ivy.chars.bayesian_models module

ivy.chars.bayesian_models.create_mk_model(tree, chars, Qtype, pi)

Create model objects to be passed to pymc.MCMC

Creates Qparams and likelihood function

ivy.chars.bayesian_models.create_multi_mk_model(tree, chars, Qtype, pi, nregime=2)

Create an mk model with multiple regimes to be sampled from with MCMC.

Regime number is fixed and the location of the regime shift is allowed to change

ivy.chars.bayesian_models.fit_mk_bayes(tree, chars, Qtype, pi, *kwargs)

Fit an mk model to a given tree and list of characters. Return posterior distributions of Q parameters and MAP estimate of Q matrix

Parameters:

• tree (Node) – Root node of a tree. All branch lengths must be greater than 0 (except root)
• chars (list) – List of character states corresponding to leaf nodes in preoder sequence. Character states must be in the form of 0,1,2,...
• pi (str) – Either “Equal”, “Equilibrium”, or “Fitzjohn”. How to weight values at root node. Defaults to “Equal” Method “Fitzjohn” is not thouroughly tested, use with caution
• Qtype –

  Either a string specifying how to esimate values for Q or a numpy array of a pre-specified Q matrix.

  Valid strings for Q:

  “Equal”: All rates equal “Sym”: Forward and reverse rates equal “ARD”: All rates different

Keyword Arguments:  

• iters (float) – Number of iterations in MCMC. Defaults to 2000
• burn (float) – Burnin to discard. Defaults to 200
• thin (float) – Thinning parameter. Defaults to 1

Returns:

The pymc MCMC object and the pymc MAP object

Return type:

tuple

ivy.chars.bayesian_models.get_indices(list_of_lists)

Get indices given list of nodes in regimes

ivy.chars.bayesian_models.hrm_bayesian(tree, chars, Qtype, nregime, pi=u'Fitzjohn', constraint=u'Rate')

Create a hidden rates model for pymc to be sampled from.

Parameters:

• tree (Node) – Root node of a tree. All branch lengths must be greater than 0 (except root)
• chars (dict) –

  Dict mapping character states to tip labels. Character states should be coded 0,1,2...

  Can also be a list with tip states in preorder sequence

• pi (str) – Either “Equal”, “Equilibrium”, or “Fitzjohn”. How to weight values at root node. Defaults to “Equal” Method “Fitzjohn” is not thouroughly tested, use with caution
• Qtype –

  Either a string specifying how to esimate values for Q or a numpy array of a pre-specified Q matrix.

  “Simple”: Symmetric rates within observed states and between

  rates.

  “STD”: State Transitions Different. Transitions between states

  within the same rate class are asymetrical

• nregime (int) – Number of hidden states. nstates = 0 is equivalent to a vanilla Mk model
• constraint (str) –

  Contraints to apply to the parameters of the Q matrix. Can be one of the following:

  “Rate”: The fastest rate in the fastest regime must be faster than

  the fastest rate in the slowest regime

  “Symmetry”: For two-regime models only. The two regimes must

  have different symmetry (a>b in regime 1, b>a in regime 2)

  “None”: No contraints

ivy.chars.bayesian_models.mk_results(mcmc_obj)

Create summary graphs and statistics for an mk model

Parameters:mcmc_obj – A pymc MCMC object that has been sampled Returns:Trace plots, histograms, and summary statistics Return type:dict

ivy.chars.bayesian_models.nshifts(node, inds, n=0)

The number of regime shifts, given indices of regimes

ivy.chars.bayesian_models.valid_indices(nobschar, nregime, x, y)

Return only the valid indices for the given subarray