statsmodels.discrete.discrete_model.BinaryResults.cov_params¶

BinaryResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None)¶

Compute the variance/covariance matrix.

The variance/covariance matrix can be of a linear contrast of the estimated parameters or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar.

Parameters:¶

r_matrix : array_like¶

Can be 1d, or 2d. Can be used alone or with other.

column : array_like, optional¶

Must be used on its own. Can be 0d or 1d see below.

scale : float, optional¶

Can be specified or not. Default is None, which means that the scale argument is taken from the model.

cov_p : ndarray, optional¶

The covariance of the parameters. If not provided, this value is read from self.normalized_cov_params or self.cov_params_default.

other : array_like, optional¶

Can be used when r_matrix is specified.

Returns:¶

The covariance matrix of the parameter estimates or of linear combination of parameter estimates. See Notes.

Return type:¶

ndarray

Notes

(The below are assumed to be in matrix notation.)

If no argument is specified returns the covariance matrix of a model (scale)*(X.T X)^(-1)

If contrast is specified it pre and post-multiplies as follows (scale) * r_matrix (X.T X)^(-1) r_matrix.T

If contrast and other are specified returns (scale) * r_matrix (X.T X)^(-1) other.T

If column is specified returns (scale) * (X.T X)^(-1)[column,column] if column is 0d

OR

(scale) * (X.T X)^(-1)[column][:,column] if column is 1d