bivariate.density: Bivariate kernel density/intensity estimation

In sparr: Spatial and Spatiotemporal Relative Risk

Bivariate kernel density/intensity estimation

Description

Provides an isotropic adaptive or fixed bandwidth kernel density/intensity estimate of bivariate/planar/2D data.

Usage

bivariate.density(
  pp,
  h0,
  hp = NULL,
  adapt = FALSE,
  resolution = 128,
  gamma.scale = "geometric",
  edge = c("uniform", "diggle", "none"),
  weights = NULL,
  intensity = FALSE,
  trim = 5,
  xy = NULL,
  pilot.density = NULL,
  leaveoneout = FALSE,
  parallelise = NULL,
  davies.baddeley = NULL,
  verbose = TRUE
)

Arguments

pp	An object of class ppp giving the observed 2D data set to be smoothed.
h0	Global bandwidth for adaptive smoothing or fixed bandwidth for constant smoothing. A numeric value > 0.
hp	Pilot bandwidth (scalar, numeric > 0) to be used for fixed bandwidth estimation of a pilot density in the case of adaptive smoothing. If NULL (default), it will take on the value of h0. Ignored when adapt = FALSE or if pilot.density is supplied as a pre-defined pixel image.
adapt	Logical value indicating whether to perform adaptive kernel estimation. See ‘Details’.
resolution	Numeric value > 0. Resolution of evaluation grid; the density/intensity will be returned on a [resolution \times resolution] grid.
gamma.scale	Scalar, numeric value > 0; controls rescaling of the variable bandwidths. Defaults to the geometric mean of the bandwidth factors given the pilot density (as per Silverman, 1986). See ‘Details’.
edge	Character string giving the type of edge correction to employ. "uniform" (default) corrects based on evaluation grid coordinate and "diggle" reweights each observation-specific kernel. Setting edge = "none" requests no edge correction. Further details can be found in the documentation for density.ppp.
weights	Optional numeric vector of nonnegative weights corresponding to each observation in pp. Must have length equal to npoints(pp).
intensity	Logical value indicating whether to return an intensity estimate (integrates to the sample size over the study region), or a density estimate (default, integrates to 1).
trim	Numeric value > 0; controls bandwidth truncation for adaptive estimation. See ‘Details’.
xy	Optional alternative specification of the evaluation grid; matches the argument of the same tag in as.mask. If supplied, resolution is ignored.
pilot.density	An optional pixel image (class im) giving the pilot density to be used for calculation of the variable bandwidths in adaptive estimation, or a ppp.object giving the data upon which to base a fixed-bandwidth pilot estimate using hp. If used, the pixel image must be defined over the same domain as the data given resolution or the supplied pre-set xy evaluation grid; or the planar point pattern data must be defined with respect to the same polygonal study region as in pp.
leaveoneout	Logical value indicating whether to compute and return the value of the density/intensity at each data point for an adaptive estimate. See ‘Details’.
parallelise	Numeric argument to invoke parallel processing, giving the number of CPU cores to use when leaveoneout = TRUE. Experimental. Test your system first using parallel::detectCores() to identify the number of cores available to you.
davies.baddeley	An optional numeric vector of length 3 to control bandwidth partitioning for approximate adaptive estimation, giving the quantile step values for the variable bandwidths for density/intensity and edge correction surfaces and the resolution of the edge correction surface. May also be provided as a single numeric value. See ‘Details’.
verbose	Logical value indicating whether to print a function progress bar to the console when adapt = TRUE.

Details

Given a data set x_1,\dots,x_n in 2D, the isotropic kernel estimate of its probability density function, \hat{f}(x), is given by

\hat{f}(y)=n^{-1}\sum_{i=1}^{n}h(x_i)^{-2}K((y-x_i)/h(x_i))

where h(x) is the bandwidth function, and K(.) is the bivariate standard normal smoothing kernel. Edge-correction factors (not shown above) are also implemented.

Fixed

The classic fixed bandwidth kernel estimator is used when adapt = FALSE. This amounts to setting h(u)=h0 for all u. Further details can be found in the documentation for density.ppp.

Adaptive

Setting adapt = TRUE requests computation of Abramson's (1982) variable-bandwidth estimator. Under this framework, we have h(u)=h0*min[\tilde{f}(u)^{-1/2},G*trim]/\gamma, where \tilde{f}(u) is a fixed-bandwidth kernel density estimate computed using the pilot bandwidth hp.

• Global smoothing of the variable bandwidths is controlled with the global bandwidth h0.

• In the above statement, G is the geometric mean of the “bandwidth factors” \tilde{f}(x_i)^{-1/2}; i=1,\dots,n. By default, the variable bandwidths are rescaled by \gamma=G, which is set with gamma.scale = "geometric". This allows h0 to be considered on the same scale as the smoothing parameter in a fixed-bandwidth estimate i.e. on the scale of the recorded data. You can use any other rescaling of h0 by setting gamma.scale to be any scalar positive numeric value; though note this only affects \gamma – see the next bullet. When using a scale-invariant h0, set gamma.scale = 1.

• The variable bandwidths must be trimmed to prevent excessive values (Hall and Marron, 1988). This is achieved through trim, as can be seen in the equation for h(u) above. The trimming of the variable bandwidths is universally enforced by the geometric mean of the bandwidth factors G independent of the choice of \gamma. By default, the function truncates bandwidth factors at five times their geometric mean. For stricter trimming, reduce trim, for no trimming, set trim = Inf.

• For even moderately sized data sets and evaluation grid resolution, adaptive kernel estimation can be rather computationally expensive. The argument davies.baddeley is used to approximate an adaptive kernel estimate by a sum of fixed bandwidth estimates operating on appropriate subsets of pp. These subsets are defined by “bandwidth bins”, which themselves are delineated by a quantile step value 0<\delta<1. E.g. setting \delta=0.05 will create 20 bandwidth bins based on the 0.05th quantiles of the Abramson variable bandwidths. Adaptive edge-correction also utilises the partitioning, with pixel-wise bandwidth bins defined using the value 0<\beta<1, and the option to decrease the resolution of the edge-correction surface for computation to a [L \times L] grid, where 0 <L \le resolution. If davies.baddeley is supplied as a vector of length 3, then the values [1], [2], and [3] correspond to the parameters \delta, \beta, and L_M=L_N in Davies and Baddeley (2018). If the argument is simply a single numeric value, it is used for both \delta and \beta, with L_M=L_N=resolution (i.e. no edge-correction surface coarsening).

• Computation of leave-one-out values (when leaveoneout = TRUE) is done by brute force, and is therefore very computationally expensive for adaptive smoothing. This is because the leave-one-out mechanism is applied to both the pilot estimation and the final estimation stages. Experimental code to do this via parallel processing using the foreach routine is implemented. Fixed-bandwidth leave-one-out can be performed directly in density.ppp.

Value

If leaveoneout = FALSE, an object of class "bivden". This is effectively a list with the following components:

z	The resulting density/intensity estimate, a pixel image object of class im.
h0	A copy of the value of h0 used.
hp	A copy of the value of hp used.
h	A numeric vector of length equal to the number of data points, giving the bandwidth used for the corresponding observation in pp.
him	A pixel image (class im), giving the ‘hypothetical’ Abramson bandwidth at each pixel coordinate conditional upon the observed data. NULL for fixed-bandwidth estimates.
q	Edge-correction weights; a pixel image if edge = "uniform", a numeric vector if edge = "diggle", and NULL if edge = "none".
gamma	The value of \gamma used in scaling the bandwidths. NA if a fixed bandwidth estimate is computed.
geometric	The geometric mean G of the untrimmed bandwidth factors \tilde{f}(x_i)^{-1/2}. NA if a fixed bandwidth estimate is computed.
pp	A copy of the ppp.object initially passed to the pp argument, containing the data that were smoothed.

Else, if leaveoneout = TRUE, simply a numeric vector of length equal to the number of data points, giving the leave-one-out value of the function at the corresponding coordinate.

Author(s)

T.M. Davies and J.C. Marshall

References

Abramson, I. (1982). On bandwidth variation in kernel estimates — a square root law, Annals of Statistics, 10(4), 1217-1223.

Davies, T.M. and Baddeley A. (2018), Fast computation of spatially adaptive kernel estimates, Statistics and Computing, 28(4), 937-956.

Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.

Davies, T.M., Jones, K. and Hazelton, M.L. (2016), Symmetric adaptive smoothing regimens for estimation of the spatial relative risk function, Computational Statistics & Data Analysis, 101, 12-28.

Diggle, P.J. (1985), A kernel method for smoothing point process data, Journal of the Royal Statistical Society, Series C, 34(2), 138-147.

Hall P. and Marron J.S. (1988) Variable window width kernel density estimates of probability densities. Probability Theory and Related Fields, 80, 37-49.

Marshall, J.C. and Hazelton, M.L. (2010) Boundary kernels for adaptive density estimators on regions with irregular boundaries, Journal of Multivariate Analysis, 101, 949-963.

Silverman, B.W. (1986), Density Estimation for Statistics and Data Analysis, Chapman & Hall, New York.

Wand, M.P. and Jones, C.M., 1995. Kernel Smoothing, Chapman & Hall, London.

Examples

data(chorley) # Chorley-Ribble data from package 'spatstat'

# Fixed bandwidth kernel density; uniform edge correction
chden1 <- bivariate.density(chorley,h0=1.5) 

# Fixed bandwidth kernel density; diggle edge correction; coarser resolution
chden2 <- bivariate.density(chorley,h0=1.5,edge="diggle",resolution=64) 


# Adaptive smoothing; uniform edge correction
chden3 <- bivariate.density(chorley,h0=1.5,hp=1,adapt=TRUE)

# Adaptive smoothing; uniform edge correction; partitioning approximation
chden4 <- bivariate.density(chorley,h0=1.5,hp=1,adapt=TRUE,davies.baddeley=0.025)
 
par(mfrow=c(2,2))
plot(chden1);plot(chden2);plot(chden3);plot(chden4)  

sparr documentation built on March 31, 2023, 8:40 p.m.