WS 7.3

*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, 27 Nov 2009 12:23:38 -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/27/t1259349921nn3ogsg9ksge4ff.htm/, Retrieved Fri, 27 Nov 2009 20:25:33 +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/27/t1259349921nn3ogsg9ksge4ff.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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100.00 100.00 94.97 106.73 107.50 104.81 124.27 96.15 107.06 88.46 79.71 88.46 163.41 91.35 144.83 92.31 166.82 91.35 154.26 87.50 132.60 85.58 157.51 86.54 104.02 97.12 106.03 99.04 113.23 98.08 117.64 92.31 113.34 88.46 66.62 89.42 185.99 90.38 174.57 90.38 208.19 88.46 163.81 86.54 162.46 86.54 148.16 86.54 113.41 94.23 105.63 96.15 111.79 94.23 132.36 89.42 110.75 86.54 67.37 86.54 178.29 87.50 156.38 87.50 189.71 87.50 152.80 88.46 150.80 84.62 160.40 79.81 127.25 80.77 108.47 77.88 117.09 74.04 147.25 75.96 116.19 75.96 75.83 76.92 181.94 75.96 179.12 73.08 183.15 68.27 197.90 65.38 155.42 62.50 162.54 66.35 125.90 78.85 105.50 83.65 121.11 79.81 137.51 75.96 97.20 72.12 69.74 75.00 152.58 79.81 146.59 80.77 161.16 78.85 152.84 74.04 121.95 69.23 140.12 70.19

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 Y[t] = + 249.743765932925 -1.05055721843048X[t] -31.0307064944671M1[t] -38.0107916100911M2[t] -30.2152583601200M3[t] -16.6075935557811M4[t] -42.9485044503155M5[t] -78.6002454534487M6[t] + 24.2010437160465M7[t] + 12.2490607972814M8[t] + 32.1316138906316M9[t] + 12.4128437972922M10[t] -9.69543105311223M11[t] -0.393724067173589t + 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) 249.743765932925 32.897048 7.5917 0 0 X -1.05055721843048 0.345681 -3.0391 0.003905 0.001953 M1 -31.0307064944671 8.026298 -3.8661 0.000345 0.000173 M2 -38.0107916100911 8.400072 -4.5251 4.2e-05 2.1e-05 M3 -30.2152583601200 8.108226 -3.7265 0.00053 0.000265 M4 -16.6075935557811 7.725342 -2.1498 0.036867 0.018434 M5 -42.9485044503155 7.565939 -5.6766 1e-06 0 M6 -78.6002454534487 7.604837 -10.3356 0 0 M7 24.2010437160465 7.729548 3.131 0.003025 0.001513 M8 12.2490607972814 7.743595 1.5818 0.12054 0.06027 M9 32.1316138906316 7.636866 4.2074 0.000118 5.9e-05 M10 12.4128437972922 7.547214 1.6447 0.106851 0.053426 M11 -9.69543105311223 7.527508 -1.288 0.204188 0.102094 t -0.393724067173589 0.174624 -2.2547 0.028956 0.014478 Multiple Linear Regression - Regression Statistics Multiple R 0.95098585591368 R-squared 0.904374098147874 Adjusted R-squared 0.877349386754882 F-TEST (value) 33.4647088361652 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.8962863267016 Sum Squared Residuals 6509.99490487596 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 113.263613528237 -13.2636135282369 2 94.97 98.8195542654022 -3.84955426540225 3 107.5 108.238433307586 -0.738433307586283 4 124.27 130.550199556360 -6.28019955635957 5 107.06 111.894349604382 -4.83434960438187 6 79.71 75.8488845340751 3.86111546592492 7 163.41 175.220339275133 -11.8103392751326 8 144.83 161.866097359501 -17.0360973595007 9 166.82 182.363461315371 -15.5434613153706 10 154.26 166.295612445815 -12.0356124458149 11 132.6 145.810683387623 -13.2106833876234 12 157.51 154.103855443869 3.40614455613121 13 104.02 111.564529511234 -7.54452951123358 14 106.03 102.173650469050 3.85634953095046 15 113.23 110.583994581540 2.64600541845966 16 117.64 129.859650469050 -12.2196504690495 17 113.34 107.169660798299 6.17033920170123 18 66.62 70.1156607982988 -3.49566079829877 19 185.99 171.514690970927 14.4753090290729 20 174.57 159.168983984988 15.4010160150115 21 208.19 180.674882870552 27.5151171294484 22 163.81 162.579458569425 1.23054143057496 23 162.46 140.077459651847 22.3825403481529 24 148.16 149.379166637786 -1.21916663778571 25 113.41 109.875951066415 3.53404893358541 26 105.63 100.485072024231 5.14492797576945 27 111.79 109.903951066415 1.88604893358541 28 132.36 128.171072024231 4.18892797576947 29 110.75 104.462041851602 6.2879581483978 30 67.37 68.4165767812954 -1.04657678129545 31 178.29 169.815606953924 8.47439304607619 32 156.38 157.469899967985 -1.08989996798516 33 189.71 176.958728994162 12.7512710058382 34 152.8 155.837699903955 -3.03769990395546 35 150.8 137.369840705150 13.4301592948495 36 160.4 151.724727911740 8.67527208826026 37 127.25 119.291762420406 7.95823757959424 38 108.47 114.954063598872 -6.4840635988723 39 117.09 126.390012500443 -9.30001250044287 40 147.25 137.586883378222 9.66311662177829 41 116.19 110.852248416514 5.3377515834864 42 75.83 73.7982484165136 2.03175158348638 43 181.94 177.214348448528 4.72565155147154 44 179.12 167.894246251670 11.2257537483304 45 183.15 192.436255498497 -9.28625549849678 46 197.9 175.359871699248 22.5401283007522 47 155.42 155.883477570750 -0.463477570749624 48 162.54 161.140539265731 1.39946073426907 49 125.9 116.584143473709 9.3158565262908 50 105.5 104.167659642445 1.33234035755463 51 121.11 115.603608544016 5.50639145598406 52 137.51 132.862194572139 4.64780542786135 53 97.2 110.161699329204 -12.9616993292035 54 69.74 71.090629469817 -1.35062946981707 55 152.58 168.445014351488 -15.8650143514880 56 146.59 155.090772435856 -8.50077243585612 57 161.16 176.596671321419 -15.4366713214193 58 152.84 161.537357381557 -8.6973573815568 59 121.95 144.088538684629 -22.1385386846294 60 140.12 152.381710740875 -12.2617107408748 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.150718425174248 0.301436850348497 0.849281574825752 18 0.153024859832122 0.306049719664243 0.846975140167878 19 0.425149483749393 0.850298967498787 0.574850516250607 20 0.618249588359308 0.763500823281384 0.381750411640692 21 0.868491643480913 0.263016713038175 0.131508356519087 22 0.826569007658813 0.346861984682373 0.173430992341187 23 0.853363959623509 0.293272080752983 0.146636040376491 24 0.874450091825156 0.251099816349689 0.125549908174844 25 0.839884354169293 0.320231291661414 0.160115645830707 26 0.787531557690269 0.424936884619462 0.212468442309731 27 0.732403290838485 0.53519341832303 0.267596709161515 28 0.676543182420977 0.646913635158046 0.323456817579023 29 0.619515948209732 0.760968103580536 0.380484051790268 30 0.629509618921376 0.740980762157249 0.370490381078624 31 0.538371676379524 0.923256647240953 0.461628323620476 32 0.535680895302763 0.928638209394474 0.464319104697237 33 0.508037818245171 0.983924363509657 0.491962181754829 34 0.63446422363152 0.73107155273696 0.36553577636848 35 0.578746528261672 0.842506943476656 0.421253471738328 36 0.481763771034391 0.963527542068783 0.518236228965609 37 0.405105855634804 0.810211711269609 0.594894144365196 38 0.486928444646557 0.973856889293114 0.513071555353443 39 0.858547290219797 0.282905419560407 0.141452709780203 40 0.830962583485379 0.338074833029243 0.169037416514621 41 0.771875183865842 0.456249632268315 0.228124816134158 42 0.740924480530347 0.518151038939306 0.259075519469653 43 0.573601301142475 0.85279739771505 0.426398698857525 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/10osob1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/10osob1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/1typ41259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/1typ41259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/2ivlg1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/2ivlg1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/33wka1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/33wka1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/4wrls1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/4wrls1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/5n00z1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/5n00z1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/6id2n1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/6id2n1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/7x4rl1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/7x4rl1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/899mu1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/899mu1259349814.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/97uxk1259349814.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259349921nn3ogsg9ksge4ff/97uxk1259349814.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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