knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The fastglm
package is intended to be a fast and stable alternative to the glm()
and glm2()
functions for fitting generalized lienar models. The The fastglm
package is compatible with R
's family
objects (see ?family
). The package can be installed via
devtools::install_github("jaredhuling/fastglm")
and loaded via:
library(fastglm)
Currently, the fastglm
package does not allow for formula-based data input and is restricted to matrices. We use the following example to demonstrate the usage of fastglm
:
data(esoph) x <- model.matrix(cbind(ncases, ncontrols) ~ agegp + unclass(tobgp) + unclass(alcgp), data = esoph) y <- cbind(esoph$ncases, esoph$ncontrols) gfit1 <- fastglm(x = x, y = y, family = binomial(link = "cloglog")) summary(gfit1)
The fastglm
package does not compromise computational stability for speed. In fact, for many situations where glm()
and even glm2()
do not converge, fastglm()
does converge.
As an example, consider the following data scenario, where the response distribution is (mildly) misspecified, but the link function is quite badly misspecified. In such scenarios, the standard IRLS algorithm tends to have convergence issues. The glm2()
package was designed to handle such cases, however, it still can have convergence issues. The fastglm()
package uses a similar step-halving technique as glm2()
, but it starts at better initialized values and thus tends to have better convergence properties in practice.
set.seed(1) x <- matrix(rnorm(10000 * 100), ncol = 100) y <- (exp(0.25 * x[,1] - 0.25 * x[,3] + 0.5 * x[,4] - 0.5 * x[,5] + rnorm(10000)) ) + 0.1 system.time(gfit1 <- fastglm(cbind(1, x), y, family = Gamma(link = "sqrt"))) system.time(gfit2 <- glm(y~x, family = Gamma(link = "sqrt")) ) system.time(gfit3 <- glm2::glm2(y~x, family = Gamma(link = "sqrt")) ) ## Note that fastglm() returns estimates with the ## largest likelihood logLik(gfit1) logLik(gfit2) logLik(gfit3) coef(gfit1)[1:5] coef(gfit2)[1:5] coef(gfit3)[1:5] ## check convergence of fastglm gfit1$converged ## number of IRLS iterations gfit1$iter ## now check convergence for glm() gfit2$converged gfit2$iter ## check convergence for glm2() gfit3$converged gfit3$iter ## increasing number of IRLS iterations for glm() does not help that much system.time(gfit2 <- glm(y~x, family = Gamma(link = "sqrt"), control = list(maxit = 100)) ) gfit2$converged gfit2$iter logLik(gfit1) logLik(gfit2)
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