Model 2

*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 13:38:54 -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/t1258663194avt1561g1ekoyrr.htm/, Retrieved Thu, 19 Nov 2009 21:40:06 +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/t1258663194avt1561g1ekoyrr.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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3 101.2 3.21 101.1 3.37 100.7 3.51 100.1 3.75 99.9 4.11 99.7 4.25 99.5 4.25 99.2 4.5 99 4.7 99 4.75 99.3 4.75 99.5 4.75 99.7 4.75 100 4.75 100.4 4.75 100.6 4.58 100.7 4.5 100.7 4.5 100.6 4.49 100.5 4.03 100.6 3.75 100.5 3.39 100.4 3.25 100.3 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.5 3.25 100.6 3.25 100.6 3.25 100.5 3.25 100.5 3.25 100.7 2.85 101.1 2.75 101.5 2.75 101.9 2.55 102.1 2.5 102.1 2.5 102.1 2.1 102.4 2 102.8 2 103.1 2 103.1 2 102.9 2 102.4 2 101.9 2 101.3 2 100.7 2 100.6 2 101 2 101.5 2 101.9 2 102.1 2 102.3 2 102.5 2 102.9 2 103.6 2 104.3

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 Tprod[t] = + 104.239014386218 -0.949163093002725Rente[t] -0.429150643259583M1[t] -0.389285793353394M2[t] -0.376879098097416M3[t] -0.35979416242337M4[t] -0.266505879121328M5[t] -0.169285793353391M6[t] -0.0816924886093766M7[t] -0.0635908147953803M8[t] -0.103455664701495M9[t] -0.0986422741895337M10[t] -0.037490385955704M11[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) 104.239014386218 0.460177 226.5196 0 0 Rente -0.949163093002725 0.101565 -9.3454 0 0 M1 -0.429150643259583 0.49181 -0.8726 0.38732 0.19366 M2 -0.389285793353394 0.491987 -0.7913 0.432772 0.216386 M3 -0.376879098097416 0.49195 -0.7661 0.447452 0.223726 M4 -0.35979416242337 0.492035 -0.7312 0.468264 0.234132 M5 -0.266505879121328 0.492105 -0.5416 0.590677 0.295339 M6 -0.169285793353391 0.491987 -0.3441 0.732316 0.366158 M7 -0.0816924886093766 0.492025 -0.166 0.868843 0.434421 M8 -0.0635908147953803 0.492015 -0.1292 0.897715 0.448857 M9 -0.103455664701495 0.491833 -0.2103 0.834306 0.417153 M10 -0.0986422741895337 0.491773 -0.2006 0.841889 0.420944 M11 -0.037490385955704 0.491592 -0.0763 0.939533 0.469767 Multiple Linear Regression - Regression Statistics Multiple R 0.811936829502331 R-squared 0.659241415102298 Adjusted R-squared 0.572239223213523 F-TEST (value) 7.57729662656181 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.54445368716338e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.77708225964867 Sum Squared Residuals 28.3812713982522 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.2 100.962374463951 0.237625536049289 2 101.1 100.802915064326 0.297084935674034 3 100.7 100.663455664701 0.0365443352985084 4 100.1 100.547657767355 -0.447657767355165 5 99.9 100.413146908337 -0.513146908336542 6 99.7 100.168668280624 -0.4686682806235 7 99.5 100.123378752347 -0.623378752347136 8 99.2 100.141480426161 -0.94148042616113 9 99 99.8643248030043 -0.864324803004337 10 99 99.6793055749158 -0.679305574915753 11 99.3 99.6929993084995 -0.39299930849945 12 99.5 99.7304896944551 -0.230489694455151 13 99.7 99.3013390511956 0.398660948804435 14 100 99.3412039011017 0.658796098898244 15 100.4 99.3536105963577 1.04638940364227 16 100.6 99.3706955320318 1.22930446796821 17 100.7 99.6253415411443 1.07465845885572 18 100.7 99.7984946743524 0.901505325647562 19 100.6 99.8860879790965 0.713912020903539 20 100.5 99.9136812838405 0.586318716159522 21 100.6 100.310431456716 0.289568543284378 22 100.5 100.581010513268 -0.081010513268341 23 100.4 100.983861114983 -0.583861114983145 24 100.3 101.154234333959 -0.85423433395924 25 100.4 100.725083690700 -0.325083690699647 26 100.4 100.764948540606 -0.364948540605836 27 100.4 100.777355235862 -0.377355235861815 28 100.4 100.794440171536 -0.394440171535861 29 100.4 100.887728454838 -0.487728454837904 30 100.5 100.984948540606 -0.484948540605845 31 100.6 101.072541845350 -0.472541845349865 32 100.6 101.090643519164 -0.490643519163862 33 100.5 101.050778669258 -0.550778669257742 34 100.5 101.055592059770 -0.555592059769703 35 100.7 101.116743948004 -0.41674394800353 36 101.1 101.533899571160 -0.433899571160332 37 101.5 101.199665237201 0.300334762798985 38 101.9 101.239530087107 0.660469912892802 39 102.1 101.441769400964 0.658230599036267 40 102.1 101.506312491288 0.593687508712085 41 102.1 101.59960077459 0.500399225410042 42 102.4 102.076486097559 0.323513902441028 43 102.8 102.258995711603 0.541004288396733 44 103.1 102.277097385417 0.822902614582734 45 103.1 102.237232535511 0.862767464488848 46 102.9 102.242045926023 0.657954073976898 47 102.4 102.303197814257 0.0968021857430683 48 101.9 102.340688200213 -0.440688200212636 49 101.3 101.911537556953 -0.61153755695306 50 100.7 101.951402406859 -1.25140240685924 51 100.6 101.963809102115 -1.36380910211523 52 101 101.980894037789 -0.980894037789271 53 101.5 102.074182321091 -0.574182321091314 54 101.9 102.171402406859 -0.271402406859245 55 102.1 102.258995711603 -0.158995711603270 56 102.3 102.277097385417 0.0229026145827360 57 102.5 102.237232535511 0.262767464488854 58 102.9 102.242045926023 0.657954073976898 59 103.6 102.303197814257 1.29680218574306 60 104.3 102.340688200213 1.95931179978736 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.310535172926722 0.621070345853444 0.689464827073278 17 0.367070722331768 0.734141444663536 0.632929277668232 18 0.40803609784493 0.81607219568986 0.59196390215507 19 0.450205515440260 0.900411030880519 0.54979448455974 20 0.525403245678041 0.949193508643918 0.474596754321959 21 0.584776897928257 0.830446204143486 0.415223102071743 22 0.559633362405663 0.880733275188675 0.440366637594337 23 0.472848271027772 0.945696542055544 0.527151728972228 24 0.393985090425049 0.787970180850097 0.606014909574951 25 0.305518483760296 0.611036967520592 0.694481516239704 26 0.236717165607875 0.473434331215749 0.763282834392125 27 0.180154029960253 0.360308059920505 0.819845970039747 28 0.125256022698981 0.250512045397962 0.874743977301019 29 0.0822495734847573 0.164499146969515 0.917750426515243 30 0.0504243856351433 0.100848771270287 0.949575614364857 31 0.0297346467966931 0.0594692935933862 0.970265353203307 32 0.0183576898787099 0.0367153797574197 0.98164231012129 33 0.0123361745475252 0.0246723490950505 0.987663825452475 34 0.0109066399323343 0.0218132798646686 0.989093360067666 35 0.0205022624493418 0.0410045248986836 0.979497737550658 36 0.177526622175992 0.355053244351985 0.822473377824008 37 0.185873924441009 0.371747848882018 0.814126075558991 38 0.159589218121650 0.319178436243301 0.84041078187835 39 0.135663122865382 0.271326245730764 0.864336877134618 40 0.107365958627557 0.214731917255113 0.892634041372443 41 0.0796688807366614 0.159337761473323 0.920331119263339 42 0.0499417203704934 0.0998834407409868 0.950058279629507 43 0.0392018293923445 0.078403658784689 0.960798170607656 44 0.0342409380334964 0.0684818760669927 0.965759061966504 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 8 0.275862068965517 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/10hwzw1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/10hwzw1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/1l7fy1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/1l7fy1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/2vrr71258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/2vrr71258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/3r00m1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/3r00m1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/4hsfl1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/4hsfl1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/5fy9f1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/5fy9f1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/68e4l1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/68e4l1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/7ap7j1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/7ap7j1258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/89qx61258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/89qx61258663129.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/912fw1258663129.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663194avt1561g1ekoyrr/912fw1258663129.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal 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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