WS 7: Multiple Regression 5

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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 04:09:40 -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/t12587154657tzecmno3qiaysj.htm/, Retrieved Fri, 20 Nov 2009 12:11:23 +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/t12587154657tzecmno3qiaysj.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:

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Dataseries X:

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113.6 123.06 83.4 79.8 112.9 123.39 113.6 83.4 104 120.28 112.9 113.6 109.9 115.33 104 112.9 99 110.4 109.9 104 106.3 114.49 99 109.9 128.9 132.03 106.3 99 111.1 123.16 128.9 106.3 102.9 118.82 111.1 128.9 130 128.32 102.9 111.1 87 112.24 130 102.9 87.5 104.53 87 130 117.6 132.57 87.5 87 103.4 122.52 117.6 87.5 110.8 131.8 103.4 117.6 112.6 124.55 110.8 103.4 102.5 120.96 112.6 110.8 112.4 122.6 102.5 112.6 135.6 145.52 112.4 102.5 105.1 118.57 135.6 112.4 127.7 134.25 105.1 135.6 137 136.7 127.7 105.1 91 121.37 137 127.7 90.5 111.63 91 137 122.4 134.42 90.5 91 123.3 137.65 122.4 90.5 124.3 137.86 123.3 122.4 120 119.77 124.3 123.3 118.1 130.69 120 124.3 119 128.28 118.1 120 142.7 147.45 119 118.1 123.6 128.42 142.7 119 129.6 136.9 123.6 142.7 151.6 143.95 129.6 123.6 110.4 135.64 151.6 129.6 99.2 122.48 110.4 151.6 130.5 136.83 99.2 110.4 136.2 153.04 130.5 99.2 129.7 142.71 136.2 130.5 128 123.46 129.7 136.2 121.6 144.37 128 129.7 135.8 146.15 121.6 128 143.8 147.61 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -37.2940689141077 + 0.73903374269513X[t] + 0.15933358934725Y1[t] + 0.234922754766544Y2[t] + 25.8124488053722M1[t] + 18.8216956913314M2[t] + 10.7186918552078M3[t] + 22.8169458116256M4[t] + 10.4075301409941M5[t] + 18.6651780109597M6[t] + 26.9840623171739M7[t] + 14.8732723526244M8[t] + 12.0424536035340M9[t] + 29.6388908477154M10[t] -5.40627929365437M11[t] -0.111511471449605t + 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) -37.2940689141077 7.498728 -4.9734 1.2e-05 6e-06 X 0.73903374269513 0.075102 9.8404 0 0 Y1 0.15933358934725 0.075192 2.119 0.040045 0.020022 Y2 0.234922754766544 0.088372 2.6583 0.011062 0.005531 M1 25.8124488053722 5.439057 4.7458 2.4e-05 1.2e-05 M2 18.8216956913314 6.631915 2.838 0.006962 0.003481 M3 10.7186918552078 4.042411 2.6516 0.011254 0.005627 M4 22.8169458116256 3.728629 6.1194 0 0 M5 10.4075301409941 4.259719 2.4432 0.018841 0.00942 M6 18.6651780109597 3.889951 4.7983 2e-05 1e-05 M7 26.9840623171739 5.330371 5.0623 9e-06 4e-06 M8 14.8732723526244 5.069271 2.934 0.005402 0.002701 M9 12.0424536035340 3.450527 3.49 0.001149 0.000574 M10 29.6388908477154 4.712545 6.2894 0 0 M11 -5.40627929365437 5.118222 -1.0563 0.296878 0.148439 t -0.111511471449605 0.038 -2.9345 0.005395 0.002697 Multiple Linear Regression - Regression Statistics Multiple R 0.97835470633545 R-squared 0.957177931408722 Adjusted R-squared 0.941884335483265 F-TEST (value) 62.5868458977316 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.06605318956999 Sum Squared Residuals 694.377118697318 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 113.6 111.387617977808 2.21238202219182 2 112.9 110.186830842854 2.71316915714603 3 104 106.657054276905 -2.65705427690547 4 109.9 113.403064862006 -3.50306486200567 5 99 96.0879570281641 2.91204297183586 6 106.3 106.906049563541 -0.606049563540803 7 128.9 126.678551420458 2.22144857954247 8 111.1 113.216895915796 -2.11689591579626 9 102.9 109.540275619302 -6.6402756193022 10 130 128.557861480146 1.44213851985418 11 87 83.9090909670136 3.09090903298642 12 87.5 83.0209709452805 4.4790290547195 13 117.6 119.422402764087 -1.82240276408679 14 103.4 109.806251481246 -6.40625148124578 15 110.8 113.258607255625 -2.45860725562543 16 112.6 117.730520549539 -5.13052054953865 17 102.5 104.581691117279 -2.08169111727950 18 112.4 112.753434559988 -0.353434559988104 19 135.6 137.104143488721 -1.50414348872073 20 105.1 110.987157232133 -5.88715723213283 21 127.7 120.223409532545 7.47659046745484 22 137 135.954763073748 1.04523692625173 23 91 96.259750824066 -5.25975082406591 24 90.5 89.2117665017755 1.28823349822453 25 122.4 120.869169317785 1.53083068221457 26 123.3 121.119263843994 2.1807361560057 27 124.3 120.697381729852 3.60261827014765 28 120 119.685767878103 0.314232121897236 29 118.1 114.784877526826 3.31512247317414 30 119 119.837040940191 -0.83704094019072 31 142.7 141.908737618777 0.791262381223005 32 123.6 119.610260606109 3.98973939389073 33 129.6 125.459334255059 4.14066574494139 34 151.6 144.623424833834 6.97657516616645 35 110.4 108.240248313456 2.15975168654358 36 99.2 102.413088805551 -3.21308880555058 37 130.5 127.255806650078 3.24419334992250 38 136.2 134.489285526859 1.71071447314124 39 129.7 126.901835340717 2.79816465928299 40 128 124.965569650216 3.03443034978389 41 121.6 126.099973060017 -4.49997306001733 42 135.8 134.142485865605 1.65751413439484 43 143.8 144.187879302930 -0.387879302929657 44 147.5 144.631617494770 2.86838250522960 45 136.2 135.947538711605 0.252461288395305 46 156.6 165.545154675918 -8.94515467591782 47 123.3 123.290909895464 0.00909010453591619 48 104.5 107.054173747393 -2.55417374739345 49 139.8 144.965003290242 -5.16500329024210 50 136.5 136.698368305047 -0.198368305047200 51 112.1 113.385121396900 -1.28512139689974 52 118.5 113.215077060137 5.28492293986319 53 94.4 94.0455012677132 0.354498732286814 54 102.3 102.160989070675 0.139010929324792 55 111.4 112.520688169115 -1.12068816911509 56 99.2 98.0540687511912 1.14593124880876 57 87.8 93.0294418814893 -5.22944188148933 58 115.8 116.318795936355 -0.518795936354544 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.119594619944799 0.239189239889598 0.880405380055201 20 0.173043641702731 0.346087283405461 0.82695635829727 21 0.716451743344577 0.567096513310846 0.283548256655423 22 0.713972901512712 0.572054196974576 0.286027098487288 23 0.753240516270224 0.493518967459551 0.246759483729776 24 0.646494500981502 0.707010998036997 0.353505499018498 25 0.642618543935289 0.714762912129422 0.357381456064711 26 0.597015303984637 0.805969392030726 0.402984696015363 27 0.6696891984702 0.6606216030596 0.3303108015298 28 0.846218585659994 0.307562828680013 0.153781414340006 29 0.786071917842032 0.427856164315935 0.213928082157968 30 0.813522371107844 0.372955257784312 0.186477628892156 31 0.727320797787165 0.54535840442567 0.272679202212835 32 0.793481037001964 0.413037925996073 0.206518962998036 33 0.825617233066176 0.348765533867648 0.174382766933824 34 0.838006928167271 0.323986143665458 0.161993071832729 35 0.746544798372633 0.506910403254735 0.253455201627367 36 0.691152798154631 0.617694403690738 0.308847201845369 37 0.65110239433112 0.69779521133776 0.34889760566888 38 0.646960654216648 0.706078691566704 0.353039345783352 39 0.538400954296748 0.923198091406504 0.461599045703252 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/20/t12587154657tzecmno3qiaysj/1050i51258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/1050i51258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/1jl9e1258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/1jl9e1258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/2gj351258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/2gj351258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/3x9qu1258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/3x9qu1258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/4u04b1258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/4u04b1258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/5ec131258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/5ec131258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/6q3ky1258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/6q3ky1258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/7er971258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/7er971258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/8otn01258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/8otn01258715376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/9jupa1258715376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587154657tzecmno3qiaysj/9jupa1258715376.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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