q3a

*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: Sun, 23 Nov 2008 08:53:10 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye.htm/, Retrieved Sun, 23 Nov 2008 15:53:51 +0000

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/2008/Nov/23/t12274556220w53llgqw0a7wye.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Original text written by user:

IsPrivate?

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User-defined keywords:

Dataseries X:

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1515 0 1510 0 1225 0 1577 0 1417 0 1224 0 1693 0 1633 0 1639 0 1914 0 1586 0 1552 0 2081 0 1500 0 1437 0 1470 0 1849 0 1387 0 1592 0 1589 0 1798 0 1935 0 1887 0 2027 0 2080 0 1556 0 1682 0 1785 0 1869 0 1781 0 2082 0 2570 1 1862 1 1936 1 1504 1 1765 1 1607 1 1577 1 1493 1 1615 1 1700 1 1335 1 1523 1 1623 1 1540 1 1637 1 1524 1 1419 1 1821 1 1593 1 1357 1 1263 1 1750 1 1405 1 1393 1 1639 1 1679 1 1551 1 1744 1 1429 1 1784 1

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 Gebouwen[t] = + 1622.60475938657 -148.643010752688Dummy[t] + 3.16784769962981t + 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) 1622.60475938657 70.705375 22.9488 0 0 Dummy -148.643010752688 123.204227 -1.2065 0.232532 0.116266 t 3.16784769962981 3.498295 0.9055 0.368926 0.184463 Multiple Linear Regression - Regression Statistics Multiple R 0.160479702443966 R-squared 0.0257537348965038 Adjusted R-squared -0.00784096390016864 F-TEST (value) 0.766601154913609 F-TEST (DF numerator) 2 F-TEST (DF denominator) 58 p-value 0.469238606944363 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 240.531615479265 Sum Squared Residuals 3355616.56661378 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1515 1625.77260708619 -110.772607086191 2 1510 1628.94045478583 -118.940454785828 3 1225 1632.10830248546 -407.108302485458 4 1577 1635.27615018509 -58.2761501850878 5 1417 1638.44399788472 -221.443997884718 6 1224 1641.61184558435 -417.611845584347 7 1693 1644.77969328398 48.2203067160228 8 1633 1647.94754098361 -14.9475409836070 9 1639 1651.11538868324 -12.1153886832368 10 1914 1654.28323638287 259.716763617133 11 1586 1657.45108408250 -71.4510840824964 12 1552 1660.61893178213 -108.618931782126 13 2081 1663.78677948176 417.213220518244 14 1500 1666.95462718139 -166.954627181386 15 1437 1670.12247488102 -233.122474881016 16 1470 1673.29032258065 -203.290322580645 17 1849 1676.45817028028 172.541829719725 18 1387 1679.62601797991 -292.626017979905 19 1592 1682.79386567953 -90.7938656795348 20 1589 1685.96171337916 -96.9617133791646 21 1798 1689.12956107879 108.870438921206 22 1935 1692.29740877842 242.702591221576 23 1887 1695.46525647805 191.534743521946 24 2027 1698.63310417768 328.366895822316 25 2080 1701.80095187731 378.199048122686 26 1556 1704.96879957694 -148.968799576943 27 1682 1708.13664727657 -26.1366472765733 28 1785 1711.30449497620 73.695505023797 29 1869 1714.47234267583 154.527657324167 30 1781 1717.64019037546 63.3598096245373 31 2082 1720.80803807509 361.191961924908 32 2570 1575.33287502203 994.667124977965 33 1862 1578.50072272166 283.499277278336 34 1936 1581.66857042129 354.331429578706 35 1504 1584.83641812092 -80.836418120924 36 1765 1588.00426582055 176.995734179446 37 1607 1591.17211352018 15.8278864798165 38 1577 1594.33996121981 -17.3399612198133 39 1493 1597.50780891944 -104.507808919443 40 1615 1600.67565661907 14.3243433809271 41 1700 1603.84350431870 96.1564956812973 42 1335 1607.01135201833 -272.011352018333 43 1523 1610.17919971796 -87.1791997179623 44 1623 1613.34704741759 9.65295258240786 45 1540 1616.51489511722 -76.514895117222 46 1637 1619.68274281685 17.3172571831483 47 1524 1622.85059051648 -98.8505905164816 48 1419 1626.01843821611 -207.018438216111 49 1821 1629.18628591574 191.813714084259 50 1593 1632.35413361537 -39.354133615371 51 1357 1635.521981315 -278.521981315001 52 1263 1638.68982901463 -375.689829014631 53 1750 1641.85767671426 108.142323285740 54 1405 1645.02552441389 -240.02552441389 55 1393 1648.19337211352 -255.19337211352 56 1639 1651.36121981315 -12.3612198131498 57 1679 1654.52906751278 24.4709324872204 58 1551 1657.69691521241 -106.696915212409 59 1744 1660.86476291204 83.1352370879608 60 1429 1664.03261061167 -235.032610611669 61 1784 1667.2004583113 116.799541688701 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.362497609282143 0.724995218564286 0.637502390717857 7 0.504583414166626 0.990833171666748 0.495416585833374 8 0.397407376866717 0.794814753733433 0.602592623133283 9 0.282676769479554 0.565353538959107 0.717323230520446 10 0.304322221411599 0.608644442823198 0.695677778588401 11 0.249350779185492 0.498701558370983 0.750649220814508 12 0.213833172946833 0.427666345893667 0.786166827053167 13 0.310168231386735 0.62033646277347 0.689831768613265 14 0.380351195731411 0.760702391462822 0.619648804268589 15 0.468260433135172 0.936520866270344 0.531739566864828 16 0.493480438808464 0.986960877616928 0.506519561191536 17 0.433870912222285 0.86774182444457 0.566129087777715 18 0.563347730930732 0.873304538138536 0.436652269069268 19 0.525694171922977 0.948611656154046 0.474305828077023 20 0.499315145622291 0.998630291244581 0.500684854377709 21 0.442989558622349 0.885979117244698 0.557010441377651 22 0.418458188588987 0.836916377177975 0.581541811411012 23 0.35813075354724 0.71626150709448 0.64186924645276 24 0.345464040526891 0.690928081053781 0.65453595947311 25 0.350310780817884 0.700621561635769 0.649689219182116 26 0.420197967346184 0.840395934692368 0.579802032653816 27 0.395769924871002 0.791539849742005 0.604230075128998 28 0.336008317213237 0.672016634426474 0.663991682786763 29 0.270536522318190 0.541073044636379 0.72946347768181 30 0.240748351843387 0.481496703686773 0.759251648156613 31 0.208625155860750 0.417250311721501 0.79137484413925 32 0.783441999258057 0.433116001483886 0.216558000741943 33 0.904706101765855 0.190587796468290 0.0952938982341448 34 0.955024254140918 0.089951491718164 0.044975745859082 35 0.976521918189697 0.0469561636206062 0.0234780818103031 36 0.97891234310086 0.0421753137982813 0.0210876568991406 37 0.9763591107863 0.0472817784274001 0.0236408892137000 38 0.971466856099566 0.0570662878008676 0.0285331439004338 39 0.967076523208838 0.065846953582325 0.0329234767911625 40 0.955942127757365 0.0881157444852691 0.0440578722426346 41 0.952890589879426 0.0942188202411482 0.0471094101205741 42 0.962829223767066 0.0743415524658685 0.0371707762329343 43 0.946090830877465 0.107818338245070 0.0539091691225348 44 0.924602454600158 0.150795090799685 0.0753975453998423 45 0.892147165332562 0.215705669334877 0.107852834667438 46 0.862349300140354 0.275301399719291 0.137650699859646 47 0.811151149314923 0.377697701370153 0.188848850685077 48 0.764504974224492 0.470990051551017 0.235495025775508 49 0.863255429933422 0.273489140133157 0.136744570066578 50 0.852986989652264 0.294026020695473 0.147013010347736 51 0.794505998818554 0.410988002362892 0.205494001181446 52 0.813158046351958 0.373683907296085 0.186841953648042 53 0.863130650550746 0.273738698898508 0.136869349449254 54 0.77088850365727 0.458222992685459 0.229111496342730 55 0.717293520188572 0.565412959622855 0.282706479811428 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 3 0.06 NOK 10% type I error level 9 0.18 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/10yfw61227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/10yfw61227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/132mf1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/132mf1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/2kxsg1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/2kxsg1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/354oh1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/354oh1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/45qsy1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/45qsy1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/5inmw1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/5inmw1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/6gjfc1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/6gjfc1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/7u5n81227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/7u5n81227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/8b5mm1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/8b5mm1227455586.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/91pwp1227455586.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274556220w53llgqw0a7wye/91pwp1227455586.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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