Multivariate regressie calculator

*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: Thu, 19 Nov 2009 02:22:31 -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/19/t1258622718oskyhpdzlqw0jg7.htm/, Retrieved Thu, 19 Nov 2009 10:25:31 +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/19/t1258622718oskyhpdzlqw0jg7.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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121.6 0 118.8 0 114.0 1 111.5 1 97.2 1 102.5 1 113.4 1 109.8 1 104.9 1 126.1 1 80.0 1 96.8 1 117.2 1 112.3 1 117.3 1 111.1 0 102.2 0 104.3 0 122.9 0 107.6 0 121.3 0 131.5 0 89.0 0 104.4 0 128.9 0 135.9 0 133.3 0 121.3 0 120.5 0 120.4 0 137.9 0 126.1 0 133.2 0 151.1 0 105.0 0 119.0 0 140.4 0 156.6 0 137.1 0 122.7 0 125.8 0 139.3 0 134.9 0 149.2 1 132.3 0 149.0 1 117.2 1 119.6 1 152.0 1 149.4 1 127.3 1 114.1 1 102.1 1 107.7 1 104.4 1 102.1 1 96.0 1 109.3 1 90.0 1 83.9 1

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 Promet[t] = + 101.309677040517 -12.0624779800352Dummy[t] + 28.1271129379527M1[t] + 30.4107848894109M2[t] + 23.7269524368761M3[t] + 11.3581287923273M4[t] + 4.48180074378546M5[t] + 9.4654726952437M6[t] + 17.0291446467019M7[t] + 15.4053121941671M8[t] + 11.2764885496183M9[t] + 29.2526560970836M10[t] -8.2036719514582M11[t] + 0.296328048541789t + 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) 101.309677040517 6.903119 14.6759 0 0 Dummy -12.0624779800352 3.397488 -3.5504 9e-04 0.00045 M1 28.1271129379527 8.183803 3.4369 0.001259 0.000629 M2 30.4107848894109 8.172433 3.7211 0.000539 0.000269 M3 23.7269524368761 8.142757 2.9139 0.005495 0.002748 M4 11.3581287923273 8.153186 1.3931 0.170288 0.085144 M5 4.48180074378546 8.145316 0.5502 0.584823 0.292412 M6 9.4654726952437 8.138622 1.163 0.250815 0.125407 M7 17.0291446467019 8.133104 2.0938 0.041817 0.020909 M8 15.4053121941671 8.104274 1.9009 0.063593 0.031796 M9 11.2764885496183 8.12561 1.3878 0.171893 0.085946 M10 29.2526560970836 8.097149 3.6127 0.000747 0.000374 M11 -8.2036719514582 8.095367 -1.0134 0.31618 0.15809 t 0.296328048541789 0.098077 3.0214 0.004101 0.00205 Multiple Linear Regression - Regression Statistics Multiple R 0.767100556821748 R-squared 0.588443264276236 Adjusted R-squared 0.472133752006477 F-TEST (value) 5.05928752337509 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.90461905464900e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.7989599352620 Sum Squared Residuals 7535.41526952436 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 121.6 129.733118027011 -8.13311802701108 2 118.8 132.313118027011 -13.5131180270111 3 114 113.863135642983 0.136864357017022 4 111.5 101.790640046976 9.70935995302416 5 97.2 95.210640046976 1.98935995302406 6 102.5 100.490640046976 2.00935995302408 7 113.4 108.350640046976 5.04935995302407 8 109.8 107.023135642983 2.77686435701701 9 104.9 103.190640046976 1.70935995302405 10 126.1 121.463135642983 4.63686435701701 11 80 84.303135642983 -4.30313564298296 12 96.8 92.803135642983 3.99686435701701 13 117.2 121.226576629477 -4.02657662947741 14 112.3 123.806576629477 -11.5065766294774 15 117.3 117.419072225484 -0.119072225484445 16 111.1 117.409054609513 -6.30905460951265 17 102.2 110.829054609513 -8.62905460951262 18 104.3 116.109054609513 -11.8090546095126 19 122.9 123.969054609513 -1.06905460951263 20 107.6 122.641550205520 -15.0415502055197 21 121.3 118.809054609513 2.49094539048737 22 131.5 137.081550205520 -5.58155020551967 23 89 99.9215502055197 -10.9215502055197 24 104.4 108.421550205520 -4.02155020551968 25 128.9 136.844991192014 -7.94499119201412 26 135.9 139.424991192014 -3.52499119201409 27 133.3 133.037486788021 0.262513211978872 28 121.3 120.964991192014 0.335008807985887 29 120.5 114.384991192014 6.1150088079859 30 120.4 119.664991192014 0.735008807985902 31 137.9 127.524991192014 10.3750088079859 32 126.1 126.197486788021 -0.0974867880211346 33 133.2 122.364991192014 10.8350088079859 34 151.1 140.637486788021 10.4625132119789 35 105 103.477486788021 1.52251321197885 36 119 111.977486788021 7.02251321197885 37 140.4 140.400927774516 -0.000927774515589896 38 156.6 142.980927774516 13.6190722254844 39 137.1 136.593423370523 0.506576629477393 40 122.7 124.520927774516 -1.82092777451557 41 125.8 117.940927774516 7.85907222548444 42 139.3 123.220927774516 16.0790722254844 43 134.9 131.080927774516 3.81907222548444 44 149.2 117.690945390487 31.5090546095126 45 132.3 125.920927774516 6.37907222548446 46 149 132.130945390487 16.8690546095126 47 117.2 94.9709453904874 22.2290546095126 48 119.6 103.470945390487 16.1290546095126 49 152 131.894386376982 20.1056136230182 50 149.4 134.474386376982 14.9256136230182 51 127.3 128.086881972989 -0.786881972988836 52 114.1 116.014386376982 -1.91438637698181 53 102.1 109.434386376982 -7.3343863769818 54 107.7 114.714386376982 -7.01438637698179 55 104.4 122.574386376982 -18.1743863769818 56 102.1 121.246881972989 -19.1468819729888 57 96 117.414386376982 -21.4143863769818 58 109.3 135.686881972989 -26.3868819729888 59 90 98.5268819729888 -8.52688197298884 60 83.9 107.026881972989 -23.1268819729888 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00603478257279155 0.0120695651455831 0.993965217427208 18 0.000915753295123846 0.00183150659024769 0.999084246704876 19 0.000542065470918489 0.00108413094183698 0.999457934529081 20 0.000324942277672244 0.000649884555344489 0.999675057722328 21 0.00101736241502918 0.00203472483005836 0.99898263758497 22 0.00027431576926143 0.00054863153852286 0.999725684230738 23 0.000116024986585688 0.000232049973171376 0.999883975013414 24 3.50899353468321e-05 7.01798706936641e-05 0.999964910064653 25 3.0052660052094e-05 6.0105320104188e-05 0.999969947339948 26 0.000519194519006628 0.00103838903801326 0.999480805480993 27 0.00044579622004586 0.00089159244009172 0.999554203779954 28 0.000224905107720563 0.000449810215441125 0.99977509489228 29 0.000424015932907489 0.000848031865814978 0.999575984067093 30 0.000835292286610938 0.00167058457322188 0.99916470771339 31 0.000749715293683688 0.00149943058736738 0.999250284706316 32 0.00102551393620651 0.00205102787241302 0.998974486063793 33 0.00112605138600281 0.00225210277200562 0.998873948613997 34 0.00089729906921231 0.00179459813842462 0.999102700930788 35 0.00454461854199947 0.00908923708399894 0.995455381458 36 0.00586864390370277 0.0117372878074055 0.994131356096297 37 0.0538733865439854 0.107746773087971 0.946126613456015 38 0.153630104054221 0.307260208108443 0.846369895945779 39 0.222360255309370 0.444720510618741 0.77763974469063 40 0.662484589996821 0.675030820006358 0.337515410003179 41 0.649515789993387 0.700968420013226 0.350484210006613 42 0.547107389922018 0.905785220155964 0.452892610077982 43 0.434520448212209 0.869040896424417 0.565479551787791 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.666666666666667 NOK 5% type I error level 20 0.740740740740741 NOK 10% type I error level 20 0.740740740740741 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/10nyg81258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/10nyg81258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/1p6cj1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/1p6cj1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/2lyrb1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/2lyrb1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/3aaod1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/3aaod1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/4p50u1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/4p50u1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/5uuqc1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/5uuqc1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/6o9ye1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/6o9ye1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/7sgkm1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/7sgkm1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/8f5gz1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/8f5gz1258622546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/9x1ye1258622546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7/9x1ye1258622546.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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