Crate linregress

source
Expand description

linregress provides an easy to use implementation of ordinary least squared linear regression with some basic statistics. See RegressionModel for details.

The builder FormulaRegressionBuilder is used to construct a model from a table of data and an R-style formula or a list of columns to use. Currently only very simple formulae are supported, see FormulaRegressionBuilder.formula for details.

§Example

use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};

let y = vec![1., 2. ,3. , 4., 5.];
let x1 = vec![5., 4., 3., 2., 1.];
let x2 = vec![729.53, 439.0367, 42.054, 1., 0.];
let x3 = vec![258.589, 616.297, 215.061, 498.361, 0.];
let data = vec![("Y", y), ("X1", x1), ("X2", x2), ("X3", x3)];
let data = RegressionDataBuilder::new().build_from(data)?;
let formula = "Y ~ X1 + X2 + X3";
let model = FormulaRegressionBuilder::new()
    .data(&data)
    .formula(formula)
    .fit()?;
let parameters: Vec<_> = model.iter_parameter_pairs().collect();
let pvalues: Vec<_> = model.iter_p_value_pairs().collect();
let standard_errors: Vec<_> = model.iter_se_pairs().collect();
assert_eq!(
    parameters,
    vec![
        ("X1", -1.0000000000000004),
        ("X2", 1.5508427875232655e-15),
        ("X3", -1.4502288259166107e-15),
    ]
);
assert_eq!(
    standard_errors,
    vec![
        ("X1", 9.22799842631787e-13),
        ("X2", 4.184801029355531e-15),
        ("X3", 2.5552590991720465e-15),
    ]
);
assert_eq!(
    pvalues,
    vec![
        ("X1", 5.874726257570879e-13),
        ("X2", 0.7740647742008093),
        ("X3", 0.6713674042015161),
    ]
);

Macros§

  • assert_almost_eq
    Only exposed for use in doc comments. This macro is not considered part of this crate’s stable API.
  • assert_slices_almost_eq
    Only exposed for use in doc comments. This macro is not considered part of this crate’s stable API.

Structs§

Enums§

  • Error
    An error that can occur in this crate.
  • InvalidValueHandling
    How to proceed if given non real f64 values (NaN or infinity or negative infinity).

Functions§