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.
- 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 ofx
(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
.
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.
Instead of the isotropic Gaussian kernel with standard deviation
sigma
, 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.
Also 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))
.
The smoothing parameter sigma
has a default value
calculated by a simple rule of thumb
that depends only on the size of the window
and the number of points.
The argument adjust
makes it easy for the user to change this
default. The value of sigma
will be multiplied by
the factor adjust
. To double the smoothing parameter, set
adjust=2
.
By default the intensity values are
computed at every location $u$ in a fine grid,
and are returned as a pixel image.
Computation is performed using the Fast Fourier Transform.
Accuracy depends on the pixel resolution, controlled by the arguments
...
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 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 inx
. Values are estimated intensity values at the points ofx
.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. (2008) 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
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>
density(cells, 0.05, at="points")