WS 7.3

*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 07:55:22 -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/t1258728970y992ndlc489d6i4.htm/, Retrieved Fri, 20 Nov 2009 15:56:22 +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/t1258728970y992ndlc489d6i4.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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9.9 8.2 9.8 8 9.3 7.5 8.3 6.8 8 6.5 8.5 6.6 10.4 7.6 11.1 8 10.9 8.1 10 7.7 9.2 7.5 9.2 7.6 9.5 7.8 9.6 7.8 9.5 7.8 9.1 7.5 8.9 7.5 9 7.1 10.1 7.5 10.3 7.5 10.2 7.6 9.6 7.7 9.2 7.7 9.3 7.9 9.4 8.1 9.4 8.2 9.2 8.2 9 8.2 9 7.9 9 7.3 9.8 6.9 10 6.6 9.8 6.7 9.3 6.9 9 7 9 7.1 9.1 7.2 9.1 7.1 9.1 6.9 9.2 7 8.8 6.8 8.3 6.4 8.4 6.7 8.1 6.6 7.7 6.4 7.9 6.3 7.9 6.2 8 6.5 7.9 6.8 7.6 6.8 7.1 6.4 6.8 6.1 6.5 5.8 6.9 6.1 8.2 7.2 8.7 7.3 8.3 6.9 7.9 6.1 7.5 5.8 7.8 6.2

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 WLVrouw[t] = + 4.27824764466338 + 0.7361871878754WLMan[t] -0.161515411368572M1[t] -0.169408779657346M2[t] -0.244788454128544M3[t] -0.405444384842235M4[t] -0.460824059313433M5[t] -0.190927477542139M6[t] + 0.518361816473883M7[t] + 0.786297216912588M8[t] + 0.593127592381327M9[t] + 0.323024174152621M10[t] + 0.039302037136374M11[t] -0.0226591441962139t + 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) 4.27824764466338 1.190597 3.5934 0.000792 0.000396 WLMan 0.7361871878754 0.145897 5.0459 8e-06 4e-06 M1 -0.161515411368572 0.311833 -0.518 0.606973 0.303486 M2 -0.169408779657346 0.311101 -0.5445 0.588696 0.294348 M3 -0.244788454128544 0.308421 -0.7937 0.431456 0.215728 M4 -0.405444384842235 0.308572 -1.3139 0.195382 0.097691 M5 -0.460824059313433 0.311452 -1.4796 0.145797 0.072899 M6 -0.190927477542139 0.316203 -0.6038 0.548934 0.274467 M7 0.518361816473883 0.307008 1.6884 0.098098 0.049049 M8 0.786297216912588 0.306859 2.5624 0.013734 0.006867 M9 0.593127592381327 0.306653 1.9342 0.059254 0.029627 M10 0.323024174152621 0.307551 1.0503 0.299062 0.149531 M11 0.039302037136374 0.308558 0.1274 0.8992 0.4496 t -0.0226591441962139 0.005221 -4.3397 7.7e-05 3.9e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.900983958337249 R-squared 0.811772093181057 Adjusted R-squared 0.758577249949617 F-TEST (value) 15.2603531445557 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.53033141714332e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.484544056241358 Sum Squared Residuals 10.8000153521861 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.9 10.1308080296768 -0.230808029676850 2 9.8 9.9530180796168 -0.153018079616801 3 9.3 9.48688566701169 -0.186885667011687 4 8.3 8.788239560589 -0.488239560589004 5 8 8.48934458555897 -0.489344585558973 6 8.5 8.8102007419216 -0.310200741921593 7 10.4 10.2330180796168 0.166981920383200 8 11.1 10.7727692110095 0.327230788990547 9 10.9 10.6305591610695 0.269440838930484 10 10 10.0433217234944 -0.0433217234944362 11 9.2 9.5897030047069 -0.389703004706897 12 9.2 9.60136054216185 -0.401360542161849 13 9.5 9.56442342417214 -0.064423424172142 14 9.6 9.53387091168715 0.0661290883128454 15 9.5 9.43583209301974 0.0641679069802581 16 9.1 9.03166086174722 0.0683391382527815 17 8.9 8.9536220430798 -0.0536220430798058 18 9 8.90638460550473 0.0936153944952737 19 10.1 9.8874896304747 0.212510369525305 20 10.3 10.1327658867172 0.167234113282815 21 10.2 9.99055583677725 0.209444163222749 22 9.6 9.77141199313987 -0.171411993139870 23 9.2 9.46503071192741 -0.265030711927410 24 9.3 9.5503069681699 -0.250306968169901 25 9.4 9.5133698501802 -0.113369850180195 26 9.4 9.55643605648275 -0.156436056482747 27 9.2 9.45839723781534 -0.258397237815336 28 9 9.27508216290543 -0.275082162905431 29 9 8.9761871878754 0.0238128121246005 30 9 8.78171231272524 0.21828768727476 31 9.8 9.17386758739489 0.626132412605112 32 10 9.19828768727476 0.80171231272524 33 9.8 9.05607763733483 0.743922362665176 34 9.3 8.91055251248499 0.389447487515016 35 9 8.67778995006006 0.322210049939937 36 9 8.68944748751502 0.310552512484984 37 9.1 8.57889165073777 0.52110834926223 38 9.1 8.47472041946524 0.625279580534758 39 9.1 8.22944416322275 0.87055583677725 40 9.2 8.11974780710039 1.08025219289961 41 8.8 7.8944715508579 0.905528449142107 42 8.3 7.84723411328281 0.452765886717186 43 8.4 8.75472041946524 -0.354720419465242 44 8.1 8.9263779569202 -0.826377956920194 45 7.7 8.56331175061764 -0.863311750617638 46 7.9 8.19693046940518 -0.296930469405178 47 7.9 7.81693046940518 0.0830695305948227 48 8 7.97582544443521 0.0241745555647900 49 7.9 8.01250704523304 -0.112507045233043 50 7.6 7.98195453274806 -0.381954532748056 51 7.1 7.58944083893048 -0.489440838930484 52 6.8 7.18526960765796 -0.38526960765796 53 6.5 6.88637463262793 -0.386374632627928 54 6.9 7.35446822656563 -0.454468226565628 55 8.2 8.85090428304838 -0.650904283048376 56 8.7 9.1697992580784 -0.469799258078408 57 8.3 8.65949561420077 -0.359495614200771 58 7.9 7.77778330147553 0.122216698524469 59 7.5 7.25054586390045 0.249454136099548 60 7.8 7.48305955771802 0.316940442281976 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0033584772096027 0.0067169544192054 0.996641522790397 18 0.000385199316939106 0.000770398633878212 0.99961480068306 19 0.00021265183350146 0.00042530366700292 0.999787348166499 20 0.000120244708648372 0.000240489417296744 0.999879755291352 21 1.93117964550586e-05 3.86235929101171e-05 0.999980688203545 22 2.98791729495879e-05 5.97583458991757e-05 0.99997012082705 23 7.60636974065905e-06 1.52127394813181e-05 0.99999239363026 24 2.16031006069256e-06 4.32062012138513e-06 0.99999783968994 25 1.11548722344000e-06 2.23097444688000e-06 0.999998884512777 26 4.04438683342522e-06 8.08877366685045e-06 0.999995955613167 27 2.14488980810463e-05 4.28977961620927e-05 0.99997855110192 28 3.93670073080929e-05 7.87340146161857e-05 0.999960632992692 29 2.50778606514929e-05 5.01557213029857e-05 0.999974922139349 30 1.58133768155014e-05 3.16267536310027e-05 0.999984186623184 31 3.37058194846855e-05 6.74116389693711e-05 0.999966294180515 32 0.000163952746605359 0.000327905493210718 0.999836047253395 33 0.00051765396794552 0.00103530793589104 0.999482346032055 34 0.000259985529153302 0.000519971058306605 0.999740014470847 35 0.000651179663465875 0.00130235932693175 0.999348820336534 36 0.00184501008694775 0.0036900201738955 0.998154989913052 37 0.000875077375232503 0.00175015475046501 0.999124922624767 38 0.000704481702071704 0.00140896340414341 0.999295518297928 39 0.00241934776545065 0.0048386955309013 0.99758065223455 40 0.0187737820619402 0.0375475641238804 0.98122621793806 41 0.0551927745559891 0.110385549111978 0.94480722544401 42 0.482194177395765 0.96438835479153 0.517805822604235 43 0.97610651429364 0.0477869714127193 0.0238934857063597 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.851851851851852 NOK 5% type I error level 25 0.925925925925926 NOK 10% type I error level 25 0.925925925925926 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/100ez11258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/100ez11258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/1h4rx1258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/1h4rx1258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/2qbh61258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/2qbh61258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/32zj71258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/32zj71258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/422s51258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/422s51258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/5wphj1258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/5wphj1258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/6g2ev1258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/6g2ev1258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/7xs2e1258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/7xs2e1258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/8x23t1258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/8x23t1258728917.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/91rz51258728917.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728970y992ndlc489d6i4/91rz51258728917.ps (open in new window)

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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