PredictionFBM {CoDaLoMic}R Documentation

Predicting using FBM

Description

This function calculates the expected value and variance of the bacteria at time point Tt. Then, this function calculates the expected value and variance of the bacteria at time point t=(Tt+1),...,K

Usage

PredictionFBM(
  paramEstimadosFinal,
  EspecieMaxima,
  alpha,
  K,
  esperanza,
  Var,
  E,
  Tt
)

Arguments

paramEstimadosFinal

The estimate parameters, in the following order: a11,a12,a13, a21, a22,a23, ...a(D-1)1,a(D-1)2,a(D-1)3,tau. Where D is the number of bacterial species present in the matrix especie.

EspecieMaxima

Row in which the bacteria chosen as reference is in especie.This bacteria is used as reference in the alr transformation that the model does and it is placed at the denominator of the balance)

alpha

Matrix that contains at the row i the Dirichlet parameter of the bacteria i at t=1,2,3,...,Tt.

K

Number. The function will calculate the value of the expected value and the variance at Tt and predict for the time points t=Tt+1,..,K. To predict all the time points available at the data we K=dim(especie.All)-1

esperanza

Matrix that contains at row i the expected value of the bacterial taxa of bacteria i at t=1,2,3,...,Tt-1.

Var

Matrix that contains at row i the variance of the bacterial taxa of bacteria i at t=1,2,3,...,Tt-1.

E

Number of bacteria available

Tt

Number of bacteria available

Details

The regression of this model is defined by

\mu_{it}=a_{i1}+a_{i2}\cdot\text{alr}(x_{i,(t-1)})+a_{i3}\cdot\text{Balance}(x_{i,(t-1)})\text{ for }i=1,\dots, D-1\text{ where } D \text{ is the number of bacteria}

Value

Returns a list with:

References

Creus-Martí, I., Moya, A., Santonja, F. J. (2021). A Dirichlet autoregressive model for the analysis of microbiota time-series data. Complexity, 2021, 1-16.

Examples


Tt=2
E=3
tau=5
EspecieMaxima=3
K=3
parms11=c(0.1,0.2,0.3,0.4,0.5,0.6,tau)
alpha=cbind(c(1.726793,1.892901,1.380306),
           c(1,1,3))
Expected=cbind(c(alpha[1,1]/tau, alpha[2,1]/tau, alpha[3,1]/tau  ),
              c(alpha[1,2]/tau,alpha[2,2]/tau,alpha[3,2]/tau))
Variance=cbind(c(0.03768101, 0.03920954, 0.03330857 ),
              c( 0.03683242,0.02784883, 0.0413761 ))
Expected.final=Expected[,-2]
Variance.final=Variance[,-2]

PredictionFBM(parms11,EspecieMaxima, alpha,K,Expected.final,Variance.final,E,Tt )


[Package CoDaLoMic version 0.1.1 Index]