WS 7 2

*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: Sat, 14 Nov 2009 06:20:47 -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/14/t12582048826gae5nbck4ewacy.htm/, Retrieved Sat, 14 Nov 2009 14:21:33 +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/14/t12582048826gae5nbck4ewacy.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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103,63 100,30 103,64 98,50 103,66 95,10 103,77 93,10 103,88 92,20 103,91 89,00 103,91 86,40 103,92 84,50 104,05 82,70 104,23 80,80 104,30 81,80 104,31 81,80 104,31 82,90 104,34 83,80 104,55 86,20 104,65 86,10 104,73 86,20 104,75 88,80 104,75 89,60 104,76 87,80 104,94 88,30 105,29 88,60 105,38 91,00 105,43 91,50 105,43 95,40 105,42 98,70 105,52 99,90 105,69 98,60 105,72 100,30 105,74 100,20 105,74 100,40 105,74 101,40 105,95 103,00 106,17 109,10 106,34 111,40 106,37 114,10 106,37 121,80 106,36 127,60 106,44 129,90 106,29 128,00 106,23 123,50 106,23 124,00 106,23 127,40 106,23 127,60 106,34 128,40 106,44 131,40 106,44 135,10 106,48 134,00 106,50 144,50 106,57 147,30 106,40 150,90 106,37 148,70 106,25 141,40 106,21 138,90 106,21 139,80 106,24 145,60 106,19 147,90 106,08 148,50 106,13 151,10 106,09 157,50

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] = + 102.005813794397 + 0.0322178805113383X[t] -0.268918412522903M1[t] -0.321797749647847M2[t] -0.313103563871682M3[t] -0.22477674310467M4[t] -0.146541763589954M5[t] -0.123144108113834M6[t] -0.140541763589957M7[t] -0.151805564727438M8[t] -0.0577137234751484M9[t] + 0.0380933100964853M10[t] + 0.0367703968692694M11[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) 102.005813794397 0.492211 207.24 0 0 X 0.0322178805113383 0.003473 9.277 0 0 M1 -0.268918412522903 0.402177 -0.6687 0.506986 0.253493 M2 -0.321797749647847 0.401801 -0.8009 0.427227 0.213613 M3 -0.313103563871682 0.401655 -0.7795 0.439573 0.219787 M4 -0.22477674310467 0.401841 -0.5594 0.578567 0.289283 M5 -0.146541763589954 0.402231 -0.3643 0.717252 0.358626 M6 -0.123144108113834 0.40235 -0.3061 0.760909 0.380455 M7 -0.140541763589957 0.402231 -0.3494 0.728346 0.364173 M8 -0.151805564727438 0.402098 -0.3775 0.707477 0.353738 M9 -0.0577137234751484 0.401975 -0.1436 0.886449 0.443225 M10 0.0380933100964853 0.401736 0.0948 0.924859 0.46243 M11 0.0367703968692694 0.401527 0.0916 0.927424 0.463712 Multiple Linear Regression - Regression Statistics Multiple R 0.810550729538045 R-squared 0.656992485154657 Adjusted R-squared 0.569416098385633 F-TEST (value) 7.50193641680406 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.77409201107537e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.634801017530965 Sum Squared Residuals 18.9396995973424 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.63 104.968348797162 -1.3383487971616 2 103.64 104.857477275116 -1.21747727511623 3 103.66 104.756630667154 -1.09663066715385 4 103.77 104.780521726898 -1.01052172689818 5 103.88 104.829760613953 -0.949760613952696 6 103.91 104.750061051793 -0.840061051792532 7 103.91 104.648896906987 -0.73889690698693 8 103.92 104.576419132878 -0.6564191328779 9 104.05 104.612518789210 -0.562518789209786 10 104.23 104.647111849810 -0.41711184980987 11 104.3 104.678006817094 -0.378006817094 12 104.31 104.641236420225 -0.331236420224725 13 104.31 104.407757676264 -0.0977576762642939 14 104.34 104.383874431600 -0.0438744315995534 15 104.55 104.469891530603 0.080108469397063 16 104.65 104.554996563319 0.095003436681194 17 104.73 104.636453330885 0.0935466691153425 18 104.75 104.743617475690 0.00638252430973915 19 104.75 104.751994124623 -0.0019941246232085 20 104.76 104.682738138565 0.077261861434686 21 104.94 104.792938920073 0.14706107992672 22 105.29 104.898411317798 0.391588682201693 23 105.38 104.974411317798 0.405588682201686 24 105.43 104.953749861185 0.476250138815298 25 105.43 104.810481182656 0.619518817343982 26 105.42 104.863920851218 0.556079148781503 27 105.52 104.911276493608 0.608723506391727 28 105.69 104.957720069711 0.732279930289457 29 105.72 105.090725446095 0.629274553905467 30 105.74 105.110901313520 0.629098686480477 31 105.74 105.099947234146 0.640052765854332 32 105.74 105.120901313520 0.619098686480474 33 105.95 105.26654176359 0.683458236410051 34 106.17 105.558877868281 0.611122131719253 35 106.34 105.631656080230 0.708343919770392 36 106.37 105.681873960741 0.68812603925905 37 106.37 105.661033228155 0.708966771844648 38 106.36 105.795017597996 0.564982402003824 39 106.44 105.877812908948 0.562187091051579 40 106.29 105.904925756744 0.385074243256118 41 106.23 105.838180273958 0.391819726042423 42 106.23 105.877686869689 0.352313130310634 43 106.23 105.969830007952 0.260169992048206 44 106.23 105.965009782917 0.264990217083420 45 106.34 106.084875928578 0.255124071422059 46 106.44 106.277336603684 0.162663396316404 47 106.44 106.395219848348 0.0447801516516683 48 106.48 106.323009782917 0.156990217083416 49 106.5 106.392379115763 0.107620884237263 50 106.57 106.429709844070 0.140290155930452 51 106.4 106.554388399687 -0.154388399686519 52 106.37 106.571835883329 -0.201835883328587 53 106.25 106.414880335111 -0.164880335110538 54 106.21 106.357733289308 -0.147733289308318 55 106.21 106.369331726292 -0.159331726292400 56 106.24 106.544931632121 -0.304931632120679 57 106.19 106.713124598549 -0.523124598549044 58 106.08 106.828262360427 -0.74826236042748 59 106.13 106.910705936530 -0.780705936529747 60 106.09 107.080129974933 -0.990129974933035 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.415926578170952 0.831853156341905 0.584073421829048 17 0.458854005575572 0.917708011151143 0.541145994424428 18 0.71922614767224 0.561547704655521 0.280773852327760 19 0.921258692356038 0.157482615287923 0.0787413076439616 20 0.980318586174372 0.0393628276512561 0.0196814138256281 21 0.996705654638364 0.00658869072327106 0.00329434536163553 22 0.99929253153344 0.00141493693312024 0.00070746846656012 23 0.999765156142492 0.000469687715015889 0.000234843857507945 24 0.999879394540365 0.000241210919270677 0.000120605459635339 25 0.999986692919187 2.66141616266778e-05 1.33070808133389e-05 26 0.999998797526025 2.40494795005038e-06 1.20247397502519e-06 27 0.999999799417483 4.01165034551103e-07 2.00582517275551e-07 28 0.9999998864946 2.27010801638524e-07 1.13505400819262e-07 29 0.999999890353088 2.19293823399220e-07 1.09646911699610e-07 30 0.999999885577427 2.28845145210344e-07 1.14422572605172e-07 31 0.999999906791593 1.86416814764832e-07 9.32084073824158e-08 32 0.99999997144156 5.71168783493193e-08 2.85584391746596e-08 33 0.999999981411591 3.7176817294992e-08 1.8588408647496e-08 34 0.999999938560595 1.22878810943075e-07 6.14394054715374e-08 35 0.999999661998674 6.76002652834084e-07 3.38001326417042e-07 36 0.999998221000957 3.5579980849274e-06 1.7789990424637e-06 37 0.99999506192862 9.8761427595945e-06 4.93807137979725e-06 38 0.99999414910794 1.17017841187050e-05 5.85089205935252e-06 39 0.999972987562555 5.40248748904874e-05 2.70124374452437e-05 40 0.999948628912521 0.000102742174958086 5.13710874790428e-05 41 0.999853732624286 0.000292534751428716 0.000146267375714358 42 0.999418845509031 0.00116230898193753 0.000581154490968766 43 0.99751787834777 0.00496424330446163 0.00248212165223081 44 0.997102521543832 0.00579495691233554 0.00289747845616777 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.827586206896552 NOK 5% type I error level 25 0.862068965517241 NOK 10% type I error level 25 0.862068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/10q3oj1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/10q3oj1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/14y7t1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/14y7t1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/2thtu1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/2thtu1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/3jnsa1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/3jnsa1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/4hljy1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/4hljy1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/5duos1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/5duos1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/6ckft1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/6ckft1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/767kf1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/767kf1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/8odpk1258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/8odpk1258204842.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/9xr321258204842.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t12582048826gae5nbck4ewacy/9xr321258204842.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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