Add model_kwargs, fit_kwargs and common arguments to yli.regress

yli/regress.py

@@ -342,6 +342,9 @@ class CategoricalTerm:
 def regress(
 	model_class, df, dep, formula, *,
 	nan_policy='warn',
+	model_kwargs=None, fit_kwargs=None,
+	family=None,  # common model_kwargs
+	cov_type=None, maxiter=None, start_params=None,  # common fit_kwargs
 	bool_baselevels=False, exp=None
 ):
 	"""
@@ -351,9 +354,25 @@ def regress(
 	exp: Report exponentiated parameters rather than raw parameters
 	"""
 	
+	# Populate model_kwargs
+	if model_kwargs is None:
+		model_kwargs = {}
+	if family is not None:
+		model_kwargs['family'] = family
+	
+	# Populate fit_kwargs
+	if fit_kwargs is None:
+		fit_kwargs = {}
+	if cov_type is not None:
+		fit_kwargs['cov_type'] = cov_type
+	if maxiter is not None:
+		fit_kwargs['maxiter'] = maxiter
+	if start_params is not None:
+		fit_kwargs['start_params'] = start_params
+	
 	# Autodetect whether to exponentiate
 	if exp is None:
-		if model_class is sm.Logit or model_class is PenalisedLogit:
+		if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
 			exp = True
 		else:
 			exp = False
@@ -372,8 +391,8 @@ def regress(
 			df[col] = df[col].astype('float64')
 	
 	# Fit model
-	model = model_class.from_formula(formula=dep + ' ~ ' + formula, data=df)
+	model = model_class.from_formula(formula=dep + ' ~ ' + formula, data=df, **model_kwargs)
-	result = model.fit()
+	result = model.fit(**fit_kwargs)
 	
 	if isinstance(result, RegressionResult):
 		# Already processed!