compare.dist: Threshold to discriminate species comparing intra- and...
In sidier: Substitution and Indel Distances to Infer Evolutionary Relationships

Description Usage Arguments Value Author(s) References Examples

This function implements the Lefebure's method to quantify the overlap between two distributions and to determine the best threshold value to discriminate them.

compare.dist(distr1=NULL,distr2=NULL,N=50,
normalize=TRUE,main=NA,col1="gray",col2="black",
col.border1="gray",col.border2="black",
col.line1="gray",col.line2="black",
Ylab=c("Abundance","Abundance","Success"),
Xlab=c("data1","data2","Threshold"))

`distr1`	a matrix containing the pairwise genetic distances between individual sequences
`distr2`	a vector containing the minimum and maximum value in the x-axis
`N`	a numeric, the number of categories for the x-axis.
`normalize`	a logic, "TRUE" to display percentage and "FALSE" for number of occurrences in the Y axis.
`main`	a vector with two elements containing the main titles of both plots.
`col1`	a string, the color to fill the histogram for distribution 1
`col2`	a string, the color to fill the histogram for distribution 2
`col.border1`	a string, the color for the border around the histogram for distribution 1
`col.border2`	a string, the color for the border around the histogram for distribution 2
`col.line1`	a string, the color for the line representing distribution 1
`col.line2`	a string, the color for the line representing distribution 2
`Ylab`	a three strings vector containing the labels of Y axes for the three plots to be represented.
`Xlab`	a three strings vector containing the labels of X axes for the three plots to be represented

The estimated threshold and its success of identification.

A.J. Muñoz-Pajares

Lefébure T, Douady CJ, Gouy M, Gibert J (2006). Relationship between morphological taxonomy and molecular divergence within Crustacea: Proposal of a molecular threshold to help species delimitation. Mol Phylogenet Evol 40: 435–447.

# ## Weak overlap
# intra<-rnorm(mean=0.08,sd=0.04,n=100)
# inter<-rnorm(mean=0.38,sd=0.10,n=1000)
# intra[intra<0]<-0
# inter[inter<0]<-0
# compare.dist(distr1=intra,distr2=inter,N=50)
# 
# # Strong overlap
# distr1<-rnorm(5000,mean=0.25,sd=0.070)
# distr2<-rnorm(5000,mean=0.31,sd=0.075)
# N<-50
# compare.dist(distr1,distr2,N)
#