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 06:14:04 -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/t125872290782lq2ugehjl46h9.htm/, Retrieved Fri, 20 Nov 2009 14:15:24 +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/t125872290782lq2ugehjl46h9.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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkl[t] = + 151.897366875482 -0.450048120480986Infl[t] -0.0787500367219569M1[t] -0.120513226375434M2[t] -0.109860556364425M3[t] -0.177209811172649M4[t] -0.115558103571257M5[t] -0.162907358379490M6[t] -0.152254688368479M7[t] -0.112601055947849M8[t] -0.0939560852137956M9[t] -0.00130534002201750M10[t] -0.0176555572398707M11[t] + 0.0383482923986089t + 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) 151.897366875482 6.788831 22.3746 0 0 Infl -0.450048120480986 0.066701 -6.7473 0 0 M1 -0.0787500367219569 0.191341 -0.4116 0.682527 0.341263 M2 -0.120513226375434 0.200525 -0.601 0.550735 0.275367 M3 -0.109860556364425 0.200315 -0.5484 0.585986 0.292993 M4 -0.177209811172649 0.20001 -0.886 0.380127 0.190063 M5 -0.115558103571257 0.199794 -0.5784 0.565765 0.282882 M6 -0.162907358379490 0.19956 -0.8163 0.418428 0.209214 M7 -0.152254688368479 0.199444 -0.7634 0.449042 0.224521 M8 -0.112601055947849 0.199418 -0.5646 0.574998 0.287499 M9 -0.0939560852137956 0.199101 -0.4719 0.639181 0.319591 M10 -0.00130534002201750 0.199043 -0.0066 0.994795 0.497398 M11 -0.0176555572398707 0.199055 -0.0887 0.9297 0.46485 t 0.0383482923986089 0.002348 16.3305 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931216785308686 R-squared 0.867164701240643 Adjusted R-squared 0.830423022860395 F-TEST (value) 23.6016627293442 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 2.22044604925031e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.3146140316187 Sum Squared Residuals 4.65215347789449 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.4 105.637023157761 -0.237023157761039 2 105.4 105.723617884603 -0.323617884602509 3 105.6 105.907633283156 -0.307633283156431 4 105.7 105.56359863641 0.136401363589883 5 105.8 105.573589012314 0.226410987686070 6 105.8 105.609592861952 0.190407138047597 7 105.8 105.748603448458 0.0513965515417788 8 105.9 105.961619809422 -0.0616198094217461 9 106.1 106.288641944843 -0.188641944843008 10 106.4 106.419640982433 -0.0196409824333840 11 106.4 106.486643869662 -0.0866438696622418 12 106.3 106.452638095205 -0.152638095204531 13 106.2 106.142207478593 0.0577925214074108 14 106.2 106.183797393386 0.0162026066141837 15 106.3 106.232798355795 0.0672016442045595 16 106.4 106.608840701819 -0.208840701818706 17 106.5 106.798850325915 -0.298850325914904 18 106.6 107.059878235794 -0.459878235793881 19 106.6 106.973864762059 -0.373864762059206 20 106.6 106.961857062782 -0.361857062782248 21 106.8 107.063855137963 -0.263855137963003 22 107 107.149849363505 -0.149849363505291 23 107.2 107.261857062782 -0.0618570627822418 24 107.3 107.227851288325 0.0721487116754713 25 107.5 107.232454356049 0.267545643950726 26 107.6 107.049020210602 0.550979789397979 27 107.6 107.053016360964 0.546983639036463 28 107.7 107.114025022650 0.585974977349888 29 107.7 107.484053894939 0.2159461050613 30 107.7 107.205024060240 0.494975939759512 31 107.7 107.299029834698 0.400970165301796 32 107.6 107.287022135421 0.312977864578741 33 107.7 107.299010586506 0.400989413494189 34 107.9 107.565024060240 0.334975939759511 35 107.9 107.407002887229 0.492997112771146 36 107.9 107.508011548915 0.39198845108457 37 107.8 107.602624240736 0.197375759263615 38 107.6 107.689218967578 -0.0892189675777174 39 107.4 107.828229554084 -0.428229554083523 40 107 107.484194907337 -0.484194907337222 41 107 107.269161223001 -0.269161223000531 42 107.2 107.440179508783 -0.240179508783302 43 107.5 107.444175659145 0.0558243408551718 44 107.8 107.567182396012 0.232817603987833 45 107.8 107.714185283241 0.0858147167589736 46 107.7 107.440141012399 0.259858987601482 47 107.6 107.642158335772 -0.0421583357716794 48 107.6 107.878181433603 -0.278181433602549 49 107.5 107.792774877231 -0.292774877231100 50 107.5 107.654345543832 -0.154345543831937 51 107.6 107.478322446001 0.121677553998932 52 107.6 107.629340731784 -0.0293407317838436 53 107.9 107.774345543832 0.125654456168065 54 107.6 107.58532533323 0.0146746667700741 55 107.5 107.634326295640 -0.134326295639540 56 107.5 107.622318596363 -0.122318596362581 57 107.6 107.634307047447 -0.0343070474471519 58 107.7 108.125344581422 -0.425344581422318 59 107.8 108.102337844555 -0.302337844554983 60 107.9 107.933317633953 -0.0333176339529607 61 107.9 107.892915889630 0.00708411037038624 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00198464167412897 0.00396928334825794 0.998015358325871 18 0.000617576502372092 0.00123515300474418 0.999382423497628 19 0.000125603039415103 0.000251206078830206 0.999874396960585 20 3.82999762714609e-05 7.65999525429218e-05 0.999961700023729 21 1.26262254317022e-05 2.52524508634043e-05 0.999987373774568 22 3.25860107899585e-05 6.5172021579917e-05 0.99996741398921 23 1.61640286269508e-05 3.23280572539016e-05 0.999983835971373 24 0.000330199504044706 0.000660399008089412 0.999669800495955 25 0.0472012981135954 0.0944025962271907 0.952798701886405 26 0.159315392088651 0.318630784177301 0.84068460791135 27 0.145592163332555 0.291184326665110 0.854407836667445 28 0.158773372309455 0.317546744618909 0.841226627690545 29 0.122792973376706 0.245585946753413 0.877207026623294 30 0.0998057929354175 0.199611585870835 0.900194207064583 31 0.0721978302314857 0.144395660462971 0.927802169768514 32 0.0482364357179943 0.0964728714359886 0.951763564282006 33 0.0378871303796307 0.0757742607592614 0.96211286962037 34 0.0362705351806109 0.0725410703612218 0.96372946481939 35 0.0495148974697172 0.0990297949394345 0.950485102530283 36 0.0646274518780178 0.129254903756036 0.935372548121982 37 0.10659273641053 0.21318547282106 0.89340726358947 38 0.183895202370519 0.367790404741037 0.816104797629482 39 0.335591986572272 0.671183973144544 0.664408013427728 40 0.662911044070256 0.674177911859489 0.337088955929744 41 0.895943402009735 0.208113195980529 0.104056597990265 42 0.897221859586557 0.205556280826886 0.102778140413443 43 0.804656957208286 0.390686085583429 0.195343042791714 44 0.799529133474307 0.400941733051386 0.200470866525693 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.285714285714286 NOK 5% type I error level 8 0.285714285714286 NOK 10% type I error level 13 0.464285714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/10u4mq1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/10u4mq1258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/12z221258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/12z221258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/26h6q1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/26h6q1258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/3ep701258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/3ep701258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/4lt701258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/4lt701258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/5670g1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/5670g1258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/6au9h1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/6au9h1258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/7ykan1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/7ykan1258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/83cj71258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/83cj71258722837.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/9r4za1258722837.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872290782lq2ugehjl46h9/9r4za1258722837.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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