sampleG: Update household (group) level latent class indexes.

In NestedCategBayesImpute: Modeling, Imputing and Generating Synthetic Versions of Nested Categorical Data in the Presence of Impossible Combinations

Description

Update household (group) level latent class indexes.

Usage

sampleG(phi, data, omega, pi, ni, HHdata, lambda, Parallel)

Arguments

phi	Matrix of posterior samples for the individual-level probabilities for each individual-level variable by each pair of group-level and individual-level latent classes.
data	Individual level data.
omega	Matrix of the probabilities for the individual-level latent classes by the group-level latent classes.
pi	Vector of the probabilities for the group-level latent classes.
ni	Vector containing the number of individuals in each household in the data..
HHdata	Household level data.
lambda	A list of matrices of the group-level probabilities for each group-level variable by the group-level latent classes. Each matrix in the list is for each group-level variable.
Parallel	Logical indicator for running the function in parallel mode.

Details

Function for obtaining a posterior sample of the household-level latent class indexes for all households in the input data based on the corresponding full conditional distribution.

Value

A list with two variables.

G	A vector for the updated values of the household-level latent class indexes for all households in the input data.
G_Individuals	The vector G expanded to a long format to match the number of individuals in data.

Author(s)

Quanli Wang

NestedCategBayesImpute documentation built on May 2, 2019, 3:26 p.m.