regress.hpp File Reference

#include <limits>
#include <memory_resource>
#include <nk/value/value.hpp>
#include <nk/value/fn_handle.hpp>
#include <tuple>

Go to the source code of this file.

Classes
struct	nk::stats::NlinfitResult
	Nonlinear least-squares fit result ([beta, R, J, CovB, MSE] = nlinfit(X, y, fun, beta0)). More...
struct	nk::stats::RobustfitResult
	Robust linear regression via iteratively-reweighted least squares ([b, stats] = robustfit(X, y)). More...
struct	nk::stats::RobustcovResult
	Result of robust covariance estimation ([sigma, mu] = robustcov(X)). More...
struct	nk::stats::GlmfitResult
	GLM fit result. More...
struct	nk::stats::LassoResult
	Lasso result. More...

Enumerations
enum class	nk::stats::RobustWeight {   Andrews , Bisquare , Cauchy , Fair ,   Huber , Logistic , Ols , Talwar ,   Welsch }
	Robust regression weight functions (MATLAB robustfit set). More...
enum class	nk::stats::GlmDistribution {   Normal , Binomial , Poisson , Gamma ,   InverseGaussian }
	GLM family of error distributions. More...
enum class	nk::stats::GlmLink {   GlmLink::Identity , GlmLink::Logit , GlmLink::Log , GlmLink::Reciprocal ,   GlmLink::Probit }
	GLM link function. More...

Functions
std::tuple< Value, Value, Value, Value >	nk::stats::regress (const Value &y, const Value &X, double alpha, std::pmr::memory_resource *mr=nullptr)
	Ordinary least-squares regression ([b, bint, r, stats] = regress(y, X, alpha)).
Value	nk::stats::ridge (const Value &y, const Value &X, const Value &k, bool scaled, std::pmr::memory_resource *mr=nullptr)
	Ridge regression (B = ridge(y, X, k, scaled)).
std::tuple< Value, Value, Value, Value >	nk::stats::lscov (const Value &A, const Value &b, const Value &w, std::pmr::memory_resource *mr=nullptr)
	Weighted least squares ([x, stdx, mse, S] = lscov(A, b, w)).
NlinfitResult	nk::stats::nlinfit (const Value &X, const Value &y, FnHandle model, const Value &beta0, std::pmr::memory_resource *mr=nullptr)
	Nonlinear least-squares fit via Levenberg-Marquardt.
Value	nk::stats::nlparci (const Value &beta, const Value &R, const Value &J, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
	Parameter confidence intervals from an nlinfit fit (ci = nlparci(beta, R, J [, alpha])).
std::tuple< Value, Value >	nk::stats::nlpredci (FnHandle model, const Value &X, const Value &beta, const Value &R, const Value &J, double alpha, std::pmr::memory_resource *mr=nullptr)
	Prediction confidence intervals from an nlinfit fit ([ypred, delta] = nlpredci(fun, X, beta, R, J [, alpha])).
RobustfitResult	nk::stats::robustfit (const Value &X, const Value &y, RobustWeight weight=RobustWeight::Bisquare, double tune=std::numeric_limits< double >::quiet_NaN(), std::pmr::memory_resource *mr=nullptr)
	Robust linear regression via Iteratively Reweighted Least Squares (IRLS) (robustfit(X, y)).
RobustcovResult	nk::stats::robustcov (const Value &X, std::pmr::memory_resource *mr=nullptr)
	Robust multivariate covariance estimate via trimmed-MCD ([sigma, mu] = robustcov(X)).
GlmfitResult	nk::stats::glmfit (const Value &X, const Value &y, GlmDistribution distr, GlmLink link=GlmLink::Identity, std::pmr::memory_resource *mr=nullptr)
	Fit a generalised linear model ([b, dev] = glmfit(X, y, distr [, link])).
Value	nk::stats::glmval (const Value &b, const Value &X, GlmLink link, std::pmr::memory_resource *mr=nullptr)
	Evaluate a fitted GLM (yhat = glmval(b, X, link)).
LassoResult	nk::stats::lasso (const Value &X, const Value &y, const Value &lambdas, double alpha=1.0, std::pmr::memory_resource *mr=nullptr)
	LASSO / elastic-net linear regression ([B, FitInfo] = lasso(X, y, lambdas, alpha)).
LassoResult	nk::stats::lassoglm (const Value &X, const Value &y, GlmDistribution distr, const Value &lambdas, double alpha=1.0, std::pmr::memory_resource *mr=nullptr)
	Lasso-regularised GLM ([B, FitInfo] = lassoglm(X, y, distr, lambdas, alpha)).