Computes the conditional probability estimator of relative risk based on a multitype point pattern using the diffusion estimate of the type-specific intensities.
relriskHeat(X, ...)# S3 method for ppp
relriskHeat(X, ..., sigmaX=NULL, weights=NULL)
A named list (of class solist)
containing pixel images,
giving the estimated conditional probability surfaces for each type.
A multitype point pattern (object of class "ppp").
Arguments passed to densityHeat
controlling the estimation of each marginal intensity.
Optional.
Numeric vector of bandwidths, one associated with each data point in
X.
Optional numeric vector of weights associated with each point of
X.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Tilman Davies Tilman.Davies@otago.ac.nz.
The function relriskHeat is generic. This file documents the
method relriskHeat.ppp for spatial point patterns (objects of
class "ppp").
This function estimates the spatially-varying conditional probability that a random point (given that it is present) will belong to a given type.
The algorithm separates X into
the sub-patterns consisting of points of each type.
It then applies densityHeat to each sub-pattern,
using the same bandwidth and smoothing regimen for each sub-pattern,
as specified by the arguments ....
If weights is specified, it should be a numeric vector
of length equal to the number of points in X, so that
weights[i] is the weight for data point X[i].
Similarly when performing lagged-arrival smoothing,
the argument sigmaX must be a numeric vector of the same length
as the number of points in X, and thus contain the
point-specific bandwidths in the order corresponding to each of these
points regardless of mark.
Agarwal, N. and Aluru, N.R. (2010) A data-driven stochastic collocation approach for uncertainty quantification in MEMS. International Journal for Numerical Methods in Engineering 83, 575--597.
Baddeley, A., Davies, T., Rakshit, S., Nair, G. and McSwiggan, G. (2022) Diffusion smoothing for spatial point patterns. Statistical Science 37, 123--142.
Barry, R.P. and McIntyre, J. (2011) Estimating animal densities and home range in regions with irregular boundaries and holes: a lattice-based alternative to the kernel density estimator. Ecological Modelling 222, 1666--1672.
Botev, Z.I. and Grotowski, J.F. and Kroese, D.P. (2010) Kernel density estimation via diffusion. Annals of Statistics 38, 2916--2957.
relrisk.ppp for the
traditional convolution-based kernel estimator of
conditional probability surfaces,
and the function risk in the sparr package for the
density-ratio-based estimator.
## bovine tuberculosis data
X <- subset(btb, select=spoligotype)
plot(X)
P <- relriskHeat(X,sigma=9)
plot(P)
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