mrs: Multi Resolution Scanning

In jacsor/MRS: Multi-Resolution Scanning for Cross-Sample Differences

Multi Resolution Scanning

Description

This function executes the Multi Resolution Scanning algorithm to detect differences across multiple distributions.

Usage

mrs(X, G, n_groups = length(unique(G)), Omega = "default", K = 6,
  init_state = NULL, beta = 1, gamma = 0.3, delta = NULL, eta = 0.3,
  alpha = 0.5, return_global_null = TRUE, return_tree = TRUE,
  min_n_node = 0)

Arguments

X	Matrix of the data. Each row represents an observation.
G	Numeric vector of the group label of each observation. Labels are integers starting from 1.
n_groups	Number of groups.
Omega	Matrix defining the vertices of the sample space. The "default" option defines a hyperrectangle containing all the data points. Otherwise the user can define a matrix where each row represents a dimension, and the two columns contain the associated lower and upper limits for each dimension.
K	Depth of the tree. Default is K = 6, while the maximum is K = 14.
init_state	Initial state of the hidden Markov process. The three states are null, altenrative and prune, respectively.
beta	Spatial clustering parameter of the transition probability matrix. Default is beta = 1.
gamma	Parameter of the transition probability matrix. Default is gamma = 0.3.
delta	Optional parameter of the transition probability matrix. Default is delta = NULL.
eta	Parameter of the transition probability matrix. Default is eta = 0.3.
alpha	Pseudo-counts of the Beta random probability assignments. Default is alpha = 0.5.
return_global_null	Boolean indicating whether to return the posterior probability of the global null hypothesis.
return_tree	Boolean indicating whether to return the posterior representative tree.
min_n_node	Node in the tree is returned if there are more than min_n_node data-points in it.

Value

An mrs object.

References

Soriano J. and Ma L. (2016). Probabilistic multi-resolution scanning for two-sample differences. Journal of the Royal Statistical Society: Series B (Statistical Methodology). http://onlinelibrary.wiley.com/doi/10.1111/rssb.12180/abstract

Examples

set.seed(1)
n = 20
p = 2
X = matrix(c(runif(p*n/2),rbeta(p*n/2, 1, 4)), nrow=n, byrow=TRUE)
G = c(rep(1,n/2), rep(2,n/2))
ans = mrs(X=X, G=G)

jacsor/MRS documentation built on Oct. 12, 2022, 8:33 p.m.