MAP.discrete {CopulaGAMM}

Estimation of latent variable in the dicrete case

Description

This function computes the estimation of a latent variables foe=r each cluster using the conditional a posteriori median.

Usage

MAP.discrete(vv, uu, family, rot, thC0k, dfC = NULL, nq = 35)

Arguments

vv

vector of values in (0,1)

uu

vector of values in (0,1)

family

copula family "gaussian" , "t" , "clayton" , "joe", "frank" , "fgm", gumbel", "plackett", "galambos", "huesler-reiss"

rot

rotation: 0 (default), 90, 180 (survival), or 270.

thC0k

vector of copula parameters

dfC

degrees of freedom for the Student copula (default is NULL)

nq

number of nodes and weighted for Gaussian quadrature of the product of conditional copulas; default is 31.

Value

condmed

Conditional a posteriori median.

Author(s)

Pavel Krupskii, Bouchra R. Nasri and Bruno N. Remillard

References

Krupskii, Nasri & Remillard (2023). On factor copula-based mixed regression models

Examples

uu = c(0.5228155, 0.3064417, 0.2789849, 0.5176489, 0.3587144)
vv = c(0.7816627, 0.6688788, 0.6351364, 0.7774917, 0.7264787)
thC0k=rep(17.54873,5)
MAP.discrete(vv,uu,"clayton",rot=90,thC0k,nq=35)