*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 10:08:20 -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/t1258221017wbthwpcatf93ysw.htm/, Retrieved Sat, 14 Nov 2009 18:50:29 +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/t1258221017wbthwpcatf93ysw.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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1.7 0 2.4 0 2.0 0 2.1 0 2.0 0 1.8 0 2.7 0 2.3 0 1.9 0 2.0 0 2.3 0 2.8 0 2.4 0 2.3 0 2.7 0 2.7 0 2.9 0 3.0 0 2.2 0 2.3 0 2.8 0 2.8 0 2.8 0 2.2 0 2.6 0 2.8 0 2.5 0 2.4 0 2.3 0 1.9 0 1.7 0 2.0 0 2.1 0 1.7 0 1.8 0 1.8 0 1.8 0 1.3 0 1.3 0 1.3 0 1.2 0 1.4 0 2.2 1 2.9 1 3.1 1 3.5 1 3.6 1 4.4 1 4.1 1 5.1 1 5.8 1 5.9 1 5.4 1 5.5 1 4.8 1 3.2 1 2.7 1 2.1 1 1.9 1 0.6 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] = + 2.16666666666667 + 1.54444444444444X[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) 2.16666666666667 0.141872 15.272 0 0 X 1.54444444444444 0.259022 5.9626 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.616464054275514 R-squared 0.380027930213804 Adjusted R-squared 0.369338756596800 F-TEST (value) 35.5526015228426 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.57079274698546e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.919436775789575 Sum Squared Residuals 49.0311111111111 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.7 2.16666666666668 -0.466666666666677 2 2.4 2.16666666666667 0.233333333333335 3 2 2.16666666666667 -0.166666666666666 4 2.1 2.16666666666667 -0.0666666666666663 5 2 2.16666666666667 -0.166666666666666 6 1.8 2.16666666666667 -0.366666666666666 7 2.7 2.16666666666667 0.533333333333334 8 2.3 2.16666666666667 0.133333333333333 9 1.9 2.16666666666667 -0.266666666666666 10 2 2.16666666666667 -0.166666666666666 11 2.3 2.16666666666667 0.133333333333333 12 2.8 2.16666666666667 0.633333333333333 13 2.4 2.16666666666667 0.233333333333334 14 2.3 2.16666666666667 0.133333333333333 15 2.7 2.16666666666667 0.533333333333334 16 2.7 2.16666666666667 0.533333333333334 17 2.9 2.16666666666667 0.733333333333333 18 3 2.16666666666667 0.833333333333333 19 2.2 2.16666666666667 0.0333333333333338 20 2.3 2.16666666666667 0.133333333333333 21 2.8 2.16666666666667 0.633333333333333 22 2.8 2.16666666666667 0.633333333333333 23 2.8 2.16666666666667 0.633333333333333 24 2.2 2.16666666666667 0.0333333333333338 25 2.6 2.16666666666667 0.433333333333334 26 2.8 2.16666666666667 0.633333333333333 27 2.5 2.16666666666667 0.333333333333334 28 2.4 2.16666666666667 0.233333333333334 29 2.3 2.16666666666667 0.133333333333333 30 1.9 2.16666666666667 -0.266666666666666 31 1.7 2.16666666666667 -0.466666666666666 32 2 2.16666666666667 -0.166666666666666 33 2.1 2.16666666666667 -0.0666666666666663 34 1.7 2.16666666666667 -0.466666666666666 35 1.8 2.16666666666667 -0.366666666666666 36 1.8 2.16666666666667 -0.366666666666666 37 1.8 2.16666666666667 -0.366666666666666 38 1.3 2.16666666666667 -0.866666666666666 39 1.3 2.16666666666667 -0.866666666666666 40 1.3 2.16666666666667 -0.866666666666666 41 1.2 2.16666666666667 -0.966666666666667 42 1.4 2.16666666666667 -0.766666666666667 43 2.2 3.71111111111111 -1.51111111111111 44 2.9 3.71111111111111 -0.811111111111111 45 3.1 3.71111111111111 -0.611111111111111 46 3.5 3.71111111111111 -0.211111111111111 47 3.6 3.71111111111111 -0.111111111111111 48 4.4 3.71111111111111 0.688888888888889 49 4.1 3.71111111111111 0.388888888888889 50 5.1 3.71111111111111 1.38888888888889 51 5.8 3.71111111111111 2.08888888888889 52 5.9 3.71111111111111 2.18888888888889 53 5.4 3.71111111111111 1.68888888888889 54 5.5 3.71111111111111 1.78888888888889 55 4.8 3.71111111111111 1.08888888888889 56 3.2 3.71111111111111 -0.511111111111111 57 2.7 3.71111111111111 -1.01111111111111 58 2.1 3.71111111111111 -1.61111111111111 59 1.9 3.71111111111111 -1.81111111111111 60 0.6 3.71111111111111 -3.11111111111111 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0355539398381246 0.0711078796762492 0.964446060161875 6 0.0119613286583257 0.0239226573166515 0.988038671341674 7 0.0215215134865314 0.0430430269730627 0.978478486513469 8 0.00810966239006212 0.0162193247801242 0.991890337609938 9 0.00307092148799074 0.00614184297598148 0.99692907851201 10 0.000965650309283763 0.00193130061856753 0.999034349690716 11 0.000335097805223025 0.000670195610446051 0.999664902194777 12 0.000619761472194523 0.00123952294438905 0.999380238527805 13 0.000240056288592228 0.000480112577184456 0.999759943711408 14 7.82059646811041e-05 0.000156411929362208 0.99992179403532 15 6.19581445815772e-05 0.000123916289163154 0.999938041855418 16 4.27222488289728e-05 8.54444976579457e-05 0.99995727775117 17 5.24807815046808e-05 0.000104961563009362 0.999947519218495 18 7.43649367009166e-05 0.000148729873401833 0.9999256350633 19 2.77190027485567e-05 5.54380054971134e-05 0.999972280997251 20 9.62046405866445e-06 1.92409281173289e-05 0.999990379535941 21 6.60022930476170e-06 1.32004586095234e-05 0.999993399770695 22 4.32718946312074e-06 8.65437892624147e-06 0.999995672810537 23 2.7502829735711e-06 5.5005659471422e-06 0.999997249717026 24 1.02457615234377e-06 2.04915230468755e-06 0.999998975423848 25 4.27923453876952e-07 8.55846907753903e-07 0.999999572076546 26 2.75203579537826e-07 5.50407159075653e-07 0.99999972479642 27 1.02373765388622e-07 2.04747530777244e-07 0.999999897626235 28 3.56522286269114e-08 7.13044572538227e-08 0.999999964347771 29 1.23351528831673e-08 2.46703057663345e-08 0.999999987664847 30 8.06740475703565e-09 1.61348095140713e-08 0.999999991932595 31 9.33327206354531e-09 1.86665441270906e-08 0.999999990666728 32 4.21456154547106e-09 8.42912309094211e-09 0.999999995785438 33 1.61961907412169e-09 3.23923814824339e-09 0.99999999838038 34 1.47837906404544e-09 2.95675812809088e-09 0.99999999852162 35 9.12215174161096e-10 1.82443034832219e-09 0.999999999087785 36 5.33550099445203e-10 1.06710019889041e-09 0.99999999946645 37 3.00206087096422e-10 6.00412174192843e-10 0.999999999699794 38 7.84637061668678e-10 1.56927412333736e-09 0.999999999215363 39 1.43683535146829e-09 2.87367070293658e-09 0.999999998563165 40 2.03100604567757e-09 4.06201209135515e-09 0.999999997968994 41 3.18455023517559e-09 6.36910047035117e-09 0.99999999681545 42 2.41091734900582e-09 4.82183469801164e-09 0.999999997589083 43 1.84720646969842e-09 3.69441293939685e-09 0.999999998152794 44 1.11675141153569e-09 2.23350282307137e-09 0.999999998883249 45 5.59576968585801e-10 1.11915393717160e-09 0.999999999440423 46 3.20800352217768e-10 6.41600704435536e-10 0.9999999996792 47 1.53281084133218e-10 3.06562168266436e-10 0.999999999846719 48 3.30318492637441e-10 6.60636985274882e-10 0.999999999669682 49 1.74186145022686e-10 3.48372290045372e-10 0.999999999825814 50 1.44629334463682e-09 2.89258668927363e-09 0.999999998553707 51 1.02162151978543e-07 2.04324303957086e-07 0.999999897837848 52 5.05731225889118e-06 1.01146245177824e-05 0.99999494268774 53 4.83224476678953e-05 9.66448953357906e-05 0.999951677552332 54 0.00229601160869069 0.00459202321738137 0.99770398839131 55 0.0688341819127632 0.137668363825526 0.931165818087237 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 46 0.901960784313726 NOK 5% type I error level 49 0.96078431372549 NOK 10% type I error level 50 0.980392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/10gc4f1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/10gc4f1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/1dwed1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/1dwed1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/2od6p1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/2od6p1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/3ropi1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/3ropi1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/4btvk1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/4btvk1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/5oi4p1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/5oi4p1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/6ss1s1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/6ss1s1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/7r5yt1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/7r5yt1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/8ksqa1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/8ksqa1258218495.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/929el1258218495.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258221017wbthwpcatf93ysw/929el1258218495.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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