rrpoint: Generate random case/control points in space or space-time

In spagmix: Artificial Spatial and Spatio-Temporal Densities on Bounded Windows

Generate random case/control points in space or space-time

Description

Generates a pair of random, independent point patterns corresponding to a case density and a control density, for relative risk analyses.

Usage

rrpoint(n, r, W = NULL, correction = 1.1, maxpass = 50)
rrstpoint(n, r, W = NULL, correction = 1.5, maxpass = 50)

Arguments

n	The number of points to be generated. This must be a numeric vector of length 2 giving the number of points to generate for the case and control densities respectively. Alternatively a single number can be supplied; then the same number of points is generated for both densities.
r	The relative risk surface object containing the definitions of the case and control probability densities: an object of class rrim or rrs for rrpoint, or an object of class rrstim or rrst for rrstpoint.
W	The polygonal owin defining the spatial window on which the density is defined. If NULL, this will be set to the as.polygonal version of the pixel images stored in r. See ‘Details’.
correction	An adjustment to the number of points generated at the initial pass of the internal loop in an effort to minimise the total number of passes required to reach n points.
maxpass	The maximum number of passes allowed before the function exits. If this is reached before n points are found with respect to the spatial or spatiotemporal domains of r, a warning is issued.

Details

These functions randomly generate a pair of independent spatial or spatiotemporal point patterns of n points based on the case and control density functions stored in r. At any given pass for each density, n * correction points are generated and rejection sampling is used to accept some of the points; this is repeated until the required number of points is found.

The argument W is optional, but is useful when the user wants the spatial window of the resulting point pattern to be a corresponding irregular polygon, as opposed to being based on the boundary of a binary image mask (which, when the pixel images in r are converted to a polygon directly, gives jagged edges based on the union of the pixels).

Value

A list with two components, cases and controls, each of which is an object of class ppp containing the n generated points. for spatiotemporal densities, the marks of the object will contain the correspondingly generated observation times.

Author(s)

T.M. Davies

Examples

# Using 'rrim' object:
set.seed(1)
gg <- rgmix(3,window=toywin,S=matrix(c(0.08^2,0,0,0.1^2),nrow=2),p0=0.2)
rho <- rrmix(g=gg,
             rhotspots=cbind(c(0.8,0.3),c(0.4,0.4),c(0.6,0.5),c(0.3,0.5)),
             rsds=c(0.005,0.025,0.01,0.025),
             rweights=c(3,2,10,5)*10)

rho.sample <- rrpoint(n=c(400,800),r=rho,W=toywin)

par(mfrow=c(2,2))
plot(rho$g,main="control density")
plot(rho$f,main="case density")
plot(rho$r,main="log relative risk surface")
plot(rho.sample$controls,main="sample data")
points(rho.sample$cases,col=2)
legend("topright",col=2:1,legend=c("cases","controls"),pch=1)



# Using 'rrs' object:
require("sparr")
data(pbc)
pbccas <- split(pbc)$case
pbccon <- split(pbc)$control
h0 <- OS(pbc,nstar="geometric")
f <- bivariate.density(pbccas,h0=h0,hp=2,adapt=TRUE,pilot.density=pbccas,
                       edge="diggle",davies.baddeley=0.05,verbose=FALSE)
g <- bivariate.density(pbccon,h0=h0,hp=2,adapt=TRUE,pilot.density=pbccas,
                       edge="diggle",davies.baddeley=0.05,verbose=FALSE)
pbcrr <- risk(f,g,tolerate=TRUE,verbose=FALSE)

pbcrr.pt <- rrpoint(n=1000,r=pbcrr)

par(mfrow=c(1,3))
plot(pbcrr)
plot(pbcrr.pt$cases)
plot(pbcrr.pt$controls)


# Using 'rrstim' object:
set.seed(321)
gg <- rgmix(7,window=shp2)
rsk <- rrstmix(g=gg,rhotspots=matrix(c(-1,-1,2,2.5,0,5),nrow=3),
               rsds=sqrt(cbind(rep(0.75,3),c(0.05,0.01,0.5))),
               rweights=c(-0.4,7),tlim=c(0,6),tres=64)
plot(rsk$r,fix.range=TRUE)

rsk.pt <- rrstpoint(1000,r=rsk,W=shp2)

par(mfrow=c(1,2))
plot(rsk.pt$cases)
plot(rsk.pt$controls)


# Using 'rrst' object:
require("sparr")
data(fmd)
fmdcas <- fmd$cases
fmdcon <- fmd$controls

f <- spattemp.density(fmdcas,h=6,lambda=8)
g <- bivariate.density(fmdcon,h0=6)
rho <- spattemp.risk(f,g)

rho.pt <- rrstpoint(1000,r=rho)

par(mfrow=c(1,2))
plot(rho.pt$cases)
plot(rho.pt$controls)

spagmix documentation built on March 28, 2022, 5:07 p.m.