WS711

*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, 22 Nov 2009 10:57:53 -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/22/t1258912734tb10rqw43xg5hd9.htm/, Retrieved Sun, 22 Nov 2009 18:59:05 +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/22/t1258912734tb10rqw43xg5hd9.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 «

104,89 124 105,15 118,63 105,24 121,86 105,57 119,97 105,62 125,03 106,17 130,09 106,27 126,65 106,41 121,7 106,94 119,24 107,16 122,63 107,32 116,66 107,32 114,12 107,35 113,11 107,55 112,61 107,87 113,4 108,37 115,18 108,38 121,01 107,92 119,44 108,03 116,68 108,14 117,07 108,3 117,41 108,64 119,58 108,66 120,92 109,04 117,09 109,03 116,77 109,03 119,39 109,54 122,49 109,75 124,08 109,83 118,29 109,65 112,94 109,82 113,79 109,95 114,43 110,12 118,7 110,15 120,36 110,21 118,27 109,99 118,34 110,14 117,82 110,14 117,65 110,81 118,18 110,97 121,02 110,99 124,78 109,73 131,16 109,81 130,14 110,02 131,75 110,18 134,73 110,21 135,35 110,25 140,32 110,36 136,35 110,51 131,6 110,6 128,9 110,95 133,89 111,18 138,25 111,19 146,23 111,69 144,76 111,7 149,3 111,83 156,8 111,77 159,08 111,73 165,12 112,01 163,14 111,86 153,43 112,04 151,01

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 AKW[t] = + 98.457446683022 + 0.0847439006773697AKB[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) 98.457446683022 1.918599 51.3174 0 0 AKB 0.0847439006773697 0.015032 5.6377 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.591692965394288 R-squared 0.350100565297086 Adjusted R-squared 0.339085320641104 F-TEST (value) 31.7832763802456 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 5.11889629128959e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.57525172461769 Sum Squared Residuals 146.403661758749 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.89 108.965690367016 -4.07569036701562 2 105.15 108.510615620378 -3.36061562037827 3 105.24 108.784338419566 -3.54433841956619 4 105.57 108.624172447286 -3.05417244728596 5 105.62 109.052976584713 -3.43297658471344 6 106.17 109.481780722141 -3.31178072214093 7 106.27 109.190261703811 -2.92026170381079 8 106.41 108.770779395458 -2.36077939545781 9 106.94 108.562309399791 -1.62230939979148 10 107.16 108.849591223088 -1.68959122308776 11 107.32 108.343670136044 -1.02367013604387 12 107.32 108.128420628323 -0.808420628323348 13 107.35 108.042829288639 -0.692829288639203 14 107.55 108.000457338301 -0.450457338300515 15 107.87 108.067405019836 -0.197405019835630 16 108.37 108.218249163041 0.151750836958652 17 108.38 108.712306103990 -0.332306103990423 18 107.92 108.579258179927 -0.659258179926946 19 108.03 108.345365014057 -0.315365014057406 20 108.14 108.378415135322 -0.23841513532158 21 108.3 108.407228061552 -0.107228061551890 22 108.64 108.591122326022 0.0488776739782215 23 108.66 108.704679152929 -0.0446791529294583 24 109.04 108.380110013335 0.659889986664877 25 109.03 108.352991965118 0.677008034881631 26 109.03 108.575020984893 0.454979015106922 27 109.54 108.837727076993 0.702272923007081 28 109.75 108.97246987907 0.777530120930057 29 109.83 108.481802694148 1.34819730585203 30 109.65 108.028422825524 1.62157717447596 31 109.82 108.100455141100 1.71954485890018 32 109.95 108.154691237533 1.79530876246668 33 110.12 108.516547693426 1.60345230657431 34 110.15 108.657222568550 1.49277743144988 35 110.21 108.480107816134 1.72989218386557 36 109.99 108.486039889182 1.50396011081815 37 110.14 108.441973060830 1.69802693917039 38 110.14 108.427566597714 1.71243340228554 39 110.81 108.472480865073 2.33751913492654 40 110.97 108.713153542997 2.25684645700281 41 110.99 109.031790609544 1.95820939045589 42 109.73 109.572456695866 0.157543304134284 43 109.81 109.486017917175 0.3239820828252 44 110.02 109.622455597265 0.397544402734627 45 110.18 109.874992421284 0.305007578716077 46 110.21 109.927533639704 0.282466360296094 47 110.25 110.348710826070 -0.098710826070427 48 110.36 110.012277540381 0.347722459618730 49 110.51 109.609744012164 0.900255987836242 50 110.6 109.380935480335 1.21906451966513 51 110.95 109.803807544715 1.14619245528506 52 111.18 110.173290951668 1.00670904833173 53 111.19 110.849547279074 0.340452720926316 54 111.69 110.724973745078 0.96502625492205 55 111.7 111.109711054153 0.590288945846794 56 111.83 111.745290309233 0.0847096907665167 57 111.77 111.938506402778 -0.168506402777889 58 111.73 112.450359562869 -0.720359562869193 59 112.01 112.282566639528 -0.272566639527999 60 111.86 111.459703363951 0.400296636049254 61 112.04 111.254623124312 0.785376875688497 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0317897267457561 0.0635794534915122 0.968210273254244 6 0.023517372006063 0.047034744012126 0.976482627993937 7 0.0239621729199152 0.0479243458398305 0.976037827080085 8 0.0770566154546982 0.154113230909396 0.922943384545302 9 0.273635407899121 0.547270815798242 0.726364592100879 10 0.48262587481490 0.96525174962980 0.5173741251851 11 0.626066998987485 0.74786600202503 0.373933001012515 12 0.652798235616423 0.694403528767155 0.347201764383577 13 0.654405351827408 0.691189296345184 0.345594648172592 14 0.653789599794339 0.692420800411322 0.346210400205661 15 0.67600506304581 0.64798987390838 0.32399493695419 16 0.770645885314655 0.45870822937069 0.229354114685345 17 0.9302415866586 0.139516826682798 0.069758413341399 18 0.970069277667168 0.0598614446656638 0.0299307223328319 19 0.983742894246686 0.0325142115066272 0.0162571057533136 20 0.993050954616583 0.0138980907668346 0.00694904538341732 21 0.997704643134295 0.00459071373140912 0.00229535686570456 22 0.999609798045813 0.000780403908374956 0.000390201954187478 23 0.999966813667035 6.63726659295798e-05 3.31863329647899e-05 24 0.99998815532192 2.36893561613249e-05 1.18446780806624e-05 25 0.999995324714568 9.35057086384403e-06 4.67528543192202e-06 26 0.99999926633137 1.46733726163204e-06 7.33668630816021e-07 27 0.999999914175898 1.71648204259309e-07 8.58241021296544e-08 28 0.999999986217951 2.75640973800162e-08 1.37820486900081e-08 29 0.999999988012535 2.39749302595767e-08 1.19874651297884e-08 30 0.999999976090406 4.78191874890581e-08 2.39095937445290e-08 31 0.999999952182502 9.5634996118215e-08 4.78174980591075e-08 32 0.999999907115195 1.85769609267445e-07 9.28848046337223e-08 33 0.999999886755767 2.26488465340668e-07 1.13244232670334e-07 34 0.999999868174906 2.63650187974645e-07 1.31825093987322e-07 35 0.999999792924127 4.14151746028776e-07 2.07075873014388e-07 36 0.999999600403221 7.99193557986938e-07 3.99596778993469e-07 37 0.999999205025128 1.58994974426861e-06 7.94974872134305e-07 38 0.999998334052871 3.33189425741787e-06 1.66594712870894e-06 39 0.999999037496184 1.92500763181952e-06 9.62503815909758e-07 40 0.999999783971639 4.32056721920273e-07 2.16028360960136e-07 41 0.999999968966522 6.20669569505116e-08 3.10334784752558e-08 42 0.999999965048679 6.9902642802868e-08 3.4951321401434e-08 43 0.999999942472304 1.15055391886091e-07 5.75276959430454e-08 44 0.9999998842993 2.31401400935608e-07 1.15700700467804e-07 45 0.999999788586793 4.22826413354572e-07 2.11413206677286e-07 46 0.99999967538409 6.4923181868023e-07 3.24615909340115e-07 47 0.999999897754829 2.04490342508172e-07 1.02245171254086e-07 48 0.999999939028787 1.21942425447111e-07 6.09712127235555e-08 49 0.99999986513723 2.69725540436906e-07 1.34862770218453e-07 50 0.999999595911132 8.08177736983105e-07 4.04088868491553e-07 51 0.99999828985201 3.42029597922619e-06 1.71014798961309e-06 52 0.999992522925257 1.49541494868900e-05 7.47707474344502e-06 53 0.999998177531503 3.64493699375667e-06 1.82246849687834e-06 54 0.999984302732237 3.13945355263534e-05 1.56972677631767e-05 55 0.999927286115542 0.000145427768916558 7.2713884458279e-05 56 0.999078689496642 0.00184262100671639 0.000921310503358196 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 36 0.692307692307692 NOK 5% type I error level 40 0.769230769230769 NOK 10% type I error level 42 0.807692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/105lqq1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/105lqq1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/1s4sn1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/1s4sn1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/2dclj1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/2dclj1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/3aw5o1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/3aw5o1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/4d1221258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/4d1221258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/5ncdf1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/5ncdf1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/6kcd11258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/6kcd11258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/7kd2y1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/7kd2y1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/88ptg1258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/88ptg1258912669.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/983841258912669.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912734tb10rqw43xg5hd9/983841258912669.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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