DNCI.ses: DNCI Function : Dispersal-Niche Continuum Index

In Corentin-Gibert-Paleontology/DNCImper: Assembly process identification based on SIMPER analysis

DNCI Function : Dispersal-Niche Continuum Index

Description

Quantitative identification of the main assembly process. This function is based on PerSIMPER function and its E index return(). The three distributions of E index (corresponding to the three hypothesis: niche, dispersal, niche+dispersal) are used to compute the DNCI index. If DNCI is significantly < 0 : dispersal || DNCI significantly > 0 : niche || DNCI +- CI ~~ 0 : dispersal+niche See Vilmi, Gibert et al. 2021 Ecography for DNCI computation and more information on process identification. More information in code and comments inside function file.

Usage

DNCI.ses(
  x,
  grouping,
  id = "no_name",
  Nperm = 1000,
  count = TRUE,
  plotSIMPER = TRUE,
  dataTYPE = "prab",
  parallelComputing = FALSE
)

Arguments

x	Sample/Taxa matrix with sample in row and taxa in column
grouping	Grouping vector, ex : c(1,1,1,1,2,2,2,2,2) : 2 groups only !!
id	Name of the dataset, default = "no_name"
Nperm	Number of permutation, default = 1000, should be change to 100 for robustness analysis
count	Display the number of permutation done, can be usefull with very large or small matrix, default = TRUE
plotSIMPER	Display the SIMPER, PerSIMPER and E index plots, default = TRUE
dataTYPE	Need to be set for presence/absence or abundance data ("count"), default = "prab" (presence_absence)
parallelComputing	Run PerSIMPER on half of the available cores/nodes

Examples

A <- DNCImper:::DNCI.ses(Matrix, Group)
#where Matrix is a presence/absence matrix with taxa in column and sample in row
#and Group is a vector with length() == number of rows/samples in Matrix, 2 groups ONLY
#
B <- DNCImper:::DNCI.ses(Matrix, Group, Nperm = 100, count = FALSE, plotSIMPER = FALSE)
#In this example, same data are analysed, with 100 permutations, with no countdown and no plots

Corentin-Gibert-Paleontology/DNCImper documentation built on Feb. 8, 2025, 10:20 a.m.