*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 12:49:17 -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/t1258573827ejpjq95eobgh8v6.htm/, Retrieved Wed, 18 Nov 2009 20:50:39 +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/18/t1258573827ejpjq95eobgh8v6.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:

» Textbox « » Textfile « » CSV «

90269 561000 90398 90390 555000 90269 88219 544000 90390 87032 537000 88219 87175 543000 87032 92603 594000 87175 93571 611000 92603 94118 613000 93571 92159 611000 94118 89528 594000 92159 89955 595000 89528 89587 591000 89955 89488 589000 89587 88521 584000 89488 86587 573000 88521 85159 567000 86587 84915 569000 85159 91378 621000 84915 92729 629000 91378 92194 628000 92729 89664 612000 92194 86285 595000 89664 86858 597000 86285 87184 593000 86858 86629 590000 87184 85220 580000 86629 84816 574000 85220 84831 573000 84816 84957 573000 84831 90951 620000 84957 92134 626000 90951 91790 620000 92134 86625 588000 91790 83324 566000 86625 82719 557000 83324 83614 561000 82719 81640 549000 83614 78665 532000 81640 77828 526000 78665 75728 511000 77828 72187 499000 75728 79357 555000 72187 81329 565000 79357 77304 542000 81329 75576 527000 77304 72932 510000 75576 74291 514000 72932 74988 517000 74291 73302 508000 74988 70483 493000 73302 69848 490000 70483 6646 etc...

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 20602.0406882828 + 0.105567859493241X[t] + 0.102513766807805Y1[t] -175.182325785885t + 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) 20602.0406882828 3776.430148 5.4554 1e-06 1e-06 X 0.105567859493241 0.007142 14.7815 0 0 Y1 0.102513766807805 0.0589 1.7405 0.08737 0.043685 t -175.182325785885 18.383412 -9.5294 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.989650661324071 R-squared 0.979408431459172 Adjusted R-squared 0.978285254993309 F-TEST (value) 871.998711891046 F-TEST (DF numerator) 3 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1134.84075998331 Sum Squared Residuals 70832495.2785724 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90269 88917.4670300972 1351.53296990283 2 90390 88095.6532714336 2294.34672856639 3 88219 86771.6286570058 1447.37134299418 4 87032 85634.9139270275 1397.08607297249 5 87175 85971.4549170002 1203.5450829998 6 92603 91194.8928940231 1408.10710597687 7 93571 93370.8089058551 200.191094144897 8 94118 93505.9956253257 612.004374674345 9 92159 93175.7526109972 -1016.75261099716 10 89528 91005.0922046497 -1477.09220464968 11 89955 90665.7640178857 -710.764017885705 12 89587 90112.0836325538 -525.083632553786 13 89488 89688.0405215962 -200.040521596147 14 88521 88974.87003543 -453.870035430083 15 86587 87539.3104427154 -952.310442715398 16 85159 86532.4593349638 -1373.45933496377 17 84915 86422.0230691628 -1507.02306916282 18 91378 91711.3560779244 -333.356077924373 19 92729 93043.2631029633 -314.263102963257 20 92194 92901.0090166415 -707.009016641473 21 89664 90981.8960737216 -1317.89607372155 22 86285 88752.7003065268 -2467.70030652682 23 86858 88442.2596816838 -1584.25968168385 24 87184 87903.5463063059 -719.546306305871 25 86629 87445.0798900196 -816.079890019607 26 85220 86157.323828723 -937.323828722979 27 84816 85204.2924485455 -388.29244854545 28 84831 84882.126701476 -51.1267014759702 29 84957 84708.4820821922 248.517917807798 30 90951 89507.9058872064 1443.09411279357 31 92134 90580.598236626 1553.40176337403 32 91790 89893.2825400143 1896.71745998573 33 86625 86304.6639746628 320.336025337213 34 83324 83277.5051344633 46.4948655367136 35 82719 81813.8141290057 905.185870994331 36 83614 81998.882412274 1615.11758772597 37 81640 80648.6355938622 991.364406137768 38 78665 78476.4374810126 188.562518987359 39 77828 77362.8695420141 465.130457985909 40 75728 75518.3653010115 209.634698988544 41 72187 73861.0897510103 -1674.08975101029 42 79357 79234.7063085795 122.293691420530 43 81329 80850.226285738 478.773714262047 44 77304 78449.1403397525 -1145.14033975251 45 75576 76277.8222101666 -701.822210166596 46 72932 74130.8424839517 -1198.84248395173 47 74291 74106.885196699 184.11480330103 48 74988 74387.7226584846 600.277341515387 49 73302 73333.8816927246 -31.8816927245972 50 70483 71402.3432637021 -919.343263702137 51 69848 70621.4710508053 -773.471050805328 52 66466 68164.2674337384 -1698.26743373842 53 67610 68592.4942840477 -982.494284047712 54 75091 73812.980682152 1278.01931784799 55 76207 75038.1110028148 1168.88899718524 56 73454 73288.2482888945 165.751711105476 57 72008 71564.0312491679 443.968750832141 58 71362 70818.3425786049 543.657421395076 59 74250 72054.8863923666 2195.11360763343 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.227252988357626 0.454505976715252 0.772747011642374 8 0.168010705404071 0.336021410808143 0.831989294595929 9 0.218215211314809 0.436430422629617 0.781784788685191 10 0.221357439913098 0.442714879826195 0.778642560086902 11 0.137887866197277 0.275775732394555 0.862112133802723 12 0.119195896430411 0.238391792860823 0.880804103569589 13 0.154543641940411 0.309087283880822 0.845456358059589 14 0.142001911529021 0.284003823058041 0.85799808847098 15 0.0996315632742445 0.199263126548489 0.900368436725756 16 0.0790763597733433 0.158152719546687 0.920923640226657 17 0.0596502493116803 0.119300498623361 0.94034975068832 18 0.0513791947117226 0.102758389423445 0.948620805288277 19 0.211345313848867 0.422690627697734 0.788654686151133 20 0.293788912804692 0.587577825609384 0.706211087195308 21 0.290980690351418 0.581961380702836 0.709019309648582 22 0.411740865669636 0.823481731339272 0.588259134330364 23 0.451065519377376 0.902131038754752 0.548934480622624 24 0.545088232547518 0.909823534904964 0.454911767452482 25 0.623545883190262 0.752908233619476 0.376454116809738 26 0.66850188198124 0.66299623603752 0.33149811801876 27 0.704375869163073 0.591248261673853 0.295624130836927 28 0.739202448858429 0.521595102283142 0.260797551141571 29 0.774913810083993 0.450172379832014 0.225086189916007 30 0.91696000151647 0.166079996967061 0.0830399984835305 31 0.978219665825825 0.0435606683483507 0.0217803341741754 32 0.988722398292302 0.0225552034153967 0.0112776017076983 33 0.985387591779208 0.0292248164415849 0.0146124082207925 34 0.979279003262974 0.0414419934740516 0.0207209967370258 35 0.972224819575215 0.0555503608495691 0.0277751804247846 36 0.97946022605303 0.0410795478939407 0.0205397739469704 37 0.9800296997948 0.0399406004103991 0.0199703002051996 38 0.97764921585646 0.0447015682870813 0.0223507841435407 39 0.987687544727929 0.0246249105441425 0.0123124552720713 40 0.999281707643554 0.00143658471289144 0.00071829235644572 41 0.999250036722272 0.00149992655545539 0.000749963277727695 42 0.998792497136182 0.00241500572763595 0.00120750286381797 43 0.997276683391334 0.00544663321733126 0.00272331660866563 44 0.998129653121768 0.00374069375646399 0.00187034687823200 45 0.997130647738677 0.00573870452264636 0.00286935226132318 46 0.9968190964571 0.00636180708580129 0.00318090354290064 47 0.992315449110853 0.0153691017782947 0.00768455088914737 48 0.99120550017562 0.0175889996487602 0.00879449982438008 49 0.992266679472251 0.0154666410554982 0.00773332052774908 50 0.988488244447597 0.0230235111048052 0.0115117555524026 51 0.983678570012143 0.0326428599757145 0.0163214299878572 52 0.972797968991257 0.0544040620174865 0.0272020310087432 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.152173913043478 NOK 5% type I error level 20 0.434782608695652 NOK 10% type I error level 22 0.478260869565217 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/1053w71258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/1053w71258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/1xim81258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/1xim81258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/2hoqy1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/2hoqy1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/31efw1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/31efw1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/48kzt1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/48kzt1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/5iez41258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/5iez41258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/68vgs1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/68vgs1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/7m6zh1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/7m6zh1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/8w1mm1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/8w1mm1258573753.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/9wyrq1258573753.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573827ejpjq95eobgh8v6/9wyrq1258573753.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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