WS 7.2

*Unverified author*

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 08:42:32 -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/t1258731778kcvrocsrj01hg3j.htm/, Retrieved Fri, 20 Nov 2009 16:43:10 +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/t1258731778kcvrocsrj01hg3j.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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9.9 8.2 9.8 8 9.3 7.5 8.3 6.8 8 6.5 8.5 6.6 10.4 7.6 11.1 8 10.9 8.1 10 7.7 9.2 7.5 9.2 7.6 9.5 7.8 9.6 7.8 9.5 7.8 9.1 7.5 8.9 7.5 9 7.1 10.1 7.5 10.3 7.5 10.2 7.6 9.6 7.7 9.2 7.7 9.3 7.9 9.4 8.1 9.4 8.2 9.2 8.2 9 8.2 9 7.9 9 7.3 9.8 6.9 10 6.6 9.8 6.7 9.3 6.9 9 7 9 7.1 9.1 7.2 9.1 7.1 9.1 6.9 9.2 7 8.8 6.8 8.3 6.4 8.4 6.7 8.1 6.6 7.7 6.4 7.9 6.3 7.9 6.2 8 6.5 7.9 6.8 7.6 6.8 7.1 6.4 6.8 6.1 6.5 5.8 6.9 6.1 8.2 7.2 8.7 7.3 8.3 6.9 7.9 6.1 7.5 5.8 7.8 6.2

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 WLMan[t] = + 2.15403889369058 + 0.566508210890234WLVrouw[t] + 0.276745894554881M1[t] + 0.270736387208297M2[t] + 0.198028522039758M3[t] + 0.161971477960242M4[t] + 0.0779334485738979M5[t] -0.178717372515126M6[t] -0.287885911840968M7[t] -0.415178046672429M8[t] -0.327885911840968M9[t] -0.278622299049265M10[t] -0.163349178910976M11[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) 2.15403889369058 0.530992 4.0566 0.000186 9.3e-05 WLVrouw 0.566508210890234 0.057849 9.7928 0 0 M1 0.276745894554881 0.250575 1.1044 0.275024 0.137512 M2 0.270736387208297 0.250198 1.0821 0.284735 0.142367 M3 0.198028522039758 0.249118 0.7949 0.430657 0.215329 M4 0.161971477960242 0.249118 0.6502 0.518742 0.259371 M5 0.0779334485738979 0.250083 0.3116 0.7567 0.37835 M6 -0.178717372515126 0.249588 -0.7161 0.477503 0.238752 M7 -0.287885911840968 0.252361 -1.1408 0.259746 0.129873 M8 -0.415178046672429 0.255275 -1.6264 0.110553 0.055276 M9 -0.327885911840968 0.252361 -1.2993 0.200188 0.100094 M10 -0.278622299049265 0.249427 -1.1171 0.269651 0.134825 M11 -0.163349178910976 0.248968 -0.6561 0.514955 0.257478 Multiple Linear Regression - Regression Statistics Multiple R 0.851403378079036 R-squared 0.724887712204394 Adjusted R-squared 0.654646277022538 F-TEST (value) 10.3199444932701 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.57805424283453e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.393546010364785 Sum Squared Residuals 7.27928772687986 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 8.03921607605878 0.160783923941219 2 8 7.97655574762316 0.0234442523768374 3 7.5 7.6205937770095 -0.120593777009507 4 6.8 7.01802852203976 -0.218028522039758 5 6.5 6.76403802938634 -0.264038029386344 6 6.6 6.79064131374244 -0.190641313742438 7 7.6 7.75783837510804 -0.157838375108038 8 8 8.02710198789974 -0.0271019878997403 9 8.1 8.00109248055315 0.0989075194468448 10 7.7 7.54049870354365 0.159501296456353 11 7.5 7.20256525496975 0.297434745030251 12 7.6 7.36591443388073 0.234085566119274 13 7.8 7.81261279170268 -0.0126127917026773 14 7.8 7.86325410544512 -0.0632541054451166 15 7.8 7.73389541918755 0.0661045808124456 16 7.5 7.47123509075194 0.0287649092480558 17 7.5 7.27389541918755 0.226104580812446 18 7.1 7.07389541918755 0.0261045808124462 19 7.5 7.58788591184097 -0.0878859118409678 20 7.5 7.57389541918755 -0.0738954191875541 21 7.6 7.60453673292999 -0.0045367329299909 22 7.7 7.31389541918755 0.386104580812446 23 7.7 7.20256525496975 0.497434745030251 24 7.9 7.42256525496975 0.477434745030251 25 8.1 7.75596197061365 0.344038029386346 26 8.2 7.74995246326707 0.450047536732929 27 8.2 7.56394295592048 0.636057044079515 28 8.2 7.41458426966292 0.785415730337078 29 7.9 7.33054624027658 0.569453759723423 30 7.3 7.07389541918755 0.226104580812446 31 6.9 7.4179334485739 -0.517933448573898 32 6.6 7.40394295592048 -0.803942955920484 33 6.7 7.3779334485739 -0.677933448573898 34 6.9 7.14394295592048 -0.243942955920484 35 7 7.0892636127917 -0.0892636127917028 36 7.1 7.25261279170268 -0.152612791702680 37 7.2 7.58600950734658 -0.386009507346583 38 7.1 7.58 -0.48 39 6.9 7.50729213483146 -0.60729213483146 40 7 7.52788591184097 -0.527885911840967 41 6.8 7.21724459809853 -0.417244598098531 42 6.4 6.67733967156439 -0.27733967156439 43 6.7 6.62482195332757 0.0751780466724284 44 6.6 6.32757735522904 0.272422644770959 45 6.4 6.18826620570441 0.211733794295592 46 6.3 6.35083146067416 -0.0508314606741578 47 6.2 6.46610458081245 -0.266104580812446 48 6.5 6.68610458081245 -0.186104580812446 49 6.8 6.9061996542783 -0.106199654278304 50 6.8 6.73023768366465 0.0697623163353499 51 6.4 6.374275713051 0.0257242869490061 52 6.1 6.16826620570441 -0.0682662057044082 53 5.8 5.914275713051 -0.114275713050994 54 6.1 5.88422817631806 0.215771823681936 55 7.2 6.51152031114952 0.688479688850476 56 7.3 6.66748228176318 0.63251771823682 57 6.9 6.52817113223855 0.371828867761452 58 6.1 6.35083146067416 -0.250831460674158 59 5.8 6.23950129645635 -0.439501296456353 60 6.2 6.5728029386344 -0.372802938634399 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00187586020558655 0.0037517204111731 0.998124139794413 17 0.000563260511974061 0.00112652102394812 0.999436739488026 18 6.01737096379554e-05 0.000120347419275911 0.999939826290362 19 7.32543213507227e-05 0.000146508642701445 0.99992674567865 20 8.93490641080047e-05 0.000178698128216009 0.999910650935892 21 2.26811025999546e-05 4.53622051999093e-05 0.9999773188974 22 5.48391946675518e-05 0.000109678389335104 0.999945160805332 23 3.32855017055639e-05 6.65710034111278e-05 0.999966714498294 24 2.37411159133906e-05 4.74822318267813e-05 0.999976258884087 25 5.81339288396695e-05 0.000116267857679339 0.99994186607116 26 0.000537372741676685 0.00107474548335337 0.999462627258323 27 0.00858893888799281 0.0171778777759856 0.991411061112007 28 0.115942089158868 0.231884178317736 0.884057910841132 29 0.30097340721868 0.60194681443736 0.69902659278132 30 0.33433626223128 0.66867252446256 0.66566373776872 31 0.339498897973555 0.678997795947111 0.660501102026445 32 0.649236495490894 0.701527009018212 0.350763504509106 33 0.81150565836872 0.376988683262558 0.188494341631279 34 0.757690476058959 0.484619047882082 0.242309523941041 35 0.839142937410947 0.321714125178106 0.160857062589053 36 0.858959802823169 0.282080394353663 0.141040197176831 37 0.799410958430767 0.401178083138467 0.200589041569233 38 0.763259102087239 0.473481795825522 0.236740897912761 39 0.785089536297089 0.429820927405823 0.214910463702911 40 0.811192129889531 0.377615740220937 0.188807870110469 41 0.755243315826568 0.489513368346865 0.244756684173432 42 0.800983703888846 0.398032592222307 0.199016296111154 43 0.992546772687967 0.0149064546240667 0.00745322731203336 44 0.98042765327301 0.0391446934539782 0.0195723467269891 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.379310344827586 NOK 5% type I error level 14 0.482758620689655 NOK 10% type I error level 14 0.482758620689655 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/107eag1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/107eag1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/1xwc81258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/1xwc81258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/2eppz1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/2eppz1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/3qiy01258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/3qiy01258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/4gy0m1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/4gy0m1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/5tfaw1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/5tfaw1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/69jhm1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/69jhm1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/7vawe1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/7vawe1258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/84y221258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/84y221258731747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/9nn7p1258731747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731778kcvrocsrj01hg3j/9nn7p1258731747.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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