paper - monthly dummies

*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: Tue, 21 Dec 2010 20:45:54 +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/21/t1292964229r00u9uoulmksjkq.htm/, Retrieved Tue, 21 Dec 2010 21:43:52 +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/21/t1292964229r00u9uoulmksjkq.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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99.2 96.7 101.0 99.0 98.1 100.1 631 923 -12 -10.8 654 294 -13 -12.2 671 833 -16 -14.1 586 840 -10 -15.2 600 969 -4 -15.8 625 568 -9 -15.8 558 110 -8 -14.9 630 577 -9 -12.6 628 654 -3 -9.9 603 184 -13 -7.8 656 255 -3 -6 600 730 -1 -5 670 326 -2 -4.5 678 423 0 -3.9 641 502 0 -2.9 625 311 -3 -1.5 628 177 0 -0.5 589 767 5 0 582 471 3 0.5 636 248 4 0.9 599 885 3 0.8 621 694 1 0.1 637 406 -1 -1 595 994 0 -2 696 308 -2 -3 674 201 -1 -3.7 648 861 2 -4.7 649 605 0 -6.4 672 392 -6 -7.5 598 396 -7 -7.8 613 177 -6 -7.7 638 104 -4 -6.6 615 632 -9 -4.2 634 465 -2 -2 638 686 -3 -0.7 604 243 2 0.1 706 669 3 0.9 677 185 1 2.1 644 328 0 3.5 644 825 1 4.9 605 707 1 5.7 600 136 3 6.2 612 166 5 6.5 599 659 5 6.5 634 210 4 6.3 618 234 11 6.2 613 576 8 6.4 627 200 -1 6.3 668 973 4 5.8 651 479 4 5.1 619 661 4 5.1 644 260 6 5.8 579 936 6 6.7 601 752 6 7.1 595 376 6 6.7 588 902 4 5.5 634 341 1 4.2 594 305 6 3 606 200 0 2.2 610 926 2 2 633 685 -2 1.8 639 696 0 1.8 659 451 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 19.0441481003162 + 0.0172098995525247Consumenten[t] -0.0549615630821821Ondernemers[t] + 23.5717575323103M1[t] + 486.023699106092M2[t] + 598.714338274397M3[t] -9.23348102571175M4[t] + 14.6033922244765M5[t] + 511.341048039299M6[t] + 598.730315681691M7[t] -6.02530340177084M8[t] + 14.2209419900258M9[t] + 612.17261403743M10[t] + 604.098910778675M11[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) 19.0441481003162 124.034951 0.1535 0.878403 0.439202 Consumenten 0.0172098995525247 0.132519 0.1299 0.897034 0.448517 Ondernemers -0.0549615630821821 0.116825 -0.4705 0.639449 0.319724 M1 23.5717575323103 111.926812 0.2106 0.833794 0.416897 M2 486.023699106092 132.582905 3.6658 0.000468 0.000234 M3 598.714338274397 105.401785 5.6803 0 0 M4 -9.23348102571175 81.202122 -0.1137 0.909784 0.454892 M5 14.6033922244765 114.630193 0.1274 0.898982 0.449491 M6 511.341048039299 136.460718 3.7472 0.000358 0.000179 M7 598.730315681691 103.305447 5.7957 0 0 M8 -6.02530340177084 79.915469 -0.0754 0.940109 0.470054 M9 14.2209419900258 114.68974 0.124 0.901665 0.450832 M10 612.17261403743 136.306879 4.4911 2.6e-05 1.3e-05 M11 604.098910778675 112.434043 5.3729 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.909561714641004 R-squared 0.827302512740683 Adjusted R-squared 0.796121021985528 F-TEST (value) 26.5318460633228 F-TEST (DF numerator) 13 F-TEST (DF denominator) 72 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 148.159422944473 Sum Squared Residuals 1580487.45172122 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.2 38.7289850480557 60.4710149519443 2 99 501.254485887984 -402.254485887984 3 631 634.30276241868 -3.30276241868004 4 -10.8 4.90724183579422 -15.7072418357942 5 -13 -3.44162927789222 -9.55837072210778 6 833 530.884795786234 302.115204213766 7 586 632.78039503695 -46.7803950369502 8 -15.2 -29.9129701965743 14.7129701965743 9 -4 -1.35780324895143 -2.64219675104857 10 568 631.930265738471 -63.9302657384712 11 558 625.475840334426 -67.4758403344261 12 -14.9 -1.82643708001216 -13.0735629199878 13 -9 7.8831992826542 -16.8831992826542 14 654 505.560336982264 148.439663017736 15 603 621.639608212447 -18.6396082124466 16 -7.8 7.08516259510437 -14.8851625951044 17 -3 0.56734307816846 -3.56734307816846 18 730 530.642794055474 199.357205944526 19 670 623.494814162295 46.5051858377047 20 -4.5 1.43841541139416 -5.93841541139416 21 0 -2.03239045359146 2.03239045359146 22 502 631.376150670684 -129.376150670684 23 625 628.660222329073 -3.66022232907284 24 -1.5 20.1237683537556 -21.6237683537556 25 0 10.2349400274451 -10.2349400274451 26 767 505.153896704171 261.846103295829 27 582 625.699464374706 -43.6994643747062 28 0.5 7.12569554562927 -6.62569554562927 29 4 0.741052948163013 3.25894705183699 30 885 530.392856587807 354.607143412193 31 621 629.663172508378 -8.66317250837788 32 0.1 1.66715610213776 -1.56715610213776 33 -1 0.545750156891232 -1.54575015689123 34 994 631.32668526391 362.67331473609 35 696 628.553631067333 67.4463689326669 36 -3 19.5963462191994 -22.5963462191994 37 -1 6.93713612702829 -7.93713612702829 38 861 505.360586351999 355.639413648001 39 649 628.170475603991 20.829524396009 40 -6.4 -0.169213154314199 -6.2307868456858 41 -6 0.651451355004006 -6.651451355004 42 396 530.693427034789 -134.693427034789 43 613 621.150385381298 -8.15038538129784 44 -7.7 18.2827580525094 -25.9827580525094 45 -4 -0.64985654224657 -3.35014345775343 46 632 631.292711606718 0.70728839328187 47 634 631.25558529708 2.74441470292057 48 -2 -7.67956825954985 5.67956825954985 49 -3 9.40707460130186 -12.4070746013019 50 243 505.096770849205 -262.096770849205 51 706 629.107024486106 76.892975513894 52 0.9 11.2938799014601 -10.3938799014601 53 1 -1.71156551107223 2.71156551107223 54 328 530.192830668828 -202.192830668828 55 644 631.917669349758 12.0823306502416 56 4.9 -15.4269911712799 20.3269911712799 57 1 0.386248668482268 0.613751331517732 58 136 630.927630145294 -494.927630145294 59 612 625.725094389299 -13.7250943892992 60 6.5 -6.86679213887939 13.3667921388794 61 5 7.8821389856146 -2.8821389856146 62 210 504.7904289572 -294.7904289572 63 618 621.1810256761 -3.18102567610041 64 6.2 -11.2975248350346 17.4975248350346 65 8 -0.703216370599211 8.70321637059921 66 200 530.021728392645 -330.021728392645 67 668 634.299849794286 33.7001502057144 68 5.8 -2.10409940912621 7.90409940912621 69 4 -0.668346969810694 4.66834696981069 70 661 631.005297764237 29.9947022357633 71 644 627.287863384154 16.7121366158456 72 5.8 -22.4353431036944 28.2353431036944 73 6 9.69931254723713 -3.69931254723713 74 752 504.78087950584 247.21912049416 75 595 623.89963922797 -28.8996392279699 76 6.7 -29.6452418886392 36.3452418886392 77 4 -1.10343622177176 5.10343622177176 78 341 530.171567474223 -189.171567474223 79 594 622.693713767035 -28.6937137670348 80 3 12.455731210939 -9.45573121093901 81 0 -0.223601610773383 0.223601610773383 82 926 631.141258810686 294.858741189314 83 633 635.041763198635 -2.04176319863484 84 1.8 -8.21197399081925 10.0119739908192 85 0 6.4272133806632 -6.4272133806632 86 451 505.002614761337 -54.0026147613372 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.870759365694903 0.258481268610194 0.129240634305097 18 0.811699600126588 0.376600799746824 0.188300399873412 19 0.706979982590616 0.586040034818767 0.293020017409384 20 0.633448819063983 0.733102361872033 0.366551180936017 21 0.513798728616749 0.972402542766502 0.486201271383251 22 0.420394081969871 0.840788163939742 0.579605918030129 23 0.331178998413237 0.662357996826474 0.668821001586763 24 0.248314141068397 0.496628282136793 0.751685858931603 25 0.173514671049045 0.347029342098089 0.826485328950955 26 0.437215366289849 0.874430732579698 0.562784633710151 27 0.352581154929445 0.70516230985889 0.647418845070555 28 0.271449190295661 0.542898380591323 0.728550809704339 29 0.20210110434381 0.40420220868762 0.79789889565619 30 0.351492179618605 0.70298435923721 0.648507820381395 31 0.276325854938103 0.552651709876206 0.723674145061897 32 0.214782310054167 0.429564620108333 0.785217689945833 33 0.159196197849661 0.318392395699322 0.840803802150339 34 0.610372496911441 0.779255006177118 0.389627503088559 35 0.56060673742268 0.87878652515464 0.43939326257732 36 0.48388091549166 0.96776183098332 0.51611908450834 37 0.407368311002936 0.814736622005872 0.592631688997064 38 0.77947577230773 0.441048455384539 0.220524227692269 39 0.722967674473431 0.554064651053138 0.277032325526569 40 0.657514490823656 0.684971018352688 0.342485509176344 41 0.586011837424159 0.827976325151682 0.413988162575841 42 0.755313012894453 0.489373974211095 0.244686987105547 43 0.69568611073396 0.60862777853208 0.30431388926604 44 0.634583587850443 0.730832824299113 0.365416412149557 45 0.56169770867051 0.876604582658979 0.438302291329489 46 0.49554812123392 0.99109624246784 0.50445187876608 47 0.42090893184324 0.84181786368648 0.57909106815676 48 0.351419177807807 0.702838355615614 0.648580822192193 49 0.283686842336547 0.567373684673094 0.716313157663453 50 0.426941661833434 0.853883323666868 0.573058338166566 51 0.367750862851337 0.735501725702674 0.632249137148663 52 0.296832433675413 0.593664867350826 0.703167566324587 53 0.232457296189444 0.464914592378888 0.767542703810556 54 0.28942080059142 0.57884160118284 0.71057919940858 55 0.224810077394356 0.449620154788711 0.775189922605644 56 0.168992553762054 0.337985107524109 0.831007446237946 57 0.121823509321778 0.243647018643556 0.878176490678222 58 0.887367828861478 0.225264342277044 0.112632171138522 59 0.835664294784403 0.328671410431194 0.164335705215597 60 0.769512746608668 0.460974506782664 0.230487253391332 61 0.687810785611706 0.624378428776587 0.312189214388294 62 0.953560641595754 0.0928787168084911 0.0464393584042455 63 0.919571885221478 0.160856229557045 0.0804281147785224 64 0.865414082251155 0.269171835497691 0.134585917748845 65 0.786167537559202 0.427664924881597 0.213832462440798 66 0.78808298536476 0.423834029270479 0.21191701463524 67 0.677289402106059 0.645421195787882 0.322710597893941 68 0.528716423585847 0.942567152828305 0.471283576414153 69 0.361000380350853 0.722000760701705 0.638999619649147 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 1 0.0188679245283019 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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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