# rmhstart

##### Determine Initial State for Metropolis-Hastings Simulation.

Builds a description of the initial state for the Metropolis-Hastings algorithm.

- Keywords
- spatial

##### Usage

```
rmhstart(start)
rmhstart(..., n.start=NULL, x.start=NULL, iseed)
```

##### Arguments

- start
- An existing description of the initial state in some format. Incompatible with the arguments listed below.
- ...
- There should be no other arguments.
- n.start
- Number of initial points (to be randomly generated).
Incompatible with
`x.start`

. - x.start
- Initial point pattern configuration.
Incompatible with
`n.start`

. - iseed
- Vector of 3 integers determining the initial state of the random number generator. This argument should not be specified, in normal use.

##### Details

Simulated realisations of many point process models
can be generated using the Metropolis-Hastings algorithm
implemented in `rmh`

.
This function `rmhstart`

creates a full description of the initial state of the
Metropolis-Hastings algorithm,
*including possibly the initial state of the random number generator*,
for use in a subsequent call to `rmh`

. It also
checks that the initial state is valid.

The initial state should be specified **either** by the
first argument `start`

**or** by the other arguments
`n.start`

, `x.start`

etc.
If `start`

is a list, then it should have components named
`n.start`

or `x.start`

and optionally `iseed`

,
with the same interpretation as described below.

The arguments are:
[object Object],[object Object],[object Object]
The parameters `n.start`

and `x.start`

are
*incompatible*.

##### Value

- An object of class
`"rmhstart"`

, which is essentially a list of parameters describing the initial point pattern and (optionally) the initial state of the random number generator. There is a`print`

method for this class, which prints a sensible description of the initial state.

##### synopsis

rmhstart(start, ...) rmhstart.default(start=NULL, ..., n.start=NULL, x.start=NULL, iseed)

##### Warnings

If `iseed`

is specified, this will fix the initial state
of the random number generator in any subsequent call
to `rmh`

.

##### See Also

##### Examples

```
# 30 random points
a <- rmhstart(n.start=30)
# a particular point pattern
data(cells)
b <- rmhstart(x.start=cells)
# set the seed
d <- rmhstart(n.start=30, iseed=c(42, 4, 2))
```

*Documentation reproduced from package spatstat, version 1.7-11, License: GPL version 2 or newer*