Struct linregress::RegressionModel

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pub struct RegressionModel {
    pub parameters: RegressionParameters,
    pub se: RegressionParameters,
    pub ssr: f64,
    pub rsquared: f64,
    pub rsquared_adj: f64,
    pub pvalues: RegressionParameters,
    pub residuals: RegressionParameters,
    pub scale: f64,
}

Expand description

A fitted regression model.

Is the result of FormulaRegressionBuilder.fit().

If a field has only one value for the model it is given as f64.

Otherwise it is given as a RegressionParameters struct.

Fields

parameters: RegressionParameters

The model’s intercept and slopes (also known as betas).

se: RegressionParameters

The standard errors of the parameter estimates.

ssr: f64

Sum of squared residuals.

rsquared: f64

R-squared of the model.

rsquared_adj: f64

Adjusted R-squared of the model.

pvalues: RegressionParameters

The two-tailed p-values for the t-statistics of the params.

residuals: RegressionParameters

The residuals of the model.

scale: f64

A scale factor for the covariance matrix.

Note that the square root of scale is often called the standard error of the regression.

Implementations

source

impl RegressionModel

source

pub fn predict<'a, I, S>(&self, new_data: I) -> Result<Vec<f64>, Error> where
I: IntoIterator<Item = (S, Vec<f64>)>,
S: Into<Cow<'a, str>>,

Evaluates the model on given new input data and returns the predicted values.

The new data is expected to have the same columns as the original data. See RegressionDataBuilder.build for details on the type of the new_data parameter.

Note

This function does no special handling of non real values (NaN or infinity or negative infinity). Such a value in new_data will result in a corresponding meaningless prediction.

Example

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).unwrap();
let formula = "Y ~ X1 + X2 + X3";
let model = FormulaRegressionBuilder::new()
    .data(&data)
    .formula(formula)
    .fit()?;
let new_data = vec![
    ("X1", vec![2.5, 3.5]),
    ("X2", vec![2.0, 8.0]),
    ("X3", vec![2.0, 1.0]),
];
let prediction: Vec<f64> = model.predict(new_data)?;
assert_eq!(prediction, vec![3.5000000000000275, 2.5000000000000533]);