density.ppp {spatstat} | R Documentation |
Compute a kernel smoothed intensity function from a point pattern.
## S3 method for class 'ppp': density(x, sigma, ..., weights, edge=TRUE, varcov=NULL)
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 .
|
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 by dividing it by the convolution of the
Gaussian kernel with the window of observation.
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(sigma^2)
.
Computation is performed using the Fast Fourier Transform.
Accuracy depends on the pixel resolution, controlled by the arguments
...
passed to as.mask
.
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
.
A pixel image (object of class "im"
).
Pixel values are estimated intensity values,
expressed in “points per unit area”.
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 “points per unit area”.
Intensity is usually a function of spatial location,
and it is this function which is estimated by 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).
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
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.
smooth.ppp
,
adaptive.density
,
ppp.object
,
im.object
data(cells) Z <- density.ppp(cells, 0.05) plot(Z)