Recently, a new approach for supertree reconstruction was proposed: matrix representation with likelihood (MRL) [ 7].
In this paper we consider a new approach for supertree estimation, called MRL (Matrix Representation with Likelihood).
We call the optimization problem in this approach to supertree estimation "matrix representation with likelihood", or MRL.
The pleiotropy test involved a comparison of the determinants of the two matrices with a likelihood-ratio statistic [ 23].
Few other applications have been reported even though maximum likelihood estimation of genetic covariance matrices with a FA structure has been considered early on in other areas [ 21].
CFA were performed using the variance-covariance matrix with the maximum-likelihood test.
We simulated 25 bootstrap matrices with Mesquite and, for each matrix, constructed a maximum likelihood tree (general-time-reversible model; model parameters, gamma shape correction and proportion of invariable sites being estimated from each matrix; rearrangements limited to 1,000).
The matrix MtPan apparently outperforms the two other matrices with this data set, as expected, as it gives higher likelihoods for most runs, and the resulting trees are more resolved and display higher posterior probabilities at most nodes.
