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.. jupyter-execute::
:hide-code:
import set_working_directory
Applying a discrete-time, non-stationary nucleotide model
---------------------------------------------------------
We fit a discrete-time Markov nucleotide model. This corresponds to a Barry and Hartigan 1987 model.
.. jupyter-execute::
from cogent3 import get_app
loader = get_app("load_aligned", format="fasta", moltype="dna")
aln = loader("data/primate_brca1.fasta")
model = get_app("model", "BH", tree="data/primate_brca1.tree")
result = model(aln)
result
.. note:: DLC stands for diagonal largest in column and the value is a check on the identifiability of the model. ``unique_Q`` is another identifiability check, but it not applicable to a discrete-time model and so remains as ``None``.
Looking at the likelihood function, we see these maximum likelihood estimated values
.. jupyter-execute::
result.lf
Get a tree with branch lengths as paralinear
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This is the only possible length metric for a discrete-time process.
.. jupyter-execute::
tree = result.tree
fig = tree.get_figure()
fig.scale_bar = "top right"
fig.show(width=500, height=500)
Getting parameter estimates
^^^^^^^^^^^^^^^^^^^^^^^^^^^
For a discrete-time model, aside from the root motif probabilities, everything is edge specific. But note that the ``tabular_result`` has different keys from the continuous-time case, as demonstrated below.
.. jupyter-execute::
tabulator = get_app("tabulate_stats")
stats = tabulator(result)
stats
.. jupyter-execute::
stats["edge motif motif2 params"]
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