scMANOVApermTest: Multivariate ANalysis Of VAriance log-likelihood Test with...

In semicontMANOVA: Multivariate ANalysis of VAriance with Ridge Regularization for Semicontinuous High-Dimensional Data

Multivariate ANalysis Of VAriance log-likelihood Test with Ridge Regularization for Semicontinuous High-Dimensional Data

Description

scMANOVApermTest uses a permutation procedure to perform a test based on a Multivariate ANalysis Of VAriance(MANOVA) Likelihood Ratio test statistic with a ridge regularization. The statistic is developed for semicontinuous and high-dimensional data, but can be used also in low-dimensional scenarios.

Usage

scMANOVApermTest(x, n, lambda = NULL, lambda0 = NULL, lambda.step = 0.1,
  ident = FALSE, tol = 1e-08, penalty = function(n, p) log(n), B = 500,
  parallel = c("no", "multicore", "snow"), ncpus = 1L, cl = NULL,
  only.pvalue = TRUE, rm.vars = NA, ...)

Arguments

x	data.frame or matrix of data with units on the rows and variables on the columns
n	vector. The length corresponds to the number of groups, the elements to the number of observations in each group
lambda	scalar or a vector of length 2. Ridge regularization parameter. The optimal value of lambda is searched in the specified interval when it is a vector of length 2, otherwise it is used as the optimal value
lambda0	NULL, a scalar or a vector of length 2. Ridge regularization parameter under null hypothesis. The optimal value of lambda0 is searched in the specified interval when it is a vector of length 2, otherwise it is used as the optimal value
lambda.step	scalar. Step size used in the optimization procedure to find the smallest value of lambda (and lambda0) that makes the covariance matrices, under the alternative and under the null hypothesis, non singular
ident	logical. If TRUE, lambda times the identity matrix is added to the raw estimated covariance matrix, if FALSE the diagonal values of the raw estimated covariance matrix are used instead
tol	scalar. Used in the optimization procedure to find the smallest value of lambda (and lambda0) that makes the covariance matrices, under the alternative and under the null hypothesis, non singular
penalty	function with two arguments: sample size (n) and number of variables (p) used as penalty function in the definition of the Information Criterion to select the optimal values for lambda and lambda0
B	scalar. Number of permutations to run in the permutation test
parallel	The type of parallel operation to be used (if any)
ncpus	integer. Number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.
cl	An optional parallel or snow cluster to use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the call
only.pvalue	logical. If TRUE only the p-value is returned
rm.vars	vector. It indicates the position of the variables to remove
...	Further parameters passed to parallel::mclapply in case of parallel="multicore"

Value

If only.pvalue=TRUE (default) a scalar which is the p-value of the Wilks statistic obtain by a permutation procedure, otherwise an object of class htest

Author(s)

Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni

References

Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054 DOI <doi:10.1002/bimj.70054> arXiv DOI <doi:10.48550/arXiv.2401.04036>

See Also

scMANOVA and scMANOVAestimation

Examples

  set.seed(1234)
  n <- c(5,5)
  p <- 20
  pmiss <- 0.1
  x <- scMANOVAsimulation(n=n, p=p, pmiss=pmiss)
  res <- scMANOVApermTest(x=x, n=n, lambda=3.59, lambda0=3.13,
    only.pvalue=FALSE)
  res

semicontMANOVA documentation built on June 11, 2025, 5:10 p.m.