Given a point pattern dataset, create a resampled point pattern by dividing the window into rectangular quadrats and randomly resampling the list of quadrats.

```
quadratresample(X, nx, ny=nx, ...,
replace = FALSE, nsamples = 1,
verbose = (nsamples > 1))
```

X

A point pattern dataset (object of class `"ppp"`

).

nx,ny

Numbers of quadrats in the \(x\) and \(y\) directions.

…

Ignored.

replace

Logical value. Specifies whether quadrats should be sampled with or without replacement.

nsamples

Number of randomised point patterns to be generated.

verbose

Logical value indicating whether to print progress reports.

A point pattern (if `nsamples = 1`

) or a
list of point patterns (if `nsamples > 1`

).

This command implements a very simple bootstrap resampling procedure
for spatial point patterns `X`

.

The dataset `X`

must be a point pattern (object of class
`"ppp"`

) and its observation window must be a rectangle.

The window is first divided into `N = nx * ny`

rectangular tiles
(quadrats) of equal size and shape.
To generate one resampled point pattern, a random sample of
`N`

quadrats is selected from the list of `N`

quadrats,
with replacement (if `replace=TRUE`

) or without replacement
(if `replace=FALSE`

). The \(i\)th quadrat in the original
dataset is then replaced by the \(i\)th sampled quadrat, after the
latter is shifted so that it
occupies the correct spatial position. The quadrats are then
reconstituted into a point pattern inside the same window as `X`

.

If `replace=FALSE`

, this procedure effectively involves a random
permutation of the quadrats. The resulting resampled point pattern has
the same number of points as `X`

.
If `replace=TRUE`

, the number of points in the resampled point
pattern is random.

See `varblock`

to estimate the variance of
a summary statistic by block resampling.

# NOT RUN { data(bei) quadratresample(bei, 6, 3) # }