WS 7.1

*Unverified author*

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 08:37:55 -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/t1258731523d9di3flyuex2ndm.htm/, Retrieved Fri, 20 Nov 2009 16:38:55 +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/t1258731523d9di3flyuex2ndm.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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9.9 8.2 9.8 8 9.3 7.5 8.3 6.8 8 6.5 8.5 6.6 10.4 7.6 11.1 8 10.9 8.1 10 7.7 9.2 7.5 9.2 7.6 9.5 7.8 9.6 7.8 9.5 7.8 9.1 7.5 8.9 7.5 9 7.1 10.1 7.5 10.3 7.5 10.2 7.6 9.6 7.7 9.2 7.7 9.3 7.9 9.4 8.1 9.4 8.2 9.2 8.2 9 8.2 9 7.9 9 7.3 9.8 6.9 10 6.6 9.8 6.7 9.3 6.9 9 7 9 7.1 9.1 7.2 9.1 7.1 9.1 6.9 9.2 7 8.8 6.8 8.3 6.4 8.4 6.7 8.1 6.6 7.7 6.4 7.9 6.3 7.9 6.2 8 6.5 7.9 6.8 7.6 6.8 7.1 6.4 6.8 6.1 6.5 5.8 6.9 6.1 8.2 7.2 8.7 7.3 8.3 6.9 7.9 6.1 7.5 5.8 7.8 6.2

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WLMan[t] = + 2.46122394441475 + 0.525724444031325WLVrouw[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) 2.46122394441475 0.504968 4.874 9e-06 4e-06 WLVrouw 0.525724444031325 0.05644 9.3147 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.77417457022882 R-squared 0.599346265188978 Adjusted R-squared 0.592438442174995 F-TEST (value) 86.7634078023937 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 4.02788913334007e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.427523556935347 Sum Squared Residuals 10.6010307206098 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.66589594032488 0.534104059675124 2 8 7.61332349592173 0.386676504078266 3 7.5 7.35046127390607 0.149538726093928 4 6.8 6.82473682987475 -0.0247368298747477 5 6.5 6.66701949666535 -0.167019496665350 6 6.6 6.92988171868101 -0.329881718681012 7 7.6 7.92875816234053 -0.32875816234053 8 8 8.29676527316246 -0.296765273162456 9 8.1 8.19162038435619 -0.091620384356192 10 7.7 7.718468384728 -0.0184683847279990 11 7.5 7.29788882950294 0.202111170497061 12 7.6 7.29788882950294 0.302111170497061 13 7.8 7.45560616271234 0.344393837287663 14 7.8 7.50817860711547 0.291821392884531 15 7.8 7.45560616271234 0.344393837287663 16 7.5 7.2453163850998 0.254683614900193 17 7.5 7.14017149629354 0.359828503706458 18 7.1 7.19274394069667 -0.0927439406966748 19 7.5 7.77104082913113 -0.271040829131131 20 7.5 7.8761857179374 -0.376185717937397 21 7.6 7.82361327353426 -0.223613273534264 22 7.7 7.50817860711547 0.191821392884531 23 7.7 7.29788882950294 0.402111170497061 24 7.9 7.35046127390607 0.549538726093928 25 8.1 7.4030337183092 0.696966281690795 26 8.2 7.4030337183092 0.796966281690795 27 8.2 7.29788882950294 0.90211117049706 28 8.2 7.19274394069667 1.00725605930332 29 7.9 7.19274394069667 0.707256059303326 30 7.3 7.19274394069667 0.107256059303325 31 6.9 7.61332349592173 -0.713323495921734 32 6.6 7.718468384728 -1.118468384728 33 6.7 7.61332349592173 -0.913323495921734 34 6.9 7.35046127390607 -0.450461273906072 35 7 7.19274394069667 -0.192743940696674 36 7.1 7.19274394069667 -0.0927439406966748 37 7.2 7.2453163850998 -0.0453163850998065 38 7.1 7.2453163850998 -0.145316385099807 39 6.9 7.2453163850998 -0.345316385099806 40 7 7.29788882950294 -0.297888829502939 41 6.8 7.08759905189041 -0.28759905189041 42 6.4 6.82473682987475 -0.424736829874747 43 6.7 6.87730927427788 -0.177309274277880 44 6.6 6.71959194106848 -0.119591941068482 45 6.4 6.50930216345595 -0.109302163455952 46 6.3 6.61444705226222 -0.314447052262217 47 6.2 6.61444705226222 -0.414447052262217 48 6.5 6.66701949666535 -0.167019496665350 49 6.8 6.61444705226222 0.185552947737783 50 6.8 6.45672971905282 0.34327028094718 51 6.4 6.19386749703716 0.206132502962843 52 6.1 6.03615016382776 0.0638498361722397 53 5.8 5.87843283061836 -0.0784328306183626 54 6.1 6.08872260823089 0.0112773917691069 55 7.2 6.77216438547161 0.427835614528386 56 7.3 7.03502660748728 0.264973392512723 57 6.9 6.82473682987475 0.0752631701252529 58 6.1 6.61444705226222 -0.514447052262218 59 5.8 6.40415727464969 -0.604157274649687 60 6.2 6.56187460785908 -0.361874607859084 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00964990237537807 0.0192998047507561 0.990350097624622 6 0.0307433421885742 0.0614866843771484 0.969256657811426 7 0.315793841156103 0.631587682312207 0.684206158843897 8 0.32515740255603 0.65031480511206 0.67484259744397 9 0.222110580480868 0.444221160961736 0.777889419519132 10 0.140750101682731 0.281500203365463 0.859249898317269 11 0.0995065923793272 0.199013184758654 0.900493407620673 12 0.0801584731108366 0.160316946221673 0.919841526889163 13 0.0680754022537612 0.136150804507522 0.931924597746239 14 0.0503453461156866 0.100690692231373 0.949654653884313 15 0.040080104810349 0.080160209620698 0.959919895189651 16 0.0261842774966516 0.0523685549933031 0.973815722503348 17 0.0198884608862351 0.0397769217724701 0.980111539113765 18 0.0132493090699937 0.0264986181399874 0.986750690930006 19 0.0117055716685091 0.0234111433370181 0.98829442833149 20 0.0124650499343162 0.0249300998686323 0.987534950065684 21 0.00853084403578716 0.0170616880715743 0.991469155964213 22 0.00520551454220788 0.0104110290844158 0.994794485457792 23 0.00467322355544211 0.00934644711088421 0.995326776444558 24 0.00697750646758477 0.0139550129351695 0.993022493532415 25 0.0182677269659801 0.0365354539319602 0.98173227303402 26 0.0609417043499772 0.121883408699954 0.939058295650023 27 0.216471047774197 0.432942095548395 0.783528952225803 28 0.635637128067663 0.728725743864674 0.364362871932337 29 0.861483903328567 0.277032193342866 0.138516096671433 30 0.865893135311293 0.268213729377414 0.134106864688707 31 0.918047101108863 0.163905797782274 0.0819528988911368 32 0.989385897761957 0.0212282044760851 0.0106141022380426 33 0.998046607612098 0.00390678477580403 0.00195339238790202 34 0.9980060801854 0.00398783962919993 0.00199391981459996 35 0.996759606717903 0.00648078656419377 0.00324039328209688 36 0.994569600939701 0.0108607981205974 0.00543039906029869 37 0.99131337241727 0.0173732551654605 0.00868662758273023 38 0.985996396063856 0.0280072078722878 0.0140036039361439 39 0.981013141098412 0.037973717803176 0.018986858901588 40 0.97258370556829 0.0548325888634185 0.0274162944317093 41 0.962961390135332 0.074077219729336 0.037038609864668 42 0.96528778725341 0.0694244254931814 0.0347122127465907 43 0.94851880897509 0.102962382049822 0.051481191024911 44 0.922052090418058 0.155895819163884 0.077947909581942 45 0.884530721851471 0.230938556297059 0.115469278148529 46 0.862395795438017 0.275208409123966 0.137604204561983 47 0.867195976743752 0.265608046512496 0.132804023256248 48 0.818589221679042 0.362821556641916 0.181410778320958 49 0.752482649704617 0.495034700590766 0.247517350295383 50 0.734719015935051 0.530561968129898 0.265280984064949 51 0.687863340897028 0.624273318205944 0.312136659102972 52 0.614655926564877 0.770688146870245 0.385344073435123 53 0.551146585732088 0.897706828535824 0.448853414267912 54 0.824095727355511 0.351808545288977 0.175904272644489 55 0.967679829668918 0.0646403406621636 0.0323201703310818 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0784313725490196 NOK 5% type I error level 18 0.352941176470588 NOK 10% type I error level 25 0.490196078431373 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/10w4kz1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/10w4kz1258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/1m1l31258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/1m1l31258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/2hf721258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/2hf721258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/3bppn1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/3bppn1258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/4ewu61258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/4ewu61258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/53p3d1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/53p3d1258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/6e3z31258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/6e3z31258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/779la1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/779la1258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/8t04f1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/8t04f1258731470.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/9p0tn1258731470.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731523d9di3flyuex2ndm/9p0tn1258731470.ps (open in new window)

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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