larch.ModelGroup.loglike

ModelGroup.loglike(x: ArrayLike | dict | None = None, *, check_if_best=True, **kwargs) → float

Compute the log likelihood of the group of models.

Note that unlike for a single model, the log likelihood cannot be computed for a slice of cases, but must be computed for all cases.

Parameters:

• x (array-like or dict, optional) – New values to set for the parameters before evaluating the log likelihood. If given as array-like, the array must be a vector with length equal to the length of the parameter frame, and the given vector will replace the current values. If given as a dictionary, the dictionary is used to update the parameters.

• check_if_best (bool, default True) – If True, check if the current log likelihood is the best found so far, and if so, update the cached best log likelihood and cached best parameters.

Returns:

float