autoregressie

*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: Fri, 24 Dec 2010 13:37:13 +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/Dec/24/t12931978014vy2mnfow0z1ld0.htm/, Retrieved Fri, 24 Dec 2010 14:36:44 +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/Dec/24/t12931978014vy2mnfow0z1ld0.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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98,1 102,8 104,7 95,9 94,6 15607,4 -7,5 15172,6 113,9 98,1 102,8 104,7 95,9 17160,9 -7,8 16858,9 80,9 113,9 98,1 102,8 104,7 14915,8 -7,7 14143,5 95,7 80,9 113,9 98,1 102,8 13768 -6,6 14731,8 113,2 95,7 80,9 113,9 98,1 17487,5 -4,2 16471,6 105,9 113,2 95,7 80,9 113,9 16198,1 -2,0 15214 108,8 105,9 113,2 95,7 80,9 17535,2 -0,7 17637,4 102,3 108,8 105,9 113,2 95,7 16571,8 0,1 17972,4 99 102,3 108,8 105,9 113,2 16198,9 0,9 16896,2 100,7 99 102,3 108,8 105,9 16554,2 2,1 16698 115,5 100,7 99 102,3 108,8 19554,2 3,5 19691,6 100,7 115,5 100,7 99 102,3 15903,8 4,9 15930,7 109,9 100,7 115,5 100,7 99 18003,8 5,7 17444,6 114,6 109,9 100,7 115,5 100,7 18329,6 6,2 17699,4 85,4 114,6 109,9 100,7 115,5 16260,7 6,5 15189,8 100,5 85,4 114,6 109,9 100,7 14851,9 6,5 15672,7 114,8 100,5 85,4 114,6 109,9 18174,1 6,3 17180,8 116,5 114,8 100,5 85,4 114,6 18406,6 6,2 17664,9 112,9 116,5 114,8 100,5 85,4 18466,5 6,4 17862,9 102 112,9 116,5 114,8 100,5 16016,5 6,3 16162,3 106 102 112,9 116,5 114,8 17428,5 5,8 17463 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation y[t] = + 22.6729257443683 + 0.0174685988429137y1[t] + 0.131437577783892y2[t] + 0.300291730285133y3[t] -0.0319892632868462y4[t] + 0.00451269410751495uitvoer[t] + 0.00879190661155511ondernemersvertrouwen[t] -0.00216823737255318invoer[t] -3.25700323627328M1[t] + 0.633033557802868M2[t] -19.234836827881M3[t] -1.94318071962852M4[t] + 2.79138694440754M5[t] + 10.1653722367121M6[t] + 0.0331127764477652M7[t] -7.23163967356985M8[t] -6.33995057650054M9[t] -3.28869162373367M10[t] + 5.43507600211532M11[t] + 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) 22.6729257443683 10.888402 2.0823 0.04303 0.021515 y1 0.0174685988429137 0.116318 0.1502 0.881294 0.440647 y2 0.131437577783892 0.093905 1.3997 0.168462 0.084231 y3 0.300291730285133 0.092809 3.2356 0.002279 0.001139 y4 -0.0319892632868462 0.104465 -0.3062 0.760849 0.380425 uitvoer 0.00451269410751495 0.001168 3.864 0.000355 0.000178 ondernemersvertrouwen 0.00879190661155511 0.069271 0.1269 0.899569 0.449784 invoer -0.00216823737255318 0.000911 -2.3797 0.021629 0.010814 M1 -3.25700323627328 2.151944 -1.5135 0.137141 0.06857 M2 0.633033557802868 2.428659 0.2607 0.79555 0.397775 M3 -19.234836827881 2.184607 -8.8047 0 0 M4 -1.94318071962852 4.195265 -0.4632 0.645465 0.322732 M5 2.79138694440754 3.584662 0.7787 0.44023 0.220115 M6 10.1653722367121 2.918588 3.483 0.001116 0.000558 M7 0.0331127764477652 2.594757 0.0128 0.989875 0.494937 M8 -7.23163967356985 2.897468 -2.4958 0.016298 0.008149 M9 -6.33995057650054 3.823156 -1.6583 0.104212 0.052106 M10 -3.28869162373367 3.52406 -0.9332 0.355692 0.177846 M11 5.43507600211532 2.718687 1.9992 0.051652 0.025826 Multiple Linear Regression - Regression Statistics Multiple R 0.971939318067388 R-squared 0.9446660380053 Adjusted R-squared 0.92253245320742 F-TEST (value) 42.6802095833921 F-TEST (DF numerator) 18 F-TEST (DF denominator) 45 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.82736393509048 Sum Squared Residuals 359.729406965265 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.1 98.2126858457704 -0.11268584577039 2 113.9 107.723404054436 6.17659594556361 3 80.9 82.4187825217305 -1.51878252173045 4 95.7 93.4145238194512 2.28547618054877 5 113.2 111.998912483449 1.20108751655118 6 105.9 108.136266679403 -2.23626667940299 7 108.8 106.467453676597 2.33254632340276 8 102.3 98.0086745310502 4.29132546894982 9 99 97.0737519255512 1.92624807444879 10 100.7 102.361043037936 -1.66104303793599 11 115.5 115.695653758072 -0.19565375807171 12 100.7 101.653218436107 -0.953218436106535 13 109.9 106.900213191253 2.99978680874656 14 114.6 114.317785615123 0.282214384877284 15 85.4 86.9312178978523 -1.53121789785226 16 100.5 100.162147264872 0.337852735128445 17 114.8 114.159978611363 0.640021388636504 18 116.5 114.848282707168 1.65171729283243 19 112.9 111.936526601124 0.963473398875858 20 102 101.273789666642 0.726210333358164 21 106 105.100946066386 0.89905393361392 22 105.3 105.967992678265 -0.667992678264846 23 118.8 118.098726514335 0.70127348566528 24 106.1 108.496166729084 -2.39616672908354 25 109.3 108.57889835095 0.721101649049747 26 117.2 118.496871933851 -1.29687193385051 27 92.5 91.264706675504 1.23529332449606 28 104.2 104.719301259494 -0.519301259494098 29 112.5 115.13701492345 -2.63701492345026 30 122.4 121.146187942434 1.2538120575657 31 113.3 111.168196872299 2.13180312770121 32 100 99.2306439637765 0.769356036223451 33 110.7 106.956991636316 3.74300836368439 34 112.8 109.065162422447 3.7348375775528 35 109.8 113.845530816836 -4.045530816836 36 117.3 115.630294788806 1.66970521119373 37 109.1 110.775007217749 -1.6750072177491 38 115.9 117.316091535858 -1.41609153585804 39 96 98.2915522879947 -2.29155228799473 40 99.8 99.9826234123155 -0.182623412315491 41 116.8 116.78335635834 0.0166436416596489 42 115.7 117.640292328459 -1.94029232845949 43 99.4 100.110975002563 -0.71097500256337 44 94.3 94.6518241568002 -0.351824156800161 45 91 91.6426872839394 -0.642687283939394 46 93.2 92.3292790927532 0.870720907246779 47 103.1 101.591124995163 1.50887500483729 48 94.1 94.757231725179 -0.657231725179031 49 91.8 91.5351055879438 0.264894412056175 50 102.7 102.295739476698 0.404260523302348 51 82.6 77.682234761797 4.91776523820299 52 89.1 87.1720980303419 1.92790196965811 53 104.5 103.720737623397 0.779262376602925 54 105.1 103.828970342536 1.27102965746434 55 95.1 99.8168478474165 -4.71684784741646 56 88.7 94.1350676817313 -5.43506768173127 57 86.3 92.2256230878077 -5.92562308780771 58 91.8 94.0765227685987 -2.27652276859874 59 111.5 109.468963915595 2.03103608440514 60 99.7 97.3630883208246 2.33691167917537 61 97.5 99.698089806333 -2.19808980633299 62 111.7 115.850107384035 -4.15010738403469 63 86.2 87.0115058551216 -0.811505855121604 64 95.4 99.2493062135257 -3.84930621352573 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.073211397064447 0.146422794128894 0.926788602935553 23 0.0225565926229421 0.0451131852458841 0.977443407377058 24 0.0435311834372226 0.0870623668744451 0.956468816562777 25 0.0196832308989213 0.0393664617978426 0.980316769101079 26 0.0347149777819045 0.069429955563809 0.965285022218096 27 0.0150868967437901 0.0301737934875801 0.98491310325621 28 0.00891700690103692 0.0178340138020738 0.991082993098963 29 0.00513539169437615 0.0102707833887523 0.994864608305624 30 0.00299041675305313 0.00598083350610626 0.997009583246947 31 0.00370764570975998 0.00741529141951997 0.99629235429024 32 0.0022165395733576 0.00443307914671519 0.997783460426642 33 0.00931990615726703 0.0186398123145341 0.990680093842733 34 0.0664838237187395 0.132967647437479 0.93351617628126 35 0.0530418636280755 0.106083727256151 0.946958136371924 36 0.145631554223126 0.291263108446252 0.854368445776874 37 0.125610751824047 0.251221503648093 0.874389248175953 38 0.228472458789349 0.456944917578699 0.77152754121065 39 0.175710305474609 0.351420610949219 0.82428969452539 40 0.102237990201056 0.204475980402113 0.897762009798944 41 0.0578656788068161 0.115731357613632 0.942134321193184 42 0.031411001128021 0.062822002256042 0.968588998871979 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.142857142857143 NOK 5% type I error level 9 0.428571428571429 NOK 10% type I error level 12 0.571428571428571 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/104joa1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/104joa1293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/18rq21293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/18rq21293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/28rq21293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/28rq21293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/38rq21293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/38rq21293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/4i07n1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/4i07n1293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/5i07n1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/5i07n1293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/6i07n1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/6i07n1293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/7ta781293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/7ta781293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/84joa1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/84joa1293197823.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/94joa1293197823.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931978014vy2mnfow0z1ld0/94joa1293197823.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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