*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 07:55:39 -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/t1258815426v0be0es7yn92hh4.htm/, Retrieved Sat, 21 Nov 2009 15:57:18 +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/21/t1258815426v0be0es7yn92hh4.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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-22 46 -20 50 -17 49 -21 48 -16 50 -11 47 -19 50 -31 49 -36 51 -33 52 -26 48 -38 55 -27 56 -21 43 -17 44 -14 50 -16 49 -16 47 -15 46 -7 50 -9 49 2 53 -6 54 0 56 7 56 4 58 -5 53 2 51 0 52 3 53 10 56 4 54 5 54 7 56 1 59 -8 62 -3 62 -16 73 -22 76 -32 80 -30 77 -32 81 -38 80 -41 80 -46 81 -58 80 -55 77 -48 71 -58 71 -58 64 -68 64 -75 47 -77 41 -75 35 -71 34 -63 33 -61 23 -53 16 -41 16 -35 8 -33 9

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 Econo[t] = -32.6419936980808 + 0.135753843215889Price[t] + 3.18763487061969M1[t] + 2.62257232884560M2[t] -0.923126133868044M3[t] -2.85161844743626M4[t] -2.46156306693402M5[t] -0.698658455074959M6[t] -1.18011076100449M7[t] -2.18011076100449M8[t] -3.76290461185907M9[t] -1.33575384321589M10[t] + 0.345698462713647M11[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) -32.6419936980808 15.819872 -2.0634 0.04451 0.022255 Price 0.135753843215889 0.206105 0.6587 0.51326 0.25663 M1 3.18763487061969 16.155716 0.1973 0.84442 0.42221 M2 2.62257232884560 16.939002 0.1548 0.877609 0.438804 M3 -0.923126133868044 16.931979 -0.0545 0.956747 0.478374 M4 -2.85161844743626 16.902852 -0.1687 0.866737 0.433368 M5 -2.46156306693402 16.88842 -0.1458 0.884726 0.442363 M6 -0.698658455074959 16.879967 -0.0414 0.967157 0.483578 M7 -1.18011076100449 16.883741 -0.0699 0.944567 0.472283 M8 -2.18011076100449 16.883741 -0.1291 0.897798 0.448899 M9 -3.76290461185907 16.875688 -0.223 0.824499 0.412249 M10 -1.33575384321589 16.875134 -0.0792 0.937238 0.468619 M11 0.345698462713647 16.874077 0.0205 0.98374 0.49187 Multiple Linear Regression - Regression Statistics Multiple R 0.127248784179139 R-squared 0.0161922530750690 Adjusted R-squared -0.229759683656164 F-TEST (value) 0.0658350297634097 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.999993785425542 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 26.6799395968464 Sum Squared Residuals 34167.3204907858 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -22 -23.2096820395302 1.20968203953021 2 -20 -23.2317292084408 3.23172920844077 3 -17 -26.9131815143703 9.91318151437029 4 -21 -28.9774276711544 7.9774276711544 5 -16 -28.3158646042204 12.3158646042204 6 -11 -26.9602215220090 15.9602215220090 7 -19 -27.0344122982908 8.03441229829085 8 -31 -28.1701661415067 -2.82983385849326 9 -36 -29.4814523059295 -6.51854769407048 10 -33 -26.9185476940705 -6.08145230592953 11 -26 -25.7801107610045 -0.219889238995511 12 -38 -25.1755323212069 -12.8244676787931 13 -27 -21.8521436073713 -5.14785639262866 14 -21 -24.182006110952 3.18200611095198 15 -17 -27.5919507304497 10.5919507304497 16 -14 -28.7059199847226 14.7059199847226 17 -16 -28.4516184474363 12.4516184474363 18 -16 -26.960221522009 10.9602215220090 19 -15 -27.5774276711544 12.5774276711544 20 -7 -28.0344122982908 21.0344122982908 21 -9 -29.7529599923613 20.7529599923613 22 2 -26.7827938508546 28.7827938508546 23 -6 -24.9655877017092 18.9655877017092 24 0 -25.0397784779910 25.0397784779910 25 7 -21.8521436073714 28.8521436073714 26 4 -22.1456984627136 26.1456984627136 27 -5 -26.3701661415067 21.3701661415067 28 2 -28.5701661415067 30.5701661415067 29 0 -28.0443569177886 28.0443569177886 30 3 -26.1456984627137 29.1456984627137 31 10 -26.2198892389955 36.2198892389955 32 4 -27.4913969254273 31.4913969254273 33 5 -29.0741907762819 34.0741907762819 34 7 -26.3755323212069 33.3755323212069 35 1 -24.2868184856297 25.2868184856297 36 -8 -24.2252554186957 16.2252554186957 37 -3 -21.037620548076 18.037620548076 38 -16 -20.1093908144753 4.10939081447532 39 -22 -23.2478277475413 1.24782774754130 40 -32 -24.6333046882460 -7.36669531175404 41 -30 -24.6505108373914 -5.34948916260862 42 -32 -22.3445908526688 -9.65540914733123 43 -38 -22.9617970018142 -15.0382029981858 44 -41 -23.9617970018142 -17.0382029981858 45 -46 -25.4088370094529 -20.5911629905471 46 -58 -23.1174400840256 -34.8825599159744 47 -55 -21.8432493077437 -33.1567506922563 48 -48 -23.0034708297527 -24.9965291702473 49 -58 -19.815835959133 -38.184164040867 50 -58 -21.3311754034183 -36.6688245965817 51 -68 -24.8768738661320 -43.123126133868 52 -75 -29.1131815143703 -45.8868184856297 53 -77 -29.5376491931634 -47.4623508068366 54 -75 -28.5892676405996 -46.4107323594004 55 -71 -29.2064737897451 -41.7935262102549 56 -63 -30.3422276329609 -32.6577723670391 57 -61 -33.2825599159744 -27.7174400840256 58 -53 -31.8056860498425 -21.1943139501575 59 -41 -30.1242337439129 -10.8757662560871 60 -35 -31.5559629523537 -3.44403704764632 61 -33 -28.2325742385181 -4.7674257614819 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00174508900529336 0.00349017801058671 0.998254910994707 17 0.000152412020282485 0.000304824040564970 0.999847587979718 18 2.5468519473877e-05 5.0937038947754e-05 0.999974531480526 19 2.88665094621285e-06 5.77330189242569e-06 0.999997113349054 20 0.000151216028306744 0.000302432056613488 0.999848783971693 21 0.000424966832576789 0.000849933665153579 0.999575033167423 22 0.00159059868907463 0.00318119737814927 0.998409401310925 23 0.000978375551097603 0.00195675110219521 0.999021624448902 24 0.00204822735250754 0.00409645470501509 0.997951772647492 25 0.00235983247565266 0.00471966495130531 0.997640167524347 26 0.00127558140828951 0.00255116281657901 0.99872441859171 27 0.00064620144511231 0.00129240289022462 0.999353798554888 28 0.000608565293968305 0.00121713058793661 0.999391434706032 29 0.00049958107218812 0.00099916214437624 0.999500418927812 30 0.000419153690383116 0.000838307380766233 0.999580846309617 31 0.000579788812586109 0.00115957762517222 0.999420211187414 32 0.00080895837338131 0.00161791674676262 0.999191041626619 33 0.00175771957546389 0.00351543915092779 0.998242280424536 34 0.00463186739766973 0.00926373479533947 0.99536813260233 35 0.00631907797991746 0.0126381559598349 0.993680922020083 36 0.00485599526517489 0.00971199053034977 0.995144004734825 37 0.00648509502028601 0.0129701900405720 0.993514904979714 38 0.0313124164418444 0.0626248328836889 0.968687583558156 39 0.0803536278534213 0.160707255706843 0.919646372146579 40 0.14702567957148 0.29405135914296 0.85297432042852 41 0.267210763996497 0.534421527992993 0.732789236003504 42 0.467213892757092 0.934427785514183 0.532786107242908 43 0.668339937552557 0.663320124894885 0.331660062447443 44 0.783348194829285 0.433303610341431 0.216651805170715 45 0.934524724303559 0.130950551392882 0.0654752756964411 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.666666666666667 NOK 5% type I error level 22 0.733333333333333 NOK 10% type I error level 23 0.766666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/10ek441258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/10ek441258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/19fg81258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/19fg81258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/266y71258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/266y71258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/3f83o1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/3f83o1258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/4u4vv1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/4u4vv1258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/5fi941258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/5fi941258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/6i8tm1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/6i8tm1258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/75zlz1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/75zlz1258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/8ikzn1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/8ikzn1258815334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/9tlvv1258815334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258815426v0be0es7yn92hh4/9tlvv1258815334.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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