PerSIMPER_overall: Per-SIMPER function adapted to overall dissimilarity analysis...

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

Per-SIMPER function adapted to overall dissimilarity analysis of 3 groups or more

Description

This function is the basis of DNCI_ses_overall. Overall dissimilarity contribution of taxa is obtained by computing the mean contribution of pairwise dissimilarity of taxa for each pairs of assemblages. -Under development- Qualitative identification of the main assembly process driving the overall dissimilarity contribution distribution (i.e. the empirical SIMPER profile structure). -Under development- Similar to PerSIMPER() function, Groups need to be larger than 2.

Usage

PerSIMPER_overall(
  matrixSIMP,
  Groups,
  count = TRUE,
  dataTYPE = "prab",
  Nperm = 1000,
  plotSIMPER = TRUE,
  parallelComputing = FALSE
)

Arguments

matrixSIMP	Sample/Taxa matrix with sample in row and taxa in column
Groups	Grouping vector, ex : c(1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4) : 3 groups or more
count	Display the number of permutation done, can be usefull with very large or small matrix, default = TRUE
dataTYPE	Need to be set for presence/absence or abundance data ("count"), default = "prab" (presence_absence)
Nperm	Number of permutation, default = 1000, should be change to 100 for robustness analysis
plotSIMPER	Display the SIMPER, PerSIMPER and E index plots, default = TRUE
parallelComputing	Run PerSIMPER on half of the available cores/nodes

Examples

A <- DNCImper:::PerSIMPER_overall(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, 3 groups or more.
#
B <- DNCImper:::PerSIMPER_overall(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.