Bayesian Immune Cell Abundance Model (BICAM)

Bayesian Immune Cell Abundance Model (BICAM)

BICAM(
  dat,
  M,
  adapt,
  burn,
  it,
  thin = 1,
  ran_eff = 1,
  chains = 4,
  cores = 4,
  v0_mu_logit = 0.01,
  ncov = 1,
  model = "Unstr",
  dis = NULL,
  tree = NULL,
  treelevels = NULL
)

Arguments

dat

data frame with dataset (proper setup displayed in tutorial)

M

number of cell types/parameters of interest

adapt

number of adaptation iterations (for compiling model)

burn

number of burn-in iterations

it

number of sampling iterations (after burn-in)

thin

number of thinning samples

ran_eff

indicate whether to use random subject effect (repeated measurements)

chains

number of chains to run

cores

number of cores

v0_mu_logit

anticipated proportion of cell types/parameters

ncov

number of covariates input into the model

model

covariance model selection

dis

distance matrix for Exp. Decay model

tree

tree-structured covariance matrix for Tree and Scaled Tree models

treelevels

list of matrices for multilevel, tree-structured covariance matrix for TreeLevels model

Value

A list of inputs and results