mcmc.distributions: Create a stochastic rcppbugs object.

In armstrtw/rcppbugs: R binding for cppbugs

Description

Create stochastic objects in the spirit of PyMC.

Usage

mcmc.normal(x, mu, tau, observed = FALSE)
mcmc.uniform(x, lower, upper, observed = FALSE)
mcmc.gamma(x, alpha, beta, observed = FALSE)
mcmc.beta(x, alpha, beta, observed = FALSE)
mcmc.bernoulli(x, p, observed = FALSE)
mcmc.binomial(x, n, p, observed = FALSE)

Arguments

x	the initial value of the object
mu	the mean for normally distributed objects
tau	the precision of normally distributed objects
lower	the lower limit of the uniform distribution
upper	the upper limit of the uniform distribution
alpha	the shape parameter of the gamma distribution
beta	the scale parameter of the gamma distribution
n	the sample size of a binomial distribution
p	the success rate in bernoulli or binomial distributions
observed	whether the object should be treated as constant data or simulated over the MCMC chain

Value

an rcppbugs object corresponding to the particular distribution requested

Author(s)

rcppbugs was written by Whit Armstrong.

References

https://github.com/armstrtw/CppBugs

See Also

logp

Examples

b <- mcmc.normal(rnorm(10),mu=0,tau=0.0001)
tau <- mcmc.gamma(runif(1),alpha=0.1,beta=0.1)
b.unif <- mcmc.uniform(runif(1),lower=0,upper=100)

armstrtw/rcppbugs documentation built on May 10, 2019, 1:42 p.m.