paper multiple regressie 6 vertragingen

*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, 28 Dec 2009 08:46:00 -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/Dec/28/t1262015221hg6snwy7swl7w5e.htm/, Retrieved Mon, 28 Dec 2009 16:47:14 +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/Dec/28/t1262015221hg6snwy7swl7w5e.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:

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User-defined keywords:

Dataseries X:

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102.3 0 106.3 97.7 98.3 91.6 104.6 111.6 106.6 0 102.3 106.3 97.7 98.3 91.6 104.6 108.1 0 106.6 102.3 106.3 97.7 98.3 91.6 93.8 0 108.1 106.6 102.3 106.3 97.7 98.3 88.2 0 93.8 108.1 106.6 102.3 106.3 97.7 108.9 0 88.2 93.8 108.1 106.6 102.3 106.3 114.2 0 108.9 88.2 93.8 108.1 106.6 102.3 102.5 0 114.2 108.9 88.2 93.8 108.1 106.6 94.2 0 102.5 114.2 108.9 88.2 93.8 108.1 97.4 0 94.2 102.5 114.2 108.9 88.2 93.8 98.5 0 97.4 94.2 102.5 114.2 108.9 88.2 106.5 0 98.5 97.4 94.2 102.5 114.2 108.9 102.9 0 106.5 98.5 97.4 94.2 102.5 114.2 97.1 0 102.9 106.5 98.5 97.4 94.2 102.5 103.7 0 97.1 102.9 106.5 98.5 97.4 94.2 93.4 0 103.7 97.1 102.9 106.5 98.5 97.4 85.8 0 93.4 103.7 97.1 102.9 106.5 98.5 108.6 0 85.8 93.4 103.7 97.1 102.9 106.5 110.2 0 108.6 85.8 93.4 103.7 97.1 102.9 101.2 0 110.2 108.6 85.8 93.4 103.7 97.1 101.2 0 101.2 110.2 108.6 85.8 93.4 103.7 96.9 0 101.2 101.2 110.2 108.6 85.8 93.4 99.4 0 96.9 101.2 101.2 110.2 108.6 85.8 118.7 0 99.4 96.9 101.2 101.2 110.2 108.6 108.0 0 118.7 99. 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 52.3800554350695 -9.6439095460792x[t] + 0.0711536770663767y1[t] + 0.449810910302228y2[t] + 0.544648532701033y3[t] -0.231596665797572y4[t] -0.212333328723183y5[t] + 0.00655193229113578y6[t] -12.9994185439502M1[t] -21.2202134716252M2[t] -14.1415161121140M3[t] -27.6143828048953M4[t] -31.0826462179462M5[t] -6.92901969997911M6[t] + 7.17864636904514M7[t] -12.6041688241142M8[t] -35.952591159114M9[t] -27.1388648742082M10[t] -14.0107696337175M11[t] + 0.164878704585040t + 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) 52.3800554350695 9.9728 5.2523 2e-06 1e-06 x -9.6439095460792 2.424779 -3.9772 0.000168 8.4e-05 y1 0.0711536770663767 0.118831 0.5988 0.551253 0.275627 y2 0.449810910302228 0.113769 3.9537 0.000182 9.1e-05 y3 0.544648532701033 0.125413 4.3428 4.7e-05 2.3e-05 y4 -0.231596665797572 0.122404 -1.8921 0.062618 0.031309 y5 -0.212333328723183 0.11596 -1.8311 0.071343 0.035671 y6 0.00655193229113578 0.121517 0.0539 0.957154 0.478577 M1 -12.9994185439502 3.066487 -4.2392 6.7e-05 3.4e-05 M2 -21.2202134716252 3.611616 -5.8755 0 0 M3 -14.1415161121140 4.073434 -3.4716 0.000891 0.000445 M4 -27.6143828048953 3.511255 -7.8645 0 0 M5 -31.0826462179462 3.445539 -9.0211 0 0 M6 -6.92901969997911 3.628507 -1.9096 0.060283 0.030141 M7 7.17864636904514 2.903272 2.4726 0.015845 0.007923 M8 -12.6041688241142 3.968128 -3.1764 0.002219 0.00111 M9 -35.952591159114 4.900745 -7.3361 0 0 M10 -27.1388648742082 5.821845 -4.6616 1.5e-05 7e-06 M11 -14.0107696337175 4.392658 -3.1896 0.002132 0.001066 t 0.164878704585040 0.046475 3.5477 0.000699 0.000349 Multiple Linear Regression - Regression Statistics Multiple R 0.960027176829339 R-squared 0.92165218025091 Adjusted R-squared 0.900386343461872 F-TEST (value) 43.3395680308228 F-TEST (DF numerator) 19 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.14323284430112 Sum Squared Residuals 1201.64648814669 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.3 101.901503041087 0.398496958912756 2 106.6 98.2653289052303 8.33467109476973 3 108.1 107.330749097980 0.769250902019542 4 93.8 92.0646510266045 1.73534897339546 5 88.2 89.8568626689535 -1.65686266895346 6 108.9 108.071398351331 0.828601648669408 7 114.2 112.222773083526 1.977226916474 8 102.5 102.264510780206 0.235489219793728 9 94.2 96.2498274072727 -2.04982740727272 10 97.4 98.5630294769689 -1.16302947696887 11 98.5 96.318423746419 2.18157625358093 12 106.5 109.211092567068 -2.71109256706838 13 102.9 103.624726963534 -0.724726963534064 14 97.1 100.454857861438 -3.35485786143805 15 103.7 109.035007560762 -5.33500756076164 16 93.4 89.5616220390823 3.83837796091768 17 85.8 84.4774334677675 1.32256653223246 18 108.6 109.376874787688 -0.776874787687585 19 110.2 115.922688949375 -5.72268894937509 20 101.2 103.481002731323 -2.28100273132333 21 101.2 96.785170708410 4.4148292915899 22 96.9 98.8527595730154 -1.95275957301541 23 99.4 101.676386666819 -2.27638666681912 24 118.7 115.989753005947 2.71024699405304 25 108 105.232500769278 2.76749923072157 26 101.2 107.395110374473 -6.19511037447317 27 119.9 120.18762302443 -0.287623024429944 28 94.8 94.2949330268657 0.505066973134304 29 95.3 92.3098759347228 2.99012406527717 30 118 119.531907946712 -1.53190794671208 31 115.9 119.016771783471 -3.11677178347118 32 111.4 111.530334428084 -0.130334428084397 33 108.2 104.781807383447 3.41819261655295 34 108.8 104.836945112819 3.96305488718069 35 109.5 109.951960356317 -0.451960356317327 36 124.8 124.341241359521 0.458758640479116 37 115.3 114.919859788099 0.380140211901409 38 109.5 113.962369490518 -4.46236949051842 39 124.2 124.542689283461 -0.342689283460508 40 92.9 100.809464850293 -7.9094648502928 41 98.4 97.6882836896421 0.711716310357883 42 120.9 119.786057922855 1.11394207714463 43 111.7 118.850840326182 -7.15084032618242 44 116.1 115.784276920198 0.315723079802093 45 109.4 106.498706011697 2.90129398830340 46 111.7 105.385149100188 6.31485089981242 47 114.3 115.613722004043 -1.31372200404292 48 133.7 128.441649598611 5.25835040138923 49 114.3 119.966844323715 -5.6668443237147 50 126.5 121.591754083615 4.90824591638514 51 131 130.40883894897 0.591161051029852 52 104 107.312581551976 -3.3125815519761 53 108.9 111.147652521231 -2.24765252123077 54 128.5 127.54172932135 0.958270678649865 55 132.4 126.947690150053 5.45230984994653 56 128 124.469868225429 3.53013177457123 57 116.4 118.030282115469 -1.63028211546881 58 120.9 120.571835590957 0.328164409042778 59 118.6 121.537885208083 -2.93788520808295 60 133.1 131.575449426556 1.52455057344398 61 121.1 124.835331713682 -3.73533171368206 62 127.6 122.587191054984 5.01280894501562 63 135.4 132.294108758053 3.10589124194674 64 114.9 113.088806672835 1.81119332716459 65 114.3 115.060769426238 -0.760769426238018 66 128.9 135.501341971638 -6.60134197163821 67 138.9 136.112305146449 2.78769485355130 68 129.4 126.580474844032 2.81952515596814 69 115 119.713845272119 -4.71384527211852 70 128 125.452893055614 2.54710694438622 71 127 122.599462216832 4.40053778316833 72 128.8 134.881053090398 -6.08105309039756 73 137.9 134.222887817830 3.67711218217032 74 128.4 127.064081083366 1.33591891663361 75 135.9 136.082259425348 -0.182259425348046 76 122.2 123.87165646493 -1.67165646493011 77 113.1 115.296605564511 -2.19660556451101 78 136.2 127.525885815617 8.67411418438279 79 138 132.226930560943 5.77306943905685 80 115.2 119.689532070727 -4.48953207072747 81 111 113.340361101586 -2.34036110158619 82 99.2 109.237388090438 -10.0373880904378 83 102.4 102.002159801487 0.397840198513061 84 112.7 113.859760951899 -1.15976095189939 85 105.5 102.596345582775 2.90365441722477 86 98.3 103.879307146374 -5.57930714637448 87 116.4 114.718723900996 1.68127609900401 88 97.4 92.396284367413 5.00371563258696 89 93.3 91.4625167269343 1.83748327306574 90 117.4 120.064803882809 -2.66480388280881 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 23 0.691850835021417 0.616298329957167 0.308149164978583 24 0.773434809517813 0.453130380964374 0.226565190482187 25 0.664748315532239 0.670503368935522 0.335251684467761 26 0.56514999188023 0.86970001623954 0.43485000811977 27 0.506358208442741 0.987283583114517 0.493641791557259 28 0.501898960935002 0.996202078129997 0.498101039064998 29 0.565357549651513 0.869284900696974 0.434642450348487 30 0.471974395668292 0.943948791336584 0.528025604331708 31 0.398207022018386 0.796414044036773 0.601792977981614 32 0.314511532938867 0.629023065877734 0.685488467061133 33 0.251879982041564 0.503759964083128 0.748120017958436 34 0.243678182258485 0.48735636451697 0.756321817741515 35 0.177311762014518 0.354623524029037 0.822688237985482 36 0.1243918330151 0.2487836660302 0.8756081669849 37 0.0887436076379744 0.177487215275949 0.911256392362026 38 0.0882195144540585 0.176439028908117 0.911780485545941 39 0.0589597651811543 0.117919530362309 0.941040234818846 40 0.175757610405629 0.351515220811257 0.824242389594371 41 0.127998358006466 0.255996716012932 0.872001641993534 42 0.091558293750645 0.18311658750129 0.908441706249355 43 0.159011888929164 0.318023777858328 0.840988111070836 44 0.124479303010362 0.248958606020725 0.875520696989638 45 0.099720136754031 0.199440273508062 0.900279863245969 46 0.157835013866546 0.315670027733093 0.842164986133454 47 0.124130961283649 0.248261922567298 0.875869038716351 48 0.141539128898213 0.283078257796425 0.858460871101787 49 0.154454644079463 0.308909288158926 0.845545355920537 50 0.156022808428416 0.312045616856832 0.843977191571584 51 0.119879019581602 0.239758039163205 0.880120980418398 52 0.115054155108706 0.230108310217413 0.884945844891294 53 0.0945063547584777 0.189012709516955 0.905493645241522 54 0.0641386517261603 0.128277303452321 0.93586134827384 55 0.0765720724566609 0.153144144913322 0.92342792754334 56 0.0603832913288986 0.120766582657797 0.939616708671101 57 0.0430184094314445 0.086036818862889 0.956981590568555 58 0.0281847856840412 0.0563695713680824 0.97181521431596 59 0.028380359277873 0.056760718555746 0.971619640722127 60 0.0192885505055393 0.0385771010110785 0.98071144949446 61 0.0385362402730393 0.0770724805460787 0.96146375972696 62 0.0538886786421115 0.107777357284223 0.946111321357888 63 0.0394264686207015 0.078852937241403 0.960573531379298 64 0.0255484946005649 0.0510969892011297 0.974451505399435 65 0.0235415867460851 0.0470831734921703 0.976458413253915 66 0.0893883862398869 0.178776772479774 0.910611613760113 67 0.125629194096006 0.251258388192011 0.874370805903995 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 2 0.0444444444444444 OK 10% type I error level 8 0.177777777777778 NOK

Charts produced by software:

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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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