review WS 7 2 maanden vertraagd + seizoenaliteit

*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, 27 Nov 2009 03:24:38 -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/27/t12593177669hc5a6okwaqk6qo.htm/, Retrieved Fri, 27 Nov 2009 11:29:38 +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/27/t12593177669hc5a6okwaqk6qo.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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94 0 106.3 101.3 102.8 1 94 106.3 102 1 102.8 94 105.1 1 102 102.8 92.4 0 105.1 102 81.4 0 92.4 105.1 105.8 1 81.4 92.4 120.3 1 105.8 81.4 100.7 1 120.3 105.8 88.8 0 100.7 120.3 94.3 0 88.8 100.7 99.9 0 94.3 88.8 103.4 1 99.9 94.3 103.3 1 103.4 99.9 98.8 0 103.3 103.4 104.2 1 98.8 103.3 91.2 0 104.2 98.8 74.7 0 91.2 104.2 108.5 1 74.7 91.2 114.5 1 108.5 74.7 96.9 0 114.5 108.5 89.6 0 96.9 114.5 97.1 0 89.6 96.9 100.3 1 97.1 89.6 122.6 1 100.3 97.1 115.4 1 122.6 100.3 109 1 115.4 122.6 129.1 1 109 115.4 102.8 1 129.1 109 96.2 0 102.8 129.1 127.7 1 96.2 102.8 128.9 1 127.7 96.2 126.5 1 128.9 127.7 119.8 1 126.5 128.9 113.2 1 119.8 126.5 114.1 1 113.2 119.8 134.1 1 114.1 113.2 130 1 134.1 114.1 121.8 1 130 134.1 132.1 1 121.8 130 105.3 1 132.1 121.8 103 1 105.3 132.1 117.1 1 103 105.3 126.3 1 117.1 103 138.1 1 126.3 117.1 119.5 1 138.1 126.3 138 1 119.5 138.1 135.5 1 138 119.5 178.6 1 135.5 138 162.2 1 178.6 135.5 176.9 1 162.2 178.6 204.9 1 176.9 162.2 132.2 1 etc...

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Omzet[t] = + 26.1376860921832 -1.44041616513782Uitvoer[t] + 0.314190804274826`Omzet-1`[t] + 0.361312695475143`Omzet-2`[t] + 14.8923044799708M1[t] + 5.18880377075971M2[t] -0.977242744491664M3[t] + 13.8940093239983M4[t] -21.3068427437488M5[t] -22.6258863164955M6[t] + 15.4496362100097M7[t] + 17.2466376332173M8[t] -0.68198202941588M9[t] -18.1300566873355M10[t] -3.72729217804567M11[t] + 0.496577843788959t + 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) 26.1376860921832 10.977887 2.3809 0.02188 0.01094 Uitvoer -1.44041616513782 4.689712 -0.3071 0.760252 0.380126 `Omzet-1` 0.314190804274826 0.142918 2.1984 0.033483 0.016742 `Omzet-2` 0.361312695475143 0.14196 2.5452 0.014684 0.007342 M1 14.8923044799708 7.321697 2.034 0.0483 0.02415 M2 5.18880377075971 7.73936 0.6704 0.506245 0.253123 M3 -0.977242744491664 7.768238 -0.1258 0.900491 0.450245 M4 13.8940093239983 7.670138 1.8114 0.077228 0.038614 M5 -21.3068427437488 7.796212 -2.733 0.009145 0.004572 M6 -22.6258863164955 8.935556 -2.5321 0.015165 0.007583 M7 15.4496362100097 7.462869 2.0702 0.044619 0.022309 M8 17.2466376332173 7.696342 2.2409 0.030378 0.015189 M9 -0.68198202941588 7.547821 -0.0904 0.928435 0.464217 M10 -18.1300566873355 7.774407 -2.332 0.024569 0.012284 M11 -3.72729217804567 8.020094 -0.4647 0.644515 0.322258 t 0.496577843788959 0.184256 2.695 0.010077 0.005039 Multiple Linear Regression - Regression Statistics Multiple R 0.93939025754965 R-squared 0.8824540559792 Adjusted R-squared 0.840473361686056 F-TEST (value) 21.0204731207443 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 8.21565038222616e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.7348292040724 Sum Squared Residuals 4839.93543770543 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94 111.526026961989 -17.5260269619891 2 102.8 98.8207045162244 3.97929548377559 3 102 91.4719687680362 10.5280312319638 4 105.1 109.767997757077 -4.66799775707655 5 92.4 77.189081035128 15.2109189648720 6 81.4 73.496461447853 7.90353855214703 7 105.8 102.583375573452 3.21662442654812 8 120.3 108.568770814528 11.7312291854723 9 100.7 104.508525427262 -3.80852542726190 10 88.8 88.078339098872 0.72166090112797 11 94.3 92.1570820497676 2.14291795023239 12 99.9 93.8093804189596 6.09061958104043 13 103.4 111.504534906634 -8.1045349066338 14 103.3 105.420630950834 -2.12063095083440 15 98.8 102.424753798245 -3.62475379824532 16 104.2 114.902177656602 -10.7021776566022 17 91.2 81.7090428112278 9.49095718877222 18 74.7 78.753185182263 -4.05318518226307 19 108.5 106.003656075708 2.49634392429214 20 114.5 112.955225051854 1.54477494814626 21 96.9 111.061113330856 -14.1611133308561 22 89.6 90.7477345343393 -1.14773453433936 23 97.1 96.9943805758494 0.105619424150556 24 100.3 99.4966827876389 0.803317212361116 25 122.6 118.600820901142 3.99917909885836 26 115.4 117.556553596569 -2.15655359656862 27 109 117.682184243423 -8.68218424342315 28 129.1 128.437741600922 0.662258399077845 29 102.8 97.7363012918471 5.0636987081529 30 96.2 97.3534187546496 -1.15341875464961 31 127.7 122.908919760596 4.7910802394042 32 128.9 132.714845572113 -3.81484557211349 33 126.5 127.041182625866 -0.541182625866083 34 119.8 109.769203116046 10.0307968839540 35 113.2 121.696316611343 -8.49631661134311 36 114.1 121.425732265280 -7.32573226528043 37 134.1 134.712722522752 -0.612722522751562 38 130 132.114797168754 -2.11479716875361 39 121.8 132.383400109267 -10.5834001092673 40 132.1 143.693483375045 -11.5934833750445 41 105.3 109.262610332221 -3.96261033222091 42 103 103.741351812092 -0.741351812091793 43 117.1 131.90763309382 -14.80763309382 44 126.3 137.800283501499 -11.5002835014988 45 138.1 128.353306088183 9.74669391181748 46 119.5 118.433337562866 1.06666243713393 47 138 131.752220763040 6.24777923696017 48 135.5 135.068204528121 0.431795471878913 49 178.6 156.355894707484 22.2441052925161 50 162.2 159.787313767619 2.41268623238103 51 176.9 164.537693081028 12.3623069189720 52 204.9 178.598599610355 26.3014003896454 53 132.2 158.002964529576 -25.8029645295762 54 142.5 144.455582803143 -1.95558280314255 55 164.3 159.996415496424 4.30358450357553 56 174.9 172.860875060006 2.03912493999372 57 175.4 166.635872527833 8.76412747216665 58 143 153.671385687877 -10.6713856878766 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0389319129226628 0.0778638258453255 0.961068087077337 20 0.0126764774878179 0.0253529549756358 0.987323522512182 21 0.00465702070976327 0.00931404141952654 0.995342979290237 22 0.00113550647740209 0.00227101295480419 0.998864493522598 23 0.000368244520717436 0.000736489041434872 0.999631755479283 24 0.000107148114961678 0.000214296229923355 0.999892851885038 25 0.0116678349238369 0.0233356698476737 0.988332165076163 26 0.00847493414777761 0.0169498682955552 0.991525065852222 27 0.00455755196792109 0.00911510393584219 0.995442448032079 28 0.0100192920011902 0.0200385840023804 0.98998070799881 29 0.0100851157076389 0.0201702314152779 0.98991488429236 30 0.00494981167190423 0.00989962334380846 0.995050188328096 31 0.00495412156091279 0.00990824312182557 0.995045878439087 32 0.00248555632221438 0.00497111264442876 0.997514443677786 33 0.00171536397878457 0.00343072795756915 0.998284636021215 34 0.0115881101011608 0.0231762202023217 0.98841188989884 35 0.00896295950746945 0.0179259190149389 0.99103704049253 36 0.0175335068706913 0.0350670137413825 0.982466493129309 37 0.0117024050689604 0.0234048101379207 0.98829759493104 38 0.0211017911441281 0.0422035822882562 0.978898208855872 39 0.0115162988848305 0.0230325977696609 0.98848370111517 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.428571428571429 NOK 5% type I error level 20 0.952380952380952 NOK 10% type I error level 21 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/10d8oj1259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/10d8oj1259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/1xsc31259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/1xsc31259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/2il1q1259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/2il1q1259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/3gpm41259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/3gpm41259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/4oie21259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/4oie21259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/5na411259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/5na411259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/6yy5g1259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/6yy5g1259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/7y5ja1259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/7y5ja1259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/8aswk1259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/8aswk1259317473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/9l8w31259317473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593177669hc5a6okwaqk6qo/9l8w31259317473.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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