MCCM (version 0.1.0)

MCCM_est: General Function to Estimate Mixed Correlation Coefficient Matrix

Description

Estimate the correlation matrix for dataframes containing continuous and ordinal variable, in pairs or simultaneously, using MLE, IRLS, or IGMM.

Usage

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

Value

Rmatrix

Estimated mixed correlation coefficient matrix.

std_matrix

Estimated standard deviation for each mixed correlation coefficient.

COV

The covariance matrix for MCCM (simultaneous estimation only).

Arguments

dataYX

a dataframe or matrix containing both continuous and ordinal variables.

order_indx

a vector to indicate the ordinal variables.

pair_est

bool value, TRUE for pairwise estimation, FALSE for simultaneous estimation.

MLE

bool value, TRUE for maximum likelihood estimation, FALSE for IRLS (pairwise) or IGMM (simultaneous) estimation.

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.

See Also

esti_polyserial, esti_polychoric, est_mixedGMM, summary_MCCM_est, draw_correlation_matrix

Examples

library(mvtnorm)
library(MASS)
library(polycor)
library(lavaan)
set.seed(1997)
n = 10000
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)

# pairwise MLE estimation
out_pair_MLE = MCCM_est(dataYX=data2,order_indx=indc,pair_est=TRUE,MLE=TRUE)
# pairwise IRLS estimation
out_pair_IRLS = MCCM_est(dataYX=data2,order_indx=indc,pair_est=TRUE,MLE=FALSE)
# simultaneous MLE estimation
out_sim_MLE = MCCM_est(dataYX=data2,order_indx=indc,pair_est=FALSE,MLE=TRUE)
# simultaneous IGMM estimation
out_sim_IGMM = MCCM_est(dataYX=data2,order_indx=indc,pair_est=FALSE,MLE=FALSE)

summary_MCCM_est(out_pair_MLE)
summary_MCCM_est(out_pair_IRLS)
summary_MCCM_est(out_sim_MLE)
summary_MCCM_est(out_sim_IGMM)