*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, 15 Nov 2009 06:55:20 -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/Nov/15/t12582934326v0ulld4rol22ka.htm/, Retrieved Sun, 15 Nov 2009 14:57:25 +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/Nov/15/t12582934326v0ulld4rol22ka.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 «

116222 344744 31899 492865 110924 338653 31384 480961 103753 327532 30650 461935 99983 326225 30400 456608 93302 318672 30003 441977 91496 317756 29896 439148 119321 337302 31557 488180 139261 349420 31883 520564 133739 336923 30830 501492 123913 330758 30354 485025 113438 321002 29756 464196 109416 320820 29934 460170 109406 327032 30599 467037 105645 324047 30378 460070 101328 316735 29925 447988 97686 315710 29471 442867 93093 313427 29567 436087 91382 310527 29419 431328 122257 330962 30796 484015 139183 339015 31475 509673 139887 341332 31708 512927 131822 339092 31917 502831 116805 323308 30871 470984 113706 325849 31512 471067 113012 330675 32362 476049 110452 332225 31928 474605 107005 331735 31699 470439 102841 328047 30363 461251 98173 326165 30386 454724 98181 327081 30364 455626 137277 346764 32806 516847 147579 344190 33423 525192 146571 343333 33071 522975 138920 345777 33888 518585 130340 344094 34805 509239 128140 348609 35489 512238 127059 354846 37259 5191 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation totaal[t] = -1.92075481494960e-10 + 1.00000000000000`-25`[t] + 1`25-50`[t] + 0.999999999999998`50+`[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) -1.92075481494960e-10 0 -0.5015 0.617637 0.308818 `-25` 1.00000000000000 0 897505385453506 0 0 `25-50` 1 0 676971031308479 0 0 `50+` 0.999999999999998 0 649215623443931 0 0 Multiple Linear Regression - Regression Statistics Multiple R 1 R-squared 1 Adjusted R-squared 1 F-TEST (value) 5.33104264565564e+30 F-TEST (DF numerator) 3 F-TEST (DF denominator) 68 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.18581389824984e-10 Sum Squared Residuals 9.56185128872081e-19 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 492865 492864.999999999 9.26673511975864e-10 2 480961 480961 -2.67969660651108e-10 3 461935 461935 6.78705994223098e-11 4 456608 456608 -5.52190891363362e-11 5 441977 441977 -1.24814347712625e-11 6 439148 439148 -1.57527062246293e-11 7 488180 488180 -1.44076740092134e-11 8 520564 520564 -1.25227797260940e-11 9 501492 501492 -6.8526252873035e-12 10 485025 485025 -8.91792840309037e-12 11 464196 464196 -4.21976078089364e-12 12 460170 460170 -5.94196110780889e-12 13 467037 467037 -1.72987645481664e-11 14 460070 460070 -7.93154427602631e-12 15 447988 447988 -7.9813038090314e-12 16 442867 442867 -1.53208092951003e-11 17 436087 436087 -6.38005931818457e-12 18 431328 431328 -3.11532157788973e-12 19 484015 484015 -6.55231796622095e-12 20 509673 509673 -9.5169330210882e-12 21 512927 512927 -6.27506190940955e-12 22 502831 502831 -5.2106696310613e-12 23 470984 470984 4.76837549624696e-12 24 471067 471067 -9.5786834145563e-12 25 476049 476049 -1.47048468851664e-11 26 474605 474605 -1.73870981885134e-11 27 470439 470439 -1.42484835685310e-11 28 461251 461251 -1.81098900816405e-11 29 454724 454724 -1.77039689672761e-11 30 455626 455626 -2.02574485347666e-11 31 516847 516847 -2.27674114187529e-11 32 525192 525192 -2.92458799436271e-12 33 522975 522975 -8.55917427145767e-12 34 518585 518585 -5.15083668725128e-12 35 509239 509239 -1.41044223765113e-11 36 512238 512238 -1.83213356946622e-11 37 519164 519164 -2.31364873242568e-11 38 517009 517009 -2.29843832264133e-11 39 509933 509933 -2.33656886785722e-11 40 509127 509127 -2.72741187608584e-11 41 500857 500857 -2.82873235955171e-11 42 506971 506971 -2.69341622287699e-11 43 569323 569323 -2.43897126520839e-11 44 579714 579714 -1.71977860099735e-11 45 577992 577992 -2.43629233848317e-11 46 565464 565464 -1.84933055870166e-11 47 547344 547344 -1.65162806140562e-11 48 554788 554788 -1.90042890853411e-11 49 562325 562325 -2.39628546342624e-11 50 560854 560854 -2.3410760808031e-11 51 555332 555332 -2.61230500579071e-11 52 543599 543599 -2.08962197915497e-11 53 536662 536662 -2.33077719012836e-11 54 542722 542722 -2.47518923570204e-11 55 593530 593530 -1.69979695174504e-11 56 610763 610763 -5.44547335686746e-14 57 612613 612613 9.85309778362473e-12 58 611324 611324 -9.21661278515814e-13 59 594167 594167 1.46734883478915e-12 60 595454 595454 -2.13412815196489e-12 61 590865 590865 -7.76527338439906e-12 62 589379 589379 -1.25238191865931e-11 63 584428 584428 -8.17291626910824e-12 64 573100 573100 5.43684996875974e-12 65 567456 567456 -1.45242816613363e-12 66 569028 569028 -3.18981807553289e-13 67 620735 620735 1.47580543229725e-11 68 628884 628884 1.60210555322746e-11 69 628232 628232 2.65098873283114e-11 70 612117 612117 2.03896997592206e-11 71 595404 595404 2.74946940527494e-11 72 597141 597141 1.71840622492783e-11 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.00897398355386216 0.0179479671077243 0.991026016446138 8 2.55068043167411e-05 5.10136086334823e-05 0.999974493195683 9 0.000425798826622817 0.000851597653245634 0.999574201173377 10 3.31593071127936e-07 6.63186142255871e-07 0.999999668406929 11 0.0446180157008825 0.089236031401765 0.955381984299118 12 0.146915156965160 0.293830313930319 0.85308484303484 13 0.068478326519986 0.136956653039972 0.931521673480014 14 0.00644063645174671 0.0128812729034934 0.993559363548253 15 0.909713662192893 0.180572675614213 0.0902863378071067 16 0.967187714192295 0.0656245716154092 0.0328122858077046 17 0.997881596102173 0.00423680779565495 0.00211840389782747 18 0.99999832064914 3.35870171748920e-06 1.67935085874460e-06 19 0.978524755290614 0.0429504894187719 0.0214752447093860 20 8.97176945126172e-07 1.79435389025234e-06 0.999999102823055 21 1 1.26377831258784e-16 6.31889156293921e-17 22 0.999934066097994 0.000131867804011872 6.59339020059359e-05 23 0.999999890194553 2.19610893055526e-07 1.09805446527763e-07 24 0.776644467886655 0.446711064226690 0.223355532113345 25 3.96806833111036e-08 7.93613666222072e-08 0.999999960319317 26 0.709762904793366 0.580474190413268 0.290237095206634 27 0.000228298463757739 0.000456596927515478 0.999771701536242 28 0.999999695368237 6.09263526198909e-07 3.04631763099455e-07 29 0.999999999999997 5.70979961696751e-15 2.85489980848375e-15 30 4.27193752751168e-09 8.54387505502336e-09 0.999999995728063 31 0.112081666632417 0.224163333264835 0.887918333367583 32 0.0516344218384952 0.103268843676990 0.948365578161505 33 0.545065228143296 0.909869543713408 0.454934771856704 34 9.49987947908819e-11 1.89997589581764e-10 0.999999999905 35 0.999928349445498 0.000143301109002945 7.16505545014724e-05 36 0.000462734807961174 0.000925469615922348 0.99953726519204 37 0.000464949318951939 0.000929898637903878 0.999535050681048 38 1.53258856527825e-09 3.0651771305565e-09 0.999999998467411 39 7.75084531353408e-19 1.55016906270682e-18 1 40 0.999161193626653 0.00167761274669350 0.000838806373346748 41 0.999999926476228 1.47047544165287e-07 7.35237720826436e-08 42 0.694786822527451 0.610426354945098 0.305213177472549 43 2.08551357439141e-09 4.17102714878282e-09 0.999999997914486 44 0.99999999902902 1.94195966671546e-09 9.70979833357729e-10 45 0.0401891061408434 0.0803782122816869 0.959810893859157 46 0.0226325774405916 0.0452651548811832 0.977367422559408 47 0.630050839712686 0.739898320574627 0.369949160287314 48 0.999999977162202 4.56755961602263e-08 2.28377980801131e-08 49 0.999989080452182 2.18390956362281e-05 1.09195478181141e-05 50 4.68750710949181e-51 9.37501421898363e-51 1 51 0.900358907098758 0.199282185802484 0.0996410929012422 52 0.0886893686472908 0.177378737294582 0.91131063135271 53 0.999999999998157 3.68620486185772e-12 1.84310243092886e-12 54 0.999261426457887 0.00147714708422589 0.000738573542112947 55 0.999999996354218 7.29156444798151e-09 3.64578222399076e-09 56 0.999999254340058 1.49131988472629e-06 7.45659942363143e-07 57 2.32231319700891e-05 4.64462639401781e-05 0.99997677686803 58 0.996669300621263 0.00666139875747314 0.00333069937873657 59 0.99999999999292 1.41607764550320e-11 7.08038822751601e-12 60 0.00028375881334316 0.00056751762668632 0.999716241186657 61 0.884225992289665 0.23154801542067 0.115774007710335 62 0.971245498651527 0.0575090026969465 0.0287545013484732 63 0.0306231638916281 0.0612463277832562 0.969376836108372 64 0.00221440878654259 0.00442881757308518 0.997785591213457 65 0.999471615527761 0.00105676894447767 0.000528384472238835 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 37 0.627118644067797 NOK 5% type I error level 41 0.694915254237288 NOK 10% type I error level 46 0.779661016949153 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/10amod1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/10amod1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/1i8rg1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/1i8rg1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/20x0o1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/20x0o1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/3ba5g1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/3ba5g1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/4pl0h1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/4pl0h1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/58dza1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/58dza1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/6p4gp1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/6p4gp1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/7db7t1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/7db7t1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/8f2zy1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/8f2zy1258293315.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/9r6cp1258293315.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/15/t12582934326v0ulld4rol22ka/9r6cp1258293315.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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