plot_DPM: Plot of a Dirichlet process mixture of gaussian distribution...

In NPflow: Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

Description

Plot of a Dirichlet process mixture of gaussian distribution partition

Usage

plot_DPM(
  z,
  U_mu = NULL,
  U_Sigma = NULL,
  m,
  c,
  i,
  alpha = "?",
  U_SS = NULL,
  dims2plot = 1:nrow(z),
  ellipses = ifelse(length(dims2plot) < 3, TRUE, FALSE),
  gg.add = list(theme())
)

Arguments

z	data matrix d x n with d dimensions in rows and n observations in columns.
U_mu	either a list or a matrix containing the current estimates of mean vectors of length d for each cluster. Default is NULL in which case U_SS has to be provided.
U_Sigma	either a list or an array containing the d x d current estimates for covariance matrix of each cluster. Default is NULL in which case U_SS has to be provided.
m	vector of length n containing the number of observations currently assigned to each clusters.
c	allocation vector of length n indicating which observation belongs to which clusters.
i	current MCMC iteration number.
alpha	current value of the DP concentration parameter.
U_SS	a list containing "mu" and "S". Default is NULL in which case U_mu and U_Sigma have to be provided.
dims2plot	index vector, subset of 1:d indicating which dimensions should be drawn. Default is all of them.
ellipses	a logical flag indicating whether ellipses should be drawn around clusters. Default is TRUE if only 2 dimensions are plotted, FALSE otherwise.
gg.add	a list of instructions to add to the ggplot2 instruction (see gg-add). Default is list(theme()), which adds nothing to the plot.

Author(s)

Boris Hejblum

NPflow documentation built on Feb. 6, 2020, 5:15 p.m.