ws8 multiple regression

*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: Tue, 30 Nov 2010 16:26:03 +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/30/t12911342673li956fm190xkok.htm/, Retrieved Tue, 30 Nov 2010 17:24:27 +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/30/t12911342673li956fm190xkok.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:

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37 0 30 0 47 0 35 0 30 0 43 0 82 0 40 0 47 0 19 0 52 0 136 0 80 0 42 0 54 0 66 0 81 0 63 0 137 0 72 0 107 0 58 0 36 0 52 0 79 0 77 0 54 0 84 0 48 0 96 0 83 0 66 0 61 0 53 0 30 0 74 0 69 0 59 0 42 0 65 0 70 0 100 0 63 0 105 0 82 0 81 0 75 0 102 0 121 1 98 1 76 1 77 1 63 1 37 1 35 1 23 1 40 1 29 1 37 1 51 1 20 1 28 1 13 1 22 1 25 1 13 1 16 1 13 1 16 1 17 1 9 1 17 1 25 1 14 1 8 1 7 1 10 1 7 1 10 1 3 1

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 y[t] = + 84.7854609929078 -33.9867021276596x[t] -7.365142688281M1[t] -19.1876055386694M2[t] -26.8672112462006M3[t] -17.9753883823033M4[t] -22.0835655184059M5[t] -17.4774569402229M6[t] -7.87134836203985M7[t] -22.6938112124282M8[t] -13.2707066869301M9[t] -29.2360266801756M10[t] -32.2013466734211M11[t] -0.0346800067544744t + 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) 84.7854609929078 13.625221 6.2227 0 0 x -33.9867021276596 12.189533 -2.7882 0.006918 0.003459 M1 -7.365142688281 15.519574 -0.4746 0.636659 0.31833 M2 -19.1876055386694 15.483608 -1.2392 0.219653 0.109826 M3 -26.8672112462006 15.451885 -1.7388 0.086739 0.043369 M4 -17.9753883823033 15.424432 -1.1654 0.248058 0.124029 M5 -22.0835655184059 15.40127 -1.4339 0.156328 0.078164 M6 -17.4774569402229 15.382421 -1.1362 0.259983 0.129992 M7 -7.87134836203985 15.367898 -0.5122 0.610225 0.305112 M8 -22.6938112124282 15.357716 -1.4777 0.144249 0.072125 M9 -13.2707066869301 15.936455 -0.8327 0.408002 0.204001 M10 -29.2360266801756 15.925964 -1.8357 0.070901 0.03545 M11 -32.2013466734211 15.919667 -2.0227 0.047155 0.023577 t -0.0346800067544744 0.258555 -0.1341 0.893708 0.446854 Multiple Linear Regression - Regression Statistics Multiple R 0.613762687419943 R-squared 0.376704636468951 Adjusted R-squared 0.253934337591623 F-TEST (value) 3.06836946650553 F-TEST (DF numerator) 13 F-TEST (DF denominator) 66 p-value 0.00135859852503972 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27.5700345856201 Sum Squared Residuals 50167.0492654509 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 37 77.3856382978724 -40.3856382978724 2 30 65.5284954407294 -35.5284954407295 3 47 57.8142097264437 -10.8142097264438 4 35 66.6713525835866 -31.6713525835866 5 30 62.5284954407295 -32.5284954407295 6 43 67.099924012158 -24.0999240121581 7 82 76.6713525835866 5.32864741641338 8 40 61.8142097264438 -21.8142097264438 9 47 71.2026342451874 -24.2026342451874 10 19 55.2026342451874 -36.2026342451874 11 52 52.2026342451874 -0.202634245187440 12 136 84.369300911854 51.6306990881459 13 80 76.9694782168186 3.03052178318137 14 42 65.1123353596758 -23.1123353596758 15 54 57.3980496453901 -3.39804964539007 16 66 66.2551925025329 -0.255192502532925 17 81 62.1123353596758 18.8876646403242 18 63 66.6837639311044 -3.68376393110436 19 137 76.255192502533 60.744807497467 20 72 61.3980496453901 10.6019503546099 21 107 70.7864741641337 36.2135258358663 22 58 54.7864741641337 3.21352583586626 23 36 51.7864741641337 -15.7864741641337 24 52 83.9531408308004 -31.9531408308004 25 79 76.553318135765 2.44668186423506 26 77 64.6961752786221 12.3038247213779 27 54 56.9818895643364 -2.98188956433638 28 84 65.8390324214792 18.1609675785208 29 48 61.6961752786221 -13.6961752786221 30 96 66.2676038500507 29.7323961499493 31 83 75.8390324214792 7.16096757852078 32 66 60.9818895643364 5.01811043566362 33 61 70.37031408308 -9.37031408308004 34 53 54.37031408308 -1.37031408308004 35 30 51.37031408308 -21.3703140830800 36 74 83.5369807497467 -9.53698074974671 37 69 76.1371580547112 -7.13715805471124 38 59 64.2800151975684 -5.2800151975684 39 42 56.5657294832827 -14.5657294832827 40 65 65.4228723404255 -0.42287234042554 41 70 61.2800151975684 8.7199848024316 42 100 65.851443768997 34.1485562310030 43 63 75.4228723404255 -12.4228723404255 44 105 60.5657294832827 44.4342705167173 45 82 69.9541540020263 12.0458459979737 46 81 53.9541540020264 27.0458459979736 47 75 50.9541540020263 24.0458459979737 48 102 83.120820668693 18.879179331307 49 121 41.7342958459979 79.265704154002 50 98 29.8771529888551 68.1228470111449 51 76 22.1628672745694 53.8371327254306 52 77 31.0200101317122 45.9799898682878 53 63 26.8771529888551 36.1228470111449 54 37 31.4485815602837 5.55141843971632 55 35 41.0200101317122 -6.02001013171225 56 23 26.1628672745694 -3.16286727456939 57 40 35.5512917933131 4.44870820668694 58 29 19.5512917933131 9.44870820668693 59 37 16.5512917933131 20.4487082066869 60 51 48.7179584599797 2.28204154002026 61 20 41.3181357649443 -21.3181357649443 62 28 29.4609929078014 -1.46099290780141 63 13 21.7467071935157 -8.74670719351572 64 22 30.6038500506586 -8.60385005065856 65 25 26.4609929078014 -1.46099290780142 66 13 31.03242147923 -18.03242147923 67 16 40.6038500506586 -24.6038500506586 68 13 25.7467071935157 -12.7467071935157 69 16 35.1351317122594 -19.1351317122594 70 17 19.1351317122594 -2.13513171225938 71 9 16.1351317122594 -7.13513171225938 72 17 48.301798378926 -31.3017983789260 73 25 40.9019756838906 -15.9019756838906 74 14 29.0448328267477 -15.0448328267477 75 8 21.330547112462 -13.3305471124620 76 7 30.1876899696049 -23.1876899696049 77 10 26.0448328267477 -16.0448328267477 78 7 30.6162613981763 -23.6162613981763 79 10 40.1876899696049 -30.1876899696049 80 3 25.330547112462 -22.330547112462 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.255075575634882 0.510151151269765 0.744924424365118 18 0.141699077080418 0.283398154160837 0.858300922919582 19 0.151050411313659 0.302100822627319 0.84894958868634 20 0.07975174856508 0.15950349713016 0.92024825143492 21 0.0843841902944365 0.168768380588873 0.915615809705563 22 0.0473000124798995 0.094600024959799 0.9526999875201 23 0.160874120996143 0.321748241992286 0.839125879003857 24 0.885926490520633 0.228147018958734 0.114073509479367 25 0.848585881225292 0.302828237549417 0.151414118774708 26 0.80118219002806 0.397635619943881 0.198817809971940 27 0.787019709116876 0.425960581766247 0.212980290883124 28 0.719815802069943 0.560368395860115 0.280184197930057 29 0.76745059920079 0.465098801598419 0.232549400799209 30 0.715565319637854 0.568869360724291 0.284434680362146 31 0.765095689549464 0.469808620901071 0.234904310450536 32 0.718768080632905 0.56246383873419 0.281231919367095 33 0.72978054993053 0.54043890013894 0.27021945006947 34 0.702078154173809 0.595843691652382 0.297921845826191 35 0.799151954890256 0.401696090219487 0.200848045109743 36 0.820548738853849 0.358902522292303 0.179451261146151 37 0.82726539217651 0.345469215646981 0.172734607823491 38 0.850934371837349 0.298131256325302 0.149065628162651 39 0.916488712065918 0.167022575868164 0.0835112879340822 40 0.927482373366724 0.145035253266552 0.072517626633276 41 0.933277051898339 0.133445896203322 0.0667229481016611 42 0.91462911790366 0.170741764192679 0.0853708820963394 43 0.953810184109267 0.0923796317814669 0.0461898158907334 44 0.95357675767042 0.0928464846591595 0.0464232423295797 45 0.930314010467917 0.139371979064166 0.0696859895320828 46 0.907702801629286 0.184594396741429 0.0922971983707143 47 0.882472075813098 0.235055848373803 0.117527924186902 48 0.83478213451145 0.330435730977098 0.165217865488549 49 0.962269850359773 0.0754602992804548 0.0377301496402274 50 0.98909870864649 0.0218025827070216 0.0109012913535108 51 0.997015342096484 0.00596931580703152 0.00298465790351576 52 0.999803550978705 0.000392898042590588 0.000196449021295294 53 0.99995458921153 9.08215769417236e-05 4.54107884708618e-05 54 0.999946228442393 0.000107543115213212 5.37715576066058e-05 55 0.999917708498355 0.000164583003289346 8.22915016446731e-05 56 0.99982149404688 0.000357011906239281 0.000178505953119641 57 0.999633226123644 0.000733547752711485 0.000366773876355743 58 0.998795714836937 0.00240857032612686 0.00120428516306343 59 0.998237731290312 0.00352453741937499 0.00176226870968749 60 0.999705575039137 0.000588849921726148 0.000294424960863074 61 0.999880016018994 0.000239967962011429 0.000119983981005715 62 0.999310605280909 0.00137878943818254 0.00068939471909127 63 0.996465129142593 0.00706974171481499 0.00353487085740750 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.276595744680851 NOK 5% type I error level 14 0.297872340425532 NOK 10% type I error level 18 0.382978723404255 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/10aqsm1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/10aqsm1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/1egud1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/1egud1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/2egud1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/2egud1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/3egud1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/3egud1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/4oqty1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/4oqty1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/5oqty1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/5oqty1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/6oqty1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/6oqty1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/7hzaj1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/7hzaj1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/8aqsm1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/8aqsm1291134353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/9aqsm1291134353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911342673li956fm190xkok/9aqsm1291134353.ps (open in new window)

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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