ws7 link4 y-1 en y-4

*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, 23 Nov 2009 15:12:22 -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/23/t125901442892rrgxkdnuma2nx.htm/, Retrieved Mon, 23 Nov 2009 23:14:00 +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/23/t125901442892rrgxkdnuma2nx.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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1.4 8.2 1.7 1.4 1.2 8.0 1.4 1.2 1.0 7.5 1.2 1 1.7 6.8 1.0 1.7 2.4 6.5 1.7 1.4 2.0 6.6 2.4 1.2 2.1 7.6 2.0 1.0 2.0 8.0 2.1 1.7 1.8 8.1 2.0 2.4 2.7 7.7 1.8 2.0 2.3 7.5 2.7 2.1 1.9 7.6 2.3 2.0 2.0 7.8 1.9 1.8 2.3 7.8 2.0 2.7 2.8 7.8 2.3 2.3 2.4 7.5 2.8 1.9 2.3 7.5 2.4 2.0 2.7 7.1 2.3 2.3 2.7 7.5 2.7 2.8 2.9 7.5 2.7 2.4 3.0 7.6 2.9 2.3 2.2 7.7 3.0 2.7 2.3 7.7 2.2 2.7 2.8 7.9 2.3 2.9 2.8 8.1 2.8 3.0 2.8 8.2 2.8 2.2 2.2 8.2 2.8 2.3 2.6 8.2 2.2 2.8 2.8 7.9 2.6 2.8 2.5 7.3 2.8 2.8 2.4 6.9 2.5 2.2 2.3 6.6 2.4 2.6 1.9 6.7 2.3 2.8 1.7 6.9 1.9 2.5 2.0 7.0 1.7 2.4 2.1 7.1 2.0 2.3 1.7 7.2 2.1 1.9 1.8 7.1 1.7 1.7 1.8 6.9 1.8 2.0 1.8 7.0 1.8 2.1 1.3 6.8 1.8 1.7 1.3 6.4 1.3 1.8 1.3 6.7 1.3 1.8 1.2 6.6 1.3 1.8 1.4 6.4 1.2 1.3 2.2 6.3 1.4 1.3 2.9 6.2 2.2 1.3 3.1 6.5 2.9 1.2 3.5 6.8 3.1 1.4 3.6 6.8 3.5 2.2 4.4 6.4 3.6 2.9 4.1 6.1 4.4 3.1 5.1 5.8 4.1 3.5 5.8 6.1 5.1 3.6 5.9 7.2 5.8 4.4 5.4 7.3 5.9 4.1 5.5 6.9 5.4 5.1 4.8 6.1 5.5 5.8 3.2 5.8 4.8 5.9 2.7 6.2 3.2 5.4 2.1 7. 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1.19733911441188 -0.114838682663652X[t] + 1.04005420661093Y1[t] -0.181531956075848Y4[t] -0.100911659309453M1[t] + 0.0562361791256351M2[t] -0.144735621419419M3[t] + 0.0688315396933528M4[t] + 0.173874541939557M5[t] -0.0284129403688838M6[t] -0.0181390830330392M7[t] -0.141918406447336M8[t] -0.0166014834555407M9[t] + 0.0167579353408157M10[t] -0.170892863225451M11[t] -0.000202430578581416t + 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) 1.19733911441188 1.064204 1.1251 0.26614 0.13307 X -0.114838682663652 0.134597 -0.8532 0.397785 0.198893 Y1 1.04005420661093 0.076994 13.5082 0 0 Y4 -0.181531956075848 0.087126 -2.0836 0.042549 0.021275 M1 -0.100911659309453 0.305046 -0.3308 0.74223 0.371115 M2 0.0562361791256351 0.307171 0.1831 0.855508 0.427754 M3 -0.144735621419419 0.305566 -0.4737 0.637887 0.318943 M4 0.0688315396933528 0.301587 0.2282 0.820436 0.410218 M5 0.173874541939557 0.317048 0.5484 0.585948 0.292974 M6 -0.0284129403688838 0.321296 -0.0884 0.929901 0.46495 M7 -0.0181390830330392 0.316879 -0.0572 0.954589 0.477295 M8 -0.141918406447336 0.316506 -0.4484 0.655889 0.327944 M9 -0.0166014834555407 0.314083 -0.0529 0.958065 0.479033 M10 0.0167579353408157 0.314581 0.0533 0.957737 0.478869 M11 -0.170892863225451 0.31541 -0.5418 0.590455 0.295227 t -0.000202430578581416 0.005288 -0.0383 0.969622 0.484811 Multiple Linear Regression - Regression Statistics Multiple R 0.930999559820629 R-squared 0.866760180386205 Adjusted R-squared 0.825122736756894 F-TEST (value) 20.8168442833037 F-TEST (DF numerator) 15 F-TEST (DF denominator) 48 p-value 4.44089209850063e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.495420919128886 Sum Squared Residuals 11.7812105813045 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.6684952394143 -0.268495239414302 2 1.2 1.57269851303543 -0.372698513035428 3 1 1.25723917313660 -0.257239173136603 4 1.7 1.21590777096007 0.484092229039932 5 2.4 2.13769747887719 0.262302521122805 6 2 2.68806803356663 -0.688068033566632 7 2.1 2.20358548623104 -0.103585486231038 8 2 2.0106013105807 -0.0106013105806990 9 1.8 1.89315414481336 -0.0931541448133602 10 2.7 1.83684854720475 0.863151452795252 11 2.3 2.58985864493489 -0.289858644934887 12 1.9 2.3511967222786 -0.451196722278602 13 2 1.84739960442863 0.152600395571366 14 2.3 1.94497167247797 0.355028327522029 15 2.8 2.12842648576795 0.671573514232046 16 2.4 2.96888270683705 -0.568882706837047 17 2.3 2.63954840025271 -0.339548400252711 18 2.7 2.3245289529473 0.375471047052698 19 2.7 2.61392061124555 0.0860793887544462 20 2.9 2.56255163968302 0.337448360316985 21 3 2.90234630075963 0.0976536992403654 22 2.2 2.9554120589418 -0.755412058941799 23 2.3 1.93551546450820 0.364484535491797 24 2.8 2.15093719006827 0.649062809931734 25 2.8 2.52872927134538 0.271270728654616 26 2.8 2.81941637579620 -0.0194163757962040 27 2.2 2.60008894906498 -0.400088949064983 28 2.6 2.09865517759469 0.501344822405311 29 2.8 2.65396903670578 0.146030963294218 30 2.5 2.72839317473914 -0.228393174739137 31 2.4 2.58130298622409 -0.18130298622409 32 2.3 2.31515463393887 -0.0151546339388751 33 1.9 2.28847344620946 -0.38847344620946 34 1.7 1.93710060207289 -0.237100602072885 35 2 1.54790585894707 0.45209414105293 36 2.1 2.03728188091844 0.0627181190815605 37 1.7 2.10130212585547 -0.401302125855473 38 1.8 1.89001611054914 -0.0900161105491406 39 1.8 1.76135544979657 0.0386445502034257 40 1.8 1.94508311645681 -0.145083116456815 41 1.3 2.14550420708751 -0.845504207087507 42 1.3 1.45076946835289 -0.150769468352894 43 1.3 1.42638929031106 -0.126389290311061 44 1.2 1.31389140458455 -0.113891404584549 45 1.4 1.44873419090732 -0.0487341909073234 46 2.2 1.70138588871365 0.49861411128635 47 2.9 2.35705989312391 0.542940106876085 48 3.1 3.23948986120693 -0.139489861206927 49 3.5 3.27562861662682 0.224371383373185 50 3.6 3.70337014226702 -0.103370142267017 51 4.4 3.52506443561684 0.874935564383159 52 4.1 4.56861774502371 -0.468617745023706 53 5.1 4.32328087707680 0.776719122923195 54 5.8 5.10824037039404 0.691759629605964 55 5.9 5.57480162598826 0.325198374011744 56 5.4 5.59780101121286 -0.197801011212862 57 5.5 5.06729191731022 0.432708082689779 58 4.8 5.16925290306692 -0.369252903066918 59 3.2 4.26966013848593 -1.06966013848593 60 2.7 2.82109434552776 -0.121094345527764 61 2.1 2.07844514232939 0.0215548576706082 62 1.9 1.66952718587424 0.23047281412576 63 0.6 1.52782550661704 -0.927825506617044 64 0.7 0.502853483127675 0.197146516872325 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.474407506356812 0.948815012713624 0.525592493643188 20 0.307264454874709 0.614528909749419 0.69273554512529 21 0.202859550504344 0.405719101008687 0.797140449495656 22 0.542813779533762 0.914372440932475 0.457186220466238 23 0.435970273035634 0.871940546071267 0.564029726964366 24 0.386306283561473 0.772612567122945 0.613693716438527 25 0.287280449375158 0.574560898750315 0.712719550624842 26 0.208789465536984 0.417578931073968 0.791210534463016 27 0.173411079708788 0.346822159417576 0.826588920291212 28 0.139020484734972 0.278040969469943 0.860979515265028 29 0.0972166165968189 0.194433233193638 0.902783383403181 30 0.0819805693073484 0.163961138614697 0.918019430692652 31 0.075142994063734 0.150285988127468 0.924857005936266 32 0.0655754536194742 0.131150907238948 0.934424546380526 33 0.0558940635135766 0.111788127027153 0.944105936486423 34 0.0367929176303844 0.0735858352607688 0.963207082369616 35 0.0390408193774086 0.0780816387548171 0.960959180622591 36 0.0305293924350834 0.0610587848701667 0.969470607564917 37 0.0167260173713707 0.0334520347427414 0.98327398262863 38 0.00897558126637926 0.0179511625327585 0.99102441873362 39 0.00496361608020473 0.00992723216040946 0.995036383919795 40 0.00516455621285709 0.0103291124257142 0.994835443787143 41 0.00518456477895924 0.0103691295579185 0.99481543522104 42 0.00578146969000712 0.0115629393800142 0.994218530309993 43 0.00502152294609622 0.0100430458921924 0.994978477053904 44 0.00301935208030213 0.00603870416060426 0.996980647919698 45 0.0185428818427238 0.0370857636854476 0.981457118157276 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0740740740740741 NOK 5% type I error level 9 0.333333333333333 NOK 10% type I error level 12 0.444444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/10rayl1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/10rayl1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/1cj531259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/1cj531259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/2aylj1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/2aylj1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/3p9iq1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/3p9iq1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/4dvwg1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/4dvwg1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/5vb1x1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/5vb1x1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/6ba111259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/6ba111259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/7jduf1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/7jduf1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/8cqef1259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/8cqef1259014337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/9jfx71259014337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t125901442892rrgxkdnuma2nx/9jfx71259014337.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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