Meervoudige Lineaire Regressie Verleden

*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: Fri, 24 Dec 2010 14:43:54 +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/24/t1293201727i7bou3cee5chvnu.htm/, Retrieved Fri, 24 Dec 2010 15:42:07 +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/24/t1293201727i7bou3cee5chvnu.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 «

35532 37903 36763 40399 44164 35533 35532 37903 36763 40399 32110 35533 35532 37903 36763 33374 32110 35533 35532 37903 35462 33374 32110 35533 35532 33508 35462 33374 32110 35533 36080 33508 35462 33374 32110 34560 36080 33508 35462 33374 38737 34560 36080 33508 35462 38144 38737 34560 36080 33508 37594 38144 38737 34560 36080 36424 37594 38144 38737 34560 36843 36424 37594 38144 38737 37246 36843 36424 37594 38144 38661 37246 36843 36424 37594 40454 38661 37246 36843 36424 44928 40454 38661 37246 36843 48441 44928 40454 38661 37246 48140 48441 44928 40454 38661 45998 48140 48441 44928 40454 47369 45998 48140 48441 44928 49554 47369 45998 48140 48441 47510 49554 47369 45998 48140 44873 47510 49554 47369 45998 45344 44873 47510 49554 47369 42413 45344 44873 47510 49554 36912 42413 45344 44873 47510 43452 36912 42413 45344 44873 42142 43452 36912 42413 45344 44382 42142 43452 36912 42413 43636 44382 42142 43452 36912 44167 43636 44382 42142 43452 44423 44167 43636 44382 42142 4 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 1342.02138911129 + 1.14792429674021X1[t] -0.0177916221452252X2[t] -0.201730547998897X3[t] + 0.0278280979719725X4[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) 1342.02138911129 943.945578 1.4217 0.158454 0.079227 X1 1.14792429674021 0.103562 11.0844 0 0 X2 -0.0177916221452252 0.15446 -0.1152 0.908546 0.454273 X3 -0.201730547998897 0.154621 -1.3047 0.195223 0.097612 X4 0.0278280979719725 0.101609 0.2739 0.78479 0.392395 Multiple Linear Regression - Regression Statistics Multiple R 0.967475350481256 R-squared 0.936008553788829 Adjusted R-squared 0.933256233521682 F-TEST (value) 340.079810101106 F-TEST (DF numerator) 4 F-TEST (DF denominator) 93 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2206.72614580172 Sum Squared Residuals 452876546.278536 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 35532 37277.0103137574 -1745.01031375739 2 35533 35163.7188406003 369.281159399662 3 32110 34875.8949120586 -2765.89491205856 4 33374 31456.5594136881 1917.4405863119 5 35462 32902.2542965313 2559.74570346871 6 33508 35967.1831116315 -2459.18311163148 7 36080 33336.7471367332 2743.25286326679 8 34560 35937.9345892357 -1377.93458923568 9 38737 34599.6161653884 4137.38383461164 10 38144 38848.3121456426 -704.312145642575 11 37594 38471.4817329173 -877.481732917261 12 36424 36965.7465937335 -541.746593733472 13 36843 35868.3247389196 974.675261080427 14 37246 36464.5709564656 781.429043534351 15 38661 37140.4490456472 1520.55095435277 16 40454 38640.5079275714 1813.49207242864 17 44928 40603.9236084978 4324.07639150223 18 48441 45433.6025316714 3007.39746832864 19 48140 49064.3347547103 -924.334754710302 20 45998 47803.660880712 -1805.66088071201 21 47369 44765.9858105667 2603.01418943334 22 49554 46536.3806791558 3017.61932084422 23 47510 49443.9335298961 -1933.93352989611 24 44873 46722.5212058093 -1849.52120580935 25 45344 43329.1819859122 2014.81801408777 26 42413 44389.9124714523 -1976.91247145234 27 36912 41492.0493264948 -4580.04932649475 28 43452 35061.0672321749 8390.93276782508 29 42142 43270.7431166064 -1128.74311660636 30 44382 42678.760668433 1703.23933156701 31 43636 43801.0179672847 -165.017967284707 32 44167 43351.0759869265 815.924013073542 33 44423 43485.565102755 937.434897244964 34 42868 43982.8122996258 -1114.81229962581 35 43908 42065.3566808511 1842.64331914890 36 42013 43249.9976216321 -1236.99762163214 37 38846 41376.9927874975 -2530.99278749752 38 35087 37522.1592014212 -2435.15920142120 39 33026 33674.6784476574 -648.678447657425 40 34646 31961.8315795754 2684.16842042463 41 37135 34528.3110171864 2606.68898281356 42 37985 37667.8330030466 317.166996953358 43 43121 38215.1281100779 4905.87188992209 44 43722 43638.7186040575 83.2813959424606 45 43630 44135.0365051137 -505.036505113709 46 42234 43006.3004936582 -772.300493658173 47 39351 41427.1200564829 -2076.12005648291 48 39327 38177.7753107927 1149.22468920733 49 35704 38480.5740343086 -2776.57403430862 50 30466 34864.8124512623 -4398.81245126226 51 28155 28841.0571586680 -686.057158667958 52 29257 27011.5985267467 2245.40147325331 53 29998 29273.5709519978 724.429048002213 54 32529 30425.0122075265 2103.98779247350 55 34787 33030.6072122584 1756.39278774164 56 33855 35458.7739065461 -1603.77390654612 57 34556 33858.7755827924 697.224417207649 58 31348 34294.9776452321 -2946.97764523214 59 30805 30850.8132901214 -45.8132901214251 60 28353 30117.2170193763 -1764.21701937627 61 24514 27978.8265892529 -3464.82658925293 62 21106 23635.8374208367 -2529.83742083667 63 21346 20271.5461014561 1074.45389854395 64 23335 21313.8908584851 2021.10914151488 65 24379 24173.5079348524 205.492065147613 66 26290 25193.2998747941 1096.7001252059 67 30084 26973.8454358885 3110.1545641115 68 29429 31139.8138225567 -1710.81382255674 69 30632 29963.9674508298 668.032549170233 70 27349 30644.38768843 -3295.38768842999 71 27264 27092.0622134361 171.937786563892 72 27474 26791.9892903016 682.010709698351 73 24482 27730.3242714401 -3248.3242714401 74 21453 24217.7859858808 -2764.78598588081 75 18788 20749.2270211058 -1961.22702110583 76 19282 18353.3212939579 928.678706042136 77 19713 19495.5907303211 217.409269678929 78 21917 20434.8776425363 1482.12235746368 79 23812 22783.4178316004 1028.58216839961 80 23785 24846.3228529256 -1061.32285292565 81 24696 24348.9935553848 347.006444615189 82 24562 25074.2867029854 -512.286702985384 83 23580 24962.4376499008 -1382.43764990075 84 24939 23653.0321799971 1285.96782000291 85 23899 25282.9159628980 -1383.91596289797 86 21454 24259.2663127995 -2805.26631279946 87 19761 21169.6156873617 -1408.61568736169 88 19815 19517.2985241884 297.701475811648 89 20780 20073.6976204706 706.302379529358 90 23462 21453.9739374498 2008.02606255023 91 25005 24457.5315664784 547.468433521618 92 24725 25987.8943642266 -1262.89436422659 93 26198 25124.8358729792 1073.16412702084 94 27543 26584.0747394767 958.925260523315 95 26471 28201.2491677828 -1730.2491677828 96 26558 26641.8036252574 -83.8036252574365 97 25317 26530.4088592677 -1213.40885926772 98 22896 25357.9708751136 -2461.97087511360 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.555773923663318 0.888452152673364 0.444226076336682 9 0.71126471011895 0.5774705797621 0.28873528988105 10 0.664078121138035 0.67184375772393 0.335921878861965 11 0.549188480798612 0.901623038402776 0.450811519201388 12 0.429592071333066 0.859184142666132 0.570407928666934 13 0.348700369387399 0.697400738774797 0.651299630612601 14 0.292071233244840 0.584142466489681 0.70792876675516 15 0.28119808459061 0.56239616918122 0.71880191540939 16 0.318325912951861 0.636651825903722 0.681674087048139 17 0.595416342874845 0.80916731425031 0.404583657125155 18 0.597439459535115 0.80512108092977 0.402560540464885 19 0.611849849511955 0.77630030097609 0.388150150488045 20 0.589160889985805 0.82167822002839 0.410839110014195 21 0.641086879054359 0.717826241891282 0.358913120945641 22 0.683499958226754 0.633000083546493 0.316500041773246 23 0.679329841507639 0.641340316984721 0.320670158492361 24 0.681832369703166 0.636335260593668 0.318167630296834 25 0.651377869247522 0.697244261504956 0.348622130752478 26 0.626204319323366 0.747591361353269 0.373795680676635 27 0.792112662044448 0.415774675911105 0.207887337955552 28 0.994214869896193 0.0115702602076135 0.00578513010380675 29 0.992572622706128 0.0148547545877438 0.00742737729387192 30 0.992698290921347 0.0146034181573058 0.00730170907865288 31 0.98912743861281 0.0217451227743790 0.0108725613871895 32 0.985214772533162 0.0295704549336762 0.0147852274668381 33 0.980167033868556 0.0396659322628874 0.0198329661314437 34 0.97303702420439 0.0539259515912203 0.0269629757956101 35 0.973155034102322 0.0536899317953552 0.0268449658976776 36 0.964965381362877 0.0700692372742466 0.0350346186371233 37 0.968677435658986 0.0626451286820277 0.0313225643410139 38 0.976003692699004 0.0479926146019913 0.0239963073009957 39 0.969454467117208 0.0610910657655834 0.0305455328827917 40 0.973687568794675 0.0526248624106493 0.0263124312053246 41 0.976128789959391 0.0477424200812177 0.0238712100406088 42 0.966414736355815 0.0671705272883695 0.0335852636441847 43 0.994733161070356 0.0105336778592878 0.00526683892964392 44 0.992086510067115 0.0158269798657694 0.00791348993288468 45 0.989757759683457 0.0204844806330866 0.0102422403165433 46 0.987207255087579 0.0255854898248429 0.0127927449124215 47 0.984612696877815 0.0307746062443695 0.0153873031221848 48 0.98953955924171 0.0209208815165797 0.0104604407582899 49 0.988964833280363 0.0220703334392741 0.0110351667196370 50 0.99544110061253 0.0091177987749388 0.0045588993874694 51 0.994308352583838 0.0113832948323250 0.00569164741616248 52 0.9960735010748 0.00785299785040158 0.00392649892520079 53 0.994636031662857 0.0107279366742857 0.00536396833714284 54 0.996273812125569 0.00745237574886223 0.00372618787443111 55 0.997407676656192 0.00518464668761562 0.00259232334380781 56 0.996792278731025 0.0064154425379508 0.0032077212689754 57 0.997926085800265 0.004147828399471 0.0020739141997355 58 0.997901681098105 0.00419663780379103 0.00209831890189551 59 0.998881402758829 0.00223719448234267 0.00111859724117133 60 0.99852155180143 0.00295689639714023 0.00147844819857011 61 0.99854326079426 0.00291347841147949 0.00145673920573975 62 0.998408038882258 0.00318392223548407 0.00159196111774203 63 0.998060008396215 0.00387998320756945 0.00193999160378473 64 0.998485208707355 0.00302958258529057 0.00151479129264529 65 0.997521956639598 0.00495608672080393 0.00247804336040197 66 0.996302333494993 0.00739533301001386 0.00369766650500693 67 0.998798148851633 0.00240370229673383 0.00120185114836692 68 0.99814411721808 0.00371176556384113 0.00185588278192056 69 0.998785380309295 0.00242923938141044 0.00121461969070522 70 0.999159507100239 0.00168098579952206 0.000840492899761031 71 0.999882489972917 0.000235020054166062 0.000117510027083031 72 0.999896082156652 0.000207835686695925 0.000103917843347962 73 0.999818578380128 0.00036284323974363 0.000181421619871815 74 0.99970611792724 0.000587764145521276 0.000293882072760638 75 0.999745684528465 0.00050863094307034 0.00025431547153517 76 0.999578316242776 0.000843367514447868 0.000421683757223934 77 0.999121462038743 0.00175707592251453 0.000878537961257266 78 0.998862130910076 0.00227573817984806 0.00113786908992403 79 0.997554692977187 0.00489061404562642 0.00244530702281321 80 0.99674715175007 0.00650569649986134 0.00325284824993067 81 0.993079580182502 0.0138408396349960 0.00692041981749799 82 0.989699488302824 0.0206010233943520 0.0103005116971760 83 0.98036082369574 0.0392783526085188 0.0196391763042594 84 0.979839574021258 0.0403208519574842 0.0201604259787421 85 0.97477525374165 0.0504494925167011 0.0252247462583505 86 0.962998539804062 0.0740029203918764 0.0370014601959382 87 0.973362007558511 0.0532759848829773 0.0266379924414886 88 0.956408842614435 0.0871823147711303 0.0435911573855651 89 0.909963446285857 0.180073107428286 0.0900365537141432 90 0.889950248782942 0.220099502434115 0.110049751217058 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 29 0.349397590361446 NOK 5% type I error level 50 0.602409638554217 NOK 10% type I error level 61 0.734939759036145 NOK

Charts produced by software:

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Parameters (Session):

par1 = additive ; par2 = 12 ;

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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