maltipoofit: Create maltipoofit object
In jsilve24/mongrel: Bayesian Multinomial Logistic Normal Regression

Description Usage Arguments Value See Also

Create maltipoofit object

maltipoofit(D, N, Q, P, 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, ellinit = NULL, names_categories = NULL,
  names_samples = NULL, names_covariates = NULL, VCScale = NULL,
  U = NULL)

`D`	number of multinomial categories
`N`	number of samples
`Q`	number of covariates
`P`	number of variance components
`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
`ellinit`	P vector initialization values for ell for optimization
`names_categories`	character vector
`names_samples`	character vector
`names_covariates`	character vector
`VCScale`	scale factors (delta) for variance components
`U`	a PQ x Q matrix of stacked variance components (each of dimension Q x Q)