cvloglk: Cross-Validated Log-Likelihood Function Calculate the...

In spatialkernel: Non-Parametric Estimation of Spatial Segregation in a Multivariate Point Process

Description

Select a common bandwidth for kernel regression estimation of type-specific probabilities of a multivariate Poisson point process with independent component processes of each categorical type by maximizing the cross-validate log-likelihood function.

Usage

cvloglk(pts, marks, t = NULL, h)

Arguments

pts	matrix containing the x,y-coordinates of the point locations.
marks	numeric/character vector of the marked labels of the type of each point.
t	numeric vector of the associated time-periods, default NULL for pure spatial data.
h	numeric vector of the kernel smoothing bandwidths at which to calculate the cross-validated log-likelihood function.

Details

Select a common bandwidth for kernel regression of type-specific probabilities for all time-periods when the argument t is not NULL, in which case the data is of a multivariate spatial-temporal point process, with t the values of associated time-periods.

Value

A list with components

cv

vector of the values of the cross-validated Log-likelihood function.

hcv

numeric value which maximizing the cross-validate log-likelihood function

...

copy of the arguments pts, marks, h.

References

1. Diggle, P.J., Zheng, P. and Durr, P. A. (2005) Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK. J. R. Stat. Soc. C, 54, 3, 645–658.

See Also

phat, mcseg.test, and mcpat.test

spatialkernel documentation built on May 2, 2019, 2:26 p.m.