*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, 20 Nov 2009 05:15:31 -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/20/t12587194252shp7opct0rwr77.htm/, Retrieved Fri, 20 Nov 2009 13:17:18 +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/20/t12587194252shp7opct0rwr77.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:

WS7,MR2

Dataseries X:

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1.3 2 1.2 2.1 1.1 2.1 1.4 2.5 1.2 2.2 1.5 2.3 1.1 2.3 1.3 2.2 1.5 2.2 1.1 1.6 1.4 1.8 1.3 1.7 1.5 1.9 1.6 1.8 1.7 1.9 1.1 1.5 1.6 1 1.3 0.8 1.7 1.1 1.6 1.5 1.7 1.7 1.9 2.3 1.8 2.4 1.9 3 1.6 3 1.5 3.2 1.6 3.2 1.6 3.2 1.7 3.5 2 4 2 4.3 1.9 4.1 1.7 4 1.8 4.1 1.9 4.2 1.7 4.5 2 5.6 2.1 6.5 2.4 7.6 2.5 8.5 2.5 8.7 2.6 8.3 2.2 8.3 2.5 8.5 2.8 8.7 2.8 8.7 2.9 8.5 3 7.9 3.1 7 2.9 5.8 2.7 4.5 2.2 3.7 2.5 3.1 2.3 2.7 2.6 2.3 2.3 1.8 2.2 1.5 1.8 1.2 1.8 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation inflatie[t] = + 1.26580173511677 + 0.165894330966839inflatie_levensmiddelen[t] -0.0127896258874350M1[t] -0.0494717392680991M2[t] -0.00615385264876214M3[t] -0.149471739268099M4[t] + 0.0203892403059319M5[t] + 0.073660786783279M6[t] + 0.0470250135446055M7[t] + 0.0536607867832790M8[t] + 0.113660786783279M9[t] + 0.0202965600219525M10[t] + 0.100296560021952M11[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) 1.26580173511677 0.21735 5.8238 1e-06 0 inflatie_levensmiddelen 0.165894330966839 0.021002 7.899 0 0 M1 -0.0127896258874350 0.26568 -0.0481 0.961814 0.480907 M2 -0.0494717392680991 0.265693 -0.1862 0.853107 0.426554 M3 -0.00615385264876214 0.265706 -0.0232 0.981623 0.490811 M4 -0.149471739268099 0.265693 -0.5626 0.576457 0.288229 M5 0.0203892403059319 0.265838 0.0767 0.939196 0.469598 M6 0.073660786783279 0.265919 0.277 0.783018 0.391509 M7 0.0470250135446055 0.265877 0.1769 0.860389 0.430195 M8 0.0536607867832790 0.265919 0.2018 0.840968 0.420484 M9 0.113660786783279 0.265919 0.4274 0.671064 0.335532 M10 0.0202965600219525 0.265964 0.0763 0.939501 0.46975 M11 0.100296560021952 0.265964 0.3771 0.707829 0.353915 Multiple Linear Regression - Regression Statistics Multiple R 0.76178189947505 R-squared 0.580311662367816 Adjusted R-squared 0.470827748202899 F-TEST (value) 5.30042853138848 F-TEST (DF numerator) 12 F-TEST (DF denominator) 46 p-value 1.6074668327537e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.395878426184873 Sum Squared Residuals 7.20910750265615 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3 1.58480077116300 -0.284800771163005 2 1.2 1.56470809087903 -0.364708090879029 3 1.1 1.60802597749836 -0.508025977498364 4 1.4 1.53106582326576 -0.131065823265763 5 1.2 1.65115850354974 -0.451158503549743 6 1.5 1.72101948312377 -0.221019483123773 7 1.1 1.6943837098851 -0.5943837098851 8 1.3 1.68443005002709 -0.384430050027089 9 1.5 1.74443005002709 -0.244430050027089 10 1.1 1.55152922468566 -0.451529224685660 11 1.4 1.66470809087903 -0.264708090879027 12 1.3 1.54782209776039 -0.247822097760391 13 1.5 1.56821133806632 -0.0682113380663236 14 1.6 1.51493979158898 0.0850602084110244 15 1.7 1.57484711130500 0.125152888695004 16 1.1 1.36517149229892 -0.265171492298924 17 1.6 1.45208530638954 0.147914693610464 18 1.3 1.47217798667352 -0.172177986673515 19 1.7 1.49531051272489 0.204689487275107 20 1.6 1.56830401835030 0.0316959816496978 21 1.7 1.66148288454367 0.0385171154563299 22 1.9 1.66765525636245 0.232344743637553 23 1.8 1.76424468945913 0.0357553105408696 24 1.9 1.76348472801728 0.136515271982719 25 1.6 1.75069510212985 -0.150695102129846 26 1.5 1.74719185494255 -0.247191854942550 27 1.6 1.79050974156189 -0.190509741561886 28 1.6 1.64719185494255 -0.0471918549425498 29 1.7 1.86682113380663 -0.166821133806632 30 2 2.0030398457674 -0.00303984576739860 31 2 2.02617237181878 -0.0261723718187767 32 1.9 1.99962927886408 -0.0996292788640825 33 1.7 2.0430398457674 -0.343039845767399 34 1.8 1.96626505210276 -0.166265052102756 35 1.9 2.06285448519944 -0.16285448519944 36 1.7 2.01232622446754 -0.312326224467539 37 2 2.18202036264363 -0.182020362643626 38 2.1 2.29464314713312 -0.194643147133117 39 2.4 2.52044479781598 -0.120444797815976 40 2.5 2.52643180906679 -0.0264318090667941 41 2.5 2.72947165483419 -0.229471654834193 42 2.6 2.71638546892480 -0.116385468924804 43 2.2 2.68974969568613 -0.489749695686131 44 2.5 2.72956433511817 -0.229564335118172 45 2.8 2.82274320131154 -0.02274320131154 46 2.8 2.72937897455021 0.0706210254497865 47 2.9 2.77620010835685 0.123799891643154 48 3 2.57636694975479 0.42363305024521 49 3.1 2.4142724259972 0.6857275740028 50 2.9 2.17851711545633 0.72148288454367 51 2.7 2.00617237181878 0.693827628181223 52 2.2 1.73013902042597 0.469860979574031 53 2.5 1.80046340141990 0.699536598580103 54 2.3 1.78737721551051 0.512622784489492 55 2.6 1.6943837098851 0.9056162901149 56 2.3 1.61807231764035 0.681927682359646 57 2.2 1.62830401835030 0.571695981649698 58 1.8 1.48517149229892 0.314828507701076 59 1.8 1.53199262610556 0.268007373894444 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.440928768546686 0.881857537093371 0.559071231453314 17 0.318219880421505 0.636439760843009 0.681780119578495 18 0.25012387443636 0.50024774887272 0.74987612556364 19 0.236813924404655 0.47362784880931 0.763186075595345 20 0.157940324320193 0.315880648640385 0.842059675679807 21 0.0953023013185387 0.190604602637077 0.904697698681461 22 0.206486668889432 0.412973337778864 0.793513331110568 23 0.171323013836142 0.342646027672284 0.828676986163858 24 0.167924507425948 0.335849014851897 0.832075492574052 25 0.130445543862962 0.260891087725924 0.869554456137038 26 0.105777965521626 0.211555931043252 0.894222034478374 27 0.0883131803212159 0.176626360642432 0.911686819678784 28 0.0705901300797084 0.141180260159417 0.929409869920292 29 0.0564828563773212 0.112965712754642 0.943517143622679 30 0.0421569950515462 0.0843139901030924 0.957843004948454 31 0.0283329921709857 0.0566659843419715 0.971667007829014 32 0.0187653215609916 0.0375306431219833 0.981234678439008 33 0.0210149615479833 0.0420299230959666 0.978985038452017 34 0.0144054760887002 0.0288109521774003 0.9855945239113 35 0.00941824995907587 0.0188364999181517 0.990581750040924 36 0.0225949267378478 0.0451898534756955 0.977405073262152 37 0.0573789028743482 0.114757805748696 0.942621097125652 38 0.116336835418457 0.232673670836913 0.883663164581543 39 0.118766031277349 0.237532062554698 0.88123396872265 40 0.0712938348058931 0.142587669611786 0.928706165194107 41 0.0567607807066243 0.113521561413249 0.943239219293376 42 0.0286700793237157 0.0573401586474313 0.971329920676284 43 0.280612585874457 0.561225171748914 0.719387414125543 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 5 0.178571428571429 NOK 10% type I error level 8 0.285714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/10qz9b1258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/10qz9b1258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/15eff1258719324.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/15eff1258719324.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/2pm5j1258719324.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/2pm5j1258719324.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/3h1ve1258719324.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/3h1ve1258719324.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/4azn21258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/4azn21258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/5ou651258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/5ou651258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/6925d1258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/6925d1258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/754d41258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/754d41258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/8agg31258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/8agg31258719325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/9usio1258719325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587194252shp7opct0rwr77/9usio1258719325.ps (open in new window)

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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