model3

*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, 20 Nov 2009 01:47:44 -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/t125870704409d1p8kpu8qhekm.htm/, Retrieved Fri, 20 Nov 2009 09:50:56 +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/t125870704409d1p8kpu8qhekm.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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106.1 97.89 106 98.69 105.9 99.01 105.8 99.18 105.7 98.45 105.6 98.13 105.4 98.29 105.4 99.1 105.5 99.26 105.6 98.85 105.7 98.05 105.9 98.53 106.1 99.34 106 100.14 105.8 100.3 105.8 100.22 105.7 99.9 105.5 99.58 105.3 99.9 105.2 100.78 105.2 100.78 105 100.46 105.1 100.06 105.1 100.28 105.2 100.78 104.9 101.58 104.8 102.06 104.5 102.02 104.5 101.68 104.4 101.32 104.4 101.81 104.2 102.3 104.1 102.12 103.9 102.1 103.8 101.75 103.9 101.5 104.2 102.16 104.1 103.47 103.8 104.05 103.6 104.09 103.7 103.55 103.5 102.77 103.4 102.89 103.1 103.6 103.1 103.76 103.1 103.92 103.2 103.35 103.3 103.32 103.5 104.2 103.6 105.44 103.5 105.81 103.3 106.25 103.2 105.94 103.1 105.82 103.2 105.96 103 106.49 103 106.32 103.1 105.88 103.4 105.07

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 Werkl[t] = + 91.491378559978 + 0.153267237582525Infl[t] + 0.0739357297362894M1[t] -0.0975590909259045M2[t] -0.235867431137875M3[t] -0.331874013777070M4[t] -0.22297054679554M5[t] -0.224489251969629M6[t] -0.261953247870372M7[t] -0.446548293832267M8[t] -0.365388945862221M9[t] -0.293576150375666M10[t] -0.0235218046077498M11[t] -0.0802397445445542t + 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) 91.491378559978 12.231892 7.4797 0 0 Infl 0.153267237582525 0.12668 1.2099 0.232645 0.116322 M1 0.0739357297362894 0.215315 0.3434 0.732909 0.366454 M2 -0.0975590909259045 0.276681 -0.3526 0.72603 0.363015 M3 -0.235867431137875 0.298492 -0.7902 0.433557 0.216778 M4 -0.331874013777070 0.294718 -1.1261 0.266104 0.133052 M5 -0.22297054679554 0.243827 -0.9145 0.365349 0.182675 M6 -0.224489251969629 0.211245 -1.0627 0.29359 0.146795 M7 -0.261953247870372 0.216299 -1.2111 0.23219 0.116095 M8 -0.446548293832267 0.252702 -1.7671 0.083994 0.041997 M9 -0.365388945862221 0.241149 -1.5152 0.136716 0.068358 M10 -0.293576150375666 0.218438 -1.344 0.185692 0.092846 M11 -0.0235218046077498 0.196627 -0.1196 0.905311 0.452656 t -0.0802397445445542 0.018546 -4.3266 8.3e-05 4.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.969649892450111 R-squared 0.940220913928512 Adjusted R-squared 0.922951400174526 F-TEST (value) 54.4439714587521 F-TEST (DF numerator) 13 F-TEST (DF denominator) 45 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.292594098675783 Sum Squared Residuals 3.85250879609523 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106.1 106.488404432123 -0.388404432122857 2 106 106.359283656982 -0.359283656982363 3 105.9 106.189781088252 -0.289781088252242 4 105.8 106.039590191458 -0.239590191457530 5 105.7 105.956368830459 -0.256368830459257 6 105.6 105.825564864714 -0.225564864714213 7 105.4 105.732383882282 -0.33238388228211 8 105.4 105.591695554218 -0.191695554217505 9 105.5 105.617137915656 -0.117137915656208 10 105.6 105.545871399189 0.0541286008106235 11 105.7 105.613072210347 0.0869277896532885 12 105.9 105.629922544450 0.270077455550484 13 106.1 105.747764992083 0.352235007916891 14 106 105.618644216942 0.381355783057624 15 105.8 105.424618890199 0.375381109800943 16 105.8 105.236111184009 0.563888815991294 17 105.7 105.215729390419 0.484270609580731 18 105.5 105.084925424674 0.41507457532578 19 105.3 105.016267200255 0.283732799744666 20 105.2 104.886307578822 0.313692421178498 21 105.2 104.887227182247 0.312772817753007 22 105 104.829754717163 0.170245282837413 23 105.1 104.958262423353 0.141737576647053 24 105.1 104.935263275684 0.164736724315702 25 105.2 105.005592879667 0.194407120332713 26 104.9 104.876472104527 0.0235278954734443 27 104.8 104.731492293810 0.0685077061903472 28 104.5 104.549115277123 -0.0491152771225977 29 104.5 104.525668138782 -0.0256681387815173 30 104.4 104.388733483533 0.0112665164668423 31 104.4 104.346130689503 0.0538693104967011 32 104.2 104.156396845412 0.0436031545877104 33 104.1 104.129728346073 -0.0297283460729360 34 103.9 104.118236052263 -0.218236052263273 35 103.8 104.254407120333 -0.454407120332761 36 103.9 104.159372371000 -0.259372371000317 37 104.2 104.254224732997 -0.054224732996521 38 104.1 104.203270249023 -0.10327024902289 39 103.8 104.073617162064 -0.273617162064228 40 103.6 103.903501524384 -0.303501524383782 41 103.7 103.849400938526 -0.149400938526185 42 103.5 103.648094043493 -0.148094043493175 43 103.4 103.548782371558 -0.148782371557775 44 103.1 103.392767319735 -0.292767319734931 45 103.1 103.418209681174 -0.318209681173628 46 103.1 103.434305490129 -0.334305490128832 47 103.2 103.536757765930 -0.336757765930146 48 103.3 103.475441808866 -0.175441808865870 49 103.5 103.604012963130 -0.104012963130227 50 103.6 103.542329772526 0.0576702274741849 51 103.5 103.380490565675 0.11950943432518 52 103.3 103.271681823027 0.0283181769726160 53 103.2 103.252832701814 -0.0528327018137714 54 103.1 103.152682183585 -0.0526821835852335 55 103.2 103.056435856401 0.143564143598519 56 103 102.872832701814 0.127167298186226 57 103 102.847696874850 0.152303125149765 58 103.1 102.771832341256 0.328167658744069 59 103.4 102.837500480037 0.562499519962565 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00475826936044502 0.00951653872089004 0.995241730639555 18 0.00189385854555843 0.00378771709111686 0.998106141454442 19 0.000426111327040718 0.000852222654081436 0.99957388867296 20 0.000312593041501181 0.000625186083002361 0.999687406958499 21 0.00135732426594403 0.00271464853188806 0.998642675734056 22 0.0303172593554905 0.060634518710981 0.96968274064451 23 0.0177926153806101 0.0355852307612201 0.98220738461939 24 0.0585337692293472 0.117067538458694 0.941466230770653 25 0.287185452589496 0.574370905178991 0.712814547410505 26 0.540667724348018 0.918664551303964 0.459332275651982 27 0.564497545663184 0.871004908673631 0.435502454336816 28 0.69142496012081 0.617150079758379 0.308575039879190 29 0.677448217804985 0.645103564390029 0.322551782195015 30 0.675101136058609 0.649797727882783 0.324898863941392 31 0.660380555172001 0.679238889655998 0.339619444827999 32 0.742145860473345 0.51570827905331 0.257854139526655 33 0.887318666488898 0.225362667022204 0.112681333511102 34 0.90693431141704 0.18613137716592 0.09306568858296 35 0.87452889705197 0.250942205896058 0.125471102948029 36 0.925742891921038 0.148514216157924 0.0742571080789618 37 0.963557214435686 0.072885571128628 0.036442785564314 38 0.967271821113423 0.0654563577731549 0.0327281788865775 39 0.95598716092845 0.0880256781431009 0.0440128390715504 40 0.935336605583656 0.129326788832688 0.0646633944163438 41 0.991021455007308 0.0179570899853831 0.00897854499269157 42 0.993578091727347 0.0128438165453064 0.00642190827265319 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.192307692307692 NOK 5% type I error level 8 0.307692307692308 NOK 10% type I error level 12 0.461538461538462 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/10obzo1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/10obzo1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/114zy1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/114zy1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/24uvl1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/24uvl1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/3qj3e1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/3qj3e1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/422561258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/422561258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/5o6wp1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/5o6wp1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/6jw251258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/6jw251258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/7quk51258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/7quk51258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/8pgbl1258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/8pgbl1258706860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/9a7381258706860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125870704409d1p8kpu8qhekm/9a7381258706860.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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