Model 2 - WZM & WZM<25j

*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, 19 Nov 2009 14:14:52 -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/19/t1258665390bq7vtvnxivrimek.htm/, Retrieved Thu, 19 Nov 2009 22:16:42 +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/19/t1258665390bq7vtvnxivrimek.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 «

8.2 20.3 8 20.3 7.5 20.3 6.8 15.8 6.5 15.8 6.6 15.8 7.6 23.2 8 23.2 8.1 23.2 7.7 20.9 7.5 20.9 7.6 20.9 7.8 19.8 7.8 19.8 7.8 19.8 7.5 20.6 7.5 20.6 7.1 20.6 7.5 21.1 7.5 21.1 7.6 21.1 7.7 22.4 7.7 22.4 7.9 22.4 8.1 20.5 8.2 20.5 8.2 20.5 8.2 18.4 7.9 18.4 7.3 18.4 6.9 17.6 6.6 17.6 6.7 17.6 6.9 18.5 7 18.5 7.1 18.5 7.2 17.3 7.1 17.3 6.9 17.3 7 16.2 6.8 16.2 6.4 16.2 6.7 18.5 6.6 18.5 6.4 18.5 6.3 16.3 6.2 16.3 6.5 16.3 6.8 16.8 6.8 16.8 6.4 16.8 6.1 14.8 5.8 14.8 6.1 14.8 7.2 21.4 7.3 21.4 6.9 21.4 6.1 16.1 5.8 16.1 6.2 16.1 7.1 19.6 7.7 19.6 7.9 19.6 7.7 18.9 7.4 18.9 7.5 18.9 8 21.9 8.1 21.9 8 21.9

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1.92944665325443 + 0.272322364476941X[t] + 0.416145636793173M1[t] + 0.482812303459842M2[t] + 0.332812303459842M3[t] + 0.535194753289615M4[t] + 0.301861419956282M5[t] + 0.151861419956281M6[t] -0.227159400887366M7[t] -0.193826067554032M8[t] -0.260492734220699M9[t] -0.12M10[t] -0.22M11[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) 1.92944665325443 0.378276 5.1006 4e-06 2e-06 X 0.272322364476941 0.018697 14.5649 0 0 M1 0.416145636793173 0.186726 2.2286 0.029869 0.014934 M2 0.482812303459842 0.186726 2.5857 0.012348 0.006174 M3 0.332812303459842 0.186726 1.7824 0.080112 0.040056 M4 0.535194753289615 0.188485 2.8395 0.006287 0.003143 M5 0.301861419956282 0.188485 1.6015 0.114889 0.057445 M6 0.151861419956281 0.188485 0.8057 0.423826 0.211913 M7 -0.227159400887366 0.189617 -1.198 0.235966 0.117983 M8 -0.193826067554032 0.189617 -1.0222 0.311083 0.155541 M9 -0.260492734220699 0.189617 -1.3738 0.174983 0.087491 M10 -0.12 0.194985 -0.6154 0.540764 0.270382 M11 -0.22 0.194985 -1.1283 0.264009 0.132004 Multiple Linear Regression - Regression Statistics Multiple R 0.906667996552816 R-squared 0.822046855973097 Adjusted R-squared 0.783914039395903 F-TEST (value) 21.5574649281151 F-TEST (DF numerator) 12 F-TEST (DF denominator) 56 p-value 1.11022302462516e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.308299121025296 Sum Squared Residuals 5.32270748939832 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.87373628892952 0.326263711070480 2 8 7.94040295559618 0.0595970444038233 3 7.5 7.79040295559617 -0.290402955596175 4 6.8 6.76733476527971 0.032665234720286 5 6.5 6.53400143194638 -0.0340014319463803 6 6.6 6.38400143194638 0.215998568053620 7 7.6 8.0201661082321 -0.420166108232099 8 8 8.05349944156543 -0.0534994415654309 9 8.1 7.98683277489876 0.113167225101235 10 7.7 7.5009840708225 0.199015929177501 11 7.5 7.4009840708225 0.0990159291775013 12 7.6 7.6209840708225 -0.0209840708224989 13 7.8 7.73757510669104 0.0624248933089629 14 7.8 7.8042417733577 -0.00424177335770586 15 7.8 7.6542417733577 0.145758226642294 16 7.5 8.07448211476903 -0.574482114769032 17 7.5 7.8411487814357 -0.341148781435699 18 7.1 7.6911487814357 -0.591148781435699 19 7.5 7.44828914283052 0.0517108571694780 20 7.5 7.48162247616385 0.0183775238361449 21 7.6 7.41495580949719 0.185044190502811 22 7.7 7.90946761753791 -0.20946761753791 23 7.7 7.80946761753791 -0.109467617537910 24 7.9 8.02946761753791 -0.12946761753791 25 8.1 7.9282007618249 0.171799238175104 26 8.2 7.99486742849156 0.205132571508435 27 8.2 7.84486742849156 0.355132571508434 28 8.2 7.47537291291976 0.724627087080239 29 7.9 7.24203957958643 0.657960420413573 30 7.3 7.09203957958643 0.207960420413573 31 6.9 6.49516086716123 0.404839132838772 32 6.6 6.52849420049456 0.0715057995054387 33 6.7 6.4618275338279 0.238172466172106 34 6.9 6.84741039607784 0.0525896039221603 35 7 6.74741039607784 0.25258960392216 36 7.1 6.96741039607784 0.132589603922160 37 7.2 7.05676919549868 0.143230804501316 38 7.1 7.12343586216535 -0.0234358621653530 39 6.9 6.97343586216535 -0.0734358621653529 40 7 6.87626371107049 0.123736288929510 41 6.8 6.64293037773716 0.157069622262843 42 6.4 6.49293037773716 -0.092930377737156 43 6.7 6.74025099519047 -0.0402509951904743 44 6.6 6.77358432852381 -0.173584328523808 45 6.4 6.70691766185714 -0.306917661857141 46 6.3 6.24830119422857 0.0516988057714302 47 6.2 6.14830119422857 0.0516988057714306 48 6.5 6.36830119422857 0.131698805771430 49 6.8 6.92060801326021 -0.120608013260214 50 6.8 6.98727467992688 -0.187274679926882 51 6.4 6.83727467992688 -0.437274679926882 52 6.1 6.49501240080277 -0.395012400802773 53 5.8 6.26167906746944 -0.46167906746944 54 6.1 6.11167906746944 -0.0116790674694396 55 7.2 7.5299858521736 -0.329985852173603 56 7.3 7.56331918550694 -0.263319185506937 57 6.9 7.49665251884027 -0.59665251884027 58 6.1 6.19383672133318 -0.0938367213331819 59 5.8 6.09383672133318 -0.293836721333182 60 6.2 6.31383672133318 -0.113836721333181 61 7.1 7.68311063379565 -0.583110633795649 62 7.7 7.74977730046232 -0.0497773004623174 63 7.9 7.59977730046232 0.300222699537682 64 7.7 7.61153409515823 0.0884659048417694 65 7.4 7.3782007618249 0.0217992381751027 66 7.5 7.22820076182490 0.271799238175102 67 8 7.66614703441207 0.333852965587926 68 8.1 7.69948036774541 0.400519632254592 69 8 7.63281370107874 0.367186298921259 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.252397425058284 0.504794850116569 0.747602574941716 17 0.167602367470976 0.335204734941952 0.832397632529024 18 0.160427153477088 0.320854306954177 0.839572846522912 19 0.107889408810491 0.215778817620982 0.892110591189509 20 0.0673327443564569 0.134665488712914 0.932667255643543 21 0.0416646709640778 0.0833293419281556 0.958335329035922 22 0.0315816442847724 0.0631632885695448 0.968418355715228 23 0.0170252199983618 0.0340504399967236 0.982974780001638 24 0.0102899764712728 0.0205799529425456 0.989710023528727 25 0.00490511864212974 0.0098102372842595 0.99509488135787 26 0.00389271904886006 0.00778543809772011 0.99610728095114 27 0.0132876977113068 0.0265753954226137 0.986712302288693 28 0.332089052361926 0.664178104723853 0.667910947638074 29 0.664902851893582 0.670194296212836 0.335097148106418 30 0.640076689422226 0.719846621155548 0.359923310577774 31 0.641576995748877 0.716846008502246 0.358423004251123 32 0.620531371363334 0.758937257273332 0.379468628636666 33 0.687936205801165 0.624127588397671 0.312063794198835 34 0.620911918508259 0.758176162983482 0.379088081491741 35 0.549463119292864 0.901073761414272 0.450536880707136 36 0.462781865975869 0.925563731951738 0.537218134024131 37 0.512536825617775 0.97492634876445 0.487463174382225 38 0.466625046734767 0.933250093469534 0.533374953265233 39 0.41389408635338 0.82778817270676 0.58610591364662 40 0.369684636476538 0.739369272953075 0.630315363523462 41 0.358403191764531 0.716806383529062 0.641596808235469 42 0.293231784004110 0.586463568008219 0.70676821599589 43 0.243622693566717 0.487245387133434 0.756377306433283 44 0.193600979099377 0.387201958198754 0.806399020900623 45 0.194039123387632 0.388078246775265 0.805960876612368 46 0.138828103401638 0.277656206803276 0.861171896598362 47 0.109496207083002 0.218992414166004 0.890503792916998 48 0.0735510812466132 0.147102162493226 0.926448918753387 49 0.144617870352999 0.289235740705997 0.855382129647001 50 0.108643846946100 0.217287693892201 0.8913561530539 51 0.103813388750168 0.207626777500337 0.896186611249832 52 0.0697966778280855 0.139593355656171 0.930203322171915 53 0.0438786204299421 0.0877572408598843 0.956121379570058 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0526315789473684 NOK 5% type I error level 5 0.131578947368421 NOK 10% type I error level 8 0.210526315789474 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/10a9yp1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/10a9yp1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/1mpuw1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/1mpuw1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/2mel31258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/2mel31258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/3mi9s1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/3mi9s1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/4b7ny1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/4b7ny1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/5grzo1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/5grzo1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/6tsbb1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/6tsbb1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/76exp1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/76exp1258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/8cxi01258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/8cxi01258665288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/9bn2f1258665288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665390bq7vtvnxivrimek/9bn2f1258665288.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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