ObtainingDIC: Writting the loglikelihood of the dirichlet

In CoDaLoMic: Compositional Models to Longitudinal Microbiome Data

Writting the loglikelihood of the dirichlet

Description

This function calculates the loglikelihood of the dirichlet for the BPBM model. Then, it calculates the loglikelihood with the parameters of each iteration of the MCMC chains and introduces the values in a vector called vectorD. DIC=(1/2)*var(vectorD)+mean(VectorD)

Usage

ObtainingDIC(cadenas, MatrizPBmodelo, E, Tt, especiemodi)

Arguments

cadenas	Matrix with the iterations (in rows) of all the Markov Chains obtained in th estimation. It is the output of "StudyingParam" adding "$AllChainsJoined".
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}
E	Number f bacteria in the dataset.
Tt	Number of time points available
especiemodi	Matrix that contains at row i the bacterial taxa of bacteria i at time points t=2,...,Tt.

Value

Returns a data.frame with the DIC value (using the rule, pD = var/2).

References

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

Examples

set.seed(314)
especie=t(gtools::rdirichlet(n=2, c(1,2,3)))
E=3
Tt=2
MatrizPBmodelo=rbind(c(1,1),c(-0.3,0.4),c(0.3,0.5))
set.seed(314)
est=Estimating_BPBM(especie,
                   Tt,
                   E,
                   MatrizPBmodelo,
                   nn.chain=3,
                   nn.burnin=1000,
                   nn.sample=5000,
                   nn.thin=10)
SumFinal=StudyingParam(est$R2jagsOutput$BUGSoutput$summary   ,est$SamplesAllChains)
cadenas=SumFinal$AllChainsJoined
especiemodi=especie[,-1]

ObtainingDIC(cadenas,MatrizPBmodelo,E,Tt,especiemodi)

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