*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: Tue, 14 Dec 2010 19:01:04 +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/14/t1292353179wpbu4k1hinnzisz.htm/, Retrieved Tue, 14 Dec 2010 19:59:49 +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/2010/Dec/14/t1292353179wpbu4k1hinnzisz.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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8.803 3 0 0.000 3 2 1.219 1 0 -0.083 3 0 7.843 4 1.8 2.356 4 0.7 -3.772 1 3.9 5.075 4 1 1.194 1 3.6 3.954 1 1.4 -0.856 4 1.5 6.142 5 0.7 -0.598 2 2.7 5.232 5 0 -2.590 1 2.1 1.099 2 0 -0.242 2 4.1 -1.609 2 1.2 0.344 1 1.3 4.094 1 6.1 6.271 5 0.3 3.320 5 0.5 -2.120 2 3.4 5.333 1 0 4.443 1 1.5 3.593 1 0 -2.293 3 3.4 0.039 4 0.8 6.256 5 0.8 4.605 1 0 3.555 4 0 -5.298 4 1.4 -4.605 1 2 4.127 1 1.9 -2.104 1 2.4 0.300 3 2.8 -3.772 3 1.3 -3.037 3 2 0.531 1 5.6 1.253 1 3.1 5.521 5 1 -0.734 2 1.8 2.303 4 0.9 0.482 2 1.8 5.257 4 1.9 0.916 5 0.9 8.364 2 0 -1.273 3 2.6 8.351 1 2.4 1.917 2 1.2 -0.288 2 0.9 1.281 3 0.5 2.697 5 0 4.016 5 0.6 0.336 2 0 -2.813 2 2.2 -0.105 2 2.3 0.693 3 0.5 -2.263 2 2.6 1.433 4 0.6 1.253 1 6.6 1.399 1 0

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation slowsleep[t] = + 2.70159405376894 -0.105498825253138gewicht[t] -0.362298441262711gevaar[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) 2.70159405376894 0.360803 7.4877 0 0 gewicht -0.105498825253138 0.053561 -1.9697 0.053576 0.026788 gevaar -0.362298441262711 0.125028 -2.8977 0.005268 0.002634 Multiple Linear Regression - Regression Statistics Multiple R 0.462886853769664 R-squared 0.214264239392778 Adjusted R-squared 0.18762912886372 F-TEST (value) 8.0444283930799 F-TEST (DF numerator) 2 F-TEST (DF denominator) 59 p-value 0.000814061694959789 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.36356875084952 Sum Squared Residuals 109.699864559306 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0 0.68599257127742 -0.68599257127742 2 2 1.61469872998080 0.385301270019198 3 0 2.21069254452265 -2.21069254452265 4 0 1.62345513247681 -1.62345513247681 5 1.8 0.424973002257728 1.37502699774227 6 0.7 1.00384505642170 -0.303845056421699 7 3.9 2.73723718136106 1.16276281863894 8 1 0.716993750558415 0.283006249441585 9 3.6 2.21333001515398 1.38666998484602 10 1.4 1.92215325745532 -0.522153257455316 11 1.5 1.34270728313478 0.157292716865221 12 0.7 0.242128062750605 0.457871937249394 13 2.7 2.04008546874489 0.659914531255109 14 0 0.338131993730962 -0.338131993730962 15 2.1 2.61253756991185 -0.512537569911854 16 0 1.86105396229032 -1.86105396229032 17 4.1 2.00252788695477 2.09747211304523 18 1.2 2.14674478107581 -0.946744781075814 19 1.3 2.30300401661915 -1.00300401661915 20 6.1 1.90738342191988 4.19261657808012 21 0.3 0.228518714292951 0.0714812857070493 22 0.5 0.539845747614962 -0.0398457476149621 23 3.4 2.20065468078017 1.19934531921983 24 0 1.77667037743124 -1.77667037743124 25 1.5 1.87056433190653 -0.370564331906531 26 0 1.9602383333717 -1.9602383333717 27 3.4 1.85660753628625 1.54339246371375 28 0.8 1.24828583453322 -0.448285834533220 29 0.8 0.230101196671748 0.569898803328252 30 0 1.85347352221552 -1.85347352221552 31 0 0.877351964943186 -0.877351964943186 32 1.4 1.81133306490922 -0.41133306490922 33 2 2.82511770279693 -0.825117702796928 34 1.9 1.90390196068652 -0.00390196068652322 35 2.4 2.56126514083883 -0.161265140838829 36 2.8 1.58304908240486 1.21695091759514 37 1.3 2.01264029883564 -0.712640298835642 38 2 1.93509866227458 0.064901337725415 39 5.6 2.28327573629681 3.31672426370319 40 3.1 2.20710558446404 0.892894415535957 41 1 0.307642833232805 0.692357166767195 42 1.8 2.05443330897932 -0.254433308979318 43 0.9 1.00943649416011 -0.109436494160115 44 1.8 1.92614673747150 -0.126146737471502 45 1.9 0.697792964362344 1.20220703563766 46 0.9 0.793464923523507 0.106535076476493 47 0 1.09460499682626 -1.09460499682626 48 2.6 1.74899873452805 0.851001265471951 49 2.4 1.45827492281727 0.941725077182734 50 1.2 1.77475592323325 -0.574755923233248 51 0.9 2.00738083291642 -1.10738083291642 52 0.5 1.47955473483153 -0.979554734831533 53 0 0.605571515747668 -0.605571515747668 54 0.6 0.466418565238778 0.133581434761222 55 0 1.94154956595846 -1.94154956595846 56 2.2 2.27376536668059 -0.0737653666805926 57 2.3 1.98807454789509 0.311925452104906 58 0.5 1.54158804408038 -1.04158804408038 59 2.6 2.21574101279137 0.384258987208633 60 0.6 1.10122047213035 -0.501220472130345 61 6.6 2.20710558446404 4.39289441553596 62 0 2.19170275597708 -2.19170275597708 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.364505089259365 0.72901017851873 0.635494910740635 7 0.728299027539819 0.543401944920363 0.271700972460181 8 0.599067378242078 0.801865243515844 0.400932621757922 9 0.659890213118794 0.680219573762413 0.340109786881206 10 0.549878575099415 0.90024284980117 0.450121424900585 11 0.434234227874639 0.868468455749278 0.565765772125361 12 0.333169229373068 0.666338458746135 0.666830770626932 13 0.263740564664092 0.527481129328184 0.736259435335908 14 0.195856573142152 0.391713146284304 0.804143426857848 15 0.140636415909614 0.281272831819227 0.859363584090386 16 0.187946710618366 0.375893421236732 0.812053289381634 17 0.305427670856494 0.610855341712987 0.694572329143506 18 0.265821596993832 0.531643193987663 0.734178403006168 19 0.222412434102041 0.444824868204082 0.777587565897959 20 0.805689975473588 0.388620049052824 0.194310024526412 21 0.745162956241168 0.509674087517663 0.254837043758832 22 0.67653958114557 0.64692083770886 0.32346041885443 23 0.659911037245919 0.680177925508163 0.340088962754081 24 0.71736214396397 0.56527571207206 0.28263785603603 25 0.652846224873807 0.694307550252386 0.347153775126193 26 0.712357644508715 0.57528471098257 0.287642355491285 27 0.717866741842365 0.56426651631527 0.282133258157635 28 0.660651485272114 0.678697029455772 0.339348514727886 29 0.599933585023225 0.80013282995355 0.400066414976775 30 0.656378890376172 0.687242219247656 0.343621109623828 31 0.614867508311141 0.770264983377717 0.385132491688859 32 0.554240045561533 0.891519908876933 0.445759954438467 33 0.504993197808691 0.990013604382618 0.495006802191309 34 0.435515578468833 0.871031156937667 0.564484421531167 35 0.365125456765535 0.73025091353107 0.634874543234465 36 0.34666861407742 0.69333722815484 0.65333138592258 37 0.293904821065730 0.587809642131461 0.70609517893427 38 0.230744408943092 0.461488817886183 0.769255591056908 39 0.541791943060626 0.916416113878749 0.458208056939374 40 0.492680438224574 0.985360876449148 0.507319561775426 41 0.433219314188232 0.866438628376464 0.566780685811768 42 0.355728526061270 0.711457052122539 0.64427147393873 43 0.282019664599905 0.56403932919981 0.717980335400095 44 0.215548332382468 0.431096664764937 0.784451667617532 45 0.207655815381696 0.415311630763392 0.792344184618304 46 0.159468277401142 0.318936554802284 0.840531722598858 47 0.140146414904539 0.280292829809078 0.85985358509546 48 0.123174448290406 0.246348896580811 0.876825551709594 49 0.0867801094703776 0.173560218940755 0.913219890529622 50 0.0590527208787065 0.118105441757413 0.940947279121294 51 0.0442858936400623 0.0885717872801246 0.955714106359938 52 0.0296256704753052 0.0592513409506105 0.970374329524695 53 0.0159822403131892 0.0319644806263783 0.98401775968681 54 0.0096328758862687 0.0192657517725374 0.990367124113731 55 0.0132678736123894 0.0265357472247787 0.98673212638761 56 0.00533070834007206 0.0106614166801441 0.994669291659928 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 4 0.0784313725490196 NOK 10% type I error level 6 0.117647058823529 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/10hbdk1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/10hbdk1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/1sayq1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/1sayq1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/2sayq1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/2sayq1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/3sayq1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/3sayq1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/43jxb1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/43jxb1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/53jxb1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/53jxb1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/63jxb1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/63jxb1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/7ebxe1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/7ebxe1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/86keh1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/86keh1292353255.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/96keh1292353255.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292353179wpbu4k1hinnzisz/96keh1292353255.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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