| summary.aghq {aghq} | R Documentation |
Summary statistics computed using AGHQ
Description
The summary.aghq method computes means, standard deviations, and
quantiles of the transformed parameter.
The associated print method
prints these along with diagnostic and other information about
the quadrature.
Usage
## S3 method for class 'aghq'
summary(object, ...)
Arguments
object |
The return value from |
... |
not used. |
Value
A list of class aghqsummary, which has a print method. Elements:
mode: the mode of the log posterior
hessian: the hessian of the log posterior at the mode
covariance: the inverse of the hessian of the log posterior at the mode
cholesky: the upper Cholesky triangle of the hessian of the log posterior at the mode
quadpoints: the number of quadrature points used in each dimension
dim: the dimension of the parameter space
summarytable: a table containing the mean, median, mode, standard deviation and quantiles of each transformed parameter, computed according to the posterior normalized using AGHQ
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.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))
funlist2d <- list(
fn = objfunc2d,
gr = function(x) numDeriv::grad(objfunc2d,x),
he = function(x) numDeriv::hessian(objfunc2d,x)
)
thequadrature <- aghq(funlist2d,3,c(0,0))
# Summarize and automatically call its print() method when called interactively:
summary(thequadrature)
# or, compute the summary and save for further processing:
ss <- summary(thequadrature)
str(ss)