Workshop 4

*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, 02 Dec 2010 13:41:54 +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/02/t1291297306as7s7507k180b0p.htm/, Retrieved Thu, 02 Dec 2010 14:41:57 +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/02/t1291297306as7s7507k180b0p.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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8.30 3.00 3.10 4.28 2649.24 8.70 3.00 2.90 3.69 2579.39 8.90 7.00 2.40 3.54 2504.58 8.90 4.00 2.40 3.13 2462.32 8.10 -4.00 2.70 3.75 2467.38 8.00 -6.00 2.50 3.85 2446.66 8.30 8.00 2.10 3.66 2656.32 8.50 2.00 1.90 3.96 2626.15 8.70 -1.00 0.80 3.93 2482.60 8.60 -2.00 0.80 4.05 2539.91 8.30 0.00 0.30 4.19 2502.66 7.90 10.00 0.00 4.32 2466.92 7.90 3.00 -0.90 4.21 2513.17 8.10 6.00 -1.00 4.24 2443.27 8.30 7.00 -0.70 4.16 2293.41 8.10 -4.00 -1.70 4.19 2070.83 7.40 -5.00 -1.00 4.20 2029.60 7.30 -7.00 -0.20 4.46 2052.02 7.70 -10.00 0.70 4.63 1864.44 8.00 -21.00 0.60 4.33 1670.07 8.00 -22.00 1.90 4.40 1810.99 7.70 -16.00 2.10 4.58 1905.41 6.90 -25.00 2.70 4.52 1862.83 6.60 -22.00 3.20 4.04 2014.45 6.90 -22.00 4.80 4.16 2197.82 7.50 -19.00 5.50 4.73 2962.34 7.90 -21.00 5.40 4.81 3047.03 7.70 -31.00 5.90 4.75 3032.60 6.50 -28.00 5.80 4.90 3504.37 6.10 -23.00 5.10 5.12 3801.06 6.40 -17.00 4.10 4.95 3857.62 6.80 -12.00 4.40 4.76 3674.40 7.10 -14.00 3.60 4.69 3720.98 7.30 -18.00 3.50 4.58 3 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 11.8776091849868 + 0.0243650510801236GeneralEconomicSituationOverNextTwelveMonths[t] + 0.0195104753475393HICPRenteOpOLO12jEnMeer[t] -0.884739521144813Bel[t] -4.62917896606381e-05`20`[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) 11.8776091849868 0.699979 16.9685 0 0 GeneralEconomicSituationOverNextTwelveMonths 0.0243650510801236 0.009285 2.6241 0.011223 0.005611 HICPRenteOpOLO12jEnMeer 0.0195104753475393 0.047095 0.4143 0.680282 0.340141 Bel -0.884739521144813 0.185007 -4.7822 1.3e-05 7e-06 `20` -4.62917896606381e-05 7.3e-05 -0.6299 0.531395 0.265698 Multiple Linear Regression - Regression Statistics Multiple R 0.79173314676329 R-squared 0.626841375683701 Adjusted R-squared 0.599702566642515 F-TEST (value) 23.0976007359946 F-TEST (DF numerator) 4 F-TEST (DF denominator) 55 p-value 3.07646130792705e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.418788040236288 Sum Squared Residuals 9.64608824547228 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.3 8.10186360046423 0.198136399535775 2 8.7 8.62319130437793 0.0768086956220665 3 8.9 8.8470702879809 0.052929712019109 4 8.9 9.13867462944095 -0.238674629440954 5 8.1 8.40083462383876 -0.300834623838760 6 8 8.26068764037629 -0.260687640376292 7 8.3 8.75238913775627 -0.452389137756270 8 8.5 8.33827150315664 0.161728496843361 9 8.7 8.2769021990741 0.423097800925895 10 8.6 8.14371542299115 0.456284577008847 11 8.3 8.06055112368221 0.239448876317786 12 7.9 8.18498682269283 -0.284986822692834 13 7.9 8.09205238937331 -0.192052389373308 14 8.1 8.13989010554186 -0.03989010554186 15 8.3 8.24782474851637 0.0521752514836275 16 8.1 7.9440601521958 0.155939847804205 17 7.4 7.92641364913521 -0.526413649135208 18 7.3 7.66222178983115 -0.362221789831151 19 7.7 7.46496375971349 0.235036240286510 20 8 7.46941674179716 0.530583258202842 21 8 7.40196010318972 0.598039896810278 22 7.7 7.38842852015415 0.311571479845853 23 6.9 7.235904821314 -0.335904821313998 24 6.6 7.7364114212293 -1.13641142122930 25 6.9 7.65297091377792 -0.752970913777916 26 7.5 7.20003087367767 0.299969126322330 27 7.9 7.07465011062472 0.825349889375276 28 7.7 6.89450719929075 0.80549280070925 29 6.5 6.81110129921645 -0.311101299216446 30 6.1 6.71089221614751 -0.610892216147513 31 6.4 6.98535950225213 -0.585359502252127 32 6.8 7.28961999097614 -0.489619990976144 33 7.1 7.28505700345561 -0.185057003455609 34 7.3 7.2772495999853 0.0227504000146947 35 7.2 7.33211753541315 -0.132117535413152 36 7 7.04099916606083 -0.04099916606083 37 7 7.36419847302746 -0.364198473027458 38 7 7.4191215166813 -0.419121516681301 39 7.3 7.32224811376027 -0.0222481137602742 40 7.5 7.59540679050298 -0.095406790502983 41 7.2 7.69233328217517 -0.492333282175171 42 7.7 7.54060689050802 0.159393109491985 43 8 7.76323792339733 0.236762076602668 44 7.9 7.76278138304212 0.137218616957876 45 8 7.9353024167379 0.0646975832620961 46 8 8.02672117267862 -0.0267211726786213 47 7.9 7.7538603026076 0.146139697392404 48 7.9 8.1346668623097 -0.234666862309691 49 8 8.35950330692978 -0.359503306929781 50 8.1 8.1896219778874 -0.0896219778874 51 8.1 8.17138249996817 -0.0713824999681715 52 8.2 8.16103771844725 0.0389622815527481 53 8 7.95442219678094 0.0455778032190585 54 8.3 7.7282609762002 0.571739023799794 55 8.5 7.79076578328884 0.709234216711163 56 8.6 7.79076973768339 0.809230262316611 57 8.7 8.083917274731 0.616082725269009 58 8.7 8.40700635783305 0.292993642166954 59 8.5 8.34191320335341 0.158086796646587 60 8.4 8.41767193076613 -0.0176719307661328 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.271464321764375 0.542928643528749 0.728535678235625 9 0.179587036901179 0.359174073802359 0.82041296309882 10 0.106270464634777 0.212540929269554 0.893729535365223 11 0.106073965775046 0.212147931550093 0.893926034224954 12 0.148948293639207 0.297896587278414 0.851051706360793 13 0.148891031405388 0.297782062810776 0.851108968594612 14 0.0901328420104733 0.180265684020947 0.909867157989527 15 0.064190418264467 0.128380836528934 0.935809581735533 16 0.0375905513149076 0.0751811026298151 0.962409448685092 17 0.0523160934543923 0.104632186908785 0.947683906545608 18 0.0370984220733622 0.0741968441467245 0.962901577926638 19 0.0450472256944068 0.0900944513888136 0.954952774305593 20 0.0554837557580325 0.110967511516065 0.944516244241968 21 0.0562044681698792 0.112408936339758 0.94379553183012 22 0.0507304058757532 0.101460811751506 0.949269594124247 23 0.129873698612080 0.259747397224159 0.87012630138792 24 0.50752521010779 0.98494957978442 0.49247478989221 25 0.752292945347382 0.495414109305236 0.247707054652618 26 0.704276034321566 0.591447931356868 0.295723965678434 27 0.756158264134937 0.487683471730125 0.243841735865063 28 0.857324624522905 0.28535075095419 0.142675375477095 29 0.907744265792935 0.184511468414131 0.0922557342070654 30 0.93715611735277 0.125687765294461 0.0628438826472303 31 0.945265163594642 0.109469672810717 0.0547348364053584 32 0.969582582505454 0.0608348349890913 0.0304174174945456 33 0.976192296741168 0.0476154065176641 0.0238077032588321 34 0.973290551733482 0.0534188965330356 0.0267094482665178 35 0.973261408621209 0.0534771827575827 0.0267385913787914 36 0.977466740947142 0.0450665181057168 0.0225332590528584 37 0.998403121558186 0.00319375688362773 0.00159687844181387 38 0.999519016231511 0.000961967536977669 0.000480983768488835 39 0.99902491205188 0.00195017589624126 0.00097508794812063 40 0.997859620708322 0.00428075858335631 0.00214037929167816 41 0.998763662743205 0.00247267451359055 0.00123633725679528 42 0.99765647994173 0.00468704011654148 0.00234352005827074 43 0.996243802923622 0.00751239415275626 0.00375619707637813 44 0.992245310643073 0.0155093787138535 0.00775468935692674 45 0.984184136873695 0.0316317262526108 0.0158158631263054 46 0.968940787777267 0.062118424445466 0.031059212222733 47 0.97861459658885 0.0427708068222984 0.0213854034111492 48 0.968572413951043 0.0628551720979136 0.0314275860489568 49 0.933511466882324 0.132977066235351 0.0664885331176757 50 0.880788055066782 0.238423889866436 0.119211944933218 51 0.997349117357282 0.00530176528543614 0.00265088264271807 52 0.99579370883605 0.00841258232789953 0.00420629116394976 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.2 NOK 5% type I error level 14 0.311111111111111 NOK 10% type I error level 22 0.488888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/10zqnz1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/10zqnz1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/1s6751291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/1s6751291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/2ly7q1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/2ly7q1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/3ly7q1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/3ly7q1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/4ly7q1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/4ly7q1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/5ly7q1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/5ly7q1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/6wp6b1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/6wp6b1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/7og5w1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/7og5w1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/8og5w1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/8og5w1291297306.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/9og5w1291297306.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297306as7s7507k180b0p/9og5w1291297306.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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