statsmodels.regression.process_regression.ProcessMLE.covariance¶
-
ProcessMLE.
covariance
(time, scale_params, smooth_params, scale_data, smooth_data)[source]¶ Returns a Gaussian process covariance matrix.
- Parameters
- timearray_like
The time points at which the fitted covariance matrix is calculated.
- scale_paramsarray_like
The regression parameters for the scaling part of the covariance structure.
- smooth_paramsarray_like
The regression parameters for the smoothing part of the covariance structure.
- scale_data
DataFrame
The data used to determine the scale parameter, must have len(time) rows.
- smooth_data
DataFrame
The data used to determine the smoothness parameter, must have len(time) rows.
- Returns
A
covariance
matrix.
Notes
If the model was fit using formulas, scale and smooth should be Dataframes, containing all variables that were present in the respective scaling and smoothing formulas used to fit the model. Otherwise, scale and smooth should contain data arrays whose columns align with the fitted scaling and smoothing parameters.
The covariance is only for the Gaussian process and does not include the white noise variance.