def test_odds_ratio():
table = [(1, 2), (3, 4)]
ratio, or_ci = analyses.odds_ratio(table)
assert np.allclose(ratio, .6667, atol=.01)
assert np.allclose(or_ci, (0.03939, 11.28), atol=.01)
def or_plot(df, risk_cols, outcome_col, risk_order, outcome_order, fig=None, ax=None):
"""
df = pandas dataframe of line listing
cols = list of columns to include in analysis
risk_order: dictionary with risk_cols as keys, and a list of values as values, e.g. {'sex':['male', 'female']}
outcome_order: list of values, e.g. ['alive', 'dead']
"""
ratio_df = []
for risk_col in risk_cols:
# if risk_order != False:
order = risk_order[risk_col]
#elif risk_order == False:
# risks = ["{}".format(val) for val in df[risk_col].dropna().unique()]
# outcome_order = ["{}".format(val) for val in df[outcome_col].dropna().unique()]
_df = df[[outcome_col, risk_col]].dropna(how='any')
if len(_df[outcome_col].unique()) > 2:
raise Exception('More than two unique values in the outcome')
if len(_df[risk_col].unique()) > 2:
raise Exception('More than two unique values in {}'.format(risk_col))
table = analyses.create_2x2(_df, risk_col, outcome_col, order, outcome_order)
ratio, or_ci = analyses.odds_ratio(table)
ratio_df.append({'names': risk_col, 'ratio':ratio, 'lower':or_ci[0], 'upper':or_ci[1]})
fig, ax = _plot(ratio_df, fig, ax)
return fig, ax
def test_odds_ratio():
table = [(1, 2),
(3, 4)]
ratio, or_ci = analyses.odds_ratio(table)
assert np.allclose(ratio, .6667, atol=.01)
assert np.allclose(or_ci, (0.03939, 11.28), atol=.01)
def or_plot(df,
risk_cols,
outcome_col,
risk_order,
outcome_order,
fig=None,
ax=None):
"""
df = pandas dataframe of line listing
cols = list of columns to include in analysis
risk_order: dictionary with risk_cols as keys, and a list of values as values, e.g. {'sex':['male', 'female']}
outcome_order: list of values, e.g. ['alive', 'dead']
RETURNS
--------
fig, ax = figure and axis objects
"""
ratio_df = []
for risk_col in risk_cols:
# if risk_order != False:
order = risk_order[risk_col]
#elif risk_order == False:
# risks = ["{}".format(val) for val in df[risk_col].dropna().unique()]
# outcome_order = ["{}".format(val) for val in df[outcome_col].dropna().unique()]
_df = df[[outcome_col, risk_col]].dropna(how='any')
if len(_df[outcome_col].unique()) > 2:
raise Exception('More than two unique values in the outcome')
if len(_df[risk_col].unique()) > 2:
raise Exception(
'More than two unique values in {}'.format(risk_col))
table = analyses.create_2x2(_df, risk_col, outcome_col, order,
outcome_order)
print risk_col + ':'
ratio, or_ci = analyses.odds_ratio(table)
print '\n'
ratio_df.append({
'names': risk_col,
'ratio': ratio,
'lower': or_ci[0],
'upper': or_ci[1]
})
fig, ax = _plot(ratio_df, fig, ax)
return fig, ax
