Result of multivariate Ordinary Least Squares regression.
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#include <LinearRegression.hpp>
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std::string | to_string () const |
| | Formats the result as string.
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| Eigen::VectorXd | predict (Eigen::Ref< const Eigen::MatrixXd > X) const |
| | Predicts Y given X. More...
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| double | predict_single (Eigen::Ref< const Eigen::VectorXd > x) const |
| | Predicts Y given X. More...
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double | var_y () const |
| | Estimated variance of observations Y, equal to rss / dof.
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| double | r2 () const |
| | R2 coefficient. More...
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| double | adjusted_r2 () const |
| | Adjusted R2 coefficient. More...
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| Eigen::VectorXd | beta |
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| Eigen::MatrixXd | cov |
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| unsigned int | n |
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| unsigned int | dof |
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| double | rss |
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| double | tss |
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Result of multivariate Ordinary Least Squares regression.
The cov matrix is calculated asuming independent Gaussian error terms.
◆ predict()
| Eigen::VectorXd ml::LinearRegression::MultivariateOLSResult::predict |
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Eigen::Ref< const Eigen::MatrixXd > |
X | ) |
const |
Predicts Y given X.
- Parameters
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| X | Matrix of independent variables with data points in columns. |
- Returns
- Vector of predicted Y(X) with size
X.cols().
- Exceptions
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| std::invalid_argument | If X.rows() != beta.size(). |
◆ predict_single()
| double ml::LinearRegression::MultivariateOLSResult::predict_single |
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Eigen::Ref< const Eigen::VectorXd > |
x | ) |
const |
Predicts Y given X.
- Parameters
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| x | Vector of independent variables. |
- Returns
- Predicted Y(X).
- Exceptions
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| std::invalid_argument | If X.size() != beta.size(). |
◆ beta
| Eigen::VectorXd ml::LinearRegression::MultivariateOLSResult::beta |
Fitted coefficients of the model \(\hat{y} = \vec{\beta} \cdot \vec{x}\).
◆ cov
| Eigen::MatrixXd ml::LinearRegression::MultivariateOLSResult::cov |
Covariance matrix of beta coefficients.
The documentation for this struct was generated from the following file:
Public Member Functions inherited from ml::LinearRegression::Result
1.8.17