dbmss (version 2.3-0)

LEnvelope: Estimation of the confidence envelope of the L function under its null hypothesis

Description

Simulates point patterns according to the null hypothesis and returns the envelope of L according to the confidence level.

Usage

LEnvelope(X, r = NULL, NumberOfSimulations = 100, Alpha = 0.05,  ReferenceType = "", NeighborType = "", SimulationType = "RandomPosition",  Global = FALSE)

Arguments

X

A point pattern (wmppp.object).

r

A vector of distances. If NULL, a sensible default value is chosen (512 intervals, from 0 to half the diameter of the window) following spatstat.

NumberOfSimulations

The number of simulations to run, 100 by default.

Alpha

The risk level, 5% by default.

ReferenceType

One of the point types. Default is all point types.

NeighborType

One of the point types. Default is all point types.

SimulationType

A string describing the null hypothesis to simulate. The null hypothesis may be "RandomPosition": points are drawn in a Poisson process (default); "RandomLabeling": randomizes point types, keeping locations unchanged; "PopulationIndependence": keeps reference points unchanged, randomizes other point locations.

Global

Logical; if TRUE, a global envelope sensu Duranton and Overman (2005) is calculated.

Value

An envelope object (envelope). There are methods for print and plot for this class.The fv contains the observed value of the function, its average simulated value and the confidence envelope.

Details

This envelope is local by default, that is to say it is computed separately at each distance. See Loosmore and Ford (2006) for a discussion. The global envelope is calculated by iteration: the simulations reaching one of the upper or lower values at any distance are eliminated at each step. The process is repeated until Alpha / Number of simulations simulations are dropped. The remaining upper and lower bounds at all distances constitute the global envelope. Interpolation is used if the exact ratio cannot be reached.

References

Duranton, G. and Overman, H. G. (2005). Testing for Localisation Using Micro-Geographic Data. Review of Economic Studies 72(4): 1077-1106. Kenkel, N. C. (1988). Pattern of Self-Thinning in Jack Pine: Testing the Random Mortality Hypothesis. Ecology 69(4): 1017-1024. Loosmore, N. B. and Ford, E. D. (2006). Statistical inference using the G or K point pattern spatial statistics. Ecology 87(8): 1925-1931. Marcon, E. and F. Puech (2012). A typology of distance-based measures of spatial concentration. HAL SHS. 00679993.

See Also

Khat

Examples

data(paracou16)
# Keep only 20% of points to run this example
X <- as.wmppp(rthin(paracou16, 0.2))
plot(X)

# Calculate confidence envelope (should be 1000 simulations, reduced to 20 to save time)
r <- 0:30
NumberOfSimulations <- 20
# Plot the envelope
plot(LEnvelope(X, r, NumberOfSimulations))