1//! Regression analysis
2
3use crate::stats::bivariate::Data;
4use crate::stats::float::Float;
5
6/// A straight line that passes through the origin `y = m * x`
7#[derive(Clone, Copy)]
8pub struct Slope<A>(pub A)
9where
10 A: Float;
11
12impl<A> Slope<A>
13where
14 A: Float,
15{
16 /// Fits the data to a straight line that passes through the origin using ordinary least
17 /// squares
18 ///
19 /// - Time: `O(length)`
20 pub fn fit(data: &Data<'_, A, A>) -> Slope<A> {
21 let xs = data.0;
22 let ys = data.1;
23
24 let xy = crate::stats::dot(xs, ys);
25 let x2 = crate::stats::dot(xs, xs);
26
27 Slope(xy / x2)
28 }
29
30 /// Computes the goodness of fit (coefficient of determination) for this data set
31 ///
32 /// - Time: `O(length)`
33 pub fn r_squared(&self, data: &Data<'_, A, A>) -> A {
34 let _0 = A::cast(0);
35 let _1 = A::cast(1);
36 let m = self.0;
37 let xs = data.0;
38 let ys = data.1;
39
40 let n = A::cast(xs.len());
41 let y_bar = crate::stats::sum(ys) / n;
42
43 let mut ss_res = _0;
44 let mut ss_tot = _0;
45
46 for (&x, &y) in data.iter() {
47 ss_res = ss_res + (y - m * x).powi(2);
48 ss_tot = ss_res + (y - y_bar).powi(2);
49 }
50
51 _1 - ss_res / ss_tot
52 }
53}
54