lm

I have 500K users and I need to compute a linear regression (with intercept) for each of them. Each user has around 30 records. I tried with dplyr and lm and this is way too slow. Around 2 sec by user. df%>% group_by(user_id, add = FALSE) %>% do(lm = lm(Y ~ x, data = .)) %>% mutate(lm_b0 = summary(lm)$coeff[1], lm_b1 = summary(lm)$coeff[2]) %>% select(user_id, lm_b0, lm_b1) %>% ungroup() ) I tried to use lm.fit which is known to be faster but it doesn't seem to be compatible with dplyr . Is there a fast way to do a linear regression by group? You can just use the basic formulas for calculating

I have data which I regularly run regressions on. Each "chunk" of data gets fit a different regression. Each state, for example, might have a different function that explains the dependent value. This seems like a typical "split-apply-combine" type of problem so I'm using the plyr package. I can easily create a list of lm() objects which works well. However I can't quite wrap my head around how I use those objects later to predict values in a separate data.frame. Here's a totally contrived example illustrating what I'm trying to do: # setting up some fake data set.seed(1) funct <- function

I get two vectors as output from R analysis, respectively> [1] "216" "217" "218" "219" "220" "221" "222" "223" "224" "225" "226" [1] 10014733 10014665 10014588 10014504 10014415 10014321 10014227 10014145 10014076 10014014 10013963 Let's call the first one a and the second one b. When I do lm(b~a), it throws out Call: lm(formula = b ~ a) Coefficients: (Intercept) a217 a218 a219 a220 a221 a222 a223 a224 10014733.4 -68.1 -145.8 -229.8 -318.5 -412.8 -506.4 -588.2 -657.4 a225 a226 -719.4 -770.8 I just want to fit a simple line, which can further be plotted with abline... Is this a data structure

I have a list for formulas I want to fit to data, rather than running a loop I'd like to do this at once, for performance's sake. The estimations should still be separate, I'm not trying to estimate a SUR or anything. The following code does what I want x <- matrix(rnorm(300),ncol=3) y <- x %*% c(1,2,3)+rnorm(100) formulae <-list(y~x[,1], y~x[,2], y~x[,1] + x[,2]) lapply(formulae,lm) Unfortunately this gets somewhat slow as the length of formulae increases is there a way to truly vectorize this? If it is any help, the only results of lm I care about are coefficients, and some standard errors.