tombolo.plots.league_table
1import jsonschema 2import matplotlib.pyplot as plt 3 4from .primitives.grid import _grid 5 6_matrix = { 7 "type": "object", 8 "additionalProperties": { 9 "type": "object", 10 "additionalProperties": {"type": ["number", "null"]}, 11 }, 12} 13 14_schema = { 15 "type": "object", 16 "required": ["league"], 17 "properties": { 18 "league": { 19 "type": "object", 20 "required": ["md", "lower", "upper"], 21 "properties": { 22 "md": _matrix, 23 "lower": _matrix, 24 "upper": _matrix, 25 "pval": _matrix, 26 }, 27 } 28 }, 29} 30 31 32def league_table(data: dict) -> plt.Figure: 33 """Grid of pairwise treatment comparisons. 34 35 Args: 36 data: Result dict from `tombolo.nma` or `tombolo.bnma`. Only `league` is used. 37 38 Returns: 39 A matrix where each cell shows the mean difference and confidence (or credible) 40 interval for the row treatment relative to the column treatment. Diagonal cells 41 show the treatment name. P-values are included for NMA results. 42 """ 43 jsonschema.validate(instance=data, schema=_schema) 44 return _grid(data["league"])
def
league_table(data: dict) -> matplotlib.figure.Figure:
33def league_table(data: dict) -> plt.Figure: 34 """Grid of pairwise treatment comparisons. 35 36 Args: 37 data: Result dict from `tombolo.nma` or `tombolo.bnma`. Only `league` is used. 38 39 Returns: 40 A matrix where each cell shows the mean difference and confidence (or credible) 41 interval for the row treatment relative to the column treatment. Diagonal cells 42 show the treatment name. P-values are included for NMA results. 43 """ 44 jsonschema.validate(instance=data, schema=_schema) 45 return _grid(data["league"])
Grid of pairwise treatment comparisons.
Args:
data: Result dict from tombolo.nma or tombolo.bnma. Only league is used.
Returns: A matrix where each cell shows the mean difference and confidence (or credible) interval for the row treatment relative to the column treatment. Diagonal cells show the treatment name. P-values are included for NMA results.