sufficient_statistics: Compute sufficient statistics

In loelschlaeger/RprobitB: Bayesian Probit Choice Modeling

Compute sufficient statistics

Description

This function computes sufficient statistics from an RprobitB_data object for the Gibbs sampler to save computation time.

Usage

sufficient_statistics(data, normalization)

Arguments

data	An object of class RprobitB_data.
normalization	An object of class RprobitB_normalization, which can be created via RprobitB_normalization.

Value

A list of sufficient statistics on the data for Gibbs sampling, containing

• the elements N, T, J, P_f and P_r from data,

• Tvec, the vector of choice occasions for each decider of length N,

• csTvec, a vector of length N with the cumulated sums of Tvec starting from 0,

• W, a list of design matrices differenced with respect to alternative number normalization$level$level for each decider in each choice occasion with covariates that are linked to a fixed coefficient (or NA if P_f = 0),

• X, a list of design matrices differenced with respect to alternative number normalization$level$level for each decider in each choice occasion with covariates that are linked to a random coefficient (or NA if P_r = 0),

• y, a matrix of dimension N x max(Tvec) with the observed choices of deciders in rows and choice occasions in columns, decoded to numeric values with respect to their appearance in data$alternatives, where rows are filled with NA in case of an unbalanced panel,

• WkW, a matrix of dimension P_f^2 x (J-1)^2, the sum over Kronecker products of each transposed element in W with itself,

• XkX, a list of length N, each element is constructed in the same way as WkW but with the elements in X and separately for each decider,

• rdiff (for the ranked case only), a list of matrices that reverse the base differencing and instead difference in such a way that the resulting utility vector is negative.

loelschlaeger/RprobitB documentation built on Oct. 15, 2024, 11:08 a.m.