Berekening 3 TVD

*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 10:03:24 -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/t1258564509cv3r2lsb7hbpp0d.htm/, Retrieved Wed, 18 Nov 2009 18:15:21 +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/t1258564509cv3r2lsb7hbpp0d.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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101.3 0 106.3 0 94 0 102.8 0 102 0 105.1 1 92.4 0 81.4 0 105.8 0 120.3 1 100.7 0 88.8 0 94.3 0 99.9 0 103.4 0 103.3 0 98.8 0 104.2 0 91.2 0 74.7 0 108.5 0 114.5 0 96.9 0 89.6 0 97.1 0 100.3 0 122.6 0 115.4 1 109 0 129.1 1 102.8 1 96.2 0 127.7 1 128.9 1 126.5 1 119.8 1 113.2 1 114.1 1 134.1 1 130 1 121.8 1 132.1 1 105.3 1 103 1 117.1 1 126.3 1 138.1 1 119.5 1 138 1 135.5 1 178.6 1 162.2 1 176.9 1 204.9 1 132.2 1 142.5 1 164.3 1 174.9 1 175.4 1 143 1

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 Omzet[t] = + 84.5368954977978 + 10.5057238206233Uitvoer[t] + 0.939725346476263t + 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) 84.5368954977978 4.50065 18.7833 0 0 Uitvoer 10.5057238206233 6.924777 1.5171 0.134763 0.067382 t 0.939725346476263 0.198927 4.724 1.6e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.781378090475809 R-squared 0.610551720275621 Adjusted R-squared 0.596886868355467 F-TEST (value) 44.6804490705931 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 2.1271873151818e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.8720308621690 Sum Squared Residuals 16225.929248597 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.3 85.4766208442743 15.8233791557257 2 106.3 86.4163461907503 19.8836538092497 3 94 87.3560715372266 6.64392846277339 4 102.8 88.2957968837028 14.5042031162971 5 102 89.235522230179 12.7644777698209 6 105.1 100.680971397279 4.41902860272126 7 92.4 91.1149729231316 1.28502707686837 8 81.4 92.0546982696079 -10.6546982696079 9 105.8 92.9944236160842 12.8055763839158 10 120.3 104.439872783184 15.8601272168162 11 100.7 94.8738743090367 5.82612569096331 12 88.8 95.813599655513 -7.01359965551296 13 94.3 96.7533250019892 -2.45332500198922 14 99.9 97.6930503484655 2.20694965153453 15 103.4 98.6327756949417 4.76722430505826 16 103.3 99.572501041418 3.72749895858199 17 98.8 100.512226387894 -1.71222638789427 18 104.2 101.451951734371 2.74804826562947 19 91.2 102.391677080847 -11.1916770808468 20 74.7 103.331402427323 -28.6314024273231 21 108.5 104.271127773799 4.22887222620068 22 114.5 105.210853120276 9.28914687972442 23 96.9 106.150578466752 -9.25057846675184 24 89.6 107.090303813228 -17.4903038132281 25 97.1 108.030029159704 -10.9300291597044 26 100.3 108.969754506181 -8.66975450618064 27 122.6 109.909479852657 12.6905201473431 28 115.4 121.354929019757 -5.95492901975651 29 109 111.788930545609 -2.78893054560943 30 129.1 123.234379712709 5.86562028729095 31 102.8 124.174105059185 -21.3741050591853 32 96.2 114.608106585038 -18.4081065850382 33 127.7 126.053555752138 1.64644424786217 34 128.9 126.993281098614 1.90671890138591 35 126.5 127.933006445090 -1.43300644509036 36 119.8 128.872731791567 -9.07273179156662 37 113.2 129.812457138043 -16.6124571380429 38 114.1 130.752182484519 -16.6521824845191 39 134.1 131.691907830995 2.40809216900459 40 130 132.631633177472 -2.63163317747167 41 121.8 133.571358523948 -11.7713585239479 42 132.1 134.511083870424 -2.4110838704242 43 105.3 135.450809216900 -30.1508092169005 44 103 136.390534563377 -33.3905345633767 45 117.1 137.330259909853 -20.230259909853 46 126.3 138.269985256329 -11.9699852563293 47 138.1 139.209710602806 -1.10971060280552 48 119.5 140.149435949282 -20.6494359492818 49 138 141.089161295758 -3.08916129575804 50 135.5 142.028886642234 -6.5288866422343 51 178.6 142.968611988711 35.6313880112894 52 162.2 143.908337335187 18.2916626648132 53 176.9 144.848062681663 32.0519373183369 54 204.9 145.787788028139 59.1122119718606 55 132.2 146.727513374616 -14.5275133746156 56 142.5 147.667238721092 -5.16723872109188 57 164.3 148.606964067568 15.6930359324319 58 174.9 149.546689414044 25.3533105859556 59 175.4 150.486414760521 24.9135852394793 60 143 151.426140106997 -8.42614010699693 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0369187853872915 0.073837570774583 0.963081214612709 7 0.0145678653759357 0.0291357307518715 0.985432134624064 8 0.0135509434642230 0.0271018869284460 0.986449056535777 9 0.0406802687131307 0.0813605374262615 0.95931973128687 10 0.0527357405836841 0.105471481167368 0.947264259416316 11 0.0294443903766678 0.0588887807533356 0.970555609623332 12 0.0175234265599504 0.0350468531199008 0.98247657344005 13 0.00806409135796622 0.0161281827159324 0.991935908642034 14 0.00444941077083988 0.00889882154167977 0.99555058922916 15 0.00293903414559156 0.00587806829118312 0.997060965854409 16 0.00167559073375287 0.00335118146750575 0.998324409266247 17 0.000722205192045536 0.00144441038409107 0.999277794807955 18 0.000388352255131824 0.000776704510263647 0.999611647744868 19 0.000237256352787268 0.000474512705574537 0.999762743647213 20 0.00162637866381486 0.00325275732762972 0.998373621336185 21 0.00194137474648179 0.00388274949296359 0.998058625253518 22 0.00355377889965697 0.00710755779931394 0.996446221100343 23 0.00182388393794562 0.00364776787589124 0.998176116062054 24 0.00121597312570685 0.00243194625141369 0.998784026874293 25 0.000587375757581616 0.00117475151516323 0.999412624242418 26 0.00028268203178128 0.00056536406356256 0.999717317968219 27 0.00130970743021519 0.00261941486043037 0.998690292569785 28 0.000706093800329859 0.00141218760065972 0.99929390619967 29 0.000463117023275857 0.000926234046551714 0.999536882976724 30 0.000484055778923812 0.000968111557847624 0.999515944221076 31 0.000524910488533667 0.00104982097706733 0.999475089511466 32 0.000285328689370647 0.000570657378741294 0.99971467131063 33 0.000247477130668648 0.000494954261337296 0.999752522869331 34 0.000218401059600540 0.000436802119201081 0.9997815989404 35 0.000153455107317987 0.000306910214635974 0.999846544892682 36 8.07697346875155e-05 0.000161539469375031 0.999919230265312 37 4.50471796582728e-05 9.00943593165457e-05 0.999954952820342 38 2.24659653022712e-05 4.49319306045424e-05 0.999977534034698 39 2.76671832684713e-05 5.53343665369427e-05 0.999972332816732 40 2.03150357273829e-05 4.06300714547658e-05 0.999979684964273 41 9.18671768696765e-06 1.83734353739353e-05 0.999990813282313 42 7.29952691281967e-06 1.45990538256393e-05 0.999992700473087 43 1.03877011842441e-05 2.07754023684881e-05 0.999989612298816 44 2.41690495123301e-05 4.83380990246603e-05 0.999975830950488 45 1.47522810212588e-05 2.95045620425176e-05 0.999985247718979 46 7.80638393640365e-06 1.56127678728073e-05 0.999992193616064 47 5.77582253327755e-06 1.15516450665551e-05 0.999994224177467 48 1.31256453239569e-05 2.62512906479138e-05 0.999986874354676 49 2.07677276747226e-05 4.15354553494451e-05 0.999979232272325 50 0.000140046031593741 0.000280092063187482 0.999859953968406 51 0.00194310114386417 0.00388620228772835 0.998056898856136 52 0.00234561019280181 0.00469122038560363 0.997654389807198 53 0.00321517145075301 0.00643034290150601 0.996784828549247 54 0.202106423805000 0.404212847609999 0.797893576195 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 40 0.816326530612245 NOK 5% type I error level 44 0.897959183673469 NOK 10% type I error level 47 0.959183673469388 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/10akwj1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/10akwj1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/1im2s1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/1im2s1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/2x7st1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/2x7st1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/35bfu1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/35bfu1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/4dmhg1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/4dmhg1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/5na351258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/5na351258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/6dm9k1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/6dm9k1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/77vtj1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/77vtj1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/8koji1258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/8koji1258563800.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/9r8p31258563800.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564509cv3r2lsb7hbpp0d/9r8p31258563800.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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