verbetering Q3 - anders bekeken

*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: Mon, 01 Dec 2008 12:40:33 -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/Dec/01/t1228160540wzhsxthogjxmlh4.htm/, Retrieved Mon, 01 Dec 2008 19:42:29 +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/Dec/01/t1228160540wzhsxthogjxmlh4.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

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 1 2570 1 1862 0 1936 0 1504 0 1765 0 1607 0 1577 0 1493 0 1615 0 1700 0 1335 0 1523 0 1623 0 1540 0 1637 0 1524 0 1419 0 1821 0 1593 0 1357 0 1263 0 1750 0 1405 0 1393 0 1639 0 1679 0 1551 0 1744 0 1429 0 1784 0

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Gebouwen[t] = + 1624.71186440678 + 701.288135593221Dummy[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) 1624.71186440678 26.761204 60.7115 0 0 Dummy 701.288135593221 147.793581 4.7451 1.4e-05 7e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.525558164934626 R-squared 0.276211384729452 Adjusted R-squared 0.263943781080798 F-TEST (value) 22.5155126168241 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.36637341620061e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 205.556710047803 Sum Squared Residuals 2492960.10169491 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1515 1624.71186440677 -109.711864406772 2 1510 1624.71186440678 -114.711864406782 3 1225 1624.71186440678 -399.71186440678 4 1577 1624.71186440678 -47.7118644067798 5 1417 1624.71186440678 -207.711864406780 6 1224 1624.71186440678 -400.71186440678 7 1693 1624.71186440678 68.2881355932202 8 1633 1624.71186440678 8.28813559322024 9 1639 1624.71186440678 14.2881355932202 10 1914 1624.71186440678 289.28813559322 11 1586 1624.71186440678 -38.7118644067798 12 1552 1624.71186440678 -72.7118644067798 13 2081 1624.71186440678 456.28813559322 14 1500 1624.71186440678 -124.711864406780 15 1437 1624.71186440678 -187.711864406780 16 1470 1624.71186440678 -154.711864406780 17 1849 1624.71186440678 224.288135593220 18 1387 1624.71186440678 -237.71186440678 19 1592 1624.71186440678 -32.7118644067798 20 1589 1624.71186440678 -35.7118644067798 21 1798 1624.71186440678 173.288135593220 22 1935 1624.71186440678 310.28813559322 23 1887 1624.71186440678 262.28813559322 24 2027 1624.71186440678 402.28813559322 25 2080 1624.71186440678 455.28813559322 26 1556 1624.71186440678 -68.7118644067798 27 1682 1624.71186440678 57.2881355932202 28 1785 1624.71186440678 160.288135593220 29 1869 1624.71186440678 244.28813559322 30 1781 1624.71186440678 156.288135593220 31 2082 2326 -244 32 2570 2326 244 33 1862 1624.71186440678 237.28813559322 34 1936 1624.71186440678 311.28813559322 35 1504 1624.71186440678 -120.711864406780 36 1765 1624.71186440678 140.288135593220 37 1607 1624.71186440678 -17.7118644067798 38 1577 1624.71186440678 -47.7118644067798 39 1493 1624.71186440678 -131.711864406780 40 1615 1624.71186440678 -9.71186440677976 41 1700 1624.71186440678 75.2881355932202 42 1335 1624.71186440678 -289.71186440678 43 1523 1624.71186440678 -101.711864406780 44 1623 1624.71186440678 -1.71186440677976 45 1540 1624.71186440678 -84.7118644067798 46 1637 1624.71186440678 12.2881355932202 47 1524 1624.71186440678 -100.711864406780 48 1419 1624.71186440678 -205.711864406780 49 1821 1624.71186440678 196.288135593220 50 1593 1624.71186440678 -31.7118644067798 51 1357 1624.71186440678 -267.71186440678 52 1263 1624.71186440678 -361.71186440678 53 1750 1624.71186440678 125.288135593220 54 1405 1624.71186440678 -219.711864406780 55 1393 1624.71186440678 -231.711864406780 56 1639 1624.71186440678 14.2881355932202 57 1679 1624.71186440678 54.2881355932202 58 1551 1624.71186440678 -73.7118644067798 59 1744 1624.71186440678 119.288135593220 60 1429 1624.71186440678 -195.711864406780 61 1784 1624.71186440678 159.288135593220 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.385879108178341 0.771758216356681 0.614120891821659 6 0.439803227489278 0.879606454978557 0.560196772510722 7 0.533077802769046 0.933844394461908 0.466922197230954 8 0.48484807508466 0.96969615016932 0.51515192491534 9 0.427985084255823 0.855970168511645 0.572014915744177 10 0.693927775333801 0.612144449332399 0.306072224666199 11 0.60026700931567 0.799465981368659 0.399732990684329 12 0.501784647595373 0.996430704809255 0.498215352404627 13 0.858571705377038 0.282856589245923 0.141428294622962 14 0.813057467172486 0.373885065655028 0.186942532827514 15 0.78257199236279 0.43485601527442 0.21742800763721 16 0.73663837515955 0.5267232496809 0.26336162484045 17 0.766882253291735 0.46623549341653 0.233117746708265 18 0.765718281877655 0.468563436244691 0.234281718122345 19 0.700115278364652 0.599769443270695 0.299884721635348 20 0.628311513437189 0.743376973125622 0.371688486562811 21 0.620746235796925 0.75850752840615 0.379253764203075 22 0.718748359578707 0.562503280842585 0.281251640421293 23 0.757019989572989 0.485960020854022 0.242980010427011 24 0.88384761213349 0.232304775733019 0.116152387866510 25 0.97040286172368 0.0591942765526399 0.0295971382763200 26 0.95701634505691 0.0859673098861798 0.0429836549430899 27 0.938483627778848 0.123032744442303 0.0615163722211517 28 0.929100027262571 0.141799945474857 0.0708999727374285 29 0.940509481099713 0.118981037800575 0.0594905189002874 30 0.932770004605195 0.134459990789611 0.0672299953948053 31 0.953019646871018 0.0939607062579648 0.0469803531289824 32 0.953032925685486 0.0939341486290275 0.0469670743145137 33 0.96466701058376 0.0706659788324805 0.0353329894162403 34 0.987185621114127 0.0256287577717466 0.0128143788858733 35 0.981630065195045 0.0367398696099102 0.0183699348049551 36 0.98115989214106 0.0376802157178805 0.0188401078589403 37 0.97073387482634 0.0585322503473214 0.0292661251736607 38 0.955450956593115 0.089098086813771 0.0445490434068855 39 0.939595451982369 0.120809096035262 0.0604045480176312 40 0.913037054762423 0.173925890475154 0.0869629452375772 41 0.89384955139835 0.212300897203301 0.106150448601650 42 0.917653204726444 0.164693590547111 0.0823467952735556 43 0.884119082014752 0.231761835970497 0.115880917985249 44 0.839128598802445 0.321742802395109 0.160871401197555 45 0.78173142868103 0.436537142637941 0.218268571318971 46 0.717378603763617 0.565242792472766 0.282621396236383 47 0.640100323754609 0.719799352490783 0.359899676245391 48 0.605705685213774 0.788588629572452 0.394294314786226 49 0.65642369610063 0.68715260779874 0.34357630389937 50 0.560290519224818 0.879418961550363 0.439709480775182 51 0.565675439052865 0.86864912189427 0.434324560947135 52 0.740026845031465 0.51994630993707 0.259973154968535 53 0.704353091665761 0.591293816668477 0.295646908334239 54 0.696480834065277 0.607038331869447 0.303519165934723 55 0.756976452953162 0.486047094093676 0.243023547046838 56 0.592985040568704 0.81402991886259 0.407014959431295 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 0.0576923076923077 NOK 10% type I error level 10 0.192307692307692 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/10i85c1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/10i85c1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/1gj7i1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/1gj7i1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/2a11c1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/2a11c1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/3ldr21228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/3ldr21228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/42xos1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/42xos1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/55n9j1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/55n9j1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/6cjgn1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/6cjgn1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/7oauw1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/7oauw1228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/8fe291228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/8fe291228160428.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/9m7nj1228160428.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228160540wzhsxthogjxmlh4/9m7nj1228160428.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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