*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, 24 Dec 2010 15:43:44 +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/24/t1293205305dwoagxh1ghh19lj.htm/, Retrieved Fri, 24 Dec 2010 16:41:48 +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/24/t1293205305dwoagxh1ghh19lj.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:

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611 120,9 594 119,6 595 125,9 591 116,1 589 107,5 584 116,7 573 112,5 567 113 569 126,4 621 114,1 629 112,5 628 112,4 612 113,1 595 116,3 597 111,7 593 118,8 590 116,5 580 125,1 574 113,1 573 119,6 573 114,4 620 114 626 117,8 620 117 588 120,9 566 115 557 117,3 561 119,4 549 114,9 532 125,8 526 117,6 511 117,6 499 114,9 555 121,9 565 117 542 106,4 527 110,5 510 113,6 514 114,2 517 125,4 508 124,6 493 120,2 490 120,8 469 111,4 478 124,1 528 120,2 534 125,5 518 116 506 117 502 105,7 516 102 528 106,4 533 96,9 536 107,6 537 98,8 524 101,1 536 105,7 587 104,6 597 103,2 581 101,6

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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation WerkL[t] = + 548.195698689663 + 0.0773540981601292Chem.Nijv.[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) 548.195698689663 87.333474 6.277 0 0 Chem.Nijv. 0.0773540981601292 0.760062 0.1018 0.919288 0.459644 Multiple Linear Regression - Regression Statistics Multiple R 0.0133623167258849 R-squared 0.000178551508282863 Adjusted R-squared -0.0170597493277811 F-TEST (value) 0.0103578368878049 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.919287527063784 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 42.1400252188591 Sum Squared Residuals 102995.340075873 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 611 557.547809157223 53.4521908427768 2 594 557.447248829615 36.5527511703855 3 595 557.934579648023 37.0654203519767 4 591 557.176509486054 33.823490513946 5 589 556.511264241877 32.4887357581231 6 584 557.22292194495 26.7770780550499 7 573 556.898034732678 16.1019652673224 8 567 556.936711781758 10.0632882182424 9 569 557.973256697103 11.0267433028966 10 621 557.021801289734 63.9781987102662 11 629 556.898034732678 72.1019652673224 12 628 556.890299322862 71.1097006771384 13 612 556.944447191574 55.0555528084263 14 595 557.191980305686 37.8080196943139 15 597 556.836151454149 40.1638485458505 16 593 557.385365551086 35.6146344489136 17 590 557.207451125318 32.7925488746819 18 580 557.872696369495 22.1273036305048 19 574 556.944447191574 17.0555528084263 20 573 557.447248829615 15.5527511703855 21 573 557.045007519182 15.9549924808182 22 620 557.014065879918 62.9859341200822 23 626 557.308011452926 68.6919885470737 24 620 557.246128174398 62.7538718256018 25 588 557.547809157223 30.4521908427773 26 566 557.091419978078 8.9085800219221 27 557 557.269334403846 -0.269334403846196 28 561 557.431778009983 3.56822199001753 29 549 557.083684568262 -8.08368456826189 30 532 557.926844238207 -25.9268442382073 31 526 557.292540633294 -31.2925406332942 32 511 557.292540633294 -46.2925406332942 33 499 557.083684568262 -58.0836845682619 34 555 557.625163255383 -2.62516325538279 35 565 557.246128174398 7.75387182560184 36 542 556.426174733901 -14.4261747339008 37 527 556.743326536357 -29.7433265363573 38 510 556.983124240654 -46.9831242406537 39 514 557.02953669955 -43.0295366995498 40 517 557.895902598943 -40.8959025989432 41 508 557.834019320415 -49.8340193204151 42 493 557.49366128851 -64.4936612885106 43 490 557.540073747407 -67.5400737474067 44 469 556.812945224701 -87.8129452247014 45 478 557.795342271335 -79.795342271335 46 528 557.49366128851 -29.4936612885106 47 534 557.903638008759 -23.9036380087593 48 518 557.168774076238 -39.168774076238 49 506 557.246128174398 -51.2461281743982 50 502 556.372026865189 -54.3720268651887 51 516 556.085816701996 -40.0858167019962 52 528 556.426174733901 -28.4261747339008 53 533 555.69131080138 -22.6913108013796 54 536 556.518999651693 -20.5189996516929 55 537 555.838283587884 -18.8382835878838 56 524 556.016198013652 -32.0161980136521 57 536 556.372026865189 -20.3720268651887 58 587 556.286937357213 30.7130626427874 59 597 556.178641619788 40.8213583802116 60 581 556.054875062732 24.9451249372678 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0122503065816005 0.024500613163201 0.9877496934184 6 0.00426883250485527 0.00853766500971053 0.995731167495145 7 0.00273319002160024 0.00546638004320049 0.9972668099784 8 0.00210008283834095 0.0042001656766819 0.99789991716166 9 0.00326239385038506 0.00652478770077011 0.996737606149615 10 0.00846681097127514 0.0169336219425503 0.991533189028725 11 0.0186682323665161 0.0373364647330323 0.981331767633484 12 0.0253756914210884 0.0507513828421769 0.974624308578912 13 0.0180452409279514 0.0360904818559028 0.981954759072049 14 0.0104588097091289 0.0209176194182578 0.98954119029087 15 0.00617795813668581 0.0123559162733716 0.993822041863314 16 0.00357064794116074 0.00714129588232147 0.99642935205884 17 0.00210745845471886 0.00421491690943773 0.997892541545281 18 0.00121783125057402 0.00243566250114804 0.998782168749426 19 0.00116107021350453 0.00232214042700907 0.998838929786495 20 0.000864508901978684 0.00172901780395737 0.999135491098021 21 0.000744648342807383 0.00148929668561477 0.999255351657193 22 0.00159143018042275 0.0031828603608455 0.998408569819577 23 0.007529417953213 0.015058835906426 0.992470582046787 24 0.0259681410155008 0.0519362820310016 0.974031858984499 25 0.0360846028353532 0.0721692056707065 0.963915397164647 26 0.0525988637957002 0.1051977275914 0.9474011362043 27 0.0811528742276169 0.162305748455234 0.918847125772383 28 0.110620684191564 0.221241368383128 0.889379315808436 29 0.167644186400428 0.335288372800857 0.832355813599572 30 0.236962734198113 0.473925468396227 0.763037265801887 31 0.356515433301626 0.713030866603253 0.643484566698374 32 0.528925679109503 0.942148641780994 0.471074320890497 33 0.735572523978037 0.528854952043927 0.264427476021963 34 0.761389964567732 0.477220070864536 0.238610035432268 35 0.810801270133347 0.378397459733306 0.189198729866653 36 0.809746047084682 0.380507905830635 0.190253952915318 37 0.809944205647858 0.380111588704284 0.190055794352142 38 0.831355588388307 0.337288823223386 0.168644411611693 39 0.830169172189043 0.339661655621914 0.169830827810957 40 0.824495228806325 0.35100954238735 0.175504771193675 41 0.810924805372785 0.37815038925443 0.189075194627215 42 0.817991845863585 0.364016308272829 0.182008154136415 43 0.821628135622747 0.356743728754506 0.178371864377253 44 0.937684494777782 0.124631010444436 0.0623155052222179 45 0.95308984913741 0.0938203017251796 0.0469101508625898 46 0.92600876167703 0.147982476645941 0.0739912383229706 47 0.900095181901536 0.199809636196927 0.0999048180984637 48 0.855217200336276 0.289565599327448 0.144782799663724 49 0.81831721805591 0.363365563888179 0.181682781944089 50 0.858712690731471 0.282574618537057 0.141287309268528 51 0.847801028931812 0.304397942136377 0.152198971068188 52 0.812799835207916 0.374400329584169 0.187200164792084 53 0.723264161914637 0.553471676170726 0.276735838085363 54 0.669419962078948 0.661160075842103 0.330580037921051 55 0.517546307056986 0.964907385886028 0.482453692943014 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.215686274509804 NOK 5% type I error level 18 0.352941176470588 NOK 10% type I error level 22 0.431372549019608 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/10l6nl1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/10l6nl1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/1e4q91293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/1e4q91293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/27w7c1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/27w7c1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/37w7c1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/37w7c1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/47w7c1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/47w7c1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/57w7c1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/57w7c1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/6i56x1293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/6i56x1293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/7ten01293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/7ten01293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/8ten01293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/8ten01293205415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/9ten01293205415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205305dwoagxh1ghh19lj/9ten01293205415.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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