Model¶

alias of larch.numba.model.NumbaModel

Attributes¶

`Model.datatree`	A source for data for the model
`Model.dataset`	Data arrays as loaded for model computation.
`Model.n_cases`	The number of cases in the attached data.

`Model.choice_ca_var`	An idca variable giving the choices as indicator values.
`Model.choice_co_vars`	A mapping giving idco expressions that evaluate to indicator values.
`Model.choice_co_code`	An idco variable giving the choices as alternative id's.

`Model.availability_ca_var`	An idca variable or expression indicating if alternatives are available.
`Model.availability_co_vars`	A mapping giving idco expressions that evaluate to availability indicators.

`Model.utility_ca`	The portion of the utility function computed from idca data.
`Model.utility_co`	The portion of the utility function computed from idco data.
`Model.quantity_ca`	The portion of the quantity function computed from idca data.

The parameter frame, unmangling on access.

`Model.most_recent_estimation_result`
`Model.possible_overspecification`

`Model.set_values`(self[, values, ...])	Set the parameter values for one or more parameters.
`Model.lock_value`(self, name, value[, note, ...])	Set a fixed value for a model parameter.
`Model.set_cap`(self[, cap])	Set the parameter values for one or more parameters.
`Model.remove_unused_parameters`([verbose])	Remove parameters that are not used in the model.

`Model.maximize_loglike`	Maximize the log likelihood.
`Model.calculate_parameter_covariance`	Compute the parameter covariance matrix.

`Model.loglike_nil`	Compute the log likelihood with no model at all.
`Model.loglike_null`	Compute the log likelihood at null values.
`Model.rho_sq_nil`	Compute the rho squared value w.r.t.
`Model.rho_sq_null`	Compute the rho squared value w.r.t.

`Model.parameter_summary`(self[, output])	Create a tabular summary of parameter values.
`Model.estimation_statistics`	Create an XHTML summary of estimation statistics.
`Model.to_xlsx`(filename[, save_now])	Write the model to an Excel file.

`Model.bhhh`([x, start_case, stop_case, ...])
`Model.check_d_loglike`	Check that the analytic and finite-difference gradients are approximately equal.
`Model.d_loglike`([x, start_case, stop_case, ...])	Compute the first derivative of log likelihood with respect to the parameters.
`Model.d_loglike_casewise`([x, start_case, ...])
`Model.loglike`([x, start_case, stop_case, ...])	Compute the log likelihood of the model.
`Model.loglike_casewise`([x, start_case, ...])
`Model.logsums`(self[, x, arr])	Returns the model logsums.
`Model.probability`(self[, x, start_case, ...])	Compute probability values.
`Model.quantity`([x, start_case, stop_case, ...])
`Model.total_weight`()	The total weight of cases in the loaded data.
`Model.utility`([x, start_case, stop_case, ...])	Compute values for the utility function contained in the model.