multi_posterior_mean {ProteoBayes}

Multivariate posterior distribution of the means

Description

Compute the multivariate posterior distribution of the means between multiple groups, for multiple correlated peptides. The function accounts for multiple imputations through the Draw identifier in the dataset.

Usage

multi_posterior_mean(
  data,
  mu_0 = NULL,
  lambda_0 = 1,
  Sigma_0 = NULL,
  nu_0 = 10,
  vectorised = FALSE
)

Arguments

data

A tibble or data frame containing imputed data sets for all groups. Required columns: Peptide, Group, Sample, Output. If missing data have been estimated from multiple imputations, each imputation should be identified in an optional Draw column.

mu_0

A vector, corresponding to the prior mean. If NULL, all groups are initialised with the same empirical mean for each peptide.

lambda_0

A number, corresponding to the prior covariance scaling parameter.

Sigma_0

A matrix, corresponding to the prior covariance parameter. If NULL, the identity matrix will be used by default.

nu_0

A number, corresponding to the prior degrees of freedom.

vectorised

A boolean, indicating whether we should used a vectorised version of the function. Default when nb_peptides < 30. If nb_peptides > 30, there is a high risk that the vectorised version would be slower.

Value

A tibble providing the parameters of the multivariate posterior t-distribution for the mean of the considered groups and draws for each peptide.

Examples

TRUE