Hosmer-Lemeshow goodness of Fit test in Python

Question

I have estimated a glm in python. How can I perform Hosmer-Lemeshow goodness

of fit test for this model in python?

Answer 1

I found a way, the code is not of the best quality however it works:

import pandas as pd
import numpy as np
from scipy.stats import chi2
pihat=model.predict()
pihatcat=pd.cut(pihat, np.percentile(pihat,[0,25,50,75,100]),labels=False,include_lowest=True) #here I've chosen only 4 groups


meanprobs =[0]*4 
expevents =[0]*4
obsevents =[0]*4 
meanprobs2=[0]*4 
expevents2=[0]*4
obsevents2=[0]*4 

for i in range(4):
   meanprobs[i]=np.mean(pihat[pihatcat==i])
   expevents[i]=np.sum(pihatcat==i)*np.array(meanprobs[i])
   obsevents[i]=np.sum(data.r[pihatcat==i])
   meanprobs2[i]=np.mean(1-pihat[pihatcat==i])
   expevents2[i]=np.sum(pihatcat==i)*np.array(meanprobs2[i])
   obsevents2[i]=np.sum(1-data.r[pihatcat==i]) 


data1={'meanprobs':meanprobs,'meanprobs2':meanprobs2}
data2={'expevents':expevents,'expevents2':expevents2}
data3={'obsevents':obsevents,'obsevents2':obsevents2}
m=pd.DataFrame(data1)
e=pd.DataFrame(data2)
o=pd.DataFrame(data3)

tt=sum(sum((np.array(o)-np.array(e))**2/np.array(e))) #the statistic for the test, which follows,under the null hypothesis, the chi-squared distribution with degrees of freedom equal to amount of groups - 2 
pvalue=1-chi2.cdf(tt,2)
pvalue