multiple regression alles

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 10 Dec 2008 03:10:26 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/10/t1228904200lcf5lc9ger6m0f2.htm/, Retrieved Wed, 10 Dec 2008 10:16:50 +0000

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/2008/Dec/10/t1228904200lcf5lc9ger6m0f2.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

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Dataseries X:

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6.4 12.5 6.8 14.8 7.5 15.9 7.5 14.8 7.6 12.9 7.6 14.3 7.4 14.2 7.3 15.9 7.1 15.3 6.9 15.5 6.8 15.1 7.5 15 7.6 12.1 7.8 15.8 8 16.9 8.1 15.1 8.2 13.7 8.3 14.8 8.2 14.7 8 16 7.9 15.4 7.6 15 7.6 15.5 8.2 15.1 8.3 11.7 8.4 16.3 8.4 16.7 8.4 15 8.6 14.9 8.9 14.6 8.8 15.3 8.3 17.9 7.5 16.4 7.2 15.4 7.5 17.9 8.8 15.9 9.3 13.9 9.3 17.8 8.7 17.9 8.2 17.4 8.3 16.7 8.5 16 8.6 16.6 8.6 19.1 8.2 17.8 8.1 17.2 8 18.6 8.6 16.3 8.7 15.1 8.8 19.2 8.5 17.7 8.4 19.1 8.5 18 8.7 17.5 8.7 17.8 8.6 21.1 8.5 17.2 8.3 19.4 8.1 19.8 8.2 17.6 8.1 16.2 8.1 19.5 7.9 19.9 7.9 20 7.9 17.3 8 18.9 8 18.6 7.9 21.4 8 18.6 7.7 19.8 7.2 20.8 7.5 19.6 7.3 17.7 7 19.8 7 22.2 7 20.7 7.2 17.9 7.3 21.2 7.1 21.4 6.8 21.7 6.6 23.2 6.2 21.5 6.2 22.9 6.8 23.2 6.9 18.6

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 Werkloosheid[t] = + 12.9065558297395 -0.363231499460388Export[t] -0.990032742897683M1[t] + 0.403999100426132M2[t] + 0.553754557928458M3[t] + 0.188451923275136M4[t] -0.281721625232212M5[t] + 0.138053810695077M6[t] + 0.104848546976968M7[t] + 0.642308396527019M8[t] -0.107172687810245M9[t] -0.398738537134718M10[t] -0.160833336991094M11[t] + 0.0292339707607532t + 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) 12.9065558297395 1.181231 10.9264 0 0 Export -0.363231499460388 0.087128 -4.1689 8.5e-05 4.3e-05 M1 -0.990032742897683 0.384794 -2.5729 0.012176 0.006088 M2 0.403999100426132 0.34778 1.1617 0.249268 0.124634 M3 0.553754557928458 0.360113 1.5377 0.128561 0.064281 M4 0.188451923275136 0.341368 0.552 0.582648 0.291324 M5 -0.281721625232212 0.347341 -0.8111 0.42003 0.210015 M6 0.138053810695077 0.337087 0.4095 0.683369 0.341685 M7 0.104848546976968 0.336569 0.3115 0.756317 0.378159 M8 0.642308396527019 0.372795 1.723 0.08925 0.044625 M9 -0.107172687810245 0.338385 -0.3167 0.752387 0.376193 M10 -0.398738537134718 0.337409 -1.1818 0.241242 0.120621 M11 -0.160833336991094 0.352469 -0.4563 0.649563 0.324782 t 0.0292339707607532 0.008358 3.4978 0.000814 0.000407 Multiple Linear Regression - Regression Statistics Multiple R 0.559488791580069 R-squared 0.313027707903726 Adjusted R-squared 0.187244048787507 F-TEST (value) 2.48861982632021 F-TEST (DF numerator) 13 F-TEST (DF denominator) 71 p-value 0.00739726870900193 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.628739665409021 Sum Squared Residuals 28.067263246964 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.4 7.40536331434775 -1.00536331434775 2 6.8 7.9931966796734 -1.1931966796734 3 7.5 7.77263145853005 -0.272631458530052 4 7.5 7.8361174440439 -0.336117444043909 5 7.6 8.08531771527205 -0.485317715272055 6 7.6 8.02580302271555 -0.425803022715553 7 7.4 8.05815487970423 -0.658154879704235 8 7.3 8.00735515093238 -0.70735515093238 9 7.1 7.5050469370321 -0.405046937032102 10 6.9 7.1700687585763 -0.270068758576304 11 6.8 7.58250052926484 -0.782500529264837 12 7.5 7.80889098696272 -0.308890986962723 13 7.6 7.90146356326092 -0.301463563260921 14 7.8 7.98077282934205 -0.180772829342051 15 8 7.7602076081987 0.239792391801297 16 8.1 8.07795564333483 0.0220443566651666 17 8.2 8.14554016483278 0.0544598351672162 18 8.3 8.1949949221144 0.105005077885604 19 8.2 8.22734677910308 -0.0273467791030806 20 8 8.32183965011538 -0.321839650115379 21 7.9 7.8195314362151 0.0804685637848987 22 7.6 7.70249215743554 -0.102492157435538 23 7.6 7.78801557860972 -0.188015578609721 24 8.2 8.12337548614572 0.0766245138542765 25 8.3 8.39756381217411 -0.0975638121741133 26 8.4 8.1499647287409 0.250035271259105 27 8.4 8.18366155721982 0.216338442780182 28 8.4 8.46508644240991 -0.06508644240991 29 8.6 8.06047001460935 0.539529985390645 30 8.9 8.61844887113551 0.281551128864487 31 8.8 8.36021552855589 0.439784471444116 32 8.3 7.98250745026968 0.317492549730320 33 7.5 7.80710758588375 -0.307107585883752 34 7.2 7.90800720678042 -0.70800720678042 35 7.5 7.26706762903383 0.232932370966173 36 8.8 8.18359793570645 0.61640206429355 37 9.3 7.9492621624903 1.35073783750970 38 9.3 7.95592512867935 1.34407487132065 39 8.7 8.0985914069964 0.601408593003608 40 8.2 7.94413849283402 0.255861507165982 41 8.3 7.7574609647097 0.542539035290306 42 8.5 8.46073242102001 0.0392675789799918 43 8.6 8.23882222838642 0.361177771613581 44 8.6 7.89743730004625 0.702562699953748 45 8.2 7.64939113576825 0.550608864231753 46 8.1 7.60499815688076 0.495001843119239 47 8 7.36361322854059 0.636386771459407 48 8.6 8.38911298505133 0.210887014948666 49 8.7 7.86419201226687 0.83580798773313 50 8.8 7.79820867856385 1.00179132143615 51 8.5 8.5220453560175 -0.0220453560175074 52 8.4 7.6774525928804 0.722547407119606 53 8.5 7.63606766454023 0.863932335459772 54 8.7 8.26669282095846 0.433307179041535 55 8.7 8.153752078163 0.546247921837008 56 8.6 7.52178195025451 1.07821804974549 57 8.5 8.21813768457352 0.281862315426481 58 8.3 7.15669650719695 1.14330349280306 59 8.1 7.27854307831717 0.821456921682834 60 8.2 8.26771968488187 -0.0677196848818679 61 8.1 7.81544501198948 0.284554988010518 62 8.1 8.04004687785477 0.0599531221452312 63 7.9 8.0737437063337 -0.173743706333691 64 7.9 7.70135189249508 0.198648107504916 65 7.9 8.24113736329154 -0.341137363291538 66 8 8.10897637084296 -0.108976370842960 67 8 8.21397452772372 -0.213974527723719 68 7.9 7.76362014954544 0.136379850454564 69 8 8.06042123445801 -0.0604212344580129 70 7.7 7.36221155654183 0.337788443458173 71 7.2 7.26611922798582 -0.0661192279858161 72 7.5 7.89206433509013 -0.392064335090129 73 7.3 7.62140541192794 -0.321405411927938 74 7 8.28188507714569 -1.28188507714569 75 7 7.58911890670384 -0.589118906703837 76 7 7.79789749200185 -0.797897492001851 77 7.2 8.37400611274435 -1.17400611274435 78 7.3 7.6243515712131 -0.324351571213105 79 7.1 7.54773397836367 -0.447733978363671 80 6.8 8.00545834883636 -1.20545834883636 81 6.6 6.74036398606927 -0.140363986069266 82 6.2 7.09552565658821 -0.895525656588206 83 6.2 6.85414072824804 -0.65414072824804 84 6.8 6.93523858616177 -0.135238586161771 85 6.9 7.64530471154263 -0.745304711542626 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0658944084663915 0.131788816932783 0.934105591533609 18 0.0217667491234461 0.0435334982468922 0.978233250876554 19 0.0066075757141444 0.0132151514282888 0.993392424285856 20 0.00273140901098240 0.00546281802196481 0.997268590989018 21 0.000810281560691463 0.00162056312138293 0.999189718439309 22 0.000441688794449975 0.00088337758889995 0.99955831120555 23 0.000141181482409983 0.000282362964819966 0.99985881851759 24 4.83247511105496e-05 9.66495022210991e-05 0.99995167524889 25 1.59840958407646e-05 3.19681916815291e-05 0.99998401590416 26 5.09649321335903e-06 1.01929864267181e-05 0.999994903506787 27 6.04011490986758e-05 0.000120802298197352 0.999939598850901 28 0.000226688534339239 0.000453377068678477 0.99977331146566 29 0.000115199394013875 0.000230398788027750 0.999884800605986 30 4.59923034114969e-05 9.19846068229937e-05 0.999954007696588 31 1.76862406491635e-05 3.5372481298327e-05 0.99998231375935 32 1.00311798704045e-05 2.00623597408090e-05 0.99998996882013 33 0.00176839998364801 0.00353679996729602 0.998231600016352 34 0.107253071054733 0.214506142109465 0.892746928945267 35 0.203401769317332 0.406803538634664 0.796598230682668 36 0.177085112793393 0.354170225586787 0.822914887206607 37 0.354613105385638 0.709226210771276 0.645386894614362 38 0.39027625136463 0.78055250272926 0.60972374863537 39 0.375635336974449 0.751270673948898 0.624364663025551 40 0.573384055920858 0.853231888158284 0.426615944079142 41 0.681762186404806 0.636475627190388 0.318237813595194 42 0.833920406899776 0.332159186200447 0.166079593100224 43 0.870151576711691 0.259696846576618 0.129848423288309 44 0.865561288581064 0.268877422837872 0.134438711418936 45 0.941208502839471 0.117582994321058 0.058791497160529 46 0.977963117892892 0.0440737642142159 0.0220368821071080 47 0.992766133622843 0.0144677327543144 0.00723386637715718 48 0.997386938529895 0.00522612294021044 0.00261306147010522 49 0.997308564849892 0.00538287030021686 0.00269143515010843 50 0.995363556931469 0.00927288613706267 0.00463644306853133 51 0.997145702138003 0.00570859572399407 0.00285429786199704 52 0.996463935590055 0.0070721288198903 0.00353606440994515 53 0.99525840919841 0.00948318160317927 0.00474159080158964 54 0.99417939947697 0.0116412010460588 0.0058206005230294 55 0.990814026145508 0.0183719477089841 0.00918597385449203 56 0.983114955489003 0.0337700890219949 0.0168850445109974 57 0.97363170684556 0.0527365863088825 0.0263682931544413 58 0.957884493960658 0.0842310120786838 0.0421155060393419 59 0.934633282682302 0.130733434635395 0.0653667173176977 60 0.951265303267407 0.0974693934651866 0.0487346967325933 61 0.95967232327045 0.0806553534591016 0.0403276767295508 62 0.959448397862964 0.0811032042740719 0.0405516021370359 63 0.948142332603905 0.103715334792190 0.0518576673960948 64 0.917684077080016 0.164631845839968 0.0823159229199838 65 0.89033053524965 0.219338929500699 0.109669464750349 66 0.851453006019482 0.297093987961037 0.148546993980518 67 0.766602648703738 0.466794702592524 0.233397351296262 68 0.655943207008453 0.688113585983093 0.344056792991547 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.384615384615385 NOK 5% type I error level 27 0.519230769230769 NOK 10% type I error level 32 0.615384615384615 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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