model 1

*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: Mon, 23 Nov 2009 05:14:44 -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/23/t1258978593tm5n28mmp0zqw63.htm/, Retrieved Mon, 23 Nov 2009 13:16:45 +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/23/t1258978593tm5n28mmp0zqw63.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:

» Textbox « » Textfile « » CSV «

5560 174 3922 70 3759 65 4138 75 4634 45 3996 313 4308 102 4143 50 4429 230 5219 147 4929 103 5755 159 5592 74 4163 58 4962 72 5208 58 4755 99 4491 46 5732 70 5731 73 5040 82 6102 175 4904 83 5369 135 5578 139 4619 167 4731 52 5011 66 5299 129 4146 78 4625 96 4736 130 4219 59 5116 75 4205 93 4121 151 5103 116 4300 80 4578 109 3809 163 5526 69 4247 106 3830 69 4394 129 4826 90 4409 141 4569 122 4106 111 4794 226 3914 78 3793 78 4405 91 4022 49 4100 167 4788 72 3163 95 3585 134 3903 155 4178 70 3863 113 4187 215

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation nietwoon[t] = + 100.823599279710 + 0.00175497853996659Woon[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) 100.823599279710 49.679344 2.0295 0.046927 0.023464 Woon 0.00175497853996659 0.010737 0.1635 0.870717 0.435359 Multiple Linear Regression - Regression Statistics Multiple R 0.0212754586064359 R-squared 0.000452645138914167 Adjusted R-squared -0.0164888354519517 F-TEST (value) 0.0267181570398407 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.870717336814817 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 52.6750217513372 Sum Squared Residuals 163704.817073727 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 174 110.581279961924 63.4187200380762 2 70 107.706625113458 -37.7066251134585 3 65 107.420563611444 -42.4205636114440 4 75 108.085700478091 -33.0857004780913 5 45 108.956169833915 -63.9561698339147 6 313 107.836493525416 205.163506474584 7 102 108.384046829886 -6.38404682988562 8 50 108.094475370791 -58.0944753707911 9 230 108.596399233222 121.403600766778 10 147 109.982832279795 37.0171677202048 11 103 109.473888503205 -6.47388850320487 12 159 110.923500777217 48.0764992227827 13 74 110.637439275203 -36.6374392752027 14 58 108.129574941590 -50.1295749415905 15 72 109.531802795024 -37.5318027950238 16 58 109.963527515856 -51.9635275158555 17 99 109.168522237251 -10.1685222372507 18 46 108.705207902699 -62.7052079026995 19 70 110.883136270798 -40.8831362707980 20 73 110.881381292258 -37.8813812922581 21 82 109.668691121141 -27.6686911211412 22 175 111.532478330586 63.4675216694143 23 83 109.430014039706 -26.4300140397057 24 135 110.246079060790 24.7539209392098 25 139 110.612869575643 28.3871304243568 26 167 108.929845155815 58.0701548441848 27 52 109.126402752291 -57.1264027522915 28 66 109.617796743482 -43.6177967434821 29 129 110.123230562993 18.8767694370075 30 78 108.099740306411 -30.0997403064110 31 96 108.940375027055 -12.9403750270550 32 130 109.135177644991 20.8648223550087 33 59 108.227853739829 -49.2278537398286 34 75 109.802069490179 -34.8020694901786 35 93 108.203284040269 -15.2032840402691 36 151 108.055865842912 42.9441341570881 37 116 109.779254769159 6.22074523084094 38 80 108.370007001566 -28.3700070015659 39 109 108.857891035677 0.142108964323403 40 163 107.508312538442 55.4916874615577 41 69 110.521610691565 -41.5216106915649 42 106 108.276993138948 -2.27699313894766 43 69 107.545167087782 -38.5451670877816 44 129 108.534974984323 20.4650250156773 45 90 109.293125713588 -19.2931257135883 46 141 108.561299662422 32.4387003375778 47 122 108.842096228817 13.1579037711831 48 111 108.029541164812 2.97045883518763 49 226 109.236966400309 116.763033599691 50 78 107.692585285139 -29.6925852851388 51 78 107.480232881803 -29.4802328818028 52 91 108.554279748262 -17.5542797482624 53 49 107.882122967455 -58.8821229674552 54 167 108.019011293573 58.9809887064274 55 72 109.226436529070 -37.2264365290696 56 95 106.374596401624 -11.3745964016239 57 134 107.115197345490 26.8848026545102 58 155 107.673280521199 47.3267194788009 59 70 108.15589961969 -38.1558996196900 60 113 107.603081379600 5.39691862039952 61 215 108.171694426550 106.828305573450 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.286052997084962 0.572105994169925 0.713947002915038 6 0.999715063586105 0.000569872827790141 0.000284936413895070 7 0.999243154470914 0.00151369105817105 0.000756845529085527 8 0.999123323034046 0.00175335393190728 0.000876676965953642 9 0.999883849590794 0.000232300818411443 0.000116150409205721 10 0.999755521622792 0.000488956754415634 0.000244478377207817 11 0.999512909669337 0.00097418066132629 0.000487090330663145 12 0.999216582165468 0.00156683566906309 0.000783417834531547 13 0.999111635127754 0.00177672974449150 0.000888364872245749 14 0.999000322972027 0.00199935405594648 0.000999677027973239 15 0.998627008334438 0.00274598333112376 0.00137299166556188 16 0.998559712443455 0.00288057511309016 0.00144028755654508 17 0.99731606186839 0.00536787626322107 0.00268393813161053 18 0.997687987078502 0.00462402584299593 0.00231201292149797 19 0.996962860089372 0.00607427982125633 0.00303713991062816 20 0.995879514963053 0.00824097007389377 0.00412048503694689 21 0.993783820876994 0.0124323582460118 0.00621617912300591 22 0.994783235067218 0.0104335298655638 0.00521676493278188 23 0.992090041419068 0.0158199171618637 0.00790995858093186 24 0.988158312705873 0.0236833745882538 0.0118416872941269 25 0.983513213622376 0.0329735727552478 0.0164867863776239 26 0.985612570305681 0.028774859388638 0.014387429694319 27 0.98624552472389 0.0275089505522185 0.0137544752761093 28 0.983721115519077 0.0325577689618468 0.0162788844809234 29 0.975891084745774 0.0482178305084512 0.0241089152542256 30 0.967677867640441 0.0646442647191176 0.0323221323595588 31 0.95220409163643 0.0955918167271396 0.0477959083635698 32 0.934398126933946 0.131203746132109 0.0656018730660543 33 0.932143848604169 0.135712302791662 0.067856151395831 34 0.917101350645218 0.165797298709564 0.0828986493547821 35 0.887861728055358 0.224276543889283 0.112138271944642 36 0.874839328101973 0.250321343796054 0.125160671898027 37 0.829761071170652 0.340477857658696 0.170238928829348 38 0.795041682137149 0.409916635725702 0.204958317862851 39 0.734114106164535 0.53177178767093 0.265885893835465 40 0.737700043574254 0.524599912851492 0.262299956425746 41 0.749578980461078 0.500842039077844 0.250421019538922 42 0.681201664065257 0.637596671869486 0.318798335934743 43 0.647053490543552 0.705893018912897 0.352946509456448 44 0.569451855265105 0.86109628946979 0.430548144734895 45 0.532580888187777 0.934838223624446 0.467419111812223 46 0.456740842177492 0.913481684354984 0.543259157822508 47 0.371664295087009 0.743328590174019 0.62833570491299 48 0.288601739147933 0.577203478295865 0.711398260852067 49 0.54352886762709 0.91294226474582 0.45647113237291 50 0.47794672670509 0.95589345341018 0.52205327329491 51 0.419690869373679 0.839381738747357 0.580309130626321 52 0.327625097415121 0.655250194830241 0.672374902584879 53 0.400281127106778 0.800562254213555 0.599718872893222 54 0.359654508353357 0.719309016706714 0.640345491646643 55 0.395672105514022 0.791344211028045 0.604327894485978 56 0.255524070538377 0.511048141076753 0.744475929461623 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.288461538461538 NOK 5% type I error level 24 0.461538461538462 NOK 10% type I error level 26 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/10pttf1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/10pttf1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/12bqf1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/12bqf1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/2awwo1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/2awwo1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/3bdbb1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/3bdbb1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/4np0u1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/4np0u1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/5m0vy1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/5m0vy1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/6tbpj1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/6tbpj1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/7jcew1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/7jcew1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/8x2cw1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/8x2cw1258978479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/9h49b1258978479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258978593tm5n28mmp0zqw63/9h49b1258978479.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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