*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 07:44:06 -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/t1258555649xselnkojxyuuzbp.htm/, Retrieved Wed, 18 Nov 2009 15:47:41 +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/t1258555649xselnkojxyuuzbp.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:

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User-defined keywords:

Dataseries X:

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8,2 25,5 8,3 25,5 8,1 25,5 7,4 20,9 7,3 20,9 7,7 20,9 8 22,3 8 22,3 7,7 22,3 6,9 19,9 6,6 19,9 6,9 19,9 7,5 24,1 7,9 24,1 7,7 24,1 6,5 13,8 6,1 13,8 6,4 13,8 6,8 16,2 7,1 16,2 7,3 16,2 7,2 18,6 7 18,6 7 18,6 7 22,4 7,3 22,4 7,5 22,4 7,2 22,6 7,7 22,6 8 22,6 7,9 20 8 20 8 20 7,9 21,8 7,9 21,8 8 21,8 8,1 28,7 8,1 28,7 8,2 28,7 8 19,5 8,3 19,5 8,5 19,5 8,6 19,4 8,7 19,4 8,7 19,4 8,5 21,7 8,4 21,7 8,5 21,7 8,7 26,2 8,7 26,2 8,6 26,2 7,9 19,1 8,1 19,1 8,2 19,1 8,5 21,3 8,6 21,3 8,5 21,3 8,3 24,1 8,2 24,1 8,7 24,1

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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3.89013097776424 + 0.18519646664636X[t] -0.690417301248855M1[t] -0.530417301248857M2[t] -0.570417301248857M3[t] -0.0421992080414256M4[t] + 0.0578007919585741M5[t] + 0.317800791958574M6[t] + 0.395571123971977M7[t] + 0.515571123971976M8[t] + 0.475571123971977M9[t] -0.0599999999999998M10[t] -0.200000000000000M11[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) 3.89013097776424 0.646 6.0219 0 0 X 0.18519646664636 0.028513 6.4952 0 0 M1 -0.690417301248855 0.341406 -2.0223 0.04886 0.02443 M2 -0.530417301248857 0.341406 -1.5536 0.126982 0.063491 M3 -0.570417301248857 0.341406 -1.6708 0.10141 0.050705 M4 -0.0421992080414256 0.32538 -0.1297 0.897364 0.448682 M5 0.0578007919585741 0.32538 0.1776 0.859768 0.429884 M6 0.317800791958574 0.32538 0.9767 0.333715 0.166858 M7 0.395571123971977 0.322548 1.2264 0.226159 0.11308 M8 0.515571123971976 0.322548 1.5984 0.116649 0.058324 M9 0.475571123971977 0.322548 1.4744 0.147036 0.073518 M10 -0.0599999999999998 0.320139 -0.1874 0.852139 0.42607 M11 -0.200000000000000 0.320139 -0.6247 0.53517 0.267585 Multiple Linear Regression - Regression Statistics Multiple R 0.728703258736597 R-squared 0.531008439293335 Adjusted R-squared 0.411265913155463 F-TEST (value) 4.43458524235517 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000103242332424536 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.506184152739685 Sum Squared Residuals 12.0424526347853 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.92222357599755 0.277776424002445 2 8.3 8.08222357599756 0.217776424002437 3 8.1 8.04222357599756 0.0577764240024357 4 7.4 7.71853792263174 -0.318537922631738 5 7.3 7.81853792263174 -0.518537922631739 6 7.7 8.07853792263174 -0.378537922631739 7 8 8.41558330795005 -0.415583307950046 8 8 8.53558330795004 -0.535583307950045 9 7.7 8.49558330795005 -0.795583307950046 10 6.9 7.5155406640268 -0.615540664026804 11 6.6 7.3755406640268 -0.775540664026805 12 6.9 7.5755406640268 -0.675540664026804 13 7.5 7.66294852269266 -0.162948522692661 14 7.9 7.82294852269266 0.0770514773073407 15 7.7 7.78294852269266 -0.0829485226926589 16 6.5 6.40364300944258 0.0963569905574161 17 6.1 6.50364300944258 -0.403643009442584 18 6.4 6.76364300944258 -0.363643009442583 19 6.8 7.28588486140725 -0.48588486140725 20 7.1 7.40588486140725 -0.30588486140725 21 7.3 7.36588486140725 -0.0658848614072498 22 7.2 7.27478525738654 -0.0747852573865374 23 7 7.13478525738654 -0.134785257386537 24 7 7.33478525738654 -0.334785257386537 25 7 7.34811452939385 -0.348114529393849 26 7.3 7.50811452939385 -0.208114529393847 27 7.5 7.46811452939385 0.0318854706061531 28 7.2 8.03337191593055 -0.833371915930551 29 7.7 8.13337191593055 -0.433371915930551 30 8 8.39337191593055 -0.393371915930551 31 7.9 7.98963143466342 -0.089631434663417 32 8 8.10963143466342 -0.109631434663417 33 8 8.06963143466342 -0.0696314346634173 34 7.9 7.86741395065489 0.0325860493451112 35 7.9 7.72741395065489 0.172586049345112 36 8 7.92741395065489 0.0725860493451112 37 8.1 8.51485226926592 -0.414852269265917 38 8.1 8.67485226926591 -0.574852269265915 39 8.2 8.63485226926591 -0.434852269265915 40 8 7.45926286932684 0.540737130673165 41 8.3 7.55926286932683 0.740737130673166 42 8.5 7.81926286932683 0.680737130673166 43 8.6 7.8785135546756 0.721486445324399 44 8.7 7.9985135546756 0.701486445324398 45 8.7 7.9585135546756 0.741486445324398 46 8.5 7.84889430399025 0.651105696009747 47 8.4 7.70889430399025 0.691105696009748 48 8.5 7.90889430399025 0.591105696009747 49 8.7 8.05186110265002 0.648138897349982 50 8.7 8.21186110265002 0.488138897349984 51 8.6 8.17186110265001 0.428138897349985 52 7.9 7.38518428266829 0.514815717331709 53 8.1 7.48518428266829 0.614815717331708 54 8.2 7.7451842826683 0.454815717331708 55 8.5 8.23038684130369 0.269613158696315 56 8.6 8.35038684130368 0.249613158696314 57 8.5 8.31038684130369 0.189613158696315 58 8.3 8.29336582394152 0.0066341760584837 59 8.2 8.15336582394152 0.0466341760584828 60 8.7 8.35336582394152 0.346634176058482 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.118676967048908 0.237353934097816 0.881323032951092 17 0.0483098369067714 0.0966196738135429 0.951690163093229 18 0.0199936094512403 0.0399872189024806 0.98000639054876 19 0.00951761194639175 0.0190352238927835 0.990482388053608 20 0.00421247271379189 0.00842494542758378 0.995787527286208 21 0.0114330992299078 0.0228661984598155 0.988566900770092 22 0.0140907796515557 0.0281815593031114 0.985909220348444 23 0.0239995202987833 0.0479990405975667 0.976000479701217 24 0.0271096941647563 0.0542193883295127 0.972890305835244 25 0.0623451206552741 0.124690241310548 0.937654879344726 26 0.120963182615449 0.241926365230898 0.87903681738455 27 0.227648667636199 0.455297335272397 0.772351332363801 28 0.310450212226311 0.620900424452622 0.689549787773689 29 0.264059010762004 0.528118021524008 0.735940989237996 30 0.204264005506072 0.408528011012145 0.795735994493928 31 0.281314917956891 0.562629835913783 0.718685082043109 32 0.381560864107304 0.763121728214609 0.618439135892696 33 0.509017727956951 0.981964544086099 0.490982272043049 34 0.65136233671344 0.69727532657312 0.34863766328656 35 0.802563226463498 0.394873547073003 0.197436773536502 36 0.974610224864031 0.050779550271938 0.025389775135969 37 0.978254676263683 0.0434906474726349 0.0217453237363174 38 0.990992365623677 0.0180152687526454 0.0090076343763227 39 0.991240631726867 0.0175187365462663 0.00875936827313316 40 0.98983572591666 0.0203285481666811 0.0101642740833405 41 0.992392791933506 0.0152144161329873 0.00760720806649367 42 0.996049123099767 0.00790175380046642 0.00395087690023321 43 0.98810434814719 0.0237913037056226 0.0118956518528113 44 0.961547315256538 0.076905369486924 0.038452684743462 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0689655172413793 NOK 5% type I error level 13 0.448275862068966 NOK 10% type I error level 17 0.586206896551724 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/10jxq01258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/10jxq01258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/12u221258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/12u221258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/2o4f61258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/2o4f61258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/33ad31258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/33ad31258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/420it1258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/420it1258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/558ml1258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/558ml1258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/67pih1258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/67pih1258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/7uysc1258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/7uysc1258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/8ksuf1258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/8ksuf1258555437.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/9mx611258555437.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555649xselnkojxyuuzbp/9mx611258555437.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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