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Data Science, Machine Learning and Predictive Analytics
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Blog about data science, machine learning, AI, predictive analytics and the R programming language. Silicon Valley, CA
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2026-03-08 21:49:11
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Data Science, Machine Learning and Predictive Analytics Data Science, Machine Learning and Predictive Analytics Silicon Valley, CA R: Improving Regression Speed with Rcpp and RcppArmadillo I am going to demonstrate how to improve speed in R when performing multiple linear regression. Below I compare three methods: The standard built in R function for regression is lm() . It is the slowest. A bare bones R implementation is lm.fit() which is substantially faster than lm() but still slow. The fastest method to perform multiple linear regression is to use Rcpp and RcppArmadillo which is the C++ Armadillo linear algebra library . A 1253 x 26 design matrix (X) is built from the cars_19 dataset and a simulation is run to compare the three methods: The cars_19 dataset from previous posts: str(cars_19) 'data.frame': 1253 obs. of 12 variables: $ fuel_economy_combined: int 21 28 21 26 28 11 15 18 17 15 ... $ eng_disp : num 3.5 1.8 4 2 2 8 6.2 6.2 6...
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