WS8-4

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 29 Nov 2011 14:48:54 -0500

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv.htm/, Retrieved Sat, 18 May 2013 23:38:32 +0000

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68] - RMPD  [Classical Decomposition] [compendium 8] [2011-11-24 14:23:11] [380049693c521f4999989215fb37aeca] - RMPD    [Multiple Regression] [WS 8 Q2] [2011-11-24 15:19:54] [380049693c521f4999989215fb37aeca] - RM          [Multiple Regression] [WS8-4] [2011-11-29 19:48:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

t1322148034e38ideuzwybad9e (pk = 146960)

Estimated Impact

22

Dataseries X:

» Textfile « » CSV « » Correlation Matrix « » Notched Boxplots «

135094 135411 135698 135880 135891 135971 136173 136358 136514 136506 136711 136891 137094 137182 137400 137479 137620 137687 137638 137612 137681 137772 137899 137983 137996 137913 137841 137656 137423 137245 137014 136747 136313 135804 135002 134383 133563 132837 132041 131381 130995 130493 130193 129962 129726 129505 129450 129320 129281 129246 129438 129715 130173 129981 129932 129873 129844 130015 130108 130260

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 'Herman Ole Andreas Wold' @ wold.wessa.net Multiple Linear Regression - Estimated Regression Equation y[t] = + 139568.525 -934.365972222166M1[t] -861.023611111117M2[t] -734.081250000006M3[t] -634.338888888894M4[t] -474.996527777783M5[t] -458.854166666671M6[t] -383.11180555556M7[t] -301.569444444448M8[t] -235.227083333337M9[t] -169.284722222226M10[t] -94.5423611111136M11[t] -161.142361111112t + 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) 139568.525 1132.34252 123.2565 0 0 M1 -934.365972222166 1377.558048 -0.6783 0.500922 0.250461 M2 -861.023611111117 1375.499869 -0.626 0.534362 0.267181 M3 -734.081250000006 1373.635049 -0.5344 0.595579 0.297789 M4 -634.338888888894 1371.964378 -0.4624 0.645958 0.322979 M5 -474.996527777783 1370.488566 -0.3466 0.730447 0.365223 M6 -458.854166666671 1369.208241 -0.3351 0.739023 0.369511 M7 -383.11180555556 1368.123954 -0.28 0.780686 0.390343 M8 -301.569444444448 1367.23617 -0.2206 0.826384 0.413192 M9 -235.227083333337 1366.545273 -0.1721 0.864072 0.432036 M10 -169.284722222226 1366.051561 -0.1239 0.901905 0.450952 M11 -94.5423611111136 1365.755248 -0.0692 0.945105 0.472553 t -161.142361111112 16.426294 -9.81 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.820999691173647 R-squared 0.674040492907223 Adjusted R-squared 0.590816788968642 F-TEST (value) 8.09914076168326 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 6.03553863554041e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2159.29246189414 Sum Squared Residuals 219139564.991665 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 135094 138473.016666666 -3379.01666666643 2 135411 138385.216666667 -2974.21666666668 3 135698 138351.016666667 -2653.01666666668 4 135880 138289.616666667 -2409.61666666668 5 135891 138287.816666667 -2396.81666666668 6 135971 138142.816666667 -2171.81666666668 7 136173 138057.416666667 -1884.41666666668 8 136358 137977.816666667 -1619.81666666668 9 136514 137883.016666667 -1369.01666666668 10 136506 137787.816666667 -1281.81666666668 11 136711 137701.416666667 -990.416666666678 12 136891 137634.816666667 -743.816666666678 13 137094 136539.308333333 554.691666666599 14 137182 136451.508333333 730.491666666662 15 137400 136417.308333333 982.691666666661 16 137479 136355.908333333 1123.09166666666 17 137620 136354.108333333 1265.89166666666 18 137687 136209.108333333 1477.89166666666 19 137638 136123.708333333 1514.29166666666 20 137612 136044.108333333 1567.89166666666 21 137681 135949.308333333 1731.69166666666 22 137772 135854.108333333 1917.89166666666 23 137899 135767.708333333 2131.29166666666 24 137983 135701.108333333 2281.89166666666 25 137996 134605.6 3390.39999999994 26 137913 134517.8 3395.2 27 137841 134483.6 3357.4 28 137656 134422.2 3233.8 29 137423 134420.4 3002.6 30 137245 134275.4 2969.6 31 137014 134190 2824 32 136747 134110.4 2636.6 33 136313 134015.6 2297.4 34 135804 133920.4 1883.6 35 135002 133834 1168 36 134383 133767.4 615.599999999998 37 133563 132671.891666667 891.108333333276 38 132837 132584.091666667 252.908333333338 39 132041 132549.891666667 -508.891666666662 40 131381 132488.491666667 -1107.49166666666 41 130995 132486.691666667 -1491.69166666666 42 130493 132341.691666667 -1848.69166666666 43 130193 132256.291666667 -2063.29166666666 44 129962 132176.691666667 -2214.69166666666 45 129726 132081.891666667 -2355.89166666666 46 129505 131986.691666667 -2481.69166666666 47 129450 131900.291666667 -2450.29166666666 48 129320 131833.691666667 -2513.69166666666 49 129281 130738.183333333 -1457.18333333339 50 129246 130650.383333333 -1404.38333333332 51 129438 130616.183333333 -1178.18333333332 52 129715 130554.783333333 -839.783333333322 53 130173 130552.983333333 -379.983333333322 54 129981 130407.983333333 -426.983333333323 55 129932 130322.583333333 -390.583333333323 56 129873 130242.983333333 -369.983333333322 57 129844 130148.183333333 -304.183333333323 58 130015 130052.983333333 -37.9833333333228 59 130108 129966.583333333 141.416666666678 60 130260 129899.983333333 360.016666666677 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00060746734076419 0.00121493468152838 0.999392532659236 17 3.9371240151208e-05 7.87424803024159e-05 0.999960628759849 18 2.34577214062529e-06 4.69154428125058e-06 0.999997654227859 19 7.0797034117476e-07 1.41594068234952e-06 0.999999292029659 20 6.53332033791137e-07 1.30666406758227e-06 0.999999346667966 21 3.81620417175415e-07 7.63240834350831e-07 0.999999618379583 22 8.21849428986936e-08 1.64369885797387e-07 0.999999917815057 23 2.06219191783918e-08 4.12438383567836e-08 0.999999979378081 24 6.47694987615527e-09 1.29538997523105e-08 0.99999999352305 25 1.59266509227882e-09 3.18533018455764e-09 0.999999998407335 26 1.23092773391168e-09 2.46185546782337e-09 0.999999998769072 27 3.16312249726122e-09 6.32624499452243e-09 0.999999996836877 28 1.48080822606901e-08 2.96161645213802e-08 0.999999985191918 29 8.06953007466768e-08 1.61390601493354e-07 0.999999919304699 30 4.49742878195913e-07 8.99485756391826e-07 0.999999550257122 31 3.16925719007608e-06 6.33851438015216e-06 0.99999683074281 32 3.01133612531012e-05 6.02267225062024e-05 0.999969886638747 33 0.000474529420544604 0.000949058841089208 0.999525470579455 34 0.00712147706677037 0.0142429541335407 0.99287852293323 35 0.0788775173297898 0.15775503465958 0.92112248267021 36 0.359604308250028 0.719208616500055 0.640395691749972 37 0.777067293640558 0.445865412718884 0.222932706359442 38 0.969935719400567 0.060128561198865 0.0300642805994325 39 0.997388201813103 0.00522359637379508 0.00261179818689754 40 0.999616613244127 0.000766773511746254 0.000383386755873127 41 0.999717166014648 0.000565667970703645 0.000282833985351823 42 0.999640224650686 0.000719550698628482 0.000359775349314241 43 0.999261670675202 0.00147665864959689 0.000738329324798444 44 0.99818323887733 0.00363352224534003 0.00181676112267001 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.827586206896552 NOK 5% type I error level 25 0.862068965517241 NOK 10% type I error level 26 0.896551724137931 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/10to631322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/10to631322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/1hi3z1322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/1hi3z1322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/2rmz61322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/2rmz61322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/3a2am1322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/3a2am1322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/4niar1322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/4niar1322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/5t7o51322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/5t7o51322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/6fzaf1322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/6fzaf1322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/7cse61322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/7cse61322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/88q3p1322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/88q3p1322596130.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/986p01322596130.png (opens in new window) http://www.freestatistics.org/blog/date/2011/Nov/29/t1322596164hinpp5wci5xftrv/986p01322596130.ps (opens in new window) Click here to open pdf file.

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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