# density.ppp

##### Kernel Smoothed Intensity of Point Pattern

Compute a kernel smoothed intensity function from a point pattern.

##### Usage

```
## S3 method for class 'ppp':
density(x, sigma, \dots, weights, edge=TRUE)
```

##### Arguments

- x
- Point pattern (object of class
`"ppp"`

) to be smoothed. - sigma
- Standard deviation of isotropic Gaussian smoothing kernel.
- weights
- Optional vector of weights to be attached to the points. May include negative values.
- ...
- Arguments passed to
`as.mask`

to determine the pixel resolution. - edge
- Logical flag: if
`TRUE`

, apply edge correction.

##### Details

This is a method for the generic function `density`

.
A kernel estimate of the intensity function of the point pattern
is computed. The result is
the convolution of the isotropic Gaussian kernel of
standard deviation `sigma`

with point masses at each of the data
points. The default is to assign
a unit weight to each point.
If `weights`

is present, the point masses have these
weights (which may be signed real numbers).

If `edge=TRUE`

, the intensity estimate is corrected for
edge effect bias by dividing it by the convolution of the
Gaussian kernel with the window of observation.

Computation is performed using the Fast Fourier Transform.

##### Value

- A pixel image (object of class
`"im"`

).

##### synopsis

## S3 method for class 'ppp': density(x, sigma, \dots, edge=TRUE)

##### See Also

##### Examples

```
data(cells)
Z <- density.ppp(cells, 0.05)
plot(Z)
```

*Documentation reproduced from package spatstat, version 1.9-1, License: GPL version 2 or newer*