evolqg: MantelModTest – R documentation

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MantelModTest

Test single modularity hypothesis using Mantel correlation

Description

Calculates the correlation and Mantel significance test between a hypothetical binary modularity matrix and a correlation matrix. Also gives mean correlation within- and between-modules. This function is usually only called by TestModularity.

Usage

MantelModTest(cor.hypothesis, cor.matrix, ...)

## Default S3 method:
MantelModTest(
  cor.hypothesis,
  cor.matrix,
  permutations = 1000,
  MHI = FALSE,
  ...,
  landmark.dim = NULL,
  withinLandmark = FALSE
)

## S3 method for class 'list'
MantelModTest(
  cor.hypothesis,
  cor.matrix,
  permutations = 1000,
  MHI = FALSE,
  landmark.dim = NULL,
  withinLandmark = FALSE,
  ...,
  parallel = FALSE
)

Arguments

`cor.hypothesis`	Hypothetical correlation matrix, with 1s within-modules and 0s between modules.
`cor.matrix`	Observed empirical correlation matrix.
`...`	aditional arguments passed to MantelCor
`permutations`	Number of permutations used in significance calculation.
`MHI`	Indicates if Modularity Hypothesis Indexshould be calculated instead of AVG Ratio.
`landmark.dim`	Used if permutations should be performed mantaining landmark structure in geometric morphomotric data. Either 2 for 2d data or 3 for 3d data. Default is NULL for non geometric morphomotric data.
`withinLandmark`	Logical. If TRUE within-landmark correlation are used in calculation of correlation. Only used if landmark.dim is passed, default is FALSE.
`parallel`	if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC.

Details

CalcAVG can be used when a significance test is not required.

Value

Returns a vector with the matrix correlation, significance via Mantel, within- and between module correlation.

Author(s)

Diogo Melo, Guilherme Garcia

References

Porto, Arthur, Felipe B. Oliveira, Leila T. Shirai, Valderes Conto, and Gabriel Marroig. 2009. "The Evolution of Modularity in the Mammalian Skull I: Morphological Integration Patterns and Magnitudes." Evolutionary Biology 36 (1): 118-35. doi:10.1007/s11692-008-9038-3.

Modularity and Morphometrics: Error Rates in Hypothesis Testing Guilherme Garcia, Felipe Bandoni de Oliveira, Gabriel Marroig bioRxiv 030874; doi: http://dx.doi.org/10.1101/030874

Examples

# Create a single modularity hypothesis:
hypot = rep(c(1, 0), each = 6)
cor.hypot = CreateHypotMatrix(hypot)

# First with an unstructured matrix:
un.cor = RandomMatrix(12)
MantelModTest(cor.hypot, un.cor)

# Now with a modular matrix:
hypot.mask = matrix(as.logical(cor.hypot), 12, 12)
mod.cor = matrix(NA, 12, 12)
mod.cor[ hypot.mask] = runif(length(mod.cor[ hypot.mask]), 0.8, 0.9) # within-modules
mod.cor[!hypot.mask] = runif(length(mod.cor[!hypot.mask]), 0.3, 0.4) # between-modules
diag(mod.cor) = 1
mod.cor = (mod.cor + t(mod.cor))/2 # correlation matrices should be symmetric

MantelModTest(cor.hypot, mod.cor)

evolqg

Tools for Evolutionary Quantitative Genetics

v0.2-8

MIT + file LICENSE

Authors

Ana Paula Assis, Diogo Melo, Edgar Zanella, Fabio Andrade Machado, Guilherme Garcia

Initial release

2020-11-14