workshop7

*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 06:01:05 -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/t1258722218zhchoas2wta1qae.htm/, Retrieved Fri, 20 Nov 2009 14:03:50 +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/t1258722218zhchoas2wta1qae.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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105.4 102.7 105.4 102.5 105.6 102.2 105.7 102.9 105.8 103.1 105.8 103 105.8 102.8 105.9 102.5 106.1 101.9 106.4 101.9 106.4 101.8 106.3 102 106.2 102.6 106.2 102.5 106.3 102.5 106.4 101.6 106.5 101.4 106.6 100.8 106.6 101.1 106.6 101.3 106.8 101.2 107 101.3 107.2 101.1 107.3 101.3 107.5 101.2 107.6 101.6 107.6 101.7 107.7 101.5 107.7 100.9 107.7 101.5 107.7 101.4 107.6 101.6 107.7 101.7 107.9 101.4 107.9 101.8 107.9 101.7 107.8 101.4 107.6 101.2 107.4 101 107 101.7 107 102.4 107.2 102 107.5 102.1 107.8 102 107.8 101.8 107.7 102.7 107.6 102.3 107.6 101.9 107.5 102 107.5 102.3 107.6 102.8 107.6 102.4 107.9 102.3 107.6 102.7 107.5 102.7 107.5 102.9 107.6 103 107.7 102.2 107.8 102.3 107.9 102.8 107.9 102.8

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 Werkl[t] = + 136.683354670492 -0.287260689331883Inflatie[t] -0.299250611551335M1[t] -0.517019144853451M2[t] -0.471273931066814M3[t] -0.497019144853448M4[t] -0.397019144853448M5[t] -0.402764358640089M6[t] -0.357019144853451M7[t] -0.285528717280176M8[t] -0.20574521378664M9[t] -0.0714904275732713M10[t] -0.0429808551465507M11[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) 136.683354670492 17.190678 7.951 0 0 Inflatie -0.287260689331883 0.168598 -1.7038 0.094882 0.047441 M1 -0.299250611551335 0.487923 -0.6133 0.542563 0.271282 M2 -0.517019144853451 0.508847 -1.0161 0.314695 0.157347 M3 -0.471273931066814 0.508947 -0.926 0.35909 0.179545 M4 -0.497019144853448 0.508847 -0.9768 0.333587 0.166794 M5 -0.397019144853448 0.508847 -0.7802 0.439084 0.219542 M6 -0.402764358640089 0.508769 -0.7916 0.432462 0.216231 M7 -0.357019144853451 0.508847 -0.7016 0.486302 0.243151 M8 -0.285528717280176 0.50907 -0.5609 0.577486 0.288743 M9 -0.20574521378664 0.508679 -0.4045 0.687664 0.343832 M10 -0.0714904275732713 0.508713 -0.1405 0.888828 0.444414 M11 -0.0429808551465507 0.508847 -0.0845 0.933036 0.466518 Multiple Linear Regression - Regression Statistics Multiple R 0.33680424337175 R-squared 0.113437098353217 Adjusted R-squared -0.108203627058479 F-TEST (value) 0.511806204128365 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.896743475231964 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.804274736354603 Sum Squared Residuals 31.0491768738368 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.4 106.882431264556 -1.48243126455624 2 105.4 106.722114869121 -1.32211486912069 3 105.6 106.854038289707 -1.2540382897069 4 105.7 106.627210593388 -0.92721059338794 5 105.8 106.669758455522 -0.869758455521573 6 105.8 106.692739310668 -0.892739310668119 7 105.8 106.795936662321 -0.995936662321133 8 105.9 106.953605296694 -1.05360529669396 9 106.1 107.205745213787 -1.10574521378664 10 106.4 107.34 -0.939999999999997 11 106.4 107.39723564136 -0.997235641359909 12 106.3 107.382764358640 -1.08276435864009 13 106.2 106.911157333490 -0.711157333489622 14 106.2 106.722114869121 -0.522114869120693 15 106.3 106.767860082907 -0.467860082907334 16 106.4 107.000649489519 -0.600649489519388 17 106.5 107.158101627386 -0.658101627385768 18 106.6 107.324712827198 -0.724712827198264 19 106.6 107.284279834185 -0.684279834185338 20 106.6 107.298318123892 -0.698318123892236 21 106.8 107.406827696319 -0.606827696318955 22 107 107.512356413599 -0.512356413599135 23 107.2 107.598318123892 -0.39831812389223 24 107.3 107.583846841172 -0.283846841172409 25 107.5 107.313322298554 0.186677701445742 26 107.6 106.980649489519 0.619350510480603 27 107.6 106.997668634373 0.602331365627158 28 107.7 107.029375558453 0.670624441547422 29 107.7 107.301731972052 0.398268027948294 30 107.7 107.123630344666 0.576369655334063 31 107.7 107.198101627386 0.501898372614238 32 107.6 107.212139917093 0.387860082907328 33 107.7 107.263197351653 0.436802648346992 34 107.9 107.483630344666 0.416369655334062 35 107.9 107.39723564136 0.502764358640092 36 107.9 107.468942565440 0.431057434560354 37 107.8 107.255870160688 0.544129839312116 38 107.6 107.095553765252 0.504446234747852 39 107.4 107.198751116905 0.20124888309485 40 107 106.971923420586 0.0280765794137966 41 107 106.870840938054 0.129159061946116 42 107.2 106.98 0.220000000000005 43 107.5 106.997019144853 0.502980855146551 44 107.8 107.09723564136 0.702764358640086 45 107.8 107.234471282720 0.565528717280173 46 107.7 107.110191448534 0.589808551465506 47 107.6 107.253605296694 0.346394703306022 48 107.6 107.411490427573 0.188509572426721 49 107.5 107.083513747089 0.416486252911247 50 107.5 106.779567006987 0.720432993012927 51 107.6 106.681681876108 0.918318123892227 52 107.6 106.770840938054 0.82915906194611 53 107.9 106.899567006987 1.00043299301293 54 107.6 106.778917517468 0.821082482532315 55 107.5 106.824662731254 0.675337268745682 56 107.5 106.838701020961 0.661298979038785 57 107.6 106.889758455522 0.71024154447843 58 107.7 107.253821793200 0.446178206799564 59 107.8 107.253605296694 0.546394703306024 60 107.9 107.152955807175 0.747044192825424 61 107.9 106.853705195623 1.04629480437676 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.894631657914402 0.210736684171196 0.105368342085598 17 0.85870721293176 0.282585574136479 0.141292787068239 18 0.800783288348445 0.398433423303109 0.199216711651554 19 0.77399878442902 0.452002431141959 0.226001215570979 20 0.804385425093904 0.391229149812192 0.195614574906096 21 0.864302624401374 0.271394751197251 0.135697375598626 22 0.907962505000051 0.184074989999898 0.092037494999949 23 0.938668958257263 0.122662083485474 0.0613310417427369 24 0.97211614089129 0.0557677182174213 0.0278838591087106 25 0.991357650304018 0.0172846993919635 0.00864234969598177 26 0.998427917139018 0.00314416572196330 0.00157208286098165 27 0.999434905189885 0.00113018962023013 0.000565094810115066 28 0.99977314704476 0.000453705910481152 0.000226852955240576 29 0.99960841458509 0.000783170829818846 0.000391585414909423 30 0.999781084404076 0.000437831191847011 0.000218915595923505 31 0.999776041291835 0.000447917416330063 0.000223958708165032 32 0.999711420485092 0.000577159029815891 0.000288579514907946 33 0.99971071642243 0.000578567155141825 0.000289283577570912 34 0.999595643525794 0.000808712948412886 0.000404356474206443 35 0.999562070411262 0.000875859177475338 0.000437929588737669 36 0.99929126051811 0.00141747896378082 0.000708739481890411 37 0.998542118278584 0.00291576344283151 0.00145788172141576 38 0.996642160152545 0.00671567969490978 0.00335783984745489 39 0.991591861576661 0.0168162768466769 0.00840813842333847 40 0.989137854754932 0.0217242904901361 0.0108621452450680 41 0.998553875369451 0.00289224926109822 0.00144612463054911 42 0.997810827258185 0.00437834548363006 0.00218917274181503 43 0.992927897801272 0.0141442043974564 0.00707210219872821 44 0.989105556554574 0.0217888868908522 0.0108944434454261 45 0.99329800505689 0.0134039898862210 0.00670199494311051 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.5 NOK 5% type I error level 21 0.7 NOK 10% type I error level 22 0.733333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/1041721258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/1041721258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/10hh71258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/10hh71258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/2dthh1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/2dthh1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/3jjpo1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/3jjpo1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/4mahb1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/4mahb1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/5qg7t1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/5qg7t1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/60kft1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/60kft1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/7zn4s1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/7zn4s1258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/8al731258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/8al731258722060.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/93g6b1258722060.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722218zhchoas2wta1qae/93g6b1258722060.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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