rtcc1: Trait selection

In RTCC: Detecting Trait Clustering in Environmental Gradients

Description

This function determines whether the selected traits exhibit or not a clustering/overdispersion signal on the tested samples. For each trait, compares the observed Mean Pairwise Distance (MPD) of each sample against a distribution of synthetic commmunities MPDs obtained by a randomization test. Each synthetic community is build maintaining the original sample richness and randomly selecting organisms form the global pool.

Usage

rtcc1(table1, table2, table3, traits_columns, repetitions)

Arguments

table1	A data frame containing organisms names on the first column and its trait values on the consecutive ones. It also has to contain two columns with the maximum and the minimum values of the tested environmental variable where the organisms have been observed.
table2	A presence-absence observations table with the organisms names on the first column and the sample names as consecutive colnames.
table3	A dataframe containing sample names on the first column and environmental parameters on the consecutive ones.
traits_columns	Table 1 column numbers where different trait values appear.
repetitions	Number of simulated synthetic communities distributions.

Value

The function returns a dataframe with trait names as colnames and the p-value distribution of the different traits.

Examples

data(group_information)
data(table_presence_absence)
data(metadata)
rtcc1(group_information, table_presence_absence, metadata, 2:11, 100)

RTCC documentation built on July 1, 2020, 7:06 p.m.