workshop7

*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 11:59:06 -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/t1258657321ryako7a0bcm1mv8.htm/, Retrieved Thu, 19 Nov 2009 20:02:13 +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/t1258657321ryako7a0bcm1mv8.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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No (this computation is public)

User-defined keywords:

Dataseries X:

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5.4 2.7 5.4 2.5 5.6 2.2 5.7 2.9 5.8 3.1 5.8 3 5.8 2.8 5.9 2.5 6.1 1.9 6.4 1.9 6.4 1.8 6.3 2 6.2 2.6 6.2 2.5 6.3 2.5 6.4 1.6 6.5 1.4 6.6 0.8 6.6 1.1 6.6 1.3 6.8 1.2 7 1.3 7.2 1.1 7.3 1.3 7.5 1.2 7.6 1.6 7.6 1.7 7.7 1.5 7.7 0.9 7.7 1.5 7.7 1.4 7.6 1.6 7.7 1.7 7.9 1.4 7.9 1.8 7.9 1.7 7.8 1.4 7.6 1.2 7.4 1 7 1.7 7 2.4 7.2 2 7.5 2.1 7.8 2 7.8 1.8 7.7 2.7 7.6 2.3 7.6 1.9 7.5 2 7.5 2.3 7.6 2.8 7.6 2.4 7.9 2.3 7.6 2.7 7.5 2.7 7.5 2.9 7.6 3 7.7 2.2 7.8 2.3 7.9 2.8 7.9 2.8

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] = + 7.9572857373039 -0.287260689331907X[t] -0.299250611551363M1[t] -0.517019144853447M2[t] -0.47127393106681M3[t] -0.497019144853447M4[t] -0.397019144853448M5[t] -0.402764358640086M6[t] -0.357019144853448M7[t] -0.285528717280171M8[t] -0.205745213786639M9[t] -0.0714904275732762M10[t] -0.0429808551465527M11[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) 7.9572857373039 0.486162 16.3676 0 0 X -0.287260689331907 0.168598 -1.7038 0.094882 0.047441 M1 -0.299250611551363 0.487923 -0.6133 0.542563 0.271282 M2 -0.517019144853447 0.508847 -1.0161 0.314695 0.157347 M3 -0.47127393106681 0.508947 -0.926 0.35909 0.179545 M4 -0.497019144853447 0.508847 -0.9768 0.333587 0.166794 M5 -0.397019144853448 0.508847 -0.7802 0.439084 0.219542 M6 -0.402764358640086 0.508769 -0.7916 0.432462 0.216231 M7 -0.357019144853448 0.508847 -0.7016 0.486302 0.243151 M8 -0.285528717280171 0.50907 -0.5609 0.577486 0.288743 M9 -0.205745213786639 0.508679 -0.4045 0.687664 0.343832 M10 -0.0714904275732762 0.508713 -0.1405 0.888828 0.444414 M11 -0.0429808551465527 0.508847 -0.0845 0.933036 0.466518 Multiple Linear Regression - Regression Statistics Multiple R 0.336804243371758 R-squared 0.113437098353223 Adjusted R-squared -0.108203627058472 F-TEST (value) 0.511806204128393 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.896743475231945 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.80427473635461 Sum Squared Residuals 31.0491768738373 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5.4 6.88243126455639 -1.48243126455639 2 5.4 6.72211486912068 -1.32211486912068 3 5.6 6.8540382897069 -1.25403828970690 4 5.7 6.62721059338792 -0.92721059338792 5 5.8 6.66975845552154 -0.869758455521539 6 5.8 6.69273931066809 -0.892739310668092 7 5.8 6.79593666232111 -0.995936662321112 8 5.9 6.95360529669396 -1.05360529669396 9 6.1 7.20574521378664 -1.10574521378664 10 6.4 7.34 -0.94 11 6.4 7.39723564135991 -0.997235641359914 12 6.3 7.38276435864009 -1.08276435864009 13 6.2 6.91115733348958 -0.711157333489578 14 6.2 6.72211486912068 -0.522114869120684 15 6.3 6.76786008290732 -0.467860082907322 16 6.4 7.0006494895194 -0.600649489519401 17 6.5 7.15810162738578 -0.658101627385783 18 6.6 7.32471282719829 -0.724712827198289 19 6.6 7.28427983418536 -0.684279834185355 20 6.6 7.29831812389225 -0.69831812389225 21 6.8 7.40682769631897 -0.606827696318973 22 7 7.51235641359914 -0.512356413599145 23 7.2 7.59831812389225 -0.39831812389225 24 7.3 7.58384684117242 -0.283846841172421 25 7.5 7.31332229855425 0.186677701445751 26 7.6 6.9806494895194 0.619350510480598 27 7.6 6.99766863437285 0.602331365627151 28 7.7 7.02937555845259 0.670624441547408 29 7.7 7.30173197205174 0.398268027948263 30 7.7 7.12363034466595 0.576369655334047 31 7.7 7.19810162738578 0.501898372614217 32 7.6 7.21213991709268 0.387860082907322 33 7.7 7.26319735165302 0.436802648346980 34 7.9 7.48363034466595 0.416369655334046 35 7.9 7.39723564135991 0.502764358640086 36 7.9 7.46894256543966 0.431057434560342 37 7.8 7.25587016068787 0.544129839312133 38 7.6 7.09555376525216 0.504446234747835 39 7.4 7.19875111690518 0.201248883094816 40 7 6.97192342058621 0.0280765794137894 41 7 6.87084093805387 0.129159061946125 42 7.2 6.98 0.220000000000001 43 7.5 6.99701914485345 0.502980855146553 44 7.8 7.09723564135991 0.702764358640085 45 7.8 7.23447128271983 0.565528717280171 46 7.7 7.11019144853447 0.589808551465526 47 7.6 7.25360529669396 0.346394703306039 48 7.6 7.41149042757328 0.188509572426723 49 7.5 7.08351374708872 0.416486252911278 50 7.5 6.77956700698707 0.720432993012934 51 7.6 6.68168187610775 0.91831812389225 52 7.6 6.77084093805388 0.829159061946125 53 7.9 6.89956700698707 1.00043299301293 54 7.6 6.77891751746767 0.821082482532335 55 7.5 6.8246627312543 0.675337268745697 56 7.5 6.8387010209612 0.661298979038803 57 7.6 6.88975845552154 0.71024154447846 58 7.7 7.25382179320043 0.446178206799573 59 7.8 7.25360529669396 0.546394703306039 60 7.9 7.15295580717456 0.747044192825441 61 7.9 6.8537051956232 1.04629480437680 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.894631657914402 0.210736684171197 0.105368342085598 17 0.8587072129318 0.282585574136401 0.141292787068200 18 0.800783288348414 0.398433423303172 0.199216711651586 19 0.773998784428965 0.452002431142071 0.226001215571035 20 0.804385425093814 0.391229149812373 0.195614574906186 21 0.864302624401351 0.271394751197297 0.135697375598649 22 0.90796250500002 0.18407498999996 0.09203749499998 23 0.938668958257312 0.122662083485376 0.0613310417426881 24 0.97211614089131 0.05576771821738 0.02788385910869 25 0.991357650304017 0.0172846993919658 0.00864234969598292 26 0.99842791713902 0.00314416572196186 0.00157208286098093 27 0.999434905189885 0.00113018962023032 0.000565094810115158 28 0.99977314704476 0.000453705910480866 0.000226852955240433 29 0.99960841458509 0.00078317082981855 0.000391585414909275 30 0.999781084404076 0.000437831191847082 0.000218915595923541 31 0.999776041291835 0.000447917416330021 0.000223958708165010 32 0.999711420485092 0.000577159029816013 0.000288579514908006 33 0.99971071642243 0.000578567155141884 0.000289283577570942 34 0.999595643525794 0.000808712948412754 0.000404356474206377 35 0.999562070411262 0.000875859177475376 0.000437929588737688 36 0.99929126051811 0.00141747896378093 0.000708739481890466 37 0.998542118278584 0.00291576344283129 0.00145788172141565 38 0.996642160152545 0.00671567969490941 0.00335783984745470 39 0.991591861576667 0.0168162768466661 0.00840813842333303 40 0.989137854754936 0.0217242904901284 0.0108621452450642 41 0.998553875369452 0.00289224926109642 0.00144612463054821 42 0.997810827258185 0.0043783454836294 0.0021891727418147 43 0.992927897801277 0.0141442043974464 0.00707210219872318 44 0.989105556554576 0.0217888868908483 0.0108944434454242 45 0.993298005056891 0.0134039898862172 0.0067019949431086 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.5 NOK 5% type I error level 21 0.7 NOK 10% type I error level 22 0.733333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/10cz1t1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/10cz1t1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/1cq3a1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/1cq3a1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/2f4om1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/2f4om1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/3zrv61258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/3zrv61258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/4sljm1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/4sljm1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/54mrh1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/54mrh1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/68dnu1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/68dnu1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/74soh1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/74soh1258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/8fl551258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/8fl551258657142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/9dm5g1258657142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258657321ryako7a0bcm1mv8/9dm5g1258657142.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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