Parsimony indices

Inference: Parsimony (CI / RI)¶

Compute parsimony indices for a discrete trait and visualize the trait values on the tips.

In [ ]:

import toytree

import toytree

Simulate a trait with high phylogenetic signal¶

In [40]:

tree = toytree.rtree.unittree(35, seed=42)
trait = tree.pcm.simulate_discrete_trait(nstates=3, tips_only=True, state_names="ABC", seed=7)

tree = toytree.rtree.unittree(35, seed=42) trait = tree.pcm.simulate_discrete_trait(nstates=3, tips_only=True, state_names="ABC", seed=7)

In [41]:

trait.head()

trait.head()

Out[41]:

0    B
1    B
2    B
3    C
4    C
Name: t0, dtype: object

In [42]:

ci = toytree.infer.consistency_and_retention_indices(tree, trait, npermutations=500, rng=7)
print(ci)

ci = toytree.infer.consistency_and_retention_indices(tree, trait, npermutations=500, rng=7) print(ci)

CI                                       0.250000
CI_permuted_mean                         0.169336
CI_p-value                               0.003992
RI                                       0.812500
RI_permuted_mean                         0.690750
RI_p-value                               0.003992
RCI                                      0.203125
RCI_permuted_mean                        0.117419
RCI_p-value                              0.003992
fitch_parsimony_score                    8.000000
fitch_parsimony_score_permuted_mean     11.896000
npermutations                          500.000000
dtype: float64

simulate a trait with low (random) phylogenetic signal¶

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