Struct OLS 

pub struct OLS;

Implementations

impl OLS

pub fn from_formula( formula: &Formula, data: &DataFrame, cov_type: CovarianceType, ) -> Result<OlsResult, GreenersError>

Fits an OLS model using a formula and DataFrame.

Examples

use greeners::{OLS, DataFrame, Formula, CovarianceType};
use ndarray::Array1;
use std::collections::HashMap;

let mut data = HashMap::new();
data.insert("y".to_string(), Array1::from(vec![1.0, 2.1, 3.2, 3.9, 5.1]));
data.insert("x1".to_string(), Array1::from(vec![1.0, 2.0, 3.0, 4.0, 5.0]));
data.insert("x2".to_string(), Array1::from(vec![2.0, 2.5, 3.0, 3.5, 4.0]));

let df = DataFrame::new(data).unwrap();
let formula = Formula::parse("y ~ x1 + x2").unwrap();

let result = OLS::from_formula(&formula, &df, CovarianceType::HC1).unwrap();
println!("R-squared: {}", result.r_squared);

pub fn detect_collinearity( x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, tolerance: f64, ) -> (ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, Vec<usize>, Vec<usize>)

Detect and remove perfectly collinear columns using QR decomposition.

Returns: (clean_x, keep_indices, omitted_indices)

pub fn fit( y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, cov_type: CovarianceType, ) -> Result<OlsResult, GreenersError>

Fits the model. Now accepts cov_type and optional variable names.

pub fn fit_with_names( y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, cov_type: CovarianceType, variable_names: Option<Vec<String>>, ) -> Result<OlsResult, GreenersError>

Fits the model with custom variable names.