hdpGLM_simParameters: Simulate the parameters of the model

In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

Simulate the parameters of the model

Description

This function generates parameters that can be used to simulate data sets from the Hierarchical Dirichlet Process of Generalized Linear Model (hdpGLM) or dpGLM

Usage

hdpGLM_simParameters(
  K,
  nCov = 2,
  nCovj = 0,
  J = 1,
  pi = NULL,
  same.K = FALSE,
  seed = NULL,
  context.effect = NULL,
  same.clusters.across.contexts = NULL,
  context.dependent.cluster = NULL
)

Arguments

K	integer, the number of clusters. If there are multiple contexts, K is the average number of clusters across contexts, and each context gets a number of clusters sampled from a Poisson distribution, except if same.K is TRUE.
nCov	integer, the number of covariates of the GLM components
nCovj	an integer indicating the number of covariates determining the average parameter of the base measure of the Dirichlet process prior
J	an integer representing the number of contexts @param parameters either NULL or a list with the parameters to generate the model. If not NULL, it must contain a sublist name beta, a vector named tau, and a vector named pi. The sublist beta must be a list of vectors, each one with size nCov+1 to be the coefficients of the GLM mixtures components that will generate the data. For the vector tau, if nCovj=0 (single-context case) then it must be a 1x1 matrix containing 1. If ncovj>0, it must be a (nCov+1)x(nCovj+1) matrix. The vector pi must add up to 1 and have length K.
pi	either NULL or a vector with length K that add up to 1. If not NULL, it determines the mixture probabilities
same.K	boolean, used when data is sampled from more than one context. If TRUE all contexts get the same number of clusters. If FALSE, each context gets a number of clusters sampled from a Poisson distribution with expectation equals to K (current not implemented)
seed	a seed for set.seed
context.effect	either NULL or a two dimensional integer vector. If it is NULL, all the coefficients (beta) of the individual level covariates are functions of context-level features (tau). If it is not NULL, the first component of the vector indicates the index of the lower level covariate (X) whose linear effect beta depends on context (tau) (0 is the intercept). The second component indicates the index context-level covariate (W) whose linear coefficient (tau) is non-zero.
same.clusters.across.contexts	boolean, if TRUE all the contexts will have the same number of clusters AND each cluster will have the same coefficient beta.
context.dependent.cluster	integer, indicates which cluster will be context-dependent. If zero, all clusters will be context-dependent

Value

The function returns a list with the parameters used to generate data sets from the hdpGLM model. This list can be used in the function hdpGLM_simulateData

Examples

pars = hdpGLM_simParameters(nCov=2, K=2, nCovj=3, J=20,
          same.clusters.across.contexts=FALSE, context.dependent.cluster=0) 

hdpGLM documentation built on Oct. 13, 2023, 1:17 a.m.