% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loglik_inad.R
\name{logL_inad_i}
\alias{logL_inad_i}
\title{INAD log likelihood contribution at time i (no fixed effect)}
\usage{
logL_inad_i(
  y,
  i,
  order = 1,
  thinning = c("binom", "pois", "nbinom"),
  innovation = c("pois", "bell", "nbinom"),
  alpha,
  theta,
  nb_inno_size = NULL
)
}
\arguments{
\item{y}{Integer matrix n_sub by n_time.}

\item{i}{Time index in 1..ncol(y).}

\item{order}{Integer in \{0, 1, 2\}.}

\item{thinning}{One of "binom", "pois", "nbinom".}

\item{innovation}{One of "pois", "bell", "nbinom".}

\item{alpha}{Thinning parameters. For order 1, numeric length 1 or n_time.
For order 2, either a matrix n_time by 2 or a list(alpha1, alpha2).}

\item{theta}{Innovation parameter at time i, or a vector length 1 or n_time.
For Poisson and negative binomial innovations, this is the innovation
mean. For Bell innovations, this is the Bell rate parameter
(mean \eqn{\theta e^\theta}).}

\item{nb_inno_size}{Size parameter for innovation "nbinom". Numeric length 1 or n_time.}
}
\value{
A scalar log likelihood contribution for time i.
}
\description{
Returns the time i contribution, summed over subjects, for the no fixed effect model.
}
\examples{
set.seed(1)
y <- simulate_inad(
  n_subjects = 50,
  n_time = 5,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  alpha = 0.3,
  theta = 2
)
fit <- fit_inad(y, order = 1, thinning = "binom", innovation = "pois", max_iter = 20)
logL_inad_i(
  y = y,
  i = 3,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  alpha = fit$alpha,
  theta = fit$theta
)
}
