Updating linear regression efficiently when adding observations and/or predictors in R

I would be interested in finding ways in R for efficiently updating a linear model when an observation or a predictor is added. biglm has an updating capability when adding observations, but my data are small enough to reside in memory (although I do have a large number of instances to update). There are ways to do this with bare hands, e.g., to update the QR factorization (see “Updating the QR Factorization and the Least Squares Problem”, by Hammarling and Lucas from 2008), but I am hoping for an existing implementation.


If the algorithm you are looking for is indeed something like Applied Statistics 274, 1992, Vol 41(2) then you could just use biglm as it does not require you to keep your data in a file.

Source : Link , Question Author : gappy , Answer Author : LudvigH

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