Package 'TVMM'

Title: Multivariate Tests for the Vector of Means
Description: This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).
Authors: Henrique Jose de Paula Alves [aut, cre], Ben Deivide de Oliveira Batista [ctb], Daniel Furtado Ferreira [aut]
Maintainer: Henrique Jose de Paula Alves <[email protected]>
License: GPL-2
Version: 3.2.1
Built: 2025-03-10 02:33:18 UTC
Source: https://github.com/cran/TVMM

Help Index


A graphical user interface (GUI) of the package TVMM

Description

A graphical user interface (GUI) of the package TVMM to perform more general hypothesis tests on the vector of multivariate population means.

https://rpubs.com/Henriqueufla/617206 for more details.

Usage

guiTVMM(gui = TRUE)

Arguments

gui

Logical argument, TRUE or FALSE. The default is TRUE

Value

guiTVMM A graphical user interface (GUI) for performing tests on the vector of multivariate population means.

References

Henrique J. P. Alves & Daniel F. Ferreira (2019): Proposition of new alternative tests adapted to the traditional T2 test, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1693596

Examples

library(TVMM)
if(interactive()){
  guiTVMM(gui=FALSE)
}

The likelihood ratio parametric bootstrap with trace test (TLRPBT). This test is more powerful.

Description

An parametric bootstrap alternative test version of the Likelihood ratio test (LRT) to test the hypotheses about a vector of population averages. This test has the advantage of being valid for high dimension data (n <p). This test should be preferred by the user, as it controlled the type I error and had greater power in all scenarios evaluated.

Usage

LRTTBoot(X, mu0, B)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

B

the number of resamples bootstrap parametric which must be at least equal to 2000.

Value

the numerical value and the p-value of the test statistic.

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
B <- 200
X <- mvrnorm(n, mu, Sigma)
LRTTBoot(X=X, mu0=mu0, B=B)

The likelihood ratio test with trace (TLRT)

Description

An asymptotic version of the Likelihood ratio test (LRT) to test the hypotheses about a vector of population averages. This test has the advantage of being valid for high dimension data (n < p).

Usage

LRTTrace(X, mu0)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

Value

the numerical value and the p-value of the test statistic.

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
X <- mvrnorm(n, mu, Sigma)
LRTTrace(X=X, mu0=mu0)

The robust likelihood ratio test parametric bootstrap with trace test (RTLRPBT).

Description

An robust alternative test version of the likelihood ratio test (LRT) parametric bootstrap with trace (RTLPBT) to test the hypotheses about a vector of population averages using the comedian robust estimator. This test has the advantage of being valid for high dimension data (n <p)

Usage

LRTTRBoot(X, mu0, B)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

B

the number of resamples bootstrap parametric which must be at least equal to 2000.

Value

the numerical value and the p-value of the test statistic.

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
B <- 200
X <- mvrnorm(n, mu, Sigma)
LRTTRBoot(X=X, mu0=mu0, B=B)

The parametric bootstrap T2 test (T2Boot).

Description

The parametric bootstrap version of the traditional T2 test.

Usage

T2Boot(X, mu0, B)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

B

the number of resamples bootstrap parametric which must be at least equal to 2000.

Value

the numerical value and the p-value of the test statistic.

References

Henrique J. P. Alves & Daniel F. Ferreira (2019): Proposition of new alternative tests adapted to the traditional T2 test, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1693596

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
B=2000
X <- mvrnorm(n, mu, Sigma)
T2Boot(X=X, mu0=mu0, B=2000)

The traditional T2 test (T2).

Description

The traditional T2 test (T2).

Usage

T2O(X, mu0)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

Value

the numerical value and the p-value of the test statistic.

References

Henrique J. P. Alves & Daniel F. Ferreira (2019): Proposition of new alternative tests adapted to the traditional T2 test, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1693596

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
X <- mvrnorm(n, mu, Sigma)
T2O(X=X, mu0=mu0)

The T2 robust parametric bootstrap test (T2RPB).

Description

The robust parametric bootstrap version of the traditional T2 test using the comedian robust estimator.

Usage

T2RobustBoot(X, mu0, B)

Arguments

X

a matrix n x p containing n observations and p variables. It should not contain missing values (NA).

mu0

a vector containing the mean population to be tested.

B

the number of resamples bootstrap parametric which must be at least equal to 2000.

Value

the numerical value and the p-value of the test statistic.

References

Henrique J. P. Alves & Daniel F. Ferreira (2019): Proposition of new alternative tests adapted to the traditional T2 test, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1693596

Examples

set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times  = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
B <- 200
X <- mvrnorm(n, mu, Sigma)
T2RobustBoot(X=X, mu0=mu0, B=B)