rhrHlscv: Calculate a Bandwidth for Kernel Density Estimation
In jmsigner/rhr: Reproducible Home Ranges with R

Description Usage Arguments Details Value References Examples

Use two dimensional least square cross validation to select the bandwidth of a kernel density estimate.

rhrHlscv(xy, range = do.call(seq, as.list(c(rhrHref(xy)$h * c(0.1, 2),
  length.out = 100))), whichMin = "global", rescale = "none",
  trast = rhrRasterFromExt(rhrExtFromPoints(xy, extendRange = 0.2), nrow =
  100, res = NULL))

`xy`	A two column numeric data.frame with two columns: the x and y coordinates.
`range`	numeric vector with different candidate h values.
`whichMin`	A character indicating if the `global` or `local` minimum should be searched for.
`rescale`	A character, indicating if and if how data are rescaled. `unitvar` rescales x and y coordinates to unit variance, `xvar` rescales x and y coordinate to variance of x and `none` uses the raw data.
`trast`	A RasterLayer with the desired extent and resolution.

Function to calcualte least square cross validation bandwidth. This implementation is based on Seaman and Powell (1996). If whichMin is "global" the global minimum is returned, else the local minimum with the largest candidate bandwidth is returned.

vector of length two

Seaman, D. E., & Powell, R. A. (1996). An evaluation of the accuracy of kernel density estimators for home range analysis. _Ecology, 77(7)_, 2075-2085.

Carr and Rodges

## Not run: 
data(datSH)
hlscv <- rhrHlscv(datSH[1:1000, 2:3])

## Plotting the different values for h
x <- datSH[, 2]
y <- datSH[, 3]


## End(Not run)