summary.OptimMCL.HCAR: Summary the output from the iterative procedure of maximising...

In mclcar: Estimating Conditional Auto-Regressive (CAR) Models using Monte Carlo Likelihood Methods

Description

The summary function summarizes the output of the output from the function OptimMCL.HCAR and the ranef.HCAR calculate the empirical Bayesian estimates of the random effects given the Monte Carlo maximum likelihood estimates.

Usage

## S3 method for class 'OptimMCL.HCAR'
summary(object, trace.all = TRUE, mc.covar =
  TRUE, ...)
  
ranef.HCAR(pars, data)

Arguments

object	an OptimMCL object returned by OptimMCL.HCAR.
trace.all	an logic value tells whether the input object given by OptimMCL.HCAR contains results from all iterations of not
mc.covar	if TRUE, the estimated covariance matrix of the MC-MLE is returned
...	arguments passed to or from other methods.
pars	the paramter values for calculating the empirical Bayesian estimates of the random effects; a list or enivironment of data for example same as described in sim.HCAR

data	A list or an environment contains the variables same as described in sim.HCAR.

Value

The summary function returns a list containing the following objects:

MC.mle,

the final MC-MLE

N.iter,

the total number of iterations

total.time,

the total time elapsed

convergence,

if TRUE the procedure converges

hessian,

the Hessian at the MC-MLE if given; the default is NULL

mc.covar

the estimated covariance matrix of the MC-MLE if given; the default is NULL

mc.samples

the Monte Carlo samples size used in the initial stage and after the first convergence.

The ranef.HCAR function return a dataframe object containing the estimated random effects and their corresponding standard deviations.

Author(s)

Zhe Sha [e-mail]

See Also

mcl.HCAR, sim.HCAR, OptimMCL.HCAR

Examples

## See examples for OptimMCL

Example output

mclcar documentation built on Jan. 8, 2022, 5:07 p.m.