meervoudige regressie huwelijken

*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 16:19:29 +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/t1292948289zjl5rsw3h4vfanx.htm/, Retrieved Tue, 21 Dec 2010 17:18:20 +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/t1292948289zjl5rsw3h4vfanx.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:

» Textbox « » Textfile « » CSV «

3111 5140 17153 2.5 766 332 2.4 3995 4749 15579 1.8 294 369 2.4 5245 3635 16755 7.3 235 384 2.4 5588 4305 16585 9.9 462 373 2.1 10681 5805 16572 13.2 919 378 2 10516 4260 16325 17.8 346 426 2 7496 3869 17913 18.8 298 423 2.1 9935 7325 17572 19.3 92 397 2.1 10249 9280 17338 13.9 516 422 2 6271 6222 17087 7.5 843 409 2 3616 3272 15864 8 395 430 2 3724 7598 15554 4 961 412 1.7 2886 1345 16229 3.6 1231 470 1.3 3318 1900 15180 4.8 794 491 1.2 4166 1480 16215 5.9 420 504 1.1 6401 1472 15801 10.4 331 484 1.4 9209 3823 15751 12.3 312 474 1.5 9820 4454 16477 15.5 692 508 1.4 7470 3357 17324 16.7 1221 492 1.1 8207 5393 16919 18.8 1272 452 1.1 9564 8329 16438 15.2 622 457 1 5309 4152 16239 11.3 479 457 1.4 3385 4042 15613 6.3 757 471 1.3 3706 7747 15821 3.2 463 451 1.2 2733 1451 15678 5.3 534 493 1.5 3045 911 14671 2.4 731 514 1.6 3449 406 15876 6.5 498 522 1.8 5542 1387 15563 10.4 629 490 1.5 10072 2150 15711 12.6 542 484 1.3 9418 1577 15583 16.8 519 506 1.6 7516 2642 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Huwelijken[t] = + 168.834470680983 + 0.279936135719047Bevolkingsgroei[t] -0.174869868049466Geboren[t] + 404.376753924984Temperatuur[t] -0.604713135632148Neerslag[t] + 6.73204838797972Werkloosheid[t] + 547.234791727878Inflatie[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) 168.834470680983 3247.534019 0.052 0.958672 0.479336 Bevolkingsgroei 0.279936135719047 0.06594 4.2453 6e-05 3e-05 Geboren -0.174869868049466 0.248924 -0.7025 0.484484 0.242242 Temperatuur 404.376753924984 34.230123 11.8135 0 0 Neerslag -0.604713135632148 0.435231 -1.3894 0.168713 0.084357 Werkloosheid 6.73204838797972 3.350672 2.0092 0.048025 0.024012 Inflatie 547.234791727878 329.279935 1.6619 0.100596 0.050298 Multiple Linear Regression - Regression Statistics Multiple R 0.872601703276923 R-squared 0.761433732561787 Adjusted R-squared 0.742844153280887 F-TEST (value) 40.9602455793144 F-TEST (DF numerator) 6 F-TEST (DF denominator) 77 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1439.39883914666 Sum Squared Residuals 159533914.396530 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3111 2704.29854949879 406.70145050121 2 3995 3121.53535536864 873.464644631355 3 5245 4964.77048276086 280.229517239143 4 5588 5857.94227989134 -269.942279891339 5 10681 7317.14593949022 3363.85406050978 6 10516 9457.60948460769 1058.39051539231 7 7496 9538.39142352316 -2042.39142352316 8 9935 10717.2073583883 -782.207358388298 9 10249 8978.94694266637 1270.05305733363 10 6271 5293.5255270026 977.474472997402 11 3616 5296.05265312918 -1680.05265312918 12 3724 4316.14407637538 -592.144076375377 13 2886 2294.20790041179 591.792099588213 14 3318 3469.17222927576 -151.172229275764 15 4166 3874.37903075742 291.620969242579 16 6401 5847.5799985366 553.420001463395 17 9209 7281.66172434203 1927.33827565797 18 9820 8569.72668881509 1250.27331118491 19 7470 8007.99761392793 -537.99761392793 20 8207 9197.83876061798 -990.838760617981 21 9564 9020.08764841896 543.91235158104 22 5309 6613.89206804146 -1304.89206804146 23 3385 4542.0988074396 -1157.09880743960 24 3706 4277.7425355004 -571.7425355004 25 2733 3793.44403657045 -1060.44403657045 26 3045 2722.64790162635 322.352098373646 27 3449 4332.70815923277 -883.708159232766 28 5542 5780.31571067854 -238.315710678545 29 10072 6760.42589452236 3311.57410547764 30 9418 8646.97210252407 771.02789747593 31 7516 8487.0156895369 -971.01568953691 32 7840 8512.52654859154 -672.526548591544 33 10081 9675.75357321416 405.246426785839 34 4956 7245.44498621325 -2289.44498621325 35 3641 4213.3326958062 -572.332695806203 36 3970 3468.11002901236 501.889970987639 37 2931 1940.78205624930 990.217943750698 38 3170 2482.2544417471 687.7455582529 39 3889 2440.04391695996 1448.95608304004 40 4850 4670.66919484856 179.330805151440 41 8037 6488.00606068233 1548.99393931767 42 12370 8233.01575987057 4136.98424012943 43 6712 9998.3747964475 -3286.3747964475 44 7297 7497.65981345879 -200.659813458788 45 10613 9512.05413606752 1100.94586393248 46 5184 6287.83722345096 -1103.83722345096 47 3506 3920.46118286234 -414.461182862343 48 3810 4091.15166758013 -281.151667580129 49 2692 3490.14930272583 -798.149302725834 50 3073 4378.18265834716 -1305.18265834716 51 3713 4457.24800242498 -744.24800242498 52 4555 6792.08268137312 -2237.08268137312 53 7807 6746.91832109005 1060.08167890995 54 10869 8357.30757117199 2511.69242882801 55 9682 7330.65477989589 2351.34522010411 56 7704 8301.68815620376 -597.688156203758 57 9826 8098.79037016824 1727.20962983176 58 5456 5779.84086024827 -323.840860248269 59 3677 3729.07803719029 -52.07803719029 60 3431 2327.45734948269 1103.54265051731 61 2765 3892.8573564778 -1127.85735647780 62 3483 4426.49356701567 -943.493567015673 63 3445 3870.42012757946 -425.420127579462 64 6081 5580.18716652338 500.812833476625 65 8767 8361.26385892522 405.736141074776 66 9407 9170.63458927308 236.365410726923 67 6551 9181.6063176063 -2630.60631760630 68 12480 9605.3691317216 2874.63086827841 69 9530 10178.6937995907 -648.693799590702 70 5960 6838.094955674 -878.094955674005 71 3252 4619.70430359626 -1367.70430359626 72 3717 3132.02858976799 584.971410232007 73 2642 1608.30468459106 1033.69531540894 74 2989 3983.80360591598 -994.803605915983 75 3607 5014.12915271932 -1407.12915271932 76 5366 7177.66208757844 -1811.66208757844 77 8898 7961.25569182029 936.74430817971 78 9435 8770.8669888226 664.1330111774 79 7328 8974.97676646753 -1646.97676646753 80 8594 10020.7976698279 -1426.7976698279 81 11349 10522.7264652362 826.273534763825 82 5797 6640.46480912358 -843.464809123577 83 3621 5755.1677636647 -2134.16776366470 84 3851 3062.12980361711 788.870196382892 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.103617549428786 0.207235098857572 0.896382450571214 11 0.0899165964287661 0.179833192857532 0.910083403571234 12 0.073579645350129 0.147159290700258 0.926420354649871 13 0.483340163440207 0.966680326880415 0.516659836559793 14 0.446351103698167 0.892702207396335 0.553648896301833 15 0.574010334381924 0.851979331236151 0.425989665618076 16 0.509108010293382 0.981783979413236 0.490891989706618 17 0.547872942609983 0.904254114780033 0.452127057390017 18 0.482181216853824 0.964362433707648 0.517818783146176 19 0.448577253153752 0.897154506307505 0.551422746846248 20 0.447496268934746 0.894992537869492 0.552503731065254 21 0.369440508393821 0.738881016787641 0.63055949160618 22 0.352520889775016 0.705041779550032 0.647479110224984 23 0.351651153279825 0.70330230655965 0.648348846720175 24 0.282884343183316 0.565768686366632 0.717115656816684 25 0.251238526068701 0.502477052137402 0.7487614739313 26 0.195280683844232 0.390561367688465 0.804719316155768 27 0.158908412121044 0.317816824242089 0.841091587878956 28 0.118084105266194 0.236168210532387 0.881915894733806 29 0.333533741532855 0.66706748306571 0.666466258467145 30 0.279962109118091 0.559924218236182 0.720037890881909 31 0.265172709769464 0.530345419538927 0.734827290230536 32 0.219009441322774 0.438018882645547 0.780990558677226 33 0.174760917139401 0.349521834278802 0.825239082860599 34 0.272128282180408 0.544256564360816 0.727871717819592 35 0.234757567512634 0.469515135025269 0.765242432487365 36 0.186760562937992 0.373521125875984 0.813239437062008 37 0.162898857772355 0.325797715544711 0.837101142227645 38 0.131162783158607 0.262325566317214 0.868837216841393 39 0.146089782299125 0.292179564598251 0.853910217700875 40 0.121678425825497 0.243356851650995 0.878321574174503 41 0.122884038230674 0.245768076461349 0.877115961769326 42 0.520859566777626 0.958280866444748 0.479140433222374 43 0.776277428326816 0.447445143346369 0.223722571673184 44 0.736353323098061 0.527293353803877 0.263646676901939 45 0.744844233570722 0.510311532858556 0.255155766429278 46 0.731039863203017 0.537920273593966 0.268960136796983 47 0.680213099597495 0.63957380080501 0.319786900402505 48 0.639045964901493 0.721908070197013 0.360954035098507 49 0.584805007967459 0.830389984065082 0.415194992032541 50 0.569726243972715 0.86054751205457 0.430273756027285 51 0.509764190616444 0.980471618767112 0.490235809383556 52 0.59077928807721 0.81844142384558 0.40922071192279 53 0.546995749901924 0.906008500196151 0.453004250098076 54 0.647774438069218 0.704451123861563 0.352225561930782 55 0.81749129715622 0.365017405687561 0.182508702843781 56 0.790573344547533 0.418853310904933 0.209426655452467 57 0.779333861342491 0.441332277315018 0.220666138657509 58 0.730084280239189 0.539831439521622 0.269915719760811 59 0.661604855948193 0.676790288103613 0.338395144051807 60 0.615874874329909 0.768250251340183 0.384125125670091 61 0.561529159046021 0.876941681907959 0.438470840953979 62 0.506560804847406 0.986878390305187 0.493439195152594 63 0.426039596895918 0.852079193791836 0.573960403104082 64 0.347274864921363 0.694549729842725 0.652725135078637 65 0.274010154736374 0.548020309472748 0.725989845263626 66 0.2123519028359 0.4247038056718 0.7876480971641 67 0.310783937461722 0.621567874923445 0.689216062538278 68 0.699375510846206 0.601248978307588 0.300624489153794 69 0.61372230911218 0.77255538177564 0.38627769088782 70 0.520600172320431 0.958799655359138 0.479399827679569 71 0.44181942852334 0.88363885704668 0.55818057147666 72 0.318769962103388 0.637539924206776 0.681230037896612 73 0.313917189443739 0.627834378887479 0.68608281055626 74 0.20272605855113 0.40545211710226 0.79727394144887 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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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