latent_to_nwishart: Transform latent variables to Normalised Wishart

In finnlindgren/multiprobit: Bayesian Multivariate Probit Regression

Description

Transform latent iid N(0,1) to Normalised Wishart matrices

Transform a Normalised Wishart matrix to latent iid N(0,1) variables

Usage

latent_to_nwishart(x, V_chol, df, lower_chol = FALSE)

latent_from_nwishart(W_chol, V_chol, df, lower_chol = FALSE)

Arguments

x	A vector of latent variables identifying the Normalised Wishart matrix, length d(d-1)/2
V_chol	The Cholesky factor of the Wishart V parameter
df	The Wishart degrees of freedom
lower_chol	TRUE if lower triangular Cholesky factors are used (default = FALSE)
W_chol	The Cholesky factor of the Normalised Wishart matrix

Value

A list with components

W_chol	The Cholesky factor of the Normalised Wishart matrix
s	Vector of scaling values
B_chol	The inner Cholesky factor of the Barlett decomposition

When lower_chol is FALSE, W_chol = (B_chol \%*\% V_chol) \%*\% diag(s).

When lower_chol is TRUE, W_chol = s * (V_chol \%*\% B_chol).

A list with components

x	A vector of latent variables identifying the Normalised Wishart matrix, length d(d-1)/2
s	Vector of scaling values
B_chol	The inner Cholesky factor of the Barlett decomposition

When lower_chol is FALSE, W_chol = (B_chol \%*\% V_chol) \%*\% diag(s).

When lower_chol is TRUE, W_chol = s * (V_chol \%*\% B_chol).

Note

This is an internal non-exported function

This is an internal non-exported function

Author(s)

Finn Lindgren

Finn Lindgren

Examples

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finnlindgren/multiprobit documentation built on June 20, 2020, 6:12 a.m.