Meervoudige Regressie Model (ASO)

*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, 21 Dec 2010 14:26:27 +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/21/t1292941510s284bnw1mkbbiw8.htm/, Retrieved Tue, 21 Dec 2010 15:25:21 +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/21/t1292941510s284bnw1mkbbiw8.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 «

4940 1 3924 1 3927 1 4535 1 3446 1 3016 1 4934 1 2743 1 3242 1 6662 1 3262 1 3381 1 7144 1 3803 1 3684 1 6759 1 3386 1 3066 1 5538 1 2940 1 3215 1 7023 1 3443 1 3712 1 7475 1 4137 1 3491 1 7019 1 3908 1 3402 1 5604 1 3222 1 3636 1 7123 1 4368 1 4092 1 8377 1 4595 1 4188 1 6988 1 4218 1 3655 1 6211 1 3622 1 3841 1 8510 1 4627 1 4582 1 8967 1 4928 1 4809 1 7917 1 4790 1 4065 1 7290 1 4670 1 3561 1 5149 1 6880 1 6981 1 8454 1 4960 1 4670 1 7638 1 4560 1 3980 0 6825 0 3939 0 4079 0 8117 0 5121 0 5167 0 7960 0 4670 0 4397 0 7191 0 4293 0 3747 0 6425 0 3709 0 3840 0 7642 0 4821 0 4865 0

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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation ASO[t] = + 5027.7 -482.780000000001Dummy[t] + 3002.82571428572M1[t] -182.888571428571M2[t] -447.317142857144M3[t] + 2249.96857142857M4[t] -528.031428571429M5[t] -1121.28571428572M6[t] + 1435.28571428571M7[t] -1133.57142857143M8[t] -1052.28571428571M9[t] + 2492.28571428571M10[t] -36.8571428571435M11[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) 5027.7 361.062934 13.9247 0 0 Dummy -482.780000000001 226.061154 -2.1356 0.036161 0.01808 M1 3002.82571428572 457.852858 6.5585 0 0 M2 -182.888571428571 457.852858 -0.3994 0.690762 0.345381 M3 -447.317142857144 457.852858 -0.977 0.331892 0.165946 M4 2249.96857142857 457.852858 4.9142 6e-06 3e-06 M5 -528.031428571429 457.852858 -1.1533 0.252664 0.126332 M6 -1121.28571428572 456.712501 -2.4551 0.016535 0.008268 M7 1435.28571428571 456.712501 3.1426 0.002443 0.001221 M8 -1133.57142857143 456.712501 -2.482 0.015431 0.007716 M9 -1052.28571428571 456.712501 -2.304 0.02415 0.012075 M10 2492.28571428571 456.712501 5.457 1e-06 0 M11 -36.8571428571435 456.712501 -0.0807 0.935907 0.467953 Multiple Linear Regression - Regression Statistics Multiple R 0.877877844813673 R-squared 0.7706695104147 Adjusted R-squared 0.731909427667889 F-TEST (value) 19.8830718563959 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 854.430850796457 Sum Squared Residuals 51833697.5942857 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4940 7547.74571428572 -2607.74571428572 2 3924 4362.03142857143 -438.031428571428 3 3927 4097.60285714286 -170.602857142862 4 4535 6794.88857142857 -2259.88857142857 5 3446 4016.88857142857 -570.888571428572 6 3016 3423.63428571428 -407.634285714284 7 4934 5980.20571428571 -1046.20571428571 8 2743 3411.34857142857 -668.34857142857 9 3242 3492.63428571429 -250.634285714287 10 6662 7037.20571428572 -375.205714285716 11 3262 4508.06285714286 -1246.06285714286 12 3381 4544.91999999999 -1163.92000000000 13 7144 7547.74571428571 -403.745714285713 14 3803 4362.03142857143 -559.031428571429 15 3684 4097.60285714286 -413.602857142856 16 6759 6794.88857142857 -35.8885714285707 17 3386 4016.88857142857 -630.888571428571 18 3066 3423.63428571429 -357.634285714286 19 5538 5980.20571428571 -442.205714285714 20 2940 3411.34857142857 -471.348571428572 21 3215 3492.63428571429 -277.634285714285 22 7023 7037.20571428571 -14.2057142857137 23 3443 4508.06285714286 -1065.06285714286 24 3712 4544.92 -832.92 25 7475 7547.74571428571 -72.7457142857131 26 4137 4362.03142857143 -225.031428571430 27 3491 4097.60285714286 -606.602857142856 28 7019 6794.88857142857 224.111428571429 29 3908 4016.88857142857 -108.888571428571 30 3402 3423.63428571429 -21.6342857142857 31 5604 5980.20571428571 -376.205714285714 32 3222 3411.34857142857 -189.348571428571 33 3636 3492.63428571429 143.365714285715 34 7123 7037.20571428571 85.7942857142863 35 4368 4508.06285714286 -140.062857142857 36 4092 4544.92 -452.92 37 8377 7547.74571428571 829.254285714287 38 4595 4362.03142857143 232.968571428570 39 4188 4097.60285714286 90.3971428571438 40 6988 6794.88857142857 193.111428571429 41 4218 4016.88857142857 201.111428571429 42 3655 3423.63428571429 231.365714285714 43 6211 5980.20571428571 230.794285714286 44 3622 3411.34857142857 210.651428571429 45 3841 3492.63428571429 348.365714285715 46 8510 7037.20571428571 1472.79428571429 47 4627 4508.06285714286 118.937142857143 48 4582 4544.92 37.0799999999996 49 8967 7547.74571428571 1419.25428571429 50 4928 4362.03142857143 565.96857142857 51 4809 4097.60285714286 711.397142857144 52 7917 6794.88857142857 1122.11142857143 53 4790 4016.88857142857 773.111428571429 54 4065 3423.63428571429 641.365714285714 55 7290 5980.20571428571 1309.79428571429 56 4670 3411.34857142857 1258.65142857143 57 3561 3492.63428571429 68.3657142857147 58 5149 7037.20571428571 -1888.20571428571 59 6880 4508.06285714286 2371.93714285714 60 6981 4544.92 2436.08 61 8454 7547.74571428571 906.254285714287 62 4960 4362.03142857143 597.96857142857 63 4670 4097.60285714286 572.397142857144 64 7638 6794.88857142857 843.11142857143 65 4560 4016.88857142857 543.111428571429 66 3980 3906.41428571429 73.5857142857133 67 6825 6462.98571428571 362.014285714285 68 3939 3894.12857142857 44.8714285714276 69 4079 3975.41428571429 103.585714285714 70 8117 7519.98571428571 597.014285714285 71 5121 4990.84285714286 130.157142857142 72 5167 5027.7 139.299999999999 73 7960 8030.52571428571 -70.525714285714 74 4670 4844.81142857143 -174.811428571430 75 4397 4580.38285714286 -183.382857142857 76 7191 7277.66857142857 -86.668571428572 77 4293 4499.66857142857 -206.668571428572 78 3747 3906.41428571429 -159.414285714287 79 6425 6462.98571428572 -37.9857142857151 80 3709 3894.12857142857 -185.128571428573 81 3840 3975.41428571429 -135.414285714287 82 7642 7519.98571428572 122.014285714285 83 4821 4990.84285714286 -169.842857142858 84 4865 5027.7 -162.700000000001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.966176197889169 0.0676476042216623 0.0338238021108312 17 0.933768788495804 0.132462423008392 0.0662312115041959 18 0.883202500439947 0.233594999120107 0.116797499560053 19 0.83771949173892 0.324561016522160 0.162280508261080 20 0.769758272941343 0.460483454117313 0.230241727058657 21 0.682536616277956 0.634926767444088 0.317463383722044 22 0.592632321274531 0.814735357450939 0.407367678725469 23 0.564875925233001 0.870248149533998 0.435124074766999 24 0.530607329523475 0.93878534095305 0.469392670476525 25 0.657015105020268 0.685969789959465 0.342984894979732 26 0.589079892366539 0.821840215266923 0.410920107633461 27 0.544237325898349 0.911525348203302 0.455762674101651 28 0.619649734200369 0.760700531599262 0.380350265799631 29 0.566464901008968 0.867070197982064 0.433535098991032 30 0.501121529862977 0.997756940274046 0.498878470137023 31 0.475969783982995 0.95193956796599 0.524030216017005 32 0.431838012305414 0.863676024610829 0.568161987694586 33 0.368767893599376 0.737535787198753 0.631232106400624 34 0.304513868468793 0.609027736937586 0.695486131531207 35 0.346643578163827 0.693287156327654 0.653356421836173 36 0.379134824440444 0.758269648880889 0.620865175559556 37 0.55220066272459 0.895598674550821 0.447799337275410 38 0.507539331116686 0.984921337766628 0.492460668883314 39 0.459042989469563 0.918085978939127 0.540957010530437 40 0.453440176622327 0.906880353244653 0.546559823377673 41 0.411203072421398 0.822406144842796 0.588796927578602 42 0.361762154418237 0.723524308836475 0.638237845581763 43 0.36218231553737 0.72436463107474 0.63781768446263 44 0.337535996873500 0.675071993747001 0.662464003126499 45 0.283852212519796 0.567704425039593 0.716147787480204 46 0.4265876785697 0.8531753571394 0.5734123214303 47 0.47538093401568 0.95076186803136 0.52461906598432 48 0.549680656871347 0.900638686257307 0.450319343128653 49 0.655963250192281 0.688073499615437 0.344036749807719 50 0.606177031596935 0.78764593680613 0.393822968403065 51 0.564452567398618 0.871094865202765 0.435547432601382 52 0.577355386269456 0.845289227461088 0.422644613730544 53 0.533829702353956 0.932340595292088 0.466170297646044 54 0.471810356975116 0.943620713950232 0.528189643024884 55 0.478797612082418 0.957595224164835 0.521202387917582 56 0.472754518478375 0.94550903695675 0.527245481521625 57 0.417025878995554 0.834051757991108 0.582974121004446 58 0.99397909384564 0.0120418123087184 0.00602090615435922 59 0.999140012597434 0.00171997480513249 0.000859987402566247 60 0.999999493277754 1.01344449304722e-06 5.0672224652361e-07 61 0.99999780833872 4.3833225589528e-06 2.1916612794764e-06 62 0.99998866974363 2.26605127410684e-05 1.13302563705342e-05 63 0.999945141601874 0.000109716796251747 5.48583981258735e-05 64 0.999767891184006 0.000464217631986989 0.000232108815993495 65 0.998958931763026 0.00208213647394807 0.00104106823697404 66 0.99633015652638 0.00733968694723855 0.00366984347361927 67 0.991170018733782 0.0176599625324357 0.00882998126621787 68 0.968776802507903 0.0624463949841941 0.0312231974920970 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.150943396226415 NOK 5% type I error level 10 0.188679245283019 NOK 10% type I error level 12 0.226415094339623 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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