loiqueverhasselt

*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, 23 Nov 2008 13:07:02 -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/Nov/23/t1227470940j9gr1hk1x1g9ye9.htm/, Retrieved Sun, 23 Nov 2008 20:09:01 +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/Nov/23/t1227470940j9gr1hk1x1g9ye9.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)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Seatbelt Q3

Dataseries X:

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99.4 0 97.5 0 94.6 0 92.6 0 92.5 0 89.8 0 88.8 0 87.4 0 85.2 0 83.1 0 84.7 0 84.8 0 85.8 0 86.3 0 89 0 89 0 89.3 0 91.9 0 94.9 0 94.4 0 96.8 0 96.9 0 98 0 97.9 0 100.9 0 103.9 0 103.1 0 102.5 0 104.3 0 102.6 0 101.7 0 102.8 0 105.4 0 110.9 1 113.5 1 116.3 1 124 1 128.8 1 133.5 1 132.6 1 128.4 1 127.3 1 126.7 1 123.3 1 123.2 1 124.4 1 128.2 1 128.7 1 135.7 1 139 1 145.4 1 142.4 1 137.7 1 137 1 137.1 1 139.3 1 139.6 1 140.4 1 142.3 1 148.3 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 9 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Grondstofprijzen[t] = + 83.0241658485503 + 16.9368028739519Wet[t] + 0.673801295896329t + 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) 83.0241658485503 1.70383 48.728 0 0 Wet 16.9368028739519 2.862575 5.9166 0 0 t 0.673801295896329 0.082233 8.1938 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.962708348317625 R-squared 0.92680736392045 Adjusted R-squared 0.924239201250991 F-TEST (value) 360.883434270959 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.59544543012153 Sum Squared Residuals 1784.61354500367 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.4 83.6979671444467 15.7020328555533 2 97.5 84.371768440343 13.1282315596571 3 94.6 85.0455697362393 9.55443026376071 4 92.6 85.7193710321356 6.88062896786439 5 92.5 86.393172328032 6.10682767196807 6 89.8 87.0669736239283 2.73302637607174 7 88.8 87.7407749198246 1.05922508017541 8 87.4 88.414576215721 -1.01457621572091 9 85.2 89.0883775116173 -3.88837751161725 10 83.1 89.7621788075136 -6.66217880751358 11 84.7 90.4359801034099 -5.7359801034099 12 84.8 91.1097813993062 -6.30978139930624 13 85.8 91.7835826952026 -5.98358269520257 14 86.3 92.4573839910989 -6.1573839910989 15 89 93.1311852869952 -4.13118528699522 16 89 93.8049865828915 -4.80498658289155 17 89.3 94.4787878787879 -5.17878787878788 18 91.9 95.1525891746842 -3.2525891746842 19 94.9 95.8263904705805 -0.926390470580529 20 94.4 96.5001917664769 -2.10019176647686 21 96.8 97.1739930623732 -0.373993062373196 22 96.9 97.8477943582695 -0.947794358269515 23 98 98.5215956541658 -0.52159565416585 24 97.9 99.1953969500622 -1.29539695006217 25 100.9 99.8691982459585 1.0308017540415 26 103.9 100.542999541855 3.35700045814517 27 103.1 101.216800837751 1.88319916224883 28 102.5 101.890602133647 0.609397866352507 29 104.3 102.564403429544 1.73559657045618 30 102.6 103.238204725440 -0.638204725440157 31 101.7 103.912006021336 -2.21200602133648 32 102.8 104.585807317233 -1.78580731723281 33 105.4 105.259608613129 0.140391386870868 34 110.9 122.870212782977 -11.9702127829774 35 113.5 123.544014078874 -10.0440140788737 36 116.3 124.21781537477 -7.91781537477001 37 124 124.891616670666 -0.891616670666342 38 128.8 125.565417966563 3.23458203343734 39 133.5 126.239219262459 7.260780737541 40 132.6 126.913020558355 5.68697944164467 41 128.4 127.586821854252 0.813178145748348 42 127.3 128.260623150148 -0.960623150147989 43 126.7 128.934424446044 -2.23442444604431 44 123.3 129.608225741941 -6.30822574194065 45 123.2 130.282027037837 -7.08202703783697 46 124.4 130.955828333733 -6.5558283337333 47 128.2 131.629629629630 -3.42962962962964 48 128.7 132.303430925526 -3.60343092552597 49 135.7 132.977232221422 2.7227677785777 50 139 133.651033517319 5.34896648268138 51 145.4 134.324834813215 11.0751651867851 52 142.4 134.998636109111 7.40136389088873 53 137.7 135.672437405008 2.02756259499239 54 137 136.346238700904 0.65376129909607 55 137.1 137.020039996800 0.0799600031997352 56 139.3 137.693841292697 1.60615870730342 57 139.6 138.367642588593 1.23235741140708 58 140.4 139.041443884489 1.35855611551076 59 142.3 139.715245180386 2.58475481961444 60 148.3 140.389046476282 7.9109535237181 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0143376599812831 0.0286753199625663 0.985662340018717 7 0.00399780860002994 0.00799561720005988 0.99600219139997 8 0.00109484343845098 0.00218968687690195 0.998905156561549 9 0.000198446545481716 0.000396893090963431 0.999801553454518 10 3.68051753433521e-05 7.36103506867043e-05 0.999963194824657 11 0.000349026392542975 0.000698052785085949 0.999650973607457 12 0.00158684067462235 0.00317368134924471 0.998413159325378 13 0.00880563238129278 0.0176112647625856 0.991194367618707 14 0.0252468550155917 0.0504937100311834 0.974753144984408 15 0.112503832506382 0.225007665012763 0.887496167493618 16 0.188095257325865 0.37619051465173 0.811904742674135 17 0.243947606037828 0.487895212075655 0.756052393962172 18 0.369122411604586 0.738244823209171 0.630877588395414 19 0.560788204870745 0.87842359025851 0.439211795129255 20 0.619877373045999 0.760245253908002 0.380122626954001 21 0.700412364579128 0.599175270841743 0.299587635420872 22 0.72279164047169 0.554416719056621 0.277208359528311 23 0.734800512962883 0.530398974074235 0.265199487037117 24 0.717034103320746 0.565931793358508 0.282965896679254 25 0.735125149279002 0.529749701441997 0.264874850720998 26 0.792624370616633 0.414751258766733 0.207375629383367 27 0.792674889316896 0.414650221366207 0.207325110683104 28 0.76291640627617 0.474167187447661 0.237083593723830 29 0.746850868695207 0.506298262609586 0.253149131304793 30 0.691489529577266 0.617020940845468 0.308510470422734 31 0.622066038051068 0.755867923897863 0.377933961948932 32 0.551199136552079 0.897601726895841 0.448800863447921 33 0.488396713569335 0.97679342713867 0.511603286430665 34 0.512131413447091 0.975737173105819 0.487868586552909 35 0.535460403113358 0.929079193773285 0.464539596886642 36 0.551934653769313 0.896130692461375 0.448065346230687 37 0.549808983315733 0.900382033368534 0.450191016684267 38 0.605910307180557 0.788179385638885 0.394089692819442 39 0.793303025761279 0.413393948477442 0.206696974238721 40 0.89025957541371 0.219480849172580 0.109740424586290 41 0.88085150198355 0.238296996032900 0.119148498016450 42 0.849024570889234 0.301950858221532 0.150975429110766 43 0.796866920008654 0.406266159982692 0.203133079991346 44 0.74997083959867 0.500058320802659 0.250029160401330 45 0.743750678361578 0.512498643276844 0.256249321638422 46 0.781120783118123 0.437758433763754 0.218879216881877 47 0.783390629648723 0.433218740702555 0.216609370351277 48 0.874799121188762 0.250401757622476 0.125200878811238 49 0.842470358070006 0.315059283859988 0.157529641929994 50 0.78300480423153 0.433990391536941 0.216995195768470 51 0.91795713188106 0.164085736237879 0.0820428681189397 52 0.98161571055947 0.0367685788810617 0.0183842894405309 53 0.973714888216482 0.0525702235670354 0.0262851117835177 54 0.938777129517767 0.122445740964466 0.0612228704822329 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.122448979591837 NOK 5% type I error level 9 0.183673469387755 NOK 10% type I error level 11 0.224489795918367 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/10hzjj1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/10hzjj1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/1yfnn1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/1yfnn1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/2emi81227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/2emi81227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/3rxjj1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/3rxjj1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/48iq51227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/48iq51227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/5h4rx1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/5h4rx1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/6oktz1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/6oktz1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/7hvq81227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/7hvq81227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/81vut1227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/81vut1227470806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/9g2p71227470806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227470940j9gr1hk1x1g9ye9/9g2p71227470806.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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