statsmodels.duration.hazard_regression.PHReg.from_formula#

classmethod PHReg.from_formula(formula, data, status=None, entry=None, strata=None, offset=None, subset=None, ties='breslow', missing='drop', *args, **kwargs)[source]#

Create a proportional hazards regression model from a formula and dataframe

Parameters:

formulastr or generic Formula object: The formula specifying the model
dataarray_like: The data for the model. See Notes.
statusarray_like, optional: The censoring status values; status=1 indicates that an event occurred (e.g., failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases.
entryarray_like, optional: The entry times, if left truncation occurs
strataarray_like, optional: Stratum labels. If None, all observations are taken to be in a single stratum.
offsetarray_like, optional: Array of offset values
subsetarray_like, optional: An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame
ties{‘breslow’, ‘efron’}, optional: The method used to handle tied times.
missingstr, optional: The method used to handle missing data
argsextra arguments: These are passed to the model
kwargsextra keyword arguments: These are passed to the model with one exception. The eval_env keyword is passed to patsy. It can be either a patsy:patsy.EvalEnvironment object or an integer indicating the depth of the namespace to use. For example, the default eval_env=0 uses the calling namespace. If you wish to use a “clean” environment set eval_env=-1.

Returns:

modelPHReg model instance

Notes

data must define __getitem__ with the keys in the formula terms args and kwargs are passed on to the model instantiation. E.g., a numpy structured or rec array, a dictionary, or a pandas DataFrame.