prior.p.dmc: Makes a list of prior distribution parameters.

In TasCL/ggdmc: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities

Description

prior.p.dmc creates a list of prior distribution an array object ("model") with a set of attributes specifying a particular model and parameterization. Call coda to summarise the model parameters in a DMC samples with multiple participants at the hyper level.

Usage

prior.p.dmc(p1, p2, lower = rep(NA, length(p1)), upper = rep(NA,
  length(p1)), dists = rep("tnorm", length(p1)), untrans = rep("identity",
  length(p1)), dist.types = c("tnorm", "beta", "gamma", "lnorm", "constant"))

Arguments

p1	the values of location parameters for each prior distribution, set as a double vector
p2	ditto for scale parameter vector
lower	lower support boundary
upper	upper support boundary
dists	indicate which prior distribution, e.g., uniform, beta etc.
untrans	whether do log transformation or not. Default is identity, namely not to transform
dist.types	allowed prior distributions in current version of DMC

TasCL/ggdmc documentation built on May 9, 2019, 4:19 p.m.