paper multiple regressie 4 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 08:21:46 -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/t1262013801odw4xfd85uc99xe.htm/, Retrieved Mon, 28 Dec 2009 16:23:33 +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/t1262013801odw4xfd85uc99xe.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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97.7 0 98.3 91.6 104.6 111.6 106.3 0 97.7 98.3 91.6 104.6 102.3 0 106.3 97.7 98.3 91.6 106.6 0 102.3 106.3 97.7 98.3 108.1 0 106.6 102.3 106.3 97.7 93.8 0 108.1 106.6 102.3 106.3 88.2 0 93.8 108.1 106.6 102.3 108.9 0 88.2 93.8 108.1 106.6 114.2 0 108.9 88.2 93.8 108.1 102.5 0 114.2 108.9 88.2 93.8 94.2 0 102.5 114.2 108.9 88.2 97.4 0 94.2 102.5 114.2 108.9 98.5 0 97.4 94.2 102.5 114.2 106.5 0 98.5 97.4 94.2 102.5 102.9 0 106.5 98.5 97.4 94.2 97.1 0 102.9 106.5 98.5 97.4 103.7 0 97.1 102.9 106.5 98.5 93.4 0 103.7 97.1 102.9 106.5 85.8 0 93.4 103.7 97.1 102.9 108.6 0 85.8 93.4 103.7 97.1 110.2 0 108.6 85.8 93.4 103.7 101.2 0 110.2 108.6 85.8 93.4 101.2 0 101.2 110.2 108.6 85.8 96.9 0 101.2 101.2 110.2 108.6 99.4 0 96.9 101.2 101.2 110.2 118.7 0 99.4 96.9 101.2 101.2 108.0 0 118.7 99.4 96.9 101.2 101.2 0 108.0 118.7 99.4 96.9 119.9 0 101.2 108.0 118.7 99.4 94.8 0 119.9 101.2 108.0 118.7 95.3 0 94.8 119.9 101.2 108.0 118.0 0 95.3 94.8 119.9 101.2 115.9 0 118.0 95.3 94.8 119.9 111. 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 26.0461632964373 -10.2213845284438dummy[t] + 0.125376118603902y1[t] + 0.337853297204397y2[t] + 0.467867221487526y3[t] -0.270421115181480y4[t] + 7.57100304186504M1[t] + 20.2817619430677M2[t] + 8.2751637314311M3[t] + 3.52015731458482M4[t] + 10.4266568702843M5[t] -3.97297815958595M6[t] -8.19458109756605M7[t] + 16.2000655477651M8[t] + 27.6669497311364M9[t] + 7.35926726402566M10[t] -9.44548316094506M11[t] + 0.152995688107594t + 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) 26.0461632964373 8.706392 2.9916 0.003768 0.001884 dummy -10.2213845284438 2.348664 -4.352 4.3e-05 2.1e-05 y1 0.125376118603902 0.109912 1.1407 0.257676 0.128838 y2 0.337853297204397 0.093056 3.6306 0.000518 0.000259 y3 0.467867221487526 0.090985 5.1422 2e-06 1e-06 y4 -0.270421115181480 0.102597 -2.6358 0.010222 0.005111 M1 7.57100304186504 2.351618 3.2195 0.001908 0.000954 M2 20.2817619430677 2.607272 7.7789 0 0 M3 8.2751637314311 3.600608 2.2983 0.024373 0.012187 M4 3.52015731458482 3.260245 1.0797 0.283773 0.141886 M5 10.4266568702843 2.641366 3.9474 0.000178 8.9e-05 M6 -3.97297815958595 2.923126 -1.3592 0.178226 0.089113 M7 -8.19458109756605 2.603545 -3.1475 0.002374 0.001187 M8 16.2000655477651 2.394955 6.7642 0 0 M9 27.6669497311364 3.60722 7.6699 0 0 M10 7.35926726402566 4.651113 1.5823 0.117855 0.058928 M11 -9.44548316094506 3.659829 -2.5809 0.011836 0.005918 t 0.152995688107594 0.041925 3.6493 0.000487 0.000244 Multiple Linear Regression - Regression Statistics Multiple R 0.958661560930972 R-squared 0.919031988406609 Adjusted R-squared 0.900431228986505 F-TEST (value) 49.4083046638048 F-TEST (DF numerator) 17 F-TEST (DF denominator) 74 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.13270459506508 Sum Squared Residuals 1263.86429798533 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.7 95.8019114224386 1.89808857756136 2 106.3 106.664731358788 -0.364731358788428 3 102.3 102.336836358256 -0.0368363582559164 4 106.6 98.046317706451 8.55368229354897 5 108.1 108.479427845339 -0.37942784533893 6 93.8 91.6765313829502 2.12346861704984 7 88.2 89.4153390959708 -1.21533909597075 8 108.9 107.968563052156 0.931436947844315 9 114.2 113.195617174347 1.00438282565310 10 102.5 101.945952582840 0.554047417159545 11 94.2 96.817129463303 -2.61712946330304 12 97.4 98.3040821402792 -0.904082140279151 13 98.5 96.7178236811219 1.78217631887813 14 106.5 110.081251661227 -3.58125166122733 15 102.9 103.343967078221 -0.443967078220730 16 97.1 100.642735075199 -3.54273507519872 17 103.7 109.204251506368 -5.50425150636793 18 93.4 89.9778545047986 3.42214549520143 19 85.8 85.1075911248806 0.692408875119415 20 108.6 109.878852125595 -1.27885212559467 21 110.2 115.185810700970 -4.98581070096981 22 101.2 102.164327491057 -0.964327491057168 23 101.2 97.6473260875808 3.55267391241919 24 96.9 98.7881113900362 -1.88811139003620 25 99.4 101.329514032334 -1.92951403233395 26 118.7 115.487729776808 3.21227022319161 27 108 104.886690532949 3.1133094670507 28 101.2 107.796202820193 -6.59620282019291 29 119.9 118.741894764162 1.15810523583802 30 94.8 94.3170796263833 0.482920373616732 31 95.3 93.1313972836017 2.16860271639827 32 118 119.849590541563 -1.84959054156286 33 115.9 117.684092840722 -1.78409284072194 34 111.4 111.956889660989 -0.556889660989302 35 108.2 104.516825836455 3.68317416354451 36 108.8 105.072680788812 3.72731921118750 37 109.5 110.253256484081 -0.753256484080629 38 124.8 123.127206244293 1.67279375570715 39 115.3 114.574423544920 0.725576455080155 40 109.5 114.115749522604 -4.61574952260375 41 124.2 124.207530663199 -0.00753066319855891 42 92.9 101.262189474720 -8.36218947471959 43 98.4 98.091123891046 0.308876108953988 44 120.9 121.199617298228 -0.299617298227808 45 111.7 118.879218547191 -7.17921854719137 46 116.1 116.210221287493 -0.110221287492951 47 109.4 106.041567488178 3.35843251182228 48 111.7 105.897727321015 5.80227267898494 49 114.3 116.192964066722 -1.89296406672194 50 133.7 125.835195857208 7.86480414279245 51 114.3 120.180224688463 -5.88022468846286 52 126.5 120.294757435524 6.20524256447606 53 131 130.703016557920 0.296983442080493 54 104 106.819586244389 -2.81958624438931 55 108.9 111.840313366300 -2.94031336629978 56 128.5 126.686524547859 1.81347545214125 57 132.4 128.569947501796 3.83005249820421 58 128 125.120071705743 2.87992829425713 59 116.4 117.079423982886 -0.679423982885972 60 120.9 120.261413654678 0.638586345321618 61 118.6 121.517248547045 -2.91724854704468 62 133.1 131.375571038529 1.72442896147103 63 121.1 125.805147083985 -4.70514708398544 64 127.6 122.304506113726 5.29549388627428 65 135.4 133.530749838492 1.86925016150806 66 114.9 112.922577825687 1.9774221743135 67 114.3 115.205206184475 -0.905206184475013 68 128.9 134.643257332984 -5.74325733298437 69 138.9 136.190353818848 2.70964618115224 70 129.4 127.484998893396 1.91500110660438 71 115 120.013818104666 -5.01381810466618 72 128 125.327798455607 2.67220154439346 73 127 122.667649491740 4.33235050825972 74 128.8 135.629833430907 -6.82983343090748 75 137.9 133.640392561612 4.25960743838776 76 128.4 126.804098728290 1.59590127170979 77 135.9 136.859567963779 -0.959567963779292 78 122.2 124.114476896314 -1.91447689631391 79 113.1 113.956545798318 -0.856545798317991 80 136.2 128.591295507698 7.60870449230236 81 138 131.594959416126 6.40504058387357 82 115.2 118.917538378482 -3.71753837848163 83 111 113.283909036931 -2.28390903693078 84 99.2 109.248186249572 -10.0481862495722 85 102.4 102.919632274518 -0.519632274518023 86 112.7 116.398480632239 -3.69848063223902 87 105.5 102.532318151594 2.96768184840633 88 98.3 105.195632598014 -6.89563259801373 89 116.4 112.873560860742 3.52643913925815 90 97.4 92.3097040447587 5.0902959552413 91 93.3 90.5524832554081 2.74751674459186 92 117.4 118.582299593918 -1.18229959391823 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.471463003174826 0.942926006349653 0.528536996825174 22 0.303641596710804 0.607283193421609 0.696358403289196 23 0.432326846962686 0.864653693925371 0.567673153037314 24 0.303782578114323 0.607565156228646 0.696217421885677 25 0.244115691420092 0.488231382840183 0.755884308579908 26 0.358827262842294 0.717654525684588 0.641172737157706 27 0.267985078542867 0.535970157085733 0.732014921457133 28 0.238541011310221 0.477082022620442 0.761458988689779 29 0.208472945013963 0.416945890027927 0.791527054986037 30 0.233001046140243 0.466002092280486 0.766998953859757 31 0.256871318551439 0.513742637102877 0.743128681448561 32 0.194974244829798 0.389948489659596 0.805025755170202 33 0.142539647257294 0.285079294514587 0.857460352742706 34 0.0997037740922991 0.199407548184598 0.900296225907701 35 0.0758734836659419 0.151746967331884 0.924126516334058 36 0.0743782635441375 0.148756527088275 0.925621736455863 37 0.0487669952234586 0.0975339904469173 0.951233004776541 38 0.0318029971401419 0.0636059942802837 0.968197002859858 39 0.0218698583585796 0.0437397167171591 0.97813014164142 40 0.0231334675775235 0.0462669351550469 0.976866532422477 41 0.0140592589894845 0.0281185179789689 0.985940741010516 42 0.0748132907844704 0.149626581568941 0.92518670921553 43 0.0507021375416097 0.101404275083219 0.94929786245839 44 0.0336306688538559 0.0672613377077118 0.966369331146144 45 0.0649872872853473 0.129974574570695 0.935012712714653 46 0.0480654835636227 0.0961309671272453 0.951934516436377 47 0.0373200226827545 0.074640045365509 0.962679977317246 48 0.0593241801105717 0.118648360221143 0.940675819889428 49 0.0441465467563569 0.0882930935127137 0.955853453243643 50 0.112583343054420 0.225166686108840 0.88741665694558 51 0.13630984169559 0.27261968339118 0.86369015830441 52 0.170487235779251 0.340974471558501 0.829512764220749 53 0.128840209025244 0.257680418050489 0.871159790974755 54 0.117171144503160 0.234342289006319 0.88282885549684 55 0.108368307003204 0.216736614006408 0.891631692996796 56 0.0772294588924382 0.154458917784876 0.922770541107562 57 0.0744061038627904 0.148812207725581 0.92559389613721 58 0.0567714243731516 0.113542848746303 0.943228575626848 59 0.0409788663666856 0.0819577327333712 0.959021133633314 60 0.0285611145488102 0.0571222290976205 0.97143888545119 61 0.0277263945235548 0.0554527890471096 0.972273605476445 62 0.0250181550293446 0.0500363100586891 0.974981844970655 63 0.0491832905874409 0.0983665811748819 0.950816709412559 64 0.0716940667335598 0.143388133467120 0.92830593326644 65 0.0493338401266425 0.098667680253285 0.950666159873357 66 0.0311174080980412 0.0622348161960824 0.96888259190196 67 0.0273903620698919 0.0547807241397838 0.972609637930108 68 0.135704018748260 0.271408037496520 0.86429598125174 69 0.107099524014314 0.214199048028629 0.892900475985686 70 0.0612507165323507 0.122501433064701 0.93874928346765 71 0.30179619507157 0.60359239014314 0.69820380492843 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 0.0588235294117647 NOK 10% type I error level 17 0.333333333333333 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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