WS7 Multiple Regression 3 (LT-trend 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 08:15:58 -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/t125873028019ebivgbpl0ygeb.htm/, Retrieved Fri, 20 Nov 2009 16:18:12 +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/t125873028019ebivgbpl0ygeb.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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Totind[t] = + 46.8345190965529 + 0.831862377966146Bouw[t] -24.5731310346029M1[t] -20.8424166591775M2[t] -19.2178354252768M3[t] -17.4348159005623M4[t] -9.18521148879648M5[t] -13.0270296530995M6[t] -18.7968268731994M7[t] -17.1531730835365M8[t] -21.9379740483817M9[t] -21.3356964790573M10[t] -21.1853261587166M11[t] -0.0143074877310507t + 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) 46.8345190965529 4.888213 9.5811 0 0 Bouw 0.831862377966146 0.091244 9.1169 0 0 M1 -24.5731310346029 4.714995 -5.2117 4e-06 2e-06 M2 -20.8424166591775 5.788456 -3.6007 0.000775 0.000387 M3 -19.2178354252768 5.614245 -3.423 0.001311 0.000655 M4 -17.4348159005623 4.813629 -3.622 0.000727 0.000363 M5 -9.18521148879648 3.436444 -2.6729 0.010371 0.005185 M6 -13.0270296530995 3.757899 -3.4666 0.001154 0.000577 M7 -18.7968268731994 4.372697 -4.2987 8.8e-05 4.4e-05 M8 -17.1531730835365 5.039153 -3.404 0.001386 0.000693 M9 -21.9379740483817 4.90522 -4.4724 5e-05 2.5e-05 M10 -21.3356964790573 4.794551 -4.45 5.4e-05 2.7e-05 M11 -21.1853261587166 5.670517 -3.736 0.000515 0.000257 t -0.0143074877310507 0.029684 -0.482 0.632092 0.316046 Multiple Linear Regression - Regression Statistics Multiple R 0.931746328205055 R-squared 0.868151220123602 Adjusted R-squared 0.830889608419402 F-TEST (value) 23.2988102343882 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 6.66133814775094e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.89283805003852 Sum Squared Residuals 697.092651856073 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.8 99.5270954872742 -2.72709548727416 2 114.1 115.555065568867 -1.45506556886731 3 110.3 113.671517327579 -3.37151732757907 4 103.9 105.125135877782 -1.22513587778233 5 101.6 103.960387930800 -2.36038793079965 6 94.6 93.532549492833 1.06745050716704 7 95.9 96.8989309426297 -0.998930942629699 8 104.7 104.517686365918 0.182313634082246 9 102.8 108.536319119783 -5.73631911978272 10 98.1 100.805665421715 -2.70566542171457 11 113.9 115.998437295511 -2.09843729551147 12 80.9 83.3479601120873 -2.44796011208735 13 95.7 99.9377092990777 -4.23770929907767 14 113.2 115.133817002705 -1.93381700270485 15 105.9 107.676790829043 -1.77679082904343 16 108.8 107.033101969925 1.76689803007489 17 102.3 101.043552230739 1.25644776926124 18 99 97.3537990542979 1.64620094570213 19 100.7 97.6422897056198 3.05771029438016 20 115.5 115.326579902298 0.173420097701714 21 100.7 98.2159082558231 2.48409174417688 22 109.9 107.538433306061 2.36156669393907 23 114.6 113.414346546637 1.18565345336296 24 85.4 83.6753876860944 1.72461231390557 25 100.5 100.098764397492 0.401235602508471 26 114.8 114.712568436542 0.0874315634576082 27 116.5 114.409558713390 2.09044128661015 28 112.9 114.264987281051 -1.36498728105120 29 102 101.870097231526 0.129902768474479 30 106 104.252939414238 1.74706058576250 31 105.3 103.543195212000 1.75680478799990 32 118.8 117.816849659017 0.983150340982653 33 106.1 101.288481677118 4.8115183228815 34 109.3 106.118949886339 3.18105011366088 35 117.2 114.157705309627 3.04229469037281 36 92.5 88.8276170523052 3.67238294769485 37 104.2 103.753641483363 0.446358516636813 38 112.5 110.464752931736 2.03524706826433 39 122.4 122.140561451296 0.259438548704380 40 113.3 111.514524056584 1.78547594341645 41 100 97.372723013329 2.62727698667105 42 110.7 109.987472445025 0.71252755497547 43 112.8 110.109590620753 2.69040937924671 44 109.8 100.508794820142 9.29120517985787 45 117.3 115.258452249770 2.04154775022963 46 109.1 106.030446271363 3.06955372863688 47 115.9 112.072731987532 3.82726801246756 48 96 93.4807289917362 2.51927100826381 49 99.8 93.6827893327934 6.11721066720655 50 116.8 115.533796060150 1.26620393985022 51 115.7 112.901571678692 2.79842832130797 52 99.4 100.362250814658 -0.962250814657801 53 94.3 95.9532395936071 -1.65323959360713 54 91 96.1732395936071 -5.17323959360713 55 93.2 99.705993518997 -6.50599351899707 56 103.1 113.730089252624 -10.6300892526245 57 94.1 97.7008386975053 -3.6008386975053 58 91.8 97.7065051145223 -5.90650511452227 59 102.7 108.656778860692 -5.95677886069186 60 82.6 88.0683061577769 -5.46830615777687 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.113874213278235 0.227748426556471 0.886125786721764 18 0.0410011311648618 0.0820022623297237 0.958998868835138 19 0.0294542609900538 0.0589085219801076 0.970545739009946 20 0.010795871589367 0.021591743178734 0.989204128410633 21 0.0150031566033695 0.0300063132067389 0.98499684339663 22 0.0205691931470765 0.0411383862941531 0.979430806852923 23 0.00942995973931924 0.0188599194786385 0.99057004026068 24 0.00511795768136993 0.0102359153627399 0.99488204231863 25 0.00294814828562365 0.0058962965712473 0.997051851714376 26 0.00243439667869610 0.00486879335739221 0.997565603321304 27 0.00166236417673400 0.00332472835346801 0.998337635823266 28 0.00254971757329334 0.00509943514658668 0.997450282426707 29 0.00285802563122181 0.00571605126244361 0.997141974368778 30 0.00133498176527175 0.00266996353054351 0.998665018234728 31 0.00068783040222106 0.00137566080444212 0.999312169597779 32 0.00032940176457545 0.0006588035291509 0.999670598235425 33 0.00027429711718506 0.00054859423437012 0.999725702882815 34 0.000121750594505441 0.000243501189010882 0.999878249405495 35 6.14326897591207e-05 0.000122865379518241 0.99993856731024 36 7.80544792508097e-05 0.000156108958501619 0.99992194552075 37 0.000346702008752065 0.000693404017504131 0.999653297991248 38 0.0062984835677087 0.0125969671354174 0.993701516432291 39 0.0829807541467307 0.165961508293461 0.917019245853269 40 0.0998126047626075 0.199625209525215 0.900187395237393 41 0.826101648425665 0.347796703148669 0.173898351574335 42 0.717742902980433 0.564514194039134 0.282257097019567 43 0.630318512645692 0.739362974708615 0.369681487354307 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.481481481481481 NOK 5% type I error level 19 0.703703703703704 NOK 10% type I error level 21 0.777777777777778 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/10gage1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/10gage1258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/1fpc61258730153.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/1fpc61258730153.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/2g9sb1258730153.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/2g9sb1258730153.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/33yo61258730153.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/33yo61258730153.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/4ix761258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/4ix761258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/5rtil1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/5rtil1258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/6mxpz1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/6mxpz1258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/7t9cr1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/7t9cr1258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/81hdj1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/81hdj1258730154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/99llc1258730154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873028019ebivgbpl0ygeb/99llc1258730154.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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