WS 7

*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: Thu, 19 Nov 2009 16:41:15 -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/t12586741395knzm14g5a05125.htm/, Retrieved Fri, 20 Nov 2009 00:42:32 +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/t12586741395knzm14g5a05125.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:

WS 7

Dataseries X:

» Textbox « » Textfile « » CSV «

286602 0 283042 0 276687 0 277915 0 277128 0 277103 0 275037 0 270150 0 267140 0 264993 0 287259 0 291186 0 292300 0 288186 0 281477 0 282656 0 280190 0 280408 0 276836 0 275216 0 274352 0 271311 0 289802 0 290726 0 292300 0 278506 0 269826 0 265861 0 269034 0 264176 0 255198 0 253353 0 246057 0 235372 0 258556 0 260993 0 254663 0 250643 0 243422 0 247105 0 248541 0 245039 0 237080 0 237085 0 225554 0 226839 0 247934 0 248333 1 246969 1 245098 1 246263 1 255765 1 264319 1 268347 1 273046 1 273963 1 267430 1 271993 1 292710 1 295881 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation nwwmb[t] = + 266613.595744681 -1219.98036006547dummy_variable[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) 266613.595744681 2643.821107 100.844 0 0 dummy_variable -1219.98036006547 5679.838858 -0.2148 0.830683 0.415342 Multiple Linear Regression - Regression Statistics Multiple R 0.0281922780235840 R-squared 0.00079480454015906 Adjusted R-squared -0.0164328712436312 F-TEST (value) 0.0461353319004817 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.830683425738388 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18125.1243374551 Sum Squared Residuals 19054167670.3961 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 266613.595744680 19988.4042553195 2 283042 266613.595744681 16428.4042553191 3 276687 266613.595744681 10073.4042553191 4 277915 266613.595744681 11301.4042553191 5 277128 266613.595744681 10514.4042553191 6 277103 266613.595744681 10489.4042553191 7 275037 266613.595744681 8423.40425531914 8 270150 266613.595744681 3536.40425531914 9 267140 266613.595744681 526.404255319142 10 264993 266613.595744681 -1620.59574468086 11 287259 266613.595744681 20645.4042553191 12 291186 266613.595744681 24572.4042553191 13 292300 266613.595744681 25686.4042553191 14 288186 266613.595744681 21572.4042553191 15 281477 266613.595744681 14863.4042553191 16 282656 266613.595744681 16042.4042553191 17 280190 266613.595744681 13576.4042553191 18 280408 266613.595744681 13794.4042553191 19 276836 266613.595744681 10222.4042553191 20 275216 266613.595744681 8602.40425531914 21 274352 266613.595744681 7738.40425531914 22 271311 266613.595744681 4697.40425531914 23 289802 266613.595744681 23188.4042553191 24 290726 266613.595744681 24112.4042553191 25 292300 266613.595744681 25686.4042553191 26 278506 266613.595744681 11892.4042553191 27 269826 266613.595744681 3212.40425531914 28 265861 266613.595744681 -752.595744680858 29 269034 266613.595744681 2420.40425531914 30 264176 266613.595744681 -2437.59574468086 31 255198 266613.595744681 -11415.5957446809 32 253353 266613.595744681 -13260.5957446809 33 246057 266613.595744681 -20556.5957446809 34 235372 266613.595744681 -31241.5957446809 35 258556 266613.595744681 -8057.59574468086 36 260993 266613.595744681 -5620.59574468086 37 254663 266613.595744681 -11950.5957446809 38 250643 266613.595744681 -15970.5957446809 39 243422 266613.595744681 -23191.5957446809 40 247105 266613.595744681 -19508.5957446809 41 248541 266613.595744681 -18072.5957446809 42 245039 266613.595744681 -21574.5957446809 43 237080 266613.595744681 -29533.5957446809 44 237085 266613.595744681 -29528.5957446809 45 225554 266613.595744681 -41059.5957446809 46 226839 266613.595744681 -39774.5957446809 47 247934 266613.595744681 -18679.5957446809 48 248333 265393.615384615 -17060.6153846154 49 246969 265393.615384615 -18424.6153846154 50 245098 265393.615384615 -20295.6153846154 51 246263 265393.615384615 -19130.6153846154 52 255765 265393.615384615 -9628.61538461538 53 264319 265393.615384615 -1074.61538461538 54 268347 265393.615384615 2953.38461538462 55 273046 265393.615384615 7652.38461538462 56 273963 265393.615384615 8569.38461538462 57 267430 265393.615384615 2036.38461538462 58 271993 265393.615384615 6599.38461538462 59 292710 265393.615384615 27316.3846153846 60 295881 265393.615384615 30487.3846153846 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0267686202906719 0.0535372405813437 0.973231379709328 6 0.00724331702714891 0.0144866340542978 0.992756682972851 7 0.0024757133828431 0.0049514267656862 0.997524286617157 8 0.00225408579966423 0.00450817159932846 0.997745914200336 9 0.00258189808369808 0.00516379616739616 0.997418101916302 10 0.00286671662914624 0.00573343325829248 0.997133283370854 11 0.00306784674628177 0.00613569349256355 0.996932153253718 12 0.00500169567999027 0.0100033913599805 0.99499830432001 13 0.00734191981545878 0.0146838396309176 0.992658080184541 14 0.00595471685363854 0.0119094337072771 0.994045283146362 15 0.00316638373929421 0.00633276747858842 0.996833616260706 16 0.0017640181070023 0.0035280362140046 0.998235981892998 17 0.000912013535613562 0.00182402707122712 0.999087986464386 18 0.000478201095184338 0.000956402190368676 0.999521798904816 19 0.000250542652921697 0.000501085305843394 0.999749457347078 20 0.000138358971021444 0.000276717942042889 0.999861641028979 21 7.9696226487747e-05 0.000159392452975494 0.999920303773512 22 5.63625522299001e-05 0.000112725104459800 0.99994363744777 23 0.000114739875428332 0.000229479750856664 0.999885260124572 24 0.000318122369347602 0.000636244738695204 0.999681877630652 25 0.00148712768807063 0.00297425537614125 0.99851287231193 26 0.00177964908075136 0.00355929816150273 0.99822035091925 27 0.00251353850393375 0.0050270770078675 0.997486461496066 28 0.00444593615769331 0.00889187231538662 0.995554063842307 29 0.00676380572847515 0.0135276114569503 0.993236194271525 30 0.0124275002865196 0.0248550005730391 0.98757249971348 31 0.0342613178597834 0.0685226357195668 0.965738682140217 32 0.0718149776446172 0.143629955289234 0.928185022355383 33 0.160910913799911 0.321821827599822 0.839089086200089 34 0.389076107085819 0.778152214171639 0.610923892914181 35 0.401726215654362 0.803452431308725 0.598273784345638 36 0.428381699316674 0.856763398633348 0.571618300683326 37 0.450007658342556 0.900015316685113 0.549992341657444 38 0.472222309567326 0.944444619134653 0.527777690432674 39 0.508001750459405 0.983996499081191 0.491998249540596 40 0.516158898479703 0.967682203040594 0.483841101520297 41 0.520448362036158 0.959103275927683 0.479551637963842 42 0.523902319756593 0.952195360486814 0.476097680243407 43 0.536874808675784 0.926250382648432 0.463125191324216 44 0.533602934998736 0.932794130002529 0.466397065001264 45 0.590687724703233 0.818624550593535 0.409312275296767 46 0.643441530555671 0.713116938888659 0.356558469444329 47 0.567638927913108 0.864722144173784 0.432361072086892 48 0.545343445062647 0.909313109874706 0.454656554937353 49 0.555852115132358 0.888295769735284 0.444147884867642 50 0.631005535291605 0.73798892941679 0.368994464708395 51 0.756652386246276 0.486695227507448 0.243347613753724 52 0.794969536086868 0.410060927826263 0.205030463913132 53 0.758577043520898 0.482845912958204 0.241422956479102 54 0.682558156947825 0.63488368610435 0.317441843052175 55 0.546431604047098 0.907136791905804 0.453568395952902 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.372549019607843 NOK 5% type I error level 25 0.490196078431373 NOK 10% type I error level 27 0.529411764705882 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/1048951258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/1048951258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/1b0661258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/1b0661258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/20t171258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/20t171258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/3sk1d1258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/3sk1d1258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/4p6h81258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/4p6h81258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/5aanz1258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/5aanz1258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/6ud9i1258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/6ud9i1258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/7unzi1258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/7unzi1258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/8ncn51258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/8ncn51258674070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/9d11c1258674070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12586741395knzm14g5a05125/9d11c1258674070.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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