sweepystats.linreg.LinearRegression

class sweepystats.linreg.LinearRegression(X, y, weights=None)

A class to perform linear regression based on the sweep operation.

Parameters:

Xarray-like

Design matrix of shape (n, p)

yarray-like

Response vector of shape (n,)

weightsarray-like, optional

Weight vector of shape (n,). If provided, performs weighted least squares. Weights should be non-negative. If None (default), performs ordinary least squares.

__init__(X, y, weights=None)

Methods

R2()	Computes the R² (coefficient of determination) of fit.
__init__(X, y[, weights])
coef()	Fitted coefficient values (beta hat).
coef_std()	Standard deviation of the fitted coefficient values
cov()	Estimated variance-covariance of beta hat, i.e. Var(b) = sigma2 * inv(X'X).
exclude_k(k[, force])	Exclude the `k`th variable in regression
f_test(k)	Tests whether the `k`th variable is significant by performing an F-test. The model must already be fitted. .
fit([verbose])	Perform least squares fitting by sweeping in all variables.
include_k(k[, force])	Include the `k`th variable in regression
is_fitted()
resid()	Estimate of residuals = ||y - yhat||^2
sigma2()	Estimate of sigma square.