paper 2

*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: Sun, 28 Nov 2010 13:36:17 +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/28/t12909512793yrw7poh9uktzmx.htm/, Retrieved Sun, 28 Nov 2010 14:34:39 +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/28/t12909512793yrw7poh9uktzmx.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 «

9939 2462 9321 9769 9336 3695 9939 9321 10195 4831 9336 9939 9464 5134 10195 9336 10010 6250 9464 10195 10213 5760 10010 9464 9563 6249 10213 10010 9890 2917 9563 10213 9305 1741 9890 9563 9391 2359 9305 9890 9928 1511 9391 9305 8686 2059 9928 9391 9843 2635 8686 9928 9627 2867 9843 8686 10074 4403 9627 9843 9503 5720 10074 9627 10119 4502 9503 10074 10000 5749 10119 9503 9313 5627 10000 10119 9866 2846 9313 10000 9172 1762 9866 9313 9241 2429 9172 9866 9659 1169 9241 9172 8904 2154 9659 9241 9755 2249 8904 9659 9080 2687 9755 8904 9435 4359 9080 9755 8971 5382 9435 9080 10063 4459 8971 9435 9793 6398 10063 8971 9454 4596 9793 10063 9759 3024 9454 9793 8820 1887 9759 9454 9403 2070 8820 9759 9676 1351 9403 8820 8642 2218 9676 9403 9402 2461 8642 9676 9610 3028 9402 8642 9294 4784 9610 9402 9448 4975 9294 9610 10319 4607 9448 9294 9548 6249 10319 9448 9801 4809 9548 10319 9596 3157 9801 9548 8923 1910 9596 9801 9746 2228 8923 9596 9829 1594 9746 8923 9125 2467 982 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation geboortes[t] = + 3636.49072146088 -0.112892682481442huwelijk[t] + 0.263683500627482`geboortes-1`[t] + 0.288035361724047`geboortes-2`[t] + 1160.87808712201M1[t] + 804.780496030499M2[t] + 1154.06440433495M3[t] + 1093.94205740715M4[t] + 1625.10788116370M5[t] + 1529.28339577008M6[t] + 984.223018622452M7[t] + 980.829801148078M8[t] + 147.756081412069M9[t] + 717.42433813678M10[t] + 1000.90518467358M11[t] + 5.07968162684446t + 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) 3636.49072146088 1159.935649 3.1351 0.002422 0.001211 huwelijk -0.112892682481442 0.086241 -1.309 0.194366 0.097183 `geboortes-1` 0.263683500627482 0.113501 2.3232 0.022777 0.011388 `geboortes-2` 0.288035361724047 0.103306 2.7882 0.006656 0.003328 M1 1160.87808712201 179.014803 6.4848 0 0 M2 804.780496030499 173.408091 4.641 1.4e-05 7e-06 M3 1154.06440433495 273.383148 4.2214 6.5e-05 3.3e-05 M4 1093.94205740715 308.809287 3.5425 0.000673 0.000336 M5 1625.10788116370 324.639934 5.0059 3e-06 2e-06 M6 1529.28339577008 331.642973 4.6112 1.5e-05 8e-06 M7 984.223018622452 310.683442 3.1679 0.002192 0.001096 M8 980.829801148078 169.087085 5.8007 0 0 M9 147.756081412069 141.153351 1.0468 0.298435 0.149218 M10 717.42433813678 167.257971 4.2893 5.1e-05 2.5e-05 M11 1000.90518467358 153.908161 6.5033 0 0 t 5.07968162684446 1.519014 3.3441 0.001271 0.000635 Multiple Linear Regression - Regression Statistics Multiple R 0.884336823751576 R-squared 0.782051617843026 Adjusted R-squared 0.740138467428223 F-TEST (value) 18.6588602885558 F-TEST (DF numerator) 15 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 262.90500426931 Sum Squared Residuals 5391285.21904797 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9939 9796.1180639714 142.881936028592 2 9336 9339.82003834253 -3.82003834253125 3 10195 9584.942243642 610.057756358004 4 9464 9548.51189946857 -84.5118994685679 5 10010 10013.4389079649 -3.43890796494149 6 10213 9911.4288605364 301.571139463594 7 9563 9527.0387014109 35.961298589096 8 9890 9791.96048661366 98.0395133863438 9 9305 8995.72976268722 309.270237312777 10 9391 9440.64273868193 -49.642738681932 11 9928 9679.11235603523 248.887643964766 12 8686 8787.7907439339 -101.790743933896 13 9843 9715.90240903992 127.097590960079 14 9627 9286.03528820429 340.96471179571 15 10074 9743.29699522327 330.703004776725 16 9503 9595.22555374235 -92.2255537423492 17 10119 10247.1628742205 -128.162874220497 18 10000 10013.6017402415 -13.6017402414689 19 9313 9633.44539823076 -320.445398230761 20 9866 9733.65963938788 132.340360612121 21 9172 8975.97795143117 196.022048568825 22 9241 9451.71367616553 -210.713676165533 23 9659 9700.8166047626 -41.8166047625996 24 8904 8723.8859526929 180.114047307107 25 9755 9800.43665483291 -45.4366548329134 26 9080 9406.8997113737 -326.899711373707 27 9435 9639.63846609964 -204.638466099642 28 8971 9368.2903601792 -397.290360179195 29 10063 9988.63922061385 74.3607793861538 30 9793 9833.28948036081 -40.2894803608146 31 9454 9740.08146850483 -286.081468504826 32 9759 9752.07697513991 6.9230248600869 33 8820 9035.22139707908 -215.221397079078 34 9403 9429.56195277316 -26.5619527731574 35 9676 9682.5545958479 -6.5545958478979 36 8642 8828.76134864618 -186.761348646177 37 9402 9773.27110965389 -371.271109653890 38 9610 9260.81394567647 349.186054323533 39 9294 9690.69102821114 -396.69102821114 40 9448 9590.67322959654 -142.673229596545 41 10319 10118.0513269249 200.948673075055 42 9548 10115.9625132757 -567.962513275689 43 9801 9786.12610160603 14.873898393965 44 9596 9818.94793898736 -222.947938987360 45 8923 9150.5489048201 -227.548904820103 46 9746 9452.89072506683 293.109274933166 47 9829 9836.18893649984 -7.18893649984465 48 9125 9000.74695489778 124.253045102215 49 9782 10032.6371814359 -250.637181435941 50 9441 9495.726071993 -54.726071993004 51 9162 9827.71450914803 -665.71450914803 52 9915 9569.61281577675 345.387184223254 53 10444 10132.8328437666 311.167156233379 54 10209 10431.2061171296 -222.206117129616 55 9985 10035.8189929045 -50.8189929044848 56 9842 10110.7978762684 -268.797876268363 57 9429 9319.99963940786 109.000360592138 58 10132 9711.3538939417 420.646106058297 59 9849 10146.2533378513 -297.253337851294 60 9172 9218.34824902134 -46.3482490213436 61 10313 10115.1339731965 197.86602680351 62 9819 9710.91320844425 108.086791555751 63 9955 10143.5476826325 -188.547682632449 64 10048 9958.25614872157 89.7438512784332 65 10082 10452.4165853727 -370.416585372677 66 10541 10422.7122701434 118.287729856578 67 10208 10042.6818157895 165.31818421052 68 10233 10343.4557969425 -110.455796942469 69 9439 9547.27191919747 -108.271919197471 70 9963 9885.53666661955 77.4633333804525 71 10158 10173.7685252058 -15.7685252057688 72 9225 9325.5388833213 -100.538883321297 73 10474 10245.7649636926 228.235036307415 74 9757 9842.11939349422 -85.1193934942221 75 10490 10236.5612466379 253.438753362083 76 10281 10061.3678626308 219.632137369163 77 10444 10751.7142609246 -307.71426092463 78 10640 10623.7684723606 16.2315276393991 79 10695 10135.4561510115 559.54384898846 80 10786 10568.3406883947 217.659311605284 81 9832 9902.3336761824 -70.333676182395 82 9747 10181.9710950787 -434.971095078689 83 10411 10291.3056437974 119.694356202639 84 9511 9379.9278674866 131.072132513392 85 10402 10430.7356441769 -28.7356441768527 86 9701 10028.6723424715 -327.672342471529 87 10540 10278.6078284056 261.392171594448 88 10112 10050.0621298842 61.9378701158072 89 10915 10691.7439802118 223.256019788158 90 11183 10775.0305459520 407.969454048017 91 10384 10502.3513705420 -118.351370541969 92 10834 10686.7605982656 147.239401734356 93 9886 9878.9167491947 7.08325080530635 94 10216 10285.3292516726 -69.329251672604 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.201182633859420 0.402365267718839 0.79881736614058 20 0.104773992835337 0.209547985670674 0.895226007164663 21 0.0706728628117468 0.141345725623494 0.929327137188253 22 0.0340786981996980 0.0681573963993961 0.965921301800302 23 0.0369507619329127 0.0739015238658253 0.963049238067087 24 0.0463920786586957 0.0927841573173914 0.953607921341304 25 0.0296777603368793 0.0593555206737587 0.97032223966312 26 0.0452378030011022 0.0904756060022044 0.954762196998898 27 0.251133967892408 0.502267935784816 0.748866032107592 28 0.253202265363622 0.506404530727244 0.746797734636378 29 0.205827469367376 0.411654938734752 0.794172530632624 30 0.161554530874446 0.323109061748892 0.838445469125554 31 0.138294937491489 0.276589874982977 0.861705062508511 32 0.103264711407728 0.206529422815455 0.896735288592272 33 0.0755784135863155 0.151156827172631 0.924421586413685 34 0.094130427734539 0.188260855469078 0.905869572265461 35 0.0669925425152411 0.133985085030482 0.933007457484759 36 0.048196860295817 0.096393720591634 0.951803139704183 37 0.0379180784555804 0.0758361569111609 0.96208192154442 38 0.143321067205704 0.286642134411409 0.856678932794296 39 0.154041480919745 0.308082961839491 0.845958519080255 40 0.168888597567549 0.337777195135099 0.83111140243245 41 0.244096986301649 0.488193972603297 0.755903013698351 42 0.264810288311703 0.529620576623407 0.735189711688297 43 0.329939281986396 0.659878563972791 0.670060718013604 44 0.333593505747288 0.667187011494576 0.666406494252712 45 0.306111148654743 0.612222297309487 0.693888851345256 46 0.434353035691275 0.86870607138255 0.565646964308725 47 0.418365676716487 0.836731353432974 0.581634323283513 48 0.514201346488832 0.971597307022336 0.485798653511168 49 0.460465929288417 0.920931858576833 0.539534070711584 50 0.407831311308255 0.81566262261651 0.592168688691745 51 0.791732530612402 0.416534938775197 0.208267469387598 52 0.843270301189233 0.313459397621534 0.156729698810767 53 0.876863273479987 0.246273453040026 0.123136726520013 54 0.85261189308446 0.294776213831081 0.147388106915540 55 0.829543458370347 0.340913083259307 0.170456541629653 56 0.886576326456198 0.226847347087604 0.113423673543802 57 0.860332345954134 0.279335308091733 0.139667654045867 58 0.933998230229001 0.132003539541998 0.0660017697709988 59 0.909252198090311 0.181495603819378 0.090747801909689 60 0.876331446088796 0.247337107822409 0.123668553911204 61 0.86423680683029 0.271526386339419 0.135763193169710 62 0.898391347483002 0.203217305033995 0.101608652516998 63 0.870320215729552 0.259359568540897 0.129679784270448 64 0.852241302104235 0.295517395791530 0.147758697895765 65 0.879465696102535 0.241068607794930 0.120534303897465 66 0.836107602374453 0.327784795251094 0.163892397625547 67 0.799848130419026 0.400303739161947 0.200151869580974 68 0.83872708747401 0.322545825051982 0.161272912525991 69 0.848150847229067 0.303698305541865 0.151849152770933 70 0.773806674706728 0.452386650586544 0.226193325293272 71 0.712434134760898 0.575131730478204 0.287565865239102 72 0.597733776226374 0.804532447547252 0.402266223773626 73 0.487497449406801 0.974994898813602 0.512502550593199 74 0.356287802738768 0.712575605477537 0.643712197261232 75 0.277259191942268 0.554518383884536 0.722740808057732 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 0 0 OK 10% type I error level 7 0.122807017543860 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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