edge_effect: Edge Effect
In dbailleul/RClone: Partially Clonal Populations Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

edge_effect tests the occurrence of Edge Effect.

1 2	edge_effect(data1, coords = NULL, center = NULL, vecpop = NULL, nbrepeat = 1, bar = FALSE, listMLL = NULL)

`data1`	a `Rclone` table with one allele per column, haploid or diploid data.
`coords`	a table with coordinates of every units in `data1`.
`center`	a vector or a list of vectors, with `c(x,y)` coordinates of the centre of the sampling area.
`vecpop`	vector, option, `vecpop` indicates the population name of each unit of `data1`, if `data1` contains several populations. If `data1` contains only one population, leave `vecpop = NULL`.
`nbrepeat`	numeric, option, the number of repeats.
`bar`	logical, option, if `TRUE`, adds a progression bar.
`listMLL`	option, a custom list of MLL.

The index of edge effect Ee estimates the effect of sampling (scheme and strategy) on genotypic richness estimation and in particular overestimation due to large clones sampled only once at the edge of the sampling area.

Ee is estimated as Ee=(Du-Da)/Da with Du average geographic distances between unique MLG/MLL and the centre, and Da between all sampling units and the centre.

As for the aggregation index Ac, coordinates of units are randomly permuted nbrepeat times to provide a upper p-value (Monte Carlo).

a list (one population) or list of lists (several populations) with

results a table with Ee value, pvalue and the number of permutations.
simulations a vector of nbrepeat values of sim-Ee.

Creator/Author: Diane Bailleul <diane.bailleul.pro@gmail.com>
Author: Sophie Arnaud-Haond <sophie.arnaud@ifremer.fr>
Contributor: Solenn Stoeckel

The R implementation of RClone was written by Diane Bailleul.

The design was inspired by GenClone program described in Arnaud-Haond & Belkhir (2007).

Arnaud-Haond et al., 2007, Standardizing methods to address clonality in population studies.

autocorrelation, clonal_sub and agg_index

data(posidonia)
data(coord_posidonia)

center1 <- c(40,10)
#Our sample quadra ranges from 0 to 80 and 0 to 20

edge_effect(posidonia, coords = coord_posidonia, center = center1, nbrepeat = 1000, 
	bar = TRUE)

#But if, for some reasons you don't know where the middle of the sampling 
##area is, you can try some of these:
center <- c(mean(coord_posidonia[,1]), mean(coord_posidonia[,2])) #or
center <- c(mean(c(min(coord_posidonia[,1]), max(coord_posidonia[,1]))), 
mean(c(min(coord_posidonia[,2]), max(coord_posidonia[,2])))) #or
center <- c((max(coord_posidonia[,1])-min(coord_posidonia[,1]))/2, 
(max(coord_posidonia[,2])-min(coord_posidonia[,2]))/2)