Implement yli.GLM

yli/__init__.py

@@ -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

yli/regress.py

@@ -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