diversitree (version 0.9-9)

make.prior: Simple Prior Functions

Description

Functions for generating prior functions for use with mcmc, etc.

Usage

make.prior.exponential(r) make.prior.uniform(lower, upper, log=TRUE)

Arguments

r
Scalar or vector of rate parameters
lower
Lower bound of the parameter
upper
Upper bound of the parameter
log
Logical: should the prior be on a log basis?

Details

The exponential prior probability distribution has probability density $$\sum_i r_i e^{-r_i x_i}$$ where the $i$ denotes the $i$th parameter. If r is a scalar, then the same rate is used for all parameters.

These functions each return a function that may be used as the prior argument to mcmc().