bmm.plot.1d: bmm.plot.1d: Overlay posterior predictive density on a...

In genome/bmm: Variational Bayesian approach to fitting mixtures of beta, binomial, or Gaussian distributions

bmm.plot.1d: Overlay posterior predictive density on a histogram of 1D data.

Description

Overlay the posterior predictive density on a histogram of 1D data.

Usage

  bmm.plot.1d(X, mu, alpha, nu, beta, E.pi, r, title, xlab, ylab)

Arguments

X	an N x D matrix with rows being the items to cluster. All entries are assumed to be proportions (i.e., between 0 and 1). Notice that there are no summation restrictions–i.e., proportions do not sum to unity across an item's dimensions.
mu	a D x N.c matrix holding the values of the shape parameters for the gamma prior distributions over the u parameters. i.e., mu[d,n] is the shape parameter governing u[d,n]. Introduced in eqn (15).
alpha	a D x N.c matrix holding the values of the rate (i.e., inverse scale) parameters for the gamma prior distributions over the u parameters. i.e., mu[d,n] is the rate parameter governing u[d,n]. Introduced in eqn (15).
nu	a D x N.c matrix holding the values of the shape parameters for the gamma prior distributions over the v parameters. i.e., nu[d,n] is the shape parameter governing v[d,n]. Introduced in eqn (16).
beta	a D x N.c matrix holding the values of the rate (i.e., inverse scale) parameters for the gamma prior distributions over the v parameters. i.e., beta[d,n] is the rate parameter governing v[d,n]. Introduced in eqn (16).
E.pi	the D-vector holding the values E[pi], i.e., the expected values of the mixing coefficients, defined in eqn (53).
r	the N x N.c matrix of responsibilities, with r[n, nc] giving the probability that item n belongs to component nc
title	plot title
xlab	x label
ylab	y label

Value

gplot object overlaying the posterior predictive density on a histogram of the data.

genome/bmm documentation built on Aug. 4, 2022, 8:01 a.m.