Create pibblefit object

pibblefit(
  D,
  N,
  Q,
  coord_system,
  iter = NULL,
  alr_base = NULL,
  ilr_base = NULL,
  Eta = NULL,
  Lambda = NULL,
  Sigma = NULL,
  Sigma_default = NULL,
  Y = NULL,
  X = NULL,
  upsilon = NULL,
  Theta = NULL,
  Xi = NULL,
  Xi_default = NULL,
  Gamma = NULL,
  init = NULL,
  names_categories = NULL,
  names_samples = NULL,
  names_covariates = NULL
)

Arguments

D

number of multinomial categories

N

number of samples

Q

number of covariates

coord_system

coordinate system objects are represented in (options include "alr", "clr", "ilr", and "proportions")

iter

number of posterior samples

alr_base

integer category used as reference (required if coord_system=="alr")

ilr_base

(D x D-1) contrast matrix (required if coord_system=="ilr")

Eta

Array of samples of Eta

Lambda

Array of samples of Lambda

Sigma

Array of samples of Sigma (null if coord_system=="proportions")

Sigma_default

Array of samples of Sigma in alr base D, used if coord_system=="proportions"

Y

DxN matrix of observed counts

X

QxN design matrix

upsilon

scalar prior dof of inverse wishart prior

Theta

prior mean of Lambda

Xi

Matrix of prior covariance for inverse wishart (null if coord_system=="proportions")

Xi_default

Matrix of prior covariance for inverse wishart in alr base D (used if coord_system=="proportions")

Gamma

QxQ covariance matrix prior for Lambda

init

matrix initial guess for Lambda used for optimization

names_categories

character vector

names_samples

character vector

names_covariates

character vector

Value

object of class pibblefit

See also