WS7-Multipleregression

*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: Thu, 19 Nov 2009 11:48:17 -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/t1258656542utomak9y5buamf7.htm/, Retrieved Thu, 19 Nov 2009 19:49:14 +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/t1258656542utomak9y5buamf7.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:

» Textbox « » Textfile « » CSV «

3922 1 3759 1 4138 1 4634 1 3996 1 4308 1 4143 1 4429 1 5219 1 4929 0 5755 1 5592 1 4163 1 4962 1 5208 1 4755 1 4491 1 5732 1 5731 1 5040 1 6102 1 4904 0 5369 0 5578 0 4619 0 4731 0 5011 0 5299 0 4146 0 4625 0 4736 0 4219 0 5116 0 4205 0 4121 0 5103 0 4300 0 4578 0 3809 0 5526 0 4247 0 3830 0 4394 0 4826 0 4409 0 4569 0 4106 0 4794 0 3914 0 3793 0 4405 0 4022 0 4100 0 4788 1 3163 1 3585 1 3903 1 4178 1 3863 1 4187 1

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 Bouw[t] = + 4555.54545454545 + 27.6397306397301Wman[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) 4555.54545454545 109.836209 41.4758 0 0 Wman 27.6397306397301 163.734153 0.1688 0.866535 0.433267 Multiple Linear Regression - Regression Statistics Multiple R 0.0221602099004452 R-squared 0.000491074902831788 Adjusted R-squared -0.0167418375988435 F-TEST (value) 0.0284963382007567 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.86653454062564 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 630.960983764151 Sum Squared Residuals 23090482.2558923 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3922 4583.18518518520 -661.185185185195 2 3759 4583.18518518518 -824.185185185185 3 4138 4583.18518518518 -445.185185185185 4 4634 4583.18518518518 50.8148148148154 5 3996 4583.18518518518 -587.185185185185 6 4308 4583.18518518518 -275.185185185185 7 4143 4583.18518518518 -440.185185185185 8 4429 4583.18518518518 -154.185185185185 9 5219 4583.18518518518 635.814814814815 10 4929 4555.54545454545 373.454545454545 11 5755 4583.18518518518 1171.81481481482 12 5592 4583.18518518518 1008.81481481482 13 4163 4583.18518518518 -420.185185185185 14 4962 4583.18518518518 378.814814814815 15 5208 4583.18518518518 624.814814814815 16 4755 4583.18518518518 171.814814814815 17 4491 4583.18518518518 -92.1851851851846 18 5732 4583.18518518518 1148.81481481482 19 5731 4583.18518518518 1147.81481481482 20 5040 4583.18518518518 456.814814814815 21 6102 4583.18518518518 1518.81481481482 22 4904 4555.54545454545 348.454545454545 23 5369 4555.54545454545 813.454545454545 24 5578 4555.54545454545 1022.45454545455 25 4619 4555.54545454545 63.4545454545454 26 4731 4555.54545454545 175.454545454545 27 5011 4555.54545454545 455.454545454545 28 5299 4555.54545454545 743.454545454545 29 4146 4555.54545454545 -409.545454545455 30 4625 4555.54545454545 69.4545454545454 31 4736 4555.54545454545 180.454545454545 32 4219 4555.54545454545 -336.545454545455 33 5116 4555.54545454545 560.454545454545 34 4205 4555.54545454545 -350.545454545455 35 4121 4555.54545454545 -434.545454545455 36 5103 4555.54545454545 547.454545454545 37 4300 4555.54545454545 -255.545454545455 38 4578 4555.54545454545 22.4545454545454 39 3809 4555.54545454545 -746.545454545455 40 5526 4555.54545454545 970.454545454545 41 4247 4555.54545454545 -308.545454545455 42 3830 4555.54545454545 -725.545454545455 43 4394 4555.54545454545 -161.545454545455 44 4826 4555.54545454545 270.454545454545 45 4409 4555.54545454545 -146.545454545455 46 4569 4555.54545454545 13.4545454545454 47 4106 4555.54545454545 -449.545454545455 48 4794 4555.54545454545 238.454545454545 49 3914 4555.54545454545 -641.545454545455 50 3793 4555.54545454545 -762.545454545455 51 4405 4555.54545454545 -150.545454545455 52 4022 4555.54545454545 -533.545454545455 53 4100 4555.54545454545 -455.545454545455 54 4788 4583.18518518518 204.814814814815 55 3163 4583.18518518518 -1420.18518518518 56 3585 4583.18518518518 -998.185185185185 57 3903 4583.18518518518 -680.185185185185 58 4178 4583.18518518518 -405.185185185185 59 3863 4583.18518518518 -720.185185185185 60 4187 4583.18518518518 -396.185185185185 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.226167231683546 0.452334463367091 0.773832768316454 6 0.123389939704840 0.246779879409679 0.87661006029516 7 0.0568259631774179 0.113651926354836 0.943174036822582 8 0.0345410234984041 0.0690820469968081 0.965458976501596 9 0.195136551077712 0.390273102155423 0.804863448922288 10 0.122464952008046 0.244929904016093 0.877535047991954 11 0.513945759090611 0.972108481818778 0.486054240909389 12 0.681352254748792 0.637295490502416 0.318647745251208 13 0.620726285584331 0.758547428831337 0.379273714415669 14 0.565627571454926 0.868744857090148 0.434372428545074 15 0.56053614643047 0.878927707139061 0.439463853569531 16 0.476533329330878 0.953066658661756 0.523466670669122 17 0.391337366388709 0.782674732777417 0.608662633611291 18 0.572192270277242 0.855615459445517 0.427807729722758 19 0.735623547229274 0.528752905541451 0.264376452770726 20 0.71335539363146 0.573289212737081 0.286644606368540 21 0.9631220435144 0.0737559129712009 0.0368779564856005 22 0.94827725803744 0.103445483925121 0.0517227419625605 23 0.953429796632116 0.0931404067357679 0.0465702033678839 24 0.972822568451529 0.0543548630969427 0.0271774315484714 25 0.964655342726213 0.0706893145475732 0.0353446572737866 26 0.952228535748717 0.0955429285025664 0.0477714642512832 27 0.944309580366475 0.111380839267049 0.0556904196335246 28 0.95760359797217 0.0847928040556614 0.0423964020278307 29 0.95846078453203 0.083078430935941 0.0415392154679705 30 0.943304399559462 0.113391200881076 0.056695600440538 31 0.926105949202144 0.147788101595712 0.0738940507978562 32 0.91309503572247 0.173809928555059 0.0869049642775293 33 0.922867371411737 0.154265257176527 0.0771326285882634 34 0.907152636297478 0.185694727405044 0.0928473637025222 35 0.893290293416875 0.213419413166251 0.106709706583125 36 0.909191647035287 0.181616705929426 0.0908083529647132 37 0.880409594595661 0.239180810808678 0.119590405404339 38 0.844054880514554 0.311890238970892 0.155945119485446 39 0.863284582189693 0.273430835620613 0.136715417810307 40 0.970863819665182 0.0582723606696367 0.0291361803348184 41 0.956200224970154 0.087599550059693 0.0437997750298465 42 0.958952140840204 0.0820957183195916 0.0410478591597958 43 0.936511292707262 0.126977414585476 0.0634887072927379 44 0.937860382826233 0.124279234347535 0.0621396171737674 45 0.90849643852428 0.183007122951441 0.0915035614757205 46 0.885618219635417 0.228763560729167 0.114381780364583 47 0.838030513781202 0.323938972437595 0.161969486218798 48 0.872105334708755 0.25578933058249 0.127894665291245 49 0.824583721690507 0.350832556618986 0.175416278309493 50 0.791392419187493 0.417215161625013 0.208607580812507 51 0.720597329106989 0.558805341786022 0.279402670893011 52 0.61456688519752 0.77086622960496 0.38543311480248 53 0.486221003189370 0.972442006378739 0.51377899681063 54 0.696227769814154 0.607544460371692 0.303772230185846 55 0.883418700273877 0.233162599452246 0.116581299726123 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 0 0 OK 10% type I error level 11 0.215686274509804 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/10wmt51258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/10wmt51258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/1z4c61258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/1z4c61258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/2o10p1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/2o10p1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/3drgp1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/3drgp1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/4ooho1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/4ooho1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/5wagc1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/5wagc1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/6y7h71258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/6y7h71258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/76eez1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/76eez1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/8prtk1258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/8prtk1258656493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/9a5691258656493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656542utomak9y5buamf7/9a5691258656493.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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