Eco. Crisis

*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 12:59:19 -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/t1258660823wgnn3dkav46453n.htm/, Retrieved Thu, 19 Nov 2009 21:00:35 +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/t1258660823wgnn3dkav46453n.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 «

105.7 0 105.7 0 111.1 0 82.4 0 60 0 107.3 0 99.3 0 113.5 0 108.9 0 100.2 0 103.9 0 138.7 0 120.2 0 100.2 0 143.2 0 70.9 0 85.2 0 133 0 136.6 0 117.9 0 106.3 0 122.3 0 125.5 0 148.4 0 126.3 0 99.6 0 140.4 0 80.3 0 92.6 0 138.5 0 110.9 0 119.6 0 105 0 109 0 129.4 0 148.6 0 101.4 0 134.8 0 143.7 0 81.6 0 90.3 0 141.5 0 140.7 0 140.2 0 100.2 0 125.7 0 119.6 0 134.7 0 109 0 116.3 0 146.9 0 97.4 0 89.4 0 132.1 0 139.8 0 129 0 112.5 0 121.9 0 121.7 0 123.1 0 131.6 0 119.3 0 132.5 0 98.3 0 85.1 0 131.7 0 129.3 0 90.7 1 78.6 1 68.9 1 79.1 1 83.5 1 74.1 1 59.7 1 93.3 1 61.3 1 56.6 1

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] = + 115.938805970149 -41.3588059701492X[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) 115.938805970149 2.409109 48.1252 0 0 X -41.3588059701492 6.685005 -6.1868 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.58129461372589 R-squared 0.337903427946732 Adjusted R-squared 0.329075473652688 F-TEST (value) 38.2765266665147 F-TEST (DF numerator) 1 F-TEST (DF denominator) 75 p-value 2.96945197320042e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.7194033393432 Sum Squared Residuals 29164.1151044776 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.7 115.938805970149 -10.2388059701494 2 105.7 115.938805970149 -10.2388059701493 3 111.1 115.938805970149 -4.83880597014926 4 82.4 115.938805970149 -33.5388059701492 5 60 115.938805970149 -55.9388059701493 6 107.3 115.938805970149 -8.63880597014925 7 99.3 115.938805970149 -16.6388059701493 8 113.5 115.938805970149 -2.43880597014925 9 108.9 115.938805970149 -7.03880597014925 10 100.2 115.938805970149 -15.7388059701492 11 103.9 115.938805970149 -12.0388059701492 12 138.7 115.938805970149 22.7611940298507 13 120.2 115.938805970149 4.26119402985075 14 100.2 115.938805970149 -15.7388059701492 15 143.2 115.938805970149 27.2611940298507 16 70.9 115.938805970149 -45.0388059701492 17 85.2 115.938805970149 -30.7388059701492 18 133 115.938805970149 17.0611940298507 19 136.6 115.938805970149 20.6611940298507 20 117.9 115.938805970149 1.96119402985075 21 106.3 115.938805970149 -9.63880597014925 22 122.3 115.938805970149 6.36119402985075 23 125.5 115.938805970149 9.56119402985075 24 148.4 115.938805970149 32.4611940298508 25 126.3 115.938805970149 10.3611940298507 26 99.6 115.938805970149 -16.3388059701493 27 140.4 115.938805970149 24.4611940298508 28 80.3 115.938805970149 -35.6388059701493 29 92.6 115.938805970149 -23.3388059701493 30 138.5 115.938805970149 22.5611940298507 31 110.9 115.938805970149 -5.03880597014925 32 119.6 115.938805970149 3.66119402985074 33 105 115.938805970149 -10.9388059701493 34 109 115.938805970149 -6.93880597014925 35 129.4 115.938805970149 13.4611940298508 36 148.6 115.938805970149 32.6611940298507 37 101.4 115.938805970149 -14.5388059701492 38 134.8 115.938805970149 18.8611940298508 39 143.7 115.938805970149 27.7611940298507 40 81.6 115.938805970149 -34.3388059701493 41 90.3 115.938805970149 -25.6388059701493 42 141.5 115.938805970149 25.5611940298507 43 140.7 115.938805970149 24.7611940298507 44 140.2 115.938805970149 24.2611940298507 45 100.2 115.938805970149 -15.7388059701492 46 125.7 115.938805970149 9.76119402985075 47 119.6 115.938805970149 3.66119402985074 48 134.7 115.938805970149 18.7611940298507 49 109 115.938805970149 -6.93880597014925 50 116.3 115.938805970149 0.361194029850745 51 146.9 115.938805970149 30.9611940298508 52 97.4 115.938805970149 -18.5388059701492 53 89.4 115.938805970149 -26.5388059701492 54 132.1 115.938805970149 16.1611940298507 55 139.8 115.938805970149 23.8611940298508 56 129 115.938805970149 13.0611940298507 57 112.5 115.938805970149 -3.43880597014925 58 121.9 115.938805970149 5.96119402985075 59 121.7 115.938805970149 5.76119402985075 60 123.1 115.938805970149 7.16119402985074 61 131.6 115.938805970149 15.6611940298507 62 119.3 115.938805970149 3.36119402985075 63 132.5 115.938805970149 16.5611940298507 64 98.3 115.938805970149 -17.6388059701493 65 85.1 115.938805970149 -30.8388059701493 66 131.7 115.938805970149 15.7611940298507 67 129.3 115.938805970149 13.3611940298508 68 90.7 74.58 16.12 69 78.6 74.58 4.02000000000000 70 68.9 74.58 -5.67999999999999 71 79.1 74.58 4.5200 72 83.5 74.58 8.92 73 74.1 74.58 -0.480000000000005 74 59.7 74.58 -14.88 75 93.3 74.58 18.72 76 61.3 74.58 -13.28 77 56.6 74.58 -17.98 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.838376215422607 0.323247569154787 0.161623784577393 6 0.767507985259906 0.464984029480188 0.232492014740094 7 0.65894066500347 0.68211866999306 0.34105933499653 8 0.613053672055702 0.773892655888596 0.386946327944298 9 0.525891493241298 0.948217013517405 0.474108506758702 10 0.42193169166389 0.84386338332778 0.57806830833611 11 0.329533386989284 0.659066773978567 0.670466613010716 12 0.584878281889326 0.830243436221347 0.415121718110674 13 0.546946629010412 0.906106741979176 0.453053370989588 14 0.470393996092672 0.940787992185343 0.529606003907328 15 0.670430855075918 0.659138289848164 0.329569144924082 16 0.83725409619559 0.325491807608822 0.162745903804411 17 0.857892943304 0.284214113392000 0.142107056696000 18 0.882767882952641 0.234464234094718 0.117232117047359 19 0.910354218454631 0.179291563090738 0.089645781545369 20 0.883055071882865 0.233889856234270 0.116944928117135 21 0.848745829750498 0.302508340499004 0.151254170249502 22 0.819519523939626 0.360960952120748 0.180480476060374 23 0.795257972820376 0.409484054359249 0.204742027179624 24 0.885726765391521 0.228546469216957 0.114273234608479 25 0.864203896361101 0.271592207277797 0.135796103638899 26 0.846442181750705 0.30711563649859 0.153557818249295 27 0.873172760715298 0.253654478569403 0.126827239284702 28 0.93101193359264 0.13797613281472 0.06898806640736 29 0.93839466822866 0.123210663542678 0.0616053317713389 30 0.946750636365034 0.106498727269932 0.053249363634966 31 0.928423248563403 0.143153502873193 0.0715767514365967 32 0.905074263689778 0.189851472620445 0.0949257363102224 33 0.885927683352896 0.228144633294208 0.114072316647104 34 0.857901256362562 0.284197487274875 0.142098743637438 35 0.838434974178709 0.323130051642582 0.161565025821291 36 0.898245717245815 0.203508565508371 0.101754282754185 37 0.887953805111421 0.224092389777159 0.112046194888579 38 0.883310201676944 0.233379596646111 0.116689798323056 39 0.90931790630311 0.181364187393779 0.0906820936968897 40 0.960198052825074 0.0796038943498515 0.0398019471749258 41 0.975412220424595 0.04917555915081 0.024587779575405 42 0.980088419350532 0.0398231612989357 0.0199115806494678 43 0.983421683718648 0.0331566325627032 0.0165783162813516 44 0.98613641065627 0.0277271786874588 0.0138635893437294 45 0.986067538558656 0.0278649228826882 0.0139324614413441 46 0.979910565263753 0.0401788694724942 0.0200894347362471 47 0.969809100792246 0.0603817984155076 0.0301908992077538 48 0.967003327039147 0.0659933459217057 0.0329966729608529 49 0.95604569443511 0.0879086111297783 0.0439543055648891 50 0.937006489648058 0.125987020703883 0.0629935103519417 51 0.962863859466782 0.0742722810664365 0.0371361405332183 52 0.9674611747945 0.0650776504109996 0.0325388252054998 53 0.98680637344312 0.0263872531137592 0.0131936265568796 54 0.982745430942176 0.0345091381156487 0.0172545690578244 55 0.985572611809059 0.0288547763818829 0.0144273881909415 56 0.979999331211268 0.0400013375774641 0.0200006687887321 57 0.969404881969363 0.0611902360612746 0.0305951180306373 58 0.952320486719722 0.095359026560556 0.047679513280278 59 0.927813698389801 0.144372603220398 0.0721863016101988 60 0.895595993586796 0.208808012826408 0.104404006413204 61 0.880195719546511 0.239608560906978 0.119804280453489 62 0.829954021916956 0.340091956166088 0.170045978083044 63 0.832479004938066 0.335041990123868 0.167520995061934 64 0.811510716782244 0.376978566435512 0.188489283217756 65 0.965483027052082 0.0690339458958366 0.0345169729479183 66 0.93929192636905 0.121416147261899 0.0607080736309493 67 0.896153517186099 0.207692965627803 0.103846482813901 68 0.895483750703015 0.209032498593970 0.104516249296985 69 0.832550186802635 0.334899626394731 0.167449813197365 70 0.733121453851501 0.533757092296997 0.266878546148499 71 0.612163308715729 0.775673382568543 0.387836691284271 72 0.517432678785983 0.965134642428034 0.482567321214017 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 10 0.147058823529412 NOK 10% type I error level 19 0.279411764705882 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/10nz6d1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/10nz6d1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/150vy1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/150vy1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/2tr8i1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/2tr8i1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/3jg7g1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/3jg7g1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/4d7711258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/4d7711258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/5jtd31258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/5jtd31258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/61woj1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/61woj1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/7opuk1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/7opuk1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/8sije1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/8sije1258660754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/9hi7w1258660754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258660823wgnn3dkav46453n/9hi7w1258660754.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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