MLX-backed generalized linear model — mlxs

Fit generalized linear models using iterative reweighted least squares (IRLS) with MLX providing the heavy lifting for weighted least squares solves. Final convergence is done at double precision on the cpu.

Usage

mlxs_glm(
  formula,
  family = mlxs_gaussian(),
  data,
  subset,
  weights,
  na.action = stats::na.exclude,
  start = NULL,
  control = list(),
  ...
)

Arguments

formula: Model formula.
family: A mlxs family object (e.g., mlxs_gaussian(), mlxs_binomial(), mlxs_poisson()). You can use "gaussian" etc.
data: Optional data frame, tibble, or environment containing the variables in the model.
subset: Optional expression for subsetting observations.
weights: Optional non-negative observation weights.
na.action: How to handle missing values.
start: Starting values for the parameters in the linear predictor.
control: Optional list of control parameters passed to mlxs_glm_control(). Control parameters can include epsilon, epsilon_f64, maxit, trace, and rank_tol.
...: Additional arguments passed to the family function when family is supplied as a function or string.

Value

An object of class c("mlxs_glm", "mlxs_model") containing elements similar to the result of stats::glm(). Unlike stats::glm(), rank-deficient model matrices are rejected rather than fit with aliased coefficients.

Examples

fit <- mlxs_glm(mpg ~ cyl + disp, family = mlxs_gaussian(), data = mtcars)
coef(fit)
#> (Intercept)         cyl        disp 
#> 34.66099167 -1.58727658 -0.02058364