get_eap_estimate_riemannsum: EAP estimate with Riemannsum

In Karel-Kroeze/ShadowCAT: Multidimensional Computer Adaptive Testing with the Shadow Testing Procedure

Description

Compute the expected aposteriori estimate and covariance matrix of the latent trait theta. Integration approximation occurs via a Riemannsumm, where grid points can be adapted to the location of the posterior distribution.

Usage

get_eap_estimate_riemannsum(dimension, likelihood, prior_form, prior_parameters,
  adapt = NULL, number_gridpoints = 50, ...)

Arguments

dimension	Number of dimensions of theta.
likelihood	Likelihood function of theta, where first argument should be theta.
prior_form	String indicating the form of the prior; one of "normal" or "uniform".
prior_parameters	List containing mu and Sigma of the normal prior: list(mu = ..., Sigma = ...), or the upper and lower bound of the uniform prior: list(lower_bound = ..., upper_bound = ...). The list element Sigma should always be in matrix form. List elements mu, lower_bound, and upper_bound should always be vectors. The length of mu, lower_bound, and upper_bound should be equal to the number of dimensions. For uniform prior, true theta should fall within lower_bound and upper_bound and be not too close to one of these bounds, in order to prevent errors.
adapt	List containing mu and Sigma for the adaptation of the grid points: list(mu = ..., Sigma = ...). If NULL, adaptation with normal prior is based on the prior parameters, and no adaptation is made with uniform prior.
number_gridpoints	Value indicating the number of grid points per dimension to use for the Riemannsum.
...	Any additional arguments to likelihood.

Value

Expected aposteriori estimate of the latent trait theta, with its covariance matrix as an attribute.

Karel-Kroeze/ShadowCAT documentation built on May 7, 2019, 12:28 p.m.