Compositional Models to Longitudinal Microbiome Data
Ridge regression matrix
Obtaining the regression value of the FBM
Calculating the balance of the FBM model
Obtains the matrix of covariates of the BPBM
CoDaLoMic
Estimating Parameters of EstParmFunc
Estimating Parameters of EstParmFunc_FBM
Estimating BPBM
Writting the loglikelihood of the dirichlet
Writting the loglikelihood of the dirichlet
Obtaining the value of the Dirichlet parameters, the expected value an...
Obtainig the value of the dirichlet parameters, the expected value and...
Obtainig the value of the dirichlet parameters, the expected value and...
Writting the parameters in the matrix form required in BPBM model
Obtaining a vector with the covariates of the prediction
Plots the time series
Plots the time series
Plots the time series
Obtaining the graphic of the SPBal at all time points
Writting the loglikelihood of the dirichlet
Putting the reference bacteria at the last row
Putting the reference bacteria at the last row
Obtaining the principal balances values
Obtaining the selected principal balances values
Writting the loglikelihood of the dirichlet
Calculating balances
Calculating balances for a composition
Obtaining the regression value of the BPBM
PCA of the estimated parameters
Percentage of variance
Plotting a dendogram
Predicting using BPBM
Predicting using dirichl-gLV
Predicting using FBM
Preparing dataset
Analysing the quality of the estimation
Ridge regression
Solving the right side of the gLV equations
Controlling quality of the convergence in BPBM
Obtainig a table with the interpretable parameters
Obtaining a table with the SPBal information
Obtainig a table with the interpretable parameters
Obtaining the value of tau and the estimate value of the rest of the p...
Alr of a bacteria
Zero replacement
Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) <doi:10.1155/2021/9951817> and Creus-Martí et al (2022) <doi:10.1155/2022/4907527>.