*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 07:39:26 -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/t1258728094jcdkexzgu30n154.htm/, Retrieved Fri, 20 Nov 2009 15:41:46 +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/t1258728094jcdkexzgu30n154.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:

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User-defined keywords:

Dataseries X:

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115.6 37.2 111.9 37.2 107 34.7 107.1 32.5 100.6 33.5 99.2 31.5 108.4 31.2 103 27 99.8 26.7 115 26.5 90.8 26 95.9 27.2 114.4 30.5 108.2 33.7 112.6 34.2 109.1 36.7 105 36.2 105 38.5 118.5 40 103.7 42.5 112.5 43.5 116.6 43.3 96.6 45.5 101.9 44.3 116.5 43 119.3 43.5 115.4 41.5 108.5 42.5 111.5 41.3 108.8 39.5 121.8 38.5 109.6 41 112.2 44.5 119.6 46 104.1 44 105.3 41.5 115 41.3 124.1 38 116.8 38 107.5 36.2 115.6 38.7 116.2 38.7 116.3 39.2 119 35.7 111.9 36.5 118.6 36.7 106.9 34.7 103.2 35 118.6 28.2 118.7 23.7 102.8 15 100.6 8.7 94.9 11 94.5 7.5 102.9 5.7 95.3 9.3 92.5 10.2 102.7 15.7 91.5 18.1 89.5 20.8

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 Ipzb[t] = + 83.9746433207604 + 0.449803219171789Cvn[t] + 15.8344486602883M1[t] + 16.6232873000092M2[t] + 12.2457874767055M3[t] + 8.49751985477916M4[t] + 7.08868121505829M5[t] + 6.75848443423008M6[t] + 15.6974411424479M7[t] + 8.15647656299695M8[t] + 7.28570876437425M9[t] + 15.3939763863006M10[t] -1.13501967808283M11[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) 83.9746433207604 2.786635 30.1348 0 0 Cvn 0.449803219171789 0.055755 8.0675 0 0 M1 15.8344486602883 2.908746 5.4437 2e-06 1e-06 M2 16.6232873000092 2.907107 5.7182 1e-06 0 M3 12.2457874767055 2.906591 4.2131 0.000113 5.7e-05 M4 8.49751985477916 2.90915 2.921 0.005346 0.002673 M5 7.08868121505829 2.907371 2.4382 0.018596 0.009298 M6 6.75848443423008 2.909636 2.3228 0.02457 0.012285 M7 15.6974411424479 2.910278 5.3938 2e-06 1e-06 M8 8.15647656299695 2.909749 2.8032 0.007331 0.003665 M9 7.28570876437425 2.907139 2.5061 0.015725 0.007862 M10 15.3939763863006 2.905975 5.2974 3e-06 2e-06 M11 -1.13501967808283 2.905973 -0.3906 0.697871 0.348936 Multiple Linear Regression - Regression Statistics Multiple R 0.882397287601617 R-squared 0.77862497316669 Adjusted R-squared 0.722103689719887 F-TEST (value) 13.7757836638569 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.33876243424425e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.59473754725227 Sum Squared Residuals 992.245817022102 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 115.6 116.541771734239 -0.941771734239318 2 111.9 117.330610373960 -5.43061037396014 3 107 111.828602502727 -4.82860250272701 4 107.1 107.090767798623 0.00923220137728752 5 100.6 106.131732378074 -5.53173237807365 6 99.2 104.901929158902 -5.70192915890184 7 108.4 113.705944901368 -5.3059449013681 8 103 104.275806801396 -1.27580680139567 9 99.8 103.270098037021 -3.47009803702143 10 115 111.288405015113 3.71159498488658 11 90.8 94.534507341144 -3.7345073411441 12 95.9 96.209290882233 -0.309290882233061 13 114.4 113.528090165788 0.871909834211727 14 108.2 115.756299106859 -7.55629910685888 15 112.6 111.603700893141 0.996299106858876 16 109.1 108.979941319144 0.120058680855773 17 105 107.346201069837 -2.34620106983746 18 105 108.050551693104 -3.05055169310437 19 118.5 117.664213230080 0.835786769920157 20 103.7 111.247756698558 -7.54775669855839 21 112.5 110.826792119107 1.67320788089253 22 116.6 118.845099097199 -2.24509909719948 23 96.6 103.305670114994 -6.70567011499398 24 101.9 103.900925930071 -2.00092593007065 25 116.5 119.150630405436 -2.65063040543564 26 119.3 120.164370654742 -0.864370654742411 27 115.4 114.887264393095 0.512735606904831 28 108.5 111.588799990341 -3.08879999034060 29 111.5 109.640197487614 1.85980251238642 30 108.8 108.500354912276 0.299645087723842 31 121.8 116.989508401322 4.81049159867784 32 109.6 110.573051869801 -0.973051869800714 33 112.2 111.276595338279 0.923404661720744 34 119.6 120.059567788963 -0.459567788963312 35 104.1 102.630965286236 1.4690347137637 36 105.3 102.641476916390 2.65852308361035 37 115 118.385964932844 -3.3859649328436 38 124.1 117.690452949298 6.40954705070242 39 116.8 113.312953125994 3.48704687400608 40 107.5 108.755039709558 -1.25503970955833 41 115.6 108.470709117767 7.12929088223307 42 116.2 108.140512336939 8.05948766306128 43 116.3 117.304370654742 -1.00437065474242 44 119 108.189094808190 10.8109051918098 45 111.9 107.678169584905 4.22183041509506 46 118.6 115.876397850666 2.72360214933432 47 106.9 98.4477953479387 8.45220465206135 48 103.2 99.717755991773 3.48224400822698 49 118.6 112.493542761693 6.10645723830683 50 118.7 111.258266915141 7.44173308485901 51 102.8 102.967479085043 -0.167479085042776 52 100.6 96.3854511823341 4.21454881766586 53 94.9 96.0111599467084 -1.11115994670838 54 94.5 94.106651898779 0.393348101221087 55 102.9 102.235962812487 0.664037187512518 56 95.3 96.314289822055 -1.01428982205500 57 92.5 95.848344920687 -3.3483449206869 58 102.7 106.430530248058 -3.73053024805811 59 91.5 90.981061909687 0.518938090313036 60 89.5 93.3305502795336 -3.83055027953362 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.192835538361676 0.385671076723352 0.807164461638324 17 0.128877047446012 0.257754094892023 0.871122952553988 18 0.0726052416138662 0.145210483227732 0.927394758386134 19 0.0540805010827415 0.108161002165483 0.945919498917259 20 0.174250369044208 0.348500738088415 0.825749630955792 21 0.148977735100687 0.297955470201374 0.851022264899313 22 0.160738461632094 0.321476923264189 0.839261538367906 23 0.170019095669223 0.340038191338446 0.829980904330777 24 0.114827660040753 0.229655320081505 0.885172339959247 25 0.0835394255488194 0.167078851097639 0.91646057445118 26 0.154999450782601 0.309998901565201 0.8450005492174 27 0.117643465621823 0.235286931243647 0.882356534378177 28 0.110270396251308 0.220540792502615 0.889729603748692 29 0.128621244702281 0.257242489404562 0.871378755297719 30 0.141630597144501 0.283261194289002 0.858369402855499 31 0.176135333335317 0.352270666670633 0.823864666664683 32 0.214341570134370 0.428683140268739 0.78565842986563 33 0.159387849089173 0.318775698178345 0.840612150910827 34 0.115687907602501 0.231375815205002 0.884312092397499 35 0.152362383011397 0.304724766022793 0.847637616988603 36 0.112124202913979 0.224248405827958 0.887875797086021 37 0.237895322277685 0.47579064455537 0.762104677722315 38 0.365441590897366 0.730883181794732 0.634558409102634 39 0.291497784289026 0.582995568578051 0.708502215710974 40 0.566113915930408 0.867772168139184 0.433886084069592 41 0.537009323603977 0.925981352792047 0.462990676396023 42 0.501338247689561 0.997323504620878 0.498661752310439 43 0.971124520400416 0.0577509591991691 0.0288754795995845 44 0.957263291143931 0.0854734177121371 0.0427367088560685 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 0 0 OK 10% type I error level 2 0.0689655172413793 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/10lfu81258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/10lfu81258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/12cdm1258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/12cdm1258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/292r41258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/292r41258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/3qau11258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/3qau11258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/4w5q61258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/4w5q61258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/5ckd91258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/5ckd91258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/6s5x61258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/6s5x61258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/7te5l1258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/7te5l1258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/88qvg1258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/88qvg1258727961.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/97m7u1258727961.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728094jcdkexzgu30n154/97m7u1258727961.ps (open in new window)

Parameters (Session):

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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