| axials {kindisperse} | R Documentation |
Estimate the axial dispersal distance of a kernel
Description
This function performs a basic estimation of axial dispersal for a numeric vector of distances between close kin dyads. The axial dispersal distance returned is interpretable as the standard deviation of one dimension of a symmetric bivariate random distribution centred on zero.
Usage
axials(valvect, composite = 1)
Arguments
valvect |
A numeric vector of distances between close kin OR an object of class |
composite |
numeric. The number of separate 'draws' (dispersal events) from the kernel required to produce the final positions of the measured individuals. For example, the displacement of a child from parent at the same lifestage would involve 1 draw and thus be composite = 1. Two full siblings would be two draws (composite = 2) from the FS kernel. Non-symmetric relationships (e.g. AV, 1C) should not be decomposed using this method, nor should any assumptions be made about different kernels (e.g. the 1C relationship would appropriately be given the value 2, but not 4) |
Value
Returns the value of the estimated axial dispersal distance of the kernel producing the dispersal distances measured. (numeric)
See Also
Other axial_helpers:
axials_add(),
axials_decompose(),
axials_subtract(),
axpermute_subtract(),
axpermute()
Examples
po_dists <- c(5, 6, 7.5)
axials(po_dists) # one 'draw' (dispersal event) goes into the parent offspring category
# so composite is left to its default of 1
fs_dists <- c(2, 3, 3)
axials(fs_dists, composite = 2) # two 'draws' (symmetric dispersal events)
# go into the full sibling category so composite is set to 2