*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: Thu, 17 Dec 2009 08:18:52 -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/Dec/17/t1261063182196g0vstixfno51.htm/, Retrieved Thu, 17 Dec 2009 16:19:54 +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/Dec/17/t1261063182196g0vstixfno51.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:

» Textbox « » Textfile « » CSV «

104.2 97.4 103.2 97 112.7 105.4 106.4 102.7 102.6 98.1 110.6 104.5 95.2 87.4 89 89.9 112.5 109.8 116.8 111.7 107.2 98.6 113.6 96.9 101.8 95.1 102.6 97 122.7 112.7 110.3 102.9 110.5 97.4 121.6 111.4 100.3 87.4 100.7 96.8 123.4 114.1 127.1 110.3 124.1 103.9 131.2 101.6 111.6 94.6 114.2 95.9 130.1 104.7 125.9 102.8 119 98.1 133.8 113.9 107.5 80.9 113.5 95.7 134.4 113.2 126.8 105.9 135.6 108.8 139.9 102.3 129.8 99 131 100.7 153.1 115.5 134.1 100.7 144.1 109.9 155.9 114.6 123.3 85.4 128.1 100.5 144.3 114.8 153 116.5 149.9 112.9 150.9 102 141 106 138.9 105.3 157.4 118.8 142.9 106.1 151.7 109.3 161 117.2 138.5 92.5 135.9 104.2 151.5 112.5 164 122.4 159.1 113.3 157 100 142.1 110.7 144.8 112.8 152.1 109.8 154.9 117.3 148.4 109.1 157.3 115.9 145.7 96 133.8 99.8 156.8 116.8

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 Omzet[t] = + 9.62892920428404 + 0.925248827042633Productie[t] + 0.676911417353666t + 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) 9.62892920428404 9.1175 1.0561 0.294776 0.147388 Productie 0.925248827042633 0.091593 10.1018 0 0 t 0.676911417353666 0.040696 16.6334 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.950714721863758 R-squared 0.903858482368483 Adjusted R-squared 0.900945103046316 F-TEST (value) 310.244009590941 F-TEST (DF numerator) 2 F-TEST (DF denominator) 66 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.1418974722568 Sum Squared Residuals 2489.71170094115 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.2 100.42507637559 3.77492362440995 2 103.2 100.731888262127 2.46811173787329 3 112.7 109.180889826639 3.51911017336147 4 106.4 107.359629410977 -0.959629410977073 5 102.6 103.780396223935 -1.18039622393463 6 110.6 110.378900134361 0.221099865638846 7 95.2 95.2340566092858 -0.0340566092857868 8 89 98.224090094246 -9.22409009424604 9 112.5 117.313453169748 -4.8134531697481 10 116.8 119.748337358483 -2.94833735848277 11 107.2 108.304489141578 -1.10448914157793 12 113.6 107.408477552959 6.19152244704086 13 101.8 106.419941081636 -4.61994108163605 14 102.6 108.854825270371 -6.25482527037073 15 122.7 124.058143272294 -1.35814327229373 16 110.3 115.667616184630 -5.3676161846296 17 110.5 111.255659053249 -0.755659053248777 18 121.6 124.886054049199 -3.28605404919932 19 100.3 103.356993617530 -3.05699361752978 20 100.7 112.731244009084 -12.0312440090842 21 123.4 129.414960134275 -6.0149601342754 22 127.1 126.575926008867 0.524073991132925 23 124.1 121.331244933148 2.76875506685211 24 131.2 119.880084048303 11.3199159516965 25 111.6 114.080253676359 -2.48025367635872 26 114.2 115.959988568868 -1.75998856886781 27 130.1 124.779089664197 5.32091033580334 28 125.9 123.698028310169 2.20197168983070 29 119 120.026270240423 -1.02627024042260 30 133.8 135.322113125050 -1.52211312504987 31 107.5 105.465813249997 2.03418675000335 32 113.5 119.836407307581 -6.33640730758128 33 134.4 136.705173198181 -2.30517319818102 34 126.8 130.627768178123 -3.82776817812348 35 135.6 133.987901193901 1.61209880609922 36 139.9 128.650695235477 11.2493047645227 37 129.8 126.274285523590 3.52571447640972 38 131 128.524119946916 2.47588005308356 39 153.1 142.894714004501 10.2052859954989 40 134.1 129.877942781624 4.22205721837622 41 144.1 139.067143407770 5.03285659223033 42 155.9 144.092724312224 11.8072756877763 43 123.3 117.752369979932 5.54763002006752 44 128.1 132.40053868563 -4.30053868562991 45 144.3 146.308508329693 -2.00850832969322 46 153 148.558342753019 4.44165724698063 47 149.9 145.904358393020 3.99564160698044 48 150.9 136.496057595609 14.4039424043915 49 141 140.873964321133 0.126035678867285 50 138.9 140.903201559557 -2.00320155955653 51 157.4 154.070972141986 3.32902785801425 52 142.9 142.997223455898 -0.097223455897966 53 151.7 146.634931119788 5.06506888021192 54 161 154.621308270779 6.37869172922146 55 138.5 132.444573660179 6.05542633982084 56 135.9 143.946896353932 -8.04689635393163 57 151.5 152.303373035739 -0.803373035739158 58 164 162.140247840815 1.8597521591851 59 159.1 154.397394932081 4.7026050679194 60 157 142.768496949767 14.2315030502328 61 142.1 153.345570816477 -11.2455708164771 62 144.8 155.965504770620 -11.1655047706203 63 152.1 153.866669706846 -1.76666970684604 64 154.9 161.482947327019 -6.58294732701945 65 148.4 154.572818362624 -6.17281836262352 66 157.3 161.541421803867 -4.24142180386710 67 145.7 143.805881563072 1.89411843692763 68 133.8 147.998738523188 -14.1987385231880 69 156.8 164.404880000266 -7.60488000026645 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0143893305690357 0.0287786611380713 0.985610669430964 7 0.00835354065314804 0.0167070813062961 0.991646459346852 8 0.0398781778289471 0.0797563556578942 0.960121822171053 9 0.0155930142064860 0.0311860284129721 0.984406985793514 10 0.00766069540344624 0.0153213908068925 0.992339304596554 11 0.0120305937954663 0.0240611875909326 0.987969406204534 12 0.102617572468486 0.205235144936973 0.897382427531514 13 0.066323224300687 0.132646448601374 0.933676775699313 14 0.0457511866692656 0.0915023733385313 0.954248813330734 15 0.0287547847876292 0.0575095695752584 0.97124521521237 16 0.0175017116089338 0.0350034232178676 0.982498288391066 17 0.0136935054241228 0.0273870108482456 0.986306494575877 18 0.00776019529469016 0.0155203905893803 0.99223980470531 19 0.00465688238787641 0.00931376477575282 0.995343117612124 20 0.0133791026128758 0.0267582052257517 0.986620897387124 21 0.0100859129357193 0.0201718258714385 0.98991408706428 22 0.0131216580222200 0.0262433160444399 0.98687834197778 23 0.0226007195121556 0.0452014390243112 0.977399280487844 24 0.162142593856764 0.324285187713528 0.837857406143236 25 0.131225007166379 0.262450014332757 0.868774992833621 26 0.105027197686373 0.210054395372746 0.894972802313627 27 0.107884318262873 0.215768636525745 0.892115681737127 28 0.0839900488958161 0.167980097791632 0.916009951104184 29 0.064856205302827 0.129712410605654 0.935143794697173 30 0.0520748860760313 0.104149772152063 0.947925113923969 31 0.0398860532497292 0.0797721064994584 0.960113946750271 32 0.063510090738119 0.127020181476238 0.936489909261881 33 0.0601047791366435 0.120209558273287 0.939895220863357 34 0.0760007153363639 0.152001430672728 0.923999284663636 35 0.0692918144645877 0.138583628929175 0.930708185535412 36 0.130526748030351 0.261053496060701 0.86947325196965 37 0.110016244805697 0.220032489611395 0.889983755194303 38 0.0932698899673663 0.186539779934733 0.906730110032634 39 0.107707416587053 0.215414833174106 0.892292583412947 40 0.0841529588858982 0.168305917771796 0.915847041114102 41 0.0623614670708201 0.124722934141640 0.93763853292918 42 0.0785870506989923 0.157174101397985 0.921412949301008 43 0.0593499468359859 0.118699893671972 0.940650053164014 44 0.111067750335679 0.222135500671357 0.888932249664321 45 0.127086972855698 0.254173945711396 0.872913027144302 46 0.0928352674517866 0.185670534903573 0.907164732548213 47 0.0664152175101398 0.132830435020280 0.93358478248986 48 0.115347051367086 0.230694102734171 0.884652948632914 49 0.0996198251946698 0.199239650389340 0.90038017480533 50 0.114637037162899 0.229274074325799 0.8853629628371 51 0.080327677003244 0.160655354006488 0.919672322996756 52 0.0721311333421328 0.144262266684266 0.927868866657867 53 0.0477290259935526 0.0954580519871053 0.952270974006447 54 0.0364556134107228 0.0729112268214457 0.963544386589277 55 0.0227505148104436 0.0455010296208871 0.977249485189556 56 0.0954338012435351 0.190867602487070 0.904566198756465 57 0.0789786623187328 0.157957324637466 0.921021337681267 58 0.0534126031780334 0.106825206356067 0.946587396821967 59 0.0404258224840095 0.080851644968019 0.95957417751599 60 0.331366124187047 0.662732248374093 0.668633875812954 61 0.364797921131135 0.729595842262271 0.635202078868865 62 0.467402747629389 0.934805495258778 0.532597252370611 63 0.316714974485999 0.633429948971997 0.683285025514001 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0172413793103448 NOK 5% type I error level 14 0.241379310344828 NOK 10% type I error level 21 0.362068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/10zcuw1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/10zcuw1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/1rb5b1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/1rb5b1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/25kfg1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/25kfg1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/39d3s1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/39d3s1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/4b0xw1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/4b0xw1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/5w6zu1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/5w6zu1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/6t6tf1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/6t6tf1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/7jheb1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/7jheb1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/8rshl1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/8rshl1261063126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/9jvqc1261063126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063182196g0vstixfno51/9jvqc1261063126.ps (open in new window)

Parameters (Session):

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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