Consumptiegoederen met seizoenale trend

*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: Tue, 16 Dec 2008 05:43:50 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/16/t1229431486y9w96rahvlz7p2s.htm/, Retrieved Tue, 16 Dec 2008 13:44:56 +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/2008/Dec/16/t1229431486y9w96rahvlz7p2s.htm/}, year = {2008}, } @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 = {2008}, 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 «

98.5 0 97.0 0 103.3 0 99.6 0 100.1 0 102.9 0 95.9 0 94.5 0 107.4 0 116.0 0 102.8 0 99.8 0 109.6 0 103.0 0 111.6 0 106.3 0 97.9 0 108.8 0 103.9 0 101.2 0 122.9 0 123.9 0 111.7 0 120.9 0 99.6 0 103.3 0 119.4 0 106.5 0 101.9 0 124.6 0 106.5 0 107.8 0 127.4 0 120.1 0 118.5 0 127.7 0 107.7 0 104.5 0 118.8 0 110.3 0 109.6 0 119.1 0 96.5 0 106.7 0 126.3 0 116.2 0 118.8 0 115.2 0 110.0 0 111.4 0 129.6 0 108.1 0 117.8 0 122.9 0 100.6 0 111.8 0 127.0 0 128.6 0 124.8 0 118.5 0 114.7 0 112.6 0 128.7 0 111.0 0 115.8 0 126.0 0 111.1 1 113.2 1 120.1 1 130.6 1 124.0 1 119.4 1 116.7 1 116.5 1 119.6 1 126.5 1 111.3 1 123.5 1 114.2 1 103.7 1 129.5 1

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] = + 115.674051896208 + 7.4556886227545X[t] -8.62486455660116M1[t] -9.8391502708868M2[t] + 1.97513544339892M3[t] -6.98200741374394M4[t] -8.96772169945823M5[t] + 1.51799258625606M6[t] -13.704248645566M7[t] -12.2471057884231M8[t] + 5.13860849729114M9[t] + 5.65000000000001M10[t] -0.149999999999998M11[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) 115.674051896208 2.959178 39.0899 0 0 X 7.4556886227545 2.084201 3.5772 0.000645 0.000323 M1 -8.62486455660116 4.005107 -2.1535 0.034831 0.017415 M2 -9.8391502708868 4.005107 -2.4567 0.016582 0.008291 M3 1.97513544339892 4.005107 0.4932 0.623493 0.311746 M4 -6.98200741374394 4.005107 -1.7433 0.085805 0.042903 M5 -8.96772169945823 4.005107 -2.2391 0.028427 0.014213 M6 1.51799258625606 4.005107 0.379 0.705858 0.352929 M7 -13.704248645566 4.012479 -3.4154 0.001078 0.000539 M8 -12.2471057884231 4.012479 -3.0523 0.003239 0.00162 M9 5.13860849729114 4.012479 1.2807 0.204665 0.102333 M10 5.65000000000001 4.155977 1.3595 0.178483 0.089242 M11 -0.149999999999998 4.155977 -0.0361 0.971314 0.485657 Multiple Linear Regression - Regression Statistics Multiple R 0.734924625676036 R-squared 0.540114205425062 Adjusted R-squared 0.458957888735367 F-TEST (value) 6.65523310391494 F-TEST (DF numerator) 12 F-TEST (DF denominator) 68 p-value 1.01711674527216e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.19836379166195 Sum Squared Residuals 3523.51800684347 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.5 107.049187339607 -8.54918733960694 2 97 105.834901625321 -8.8349016253208 3 103.3 117.649187339606 -14.3491873396065 4 99.6 108.692044482464 -9.09204448246365 5 100.1 106.706330196749 -6.60633019674938 6 102.9 117.192044482464 -14.2920444824636 7 95.9 101.969803250642 -6.06980325064156 8 94.5 103.426946107784 -8.9269461077844 9 107.4 120.812660393499 -13.4126603934987 10 116 121.324051896208 -5.32405189620757 11 102.8 115.524051896208 -12.7240518962076 12 99.8 115.674051896208 -15.8740518962076 13 109.6 107.049187339606 2.55081266039357 14 103 105.834901625321 -2.83490162532078 15 111.6 117.649187339606 -6.04918733960651 16 106.3 108.692044482464 -2.39204448246365 17 97.9 106.706330196749 -8.80633019674935 18 108.8 117.192044482464 -8.39204448246365 19 103.9 101.969803250642 1.93019674935843 20 101.2 103.426946107784 -2.22694610778443 21 122.9 120.812660393499 2.08733960650129 22 123.9 121.324051896208 2.57594810379243 23 111.7 115.524051896208 -3.82405189620758 24 120.9 115.674051896208 5.22594810379243 25 99.6 107.049187339606 -7.44918733960643 26 103.3 105.834901625321 -2.53490162532078 27 119.4 117.649187339606 1.75081266039351 28 106.5 108.692044482464 -2.19204448246364 29 101.9 106.706330196749 -4.80633019674935 30 124.6 117.192044482464 7.40795551753635 31 106.5 101.969803250642 4.53019674935842 32 107.8 103.426946107784 4.37305389221556 33 127.4 120.812660393499 6.58733960650129 34 120.1 121.324051896208 -1.22405189620759 35 118.5 115.524051896208 2.97594810379242 36 127.7 115.674051896208 12.0259481037924 37 107.7 107.049187339606 0.650812660393578 38 104.5 105.834901625321 -1.33490162532078 39 118.8 117.649187339606 1.15081266039350 40 110.3 108.692044482464 1.60795551753635 41 109.6 106.706330196749 2.89366980325064 42 119.1 117.192044482464 1.90795551753635 43 96.5 101.969803250642 -5.46980325064157 44 106.7 103.426946107784 3.27305389221557 45 126.3 120.812660393499 5.48733960650128 46 116.2 121.324051896208 -5.12405189620758 47 118.8 115.524051896208 3.27594810379242 48 115.2 115.674051896208 -0.474051896207581 49 110 107.049187339606 2.95081266039357 50 111.4 105.834901625321 5.56509837467923 51 129.6 117.649187339606 11.9508126603935 52 108.1 108.692044482464 -0.592044482463647 53 117.8 106.706330196749 11.0936698032506 54 122.9 117.192044482464 5.70795551753636 55 100.6 101.969803250642 -1.36980325064158 56 111.8 103.426946107784 8.37305389221557 57 127 120.812660393499 6.18733960650128 58 128.6 121.324051896208 7.27594810379242 59 124.8 115.524051896208 9.27594810379241 60 118.5 115.674051896208 2.82594810379242 61 114.7 107.049187339606 7.65081266039358 62 112.6 105.834901625321 6.76509837467921 63 128.7 117.649187339606 11.0508126603935 64 111 108.692044482464 2.30795551753636 65 115.8 106.706330196749 9.09366980325064 66 126 117.192044482464 8.80795551753635 67 111.1 109.425491873396 1.67450812660392 68 113.2 110.882634730539 2.31736526946107 69 120.1 128.268349016253 -8.16834901625322 70 130.6 128.779740518962 1.82025948103792 71 124 122.979740518962 1.02025948103792 72 119.4 123.129740518962 -3.72974051896207 73 116.7 114.504875962361 2.19512403763908 74 116.5 113.290590248075 3.20940975192472 75 119.6 125.104875962361 -5.504875962361 76 126.5 116.147733105218 10.3522668947819 77 111.3 114.162018819504 -2.86201881950385 78 123.5 124.647733105218 -1.14773310521814 79 114.2 109.425491873396 4.77450812660393 80 103.7 110.882634730539 -7.18263473053893 81 129.5 128.268349016253 1.23165098374679 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.759201345468235 0.48159730906353 0.240798654531765 17 0.666129117628915 0.667741764742171 0.333870882371085 18 0.653645761072549 0.692708477854902 0.346354238927451 19 0.64218376694543 0.71563246610914 0.35781623305457 20 0.61280379582226 0.774392408355479 0.387196204177740 21 0.803366551359757 0.393266897280486 0.196633448640243 22 0.78095429995189 0.43809140009622 0.21904570004811 23 0.800404320552861 0.399191358894278 0.199595679447139 24 0.945132142852864 0.109735714294273 0.0548678571471364 25 0.949028455216135 0.101943089567730 0.0509715447838651 26 0.936348743220273 0.127302513559454 0.063651256779727 27 0.95272073974981 0.0945585205003818 0.0472792602501909 28 0.941487883902285 0.117024232195429 0.0585121160977147 29 0.94628513818247 0.107429723635059 0.0537148618175294 30 0.982523613228022 0.0349527735439554 0.0174763867719777 31 0.977091496261308 0.0458170074773842 0.0229085037386921 32 0.97506455353818 0.0498708929236417 0.0249354464618208 33 0.978195608296649 0.0436087834067028 0.0218043917033514 34 0.967948376754861 0.0641032464902771 0.0320516232451385 35 0.967922729845314 0.0641545403093728 0.0320772701546864 36 0.989511914494134 0.0209761710117322 0.0104880855058661 37 0.986178575233241 0.0276428495335170 0.0138214247667585 38 0.984922694687998 0.0301546106240037 0.0150773053120018 39 0.98348942773924 0.0330211445215196 0.0165105722607598 40 0.978297260618218 0.0434054787635646 0.0217027393817823 41 0.97618203511884 0.0476359297623186 0.0238179648811593 42 0.97001341113495 0.0599731777301009 0.0299865888650504 43 0.977800742481235 0.0443985150375301 0.0221992575187651 44 0.967704354315303 0.0645912913693942 0.0322956456846971 45 0.956795410130118 0.0864091797397632 0.0432045898698816 46 0.973828909700437 0.0523421805991254 0.0261710902995627 47 0.968423904240614 0.0631521915187728 0.0315760957593864 48 0.953097876609473 0.0938042467810548 0.0469021233905274 49 0.942530655134615 0.114938689730770 0.0574693448653851 50 0.928669162154933 0.142661675690135 0.0713308378450674 51 0.944460088381428 0.111079823237143 0.0555399116185717 52 0.955527501054448 0.0889449978911048 0.0444724989455524 53 0.957726678423675 0.0845466431526504 0.0422733215763252 54 0.939191187280319 0.121617625439362 0.060808812719681 55 0.963814806099724 0.0723703878005512 0.0361851939002756 56 0.95015127676317 0.0996974464736586 0.0498487232368293 57 0.924767491434344 0.150465017131313 0.0752325085656564 58 0.889127150391562 0.221745699216877 0.110872849608438 59 0.84634432715803 0.307311345683939 0.153655672841970 60 0.768047962775252 0.463904074449495 0.231952037224748 61 0.683426412541216 0.633147174917569 0.316573587458784 62 0.590299603188934 0.819400793622132 0.409700396811066 63 0.602515502792666 0.794968994414668 0.397484497207334 64 0.80091079729935 0.398178405401298 0.199089202700649 65 0.660450688379071 0.679098623241858 0.339549311620929 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 11 0.22 NOK 10% type I error level 24 0.48 NOK

Charts produced by software:

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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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