Large fixed effects binomial regression in R

Question

I need to run a logistic regression on a relatively large data frame with 480.000 entries with 3 fixed effect variables. Fixed effect var A has 3233 levels, var B has 2326 level

Answer 1

Check out

glmmboot{glmmML}

http://cran.r-project.org/web/packages/glmmML/glmmML.pdf

There is also a nice document by Brostrom and Holmberg (http://cran.r-project.org/web/packages/eha/vignettes/glmmML.pdf)

Here is the example from their document:

dat <- data.frame(y = rbinom(5000, size = 1, prob = 0.5),
               x = rnorm(5000), group = rep(1:1000, each = 5))
fit1 <- glm(y ~ factor(group) + x, data = dat, family = binomial)

require(glmmML)
fit2 <- glmmboot(y ~ x, cluster = group,data = dat)

The computing time difference is "huge"!