*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: Wed, 18 Nov 2009 09:30:58 -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/18/t1258561932co23m5pku9rgyev.htm/, Retrieved Wed, 18 Nov 2009 17:32:24 +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/18/t1258561932co23m5pku9rgyev.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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521 104.29 501 104.56 518 104.79 547 105.08 629 105.21 572 105.43 582 105.69 574 105.74 461 106.2 576 106.04 460 106.45 455 106.4 444 106.48 488 106.83 513 107.14 468 107.94 488 108.46 536 108.81 486 108.92 460 108.99 376 109.16 503 109.22 369 109.43 353 109.23 359 109.93 400 110.09 374 110.33 430 110.11 433 110.35 418 110.09 438 110.44 389 110.39 368 110.62 386 110.43 261 110.46 294 110.55 263 110.94 293 111.56 303 111.82 326 111.73 314 111.57 332 111.85 347 112.06 290 112.2 340 112.47 371 112.15 340 112.36 376 112.32 322 112.67 364 113.02 379 113.05 343 113.5 358 113.67 433 113.65 344 114 357 114.03 385 114.08 392 114.49 308 114.48 294 114.25

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 AvgBouw[t] = + 3066.15011706504 -24.1528003127978Gzhidx[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) 3066.15011706504 269.397304 11.3815 0 0 Gzhidx -24.1528003127978 2.44887 -9.8628 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.791498856641332 R-squared 0.626470440064537 Adjusted R-squared 0.620030275238063 F-TEST (value) 97.275528956854 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 5.17363929475323e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 55.4405727636788 Sum Squared Residuals 178272.112285156 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 521 547.254572443359 -26.2545724433594 2 501 540.733316358902 -39.7333163589025 3 518 535.178172286959 -17.1781722869590 4 547 528.173860196248 18.8261398037522 5 629 525.033996155584 103.966003844416 6 572 519.720380086768 52.2796199132316 7 582 513.440652005441 68.5593479945588 8 574 512.233011989801 61.7669880101986 9 461 501.122723845914 -40.1227238459142 10 576 504.987171895962 71.0128281040382 11 460 495.084523767715 -35.0845237677148 12 455 496.292163783355 -41.2921637833546 13 444 494.359939758331 -50.3599397583308 14 488 485.906459648852 2.09354035114829 15 513 478.419091551884 34.5809084481157 16 468 459.096851301646 8.90314869835381 17 488 446.537395138991 41.4626048610086 18 536 438.083915029512 97.916084970488 19 486 435.427106995104 50.5728930048957 20 460 433.736410973209 26.2635890267914 21 376 429.630434920033 -53.6304349200329 22 503 428.181266901265 74.818733098735 23 369 423.109178835577 -54.1091788355773 24 353 427.939738898137 -74.9397388981369 25 359 411.032778679178 -52.0327786791784 26 400 407.168330629131 -7.16833062913084 27 374 401.371658554059 -27.3716585540595 28 430 406.685274622875 23.3147253771250 29 433 400.888602547804 32.1113974521964 30 418 407.168330629131 10.8316693708692 31 438 398.714850519652 39.2851494803483 32 389 399.922490535292 -10.9224905352916 33 368 394.367346463348 -26.367346463348 34 386 398.956378522780 -12.9563785227795 35 261 398.231794513396 -137.231794513396 36 294 396.058042485244 -102.058042485244 37 263 386.638450363253 -123.638450363253 38 293 371.663714169318 -78.6637141693181 39 303 365.383986087991 -62.3839860879909 40 326 367.557738116142 -41.5577381161425 41 314 371.422186166190 -57.4221861661904 42 332 364.659402078607 -32.659402078607 43 347 359.587314012919 -12.5873140129192 44 290 356.205921969128 -66.2059219691275 45 340 349.684665884672 -9.68466588467223 46 371 357.413561984767 13.5864380152326 47 340 352.34147391908 -12.3414739190800 48 376 353.307585931592 22.6924140684080 49 322 344.854105822113 -22.8541058221126 50 364 336.400625712634 27.5993742873665 51 379 335.676041703250 43.3239582967504 52 343 324.807281562491 18.1927184375095 53 358 320.701305509315 37.2986944906852 54 433 321.184361515571 111.815638484429 55 344 312.730881406092 31.2691185939084 56 357 312.006297396708 44.9937026032923 57 385 310.798657381068 74.2013426189322 58 392 300.896009252821 91.1039907471792 59 308 301.137537255949 6.86246274405142 60 294 306.692681327892 -12.6926813278922 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.291782906170642 0.583565812341284 0.708217093829358 6 0.197473885603905 0.39494777120781 0.802526114396095 7 0.130056051664860 0.260112103329721 0.86994394833514 8 0.0875194432112066 0.175038886422413 0.912480556788793 9 0.526590357195366 0.946819285609268 0.473409642804634 10 0.472743248033732 0.945486496067463 0.527256751966268 11 0.578337557353468 0.843324885293063 0.421662442646532 12 0.590034449889854 0.819931100220291 0.409965550110146 13 0.582191828876306 0.835616342247387 0.417808171123694 14 0.490749341609823 0.981498683219646 0.509250658390177 15 0.444529290349742 0.889058580699483 0.555470709650258 16 0.364635020559432 0.729270041118864 0.635364979440568 17 0.331850645242181 0.663701290484363 0.668149354757819 18 0.478262733269559 0.956525466539119 0.521737266730441 19 0.473796511888009 0.947593023776019 0.526203488111991 20 0.454736459498901 0.909472918997802 0.545263540501099 21 0.537885885748668 0.924228228502664 0.462114114251332 22 0.688939985963933 0.622120028072134 0.311060014036067 23 0.728845829419162 0.542308341161676 0.271154170580838 24 0.775038009520112 0.449923980959777 0.224961990479888 25 0.752644212528742 0.494711574942516 0.247355787471258 26 0.70569476405419 0.588610471891621 0.294305235945810 27 0.646776397512441 0.706447204975119 0.353223602487559 28 0.660135161813775 0.67972967637245 0.339864838186225 29 0.709795646435981 0.580408707128038 0.290204353564019 30 0.741695103474354 0.516609793051292 0.258304896525646 31 0.869991881616956 0.260016236766089 0.130008118383044 32 0.89597864191445 0.2080427161711 0.10402135808555 33 0.90396158199664 0.192076836006718 0.096038418003359 34 0.955943148491154 0.0881137030176915 0.0440568515088458 35 0.982737639308472 0.0345247213830564 0.0172623606915282 36 0.982470200509979 0.0350595989800423 0.0175297994900212 37 0.990358952499612 0.0192820950007759 0.00964104750038794 38 0.9888459958047 0.0223080083906015 0.0111540041953008 39 0.985446401148956 0.0291071977020885 0.0145535988510442 40 0.975993330916608 0.0480133381667832 0.0240066690833916 41 0.966163966533795 0.0676720669324093 0.0338360334662047 42 0.947492496185051 0.105015007629898 0.052507503814949 43 0.920454228495962 0.159091543008076 0.0795457715040379 44 0.945838648651054 0.108322702697893 0.0541613513489463 45 0.92410871557419 0.151782568851620 0.0758912844258101 46 0.891809393192058 0.216381213615885 0.108190606807942 47 0.855176070937519 0.289647858124962 0.144823929062481 48 0.803462726119763 0.393074547760475 0.196537273880237 49 0.81519299639628 0.369614007207438 0.184807003603719 50 0.760098301956011 0.479803396087978 0.239901698043989 51 0.686709934049591 0.626580131900818 0.313290065950409 52 0.664739171229742 0.670521657540516 0.335260828770258 53 0.612585169673665 0.77482966065267 0.387414830326335 54 0.635110226599586 0.729779546800828 0.364889773400414 55 0.473654792576506 0.947309585153012 0.526345207423494 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 6 0.117647058823529 NOK 10% type I error level 8 0.156862745098039 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/10u2661258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/10u2661258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/143mv1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/143mv1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/2el571258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/2el571258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/32ju81258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/32ju81258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/4xx0q1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/4xx0q1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/50k9a1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/50k9a1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/6wvcw1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/6wvcw1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/7fock1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/7fock1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/8v5us1258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/8v5us1258561853.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/9ury71258561853.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561932co23m5pku9rgyev/9ury71258561853.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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