Seatbelt Q3: eigen tijdreeks met 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: Thu, 27 Nov 2008 07:47:40 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227797302wn972ab8gi234o4.htm/, Retrieved Thu, 27 Nov 2008 14:48:33 +0000

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/2008/Nov/27/t1227797302wn972ab8gi234o4.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

101,02 0 100,67 0 100,47 0 100,38 0 100,33 0 100,34 0 100,37 0 100,39 0 100,21 0 100,21 0 100,22 0 100,28 0 100,25 0 100,25 0 100,21 0 100,16 0 100,18 0 100,1 1 99,96 1 99,88 1 99,88 1 99,86 1 99,84 1 99,8 1 99,82 1 99,81 1 99,92 1 100,03 1 99,99 1 100,02 1 100,01 1 100,13 1 100,33 1 100,13 1 99,96 1 100,05 1 99,83 1 99,8 1 100,01 1 100,1 1 100,13 1 100,16 1 100,41 1 101,34 1 101,65 1 101,85 1 102,07 1 102,12 1 102,14 1 102,21 1 102,28 1 102,19 1 102,33 1 102,54 1 102,44 1 102,78 1 102,9 1 103,08 1 102,77 1 102,65 1 102,71 1 103,29 1 102,86 1 103,45 1 103,72 1 103,65 1 103,83 1 104,45 1 105,14 1 105,07 1 105,31 1 105,19 1 105,3 1 105,02 1 105,17 1 105,28 1 105,45 1 105,38 1 105,8 1 105,96 1 105,08 1 105,11 1 105,61 1 105,5 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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Suiker[t] = + 100.608354037267 + 1.88858695652173Cotonou[t] -0.375916149068313M1[t] -0.378773291925466M2[t] -0.397344720496895M3[t] -0.301630434782610M4[t] -0.224487577639753M5[t] -0.485714285714287M6[t] -0.395714285714287M7[t] -0.0942857142857158M8[t] -0.0571428571428576M9[t] -0.0400000000000039M10[t] + 0.0271428571428549M11[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) 100.608354037267 0.919321 109.4377 0 0 Cotonou 1.88858695652173 0.568801 3.3203 0.001422 0.000711 M1 -0.375916149068313 1.105218 -0.3401 0.734764 0.367382 M2 -0.378773291925466 1.105218 -0.3427 0.732827 0.366413 M3 -0.397344720496895 1.105218 -0.3595 0.720275 0.360138 M4 -0.301630434782610 1.105218 -0.2729 0.785711 0.392855 M5 -0.224487577639753 1.105218 -0.2031 0.839625 0.419813 M6 -0.485714285714287 1.102227 -0.4407 0.660793 0.330397 M7 -0.395714285714287 1.102227 -0.359 0.720651 0.360325 M8 -0.0942857142857158 1.102227 -0.0855 0.932072 0.466036 M9 -0.0571428571428576 1.102227 -0.0518 0.958799 0.4794 M10 -0.0400000000000039 1.102227 -0.0363 0.971153 0.485576 M11 0.0271428571428549 1.102227 0.0246 0.980423 0.490211 Multiple Linear Regression - Regression Statistics Multiple R 0.386516860213904 R-squared 0.149395283229615 Adjusted R-squared 0.0056311057472962 F-TEST (value) 1.03916904646144 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0.423929093603733 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.06207823139742 Sum Squared Residuals 301.903830900621 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.02 100.232437888199 0.78756211180132 2 100.67 100.229580745342 0.440419254658387 3 100.47 100.211009316770 0.258990683229809 4 100.38 100.306723602484 0.0732763975155205 5 100.33 100.383866459627 -0.0538664596273344 6 100.34 100.122639751553 0.217360248447202 7 100.37 100.212639751553 0.157360248447205 8 100.39 100.514068322981 -0.124068322981370 9 100.21 100.551211180124 -0.341211180124234 10 100.21 100.568354037267 -0.358354037267088 11 100.22 100.63549689441 -0.415496894409942 12 100.28 100.608354037267 -0.328354037267084 13 100.25 100.232437888199 0.0175621118012270 14 100.25 100.229580745342 0.0204192546583813 15 100.21 100.211009316770 -0.00100931677019619 16 100.16 100.306723602484 -0.146723602484477 17 100.18 100.383866459627 -0.203866459627325 18 100.1 102.011226708075 -1.91122670807454 19 99.96 102.101226708075 -2.14122670807454 20 99.88 102.402655279503 -2.52265527950311 21 99.88 102.439798136646 -2.55979813664597 22 99.86 102.456940993789 -2.59694099378882 23 99.84 102.524083850932 -2.68408385093167 24 99.8 102.496940993789 -2.69694099378882 25 99.82 102.121024844721 -2.30102484472051 26 99.81 102.118167701863 -2.30816770186335 27 99.92 102.099596273292 -2.17959627329192 28 100.03 102.195310559006 -2.16531055900621 29 99.99 102.272453416149 -2.28245341614907 30 100.02 102.011226708075 -1.99122670807454 31 100.01 102.101226708075 -2.09122670807453 32 100.13 102.402655279503 -2.27265527950311 33 100.33 102.439798136646 -2.10979813664596 34 100.13 102.456940993789 -2.32694099378882 35 99.96 102.524083850932 -2.56408385093168 36 100.05 102.496940993789 -2.44694099378882 37 99.83 102.121024844721 -2.29102484472051 38 99.8 102.118167701863 -2.31816770186336 39 100.01 102.099596273292 -2.08959627329192 40 100.1 102.195310559006 -2.09531055900621 41 100.13 102.272453416149 -2.14245341614907 42 100.16 102.011226708075 -1.85122670807454 43 100.41 102.101226708075 -1.69122670807454 44 101.34 102.402655279503 -1.0626552795031 45 101.65 102.439798136646 -0.789798136645956 46 101.85 102.456940993789 -0.606940993788821 47 102.07 102.524083850932 -0.454083850931681 48 102.12 102.496940993789 -0.376940993788815 49 102.14 102.121024844721 0.0189751552794941 50 102.21 102.118167701863 0.091832298136641 51 102.28 102.099596273292 0.180403726708077 52 102.19 102.195310559006 -0.00531055900621019 53 102.33 102.272453416149 0.0575465838509321 54 102.54 102.011226708075 0.528773291925474 55 102.44 102.101226708075 0.338773291925465 56 102.78 102.402655279503 0.377344720496897 57 102.9 102.439798136646 0.460201863354044 58 103.08 102.456940993789 0.623059006211183 59 102.77 102.524083850932 0.245916149068321 60 102.65 102.496940993789 0.153059006211186 61 102.71 102.121024844721 0.588975155279488 62 103.29 102.118167701863 1.17183229813665 63 102.86 102.099596273292 0.760403726708075 64 103.45 102.195310559006 1.25468944099380 65 103.72 102.272453416149 1.44754658385093 66 103.65 102.011226708075 1.63877329192547 67 103.83 102.101226708075 1.72877329192547 68 104.45 102.402655279503 2.0473447204969 69 105.14 102.439798136646 2.70020186335404 70 105.07 102.456940993789 2.61305900621118 71 105.31 102.524083850932 2.78591614906833 72 105.19 102.496940993789 2.69305900621118 73 105.3 102.121024844721 3.17897515527949 74 105.02 102.118167701863 2.90183229813664 75 105.17 102.099596273292 3.07040372670808 76 105.28 102.195310559006 3.08468944099379 77 105.45 102.272453416149 3.17754658385094 78 105.38 102.011226708075 3.36877329192546 79 105.8 102.101226708075 3.69877329192546 80 105.96 102.402655279503 3.55734472049689 81 105.08 102.439798136646 2.64020186335404 82 105.11 102.456940993789 2.65305900621118 83 105.61 102.524083850932 3.08591614906832 84 105.5 102.496940993789 3.00305900621118 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0060733698582586 0.0121467397165172 0.99392663014174 17 0.000840222199133494 0.00168044439826699 0.999159777800867 18 0.000104132567705133 0.000208265135410265 0.999895867432295 19 1.26654166229064e-05 2.53308332458128e-05 0.999987334583377 20 1.56166698075766e-06 3.12333396151532e-06 0.99999843833302 21 1.74593420198672e-07 3.49186840397344e-07 0.99999982540658 22 1.88535028623064e-08 3.77070057246129e-08 0.999999981146497 23 2.00985251341615e-09 4.01970502683231e-09 0.999999997990147 24 2.23930211638101e-10 4.47860423276201e-10 0.99999999977607 25 6.87829496969514e-11 1.37565899393903e-10 0.999999999931217 26 9.16440083811527e-12 1.83288016762305e-11 0.999999999990836 27 9.74142818819534e-13 1.94828563763907e-12 0.999999999999026 28 1.48103815054399e-13 2.96207630108798e-13 0.999999999999852 29 2.04876796536002e-14 4.09753593072003e-14 0.99999999999998 30 2.24363956995287e-15 4.48727913990574e-15 0.999999999999998 31 2.64072137606163e-16 5.28144275212327e-16 1 32 5.00876163194272e-17 1.00175232638854e-16 1 33 5.05509968003036e-17 1.01101993600607e-16 1 34 1.60610882935057e-17 3.21221765870114e-17 1 35 3.72125714148495e-18 7.4425142829699e-18 1 36 1.09277940449255e-18 2.18555880898509e-18 1 37 4.53360548298292e-19 9.06721096596583e-19 1 38 1.5005993767955e-19 3.001198753591e-19 1 39 3.76622360725541e-20 7.53244721451081e-20 1 40 1.41111228206362e-20 2.82222456412724e-20 1 41 8.27291189937257e-21 1.65458237987451e-20 1 42 4.54569132590335e-21 9.0913826518067e-21 1 43 1.31630687040037e-20 2.63261374080074e-20 1 44 2.75925241125949e-16 5.51850482251898e-16 1 45 6.39042594885484e-13 1.27808518977097e-12 0.99999999999936 46 3.01241398047251e-10 6.02482796094503e-10 0.999999999698759 47 4.84910208543394e-08 9.69820417086788e-08 0.99999995150898 48 1.4066112905666e-06 2.8132225811332e-06 0.99999859338871 49 9.65055820512143e-06 1.93011164102429e-05 0.999990349441795 50 5.62989147498402e-05 0.000112597829499680 0.99994370108525 51 0.000204925279588473 0.000409850559176945 0.999795074720412 52 0.000616715617479875 0.00123343123495975 0.99938328438252 53 0.00185976356187061 0.00371952712374123 0.99814023643813 54 0.00499512883303037 0.00999025766606074 0.99500487116697 55 0.0126924090842427 0.0253848181684854 0.987307590915757 56 0.0307030393905515 0.061406078781103 0.969296960609448 57 0.0591922167691091 0.118384433538218 0.94080778323089 58 0.0997588538336161 0.199517707667232 0.900241146166384 59 0.200355290268594 0.400710580537187 0.799644709731406 60 0.364650535895061 0.729301071790121 0.63534946410494 61 0.506364455595714 0.987271088808573 0.493635544404286 62 0.566135016220607 0.867729967558786 0.433864983779393 63 0.688880251670014 0.622239496659973 0.311119748329986 64 0.756591516093737 0.486816967812527 0.243408483906263 65 0.810168986199604 0.379662027600792 0.189831013800396 66 0.860778989452975 0.278442021094049 0.139221010547025 67 0.951220173881713 0.0975596522365732 0.0487798261182866 68 0.997054399387278 0.00589120122544398 0.00294560061272199 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.735849056603774 NOK 5% type I error level 41 0.773584905660377 NOK 10% type I error level 43 0.811320754716981 NOK

Charts produced by software:

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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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