run_mcmc: Run Markov-Chain Monte-Carlo sampling algorithm

In jshamsho/LFBayes: Bayesian analysis of longitudinal and multidimensional functional data

Description

This function will generate samples from the posterior distribution.

Usage

run_mcmc(y, missing, X, splineS, splineT, q1, q2, iter, thin, burnin, nchains)

Arguments

y	A list of of length n containing responses
missing	A list of length n containing missing indices for each response
X	An n by p design matrix
splineS	Basis matrix for longitudinal direction
splineT	Basis matrix for functional direction
q1	Number of latent factors for longitudinal direction
q2	Number of latent factors for functional direction
iter	Number of posterior samples to keep
thin	Keep every thin samples
burnin	Number of burnin samples to discard for posterior inference
nchains	How many chains to run

Value

A list of samples for each parameter. This list can be used as input to eigenLFChains for further post-processing

Examples

See the example in Root/Example

jshamsho/LFBayes documentation built on June 8, 2021, 4:38 a.m.