Paper multiple regression

*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, 17 Dec 2010 10:27:45 +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/17/t1292581748ga435l9mcns3h1s.htm/, Retrieved Fri, 17 Dec 2010 11:29:11 +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/17/t1292581748ga435l9mcns3h1s.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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5140 3111 17153 2,5 766 332 2,4 4749 3995 15579 1,8 294 369 2,4 3635 5245 16755 7,3 235 384 2,4 4305 5588 16585 9,9 462 373 2,1 5805 10681 16572 13,2 919 378 2 4260 10516 16325 17,8 346 426 2 3869 7496 17913 18,8 298 423 2,1 7325 9935 17572 19,3 92 397 2,1 9280 10249 17338 13,9 516 422 2 6222 6271 17087 7,5 843 409 2 3272 3616 15864 8 395 430 2 7598 3724 15554 4 961 412 1,7 1345 2886 16229 3,6 1231 470 1,3 1900 3318 15180 4,8 794 491 1,2 1480 4166 16215 5,9 420 504 1,1 1472 6401 15801 10,4 331 484 1,4 3823 9209 15751 12,3 312 474 1,5 4454 9820 16477 15,5 692 508 1,4 3357 7470 17324 16,7 1221 492 1,1 5393 8207 16919 18,8 1272 452 1,1 8329 9564 16438 15,2 622 457 1 4152 5309 16239 11,3 479 457 1,4 4042 3385 15613 6,3 757 471 1,3 7747 3706 15821 3,2 463 451 1,2 1451 2733 15678 5,3 534 493 1,5 911 3045 14671 2,4 731 514 1,6 406 3449 15876 6,5 498 522 1,8 1387 5542 15563 10,4 629 490 1,5 2150 10072 15711 12,6 542 484 1,3 1577 9418 15583 16,8 519 506 1,6 2642 7516 16405 17,7 1585 501 1 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 Huwelijken[t] = -1673.55606693331 + 0.263089189753331Bevolkingsgroei[t] -0.0106172913357292Geborenen[t] + 392.801077158716Temperatuur[t] -0.645368338285581Neerslag[t] + 5.72015160621913Werkloosheid[t] + 482.845499276914Inflatie[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) -1673.55606693331 1991.140262 -0.8405 0.40323 0.201615 Bevolkingsgroei 0.263089189753331 0.062145 4.2335 6.3e-05 3.2e-05 Geborenen -0.0106172913357292 0.099615 -0.1066 0.915396 0.457698 Temperatuur 392.801077158716 30.041574 13.0752 0 0 Neerslag -0.645368338285581 0.452723 -1.4255 0.158047 0.079023 Werkloosheid 5.72015160621913 3.093241 1.8492 0.068261 0.03413 Inflatie 482.845499276914 320.38452 1.5071 0.135882 0.067941 Multiple Linear Regression - Regression Statistics Multiple R 0.871745448513117 R-squared 0.759940127003336 Adjusted R-squared 0.741234162873725 F-TEST (value) 40.6255524568447 F-TEST (DF numerator) 6 F-TEST (DF denominator) 77 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1443.89767217794 Sum Squared Residuals 160532717.554508 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3111 3042.17404741643 68.8259525835727 2 3995 3197.31650187511 797.683498124887 3 5245 5176.03414030416 68.9658596958441 4 5588 6021.11770733631 -433.117707336312 5 10681 7397.51594888433 3283.48405111567 6 10516 9444.9139115416 1071.08608845839 7 7496 9780.08863221237 -2284.08863221237 8 9935 10873.5678428499 -938.567842849858 9 10249 9090.34890312782 1158.65109687218 10 6271 5489.16278967138 781.837210328616 11 3616 5331.68336506455 -1715.68336506455 12 3724 4288.79939345202 -564.799393452021 13 2886 2443.79672952217 442.203270477826 14 3318 3426.17465867062 -108.174658670621 15 4166 4004.21466678829 161.785333211705 16 6401 5861.99875886359 539.001241136412 17 9209 7230.71938743494 1978.28061256506 18 9820 8546.94459570268 1273.05540429732 19 7470 8142.92827493672 -672.928274936722 20 8207 9246.04028079745 -1039.04028079745 21 9564 9009.29880926337 554.70119073663 22 5309 6665.98977580613 -1356.98977580613 23 3385 4532.07617803183 -1147.07617803183 24 3706 4313.98059968196 -607.980599681963 25 2733 3823.2504609153 -1090.2504609153 26 3045 2594.02095807934 450.979041920658 27 3449 4351.55263307077 -902.552633070767 28 5542 5732.44878782836 -190.448787828362 29 10072 6721.03388617979 3350.96611382021 30 9418 8506.94777470917 911.052225290828 31 7516 8415.36791111782 -899.367911117818 32 7840 8531.54191762652 -691.541917626521 33 10081 9563.50989219416 517.490107805844 34 4956 7241.21501557227 -2285.21501557227 35 3641 4066.3478234923 -425.347823492304 36 3970 3384.17312084045 585.826879159546 37 2931 2046.52818919096 884.471810809035 38 3170 2366.91196601555 803.088033984453 39 3889 2522.01591385612 1366.98408614388 40 4850 4583.40869767775 266.591302322254 41 8037 6500.68586587604 1536.31413412396 42 12370 8145.27561422789 4224.72438577211 43 6712 10001.2019450426 -3289.20194504257 44 7297 7675.1198481444 -378.119848144401 45 10613 9544.9852273881 1068.01477261189 46 5184 6389.44243972984 -1205.44243972984 47 3506 3940.66761137263 -434.667611372631 48 3810 4069.43530288549 -259.435302885494 49 2692 3487.28558649729 -795.28558649729 50 3073 4150.7937087924 -1077.7937087924 51 3713 4436.81276751036 -723.812767510362 52 4555 6639.97090021167 -2084.97090021167 53 7807 6642.61697685495 1164.38302314505 54 10869 8161.85792878968 2707.14207121032 55 9682 7340.36780882019 2341.63219117981 56 7704 8435.8122414998 -731.812241499797 57 9826 8116.39487138927 1709.60512861073 58 5456 5872.1347444735 -416.134744473498 59 3677 3700.31229844708 -23.3122984470848 60 3431 2329.37320529422 1101.62679470578 61 2765 3892.43977249297 -1127.43977249297 62 3483 4278.15519251866 -795.155192518663 63 3445 3812.85673193696 -367.856731936956 64 6081 5529.22915607541 551.770843924589 65 8767 8344.92155946276 422.07844053724 66 9407 9066.16287249454 340.83712750546 67 6551 9236.76871475977 -2685.76871475977 68 12480 9582.12238285522 2897.87761714478 69 9530 10084.2714364012 -554.271436401155 70 5960 6807.19944205875 -847.199442058754 71 3252 4518.95974315539 -1266.95974315539 72 3717 3177.03135910754 539.968640892457 73 2642 1757.80214787432 884.197852125678 74 2989 3740.47383536903 -751.473835369028 75 3607 5014.49745736459 -1407.49745736459 76 5366 7099.75716598692 -1733.75716598692 77 8898 7851.58958913711 1046.41041086289 78 9435 8677.32631470726 757.673685292735 79 7328 9057.293111179 -1729.29311117901 80 8594 10047.7428396999 -1453.74283969986 81 11349 10471.6842089115 877.315791088515 82 5797 6608.48745884375 -811.487458843747 83 3621 5619.42851020535 -1998.42851020535 84 3851 3036.09125655246 814.908743447538 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.101913614951268 0.203827229902536 0.898086385048732 11 0.0885907577051257 0.177181515410251 0.911409242294874 12 0.0722796172734551 0.14455923454691 0.927720382726545 13 0.476097565021549 0.952195130043099 0.523902434978451 14 0.438694584586728 0.877389169173455 0.561305415413272 15 0.564524143023877 0.870951713952247 0.435475856976123 16 0.498399992800726 0.996799985601453 0.501600007199274 17 0.535814167506613 0.928371664986773 0.464185832493387 18 0.468514146829519 0.937028293659038 0.531485853170481 19 0.435931574463868 0.871863148927736 0.564068425536132 20 0.435955343664194 0.871910687328388 0.564044656335806 21 0.357708201508279 0.715416403016557 0.642291798491721 22 0.34368772258065 0.6873754451613 0.65631227741935 23 0.343504048541365 0.68700809708273 0.656495951458635 24 0.27620884156194 0.552417683123879 0.72379115843806 25 0.246826577824641 0.493653155649283 0.753173422175359 26 0.191677718497554 0.383355436995107 0.808322281502446 27 0.157040693758596 0.314081387517192 0.842959306241404 28 0.116641816412797 0.233283632825593 0.883358183587203 29 0.327849012951222 0.655698025902445 0.672150987048778 30 0.275693693847509 0.551387387695018 0.724306306152491 31 0.261203316415361 0.522406632830721 0.738796683584639 32 0.216414744965706 0.432829489931412 0.783585255034294 33 0.17214459345734 0.344289186914679 0.82785540654266 34 0.271966978730623 0.543933957461245 0.728033021269377 35 0.234460745692797 0.468921491385594 0.765539254307203 36 0.186448458499177 0.372896916998354 0.813551541500823 37 0.158903916813858 0.317807833627715 0.841096083186142 38 0.127905204430351 0.255810408860702 0.872094795569649 39 0.12899363674965 0.2579872734993 0.87100636325035 40 0.106725953689889 0.213451907379778 0.89327404631011 41 0.101804145391268 0.203608290782537 0.898195854608732 42 0.465369480829318 0.930738961658635 0.534630519170682 43 0.746004184991224 0.507991630017553 0.253995815008776 44 0.793005253054967 0.413989493890067 0.206994746945033 45 0.800517202489343 0.398965595021315 0.199482797510657 46 0.788585968848949 0.422828062302103 0.211414031151051 47 0.739181633819402 0.521636732361196 0.260818366180598 48 0.68946946710237 0.621061065795261 0.31053053289763 49 0.631704456205118 0.736591087589765 0.368295543794882 50 0.588121469424169 0.823757061151663 0.411878530575831 51 0.525315741344099 0.949368517311802 0.474684258655901 52 0.59207296081503 0.81585407836994 0.40792703918497 53 0.557396805729808 0.885206388540383 0.442603194270192 54 0.676808899671785 0.64638220065643 0.323191100328215 55 0.835234057102072 0.329531885795857 0.164765942897928 56 0.812305459657525 0.37538908068495 0.187694540342475 57 0.80357566159113 0.392848676817739 0.19642433840887 58 0.757489710368307 0.485020579263386 0.242510289631693 59 0.691519238335876 0.616961523328249 0.308480761664124 60 0.648774951337826 0.702450097324348 0.351225048662174 61 0.592234882360542 0.815530235278916 0.407765117639458 62 0.528623679698641 0.942752640602717 0.471376320301359 63 0.446264450249171 0.892528900498341 0.553735549750829 64 0.367403253389659 0.734806506779319 0.63259674661034 65 0.292544482725871 0.585088965451742 0.707455517274129 66 0.228901913278958 0.457803826557916 0.771098086721042 67 0.330849570870295 0.66169914174059 0.669150429129705 68 0.718822202584917 0.562355594830167 0.281177797415083 69 0.6296385891861 0.7407228216278 0.3703614108139 70 0.53567317198801 0.92865365602398 0.46432682801199 71 0.453407157190684 0.906814314381369 0.546592842809316 72 0.328858520391883 0.657717040783767 0.671141479608117 73 0.321245090085025 0.64249018017005 0.678754909914975 74 0.206107786712349 0.412215573424697 0.793892213287651 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 0 0 OK

Charts produced by software:

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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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