A tool for the visual estimation of lambda(s) via a 2
dimensional smoothing of the case locations. For
parameter estimation, the alternative is to estimate
lambda(s) by some other means, convert it into a
spatialAtRisk object and then into a pixel image object
using the build in coercion methods, this im
object can then be fed to ginhomAverage,
KinhomAverage or thetaEst for instance.
Usage
## S3 method for class 'stppp':
lambdaEst(xyt, weights = c(),
edge = TRUE, bw = NULL, ...)
Arguments
xyt
object of class stppp
weights
Optional vector of weights to be attached
to the points. May include negative values. See
?density.ppp.
edge
Logical flag: if TRUE, apply edge correction.
See ?density.ppp.
bw
optional bandwidth. Set to NULL by default,
which calls teh resolve.2D.kernel function for computing
an initial value of this
...
arguments to be passed to plot
Value
This is an rpanel function for visual choice of
lambda(s), the output is a variable, varname, with the
density *per unit time* the variable varname can be fed
to the function ginhomAverage or KinhomAverage as the
argument density (see for example ?ginhomAverage), or
into the function thetaEst as the argument
spatial.intensity.
Details
The function lambdaEst is built directly on the
density.ppp function and as such, implements a bivariate
Gaussian smoothing kernel. The bandwidth is initially
that which is automatically chosen by the default method
of density.ppp. Since image plots of these kernel density
estimates may not have appropriate colour scales, the
ability to adjust this is given with the slider 'colour
adjustment'. With colour adjustment set to 1, the default
image.plot for the equivalent pixel image object is shown
and for values less than 1, the colour scheme is more
spread out, allowing the user to get a better feel for
the density that is being fitted. NOTE: colour adjustment
does not affect the returned density and the user should
be aware that the returned density will 'look like' that
displayed when colour adjustment is set equal to 1.
References
Benjamin M. Taylor, Tilman M. Davies,
Barry S. Rowlingson, Peter J. Diggle (2013). Journal of
Statistical Software, 52(4), 1-40. URL
http://www.jstatsoft.org/v52/i04/
Brix A, Diggle PJ
(2001). Spatiotemporal Prediction for log-Gaussian Cox
processes. Journal of the Royal Statistical Society,
Series B, 63(4), 823-841.
Diggle P, Rowlingson B,
Su T (2005). Point Process Methodology for On-line
Spatio-temporal Disease Surveillance. Environmetrics,
16(5), 423-434.