P-values for glmer mixed effects logistic regression in Python - logistic-regression

Question

I have a dataset for one year for all employees with individual-level data (e.g. age, gender, promotions, etc.). Each employee is in a team of a certain manager. I have some variables on the team- and manager-levels as well (e.g. manager's tenure, team diversity, etc.). I want to explain the termination of employees (binary: left the company or not). I am running a multilevel logistic regression, where employees are grouped by their managers, therefore they share the same team- and manager-level characteristics.
So, my model looks like:
Termination ~ Age + Time in company + Promotions + Manager tenure + Percent of employees who completed training", data, groups=data[Manager_ID]
Dataset example:
data = {'Employee': ['ID1', 'ID2','ID3','ID4','ID5','ID6','ID7', 'ID8'],
'Manager_ID': ['MID1', 'MID2','MID2','MID1','MID3','MID3','MID3', 'MID1'],
'Termination': ['0', '0', '0', '0', '1', '1', '1', '0'],
'Age': ['35', '40','50','24','33','46','44', '31'],
'TimeinCompany': ['1', '3', '10', '20', '4', '0', '4', '9'],
'Promotions': ['1', '0', '0', '0', '1', '1', '1', '0'],
'Manager_Tenure': ['10', '5', '5', '10', '8', '8', '8', '10'],
'PercentCompletedTrainingTeam': ['40', '20', '20', '40', '49', '49', '49', '40']}
columns = ['Employee','Manager_ID','Age', 'TimeinCompany', 'Promotions', 'Manager_Tenure', 'AverageAgeTeam', 'PercentCompletedTrainingTeam']
data = pd.DataFrame(data, columns=columns)
I managed to run mixed effects logistic regression using lme4 package from R in Python.
importr('lme4')
model1 = r.glmer(formula=Formula('Termination ~ Age + TimeinCompany + Promotions + Manager_Tenure + PercentCompletedTrainingTeam + (1 | Manager_ID)'),
data=data)
print(r.summary(model1))
I receive the following output for the full sample:
REML criterion at convergence: 54867.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.9075 -0.3502 -0.2172 -0.0929 3.9378
Random effects:
Groups Name Variance Std.Dev.
Manager_ID (Intercept) 0.005033 0.07094
Residual 0.072541 0.26933
Number of obs: 211974, groups: Manager_ID, 24316
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.14635573 0.00299341 48.893
Age -0.00112153 0.00008079 -13.882
TimeinCompany -0.00238352 0.00010314 -23.110
Promotions -0.01754085 0.00491545 -3.569
Manager_Tenure -0.00044373 0.00010834 -4.096
PercentCompletedTrainingTeam -0.00014393 0.00002598 -5.540
Correlation of Fixed Effects:
(Intr) Age TmnCmpny Promotions Mngr_Tenure
Age -0.817
TmnCmpny 0.370 -0.616
Promotions -0.011 -0.009 -0.033
Mngr_Tenure -0.279 0.013 -0.076 0.035
PrcntCmpltT -0.309 -0.077 -0.021 -0.042 0.052
But, there are no p-values displayed. I read a lot that lme4 does not provide p-values for a number of reasons, however I have to have them for the work presentation.
I tried several possible solutions that I found, but none of them worked:
importr('lmerTest')
importr('afex')
print(r.anova(model1))
does not display any output
print(r.anova(model1, ddf="Kenward-Roger"))
only displays npar, Sum Sq, Mean Sq, F value
print(r.summary(model1, ddf="merModLmerTest"))
Provides the same output as with just summary
print(r.anova(model1, "merModLmerTest"))
only displays npar, Sum Sq, Mean Sq, F value
Any ideas on how to get p-values are much appreciated.