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.