sidier (version 4.1.0)

compare.dist: Threshold to discriminate species comparing intra- and interspecific distance distributions

Description

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

Usage

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"))

Arguments

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

Value

The estimated threshold and its success of identification.

References

Lef<U+00E9>bure 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<U+2013>447.

Examples

# NOT RUN {
# ## 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)
# 
# }