evmSim: MCMC simulation around an evmOpt fit

In texmex: Statistical Modelling of Extreme Values

MCMC simulation around an evmOpt fit

Description

MCMC simulation around an evmOpt fit

Usage

evmSim(
  o,
  priorParameters,
  prop.dist,
  jump.const,
  jump.cov,
  iter,
  start,
  thin,
  burn,
  chains,
  export = NULL,
  verbose,
  trace,
  theCall,
  ...
)

Arguments

o	a fit evmOpt object
priorParameters	A list with two components. The first should be a vector of means, the second should be a covariance matrix if the penalty/prior is "gaussian" or "quadratic" and a diagonal precision matrix if the penalty/prior is "lasso", "L1" or "Laplace". If method = "simulate" then these represent the parameters in the Gaussian prior distribution. If method = 'optimize' then these represent the parameters in the penalty function. If not supplied: all default prior means are zero; all default prior variances are 10^4; all covariances are zero.
prop.dist	The proposal distribution to use, either multivariate gaussian or a multivariate Cauchy.
jump.const	Control parameter for the Metropolis algorithm.
jump.cov	Covariance matrix for proposal distribution of Metropolis algorithm. This is scaled by jump.const.
iter	Number of simulations to generate
start	Starting values for the chain; if missing, defaults to the MAP/ML estimates in o.
thin	The degree of thinning of the resulting Markov chains.
burn	The number of initial steps to be discarded.
chains	The number of Markov chains to run. Defaults to 1. If you run more, the function will try to figure out how to do it in parallel using the same number of cores as chains.
export	Character vector of names of variables to export. See the help file for parallel::export. Defaults to export = NULL and most users will never need to use it. Only matters on Windows.
verbose	Whether or not to print progress to screen. Defaults to verbose=TRUE.
trace	How frequently to talk to the user
theCall	(internal use only)
...	ignored

Value

an object of class evmSim:

call	The call to evmSim that produced the object.
threshold	The threshold above which the model was fit.
map	The point estimates found by maximum penalized likelihood and which were used as the starting point for the Markov chain. This is of class evmOpt and methods for this class (such as resid and plot) may be useful.
burn	The number of steps of the Markov chain that are to be treated as the burn-in and not used in inferences.
thin	The degree of thinning used.
chains	The entire Markov chain generated by the Metropolis algorithm.
y	The response data above the threshold for fitting.
seed	The seed used by the random number generator.
param	The remainder of the chain after deleting the burn-in and applying any thinning.

Note

it is not expected that the user should call this directly

texmex documentation built on June 22, 2024, 12:26 p.m.