Estimating_BPBM: Estimating BPBM

In CoDaLoMic: Compositional Models to Longitudinal Microbiome Data

Estimating BPBM

Description

The estimation of the BPBM model is carried out using MCMC. To execute this function it is necessary to have the program Just Another Gibbs Sampler (JAGS) (Plummer, 2003) program installed.

Usage

Estimating_BPBM(
  especie,
  Tt,
  E,
  MatrizPBmodelo,
  nn.chain = 3,
  nn.burnin = 5000,
  nn.sample = 20000,
  nn.thin = 10,
  seed = NULL
)

Arguments

especie	Matrix that contains at row i the bacterial taxa of bacteria i at all time points.
Tt	Number of time points available.
E	Number of bacteria in the dataset.
MatrizPBmodelo	Matrix with the covariates of the model. In an example with two SPBal and three time points, the covariates are written in the following order: 1 1 1 SPBal_{1,t-1} SPBal_{1,t-2} SPBal_{1,t-3} SPBal_{2,t-1} SPBal_{2,t-2} SPBal_{2,t-3}
nn.chain	the number of chains to use with the simulation. Default is 3, minimum2.
nn.burnin	the number of burnin iterations. Default is 5000.
nn.sample	the number of iterations to take. Default: 20000. The markov chain will have ("sample"-"burnin")/"thin" iterations.
nn.thin	the thinning interval to be used. Default: 10.
seed	Number. Set a seed. Default seed=NULL.

Value

Returns a list with:

• List with:

  • R2jagsOutput: R2jags object with the information of the estimation.

  • SamplesAllChains: Matrix. Matrix that has the iterations of all the Markov chains joined.

References

• Creus-Martí, I., Moya, A., Santonja, F. J. (2022). Bayesian hierarchical compositional models for analysing longitudinal abundance data from microbiome studies. Complexity, 2022.

• Plummer, M. (2003, March). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, No. 125.10, pp. 1-10).

Examples

set.seed(314)
especie=t(gtools::rdirichlet(n=6, c(6,6,1,6,6)))
E=5
Tt=6
MatrizPBmodelo=rbind(c(1,1,1,1,1,1),c(-0.3,0.4,0.3,-0.7,-0.4,-0.6),c(0.3,0.5,-0.3,0.1,0.4,0.1))



Estimating_BPBM(especie,
               Tt,
               E,
               MatrizPBmodelo,
               nn.chain=3,
               nn.burnin=1000,
               nn.sample=2000,
               nn.thin=10,
               714)

CoDaLoMic documentation built on April 12, 2025, 2:18 a.m.