ws7 4

*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: Wed, 18 Nov 2009 13:49:31 -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/18/t125857743565b6kod5lorlcad.htm/, Retrieved Wed, 18 Nov 2009 21:50:47 +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/18/t125857743565b6kod5lorlcad.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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103.91 89.00 103.88 103.77 103.66 103.64 103.63 103.91 86.40 103.91 103.88 103.77 103.66 103.64 103.92 84.50 103.91 103.91 103.88 103.77 103.66 104.05 82.70 103.92 103.91 103.91 103.88 103.77 104.23 80.80 104.05 103.92 103.91 103.91 103.88 104.30 81.80 104.23 104.05 103.92 103.91 103.91 104.31 81.80 104.30 104.23 104.05 103.92 103.91 104.31 82.90 104.31 104.30 104.23 104.05 103.92 104.34 83.80 104.31 104.31 104.30 104.23 104.05 104.55 86.20 104.34 104.31 104.31 104.30 104.23 104.65 86.10 104.55 104.34 104.31 104.31 104.30 104.73 86.20 104.65 104.55 104.34 104.31 104.31 104.75 88.80 104.73 104.65 104.55 104.34 104.31 104.75 89.60 104.75 104.73 104.65 104.55 104.34 104.76 87.80 104.75 104.75 104.73 104.65 104.55 104.94 88.30 104.76 104.75 104.75 104.73 104.65 105.29 88.60 104.94 104.76 104.75 104.75 104.73 105.38 91.00 105.29 104.94 104.76 104.75 104.75 105.43 91.50 105.38 105.29 104.94 104.76 104.75 105.43 95.40 105.43 105.38 105.29 104.94 104.76 105.42 98.70 105.43 105.43 105.38 105.29 104 etc...

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Y[t] = -0.882021848595453 -0.00452130769895071X[t] + 1.28776785397041Y1[t] -0.416249054274984Y2[t] -0.111638282342769Y3[t] + 0.271637226912727Y4[t] -0.0188616710391742`Y5 `[t] + 0.0035299223429012t + 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) -0.882021848595453 4.04946 -0.2178 0.828518 0.414259 X -0.00452130769895071 0.001859 -2.4321 0.018876 0.009438 Y1 1.28776785397041 0.138486 9.2989 0 0 Y2 -0.416249054274984 0.229841 -1.811 0.07653 0.038265 Y3 -0.111638282342769 0.243676 -0.4581 0.648961 0.32448 Y4 0.271637226912727 0.236105 1.1505 0.255761 0.12788 `Y5 ` -0.0188616710391742 0.149062 -0.1265 0.899847 0.449924 t 0.0035299223429012 0.003991 0.8845 0.38095 0.190475 Multiple Linear Regression - Regression Statistics Multiple R 0.996238049982534 R-squared 0.992490252233001 Adjusted R-squared 0.99137177916132 F-TEST (value) 887.361776838902 F-TEST (DF numerator) 7 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.07894860758537 Sum Squared Residuals 0.292945484064431 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.91 103.923694876667 -0.0136948766666860 2 103.91 103.924789755446 -0.0147897554456499 3 103.92 103.941645341270 -0.0216453412702114 4 104.05 103.990647458687 0.0593525413132544 5 104.23 104.172089529124 0.0579104708758845 6 104.3 104.347101747472 -0.0471017474724066 7 104.31 104.354053985388 -0.0440539853882922 8 104.31 104.351380045969 -0.0413800459693747 9 104.34 104.385306304686 -0.0453063046856742 10 104.55 104.431121246444 0.118878753556377 11 104.65 104.694443132558 -0.0444431325583265 12 104.73 104.73534764295 -0.0053476429499675 13 104.75 104.773223765681 -0.0232237656811312 14 104.75 104.810906213889 -0.0609062138884949 15 104.76 104.828521218190 -0.0685212181896317 16 104.94 104.860280210625 0.0797197893749793 17 105.29 105.094013274685 0.195986725314739 18 105.38 105.460992361427 -0.0809923614266413 19 105.43 105.415095049229 0.0149049507714379 20 105.43 105.437550534673 -0.00755053467327394 21 105.42 105.486978172117 -0.0669781721174468 22 105.52 105.484468698123 0.0355313018768063 23 105.69 105.638699907367 0.051300092633371 24 105.72 105.812012535640 -0.0920125356402588 25 105.74 105.769985084642 -0.02998508464202 26 105.74 105.794252462300 -0.0542524622996764 27 105.74 105.825879108859 -0.0858791088590926 28 105.95 105.824884805968 0.125115194032468 29 106.17 106.076132895088 0.0938671049122893 30 106.34 106.264783202778 0.0752167972220204 31 106.37 106.360007298276 0.00999270172379286 32 106.37 106.329077243266 0.0409227567341805 33 106.36 106.330716840331 0.0292831596691363 34 106.44 106.349649688903 0.090350311097268 35 106.29 106.473896647465 -0.183896647464736 36 106.23 106.271857884708 -0.0418578847076219 37 106.23 106.246651005248 -0.0166510052475141 38 106.23 106.298448761885 -0.0684487618853065 39 106.23 106.265518201909 -0.0355182019089434 40 106.34 106.251962095134 0.0880379048662037 41 106.44 106.384714258579 0.0552857414210537 42 106.44 106.454504731863 -0.0145047318625162 43 106.48 106.409102976189 0.0708970238109437 44 106.5 106.435386148578 0.064613851422073 45 106.57 106.460450743149 0.109549256850864 46 106.4 106.523171028071 -0.123171028070596 47 106.37 106.297222581807 0.0727774181933546 48 106.25 106.362750951892 -0.112750951892213 49 106.21 106.273155353096 -0.0631553530956664 50 106.21 106.226905773786 -0.0169057737860680 51 106.24 106.229316034796 0.0106839652035338 52 106.19 106.233514899246 -0.0435148992462501 53 106.08 106.148854084091 -0.0688540840912035 54 106.13 106.017191913565 0.112808086434877 55 106.09 106.115692286228 -0.0256922862280168 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.071265950870845 0.14253190174169 0.928734049129155 12 0.0218894858032215 0.0437789716064431 0.978110514196779 13 0.00814670263366301 0.0162934052673260 0.991853297366337 14 0.00493308734657163 0.00986617469314327 0.995066912653428 15 0.00216667796840162 0.00433335593680323 0.997833322031598 16 0.000635912631242297 0.00127182526248459 0.999364087368758 17 0.106632233816174 0.213264467632349 0.893367766183826 18 0.0938331915573314 0.187666383114663 0.906166808442669 19 0.238414354198204 0.476828708396409 0.761585645801796 20 0.197930614162767 0.395861228325534 0.802069385837233 21 0.16020519792714 0.32041039585428 0.83979480207286 22 0.150141424871728 0.300282849743456 0.849858575128272 23 0.108163703551091 0.216327407102182 0.891836296448909 24 0.16718194738908 0.33436389477816 0.83281805261092 25 0.151798772285462 0.303597544570924 0.848201227714538 26 0.173767408281790 0.347534816563580 0.82623259171821 27 0.339235750299163 0.678471500598327 0.660764249700837 28 0.294231866630903 0.588463733261806 0.705768133369097 29 0.238637280098202 0.477274560196403 0.761362719901798 30 0.200214296265097 0.400428592530195 0.799785703734903 31 0.147472425502857 0.294944851005715 0.852527574497143 32 0.133016256970895 0.266032513941791 0.866983743029105 33 0.124449598043578 0.248899196087157 0.875550401956422 34 0.0920529696497502 0.184105939299500 0.90794703035025 35 0.556351878180809 0.887296243638382 0.443648121819191 36 0.627049447585381 0.745901104829238 0.372950552414619 37 0.592274321104015 0.81545135779197 0.407725678895985 38 0.648157640811814 0.703684718376371 0.351842359188186 39 0.686429692755743 0.627140614488514 0.313570307244257 40 0.696157143246208 0.607685713507583 0.303842856753792 41 0.64875431329439 0.702491373411219 0.351245686705610 42 0.851394470904742 0.297211058190516 0.148605529095258 43 0.755849871146904 0.488300257706191 0.244150128853096 44 0.955787335316 0.0884253293680012 0.0442126646840006 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.088235294117647 NOK 5% type I error level 5 0.147058823529412 NOK 10% type I error level 6 0.176470588235294 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/10r1j01258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/10r1j01258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/1zp7b1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/1zp7b1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/2zq4z1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/2zq4z1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/3bmps1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/3bmps1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/45rlj1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/45rlj1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/51bgf1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/51bgf1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/68zr41258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/68zr41258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/7o7hz1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/7o7hz1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/8ejgs1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/8ejgs1258577366.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/9r8va1258577366.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125857743565b6kod5lorlcad/9r8va1258577366.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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