Struct LinearRegression 

pub struct LinearRegression<T = f64>
where
    T: Float + Debug + Default + Serialize,
{
    pub slope: T,
    pub intercept: T,
    pub r_squared: T,
    pub standard_error: T,
    pub n: usize,
}

Linear regression model that fits a line to data points.

Fields

slope: T

Slope of the regression line (coefficient of x)

intercept: T

Y-intercept of the regression line

r_squared: T

Coefficient of determination (R²) - goodness of fit

standard_error: T

Standard error of the estimate

n: usize

Number of data points used for regression

Implementations

impl<T> LinearRegression<T>

where T: Float + Debug + Default + NumCast + Serialize + for<'de> Deserialize<'de>,

pub fn new() -> Self

Create a new linear regression model without fitting any data

pub fn fit<U, V>(&mut self, x_values: &[U], y_values: &[V]) -> StatsResult<()>

where U: NumCast + Copy, V: NumCast + Copy,

Fit a linear model to the provided x and y data points

Arguments

• x_values - Independent variable values
• y_values - Dependent variable values (observations)

Returns

• StatsResult<()> - Ok if successful, Err with StatsError if the inputs are invalid

Errors

Returns StatsError::DimensionMismatch if X and Y arrays have different lengths. Returns StatsError::EmptyData if the input arrays are empty. Returns StatsError::ConversionError if value conversion fails. Returns StatsError::InvalidParameter if there’s no variance in X values.

pub fn predict<U>(&self, x: U) -> StatsResult<T>

where U: NumCast + Copy,

Predict y value for a given x using the fitted model

Arguments

• x - The x value to predict for

Returns

• StatsResult<T> - The predicted y value

Errors

Returns StatsError::NotFitted if the model has not been fitted (n == 0). Returns StatsError::ConversionError if type conversion fails.

Examples

use rs_stats::regression::linear_regression::LinearRegression;

let mut model = LinearRegression::<f64>::new();
model.fit(&[1.0, 2.0, 3.0], &[2.0, 4.0, 6.0]).unwrap();

let prediction = model.predict(4.0).unwrap();
assert!((prediction - 8.0).abs() < 1e-10);

pub fn predict_many<U>(&self, x_values: &[U]) -> StatsResult<Vec<T>>

where U: NumCast + Copy,

Calculate predictions for multiple x values

Arguments

• x_values - Slice of x values to predict for

Returns

• StatsResult<Vec<T>> - Vector of predicted y values

Errors

Returns StatsError::NotFitted if the model has not been fitted. Returns StatsError::ConversionError if type conversion fails for any value.

Examples

use rs_stats::regression::linear_regression::LinearRegression;

let mut model = LinearRegression::<f64>::new();
model.fit(&[1.0, 2.0, 3.0], &[2.0, 4.0, 6.0]).unwrap();

let predictions = model.predict_many(&[4.0, 5.0]).unwrap();
assert_eq!(predictions.len(), 2);

pub fn confidence_interval<U>( &self, x: U, confidence_level: f64, ) -> StatsResult<(T, T)>

where U: NumCast + Copy,

Calculate confidence intervals for the regression line

Arguments

• x - The x value to calculate confidence interval for
• confidence_level - Confidence level (0.95 for 95% confidence)

Returns

• StatsResult<(T, T)> - Tuple of (lower_bound, upper_bound), or an error if invalid

Errors

Returns StatsError::InvalidInput if there are fewer than 3 data points. Returns StatsError::InvalidParameter if confidence level is not supported (only 0.90, 0.95, 0.99). Returns StatsError::ConversionError if value conversion fails.

pub fn correlation_coefficient(&self) -> StatsResult<T>

Get the correlation coefficient (r)

The correlation coefficient ranges from -1 to 1, indicating the strength and direction of the linear relationship between x and y.

Returns

• StatsResult<T> - The correlation coefficient

Errors

Returns StatsError::NotFitted if the model has not been fitted (n == 0).

Examples

use rs_stats::regression::linear_regression::LinearRegression;

let mut model = LinearRegression::<f64>::new();
model.fit(&[1.0, 2.0, 3.0], &[2.0, 4.0, 6.0]).unwrap();

let r = model.correlation_coefficient().unwrap();
assert!((r - 1.0).abs() < 1e-10); // Perfect positive correlation

pub fn save<P: AsRef<Path>>(&self, path: P) -> Result<(), Error>

Save the model to a file

Arguments

• path - Path where to save the model

Returns

• Result<(), io::Error> - Ok if successful, Err with IO error if saving fails

pub fn save_binary<P: AsRef<Path>>(&self, path: P) -> Result<(), Error>

Save the model in binary format

Arguments

• path - Path where to save the model

Returns

• Result<(), io::Error> - Ok if successful, Err with IO error if saving fails

pub fn load<P: AsRef<Path>>(path: P) -> Result<Self, Error>

Load a model from a file

Arguments

• path - Path to the saved model file

Returns

• Result<Self, io::Error> - Loaded model or IO error

pub fn load_binary<P: AsRef<Path>>(path: P) -> Result<Self, Error>

Load a model from a binary file

Arguments

• path - Path to the saved model file

Returns

• Result<Self, io::Error> - Loaded model or IO error

pub fn to_json(&self) -> Result<String, String>

Save the model to a string in JSON format

Returns

• Result<String, String> - JSON string representation or error message

pub fn from_json(json: &str) -> Result<Self, String>

Load a model from a JSON string

Arguments

• json - JSON string containing the model data

Returns

• Result<Self, String> - Loaded model or error message

Trait Implementations

impl<T> Clone for LinearRegression<T>

where T: Float + Debug + Default + Serialize + Clone,

fn clone(&self) -> LinearRegression<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T> Debug for LinearRegression<T>

where T: Float + Debug + Default + Serialize + Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T> Default for LinearRegression<T>

where T: Float + Debug + Default + NumCast + Serialize + for<'de> Deserialize<'de>,

fn default() -> Self

Returns the “default value” for a type.

impl<'de, T> Deserialize<'de> for LinearRegression<T>

where T: Float + Debug + Default + Serialize + Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T> Serialize for LinearRegression<T>

where T: Float + Debug + Default + Serialize + Serialize,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.