*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: Fri, 20 Nov 2009 01:50:21 -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/t1258707209wb0fv06zi550fe5.htm/, Retrieved Fri, 20 Nov 2009 09:53:41 +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/t1258707209wb0fv06zi550fe5.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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8.2 267722 8 266003 7.9 262971 7.6 265521 7.6 264676 8.3 270223 8.4 269508 8.4 268457 8.4 265814 8.4 266680 8.6 263018 8.9 269285 8.8 269829 8.3 270911 7.5 266844 7.2 271244 7.4 269907 8.8 271296 9.3 270157 9.3 271322 8.7 267179 8.2 264101 8.3 265518 8.5 269419 8.6 268714 8.5 272482 8.2 268351 8.1 268175 7.9 270674 8.6 272764 8.7 272599 8.7 270333 8.5 270846 8.4 270491 8.5 269160 8.7 274027 8.7 273784 8.6 276663 8.5 274525 8.3 271344 8 271115 8.2 270798 8.1 273911 8.1 273985 8 271917 7.9 273338 7.9 270601 8 273547 8 275363 7.9 281229 8 277793 7.7 279913 7.2 282500 7.5 280041 7.3 282166 7 290304 7 283519 7 287816 7.2 285226 7.3 287595

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 wkh[t] = + 23.7242314611506 -5.62069109049765e-05los[t] -0.0275271564433608M1[t] -0.0940245016618537M2[t] -0.522924687831299M3[t] -0.698702671431273M4[t] -0.82863197409711M5[t] -0.0983733354658894M6[t] + 0.0178126737747341M7[t] + 0.0259354497915659M8[t] -0.324101697078169M9[t] -0.428680101825853M10[t] -0.408762127383254M11[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) 23.7242314611506 2.450619 9.6809 0 0 los -5.62069109049765e-05 9e-06 -6.3201 0 0 M1 -0.0275271564433608 0.263103 -0.1046 0.917119 0.458559 M2 -0.0940245016618537 0.261309 -0.3598 0.720592 0.360296 M3 -0.522924687831299 0.26434 -1.9782 0.053781 0.026891 M4 -0.698702671431273 0.262932 -2.6573 0.010727 0.005363 M5 -0.82863197409711 0.262406 -3.1578 0.002776 0.001388 M6 -0.0983733354658894 0.26151 -0.3762 0.70848 0.35424 M7 0.0178126737747341 0.261231 0.0682 0.945926 0.472963 M8 0.0259354497915659 0.261048 0.0994 0.921282 0.460641 M9 -0.324101697078169 0.262334 -1.2355 0.222801 0.111401 M10 -0.428680101825853 0.261839 -1.6372 0.10827 0.054135 M11 -0.408762127383254 0.263543 -1.551 0.127604 0.063802 Multiple Linear Regression - Regression Statistics Multiple R 0.747816602639393 R-squared 0.559229671183124 Adjusted R-squared 0.446692565953283 F-TEST (value) 4.96929141762602 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 3.04602975735868e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.412749828750751 Sum Squared Residuals 8.00703379328739 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 8.6488777034051 -0.448877703405109 2 8 8.67900003803224 -0.679000038032239 3 7.9 8.42051920572668 -0.520519205726682 4 7.6 8.10141359931902 -0.501413599319018 5 7.6 8.01897913636789 -0.418979136367885 6 8.3 8.4374580402092 -0.137458040209200 7 8.4 8.59383199074688 -0.193831990746883 8 8.4 8.66102823012485 -0.261028230124845 9 8.4 8.45954594877696 -0.0595459487769628 10 8.4 8.30629235918557 0.0937076408144305 11 8.6 8.5320400413622 0.0679599586378067 12 8.9 8.58855345810396 0.311446541896041 13 8.8 8.53044974212829 0.269550257871709 14 8.3 8.40313651931061 -0.103136519310613 15 7.5 8.2028298397917 -0.702829839791707 16 7.2 7.77974144820984 -0.579741448209837 17 7.4 7.72496078542395 -0.324960785423953 18 8.8 8.37714802480816 0.422851975191839 19 9.3 8.55735370556955 0.742646294430447 20 9.3 8.49999543038209 0.800004569617913 21 8.7 8.38282351539167 0.317176484608329 22 8.2 8.4512499824095 -0.251249982409505 23 8.3 8.39152276409975 -0.0915227640997506 24 8.5 8.5810217320427 -0.0810217320426921 25 8.6 8.59312044778734 0.00687955221266002 26 8.5 8.3148354622789 0.185164537721105 27 8.2 8.11812602505791 0.0818739749420914 28 8.1 7.95224045777721 0.147759542222789 29 7.9 7.68185008475984 0.218149915240164 30 8.6 8.29463627959966 0.305363720400343 31 8.7 8.4200964291396 0.279903570860399 32 8.7 8.55558406526711 0.14441593473289 33 8.5 8.17671277310312 0.323287226896879 34 8.4 8.0920878217267 0.307912178273296 35 8.5 8.18681719458383 0.313182805416173 36 8.7 8.32202028659256 0.377979713407439 37 8.7 8.30815140949911 0.391848590500891 38 8.6 8.07983436778519 0.520165632214811 39 8.5 7.77110455713058 0.728895442869417 40 8.3 7.77412075711934 0.525879242880661 41 8 7.65706283705074 0.342937162949258 42 8.2 8.40513906643884 -0.205139066438841 43 8.1 8.34635296203227 -0.246352962032272 44 8.1 8.35031642664213 -0.250316426642135 45 8 8.1165151715239 -0.116515171523891 46 7.9 7.93206674638024 -0.0320667463802358 47 7.9 8.10582303596976 -0.205823035969755 48 8 8.34899960382695 -0.348999603826949 49 8 8.21940069718015 -0.219400697180151 50 7.9 7.82319361259307 0.0768063874069349 51 8 7.58742037229312 0.412579627706881 52 7.7 7.2924837375746 0.407516262425405 53 7.2 7.01714715639758 0.182852843602416 54 7.5 7.88561858894414 -0.385618588944142 55 7.3 7.88236491251169 -0.58236491251169 56 7 7.43307584758382 -0.433075847583823 57 7 7.46440259120435 -0.464402591204354 58 7 7.11830309029799 -0.118303090297986 59 7.2 7.28379696398447 -0.0837969639844735 60 7.3 7.55940491943384 -0.259404919433839 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.610570081041099 0.778859837917803 0.389429918958901 17 0.488852709692011 0.977705419384021 0.511147290307989 18 0.517560905691002 0.964878188617996 0.482439094308998 19 0.806755560482881 0.386488879034237 0.193244439517119 20 0.946883033266664 0.106233933466673 0.0531169667333364 21 0.92532011137493 0.149359777250141 0.0746798886250707 22 0.912238451972645 0.175523096054711 0.0877615480273554 23 0.88869572141976 0.222608557160482 0.111304278580241 24 0.861563964146174 0.276872071707653 0.138436035853826 25 0.806224575803617 0.387550848392766 0.193775424196383 26 0.763368054258309 0.473263891483382 0.236631945741691 27 0.82860157226616 0.34279685546768 0.17139842773384 28 0.903552927713268 0.192894144573465 0.0964470722867324 29 0.87369414307054 0.252611713858919 0.126305856929459 30 0.885098113575207 0.229803772849587 0.114901886424793 31 0.917639470315607 0.164721059368786 0.082360529684393 32 0.888493537829144 0.223012924341712 0.111506462170856 33 0.907608800869399 0.184782398261203 0.0923911991306014 34 0.870786944078094 0.258426111843813 0.129213055921907 35 0.850890688711885 0.29821862257623 0.149109311288115 36 0.937645018071944 0.124709963856113 0.0623549819280564 37 0.981314382815044 0.0373712343699129 0.0186856171849565 38 0.989550603140814 0.0208987937183719 0.0104493968591860 39 0.991214885257098 0.0175702294858041 0.00878511474290203 40 0.98353943170869 0.03292113658262 0.01646056829131 41 0.96332990698207 0.0733401860358602 0.0366700930179301 42 0.929297472176128 0.141405055647744 0.070702527823872 43 0.942664984313462 0.114670031373075 0.0573350156865377 44 0.893010594771731 0.213978810456539 0.106989405228269 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 4 0.137931034482759 NOK 10% type I error level 5 0.172413793103448 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/10a4ba1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/10a4ba1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/143521258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/143521258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/2xh171258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/2xh171258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/3g49y1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/3g49y1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/4wr9n1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/4wr9n1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/50gje1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/50gje1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/6z3wq1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/6z3wq1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/7yvvo1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/7yvvo1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/8ryla1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/8ryla1258707016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/9kt3p1258707016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258707209wb0fv06zi550fe5/9kt3p1258707016.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 = 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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