This function generates parameters that can be used to simulate data sets from the Hierarchical Dirichlet Process of Generalized Linear Model (hdpGLM) or dpGLM
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
)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.
integer, the number of covariates of the GLM components
an integer indicating the number of covariates determining the average parameter of the base measure of the Dirichlet process prior
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.
either NULL or a vector with length K that add up to 1. If not NULL, it determines the mixture probabilities
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)
a seed for set.seed
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.
boolean, if TRUE all the contexts will have the same number of clusters AND each cluster will have the same coefficient beta.
integer, indicates which cluster will be context-dependent. If zero, all clusters will be context-dependent
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
pars = hdpGLM_simParameters(nCov=2, K=2, nCovj=3, J=20,
same.clusters.across.contexts=FALSE, context.dependent.cluster=0)