*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, 15 Dec 2009 07:54:02 -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/15/t1260888959lgmh6dxrs2cv9re.htm/, Retrieved Tue, 15 Dec 2009 15:56:11 +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/15/t1260888959lgmh6dxrs2cv9re.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:

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8.7 0 8.2 0 8.3 0 8.5 0 8.6 0 8.5 0 8.2 0 8.1 0 7.9 0 8.6 0 8.7 0 8.7 0 8.5 0 8.4 0 8.5 0 8.7 0 8.7 0 8.6 0 8.5 0 8.3 0 8 0 8.2 0 8.1 0 8.1 0 8 0 7.9 0 7.9 0 8 0 8 0 7.9 0 8 0 7.7 0 7.2 0 7.5 0 7.3 0 7 0 7 0 7 0 7.2 0 7.3 0 7.1 0 6.8 0 6.4 0 6.1 0 6.5 0 7.7 0 7.9 0 7.5 0 6.9 1 6.6 1 6.9 1 7.7 1 8 1 8 1 7.7 1 7.3 1 7.4 1 8.1 1 8.3 1 8.2 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] = + 7.88541666666666 -0.293749999999999X[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) 7.88541666666666 0.094476 83.4651 0 0 X -0.293749999999999 0.211254 -1.3905 0.169687 0.084844 Multiple Linear Regression - Regression Statistics Multiple R 0.179613316610986 R-squared 0.0322609435039984 Adjusted R-squared 0.0155757873575157 F-TEST (value) 1.93351163278139 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.169687496829789 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.654545885398383 Sum Squared Residuals 24.8489583333333 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.7 7.8854166666667 0.814583333333301 2 8.2 7.88541666666667 0.314583333333333 3 8.3 7.88541666666667 0.414583333333335 4 8.5 7.88541666666667 0.614583333333334 5 8.6 7.88541666666667 0.714583333333334 6 8.5 7.88541666666667 0.614583333333334 7 8.2 7.88541666666667 0.314583333333333 8 8.1 7.88541666666667 0.214583333333334 9 7.9 7.88541666666667 0.0145833333333344 10 8.6 7.88541666666667 0.714583333333334 11 8.7 7.88541666666667 0.814583333333333 12 8.7 7.88541666666667 0.814583333333333 13 8.5 7.88541666666667 0.614583333333334 14 8.4 7.88541666666667 0.514583333333334 15 8.5 7.88541666666667 0.614583333333334 16 8.7 7.88541666666667 0.814583333333333 17 8.7 7.88541666666667 0.814583333333333 18 8.6 7.88541666666667 0.714583333333334 19 8.5 7.88541666666667 0.614583333333334 20 8.3 7.88541666666667 0.414583333333335 21 8 7.88541666666667 0.114583333333334 22 8.2 7.88541666666667 0.314583333333333 23 8.1 7.88541666666667 0.214583333333334 24 8.1 7.88541666666667 0.214583333333334 25 8 7.88541666666667 0.114583333333334 26 7.9 7.88541666666667 0.0145833333333344 27 7.9 7.88541666666667 0.0145833333333344 28 8 7.88541666666667 0.114583333333334 29 8 7.88541666666667 0.114583333333334 30 7.9 7.88541666666667 0.0145833333333344 31 8 7.88541666666667 0.114583333333334 32 7.7 7.88541666666667 -0.185416666666666 33 7.2 7.88541666666667 -0.685416666666666 34 7.5 7.88541666666667 -0.385416666666666 35 7.3 7.88541666666667 -0.585416666666666 36 7 7.88541666666667 -0.885416666666666 37 7 7.88541666666667 -0.885416666666666 38 7 7.88541666666667 -0.885416666666666 39 7.2 7.88541666666667 -0.685416666666666 40 7.3 7.88541666666667 -0.585416666666666 41 7.1 7.88541666666667 -0.785416666666666 42 6.8 7.88541666666667 -1.08541666666667 43 6.4 7.88541666666667 -1.48541666666667 44 6.1 7.88541666666667 -1.78541666666667 45 6.5 7.88541666666667 -1.38541666666667 46 7.7 7.88541666666667 -0.185416666666666 47 7.9 7.88541666666667 0.0145833333333344 48 7.5 7.88541666666667 -0.385416666666666 49 6.9 7.59166666666667 -0.691666666666666 50 6.6 7.59166666666667 -0.991666666666667 51 6.9 7.59166666666667 -0.691666666666666 52 7.7 7.59166666666667 0.108333333333333 53 8 7.59166666666667 0.408333333333333 54 8 7.59166666666667 0.408333333333333 55 7.7 7.59166666666667 0.108333333333333 56 7.3 7.59166666666667 -0.291666666666667 57 7.4 7.59166666666667 -0.191666666666666 58 8.1 7.59166666666667 0.508333333333333 59 8.3 7.59166666666667 0.708333333333334 60 8.2 7.59166666666667 0.608333333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.060056951827215 0.12011390365443 0.939943048172785 6 0.0182840807693824 0.0365681615387647 0.981715919230618 7 0.00986483843653334 0.0197296768730667 0.990135161563467 8 0.00708088204392966 0.0141617640878593 0.99291911795607 9 0.0100967725052657 0.0201935450105314 0.989903227494734 10 0.0060119155015735 0.012023831003147 0.993988084498427 11 0.00478756042831671 0.00957512085663343 0.995212439571683 12 0.00361931532860224 0.00723863065720449 0.996380684671398 13 0.00171048155461316 0.00342096310922631 0.998289518445387 14 0.000749397986526177 0.00149879597305235 0.999250602013474 15 0.000355274445455336 0.000710548890910673 0.999644725554545 16 0.000308221183930845 0.000616442367861689 0.999691778816069 17 0.000279767277200655 0.00055953455440131 0.9997202327228 18 0.000196111265212983 0.000392222530425966 0.999803888734787 19 0.000122174265884336 0.000244348531768673 0.999877825734116 20 8.20704842270747e-05 0.000164140968454149 0.999917929515773 21 0.000148632170294652 0.000297264340589304 0.999851367829705 22 0.000122487401785548 0.000244974803571096 0.999877512598214 23 0.000129877039221647 0.000259754078443294 0.999870122960778 24 0.000137143166708917 0.000274286333417834 0.99986285683329 25 0.000187457695128190 0.000374915390256381 0.999812542304872 26 0.000323752406290583 0.000647504812581166 0.99967624759371 27 0.000495834840805756 0.000991669681611511 0.999504165159194 28 0.000603284245386764 0.00120656849077353 0.999396715754613 29 0.000773181375857579 0.00154636275171516 0.999226818624142 30 0.00117241428254717 0.00234482856509434 0.998827585717453 31 0.00175947508729198 0.00351895017458396 0.998240524912708 32 0.00380329606118825 0.00760659212237651 0.996196703938812 33 0.0223457319757783 0.0446914639515567 0.977654268024222 34 0.0359365786413168 0.0718731572826336 0.964063421358683 35 0.0632353319493416 0.126470663898683 0.936764668050658 36 0.132149878330901 0.264299756661802 0.867850121669099 37 0.198644137088814 0.397288274177629 0.801355862911186 38 0.250865513596792 0.501731027193585 0.749134486403208 39 0.256956972329371 0.513913944658742 0.743043027670629 40 0.248306710010757 0.496613420021515 0.751693289989243 41 0.248774392111804 0.497548784223608 0.751225607888196 42 0.283042481553338 0.566084963106676 0.716957518446662 43 0.423209067091448 0.846418134182896 0.576790932908552 44 0.723915122273933 0.552169755452134 0.276084877726067 45 0.865020702186137 0.269958595627726 0.134979297813863 46 0.805774708310479 0.388450583379042 0.194225291689521 47 0.743815315119107 0.512369369761786 0.256184684880893 48 0.659510661976787 0.680978676046426 0.340489338023213 49 0.66858567273777 0.662828654524459 0.331414327262229 50 0.844998632742291 0.310002734515418 0.155001367257709 51 0.94048707934305 0.119025841313900 0.0595129206569498 52 0.89925756896168 0.201484862076641 0.100742431038320 53 0.83224240287168 0.335515194256639 0.167757597128319 54 0.727854259839694 0.544291480320612 0.272145740160306 55 0.571088885259033 0.857822229481933 0.428911114740966 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.431372549019608 NOK 5% type I error level 28 0.549019607843137 NOK 10% type I error level 29 0.568627450980392 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/10phwn1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/10phwn1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/1cc9w1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/1cc9w1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/2o5c21260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/2o5c21260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/320ma1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/320ma1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/4cscu1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/4cscu1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/53zdu1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/53zdu1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/69al11260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/69al11260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/7vkfq1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/7vkfq1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/8ag7g1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/8ag7g1260888837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/91ejf1260888837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260888959lgmh6dxrs2cv9re/91ejf1260888837.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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