statsmodels.stats.mediation.Mediation¶
-
class
statsmodels.stats.mediation.
Mediation
(outcome_model, mediator_model, exposure, mediator=None, moderators=None, outcome_fit_kwargs=None, mediator_fit_kwargs=None)[source]¶ Conduct a mediation analysis.
- Parameters
- outcome_model
statsmodels
model
Regression model for the outcome. Predictor variables include the treatment/exposure, the mediator, and any other variables of interest.
- mediator_model
statsmodels
model
Regression model for the mediator variable. Predictor variables include the treatment/exposure and any other variables of interest.
- exposure
str
or
(int
,int
)tuple
The name or column position of the treatment/exposure variable. If positions are given, the first integer is the column position of the exposure variable in the outcome model and the second integer is the position of the exposure variable in the mediator model. If a string is given, it must be the name of the exposure variable in both regression models.
- mediator{
str
,int
} The name or column position of the mediator variable in the outcome regression model. If None, infer the name from the mediator model formula (if present).
- moderators
dict
Map from variable names or index positions to values of moderator variables that are held fixed when calculating mediation effects. If the keys are index position they must be tuples (i, j) where i is the index in the outcome model and j is the index in the mediator model. Otherwise the keys must be variable names.
- outcome_fit_kwargsdict-like
Keyword arguments to use when fitting the outcome model.
- mediator_fit_kwargsdict-like
Keyword arguments to use when fitting the mediator model.
- Returns a ``MediationResults`` object.
- outcome_model
Notes
The mediator model class must implement
get_distribution
.References
Imai, Keele, Tingley (2010). A general approach to causal mediation analysis. Psychological Methods 15:4, 309-334. http://imai.princeton.edu/research/files/BaronKenny.pdf
Tingley, Yamamoto, Hirose, Keele, Imai (2014). mediation : R package for causal mediation analysis. Journal of Statistical Software 59:5. http://www.jstatsoft.org/v59/i05/paper
Examples
A basic mediation analysis using formulas:
>>> import statsmodels.api as sm >>> import statsmodels.genmod.families.links as links >>> probit = links.probit >>> outcome_model = sm.GLM.from_formula("cong_mesg ~ emo + treat + age + educ + gender + income", ... data, family=sm.families.Binomial(link=probit())) >>> mediator_model = sm.OLS.from_formula("emo ~ treat + age + educ + gender + income", data) >>> med = Mediation(outcome_model, mediator_model, "treat", "emo").fit() >>> med.summary()
A basic mediation analysis without formulas. This may be slightly faster than the approach using formulas. If there are any interactions involving the treatment or mediator variables this approach will not work, you must use formulas.
>>> import patsy >>> outcome = np.asarray(data["cong_mesg"]) >>> outcome_exog = patsy.dmatrix("emo + treat + age + educ + gender + income", data, ... return_type='dataframe') >>> probit = sm.families.links.probit >>> outcome_model = sm.GLM(outcome, outcome_exog, family=sm.families.Binomial(link=probit())) >>> mediator = np.asarray(data["emo"]) >>> mediator_exog = patsy.dmatrix("treat + age + educ + gender + income", data, ... return_type='dataframe') >>> mediator_model = sm.OLS(mediator, mediator_exog) >>> tx_pos = [outcome_exog.columns.tolist().index("treat"), ... mediator_exog.columns.tolist().index("treat")] >>> med_pos = outcome_exog.columns.tolist().index("emo") >>> med = Mediation(outcome_model, mediator_model, tx_pos, med_pos).fit() >>> med.summary()
A moderated mediation analysis. The mediation effect is computed for people of age 20.
>>> fml = "cong_mesg ~ emo + treat*age + emo*age + educ + gender + income", >>> outcome_model = sm.GLM.from_formula(fml, data, ... family=sm.families.Binomial()) >>> mediator_model = sm.OLS.from_formula("emo ~ treat*age + educ + gender + income", data) >>> moderators = {"age" : 20} >>> med = Mediation(outcome_model, mediator_model, "treat", "emo", ... moderators=moderators).fit()
Methods
fit
([method, n_rep])Fit a regression model to assess mediation.