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 10:00:23 -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/t1258736644r6tv9k8webn8cy1.htm/, Retrieved Fri, 20 Nov 2009 18:04:15 +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/t1258736644r6tv9k8webn8cy1.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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363 14.3 364 14.2 363 15.9 358 15.3 357 15.5 357 15.1 380 15 378 12.1 376 15.8 380 16.9 379 15.1 384 13.7 392 14.8 394 14.7 392 16 396 15.4 392 15 396 15.5 419 15.1 421 11.7 420 16.3 418 16.7 410 15 418 14.9 426 14.6 428 15.3 430 17.9 424 16.4 423 15.4 427 17.9 441 15.9 449 13.9 452 17.8 462 17.9 455 17.4 461 16.7 461 16 463 16.6 462 19.1 456 17.8 455 17.2 456 18.6 472 16.3 472 15.1 471 19.2 465 17.7 459 19.1 465 18 468 17.5 467 17.8 463 21.1 460 17.2 462 19.4 461 19.8 476 17.6 476 16.2 471 19.5 453 19.9 443 20 442 17.3

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WK>25j[t] = + 390.495158429888 -2.19378103570580ExpBE[t] + 10.6069652991252M1[t] + 10.2304297706313M2[t] + 11.8414563135492M3[t] + 2.98448805864265M4[t] -0.0308036769922362M5[t] + 1.30892941593749M6[t] + 14.246841747458M7[t] + 8.873604871128M8[t] + 14.0824323126034M9[t] + 9.7110161976826M10[t] + 0.0233314613383325M11[t] + 2.19079421849136t + 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) 390.495158429888 41.993521 9.2989 0 0 ExpBE -2.19378103570580 3.135346 -0.6997 0.487642 0.243821 M1 10.6069652991252 9.525558 1.1135 0.271267 0.135634 M2 10.2304297706313 9.577269 1.0682 0.291005 0.145502 M3 11.8414563135492 12.538244 0.9444 0.349886 0.174943 M4 2.98448805864265 9.926101 0.3007 0.76502 0.38251 M5 -0.0308036769922362 9.914293 -0.0031 0.997534 0.498767 M6 1.30892941593749 10.925694 0.1198 0.905161 0.452581 M7 14.246841747458 9.472688 1.504 0.139419 0.06971 M8 8.873604871128 11.307812 0.7847 0.436633 0.218317 M9 14.0824323126034 11.06017 1.2733 0.209324 0.104662 M10 9.7110161976826 11.086558 0.8759 0.385621 0.19281 M11 0.0233314613383325 10.240284 0.0023 0.998192 0.499096 t 2.19079421849136 0.281584 7.7803 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.937327976220717 R-squared 0.878583735006024 Adjusted R-squared 0.844270442725118 F-TEST (value) 25.604763536343 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.11022302462516e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.8903066765169 Sum Squared Residuals 10199.1767143533 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 363 371.921849136911 -8.92184913691128 2 364 373.95548593048 -9.95548593048008 3 363 374.027878931189 -11.0278789311894 4 358 368.677973516198 -10.6779735161977 5 357 367.414719791913 -10.4147197919130 6 357 371.822759517616 -14.8227595176165 7 380 387.170844171199 -7.17084417119895 8 378 390.350366516907 -12.3503665169071 9 376 389.632998344762 -13.6329983447624 10 380 385.039217309057 -5.03921730905656 11 379 381.491132655474 -2.49113265547411 12 384 386.729888862615 -2.72988886261529 13 392 397.114489240955 -5.11448924095547 14 394 399.148126034524 -5.14812603452353 15 392 400.098031449515 -8.09803144951522 16 396 394.748126034524 1.25187396547647 17 392 394.801140931662 -2.80114093166232 18 396 397.234777725231 -1.23477772523054 19 419 413.240996689525 5.75900331047528 20 421 417.517409553086 3.4825904469142 21 420 414.825638448806 5.17436155119415 22 418 411.767504138094 6.23249586190588 23 410 408.000041380941 1.99995861905893 24 418 410.386882241665 7.6131177583353 25 426 423.842776069993 2.15722393000698 26 428 424.121388034996 3.87861196500358 27 430 422.219378103571 7.7806218964294 28 424 418.843875620714 5.15612437928589 29 423 420.213159139276 2.78684086072363 30 427 418.259233861433 8.74076613856701 31 441 437.775502482856 3.22449751714354 32 449 438.980621896429 10.0193781035706 33 452 437.824497517144 14.1755024828565 34 462 435.424497517144 26.5755024828565 35 455 429.024497517144 25.9755024828565 36 461 432.727606999291 28.2723930007094 37 461 447.061013241901 13.9389867580987 38 463 447.559003310475 15.4409966895247 39 462 445.87637148262 16.12362851738 40 456 442.062112792622 13.9378872073776 41 455 442.553883896902 12.4461161030977 42 456 443.013117758335 12.9868822416647 43 472 463.187520690471 8.8124793095295 44 472 462.637615275479 9.36238472452115 45 471 461.042734689052 9.9572653109482 46 465 462.152784346181 2.84721565381893 47 459 451.58460037834 7.41539962165995 48 465 456.165222274769 8.83477772523053 49 468 470.059872310239 -2.05987231023896 50 467 471.215996689525 -4.21599668952469 51 463 467.778340033105 -4.77834003310478 52 460 469.667912035942 -9.66791203594225 53 462 464.017096240246 -2.01709624024594 54 461 466.670111137385 -5.67011113738472 55 476 486.625135965949 -10.6251359659494 56 476 486.513986758099 -10.5139867580988 57 471 486.674131000236 -15.6741310002364 58 453 483.615996689525 -30.6159966895247 59 443 475.899728068101 -32.8997280681012 60 442 483.99039962166 -41.9903996216599 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0131851026298001 0.0263702052596002 0.9868148973702 18 0.0105625755656915 0.0211251511313831 0.989437424434308 19 0.00472278005168832 0.00944556010337664 0.995277219948312 20 0.00204835481270120 0.00409670962540241 0.997951645187299 21 0.00304232077198455 0.0060846415439691 0.996957679228015 22 0.00115514368199335 0.00231028736398671 0.998844856318007 23 0.000549541442532897 0.00109908288506579 0.999450458557467 24 0.000182122977359863 0.000364245954719725 0.99981787702264 25 0.000117580008112125 0.00023516001622425 0.999882419991888 26 5.74731260398207e-05 0.000114946252079641 0.99994252687396 27 2.64589758728815e-05 5.2917951745763e-05 0.999973541024127 28 2.75281750802481e-05 5.50563501604961e-05 0.99997247182492 29 2.26462357928356e-05 4.52924715856713e-05 0.999977353764207 30 2.85361502470566e-05 5.70723004941132e-05 0.999971463849753 31 0.000808416445635874 0.00161683289127175 0.999191583554364 32 0.00300859417020058 0.00601718834040116 0.9969914058298 33 0.018960042468634 0.037920084937268 0.981039957531366 34 0.0459113128460017 0.0918226256920034 0.954088687153998 35 0.0477723825563165 0.095544765112633 0.952227617443683 36 0.0376573262029424 0.0753146524058848 0.962342673797058 37 0.0268985281094806 0.0537970562189612 0.97310147189052 38 0.0171155598416064 0.0342311196832127 0.982884440158394 39 0.00909906306947408 0.0181981261389482 0.990900936930526 40 0.0209416955685161 0.0418833911370323 0.979058304431484 41 0.0129061039244713 0.0258122078489426 0.987093896075529 42 0.0141686549465306 0.0283373098930611 0.98583134505347 43 0.0202078647078571 0.0404157294157142 0.979792135292143 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.518518518518518 NOK 5% type I error level 23 0.851851851851852 NOK 10% type I error level 27 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/104mok1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/104mok1258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/1zl301258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/1zl301258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/2wqmg1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/2wqmg1258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/3lrsi1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/3lrsi1258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/422l31258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/422l31258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/5yoq11258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/5yoq11258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/6vkug1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/6vkug1258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/7733z1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/7733z1258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/8xzw81258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/8xzw81258736416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/9kzgs1258736416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736644r6tv9k8webn8cy1/9kzgs1258736416.ps (open in new window)

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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