prepare_PAM_CS {biosurvey} R Documentation

## Preparing data for new range-diversity plot

### Description

Preparation of data and details to create range-diversity plots.

### Usage

prepare_PAM_CS(PAM, exclude_column = NULL, id_column = NULL,
significance_test = FALSE, randomization_iterations = 100,
CL = 0.05, picante_iterations = NULL,
keep_randomizations = FALSE, parallel = FALSE,
n_cores = NULL)


### Arguments

 PAM matrix, data.frame, or base_PAM object containing information on presence and absence of species for a set of sites. Sites are organized in the rows and species in the columns. See details. exclude_column (optional) name or numeric index of columns to be excluded. Default = NULL. id_column (optional) name or numeric index of column containing the ID of sites (cells of the PAM). Default = NULL. significance_test (logical) whether to perform a test to detect sites (cells) that are statistically significant (i.e., the pattern detected can be distinguished from random expectations). Default = FALSE. randomization_iterations (numeric) number of iterations for the randomization test used to calculate statistical significance. Default = 100. CL (numeric) confidence limit to detect statistically significant values. Default = 0.05. picante_iterations (numeric) number of iterations to be used for each matrix randomization process (to be done randomization_iterations times). This process is done using the function randomizeMatrix from the package picante. The default, NULL, uses 2 * sum(PAM). keep_randomizations (logical) whether to keep a matrix with all values from the randomization process. Default = FALSE. parallel (logical) whether to perform analyses in parallel. Default = FALSE. n_cores (numeric) number of cores to be used when parallel = TRUE. The default, NULL, uses available cores - 1.

### Details

Range-diversity plot allow explorations of patterns of biodiversity in a region based on the data of presence-absence matrices. The plots to be produced using the information prepared here are a modification of those presented in Arita et al. (2011) doi: 10.1111/j.1466-8238.2011.00662.x.

### Value

An S3 object of class PAM_CS if PAM is a matrix or data.frame, otherwise, an object of class base_PAM that contains the PAM_CS object as a part of PAM_indices.

Significant values are presented as a vector in which 0 means non-significant, and 1 and 2 represent significant values below and above confidence limits of random expectations, respectively.

### Examples

# Data
data("b_pam", package = "biosurvey")

# Preparing data for CS diagram
pcs <- prepare_PAM_CS(PAM = b_pam)

summary(pcs$PAM_indices$CS_diagram)


[Package biosurvey version 0.1.1 Index]