MLX-backed linear regression — mlxs

Fit a linear model via QR decomposition using MLX arrays on Apple Silicon devices. The interface mirrors stats::lm() for the common arguments.

Usage

mlxs_lm(
  formula,
  data,
  subset,
  weights,
  na.action = stats::na.exclude,
  rank_tol = NULL
)

Arguments

formula: Model formula.
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.
rank_tol: Optional relative tolerance used to detect rank-deficient systems. NULL uses the package default, which varies by dtype and is 1e-6 for float32 matrices. Set to FALSE to skip rank checks entirely. Note that higher numbers indicate lower tolerance.

Value

An object of class c("mlxs_lm", "mlxs_model") containing components similar to an "lm" fit, along with MLX intermediates stored in the mlx element. Note that MLX currently operates in single precision, so fitted values and diagnostics may differ from stats::lm() at around the 1e-6 level. Unlike stats::lm(), rank-deficient model matrices are rejected rather than fit with aliased coefficients.

Examples

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