ws 7

*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, 23 Nov 2010 19:56:37 +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/Nov/23/t1290542114jo5fk6bgjylfcoe.htm/, Retrieved Tue, 23 Nov 2010 20:55:25 +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/Nov/23/t1290542114jo5fk6bgjylfcoe.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:

» Textbox « » Textfile « » CSV «

14544.5 94.6 15116.3 95.9 17413.2 104.7 16181.5 102.8 15607.4 98.1 17160.9 113.9 14915.8 80.9 13768 95.7 17487.5 113.2 16198.1 105.9 17535.2 108.8 16571.8 102.3 16198.9 99 16554.2 100.7 19554.2 115.5 15903.8 100.7 18003.8 109.9 18329.6 114.6 16260.7 85.4 14851.9 100.5 18174.1 114.8 18406.6 116.5 18466.5 112.9 16016.5 102 17428.5 106 17167.2 105.3 19630 118.8 17183.6 106.1 18344.7 109.3 19301.4 117.2 18147.5 92.5 16192.9 104.2 18374.4 112.5 20515.2 122.4 18957.2 113.3 16471.5 100 18746.8 110.7 19009.5 112.8 19211.2 109.8 20547.7 117.3 19325.8 109.1 20605.5 115.9 20056.9 96 16141.4 99.8 20359.8 116.8 19711.6 115.7 15638.6 99.4 14384.5 94.3 13855.6 91 14308.3 93.2 15290.6 103.1 14423.8 94.1 13779.7 91.8 15686.3 102.7 14733.8 82.6 12522.5 89.1 16189.4 104.5 16059.1 105.1 16007.1 95.1 15806.8 88.7 15160 86.3 15692.1 91.8 18908.9 111.5 16969.9 99.7 16997.5 97.5 19858.9 111.7 17681.2 86.2 16006.9 95.4

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation productie[t] = + 35.3845145648451 + 0.00398530347372131uitvoer[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) 35.3845145648451 6.782018 5.2174 2e-06 1e-06 uitvoer 0.00398530347372131 0.000396 10.059 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.777960787487783 R-squared 0.605222986868611 Adjusted R-squared 0.599241516972681 F-TEST (value) 101.182986355981 F-TEST (DF numerator) 1 F-TEST (DF denominator) 66 p-value 5.99520433297585e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.29892155690996 Sum Squared Residuals 2618.64324348693 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94.6 93.3487609383847 1.25123906161525 2 95.9 95.6275574646586 0.272442535341443 3 104.7 104.781401013449 -0.0814010134490508 4 102.8 99.8727027248665 2.92729727513349 5 98.1 97.5847400006031 0.515259999396888 6 113.9 103.775908947029 10.1240910529708 7 80.9 94.8285041181774 -13.9285041181774 8 95.7 90.2541727910401 5.44582720895987 9 113.2 105.077509061547 8.12249093845346 10 105.9 99.9388587625303 5.96114123746972 11 108.8 105.267608037243 3.53239196275694 12 102.3 101.42816667066 0.87183332934006 13 99 99.9420470053093 -0.942047005309262 14 100.7 101.358025329522 -0.658025329522445 15 115.5 113.313935750686 2.18606424931362 16 100.7 98.765983950214 1.9340160497859 17 109.9 107.135121245029 2.76487875497115 18 114.6 108.433533116767 6.16646688323274 19 85.4 100.188338759985 -14.7883387599852 20 100.5 94.5738432262067 5.92615677379334 21 114.8 107.813818426604 6.9861815733964 22 116.5 108.740401484244 7.7595985157562 23 112.9 108.979121162320 3.9208788376803 24 102 99.2151276517025 2.78487234829750 25 106 104.842376156597 1.15762384340301 26 105.3 103.801016358914 1.49898364108639 27 118.8 113.616021753994 5.18397824600555 28 106.1 103.866375335883 2.23362466411736 29 109.3 108.493711199220 0.806288800779543 30 117.2 112.306451032530 4.89354896747037 31 92.5 107.707809354203 -15.2078093542026 32 104.2 99.918135184467 4.28186481553307 33 112.5 108.61207471239 3.88792528761002 34 122.4 117.143812388933 5.25618761106745 35 113.3 110.934709576875 2.36529042312524 36 100 101.028440732246 -1.02844073224569 37 110.7 110.096201726004 0.603798273996215 38 112.8 111.143140948550 1.65685905144962 39 109.8 111.9469766592 -2.14697665919997 40 117.3 117.273334751829 0.0266652481714963 41 109.1 112.403692437288 -3.30369243728843 42 115.9 117.503685292610 -1.60368529260959 43 96 115.317347806926 -19.3173478069261 44 99.8 99.7128920555703 0.0871079444297108 45 116.8 116.524496229116 0.275503770883736 46 115.7 113.94122251745 1.75877748254990 47 99.4 97.7090814689832 1.69091853101679 48 94.3 92.7111123825893 1.58888761741068 49 91 90.6032853753381 0.396714624661882 50 93.2 92.4074322578918 0.792567742108253 51 103.1 96.3221958601282 6.7778041398718 52 94.1 92.8677348091066 1.23226519089343 53 91.8 90.3008008416827 1.49919915831732 54 102.7 97.8991804446797 4.80081955532029 55 82.6 94.1031788859602 -11.5031788859602 56 89.1 85.2904773145202 3.80952268547976 57 104.5 99.904186622309 4.59581337769109 58 105.1 99.384901579683 5.71509842031697 59 95.1 99.1776657990495 -4.07766579904952 60 88.7 98.3794095132631 -9.67940951326313 61 86.3 95.8017152264602 -9.5017152264602 62 91.8 97.9222952048273 -6.12229520482731 63 111.5 110.742219419094 0.75778058090598 64 99.7 103.014715983548 -3.3147159835484 65 97.5 103.124710359423 -5.6247103594231 66 111.7 114.528257719129 -2.82825771912926 67 86.2 105.849462344406 -19.6494623444064 68 95.4 99.1768687383548 -3.77686873835476 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0122393863329949 0.0244787726659899 0.987760613667005 6 0.165243659898172 0.330487319796345 0.834756340101828 7 0.646919620877993 0.706160758244014 0.353080379122007 8 0.733969676635054 0.532060646729892 0.266030323364946 9 0.680206359947278 0.639587280105444 0.319793640052722 10 0.611755746718424 0.776488506563153 0.388244253281576 11 0.518024712866499 0.963950574267002 0.481975287133501 12 0.427313566643062 0.854627133286124 0.572686433356938 13 0.353261540136958 0.706523080273915 0.646738459863042 14 0.287505983457042 0.575011966914085 0.712494016542958 15 0.240848041632738 0.481696083265476 0.759151958367262 16 0.177141985825056 0.354283971650113 0.822858014174944 17 0.127499227022808 0.254998454045616 0.872500772977192 18 0.0996552749632639 0.199310549926528 0.900344725036736 19 0.473400443057507 0.946800886115014 0.526599556942493 20 0.474605184161983 0.949210368323967 0.525394815838017 21 0.447695944746443 0.895391889492886 0.552304055253557 22 0.433915520470849 0.867831040941698 0.566084479529151 23 0.374588963455935 0.749177926911869 0.625411036544065 24 0.314880014073218 0.629760028146437 0.685119985926782 25 0.25738699208978 0.51477398417956 0.74261300791022 26 0.204903020632474 0.409806041264949 0.795096979367526 27 0.175786571048042 0.351573142096085 0.824213428951958 28 0.136835493756481 0.273670987512962 0.86316450624352 29 0.108577384494526 0.217154768989052 0.891422615505474 30 0.0919941774495026 0.183988354899005 0.908005822550497 31 0.443766142524199 0.887532285048398 0.556233857475801 32 0.408575341213732 0.817150682427463 0.591424658786268 33 0.368806318486194 0.737612636972387 0.631193681513806 34 0.360183015665194 0.720366031330387 0.639816984334806 35 0.319407909798925 0.63881581959785 0.680592090201075 36 0.263303915139658 0.526607830279315 0.736696084860342 37 0.222138783337289 0.444277566674577 0.777861216662711 38 0.190805328870343 0.381610657740686 0.809194671129657 39 0.163709344449996 0.327418688899992 0.836290655550004 40 0.143880196622321 0.287760393244642 0.856119803377679 41 0.123294838535306 0.246589677070613 0.876705161464694 42 0.106512578694549 0.213025157389098 0.893487421305451 43 0.501286019050733 0.997427961898534 0.498713980949267 44 0.431168981571235 0.86233796314247 0.568831018428765 45 0.381273240869067 0.762546481738135 0.618726759130933 46 0.368546279425729 0.737092558851457 0.631453720574271 47 0.313300750793685 0.626601501587371 0.686699249206315 48 0.253168890410468 0.506337780820937 0.746831109589532 49 0.195091007769342 0.390182015538685 0.804908992230658 50 0.146867298124464 0.293734596248929 0.853132701875536 51 0.175711200525834 0.351422401051668 0.824288799474166 52 0.135025731859071 0.270051463718143 0.864974268140929 53 0.102106453355649 0.204212906711297 0.897893546644351 54 0.115940602998100 0.231881205996201 0.8840593970019 55 0.178464230118907 0.356928460237814 0.821535769881093 56 0.167257850549333 0.334515701098665 0.832742149450667 57 0.224899163613997 0.449798327227994 0.775100836386003 58 0.447505458958561 0.895010917917122 0.552494541041439 59 0.378632433384741 0.757264866769482 0.621367566615259 60 0.31533743655294 0.63067487310588 0.68466256344706 61 0.242825814954185 0.485651629908371 0.757174185045815 62 0.158503843757859 0.317007687515718 0.841496156242141 63 0.134888000646332 0.269776001292665 0.865111999353668 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 1 0.0169491525423729 OK 10% type I error level 1 0.0169491525423729 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/10iwq51290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/10iwq51290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/1bvbb1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/1bvbb1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/2bvbb1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/2bvbb1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/3mnae1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/3mnae1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/4mnae1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/4mnae1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/5mnae1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/5mnae1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/6we9z1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/6we9z1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/7we9z1290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/7we9z1290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/875q11290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/875q11290542189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/975q11290542189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe/975q11290542189.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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