WS7 Multiple Regression 2 (seizoenaliteit filteren)

*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 07:38:05 -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/t125872794222z7lfzdcl6vnr4.htm/, Retrieved Fri, 20 Nov 2009 15:39:14 +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/t125872794222z7lfzdcl6vnr4.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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96.8 92.9 114.1 107.7 110.3 103.5 103.9 91.1 101.6 79.8 94.6 71.9 95.9 82.9 104.7 90.1 102.8 100.7 98.1 90.7 113.9 108.8 80.9 44.1 95.7 93.6 113.2 107.4 105.9 96.5 108.8 93.6 102.3 76.5 99 76.7 100.7 84 115.5 103.3 100.7 88.5 109.9 99 114.6 105.9 85.4 44.7 100.5 94 114.8 107.1 116.5 104.8 112.9 102.5 102 77.7 106 85.2 105.3 91.3 118.8 106.5 106.1 92.4 109.3 97.5 117.2 107 92.5 51.1 104.2 98.6 112.5 102.2 122.4 114.3 113.3 99.4 100 72.5 110.7 92.3 112.8 99.4 109.8 85.9 117.3 109.4 109.1 97.6 115.9 104.7 96 56.9 99.8 86.7 116.8 108.5 115.7 103.4 99.4 86.2 94.3 71 91 75.9 93.2 87.1 103.1 102 94.1 88.5 91.8 87.8 102.7 100.8 82.6 50.6

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Totind[t] = + 46.4685613985971 + 0.828848799543308Bouw[t] -24.2841155640517M1[t] -20.5272664539229M2[t] -18.9232609508728M3[t] -17.1845038834123M4[t] -9.00664576411686M5[t] -12.8480048818791M6[t] -18.6063736299789M7[t] -16.9510502820422M8[t] -21.7551612748004M9[t] -21.1713499314306M10[t] -21.0023788224435M11[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) 46.4685613985971 4.78929 9.7026 0 0 Bouw 0.828848799543308 0.090283 9.1805 0 0 M1 -24.2841155640517 4.638361 -5.2355 4e-06 2e-06 M2 -20.5272664539229 5.704246 -3.5986 0.000767 0.000384 M3 -18.9232609508728 5.535117 -3.4188 0.001309 0.000655 M4 -17.1845038834123 4.746291 -3.6206 0.000718 0.000359 M5 -9.00664576411686 3.3884 -2.6581 0.010707 0.005353 M6 -12.8480048818791 3.708833 -3.4642 0.001145 0.000573 M7 -18.6063736299789 4.319096 -4.3079 8.3e-05 4.2e-05 M8 -16.9510502820422 4.980495 -3.4035 0.001369 0.000685 M9 -21.7551612748004 4.85043 -4.4852 4.7e-05 2.3e-05 M10 -21.1713499314306 4.743194 -4.4635 5e-05 2.5e-05 M11 -21.0023788224435 5.611403 -3.7428 0.000495 0.000248 Multiple Linear Regression - Regression Statistics Multiple R 0.931388917304204 R-squared 0.867485315277098 Adjusted R-squared 0.833651778752102 F-TEST (value) 25.6398060733674 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.11022302462516e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.86091525901995 Sum Squared Residuals 700.613331954655 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.8 99.1844993121189 -2.38449931211888 2 114.1 115.208310655489 -1.10831065548852 3 110.3 113.331151200457 -3.03115120045669 4 103.9 104.792183153580 -0.892183153580134 5 101.6 103.604049838036 -2.00404983803623 6 94.6 93.2147852038819 1.38521479611812 7 95.9 96.5737532507584 -0.673753250758427 8 104.7 104.196787955407 0.503212044593087 9 102.8 108.178474237808 -5.37847423780787 10 98.1 100.473797585745 -2.37379758574459 11 113.9 115.644931966466 -1.74493196646551 12 80.9 83.020793458457 -2.120793458457 13 95.7 99.764693471799 -4.06469347179901 14 113.2 114.959656015626 -1.75965601562551 15 105.9 107.529209603654 -1.62920960365353 16 108.8 106.864305152438 1.93569484756157 17 102.3 100.868848799543 1.43115120045669 18 99 97.1932594416898 1.80674055831024 19 100.7 97.485486930256 3.21451306974394 20 115.5 115.137592109379 0.362407890621414 21 100.7 98.0665188833795 2.63348111662049 22 109.9 107.353242621954 2.546757378046 23 114.6 113.24127044779 1.35872955221007 24 85.4 83.518102738183 1.88189726181702 25 100.5 100.096232991616 0.403767008383655 26 114.8 114.711001375763 0.0889986242374845 27 116.5 114.408654639863 2.09134536013700 28 112.9 114.241059468374 -1.34105946837387 29 102 101.863467358995 0.136532641004724 30 106 104.238474237808 1.76152576219212 31 105.3 103.536083166922 1.76391683307779 32 118.8 117.789908267917 1.01009173208283 33 106.1 101.299029201598 4.80097079840157 34 109.3 106.109969422639 3.19003057736094 35 117.2 114.153004127288 3.04699587271244 36 92.5 88.8227350552602 3.67726494473985 37 104.2 103.908937469516 0.291062530484445 38 112.5 110.649642258000 1.85035774199969 39 122.4 122.282718235524 0.117281764475588 40 113.3 111.671628189790 1.62837181021038 41 100 97.55345360137 2.44654639862993 42 110.7 110.123300714565 0.57669928543464 43 112.8 110.249758443223 2.55024155677699 44 109.8 100.715622997325 9.08437700267496 45 117.3 115.389458793835 1.91054120616534 46 109.1 106.192854302593 2.90714569740662 47 115.9 112.246651888338 3.65334811166205 48 96 93.6300580926113 2.36994190738866 49 99.8 94.0456367549502 5.7543632450498 50 116.8 115.871389695123 0.928610304876852 51 115.7 113.248266320502 2.45173367949763 52 99.4 100.730824035818 -1.33082403581795 53 94.3 96.310180402055 -2.01018040205512 54 91 96.5301804020551 -5.53018040205512 55 93.2 100.054918208840 -6.8549182088403 56 103.1 114.060088669972 -10.9600886699723 57 94.1 98.0665188833795 -3.96651888337952 58 91.8 98.070136067069 -6.27013606706896 59 102.7 109.014141570119 -6.31414157011906 60 82.6 88.4083106554885 -5.80831065548851 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0505399999794005 0.101079999958801 0.9494600000206 17 0.0347976998395998 0.0695953996791996 0.9652023001604 18 0.0130349936021721 0.0260699872043442 0.986965006397828 19 0.0165524153570222 0.0331048307140443 0.983447584642978 20 0.00598459402073919 0.0119691880414784 0.99401540597926 21 0.0173444818698010 0.0346889637396019 0.9826555181302 22 0.0340137112741694 0.0680274225483389 0.96598628872583 23 0.0202915142567267 0.0405830285134535 0.979708485743273 24 0.0158329099633864 0.0316658199267729 0.984167090036614 25 0.0128509894414229 0.0257019788828458 0.987149010558577 26 0.00657285171888726 0.0131457034377745 0.993427148281113 27 0.00717385472292407 0.0143477094458481 0.992826145277076 28 0.00349448061473013 0.00698896122946026 0.99650551938527 29 0.00153639183820144 0.00307278367640289 0.998463608161799 30 0.000766656293644214 0.00153331258728843 0.999233343706356 31 0.000367185995418008 0.000734371990836015 0.999632814004582 32 0.00014939404077243 0.00029878808154486 0.999850605959228 33 0.000365639265073964 0.000731278530147929 0.999634360734926 34 0.000254788745595835 0.00050957749119167 0.999745211254404 35 0.000166675137678827 0.000333350275357655 0.999833324862321 36 0.000156543339555451 0.000313086679110902 0.999843456660445 37 0.0001075580840502 0.0002151161681004 0.99989244191595 38 5.11361248403169e-05 0.000102272249680634 0.99994886387516 39 2.10439500429032e-05 4.20879000858065e-05 0.999978956049957 40 7.31815082770792e-06 1.46363016554158e-05 0.999992681849172 41 3.27988317841344e-06 6.55976635682688e-06 0.999996720116822 42 1.04301186306898e-06 2.08602372613796e-06 0.999998956988137 43 7.50727420375316e-07 1.50145484075063e-06 0.99999924927258 44 0.0420685503580396 0.0841371007160792 0.95793144964196 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.551724137931034 NOK 5% type I error level 25 0.862068965517241 NOK 10% type I error level 28 0.96551724137931 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/10wnq01258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/10wnq01258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/1oje71258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/1oje71258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/2bp9f1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/2bp9f1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/3q0yt1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/3q0yt1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/4l01h1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/4l01h1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/5n8i81258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/5n8i81258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/6mzxj1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/6mzxj1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/7dmna1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/7dmna1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/86nwy1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/86nwy1258727878.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/9tt8h1258727878.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872794222z7lfzdcl6vnr4/9tt8h1258727878.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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