| MAP.discrete {CopulaGAMM} | R Documentation |
This function computes the estimation of a latent variables foe=r each cluster using the conditional a posteriori median.
MAP.discrete(vv, uu, family, rot, thC0k, dfC = NULL, adj = 1, nq = 35)
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) |
adj |
tuning parameter (>= 1) that can be used to prevent overflow when the cluster size n is very large; when n<=100 OR Bernoulli marginals, no adjustment is required; when n>=500 for the Poisson likelihood fails due to overflow problem; adj=3 prevents this in 100% cases |
nq |
number of nodes and weighted for Gaussian quadrature of the product of conditional copulas; default is 31. |
condmed |
Conditional a posteriori median. |
Pavel Krupskii, Bouchra R. Nasri and Bruno N. Remillard
Krupskii, Nasri & Remillard (2023). On factor copula-based mixed regression models
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)