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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 10:29:00 -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/t1258566083b72rzlu04krp775.htm/, Retrieved Wed, 18 Nov 2009 18:41:35 +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/t1258566083b72rzlu04krp775.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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102 1 102,8 94 106,3 101,3 105,1 1 102 102,8 94 106,3 92,4 0 105,1 102 102,8 94 81,4 0 92,4 105,1 102 102,8 105,8 1 81,4 92,4 105,1 102 120,3 1 105,8 81,4 92,4 105,1 100,7 1 120,3 105,8 81,4 92,4 88,8 0 100,7 120,3 105,8 81,4 94,3 0 88,8 100,7 120,3 105,8 99,9 0 94,3 88,8 100,7 120,3 103,4 1 99,9 94,3 88,8 100,7 103,3 1 103,4 99,9 94,3 88,8 98,8 0 103,3 103,4 99,9 94,3 104,2 1 98,8 103,3 103,4 99,9 91,2 0 104,2 98,8 103,3 103,4 74,7 0 91,2 104,2 98,8 103,3 108,5 1 74,7 91,2 104,2 98,8 114,5 1 108,5 74,7 91,2 104,2 96,9 0 114,5 108,5 74,7 91,2 89,6 0 96,9 114,5 108,5 74,7 97,1 0 89,6 96,9 114,5 108,5 100,3 1 97,1 89,6 96,9 114,5 122,6 1 100,3 97,1 89,6 96,9 115,4 1 122,6 100,3 97,1 89,6 109 1 115,4 122,6 100,3 97,1 129,1 1 109 115,4 122,6 100,3 102,8 1 129,1 109 115,4 122,6 96,2 0 102,8 129,1 109 115,4 127,7 1 96,2 102,8 129,1 109 128,9 1 127,7 96,2 102,8 129,1 126,5 1 128,9 127,7 96,2 102,8 119,8 1 126,5 128,9 127,7 96,2 113,2 1 119,8 126,5 128,9 127,7 114,1 1 113,2 119,8 126, etc...

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 Omzet[t] = + 35.4477969963449 + 14.5473227020697Uitvoer[t] + 0.363436889522976`Omzet-1`[t] -0.068063425825551`Omzet-2`[t] + 0.176361769468053`Omzet-3`[t] + 0.0238595995590367`Omzet-4`[t] + 0.512233932531202t + 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) 35.4477969963449 16.074861 2.2052 0.032166 0.016083 Uitvoer 14.5473227020697 5.844839 2.4889 0.016259 0.008129 `Omzet-1` 0.363436889522976 0.138115 2.6314 0.011337 0.005668 `Omzet-2` -0.068063425825551 0.151597 -0.449 0.655429 0.327715 `Omzet-3` 0.176361769468053 0.147022 1.1996 0.236078 0.118039 `Omzet-4` 0.0238595995590367 0.137309 0.1738 0.862766 0.431383 t 0.512233932531202 0.261197 1.9611 0.055563 0.027782 Multiple Linear Regression - Regression Statistics Multiple R 0.848001404722238 R-squared 0.719106382410888 Adjusted R-squared 0.684711245563242 F-TEST (value) 20.9072109698582 F-TEST (DF numerator) 6 F-TEST (DF denominator) 49 p-value 5.55799850587846e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15.1831187458836 Sum Squared Residuals 11295.8276477285 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102 102.634937376090 -0.634937376090317 2 105.1 100.207511883076 4.89248811692351 3 92.4 88.6120387084624 3.78796129153762 4 81.4 84.3665025845377 -2.96650258453768 5 105.8 96.820292748074 8.97970725192594 6 120.3 104.783254755436 15.5167452445643 7 100.7 106.661579617358 -5.96157961735826 8 88.8 88.55697971857 0.243020281429958 9 94.3 89.2177776984859 5.08222230151411 10 99.9 89.4281428027497 10.4718571972503 11 103.4 103.582243968612 -0.182243968611788 12 103.3 105.671412327172 -2.37141232717209 13 98.8 92.4806115848877 6.31938841511234 14 104.2 106.662388509887 -2.46238850988651 15 91.2 94.9620167814969 -3.76201678149689 16 74.7 89.5860147282093 -14.8860147282093 17 108.5 100.378672578525 8.12132742147494 18 114.5 112.134258737589 2.36574126241144 19 96.9 94.759103521794 2.14089647820606 20 89.6 94.0338120590635 -4.43381205906356 21 97.1 94.9554980745105 2.14450192548952 22 100.3 110.276884843777 -9.97688484377671 23 122.6 109.734271259734 12.8657287402660 24 115.4 119.281883060215 -3.8818830602152 25 109 116.402861651262 -7.40286165126174 26 129.1 119.088374334516 10.0116256654836 27 102.8 126.603560001739 -23.8035600017395 28 96.2 100.341501737233 -4.14150173723250 29 127.7 118.184613129324 9.51538687067622 30 128.9 126.435591106404 2.46440889359579 31 126.5 123.448456245966 3.05154375403368 32 119.8 128.404687913806 -8.60468791380575 33 113.2 127.608458417986 -14.4084584179856 34 114.1 125.783397105444 -11.6833971054439 35 134.1 125.833055954617 8.26694404538321 36 130 132.228923598830 -2.22892359882982 37 121.8 129.891050003237 -8.09105000323741 38 132.1 131.250870516529 0.849129483470838 39 105.3 135.818733239278 -30.5187332392783 40 103 124.345814378760 -21.3458143787604 41 117.1 127.467120786650 -10.3671207866504 42 126.3 128.779619194569 -2.47961919456859 43 138.1 130.630708868612 7.4692911313878 44 119.5 137.237138450433 -17.7371384504332 45 138 132.145246445984 5.85475355401594 46 135.5 142.947619750712 -7.44761975071174 47 178.6 138.293302444354 40.3066975556463 48 162.2 157.45872906325 4.74127093675008 49 176.9 149.077562522695 27.8224374773049 50 204.9 163.590102179929 41.3098978200714 51 132.2 171.414052381186 -39.2140523811859 52 142.5 145.799869100694 -3.29986910069350 53 164.3 160.292579711452 4.00742028854775 54 174.9 155.873252696907 19.0267473030933 55 175.4 158.836068312963 16.5639316870366 56 143 162.898988826367 -19.8989888263669 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0752637477085938 0.150527495417188 0.924736252291406 11 0.0401389481986491 0.0802778963972982 0.95986105180135 12 0.0151407415338111 0.0302814830676222 0.984859258466189 13 0.00587431100229299 0.0117486220045860 0.994125688997707 14 0.00175410068284580 0.00350820136569161 0.998245899317154 15 0.00101502263743772 0.00203004527487544 0.998984977362562 16 0.00225118143683047 0.00450236287366094 0.99774881856317 17 0.00101507841630092 0.00203015683260183 0.9989849215837 18 0.000482580400967143 0.000965160801934287 0.999517419599033 19 0.000372108001922049 0.000744216003844099 0.999627891998078 20 0.000124926741202036 0.000249853482404073 0.999875073258798 21 4.83146649568979e-05 9.66293299137958e-05 0.999951685335043 22 2.1873610976974e-05 4.3747221953948e-05 0.999978126389023 23 0.000101418444013600 0.000202836888027201 0.999898581555986 24 3.73532934472415e-05 7.4706586894483e-05 0.999962646706553 25 1.82693960305546e-05 3.65387920611092e-05 0.99998173060397 26 4.38637024532023e-05 8.77274049064046e-05 0.999956136297547 27 7.10012874413408e-05 0.000142002574882682 0.999928998712559 28 3.88564034320028e-05 7.77128068640056e-05 0.999961143596568 29 3.42717438638465e-05 6.8543487727693e-05 0.999965728256136 30 2.61699795083907e-05 5.23399590167814e-05 0.999973830020492 31 2.46052701905289e-05 4.92105403810577e-05 0.99997539472981 32 9.6241959954339e-06 1.92483919908678e-05 0.999990375804005 33 4.08665361226412e-06 8.17330722452823e-06 0.999995913346388 34 1.50211348500845e-06 3.00422697001691e-06 0.999998497886515 35 2.45731701850252e-06 4.91463403700503e-06 0.999997542682981 36 1.14702333239418e-06 2.29404666478836e-06 0.999998852976668 37 3.86058529791856e-07 7.72117059583712e-07 0.99999961394147 38 2.62615688485312e-07 5.25231376970623e-07 0.999999737384311 39 8.00393960022358e-07 1.60078792004472e-06 0.99999919960604 40 6.76320481256598e-07 1.35264096251320e-06 0.999999323679519 41 3.30650752654412e-07 6.61301505308824e-07 0.999999669349247 42 1.19558133335682e-07 2.39116266671364e-07 0.999999880441867 43 5.6035888033728e-08 1.12071776067456e-07 0.999999943964112 44 1.75603242527282e-07 3.51206485054564e-07 0.999999824396757 45 3.011239569825e-07 6.02247913965e-07 0.999999698876043 46 9.55232393642934e-06 1.91046478728587e-05 0.999990447676063 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.891891891891892 NOK 5% type I error level 35 0.945945945945946 NOK 10% type I error level 36 0.972972972972973 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/10k2hf1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/10k2hf1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/1c42q1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/1c42q1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/2tcq81258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/2tcq81258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/3ff8t1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/3ff8t1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/421a71258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/421a71258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/56fsp1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/56fsp1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/6ln6y1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/6ln6y1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/7hgw41258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/7hgw41258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/86qez1258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/86qez1258565336.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/97px81258565336.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566083b72rzlu04krp775/97px81258565336.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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