WS 7.4

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

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 09:06:40 -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/Nov/20/t1258734866sp3i3mfxwo20ntw.htm/, Retrieved Fri, 20 Nov 2009 17:34:38 +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/Nov/20/t1258734866sp3i3mfxwo20ntw.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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8 6,5 8,3 9,3 9,8 9,9 8,5 6,6 8 8,3 9,3 9,8 10,4 7,6 8,5 8 8,3 9,3 11,1 8 10,4 8,5 8 8,3 10,9 8,1 11,1 10,4 8,5 8 10 7,7 10,9 11,1 10,4 8,5 9,2 7,5 10 10,9 11,1 10,4 9,2 7,6 9,2 10 10,9 11,1 9,5 7,8 9,2 9,2 10 10,9 9,6 7,8 9,5 9,2 9,2 10 9,5 7,8 9,6 9,5 9,2 9,2 9,1 7,5 9,5 9,6 9,5 9,2 8,9 7,5 9,1 9,5 9,6 9,5 9 7,1 8,9 9,1 9,5 9,6 10,1 7,5 9 8,9 9,1 9,5 10,3 7,5 10,1 9 8,9 9,1 10,2 7,6 10,3 10,1 9 8,9 9,6 7,7 10,2 10,3 10,1 9 9,2 7,7 9,6 10,2 10,3 10,1 9,3 7,9 9,2 9,6 10,2 10,3 9,4 8,1 9,3 9,2 9,6 10,2 9,4 8,2 9,4 9,3 9,2 9,6 9,2 8,2 9,4 9,4 9,3 9,2 9 8,2 9,2 9,4 9,4 9,3 9 7,9 9 9,2 9,4 9,4 9 7,3 9 9 9,2 9,4 9,8 6,9 9 9 9 9,2 10 6,6 9,8 9 9 9 9,8 6,7 10 9,8 9 9 9,3 6,9 9,8 10 9,8 9 9 7 9,3 9,8 10 9,8 9 7,1 9 9,3 9,8 10 9,1 7,2 9 9 9,3 9,8 9,1 7,1 9,1 9 9 9,3 9,1 6,9 9,1 9,1 9 9 9,2 7 9,1 9,1 9,1 9 8,8 6,8 9,2 9,1 9,1 9,1 8,3 6,4 8,8 9,2 9,1 9,1 8,4 6,7 8,3 8,8 9,2 9,1 8,1 6,6 8,4 8,3 8,8 9,2 7,7 6,4 8,1 8,4 8,3 8,8 7,9 6,3 7,7 8,1 8,4 8,3 7,9 6,2 7,9 7,7 8,1 8,4 8 6,5 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WLMan[t] = + 4.74589368190231 + 0.252977925543289WLVrouw[t] + 0.286731211867211`Yt-1`[t] -0.140281941699051`Yt-2`[t] + 0.0307676256758232`Yt-3`[t] -0.0942370090570313`Yt-4`[t] -0.211902844334609M1[t] -0.403473617050317M2[t] -0.249269058051509M3[t] -0.592689012093239M4[t] -0.528197626041862M5[t] -0.515370882802887M6[t] -0.326476270103165M7[t] -0.038971385595141M8[t] + 0.131030026188113M9[t] + 0.104218769786131M10[t] + 0.00936951584773028M11[t] -0.0130676658698532t + 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) 4.74589368190231 1.484975 3.1959 0.002804 0.001402 WLVrouw 0.252977925543289 0.358825 0.705 0.485099 0.242549 `Yt-1` 0.286731211867211 0.576107 0.4977 0.62156 0.31078 `Yt-2` -0.140281941699051 0.572234 -0.2451 0.807661 0.403831 `Yt-3` 0.0307676256758232 0.555972 0.0553 0.956157 0.478079 `Yt-4` -0.0942370090570313 0.316676 -0.2976 0.767643 0.383821 M1 -0.211902844334609 0.286808 -0.7388 0.464549 0.232275 M2 -0.403473617050317 0.291209 -1.3855 0.173975 0.086988 M3 -0.249269058051509 0.414358 -0.6016 0.551027 0.275513 M4 -0.592689012093239 0.40213 -1.4739 0.148752 0.074376 M5 -0.528197626041862 0.394484 -1.339 0.188539 0.09427 M6 -0.515370882802887 0.35084 -1.469 0.150074 0.075037 M7 -0.326476270103165 0.292308 -1.1169 0.271054 0.135527 M8 -0.038971385595141 0.308159 -0.1265 0.900031 0.450015 M9 0.131030026188113 0.325835 0.4021 0.689837 0.344918 M10 0.104218769786131 0.328711 0.3171 0.752939 0.376469 M11 0.00936951584773028 0.298661 0.0314 0.975137 0.487569 t -0.0130676658698532 0.007412 -1.763 0.085946 0.042973 Multiple Linear Regression - Regression Statistics Multiple R 0.85370008363738 R-squared 0.728803832802469 Adjusted R-squared 0.607479231687784 F-TEST (value) 6.00705731654168 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 2.25105635887068e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.416181974052504 Sum Squared Residuals 6.58188254999708 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.5 6.98856991869931 -0.488569918699314 2 6.6 6.95872290909206 -0.358722909092058 3 7.6 7.78231892804928 -0.182318928049276 4 8 8.16257090907046 -0.162570909070454 5 8.1 8.14123011877719 -0.0412301187771918 6 7.7 7.76910544585012 -0.0691054458501248 7 7.5 7.3550353706694 0.144964629330602 8 7.6 7.45422193586786 0.145778064132140 9 7.8 7.79043115150665 0.00956884849334667 10 7.8 7.92206859295998 -0.122068592959981 11 7.8 7.85083202652003 -0.0508320265200283 12 7.5 7.69373264693125 -0.19373264693125 13 7.5 7.29230792089162 0.207692079108376 14 7.1 7.09923334569328 0.000766654306715076 15 7.5 7.57249211708176 -0.0724921170817615 16 7.5 7.59951749965051 -0.0995174996505113 17 7.6 7.55060369816118 0.049396301838819 18 7.7 7.3662671980155 0.3337328019845 19 7.7 7.18538525685006 0.514614743149939 20 7.9 7.43267278393612 0.467327216063881 21 8.1 7.6906533457704 0.4093466542296 22 8.2 7.70965450567927 0.490345494320729 23 8.2 7.57788537278285 0.622114627217151 24 8.2 7.44115942524504 0.758840574754956 25 7.9 7.17747536010125 0.722524639898754 26 7.3 6.99473978472033 0.305260215279669 27 6.9 7.35095289496016 -0.450952894960158 28 6.6 7.29329323146241 -0.693293231462409 29 6.7 7.23924205554948 -0.539242055549475 30 6.9 7.05172363997436 -0.151723639974359 31 7 6.96711190943698 0.0328880905630153 32 7.1 7.20066980841795 -0.100669808417948 33 7.2 7.42844951836889 -0.228449518368886 34 7.1 7.45513193410954 -0.355131934109542 35 6.9 7.36145792284849 -0.461457922848491 36 7 7.36739529625282 -0.367395296252819 37 6.8 7.06048303611206 -0.260483036112060 38 6.4 6.60063495583807 -0.200634955838065 39 6.7 6.68289357483495 0.0171064251650543 40 6.6 6.32759591812059 0.27240408187941 41 6.4 6.20009190113963 0.199908098860370 42 6.3 6.22803392847634 0.0719660715236602 43 6.2 6.49866590575082 -0.298665905750820 44 6.5 6.78630858105029 -0.28630858105029 45 6.8 6.99046598435406 -0.19046598435406 46 6.8 6.8131449672512 -0.0131449672512064 47 6.4 6.50982467784863 -0.109824677848631 48 6.1 6.29771263157089 -0.197712631570886 49 5.8 5.98116376419576 -0.181163764195756 50 6.1 5.84666900465626 0.253330995343740 51 7.2 6.51134248507386 0.688657514926141 52 7.3 6.61702244169604 0.682977558303964 53 6.9 6.56883222637252 0.331167773627478 54 6.1 6.28486978768368 -0.184869787683677 55 5.8 6.19380155729274 -0.393801557292736 56 6.2 6.42612689072778 -0.226126890727784 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.00750110273949797 0.0150022054789959 0.992498897260502 22 0.00798305414028374 0.0159661082805675 0.992016945859716 23 0.00740345469345923 0.0148069093869185 0.99259654530654 24 0.0222575613231755 0.0445151226463509 0.977742438676825 25 0.0294047426895619 0.0588094853791237 0.970595257310438 26 0.0166854491952308 0.0333708983904616 0.98331455080477 27 0.0607729768606942 0.121545953721388 0.939227023139306 28 0.56548563579269 0.869028728414619 0.434514364207309 29 0.921740491745673 0.156519016508654 0.078259508254327 30 0.90200668725924 0.195986625481521 0.0979933127407606 31 0.992898209780781 0.0142035804384374 0.00710179021921872 32 0.992791368309498 0.0144172633810042 0.00720863169050208 33 0.986118559304374 0.0277628813912513 0.0138814406956256 34 0.983446379776874 0.0331072404462522 0.0165536202231261 35 0.978707988944746 0.0425840221105079 0.0212920110552540 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 10 0.666666666666667 NOK 10% type I error level 11 0.733333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/106x7r1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/106x7r1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/1lhzd1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/1lhzd1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/25q9c1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/25q9c1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/37d3y1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/37d3y1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/4gyf71258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/4gyf71258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/5ctgr1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/5ctgr1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/6fbcn1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/6fbcn1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/73sbq1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/73sbq1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/8su2l1258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/8su2l1258733196.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/9lap21258733196.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734866sp3i3mfxwo20ntw/9lap21258733196.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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