Getting confidence interval for prediction from statsmodel Robust Linear model I'm using statsmodels to fit a statistical model. I have a formula that is fitted like this:
formula = "Y ~ X1 + X2 + X1:X2"
model =  rlm(formula, data=x_train)
result = model.fit()

After I fit the model I want to get, not only the predictions but the confidence interval for the predictions. If I was using the regular ols I could do something like this:
predictions = result.get_prediction(x_test)
predictions.summary_frame(alpha=0.05)

But with the robust model I get the error below:
AttributeError: 'RLMResults' object has no attribute 'get_prediction'

How can I get a confidence interval for my prediction with this model?

(preliminary answer)
The confidence interval for the predicted mean or conditional expectation X b depends on the estimated covariance of the parameters V(b).
The variance of a linear prediction or a linear combination of parameters is x V(b) x. This is the same as in the t- or z-test.
So in statsmodels, the confidence interval for the predicted mean can be obtained by
results.t_test(x_test)
Prediction interval, i.e. confidence interval for a new observation y, would depend on distributional assumptions and is not directly available in statsmodels.
Prediction interval for robust regression with MM-estimator
as follow-up, I opened
https://github.com/statsmodels/statsmodels/issues/8304
