Consumptiegoederen met seizoenale en lineaire 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 06:42:41 -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/t1229435022sba091cb22sunr3.htm/, Retrieved Tue, 16 Dec 2008 14:43:54 +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/t1229435022sba091cb22sunr3.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:

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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] = + 105.892771084337 -3.86265060240966X[t] -7.50534040925615M1[t] -8.99742780646395M2[t] + 2.5390562248996M3[t] -6.69588831516543M4[t] -8.95940428380188M5[t] + 1.24850831899023M6[t] -12.6346289921591M7[t] -11.4552878179384M8[t] + 5.65262478485371M9[t] + 6.20560336584434M10[t] + 0.127801682922166M11[t] + 0.277801682922165t + 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) 105.892771084337 2.343016 45.1951 0 0 X -3.86265060240966 1.950616 -1.9802 0.051788 0.025894 M1 -7.50534040925615 2.778922 -2.7008 0.008751 0.004376 M2 -8.99742780646395 2.777607 -3.2393 0.001866 0.000933 M3 2.5390562248996 2.776664 0.9144 0.363772 0.181886 M4 -6.69588831516543 2.776094 -2.412 0.018612 0.009306 M5 -8.95940428380188 2.775896 -3.2276 0.001934 0.000967 M6 1.24850831899023 2.776071 0.4497 0.65435 0.327175 M7 -12.6346289921591 2.783763 -4.5387 2.4e-05 1.2e-05 M8 -11.4552878179384 2.782516 -4.1169 0.000108 5.4e-05 M9 5.65262478485371 2.781642 2.0321 0.046109 0.023055 M10 6.20560336584434 2.881181 2.1538 0.034854 0.017427 M11 0.127801682922166 2.880642 0.0444 0.964745 0.482372 t 0.277801682922165 0.032173 8.6346 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.884495522192808 R-squared 0.782332328779128 Adjusted R-squared 0.740098303019854 F-TEST (value) 18.5237451252760 F-TEST (DF numerator) 13 F-TEST (DF denominator) 67 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.98910695485532 Sum Squared Residuals 1667.70960986804 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.5 98.6652323580039 -0.165232358003882 2 97 97.4509466437177 -0.450946643717700 3 103.3 109.265232358003 -5.96523235800342 4 99.6 100.308089500861 -0.708089500860571 5 100.1 98.3223752151463 1.77762478485369 6 102.9 108.808089500861 -5.90808950086056 7 95.9 95.2027538726334 0.697246127366638 8 94.5 96.6598967297762 -2.1598967297762 9 107.4 114.045611015491 -6.6456110154905 10 116 114.876391279403 1.12360872059669 11 102.8 109.076391279403 -6.27639127940332 12 99.8 109.226391279403 -9.42639127940333 13 109.6 101.998852553069 7.60114744693066 14 103 100.784566838784 2.21543316121630 15 111.6 112.598852553069 -0.99885255306941 16 106.3 103.641709695927 2.65829030407345 17 97.9 101.655995410212 -3.75599541021226 18 108.8 112.141709695927 -3.34170969592656 19 103.9 98.5363740676994 5.36362593230064 20 101.2 99.9935169248422 1.20648307515778 21 122.9 117.379231210556 5.5207687894435 22 123.9 118.210011474469 5.68998852553071 23 111.7 112.410011474469 -0.710011474469298 24 120.9 112.560011474469 8.33998852553071 25 99.6 105.332472748135 -5.73247274813532 26 103.3 104.118187033850 -0.818187033849684 27 119.4 115.932472748135 3.46752725186462 28 106.5 106.975329890993 -0.475329890992534 29 101.9 104.989615605278 -3.08961560527824 30 124.6 115.475329890993 9.12467010900746 31 106.5 101.869994262765 4.63000573723466 32 107.8 103.327137119908 4.47286288009179 33 127.4 120.712851405622 6.68714859437752 34 120.1 121.543631669535 -1.44363166953529 35 118.5 115.743631669535 2.75636833046472 36 127.7 115.893631669535 11.8063683304647 37 107.7 108.666092943201 -0.966092943201298 38 104.5 107.451807228916 -2.95180722891567 39 118.8 119.266092943201 -0.466092943201378 40 110.3 110.308950086059 -0.00895008605852083 41 109.6 108.323235800344 1.27676419965576 42 119.1 118.808950086059 0.29104991394147 43 96.5 105.203614457831 -8.70361445783133 44 106.7 106.660757314974 0.0392426850258081 45 126.3 124.046471600688 2.25352839931152 46 116.2 124.877251864601 -8.67725186460127 47 118.8 119.077251864601 -0.277251864601271 48 115.2 119.227251864601 -4.02725186460127 49 110 111.999713138267 -1.99971313826729 50 111.4 110.785427423982 0.614572576018355 51 129.6 122.599713138267 7.00028686173263 52 108.1 113.642570281125 -5.54257028112451 53 117.8 111.656855995410 6.14314400458978 54 122.9 122.142570281125 0.757429718875495 55 100.6 108.537234652897 -7.93723465289732 56 111.8 109.994377510040 1.80562248995982 57 127 127.380091795754 -0.380091795754462 58 128.6 128.210872059667 0.389127940332743 59 124.8 122.410872059667 2.38912794033274 60 118.5 122.560872059667 -4.06087205966726 61 114.7 115.333333333333 -0.63333333333327 62 112.6 114.119047619048 -1.51904761904764 63 128.7 125.933333333333 2.76666666666664 64 111 116.976190476190 -5.97619047619049 65 115.8 114.990476190476 0.809523809523795 66 126 125.476190476190 0.523809523809504 67 111.1 108.008204245554 3.09179575444635 68 113.2 109.465347102697 3.7346528973035 69 120.1 126.851061388411 -6.7510613884108 70 130.6 127.681841652324 2.91815834767641 71 124 121.881841652324 2.11815834767642 72 119.4 122.031841652324 -2.63184165232358 73 116.7 114.804302925990 1.89569707401040 74 116.5 113.590017211704 2.90998278829603 75 119.6 125.404302925990 -5.80430292598968 76 126.5 116.447160068847 10.0528399311532 77 111.3 114.461445783133 -3.16144578313253 78 123.5 124.947160068847 -1.44716006884682 79 114.2 111.341824440620 2.85817555938037 80 103.7 112.798967297762 -9.09896729776249 81 129.5 130.184681583477 -0.684681583476776 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.443197959001883 0.886395918003766 0.556802040998117 18 0.293637714380313 0.587275428760626 0.706362285619687 19 0.184378734042931 0.368757468085862 0.815621265957069 20 0.101737048492151 0.203474096984302 0.89826295150785 21 0.193054810742707 0.386109621485413 0.806945189257294 22 0.124950959661492 0.249901919322984 0.875049040338508 23 0.0794626580502058 0.158925316100412 0.920537341949794 24 0.303302980484653 0.606605960969306 0.696697019515347 25 0.741684459691485 0.51663108061703 0.258315540308515 26 0.714190036556712 0.571619926886577 0.285809963443288 27 0.647168441464933 0.705663117070134 0.352831558535067 28 0.607442917811574 0.785114164376851 0.392557082188426 29 0.607645513705228 0.784708972589544 0.392354486294772 30 0.697699192924441 0.604601614151118 0.302300807075559 31 0.647264178779515 0.705471642440971 0.352735821220485 32 0.580276369433407 0.839447261133187 0.419723630566593 33 0.556044898144487 0.887910203711026 0.443955101855513 34 0.568172852919518 0.863654294160965 0.431827147080482 35 0.497480231441585 0.99496046288317 0.502519768558415 36 0.72210489605163 0.555790207896739 0.277895103948370 37 0.701249575045123 0.597500849909754 0.298750424954877 38 0.703380293072697 0.593239413854606 0.296619706927303 39 0.645737519089193 0.708524961821613 0.354262480910807 40 0.587158312068208 0.825683375863585 0.412841687931792 41 0.509267250888222 0.981465498223557 0.490732749111778 42 0.439453914828865 0.87890782965773 0.560546085171135 43 0.651963402053491 0.696073195893017 0.348036597946509 44 0.584625955522581 0.830748088954839 0.415374044477419 45 0.53651445307025 0.9269710938595 0.46348554692975 46 0.696046588701575 0.60790682259685 0.303953411298425 47 0.634890347603866 0.730219304792268 0.365109652396134 48 0.609938952073974 0.780122095852052 0.390061047926026 49 0.555056762814097 0.889886474371805 0.444943237185903 50 0.475759274952312 0.951518549904624 0.524240725047688 51 0.496015990848797 0.992031981697594 0.503984009151203 52 0.560801619463749 0.878396761072503 0.439198380536251 53 0.542437899868594 0.915124200262811 0.457562100131406 54 0.451504826589042 0.903009653178084 0.548495173410958 55 0.618845682158513 0.762308635682975 0.381154317841487 56 0.54717903101317 0.905641937973661 0.452820968986831 57 0.469622857180198 0.939245714360396 0.530377142819802 58 0.370837367137837 0.741674734275675 0.629162632862163 59 0.281710293013062 0.563420586026124 0.718289706986938 60 0.208954583495357 0.417909166990715 0.791045416504643 61 0.136405290585184 0.272810581170368 0.863594709414816 62 0.0871535678789722 0.174307135757944 0.912846432121028 63 0.0954335419662151 0.190867083932430 0.904566458033785 64 0.310435796761489 0.620871593522977 0.689564203238511 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

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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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