| EstDiscrete {CopulaGAMM} | R Documentation |
This function computes the estimation of a copula-based 2-level hierarchical model.
EstDiscrete(
y,
disc,
family,
rot = 0,
clu,
xc = NULL,
xm = NULL,
start,
LB,
UB,
nq = 25,
dfC = NULL,
offset = NULL,
adj = 1,
prediction = TRUE
)
y |
n x 1 vector of response variable (assumed continuous). |
disc |
function for margins: 1 (Bernoulli), 2 (Poisson), 3 (Negative Binomial), 4 (Geometric). |
family |
copula family: "gaussian" , "t" , "clayton" , "frank" , "fgm", gumbel". |
rot |
rotation: 0 (default), 90, 180 (survival), or 270 |
clu |
variable of size n defining the clusters; can be a factor |
xc |
covariates of size n for the estimation of the copula, in addition to the constant; default is NULL. |
xm |
covariates of size n for the estimation of the mean of the margin, in addition to the constant; default is NULL. |
start |
starting point for the estimation; could be the ones associated with a Gaussian-copula model defined by lmer. |
LB |
lower bound for the parameters. |
UB |
upper bound for the parameters. |
nq |
number of nodes and weighted for Gaussian quadrature of the product of conditional copulas; default is 25. |
dfC |
degrees of freedom for a Student margin; default is 0. |
offset |
offset (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 |
prediction |
logical variable for prediction of latent variables V (default is TRUE). |
coefficients |
Estimated parameters |
sd |
Standard deviations of the estimated parameters |
tstat |
T statistics for the estimated parameters |
pval |
P-values of the t statistics for the estimated parameters |
gradient |
Gradient of the log-likelihood |
loglik |
Log-likelihood |
aic |
AIC coefficient |
bic |
BIC coefficient |
cov |
Covariance matrix of the estimations |
grd |
Gradients by clusters |
clu |
Cluster values |
Matxc |
Matrix of covariates defining the copula parameters, including a constant |
Matxm |
Matrix of covariates defining the margin parameters, including a constant |
V |
Estimated value of the latent variable by clusters (if prediction=TRUE) |
cluster |
Unique clusters |
family |
Copula family |
thC0 |
Estimated parameters of the copula by observation |
thF |
Estimated parameters of the margins by observation |
disc |
Discrete margin number |
rot |
rotation |
dfC |
Degrees of freedom for the Student copula |
Pavel Krupskii, Bouchra R. Nasri and Bruno N. Remillard
Krupskii, Nasri & Remillard (2023). On factor copula-based mixed regression models
data(poisson) #simulated data with poisson margins
start=c(0,0,0); LB=rep(-10,3);UB=rep(10,3)
y=poisson$y; clu=poisson$clu;xm=poisson$xm
EstDiscrete(y,disc=2,family="clayton",rot=90,clu=clu,xm=xm,start=start,LB=LB,UB=UB)