SHW WS7 - Fixed Saisonal Effect

*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: Wed, 18 Nov 2009 07:34:12 -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/18/t12585550077ppisrtv9wbcysg.htm/, Retrieved Wed, 18 Nov 2009 15:36:59 +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/18/t12585550077ppisrtv9wbcysg.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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8.6 1.59 8.5 1.26 8.3 1.13 7.8 1.92 7.8 2.61 8 2.26 8.6 2.41 8.9 2.26 8.9 2.03 8.6 2.86 8.3 2.55 8.3 2.27 8.3 2.26 8.4 2.57 8.5 3.07 8.4 2.76 8.6 2.51 8.5 2.87 8.5 3.14 8.5 3.11 8.5 3.16 8.5 2.47 8.5 2.57 8.5 2.89 8.5 2.63 8.5 2.38 8.5 1.69 8.5 1.96 8.6 2.19 8.4 1.87 8.1 1.6 8 1.63 8 1.22 8 1.21 8 1.49 7.9 1.64 7.8 1.66 7.8 1.77 7.9 1.82 8.1 1.78 8 1.28 7.6 1.29 7.3 1.37 7 1.12 6.8 1.51 7 2.24 7.1 2.94 7.2 3.09 7.1 3.46 6.9 3.64 6.7 4.39 6.7 4.15 6.6 5.21 6.9 5.8 7.3 5.91 7.5 5.39 7.3 5.46 7.1 4.72 6.9 3.14 7.1 2.63

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] = + 8.39940223094786 -0.23937788775873X[t] + 0.215954468652391M1[t] + 0.176911980203429M2[t] + 0.159892257428267M3[t] + 0.102393778877587M4[t] + 0.181280739266235M5[t] + 0.155164656756241M6[t] + 0.251442353123835M7[t] + 0.227396821776229M8[t] + 0.141172996694502M9[t] + 0.0869180660007111M10[t] -0.031861151816203M11[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) 8.39940223094786 0.344243 24.3996 0 0 X -0.23937788775873 0.071835 -3.3323 0.001685 0.000842 M1 0.215954468652391 0.415297 0.52 0.605504 0.302752 M2 0.176911980203429 0.415288 0.426 0.672053 0.336027 M3 0.159892257428267 0.41513 0.3852 0.701855 0.350928 M4 0.102393778877587 0.415087 0.2467 0.80623 0.403115 M5 0.181280739266235 0.415493 0.4363 0.664615 0.332308 M6 0.155164656756241 0.415699 0.3733 0.710631 0.355316 M7 0.251442353123835 0.415992 0.6044 0.548458 0.274229 M8 0.227396821776229 0.41533 0.5475 0.58662 0.29331 M9 0.141172996694502 0.41527 0.34 0.735405 0.367703 M10 0.0869180660007111 0.415325 0.2093 0.835136 0.417568 M11 -0.031861151816203 0.415093 -0.0768 0.939143 0.469572 Multiple Linear Regression - Regression Statistics Multiple R 0.452704337859653 R-squared 0.204941217516946 Adjusted R-squared 0.00194748581914561 F-TEST (value) 1.00959382244396 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.455531488772312 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.656308937340636 Sum Squared Residuals 20.2448467979601 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.23474585806389 0.365254141936114 2 8.5 8.27469807257529 0.225301927424711 3 8.3 8.28879747520876 0.0112025247912387 4 7.8 8.04219046532869 -0.242190465328686 5 7.8 7.95590668316381 -0.155906683163810 6 8 8.01357286136937 -0.0135728613693714 7 8.6 8.07394387457316 0.526056125426844 8 8.9 8.08580502638936 0.814194973610642 9 8.9 8.05463811549214 0.84536188450786 10 8.6 7.8016995379586 0.798300462041397 11 8.3 7.7571274653469 0.542872534653105 12 8.3 7.85601442573554 0.443985574264458 13 8.3 8.07436267326552 0.22563732673448 14 8.4 7.96111303961135 0.438886960388648 15 8.5 7.82440437295683 0.675595627043174 16 8.4 7.84111303961135 0.558886960388648 17 8.6 7.97984447193968 0.620155528060317 18 8.5 7.86755234983655 0.632447650163454 19 8.5 7.89919801650928 0.600801983490718 20 8.5 7.88233382179444 0.617666178205562 21 8.5 7.78414110232477 0.715858897675225 22 8.5 7.8950569141845 0.604943085815492 23 8.5 7.75233990759172 0.747660092408279 24 8.5 7.70760013532513 0.79239986467487 25 8.5 7.98579285479479 0.514207145205209 26 8.5 8.00659483828551 0.493405161714489 27 8.5 8.15474585806387 0.345254141936127 28 8.5 8.03261534981834 0.467384650181663 29 8.6 8.05644539602248 0.543554603977524 30 8.4 8.10693023759528 0.293069762404724 31 8.1 8.26783996365773 -0.167839963657727 32 8 8.23661309567736 -0.236613095677359 33 8 8.24853420457671 -0.248534204576711 34 8 8.19667305276051 -0.196673052760508 35 8 8.01086802637115 -0.0108680263711493 36 7.9 8.00682249502354 -0.106822495023542 37 7.8 8.21798940592076 -0.417989405920759 38 7.8 8.15261534981834 -0.352615349818337 39 7.9 8.12362673265524 -0.223626732655238 40 8.1 8.0757033696149 0.0242966303850916 41 8 8.27427927388292 -0.274279273882920 42 7.6 8.24576941249534 -0.64576941249534 43 7.3 8.32289687784223 -1.02289687784223 44 7 8.35869581843431 -1.35869581843431 45 6.8 8.17911461712668 -1.37911461712668 46 7 7.95011382836902 -0.950113828369016 47 7.1 7.66377008912099 -0.563770089120991 48 7.2 7.65972455777338 -0.459724557773384 49 7.1 7.78710920795504 -0.687109207955045 50 6.9 7.70497869970951 -0.80497869970951 51 6.7 7.5084255611153 -0.808425561115302 52 6.7 7.50837777562672 -0.808377775626717 53 6.6 7.33352417499111 -0.733524174991111 54 6.9 7.16617513870347 -0.266175138703466 55 7.3 7.2361212674176 0.0638787325824002 56 7.5 7.33655223770453 0.163447762295466 57 7.3 7.2335719604797 0.0664280395203048 58 7.1 7.35645666672737 -0.256456666727366 59 6.9 7.61589451156924 -0.715894511569244 60 7.1 7.7698383861424 -0.6698383861424 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0749880350471168 0.149976070094234 0.925011964952883 17 0.114766839796605 0.22953367959321 0.885233160203395 18 0.0740275536978857 0.148055107395771 0.925972446302114 19 0.0382151233098422 0.0764302466196844 0.961784876690158 20 0.0287781383228749 0.0575562766457497 0.971221861677125 21 0.0230102082088502 0.0460204164177004 0.97698979179115 22 0.0134497477470832 0.0268994954941665 0.986550252252917 23 0.00930733380084356 0.0186146676016871 0.990692666199156 24 0.00717503601395457 0.0143500720279091 0.992824963986045 25 0.00472334017103899 0.00944668034207799 0.995276659828961 26 0.00336914139352412 0.00673828278704824 0.996630858606476 27 0.00222664733224538 0.00445329466449077 0.997773352667755 28 0.00269115534650029 0.00538231069300058 0.9973088446535 29 0.00441379247805367 0.00882758495610735 0.995586207521946 30 0.00365618865529410 0.00731237731058821 0.996343811344706 31 0.00378898222814123 0.00757796445628246 0.99621101777186 32 0.0065521261433684 0.0131042522867368 0.993447873856632 33 0.00846960326206076 0.0169392065241215 0.99153039673794 34 0.00749382288942243 0.0149876457788449 0.992506177110578 35 0.0078971143598341 0.0157942287196682 0.992102885640166 36 0.0072667800485661 0.0145335600971322 0.992733219951434 37 0.00852404391186397 0.0170480878237279 0.991475956088136 38 0.0127259949013215 0.0254519898026429 0.987274005098679 39 0.0244834643244305 0.0489669286488609 0.97551653567557 40 0.0805455017527093 0.161091003505419 0.91945449824729 41 0.494402023169639 0.988804046339278 0.505597976830361 42 0.931163639625597 0.137672720748807 0.0688363603744033 43 0.981553754481723 0.0368924910365537 0.0184462455182769 44 0.955543002818865 0.0889139943622694 0.0444569971811347 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.241379310344828 NOK 5% type I error level 20 0.689655172413793 NOK 10% type I error level 23 0.793103448275862 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/1040io1258554848.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/1040io1258554848.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/1vpai1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/1vpai1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/2snm91258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/2snm91258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/31ngb1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/31ngb1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/4sjzd1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/4sjzd1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/5ff1n1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/5ff1n1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/67txd1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/67txd1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/72g331258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/72g331258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/890cg1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/890cg1258554847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/92c1z1258554847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585550077ppisrtv9wbcysg/92c1z1258554847.ps (open in new window)

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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