preexplorationAMH: Pre exploration Adapative Metropolis-Hastings
In PAWL: Implementation of the PAWL algorithm

Description Usage Arguments Details Value Author(s) See Also

This function takes a target distribution, an integer representing the number of parallel chains, and an integer representing a number of iterations, and runs adaptive Metropolis-Hastings algorithm using them. The chains are then used to create a range called SuggestedRange, to be used to bin the state space according to the energy levels. The energy is here defined as minus the log density of the target distribution.

1	preexplorationAMH(target, nchains, niterations, proposal, verbose)

`target`	Object of class `"target"`: this argument describes the target distribution. See `target` for details.
`nchains`	Object of class `"numeric"`: specifies the number of parallel chains.
`niterations`	Object of class `"numeric"`: specifies the number of iterations.
`proposal`	Object of class `"proposal"`: specifies the proposal distribution to be used to propose new values and to compute the acceptance rate. See the help of `proposal`. If this is not specified and the target is continuous, then the default is an adaptive gaussian random walk.
`verbose`	Object of class `"logical"`: if TRUE (default) then prints some indication of progress in the console.

The adaptive Metropolis-Hastings algorithm used in the function is described in more details in the help page of adaptiveMH

The function returns a list holding the following entries:

`LogEnergyRange`	This holds the minimum and maximum energy values seen by the chains during the exploration.
`LogEnergyQtile`	Returns the first 10% quantile of the energy values seen by the chains during the exploration.
`SuggestedRange`	This holds the suggested range, that is, the first 10% quantile and the maximum value of the energy values seen during the exploration. This can be passed as the `binrange` argument of the `binning` class, see the `trimodal` example.
`finalchains`	The last point of each chain.