Generates simulated realisations from a fitted spatial logistic regresson model

```
# S3 method for slrm
simulate(object, nsim = 1, seed=NULL, ...,
window=NULL, covariates=NULL, verbose=TRUE, drop=FALSE)
```

object

Fitted spatial logistic regression model. An object of class `"slrm"`

.

nsim

Number of simulated realisations.

seed

an object specifying whether and how to initialise
the random number generator. Either `NULL`

or an integer that will
be used in a call to `set.seed`

before simulating the point patterns.

…

Ignored.

window

Optional. Window (object of class `"owin"`

) in which the
model should be simulated.

covariates

Optional. A named list containing new values for the covariates in the model.

verbose

Logical. Whether to print progress reports (when `nsim > 1`

).

drop

Logical. If `nsim=1`

and `drop=TRUE`

, the
result will be a point pattern, rather than a list
containing a point pattern.

A list of length `nsim`

containing simulated point patterns
(objects of class `"ppp"`

).

The return value also carries an attribute `"seed"`

that
captures the initial state of the random number generator.
See Details.

This function is a method for the generic function
`simulate`

for the class `"slrm"`

of fitted
spatial logistic regression models.

Simulations are performed by `rpoispp`

after the intensity has been computed by `predict.slrm`

.

The return value is a list of point patterns.
It also carries an attribute `"seed"`

that
captures the initial state of the random number generator.
This follows the convention used in
`simulate.lm`

(see `simulate`

).
It can be used to force a sequence of simulations to be
repeated exactly, as shown in the examples for `simulate`

.

# NOT RUN { X <- copper$SouthPoints fit <- slrm(X ~ 1) simulate(fit, 2) fitxy <- slrm(X ~ x+y) simulate(fitxy, 2, window=square(2)) # }