mcovxi: Mcov - XI E-Step For Variance Estimate

In JoaoPedro2536/univ.em:

Description

The function to find the covariance matrices using the current theta of the truncated multivariate normal distributions for the rectangles which will be used to calculate the estimate of the covariance matrix.

Usage

mcovxi(data, mu, sigma)

Arguments

data	Data in the form of multivariate grouped data.
mu	Mean vector.
sigma	Covariance matrix

Value

returns a list containing in each item a variation and estimated covariance matrix.

Examples

library(MASS)
library(em.univ)
library(tmvtnorm)

simulateddata = em.univ::mult_simul(mm = c(68,68),
                          ss = base::matrix(c(3,2,2,6),2,2) ,
                          n_data_sets = 1,
                          breaks_x = c(-Inf,64,65,66,67,68,69,70,71,72,Inf),
                          breaks_y = c(-Inf,64.2,65.2,66.2,
                                       67.2,68.2,69.2,70.2,
                                       71.2,72.2,Inf),
                          lower_x = base::rep(c(-Inf,64,65,66,67,
                                          68,69,70,71,72),10),
                          lower_y = c(base::rep(-Inf,10),
                                      base::rep(64.2,10),
                                      base::rep(65.2,10),
                                      base::rep(66.2,10),
                                      base::rep(67.2,10),
                                      base::rep(68.2,10),
                                      base::rep(69.2,10),
                                      base::rep(70.2,10),
                                      base::rep(71.2,10),
                                      base::rep(72.2,10)),
                          upper_x = base::rep(c(64,65,66,67,68,69,70,
                                                71,72,Inf),10),
                          upper_y = c(base::rep(64.2,10),
                                      base::rep(65.2,10),
                                      base::rep(66.2,10),
                                      base::rep(67.2,10),
                                      base::rep(68.2,10),
                                      base::rep(69.2,10),
                                      base::rep(70.2,10),
                                      base::rep(71.2,10),
                                      base::rep(72,2,10),
                                      base::rep(Inf,10))
)


mu2<- c(67,67)
sigma2<- base::matrix(c(3.1,2.2,2.2,4.3),2,2)


out<- em.univ::mcovxi(data = simulateddata[,,1],
             mu = mu2,
             sigma = sigma2)

out

JoaoPedro2536/univ.em documentation built on Dec. 18, 2021, 1:38 a.m.