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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: Wed, 18 Nov 2009 07:52:22 -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/18/t1258556011gym7b278la6ff0g.htm/, Retrieved Wed, 18 Nov 2009 15:53:43 +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/18/t1258556011gym7b278la6ff0g.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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8,2 25,5 8,3 25,5 8,1 25,5 7,4 20,9 7,3 20,9 7,7 20,9 8 22,3 8 22,3 7,7 22,3 6,9 19,9 6,6 19,9 6,9 19,9 7,5 24,1 7,9 24,1 7,7 24,1 6,5 13,8 6,1 13,8 6,4 13,8 6,8 16,2 7,1 16,2 7,3 16,2 7,2 18,6 7 18,6 7 18,6 7 22,4 7,3 22,4 7,5 22,4 7,2 22,6 7,7 22,6 8 22,6 7,9 20 8 20 8 20 7,9 21,8 7,9 21,8 8 21,8 8,1 28,7 8,1 28,7 8,2 28,7 8 19,5 8,3 19,5 8,5 19,5 8,6 19,4 8,7 19,4 8,7 19,4 8,5 21,7 8,4 21,7 8,5 21,7 8,7 26,2 8,7 26,2 8,6 26,2 7,9 19,1 8,1 19,1 8,2 19,1 8,5 21,3 8,6 21,3 8,5 21,3 8,3 24,1 8,2 24,1 8,7 24,1

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 Y[t] = + 4.16181097906056 + 0.140421618562535X[t] -0.296852126957176M1[t] -0.155697745708356M2[t] -0.214543364459535M3[t] + 0.0172250518770039M4[t] + 0.0983794331258246M5[t] + 0.339533814374645M6[t] + 0.428009927372194M7[t] + 0.529164308621014M8[t] + 0.470318689869835M9[t] -0.0223087624976419M10[t] -0.181154381248821M11[t] + 0.018845618751179t + 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) 4.16181097906056 0.483081 8.6151 0 0 X 0.140421618562535 0.02242 6.2632 0 0 M1 -0.296852126957176 0.262005 -1.133 0.263085 0.131542 M2 -0.155697745708356 0.261291 -0.5959 0.554177 0.277089 M3 -0.214543364459535 0.26061 -0.8232 0.414622 0.207311 M4 0.0172250518770039 0.242513 0.071 0.943684 0.471842 M5 0.0983794331258246 0.242413 0.4058 0.686746 0.343373 M6 0.339533814374645 0.24235 1.401 0.167923 0.083962 M7 0.428009927372194 0.240273 1.7814 0.081458 0.040729 M8 0.529164308621014 0.240226 2.2028 0.032659 0.01633 M9 0.470318689869835 0.240218 1.9579 0.056325 0.028163 M10 -0.0223087624976419 0.238499 -0.0935 0.925882 0.462941 M11 -0.181154381248821 0.238441 -0.7597 0.451284 0.225642 t 0.018845618751179 0.003028 6.2241 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.863371612166365 R-squared 0.745410540694749 Adjusted R-squared 0.6734613456737 F-TEST (value) 10.3602346138365 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.02096775478344e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.376978214389973 Sum Squared Residuals 6.53717840973402 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.4645557441992 0.735444255800796 2 8.3 7.6245557441992 0.675444255800792 3 8.1 7.5845557441992 0.515444255800792 4 7.4 7.18923033389926 0.210769666100737 5 7.3 7.28923033389926 0.0107696661007361 6 7.7 7.54923033389926 0.150769666100736 7 8 7.85314233163554 0.14685766836446 8 8 7.97314233163554 0.0268576683644611 9 7.7 7.93314233163554 -0.23314233163554 10 6.9 7.12234861346916 -0.222348613469157 11 6.6 6.98234861346916 -0.382348613469158 12 6.9 7.18234861346916 -0.282348613469157 13 7.5 7.4941129032258 0.00588709677419252 14 7.9 7.6541129032258 0.245887096774195 15 7.7 7.6141129032258 0.0858870967741937 16 6.5 6.41838426711941 0.0816157328805864 17 6.1 6.51838426711941 -0.418384267119414 18 6.4 6.77838426711941 -0.378384267119413 19 6.8 7.22271788341822 -0.422717883418224 20 7.1 7.34271788341822 -0.242717883418224 21 7.3 7.30271788341822 -0.00271788341822402 22 7.2 7.16594793435201 0.0340520656479897 23 7 7.02594793435201 -0.0259479343520098 24 7 7.22594793435201 -0.225947934352010 25 7 7.48154357668365 -0.481543576683646 26 7.3 7.64154357668365 -0.341543576683646 27 7.5 7.60154357668365 -0.101543576683645 28 7.2 7.88024193548387 -0.68024193548387 29 7.7 7.98024193548387 -0.28024193548387 30 8 8.24024193548387 -0.24024193548387 31 7.9 7.98246745897 -0.0824674589700051 32 8 8.10246745897 -0.102467458970005 33 8 8.06246745897 -0.0624674589700053 34 7.9 7.84144453876627 0.0585554612337295 35 7.9 7.70144453876627 0.19855546123373 36 8 7.90144453876627 0.0985554612337294 37 8.1 8.59234719864176 -0.492347198641766 38 8.1 8.75234719864176 -0.652347198641765 39 8.2 8.71234719864176 -0.512347198641765 40 8 7.67108234295416 0.32891765704584 41 8.3 7.77108234295416 0.528917657045841 42 8.5 8.03108234295416 0.468917657045841 43 8.6 8.12436191284663 0.475638087153367 44 8.7 8.24436191284663 0.455638087153367 45 8.7 8.20436191284663 0.495638087153367 46 8.5 8.05354980192416 0.446450198075835 47 8.4 7.91354980192417 0.486450198075836 48 8.5 8.11354980192417 0.386450198075835 49 8.7 8.46744057724958 0.232559422750423 50 8.7 8.62744057724958 0.0725594227504237 51 8.6 8.58744057724958 0.0125594227504248 52 7.9 7.8410611205433 0.0589388794567062 53 8.1 7.9410611205433 0.158938879456706 54 8.2 8.2010611205433 -0.00106112054329452 55 8.5 8.6173104131296 -0.117310413129598 56 8.6 8.7373104131296 -0.137310413129598 57 8.5 8.6973104131296 -0.197310413129597 58 8.3 8.6167091114884 -0.316709111488397 59 8.2 8.4767091114884 -0.276709111488398 60 8.7 8.6767091114884 0.0232908885116015 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0901559833530818 0.180311966706164 0.909844016646918 18 0.0626714840129637 0.125342968025927 0.937328515987036 19 0.0375524347403779 0.0751048694807557 0.962447565259622 20 0.0202053548857456 0.0404107097714912 0.979794645114254 21 0.116598970745293 0.233197941490585 0.883401029254707 22 0.262965494130865 0.52593098826173 0.737034505869135 23 0.348805228094003 0.697610456188006 0.651194771905997 24 0.330681039319336 0.661362078638671 0.669318960680664 25 0.562627480418099 0.874745039163802 0.437372519581901 26 0.689328183486594 0.621343633026813 0.310671816513406 27 0.821017298636882 0.357965402726236 0.178982701363118 28 0.77147291672345 0.457054166553099 0.228527083276550 29 0.789987859820294 0.420024280359412 0.210012140179706 30 0.79849396548533 0.403012069029339 0.201506034514670 31 0.818380219105684 0.363239561788632 0.181619780894316 32 0.824815865134824 0.350368269730351 0.175184134865176 33 0.829934701492678 0.340130597014645 0.170065298507322 34 0.84377905761126 0.312441884777478 0.156220942388739 35 0.869221753381783 0.261556493236435 0.130778246618217 36 0.962509343486065 0.0749813130278698 0.0374906565139349 37 0.962410431048413 0.0751791379031735 0.0375895689515867 38 0.98376973854779 0.0324605229044204 0.0162302614522102 39 0.99805526561513 0.00388946876973915 0.00194473438486958 40 0.997167047132083 0.0056659057358346 0.0028329528679173 41 0.994746979717452 0.0105060405650971 0.00525302028254853 42 0.984606810537072 0.0307863789258555 0.0153931894629278 43 0.95212084769959 0.0957583046008193 0.0478791523004096 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0740740740740741 NOK 5% type I error level 6 0.222222222222222 NOK 10% type I error level 10 0.370370370370370 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/10g36q1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/10g36q1258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/1r94h1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/1r94h1258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/2d9q41258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/2d9q41258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/3cmv31258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/3cmv31258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/4q6x91258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/4q6x91258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/52n001258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/52n001258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/6j65m1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/6j65m1258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/7o71k1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/7o71k1258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/8bdfc1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/8bdfc1258555937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/97l2s1258555937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556011gym7b278la6ff0g/97l2s1258555937.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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