Paper: Multiple Linear Regression met interactievariabelen

*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: Sun, 12 Dec 2010 15:24:47 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292167513gefj54mo1fzxa28.htm/, Retrieved Sun, 12 Dec 2010 16:25:23 +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/2010/Dec/12/t1292167513gefj54mo1fzxa28.htm/}, year = {2010}, } @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 = {2010}, 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 «

24 24 14 14 11 11 12 12 24 24 26 26 10 10 25 0 11 0 7 0 8 0 25 0 23 0 14 0 17 0 6 0 17 0 8 0 30 0 25 0 18 0 18 18 12 12 10 10 8 8 19 19 23 23 15 15 18 0 8 0 12 0 9 0 22 0 19 0 18 0 16 0 10 0 12 0 7 0 22 0 29 0 11 0 20 0 10 0 11 0 4 0 25 0 25 0 17 0 16 0 11 0 11 0 11 0 23 0 21 0 19 0 18 0 16 0 12 0 7 0 17 0 22 0 7 0 17 0 11 0 13 0 7 0 21 0 25 0 12 0 23 23 13 13 14 14 12 12 19 19 24 24 13 13 30 0 12 0 16 0 10 0 19 0 18 0 15 0 23 0 8 0 11 0 10 0 15 0 22 0 14 0 18 0 12 0 10 0 8 0 16 0 15 0 14 0 15 15 11 11 11 11 8 8 23 23 22 22 16 16 12 12 4 4 15 15 4 4 27 27 28 28 16 16 21 0 9 0 9 0 9 0 22 0 20 0 12 0 15 15 8 8 11 11 8 8 14 14 12 12 12 12 20 20 8 8 17 17 7 7 22 22 24 24 13 13 31 0 14 0 17 0 11 0 23 0 20 0 16 0 27 0 15 0 11 0 9 0 23 0 21 0 9 0 21 0 9 0 14 0 13 0 19 0 21 0 11 0 31 31 14 14 10 10 8 8 18 18 23 23 12 12 19 19 11 11 11 11 8 8 20 20 28 28 11 11 16 0 8 0 15 0 9 0 23 0 24 0 14 0 20 0 9 0 15 0 6 0 25 0 24 0 18 0 21 21 9 9 13 13 9 9 19 19 24 24 11 11 22 22 9 9 16 16 9 9 24 24 23 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 12 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation PS[t] = + 3.02312027804749 + 0.289809759090523CM[t] -0.293633940943942CM_G[t] -0.199249052781833D[t] + 0.153536202361446D_G[t] + 0.255960697716122PE[t] -0.283226764542751PE_G[t] + 0.0132743157206869PC[t] -0.0117482321641987PC_G[t] + 0.978572091873339PS_G[t] + 0.415924298586412O[t] -0.444540756555795O_G[t] + 0.178141200721764H[t] -0.247978402414856H_G[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) 3.02312027804749 2.612232 1.1573 0.249123 0.124561 CM 0.289809759090523 0.063461 4.5667 1.1e-05 5e-06 CM_G -0.293633940943942 0.106148 -2.7663 0.006436 0.003218 D -0.199249052781833 0.130029 -1.5323 0.127694 0.063847 D_G 0.153536202361446 0.188746 0.8135 0.417338 0.208669 PE 0.255960697716122 0.111222 2.3013 0.022849 0.011424 PE_G -0.283226764542751 0.180928 -1.5654 0.119744 0.059872 PC 0.0132743157206869 0.131064 0.1013 0.919472 0.459736 PC_G -0.0117482321641987 0.229855 -0.0511 0.959309 0.479655 PS_G 0.978572091873339 0.113793 8.5996 0 0 O 0.415924298586412 0.072156 5.7642 0 0 O_G -0.444540756555795 0.139406 -3.1888 0.001763 0.000881 H 0.178141200721764 0.125845 1.4156 0.159124 0.079562 H_G -0.247978402414856 0.172404 -1.4384 0.152564 0.076282 Multiple Linear Regression - Regression Statistics Multiple R 0.776966426907153 R-squared 0.603676828540868 Adjusted R-squared 0.566875391191092 F-TEST (value) 16.4036209456513 F-TEST (DF numerator) 13 F-TEST (DF denominator) 140 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.80575699262366 Sum Squared Residuals 1102.11812223192 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 24.0530765560902 -0.0530765560901563 2 25 22.0347797620809 2.96522023791906 3 30 24.8165673304871 5.18343266951292 4 19 19.0324119867282 -0.0324119867281819 5 22 20.9458040196355 1.05419598036454 6 22 22.8533923452611 -0.85339234526115 7 25 23.12199774673 1.87800225327001 8 23 20.5490150747288 2.45098492527124 9 17 18.6134826537593 -1.61348265375926 10 21 21.7143577556621 -0.714357755662085 11 19 18.9756762393769 0.0243237606230659 12 19 23.7132941234279 -4.71329412342788 13 15 22.6873745259648 -7.68737452596483 14 16 17.2473501001225 -1.24735010012250 15 23 22.9353989396518 0.0646010603481655 16 27 26.8942824562994 0.105717543700623 17 22 20.1951792452327 1.80482075476734 18 14 14.8309020505191 -0.830902050519145 19 22 22.0620006943968 -0.0620006943968054 20 23 24.8838305882861 -1.88383058828613 21 23 21.1319655749382 1.86803442506183 22 19 21.7658630945607 -2.76586309456067 23 18 18.1222114349989 -0.122211434998897 24 20 20.1618731972673 -0.161873197267308 25 23 22.5011232846478 0.498876715352215 26 25 24.1338551239530 0.866144876046962 27 19 19.3285382243087 -0.328538224308664 28 24 23.9548811542322 0.0451188457678439 29 22 22.1584389519411 -0.158438951941049 30 26 24.0199385959575 1.98006140404252 31 29 23.0207127559547 5.97928724404534 32 32 24.7309046904592 7.26909530954084 33 25 22.0339736913512 2.96602630864875 34 29 28.7994562904100 0.200543709589977 35 28 27.8807412938904 0.119258706109636 36 17 16.9508805859068 0.0491194140932041 37 28 25.7825160599411 2.21748394005887 38 29 28.9448170203572 0.0551829796428413 39 26 25.1938589519412 0.80614104805876 40 25 23.5469889908283 1.45301100917168 41 14 14.6853688520440 -0.68536885204396 42 25 22.7349055286470 2.26509447135304 43 26 26.1058683703617 -0.105868370361661 44 20 19.9379576413964 0.0620423586036247 45 18 21.6877580201582 -3.68775802015815 46 32 31.9498442026493 0.0501557973507456 47 25 25.029353741249 -0.0293537412489835 48 25 22.3937559491003 2.60624405089968 49 23 23.253888249809 -0.253888249809004 50 21 21.1133833318065 -0.113383331806463 51 20 24.5006874023663 -4.50068740236634 52 15 15.9313015487408 -0.931301548740772 53 30 29.7335892231885 0.266410776811461 54 24 25.5232994949851 -1.52329949498510 55 26 24.3081002205174 1.69189977948256 56 24 23.9586108514866 0.0413891485133683 57 22 22.1522491819777 -0.152249181977660 58 14 17.0304526985932 -3.03045269859321 59 24 23.9559962131739 0.0440037868260885 60 24 24.0314331791993 -0.0314331791993214 61 24 23.4203206138149 0.579679386185118 62 24 23.8371641532218 0.162835846778242 63 19 18.9829124215140 0.0170875784859807 64 31 30.9504825580780 0.0495174419220362 65 22 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137 29 25.2991718207792 3.70082817922083 138 21 19.5382658099568 1.46173419004319 139 21 21.3784089773461 -0.378408977346122 140 23 22.8386894466138 0.161310553386206 141 27 23.6109919925434 3.38900800745662 142 25 24.9031793200022 0.0968206799977803 143 21 20.9251658522983 0.0748341477016993 144 10 16.7806836535453 -6.78068365354529 145 20 22.3335982628498 -2.33359826284984 146 26 22.8946259893632 3.10537401063678 147 24 25.1619579556401 -1.16195795564013 148 29 32.2243102755247 -3.22431027552474 149 19 19.1101399599375 -0.110139959937496 150 24 22.7318187197821 1.26818128021791 151 19 20.4358843511594 -1.43588435115941 152 24 23.8793968053101 0.120603194689921 153 22 22.4251622938296 -0.425162293829623 154 17 24.8854842560364 -7.88548425603638 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.9781439190994 0.0437121618011982 0.0218560809005991 18 0.952678714586764 0.0946425708264729 0.0473212854132364 19 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4.49052739001114e-06 8.98105478002227e-06 0.99999550947261 104 3.28687920748496e-06 6.57375841496991e-06 0.999996713120793 105 1.79588682881190e-06 3.59177365762379e-06 0.999998204113171 106 9.39938048657008e-07 1.87987609731402e-06 0.99999906006195 107 3.37899972234571e-06 6.75799944469141e-06 0.999996621000278 108 0.000360074540067842 0.000720149080135683 0.999639925459932 109 0.000219138956284162 0.000438277912568324 0.999780861043716 110 0.0632485219714483 0.126497043942897 0.936751478028552 111 0.464922955463394 0.929845910926788 0.535077044536606 112 0.415909187218632 0.831818374437263 0.584090812781368 113 0.361588090503173 0.723176181006345 0.638411909496827 114 0.305875462980007 0.611750925960013 0.694124537019994 115 0.254077437629412 0.508154875258824 0.745922562370588 116 0.239451286850621 0.478902573701242 0.76054871314938 117 0.193732305390424 0.387464610780848 0.806267694609576 118 0.26079500319118 0.52159000638236 0.73920499680882 119 0.211947516692657 0.423895033385315 0.788052483307343 120 0.166686983300123 0.333373966600246 0.833313016699877 121 0.127652654362295 0.255305308724590 0.872347345637705 122 0.0998213697896844 0.199642739579369 0.900178630210316 123 0.0777366484025935 0.155473296805187 0.922263351597407 124 0.0583586884614695 0.116717376922939 0.94164131153853 125 0.0414177901424689 0.0828355802849379 0.95858220985753 126 0.0435735189374731 0.0871470378749462 0.956426481062527 127 0.0525358887988513 0.105071777597703 0.947464111201149 128 0.0344645801326764 0.0689291602653527 0.965535419867324 129 0.0239851270000138 0.0479702540000276 0.976014872999986 130 0.0143835707712485 0.0287671415424969 0.985616429228751 131 0.0081525596018272 0.0163051192036544 0.991847440398173 132 0.00450649590766619 0.00901299181533239 0.995493504092334 133 0.00421390984816502 0.00842781969633004 0.995786090151835 134 0.00213309845124953 0.00426619690249906 0.99786690154875 135 0.00139834635399111 0.00279669270798222 0.99860165364601 136 0.000808516029532804 0.00161703205906561 0.999191483970467 137 0.000956961837372133 0.00191392367474427 0.999043038162628 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 41 0.338842975206612 NOK 5% type I error level 57 0.471074380165289 NOK 10% type I error level 64 0.528925619834711 NOK

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Parameters (Session):

par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 9 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa

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