bssm (version 1.1.7-1)

uniform_prior: Prior objects for bssm models

Description

These simple objects of class bssm_prior are used to construct a prior distributions for the some of the model objects of bssm package. Currently supported priors are uniform (uniform()), half-normal (halfnormal()), normal (normal()), gamma (gamma), and truncated normal distribution (tnormal()). All parameters are vectorized so for regression coefficient vector beta you can define prior for example as normal(0, 0, c(10, 20)).

Usage

uniform_prior(init, min, max)
uniform(init, min, max)
halfnormal_prior(init, sd)
halfnormal(init, sd)
normal_prior(init, mean, sd)
normal(init, mean, sd)
tnormal_prior(init, mean, sd, min = -Inf, max = Inf)
tnormal(init, mean, sd, min = -Inf, max = Inf)
gamma_prior(init, shape, rate)
gamma(init, shape, rate)

Arguments

init

Initial value for the parameter, used in initializing the model components and as a starting values in MCMC.

min

Lower bound of the uniform and truncated normal prior.

max

Upper bound of the uniform and truncated normal prior.

sd

Positive value defining the standard deviation of the (underlying i.e. non-truncated) Normal distribution.

mean

Mean of the Normal prior.

shape

Positive shape parameter of the Gamma prior.

rate

Positive rate parameter of the Gamma prior.

Value

object of class bssm_prior or bssm_prior_list in case of multiple priors (i.e. multiple regression coefficients).

Details

The longer name versions of the prior functions with _prior ending are identical with shorter versions and they are available only to avoid clash with R's primitive function gamma (other long prior names are just for consistent naming).

Examples

# NOT RUN {
# create uniform prior on [-1, 1] for one parameter with initial value 0.2:
uniform(init = 0.2, min = -1.0, max = 1.0)
# two normal priors at once i.e. for coefficients beta:
normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8))
# Gamma prior
gamma(init = 0.1, shape = 2.5, rate = 1.1)
# Same as
gamma_prior(init = 0.1, shape = 2.5, rate = 1.1)
# Half-normal
halfnormal(init = 0.01, sd = 0.1)
# Truncated normal
tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5)

# }