rgeneric_get: 'rgeneric_get' is an internal function used by 'graph',...

In INLAtools: Functionalities for the 'INLA' Package

rgeneric_get is an internal function used by graph, pred, initial, mu or prior methods for rgeneric.

Description

The generic_get retrieve a model property specified by cmd on an rgeneric object. The functions listed below are for each cmd case.

Usage

rgeneric_get(
  model,
  cmd = c("graph", "Q", "initial", "mu", "log_prior"),
  theta,
  ...
)

## S3 method for class 'rgeneric'
initial(model)

## S3 method for class 'rgeneric'
mu(model, theta)

## S3 method for class 'rgeneric'
graph(model, optimize)

## S3 method for class 'rgeneric'
prec(model, theta, optimize)

## S3 method for class 'rgeneric'
prior(model, theta)

Arguments

model	a rgeneric object.
cmd	an string to specify which model element to get
theta	numeric vector with the model parameters. If missing, the initial() will be used.
...	additional arguments passed on to methods. E.g.: optimize = FALSE return the graph and precision as a sparse matrix whereas optimize = TRUE retur the graph as arow/col indexes and the precision as a numeric vector with its elements.
optimize	logical indicating if it is to be returned only the elements and not as a sparse matrix.

Value

depends on cmd

numeric scalar (if numeric vector is provided for theta) or vector (if numeric matrix is provided for theta).

Functions

• initial(rgeneric): Retrive the initial parameter(s) of an rgeneric model.

• mu(rgeneric): Evaluate the mean for an rgeneric model.

• graph(rgeneric): Retrieve the graph of an rgeneric object

• prec(rgeneric): Retrieve the precision of an rgeneric object

• prior(rgeneric): Evaluate the prior for an rgeneric model

Examples

library(INLAtools)
old.par <- par(no.readonly = TRUE)

## Setting the prior parameters
prior.par <- c(1, 0.5) # P(sigma > 1) = 0.5
cmodel <- cgeneric(
  model = "iid", n = 10,
  param = prior.par)

## prior summaries: sigma and log-precision
(lamb <- -log(prior.par[2])/prior.par[1])
(smedian <- qexp(0.5, lamb))
(smean <- 1/lamb)

## mode: at the minimum of - log-prior
(lpmode <- optimize(function(x)
  -prior(cmodel, theta = x),
  c(-10, 30))$minimum)
## mean: integral of x*f(x)dx
(lpmean <- integrate(function(x)
  exp(prior(cmodel, theta = matrix(x, 1)))*x,
  -10, 30)$value)

## prior visualization: log(precision) and sigma
par(mfrow = c(1, 2))
plot(function(x)
 exp(prior(cmodel, theta = matrix(x, nrow=1))),
  -3, 3, n = 601, xlab = "log-precision",
  ylab = "density")
abline(v = lpmode, lwd = 3, col = 2)
rug(-2*log(smedian), lwd = 3, col = 3)
rug(lpmean, lwd = 3, col = 4)
plot(function(x)
 exp(prior(cmodel,
  theta = matrix(
    -2*log(x),
    nrow = 1))+log(2)-log(x)),
  1/100, 10, n = 1000,
  xlab = expression(sigma),
  ylab = "density")
plot(function(x) dexp(x, lamb),
   1/100, 10, n = 1000,
   add = TRUE, lty = 2, col = 2)
rug(smedian, lwd = 3, col = 3)
rug(smean, lwd = 3, col = 4)

par(old.par)

INLAtools documentation built on April 22, 2026, 5:10 p.m.