TSA faillissementen

*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: Sat, 18 Dec 2010 12:30:39 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6.htm/, Retrieved Sat, 18 Dec 2010 13:30:02 +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/2010/Dec/18/t1292675400cd8ng00zjlpekr6.htm/}, year = {2010}, } @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 = {2010}, 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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46 62 66 59 58 61 41 27 58 70 49 59 44 36 72 45 56 54 53 35 61 52 47 51 52 63 74 45 51 64 36 30 55 64 39 40 63 45 59 55 40 64 27 28 45 57 45 69 60 56 58 50 51 53 37 22 55 70 62 58 39 49 58 47 42 62 39 40 72 70 54 65

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Faillissementen[t] = + 57 -6.33333333333331M1[t] -5.16666666666667M2[t] + 7.5M3[t] -6.83333333333333M4[t] -7.33333333333334M5[t] + 2.66666666666666M6[t] -18.1666666666667M7[t] -26.6666666666667M8[t] + 0.666666666666666M9[t] + 6.83333333333333M10[t] -7.66666666666667M11[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) 57 3.295971 17.2938 0 0 M1 -6.33333333333331 4.661207 -1.3587 0.179318 0.089659 M2 -5.16666666666667 4.661207 -1.1084 0.272095 0.136048 M3 7.5 4.661207 1.609 0.11286 0.05643 M4 -6.83333333333333 4.661207 -1.466 0.14787 0.073935 M5 -7.33333333333334 4.661207 -1.5733 0.120915 0.060458 M6 2.66666666666666 4.661207 0.5721 0.569393 0.284697 M7 -18.1666666666667 4.661207 -3.8974 0.000248 0.000124 M8 -26.6666666666667 4.661207 -5.721 0 0 M9 0.666666666666666 4.661207 0.143 0.88675 0.443375 M10 6.83333333333333 4.661207 1.466 0.14787 0.073935 M11 -7.66666666666667 4.661207 -1.6448 0.105245 0.052622 Multiple Linear Regression - Regression Statistics Multiple R 0.788687254124398 R-squared 0.622027584818283 Adjusted R-squared 0.552732642034968 F-TEST (value) 8.97652209286559 F-TEST (DF numerator) 11 F-TEST (DF denominator) 60 p-value 3.4137527249456e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.07344756318857 Sum Squared Residuals 3910.83333333333 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 46 50.6666666666666 -4.66666666666656 2 62 51.8333333333333 10.1666666666667 3 66 64.5 1.5 4 59 50.1666666666667 8.83333333333334 5 58 49.6666666666667 8.33333333333333 6 61 59.6666666666667 1.33333333333334 7 41 38.8333333333333 2.16666666666667 8 27 30.3333333333333 -3.33333333333333 9 58 57.6666666666667 0.33333333333334 10 70 63.8333333333333 6.16666666666667 11 49 49.3333333333333 -0.333333333333334 12 59 57 2 13 44 50.6666666666667 -6.66666666666669 14 36 51.8333333333333 -15.8333333333333 15 72 64.5 7.5 16 45 50.1666666666667 -5.16666666666667 17 56 49.6666666666667 6.33333333333333 18 54 59.6666666666667 -5.66666666666667 19 53 38.8333333333333 14.1666666666667 20 35 30.3333333333333 4.66666666666667 21 61 57.6666666666667 3.33333333333333 22 52 63.8333333333333 -11.8333333333333 23 47 49.3333333333333 -2.33333333333333 24 51 57 -6 25 52 50.6666666666667 1.33333333333332 26 63 51.8333333333333 11.1666666666667 27 74 64.5 9.5 28 45 50.1666666666667 -5.16666666666667 29 51 49.6666666666667 1.33333333333333 30 64 59.6666666666667 4.33333333333333 31 36 38.8333333333333 -2.83333333333333 32 30 30.3333333333333 -0.333333333333333 33 55 57.6666666666667 -2.66666666666667 34 64 63.8333333333333 0.166666666666664 35 39 49.3333333333333 -10.3333333333333 36 40 57 -17 37 63 50.6666666666667 12.3333333333333 38 45 51.8333333333333 -6.83333333333333 39 59 64.5 -5.5 40 55 50.1666666666667 4.83333333333333 41 40 49.6666666666667 -9.66666666666667 42 64 59.6666666666667 4.33333333333333 43 27 38.8333333333333 -11.8333333333333 44 28 30.3333333333333 -2.33333333333333 45 45 57.6666666666667 -12.6666666666667 46 57 63.8333333333333 -6.83333333333333 47 45 49.3333333333333 -4.33333333333333 48 69 57 12 49 60 50.6666666666667 9.33333333333331 50 56 51.8333333333333 4.16666666666667 51 58 64.5 -6.5 52 50 50.1666666666667 -0.166666666666667 53 51 49.6666666666667 1.33333333333333 54 53 59.6666666666667 -6.66666666666667 55 37 38.8333333333333 -1.83333333333333 56 22 30.3333333333333 -8.33333333333333 57 55 57.6666666666667 -2.66666666666667 58 70 63.8333333333333 6.16666666666667 59 62 49.3333333333333 12.6666666666667 60 58 57 0.999999999999999 61 39 50.6666666666667 -11.6666666666667 62 49 51.8333333333333 -2.83333333333333 63 58 64.5 -6.5 64 47 50.1666666666667 -3.16666666666667 65 42 49.6666666666667 -7.66666666666667 66 62 59.6666666666667 2.33333333333333 67 39 38.8333333333333 0.166666666666664 68 40 30.3333333333333 9.66666666666666 69 72 57.6666666666667 14.3333333333333 70 70 63.8333333333333 6.16666666666667 71 54 49.3333333333333 4.66666666666667 72 65 57 8 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.823536690323427 0.352926619353145 0.176463309676573 16 0.812257368621997 0.375485262756007 0.187742631378003 17 0.718430523191285 0.56313895361743 0.281569476808715 18 0.637564107551097 0.724871784897806 0.362435892448903 19 0.670969524616548 0.658060950766904 0.329030475383452 20 0.606056232676555 0.78788753464689 0.393943767323445 21 0.50670033061216 0.98659933877568 0.49329966938784 22 0.628092519446701 0.743814961106598 0.371907480553299 23 0.533949610753664 0.932100778492672 0.466050389246336 24 0.482551298894477 0.965102597788954 0.517448701105523 25 0.422199974745293 0.844399949490586 0.577800025254707 26 0.500130338962283 0.999739322075433 0.499869661037717 27 0.495999877384309 0.991999754768618 0.504000122615691 28 0.446703143077069 0.893406286154138 0.553296856922931 29 0.39540117508548 0.79080235017096 0.60459882491452 30 0.3425057531196 0.685011506239201 0.6574942468804 31 0.327272396213846 0.654544792427692 0.672727603786154 32 0.254748152207466 0.509496304414931 0.745251847792534 33 0.200598892197257 0.401197784394514 0.799401107802743 34 0.150124493276289 0.300248986552578 0.84987550672371 35 0.17148144293878 0.34296288587756 0.82851855706122 36 0.393100998423107 0.786201996846213 0.606899001576893 37 0.512595046010505 0.97480990797899 0.487404953989495 38 0.490486588270775 0.98097317654155 0.509513411729225 39 0.467291137179449 0.934582274358898 0.532708862820551 40 0.416241896229767 0.832483792459534 0.583758103770233 41 0.441154936435083 0.882309872870165 0.558845063564917 42 0.385308623867449 0.770617247734899 0.61469137613255 43 0.452649181931322 0.905298363862643 0.547350818068678 44 0.374563679032318 0.749127358064636 0.625436320967682 45 0.530142134828762 0.939715730342476 0.469857865171238 46 0.556040602763667 0.887918794472667 0.443959397236333 47 0.58420831405905 0.8315833718819 0.41579168594095 48 0.623355783871116 0.753288432257769 0.376644216128884 49 0.801752686812514 0.396494626374972 0.198247313187486 50 0.75510735419549 0.48978529160902 0.24489264580451 51 0.679917635928171 0.640164728143658 0.320082364071829 52 0.57967309667252 0.840653806654959 0.420326903327479 53 0.525788144497149 0.948423711005702 0.474211855502851 54 0.479053982724936 0.958107965449871 0.520946017275064 55 0.352689233330448 0.705378466660895 0.647310766669552 56 0.535119630587148 0.929760738825705 0.464880369412852 57 0.821934440348795 0.35613111930241 0.178065559651205 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/10dfcj1292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/10dfcj1292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/1w5y41292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/1w5y41292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/2w5y41292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/2w5y41292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/36wf71292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/36wf71292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/46wf71292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/46wf71292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/56wf71292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/56wf71292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/6h5es1292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/6h5es1292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/7sxev1292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/7sxev1292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/8sxev1292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/8sxev1292675429.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/93odg1292675429.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675400cd8ng00zjlpekr6/93odg1292675429.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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