OrdinaryLeastSquares class

Implements Ordinary Least Squares method for linear regression.

This class inherits all the attributes and methods from LeastSquares.

Example:

var A = Matrix.fromList([[1, 2], [3, 4], [5, 6]]);
var b = Column.fromList([7, 8, 9]);
var ols = OrdinaryLeastSquares(A, b);
ols.fit();
print(ols.beta);  // Prints the coefficients of the regression model
print(ols.residuals); // Prints the residuals of the model
print(ols.standardError());  // Prints the standard error of the residuals
Inheritance

Constructors

OrdinaryLeastSquares.new(Matrix A, ColumnMatrix b, {DiagonalMatrix? W, EquationMethod method = EquationMethod.linear})
Creates an instance of the OrdinaryLeastSquares class.

Properties

A → Matrix
The designed or input matrix for the model.
finalinherited
b → ColumnMatrix
The absolute terms or output column matrix for the model.
finalinherited
beta ↔ Matrix
Coefficients vector, solved in the fit method.
getter/setter pairinherited
hashCode → int
The hash code for this object.
no setterinherited
method ↔ EquationMethod
Equation solving method.
getter/setter pairinherited
residuals → Matrix
Computes residuals of the fitted model.
no setterinherited
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
W → DiagonalMatrix?
An optional diagonal matrix of weights.
finalinherited

Methods

confidenceLevel() → dynamic
Compute confidence level. Actual implementation depends on the fit and residuals.
inherited
covariance([bool isOnDesignMatrix = true]) → Matrix
Compute covariance matrix either of the coefficients or of the residuals.
inherited
detectOutliers(double confidenceLevel) → List<int>
Detect outliers in the data using Chauvenet's criterion with a given confidence level.
inherited
errorEllipse() → Eigen
Compute error ellipse parameters using eigenvalue decomposition on the covariance matrix.
inherited
fit({LinearSystemMethod linear = LinearSystemMethod.leastSquares, DecompositionMethod decomposition = DecompositionMethod.cholesky}) → void
Fits the model to the data using the chosen method. The method can be either linear or using a matrix decomposition.
inherited
normal() → Matrix
Computes the normal equation matrix.
inherited
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
predict(Matrix xNew) → Matrix
Uses the fitted model to predict new outputs given new inputs xNew.
inherited
standardDeviation() → dynamic
Compute standard deviation of residuals.
inherited
standardError() → dynamic
Compute standard error of residuals, which is the standard deviation divided by the square root of the number of observations.
inherited
toString() → String
A string representation of this object.
inherited
unitVariance() → dynamic
Compute unit variance, also known as the mean square error (MSE).
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited