andova | R Documentation |
This function executes the Multi Resolution Scanning algorithm to detect differences across the distributions of multiple groups having multiple replicates.
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)
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 grop. |
Omega |
Matrix defining the vertices of the sample space.
The |
K |
Depth of the tree. Default is |
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 |
gamma |
Parameter of the transition probability matrix. Default is |
delta |
Parameter of the transition probability matrix. Default is |
eta |
Parameter of the transition probability matrix. Default is |
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. |
An mrs
object.
Ma L. and Soriano J. (2016). Analysis of distributional variation through multi-scale Beta-Binomial modeling. arXiv. http://arxiv.org/abs/1604.01443
set.seed(1) 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)
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