plot_DPMsn: Plot of a Dirichlet process mixture of skew normal...

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

Plot of a Dirichlet process mixture of skew normal distribution partition

Description

Plot of a Dirichlet process mixture of skew normal distribution partition

Usage

plot_DPMsn(
  z,
  c,
  i = "",
  alpha = "?",
  U_SS,
  dims2plot = 1:nrow(z),
  ellipses = ifelse(length(dims2plot) < 3, TRUE, FALSE),
  gg.add = list(theme()),
  nbsim_dens = 1000
)

Arguments

z	data matrix d x n with d dimensions in rows and n observations in columns.
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 "xi", "psi", "S", and "df".
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.
nbsim_dens	number of simulated points used for computing clusters density contours in 2D plots. Default is 1000 points.

Author(s)

Boris Hejblum

borishejblum/NPflow documentation built on Feb. 2, 2024, 1:51 a.m.