# Crate linregress[−][src]

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 = model.parameters;
let standard_errors = model.se;
let pvalues = model.pvalues;
assert_eq!(
parameters.pairs(),
vec![
("X1", -0.9999999999999745),
("X2", 0.00000000000000005637851296924623),
("X3", 0.00000000000000008283304597789254),
]
);
assert_eq!(
standard_errors.pairs(),
vec![
("X1", 0.00000000000019226371555402852),
("X2", 0.0000000000000008718958950659518),
("X3", 0.0000000000000005323837152041135),
]
);
assert_eq!(
pvalues.pairs(),
vec![
("X1", 0.00000000000012239888283055414),
("X2", 0.9588921357097694),
("X3", 0.9017368322742073),
]
);``````

## Structs

A builder to create and fit a linear regression model.

A fitted regression model

A container struct for the regression data.

A builder to create a RegressionData struct for use with a `FormulaRegressionBuilder`.

A fitted regression model.

A parameter of a fitted `RegressionModel` given for the intercept and each regressor.

## Enums

An error that can occur in this crate.

How to proceed if given non real `f64` values (NaN or infinity or negative infinity).

## Functions

Fit a regression model directly on a matrix of input data

Like `fit_low_level_regression_model` but does not compute any statistics after fitting the model.