Implement yli.GLM
This commit is contained in:
parent
4b9537643a
commit
d17412ca07
@ -20,7 +20,7 @@ from .descriptives import auto_correlations, auto_descriptives
|
||||
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
|
||||
from .graphs import init_fonts, HorizontalEffectPlot
|
||||
from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
|
||||
from .regress import IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, Poisson, regress, vif
|
||||
from .regress import GLM, IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, Poisson, regress, vif
|
||||
from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind, ttest_ind_multiple
|
||||
from .survival import kaplanmeier, logrank, turnbull
|
||||
from .utils import as_ordinal
|
||||
|
@ -101,7 +101,7 @@ def regress(
|
||||
model_class, df, dep, formula,
|
||||
*,
|
||||
nan_policy='warn',
|
||||
exposure=None,
|
||||
exposure=None, family=None,
|
||||
method=None, maxiter=None, start_params=None, tolerance=None,
|
||||
reduced=None,
|
||||
bool_baselevels=False, exp=None
|
||||
@ -121,6 +121,8 @@ def regress(
|
||||
:type nan_policy: str
|
||||
:param exposure: Column in *df* for the exposure variable (numeric, some models only)
|
||||
:type exposure: str
|
||||
:param family: See :class:`yli.GLM`
|
||||
:type family: str
|
||||
:param method: See statsmodels *model.fit*
|
||||
:param maxiter: See statsmodels *model.fit*
|
||||
:param start_params: See statsmodels *model.fit*
|
||||
@ -146,6 +148,8 @@ def regress(
|
||||
fit_kwargs = {}
|
||||
if exposure is not None:
|
||||
fit_kwargs['exposure'] = exposure
|
||||
if family is not None:
|
||||
fit_kwargs['family'] = family
|
||||
if method is not None:
|
||||
fit_kwargs['method'] = method
|
||||
if maxiter is not None:
|
||||
@ -639,6 +643,53 @@ def raw_terms_from_statsmodels_result(raw_result):
|
||||
# ------------------------
|
||||
# Concrete implementations
|
||||
|
||||
class GLM(RegressionModel):
|
||||
# TODO: Documentation
|
||||
|
||||
@property
|
||||
def model_long_name(self):
|
||||
return 'Generalised Linear Model'
|
||||
|
||||
@property
|
||||
def model_short_name(self):
|
||||
return 'GLM'
|
||||
|
||||
@classmethod
|
||||
def fit(cls, data_dep, data_ind, family, method='irls', maxiter=None, start_params=None):
|
||||
result = cls()
|
||||
result.exp = True
|
||||
result.cov_type = 'nonrobust'
|
||||
|
||||
if family == 'binomial':
|
||||
sm_family = sm.families.Binomial()
|
||||
elif family == 'gamma':
|
||||
sm_family = sm.families.Gamma()
|
||||
elif family == 'gaussian':
|
||||
sm_family = sm.families.Gaussian()
|
||||
elif family == 'inverse_gaussian':
|
||||
sm_family = sm.families.InverseGaussian()
|
||||
elif family == 'negative_binomial':
|
||||
sm_family = sm.families.NegativeBinomial()
|
||||
elif family == 'poisson':
|
||||
sm_family = sm.families.Poisson()
|
||||
elif family == 'tweedie':
|
||||
sm_family = sm.families.Tweedie()
|
||||
else:
|
||||
raise ValueError('Unknown GLM family')
|
||||
|
||||
# Perform regression
|
||||
raw_result = sm.GLM(endog=data_dep, exog=data_ind, family=sm_family, missing='raise').fit(disp=False, method=method, start_params=start_params)
|
||||
|
||||
result.dof_model = raw_result.df_model
|
||||
result.dof_resid = raw_result.df_resid
|
||||
result.ll_model = raw_result.llf
|
||||
result.ll_null = raw_result.llnull
|
||||
|
||||
result.terms = raw_terms_from_statsmodels_result(raw_result)
|
||||
result.vcov = raw_result.cov_params()
|
||||
|
||||
return result
|
||||
|
||||
class IntervalCensoredCox(RegressionModel):
|
||||
"""
|
||||
Interval-censored Cox regression
|
||||
|
Loading…
Reference in New Issue
Block a user