statsmodels.regression.mixed_linear_model.MixedLM.steepest_ascent MixedLM.steepest_ascent(params, n_iter) [source] Take steepest ascent steps to increase the log-likelihood function. Parameters: params : array-like The starting point of the optimization. n_iter: non-negative integer : Return once this number of iterations have occured. Returns: A MixedLMParameters object containing the final value of the : optimization. :

statsmodels.regression.mixed_linear_model.MixedLM.score_full MixedLM.score_full(params) [source] Calculates the score vector for the profiled log-likelihood of the mixed effects model with respect to the parameterization in which the random effects covariance matrix is represented in its full form (not using the Cholesky factor). Parameters: params : MixedLMParams or array-like The parameter at which the score function is evaluated. If array-like, must contain packed parameter values that

statsmodels.regression.mixed_linear_model.MixedLM.score_sqrt MixedLM.score_sqrt(params) [source] Returns the score vector with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters: params : MixedLMParams or array-like The model parameters. If array-like must contain packed parameters that are compatible with this model instance. Returns: The score vector. : Notes The input, if provided as a MixedLMPara

statsmodels.regression.mixed_linear_model.MixedLM.score MixedLM.score(params) [source] Returns the score vector of the profile log-likelihood. Notes The score vector that is returned is computed with respect to the parameterization defined by this model instance?s use_sqrt attribute. The input value params can be with respect to any parameterization.

statsmodels.regression.mixed_linear_model.MixedLM.predict MixedLM.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.regression.mixed_linear_model.MixedLM.loglike MixedLM.loglike(params) [source] Evaluate the (profile) log-likelihood of the linear mixed effects model. Parameters: params : MixedLMParams, or array-like. The parameter value. If array-like, must be a packed parameter vector compatible with this model. Returns: The log-likelihood value at `params`. : Notes This is the profile likelihood in which the scale parameter scale has been profiled out. The input parameter state, if pro

statsmodels.regression.mixed_linear_model.MixedLM.initialize MixedLM.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.regression.mixed_linear_model.MixedLM.information MixedLM.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.mixed_linear_model.MixedLM.hessian_sqrt MixedLM.hessian_sqrt(params) [source] Returns the Hessian matrix of the log-likelihood evaluated at a given point, calculated with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters: params : MixedLMParams or array-like The model parameters. If array-like, must contain packed parameters that are compatible with this model. Returns: The Hes

statsmodels.regression.mixed_linear_model.MixedLM.group_list MixedLM.group_list(array) [source] Returns array split into subarrays corresponding to the grouping structure.