# axials: Estimate the axial dispersal distance of a kernel In kindisperse: Simulate and Estimate Close-Kin Dispersal Kernels

## 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

 `1` ```axials(valvect, composite = 1) ```

## Arguments

 `valvect` A numeric vector of distances between close kin OR an object of class `KinPairData` `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)

Other axial_helpers: `axials_add()`, `axials_decompose()`, `axials_subtract()`, `axpermute_subtract()`, `axpermute()`
 ```1 2 3 4 5 6 7``` ```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 ```