Fitmodel: High Level Model-Fitting Wrapper

class fitr.inference.fitmodel.FitModel(name='Anon Model', loglik_func=None, params=None, generative_model=None)

An object representing a model to be fit to behavioural data. This should be viewed as a high level wrapper for multiple potential model fitting algorithms which themselves can be run by using their respective classes.

Attributes

name	(str) Name of the model. We suggest identifying model based on free parameters.
loglik_func	(function) The log-likelihood function to be used to fit the data
params	(list) List of reinforcement learning parameter objects from the rlparams module.
generative_model	(GenerativeModel object) Object representing a generative model

Methods

fit(data, method=’EM’, c_limit=0.01)

Runs the specified model fitting algorithm with the given data.

fit(data, method='EM', c_limit=0.01, verbose=True)

Runs model fitting

Parameters:

data : dict

Behavioural data.

method : {‘EM’, ‘MLE’, ‘EmpiricalPriors’, ‘MCMC’}

The inference algorithm to use. Note that the data formats for ‘MCMC’ compared to the other methods is distinct, and should correspond appropriately to the method being employed

c_limit : float

Limit at which convergence of log-posterior probability is determined (only for methods ‘EM’ and ‘EmpiricalPriors’)

verbose : bool

Controls amount of printed output during model fitting

Returns:

fitrfit : object

Representation of the model fitting results