run sequence plot en jaren

*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, 19 Nov 2010 12:01:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey.htm/, Retrieved Fri, 19 Nov 2010 13:03:21 +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/2010/Nov/19/t12901681970zuuufnvxsdc1ey.htm/}, year = {2010}, } @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 = {2010}, 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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6 101,82 107,34 93,63 99,85 101,76 6 101,68 107,34 93,63 99,91 102,37 6 101,68 107,34 93,63 99,87 102,38 6 102,45 107,34 96,13 99,86 102,86 6 102,45 107,34 96,13 100,10 102,87 6 102,45 107,34 96,13 100,10 102,92 6 102,45 107,34 96,13 100,12 102,95 6 102,45 107,34 96,13 99,95 103,02 6 102,45 112,60 96,13 99,94 104,08 6 102,52 112,60 96,13 100,18 104,16 6 102,52 112,60 96,13 100,31 104,24 6 102,85 112,60 96,13 100,65 104,33 7 102,85 112,61 96,13 100,65 104,73 7 102,85 112,61 96,13 100,69 104,86 7 103,25 112,61 96,13 101,26 105,03 7 103,25 112,61 98,73 101,26 105,62 7 103,25 112,61 98,73 101,38 105,63 7 103,25 112,61 98,73 101,38 105,63 7 104,45 112,61 98,73 101,38 105,94 7 104,45 112,61 98,73 101,44 106,61 7 104,45 118,65 98,73 101,40 107,69 7 104,80 118,65 98,73 101,40 107,78 7 104,80 118,65 98,73 100,58 107,93 7 105,29 118,65 98,73 100,58 108,48 8 105,29 114,29 98,73 100,58 108,14 8 105,29 114,29 98,73 100,59 108,48 8 105,29 114,29 98,73 100,81 108,48 8 106,04 114,29 101,67 100,75 1 etc...

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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Cultuurenvrijetijdsbesteding[t] = + 43.1196518887865 + 1.06911178353785jaar[t] + 0.0955092964682527bioscoop[t] + 0.293827546929861schouwburgabonnement[t] + 0.271011310340612eendagsattractie[t] -0.140287440271962huurDVD[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) 43.1196518887865 11.20716 3.8475 0.000328 0.000164 jaar 1.06911178353785 0.142107 7.5233 0 0 bioscoop 0.0955092964682527 0.030258 3.1565 0.002656 0.001328 schouwburgabonnement 0.293827546929861 0.024122 12.1808 0 0 eendagsattractie 0.271011310340612 0.040701 6.6585 0 0 huurDVD -0.140287440271962 0.134669 -1.0417 0.302359 0.15118 Multiple Linear Regression - Regression Statistics Multiple R 0.996983707496076 R-squared 0.99397651301262 Adjusted R-squared 0.993397331571527 F-TEST (value) 1716.17466045679 F-TEST (DF numerator) 5 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.452997679974217 Sum Squared Residuals 10.6707586992252 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.76 102.165616119899 -0.405616119898497 2 102.37 102.143827571977 0.226172428023485 3 102.38 102.149439069587 0.230560930412597 4 102.86 102.901912378122 -0.0419123781222023 5 102.87 102.868243392457 0.00175660754307266 6 102.92 102.868243392457 0.0517566075430692 7 102.95 102.865437643651 0.084562356348511 8 103.02 102.889286508498 0.13071349150227 9 104.08 104.436222279752 -0.356222279751516 10 104.16 104.409238944839 -0.249238944839022 11 104.24 104.391001577604 -0.15100157760367 12 104.33 104.374821915746 -0.0448219157457218 13 104.73 105.446871974753 -0.716871974752862 14 104.86 105.441260477142 -0.581260477141989 15 105.03 105.399500354774 -0.369500354774269 16 105.62 106.10412976166 -0.484129761659859 17 105.63 106.087295268827 -0.457295268827234 18 105.63 106.087295268827 -0.457295268827234 19 105.94 106.201906424589 -0.261906424589135 20 106.61 106.193489178173 0.416510821827185 21 107.69 107.97381905924 -0.283819059240059 22 107.78 108.007247313004 -0.227247313003943 23 107.93 108.122283014027 -0.192283014026947 24 108.48 108.169082569296 0.310917430703605 25 108.14 107.95710624822 0.182893751779952 26 108.48 107.955703373817 0.524296626182676 27 108.48 107.924840136957 0.555159863042507 28 108.89 108.801662608126 0.0883373918735987 29 108.93 108.79211167848 0.137888321520431 30 109.21 108.762651316022 0.447348683977529 31 109.47 108.713550711927 0.756449288072722 32 109.8 108.699729017437 1.10027098256326 33 111.73 111.400283640225 0.329716359774718 34 111.85 111.430217631844 0.419782368155681 35 112.12 111.520951463489 0.599048536510851 36 112.15 111.541218076831 0.608781923168915 37 112.17 112.592092493134 -0.42209249313358 38 112.67 112.633549741964 0.0364502580356429 39 112.8 112.612506625924 0.187493374076434 40 113.44 114.311747541759 -0.8717475417592 41 113.53 114.306136044148 -0.776136044148316 42 114.53 114.349115227559 0.18088477244097 43 114.51 114.294403125853 0.215596874147037 44 115.05 114.651398233561 0.398601766439368 45 116.67 116.440010998665 0.229989001334743 46 117.07 116.545662722606 0.524337277394332 47 116.92 116.548468471411 0.3715315285889 48 117 116.569151795373 0.430848204626704 49 117.02 117.649724694487 -0.629724694487336 50 117.35 117.67601951606 -0.326019516060412 51 117.36 117.858631308768 -0.498631308768392 52 117.82 118.489531563584 -0.669531563583711 53 117.88 118.503291116439 -0.623291116438692 54 118.24 118.551105294761 -0.311105294761428 55 118.5 118.554418355094 -0.0544183550935076 56 118.8 118.568447099121 0.231552900879294 57 119.76 119.705559705739 0.0544402942607374 58 120.09 119.691530961712 0.398469038287931 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.00502072491588492 0.0100414498317698 0.994979275084115 10 0.00669688092432533 0.0133937618486507 0.993303119075675 11 0.0017401581893518 0.0034803163787036 0.998259841810648 12 0.00741959649461732 0.0148391929892346 0.992580403505383 13 0.00279602970144417 0.00559205940288833 0.997203970298556 14 0.00110194305715108 0.00220388611430216 0.99889805694285 15 0.000476665719152913 0.000953331438305827 0.999523334280847 16 0.000649925724005584 0.00129985144801117 0.999350074275994 17 0.000320809571281456 0.000641619142562911 0.999679190428719 18 0.000172828219287069 0.000345656438574138 0.999827171780713 19 0.000119458066808968 0.000238916133617936 0.99988054193319 20 0.00186151063345142 0.00372302126690283 0.998138489366549 21 0.00171554117483145 0.00343108234966289 0.998284458825169 22 0.00179872864038887 0.00359745728077775 0.998201271359611 23 0.00384167320135224 0.00768334640270448 0.996158326798648 24 0.00965278179992281 0.0193055635998456 0.990347218200077 25 0.00793658829058765 0.0158731765811753 0.992063411709412 26 0.010108141599005 0.02021628319801 0.989891858400995 27 0.00923009430780605 0.0184601886156121 0.990769905692194 28 0.012206267319817 0.0244125346396341 0.987793732680183 29 0.0124905485051236 0.0249810970102473 0.987509451494876 30 0.011428355761158 0.0228567115223159 0.988571644238842 31 0.0190947554300469 0.0381895108600937 0.980905244569953 32 0.0535432595351472 0.107086519070294 0.946456740464853 33 0.05929021428084 0.11858042856168 0.94070978571916 34 0.0416617970909452 0.0833235941818904 0.958338202909055 35 0.0409780145515397 0.0819560291030794 0.95902198544846 36 0.0371994480109658 0.0743988960219316 0.962800551989034 37 0.146917170391727 0.293834340783455 0.853082829608273 38 0.133094861081901 0.266189722163802 0.866905138918099 39 0.10750525125286 0.21501050250572 0.89249474874714 40 0.392622786231374 0.785245572462749 0.607377213768626 41 0.769805673908009 0.460388652183982 0.230194326091991 42 0.692911364555042 0.614177270889917 0.307088635444958 43 0.601605055524916 0.796789888950169 0.398394944475084 44 0.845896298593003 0.308207402813993 0.154103701406997 45 0.969961559340205 0.06007688131959 0.030038440659795 46 0.95948545337446 0.081029093251079 0.0405145466255395 47 0.933478182701915 0.13304363459617 0.0665218172980849 48 0.857549983752707 0.284900032494585 0.142450016247293 49 0.777445777511202 0.445108444977597 0.222554222488798 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.292682926829268 NOK 5% type I error level 23 0.560975609756098 NOK 10% type I error level 28 0.682926829268293 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/10n6bn1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/10n6bn1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/1mq3i1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/1mq3i1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/2ezkk1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/2ezkk1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/3ezkk1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/3ezkk1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/4ezkk1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/4ezkk1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/52nuh1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/52nuh1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/62nuh1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/62nuh1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/7vfu21290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/7vfu21290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/8n6bn1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/8n6bn1290168107.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/9n6bn1290168107.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t12901681970zuuufnvxsdc1ey/9n6bn1290168107.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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