008939 41 1.03 1.02980730312363 0.000192696876374768 42 1.02 1.01511951899105 0.00488048100894553 43 1.01 1.00777562692477 0.00222437307523091 44 1.03 1.02980730312363 0.000192696876374768 45 1.02 1.02246341105734 -0.00246341105733985 46 1.01 1.01511951899105 -0.00511951899105448 47 1.02 1.01511951899105 0.00488048100894553 48 1.01 1.00777562692477 0.00222437307523091 49 1.02 1.02246341105734 -0.00246341105733985 50 1.03 1.03715119518991 -0.00715119518991061 51 1.04 1.05918287138877 -0.0191828713887668 52 1.04 1.05183897932248 -0.0118389793224814 53 1.03 1.05918287138877 -0.0291828713887668 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.347973323759285 0.695946647518571 0.652026676240715 6 0.201198821532569 0.402397643065139 0.798801178467431 7 0.106381748963203 0.212763497926405 0.893618251036797 8 0.309007678706755 0.61801535741351 0.690992321293245 9 0.218950188093510 0.437900376187021 0.78104981190649 10 0.594880177958573 0.810239644082854 0.405119822041427 11 0.992922958464653 0.0141540830706935 0.00707704153534675 12 0.998063560906294 0.00387287818741149 0.00193643909370575 13 0.996808719594157 0.00638256081168678 0.00319128040584339 14 0.99605468341386 0.0078906331722805 0.00394531658614025 15 0.996318816225984 0.00736236754803192 0.00368118377401596 16 0.99793493768338 0.00413012463323948 0.00206506231661974 17 0.996063830737429 0.00787233852514246 0.00393616926257123 18 0.994091015194252 0.0118179696114954 0.0059089848057477 19 0.990403450871431 0.0191930982571382 0.00959654912856912 20 0.98405027261104 0.0318994547779207 0.0159497273889604 21 0.977247873145573 0.0455042537088539 0.0227521268544269 22 0.96965693086991 0.0606861382601808 0.0303430691300904 23 0.96375457292605 0.0724908541479017 0.0362454270739508 24 0.946657909768944 0.106684180462112 0.0533420902310559 25 0.922879356545837 0.154241286908325 0.0771206434541626 26 0.902695972840033 0.194608054319934 0.097304027159967 27 0.901725406751018 0.196549186497963 0.0982745932489817 28 0.886688170591792 0.226623658816417 0.113311829408209 29 0.849914274717306 0.300171450565387 0.150085725282694 30 0.80974300336621 0.380513993267581 0.190256996633790 31 0.836149023535563 0.327701952928875 0.163850976464437 32 0.929558351718444 0.140883296563113 0.0704416482815563 33 0.942267822705711 0.115464354588578 0.0577321772942889 34 0.944782943096855 0.110434113806290 0.0552170569031448 35 0.960130636186654 0.079738727626693 0.0398693638133465 36 0.987420851004243 0.0251582979915147 0.0125791489957573 37 0.990944013382798 0.0181119732344032 0.00905598661720162 38 0.985887158957434 0.0282256820851320 0.0141128410425660 39 0.978734993339433 0.0425300133211332 0.0212650066605666 40 0.991576267476562 0.0168474650468756 0.0084237325234378 41 0.989515760825935 0.0209684783481307 0.0104842391740653 42 0.981989232867816 0.0360215342643676 0.0180107671321838 43 0.965251089421542 0.0694978211569151 0.0347489105784576 44 0.961838195896234 0.0763236082075321 0.0381618041037660 45 0.923041851172113 0.153916297655774 0.0769581488278869 46 0.914090629739952 0.171818740520096 0.085909370260048 47 0.848936088395094 0.302127823209811 0.151063911604906 48 0.746607166687555 0.50678566662489 0.253392833312445 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.136363636363636 NOK 5% type I error level 18 0.409090909090909 NOK 10% type I error level 23 0.522727272727273 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/10j0br1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/10j0br1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/1czdg1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/1czdg1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/24qd11290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/24qd11290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/34qd11290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/34qd11290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/44qd11290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/44qd11290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/5xzul1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/5xzul1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/6xzul1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/6xzul1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/789tp1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/789tp1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/889tp1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/889tp1290501388.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/9j0br1290501388.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129050131371j1ktvzq5p2h25/9j0br1290501388.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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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