SHW WS7

*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: Fri, 20 Nov 2009 03:54:07 -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/t1258714510oqkn6sqjvkro6eq.htm/, Retrieved Fri, 20 Nov 2009 11:55:22 +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/t1258714510oqkn6sqjvkro6eq.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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8.6 0 8.5 0 8.3 0 7.8 0 7.8 0 8 0 8.6 0 8.9 0 8.9 0 8.6 0 8.3 0 8.3 0 8.3 0 8.4 0 8.5 0 8.4 0 8.6 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.6 0 8.4 0 8.1 0 8 0 8 0 8 0 8 0 7.9 0 7.8 0 7.8 0 7.9 0 8.1 0 8 0 7.6 0 7.3 0 7 0 6.8 0 7 0 7.1 0 7.2 0 7.1 1 6.9 1 6.7 1 6.7 1 6.6 1 6.9 1 7.3 1 7.5 1 7.3 1 7.1 1 6.9 1 7.1 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 Y[t] = + 8.14375 -1.13541666666667X[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) 8.14375 0.068563 118.7775 0 0 X -1.13541666666667 0.153312 -7.4059 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.697161272237485 R-squared 0.486033839507789 Adjusted R-squared 0.477172353982061 F-TEST (value) 54.8478963370955 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 6.0921212519105e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.475018904644978 Sum Squared Residuals 13.0872916666667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.14375000000002 0.456249999999984 2 8.5 8.14375 0.356250000000003 3 8.3 8.14375 0.156250000000001 4 7.8 8.14375 -0.34375 5 7.8 8.14375 -0.34375 6 8 8.14375 -0.143750000000000 7 8.6 8.14375 0.45625 8 8.9 8.14375 0.75625 9 8.9 8.14375 0.75625 10 8.6 8.14375 0.45625 11 8.3 8.14375 0.156250000000001 12 8.3 8.14375 0.156250000000001 13 8.3 8.14375 0.156250000000001 14 8.4 8.14375 0.256250000000001 15 8.5 8.14375 0.35625 16 8.4 8.14375 0.256250000000001 17 8.6 8.14375 0.45625 18 8.5 8.14375 0.35625 19 8.5 8.14375 0.35625 20 8.5 8.14375 0.35625 21 8.5 8.14375 0.35625 22 8.5 8.14375 0.35625 23 8.5 8.14375 0.35625 24 8.5 8.14375 0.35625 25 8.5 8.14375 0.35625 26 8.5 8.14375 0.35625 27 8.5 8.14375 0.35625 28 8.5 8.14375 0.35625 29 8.6 8.14375 0.45625 30 8.4 8.14375 0.256250000000001 31 8.1 8.14375 -0.0437500000000001 32 8 8.14375 -0.143750000000000 33 8 8.14375 -0.143750000000000 34 8 8.14375 -0.143750000000000 35 8 8.14375 -0.143750000000000 36 7.9 8.14375 -0.243749999999999 37 7.8 8.14375 -0.34375 38 7.8 8.14375 -0.34375 39 7.9 8.14375 -0.243749999999999 40 8.1 8.14375 -0.0437500000000001 41 8 8.14375 -0.143750000000000 42 7.6 8.14375 -0.54375 43 7.3 8.14375 -0.84375 44 7 8.14375 -1.14375 45 6.8 8.14375 -1.34375 46 7 8.14375 -1.14375 47 7.1 8.14375 -1.04375 48 7.2 8.14375 -0.94375 49 7.1 7.00833333333333 0.0916666666666664 50 6.9 7.00833333333333 -0.108333333333333 51 6.7 7.00833333333333 -0.308333333333333 52 6.7 7.00833333333333 -0.308333333333333 53 6.6 7.00833333333333 -0.408333333333334 54 6.9 7.00833333333333 -0.108333333333333 55 7.3 7.00833333333333 0.291666666666667 56 7.5 7.00833333333333 0.491666666666667 57 7.3 7.00833333333333 0.291666666666667 58 7.1 7.00833333333333 0.0916666666666664 59 6.9 7.00833333333333 -0.108333333333333 60 7.1 7.00833333333333 0.0916666666666664 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.51160000178527 0.97679999642946 0.48839999821473 6 0.363224269999252 0.726448539998505 0.636775730000748 7 0.333151710557348 0.666303421114696 0.666848289442652 8 0.452247713156941 0.904495426313883 0.547752286843059 9 0.51415307872714 0.97169384254572 0.48584692127286 10 0.435193415959257 0.870386831918514 0.564806584040743 11 0.335806122238497 0.671612244476993 0.664193877761504 12 0.249591268221266 0.499182536442532 0.750408731778734 13 0.178858883745672 0.357717767491345 0.821141116254328 14 0.12469836067383 0.24939672134766 0.87530163932617 15 0.0895642760226075 0.179128552045215 0.910435723977392 16 0.0595570366010637 0.119114073202127 0.940442963398936 17 0.0472257754246319 0.0944515508492638 0.952774224575368 18 0.0330415809180044 0.0660831618360087 0.966958419081996 19 0.0230863298592111 0.0461726597184222 0.976913670140789 20 0.0162014827860769 0.0324029655721538 0.983798517213923 21 0.0114970995069190 0.0229941990138379 0.988502900493081 22 0.00831654652751608 0.0166330930550322 0.991683453472484 23 0.00619207567012026 0.0123841513402405 0.99380792432988 24 0.00480229504966894 0.00960459009933788 0.995197704950331 25 0.00393777937997768 0.00787555875995535 0.996062220620022 26 0.00347909825566418 0.00695819651132836 0.996520901744336 27 0.00339418322373752 0.00678836644747505 0.996605816776262 28 0.00377643531336636 0.00755287062673273 0.996223564686634 29 0.00700452904333759 0.0140090580866752 0.992995470956662 30 0.00936664952961359 0.0187332990592272 0.990633350470386 31 0.0123329038441341 0.0246658076882681 0.987667096155866 32 0.0180526126725443 0.0361052253450886 0.981947387327456 33 0.0252210580364163 0.0504421160728326 0.974778941963584 34 0.0346595376745959 0.0693190753491918 0.965340462325404 35 0.0481197654646686 0.0962395309293373 0.951880234535331 36 0.0690620583392573 0.138124116678515 0.930937941660743 37 0.0998031317827769 0.199606263565554 0.900196868217223 38 0.135438517292546 0.270877034585093 0.864561482707454 39 0.187441836780236 0.374883673560473 0.812558163219764 40 0.378628353483253 0.757256706966506 0.621371646516747 41 0.713247590918721 0.573504818162558 0.286752409081279 42 0.871122367205516 0.257755265588969 0.128877632794485 43 0.93238730314995 0.135225393700100 0.0676126968500502 44 0.963438813248428 0.0731223735031431 0.0365611867515716 45 0.985024126533375 0.0299517469332498 0.0149758734666249 46 0.986110327046495 0.0277793459070089 0.0138896729535044 47 0.982706947925111 0.0345861041497785 0.0172930520748892 48 0.974454319788982 0.0510913604220368 0.0255456802110184 49 0.952872703749508 0.0942545925009834 0.0471272962504917 50 0.91769303437152 0.164613931256959 0.0823069656284795 51 0.898691162019863 0.202617675960273 0.101308837980137 52 0.886310509019147 0.227378981961706 0.113689490980853 53 0.940886975597941 0.118226048804118 0.0591130244020589 54 0.920229629623733 0.159540740752534 0.079770370376267 55 0.831684607495008 0.336630785009983 0.168315392504992 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0980392156862745 NOK 5% type I error level 17 0.333333333333333 NOK 10% type I error level 25 0.490196078431373 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/10qela1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/10qela1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/1h49w1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/1h49w1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/2hjue1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/2hjue1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/381981258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/381981258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/4kp6b1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/4kp6b1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/53rgw1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/53rgw1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/65y621258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/65y621258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/73n4u1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/73n4u1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/8hd1u1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/8hd1u1258714442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/99n9x1258714442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258714510oqkn6sqjvkro6eq/99n9x1258714442.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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