Implement Poisson regression

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
 from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
-from .regress import IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, regress, vif
+from .regress import 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

@@ -97,7 +97,13 @@ def vif(df, formula=None, *, nan_policy='warn'):
 	
 	return pd.Series(vifs)
 
-def regress(model_class, df, dep, formula, *, nan_policy='warn', bool_baselevels=False, exp=None):
+def regress(
+	model_class, df, dep, formula,
+	*,
+	nan_policy='warn',
+	exposure=None,
+	bool_baselevels=False, exp=None
+):
 	"""
 	Fit a statsmodels regression model
 	
@@ -111,6 +117,8 @@ def regress(model_class, df, dep, formula, *, nan_policy='warn', bool_baselevels
 	:type formula: str
 	:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
 	:type nan_policy: str
+	:param exposure: Column in *df* for the exposure variable (numeric, some models only)
+	:type exposure: str
 	:param bool_baselevels: Show reference categories for boolean independent variables even if reference category is *False*
 	:type bool_baselevels: bool
 	:param exp: Report exponentiated parameters rather than raw parameters, default (*None*) is to autodetect based on *model_class*
@@ -128,7 +136,12 @@ def regress(model_class, df, dep, formula, *, nan_policy='warn', bool_baselevels
 	dmatrices, dep_categories = df_to_dmatrices(df, dep, formula, nan_policy)
 	
 	# Fit model
-	result = model_class.fit(dmatrices[0], dmatrices[1])
+	if exposure is None:
+		result = model_class.fit(dmatrices[0], dmatrices[1])
+	else:
+		result = model_class.fit(dmatrices[0], dmatrices[1], exposure=exposure)
+	
+	# Fill in general information
 	result.df = df_ref
 	result.dep = dep
 	result.formula = formula
@@ -204,6 +217,8 @@ def df_to_dmatrices(df, dep, formula, nan_policy):
 	return dmatrices, dep_categories
 
 class RegressionModel:
+	# TODO: Documentation
+	
 	def __init__(self):
 		# Model configuration (all set in yli.regress)
 		self.df = None
@@ -1087,6 +1102,40 @@ class PenalisedLogit(RegressionModel):
 		
 		return result
 
+class Poisson(RegressionModel):
+	"""
+	Poisson regression
+	"""
+	
+	# TODO: Document example
+	
+	@property
+	def model_long_name(self):
+		return 'Poisson Regression'
+	
+	@property
+	def model_short_name(self):
+		return 'Poisson'
+	
+	@classmethod
+	def fit(cls, data_dep, data_ind, exposure=None):
+		result = cls()
+		result.exp = True
+		result.cov_type = 'nonrobust'
+		
+		# Perform regression
+		raw_result = sm.Poisson(endog=data_dep, exog=data_ind, exposure=exposure, missing='raise').fit(disp=False)
+		
+		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
+
 # ------------------
 # Brant test helpers