paper multiple regressie 4 vertragingen

*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: Mon, 28 Dec 2009 07:18:34 -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/28/t1262009976ln7t2g7189j7g72.htm/, Retrieved Mon, 28 Dec 2009 15:19:49 +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/28/t1262009976ln7t2g7189j7g72.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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97.7 0 98.3 91.6 104.6 111.6 106.3 0 97.7 98.3 91.6 104.6 102.3 0 106.3 97.7 98.3 91.6 106.6 0 102.3 106.3 97.7 98.3 108.1 0 106.6 102.3 106.3 97.7 93.8 0 108.1 106.6 102.3 106.3 88.2 0 93.8 108.1 106.6 102.3 108.9 0 88.2 93.8 108.1 106.6 114.2 0 108.9 88.2 93.8 108.1 102.5 0 114.2 108.9 88.2 93.8 94.2 0 102.5 114.2 108.9 88.2 97.4 0 94.2 102.5 114.2 108.9 98.5 0 97.4 94.2 102.5 114.2 106.5 0 98.5 97.4 94.2 102.5 102.9 0 106.5 98.5 97.4 94.2 97.1 0 102.9 106.5 98.5 97.4 103.7 0 97.1 102.9 106.5 98.5 93.4 0 103.7 97.1 102.9 106.5 85.8 0 93.4 103.7 97.1 102.9 108.6 0 85.8 93.4 103.7 97.1 110.2 0 108.6 85.8 93.4 103.7 101.2 0 110.2 108.6 85.8 93.4 101.2 0 101.2 110.2 108.6 85.8 96.9 0 101.2 101.2 110.2 108.6 99.4 0 96.9 101.2 101.2 110.2 118.7 0 99.4 96.9 101.2 101.2 108.0 0 118.7 99.4 96.9 101.2 101.2 0 108.0 118.7 99.4 96.9 119.9 0 101.2 108.0 118.7 99.4 94.8 0 119.9 101.2 108.0 118.7 95.3 0 94.8 119.9 101.2 108.0 118.0 0 95.3 94.8 119.9 101.2 115.9 0 118.0 95.3 94.8 119.9 111. 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 6.22104629349978 -2.97334629831363x[t] + 0.303288878357398y1[t] + 0.430522582438265y2[t] + 0.493879466224932y3[t] -0.342943786844925y4[t] + 8.26140347753671M1[t] + 20.3893106924825M2[t] + 5.65105926012792M3[t] + 1.25757816772481M4[t] + 8.94165957078786M5[t] -5.91154539630798M6[t] -7.84346025105621M7[t] + 17.2355755305452M8[t] + 26.470150117162M9[t] + 2.61838399538435M10[t] -13.4368118308086M11[t] + 0.0484069423384669t + 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) 6.22104629349978 7.905072 0.787 0.433813 0.216907 x -2.97334629831363 1.823726 -1.6304 0.107273 0.053636 y1 0.303288878357398 0.110591 2.7424 0.007644 0.003822 y2 0.430522582438265 0.099195 4.3402 4.4e-05 2.2e-05 y3 0.493879466224932 0.100211 4.9284 5e-06 2e-06 y4 -0.342943786844925 0.111269 -3.0821 0.002887 0.001443 M1 8.26140347753671 2.582991 3.1984 0.002035 0.001017 M2 20.3893106924825 2.874936 7.0921 0 0 M3 5.65105926012792 3.903907 1.4475 0.151969 0.075985 M4 1.25757816772481 3.539568 0.3553 0.723382 0.361691 M5 8.94165957078786 2.879657 3.1051 0.002695 0.001348 M6 -5.91154539630798 3.17459 -1.8621 0.066553 0.033276 M7 -7.84346025105621 2.865403 -2.7373 0.007753 0.003877 M8 17.2355755305452 2.631435 6.5499 0 0 M9 26.470150117162 3.958334 6.6872 0 0 M10 2.61838399538435 4.949987 0.529 0.598411 0.299205 M11 -13.4368118308086 3.875828 -3.4668 0.000881 0.00044 t 0.0484069423384669 0.037832 1.2795 0.204711 0.102356 Multiple Linear Regression - Regression Statistics Multiple R 0.949649820599297 R-squared 0.901834781764276 Adjusted R-squared 0.879283312710123 F-TEST (value) 39.9900680349782 F-TEST (DF numerator) 17 F-TEST (DF denominator) 74 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.55047197789962 Sum Squared Residuals 1532.30284640208 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.7 97.1672875624866 0.532712437513421 2 106.3 108.026303142083 -1.72630314208309 3 102.3 103.453691109169 -1.15369110916869 4 106.6 99.0039046030477 7.59609539695233 5 108.1 110.771574477274 -2.67157447727433 6 93.8 93.3481224427715 0.451877557228548 7 88.2 91.2688242956552 -3.06882429565520 8 108.9 107.807537287831 1.09246271216933 9 114.2 113.380780089846 0.81921991015409 10 102.5 102.235040563196 0.264959436804194 11 94.2 97.1053316466703 -2.90533164667025 12 97.4 98.5547632982254 -1.15476329822538 13 98.5 96.6657688694968 1.83423113050318 14 106.5 110.466615793195 -3.96661579319535 15 102.9 103.103504893453 -0.203504893453175 16 97.1 100.556618735752 -3.4566187357517 17 103.7 108.553947854173 -4.8539478541726 18 93.4 88.732309075263 4.66769092473705 19 85.8 84.9364714884017 0.863528511598348 20 108.6 108.573214578496 0.026785421503674 21 110.2 114.148823412176 -3.94882341217615 22 101.2 100.425478378894 0.774521621105549 23 101.2 96.2447503316745 4.95524966832547 24 96.9 98.8263546687728 -1.92635466877279 25 99.4 100.838397656735 -1.43839765673487 26 118.7 115.008180987032 3.6918190129676 27 108 105.124436600643 2.87556339935744 28 101.2 108.552614242208 -7.35261424220776 29 119.9 118.290660813718 1.60933918628157 30 94.8 94.3264858789504 0.473514121049625 31 95.3 93.1923175602766 2.10768243972339 32 118 119.232851673146 -1.23285167314649 33 115.9 116.806328615788 -0.906328615787921 34 111.4 110.993754196067 0.406245803933119 35 108.2 103.757659926367 4.44234007363271 36 108.8 105.513031827346 3.28696817265369 37 109.5 111.124867664796 -1.62486766479561 38 124.8 123.734630335275 1.06536966472463 39 115.3 115.380219289473 -0.0802192894729799 40 109.5 114.880845660569 -5.380845660569 41 124.2 124.080589160784 0.119410839215884 42 92.9 101.298211801874 -8.39821180187434 43 98.4 96.6439090296427 1.75609097035732 44 120.9 119.213185871438 1.68681412856235 45 111.7 117.188340407384 -5.48834040738406 46 116.1 113.731959244401 2.36804075559896 47 109.4 104.324930829301 5.07506917069902 48 111.7 105.412487186902 6.28751281309837 49 114.3 116.863513215025 -2.56351321502546 50 133.7 126.000635309822 7.69936469017775 51 114.3 121.747599918457 -7.4475999184575 52 126.5 120.366175530003 6.13382446999685 53 131 132.136257891029 -1.13625789102949 54 104 107.714264215072 -3.714264215072 55 108.9 112.257747160720 -3.35774716072038 56 128.5 125.285739061282 3.2142609387175 57 132.4 127.744750631115 4.65524936888507 58 128 125.241952322375 2.75804767762528 59 116.4 117.579343427725 -1.17934342772548 60 120.9 120.856543545315 0.0434564546849588 61 118.6 122.026541541430 -3.42654154142973 62 133.1 131.222593753373 1.87740624662739 63 121.1 126.140841585344 -5.0408415853441 64 127.6 121.719708527226 5.880291472774 65 135.4 134.207326562696 1.19267343730371 66 114.9 113.667340071025 1.23265992897527 67 114.3 116.250028267908 -1.95002826790794 68 128.9 133.992909946911 -5.09290994691142 69 138.9 134.646104955420 4.25389504457978 70 129.4 126.895284213740 2.50471578626034 71 115 119.728883288864 -4.72888328886355 72 128 121.715246756500 6.28475324349969 73 127 119.646994610325 7.35300538967546 74 128.8 133.262915122337 -4.46291512233657 75 137.9 130.047291622417 7.8527083775834 76 128.4 124.284938218584 4.11506178141581 77 135.9 134.285864545828 1.61413545417155 78 122.2 121.542772901914 0.657227098085938 79 113.1 110.920483334869 2.17951666513075 80 136.2 134.351899858066 1.84810014193362 81 138 137.384871888271 0.615128111729184 82 115.2 124.276531081327 -9.07653108132743 83 111 116.659100549398 -5.65910054939792 84 99.2 112.021572716939 -12.8215727169386 85 102.4 103.066628879706 -0.666628879706384 86 112.7 116.878125556882 -4.17812555688236 87 105.5 102.302414981044 3.1975850189556 88 98.3 105.835194482611 -7.53519448261052 89 116.4 112.273778694496 4.12622130550369 90 97.4 92.77049361313 4.62950638686991 91 93.3 91.8302188625263 1.46978113747370 92 117.4 118.942661722829 -1.54266172282857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.379275916352884 0.758551832705767 0.620724083647116 22 0.222053439825174 0.444106879650349 0.777946560174826 23 0.317941490607155 0.635882981214309 0.682058509392846 24 0.204646398758273 0.409292797516546 0.795353601241727 25 0.153306251259958 0.306612502519915 0.846693748740042 26 0.230074848927876 0.460149697855753 0.769925151072124 27 0.154142816806066 0.308285633612131 0.845857183193934 28 0.134234863681771 0.268469727363543 0.865765136318228 29 0.110934715456311 0.221869430912622 0.88906528454369 30 0.121348361999912 0.242696723999825 0.878651638000088 31 0.130828849695372 0.261657699390744 0.869171150304628 32 0.0905703865222793 0.181140773044559 0.90942961347772 33 0.0596851948658832 0.119370389731766 0.940314805134117 34 0.0376562316425495 0.075312463285099 0.96234376835745 35 0.0260181446719743 0.0520362893439486 0.973981855328026 36 0.0233502595858833 0.0467005191717667 0.976649740414117 37 0.0141119101009852 0.0282238202019704 0.985888089899015 38 0.00804208071924888 0.0160841614384978 0.99195791928075 39 0.00483236698777625 0.0096647339755525 0.995167633012224 40 0.0058323026874348 0.0116646053748696 0.994167697312565 41 0.00327408122317157 0.00654816244634314 0.996725918776828 42 0.0255078971753934 0.0510157943507868 0.974492102824607 43 0.0157881722207151 0.0315763444414301 0.984211827779285 44 0.00946956041097194 0.0189391208219439 0.990530439589028 45 0.0157974681951415 0.031594936390283 0.984202531804859 46 0.00988594507693973 0.0197718901538795 0.99011405492306 47 0.00703063867068544 0.0140612773413709 0.992969361329315 48 0.0110440165018774 0.0220880330037548 0.988955983498123 49 0.00849392551487878 0.0169878510297576 0.991506074485121 50 0.0183504089337297 0.0367008178674595 0.98164959106627 51 0.0386986777120617 0.0773973554241234 0.961301322287938 52 0.0445922150739818 0.0891844301479636 0.955407784926018 53 0.0372150805074827 0.0744301610149655 0.962784919492517 54 0.0481813120205722 0.0963626240411444 0.951818687979428 55 0.063647888939907 0.127295777879814 0.936352111060093 56 0.0430074092489284 0.0860148184978567 0.956992590751072 57 0.0535809008204114 0.107161801640823 0.946419099179589 58 0.044900338478116 0.089800676956232 0.955099661521884 59 0.0695744291436213 0.139148858287243 0.930425570856379 60 0.0478239916383844 0.0956479832767687 0.952176008361616 61 0.0692497706436642 0.138499541287328 0.930750229356336 62 0.0491220845586259 0.0982441691172519 0.950877915441374 63 0.166153927529133 0.332307855058265 0.833846072470867 64 0.171813504709340 0.343627009418679 0.82818649529066 65 0.167396798181648 0.334793596363295 0.832603201818352 66 0.120081537676271 0.240163075352542 0.879918462323729 67 0.122201698528169 0.244403397056337 0.877798301471831 68 0.561366632309616 0.877266735380768 0.438633367690384 69 0.5482459928617 0.9035080142766 0.4517540071383 70 0.414847250670747 0.829694501341494 0.585152749329253 71 0.283738508959182 0.567477017918363 0.716261491040818 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0392156862745098 NOK 5% type I error level 14 0.274509803921569 NOK 10% type I error level 25 0.490196078431373 NOK

Charts produced by software:

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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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