reg_lm: Linear regression (lm)

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Linear regression (lm)

Description

Linear regression using stats::lm.

Usage

reg_lm(formula = NULL, attribute = NULL, features = NULL)

Arguments

formula	optional regression formula (e.g., y ~ x1 + x2).
attribute	target attribute name (used when formula is NULL)
features	optional vector of feature names (used when formula is NULL)

Value

returns a reg_lm object

Examples

if (requireNamespace("MASS", quietly = TRUE)) {
 data(Boston, package = "MASS")

 # Simple linear regression
 model_simple <- reg_lm(formula = medv ~ lstat)
 model_simple <- fit(model_simple, Boston)
 pred_simple <- predict(model_simple, Boston)
 head(pred_simple)

 # Polynomial regression (degree 2)
 model_poly <- reg_lm(formula = medv ~ poly(lstat, 2, raw = TRUE))
 model_poly <- fit(model_poly, Boston)
 pred_poly <- predict(model_poly, Boston)
 head(pred_poly)

 # Multiple regression
 model_multi <- reg_lm(formula = medv ~ lstat + rm + ptratio)
 model_multi <- fit(model_multi, Boston)
 pred_multi <- predict(model_multi, Boston)
 head(pred_multi)
}

daltoolbox documentation built on Feb. 10, 2026, 9:06 a.m.