ws7

*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, 20 Nov 2009 07:51:00 -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/20/t1258729323mq8mbz48cv8xay3.htm/, Retrieved Fri, 20 Nov 2009 16:02:15 +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/20/t1258729323mq8mbz48cv8xay3.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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15335,63636 13845,66667 12823,61538 12792 10284,5 11188,5 15335,63636 13845,66667 12823,61538 12792 13633,25 11188,5 15335,63636 13845,66667 12823,61538 12298,46667 13633,25 11188,5 15335,63636 13845,66667 15353,63636 12298,46667 13633,25 11188,5 15335,63636 12696,15385 15353,63636 12298,46667 13633,25 11188,5 12213,93333 12696,15385 15353,63636 12298,46667 13633,25 13683,72727 12213,93333 12696,15385 15353,63636 12298,46667 11214,14286 13683,72727 12213,93333 12696,15385 15353,63636 13950,23077 11214,14286 13683,72727 12213,93333 12696,15385 11179,13333 13950,23077 11214,14286 13683,72727 12213,93333 11801,875 11179,13333 13950,23077 11214,14286 13683,72727 11188,82353 11801,875 11179,13333 13950,23077 11214,14286 16456,27273 11188,82353 11801,875 11179,13333 13950,23077 11110,0625 16456,27273 11188,82353 11801,875 11179,13333 16530,69231 11110,0625 16456,27273 11188,82353 11801,875 10038,41176 16530,69231 11110,0625 16456,27273 11188,82353 11681,25 10038,41176 16530,69231 11110,0625 16456,2 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 Yt[t] = + 17424.9461206477 -0.0769106839892755`Yt-1`[t] + 0.289791286320848`Yt-2`[t] -0.229658583665953`Yt-3`[t] -0.248195965086348`Yt-4`[t] -482.673839824678M1[t] + 682.241579160312M2[t] -52.7929730074228M3[t] + 704.745486669194M4[t] + 478.005060254843M5[t] -46.9907665014013M6[t] + 655.375475998909M7[t] + 604.825547340014M8[t] -1081.26087721467M9[t] + 581.745707803682M10[t] + 738.588003595692M11[t] -155.411865696257t + 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) 17424.9461206477 4246.586402 4.1033 0.000201 1e-04 `Yt-1` -0.0769106839892755 0.15908 -0.4835 0.631464 0.315732 `Yt-2` 0.289791286320848 0.15753 1.8396 0.073448 0.036724 `Yt-3` -0.229658583665953 0.153446 -1.4967 0.142529 0.071264 `Yt-4` -0.248195965086348 0.154441 -1.6071 0.11611 0.058055 M1 -482.673839824678 1029.309839 -0.4689 0.641731 0.320865 M2 682.241579160312 1012.524968 0.6738 0.504411 0.252206 M3 -52.7929730074228 1017.625576 -0.0519 0.95889 0.479445 M4 704.745486669194 1009.209135 0.6983 0.489124 0.244562 M5 478.005060254843 1029.194885 0.4644 0.644911 0.322455 M6 -46.9907665014013 1006.65 -0.0467 0.963006 0.481503 M7 655.375475998909 1037.091521 0.6319 0.531116 0.265558 M8 604.825547340014 1024.984216 0.5901 0.558539 0.279269 M9 -1081.26087721467 1068.706898 -1.0117 0.317895 0.158948 M10 581.745707803682 1106.005417 0.526 0.601875 0.300937 M11 738.588003595692 1086.066772 0.6801 0.500485 0.250243 t -155.411865696257 39.28188 -3.9563 0.000312 0.000156 Multiple Linear Regression - Regression Statistics Multiple R 0.866131590033882 R-squared 0.75018393125462 Adjusted R-squared 0.647695287666772 F-TEST (value) 7.31967860040609 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 2.06504179978140e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1472.27954328209 Sum Squared Residuals 84536675.0891104 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15335.63636 13947.7887123181 1387.84764768191 2 11188.5 14509.2671097938 -3320.7671097938 3 13633.25 14126.9903575724 -493.740357572352 4 12298.46667 12743.4322415577 -444.965571557676 5 15353.63636 13755.0272931160 1598.60906688404 6 12696.15385 12920.6805207395 -224.526670739477 7 12213.93333 14257.1526128946 -2043.21928289462 8 13683.72727 12947.8053465242 735.921923475761 9 11214.14286 10705.5537738950 508.589086104975 10 13950.23077 13599.3419137661 350.888856233935 11 11179.13333 12456.8083012018 -1277.67497120176 12 11801.875 12771.1941987747 -969.319198774715 13 11188.82353 11266.7479256135 -77.9243956134569 14 16456.27273 12461.1871302626 3995.08559973738 15 11110.0625 11532.7178510228 -422.655351022818 16 16530.69231 14058.7165723701 2471.97573762993 17 10038.41176 10652.8167728662 -614.405012866218 18 11681.25 11963.0295351384 -281.77953513844 19 11148.88235 10584.2394103273 564.642939672658 20 8631 11040.9320916604 -2409.93209166037 21 9386.444444 10472.8762823912 -1086.43183839125 22 9764.736842 10907.2258658294 -1142.48902382937 23 12043.75 11808.8675830287 234.882416971303 24 12948.06667 11300.647032341 1647.41963765900 25 10987.125 10979.0715086024 8.0534913975518 26 11648.3125 11784.1699372059 -135.857437205851 27 10633.35294 9501.28137171024 1132.07156828976 28 10219.3 10598.9749137253 -379.67491372535 29 9037.6 10289.3917054579 -1251.79170545792 30 10296.31579 9648.8705395503 647.445250449693 31 11705.41176 10103.5695402330 1601.84221976705 32 10681.94444 10528.1518962732 153.792543726773 33 9362.947368 9177.93119771314 185.016170286857 34 11306.35294 9854.35980786439 1451.99313213561 35 10984.45 9209.4038489669 1775.04615103311 36 10062.61905 9460.28321415828 602.335835841715 37 8118.583333 8680.86147054834 -562.278137548336 38 8867.48 9164.32591554183 -296.84591554183 39 8346.72 7944.51813291235 402.201867087648 40 8529.307692 9478.97967478225 -949.671982782253 41 10697.18182 9242.38400121523 1454.79781878477 42 8591.84 8381.8798219205 209.960178079504 43 8695.607143 9806.3062103056 -1110.69906730559 44 8125.571429 8439.06546961245 -313.494040612452 45 7009.758621 6616.93203900059 392.826581999415 46 7883.466667 8543.85963154018 -660.392964540184 47 7527.645161 8259.89875780266 -732.253596802657 48 6763.758621 8044.194895726 -1280.43627472600 49 6682.333333 7438.03193891767 -755.698605917668 50 7855.681818 8097.29695519589 -241.615137195890 51 6738.88 7356.75772678224 -617.877726782237 52 7895.434783 8593.09805256466 -697.663269564656 53 6361.884615 7549.09478234467 -1187.21016734467 54 6935.956522 7287.05574465128 -351.099222651279 55 8344.454545 7357.0213542395 987.433190760505 56 9107.944444 7274.23277892971 1833.71166507029 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.97612878428926 0.0477424314214779 0.0238712157107390 21 0.999620903367533 0.000758193264934088 0.000379096632467044 22 0.999737789650786 0.000524420698427547 0.000262210349213774 23 0.99915982928248 0.00168034143503955 0.000840170717519775 24 0.998472940966378 0.00305411806724405 0.00152705903362203 25 0.99604498485283 0.0079100302943398 0.0039550151471699 26 0.991336782343626 0.0173264353127479 0.00866321765637397 27 0.983390571312652 0.0332188573746952 0.0166094286873476 28 0.97057254211588 0.0588549157682404 0.0294274578841202 29 0.973959512124225 0.0520809757515499 0.0260404878757749 30 0.948895757802278 0.102208484395445 0.0511042421977225 31 0.915054702655165 0.169890594689670 0.0849452973448348 32 0.87294780381496 0.254104392370081 0.127052196185040 33 0.788129886328093 0.423740227343813 0.211870113671907 34 0.764206198261504 0.471587603476992 0.235793801738496 35 0.685356244109053 0.629287511781894 0.314643755890947 36 0.603496369793614 0.793007260412773 0.396503630206386 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.294117647058824 NOK 5% type I error level 8 0.470588235294118 NOK 10% type I error level 10 0.588235294117647 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/10ik6m1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/10ik6m1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/1pq141258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/1pq141258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/2ptuz1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/2ptuz1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/34uwm1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/34uwm1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/4fl4n1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/4fl4n1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/528d21258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/528d21258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/6nmse1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/6nmse1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/7r0ln1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/7r0ln1258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/8x1971258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/8x1971258728656.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/97vjp1258728656.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729323mq8mbz48cv8xay3/97vjp1258728656.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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