An object specifying a linear mixed effects model. Use the fit method to fit the model and obtain a results object.
Parameters:  endog : 1d arraylike
exog : 2d arraylike
groups : 1d arraylike
exog_re : 2d arraylike
use_sqrt : bool
missing : string


Notes
The covariates in exog and exog_re may (but need not) partially or wholly overlap.
use_sqrt should almost always be set to True. The main use case for use_sqrt=False is when complicated patterns of fixed values in the covariance structure are set (using the free argument to fit) that cannot be expressed in terms of the Cholesky factor L.
Methods
EM(fe_params, cov_re, scale[, niter_em, hist])  Run the EM algorithm from a given starting point. 
Estep(fe_params, cov_re, scale)  The Estep of the EM algorithm. 
fit([start_params, reml, niter_em, ...])  Fit a linear mixed model to the data. 
fit_regularized([start_params, method, ...])  Fit a model in which the fixed effects parameters are penalized. 
from_formula(formula, data[, re_formula, subset])  Create a Model from a formula and dataframe. 
get_scale(fe_params, cov_re)  Returns the estimated error variance based on given estimates of the slopes and random effects covariance matrix. 
group_list(array)  Returns array split into subarrays corresponding to the grouping structure. 
hessian(params)  Returns the Hessian matrix of the profile loglikelihood. 
hessian_full(params)  Calculates the Hessian matrix for the mixed effects model with respect to the parameterization in which the covariance matrix is represented directly (without squareroot transformation). 
hessian_sqrt(params)  Returns the Hessian matrix of the loglikelihood 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. 
information(params)  Fisher information matrix of model 
initialize()  Initialize (possibly reinitialize) a Model instance. 
loglike(params)  Evaluate the (profile) loglikelihood of the linear mixed effects model. 
predict(params[, exog])  After a model has been fit predict returns the fitted values. 
score(params)  Returns the score vector of the profile loglikelihood. 
score_full(params)  Calculates the score vector for the profiled loglikelihood 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). 
score_sqrt(params)  Returns the score vector with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. 
starting_values(start_params)  
steepest_ascent(params, n_iter)  Take steepest ascent steps to increase the loglikelihood function. 
Attributes
endog_names  
exog_names 