*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 16:13:35 +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/t1291306342vssbvjeydb3rs6d.htm/, Retrieved Thu, 02 Dec 2010 17:12:37 +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/t1291306342vssbvjeydb3rs6d.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:

» Textbox « » Textfile « » CSV «

1 162556 1081 213118 6282929 1 29790 309 81767 4324047 1 87550 458 153198 4108272 0 84738 588 -26007 -1212617 1 54660 299 126942 1485329 1 42634 156 157214 1779876 0 40949 481 129352 1367203 1 42312 323 234817 2519076 1 37704 452 60448 912684 1 16275 109 47818 1443586 0 25830 115 245546 1220017 0 12679 110 48020 984885 1 18014 239 -1710 1457425 0 43556 247 32648 -572920 1 24524 497 95350 929144 0 6532 103 151352 1151176 0 7123 109 288170 790090 1 20813 502 114337 774497 1 37597 248 37884 990576 0 17821 373 122844 454195 1 12988 119 82340 876607 1 22330 84 79801 711969 0 13326 102 165548 702380 0 16189 295 116384 264449 0 7146 105 134028 450033 0 15824 64 63838 541063 1 26088 267 74996 588864 0 11326 129 31080 -37216 0 8568 37 32168 783310 0 14416 361 49857 467359 1 3369 28 87161 688779 1 11819 85 106113 608419 1 6620 44 80570 696348 1 4519 49 102129 597793 0 2220 22 301670 821730 0 18562 155 102313 377934 0 10327 91 88577 651939 1 5336 81 112477 697458 1 2365 79 191778 70 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 29 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Group[t] = + 0.0761928261752459 + 7.32009843802612e-07Costs[t] + 0.0001518049473316Trades[t] + 8.28576137963525e-07Dividends[t] + 1.68976894851889e-07`Wealth `[t] -0.0244610052209563M1[t] + 0.0377799205961304M2[t] + 0.102111416123679M3[t] + 0.200028069046625M4[t] + 0.0592382954538094M5[t] + 0.0911585426152266M6[t] + 0.0702732756616937M7[t] + 0.0572242088453046M8[t] -0.00739757344270558M9[t] + 0.183110754289409M10[t] + 0.0121927684361800M11[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) 0.0761928261752459 0.087143 0.8743 0.382438 0.191219 Costs 7.32009843802612e-07 3e-06 0.215 0.82986 0.41493 Trades 0.0001518049473316 0.000308 0.4935 0.621912 0.310956 Dividends 8.28576137963525e-07 1e-06 1.0859 0.278141 0.139071 `Wealth ` 1.68976894851889e-07 0 2.365 0.018487 0.009243 M1 -0.0244610052209563 0.104844 -0.2333 0.815637 0.407819 M2 0.0377799205961304 0.104297 0.3622 0.717361 0.35868 M3 0.102111416123679 0.104471 0.9774 0.328936 0.164468 M4 0.200028069046625 0.104664 1.9112 0.056674 0.028337 M5 0.0592382954538094 0.104664 0.566 0.571709 0.285855 M6 0.0911585426152266 0.104493 0.8724 0.3835 0.19175 M7 0.0702732756616937 0.104267 0.674 0.500705 0.250352 M8 0.0572242088453046 0.10464 0.5469 0.584765 0.292382 M9 -0.00739757344270558 0.104262 -0.071 0.94347 0.471735 M10 0.183110754289409 0.104485 1.7525 0.080426 0.040213 M11 0.0121927684361800 0.105103 0.116 0.907703 0.453851 Multiple Linear Regression - Regression Statistics Multiple R 0.275541529327050 R-squared 0.0759231343838893 Adjusted R-squared 0.0425227657471624 F-TEST (value) 2.27312264752685 F-TEST (DF numerator) 15 F-TEST (DF denominator) 415 p-value 0.0042973313924235 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.439098732002138 Sum Squared Residuals 80.0151940250423 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.57307988355429 -0.573079883554292 2 1 0.981101269070211 0.0188987309297888 3 1 1.13305762295241 -0.133057622952406 4 0 0.201058219472366 -0.201058219472366 5 1 0.576999853302284 0.423000146697716 6 1 0.653263136910083 0.346736863089917 7 0 0.587663050767355 -0.587663050767355 8 1 0.833652236883733 0.166347763116267 9 1 0.369319066765776 0.630680933234224 10 1 0.571317313428482 0.428682686571518 11 0 0.534359218518887 -0.534359218518887 12 0 0.308383558422506 -0.308383558422506 13 1 0.3460539144764 0.6539460855236 14 0 0.113593100672637 -0.113593100672637 15 1 0.507711713277228 0.492288286722772 16 0 0.616567094637816 -0.616567094637816 17 0 0.529469707536083 -0.529469707536083 18 1 0.484401781248439 0.515598218751561 19 1 0.410409337878037 0.589590662121963 20 0 0.381619495650889 -0.381619495650889 21 1 0.312718673381654 0.687281326618346 22 1 0.474828491089052 0.525171508910948 23 0 0.369479576311417 -0.369479576311417 24 0 0.273944569106822 -0.273944569106822 25 0 0.259999864307775 -0.259999864307775 26 0 0.279593376323493 -0.279593376323493 27 1 0.399577142294359 0.600422857705641 28 0 0.323557979167653 -0.323557979167653 29 0 0.306334693734467 -0.306334693734467 30 0 0.35298880179697 -0.35298880179697 31 1 0.341790042946221 0.658209957053779 32 1 0.345703533004254 0.654296466995746 33 1 0.264745677793147 0.735254322206853 34 1 0.455684832666318 0.544315167333682 35 0 0.482160312652062 -0.482160312652062 36 0 0.261946383914723 -0.261946383914723 37 0 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0.999981340734183 3.73185316348476e-05 1.86592658174238e-05 326 0.99997309394516 5.38121096813081e-05 2.69060548406541e-05 327 0.999994907294893 1.01854102136432e-05 5.09270510682161e-06 328 0.99999796684746 4.06630507823403e-06 2.03315253911702e-06 329 0.999996966094845 6.06781030976958e-06 3.03390515488479e-06 330 0.999995845111532 8.30977693525848e-06 4.15488846762924e-06 331 0.999993802828905 1.23943421890394e-05 6.19717109451972e-06 332 0.99999091189589 1.81762082211596e-05 9.08810411057978e-06 333 0.999986437165617 2.71256687658756e-05 1.35628343829378e-05 334 0.999981022265487 3.79554690250906e-05 1.89777345125453e-05 335 0.999971812443868 5.63751122646444e-05 2.81875561323222e-05 336 0.999958481114134 8.30377717329506e-05 4.15188858664753e-05 337 0.999938895043363 0.00012220991327352 6.110495663676e-05 338 0.999911751292087 0.000176497415826116 8.82487079130581e-05 339 0.999875919707029 0.000248160585942305 0.000124080292971152 340 0.999874325507186 0.000251348985627810 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0.365595913417326 0.182797956708663 374 0.78657755550278 0.42684488899444 0.21342244449722 375 0.75542622001874 0.48914755996252 0.24457377998126 376 0.74511428841429 0.509771423171419 0.254885711585710 377 0.721053959169212 0.557892081661575 0.278946040830788 378 0.715126270383485 0.569747459233031 0.284873729616515 379 0.672471439538701 0.655057120922598 0.327528560461299 380 0.638935880631725 0.72212823873655 0.361064119368275 381 0.590971842920141 0.818056314159718 0.409028157079859 382 0.572539102303524 0.854921795392952 0.427460897696476 383 0.572830695692863 0.854338608614274 0.427169304307137 384 0.520888131792408 0.958223736415184 0.479111868207592 385 0.468239421560492 0.936478843120983 0.531760578439508 386 0.426259723657524 0.852519447315048 0.573740276342476 387 0.386524959194671 0.773049918389342 0.613475040805329 388 0.40118909854872 0.80237819709744 0.59881090145128 389 0.368517443742596 0.737034887485191 0.631482556257404 390 0.394556790290862 0.789113580581725 0.605443209709138 391 0.341762682642705 0.68352536528541 0.658237317357295 392 0.32937891414597 0.65875782829194 0.67062108585403 393 0.277110497121317 0.554220994242634 0.722889502878683 394 0.249393855234862 0.498787710469723 0.750606144765138 395 0.4991484028373 0.9982968056746 0.5008515971627 396 0.434071465841796 0.868142931683593 0.565928534158204 397 0.371227292900288 0.742454585800577 0.628772707099712 398 0.524676186326052 0.950647627347897 0.475323813673948 399 0.464422299488735 0.92884459897747 0.535577700511265 400 0.398665797639001 0.797331595278002 0.601334202360999 401 0.363666354321201 0.727332708642402 0.636333645678799 402 0.598790971365715 0.80241805726857 0.401209028634285 403 0.523265650242865 0.95346869951427 0.476734349757135 404 0.619615623456345 0.76076875308731 0.380384376543655 405 0.530995247618962 0.938009504762077 0.469004752381038 406 0.482393160851131 0.964786321702261 0.517606839148869 407 0.384106300688491 0.768212601376983 0.615893699311509 408 0.288890681838355 0.57778136367671 0.711109318161645 409 0.202777112729789 0.405554225459578 0.797222887270211 410 0.133680862381548 0.267361724763096 0.866319137618452 411 0.151561773368587 0.303123546737174 0.848438226631413 412 0.313563847284632 0.627127694569264 0.686436152715368 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 51 0.129441624365482 NOK 5% type I error level 116 0.294416243654822 NOK 10% type I error level 149 0.378172588832487 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/104vvm1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/104vvm1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/1xbya1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/1xbya1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/2qlfv1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/2qlfv1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/3qlfv1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/3qlfv1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/4icwg1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/4icwg1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/5icwg1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/5icwg1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/6icwg1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/6icwg1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/7b3e11291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/7b3e11291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/84vvm1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/84vvm1291306384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/94vvm1291306384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291306342vssbvjeydb3rs6d/94vvm1291306384.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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