Implement yli.GLM

This commit is contained in:
RunasSudo 2023-07-16 16:22:20 +10:00
parent 4b9537643a
commit d17412ca07
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
2 changed files with 53 additions and 2 deletions

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@ -20,7 +20,7 @@ from .descriptives import auto_correlations, auto_descriptives
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
from .graphs import init_fonts, HorizontalEffectPlot from .graphs import init_fonts, HorizontalEffectPlot
from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted 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 .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind, ttest_ind_multiple
from .survival import kaplanmeier, logrank, turnbull from .survival import kaplanmeier, logrank, turnbull
from .utils import as_ordinal from .utils import as_ordinal

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@ -101,7 +101,7 @@ def regress(
model_class, df, dep, formula, model_class, df, dep, formula,
*, *,
nan_policy='warn', nan_policy='warn',
exposure=None, exposure=None, family=None,
method=None, maxiter=None, start_params=None, tolerance=None, method=None, maxiter=None, start_params=None, tolerance=None,
reduced=None, reduced=None,
bool_baselevels=False, exp=None bool_baselevels=False, exp=None
@ -121,6 +121,8 @@ def regress(
:type nan_policy: str :type nan_policy: str
:param exposure: Column in *df* for the exposure variable (numeric, some models only) :param exposure: Column in *df* for the exposure variable (numeric, some models only)
:type exposure: str :type exposure: str
:param family: See :class:`yli.GLM`
:type family: str
:param method: See statsmodels *model.fit* :param method: See statsmodels *model.fit*
:param maxiter: See statsmodels *model.fit* :param maxiter: See statsmodels *model.fit*
:param start_params: See statsmodels *model.fit* :param start_params: See statsmodels *model.fit*
@ -146,6 +148,8 @@ def regress(
fit_kwargs = {} fit_kwargs = {}
if exposure is not None: if exposure is not None:
fit_kwargs['exposure'] = exposure fit_kwargs['exposure'] = exposure
if family is not None:
fit_kwargs['family'] = family
if method is not None: if method is not None:
fit_kwargs['method'] = method fit_kwargs['method'] = method
if maxiter is not None: if maxiter is not None:
@ -639,6 +643,53 @@ def raw_terms_from_statsmodels_result(raw_result):
# ------------------------ # ------------------------
# Concrete implementations # 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): class IntervalCensoredCox(RegressionModel):
""" """
Interval-censored Cox regression Interval-censored Cox regression