workshop 7

*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 01:30:37 -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/t1258706006b09g589p7w3144p.htm/, Retrieved Fri, 20 Nov 2009 09:33:39 +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/t1258706006b09g589p7w3144p.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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8,9 8,6 8,9 8,5 8,9 8,3 8,9 7,8 9 7,8 9 8 9 8,6 9 8,9 9 8,9 9 8,6 9 8,3 9,1 8,3 9 8,3 9,1 8,4 9,1 8,5 9 8,4 9 8,6 9 8,5 9 8,5 8,9 8,4 8,9 8,5 8,9 8,5 8,9 8,5 8,8 8,5 8,8 8,5 8,7 8,5 8,7 8,5 8,5 8,5 8,5 8,6 8,4 8,4 8,2 8,1 8,2 8 8,1 8 8,1 8 8 8 7,9 7,9 7,8 7,8 7,7 7,8 7,6 7,9 7,5 8,1 7,5 8 7,5 7,6 7,5 7,3 7,5 7 7,4 6,8 7,4 7 7,3 7,1 7,3 7,2 7,3 7,1 7,2 6,9 7,2 6,7 7,3 6,7 7,4 6,6 7,4 6,9 7,5 7,3 7,6 7,5 7,7 7,3 7,9 7,1 8 6,9 8,2 7,1

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1.27666936157067 + 0.883343521385048X[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) 1.27666936157067 0.57852 2.2068 0.031301 0.015651 X 0.883343521385048 0.072846 12.1261 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.846836840542365 R-squared 0.717132634499774 Adjusted R-squared 0.712255610956667 F-TEST (value) 147.043094658276 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.366819119890273 Sum Squared Residuals 7.80426346959031 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.87342364548213 0.0265763545178733 2 8.9 8.78508929334359 0.114910706656415 3 8.9 8.60842058906658 0.291579410933423 4 8.9 8.16674882837405 0.733251171625948 5 9 8.16674882837405 0.833251171625948 6 9 8.34341753265106 0.656582467348938 7 9 8.8734236454821 0.126576354517910 8 9 9.1384267018976 -0.138426701897606 9 9 9.1384267018976 -0.138426701897606 10 9 8.8734236454821 0.126576354517910 11 9 8.60842058906658 0.391579410933423 12 9.1 8.60842058906658 0.491579410933423 13 9 8.60842058906658 0.391579410933423 14 9.1 8.69675494120508 0.403245058794918 15 9.1 8.78508929334359 0.314910706656414 16 9 8.69675494120508 0.303245058794919 17 9 8.8734236454821 0.126576354517910 18 9 8.78508929334359 0.214910706656414 19 9 8.78508929334359 0.214910706656414 20 8.9 8.69675494120508 0.203245058794919 21 8.9 8.78508929334359 0.114910706656414 22 8.9 8.78508929334359 0.114910706656414 23 8.9 8.78508929334359 0.114910706656414 24 8.8 8.78508929334359 0.0149107066564148 25 8.8 8.78508929334359 0.0149107066564148 26 8.7 8.78508929334359 -0.0850892933435866 27 8.7 8.78508929334359 -0.0850892933435866 28 8.5 8.78508929334359 -0.285089293343586 29 8.5 8.8734236454821 -0.373423645482090 30 8.4 8.69675494120508 -0.296754941205081 31 8.2 8.43175188478957 -0.231751884789567 32 8.2 8.34341753265106 -0.143417532651063 33 8.1 8.34341753265106 -0.243417532651062 34 8.1 8.34341753265106 -0.243417532651062 35 8 8.34341753265106 -0.343417532651062 36 7.9 8.25508318051256 -0.355083180512557 37 7.8 8.16674882837405 -0.366748828374052 38 7.7 8.16674882837405 -0.466748828374052 39 7.6 8.25508318051256 -0.655083180512558 40 7.5 8.43175188478957 -0.931751884789566 41 7.5 8.34341753265106 -0.843417532651062 42 7.5 7.99008012409704 -0.490080124097042 43 7.5 7.72507706768153 -0.225077067681528 44 7.5 7.46007401126601 0.0399259887339864 45 7.4 7.283405306989 0.116594693010997 46 7.4 7.46007401126601 -0.0600740112660132 47 7.3 7.54840836340452 -0.248408363404518 48 7.3 7.63674271554302 -0.336742715543024 49 7.3 7.54840836340452 -0.248408363404518 50 7.2 7.37173965912751 -0.171739659127509 51 7.2 7.1950709548505 0.00492904514950071 52 7.3 7.1950709548505 0.104929045149500 53 7.4 7.106736602712 0.293263397288006 54 7.4 7.37173965912751 0.0282603408724912 55 7.5 7.72507706768153 -0.225077067681528 56 7.6 7.90174577195854 -0.301745771958538 57 7.7 7.72507706768153 -0.0250770676815277 58 7.9 7.54840836340452 0.351591636595482 59 8 7.37173965912751 0.628260340872491 60 8.2 7.54840836340452 0.651591636595481 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00240539709695284 0.00481079419390569 0.997594602903047 6 0.00073914716324368 0.00147829432648736 0.999260852836756 7 0.000394111152431112 0.000788222304862224 0.999605888847569 8 0.000107335517347592 0.000214671034695184 0.999892664482652 9 2.06905249978744e-05 4.13810499957488e-05 0.999979309475002 10 3.85605238218764e-06 7.71210476437528e-06 0.999996143947618 11 8.49735665809041e-07 1.69947133161808e-06 0.999999150264334 12 2.13393674769187e-06 4.26787349538374e-06 0.999997866063252 13 5.17407723027196e-07 1.03481544605439e-06 0.999999482592277 14 6.25513369209887e-07 1.25102673841977e-06 0.999999374486631 15 5.10484861887215e-07 1.02096972377443e-06 0.999999489515138 16 1.46677267589816e-07 2.93354535179631e-07 0.999999853322732 17 3.56336182684978e-08 7.12672365369956e-08 0.999999964366382 18 9.86601137741827e-09 1.97320227548365e-08 0.99999999013399 19 2.92799903500900e-09 5.85599807001799e-09 0.999999997072 20 2.43378104015101e-09 4.86756208030203e-09 0.999999997566219 21 1.82779427803205e-09 3.65558855606411e-09 0.999999998172206 22 1.41611323111640e-09 2.83222646223281e-09 0.999999998583887 23 1.21951784991717e-09 2.43903569983434e-09 0.999999998780482 24 7.44840260963452e-09 1.48968052192690e-08 0.999999992551597 25 3.06341786583657e-08 6.12683573167314e-08 0.999999969365821 26 6.04700862125556e-07 1.20940172425111e-06 0.999999395299138 27 5.34097803824134e-06 1.06819560764827e-05 0.999994659021962 28 0.000326031791112512 0.000652063582225023 0.999673968208888 29 0.00265149253766517 0.00530298507533034 0.997348507462335 30 0.0226659489670935 0.0453318979341869 0.977334051032906 31 0.161799853117722 0.323599706235445 0.838200146882278 32 0.363835174646956 0.727670349293912 0.636164825353044 33 0.561345823671366 0.877308352657268 0.438654176328634 34 0.701772620920233 0.596454758159534 0.298227379079767 35 0.8008679332679 0.398264133464201 0.199132066732101 36 0.854120438528073 0.291759122943854 0.145879561471927 37 0.875169064827548 0.249661870344903 0.124830935172452 38 0.883833127581745 0.23233374483651 0.116166872418255 39 0.89829229135121 0.203415417297582 0.101707708648791 40 0.934802878214215 0.130394243571569 0.0651971217857846 41 0.946340974387085 0.107318051225831 0.0536590256129155 42 0.930080636889735 0.139838726220529 0.0699193631102646 43 0.897686569491577 0.204626861016846 0.102313430508423 44 0.857761782448688 0.284476435102624 0.142238217551312 45 0.808269533970753 0.383460932058494 0.191730466029247 46 0.742586978176477 0.514826043647045 0.257413021823523 47 0.698577203367617 0.602845593264766 0.301422796632383 48 0.678586605366877 0.642826789266245 0.321413394633123 49 0.650241899535645 0.69951620092871 0.349758100464355 50 0.639844411009132 0.720311177981735 0.360155588990868 51 0.617850822313662 0.764298355372676 0.382149177686338 52 0.591066964394894 0.817866071210211 0.408933035605106 53 0.617129069810837 0.765741860378327 0.382870930189163 54 0.883025827435678 0.233948345128644 0.116974172564322 55 0.8766018328628 0.246796334274398 0.123398167137199 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.490196078431373 NOK 5% type I error level 26 0.509803921568627 NOK 10% type I error level 26 0.509803921568627 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/10nfj31258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/10nfj31258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/1s27u1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/1s27u1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/27nv91258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/27nv91258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/3v2ij1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/3v2ij1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/4o7a01258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/4o7a01258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/5cnre1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/5cnre1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/6jvtr1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/6jvtr1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/7vpux1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/7vpux1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/8x3mv1258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/8x3mv1258705833.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/9jkk51258705833.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706006b09g589p7w3144p/9jkk51258705833.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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