Model2

*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 15:52:44 -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/t1258757703xs8mix3ikv1zve7.htm/, Retrieved Fri, 20 Nov 2009 23:55:15 +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/t1258757703xs8mix3ikv1zve7.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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562 13.9 561 15.9 555 18.2 544 19.7 537 20.1 543 19.9 594 20 611 22.6 613 20.6 611 20.1 594 20.2 595 21.8 591 22 589 19.5 584 17.5 573 18.2 567 18.8 569 19.7 621 18.8 629 18.5 628 18.7 612 18.5 595 19.3 597 18.9 593 21.4 590 22.5 580 25 574 22.9 573 22.9 573 21.3 620 22.3 626 20.9 620 19.9 588 20.2 566 19.8 557 17.7 561 18.1 549 17.6 532 18.2 526 16 511 16.3 499 17.3 555 19 565 18.6 542 18 527 17.9 510 17.8 514 18.5 517 17.4 508 19 493 17.4 490 20.6 469 18.5 478 20 528 18.8 534 18.8 518 19.7 506 15.3 502 10.6 516 6.1 528 0.9

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 471.932069124408 + 5.05228499250553X[t] + 7.83474690929715M1[t] -8.0202554827627M2[t] -20.4390780800647M3[t] -28.9505807784159M4[t] -38.1422151796150M5[t] -38.7589463772168M6[t] + 11.7337337238324M7[t] + 20.6285052245819M8[t] + 14.3546477208346M9[t] + 3.90588701349005M10[t] -7.14914789295519M11[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) 471.932069124408 28.719326 16.4326 0 0 X 5.05228499250553 1.415407 3.5695 0.000824 0.000412 M1 7.83474690929715 22.404858 0.3497 0.728101 0.364051 M2 -8.0202554827627 23.5817 -0.3401 0.735261 0.367631 M3 -20.4390780800647 23.657426 -0.864 0.391907 0.195953 M4 -28.9505807784159 23.708976 -1.2211 0.228019 0.11401 M5 -38.1422151796150 23.67109 -1.6113 0.113662 0.056831 M6 -38.7589463772168 23.748961 -1.632 0.109218 0.054609 M7 11.7337337238324 23.785661 0.4933 0.624042 0.312021 M8 20.6285052245819 23.812851 0.8663 0.390648 0.195324 M9 14.3546477208346 23.685051 0.6061 0.547328 0.273664 M10 3.90588701349005 23.49446 0.1662 0.868661 0.43433 M11 -7.14914789295519 23.393779 -0.3056 0.76123 0.380615 Multiple Linear Regression - Regression Statistics Multiple R 0.61326202686736 R-squared 0.376090313597463 Adjusted R-squared 0.220112891996828 F-TEST (value) 2.41118432230792 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.0155093066966934 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 36.9289428676062 Sum Squared Residuals 65459.8474233084 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562 549.993577429532 12.006422570468 2 561 544.243145022483 16.7568549775166 3 555 543.444577907944 11.5554220920558 4 544 542.511502698351 1.48849730164874 5 537 535.340782294154 1.65921770584570 6 543 533.713594098051 9.28640590194859 7 594 584.711502698351 9.28849730164879 8 611 606.742215179615 4.25778482038498 9 613 590.363787690857 22.6362123091432 10 611 577.388884487259 33.6111155127406 11 594 566.839078080065 27.1609219199353 12 595 582.071881961029 12.9281180389712 13 591 590.917085868827 0.08291413117299 14 589 562.431370995503 26.5686290044967 15 584 539.90797841319 44.0920215868097 16 573 534.933075209593 38.0669247904071 17 567 528.772811803897 38.2271881961029 18 569 532.70313709955 36.2968629004497 19 621 578.648760707345 42.3512392926554 20 629 586.027846710342 42.9721532896576 21 628 580.764446205096 47.2355537949038 22 612 569.30522849925 42.6947715007494 23 595 562.29202158681 32.7079784131903 24 597 567.420255482763 29.5797445172373 25 593 587.885714873324 5.11428512667632 26 590 577.58822597302 12.4117740269801 27 580 577.800115856982 2.19988414301825 28 574 558.678814674369 15.3211853256311 29 573 549.48718027317 23.5128197268302 30 573 540.786793087559 32.2132069124408 31 620 596.331758181114 23.6682418188861 32 626 598.153330692356 27.8466693076443 33 620 586.827188196103 33.1728118038971 34 588 577.89411298651 10.1058870134900 35 566 564.818164083062 1.18183591693749 36 557 561.357513491756 -4.35751349175607 37 561 571.213174398055 -10.2131743980554 38 549 552.832029509743 -3.83202950974282 39 532 543.444577907944 -11.4445779079441 40 526 523.818048226081 2.18195177391925 41 511 516.142099322633 -5.14209932263331 42 499 520.577653117537 -21.5776531175371 43 555 579.659217705846 -24.6592177058457 44 565 586.533075209593 -21.5330752095929 45 542 577.227846710342 -35.2278467103424 46 527 566.273857503747 -39.2738575037472 47 510 554.713594098051 -44.7135940980514 48 514 565.39934148576 -51.3993414857605 49 517 567.676574903302 -50.6765749033016 50 508 559.90522849925 -51.9052284992505 51 493 539.40274991394 -46.4027499139397 52 490 547.058559191606 -57.0585591916062 53 469 527.257126306145 -58.2571263061455 54 478 534.218822597302 -56.218822597302 55 528 578.648760707345 -50.6487607073446 56 534 587.543532208094 -53.543532208094 57 518 585.816731197602 -67.8167311976017 58 506 553.137916523233 -47.1379165232329 59 502 518.337142152012 -16.3371421520116 60 516 502.751007578692 13.2489924213081 61 528 484.31387252696 43.6861274730397 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.126485914807531 0.252971829615062 0.873514085192469 17 0.0980772438102142 0.196154487620428 0.901922756189786 18 0.0632981030769038 0.126596206153808 0.936701896923096 19 0.0476889998018185 0.095377999603637 0.952311000198181 20 0.0328298485790637 0.0656596971581275 0.967170151420936 21 0.0215490782978737 0.0430981565957474 0.978450921702126 22 0.0125445939740594 0.0250891879481188 0.98745540602594 23 0.00653358099005956 0.0130671619801191 0.99346641900994 24 0.00321247040903532 0.00642494081807064 0.996787529590965 25 0.00152722215206209 0.00305444430412419 0.998472777847938 26 0.000745262395086358 0.00149052479017272 0.999254737604914 27 0.000320869892838487 0.000641739785676973 0.999679130107161 28 0.000168931002977679 0.000337862005955359 0.999831068997022 29 0.000142324820734332 0.000284649641468665 0.999857675179266 30 0.000176206589977457 0.000352413179954915 0.999823793410022 31 0.000186688021276992 0.000373376042553985 0.999813311978723 32 0.000259207699964295 0.000518415399928591 0.999740792300036 33 0.00116293403647270 0.00232586807294540 0.998837065963527 34 0.00611908282037922 0.0122381656407584 0.99388091717962 35 0.0250412081384126 0.0500824162768252 0.974958791861587 36 0.0611072935417523 0.122214587083505 0.938892706458248 37 0.10966572648902 0.21933145297804 0.89033427351098 38 0.175909046547350 0.351818093094701 0.82409095345265 39 0.312788433196991 0.625576866393982 0.687211566803009 40 0.376067517426448 0.752135034852895 0.623932482573552 41 0.590588256168092 0.818823487663817 0.409411743831908 42 0.654609245761472 0.690781508477056 0.345390754238528 43 0.743613503918349 0.512772992163302 0.256386496081651 44 0.849964376221882 0.300071247556236 0.150035623778118 45 0.900355685967451 0.199288628065097 0.0996443140325487 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.333333333333333 NOK 5% type I error level 14 0.466666666666667 NOK 10% type I error level 17 0.566666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/10n29a1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/10n29a1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/1oodq1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/1oodq1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/27mqb1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/27mqb1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/3rj741258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/3rj741258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/4lk7m1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/4lk7m1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/5lbk51258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/5lbk51258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/63nch1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/63nch1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/7hxu91258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/7hxu91258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/883gr1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/883gr1258757559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/9j8oy1258757559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757703xs8mix3ikv1zve7/9j8oy1258757559.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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