| print.marginallaplacesummary {aghq} | R Documentation |
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 class 'marginallaplacesummary'
print(x, ...)
Arguments
x |
Object of class |
... |
not used. |
Value
Nothing. Prints contents.
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(),
summary.aghq(),
summary.laplace(),
summary.marginallaplace()
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)