# 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, varcov=NULL,
at="pixels", leaveoneout=TRUE,
adjust=1, diggle=FALSE)
```

##### Arguments

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

). - sigma
- Standard deviation of isotropic Gaussian smoothing kernel.
Either a numerical value, or a function that computes an
appropriate value of
`sigma`

. - 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. - varcov
- Variance-covariance matrix of anisotropic Gaussian kernel.
Incompatible with
`sigma`

. - at
- String specifying whether to compute the intensity values
at a grid of pixel locations (
`at="pixels"`

) or only at the points of`x`

(`at="points"`

). - leaveoneout
- Logical value indicating whether to compute a leave-one-out
estimator. Applicable only when
`at="points"`

. - adjust
- Optional. Adjustment factor for the smoothing parameter.
- diggle
- Logical. If
`TRUE`

, use Diggle's edge correction, which is more accurate but slower to compute than the correction described under Details.

##### Details

This is a method for the generic function `density`

.

It computes a fixed-bandwidth kernel estimate
(Diggle, 1985) of the intensity function of the point process
that generated the point pattern `x`

.
By default it computes the convolution of the
isotropic Gaussian kernel of standard deviation `sigma`

with point masses at each of the data points in `x`

.
Anisotropic Gaussian kernels are also supported.
Each point has unit weight, unless the argument `weights`

is
given (it should be a numeric vector; weights can be negative or zero).

If `edge=TRUE`

, the intensity estimate is corrected for
edge effect bias in one of two ways:

- If
`diggle=FALSE`

(the default) the intensity estimate is correted by dividing it by the convolution of the Gaussian kernel with the window of observation. Thus the intensity value at a point$u$is$$\hat\lambda(u) = e(u) \sum_i k(x_i - u) w_i$$where$k$is the Gaussian smoothing kernel,$e(u)$is an edge correction factor, and$w_i$are the weights. - If
`diggle=TRUE`

then the method of Diggle (1985) is followed exactly. The intensity value at a point$u$is$$\hat\lambda(u) = \sum_i k(x_i - u) w_i e(x_i)$$where again$k$is the Gaussian smoothing kernel,$e(x_i)$is an edge correction factor, and$w_i$are the weights. This computation is slightly slower but more accurate.

The smoothing kernel is determined by the arguments
`sigma`

, `varcov`

and `adjust`

.

- if
`sigma`

is a single numerical value, this is taken as the standard deviation of the isotropic Gaussian kernel. - alternatively
`sigma`

may be a function that computes an appropriate bandwidth for the isotropic Gaussian kernel from the data point pattern by calling`sigma(x)`

. To perform automatic bandwidth selection using cross-validation, it is recommended to use the function`bw.diggle`

. - The smoothing kernel may be chosen to be any Gaussian
kernel, by giving the variance-covariance matrix
`varcov`

. The arguments`sigma`

and`varcov`

are incompatible. - Alternatively
`sigma`

may be a vector of length 2 giving the standard deviations of two independent Gaussian coordinates, thus equivalent to`varcov = diag(rep(sigma^2, 2))`

. - if neither
`sigma`

nor`varcov`

is specified, an isotropic Gaussian kernel will be used, with a default value of`sigma`

calculated by a simple rule of thumb that depends only on the size of the window. - The argument
`adjust`

makes it easy for the user to change the bandwidth specified by any of the rules above. The value of`sigma`

will be multiplied by the factor`adjust`

. The matrix`varcov`

will be multiplied by`adjust^2`

. To double the smoothing bandwidth, set`adjust=2`

.

`...`

passed to `as.mask`

. If `at="points"`

, the intensity values are computed
to high accuracy at the points of `x`

only. Computation is
performed by directly evaluating and summing the Gaussian kernel
contributions without discretising the data. The result is a numeric
vector giving the density values.
The intensity value at a point $x_i$ is (if `diggle=FALSE`

)
$$\hat\lambda(x_i) = e(x_i) \sum_j k(x_j - x_i) w_j$$
or (if `diggle=TRUE`

)
$$\hat\lambda(x_i) = \sum_j k(x_j - x_i) w_j e(x_j)$$
If `leaveoneout=TRUE`

(the default), then the sum in the equation
is taken over all $j$ not equal to $i$,
so that the intensity value at a
data point is the sum of kernel contributions from
all *other* data points.
If `leaveoneout=FALSE`

then the sum is taken over all $j$,
so that the intensity value at a data point includes a contribution
from the same point.
To select the bandwidth `sigma`

automatically by
cross-validation, use `bw.diggle`

.
To perform spatial interpolation of values that were observed
at the points of a point pattern, use `smooth.ppp`

.

For adaptive nonparametric estimation, see
`adaptive.density`

.
For data sharpening, see `sharpen.ppp`

.

To compute a relative risk surface or probability map for
two (or more) types of points, use `relrisk`

.

##### Value

- By default, the result is
a pixel image (object of class
`"im"`

). Pixel values are estimated intensity values, expressed inpoints per unit area .If

`at="points"`

, the result is a numeric vector of length equal to the number of points in`x`

. Values are estimated intensity values at the points of`x`

.In either case, the return value has attributes

`"sigma"`

and`"varcov"`

which report the smoothing bandwidth that was used.

##### Note

This function is often misunderstood.

The result of `density.ppp`

is not a spatial smoothing
of the marks or weights attached to the point pattern.
To perform spatial interpolation of values that were observed
at the points of a point pattern, use `smooth.ppp`

.

The result of `density.ppp`

is not a probability density.
It is an estimate of the *intensity function* of the
point process that generated the point pattern data.
Intensity is the expected number of random points
per unit area.
The units of intensity are `density.ppp`

.
The integral of the intensity function over a spatial region gives the
expected number of points falling in this region.

Inspecting an estimate of the intensity function is usually the first step in exploring a spatial point pattern dataset. For more explanation, see the workshop notes (Baddeley, 2008) or Diggle (2003).

If you have two (or more) types of points, and you want a
probability map or relative risk surface (the spatially-varying
probability of a given type), use `relrisk`

.

##### References

Baddeley, A. (2010) Analysing spatial point patterns in R.
Workshop notes. CSIRO online technical publication.
URL: `www.csiro.au/resources/pf16h.html`

Diggle, P.J. (1985)
A kernel method for smoothing point process data.
*Applied Statistics* (Journal of the Royal Statistical Society,
Series C) **34** (1985) 138--147.

Diggle, P.J. (2003)
*Statistical analysis of spatial point patterns*,
Second edition. Arnold.

##### See Also

`bw.diggle`

,
`smooth.ppp`

,
`sharpen.ppp`

,
`adaptive.density`

,
`relrisk`

,
`ppp.object`

,
`im.object`

##### Examples

```
data(cells)
if(interactive()) {
opa <- par(mfrow=c(1,2))
plot(density(cells, 0.05))
plot(density(cells, 0.05, diggle=TRUE))
par(opa)
v <- diag(c(0.05, 0.07)^2)
plot(density(cells, varcov=v))
}
<testonly>Z <- density(cells, 0.05)
Z <- density(cells, 0.05, diggle=TRUE)
Z <- density(cells, varcov=diag(c(0.05^2, 0.07^2)))</testonly>
# automatic bandwidth selection
plot(density(cells, sigma=bw.diggle(cells)))
# equivalent:
plot(density(cells, bw.diggle))
# evaluate intensity at points
density(cells, 0.05, at="points")
```

*Documentation reproduced from package spatstat, version 1.31-3, License: GPL (>= 2)*