apollo_unconditionals: Returns unconditionals for models with random heterogeneity

In apollo: Tools for Choice Model Estimation and Application

Returns unconditionals for models with random heterogeneity

Description

Returns unconditionals for random parameters in model, both for continuous mixtures and latent class.

Usage

apollo_unconditionals(model, apollo_probabilities, apollo_inputs)

Arguments

model	Model object. Estimated model object as returned by function apollo_estimate.
apollo_probabilities	Function. Returns probabilities of the model to be estimated. Must receive three arguments: apollo_beta: Named numeric vector. Names and values of model parameters. apollo_inputs: List containing options of the model. See apollo_validateInputs. functionality: Character. Can be either "components", "conditionals", "estimate" (default), "gradient", "output", "prediction", "preprocess", "raw", "report", "shares_LL", "validate" or "zero_LL".
apollo_inputs	List grouping most common inputs. Created by function apollo_validateInputs.

Details

This functions is only meant for use with models using continuous distributions or latent classes, or both at the same time.

Value

Depends on whether the model uses continuous mixtures or latent class.

• If the model contains a continuous mixture, it returns a list with one object per random coefficient. When using inter-individual draws only, each element will be a matrix with one row per individual, and one column per draw. When using intra- individual draws, each element will be a three-dimensional array, with one row per observation, inter-individual draws in the second dimension, and intra- individual draws in the third dimension.

• If the model contains latent classes, it returns a list with as many elements as random coefficients in the model, plus one additional element containing the class allocation probabilities.

• If the model contains both continuous mixing and latent classes, a list with the two elements described above will be returned.

apollo documentation built on Oct. 13, 2023, 1:15 a.m.