MCCM (version 0.1.0)

est_mixedGMM: Estimating Mixed Correlation Matrix by IGMM

Description

An accelerated function to estimate a mixed correlation coefficient matrix, as well as its covariance matrix, for dataframes containing continuous and ordinal variable.

Usage

est_mixedGMM(
  dataYX,
  order_indx,
  R0 = NULL,
  app = TRUE,
  korder = 2,
  max_iter = 1000,
  max_tol = 1e-08,
  show_log = FALSE
)

Value

Rhat

The estimated correlation coefficients.

COV

The estimated covariance matrix for Rhat

Arguments

dataYX

a dataframe or matrix containing both continuous and ordinal variables.

order_indx

a vector to indicate the ordinal variables.

R0

the initial value for correlation vector, default Pearson correlation matrix.

app

bool value for approximation, TRUE for Legendre approximation, FALSE for common integral.

korder

the order of Legendre approximation.

max_iter

max iteration number for IGMM.

max_tol

max tolerance for iteration algorithm.

show_log

bool value, TRUE for showing calculation log.

References

arXiv:2404.06781

Examples

library(mvtnorm)
library(MASS)
set.seed(1997)
n = 500
rho12=0.3
rho13=0.4
rho14=0.5
rho23=0.6
rho24=0.7
rho34=0.8

R = matrix(c(1,rho12,rho13,rho14,rho12,1,rho23,rho24,rho13,rho23,1,rho34,
rho14,rho24,rho34,1),4,4)
indc = c(3,4)
thresholds = list(c(),c(),0,0)
data1 = gen_mixed(n=n,R=R,indc=indc,thresholds=thresholds)
data2 = data.frame(data1$observed)
out1 = est_mixedGMM(dataYX = data2,order_indx = indc)
print(out1$Rhat)
print(out1$COV)