summary.bayesian.vam: Summarizing Bayesian method for a virtual age model

In rcqls/VAM: Virtual Age Models

Summarizing Bayesian method for a virtual age model

Description

For bayesian.vam object, produce a summary of the Bayesian estimation method corresponding to the posterior distribution sampled obtained with run.bayesian.vam.

Usage

summary.bayesian.vam(obj,alpha=0.05,new.run=FALSE,digits=4,...)

Arguments

obj	an object of class bayesian.vam specifying the virtual age model considered, the prior distribution of the parameters and the data set of observations.
alpha	an optional argument specifying the level (1-alpha) of the credibility intervals for the parameters.
new.run	an optional argument specifying if run.bayesian.vam methods has to be relaunched even if it has already been executed.
digits	an optional argument specifying the number of significant digits to be printed for estimated characteristics.
...	some supplementary arguments for the run.bayesian.vam method.

Details

The acceptation rate of the Metropolis Hasting step can be used to calibrate the standard deviation of the instrumental distribution (see the argument sigma.proposal of the run.bayesian.vam method).

Value

Print a summary of the Bayesian method run.bayesian.vam applied to the object obj:

• The initialization values of the Gibbs algorithm.

• The Bayesian point estimates of the parameters, corresponding to the means of the sampled marginal posterior distributions.

• A (1-alpha) credibility interval for the parameters, corresponding to the alpha/2 and 1-alpha/2 quantiles of the marginal posterior distribution.

• The number of accepted marginal simulated parameters values of the posterior distribution in the Metropolis Hasting step.

• The acceptation rate of the Metropolis Hasting step.

The returned value is a data frame with in line the parameters and in column the previous detailed characteristics.

Author(s)

R. Drouilhet et L. Doyen

See Also

bayesian.vam to define the Bayesian object. run.bayesian.vam to compute the Bayesian method. coef.bayesian.vam to extract the parameters estimation values of the Bayesian method. hist.bayesian.vam for plotting the histogram of the posterior distribution of the parameters. plot.bayesian.vam for plotting estimating characteristics of the model.

Examples

simARAInf<-sim.vam(  ~ (ARAInf(.4) | Weibull(.001,2.5)))
simData<-simulate(simARAInf,30)
bayesARAInf <- bayesian.vam(Time & Type ~ (ARAInf(~Unif(0,1)) | Weibull(~Unif(0,1),~Unif(2,4))),data=simData)
summary(bayesARAInf)
run(bayesARAInf,profile.alpha=TRUE)
summary(bayesARAInf)
summary(bayesARAInf,par0=c(1e-2,2.5,0.5),fixed=2)

rcqls/VAM documentation built on Jan. 14, 2024, 9:07 p.m.