INLAtools (version 0.0.8)

cgeneric_get: cgeneric_get is an internal function used by graph, pred, initial, mu or prior methods for cgeneric.

Description

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

Usage

cgeneric_get(
  model,
  cmd = c("graph", "Q", "initial", "mu", "log_prior"),
  theta,
  optimize = TRUE
)
# S3 method for cgeneric
initial(model)
# S3 method for cgeneric
mu(model, theta)
# S3 method for cgeneric
graph(model, optimize)
# S3 method for cgeneric
prec(model, theta, optimize)
# S3 method for cgeneric
prior(model, theta)

Value

depends on cmd

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

Arguments

model

a cgeneric 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.

optimize

logical indicating if it is to be returned only the elements and not as a sparse matrix.

Functions

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

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

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

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

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

See Also

check the examples in cgeneric_generic0()

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)