WS7 Include dummies

*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, 18 Nov 2009 08:42:15 -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/18/t1258559074jupa2qtfiz846re.htm/, Retrieved Wed, 18 Nov 2009 16:44:46 +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/18/t1258559074jupa2qtfiz846re.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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7,55 42,97 7,55 42,98 7,59 43,01 7,59 43,09 7,59 43,14 7,57 43,39 7,57 43,46 7,59 43,54 7,6 43,62 7,64 44,01 7,64 44,5 7,76 44,73 7,76 44,89 7,76 45,09 7,77 45,17 7,83 45,24 7,94 45,42 7,94 45,67 7,94 45,68 8,09 46,56 8,18 46,72 8,26 47,01 8,28 47,26 8,28 47,49 8,28 47,51 8,29 47,52 8,3 47,66 8,3 47,71 8,31 47,87 8,33 48 8,33 48 8,34 48,05 8,48 48,25 8,59 48,72 8,67 48,94 8,67 49,16 8,67 49,18 8,71 49,25 8,72 49,34 8,72 49,49 8,72 49,57 8,74 49,63 8,74 49,67 8,74 49,7 8,74 49,8 8,79 50,09 8,85 50,49 8,86 50,73 8,87 51,12 8,92 51,15 8,96 51,41 8,97 51,61 8,99 52,06 8,98 52,17 8,98 52,18 9,01 52,19 9,01 52,74 9,03 53,05 9,05 53,38 9,05 53,78

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] = + 0.39230876561183 + 0.165352215104075X[t] + 0.0399799276727355M1[t] + 0.0493973859060674M2[t] + 0.051555120093579M3[t] + 0.0473663764321304M4[t] + 0.0449415688529809M5[t] + 0.0204852144363287M6[t] + 0.0161860568436230M7[t] + 0.0234620916717671M8[t] + 0.035415308779079M9[t] + 0.0375420334926525M10[t] + 0.0176529847874754M11[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) 0.39230876561183 0.209051 1.8766 0.066787 0.033393 X 0.165352215104075 0.004159 39.758 0 0 M1 0.0399799276727355 0.061744 0.6475 0.520452 0.260226 M2 0.0493973859060674 0.061708 0.8005 0.427448 0.213724 M3 0.051555120093579 0.061643 0.8363 0.407193 0.203596 M4 0.0473663764321304 0.061588 0.7691 0.445688 0.222844 M5 0.0449415688529809 0.061502 0.7307 0.468568 0.234284 M6 0.0204852144363287 0.061435 0.3334 0.740281 0.370141 M7 0.0161860568436230 0.061425 0.2635 0.793309 0.396655 M8 0.0234620916717671 0.061349 0.3824 0.703863 0.351932 M9 0.035415308779079 0.061284 0.5779 0.566099 0.283049 M10 0.0375420334926525 0.061207 0.6134 0.542596 0.271298 M11 0.0176529847874754 0.061166 0.2886 0.77415 0.387075 Multiple Linear Regression - Regression Statistics Multiple R 0.985965174331125 R-squared 0.972127324993806 Adjusted R-squared 0.96501089733265 F-TEST (value) 136.6032750252 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0966961826717787 Sum Squared Residuals 0.439457131934818 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.55 7.5374733763066 0.0125266236933963 2 7.55 7.54854435669101 0.00145564330898701 3 7.59 7.55566265733165 0.0343373426683528 4 7.59 7.56470209087853 0.0252979091214744 5 7.59 7.57054489405458 0.0194551059454208 6 7.57 7.58742659341395 -0.0174265934139454 7 7.57 7.59470209087852 -0.0247020908785247 8 7.59 7.615206302915 -0.0252063029149949 9 7.6 7.64038769723063 -0.0403876972306327 10 7.64 7.7070017858348 -0.0670017858347955 11 7.64 7.76813532253061 -0.128135322530615 12 7.76 7.78851334721708 -0.0285133472170763 13 7.76 7.85494962930646 -0.0949496293064644 14 7.76 7.89743753056061 -0.137437530560612 15 7.77 7.91282344195645 -0.142823441956449 16 7.83 7.92020935335229 -0.0902093533522853 17 7.94 7.94754794449187 -0.0075479444918688 18 7.94 7.96442964385124 -0.0244296438512353 19 7.94 7.96178400840957 -0.0217840084095699 20 8.09 8.1145699925293 -0.0245699925293005 21 8.18 8.15297956405326 0.027020435946736 22 8.26 8.20305843114702 0.0569415688529811 23 8.28 8.22450743621786 0.0554925637821391 24 8.28 8.24488546090432 0.0351145390956768 25 8.28 8.28817243287914 -0.0081724328791396 26 8.29 8.29924341326351 -0.00924341326351327 27 8.3 8.3245504575656 -0.0245504575655927 28 8.3 8.32862932465935 -0.0286293246593485 29 8.31 8.35266087149685 -0.0426608714968505 30 8.33 8.34970030504373 -0.0197003050437290 31 8.33 8.34540114745102 -0.0154011474510232 32 8.34 8.36094479303437 -0.0209447930343707 33 8.48 8.4059684531625 0.0740315468375025 34 8.59 8.48581071897499 0.104189281025014 35 8.67 8.5022991575927 0.167700842407295 36 8.67 8.52102366012813 0.148976339871874 37 8.67 8.56431063210294 0.105689367897056 38 8.71 8.58530274539356 0.12469725460644 39 8.72 8.60234217894044 0.117657821059561 40 8.72 8.6229562675446 0.0970437324553985 41 8.72 8.63375963717378 0.0862403628262224 42 8.74 8.61922441566337 0.120775584336629 43 8.74 8.62153934667483 0.118460653325172 44 8.74 8.6337759479561 0.106224052043906 45 8.74 8.66226438657381 0.0777356134261873 46 8.79 8.71234325366757 0.07765674633243 47 8.85 8.75859509100402 0.091404908995978 48 8.86 8.78062663784152 0.0793733621584761 49 8.87 8.88509392940485 -0.0150939294048486 50 8.92 8.8994719540913 0.0205280459086978 51 8.96 8.94462126420587 0.0153787357941280 52 8.97 8.97350296356524 -0.00350296356523906 53 8.99 9.04548665278292 -0.0554866527829239 54 8.98 9.03921904202772 -0.0592190420277196 55 8.98 9.03657340658605 -0.0565734065860544 56 9.01 9.04550296356524 -0.0355029635652394 57 9.01 9.14839989897979 -0.138399898979793 58 9.03 9.20178581037563 -0.171785810375629 59 9.05 9.2364629926548 -0.186462992654796 60 9.05 9.28495089390895 -0.234950893908950 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0113845235420079 0.0227690470840158 0.988615476457992 17 0.0548173214838672 0.109634642967734 0.945182678516133 18 0.0696546400006996 0.139309280001399 0.9303453599993 19 0.0675959679072768 0.135191935814554 0.932404032092723 20 0.0698029607081875 0.139605921416375 0.930197039291813 21 0.103708174171031 0.207416348342062 0.896291825828969 22 0.160797238886064 0.321594477772128 0.839202761113936 23 0.260577277610137 0.521154555220274 0.739422722389863 24 0.202389887314129 0.404779774628257 0.797610112685871 25 0.161297171641123 0.322594343282246 0.838702828358877 26 0.150957073124789 0.301914146249578 0.849042926875211 27 0.152491294350280 0.304982588700559 0.84750870564972 28 0.168168684702845 0.336337369405689 0.831831315297155 29 0.225679098683991 0.451358197367981 0.77432090131601 30 0.315675036818133 0.631350073636266 0.684324963181867 31 0.541395016361897 0.917209967276206 0.458604983638103 32 0.96922891430511 0.0615421713897799 0.0307710856948899 33 0.992508530638045 0.0149829387239102 0.00749146936195508 34 0.994360460032078 0.0112790799358444 0.00563953996792221 35 0.995747093014405 0.00850581397119026 0.00425290698559513 36 0.993111162898822 0.0137776742023565 0.00688883710117825 37 0.988186248299902 0.0236275034001959 0.0118137517000979 38 0.984109228848038 0.0317815423039237 0.0158907711519619 39 0.980896017517221 0.0382079649655579 0.0191039824827789 40 0.978794003894684 0.0424119922106319 0.0212059961053160 41 0.972648182011313 0.0547036359773743 0.0273518179886872 42 0.945558962153628 0.108882075692744 0.0544410378463722 43 0.899644518771624 0.200710962456751 0.100355481228376 44 0.922628619279899 0.154742761440202 0.077371380720101 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0344827586206897 NOK 5% type I error level 9 0.310344827586207 NOK 10% type I error level 11 0.379310344827586 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/10xlbn1258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/10xlbn1258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/1krd71258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/1krd71258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/2xjsg1258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/2xjsg1258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/3i7731258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/3i7731258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/4ywk41258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/4ywk41258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/5kqw01258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/5kqw01258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/67mqw1258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/67mqw1258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/72o9g1258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/72o9g1258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/8cfve1258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/8cfve1258558931.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/9rel51258558931.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559074jupa2qtfiz846re/9rel51258558931.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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