# cvloglk

##### Cross-Validated Log-Likelihood Function Calculate the cross-validated log-likelihood function.

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.

- Keywords
- smooth

##### 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

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`

*Documentation reproduced from package spatialkernel, version 0.4-23, License: CC BY-NC-SA 4.0*