andova: Multi Resolution Scanning for one-way ANDOVA using the...

In MRS: Multi-Resolution Scanning for Cross-Sample and Cross-Group Differences

Multi Resolution Scanning for one-way ANDOVA using the multi-scale Beta-Binomial model

Description

This function executes the Multi Resolution Scanning algorithm to detect differences across the distributions of multiple groups having multiple replicates.

Usage

andova(
  X,
  G,
  H,
  n_groups = length(unique(G)),
  n_subgroups = NULL,
  Omega = "default",
  K = 6,
  init_state = c(0.8, 0.2, 0),
  beta = 1,
  gamma = 0.07,
  delta = 0.4,
  eta = 0,
  alpha = 0.5,
  nu_vec = 10^(seq(-1, 4)),
  return_global_null = TRUE,
  return_tree = TRUE,
  n_post_samples = 0,
  baseline = 0,
  method = "newton",
  n_grid_theta = 4
)

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.
H	Numeric vector of the replicate label of each observation. Labels are integers starting from 1.
n_groups	Number of groups.
n_subgroups	Vector indicating the number of replicates for each group.
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 limit.
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, alternative and prune, respectively.
beta	Spatial clustering parameter of the transition probability matrix. Default is beta = 1.0.
gamma	Parameter of the transition probability matrix. Default is gamma = 0.07.
delta	Parameter of the transition probability matrix. Default is delta = 0.4.
eta	Parameter of the transition probability matrix. Default is eta = 0.0.
alpha	Pseudo-counts of the Beta random probability assignments.
nu_vec	The support of the discrete uniform prior on nu.
return_global_null	Boolean indicating whether to return the marginal posterior probability of the global null.
return_tree	Boolean indicating whether to return the posterior representative tree.
n_post_samples	Number of posterior trees to sample. Default is zero.
baseline	Group used as the baseline for effect sizes. A value of zero uses the pooled mean as the baseline.
method	Numerical integration method, either "newton" or "riemann".
n_grid_theta	Number of grid points used by the numerical integration. The default of four uses the same midpoint quadrature rule as version 1.2.6.

Value

An mrs object.

References

Ma L. and Soriano J. (2018). Analysis of distributional variation through multi-scale Beta-Binomial modeling. Journal of Computational and Graphical Statistics, 27(3), 529–541. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2017.1402774")}

Examples

set.seed(12345)
n = 1000
M = 5
class_1 = sample(M, n, prob= 1:5, replace=TRUE  )
class_2 = sample(M, n, prob = 5:1, replace=TRUE )

Y_1 = rnorm(n, mean=class_1, sd = .2)
Y_2 = rnorm(n, mean=class_2, sd = .2)

X = matrix( c(Y_1, Y_2), ncol = 1)
G = c(rep(1,n),rep(2,n))
H = sample(3,2*n, replace = TRUE  )

ans = andova(X, G, H)
ans$PostGlobNull
plot1D(ans)

MRS documentation built on July 22, 2026, 5:10 p.m.