potential_analysis: Bootstrapped Potential Analysis

In microbiome: Microbiome Analytics

Description

Analysis of multimodality based on bootstrapped potential analysis of Livina et al. (2010) as described in Lahti et al. (2014).

Usage

potential_analysis(
  x,
  peak.threshold = 0,
  bw.adjust = 1,
  bs.iter = 100,
  min.density = 1
)

Arguments

x	Input data vector
peak.threshold	Mode detection threshold
bw.adjust	Bandwidth adjustment
bs.iter	Bootstrap iterations
min.density	minimum accepted density for a maximum; as a multiple of kernel height

Value

List with following elements:

• modesNumber of modes for the input data vector (the most frequent number of modes from bootstrap)

• minimaAverage of potential minima across the bootstrap samples (for the most frequent number of modes)

• maximaAverage of potential maxima across the bootstrap samples (for the most frequent number of modes)

• unimodality.supportFraction of bootstrap samples exhibiting unimodality

• bwsBandwidths

References

• Livina et al. (2010). Potential analysis reveals changing number of climate states during the last 60 kyr. Climate of the Past, 6, 77-82.

• Lahti et al. (2014). Tipping elements of the human intestinal ecosystem. Nature Communications 5:4344.

See Also

plot_potential

Examples

# Example data; see help(peerj32) for details
data(peerj32)

# Log10 abundance of Dialister
x <- abundances(transform(peerj32$phyloseq, "clr"))['Dialister',]

# Bootstrapped potential analysis
# In practice, use more bootstrap iterations
# res <- potential_analysis(x, peak.threshold=0, bw.adjust=1,
#    bs.iter=9, min.density=1)

microbiome documentation built on Nov. 8, 2020, 5:08 p.m.