determine optimal spatial data discretization for individual variables

Function for determining optimal spatial data discretization for individual variables based on locally estimated scatterplot smoothing (LOESS) model.

Usage

loess_optnum(qvec, discnumvec, increase_rate = 0.05)

Arguments

qvec: A numeric vector of q statistics.
discnumvec: A numeric vector of break numbers corresponding to qvec.
increase_rate: (optional) The critical increase rate of the number of discretization. Default is 0.05.

Value

A two element numeric vector.

discnum: optimal number of spatial data discretization
increase_rate: the critical increase rate of the number of discretization

Note

When increase_rate is not satisfied by the calculation, the discrete number corresponding to the highest q statistic is selected as a return.

Note that sdsfun sorts discnumvec from smallest to largest and keeps qvec in one-to-one correspondence with discnumvec.

Examples

qv = c(0.26045642,0.64120405,0.43938704,0.95165535,0.46347836,
       0.25385338,0.78778726,0.95938330,0.83247885,0.09285196)
loess_optnum(qv,3:12)
#>       discnum increase_rate 
#>          6.00          0.05