statsmodels.genmod.cov_struct.Stationary

class statsmodels.genmod.cov_struct.Stationary(max_lag=1, grid=None)

A stationary covariance structure.

The correlation between two observations is an arbitrary function of the distance between them. Distances up to a given maximum value are included in the covariance model.

Parameters:

max_lagint, optional

The largest distance that is included in the covariance model.

gridNone or bool, optional

If True, the index positions in the data (after dropping missing values) are used to define distances, and the time variable is ignored. If False, the time variable is used to determine distances. If None (the default), behaves as False and a FutureWarning is issued because the default will change to True in a future version.

Methods

covariance_matrix(endog_expval, index)	Returns the working covariance or correlation matrix for a given cluster of data.
covariance_matrix_solve(expval, index, ...)	Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class.
initialize(model)	Called by GEE, used by implementations that need additional setup prior to running fit.
summary()	Returns a text summary of the current estimate of the dependence structure.
update(params)	Update the association parameter values based on the current regression coefficients.

covariance_matrix_grid
update_grid
update_nogrid

Methods

covariance_matrix(endog_expval, index)	Returns the working covariance or correlation matrix for a given cluster of data.
covariance_matrix_grid(endog_expval, index)
covariance_matrix_solve(expval, index, ...)	Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class.
initialize(model)	Called by GEE, used by implementations that need additional setup prior to running fit.
summary()	Returns a text summary of the current estimate of the dependence structure.
update(params)	Update the association parameter values based on the current regression coefficients.
update_grid(params)
update_nogrid(params)