box cox wlh

*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, 25 Dec 2009 12:20:04 -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/Dec/25/t1261768867lp6lpej8p1ek55o.htm/, Retrieved Fri, 25 Dec 2009 20:21:19 +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/Dec/25/t1261768867lp6lpej8p1ek55o.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:

» Textbox « » Textfile « » CSV «

612613 1 611324 1 594167 1 595454 1 590865 1 589379 1 584428 1 573100 1 567456 1 569028 1 620735 1 628884 1 628232 1 612117 1 595404 1 597141 1 593408 1 590072 1 579799 1 574205 1 572775 1 572942 1 619567 1 625809 1 619916 1 587625 0 565742 0 557274 0 560576 0 548854 0 531673 0 525919 0 511038 0 498662 0 555362 0 564591 0 541657 0 527070 0 509846 0 514258 0 516922 0 507561 0 492622 0 490243 0 469357 0 477580 0 528379 0 533590 0 517945 0 506174 0 501866 0 516141 0 528222 0 532638 0 536322 0 536535 0 523597 0 536214 0 586570 0 596594 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wlh[t] = + 529577.685714286 + 67175.1142857143dummies[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) 529577.685714286 4430.83393 119.521 0 0 dummies 67175.1142857143 6864.218408 9.7863 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.789186974101873 R-squared 0.62281608009207 Adjusted R-squared 0.616312909059174 F-TEST (value) 95.7711364104749 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 6.87228052242972e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 26213.1670354156 Sum Squared Residuals 39853547309.5428 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 612613 596752.8 15860.1999999995 2 611324 596752.8 14571.2000000001 3 594167 596752.8 -2585.79999999999 4 595454 596752.8 -1298.79999999998 5 590865 596752.8 -5887.79999999999 6 589379 596752.8 -7373.79999999999 7 584428 596752.8 -12324.8000000000 8 573100 596752.8 -23652.8 9 567456 596752.8 -29296.8 10 569028 596752.8 -27724.8 11 620735 596752.8 23982.2 12 628884 596752.8 32131.2 13 628232 596752.8 31479.2 14 612117 596752.8 15364.2 15 595404 596752.8 -1348.79999999998 16 597141 596752.8 388.200000000015 17 593408 596752.8 -3344.79999999999 18 590072 596752.8 -6680.79999999999 19 579799 596752.8 -16953.8 20 574205 596752.8 -22547.8 21 572775 596752.8 -23977.8 22 572942 596752.8 -23810.8 23 619567 596752.8 22814.2 24 625809 596752.8 29056.2 25 619916 596752.8 23163.2 26 587625 529577.685714286 58047.3142857143 27 565742 529577.685714286 36164.3142857143 28 557274 529577.685714286 27696.3142857143 29 560576 529577.685714286 30998.3142857143 30 548854 529577.685714286 19276.3142857143 31 531673 529577.685714286 2095.31428571428 32 525919 529577.685714286 -3658.68571428572 33 511038 529577.685714286 -18539.6857142857 34 498662 529577.685714286 -30915.6857142857 35 555362 529577.685714286 25784.3142857143 36 564591 529577.685714286 35013.3142857143 37 541657 529577.685714286 12079.3142857143 38 527070 529577.685714286 -2507.68571428572 39 509846 529577.685714286 -19731.6857142857 40 514258 529577.685714286 -15319.6857142857 41 516922 529577.685714286 -12655.6857142857 42 507561 529577.685714286 -22016.6857142857 43 492622 529577.685714286 -36955.6857142857 44 490243 529577.685714286 -39334.6857142857 45 469357 529577.685714286 -60220.6857142857 46 477580 529577.685714286 -51997.6857142857 47 528379 529577.685714286 -1198.68571428572 48 533590 529577.685714286 4012.31428571428 49 517945 529577.685714286 -11632.6857142857 50 506174 529577.685714286 -23403.6857142857 51 501866 529577.685714286 -27711.6857142857 52 516141 529577.685714286 -13436.6857142857 53 528222 529577.685714286 -1355.68571428572 54 532638 529577.685714286 3060.31428571428 55 536322 529577.685714286 6744.31428571428 56 536535 529577.685714286 6957.31428571428 57 523597 529577.685714286 -5980.68571428572 58 536214 529577.685714286 6636.31428571428 59 586570 529577.685714286 56992.3142857143 60 596594 529577.685714286 67016.3142857143 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0968246067059483 0.193649213411897 0.903175393294052 6 0.0502369456009515 0.100473891201903 0.949763054399049 7 0.0333103770233112 0.0666207540466224 0.966689622976689 8 0.0476983717446572 0.0953967434893144 0.952301628255343 9 0.0691101395815285 0.138220279163057 0.930889860418471 10 0.0696619462231217 0.139323892446243 0.930338053776878 11 0.106902022257277 0.213804044514555 0.893097977742723 12 0.179308899840252 0.358617799680503 0.820691100159748 13 0.227434732687426 0.454869465374851 0.772565267312574 14 0.180484255786876 0.360968511573752 0.819515744213124 15 0.123938822890716 0.247877645781431 0.876061177109284 16 0.081597689045379 0.163195378090758 0.918402310954621 17 0.0524078914434704 0.104815782886941 0.94759210855653 18 0.0335400823788611 0.0670801647577222 0.96645991762114 19 0.0262799516986992 0.0525599033973984 0.97372004830130 20 0.0249540736318012 0.0499081472636025 0.97504592636820 21 0.0255810113927698 0.0511620227855395 0.97441898860723 22 0.0286490010021718 0.0572980020043437 0.971350998997828 23 0.0276093440227951 0.0552186880455902 0.972390655977205 24 0.0309633372221442 0.0619266744442884 0.969036662777856 25 0.0269142010473296 0.0538284020946593 0.97308579895267 26 0.0332439830797834 0.0664879661595669 0.966756016920217 27 0.0333164067674893 0.0666328135349785 0.96668359323251 28 0.0305782282852281 0.0611564565704562 0.969421771714772 29 0.0275233154912567 0.0550466309825133 0.972476684508743 30 0.0240944362415531 0.0481888724831061 0.975905563758447 31 0.0250647830458069 0.0501295660916138 0.974935216954193 32 0.0254689307565769 0.0509378615131537 0.974531069243423 33 0.0353861388661131 0.0707722777322262 0.964613861133887 34 0.0624507381467138 0.124901476293428 0.937549261853286 35 0.0564131382626615 0.112826276525323 0.943586861737338 36 0.0683548834482669 0.136709766896534 0.931645116551733 37 0.0522874590108435 0.104574918021687 0.947712540989156 38 0.0389426692266325 0.077885338453265 0.961057330773367 39 0.0377445438997684 0.0754890877995367 0.962255456100232 40 0.0308835089874907 0.0617670179749814 0.96911649101251 41 0.0229354581386802 0.0458709162773603 0.97706454186132 42 0.0202117236824956 0.0404234473649913 0.979788276317504 43 0.0292597370888772 0.0585194741777543 0.970740262911123 44 0.0438112066684967 0.0876224133369934 0.956188793331503 45 0.167883850386535 0.335767700773069 0.832116149613465 46 0.362712402111018 0.725424804222035 0.637287597888982 47 0.281668301040439 0.563336602080879 0.71833169895956 48 0.207283563416722 0.414567126833445 0.792716436583278 49 0.162084876940644 0.324169753881288 0.837915123059356 50 0.165870416372674 0.331740832745347 0.834129583627326 51 0.218168881440589 0.436337762881179 0.78183111855941 52 0.210672167633547 0.421344335267095 0.789327832366453 53 0.162482705967114 0.324965411934228 0.837517294032886 54 0.113990884206547 0.227981768413093 0.886009115793453 55 0.0713588142854432 0.142717628570886 0.928641185714557 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 4 0.0784313725490196 NOK 10% type I error level 25 0.490196078431373 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/10u0td1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/10u0td1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/195wm1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/195wm1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/217qd1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/217qd1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/3um7k1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/3um7k1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/4gfy61261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/4gfy61261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/5a51b1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/5a51b1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/6ev8w1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/6ev8w1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/72t641261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/72t641261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/8fi0c1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/8fi0c1261768798.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/9qz0d1261768798.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t1261768867lp6lpej8p1ek55o/9qz0d1261768798.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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