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.. jupyter-execute::
:hide-code:
import set_working_directory
Extracting maximum likelihood estimates from a ``model_result``
---------------------------------------------------------------
If you want to get the stats from a fitted model, use the ``tabulate_stats`` app.
We demonstrate this by first fitting a 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", "GN", tree="data/primate_brca1.tree")
result = model(aln)
Create and apply ``tabulate_stats`` app
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. jupyter-execute::
tabulator = get_app("tabulate_stats")
tabulated = tabulator(result)
tabulated
``tabulated`` is a ``tabular_result`` instance which, like other result types, has ``dict`` like behaviour. It also contains key/value pairs for each model parameter type.
Edge parameters
^^^^^^^^^^^^^^^
These are all parameters that differ between edges. Since the current model is time-homogeneous (a single rate matrix), the table only has entries for the branch scalar (denoted “length”).
.. jupyter-execute::
tabulated["edge params"]
.. note:: Unless the model is time-reversible, the lengths in that table are not ENS (`Kaehler et al <https://www.ncbi.nlm.nih.gov/pubmed/28175284>`__). As we used a non-stationary nucleotide model in this example, the length values are a scalar used to adjust the matrices during optimisation.
Global parameters
^^^^^^^^^^^^^^^^^
These are the elements of the rate matrix.
.. jupyter-execute::
tabulated["global params"]
Motif parameters
^^^^^^^^^^^^^^^^
These are estimates of the nucleotide probabilities in the unobserved ancestor.
.. jupyter-execute::
tabulated["motif params"]
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