SHW WS7

*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 07:54:02 -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/t12585561557hxnh3guj007kgz.htm/, Retrieved Wed, 18 Nov 2009 15:56:07 +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/t12585561557hxnh3guj007kgz.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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7.8 2.61 7.8 8.3 8 2.26 7.8 7.8 8.6 2.41 8 7.8 8.9 2.26 8.6 8 8.9 2.03 8.9 8.6 8.6 2.86 8.9 8.9 8.3 2.55 8.6 8.9 8.3 2.27 8.3 8.6 8.3 2.26 8.3 8.3 8.4 2.57 8.3 8.3 8.5 3.07 8.4 8.3 8.4 2.76 8.5 8.4 8.6 2.51 8.4 8.5 8.5 2.87 8.6 8.4 8.5 3.14 8.5 8.6 8.5 3.11 8.5 8.5 8.5 3.16 8.5 8.5 8.5 2.47 8.5 8.5 8.5 2.57 8.5 8.5 8.5 2.89 8.5 8.5 8.5 2.63 8.5 8.5 8.5 2.38 8.5 8.5 8.5 1.69 8.5 8.5 8.5 1.96 8.5 8.5 8.6 2.19 8.5 8.5 8.4 1.87 8.6 8.5 8.1 1.6 8.4 8.6 8 1.63 8.1 8.4 8 1.22 8 8.1 8 1.21 8 8 8 1.49 8 8 7.9 1.64 8 8 7.8 1.66 7.9 8 7.8 1.77 7.8 7.9 7.9 1.82 7.8 7.8 8.1 1.78 7.9 7.8 8 1.28 8.1 7.9 7.6 1.29 8 8.1 7.3 1.37 7.6 8 7 1.12 7.3 7.6 6.8 1.51 7 7.3 7 2.24 6.8 7 7.1 2.94 7 6.8 7.2 3.09 7.1 7 7.1 3.46 7.2 7.1 6.9 3.64 7.1 7.2 6.7 4.39 6.9 7.1 6.7 4.15 6.7 6.9 6.6 5.21 6.7 6.7 6.9 5.8 6.6 6.7 7.3 5.91 6.9 6.6 7.5 5.39 7.3 6.9 7.3 5.46 7.5 7.3 7.1 4.72 7.3 7.5 6.9 3.14 7.1 7.3 7.1 2.63 6.9 7.1

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.31529380916423 -0.00404322630682142X[t] + 1.32252823053763Y1[t] -0.575637866483151Y2[t] -0.00521518864188663M1[t] -0.108150188940405M2[t] + 0.0458707235047182M3[t] -0.0543689167329827M4[t] -0.105541489403970M5[t] -0.0387973496948420M6[t] -0.07681960133785M7[t] + 0.0436390019268301M8[t] -0.0846599530284503M9[t] -0.0338476726946303M10[t] -0.0196176817384662M11[t] -0.00933208650509082t + 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) 2.31529380916423 0.556634 4.1595 0.000164 8.2e-05 X -0.00404322630682142 0.02108 -0.1918 0.848864 0.424432 Y1 1.32252823053763 0.114008 11.6003 0 0 Y2 -0.575637866483151 0.118888 -4.8418 2e-05 1e-05 M1 -0.00521518864188663 0.102256 -0.051 0.959578 0.479789 M2 -0.108150188940405 0.101638 -1.0641 0.293677 0.146838 M3 0.0458707235047182 0.101752 0.4508 0.654562 0.327281 M4 -0.0543689167329827 0.101653 -0.5348 0.595715 0.297857 M5 -0.105541489403970 0.1013 -1.0419 0.303728 0.151864 M6 -0.0387973496948420 0.10172 -0.3814 0.704917 0.352458 M7 -0.07681960133785 0.101453 -0.7572 0.453372 0.226686 M8 0.0436390019268301 0.101675 0.4292 0.670081 0.335041 M9 -0.0846599530284503 0.106811 -0.7926 0.432678 0.216339 M10 -0.0338476726946303 0.106908 -0.3166 0.753189 0.376594 M11 -0.0196176817384662 0.106807 -0.1837 0.855197 0.427599 t -0.00933208650509082 0.002232 -4.1805 0.000154 7.7e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.981193454555963 R-squared 0.962740595263465 Adjusted R-squared 0.948768318487264 F-TEST (value) 68.9036304307483 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.150924278622669 Sum Squared Residuals 0.91112551511092 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.8 7.82811961973983 -0.02811961973983 2 8 8.00508659538519 -0.00508659538518576 3 8.6 8.41367458348672 0.186325416513282 4 8.9 8.9830987057159 -0.0830987057159018 5 8.9 8.9748997378618 -0.0748997378617924 6 8.6 8.85626455328622 -0.256264553286223 7 8.3 8.41340514613195 -0.113405146131947 8 8.3 8.3015966570411 -0.00159665704110306 9 8.3 8.33669740778875 -0.0366974077887451 10 8.4 8.37692420146236 0.02307579853764 11 8.5 8.51205331581379 -0.0120533158137856 12 8.4 8.59828134760772 -0.198281347607724 13 8.6 8.39492826933537 0.205071730664626 14 8.5 8.60327505381715 -0.103275053817150 15 8.5 8.49949181230395 0.000508187696052827 16 8.5 8.44760516899868 0.052394831001325 17 8.5 8.38689834850726 0.113101651492744 18 8.5 8.447100227863 0.0528997721370002 19 8.5 8.39934156708422 0.100658432915781 20 8.5 8.50917425142563 -0.0091742514256252 21 8.5 8.37259444880503 0.127405551194972 22 8.5 8.41508544921046 0.0849145507895377 23 8.5 8.42277317981324 0.0772268201867576 24 8.5 8.43196710394378 0.068032896056224 25 8.6 8.41648988674623 0.183510113253770 26 8.4 8.43776945541457 -0.0377694554145661 27 8.1 8.2614805197016 -0.1614805197016 28 8 7.87015660030494 0.129843399695058 29 8 7.85174820080584 0.148251799194156 30 8 7.96676447292126 0.0332355270787357 31 8 7.91827803140726 0.0817219685927446 32 7.9 8.02879806422082 -0.128798064220821 33 7.8 7.75883333518055 0.0411666648194488 34 7.8 7.72517973771008 0.0748202622899191 35 7.9 7.78743926749413 0.112560732505872 36 8.1 7.93013941483354 0.169860585166459 37 8 8.12455561229918 -0.124555612299184 38 7.6 7.76486769688211 -0.164867696882115 39 7.3 7.43778555915086 -0.137785559150862 40 7 7.16272131641675 -0.162721316416747 41 6.8 6.87657268976466 -0.0765726897646634 42 7 6.83921890160214 0.160781098397860 43 7.1 7.16866752444342 -0.0686675244434232 44 7.2 7.29631280701412 -0.0963128070141214 45 7.1 7.23187480822568 -0.131874808225676 46 6.9 7.0828106116171 -0.182810611617097 47 6.7 6.87773423687884 -0.177734236878844 48 6.7 6.73961213361496 -0.0396121336149595 49 6.6 6.83590661187938 -0.235906611879382 50 6.9 6.58900119850098 0.310998801499016 51 7.3 7.18756752535687 0.112432474643128 52 7.5 7.43641820856373 0.0635817914362655 53 7.3 7.40988102306045 -0.109881023060445 54 7.1 7.09065184432737 0.0093481556726265 55 6.9 6.90030773093316 -0.000307730933155562 56 7.1 6.86411822029833 0.235881779701671 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.172031675731058 0.344063351462115 0.827968324268942 20 0.107289737296486 0.214579474592972 0.892710262703514 21 0.0522927974075045 0.104585594815009 0.947707202592496 22 0.0294886251678501 0.0589772503357001 0.97051137483215 23 0.0170735530663957 0.0341471061327913 0.982926446933604 24 0.00874845003284478 0.0174969000656896 0.991251549967155 25 0.00743440786090417 0.0148688157218083 0.992565592139096 26 0.00481740058772041 0.00963480117544082 0.99518259941228 27 0.0409371538238512 0.0818743076477024 0.959062846176149 28 0.0255389257033212 0.0510778514066424 0.974461074296679 29 0.0279773593937619 0.0559547187875239 0.972022640606238 30 0.0189927398688399 0.0379854797376798 0.98100726013116 31 0.0172208166354359 0.0344416332708718 0.982779183364564 32 0.0278677610307252 0.0557355220614505 0.972132238969275 33 0.0281930351347658 0.0563860702695316 0.971806964865234 34 0.0502350641314403 0.100470128262881 0.94976493586856 35 0.0704370471962732 0.140874094392546 0.929562952803727 36 0.183441874191265 0.36688374838253 0.816558125808735 37 0.792836601326088 0.414326797347823 0.207163398673912 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0526315789473684 NOK 5% type I error level 6 0.315789473684211 NOK 10% type I error level 12 0.631578947368421 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/106gyh1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/106gyh1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/1tkvz1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/1tkvz1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/2woa31258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/2woa31258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/3x8kz1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/3x8kz1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/444sf1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/444sf1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/5knr01258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/5knr01258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/6w46g1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/6w46g1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/70xor1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/70xor1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/83axy1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/83axy1258556037.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/9056o1258556037.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585561557hxnh3guj007kgz/9056o1258556037.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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