Title:	Ecological Indices Calculator for Nematode Communities
Version:	0.2.0
Date:	2025-08-08
Description:	Nematode communities serve as crucial bioindicators in ecological studies, reflecting soil health, ecosystem functioning, and trophic interactions. To standardize these assessments, we developed a computational toolkit for quantifying nematode-based ecological indicators, including metabolic footprints, energy flow metrics, and community structure analysis.
License:	GPL (≥ 3)
Depends:	R (≥ 3.5)
Imports:	dplyr (≥ 1.1.4), purrr (≥ 1.0.4), stats, stringdist (≥ 0.9.15), utils, vegan (≥ 2.7-1)
BuildManual:	no
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-08-18 09:40:15 UTC; 15225
Author:	Yuxuan He [aut, cre, cph], Dong Wang [ths, cph]
Maintainer:	Yuxuan He <heyuxuan@henu.edu.cn>
Repository:	CRAN
Date/Publication:	2025-08-20 16:50:07 UTC

Calculate Ecological Indices of Nematodes

Description

This function calculates various ecological indices based on the provided nematode genus abundance data. It supports a range of indices, including taxonomic diversity, Shannon diversity index, Pielou's evenness index, Simpson's index, and more. Users can specify which indices to calculate or use the default option to calculate all supported indices.

Usage

Ecological.Indices(
  data,
  indices = "All",
  total.abundance = NULL,
  method = NULL,
  ...
)

## S3 method for class 'data.frame'
Ecological.Indices(
  data,
  indices = "All",
  total.abundance = NULL,
  method = NULL,
  ...
)

## S3 method for class 'matrix'
Ecological.Indices(
  data,
  indices = "All",
  total.abundance = NULL,
  method = NULL,
  ...
)

## Default S3 method:
Ecological.Indices(
  data,
  indices = "All",
  total.abundance = NULL,
  method = NULL,
  ...
)

Arguments

Value

A data frame containing the calculated indices. The data frame includes a Sample.ID column and additional columns for each requested index.

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
abundance <- data.frame(
  abundance = c(100, 150, 120),
  row.names = c("A", "B", "C")
)
Ecological.Indices(data = df, indices = "All", total.abundance = abundance, method = "Menhinick")

Nematode Energy Footprints (NEF) Calculation

Description

Nematode Energy Footprints (NEF) Calculation

Usage

NEF(data, abundance, AE = list(Ba = 0.6, Fu = 0.38, Pp = 0.25, Op = 0.5), ...)

## S3 method for class 'data.frame'
NEF(data, abundance, AE = list(Ba = 0.6, Fu = 0.38, Pp = 0.25, Op = 0.5), ...)

## S3 method for class 'matrix'
NEF(data, abundance, AE = list(Ba = 0.6, Fu = 0.38, Pp = 0.25, Op = 0.5), ...)

## Default S3 method:
NEF(data, abundance, AE = list(Ba = 0.6, Fu = 0.38, Pp = 0.25, Op = 0.5), ...)

Arguments

Value

A list object of class "NEF" containing the following components:

data

A list with original input data:

• data - Original genus abundance data.frame or matrix of nematode genera

• Abundance - Total abundance data used for calculations

Energy.flux

A list containing energy flow calculations:

• Energy.flux: Data frame of energy flows (\mu g~C~100g^{-1}~dry~soil) per feeding group. Columns:

  • Sample.ID - Sample identifier

  • BaEF - Bacterial feeders energy flows

  • FuEF - Fungal feeders energy flows

  • PpEF - Plant feeders energy flows

  • OpEF - Omnivores/Predators energy flows

  • TNEF - Total energy flows of nematodes

• C.flux.node: Data frame of Biomass (\mu g~C~100g^{-1}~dry~soil) per feeding group. Columns:

  • Sample.ID - Sample identifier

  • Ba - Bacterial feeders biomass

  • Fu - Fungal feeders biomass

  • Pp - Plant feeders biomass

  • Op - Omnivores/Predators biomass

• C.flux.path: Data frame of energy flows (\mu g~C~100g^{-1}~dry~soil~day^{-1}). Columns:

  • Sample.ID - Sample identifier

  • R.to.Ba - Carbon flux from Resources to bacterial feeders

  • R.to.Fu - Carbon flux from Resources to fungal feeders

  • R.to.Pp - Carbon flux from Resources to plant feeders

  • Ba.to.Op - Carbon flux from bacterial to omnivorous channels

  • Fu.to.Op - Carbon flux from fungal to omnivorous channels

  • Pp.to.Op - Carbon flux from plant to omnivorous channels

• U: Data frame of ecosystem stability indices. Columns:

  • Sample.ID - Sample identifier

  • U - Energy flow uniformity index

Examples

data <- data.frame(
  Cephalobus = c(10, 20, 30),
  Eucephalobus = c(5, 10, 12),
  Acrobeloides = c(1, 2, 3),
  Caenorhabditis = c(5, 8, 15),
  Aphelenchus = c(5, 13, 11),
  Leptonchus = c(3, 10, 15),
  Pratylenchus = c(9, 2, 15),
  Tylenchus = c(5, 0, 15),
  Mesodorylaimus = c(7, 10, 18),
  Discolaimus = c(1, 10, 25),
  row.names = c("Sample1", "Sample2", "Sample3")
)
abundance <- data.frame(
  Abundance = c(100, 200, 300),
  row.names = c("Sample1", "Sample2", "Sample3")
)
result <- NEF(data, abundance)
print(result)

Nematode Metabolic Footprints (NMF) Calculation

Description

This function calculates various Nematode Metabolic Footprints (NMF) based on the input data and abundance information. It supports multiple types of NMF calculations and can handle data in different formats (data.frame or matrix).

Usage

NMF(data, abundance, type = "All", ...)

## S3 method for class 'data.frame'
NMF(data, abundance, type = "All", ...)

## S3 method for class 'matrix'
NMF(data, abundance, type = "All", ...)

## Default S3 method:
NMF(data, abundance, type = "all", ...)

Arguments

Value

A data.frame containing the calculated NMF values for each sample. The columns represent different NMF types, and the rows correspond to samples.

Examples

data <- data.frame(
  Cephalobus = c(10, 20, 30),
  Eucephalobus = c(5, 10, 12),
  Acrobeloides = c(1, 2, 3),
  Caenorhabditis = c(5, 8, 15),
  Aphelenchus = c(5, 13, 11),
  Leptonchus = c(3, 10, 15),
  Pratylenchus = c(9, 2, 15),
  Tylenchus = c(5, 0, 15),
  Mesodorylaimus = c(7, 10, 18),
  Discolaimus = c(1, 10, 25),
  row.names = c("Sample1", "Sample2", "Sample3")
)
abundance <- data.frame(
  Abundance = c(100, 200, 300),
  row.names = c("Sample1", "Sample2", "Sample3")
)
result <- NMF(data, abundance, type = "All")
print(result)

Calculate Basic Index (BI)

Description

This function calculates the Basic Index (BI) for ecological communities.

Usage

cal.BI(data, ...)

## S3 method for class 'data.frame'
cal.BI(data, ...)

## S3 method for class 'matrix'
cal.BI(data, ...)

## Default S3 method:
cal.BI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.BI(data = df)

Calculate Channel Index (CI)

Description

This function calculates the Channel Index (CI) for ecological communities.

Usage

cal.CI(data, ...)

## S3 method for class 'data.frame'
cal.CI(data, ...)

## S3 method for class 'matrix'
cal.CI(data, ...)

## Default S3 method:
cal.CI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.CI(data = df)

Calculate Enrichment Index (EI)

Description

This function calculates the Enrichment Index (EI) for ecological communities.

Usage

cal.EI(data, ...)

## S3 method for class 'data.frame'
cal.EI(data, ...)

## S3 method for class 'matrix'
cal.EI(data, ...)

## Default S3 method:
cal.EI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.EI(data = df)

Calculate Shannon-Wiener Index (H)

Description

This function calculates the Shannon-Wiener Index (H) for ecological communities.

Usage

cal.H(data, ...)

## S3 method for class 'data.frame'
cal.H(data, ...)

## S3 method for class 'matrix'
cal.H(data, ...)

## Default S3 method:
cal.H(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.H(data = df)

Calculate Pielou's Evenness Index (J)

Description

This function calculates the Pielou's Evenness Index (J) for ecological communities.

Usage

cal.J(data, ...)

## S3 method for class 'data.frame'
cal.J(data, ...)

## S3 method for class 'matrix'
cal.J(data, ...)

## Default S3 method:
cal.J(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.J(data = df)

Calculate Maturity Index (MI)

Description

This function calculates the Maturity Index (MI) for ecological communities.

Usage

cal.MI(data, ...)

## S3 method for class 'data.frame'
cal.MI(data, ...)

## S3 method for class 'matrix'
cal.MI(data, ...)

## Default S3 method:
cal.MI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.MI(data = df)

Calculate Nematode Channel Ratio (NCR)

Description

This function calculates the Nematode Channel Ratio (NCR) for ecological communities.

Usage

cal.NCR(data, ...)

## S3 method for class 'data.frame'
cal.NCR(data, ...)

## S3 method for class 'matrix'
cal.NCR(data, ...)

## Default S3 method:
cal.NCR(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.NCR(data = df)

Calculate Plant Parasite Index (PPI)

Description

This function calculates the Plant Parasite Index (PPI) for ecological communities.

Usage

cal.PPI(data, ...)

## S3 method for class 'data.frame'
cal.PPI(data, ...)

## S3 method for class 'matrix'
cal.PPI(data, ...)

## Default S3 method:
cal.PPI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.PPI(data = df)

Calculate Structure Index (SI)

Description

This function calculates the Structure Index (SI) for ecological communities.

Usage

cal.SI(data, ...)

## S3 method for class 'data.frame'
cal.SI(data, ...)

## S3 method for class 'matrix'
cal.SI(data, ...)

## Default S3 method:
cal.SI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.SI(data = df)

Calculate Species Richness Index (SRI)

Description

This function calculates the Species Richness Index (SRI) for ecological communities.

Usage

cal.SRI(data, total.abundance, method = "Margalef", ...)

## S3 method for class 'data.frame'
cal.SRI(data, total.abundance, method = "Margalef", ...)

## S3 method for class 'matrix'
cal.SRI(data, total.abundance, method = "Margalef", ...)

## Default S3 method:
cal.SRI(data, total.abundance, method = "Margalef", ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
abundance <- data.frame(
  abundance = c(100, 150, 120),
  row.names = c("A", "B", "C")
)
cal.SRI(data = df, total.abundance = abundance, method = "Margalef")

Calculate Simpson Index

Description

This function calculates the Simpson Index for ecological communities.

Usage

cal.Simpson(data, ...)

## S3 method for class 'data.frame'
cal.Simpson(data, ...)

## S3 method for class 'matrix'
cal.Simpson(data, ...)

## Default S3 method:
cal.Simpson(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.Simpson(data = df)

Calculate Trophic Diversity (TD) Index

Description

This function calculates the Trophic Diversity (TD) Index for ecological communities.

Usage

cal.TD(data, ...)

## S3 method for class 'data.frame'
cal.TD(data, ...)

## S3 method for class 'matrix'
cal.TD(data, ...)

## Default S3 method:
cal.TD(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.TD(data = df)

Calculate Wasilewska Index (WI)

Description

This function calculates the Wasilewska Index (WI) for ecological communities.

Usage

cal.WI(data, ...)

## S3 method for class 'data.frame'
cal.WI(data, ...)

## S3 method for class 'matrix'
cal.WI(data, ...)

## Default S3 method:
cal.WI(data, ...)

Arguments

Value

A data frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
cal.WI(data = df)

Check validity of nematode genus names against reference database

Description

This generic function validates nematode genus names by checking their existence in a reference database (nematode.info). It supports multiple input types and provides flexible output formats.

Usage

check_nematode_genus(Query.genus, Query.col = NULL, show.details = TRUE, ...)

## S3 method for class 'character'
check_nematode_genus(Query.genus, Query.col = NULL, show.details = TRUE, ...)

## S3 method for class 'data.frame'
check_nematode_genus(Query.genus, Query.col, show.details = TRUE, ...)

## Default S3 method:
check_nematode_genus(Query.genus, Query.col = NULL, show.details = TRUE, ...)

Arguments

Value

Output varies by input type and show.details:

• For character vector input:

  • show.details = TRUE: data.frame with query, existence, and reference data

  • show.details = FALSE: character vector of invalid genera

• For data.frame input: same as character input for the specified column

• For unsupported types: error message

Examples

# Check character vector
check_nematode_genus(c("Caenorhabditis", "Wrong"))

# Check data.frame column
df <- data.frame(genus = c("Meloidogyne", "XXX"))
check_nematode_genus(Query.genus = df, Query.col = "genus")

Calculate Diet Relative or Absolute Abundance

Description

TThis function calculates the relative or absolute abundance of four feeding types of nematodes in each sample. The feeding types include bacterial feeders (Ba), fungus feeders (Fu), plant feeders (Pp), and omnivores/predators (Op).

Usage

diet_rel_abundance(data, total.abundance = NULL, relative = TRUE, ...)

## S3 method for class 'data.frame'
diet_rel_abundance(data, total.abundance = NULL, relative = TRUE, ...)

## S3 method for class 'matrix'
diet_rel_abundance(data, total.abundance = NULL, relative = TRUE, ...)

## Default S3 method:
diet_rel_abundance(data, total.abundance = NULL, relative = TRUE, ...)

Arguments

Value

A data frame with five columns:

Examples

# Example with a data frame
df <- data.frame(
  Cephalobus = c(10, NA, 15),
  Caenorhabditis = c(5, 10, NA),
  Pratylenchus = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
abundance <- data.frame(
  abundance = c(100, 150, 120),
  row.names = c("A", "B", "C")
)
diet_rel_abundance(df, abundance, relative = FALSE)

# Example with a matrix
mat <- matrix(c(10, NA, 15, 5, 10, NA, 8, 12, 10), nrow = 3, byrow = TRUE)
colnames(mat) <- c("Cephalobus", "Caenorhabditis", "Pratylenchus")
row.names(mat) <- c("A", "B", "C")
diet_rel_abundance(mat)

Fuzzy Matching of Nematode Genus Names

Description

This function performs fuzzy matching of nematode genus names against a reference database using Levenshtein distance (edit distance) with case insensitivity.

Usage

fuzzy_genus_match(Query.genus, max_dist = 2, ...)

Arguments

Value

A data frame containing:

• Query.genus - Original query genus name

• CorrectName - Matched genus name from reference

• Distance - Edit distance between query and match

• Additional columns - All columns from nematode.info for matched records

Examples

fuzzy_genus_match(c("Harterta", "Meloidogyne"))

Nematode Genus and Family Average Body Mass

Description

A dataset containing the average dry body mass (in micrograms) of nematode genera and families, compiled from morphological measurements and allometric scaling. Essential for metabolic rate calculations and size-spectrum analyses in soil ecology.

Usage

nematode.ave.mass

Format

A data frame with 987 rows and 4 variables:

Genus

Nematode genus name (character), taxonomically validated against Nemaplex database

Family

Corresponding taxonomic family (character)

Genus.Average.Mass

Mean dry mass per genus (numeric, \mug)

Family.Average.Mass

Mean dry mass per family (numeric, \mug)

Source

Nemaplex.UCDavis.edu; Revision Date: 07/08/2025; Accessed 07/16/2025

• Website: http://nemaplex.ucdavis.edu/

See Also

Use nematode.info for complementary trait data.

Examples

# Load data
data(nematode.ave.mass)

# Find mass range within a family (e.g. Rhabditidae)
rhabditidae <- subset(nematode.ave.mass, Family == "Rhabditidae")
range(rhabditidae$Genus.Average.Mass, na.rm = TRUE)

# Convert to biomass (example: 100 individuals of Acanthopharynx)
100 * subset(nematode.ave.mass, Genus == "Acanthopharynx")$Genus.Average.Mass

Nematode Taxonomic and Functional Traits

Description

A dataset containing taxonomic classification and functional traits of nematode genera, including feeding habits and ecological group (c-p value).

Usage

nematode.info

Format

A data frame with 2484 rows and 4 variables:

Genus

Nematode genus name (character), e.g. "Parascaris", "Heterakis"

Family

Taxonomic family name (character), e.g. "Ascarididae", "Heterakidae"

Feeding_habit

Feeding behavior category (character), e.g. "Bacterial feeders", "Omnivores"

CP_group

Colonizer-Persister group (numeric, 1-5)

Details

This dataset is particularly useful for:

• Ecological studies of soil nematode communities

• Trophic network analysis

• Calculating nematode maturity indices (e.g. MI, PPI)

Source

Nemaplex.UCDavis.edu; Revision Date: 07/08/2025; Accessed 07/16/2025

• Website: http://nemaplex.ucdavis.edu/

Examples

# Load the data
data(nematode.info)

# Count nematodes by feeding habit
table(nematode.info$Feeding_habit)

# Find all genera in Ascarididae family
subset(nematode.info, Family == "Ascarididae")

Calculate Number of Species

Description

This function calculates the number of nematode species present in each sample. It counts the number of non-zero and non-empty nematode species for each sample.

Usage

num_species(data, ...)

## S3 method for class 'data.frame'
num_species(data, ...)

## S3 method for class 'matrix'
num_species(data, ...)

## Default S3 method:
num_species(data, ...)

Arguments

Value

A data.frame with two columns:

Examples

# Example with a data frame
df <- data.frame(
  Species1 = c(10, NA, 15),
  Species2 = c(5, 10, NA),
  Species3 = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
num_species(df)

# Example with a matrix
mat <- matrix(c(10, NA, 15, 5, 10, NA, 8, 12, 10), nrow = 3, byrow = TRUE)
colnames(mat) <- c("Species1", "Species2", "Species3")
row.names(mat) <- c("A", "B", "C")
num_species(mat)

Calculate the Relative Abundance of Nematodes

Description

This function calculates the relative abundance of nematodes for each sample. The relative abundance is defined as the proportion of each nematode's count to the total count of all nematodes in a sample.

Usage

rel_abundance(data, ...)

## S3 method for class 'data.frame'
rel_abundance(data, ...)

## S3 method for class 'matrix'
rel_abundance(data, ...)

## Default S3 method:
rel_abundance(data, ...)

Arguments

Value

A data.frame or matrix (matching the input type) containing the relative abundance of each nematode in each sample.

Examples

# Example with a data frame
df <- data.frame(
  Species1 = c(10, NA, 15),
  Species2 = c(5, 10, NA),
  Species3 = c(8, 12, 10),
  row.names = c("A", "B", "C")
)
rel_abundance(df)

# Example with a matrix
mat <- matrix(c(10, NA, 15, 5, 10, NA, 8, 12, 10), nrow = 3, byrow = TRUE)
colnames(mat) <- c("Species1", "Species2", "Species3")
row.names(mat) <- c("A", "B", "C")
rel_abundance(mat)

Non-Metric Multidimensional Scaling (NMDS) Analysis

Description

This function performs NMDS analysis on a dataset using the specified distance metric, and optionally runs PERMANOVA (adonis2) and ANOSIM tests for group differences. It supports both data.frame and matrix inputs.

Usage

runNMDS(
  data,
  group,
  distance = "bray",
  k = 2,
  decostand.method = "hellinger",
  autotransform = TRUE,
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## S3 method for class 'data.frame'
runNMDS(
  data,
  group,
  distance = "bray",
  k = 2,
  decostand.method = "hellinger",
  autotransform = TRUE,
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## S3 method for class 'matrix'
runNMDS(
  data,
  group,
  distance = "bray",
  k = 2,
  decostand.method = "hellinger",
  autotransform = TRUE,
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## Default S3 method:
runNMDS(
  data,
  group,
  distance = "bray",
  k = 2,
  decostand.method = "hellinger",
  autotransform = TRUE,
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

Arguments

Value

An object of class "NMDS" containing:

• data - List containing the input data and group information

• call - The function call

• NMDS - NMDS results from metaMDS

• adonis2 - PERMANOVA results (if adonis2 = TRUE)

• anosim - ANOSIM results (if anosim = TRUE)

• SIMPER - SIMPER results (if simper = TRUE)

See Also

• metaMDS for details on NMDS implementation and distance measures

• decostand for details on standardization methods

• vegdist for available distance metrics

• adonis2 for PERMANOVA

• anosim for ANOSIM

• simper for SIMPER

Examples

# Example with default Bray-Curtis distance
data <- data.frame(
  Cephalobus = c(10, 20, 30, 1, 6, 5),
  Eucephalobus = c(5, 10, 12, 30, 1, 6),
  Acrobeloides = c(1, 2, 3, 12, 30, 1),
  Caenorhabditis = c(5, 8, 15, 2, 3, 12),
  Aphelenchus = c(5, 13, 11, 15, 2, 3),
  Leptonchus = c(3, 10, 15, 0, 15, 11),
  Pratylenchus = c(9, 2, 15, 15, 0, 15),
  Tylenchus = c(5, 0, 15, 11, 15, 2),
  Mesodorylaimus = c(7, 10, 18, 3, 12, 30),
  Discolaimus = c(1, 10, 25, 10, 18, 3),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)
group_df <- data.frame(
  group = c("A", "A", "B", "B", "C", "C"),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)

nmds <- runNMDS(data, group = group_df)

Principal Coordinates Analysis (PCoA) Analysis

Description

This function performs PCoA analysis on a dataset using the specified distance metric, and optionally runs PERMANOVA (adonis2) and ANOSIM tests for group differences. It supports both data.frame and matrix inputs.

Usage

runPCoA(
  data,
  group,
  k = 2,
  distance = "bray",
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## S3 method for class 'data.frame'
runPCoA(
  data,
  group,
  k = 2,
  distance = "bray",
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## S3 method for class 'matrix'
runPCoA(
  data,
  group,
  k = 2,
  distance = "bray",
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

## Default S3 method:
runPCoA(
  data,
  group,
  k = 2,
  distance = "bray",
  adonis2 = TRUE,
  anosim = TRUE,
  simper = TRUE,
  ...
)

Arguments

Value

An object of class "PCoA" containing:

• data - List containing the input data and group information

• call - The function call

• Points - Sample coordinates in the reduced space.

• Eigenvalues - Variance explained by each principal coordinate axis.

• adonis2 - PERMANOVA results (if adonis2 = TRUE)

• anosim - ANOSIM results (if anosim = TRUE)

• SIMPER - SIMPER results (if simper = TRUE)

See Also

• cmdscale for details on cmdscale implementation

• vegdist for available distance metrics

• adonis2 for PERMANOVA

• anosim for ANOSIM

• simper for SIMPER

Examples

# Example with default Bray-Curtis distance
data <- data.frame(
  Cephalobus = c(10, 20, 30, 1, 6, 5),
  Eucephalobus = c(5, 10, 12, 30, 1, 6),
  Acrobeloides = c(1, 2, 3, 12, 30, 1),
  Caenorhabditis = c(5, 8, 15, 2, 3, 12),
  Aphelenchus = c(5, 13, 11, 15, 2, 3),
  Leptonchus = c(3, 10, 15, 0, 15, 11),
  Pratylenchus = c(9, 2, 15, 15, 0, 15),
  Tylenchus = c(5, 0, 15, 11, 15, 2),
  Mesodorylaimus = c(7, 10, 18, 3, 12, 30),
  Discolaimus = c(1, 10, 25, 10, 18, 3),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)
group_df <- data.frame(
  group = c("A", "A", "B", "B", "C", "C"),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)

pcoa <- runPCoA(data, group = group_df)

Similarity Percentages Analysis

Description

Discriminating species between two groups using Bray-Curtis dissimilarities

Usage

runSimper(object, ...)

## S3 method for class 'Ordination'
runSimper(object, ...)

## Default S3 method:
runSimper(object, ...)

Arguments

Value

The object of class "Ordination" containing (See runNMDS for details):

• data - List containing the input data and group information

• call - The function call

• NMDS - NMDS results from metaMDS

• SIMPER - SIMPER results

Examples

# Example with default Bray-Curtis distance
data <- data.frame(
  Cephalobus = c(10, 20, 30, 1, 6, 5),
  Eucephalobus = c(5, 10, 12, 30, 1, 6),
  Acrobeloides = c(1, 2, 3, 12, 30, 1),
  Caenorhabditis = c(5, 8, 15, 2, 3, 12),
  Aphelenchus = c(5, 13, 11, 15, 2, 3),
  Leptonchus = c(3, 10, 15, 0, 15, 11),
  Pratylenchus = c(9, 2, 15, 15, 0, 15),
  Tylenchus = c(5, 0, 15, 11, 15, 2),
  Mesodorylaimus = c(7, 10, 18, 3, 12, 30),
  Discolaimus = c(1, 10, 25, 10, 18, 3),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)
group_df <- data.frame(
  group = c("A", "A", "B", "B", "C", "C"),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)
nmds <- runNMDS(data, group = group_df, simper = FALSE)

# Example
nmds_simper <- runSimper(nmds)
print(nmds_simper$SIMPER)

Summarize NMDS Results

Description

Provides a concise summary of Non-Metric Multidimensional Scaling (NMDS) analysis results, including stress value, PERMANOVA (adonis2) and ANOSIM test statistics.

Provides a concise summary of Principal Coordinates Analysis (PCoA) analysis results, including PERMANOVA (adonis2) and ANOSIM test statistics.

Usage

## S3 method for class 'NMDS'
summary(object, ...)

## S3 method for class 'PCoA'
summary(object, ...)

Arguments

Value

A list containing:

• stress - NMDS stress value

• points - Sample coordinates in the reduced space

• adonis2 - PERMANOVA results (R2, p-value, significance)

• anosim - ANOSIM results (R statistic, p-value, significance)

A list containing:

• points - Sample coordinates in the reduced space.

• eig - Variance explained by each principal coordinate axis.

• adonis2 - PERMANOVA results (R2, p-value, significance)

• anosim - ANOSIM results (R statistic, p-value, significance)

Examples

# Example with default Bray-Curtis distance
data <- data.frame(
  Cephalobus = c(10, 20, 30, 1, 6, 5),
  Eucephalobus = c(5, 10, 12, 30, 1, 6),
  Acrobeloides = c(1, 2, 3, 12, 30, 1),
  Caenorhabditis = c(5, 8, 15, 2, 3, 12),
  Aphelenchus = c(5, 13, 11, 15, 2, 3),
  Leptonchus = c(3, 10, 15, 0, 15, 11),
  Pratylenchus = c(9, 2, 15, 15, 0, 15),
  Tylenchus = c(5, 0, 15, 11, 15, 2),
  Mesodorylaimus = c(7, 10, 18, 3, 12, 30),
  Discolaimus = c(1, 10, 25, 10, 18, 3),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)
group_df <- data.frame(
  group = c("A", "A", "B", "B", "C", "C"),
  row.names = c("Sample1", "Sample2", "Sample3", "Sample4", "Sample5", "Sample6")
)

# Example for summary.NMDS
nmds <- runNMDS(data, group = group_df)
summary(nmds)

# Example for summary.PCoA
pcoa <- runPCoA(data, group = group_df)
summary(pcoa)