get_SigmaS: Computes the collection of patterns, means, variances,...

In MCARtest: Optimal Nonparametric Testing of Missing Completely at Random

Computes the collection of patterns, means, variances, covariance and correlation matrices for a given dataset with missing values.

Description

Using the same the notation of \insertCiteBB2024;textualMCARtest, computes the collection of patterns \mathbb{S}, means \mu_\mathbb{S}, variances \sigma^2_\mathbb{S}, covariance matrices \Omega_\mathbb{S} and correlation matrices \Sigma_\mathbb{S} for a dataset with missing values.

Usage

get_SigmaS(X, min_diff = 0)

Arguments

X	The dataset with incomplete data.
min_diff	A natural number such that patterns with n_S \leq |S| + min_diff are discarded. Default to zero.

Value

patterns The collection of patterns \mathbb{S}.

n_pattern The cardinality of \mathbb{S}.

data_pattern A vector where the data are grouped according to \mathbb{S}.

mu_S The collection of means.

C_S The collection of covariance matrices.

sigma_squared_S The collection of variances.

SigmaS The collection of correlation matrices.

ambient_dimension The dimension d of the data.

References

\insertRef

BB2024MCARtest

Examples

library(copula)
library(missMethods)
n = 100

cp = claytonCopula(param = c(1), dim = 5)
P = mvdc(copula = cp, margins = c("exp", "exp", "exp", "exp", "exp"),
         paramMargins = list(list(1), list(1), list(1), list(1), list(1)))
X = rMvdc(n, P)
X = delete_MCAR(X, 0.1, c(1,4,5))

get_SigmaS(X)
get_SigmaS(X, min_diff = 20)

MCARtest documentation built on June 26, 2025, 5:08 p.m.