smacpod (version 1.4.1)

kdest: Difference of estimated K functions

Description

kdest determines the difference in estimated K functions for a set of cases and controls.

Usage

kdest(x, case = 2, nsim = 0, level = 0.95, r = NULL, breaks = NULL,
  correction = c("border", "isotropic", "Ripley", "translate"),
  nlarge = 3000, domain = NULL, var.approx = FALSE, ratio = FALSE)

Arguments

x

A ppp object from the spatstat package with marks for the case and control groups.

case

The position of the name of the "case" group in levels(x$marks). The default is 2.

nsim

An non-negative integer. Default is 0. The difference in estimated K functions will be calculated for nsim data sets generated under the random labeling hypothesis.

level

Confidence level of confidence envelopes. Ignoried if nsim is 0.

r

Optional. Vector of values for the argument r at which K(r) should be evaluated. Users are advised not to specify this argument; there is a sensible default.

breaks

This argument is for internal use only.

correction

Optional. A character vector containing any selection of the options "none", "border", "bord.modif", "isotropic", "Ripley", "translate", "translation", "none", "good" or "best". It specifies the edge correction(s) to be applied.

nlarge

Optional. Efficiency threshold. If the number of points exceeds nlarge, then only the border correction will be computed (by default), using a fast algorithm.

domain

Optional. Calculations will be restricted to this subset of the window. See Details.

var.approx

Logical. If TRUE, the approximate variance of Kest(r) under CSR will also be computed.

ratio

Logical. If TRUE, the numerator and denominator of each edge-corrected estimate will also be saved, for use in analysing replicated point patterns.

Value

Returns an fv object. See documentation for spatstat::Kest.

Details

This function relies internally on the Kest and eval.fv functions from the spatstat package. So the arguments are essentially the same as the Kest function. See the documentation of the Kdest for more details about the various arguments.

References

Waller, L.A. and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley. Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics -- Theory and Methods 26, 1481-1496.

See Also

Kest

Examples

# NOT RUN {
data(grave)
kd1 = kdest(grave)
plot(kd1, iso ~ r, ylab = "difference", legend = FALSE, main = "")
kd2 = kdest(grave, nsim = 9, level = 0.8)
plot(kd2)
# }