I found a fix to my problem using the fastLm() from package RcppEigen, using the Jacobi singular value decomposition (SVD) (method 4) or a method based on the eigenvalue-eigenvector decomposition of X'X - method 5 of the fastLm function
install.packages("RcppEigen") library(RcppEigen) n_obs <- 1500 y <- rnorm(n_obs, 10,2.89) x1 <- rnorm(n_obs, 0.00000000000001235657,0.000000000000000045) x2 <- rnorm(n_obs, 10,3.21) X <- cbind(x1,x2) bFE <- fastLm(y ~ x1 + x2, method =4) bFE Call: fastLm.formula(formula = y ~ x1 + x2, method = 4) Coefficients: (Intercept) x1 x2 9.94832839474159414 0.00000000000012293 0.00440078989949841 Best, Raluca -- View this message in context: http://r.789695.n4.nabble.com/Error-in-lm-with-very-small-close-to-zero-regressor-tp4705185p4705328.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.