*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, 20 Nov 2009 05:33:37 -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/20/t1258721564mtduta3s3e9cqk7.htm/, Retrieved Fri, 20 Nov 2009 13:52:56 +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/20/t1258721564mtduta3s3e9cqk7.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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10144 112 10751 304 11752 794 13808 901 16203 1232 17432 1240 18014 1032 16956 1145 17982 1588 19435 2264 19990 2209 20154 2917 10327 243 9807 558 10862 1238 13743 1502 16458 2000 18466 2146 18810 2066 17361 2046 17411 1952 18517 2771 18525 3278 17859 4000 9499 410 9490 1107 9255 1622 10758 1986 12375 2036 14617 2400 15427 2736 14136 2901 14308 2883 15293 3747 15679 4075 16319 4996 11196 575 11169 999 12158 1411 14251 1493 16237 1846 19706 2899 18960 2372 18537 2856 19103 3468 19691 4193 19464 4440 17264 4186 8957 655 9703 1453 9166 1989 9519 2209 10535 2667 11526 3005 9630 2195 7061 2236 6021 2489 4728 2651 2657 2636 1264 2819

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 Y[t] = + 10746.1453026392 + 1.41721562690964X[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) 10746.1453026392 1219.242775 8.8138 0 0 X 1.41721562690964 0.505825 2.8018 0.006896 0.003448 Multiple Linear Regression - Regression Statistics Multiple R 0.345269196870399 R-squared 0.119210818307530 Adjusted R-squared 0.104024797933522 F-TEST (value) 7.85003676878821 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.00689613986257798 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4373.91726887107 Sum Squared Residuals 1109606831.94586 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10144 10904.8734528531 -760.873452853079 2 10751 11176.9788532197 -425.978853219690 3 11752 11871.4145104054 -119.414510405411 4 13808 12023.0565824847 1784.94341751526 5 16203 12492.1549549918 3710.84504500817 6 17432 12503.4926800071 4928.50731999289 7 18014 12208.7118296099 5805.28817039009 8 16956 12368.8571954507 4587.14280454931 9 17982 12996.6837181717 4985.31628182834 10 19435 13954.7214819626 5480.27851803742 11 19990 13876.7746224825 6113.22537751745 12 20154 14880.1632863346 5273.83671366543 13 10327 11090.5286999782 -763.528699978201 14 9807 11536.9516224547 -1729.95162245474 15 10862 12500.6582487533 -1638.65824875329 16 13743 12874.8031742574 868.196825742565 17 16458 13580.5765564584 2877.42344354157 18 18466 13787.4900379872 4678.50996201276 19 18810 13674.1127878345 5135.88721216553 20 17361 13645.7684752963 3715.23152470372 21 17411 13512.5502063668 3898.44979363323 22 18517 14673.2498048058 3843.75019519424 23 18525 15391.7781276490 3133.22187235105 24 17859 16415.0078102777 1443.99218972229 25 9499 11327.2037096721 -1828.20370967211 26 9490 12315.0030016281 -2825.00300162813 27 9255 13044.8690494866 -3789.86904948659 28 10758 13560.7355376817 -2802.7355376817 29 12375 13631.5963190272 -1256.59631902718 30 14617 14147.4628072223 469.537192777711 31 15427 14623.6472578639 803.352742136073 32 14136 14857.4878363040 -721.487836304017 33 14308 14831.9779550196 -523.977955019644 34 15293 16056.4522566696 -763.45225666957 35 15679 16521.2989822959 -842.298982295932 36 16319 17826.5545746797 -1507.55457467971 37 11196 11561.0442881122 -365.044288112201 38 11169 12161.9437139219 -992.943713921887 39 12158 12745.8365522087 -587.836552208658 40 14251 12862.0482336152 1388.95176638475 41 16237 13362.3253499144 2874.67465008565 42 19706 14854.6534050502 4851.3465949498 43 18960 14107.7807696688 4852.21923033118 44 18537 14793.7131330931 3743.28686690692 45 19103 15661.0490967618 3441.95090323822 46 19691 16688.5304262713 3002.46957372873 47 19464 17038.5826861179 2425.41731388205 48 17264 16678.6099168829 585.390083117098 49 8957 11674.4215382650 -2717.42153826497 50 9703 12805.3596085389 -3102.35960853886 51 9166 13564.9871845624 -4398.98718456243 52 9519 13876.7746224825 -4357.77462248255 53 10535 14525.8593796072 -3990.85937960716 54 11526 15004.8782615026 -3478.87826150262 55 9630 13856.9336037058 -4226.93360370581 56 7061 13915.0394444091 -6854.03944440911 57 6021 14273.5949980172 -8252.59499801725 58 4728 14503.1839295766 -9775.1839295766 59 2657 14481.9256951730 -11824.9256951730 60 1264 14741.2761548974 -13477.2761548974 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0134212683948824 0.0268425367897647 0.986578731605118 6 0.00799445963605446 0.0159889192721089 0.992005540363946 7 0.0148401486796582 0.0296802973593164 0.985159851320342 8 0.0052972107328393 0.0105944214656786 0.99470278926716 9 0.00229132179119115 0.00458264358238229 0.997708678208809 10 0.00273310757909739 0.00546621515819477 0.997266892420903 11 0.00121654530261787 0.00243309060523574 0.998783454697382 12 0.00185020893073329 0.00370041786146659 0.998149791069267 13 0.000916952578270615 0.00183390515654123 0.99908304742173 14 0.00107211844999425 0.00214423689998851 0.998927881550006 15 0.0032587687193253 0.0065175374386506 0.996741231280675 16 0.00244686840228203 0.00489373680456405 0.997553131597718 17 0.00150426268603524 0.00300852537207048 0.998495737313965 18 0.000853343539560415 0.00170668707912083 0.99914665646044 19 0.000542001408531942 0.00108400281706388 0.999457998591468 20 0.000319211419403156 0.000638422838806313 0.999680788580597 21 0.000189554002515980 0.000379108005031960 0.999810445997484 22 0.000182659897913647 0.000365319795827293 0.999817340102086 23 0.000309291833493604 0.000618583666987207 0.999690708166506 24 0.00115415411476263 0.00230830822952527 0.998845845885237 25 0.000975129616077955 0.00195025923215591 0.999024870383922 26 0.00162796921800542 0.00325593843601084 0.998372030781995 27 0.0047519978027026 0.0095039956054052 0.995248002197297 28 0.00770759356311508 0.0154151871262302 0.992292406436885 29 0.00704415327376774 0.0140883065475355 0.992955846726232 30 0.00515575861356541 0.0103115172271308 0.994844241386435 31 0.00374205336670692 0.00748410673341384 0.996257946633293 32 0.00331329305804467 0.00662658611608934 0.996686706941955 33 0.00259832868863594 0.00519665737727188 0.997401671311364 34 0.002215478083035 0.00443095616607 0.997784521916965 35 0.00172170441586316 0.00344340883172631 0.998278295584137 36 0.00139433103047864 0.00278866206095727 0.998605668969521 37 0.00091383782172331 0.00182767564344662 0.999086162178277 38 0.00060356772745295 0.0012071354549059 0.999396432272547 39 0.000387860250910582 0.000775720501821164 0.99961213974909 40 0.000321051035832586 0.000642102071665173 0.999678948964167 41 0.000426629484409001 0.000853258968818003 0.99957337051559 42 0.000835742354980828 0.00167148470996166 0.99916425764502 43 0.00312305514887167 0.00624611029774334 0.996876944851128 44 0.00627936628292355 0.0125587325658471 0.993720633717077 45 0.0095102325846023 0.0190204651692046 0.990489767415398 46 0.0120223062402335 0.024044612480467 0.987977693759767 47 0.0260269080720892 0.0520538161441785 0.973973091927911 48 0.212809332570543 0.425618665141087 0.787190667429457 49 0.184247439834151 0.368494879668301 0.81575256016585 50 0.145703791952100 0.291407583904199 0.8542962080479 51 0.125425768824986 0.250851537649972 0.874574231175014 52 0.114534302947803 0.229068605895607 0.885465697052197 53 0.151230300696335 0.302460601392671 0.848769699303665 54 0.966915485183038 0.0661690296339246 0.0330845148169623 55 0.939472335134497 0.121055329731006 0.0605276648655028 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 32 0.627450980392157 NOK 5% type I error level 42 0.823529411764706 NOK 10% type I error level 44 0.862745098039216 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/107vwv1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/107vwv1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/1e6te1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/1e6te1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/2qaow1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/2qaow1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/3yoz41258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/3yoz41258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/4gyg91258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/4gyg91258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/58w5v1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/58w5v1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/6w2ai1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/6w2ai1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/7kjbs1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/7kjbs1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/8ffvo1258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/8ffvo1258720412.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/9ua611258720412.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721564mtduta3s3e9cqk7/9ua611258720412.ps (open in new window)

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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