ModelFitResult

class fitr.inference.modelfitresult.MCMCFitResult(method, nsubjects, nparams, name)

Results of model fitting with MCMC

Attributes

name	(str) Model identifier. We suggest using free-parameters as identifiers
method	(str) Method employed in optimization.
nsubjects	(int) Number of subjects fitted.
nparams	(int) Number of free parameters in the fitted model.
params	(ndarray(shape=(nsubjects, nparams))) Array of parameter estimates
paramnames	(list) List of parameter names
stanfit :	Stan fit object
summary	(pandas.DataFrame) Summary of the MCMC fit results

Methods

get_paramestimates(self, FUN=np.mean)	Extracts parameter estimates
trace_plot(self, figsize=None, save_figure=False, filename=’fitr-mcstan-traceplot.pdf’)	Trace plot for fit results

get_paramestimates(FUN=<Mock name='mock.median' id='139936813788240'>)

Extracts parameter estimates

Parameters:FUN : {numpy.mean, numpy.median}

make_summary()

Creates summary of Stan fitting results

trace_plot(figsize=None, save_figure=False, filename='fitr-mcstan-traceplot.pdf')

Easy wrapper for Stan Traceplot

Parameters:

figsize : (optional) list [width in inches, height in inches]

Controls figure size

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output

class fitr.inference.modelfitresult.ModelFitResult(method, nsubjects, nparams, name=None)

Class representing the results of a fitrmodel fitting.

Attributes

name	(str) Model identifier. We suggest using free-parameters as identifiers
method	(str) Method employed in optimization.
nsubjects	(int) Number of subjects fitted.
nparams	(int) Number of free parameters in the fitted model.
params	(ndarray(shape=(nsubjects, nparams))) Array of parameter estimates
paramnames	(list) List of parameter names

Methods

set_paramnames(params)	Sets names of RL parameters to the fitrfit object
plot_ae(actual, save_figure=False, filename=’actual-estimate.pdf’)	Plots estimated parameters against actual simulated parameters
summary_table(write_csv=False, filename=’summary-table.csv’, delimiter=’,’)	Writes a CSV file with summary statistics from the present model

ae_metrics(actual, matches=None)

Computes metrics summarizing the ability of the model to fit data generated from a known model

Parameters:

matches : list

List consisting of [rlparams object, column index in actual, column index in estimates]. Ensures comparisons are being made between the same parameters, particularly when the models have different numbers of free parameters.

Returns:

DataFrame

Including summary statistics of the parameter matching

plot_ae(actual, save_figure=False, filename='actual-estimate.pdf')

Plots actual parameters (if provided) against estimates

Parameters:

actual : ndarray(shape=(nsubjects, nparams))

Array of actual parameters from a simulation

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output

set_paramnames(params)

Sets the names of the RL parameters to the fitrfit object

Parameters:

params : list

List of parameters from the rlparams module

class fitr.inference.modelfitresult.OptimizationFitResult(method, nsubjects, nparams, name)

Results of model fitting with optimization methods

Attributes

name	(str) Model identifier. We suggest using free-parameters as identifiers
method	(str) Method employed in optimization.
nsubjects	(int) Number of subjects fitted.
nparams	(int) Number of free parameters in the fitted model.
params	(ndarray(shape=(nsubjects, nparams))) Array of parameter estimates
paramnames	(list) List of parameter names
errs	(ndarray(shape=(nsubjects, nparams))) Array of parameter estimate errors
nlogpost	(ndarray(shape=(nsubjects))) Subject level negative log-posterior probability
nloglik	(float) Subject level negative log-likelihood
LME	(float) Log-model evidence
BIC	(ndarray(shape=(nsubjects))) Subject-level Bayesian Information Criterion
AIC	(ndarray(shape=(nsubjects))) Subject-level Aikake Information Criterion
summary	(DataFrame) Summary of means and standard deviations for each free parameter, as well as negative log-likelihood, log-model-evidence, BIC, and AIC for the model

Methods

plot_fit_ts(save_figure=False, filename=’fit-stats.pdf’) :	Plots the evolution of log-likelihood, log-model-evidence, AIC, and BIC over optimization iterations
param_hist(save_figure=False, filename=’param-hist.pdf’) :	Plots hitograms of parameters in the model
summary_table(write_csv=False, filename=’summary-table.csv’, delimiter=’,’)	Writes a CSV file with summary statistics from the present model

param_hist(save_figure=False, filename='param-hist.pdf')

Plots histograms of the parameter estimates

Parameters:

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output

plot_fit_ts(save_figure=False, filename='fit-stats.pdf')

Plots the log-model-evidence, BIC, and AIC over optimization iterations

Parameters:

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output

summary_table()

Generates a table summarizing the model-fitting results