fuzzyqCI: Compute Confidence Intervals of Clustering Metrics

In Ligophorus/FuzzyQ: Fuzzy Quantification of Common and Rare Species

Description

Computes confidence intervals of clustering metrics based on the bootstrap replicates produced by fuzzyqBoot.

Usage

fuzzyqCI(fq.bs, fq = NULL, method = "pct", c.level = 0.95)

Arguments

fq.bs	A list returned by FuzzyQ::fuzzyqBoot.
fq	A list of class fuzzyq returned by FuzzyQ::fuzzyq. Required only if method = "bc" or method = "bca".
method	String. Specify the method to compute confidence intervals. Any of the following: "pct" (percentile, the default), "bc" (bias corrected), "bca" (bias corrected and accelerated).
c.level	Number within [0,1]. Specify the confidence interval level. Default is 0.95.

Value

A matrix with upper and lower confidence interval limits of clustering metrics.

Examples

data(antsA)
FQAnts <- fuzzyq(antsA, sorting = TRUE)

# Compute species Commonness Indices of species of 1,000 bootstrap
# replicates:
BS.FQAnts <- fuzzyqBoot (antsA, N = 1e3, level='spp')

# Compute 95 % confidence intervals, percentile method, default values:
BS.sppCI1 <- fuzzyqCI(BS.FQAnts)

# Alternatively, 95 % confidence intervals, bias corrected and accelerated
# method:
BS.sppCI2 <- fuzzyqCI(BS.FQAnts, fq=FQAnts, method = "bca")

# Compute global metrics of 1,000 boostrap replicates:
BS.global <- fuzzyqBoot (antsA, N = 1e3, level='global')

# Compute 95 % confidence intervals, bias corrected and accelerated method:
BS.globalCI <- fuzzyqCI(BS.global, fq=FQAnts, method = "bca")

Ligophorus/FuzzyQ documentation built on June 3, 2021, 4:37 a.m.