def run_mediation(self, predict_col_categorical=False):
if predict_col_categorical:
probit = links.probit
outcome_model = sm.GLM.from_formula(
"cont1 ~ rdt1 + jelt1",
self.data.X,
family=sm.families.Binomial(link=probit()))
else:
outcome_model = sm.GLM.from_formula("cont1 ~ rdt1 + jelt1",
self.data.X)
mediator_model = sm.OLS.from_formula("rdt1 ~ jelt1", self.data.X)
med = Mediation(outcome_model,
mediator_model,
"jelt1",
mediator="rdt1").fit()
with pd.option_context('display.max_rows', None, 'display.max_columns',
None):
print(med.summary())
df_f.rename({'Full Scale IQ_2': 'IQ', 'le-rCEN': 'le_rCEN'}, axis=1, inplace=True)
df_f.rename({('2', 'GID Post'): 'GIDPost'}, axis=1, inplace=True)
big_df.rename({('2', 'GID Post'): 'GIDPost'}, axis=1, inplace=True)
import statsmodels.api as sm
from statsmodels.stats.mediation import Mediation, MediationResults
outcome_model = sm.GLM.from_formula("Phy48Grade ~ le_rCEN + IQ",
no_na_m)
mediator_model = sm.OLS.from_formula("IQ ~ le_rCEN", no_na_m)
med = Mediation(outcome_model, mediator_model, "le_rCEN", "IQ").fit()
med.summary(alpha=0.01)
outcome_model = sm.GLM.from_formula("Phy48Grade ~ le_rCEN + GIDPost",
no_na_m)
mediator_model = sm.OLS.from_formula("GIDPost ~ le_rCEN", no_na_m)
med = Mediation(outcome_model, mediator_model, "le_rCEN", "GIDPost").fit()
med.summary(alpha=0.01)
#average causal mediation effect (ACME) = a*b = c - c'
#average direct effect (ADE) = c'
#total effect = a*b + c' = c
df_f['HcDMN_phy_minus_gen'] = df_f['fc hippo-default mode phy'] - df_f['fc hippo-default mode gen']
spearmanr(df_f['HcDMN_phy_minus_gen'], df_f['GIDPost'], nan_policy='omit')
