`redwood`

.
Strauss (1975) divided the sampling region into two subregions I and II
demarcated by a diagonal line across the region. The spatial pattern
appears to be slightly regular in region I and strongly clustered in
region II. The dataset `redwoodfull`

contains the full point pattern
of 195 trees.
The auxiliary information about the subregions is contained in
`redwoodfull.extra`

, which is a list with entries
`diag`

The coordinates of the diagonal boundary
between regions I and II
`regionI`

Region I as a window object
`regionII`

Region II as a window object
`regionR`

Ripley's subrectangle (approximate)
`plot`

Function to plot the full data and auxiliary markings
}

Ripley (1977) extracted a subset of these data, containing 62 points,
lying within a square subregion which overlaps regions I and II.
He rescaled the data to the unit square.
This has been re-analysed many times, and is the dataset usually known as
``the redwood data'' in the spatial statistics literature.
The exact dataset used by Ripley is supplied in the `redwood`

.
There are some minor inconsistencies with
`redwood`

since it originates from a different digitisation.

The approximate position of the square chosen by Ripley
within the `redwoodfull`

pattern
is indicated by the window `redwoodfull.extra$regionR`

.

`data(redwoodfull)`

`redwoodfull`

is an object of class `"ppp"`

representing the point pattern of tree locations.
The window has been rescaled to the unit square.
See `ppp.object`

for details of the format of a
point pattern object. The dataset `redwoodfull.extra`

is a list with entries
`diag`

coordinates of endpoints of a line,
in format `list(x=numeric(2),y=numeric(2))`

`regionI`

a window object
`regionII`

a window object
`regionR`

a window object
`plot`

Function with no arguments
}

Ripley, B.D. (1977)
Modelling spatial patterns (with discussion).
*Journal of the Royal Statistical Society, Series B*
**39**, 172--212.

Strauss, D.J. (1975)
A model for clustering.
*Biometrika* **63**, 467--475.

`redwood`

```
data(redwoodfull)
plot(redwoodfull)
redwoodfull.extra$plot()
# extract the pattern in region II
redwoodII <- redwoodfull[, redwoodfull.extra$regionII]
```

Run the code above in your browser using DataCamp Workspace