meervoudig regressie model

*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: Tue, 14 Dec 2010 14:29:53 +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/14/t1292336886ib30fefhoqc1zkm.htm/, Retrieved Tue, 14 Dec 2010 15:28:10 +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/14/t1292336886ib30fefhoqc1zkm.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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1606 6 3,74 16 1391 1634 6,81 4,17 29 1621 2013 9,75 4,84 22 1837 1654 6,96 4,21 30 2132 1003 3,94 3,93 20 1489 1029 5 4,9 39 1817 1052 4,9 4,7 18 1586 1653 5,7 3,5 9,6 1565 1918 6,5 3,4 10,2 1787 1926 7,1 3,7 20,2 1804 1862 7,5 4 50 1763 1816 7,8 4,3 120 1675 1712 7 4,1 19,8 1575 1646 7,4 4,5 18 1524 1555 8,55 5,5 3 1686 1402 7,43 5,3 11 1800 1047 4,7 4,5 15 1442 891 4,7 5,3 27 1345 940 5,3 5,6 28 1500 1372 6,2 4,5 14 1556 2012 7,4 3,7 5,6 2012 1879 7,5 4 6,5 1618 1667 7,32 4,4 8,5 1487 1856 8,15 4,4 87,9 1607 1771 7,24 4,1 5,8 1308 1721 7,4 4,3 25,2 1429 1773 9,4 5,3 7,5 1596 1507 8,9 5,9 13,7 1884 1033 4,5 4,4 34 1262 1011 4,9 4,9 17 1283 1111 5,6 5,1 9 1346 1736 6,4 3,7 9,2 1505 1865 6 3,2 5 1151 2078 6,9 3,3 24 1600 1947 6,7 3,5 40 1420 1428 5,4 3,8 86,5 1073 1500 5,6 3,8 0,54 1076 1950 6,9 3,5 14 1510 1591 6,9 4,3 4,8 1345 1613 7 4,3 28 1631 1077 4 3,7 16 1135 880 3,7 4,2 5,8 1009 1128 4,9 4,3 16 1155 1320 5 3,8 9,1 1184 1692 5,7 3,4 6 1285 1575 6,1 3,9 17 12 etc...

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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation aanvoer[t] = + 1496.18336595151 + 225.734876692285aanvoerwaarde[t] -346.457827131305prijs[t] -0.133038531248398interventie[t] + 0.0407046617575528visserijen[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) 1496.18336595151 47.461652 31.524 0 0 aanvoerwaarde 225.734876692285 5.62315 40.1439 0 0 prijs -346.457827131305 10.687147 -32.4182 0 0 interventie -0.133038531248398 0.21557 -0.6171 0.539683 0.269842 visserijen 0.0407046617575528 0.026303 1.5475 0.127471 0.063735 Multiple Linear Regression - Regression Statistics Multiple R 0.98992726970624 R-squared 0.97995599930805 Adjusted R-squared 0.978498253803182 F-TEST (value) 672.240796514138 F-TEST (DF numerator) 4 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 49.1182655955212 Sum Squared Residuals 132693.220831169 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1606 1609.33192063893 -3.33192063892801 2 1634 1650.83287639122 -16.8328763912236 3 2013 2092.09014634694 -79.0901463469379 4 1654 1691.50183843668 -37.5018384366752 5 1003 1081.94799022512 -78.9479902251169 6 1029 995.986264164331 33.013735835669 7 1052 1036.09537421159 15.9046257884146 8 1653 1632.69539388856 20.3046061114426 9 1918 1856.88568974694 61.1143102530564 10 1926 1887.75086156032 38.2491384396825 11 1862 1868.47402473458 -6.47402473457829 12 1816 1819.36243218082 -3.36243218081984 13 1712 1717.32609090859 -5.32609090858704 14 1646 1667.20044233959 -21.200442339591 15 1555 1588.92745657786 -33.9274565778636 16 1402 1408.97198329914 -6.97198329913882 17 1047 1054.77760860005 -7.77760860004696 18 891 772.06653232954 118.93346767046 19 940 809.746294246691 130.253705753308 20 1372 1398.15329361008 -26.1532936100841 21 2012 1965.8802567698 46.1197432301992 22 1879 1868.35902488904 10.6409751109616 23 1667 1683.54522847917 -16.5452284791689 24 1856 1865.22647616355 -9.22647616354907 25 1771 1762.49685606295 8.5031439370535 26 1721 1731.6671874739 -10.6671874738961 27 1773 1845.83157424377 -72.8315742437694 28 1507 1535.98754331128 -28.9875433112789 29 1033 1034.42184476464 -1.42184476464119 30 1011 954.603334804034 56.3966651959658 31 1111 1046.95488500309 64.045114996914 32 1736 1719.02917785394 16.9708221460578 33 1865 1788.11345231175 76.88654768825 34 2078 1972.3777196571 105.622280342902 35 1947 1848.48372327605 98.516276723954 36 1428 1430.78022610376 -2.78022610376268 37 1500 1487.4853075736 12.5146924263955 38 1950 1900.75311998514 49.2468800148587 39 1591 1618.09454357759 -27.0945435775864 40 1613 1649.22307058451 -36.2230705845121 41 1077 1161.30008692967 -84.3000869296741 42 880 916.578915993618 -36.5789159936182 43 1128 1157.4008729091 -29.400872909099 44 1320 1355.30167520056 -35.3016752005629 45 1692 1656.42281002207 35.5771899779326 46 1575 1570.88469276038 4.11530723961504 47 1478 1487.90800538326 -9.9080053832611 48 1500 1529.08411145317 -29.0841114531661 49 1368 1331.8603048779 36.139695122101 50 1563 1572.24782535819 -9.24782535818962 51 1424 1454.98087693821 -30.9808769382089 52 1274 1353.02558065204 -79.0255806520363 53 1047 1089.49119661336 -42.4911966133609 54 1049 1119.52612881511 -70.5261288151116 55 1069 1071.69104919976 -2.69104919976373 56 981 1010.37858397075 -29.3785839707532 57 1540 1562.2078259486 -22.2078259486044 58 1559 1571.76682430922 -12.7668243092166 59 1459 1460.6506476573 -1.65064765730372 60 1559 1539.85291768151 19.1470823184917 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.767755256461055 0.464489487077889 0.232244743538945 9 0.796939399050459 0.406121201899083 0.203060600949541 10 0.738538318296858 0.522923363406283 0.261461681703142 11 0.627844453258777 0.744311093482446 0.372155546741223 12 0.512562831397864 0.974874337204272 0.487437168602136 13 0.400707211546233 0.801414423092465 0.599292788453767 14 0.304289967384277 0.608579934768553 0.695710032615723 15 0.242462892205325 0.484925784410651 0.757537107794675 16 0.183699707919294 0.367399415838588 0.816300292080706 17 0.123593692681044 0.247187385362089 0.876406307318956 18 0.499986294368171 0.999972588736342 0.500013705631829 19 0.860414927569754 0.279170144860492 0.139585072430246 20 0.82671128413514 0.346577431729719 0.173288715864859 21 0.852062352422219 0.295875295155562 0.147937647577781 22 0.807343798409095 0.385312403181811 0.192656201590905 23 0.750550409573865 0.49889918085227 0.249449590426135 24 0.685951463688815 0.62809707262237 0.314048536311185 25 0.617578909872743 0.764842180254515 0.382421090127257 26 0.545231877758411 0.909536244483179 0.454768122241589 27 0.713132078156497 0.573735843687005 0.286867921843503 28 0.80386727284448 0.392265454311041 0.196132727155521 29 0.775864685499664 0.448270629000671 0.224135314500335 30 0.839986725100439 0.320026549799123 0.160013274899561 31 0.941445739933196 0.117108520133608 0.0585542600668041 32 0.915240751140544 0.169518497718912 0.084759248859456 33 0.933105549002083 0.133788901995834 0.0668944509979172 34 0.98605923616594 0.0278815276681219 0.0139407638340609 35 0.998793220534277 0.00241355893144557 0.00120677946572278 36 0.997949605677898 0.0041007886442047 0.00205039432210235 37 0.996567071541786 0.00686585691642822 0.00343292845821411 38 0.998108337853 0.00378332429399963 0.00189166214699982 39 0.997882825881575 0.00423434823685107 0.00211717411842553 40 0.999278778381034 0.00144244323793286 0.000721221618966432 41 0.999774255948285 0.00045148810342911 0.000225744051714555 42 0.999599811149218 0.000800377701564617 0.000400188850782309 43 0.999148930542657 0.00170213891468654 0.00085106945734327 44 0.998435867166545 0.00312826566691014 0.00156413283345507 45 0.999557214132084 0.000885571735831923 0.000442785867915962 46 0.998641482431024 0.00271703513795217 0.00135851756897609 47 0.996197318244725 0.00760536351054992 0.00380268175527496 48 0.994074498425548 0.0118510031489032 0.00592550157445158 49 0.992955994188807 0.0140880116223854 0.00704400581119272 50 0.987700404596183 0.0245991908076344 0.0122995954038172 51 0.962714801895197 0.074570396209607 0.0372851981048035 52 0.937620892086513 0.124758215826975 0.0623791079134873 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.288888888888889 NOK 5% type I error level 17 0.377777777777778 NOK 10% type I error level 18 0.4 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/10jhuw1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/10jhuw1292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/1hj4q1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/1hj4q1292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/257w51292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/257w51292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/357w51292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/357w51292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/457w51292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/457w51292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/557w51292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/557w51292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/6fgvq1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/6fgvq1292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/7qqcb1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/7qqcb1292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/8qqcb1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/8qqcb1292336984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/9qqcb1292336984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336886ib30fefhoqc1zkm/9qqcb1292336984.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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