rats


 This example is taken from section 6 of Gelfand and Smith (1990), and concerns 
 30 young rats whose weights were measured weekly for five weeks. 


datasets

This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian
nonparametric and semiparametric models. Although the name of
the package was motivated by the Dirichlet Process prior, the
package considers and will consider other priors on functional
spaces. So far, DPpackage includes models considering Dirichlet
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
Trees, Linear Dependent Tailfree Processes, Mixtures of
Triangular distributions, Random Bernstein polynomials priors
and Dependent Bernstein Polynomials.  The package also includes
models considering Penalized B-Splines. Currently the package
includes semiparametric models for marginal and conditional
density estimation, ROC curve analysis, interval censored data,
binary regression models, generalized linear mixed models, IRT
type models, and generalized additive models. The package also
contains functions to compute Pseudo-Bayes factors for model
comparison, and to elicitate the precision parameter of the
Dirichlet Process. To maximize computational efficiency, the
actual sampling for each model is done in compiled FORTRAN. The
functions return objects which can be subsequently analyzed
with functions provided in the coda package.

Alejandro Jara

DPpackage

Bayesian nonparametric modeling in R

rats function

A data frame with 150 observations on the following 3 variables.
  <dl>
    <dt><code>weight</code></dt><dd>a numeric vector giving the weight of the rat</dd> <dt><code>day</code></dt><dd>a numeric vector giving the day of the weight evaluation</dd> <dt><code>rat</code></dt><dd>an ordered factor giving a unique identifier for the subject in the study</dd> </dl> 

Format

Gelfand, A.E. (with S. Hills, A. Racine-Poon and A.F.M. Smith) 1990. Illustration of 
 Bayesian Inference in Normal Data Models Using Gibbs Sampling. Journal Amer. Stat. 
 Assoc., 85, 972-985. 

rats: Rats

Description

Usage

Arguments

Format

Source

References

Examples