Completeness: Main function for STEP 1: Assessment of sample completeness

In iNEXT.4steps: Four-Step Biodiversity Analysis Based on 'iNEXT'

Main function for STEP 1: Assessment of sample completeness

Description

Completeness computes sample completeness estimates of orders q = 0 to 2 in increments of 0.2 (by default).

Usage

Completeness(
  data,
  q = seq(0, 2, 0.2),
  datatype = "abundance",
  nboot = 30,
  conf = 0.95,
  nT = NULL
)

Arguments

data	(a) For datatype = "abundance", data can be input as a vector of species abundances (for a single assemblage), matrix/data.frame (species by assemblages), or a list of species abundance vectors. (b) For datatype = "incidence_raw", data can be input as a list of matrix/data.frame (species by sampling units); data can also be input as a matrix/data.frame by merging all sampling units across assemblages based on species identity; in this case, the number of sampling units (nT, see below for this argument) must be input.
q	a numerical vector specifying the orders of sample completeness. Default is seq(0, 2, by = 0.2).
datatype	data type of input data: individual-based abundance data (datatype = "abundance") or species by sampling-units incidence matrix (datatype = "incidence_raw") with all entries being 0 (non-detection) or 1 (detection).
nboot	a positive integer specifying the number of bootstrap replications when assessing sampling uncertainty and constructing confidence intervals. Enter 0 to skip the bootstrap procedures. Default is 30.
conf	a positive number < 1 specifying the level of confidence interval. Default is 0.95.
nT	(required only when datatype = "incidence_raw" and input data in a single matrix/data.frame) a vector of positive integers specifying the number of sampling units in each assemblage. If assemblage names are not specified (i.e., names(nT) = NULL), then assemblages are automatically named as "Assemblage1", "Assemblage2",..., etc.

Value

a matrix of estimated sample completeness of order q:

Order.q	the order of sample completeness.
Estimate.SC	the estimated sample completeness of order q.
s.e.	standard error of sample completeness estimate.
SC.LCL, SC.UCL	the bootstrap lower and upper confidence limits for the sample completeness of order q at the specified level (with a default value of 0.95).
Assemblage	the assemblage name.

Examples

## Sample completeness for abundance data
data(Data_spider)
SC_out1 <- Completeness(data = Data_spider, datatype = "abundance")
SC_out1


## Sample completeness for incidence raw data
data(Data_woody_plant)
SC_out2 <- Completeness(data = Data_woody_plant, datatype = "incidence_raw")
SC_out2

iNEXT.4steps documentation built on June 22, 2024, 12:23 p.m.