spattemp.risk: Spatiotemporal relative risk/density ratio

In tilmandavies/sparr: Spatial and Spatiotemporal Relative Risk

Spatiotemporal relative risk/density ratio

Description

Produces a spatiotemporal relative risk surface based on the ratio of two kernel estimates of spatiotemporal densities.

Usage

spattemp.risk(f, g, log = TRUE, tolerate = FALSE, finiteness = TRUE, verbose = TRUE)

Arguments

f	An object of class stden representing the ‘case’ (numerator) density estimate.
g	Either an object of class stden, or an object of class bivden for the ‘control’ (denominator) density estimate. This object must match the spatial (and temporal, if stden) domain of f completely; see ‘Details’.
log	Logical value indicating whether to return the log relative risk (default) or the raw ratio.
tolerate	Logical value indicating whether to compute and return asymptotic p-value surfaces for elevated risk; see ‘Details’.
finiteness	Logical value indicating whether to internally correct infinite risk (on the log-scale) to the nearest finite value to avoid numerical problems. A small extra computational cost is required.
verbose	Logical value indicating whether to print function progress during execution.

Details

Fernando & Hazelton (2014) generalise the spatial relative risk function (e.g. Kelsall & Diggle, 1995) to the spatiotemporal domain. This is the implementation of their work, yielding the generalised log-relative risk function for x\in W\subset R^2 and t\in T\subset R. It produces

\hat{\rho}(x,t)=\log(\hat{f}(x,t))-\log(\hat{g}(x,t)),

where \hat{f}(x,t) is a fixed-bandwidth kernel estimate of the spatiotemporal density of the cases (argument f) and \hat{g}(x,t) is the same for the controls (argument g).

• When argument g is an object of class stden arising from a call to spattemp.density, the resolution, spatial domain, and temporal domain of this spatiotemporal estimate must match that of f exactly, else an error will be thrown.

• When argument g is an object of class bivden arising from a call to bivariate.density, it is assumed the ‘at-risk’ control density is static over time. In this instance, the above equation for the relative risk becomes \hat{\rho}=\log(\hat{f}(x,t))+\log|T|-\log(g(x)). The spatial density estimate in g must match the spatial domain of f exactly, else an error will be thrown.

• The estimate \hat{\rho}(x,t) represents the joint or unconditional spatiotemporal relative risk over W\times T. This means that the raw relative risk \hat{r}(x,t)=\exp{\hat{\rho}(x,t)} integrates to 1 with respect to the control density over space and time: \int_W \int_T r(x,t)g(x,t) dt dx = 1. This function also computes the conditional spatiotemporal relative risk at each time point, namely

  \hat{\rho}(x|t)=\log{\hat{f}(x|t)}-\log{\hat{g}(x|t)},

  where \hat{f}(x|t) and \hat{g}(x|t) are the conditional densities over space of the cases and controls given a specific time point t (see the documentation for spattemp.density). In terms of normalisation, we therefore have \int_W r(x|t)g(x|t) dx = 1. In the case where \hat{g} is static over time, one may simply replace \hat{g}(x|t) with \hat{g}(x) in the above.

• Based on the asymptotic properties of the estimator, Fernando & Hazelton (2014) also define the calculation of tolerance contours for detecting statistically significant fluctuations in such spatiotemporal log-relative risk surfaces. This function can produce the required p-value surfaces by setting tolerate = TRUE; and if so, results are returned for both the unconditional (x,t) and conditional (x|t) surfaces. See the examples in the documentation for plot.rrst for details on how one may superimpose contours at specific p-values for given evaluation times t on a plot of relative risk on the spatial margin.

Value

An object of class “rrst”. This is effectively a list with the following members:

rr	A named (by time-point) list of pixel images corresponding to the joint spatiotemporal relative risk over space at each discretised time.
rr.cond	A named list of pixel images corresponding to the conditional spatial relative risk given each discretised time.
P	A named list of pixel images of the p-value surfaces testing for elevated risk for the joint estimate. If tolerate = FALSE, this will be NULL.
P.cond	As above, for the conditional relative risk surfaces.
f	A copy of the object f used in the initial call.
g	As above, for g.
tlim	A numeric vector of length two giving the temporal bound of the density estimate.

Author(s)

T.M. Davies

References

Fernando, W.T.P.S. and Hazelton, M.L. (2014), Generalizing the spatial relative risk function, Spatial and Spatio-temporal Epidemiology, 8, 1-10.

See Also

spattemp.density, spattemp.slice, bivariate.density

Examples

data(fmd)
fmdcas <- fmd$cases
fmdcon <- fmd$controls

f <- spattemp.density(fmdcas,h=6,lambda=8) # stden object as time-varying case density
g <- bivariate.density(fmdcon,h0=6) # bivden object as time-static control density
rho <- spattemp.risk(f,g,tolerate=TRUE) 
print(rho)

par(mfrow=c(2,3))
plot(rho$f$spatial.z,main="Spatial margin (cases)") # spatial margin of cases
plot(rho$f$temporal.z,main="Temporal margin (cases)") # temporal margin of cases
plot(rho$g$z,main="Spatial margin (controls)") # spatial margin of controls
plot(rho,tselect=50,type="conditional",tol.args=list(levels=c(0.05,0.0001),
     lty=2:1,lwd=1:2),override.par=FALSE)
plot(rho,tselect=100,type="conditional",tol.args=list(levels=c(0.05,0.0001),
     lty=2:1,lwd=1:2),override.par=FALSE)
plot(rho,tselect=200,type="conditional",tol.args=list(levels=c(0.05,0.0001),
     lty=2:1,lwd=1:2),override.par=FALSE)

tilmandavies/sparr documentation built on March 21, 2023, 11:34 a.m.