% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{loo.dcvar}
\alias{loo.dcvar}
\alias{loo.dcvar_fit}
\alias{loo.dcvar_covariate_fit}
\alias{loo.dcvar_hmm_fit}
\alias{loo.dcvar_constant_fit}
\alias{loo.dcvar_multilevel_fit}
\alias{loo.dcvar_sem_fit}
\title{Compute LOO-CV for a fitted model}
\usage{
\method{loo}{dcvar_fit}(x, ...)

\method{loo}{dcvar_covariate_fit}(x, ...)

\method{loo}{dcvar_hmm_fit}(x, ...)

\method{loo}{dcvar_constant_fit}(x, ...)

\method{loo}{dcvar_multilevel_fit}(x, ...)

\method{loo}{dcvar_sem_fit}(x, ...)
}
\arguments{
\item{x}{A fitted model object.}

\item{...}{Additional arguments passed to \code{\link[loo:loo]{loo::loo()}}.}
}
\value{
A \code{loo} object from the loo package.
}
\description{
Compute LOO-CV for a fitted model
}
\details{
PSIS-LOO is available for Gaussian and Clayton single-level fits,
covariate fits, multilevel fits, and naive SEM score fits. Indicator SEM
fits are not supported because their stored \code{log_lik} conditions on
latent innovations rather than being an observation-level predictive
density. Multilevel \code{log_lik} values are conditional one-step-ahead
densities given the unit-level random effects.

Note that the pointwise \code{log_lik} estimands differ across model families:
HMM fits store the state-marginalized one-step-ahead predictive density,
while dynamic (DC-VAR and covariate) fits condition on the smoothed
latent \code{rho} trajectory, which is itself informed by the observation.
Comparisons across these families can systematically favor the
latent-conditioned models; see \code{\link[=dcvar_compare]{dcvar_compare()}}.
}
