mcmc.fmem: MCMC algorithm for Flexible Measurement Error Models
In BayesGESM: Bayesian Analysis of Generalized Elliptical Semi-Parametric Models and Flexible Measurement Error Models
Description
Usage
Arguments
Author(s)
Description
This function implements the MCMC algorithm derived in order to draw samples of
the posterior distribution of the interest parameters in flexible measurement error models
under the presence of homocedastic and heterocedastic random errors.
Usage
1
mcmc.fmem(params)
Arguments
params
An object type list, which provides the setup (i.e., values of hyperparameters, initial values, model matrices, basis functions for
the B-splines, burn-in and posterior sample size) of the model requested by the user.
Author(s)
Luz Marina Rondon <lumarp@gmail.com> and Heleno Bolfarine
BayesGESM documentation built on May 2, 2019, 11:27 a.m.
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Description Usage Arguments Author(s)
Description
This function implements the MCMC algorithm derived in order to draw samples of the posterior distribution of the interest parameters in flexible measurement error models under the presence of homocedastic and heterocedastic random errors.
Usage
1 | mcmc.fmem(params)
|
Arguments
params |
An object type list, which provides the setup (i.e., values of hyperparameters, initial values, model matrices, basis functions for the B-splines, burn-in and posterior sample size) of the model requested by the user. |
Author(s)
Luz Marina Rondon <lumarp@gmail.com> and Heleno Bolfarine
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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