relrisk

0th

Percentile

Nonparametric Estimate of Spatially-Varying Relative Risk

Given a multitype point pattern, this function estimates the spatially-varying probability of each type of point, using kernel smoothing. The default smoothing bandwidth is selected by cross-validation.

Keywords
methods, smooth, spatial
Usage
relrisk(X, sigma = NULL, ..., varcov = NULL, at = "pixels",
casecontrol=TRUE, case=2)
Arguments
X
A multitype point pattern (object of class "ppp" which has factor valued marks).
sigma
Optional. The numeric value of the smoothing bandwidth (the standard deviation of isotropic Gaussian smoothing kernel). Alternatively sigma may be a function which can be used to select a different bandwidth for each type of p
...
Arguments passed to bw.relrisk to select the bandwidth, or passed to density.ppp to control the pixel resolution.
varcov
Optional. Variance-covariance matrix of anisotopic Gaussian smoothing kernel. Incompatible with sigma.
at
String specifying whether to compute the probability values at a grid of pixel locations (at="pixels") or only at the points of X (at="points").
casecontrol
Logical. Whether to treat a bivariate point pattern as consisting of cases and controls. See Details.
case
Integer, or character string, identifying which mark value corresponds to a case (rather than a control).
Details

If X is a bivariate point pattern (a multitype point pattern consisting of two types of points) then by default, the points of the first type (the first level of marks(X)) are treated as controls or non-events, and points of the second type are treated as cases or events. Then this command computes the spatially-varying risk of an event, i.e. the probability $p(u)$ that a point at spatial location $u$ will be a case.

If X is a multitype point pattern with $m > 2$ types, or if X is a bivariate point pattern and casecontrol=FALSE, then this command computes, for each type $j$, a nonparametric estimate of the spatially-varying risk of an event of type $j$. This is the probability $p_j(u)$ that a point at spatial location $u$ will belong to type $j$.

If at = "pixels" the calculation is performed for every spatial location $u$ on a fine pixel grid, and the result is a pixel image representing the function $p(u)$ or a list of pixel images representing the functions $p_j(u)$ for $j = 1,\ldots,m$.

If at = "points" the calculation is performed only at the data points $x_i$. The result is a vector of values $p(x_i)$ giving the estimated probability of a case at each data point, or a matrix of values $p_j(x_i)$ giving the estimated probability of each possible type $j$ at each data point.

Estimation is performed by a simple Nadaraja-Watson type kernel smoother (Diggle, 2003). The smoothing bandwidth can be specified in any of the following ways:

• sigmais a single numeric value, giving the standard deviation of the isotropic Gaussian kernel.
• sigmais a numeric vector of length 2, giving the standard deviations in the$x$and$y$directions of a Gaussian kernel.
• varcovis a 2 by 2 matrix giving the variance-covariance matrix of the Gaussian kernel.
• sigmais afunctionwhich selects the bandwidth. Bandwidth selection will be appliedseparately to each type of point. An example of such a function isbw.diggle.
• sigmaandvarcovare both missing or null. Then acommonsmoothing bandwidthsigmawill be selected by cross-validation usingbw.relrisk.

Value

• If X consists of only two types of points, the result is a pixel image (if at="pixels") or a vector of probabilities (if at="points").

If X consists of more than two types of points, the result is:

• (ifat="pixels") a list of pixel images, with one image for each possible type of point. The result also belongs to the class"listof"so that it can be printed and plotted.
• (ifat="points") a matrix of probabilities, with rows corresponding to data points$x_i$, and columns corresponding to types$j$.

References

Diggle, P.J. (2003) Statistical analysis of spatial point patterns, Second edition. Arnold.

bw.relrisk, density.ppp, Smooth.ppp, eval.im

• relrisk
Examples
data(urkiola)
p <- relrisk(urkiola, 20)
if(interactive()) {
plot(p, main="proportion of oak")
plot(eval.im(p > 0.3), main="More than 30 percent oak")
plot(split(lansing), main="Lansing Woods")
rr <- relrisk(lansing, 0.05)
plot(rr, main="Lansing Woods relative risk")
wh <- im.apply(rr, which.max)
types <- levels(marks(lansing))
wh <- eval.im(types[wh])
plot(wh, main="Most common species")
}
Documentation reproduced from package spatstat, version 1.37-0, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.