Weighted Linear Regression- R to Python - Statsmodels

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votes

I'm attempting to translate R code into Python and running into trouble trying to replicate the R lm{stats} function which contains 'weights', allowing for weights to be used in the fitting process.

My ultimate goal is to simply run a weighted linear regression in Python using the statsmodels library.

Searching through the Statsmodels issues I've located caseweights in linear models #743 and SUMM/ENH rare events, unbalanced sample, matching, weights #2701 which make me think this may not be possible with Statsmodels.

Is it possible to add weights to GLM models in Statsmodels or alternatively, is there a better way to run a weighted linear regression in python?

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pythonrlinear-regressionstatsmodels

1 Answers

1
votes

WLS has weights for the linear model, where weights are interpreted as inverse variance for the result statistics. http://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.WLS.html

The unreleased version of statsmodels has frequency weights for GLM, but no variance weights. see freq_weights in http://www.statsmodels.org/dev/generated/statsmodels.genmod.generalized_linear_model.GLM.html

(There are many open issues to expand the types of weights and adding weights to other models, but those are not available yet.)