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.. jupyter-execute::
:hide-code:
import set_working_directory
Testing a hypothesis – non-stationary or time-reversible
--------------------------------------------------------
We test the hypothesis that the GTR model is sufficient for a data set, compared with the GN (non-stationary general nucleotide model).
.. jupyter-execute::
from cogent3 import get_app
loader = get_app("load_aligned", format="fasta", moltype="dna")
aln = loader("data/primate_brca1.fasta")
tree = "data/primate_brca1.tree"
null = get_app("model", "GTR", tree=tree, optimise_motif_probs=True)
alt = get_app("model", "GN", tree=tree, optimise_motif_probs=True)
hyp = get_app("hypothesis", null, alt)
result = hyp(aln)
type(result)
``result`` is a ``hypothesis_result`` object. The ``repr()`` displays the likelihood ratio test statistic, degrees of freedom and associated p-value>
.. jupyter-execute::
result
In this case, we accept the null given the p-value is > 0.05. We use this object to demonstrate the properties of a ``hypothesis_result``.
``hypothesis_result`` has attributes and keys
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Accessing the test statistics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. jupyter-execute::
result.LR, result.df, result.pvalue
The null hypothesis
~~~~~~~~~~~~~~~~~~~
This model is accessed via the ``null`` attribute.
.. jupyter-execute::
result.null
.. jupyter-execute::
result.null.lf
The alternate hypothesis
~~~~~~~~~~~~~~~~~~~~~~~~
.. jupyter-execute::
result.alt.lf
Saving hypothesis results
^^^^^^^^^^^^^^^^^^^^^^^^^
You are advised to save these results as serialised data since this provides maximum flexibility for downstream analyses.
The following would write the result into a ``sqlitedb``.
.. code-block:: python
from cogent3 import get_app, open_data_store
output = open_data_store("path/to/myresults.sqlitedb", mode="w")
writer = get_app("write_db", data_store=output)
writer(result)
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