w7

*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: Sun, 22 Nov 2009 06:02:44 -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/22/t12588951154x0wy8rxdesgvys.htm/, Retrieved Sun, 22 Nov 2009 14:05:27 +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/22/t12588951154x0wy8rxdesgvys.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:

» Textbox « » Textfile « » CSV «

8 0 8,1 0 7,7 0 7,5 0 7,6 0 7,8 0 7,8 0 7,8 0 7,5 0 7,5 0 7,1 0 7,5 0 7,5 0 7,6 0 7,7 0 7,7 0 7,9 0 8,1 0 8,2 0 8,2 0 8,2 0 7,9 0 7,3 0 6,9 0 6,6 0 6,7 0 6,9 0 7 0 7,1 0 7,2 0 7,1 0 6,9 0 7 0 6,8 0 6,4 0 6,7 0 6,6 0 6,4 0 6,3 0 6,2 0 6,5 0 6,8 1 6,8 1 6,4 1 6,1 1 5,8 1 6,1 1 7,2 1 7,3 1 6,9 1 6,1 1 5,8 1 6,2 1 7,1 1 7,7 1 7,9 1 7,7 1 7,4 1 7,5 1 8 1 8,1 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] = + 7.30487804878049 -0.359878048780488X[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) 7.30487804878049 0.100902 72.3955 0 0 X -0.359878048780488 0.176218 -2.0422 0.045607 0.022803 Multiple Linear Regression - Regression Statistics Multiple R 0.256948656215148 R-squared 0.0660226119307705 Adjusted R-squared 0.0501924867092582 F-TEST (value) 4.17069423058316 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0456065207076984 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.646090254363734 Sum Squared Residuals 24.6285243902439 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.30487804878048 0.695121951219518 2 8.1 7.30487804878049 0.795121951219512 3 7.7 7.30487804878049 0.395121951219512 4 7.5 7.30487804878049 0.195121951219512 5 7.6 7.30487804878049 0.295121951219512 6 7.8 7.30487804878049 0.495121951219512 7 7.8 7.30487804878049 0.495121951219512 8 7.8 7.30487804878049 0.495121951219512 9 7.5 7.30487804878049 0.195121951219512 10 7.5 7.30487804878049 0.195121951219512 11 7.1 7.30487804878049 -0.204878048780488 12 7.5 7.30487804878049 0.195121951219512 13 7.5 7.30487804878049 0.195121951219512 14 7.6 7.30487804878049 0.295121951219512 15 7.7 7.30487804878049 0.395121951219512 16 7.7 7.30487804878049 0.395121951219512 17 7.9 7.30487804878049 0.595121951219513 18 8.1 7.30487804878049 0.795121951219512 19 8.2 7.30487804878049 0.895121951219511 20 8.2 7.30487804878049 0.895121951219511 21 8.2 7.30487804878049 0.895121951219511 22 7.9 7.30487804878049 0.595121951219513 23 7.3 7.30487804878049 -0.00487804878048807 24 6.9 7.30487804878049 -0.404878048780487 25 6.6 7.30487804878049 -0.704878048780488 26 6.7 7.30487804878049 -0.604878048780488 27 6.9 7.30487804878049 -0.404878048780487 28 7 7.30487804878049 -0.304878048780488 29 7.1 7.30487804878049 -0.204878048780488 30 7.2 7.30487804878049 -0.104878048780488 31 7.1 7.30487804878049 -0.204878048780488 32 6.9 7.30487804878049 -0.404878048780487 33 7 7.30487804878049 -0.304878048780488 34 6.8 7.30487804878049 -0.504878048780488 35 6.4 7.30487804878049 -0.904878048780488 36 6.7 7.30487804878049 -0.604878048780488 37 6.6 7.30487804878049 -0.704878048780488 38 6.4 7.30487804878049 -0.904878048780488 39 6.3 7.30487804878049 -1.00487804878049 40 6.2 7.30487804878049 -1.10487804878049 41 6.5 7.30487804878049 -0.804878048780488 42 6.8 6.945 -0.145000000000000 43 6.8 6.945 -0.145000000000000 44 6.4 6.945 -0.545 45 6.1 6.945 -0.845 46 5.8 6.945 -1.145 47 6.1 6.945 -0.845 48 7.2 6.945 0.255 49 7.3 6.945 0.355 50 6.9 6.945 -0.0449999999999997 51 6.1 6.945 -0.845 52 5.8 6.945 -1.145 53 6.2 6.945 -0.745 54 7.1 6.945 0.155000000000000 55 7.7 6.945 0.755 56 7.9 6.945 0.955 57 7.7 6.945 0.755 58 7.4 6.945 0.455 59 7.5 6.945 0.555 60 8 6.945 1.055 61 8.1 6.945 1.155 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.110302267475276 0.220604534950553 0.889697732524724 6 0.0406779196453908 0.0813558392907816 0.95932208035461 7 0.0137628158657740 0.0275256317315480 0.986237184134226 8 0.00433976423617628 0.00867952847235256 0.995660235763824 9 0.00267857615942861 0.00535715231885722 0.997321423840571 10 0.00142888888723015 0.0028577777744603 0.99857111111277 11 0.00532505267216458 0.0106501053443292 0.994674947327835 12 0.00243958605574751 0.00487917211149502 0.997560413944252 13 0.00107012896945466 0.00214025793890931 0.998929871030545 14 0.000411761434482467 0.000823522868964933 0.999588238565517 15 0.000159184406919298 0.000318368813838596 0.99984081559308 16 6.02970242099036e-05 0.000120594048419807 0.99993970297579 17 3.81328847550035e-05 7.6265769510007e-05 0.999961867115245 18 6.57123831294717e-05 0.000131424766258943 0.99993428761687 19 0.000177807812158332 0.000355615624316664 0.999822192187842 20 0.000419687217221266 0.000839374434442532 0.999580312782779 21 0.00099501251094332 0.00199002502188664 0.999004987489057 22 0.000945005598180436 0.00189001119636087 0.99905499440182 23 0.00119069200007553 0.00238138400015106 0.998809307999925 24 0.0048585061497506 0.0097170122995012 0.99514149385025 25 0.0270769676793208 0.0541539353586415 0.972923032320679 26 0.0535750066316422 0.107150013263284 0.946424993368358 27 0.0611394564567509 0.122278912913502 0.93886054354325 28 0.0593573484381841 0.118714696876368 0.940642651561816 29 0.052755358193442 0.105510716386884 0.947244641806558 30 0.0454798851328147 0.0909597702656294 0.954520114867185 31 0.0406686001204387 0.0813372002408774 0.959331399879561 32 0.0403260453934386 0.0806520907868771 0.959673954606561 33 0.0373845163174436 0.0747690326348872 0.962615483682556 34 0.0385390391116068 0.0770780782232136 0.961460960888393 35 0.0574796525920831 0.114959305184166 0.942520347407917 36 0.0567060347831372 0.113412069566274 0.943293965216863 37 0.0578974434468685 0.115794886893737 0.942102556553132 38 0.0658303875666323 0.131660775133265 0.934169612433368 39 0.0757368179422416 0.151473635884483 0.924263182057758 40 0.0889984380819412 0.177996876163882 0.911001561918059 41 0.0782648382976196 0.156529676595239 0.92173516170238 42 0.0534942245223366 0.106988449044673 0.946505775477663 43 0.0352760202704949 0.0705520405409898 0.964723979729505 44 0.0278423535849081 0.0556847071698163 0.972157646415092 45 0.0311113796344805 0.0622227592689609 0.96888862036552 46 0.062286170849134 0.124572341698268 0.937713829150866 47 0.084810421641936 0.169620843283872 0.915189578358064 48 0.0670700816622988 0.134140163324598 0.932929918337701 49 0.0510907359076763 0.102181471815353 0.948909264092324 50 0.0339365944875603 0.0678731889751206 0.96606340551244 51 0.0627607417239202 0.125521483447840 0.93723925827608 52 0.342155754363544 0.684311508727088 0.657844245636456 53 0.879923465737701 0.240153068524598 0.120076534262299 54 0.942371536573815 0.115256926852369 0.0576284634261846 55 0.888037592171673 0.223924815656654 0.111962407828327 56 0.800727562599092 0.398544874801816 0.199272437400908 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.307692307692308 NOK 5% type I error level 18 0.346153846153846 NOK 10% type I error level 29 0.557692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/10b0r71258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/10b0r71258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/1y3xk1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/1y3xk1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/2p8ka1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/2p8ka1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/3ireq1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/3ireq1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/4xfef1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/4xfef1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/574m51258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/574m51258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/6f2fw1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/6f2fw1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/7098r1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/7098r1258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/8ggm51258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/8ggm51258894959.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/9qjvw1258894959.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588951154x0wy8rxdesgvys/9qjvw1258894959.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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