ws7model4

*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: Fri, 27 Nov 2009 05:02:57 -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/27/t1259323470sak993azii8wpnc.htm/, Retrieved Fri, 27 Nov 2009 13:04:42 +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/27/t1259323470sak993azii8wpnc.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:

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2.3 0 2.0 1.9 2.3 2.7 2.8 0 2.3 2.0 1.9 2.3 2.4 0 2.8 2.3 2.0 1.9 2.3 0 2.4 2.8 2.3 2.0 2.7 0 2.3 2.4 2.8 2.3 2.7 0 2.7 2.3 2.4 2.8 2.9 0 2.7 2.7 2.3 2.4 3.0 0 2.9 2.7 2.7 2.3 2.2 0 3.0 2.9 2.7 2.7 2.3 0 2.2 3.0 2.9 2.7 2.8 0 2.3 2.2 3.0 2.9 2.8 0 2.8 2.3 2.2 3.0 2.8 0 2.8 2.8 2.3 2.2 2.2 0 2.8 2.8 2.8 2.3 2.6 0 2.2 2.8 2.8 2.8 2.8 0 2.6 2.2 2.8 2.8 2.5 0 2.8 2.6 2.2 2.8 2.4 0 2.5 2.8 2.6 2.2 2.3 0 2.4 2.5 2.8 2.6 1.9 0 2.3 2.4 2.5 2.8 1.7 0 1.9 2.3 2.4 2.5 2.0 0 1.7 1.9 2.3 2.4 2.1 0 2.0 1.7 1.9 2.3 1.7 0 2.1 2.0 1.7 1.9 1.8 0 1.7 2.1 2.0 1.7 1.8 0 1.8 1.7 2.1 2.0 1.8 0 1.8 1.8 1.7 2.1 1.3 0 1.8 1.8 1.8 1.7 1.3 0 1.3 1.8 1.8 1.8 1.3 1 1.3 1.3 1.8 1.8 1.2 1 1.3 1.3 1.3 1.8 1.4 1 1.2 1.3 1.3 1.3 2.2 1 1.4 1.2 1.3 1.3 2.9 1 2.2 1.4 1.2 1.3 3.1 1 2.9 2.2 1.4 1.2 3.5 1 3.1 2.9 2.2 1.4 3.6 1 3.5 3.1 2.9 2.2 4.4 1 3.6 3.5 3.1 2.9 4.1 1 4.4 3.6 3.5 3.1 5.1 1 4.1 4.4 3.6 3.5 5.8 1 5.1 4.1 4.4 3.6 5.9 1 5.8 5.1 4.1 4.4 5.4 1 5.9 5.8 5.1 4.1 5.5 1 5.4 5.9 5.8 5.1 4.8 1 5.5 5. 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 Y[t] = + 0.539091205777179 + 0.573438557956526X[t] + 1.02829851557690Y1[t] + 0.0269671841106094Y2[t] + 0.0328180088795993Y3[t] -0.210413134181968Y4[t] + 0.136004659282262M1[t] -0.0605367365174084M2[t] + 0.0399923932085476M3[t] + 0.0210278026729679M4[t] + 0.120045150787521M5[t] -0.137283584804756M6[t] + 0.119908563240552M7[t] -0.0320928698935266M8[t] -0.136438669654607M9[t] -0.00629145184081034M10[t] + 0.196689637957487M11[t] -0.0196778006990444t + 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) 0.539091205777179 0.375288 1.4365 0.15905 0.079525 X 0.573438557956526 0.32511 1.7638 0.0858 0.0429 Y1 1.02829851557690 0.160039 6.4253 0 0 Y2 0.0269671841106094 0.242223 0.1113 0.911939 0.455969 Y3 0.0328180088795993 0.254511 0.1289 0.898081 0.44904 Y4 -0.210413134181968 0.175993 -1.1956 0.239269 0.119634 M1 0.136004659282262 0.367371 0.3702 0.713281 0.356641 M2 -0.0605367365174084 0.366645 -0.1651 0.869733 0.434866 M3 0.0399923932085476 0.368788 0.1084 0.914215 0.457108 M4 0.0210278026729679 0.369717 0.0569 0.954943 0.477471 M5 0.120045150787521 0.367397 0.3267 0.745653 0.372827 M6 -0.137283584804756 0.369555 -0.3715 0.712341 0.35617 M7 0.119908563240552 0.371871 0.3224 0.748882 0.374441 M8 -0.0320928698935266 0.369313 -0.0869 0.931208 0.465604 M9 -0.136438669654607 0.386666 -0.3529 0.726144 0.363072 M10 -0.00629145184081034 0.388555 -0.0162 0.987166 0.493583 M11 0.196689637957487 0.387853 0.5071 0.614998 0.307499 t -0.0196778006990444 0.010447 -1.8836 0.067289 0.033645 Multiple Linear Regression - Regression Statistics Multiple R 0.959090455556404 R-squared 0.919854501939391 Adjusted R-squared 0.88399993701754 F-TEST (value) 25.6551572706100 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 9.9920072216264e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.542700801466578 Sum Squared Residuals 11.1919180766737 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.3 2.27061870345612 0.0293812965438833 2 2.8 2.43662383016248 0.363376169837522 3 2.4 3.12716162677177 -0.727161626771766 4 2.3 2.67948751060737 -0.379487510607374 5 2.7 2.59849539700616 0.101504602993843 6 2.7 2.61177777789171 0.0882222221082889 7 2.9 2.94096245166705 -0.0409624516670454 8 3 3.00911143791934 -0.00911143791933824 9 2.2 2.90914587216624 -0.709145872166238 10 2.3 2.20623679700645 0.0937632029935468 11 2.8 2.43199536442647 0.368004635573525 12 2.8 2.68517818144758 0.114821818552423 13 2.8 2.98660094031963 -0.186600940319633 14 2.2 2.76574943484252 -0.565749434842521 15 2.6 2.12441508743231 0.475584912567689 16 2.8 2.48091179196208 0.31908820803792 17 2.5 2.75700711080945 -0.257007110809452 18 2.4 2.31627954072820 0.0837204592717956 19 2.3 2.36527222938673 -0.0652722293867272 20 1.9 2.03613839608458 -0.136138396084581 21 1.7 1.55794081034927 0.142059189650733 22 2 1.46972316323463 0.530276836765368 23 2.1 1.96403668005119 0.13596331994881 24 1.7 1.9361909000824 -0.236190900082398 25 1.8 1.69582310034619 0.104176899653810 26 1.8 1.51180474239429 0.288195257605708 27 1.8 1.56118427286223 0.238815727137772 28 1.3 1.60998893618835 -0.309988936188351 29 1.3 1.15413791239721 0.145862087602786 30 1.3 1.43708634200711 -0.137086342007114 31 1.2 1.65819168491358 -0.458191684913577 32 1.4 1.48888916661375 -0.0888891666137487 33 2.2 1.56782855085794 0.632171449142057 34 2.9 2.50304841636838 0.396951583631625 35 3.1 3.45533932885406 -0.355339328854061 36 3.5 3.44768040245762 0.0523195975423788 37 3.6 3.83536220296386 -0.235362202963864 38 4.4 3.59203413951563 0.807965860484374 39 4.1 4.46926557613056 -0.369265576130564 40 5.1 4.06282392472653 1.03717607527347 41 5.8 5.16758492617124 0.632415073828764 42 5.9 5.4591786248849 0.440821375115102 43 5.4 5.91434180180047 -0.514341801800468 44 5.5 5.04376950062371 0.456230499376291 45 4.8 4.86508476662655 -0.0650847666265526 46 3.2 4.22099162339054 -1.02099162339054 47 2.7 2.84862862666827 -0.148628626668274 48 2.1 2.03095051601240 0.0690494839875975 49 1.9 1.61159505291420 0.288404947085805 50 0.6 1.49378785308508 -0.893787853085084 51 0.7 0.317973436803132 0.382026563196868 52 -0.2 0.466787836515665 -0.666787836515665 53 -1 -0.377225346384058 -0.622774653615942 54 -1.7 -1.22432228551193 -0.475677714488072 55 -0.7 -1.77876816776782 1.07876816776782 56 -1 -0.777908501241377 -0.222091498758623 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.385112939108984 0.770225878217968 0.614887060891016 22 0.238982298380870 0.477964596761741 0.76101770161913 23 0.131524843899220 0.263049687798441 0.86847515610078 24 0.072512960826465 0.14502592165293 0.927487039173535 25 0.033009243457306 0.066018486914612 0.966990756542694 26 0.0140635348849267 0.0281270697698534 0.985936465115073 27 0.00546772728398714 0.0109354545679743 0.994532272716013 28 0.00280841230546540 0.00561682461093079 0.997191587694535 29 0.000934217956680162 0.00186843591336032 0.99906578204332 30 0.000279459674841270 0.000558919349682541 0.999720540325159 31 0.000196752440627175 0.000393504881254349 0.999803247559373 32 0.000999882951486526 0.00199976590297305 0.999000117048513 33 0.0167651151639226 0.0335302303278452 0.983234884836077 34 0.00908276349618909 0.0181655269923782 0.99091723650381 35 0.00683473958062108 0.0136694791612422 0.993165260419379 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.333333333333333 NOK 5% type I error level 10 0.666666666666667 NOK 10% type I error level 11 0.733333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/10cano1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/10cano1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/1t2pg1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/1t2pg1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/2es1u1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/2es1u1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/3us671259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/3us671259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/4hv6s1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/4hv6s1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/5c58f1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/5c58f1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/6oaxb1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/6oaxb1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/779hv1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/779hv1259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/8jec41259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/8jec41259323372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/9d69w1259323372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259323470sak993azii8wpnc/9d69w1259323372.ps (open in new window)

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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