aghq (version 0.4.1)

summary.marginallaplace: Summary statistics for models using marginal Laplace approximations

Description

The summary.marginallaplace calls summary.aghq, but also computes summary statistics of the random effects, by drawing from their approximate posterior using aghq::sample_marginal with the specified number of samples.

Usage

# S3 method for marginallaplace
summary(object, M = 1000, max_print = 30, ...)

Value

A list containing an object of class aghqsummary (see summary.aghq).

Arguments

object

Object inheriting from both classes aghq and marginallaplace, for example as returned by aghq::marginal_laplace or aghq::marginal_laplace_tmb.

M

Number of samples to use to compute summary statistics of the random effects. Default 1000. Lower runs faster, higher is more accurate.

max_print

Sometimes there are a lot of random effects. If there are more random effects than max_print, the random effects aren't summarized, and a note is printed to this effect. Default 30.

...

not used.

See Also

Other quadrature: aghq(), get_hessian(), get_log_normconst(), get_mode(), get_nodesandweights(), get_numquadpoints(), get_opt_results(), get_param_dim(), laplace_approximation(), marginal_laplace_tmb(), marginal_laplace(), nested_quadrature(), normalize_logpost(), optimize_theta(), plot.aghq(), print.aghqsummary(), print.aghq(), print.laplacesummary(), print.laplace(), print.marginallaplacesummary(), summary.aghq(), summary.laplace()

Examples

logfteta2d <- function(eta,y) {
  # eta is now (eta1,eta2)
  # y is now (y1,y2)
  n <- length(y)
  n1 <- ceiling(n/2)
  n2 <- floor(n/2)
  y1 <- y[1:n1]
  y2 <- y[(n1+1):(n1+n2)]
  eta1 <- eta[1]
  eta2 <- eta[2]
  sum(y1) * eta1 - (length(y1) + 1) * exp(eta1) - sum(lgamma(y1+1)) + eta1 +
    sum(y2) * eta2 - (length(y2) + 1) * exp(eta2) - sum(lgamma(y2+1)) + eta2
}
set.seed(84343124)
n1 <- 5
n2 <- 5
n <- n1+n2
y1 <- rpois(n1,5)
y2 <- rpois(n2,5)
objfunc2d <- function(x) logfteta2d(x,c(y1,y2))
objfunc2dmarg <- function(W,theta) objfunc2d(c(W,theta))
objfunc2dmarggr <- function(W,theta) {
  fn <- function(W) objfunc2dmarg(W,theta)
  numDeriv::grad(fn,W)
}
objfunc2dmarghe <- function(W,theta) {
  fn <- function(W) objfunc2dmarg(W,theta)
  numDeriv::hessian(fn,W)
}

funlist2dmarg <- list(
  fn = objfunc2dmarg,
  gr = objfunc2dmarggr,
  he = objfunc2dmarghe
)

themarginallaplace <- aghq::marginal_laplace(funlist2dmarg,3,list(W = 0,theta = 0))
summary(themarginallaplace)