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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 12:35:13 -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/t1258659469v2nkickzzhoqtj3.htm/, Retrieved Thu, 19 Nov 2009 20:38:01 +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/t1258659469v2nkickzzhoqtj3.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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19 24.4 19 23 22 22.5 19 19 23 19.4 22 18 20 18.1 23 19 14 18.1 20 19 14 20.7 14 22 14 19.1 14 23 15 18.3 14 20 11 16.9 15 14 17 17.9 11 14 16 20.2 17 14 20 21.2 16 15 24 23.8 20 11 23 24 24 17 20 26.6 23 16 21 25.3 20 20 19 27.6 21 24 23 24.7 19 23 23 26.6 23 20 23 24.4 23 21 23 24.6 23 19 27 26 23 23 26 24.8 27 23 17 24 26 23 24 22.7 17 23 26 23 24 27 24 24.1 26 26 27 24 24 17 27 22.7 27 24 26 22.6 27 26 24 23.1 26 24 23 24.4 24 27 23 23 23 27 24 22 23 26 17 21.3 24 24 21 21.5 17 23 19 21.3 21 23 22 23.2 19 24 22 21.8 22 17 18 23.3 22 21 16 21 18 19 14 22.4 16 22 12 20.4 14 22 14 19.9 12 18 16 21.3 14 16 8 18.9 16 14 3 15.6 8 12 0 12.5 3 14 5 7.8 0 16 1 5.5 5 8 1 4 1 3 3 3.3 1 0 6 3.7 3 5 7 3.1 6 1 8 5 7 1 14 6.3 8 3 14 20 14 6

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] = + 0.633709699169343 + 0.208586394090889X[t] + 0.75975279754674`Y(t-1)`[t] -0.035428460457972`Y(t-4)`[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) 0.633709699169343 1.405333 0.4509 0.653878 0.326939 X 0.208586394090889 0.154012 1.3543 0.181371 0.090686 `Y(t-1)` 0.75975279754674 0.123171 6.1683 0 0 `Y(t-4)` -0.035428460457972 0.109207 -0.3244 0.7469 0.37345 Multiple Linear Regression - Regression Statistics Multiple R 0.898579324435252 R-squared 0.807444802302514 Adjusted R-squared 0.796545451489449 F-TEST (value) 74.0819170013884 F-TEST (DF numerator) 3 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.34001626392747 Sum Squared Residuals 591.252558094901 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19 19.3436662778418 -0.343666277841756 2 22 19.0890659709010 2.91093402909904 3 23 20.7571350023174 2.24286499768262 4 20 21.210297027088 -1.21029702708800 5 14 18.9310386344478 -4.93103863444777 6 14 14.8085610924297 -0.80856109242973 7 14 14.4393944014263 -0.439394401426334 8 15 14.3788106675275 0.621189332472461 9 11 15.0591132760949 -4.05911327609487 10 17 12.2286884799988 4.77131152000121 11 16 17.2669539716883 -1.26695397168828 12 20 16.6803591077745 3.31964089222554 13 24 20.4034087644296 3.59659123557038 14 23 23.2715664706869 -0.271566470686929 15 20 23.0895667582345 -3.08956675823447 16 21 20.3974322114442 0.602567788555793 17 19 21.4952198735681 -2.49521987356811 18 23 19.406242196069 3.59375780393098 19 23 22.9478529164026 0.0521470835974146 20 23 22.4535343889447 0.546465611055345 21 23 22.5661085886788 0.433891411321222 22 27 22.7164156985741 4.28358430142586 23 26 25.5051232158520 0.49487678414797 24 17 24.5785013030326 -7.57850130303258 25 24 17.4695638127938 6.53043618720624 26 26 22.7086954720163 3.29130452798368 27 24 24.4930745610678 -0.493074561067751 28 27 23.2715664706869 3.72843352931307 29 27 25.0316633278032 1.96833667219681 30 26 24.9399477674782 1.06005223252184 31 24 24.3553450878928 -0.355345087892805 32 23 23.0007164237436 -0.000716423743563926 33 23 21.9489426744696 1.05105732553042 34 24 21.7757847408367 2.22421525916334 35 17 22.4603839834357 -5.46038398343572 36 21 17.2192601398847 3.78073986011531 37 19 20.2165540512535 -1.21655405125347 38 22 19.0579341444747 2.94206585552529 39 22 21.2931708085935 0.706829191406509 40 18 21.4643365578979 -3.46433655789794 41 16 18.0164335822179 -2.01643358221787 42 14 16.6826635574777 -2.68266355747772 43 12 14.7459851742025 -2.74598517420246 44 14 13.2639002238954 0.736099776104575 45 16 15.1462836916321 0.853716308367904 46 8 16.2360388618234 -8.23603886182338 47 3 9.54053830186547 -6.54053830186547 48 0 5.02429957153407 -5.02429957153407 49 5 1.69382820575072 3.30617179424928 50 1 5.29627117073916 -4.29627117073916 51 1 2.12152269170572 -1.12152269170572 52 3 2.08179759721601 0.918202402783987 53 6 3.50759544765599 2.49240455234401 54 7 5.80341584567357 1.19658415432643 55 8 6.959482791993 1.04051720800701 56 14 7.91954098094195 6.08045901905805 57 14 15.2294059838937 -1.22940598389366 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.418722933353165 0.83744586670633 0.581277066646835 8 0.258308143070212 0.516616286140424 0.741691856929788 9 0.327120861317972 0.654241722635943 0.672879138682028 10 0.429177539777828 0.858355079555657 0.570822460222172 11 0.391815798834922 0.783631597669844 0.608184201165078 12 0.314631365519566 0.629262731039133 0.685368634480434 13 0.241748253677365 0.483496507354729 0.758251746322635 14 0.177013646092692 0.354027292185384 0.822986353907308 15 0.254554731821963 0.509109463643927 0.745445268178037 16 0.181758439671971 0.363516879343941 0.81824156032803 17 0.145597433062819 0.291194866125637 0.854402566937181 18 0.175807969722388 0.351615939444776 0.824192030277612 19 0.122315368267791 0.244630736535581 0.87768463173221 20 0.0873241818779186 0.174648363755837 0.912675818122081 21 0.0580794277083486 0.116158855416697 0.941920572291651 22 0.092376339394081 0.184752678788162 0.907623660605919 23 0.0657212102749824 0.131442420549965 0.934278789725018 24 0.206041827305519 0.412083654611038 0.79395817269448 25 0.412172549812747 0.824345099625494 0.587827450187253 26 0.44756218061329 0.89512436122658 0.55243781938671 27 0.370299666964394 0.740599333928788 0.629700333035606 28 0.427098551420283 0.854197102840567 0.572901448579717 29 0.394533810284322 0.789067620568644 0.605466189715678 30 0.330527779809453 0.661055559618906 0.669472220190547 31 0.259861030495449 0.519722060990898 0.740138969504551 32 0.199051280122852 0.398102560245704 0.800948719877148 33 0.152073884165978 0.304147768331956 0.847926115834022 34 0.131662967148592 0.263325934297183 0.868337032851408 35 0.210716517155643 0.421433034311287 0.789283482844357 36 0.243716531624212 0.487433063248423 0.756283468375789 37 0.188121578095851 0.376243156191703 0.811878421904149 38 0.215025772435994 0.430051544871988 0.784974227564006 39 0.180889391648185 0.361778783296369 0.819110608351815 40 0.155262919122995 0.310525838245990 0.844737080877005 41 0.116921003777538 0.233842007555076 0.883078996222462 42 0.0967929243852032 0.193585848770406 0.903207075614797 43 0.0737067406226881 0.147413481245376 0.926293259377312 44 0.0731011400142706 0.146202280028541 0.92689885998573 45 0.135135595844001 0.270271191688002 0.864864404156 46 0.210926534123702 0.421853068247404 0.789073465876298 47 0.255982978891898 0.511965957783795 0.744017021108102 48 0.271971785167515 0.543943570335031 0.728028214832485 49 0.456311298185138 0.912622596370277 0.543688701814862 50 0.625861562619132 0.748276874761737 0.374138437380868 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/10gw901258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/10gw901258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/1hfr41258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/1hfr41258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/24ebx1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/24ebx1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/30fsb1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/30fsb1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/404gy1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/404gy1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/5i33z1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/5i33z1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/655pu1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/655pu1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/76j1f1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/76j1f1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/8stqo1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/8stqo1258659309.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/9oikg1258659309.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258659469v2nkickzzhoqtj3/9oikg1258659309.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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