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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, 29 Dec 2010 20:40:53 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4.htm/, Retrieved Wed, 29 Dec 2010 21:40:36 +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/2010/Dec/29/t1293655227bmr0ydipjlofcp4.htm/}, year = {2010}, } @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 = {2010}, 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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-2 3 16 0 6 0 8 17 2 6 -2 3 23 3 7 -4 3 24 1 4 -4 7 27 1 3 -7 4 31 0 0 -9 -4 40 1 6 -13 -6 47 -1 3 -8 8 43 2 1 -13 2 60 2 6 -15 -1 64 0 5 -15 -2 65 1 7 -15 0 65 1 4 -10 10 55 3 3 -12 3 57 3 6 -11 6 57 1 6 -11 7 57 1 5 -17 -4 65 -2 2 -18 -5 69 1 3 -19 -7 70 1 -2 -22 -10 71 -1 -4 -24 -21 71 -4 0 -24 -22 73 -2 1 -20 -16 68 -1 4 -25 -25 65 -5 -3 -22 -22 57 -4 -3 -17 -22 41 -5 0 -9 -19 21 0 6 -11 -21 21 -2 -1 -13 -31 17 -4 0 -11 -28 9 -6 -1 -9 -23 11 -2 1 -7 -17 6 -2 -4 -3 -12 -2 -2 -1 -3 -14 0 1 -1 -6 -18 5 -2 0 -4 -16 3 0 3 -8 -22 7 -1 0 -1 -9 4 2 8 -2 -10 8 3 8 -2 -10 9 2 8 -1 0 14 3 8 1 3 12 4 11 2 2 12 5 13 2 4 7 5 5 -1 -3 15 4 12 1 0 14 5 13 -1 -1 19 6 9 -8 -7 39 4 11 1 2 12 6 7 2 3 11 6 12 -2 -3 17 3 11 -2 -5 16 5 10 -2 0 25 5 13 -2 -3 24 5 14 -6 -7 28 3 10 -4 -7 25 5 13 -5 -7 31 5 12 -2 -4 24 6 13 -1 -3 24 6 17

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' @ www.wessa.org Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 0.59572539246056 -3.94502235891268indicator[t] + 0.9968486769921economie[t] + 1.06507538235466`finaciën`[t] + 0.880345457337178spaarvermogen[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) 0.59572539246056 0.456224 1.3058 0.197065 0.098532 indicator -3.94502235891268 0.030602 -128.9139 0 0 economie 0.9968486769921 0.022312 44.6775 0 0 `finaciën` 1.06507538235466 0.12733 8.3647 0 0 spaarvermogen 0.880345457337178 0.059472 14.8027 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998682644366743 R-squared 0.99736702415935 Adjusted R-squared 0.997175535007303 F-TEST (value) 5208.47793985421 F-TEST (DF numerator) 4 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.23256411359052 Sum Squared Residuals 83.5567861761153 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16 16.7583888852851 -0.758388885285125 2 17 15.9827383171297 1.01726168287027 3 23 20.8339604896864 2.16603951031357 4 24 23.9528180707909 0.0471819292090527 5 27 27.0598673214222 -0.0598673214221827 6 31 32.1982766128177 -1.19827661281774 7 40 38.460680041084 1.53931995891598 8 47 47.4758849860297 -0.475884986029709 9 43 43.1411899017453 -0.141189901745318 10 60 61.286936921042 -1.28693692104203 11 64 63.1759393858446 0.82406061415541 12 65 65.0048570058815 -0.00485700588150435 13 65 64.3575179878542 0.642482012145825 14 55 55.8506982705839 -0.850698270583905 15 57 59.4038386214761 -2.4038386214761 16 57 56.3192115288304 0.680788471169607 17 57 56.4357147484853 0.564285251514684 18 65 63.3042509359728 1.69574906402718 19 69 70.3279962222946 -1.32799622229456 20 70 67.8775939405372 2.12240605946285 21 71 72.8312733069152 -1.83127330691523 22 71 70.0821382601122 0.917861739887776 23 73 72.0957858051666 0.904214194833377 24 68 66.0029001858347 1.99709981416532 25 65 66.3336541566903 -1.33365415669032 26 57 58.5542084932832 -1.55420849328322 27 41 40.4050576883767 0.594942311623332 28 21 22.4428745038479 -1.44287450384786 29 21 20.0466529016195 0.953347098380537 30 17 16.7184055421517 0.28159445784831 31 9 8.80841063325611 0.191589366743885 32 11 11.9236017444842 -0.923601744484246 33 6 5.6129218019256 0.387078198074401 34 -2 -2.54188787675311 0.541887876753107 35 0 -1.34035908367333 1.34035908367333 36 5 4.19243259536952 0.807567404630477 37 3 3.06727236824921 -0.067272368249206 38 7 9.16015798758115 -2.16015798758115 39 4 4.74202408185106 -0.742024081851062 40 8 8.7552731461263 -0.755273146126306 41 9 7.69019776377165 1.30980223622835 42 14 14.7787375571346 -0.778737557134626 43 12 13.5853506246518 -1.58535062465175 44 12 11.469245885776 0.530754114224019 45 7 6.42017958106277 0.579820418937235 46 15 16.3746487378617 -1.37464873786169 47 14 13.4205708907045 0.579429109295531 48 19 17.8574604845437 1.14253951545631 49 39 39.1220650849449 -0.122065084944903 50 12 11.1972708830203 0.802729116979733 51 11 12.6508244877856 -1.65082448778557 52 17 18.3742502570825 -1.37425025708254 53 16 17.6303582104705 -1.63035821047048 54 25 25.2556379674425 -0.255637967442515 55 24 23.1454373938034 0.854562606196609 56 28 29.2865995274277 -1.2865995274277 57 25 26.1677419463232 -1.16774194632318 58 31 29.2324188478987 1.76758115210131 59 24 22.3333186418288 1.66668135817123 60 24 22.9065267892569 1.0934732107431 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00139581978660337 0.00279163957320674 0.998604180213397 9 0.0110730367183872 0.0221460734367743 0.988926963281613 10 0.0428564707246797 0.0857129414493594 0.95714352927532 11 0.298110046580894 0.596220093161788 0.701889953419106 12 0.196650813946822 0.393301627893645 0.803349186053178 13 0.156252385408925 0.31250477081785 0.843747614591075 14 0.111256292522323 0.222512585044645 0.888743707477677 15 0.467775068305249 0.935550136610499 0.532224931694751 16 0.444820369783773 0.889640739567546 0.555179630216227 17 0.385648076041866 0.771296152083732 0.614351923958134 18 0.478667531613046 0.957335063226092 0.521332468386954 19 0.440307615047069 0.880615230094138 0.559692384952931 20 0.650862167753021 0.698275664493958 0.349137832246979 21 0.748599495786612 0.502801008426775 0.251400504213388 22 0.691811395609513 0.616377208780974 0.308188604390487 23 0.62657405863984 0.746851882720321 0.37342594136016 24 0.712169023883214 0.575661952233572 0.287830976116786 25 0.742381158222039 0.515237683555923 0.257618841777961 26 0.764993307649475 0.470013384701049 0.235006692350524 27 0.72484298192719 0.550314036145621 0.27515701807281 28 0.794232070796536 0.411535858406929 0.205767929203464 29 0.77782454733281 0.444350905334381 0.22217545266719 30 0.718140799187504 0.563718401624992 0.281859200812496 31 0.684678079836789 0.630643840326421 0.315321920163211 32 0.638944749803544 0.722110500392912 0.361055250196456 33 0.584218990219321 0.831562019561358 0.415781009780679 34 0.569725935398716 0.860548129202568 0.430274064601284 35 0.559757808493413 0.880484383013174 0.440242191506587 36 0.659910976105238 0.680178047789524 0.340089023894762 37 0.632606053228215 0.73478789354357 0.367393946771785 38 0.674208427691289 0.651583144617422 0.325791572308711 39 0.609183498950494 0.781633002099012 0.390816501049506 40 0.588802260780143 0.822395478439714 0.411197739219857 41 0.711572882272582 0.576854235454836 0.288427117727418 42 0.667785589697011 0.664428820605978 0.332214410302989 43 0.633160447748526 0.733679104502947 0.366839552251474 44 0.585321344727905 0.82935731054419 0.414678655272095 45 0.568905641527201 0.862188716945598 0.431094358472799 46 0.485782593986516 0.971565187973033 0.514217406013484 47 0.473899680505433 0.947799361010867 0.526100319494567 48 0.405367680376904 0.810735360753807 0.594632319623096 49 0.337056668196691 0.674113336393381 0.66294333180331 50 0.432612821320772 0.865225642641544 0.567387178679228 51 0.361542924754275 0.723085849508549 0.638457075245725 52 0.349680075158762 0.699360150317523 0.650319924841238 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0222222222222222 NOK 5% type I error level 2 0.0444444444444444 OK 10% type I error level 3 0.0666666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/10pfor1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/10pfor1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/1iwag1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/1iwag1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/2b5rj1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/2b5rj1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/3b5rj1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/3b5rj1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/4b5rj1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/4b5rj1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/5meq41293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/5meq41293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/6meq41293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/6meq41293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/7wopp1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/7wopp1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/8wopp1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/8wopp1293655249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/9wopp1293655249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293655227bmr0ydipjlofcp4/9wopp1293655249.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

Parameters (R input):

par1 = 3 ; 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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