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.
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#
tabulator = get_app("tabulate_stats")
tabulated = tabulator(result)
tabulated
3x tabular_result('global params': Table, 'edge params': Table, 'motif params': Table)
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”).
tabulated["edge params"]
| edge | parent | length |
|---|---|---|
| Galago | root | 0.1734 |
| HowlerMon | root | 0.0450 |
| Rhesus | edge.3 | 0.0215 |
| Orangutan | edge.2 | 0.0078 |
| Gorilla | edge.1 | 0.0025 |
| Human | edge.0 | 0.0061 |
| Chimpanzee | edge.0 | 0.0028 |
| edge.0 | edge.1 | 0.0000 |
| edge.1 | edge.2 | 0.0033 |
| edge.2 | edge.3 | 0.0121 |
| edge.3 | root | 0.0077 |
11 rows x 3 columns
Note
Unless the model is time-reversible, the lengths in that table are not ENS (Kaehler et al). 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.
tabulated["global params"]
| A>C | A>G | A>T | C>A | C>G | C>T | G>A | G>C | G>T | T>A |
|---|---|---|---|---|---|---|---|---|---|
| 0.8699 | 3.6640 | 0.9110 | 1.5907 | 2.1245 | 6.0240 | 8.2212 | 1.2295 | 0.6296 | 1.2502 |
| T>C |
|---|
| 3.4168 |
1 rows x 11 columns
Motif parameters#
These are estimates of the nucleotide probabilities in the unobserved ancestor.
tabulated["motif params"]
| A | C | G | T |
|---|---|---|---|
| 0.3757 | 0.1742 | 0.2095 | 0.2406 |
1 rows x 4 columns