LF.gof {idar}

Loosmore and Ford Goodness of Fit Test

Description

Performs the Loosmore and Ford (2006) test or the Maximum Absolute Deviation test for a spatial point pattern.

Usage

  LF.gof(X, rmin=NULL, rmax=NULL, na.rm=TRUE)

Arguments

X

An object resulting from the function envelope, i.e., with an attribute "simfuns" (obtained using the argument savefuns=TRUE in envelope) ,which is an object of class "fv" containing the summary functions computed for each of the simulated patterns.

rmin

Minimum value of the function argument r over which the maximum absolute deviation, or the integral, will be computed for the test.

rmax

Maximum value of the function argument r over which the maximum absolute deviation, or the integral, will be computed for the test.

na.rm

Should NA's be removed to compute the integral?

Details

These function perform a tests for goodness-of-fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model. The simulations should have been previously computed with the function envelope, applied with the argument savefuns=TRUE in order to save all the simulated functions, required for the computation of the test.

The test, popularized in the ecological field by Loosmore and Ford (2006) is also described in Diggle (2003, page 14), and according to Baddeley and Turner (2005) also in Diggle (1986) and Cressie (1991, page 667, equation (8.5.42)). If the arguments rmin and rmax are set to NULL, the integral of the GoF statistics will be computed over the complete range of r values.

Value

A list with the following components:

u

The GoF statistic, i.e., the value of the integral over the range of r's

p

The p-value of the test

na.count.by.r

Number of NA values for each r. It helps to evaluate the reliability of the computed u's, specially for small r's

Author(s)

Marcelino de la Cruz [e-mail]

References

Cressie, N.A.C. (1991) Statistics for spatial data. John Wiley and Sons, 1991.

Diggle, P. J. (1986). Displaced amacrine cells in the retina of a rabbit : analysis of a bivariate spatial point pattern. J. Neuroscience Methods 18, 115-125.

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

Loosmore, N.B. and Ford, E.D. (2006) Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925-1931.

See Also

dclf.test for an alternative implementation of the test in spatstat.

Examples

# some envelopesfor some idar function
# The argument "savefuns" must be set to "TRUE"
# BEWARE: in real tests nsim shpuld be 199 or higher
data(lansing)
maple.lambda<- density(unmark(lansing[lansing$marks=="maple"]))
multi.maple.sim <- multifocalsimulator(lansing, "maple", nsim=10,
                               simulate=expression(rpoispp(maple.lambda)))

isar.maple.env<- envelope(lansing, fun=isar, mimark="maple", nsim=10,
                                     simulate=multi.maple.sim, r=seq(0.01, 0.25, le=100),
                                      savefuns=TRUE)
# Estimate GoF test
LF.gof(isar.maple.env)