multivariate.hpp File Reference

Multivariate analysis functions. More...

#include <algorithm>
#include <cmath>
#include <cstddef>
#include <limits>
#include <stdexcept>
#include <utility>
#include <vector>
#include "statcpp/linear_regression.hpp"

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Classes
struct	statcpp::pca_result
	PCA result. More...

Namespaces
namespace	statcpp

Functions
std::vector< std::vector< double > >	statcpp::covariance_matrix (const std::vector< std::vector< double > > &data)
	Calculate sample covariance matrix.
std::vector< std::vector< double > >	statcpp::correlation_matrix (const std::vector< std::vector< double > > &data)
	Calculate Pearson correlation matrix.
std::vector< std::vector< double > >	statcpp::standardize (const std::vector< std::vector< double > > &data)
	Z-score standardization.
std::vector< std::vector< double > >	statcpp::min_max_scale (const std::vector< std::vector< double > > &data)
	Min-Max normalization (0-1 scaling)
std::pair< double, std::vector< double > >	statcpp::power_iteration (const std::vector< std::vector< double > > &matrix, std::size_t max_iter=1000, double tol=1e-10)
	Find largest eigenvalue and eigenvector using power iteration.
pca_result	statcpp::pca (const std::vector< std::vector< double > > &data, std::size_t n_components)
	Principal Component Analysis.
std::vector< std::vector< double > >	statcpp::pca_transform (const std::vector< std::vector< double > > &data, const pca_result &pca)
	Project data onto principal component space.

Detailed Description

Multivariate analysis functions.

Provides functions for multivariate data analysis including covariance matrices, correlation matrices, principal component analysis (PCA), and data standardization.

Definition in file multivariate.hpp.