WS72

*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: Sun, 22 Nov 2009 11:36:31 -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/22/t1258915038hdursbk6ertht9g.htm/, Retrieved Sun, 22 Nov 2009 19:37:30 +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/22/t1258915038hdursbk6ertht9g.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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104.89 124 105.15 118.63 105.24 121.86 105.57 119.97 105.62 125.03 106.17 130.09 106.27 126.65 106.41 121.7 106.94 119.24 107.16 122.63 107.32 116.66 107.32 114.12 107.35 113.11 107.55 112.61 107.87 113.4 108.37 115.18 108.38 121.01 107.92 119.44 108.03 116.68 108.14 117.07 108.3 117.41 108.64 119.58 108.66 120.92 109.04 117.09 109.03 116.77 109.03 119.39 109.54 122.49 109.75 124.08 109.83 118.29 109.65 112.94 109.82 113.79 109.95 114.43 110.12 118.7 110.15 120.36 110.21 118.27 109.99 118.34 110.14 117.82 110.14 117.65 110.81 118.18 110.97 121.02 110.99 124.78 109.73 131.16 109.81 130.14 110.02 131.75 110.18 134.73 110.21 135.35 110.25 140.32 110.36 136.35 110.51 131.6 110.6 128.9 110.95 133.89 111.18 138.25 111.19 146.23 111.69 144.76 111.7 149.3 111.83 156.8 111.77 159.08 111.73 165.12 112.01 163.14 111.86 153.43 112.04 151.01

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 AKW[t] = + 109.731009518592 -0.0343479895600967AKB[t] + 0.123957829007378t + 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) 109.731009518592 0.696526 157.5405 0 0 AKB -0.0343479895600967 0.006265 -5.4828 1e-06 0 t 0.123957829007378 0.004774 25.9647 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.973918245314072 R-squared 0.948516748555642 Adjusted R-squared 0.946741464023078 F-TEST (value) 534.289986285004 F-TEST (DF numerator) 2 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.44716919389781 Sum Squared Residuals 11.5976967023306 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.89 105.595816642147 -0.705816642147231 2 105.15 105.904223175093 -0.754223175092508 3 105.24 105.917236997821 -0.677236997820783 4 105.57 106.106112527097 -0.536112527096747 5 105.62 106.05626952893 -0.436269528930024 6 106.17 106.006426530763 0.163573469236684 7 106.27 106.248541443857 0.0214585561425686 8 106.41 106.542521821187 -0.132521821187287 9 106.94 106.750975704512 0.189024295487498 10 107.16 106.758493848911 0.401506151088847 11 107.32 107.087509175592 0.232490824407689 12 107.32 107.298710898082 0.0212891019176655 13 107.35 107.457360196545 -0.107360196545409 14 107.55 107.598492020333 -0.0484920203328324 15 107.87 107.695314937588 0.174685062412274 16 108.37 107.758133345178 0.611866654821868 17 108.38 107.681842395050 0.698157604949845 18 107.92 107.859726567667 0.060273432333121 19 108.03 108.078484847860 -0.048484847860124 20 108.14 108.189046960939 -0.0490469609390653 21 108.3 108.301326473496 -0.00132647349601354 22 108.64 108.350749165158 0.289250834842022 23 108.66 108.428680688155 0.23131931184517 24 109.04 108.684191317177 0.355808682822631 25 109.03 108.819140502844 0.210859497156017 26 109.03 108.853106599204 0.176893400796093 27 109.54 108.870585660575 0.66941433942502 28 109.75 108.939930186182 0.81006981381819 29 109.83 109.262762874742 0.567237125257851 30 109.65 109.570482447896 0.0795175521039623 31 109.82 109.665244485777 0.154755514222655 32 109.95 109.767219601466 0.182780398533748 33 110.12 109.744511515052 0.375488484947985 34 110.15 109.811451681390 0.338548318610368 35 110.21 110.007196808578 0.202803191422377 36 109.99 110.128750278316 -0.138750278315793 37 110.14 110.270569061894 -0.130569061894416 38 110.14 110.400366049127 -0.26036604912701 39 110.81 110.506119443668 0.303880556332466 40 110.97 110.532528982324 0.437471017675758 41 110.99 110.527338370586 0.46266162941434 42 109.73 110.432156026200 -0.702156026199612 43 109.81 110.591148804558 -0.781148804558290 44 110.02 110.659806370374 -0.639806370373918 45 110.18 110.681407190492 -0.501407190492198 46 110.21 110.784069265972 -0.574069265972329 47 110.25 110.737317586866 -0.48731758686602 48 110.36 110.997636934427 -0.637636934426982 49 110.51 111.284747713845 -0.774747713844813 50 110.6 111.501445114664 -0.901445114664462 51 110.95 111.454006475767 -0.50400647576695 52 111.18 111.428207070292 -0.248207070292302 53 111.19 111.278067942610 -0.0880679426101177 54 111.69 111.452517316271 0.237482683729163 55 111.7 111.420535272675 0.279464727324629 56 111.83 111.286883179982 0.543116820017972 57 111.77 111.332527592792 0.437472407207611 58 111.73 111.249023564857 0.480976435143226 59 112.01 111.440990413193 0.569009586806857 60 111.86 111.898467220829 -0.0384672208290643 61 112.04 112.105547184572 -0.0655471845718699 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.052001162738918 0.104002325477836 0.947998837261082 7 0.0153089586708070 0.0306179173416139 0.984691041329193 8 0.00436935562053882 0.00873871124107763 0.99563064437946 9 0.00593000818568103 0.0118600163713621 0.994069991814319 10 0.00244536509717063 0.00489073019434126 0.99755463490283 11 0.000736655965447165 0.00147331193089433 0.999263344034553 12 0.000759462453610602 0.00151892490722120 0.99924053754639 13 0.00177622226027771 0.00355244452055542 0.998223777739722 14 0.00174240001777443 0.00348480003554885 0.998257599982226 15 0.00081407208301982 0.00162814416603964 0.99918592791698 16 0.000427656445507792 0.000855312891015584 0.999572343554492 17 0.000251675429203963 0.000503350858407926 0.999748324570796 18 0.0132807213352121 0.0265614426704242 0.986719278664788 19 0.0513832772586011 0.102766554517202 0.948616722741399 20 0.106056278300795 0.212112556601589 0.893943721699205 21 0.148225362639775 0.296450725279550 0.851774637360225 22 0.124368979275466 0.248737958550932 0.875631020724534 23 0.120859639214345 0.241719278428689 0.879140360785655 24 0.0885585991780521 0.177117198356104 0.911441400821948 25 0.0753837672491183 0.150767534498237 0.924616232750882 26 0.0766753704776331 0.153350740955266 0.923324629522367 27 0.0541794047251646 0.108358809450329 0.945820595274835 28 0.0385551327242827 0.0771102654485654 0.961444867275717 29 0.0257146571441168 0.0514293142882337 0.974285342855883 30 0.0265950858361949 0.0531901716723898 0.973404914163805 31 0.0231689960488151 0.0463379920976302 0.976831003951185 32 0.0198376277432300 0.0396752554864599 0.98016237225677 33 0.0162152467819164 0.0324304935638328 0.983784753218084 34 0.0147705492908951 0.0295410985817901 0.985229450709105 35 0.0162989992320634 0.0325979984641268 0.983701000767937 36 0.0277625199995066 0.0555250399990131 0.972237480000493 37 0.0373084828935053 0.0746169657870105 0.962691517106495 38 0.0515280839599898 0.103056167919980 0.94847191604001 39 0.100041437105112 0.200082874210223 0.899958562894889 40 0.395983923505107 0.791967847010215 0.604016076494893 41 0.99724763040462 0.00550473919076133 0.00275236959538067 42 0.998762002823673 0.00247599435265345 0.00123799717632673 43 0.9988491605804 0.0023016788391994 0.0011508394195997 44 0.998036295648763 0.00392740870247357 0.00196370435123678 45 0.99595237394728 0.00809525210544047 0.00404762605272023 46 0.991945242775261 0.0161095144494779 0.00805475722473897 47 0.989148761259929 0.0217024774801422 0.0108512387400711 48 0.989487638366013 0.0210247232679731 0.0105123616339866 49 0.989218079717433 0.0215638405651334 0.0107819202825667 50 0.99352716237443 0.0129456752511382 0.00647283762556909 51 0.99000608522291 0.0199878295541820 0.00999391477709102 52 0.981431988031493 0.0371360239370138 0.0185680119685069 53 0.997980072122548 0.00403985575490325 0.00201992787745162 54 0.992160737833225 0.0156785243335508 0.00783926216677538 55 0.971079792632564 0.0578404147348727 0.0289202073674363 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.3 NOK 5% type I error level 31 0.62 NOK 10% type I error level 37 0.74 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/10ime41258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/10ime41258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/16jm21258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/16jm21258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/2rry51258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/2rry51258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/3zthp1258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/3zthp1258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/4jr1y1258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/4jr1y1258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/519oi1258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/519oi1258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/6gq641258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/6gq641258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/7pqsn1258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/7pqsn1258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/8dyl41258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/8dyl41258914986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/9of1v1258914986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915038hdursbk6ertht9g/9of1v1258914986.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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