*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: Wed, 22 Dec 2010 15:58:26 +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/Dec/22/t1293033417xnfvtl3gog0rmkv.htm/, Retrieved Wed, 22 Dec 2010 16:57:00 +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/Dec/22/t1293033417xnfvtl3gog0rmkv.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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1143.94 9.70 1227.85 9.20 1261.26 9.90 1408.95 10.00 1162.58 9.90 1259.39 9.20 1253.85 9.60 1475.32 9.40 1211.75 9.10 1303.83 8.70 1299.37 9.50 1430.73 9.50 1244.95 9.40 1318.58 9.00 1318.74 9.60 1525.05 9.30 1275.88 9.00 1360.09 8.50 1349.81 8.50 1574.04 7.90 1294.58 7.20 1380.60 6.50 1369.22 7.10 1565.98 6.80 1338.96 6.20 1457.57 6.20 1456.21 6.50 1654.44 7.50 1428.47 7.40 1530.39 6.90 1514.13 7.60 1698.25 8.10 1454.22 8.20 1578.06 7.70 1526.53 8.30 1714.21 8.50 1492.86 8.70 1593.42 7.40 1555.50 9.10 1820.55 8.40 1534.57 8.60 1636.03 8.10 1594.58 8.70 1805.13 8.50 1565.37 8.70 1679.57 8.30 1638.26 8.10 1854.64 7.90 1628.72 8.00 1744.97 7.60 1694.35 7.30 1920.88 7.10 1680.26 7.10 1778.62 6.30 1740.89 7.70 2010.56 6.80

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 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation WERKLOOSHEIDSGRAAD [t] = + 13.8797415087597 -0.00335126330891042LOONKOSTEN[t] -0.850748734443006Q1[t] -1.06042806664729Q2[t] -0.562274746243909Q3[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) 13.8797415087597 1.170768 11.8552 0 0 LOONKOSTEN -0.00335126330891042 0.000685 -4.8959 1e-05 5e-06 Q1 -0.850748734443006 0.385495 -2.2069 0.031848 0.015924 Q2 -1.06042806664729 0.355821 -2.9802 0.004404 0.002202 Q3 -0.562274746243909 0.360885 -1.558 0.125408 0.062704 Multiple Linear Regression - Regression Statistics Multiple R 0.593533849375111 R-squared 0.352282430354037 Adjusted R-squared 0.301481052342589 F-TEST (value) 6.93450540405913 F-TEST (DF numerator) 4 F-TEST (DF denominator) 51 p-value 0.000154740728753544 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.878800208164375 Sum Squared Residuals 39.3867800993572 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.7 9.19534862472172 0.504651375278283 2 9.2 8.70446478826677 0.495535211733225 3 9.9 9.09065240151946 0.809347598480544 4 10 9.15797906967039 0.842020930329614 5 9.9 9.13288107664364 0.767118923356361 6 9.2 8.59876594350374 0.601234056496261 7 9.6 9.11548526263848 0.484514737361516 8 9.4 8.935555723858 0.464444276141998 9 9.1 8.96809945974451 0.131900540255485 10 8.7 8.44983580205576 0.250164197944239 11 9.5 8.96293575681688 0.537064243183119 12 9.5 9.08498855480232 0.415011445197682 13 9.4 8.85683751788869 0.543162482111312 14 9 8.40040466824933 0.599595331750668 15 9.6 8.89802178652329 0.701978213476714 16 9.3 8.76889739950589 0.531102600494113 17 9 8.75318294374409 0.246817056255911 18 8.5 8.26129372829646 0.23870627170354 19 8.5 8.79389803551544 -0.29389803551544 20 7.9 8.60471901000237 -0.704719010002366 21 7.2 8.69051431986746 -1.49051431986746 22 6.5 8.1925593178307 -1.69255931783071 23 7.1 8.72885001468949 -1.62885001468949 24 6.8 8.63173019227218 -1.83173019227218 25 6.2 8.54178525421802 -2.34178525421802 26 6.2 7.93461258094387 -1.73461258094387 27 6.5 8.43732361944737 -1.93732361944737 28 7.5 8.33527743996597 -0.835277439965968 29 7.4 8.24181367543745 -0.841813675437449 30 6.9 7.69057358678902 -0.790573586789016 31 7.6 8.24321844859528 -0.64321844859528 32 8.1 8.1884585944026 -0.0884585944026037 33 8.2 8.155518645233 0.0444813547669934 34 7.7 7.53081886485326 0.169181135146743 35 8.3 8.20166278356479 0.0983372164352098 36 8.5 8.1349724319924 0.365027568007607 37 8.7 8.02602583097671 0.673974169023291 38 7.4 7.47934346042839 -0.0793434604283924 39 9.1 8.10457668550566 0.995423314494344 40 8.4 7.77859909172286 0.62140090827714 41 8.6 7.88624463836205 0.713755361637945 42 8.1 7.33654613083572 0.76345386916428 43 8.7 7.97360931539344 0.726390684606562 44 8.5 7.83027557194626 0.669724428053742 45 8.7 7.78302572844761 0.916974271552386 46 8.3 7.19063212636576 1.10936787363424 47 8.1 7.82722613406023 0.272773865939769 48 7.9 7.6643545255221 0.235645474477897 49 8 7.57072319782814 0.429276802171862 50 7.6 6.97145950596302 0.62854049403698 51 7.3 7.63925377506345 -0.339253775063446 52 7.1 7.44236684393988 -0.342366843939878 53 7.1 7.3979990868869 -0.297999086886896 54 6.3 6.85868949561818 -0.558689495618185 55 7.7 7.48328598066675 0.216714019333246 56 6.8 7.14182555039679 -0.341825550396792 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.020256608324744 0.0405132166494879 0.979743391675256 9 0.00700773572207111 0.0140154714441422 0.99299226427793 10 0.00148026596675053 0.00296053193350105 0.99851973403325 11 0.000331954847594723 0.000663909695189445 0.999668045152405 12 0.0001546977112812 0.0003093954225624 0.999845302288719 13 0.000102830063072918 0.000205660126145835 0.999897169936927 14 5.59900372578025e-05 0.000111980074515605 0.999944009962742 15 2.4186093912833e-05 4.8372187825666e-05 0.999975813906087 16 9.02997775057043e-06 1.80599555011409e-05 0.99999097002225 17 3.32291302726378e-06 6.64582605452756e-06 0.999996677086973 18 1.44890936691118e-06 2.89781873382237e-06 0.999998551090633 19 3.89137630508911e-05 7.78275261017822e-05 0.99996108623695 20 0.000235137777001643 0.000470275554003286 0.999764862222998 21 0.0086021712752016 0.0172043425504032 0.991397828724798 22 0.0438525100296243 0.0877050200592485 0.956147489970376 23 0.0982490725632267 0.196498145126453 0.901750927436773 24 0.153350151726934 0.306700303453867 0.846649848273066 25 0.305682571698309 0.611365143396619 0.69431742830169 26 0.351346690561693 0.702693381123387 0.648653309438307 27 0.554479315648733 0.891041368702534 0.445520684351267 28 0.6566280250322 0.686743949935601 0.343371974967801 29 0.84608883677715 0.307822326445702 0.153911163222851 30 0.941725615386448 0.116548769227105 0.0582743846135524 31 0.978654083680025 0.042691832639949 0.0213459163199745 32 0.987015809843431 0.0259683803131371 0.0129841901565686 33 0.994649660616503 0.0107006787669933 0.00535033938349665 34 0.996616748581965 0.00676650283607017 0.00338325141803508 35 0.997622570825241 0.00475485834951784 0.00237742917475892 36 0.997660705467477 0.00467858906504627 0.00233929453252313 37 0.997421401392436 0.00515719721512844 0.00257859860756422 38 0.999759326371944 0.000481347256111183 0.000240673628055592 39 0.999583728869726 0.000832542260546972 0.000416271130273486 40 0.99908980100909 0.00182039798181966 0.000910198990909828 41 0.998130181175688 0.0037396376486249 0.00186981882431245 42 0.996366893688652 0.00726621262269685 0.00363310631134843 43 0.991385531864946 0.0172289362701071 0.00861446813505355 44 0.979877904124215 0.0402441917515708 0.0201220958757854 45 0.96072612010716 0.0785477597856813 0.0392738798928407 46 0.949629203649625 0.10074159270075 0.050370796350375 47 0.884426114601325 0.231147770797351 0.115573885398675 48 0.761478062988039 0.477043874023923 0.238521937011961 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.48780487804878 NOK 5% type I error level 28 0.682926829268293 NOK 10% type I error level 30 0.73170731707317 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/10xyen1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/10xyen1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/18fzb1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/18fzb1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/21oge1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/21oge1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/31oge1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/31oge1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/41oge1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/41oge1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/5cyyz1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/5cyyz1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/6cyyz1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/6cyyz1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/747xk1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/747xk1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/847xk1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/847xk1293033497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/9xyen1293033497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293033417xnfvtl3gog0rmkv/9xyen1293033497.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Quarterly 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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