Workshop 7

*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, 21 Nov 2009 06:45:34 -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/21/t12588111876c99wzga0rbt6ds.htm/, Retrieved Sat, 21 Nov 2009 14:46:39 +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/21/t12588111876c99wzga0rbt6ds.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:

WS 7 part 3

Dataseries X:

» Textbox « » Textfile « » CSV «

9.3 98.3 9.3 112.3 8.7 113.9 8.2 106.2 8.3 98.6 8.5 96.5 8.6 95.9 8.5 103.7 8.2 103.1 8.1 103.7 7.9 112.1 8.6 86.9 8.7 95 8.7 111.8 8.5 108.8 8.4 109.3 8.5 101.4 8.7 100.5 8.7 100.7 8.6 113.5 8.5 106.1 8.3 111.6 8 114.9 8.2 88.6 8.1 99.5 8.1 115.1 8 118 7.9 111.4 7.9 107.3 8 105.3 8 105.3 7.9 117.9 8 110.2 7.7 112.4 7.2 117.5 7.5 93 7.3 103.5 7 116.3 7 120 7 114.3 7.2 104.7 7.3 109.8 7.1 112.6 6.8 114.4 6.4 115.7 6.1 114.7 6.5 118.4 7.7 94.9 7.9 103.8 7.5 115.1 6.9 113.7 6.6 104 6.9 94.3 7.7 92.5 8 93.2 8 104.7 7.7 94 7.3 98.1 7.4 102.7 8.1 82.4

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 12.9761973202013 -0.0437302424418521X[t] + 0.391874378892299M1[t] + 0.897837711324807M2[t] + 0.660439609582997M3[t] + 0.234421907724963M4[t] + 0.063567535529736M5[t] + 0.358066167101888M6[t] + 0.454545831418218M7[t] + 0.770604000129826M8[t] + 0.380445097074110M9[t] + 0.249516963843914M10[t] + 0.398409694904394M11[t] -0.0293669140023821t + 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) 12.9761973202013 0.911265 14.2398 0 0 X -0.0437302424418521 0.009977 -4.383 6.7e-05 3.4e-05 M1 0.391874378892299 0.278271 1.4082 0.165781 0.082891 M2 0.897837711324807 0.357388 2.5122 0.015568 0.007784 M3 0.660439609582997 0.362427 1.8223 0.074919 0.03746 M4 0.234421907724963 0.323371 0.7249 0.472166 0.236083 M5 0.063567535529736 0.282031 0.2254 0.822673 0.411336 M6 0.358066167101888 0.280339 1.2773 0.20792 0.10396 M7 0.454545831418218 0.282778 1.6074 0.114804 0.057402 M8 0.770604000129826 0.333843 2.3083 0.025531 0.012765 M9 0.380445097074110 0.303591 1.2531 0.216484 0.108242 M10 0.249516963843914 0.316514 0.7883 0.434549 0.217274 M11 0.398409694904394 0.348667 1.1427 0.25909 0.129545 t -0.0293669140023821 0.003053 -9.6195 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.865878546514659 R-squared 0.749745657314338 Adjusted R-squared 0.679021603946651 F-TEST (value) 10.6009995413652 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 7.08769265500564e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.401166295786969 Sum Squared Residuals 7.40298225627013 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 9.0400219530572 0.259978046942795 2 9.3 8.90439497730134 0.395605022698658 3 8.7 8.56766157365019 0.132338426349814 4 8.2 8.44899982459203 -0.248999824592030 5 8.3 8.5811283809525 -0.281128380952497 6 8.5 8.93809360765016 -0.438093607650156 7 8.6 9.03144450342922 -0.431444503429216 8 8.5 8.977039867092 -0.477039867091993 9 8.2 8.58375219549901 -0.383752195499008 10 8.1 8.39721900280132 -0.297219002801319 11 7.9 8.14941078334786 -0.249410783347860 12 8.6 8.82363628397576 -0.223636283975755 13 8.7 8.83192878508667 -0.131928785086671 14 8.7 8.57385713049368 0.126142869506320 15 8.5 8.43828284207505 0.0617171579249544 16 8.4 7.9610331049937 0.438966895006296 17 8.5 8.10628073408673 0.393719265913274 18 8.7 8.41076966985416 0.289230330145837 19 8.7 8.46913637167974 0.230863628320259 20 8.6 8.19608052313326 0.403919476866741 21 8.5 8.10015850014487 0.399841499855133 22 8.3 7.6993471194821 0.600652880517898 23 8 7.67456313648209 0.325436863517912 24 8.2 8.39689190379602 -0.196891903796023 25 8.1 8.28273972606975 -0.182739726069751 26 8.1 8.07714436240698 0.0228556375930144 27 8 7.68356164358142 0.316438356418579 28 7.9 7.51679662783723 0.383203372162772 29 7.9 7.49586933565121 0.404130664348787 30 8 7.84846153810469 0.151538461895312 31 8 7.91557428841864 0.0844257115813641 32 7.9 7.65126448836052 0.248735511639476 33 8 7.56846153810469 0.431538461895313 34 7.7 7.31195995750004 0.388040042499964 35 7.2 7.20846153810469 -0.00846153810468793 36 7.5 7.85207586902329 -0.352075869023288 37 7.3 7.75541578827376 -0.455415788273757 38 7 7.67226510344818 -0.672265103448178 39 7 7.24369819066913 -0.243698190669132 40 7 7.03757595672727 -0.0375759567272731 41 7.2 7.25716499797144 -0.057164997971444 42 7.3 7.29927247908777 0.000727520912231643 43 7.1 7.24394055056453 -0.143940550564531 44 6.8 7.45191736887842 -0.651917368878422 45 6.4 6.97554223664592 -0.575542236645916 46 6.1 6.8589774318552 -0.758977431855192 47 6.5 6.81670135187844 -0.316701351878436 48 7.7 7.41658544035518 0.283414559644817 49 7.9 7.38989374751262 0.510106252487384 50 7.5 7.37233842634982 0.127661573650185 51 6.9 7.16679575002421 -0.266795750024215 52 6.6 7.13559448584977 -0.535594485849765 53 6.9 7.35955655133812 -0.45955655133812 54 7.7 7.70340270530322 -0.00340270530322409 55 8 7.73990428590788 0.260095714092124 56 8 7.5236977525358 0.476302247464198 57 7.7 7.57208552960552 0.127914470394478 58 7.3 7.23249648836135 0.0675035116386488 59 7.4 7.15086319018693 0.249136809813071 60 8.1 7.61081050284975 0.48918949715025 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.228883199574839 0.457766399149679 0.771116800425161 18 0.107884880748169 0.215769761496338 0.892115119251831 19 0.0486822304455969 0.0973644608911938 0.951317769554403 20 0.0336613542641461 0.0673227085282921 0.966338645735854 21 0.0226780109952999 0.0453560219905997 0.9773219890047 22 0.0108546113049305 0.0217092226098610 0.98914538869507 23 0.00454749245497722 0.00909498490995444 0.995452507545023 24 0.00454238395323441 0.00908476790646883 0.995457616046766 25 0.0354636394783131 0.0709272789566263 0.964536360521687 26 0.0540022338738688 0.108004467747738 0.945997766126131 27 0.0483502374169468 0.0967004748338936 0.951649762583053 28 0.0319372727569500 0.0638745455138999 0.96806272724305 29 0.0308586379432745 0.061717275886549 0.969141362056726 30 0.0215305423199142 0.0430610846398283 0.978469457680086 31 0.0147851047029676 0.0295702094059352 0.985214895297032 32 0.0128191165273892 0.0256382330547784 0.98718088347261 33 0.0144021861658246 0.0288043723316492 0.985597813834175 34 0.0432961307648881 0.0865922615297763 0.956703869235112 35 0.0541335471204317 0.108267094240863 0.945866452879568 36 0.0444713452338657 0.0889426904677313 0.955528654766134 37 0.0911090655363041 0.182218131072608 0.908890934463696 38 0.226298610698753 0.452597221397506 0.773701389301247 39 0.188982608484558 0.377965216969117 0.811017391515442 40 0.244152924459742 0.488305848919484 0.755847075540258 41 0.508492298217097 0.983015403565807 0.491507701782903 42 0.618088223471293 0.763823553057415 0.381911776528708 43 0.518257269423754 0.96348546115249 0.481742730576246 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0740740740740741 NOK 5% type I error level 8 0.296296296296296 NOK 10% type I error level 16 0.592592592592593 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/10dq1j1258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/10dq1j1258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/1yw4n1258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/1yw4n1258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/2tn881258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/2tn881258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/3fte61258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/3fte61258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/4nlt61258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/4nlt61258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/5i6g71258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/5i6g71258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/6lnl21258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/6lnl21258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/7jawb1258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/7jawb1258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/8f1kd1258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/8f1kd1258811128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/9nfvm1258811128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588111876c99wzga0rbt6ds/9nfvm1258811128.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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