3.7 Numerical parameters estimation

Some programs allow you to (re-)estimate numerical parameters, including

• Branch lengths
• Entries of the substitution matrices, included base frequencies values)
• Parameters of the rate distribution (currently shape parameter of the gamma law, proportion of invariant sites).

optimization = {method}

where “method” can be one of

None

No optimization is performed, initial values are kept “as is”.

FullD(derivatives={Newton|Gradient})

Full-derivatives method. Branch length derivatives are computed analytically, others numerically. The derivatives arguments specifies if first or second order derivatives should be used. In the first case, the optimization method used is the so-called conjugate gradient method, otherwise the Newton-Raphson method will be used.

D-Brent(derivatives={Newton|Gradient}, nstep={int>0})

Branch lengths parameters are optimized using either the conjugate gradient or the Newton-Raphson method, other parameters are estimated using the Brent method in one dimension. The algorithm then loops over all parameters until convergence. The nstep arguments allow to specify a number of progressive steps to perform during optimization. If nstep=3 and precision=E-6, a first optimization with precision=E-2, will be performed, then a round with precision set to E-4 and finally precision will be set to E-6. This approach generally increases convergence time.

D-BFGS(derivatives={Newton|Gradient}, nstep={int>0})

Branch lengths parameters are optimized using either the conjugate gradient or the Newton-Raphson method, other parameters are estimated using the BFGS method. The algorithm then loops over all parameters until convergence. The nstep arguments allow to specify a number of progressive steps to perform during optimization. If nstep=3 and precision=E-6, a first optimization with precision=E-2, will be performed, then a round with precision set to E-4 and finally precision will be set to E-6. This approach generally increases convergence time.

optimization.reparametrization = {boolean}

Tells if parameters should be transformed in order to remove constraints (for instance positivie-only parameters will be log transformed in order to obtain parameters defined from -inf to +inf). This may improve the optimization, particularly for parameter-rich models, but the likelihood calculations will take a bit more time.

optimization.final = {powell|simplex}

Optional final optimization step, useful if numerical derivatives are to be used. Leave the field empty in order to skip this step.

optimization.profiler = {{path}|std|none}

A file where to dump optimization steps (a file path or std for standard output or none for no output).

optimization.message_handler = {{path}|std|none}

A file where to dump warning messages.

optimization.max_number_f_eval = {int<0}

The maximum number of likelihood evaluations to perform.

optimization.ignore_parameter = {list<chars>}

A list of parameters to ignore during the estimation process. The parameter name should include there "namespace", that is their model name, for instance K80.kappa, TN93.theta, GTR.a, Gamma.alpha, etc. For nested models, the syntax is the following: G01.rdist_Gamma.alpha, TS98.model_T92.kappa, RE08.lamba, RE08.model_G01.model_GTR.a, etc. ’Ancient’ will ignore all parameters in the ancestral frequency set (non-homogeneous models), ’BrLen’ will ignore all branch lengths and ’Model’ will ignore all model parameters.

The ’*’ wildcard can be used, as in *theta* for all the parameters whose name has theta in it.

optimization.constrain_parameter = {list<chars=interval>}

A list of parameters on which the authorized values are limited to a given interval.

optimization.constrain_parameter = YN98.omega = [-inf;1.9[, *theta* = [0.1;0.7[, BrLen*=[0.01;inf]

optimization.tolerance = {float>0}

The precision on the log-likelihood to reach.

output.infos = {{path}|none}

A text file containing several statistics for each site in the alignment. These statistics include the posterior rate, rate class with maximum posterior probability and whether the site is conserved or not.

The resulting tree will be written to a file specified by the general tree writing options (WritingTrees).